From 8d579aacb836e4061632268401ff5052b8ff7d9b Mon Sep 17 00:00:00 2001 From: klessard Date: Sun, 25 Apr 2021 22:09:16 -0400 Subject: [PATCH 01/63] Move NdArray library to subfolder --- ndarray/pom.xml | 68 + .../tensorflow/ndarray/BooleanNdArray.java | 108 + .../org/tensorflow/ndarray/ByteNdArray.java | 108 + .../org/tensorflow/ndarray/DoubleNdArray.java | 108 + .../org/tensorflow/ndarray/FloatNdArray.java | 108 + .../ndarray/IllegalRankException.java | 27 + .../org/tensorflow/ndarray/IntNdArray.java | 108 + .../org/tensorflow/ndarray/LongNdArray.java | 108 + .../java/org/tensorflow/ndarray/NdArray.java | 302 ++ .../tensorflow/ndarray/NdArraySequence.java | 67 + .../java/org/tensorflow/ndarray/NdArrays.java | 495 +++ .../java/org/tensorflow/ndarray/Shape.java | 447 ++ .../java/org/tensorflow/ndarray/Shaped.java | 51 + .../org/tensorflow/ndarray/ShortNdArray.java | 108 + .../org/tensorflow/ndarray/StdArrays.java | 3809 +++++++++++++++++ .../ndarray/buffer/BooleanDataBuffer.java | 165 + .../ndarray/buffer/ByteDataBuffer.java | 232 + .../tensorflow/ndarray/buffer/DataBuffer.java | 324 ++ .../ndarray/buffer/DataBufferWindow.java | 93 + .../ndarray/buffer/DataBuffers.java | 457 ++ .../ndarray/buffer/DataStorageVisitor.java | 147 + .../ndarray/buffer/DoubleDataBuffer.java | 165 + .../ndarray/buffer/FloatDataBuffer.java | 165 + .../ndarray/buffer/IntDataBuffer.java | 165 + .../ndarray/buffer/LongDataBuffer.java | 165 + .../ndarray/buffer/ShortDataBuffer.java | 165 + .../buffer/layout/BooleanDataLayout.java | 65 + .../ndarray/buffer/layout/ByteDataLayout.java | 65 + .../ndarray/buffer/layout/DataLayout.java | 129 + .../ndarray/buffer/layout/DataLayouts.java | 96 + .../buffer/layout/DoubleDataLayout.java | 65 + .../buffer/layout/FloatDataLayout.java | 65 + .../ndarray/buffer/layout/IntDataLayout.java | 66 + .../ndarray/buffer/layout/LongDataLayout.java | 65 + .../buffer/layout/ShortDataLayout.java | 65 + .../ndarray/impl/AbstractNdArray.java | 92 + .../tensorflow/ndarray/impl/Validator.java | 55 + .../impl/buffer/AbstractDataBuffer.java | 182 + .../impl/buffer/AbstractDataBufferWindow.java | 48 + .../ndarray/impl/buffer/Validator.java | 132 + .../adapter/AbstractDataBufferAdapter.java | 68 + .../adapter/BooleanDataBufferAdapter.java | 116 + .../buffer/adapter/ByteDataBufferAdapter.java | 135 + .../buffer/adapter/DataBufferAdapter.java | 47 + .../adapter/DataBufferAdapterFactory.java | 142 + .../adapter/DoubleDataBufferAdapter.java | 116 + .../adapter/FloatDataBufferAdapter.java | 116 + .../buffer/adapter/IntDataBufferAdapter.java | 116 + .../buffer/adapter/LongDataBufferAdapter.java | 116 + .../adapter/ShortDataBufferAdapter.java | 116 + .../impl/buffer/layout/Bfloat16Layout.java | 54 + .../impl/buffer/layout/BoolLayout.java | 47 + .../impl/buffer/layout/Float16Layout.java | 119 + .../impl/buffer/layout/StringLayout.java | 48 + .../impl/buffer/misc/ArrayDataBuffer.java | 126 + .../impl/buffer/misc/BitSetDataBuffer.java | 183 + .../buffer/misc/BooleanArrayDataBuffer.java | 176 + .../buffer/misc/MiscDataBufferFactory.java | 40 + .../buffer/nio/AbstractNioDataBuffer.java | 41 + .../impl/buffer/nio/ByteNioDataBuffer.java | 185 + .../impl/buffer/nio/DoubleNioDataBuffer.java | 147 + .../impl/buffer/nio/FloatNioDataBuffer.java | 147 + .../impl/buffer/nio/IntNioDataBuffer.java | 147 + .../impl/buffer/nio/LongNioDataBuffer.java | 147 + .../impl/buffer/nio/NioDataBufferFactory.java | 61 + .../impl/buffer/nio/ShortNioDataBuffer.java | 147 + .../buffer/raw/AbstractRawDataBuffer.java | 94 + .../impl/buffer/raw/BooleanRawDataBuffer.java | 146 + .../impl/buffer/raw/ByteRawDataBuffer.java | 185 + .../impl/buffer/raw/DoubleRawDataBuffer.java | 149 + .../impl/buffer/raw/FloatRawDataBuffer.java | 150 + .../impl/buffer/raw/IntRawDataBuffer.java | 149 + .../impl/buffer/raw/LongRawDataBuffer.java | 149 + .../impl/buffer/raw/RawDataBufferFactory.java | 148 + .../impl/buffer/raw/RawDataBufferWindow.java | 19 + .../impl/buffer/raw/ShortRawDataBuffer.java | 149 + .../impl/buffer/raw/UnsafeMemoryHandle.java | 195 + .../impl/buffer/raw/UnsafeReference.java | 64 + .../impl/dense/AbstractDenseNdArray.java | 163 + .../impl/dense/BooleanDenseNdArray.java | 91 + .../ndarray/impl/dense/ByteDenseNdArray.java | 91 + .../ndarray/impl/dense/DataTransfer.java | 136 + .../ndarray/impl/dense/DenseNdArray.java | 63 + .../impl/dense/DoubleDenseNdArray.java | 91 + .../ndarray/impl/dense/FloatDenseNdArray.java | 91 + .../ndarray/impl/dense/IntDenseNdArray.java | 91 + .../ndarray/impl/dense/LongDenseNdArray.java | 91 + .../ndarray/impl/dense/ShortDenseNdArray.java | 91 + .../ndarray/impl/dense/Validator.java | 49 + .../impl/dimension/AbstractDimension.java | 45 + .../ndarray/impl/dimension/Axis.java | 61 + .../ndarray/impl/dimension/Dimension.java | 36 + .../impl/dimension/DimensionalSpace.java | 224 + .../impl/dimension/IndexedDimension.java | 69 + .../impl/dimension/ReducedDimension.java | 62 + .../dimension/RelativeDimensionalSpace.java | 32 + .../impl/sequence/CoordinatesIncrementor.java | 38 + .../impl/sequence/FastElementSequence.java | 81 + .../sequence/IndexedPositionIterator.java | 28 + .../IndexedSequentialPositionIterator.java | 51 + .../impl/sequence/NdPositionIterator.java | 70 + .../impl/sequence/PositionIterator.java | 42 + .../sequence/SequentialPositionIterator.java | 55 + .../impl/sequence/SingleElementSequence.java | 71 + .../impl/sequence/SlicingElementSequence.java | 77 + .../org/tensorflow/ndarray/index/All.java | 57 + .../java/org/tensorflow/ndarray/index/At.java | 74 + .../tensorflow/ndarray/index/Ellipsis.java | 48 + .../tensorflow/ndarray/index/Hyperslab.java | 90 + .../org/tensorflow/ndarray/index/Index.java | 131 + .../org/tensorflow/ndarray/index/Indices.java | 363 ++ .../org/tensorflow/ndarray/index/NewAxis.java | 53 + .../tensorflow/ndarray/index/Sequence.java | 52 + .../org/tensorflow/ndarray/index/Slice.java | 89 + .../tensorflow/ndarray/index/SliceFrom.java | 86 + .../org/tensorflow/ndarray/index/SliceTo.java | 86 + .../org/tensorflow/ndarray/index/Step.java | 83 + .../ndarray/BooleanNdArrayTestBase.java | 57 + .../ndarray/ByteNdArrayTestBase.java | 55 + .../ndarray/DoubleNdArrayTestBase.java | 55 + .../ndarray/FloatNdArrayTestBase.java | 55 + .../org/tensorflow/ndarray/IndexTest.java | 205 + .../ndarray/IntNdArrayTestBase.java | 55 + .../ndarray/LongNdArrayTestBase.java | 55 + .../tensorflow/ndarray/NdArrayTestBase.java | 338 ++ .../org/tensorflow/ndarray/ShapeTest.java | 170 + .../ndarray/ShortNdArrayTestBase.java | 55 + .../org/tensorflow/ndarray/StdArraysTest.java | 211 + .../ndarray/benchmark/NdArrayBenchmark.java | 163 + .../buffer/BooleanDataBufferTestBase.java | 142 + .../buffer/ByteDataBufferTestBase.java | 145 + .../ndarray/buffer/DataBufferTestBase.java | 293 ++ .../buffer/DoubleDataBufferTestBase.java | 135 + .../buffer/FloatDataBufferTestBase.java | 135 + .../ndarray/buffer/IntDataBufferTestBase.java | 135 + .../buffer/LongDataBufferTestBase.java | 135 + .../buffer/ShortDataBufferTestBase.java | 135 + .../BigIntegerDataBufferAdapterTest.java | 67 + .../adapter/BooleanDataBufferAdapterTest.java | 45 + .../adapter/ByteDataBufferAdapterTest.java | 27 + .../adapter/DoubleDataBufferAdapterTest.java | 61 + .../adapter/FloatDataBufferAdapterTest.java | 52 + .../adapter/IntDataBufferAdapterTest.java | 51 + .../adapter/LongDataBufferAdapterTest.java | 60 + .../adapter/ShortDataBufferAdapterTest.java | 45 + .../buffer/layout/Bfloat16LayoutTest.java | 84 + .../impl/buffer/layout/BoolLayoutTest.java | 42 + .../impl/buffer/layout/Float16LayoutTest.java | 90 + .../impl/buffer/misc/ArrayDataBufferTest.java | 265 ++ .../buffer/misc/BitSetDataBufferTest.java | 34 + .../misc/StringArrayDataBufferTest.java | 33 + .../buffer/nio/ByteNioDataBufferTest.java | 29 + .../buffer/nio/DoubleNioDataBufferTest.java | 29 + .../buffer/nio/FloatNioDataBufferTest.java | 29 + .../impl/buffer/nio/IntNioDataBufferTest.java | 29 + .../buffer/nio/LongNioDataBufferTest.java | 29 + .../buffer/nio/ShortNioDataBufferTest.java | 29 + .../buffer/raw/BooleanRawDataBufferTest.java | 28 + .../buffer/raw/ByteRawDataBufferTest.java | 28 + .../buffer/raw/DoubleRawDataBufferTest.java | 28 + .../buffer/raw/FloatRawDataBufferTest.java | 28 + .../impl/buffer/raw/IntRawDataBufferTest.java | 28 + .../buffer/raw/LongRawDataBufferTest.java | 28 + .../buffer/raw/ShortRawDataBufferTest.java | 28 + .../impl/dense/BooleanDenseNdArrayTest.java | 35 + .../impl/dense/ByteDenseNdArrayTest.java | 35 + .../ndarray/impl/dense/DenseNdArrayTest.java | 51 + .../impl/dense/DoubleDenseNdArrayTest.java | 35 + .../impl/dense/FloatDenseNdArrayTest.java | 35 + .../impl/dense/IntDenseNdArrayTest.java | 35 + .../impl/dense/LongDenseNdArrayTest.java | 35 + .../impl/dense/ShortDenseNdArrayTest.java | 35 + .../impl/dense/StringDenseNdArrayTest.java | 43 + .../impl/sequence/ElementSequenceTest.java | 145 + ndarray/src/test/resources/COPYRIGHT.txt | 1 + ndarray/src/test/resources/castle.jpg | Bin 0 -> 436164 bytes 176 files changed, 21797 insertions(+) create mode 100644 ndarray/pom.xml create mode 100644 ndarray/src/main/java/org/tensorflow/ndarray/BooleanNdArray.java create mode 100644 ndarray/src/main/java/org/tensorflow/ndarray/ByteNdArray.java create mode 100644 ndarray/src/main/java/org/tensorflow/ndarray/DoubleNdArray.java create mode 100644 ndarray/src/main/java/org/tensorflow/ndarray/FloatNdArray.java create mode 100644 ndarray/src/main/java/org/tensorflow/ndarray/IllegalRankException.java create mode 100644 ndarray/src/main/java/org/tensorflow/ndarray/IntNdArray.java create mode 100644 ndarray/src/main/java/org/tensorflow/ndarray/LongNdArray.java create mode 100644 ndarray/src/main/java/org/tensorflow/ndarray/NdArray.java create mode 100644 ndarray/src/main/java/org/tensorflow/ndarray/NdArraySequence.java create mode 100644 ndarray/src/main/java/org/tensorflow/ndarray/NdArrays.java create mode 100644 ndarray/src/main/java/org/tensorflow/ndarray/Shape.java create mode 100644 ndarray/src/main/java/org/tensorflow/ndarray/Shaped.java create mode 100644 ndarray/src/main/java/org/tensorflow/ndarray/ShortNdArray.java create mode 100644 ndarray/src/main/java/org/tensorflow/ndarray/StdArrays.java create mode 100644 ndarray/src/main/java/org/tensorflow/ndarray/buffer/BooleanDataBuffer.java create mode 100644 ndarray/src/main/java/org/tensorflow/ndarray/buffer/ByteDataBuffer.java create mode 100644 ndarray/src/main/java/org/tensorflow/ndarray/buffer/DataBuffer.java create mode 100644 ndarray/src/main/java/org/tensorflow/ndarray/buffer/DataBufferWindow.java create mode 100644 ndarray/src/main/java/org/tensorflow/ndarray/buffer/DataBuffers.java create mode 100644 ndarray/src/main/java/org/tensorflow/ndarray/buffer/DataStorageVisitor.java create mode 100644 ndarray/src/main/java/org/tensorflow/ndarray/buffer/DoubleDataBuffer.java create mode 100644 ndarray/src/main/java/org/tensorflow/ndarray/buffer/FloatDataBuffer.java create mode 100644 ndarray/src/main/java/org/tensorflow/ndarray/buffer/IntDataBuffer.java create mode 100644 ndarray/src/main/java/org/tensorflow/ndarray/buffer/LongDataBuffer.java create mode 100644 ndarray/src/main/java/org/tensorflow/ndarray/buffer/ShortDataBuffer.java create mode 100644 ndarray/src/main/java/org/tensorflow/ndarray/buffer/layout/BooleanDataLayout.java create mode 100644 ndarray/src/main/java/org/tensorflow/ndarray/buffer/layout/ByteDataLayout.java create mode 100644 ndarray/src/main/java/org/tensorflow/ndarray/buffer/layout/DataLayout.java create mode 100644 ndarray/src/main/java/org/tensorflow/ndarray/buffer/layout/DataLayouts.java create mode 100644 ndarray/src/main/java/org/tensorflow/ndarray/buffer/layout/DoubleDataLayout.java create mode 100644 ndarray/src/main/java/org/tensorflow/ndarray/buffer/layout/FloatDataLayout.java create mode 100644 ndarray/src/main/java/org/tensorflow/ndarray/buffer/layout/IntDataLayout.java create mode 100644 ndarray/src/main/java/org/tensorflow/ndarray/buffer/layout/LongDataLayout.java create mode 100644 ndarray/src/main/java/org/tensorflow/ndarray/buffer/layout/ShortDataLayout.java create mode 100644 ndarray/src/main/java/org/tensorflow/ndarray/impl/AbstractNdArray.java create mode 100644 ndarray/src/main/java/org/tensorflow/ndarray/impl/Validator.java create mode 100644 ndarray/src/main/java/org/tensorflow/ndarray/impl/buffer/AbstractDataBuffer.java create mode 100644 ndarray/src/main/java/org/tensorflow/ndarray/impl/buffer/AbstractDataBufferWindow.java create mode 100644 ndarray/src/main/java/org/tensorflow/ndarray/impl/buffer/Validator.java create mode 100644 ndarray/src/main/java/org/tensorflow/ndarray/impl/buffer/adapter/AbstractDataBufferAdapter.java create mode 100644 ndarray/src/main/java/org/tensorflow/ndarray/impl/buffer/adapter/BooleanDataBufferAdapter.java create mode 100644 ndarray/src/main/java/org/tensorflow/ndarray/impl/buffer/adapter/ByteDataBufferAdapter.java create mode 100644 ndarray/src/main/java/org/tensorflow/ndarray/impl/buffer/adapter/DataBufferAdapter.java create mode 100644 ndarray/src/main/java/org/tensorflow/ndarray/impl/buffer/adapter/DataBufferAdapterFactory.java create mode 100644 ndarray/src/main/java/org/tensorflow/ndarray/impl/buffer/adapter/DoubleDataBufferAdapter.java create mode 100644 ndarray/src/main/java/org/tensorflow/ndarray/impl/buffer/adapter/FloatDataBufferAdapter.java create mode 100644 ndarray/src/main/java/org/tensorflow/ndarray/impl/buffer/adapter/IntDataBufferAdapter.java create mode 100644 ndarray/src/main/java/org/tensorflow/ndarray/impl/buffer/adapter/LongDataBufferAdapter.java create mode 100644 ndarray/src/main/java/org/tensorflow/ndarray/impl/buffer/adapter/ShortDataBufferAdapter.java create mode 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ndarray/src/main/java/org/tensorflow/ndarray/impl/sequence/FastElementSequence.java create mode 100644 ndarray/src/main/java/org/tensorflow/ndarray/impl/sequence/IndexedPositionIterator.java create mode 100644 ndarray/src/main/java/org/tensorflow/ndarray/impl/sequence/IndexedSequentialPositionIterator.java create mode 100644 ndarray/src/main/java/org/tensorflow/ndarray/impl/sequence/NdPositionIterator.java create mode 100644 ndarray/src/main/java/org/tensorflow/ndarray/impl/sequence/PositionIterator.java create mode 100644 ndarray/src/main/java/org/tensorflow/ndarray/impl/sequence/SequentialPositionIterator.java create mode 100644 ndarray/src/main/java/org/tensorflow/ndarray/impl/sequence/SingleElementSequence.java create mode 100644 ndarray/src/main/java/org/tensorflow/ndarray/impl/sequence/SlicingElementSequence.java create mode 100644 ndarray/src/main/java/org/tensorflow/ndarray/index/All.java create mode 100644 ndarray/src/main/java/org/tensorflow/ndarray/index/At.java create mode 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create mode 100644 ndarray/src/test/java/org/tensorflow/ndarray/DoubleNdArrayTestBase.java create mode 100644 ndarray/src/test/java/org/tensorflow/ndarray/FloatNdArrayTestBase.java create mode 100644 ndarray/src/test/java/org/tensorflow/ndarray/IndexTest.java create mode 100644 ndarray/src/test/java/org/tensorflow/ndarray/IntNdArrayTestBase.java create mode 100644 ndarray/src/test/java/org/tensorflow/ndarray/LongNdArrayTestBase.java create mode 100644 ndarray/src/test/java/org/tensorflow/ndarray/NdArrayTestBase.java create mode 100644 ndarray/src/test/java/org/tensorflow/ndarray/ShapeTest.java create mode 100644 ndarray/src/test/java/org/tensorflow/ndarray/ShortNdArrayTestBase.java create mode 100644 ndarray/src/test/java/org/tensorflow/ndarray/StdArraysTest.java create mode 100644 ndarray/src/test/java/org/tensorflow/ndarray/benchmark/NdArrayBenchmark.java create mode 100644 ndarray/src/test/java/org/tensorflow/ndarray/buffer/BooleanDataBufferTestBase.java create mode 100644 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mode 100644 ndarray/src/test/java/org/tensorflow/ndarray/impl/buffer/misc/ArrayDataBufferTest.java create mode 100644 ndarray/src/test/java/org/tensorflow/ndarray/impl/buffer/misc/BitSetDataBufferTest.java create mode 100644 ndarray/src/test/java/org/tensorflow/ndarray/impl/buffer/misc/StringArrayDataBufferTest.java create mode 100644 ndarray/src/test/java/org/tensorflow/ndarray/impl/buffer/nio/ByteNioDataBufferTest.java create mode 100644 ndarray/src/test/java/org/tensorflow/ndarray/impl/buffer/nio/DoubleNioDataBufferTest.java create mode 100644 ndarray/src/test/java/org/tensorflow/ndarray/impl/buffer/nio/FloatNioDataBufferTest.java create mode 100644 ndarray/src/test/java/org/tensorflow/ndarray/impl/buffer/nio/IntNioDataBufferTest.java create mode 100644 ndarray/src/test/java/org/tensorflow/ndarray/impl/buffer/nio/LongNioDataBufferTest.java create mode 100644 ndarray/src/test/java/org/tensorflow/ndarray/impl/buffer/nio/ShortNioDataBufferTest.java create mode 100644 ndarray/src/test/java/org/tensorflow/ndarray/impl/buffer/raw/BooleanRawDataBufferTest.java create mode 100644 ndarray/src/test/java/org/tensorflow/ndarray/impl/buffer/raw/ByteRawDataBufferTest.java create mode 100644 ndarray/src/test/java/org/tensorflow/ndarray/impl/buffer/raw/DoubleRawDataBufferTest.java create mode 100644 ndarray/src/test/java/org/tensorflow/ndarray/impl/buffer/raw/FloatRawDataBufferTest.java create mode 100644 ndarray/src/test/java/org/tensorflow/ndarray/impl/buffer/raw/IntRawDataBufferTest.java create mode 100644 ndarray/src/test/java/org/tensorflow/ndarray/impl/buffer/raw/LongRawDataBufferTest.java create mode 100644 ndarray/src/test/java/org/tensorflow/ndarray/impl/buffer/raw/ShortRawDataBufferTest.java create mode 100644 ndarray/src/test/java/org/tensorflow/ndarray/impl/dense/BooleanDenseNdArrayTest.java create mode 100644 ndarray/src/test/java/org/tensorflow/ndarray/impl/dense/ByteDenseNdArrayTest.java create mode 100644 ndarray/src/test/java/org/tensorflow/ndarray/impl/dense/DenseNdArrayTest.java create mode 100644 ndarray/src/test/java/org/tensorflow/ndarray/impl/dense/DoubleDenseNdArrayTest.java create mode 100644 ndarray/src/test/java/org/tensorflow/ndarray/impl/dense/FloatDenseNdArrayTest.java create mode 100644 ndarray/src/test/java/org/tensorflow/ndarray/impl/dense/IntDenseNdArrayTest.java create mode 100644 ndarray/src/test/java/org/tensorflow/ndarray/impl/dense/LongDenseNdArrayTest.java create mode 100644 ndarray/src/test/java/org/tensorflow/ndarray/impl/dense/ShortDenseNdArrayTest.java create mode 100644 ndarray/src/test/java/org/tensorflow/ndarray/impl/dense/StringDenseNdArrayTest.java create mode 100644 ndarray/src/test/java/org/tensorflow/ndarray/impl/sequence/ElementSequenceTest.java create mode 100644 ndarray/src/test/resources/COPYRIGHT.txt create mode 100644 ndarray/src/test/resources/castle.jpg diff --git a/ndarray/pom.xml b/ndarray/pom.xml new file mode 100644 index 00000000000..d852f9bcd32 --- /dev/null +++ b/ndarray/pom.xml @@ -0,0 +1,68 @@ + + + 4.0.0 + + + org.tensorflow + tensorflow-java-ndarray + 0.4.0-SNAPSHOT + + ndarray + jar + + Java NdArray Library + + Utility library for N-dimensional data I/O operations in Java. + + + + org.tensorflow.ndarray + + + + + + maven-jar-plugin + 3.2.0 + + + + ${java.module.name} + + + + + + org.apache.maven.plugins + maven-surefire-plugin + 2.22.2 + + 1 + false + -Xmx2G -XX:MaxPermSize=256m + + **/*Test.java + + + + + + + diff --git a/ndarray/src/main/java/org/tensorflow/ndarray/BooleanNdArray.java b/ndarray/src/main/java/org/tensorflow/ndarray/BooleanNdArray.java new file mode 100644 index 00000000000..5b4bedb1c84 --- /dev/null +++ b/ndarray/src/main/java/org/tensorflow/ndarray/BooleanNdArray.java @@ -0,0 +1,108 @@ +/* + Copyright 2019 The TensorFlow Authors. All Rights Reserved. + + Licensed under the Apache License, Version 2.0 (the "License"); + you may not use this file except in compliance with the License. + You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + + Unless required by applicable law or agreed to in writing, software + distributed under the License is distributed on an "AS IS" BASIS, + WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + See the License for the specific language governing permissions and + limitations under the License. + ======================================================================= + */ +package org.tensorflow.ndarray; + +import org.tensorflow.ndarray.buffer.BooleanDataBuffer; +import org.tensorflow.ndarray.buffer.DataBuffer; +import org.tensorflow.ndarray.index.Index; + +/** + * An {@link NdArray} of booleans. + */ +public interface BooleanNdArray extends NdArray { + + /** + * Returns the boolean value of the scalar found at the given coordinates. + * + *

To access the scalar element, the number of coordinates provided must be equal to the number + * of dimensions of this array (i.e. its rank). For example: + * 

{@code
+   *  BooleanNdArray matrix = NdArrays.ofBooleans(shape(2, 2));  // matrix rank = 2
+   *  matrix.getBoolean(0, 1);  // succeeds, returns false
+   *  matrix.getBoolean(0);  // throws IllegalRankException
+   *
+   *  BooleanNdArray scalar = matrix.get(0, 1);  // scalar rank = 0
+   *  scalar.getBoolean();  // succeeds, returns false
+   * }
+ * + * @param coordinates coordinates of the scalar to resolve + * @return value of that scalar + * @throws IndexOutOfBoundsException if some coordinates are outside the limits of their respective dimension + * @throws IllegalRankException if number of coordinates is not sufficient to access a scalar element + */ + boolean getBoolean(long... coordinates); + + /** + * Assigns the boolean value of the scalar found at the given coordinates. + * + *

To access the scalar element, the number of coordinates provided must be equal to the number + * of dimensions of this array (i.e. its rank). For example: + * 

{@code
+   *  BooleanNdArray matrix = NdArrays.ofBooleans(shape(2, 2));  // matrix rank = 2
+   *  matrix.setBoolean(true, 0, 1);  // succeeds
+   *  matrix.setBoolean(true, 0);  // throws IllegalRankException
+   *
+   *  BooleanNdArray scalar = matrix.get(0, 1);  // scalar rank = 0
+   *  scalar.setBoolean(true);  // succeeds
+   * }
+ * + * @param value the value to assign + * @param coordinates coordinates of the scalar to assign + * @return this array + * @throws IndexOutOfBoundsException if some coordinates are outside the limits of their respective dimension + * @throws IllegalRankException if number of coordinates is not sufficient to access a scalar element + */ + BooleanNdArray setBoolean(boolean value, long... coordinates); + + @Override + BooleanNdArray slice(Index... indices); + + @Override + BooleanNdArray get(long... coordinates); + + @Override + BooleanNdArray set(NdArray src, long... coordinates); + + @Override + default Boolean getObject(long... coordinates) { + return getBoolean(coordinates); + } + + @Override + default BooleanNdArray setObject(Boolean value, long... coordinates) { + return setBoolean(value, coordinates); + } + + @Override + NdArraySequence elements(int dimensionIdx); + + @Override + NdArraySequence scalars(); + + @Override + BooleanNdArray copyTo(NdArray dst); + + @Override + BooleanNdArray read(DataBuffer dst); + + BooleanNdArray read(BooleanDataBuffer dst); + + @Override + BooleanNdArray write(DataBuffer src); + + BooleanNdArray write(BooleanDataBuffer src); +} diff --git a/ndarray/src/main/java/org/tensorflow/ndarray/ByteNdArray.java b/ndarray/src/main/java/org/tensorflow/ndarray/ByteNdArray.java new file mode 100644 index 00000000000..0e6f118f5ef --- /dev/null +++ b/ndarray/src/main/java/org/tensorflow/ndarray/ByteNdArray.java @@ -0,0 +1,108 @@ +/* + Copyright 2019 The TensorFlow Authors. All Rights Reserved. + + Licensed under the Apache License, Version 2.0 (the "License"); + you may not use this file except in compliance with the License. + You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + + Unless required by applicable law or agreed to in writing, software + distributed under the License is distributed on an "AS IS" BASIS, + WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + See the License for the specific language governing permissions and + limitations under the License. + ======================================================================= + */ +package org.tensorflow.ndarray; + +import org.tensorflow.ndarray.buffer.ByteDataBuffer; +import org.tensorflow.ndarray.buffer.DataBuffer; +import org.tensorflow.ndarray.index.Index; + +/** + * An {@link NdArray} of bytes. + */ +public interface ByteNdArray extends NdArray { + + /** + * Returns the byte value of the scalar found at the given coordinates. + * + *

To access the scalar element, the number of coordinates provided must be equal to the number + * of dimensions of this array (i.e. its rank). For example: + * 

{@code
+   *  ByteNdArray matrix = NdArrays.ofBytes(shape(2, 2));  // matrix rank = 2
+   *  matrix.getByte(0, 1);  // succeeds, returns 0
+   *  matrix.getByte(0);  // throws IllegalRankException
+   *
+   *  ByteNdArray scalar = matrix.get(0, 1);  // scalar rank = 0
+   *  scalar.getByte();  // succeeds, returns 0
+   * }
+ * + * @param coordinates coordinates of the scalar to resolve + * @return value of that scalar + * @throws IndexOutOfBoundsException if some coordinates are outside the limits of their respective dimension + * @throws IllegalRankException if number of coordinates is not sufficient to access a scalar element + */ + byte getByte(long... coordinates); + + /** + * Assigns the byte value of the scalar found at the given coordinates. + * + *

To access the scalar element, the number of coordinates provided must be equal to the number + * of dimensions of this array (i.e. its rank). For example: + * 

{@code
+   *  ByteNdArray matrix = NdArrays.ofBytes(shape(2, 2));  // matrix rank = 2
+   *  matrix.setByte(10, 0, 1);  // succeeds
+   *  matrix.setByte(10, 0);  // throws IllegalRankException
+   *
+   *  ByteNdArray scalar = matrix.get(0, 1);  // scalar rank = 0
+   *  scalar.setByte(10);  // succeeds
+   * }
+ * + * @param value the value to assign + * @param coordinates coordinates of the scalar to assign + * @return this array + * @throws IndexOutOfBoundsException if some coordinates are outside the limits of their respective dimension + * @throws IllegalRankException if number of coordinates is not sufficient to access a scalar element + */ + ByteNdArray setByte(byte value, long... coordinates); + + @Override + ByteNdArray slice(Index... indices); + + @Override + ByteNdArray get(long... coordinates); + + @Override + ByteNdArray set(NdArray src, long... coordinates); + + @Override + default Byte getObject(long... coordinates) { + return getByte(coordinates); + } + + @Override + default ByteNdArray setObject(Byte value, long... coordinates) { + return setByte(value, coordinates); + } + + @Override + NdArraySequence elements(int dimensionIdx); + + @Override + NdArraySequence scalars(); + + @Override + ByteNdArray copyTo(NdArray dst); + + @Override + ByteNdArray read(DataBuffer dst); + + ByteNdArray read(ByteDataBuffer dst); + + @Override + ByteNdArray write(DataBuffer src); + + ByteNdArray write(ByteDataBuffer src); +} diff --git a/ndarray/src/main/java/org/tensorflow/ndarray/DoubleNdArray.java b/ndarray/src/main/java/org/tensorflow/ndarray/DoubleNdArray.java new file mode 100644 index 00000000000..80e99b01877 --- /dev/null +++ b/ndarray/src/main/java/org/tensorflow/ndarray/DoubleNdArray.java @@ -0,0 +1,108 @@ +/* + Copyright 2019 The TensorFlow Authors. All Rights Reserved. + + Licensed under the Apache License, Version 2.0 (the "License"); + you may not use this file except in compliance with the License. + You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + + Unless required by applicable law or agreed to in writing, software + distributed under the License is distributed on an "AS IS" BASIS, + WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + See the License for the specific language governing permissions and + limitations under the License. + ======================================================================= + */ +package org.tensorflow.ndarray; + +import org.tensorflow.ndarray.buffer.DataBuffer; +import org.tensorflow.ndarray.buffer.DoubleDataBuffer; +import org.tensorflow.ndarray.index.Index; + +/** + * An {@link NdArray} of doubles. + */ +public interface DoubleNdArray extends NdArray { + + /** + * Returns the double value of the scalar found at the given coordinates. + * + *

To access the scalar element, the number of coordinates provided must be equal to the number + * of dimensions of this array (i.e. its rank). For example: + * 

{@code
+   *  DoubleNdArray matrix = NdArrays.ofDoubles(shape(2, 2));  // matrix rank = 2
+   *  matrix.getDouble(0, 1);  // succeeds, returns 0.0
+   *  matrix.getDouble(0);  // throws IllegalRankException
+   *
+   *  DoubleNdArray scalar = matrix.get(0, 1);  // scalar rank = 0
+   *  scalar.getDouble();  // succeeds, returns 0.0
+   * }
+ * + * @param coordinates coordinates of the scalar to resolve + * @return value of that scalar + * @throws IndexOutOfBoundsException if some coordinates are outside the limits of their respective dimension + * @throws IllegalRankException if number of coordinates is not sufficient to access a scalar element + */ + double getDouble(long... coordinates); + + /** + * Assigns the double value of the scalar found at the given coordinates. + * + *

To access the scalar element, the number of coordinates provided must be equal to the number + * of dimensions of this array (i.e. its rank). For example: + * 

{@code
+   *  DoubleNdArray matrix = NdArrays.ofDoubles(shape(2, 2));  // matrix rank = 2
+   *  matrix.setDouble(10.0, 0, 1);  // succeeds
+   *  matrix.setDouble(10.0, 0);  // throws IllegalRankException
+   *
+   *  DoubleNdArray scalar = matrix.get(0, 1);  // scalar rank = 0
+   *  scalar.setDouble(10.0);  // succeeds
+   * }
+ * + * @param value value to assign + * @param coordinates coordinates of the scalar to assign + * @return this array + * @throws IndexOutOfBoundsException if some coordinates are outside the limits of their respective dimension + * @throws IllegalRankException if number of coordinates is not sufficient to access a scalar element + */ + DoubleNdArray setDouble(double value, long... coordinates); + + @Override + DoubleNdArray slice(Index... indices); + + @Override + DoubleNdArray get(long... coordinates); + + @Override + DoubleNdArray set(NdArray src, long... coordinates); + + @Override + default Double getObject(long... coordinates) { + return getDouble(coordinates); + } + + @Override + default DoubleNdArray setObject(Double value, long... coordinates) { + return setDouble(value, coordinates); + } + + @Override + NdArraySequence elements(int dimensionIdx); + + @Override + NdArraySequence scalars(); + + @Override + DoubleNdArray copyTo(NdArray dst); + + @Override + DoubleNdArray read(DataBuffer dst); + + DoubleNdArray read(DoubleDataBuffer dst); + + @Override + DoubleNdArray write(DataBuffer src); + + DoubleNdArray write(DoubleDataBuffer src); +} diff --git a/ndarray/src/main/java/org/tensorflow/ndarray/FloatNdArray.java b/ndarray/src/main/java/org/tensorflow/ndarray/FloatNdArray.java new file mode 100644 index 00000000000..8d4fbf5c1ed --- /dev/null +++ b/ndarray/src/main/java/org/tensorflow/ndarray/FloatNdArray.java @@ -0,0 +1,108 @@ +/* + Copyright 2019 The TensorFlow Authors. All Rights Reserved. + + Licensed under the Apache License, Version 2.0 (the "License"); + you may not use this file except in compliance with the License. + You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + + Unless required by applicable law or agreed to in writing, software + distributed under the License is distributed on an "AS IS" BASIS, + WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + See the License for the specific language governing permissions and + limitations under the License. + ======================================================================= + */ +package org.tensorflow.ndarray; + +import org.tensorflow.ndarray.buffer.DataBuffer; +import org.tensorflow.ndarray.buffer.FloatDataBuffer; +import org.tensorflow.ndarray.index.Index; + +/** + * An {@link NdArray} of floats. + */ +public interface FloatNdArray extends NdArray { + + /** + * Returns the float value of the scalar found at the given coordinates. + * + *

To access the scalar element, the number of coordinates provided must be equal to the number + * of dimensions of this array (i.e. its rank). For example: + * 

{@code
+   *  FloatNdArray matrix = NdArrays.ofFloats(shape(2, 2));  // matrix rank = 2
+   *  matrix.getFloat(0, 1);  // succeeds, returns 0.0f
+   *  matrix.getFloat(0);  // throws IllegalRankException
+   *
+   *  FloatNdArray scalar = matrix.get(0, 1);  // scalar rank = 0
+   *  scalar.getFloat();  // succeeds, returns 0.0f
+   * }
+ * + * @param coordinates coordinates of the scalar to resolve + * @return value of that scalar + * @throws IndexOutOfBoundsException if some coordinates are outside the limits of their respective dimension + * @throws IllegalRankException if number of coordinates is not sufficient to access a scalar element + */ + float getFloat(long... coordinates); + + /** + * Assigns the float value of the scalar found at the given coordinates. + * + *

To access the scalar element, the number of coordinates provided must be equal to the number + * of dimensions of this array (i.e. its rank). For example: + * 

{@code
+   *  FloatNdArray matrix = NdArrays.ofFloats(shape(2, 2));  // matrix rank = 2
+   *  matrix.setFloat(10.0f, 0, 1);  // succeeds
+   *  matrix.setFloat(10.0f, 0);  // throws IllegalRankException
+   *
+   *  FloatNdArray scalar = matrix.get(0, 1);  // scalar rank = 0
+   *  scalar.setFloat(10.0f);  // succeeds
+   * }
+ * + * @param value value to assign + * @param coordinates coordinates of the scalar to assign + * @return this array + * @throws IndexOutOfBoundsException if some coordinates are outside the limits of their respective dimension + * @throws IllegalRankException if number of coordinates is not sufficient to access a scalar element + */ + FloatNdArray setFloat(float value, long... coordinates); + + @Override + FloatNdArray slice(Index... coordinates); + + @Override + FloatNdArray get(long... coordinates); + + @Override + FloatNdArray set(NdArray src, long... coordinates); + + @Override + default Float getObject(long... coordinates) { + return getFloat(coordinates); + } + + @Override + default FloatNdArray setObject(Float value, long... coordinates) { + return setFloat(value, coordinates); + } + + @Override + NdArraySequence elements(int dimensionIdx); + + @Override + NdArraySequence scalars(); + + @Override + FloatNdArray copyTo(NdArray dst); + + @Override + FloatNdArray read(DataBuffer dst); + + FloatNdArray read(FloatDataBuffer dst); + + @Override + FloatNdArray write(DataBuffer src); + + FloatNdArray write(FloatDataBuffer src); +} diff --git a/ndarray/src/main/java/org/tensorflow/ndarray/IllegalRankException.java b/ndarray/src/main/java/org/tensorflow/ndarray/IllegalRankException.java new file mode 100644 index 00000000000..b816d338d7e --- /dev/null +++ b/ndarray/src/main/java/org/tensorflow/ndarray/IllegalRankException.java @@ -0,0 +1,27 @@ +/* + Copyright 2019 The TensorFlow Authors. All Rights Reserved. + + Licensed under the Apache License, Version 2.0 (the "License"); + you may not use this file except in compliance with the License. + You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + + Unless required by applicable law or agreed to in writing, software + distributed under the License is distributed on an "AS IS" BASIS, + WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + See the License for the specific language governing permissions and + limitations under the License. + ======================================================================= + */ +package org.tensorflow.ndarray; + +/** + * Exception thrown when an operation cannot be completed because of the rank of the targeted array. + */ +public class IllegalRankException extends IllegalArgumentException { + + public IllegalRankException(String message) { + super(message); + } +} diff --git a/ndarray/src/main/java/org/tensorflow/ndarray/IntNdArray.java b/ndarray/src/main/java/org/tensorflow/ndarray/IntNdArray.java new file mode 100644 index 00000000000..aa2cc652d69 --- /dev/null +++ b/ndarray/src/main/java/org/tensorflow/ndarray/IntNdArray.java @@ -0,0 +1,108 @@ +/* + Copyright 2019 The TensorFlow Authors. All Rights Reserved. + + Licensed under the Apache License, Version 2.0 (the "License"); + you may not use this file except in compliance with the License. + You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + + Unless required by applicable law or agreed to in writing, software + distributed under the License is distributed on an "AS IS" BASIS, + WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + See the License for the specific language governing permissions and + limitations under the License. + ======================================================================= + */ +package org.tensorflow.ndarray; + +import org.tensorflow.ndarray.buffer.DataBuffer; +import org.tensorflow.ndarray.buffer.IntDataBuffer; +import org.tensorflow.ndarray.index.Index; + +/** + * An {@link NdArray} of integers. + */ +public interface IntNdArray extends NdArray { + + /** + * Returns the integer value of the scalar found at the given coordinates. + * + *

To access the scalar element, the number of coordinates provided must be equal to the number + * of dimensions of this array (i.e. its rank). For example: + * 

{@code
+   *  IntNdArray matrix = NdArrays.ofInts(shape(2, 2));  // matrix rank = 2
+   *  matrix.getInt(0, 1);  // succeeds, returns 0
+   *  matrix.getInt(0);  // throws IllegalRankException
+   *
+   *  IntNdArray scalar = matrix.get(0, 1);  // scalar rank = 0
+   *  scalar.getInt();  // succeeds, returns 0
+   * }
+ * + * @param coordinates coordinates of the scalar to resolve + * @return value of that scalar + * @throws IndexOutOfBoundsException if some coordinates are outside the limits of their respective dimension + * @throws IllegalRankException if number of coordinates is not sufficient to access a scalar element + */ + int getInt(long... coordinates); + + /** + * Assigns the integer value of the scalar found at the given coordinates. + * + *

To access the scalar element, the number of coordinates provided must be equal to the number + * of dimensions of this array (i.e. its rank). For example: + * 

{@code
+   *  IntNdArray matrix = NdArrays.ofInts(shape(2, 2));  // matrix rank = 2
+   *  matrix.setInt(10, 0, 1);  // succeeds
+   *  matrix.setInt(10, 0);  // throws IllegalRankException
+   *
+   *  IntNdArray scalar = matrix.get(0, 1);  // scalar rank = 0
+   *  scalar.setInt(10);  // succeeds
+   * }
+ * + * @param value value to assign + * @param coordinates coordinates of the scalar to assign + * @return this array + * @throws IndexOutOfBoundsException if some coordinates are outside the limits of their respective dimension + * @throws IllegalRankException if number of coordinates is not sufficient to access a scalar element + */ + IntNdArray setInt(int value, long... coordinates); + + @Override + IntNdArray slice(Index... indices); + + @Override + IntNdArray get(long... coordinates); + + @Override + IntNdArray set(NdArray src, long... coordinates); + + @Override + default Integer getObject(long... coordinates) { + return getInt(coordinates); + } + + @Override + default IntNdArray setObject(Integer value, long... coordinates) { + return setInt(value, coordinates); + } + + @Override + NdArraySequence elements(int dimensionIdx); + + @Override + NdArraySequence scalars(); + + @Override + IntNdArray copyTo(NdArray dst); + + @Override + IntNdArray read(DataBuffer dst); + + IntNdArray read(IntDataBuffer dst); + + @Override + IntNdArray write(DataBuffer src); + + IntNdArray write(IntDataBuffer src); +} diff --git a/ndarray/src/main/java/org/tensorflow/ndarray/LongNdArray.java b/ndarray/src/main/java/org/tensorflow/ndarray/LongNdArray.java new file mode 100644 index 00000000000..3e5be6dc7ec --- /dev/null +++ b/ndarray/src/main/java/org/tensorflow/ndarray/LongNdArray.java @@ -0,0 +1,108 @@ +/* + Copyright 2019 The TensorFlow Authors. All Rights Reserved. + + Licensed under the Apache License, Version 2.0 (the "License"); + you may not use this file except in compliance with the License. + You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + + Unless required by applicable law or agreed to in writing, software + distributed under the License is distributed on an "AS IS" BASIS, + WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + See the License for the specific language governing permissions and + limitations under the License. + ======================================================================= + */ +package org.tensorflow.ndarray; + +import org.tensorflow.ndarray.buffer.DataBuffer; +import org.tensorflow.ndarray.buffer.LongDataBuffer; +import org.tensorflow.ndarray.index.Index; + +/** + * An {@link NdArray} of longs. + */ +public interface LongNdArray extends NdArray { + + /** + * Returns the long value of the scalar found at the given coordinates. + * + *

To access the scalar element, the number of coordinates provided must be equal to the number + * of dimensions of this array (i.e. its rank). For example: + * 

{@code
+   *  LongNdArray matrix = NdArrays.ofLongs(shape(2, 2));  // matrix rank = 2
+   *  matrix.getLong(0, 1);  // succeeds, returns 0L
+   *  matrix.getLong(0);  // throws IllegalRankException
+   *
+   *  LongNdArray scalar = matrix.get(0, 1);  // scalar rank = 0
+   *  scalar.getLong();  // succeeds, returns 0L
+   * }
+ * + * @param coordinates coordinates of the scalar to resolve + * @return value of that scalar + * @throws IndexOutOfBoundsException if some coordinates are outside the limits of their respective dimension + * @throws IllegalRankException if number of coordinates is not sufficient to access a scalar element + */ + long getLong(long... coordinates); + + /** + * Assigns the long value of the scalar found at the given coordinates. + * + *

To access the scalar element, the number of coordinates provided must be equal to the number + * of dimensions of this array (i.e. its rank). For example: + * 

{@code
+   *  LongNdArray matrix = NdArrays.ofLongs(shape(2, 2));  // matrix rank = 2
+   *  matrix.setLong(10L, 0, 1);  // succeeds
+   *  matrix.setLong(10L, 0);  // throws IllegalRankException
+   *
+   *  LongNdArray scalar = matrix.get(0, 1);  // scalar rank = 0
+   *  scalar.setLong(10L);  // succeeds
+   * }
+ * + * @param value value to assign + * @param coordinates coordinates of the scalar to assign + * @return this array + * @throws IndexOutOfBoundsException if some coordinates are outside the limits of their respective dimension + * @throws IllegalRankException if number of coordinates is not sufficient to access a scalar element + */ + LongNdArray setLong(long value, long... coordinates); + + @Override + LongNdArray slice(Index... indices); + + @Override + LongNdArray get(long... coordinates); + + @Override + LongNdArray set(NdArray src, long... coordinates); + + @Override + default Long getObject(long... coordinates) { + return getLong(coordinates); + } + + @Override + default LongNdArray setObject(Long value, long... coordinates) { + return setLong(value, coordinates); + } + + @Override + NdArraySequence elements(int dimensionIdx); + + @Override + NdArraySequence scalars(); + + @Override + LongNdArray copyTo(NdArray dst); + + @Override + LongNdArray read(DataBuffer dst); + + LongNdArray read(LongDataBuffer dst); + + @Override + LongNdArray write(DataBuffer src); + + LongNdArray write(LongDataBuffer src); +} diff --git a/ndarray/src/main/java/org/tensorflow/ndarray/NdArray.java b/ndarray/src/main/java/org/tensorflow/ndarray/NdArray.java new file mode 100644 index 00000000000..6686abd9148 --- /dev/null +++ b/ndarray/src/main/java/org/tensorflow/ndarray/NdArray.java @@ -0,0 +1,302 @@ +/* + Copyright 2019 The TensorFlow Authors. All Rights Reserved. + + Licensed under the Apache License, Version 2.0 (the "License"); + you may not use this file except in compliance with the License. + You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + + Unless required by applicable law or agreed to in writing, software + distributed under the License is distributed on an "AS IS" BASIS, + WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + See the License for the specific language governing permissions and + limitations under the License. + ======================================================================= + */ +package org.tensorflow.ndarray; + +import java.util.function.BiConsumer; +import java.util.function.Consumer; +import org.tensorflow.ndarray.buffer.DataBuffer; +import org.tensorflow.ndarray.index.Index; + +/** + * A data structure of N-dimensions. + * + *

The `NdArray` interface creates an abstraction between the physical storage of a data record, + * which can be linear or segmented, and its logical representation. In general, they achieve + * better performances than standard multi-dimensional arrays in Java by mapping directly linear + * data segments in memory. + * + *

Like {@link DataBuffer}, {@code NdArray} instances support 64-bits indexing so they can be + * used to map very large data records. They also support special coordinates that allow traversing + * their values in any direction or to select only a subset of them. + * + *

Example of usage: + * 

{@code
+ *    // Creates a 2x3x2 matrix (of rank 3)
+ *    FloatNdArray matrix3d = NdArrays.ofFloats(shape(2, 3, 2));
+ *
+ *    // Initialize sub-matrices data with vectors
+ *    matrix.set(NdArrays.vectorOf(1.0f, 2.0f), 0, 0)
+ *          .set(NdArrays.vectorOf(3.0f, 4.0f), 0, 1)
+ *          .set(NdArrays.vectorOf(5.0f, 6.0f), 0, 2)
+ *          .set(NdArrays.vectorOf(7.0f, 8.0f), 1, 0)
+ *          .set(NdArrays.vectorOf(9.0f, 10.0f), 1, 1)
+ *          .set(NdArrays.vectorOf(11.0f, 12.0f), 1, 2);
+ *
+ *    // Access the second 3x2 matrix (of rank 2)
+ *    FloatNdArray matrix = matrix3d.get(1);
+ *
+ *    // Access directly the float value at (1, 0) from the second matrix
+ *    assertEquals(9.0f, matrix.getFloat(1, 0));
+ * }
+ * + * @param the type of values to be mapped + */ +public interface NdArray extends Shaped { + + /** + * Returns a sequence of all elements at a given dimension. + * + *

Logically, the N-dimensional array can be flatten in a single vector, where the scalars of + * the {@code (n - 1)}th element precedes those of the {@code (n)}th element, for a total of + * {@link #size()} values. + * + *

For example, given a {@code n x m} matrix on the {@code [x, y]} axes, elements are iterated in + * the following order: + *

x0y0, x0y1, ..., x0ym-1, x1y0, x1y1, ..., xn-1ym-1 + * + *

The returned sequence can then be iterated to visit each elements, either by calling + * {@link NdArraySequence#forEach(Consumer)} or {@link NdArraySequence#forEachIndexed(BiConsumer)}. + * 

{@code
+   *    // Iterate matrix for initializing each of its vectors
+   *    matrixOfFloats.elements(0).forEach(v -> {
+   *      v.set(vector(1.0f, 2.0f, 3.0f));
+   *    });
+   *
+   *    // Iterate a vector for reading each of its scalar
+   *    vectorOfFloats.scalars().forEachIdx((coords, s) -> {
+   *      System.out.println("Value " + s.getFloat() + " found at " + coords);
+   *    });
+   * }
+ * + * @param dimensionIdx index of the dimension + * @return an {@code NdArray} sequence + * @throws IllegalArgumentException if {@code dimensionIdx} is greater or equal to the total + * number of dimensions of this array + */ + NdArraySequence> elements(int dimensionIdx); + + /** + * Returns a sequence of all scalars in this array. + * + *

This is equivalent to call {@code elements(shape().numDimensions() - 1)} + * + * @return an {@code NdArray} sequence + */ + NdArraySequence> scalars(); + + /** + * Creates a multi-dimensional view (or slice) of this array by mapping one or more dimensions + * to the given index selectors. + * + *

Slices allow to traverse an N-dimensional array in any of its axis and/or to filter only + * elements of interest. For example, for a given matrix on the {@code [x, y]} axes, it is + * possible to iterate elements at {@code y=0} for all {@code x}. + * + *

Any changes applied to the returned slice affect the data of this array as well, as there + * is no copy involved. + * + *

Example of usage: + * 

{@code
+   *    FloatNdArray matrix3d = NdArrays.ofFloats(shape(3, 2, 4));  // with [x, y, z] axes
+   *
+   *    // Iterates elements on the x axis by preserving only the 3rd value on the z axis,
+   *    // (i.e. [x, y, 2])
+   *    matrix3d.slice(all(), all(), at(2)).elements(0).forEach(m -> {
+   *      assertEquals(shape(2), m); // y=2, z=0 (scalar)
+   *    });
+   *
+   *    // Creates a slice that contains only the last element of the y axis and elements with an
+   *    // odd `z` coordinate.
+   *    FloatNdArray slice = matrix3d.slice(all(), at(1), odd());
+   *    assertEquals(shape(3, 2), slice.shape());  // x=3, y=0 (scalar), z=2 (odd coordinates)
+   *
+   *    // Iterates backward the elements on the x axis
+   *    matrix3d.slice(flip()).elements(0).forEach(m -> {
+   *      assertEquals(shape(2, 4), m);  // y=2, z=4
+   *    });
+   * }
+ * + * @param indices index selectors per dimensions, starting from dimension 0 of this array. + * @return the element resulting of the index selection + * @throws IndexOutOfBoundsException if some coordinates are outside the limits of their + * respective dimension + */ + NdArray slice(Index... indices); + + /** + * Returns the N-dimensional element of this array at the given coordinates. + * + *

Elements of any of the dimensions of this array can be retrieved. For example, if the number + * of coordinates is equal to the number of dimensions of this array, then a rank-0 (scalar) array + * is returned, which value can then be obtained by calling `array.getObject()`. + * + *

Any changes applied to the returned elements affect the data of this array as well, as there + * is no copy involved. + * + *

Note that invoking this method is an equivalent and more efficient way to slice this array + * on single scalar, i.e. {@code array.get(x, y, z)} is equal to + * {@code array.slice(at(x), at(y), at(z))} + * + * @param coordinates coordinates of the element to access, none will return this array + * @return the element at this index + * @throws IndexOutOfBoundsException if some coordinates are outside the limits of their + * respective dimension + */ + NdArray get(long... coordinates); + + /** + * Assigns the value of the N-dimensional element found at the given coordinates. + * + *

The number of coordinates provided can be anywhere between 0 and rank - 1. For example: + * 

{@code
+   *  FloatNdArray matrix = NdArrays.ofFloats(shape(2, 2));  // matrix rank = 2
+   *  matrix.set(vector(10.0f, 20.0f), 0);  // success
+   *  matrix.set(scalar(10.0f), 1, 0); // success
+   * }
+ * + * @param src an array of the values to assign + * @param coordinates coordinates of the element to assign + * @return this array + * @throws IndexOutOfBoundsException if some coordinates are outside the limits of their + * respective dimension + */ + NdArray set(NdArray src, long... coordinates); + + /** + * Returns the value of the scalar found at the given coordinates. + * + *

To access the scalar element, the number of coordinates provided must be equal to the number + * of dimensions of this array (i.e. its rank). For example: + * 

{@code
+   *  FloatNdArray matrix = NdArrays.ofFloats(shape(2, 2));  // matrix rank = 2
+   *  matrix.getObject(0, 1);  // succeeds, returns 0.0f
+   *  matrix.getObject(0);  // throws IllegalRankException
+   *
+   *  FloatNdArray scalar = matrix.get(0, 1);  // scalar rank = 0
+   *  scalar.getObject();  // succeeds, returns 0.0f
+   * }
+ * + * Note: if this array stores values of a primitive type, prefer the usage of the specialized + * method in the subclass for that type. For example, {@code floatArray.getFloat(0); }. + * + * @param coordinates coordinates of the scalar to resolve + * @return value of that scalar + * @throws IndexOutOfBoundsException if some coordinates are outside the limits of their + * respective dimension + * @throws IllegalRankException if number of coordinates is not sufficient to access a scalar + * element + */ + T getObject(long... coordinates); + + /** + * Assigns the value of the scalar found at the given coordinates. + * + *

To access the scalar element, the number of coordinates provided must be equal to the number + * of dimensions of this array (i.e. its rank). For example: + * 

{@code
+   *  FloatNdArray matrix = NdArrays.ofFloats(shape(2, 2));  // matrix rank = 2
+   *  matrix.setObject(10.0f, 0, 1);  // succeeds
+   *  matrix.setObject(10.0f, 0);  // throws IllegalRankException
+   *
+   *  FloatNdArray scalar = matrix.get(0, 1);  // scalar rank = 0
+   *  scalar.setObject(10.0f);  // succeeds
+   * }
+ * + * Note: if this array stores values of a primitive type, prefer the usage of the specialized + * method in the subclass for that type. For example, {@code floatArray.setFloat(10.0f, 0); } + * + * @param value the value to assign + * @param coordinates coordinates of the scalar to assign + * @return this array + * @throws IndexOutOfBoundsException if some coordinates are outside the limits of their + * respective dimension + * @throws IllegalRankException if number of coordinates is not sufficient to access a scalar + * element + */ + NdArray setObject(T value, long... coordinates); + + /** + * Copy the content of this array to the destination array. + * + *

The {@link #shape()} of the destination array must be equal to the shape of this array, or + * an exception is thrown. After the copy, the content of both arrays can be altered + * independently, without affecting each other. + * + * @param dst array to receive a copy of the content of this array + * @return this array + * @throws IllegalArgumentException if the shape of {@code dst} is not equal to the shape of this + * array + */ + NdArray copyTo(NdArray dst); + + /** + * Read the content of this N-dimensional array into the destination buffer. + * + *

The size of the buffer must be equal or greater to the {@link #size()} of this + * array, or an exception is thrown. After the copy, content of the buffer and of the array can be + * altered independently, without affecting each other. + * + * @param dst the destination buffer + * @return this array + * @throws java.nio.BufferOverflowException if the buffer cannot hold the content of this array + * @see DataBuffer#size() + */ + NdArray read(DataBuffer dst); + + /** + * Write the content of this N-dimensional array from the source buffer. + * + *

The size of the buffer must be equal or greater to the {@link #size()} of this + * array, or an exception is thrown. After the copy, content of the buffer and of the array can be + * altered independently, without affecting each other. + * + * @param src the source buffer + * @return this array + * @throws java.nio.BufferUnderflowException if the buffer has not enough remaining data to write + * into this array + * @see DataBuffer#size() + */ + NdArray write(DataBuffer src); + + /** + * Checks equality between n-dimensional arrays. + * + *

An array is equal to another object if this object is another {@link NdArray} of the + * same shape, type and the elements are equal and in the same order. For example: + * + * 

{@code
+   * IntNdArray array = NdArrays.ofInts(Shape.of(2, 2))
+   *    .set(NdArrays.vectorOf(1, 2), 0)
+   *    .set(NdArrays.vectorOf(3, 4), 1);
+   *
+   * assertEquals(array, StdArrays.ndCopyOf(new int[][] {{1, 2}, {3, 4}}));  // true
+   * assertEquals(array, StdArrays.ndCopyOf(new Integer[][] {{1, 2}, {3, 4}}));  // true, as Integers are equal to ints
+   * assertNotEquals(array, NdArrays.vectorOf(1, 2, 3, 4));  // false, different shapes
+   * assertNotEquals(array, StdArrays.ndCopyOf(new int[][] {{3, 4}, {1, 2}}));  // false, different order
+   * assertNotEquals(array, StdArrays.ndCopyOf(new long[][] {{1L, 2L}, {3L, 4L}}));  // false, different types
+   * }
+ * + *

Note that the computation required to verify equality between two arrays can be expensive + * in some cases and therefore, it is recommended to not use this method in a critical path + * where performances matter. + * + * @param obj object to compare this array with + * @return true if this array is equal to the provided object + */ + @Override + boolean equals(Object obj); +} diff --git a/ndarray/src/main/java/org/tensorflow/ndarray/NdArraySequence.java b/ndarray/src/main/java/org/tensorflow/ndarray/NdArraySequence.java new file mode 100644 index 00000000000..afb930e278b --- /dev/null +++ b/ndarray/src/main/java/org/tensorflow/ndarray/NdArraySequence.java @@ -0,0 +1,67 @@ +/* + * Copyright 2019 The TensorFlow Authors. All Rights Reserved. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + * ======================================================================= + */ + +package org.tensorflow.ndarray; + +import java.util.function.BiConsumer; +import org.tensorflow.ndarray.buffer.DataBufferWindow; + +/** + * A sequence of elements of an N-dimensional array. + * + *

An {@code NdArraySequence} is used to traverse an {@code NdArray} in a given dimension + * and visit each of its elements. For example, given a {@code n x m} matrix on the {@code [x, y]} axes, + * elements are iterated in the following order: + *

x0y0, x0y1, ..., x0ym-1, x1y0, x1y1, ..., xn-1ym-1 + * + * @param data type of the array being iterated + */ +public interface NdArraySequence> extends Iterable { + + /** + * Visit each elements of this iteration and their respective coordinates. + * + *

Important: the consumer method should not keep a reference to the coordinates + * as they might be mutable and reused during the iteration to improve performance. + * + * @param consumer method to invoke for each elements + */ + void forEachIndexed(BiConsumer consumer); + + /** + * Returns each element as a new slice. + * + *

Unlike conventional Java collections, elements of a {@code NdArraySequence} are transient, i.e. new {@code NdArray} + * instances are allocated for each iteration. To improve performance, the same instance can be recycled to view + * all elements of this sequence, using a {@link DataBufferWindow}. + * + *

In some cases though, it might be preferable to disable such optimizations to ensure that each element returned is a + * new slice of the original array. For example, if one or more elements visited must live beyond the scope of the sequence + * iteration, {@code asSlices()} makes sure that all elements returned by the sequence are unique instances. + * + * 

{@code
+   *     final List vectors = new ArrayList<>();
+   *     IntNdArray matrix = NdArrays.ofInts(Shape.of(6, 6));
+   *     ndArray.elements(0).forEach(e -> vectors::add);  // Not safe, as `e` might always be the same recycled instance
+   *     ndArray.elements(0).asSlices().forEach(e -> vectors::add);  // Safe, each `e` is a distinct NdArray instance
+   * }
+ * + * @return a sequence that returns each elements iterated as a new slice + * @see DataBufferWindow + */ + NdArraySequence asSlices(); +} diff --git a/ndarray/src/main/java/org/tensorflow/ndarray/NdArrays.java b/ndarray/src/main/java/org/tensorflow/ndarray/NdArrays.java new file mode 100644 index 00000000000..8ad55cae7ed --- /dev/null +++ b/ndarray/src/main/java/org/tensorflow/ndarray/NdArrays.java @@ -0,0 +1,495 @@ +/* + Copyright 2019 The TensorFlow Authors. All Rights Reserved. + + Licensed under the Apache License, Version 2.0 (the "License"); + you may not use this file except in compliance with the License. + You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + + Unless required by applicable law or agreed to in writing, software + distributed under the License is distributed on an "AS IS" BASIS, + WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + See the License for the specific language governing permissions and + limitations under the License. + ======================================================================= + */ +package org.tensorflow.ndarray; + +import org.tensorflow.ndarray.impl.dense.DenseNdArray; +import org.tensorflow.ndarray.impl.dense.IntDenseNdArray; +import org.tensorflow.ndarray.buffer.BooleanDataBuffer; +import org.tensorflow.ndarray.buffer.ByteDataBuffer; +import org.tensorflow.ndarray.buffer.DataBuffer; +import org.tensorflow.ndarray.buffer.DataBuffers; +import org.tensorflow.ndarray.buffer.DoubleDataBuffer; +import org.tensorflow.ndarray.buffer.FloatDataBuffer; +import org.tensorflow.ndarray.buffer.IntDataBuffer; +import org.tensorflow.ndarray.buffer.LongDataBuffer; +import org.tensorflow.ndarray.buffer.ShortDataBuffer; +import org.tensorflow.ndarray.impl.dense.BooleanDenseNdArray; +import org.tensorflow.ndarray.impl.dense.ByteDenseNdArray; +import org.tensorflow.ndarray.impl.dense.DoubleDenseNdArray; +import org.tensorflow.ndarray.impl.dense.FloatDenseNdArray; +import org.tensorflow.ndarray.impl.dense.LongDenseNdArray; +import org.tensorflow.ndarray.impl.dense.ShortDenseNdArray; + +/** + * Utility class for instantiating {@link NdArray} objects. + */ +public final class NdArrays { + + // BYTE ARRAYS + + /** + * Creates byte scalar (rank 0) initialized with the given value. + * + * @param value scalar value + * @return new byte scalar + */ + public static ByteNdArray scalarOf(byte value) { + return ofBytes(Shape.scalar()).setByte(value); + } + + /** + * Creates a byte vector (rank 1) initialized with the given values. + * + *

Modifying the data of the returned vector will also impact the values in the array + * passed in parameter. + * + * @param values vector values + * @return new byte vector + * @throws IllegalArgumentException if values is null + */ + public static ByteNdArray vectorOf(byte... values) { + if (values == null) { + throw new IllegalArgumentException("Values cannot be null"); + } + return wrap(Shape.of(values.length), DataBuffers.of(values, false, false)); + } + + /** + * Creates an N-dimensional array of bytes of the given shape. + * + *

All values are initialized to zeros. + * + * @param shape shape of the array + * @return new byte N-dimensional array + * @throws IllegalArgumentException if shape is null or has unknown dimensions + */ + public static ByteNdArray ofBytes(Shape shape) { + if (shape == null) { + throw new IllegalArgumentException("Shape cannot be null"); + } + return wrap(shape, DataBuffers.ofBytes(shape.size())); + } + + /** + * Wraps a buffer in a byte N-dimensional array of a given shape. + * + * @param shape shape of the array + * @param buffer buffer to wrap + * @return new byte N-dimensional array + * @throws IllegalArgumentException if shape is null, has unknown dimensions or has size bigger + * in the buffer size + */ + public static ByteNdArray wrap(Shape shape, ByteDataBuffer buffer) { + return ByteDenseNdArray.create(buffer, shape); + } + + // LONG ARRAYS + + /** + * Creates long scalar (rank 0) initialized with the given value. + * + * @param value scalar value + * @return new long scalar + */ + public static LongNdArray scalarOf(long value) { + return ofLongs(Shape.scalar()).setLong(value); + } + + /** + * Creates a long vector (rank 1) initialized with the given values. + * + *

Modifying the data of the returned vector will also impact the values in the array + * passed in parameter. + * + * @param values vector values + * @return new long vector + * @throws IllegalArgumentException if values is null + */ + public static LongNdArray vectorOf(long... values) { + if (values == null) { + throw new IllegalArgumentException(); + } + return wrap(Shape.of(values.length), DataBuffers.of(values, false, false)); + } + + /** + * Creates an N-dimensional array of longs of the given shape. + * + *

All values are initialized to zeros. + * + * @param shape shape of the array + * @return new long N-dimensional array + * @throws IllegalArgumentException if shape is null or has unknown dimensions + */ + public static LongNdArray ofLongs(Shape shape) { + return wrap(shape, DataBuffers.ofLongs(shape.size())); + } + + /** + * Wraps a buffer in a long N-dimensional array of a given shape. + * + * @param shape shape of the array + * @param buffer buffer to wrap + * @return new long N-dimensional array + * @throws IllegalArgumentException if shape is null, has unknown dimensions or has size bigger + * in the buffer size + */ + public static LongNdArray wrap(Shape shape, LongDataBuffer buffer) { + return LongDenseNdArray.create(buffer, shape); + } + + // INT ARRAYS + + /** + * Creates long scalar (rank 0) initialized with the given value. + * + * @param value scalar value + * @return new long scalar + */ + public static IntNdArray scalarOf(int value) { + return ofInts(Shape.scalar()).setInt(value); + } + + /** + * Creates a int vector (rank 1) initialized with the given values. + * + *

Modifying the data of the returned vector will also impact the values in the array + * passed in parameter. + * + * @param values vector values + * @return new int vector + * @throws IllegalArgumentException if values is null + */ + public static IntNdArray vectorOf(int... values) { + if (values == null) { + throw new IllegalArgumentException(); + } + return wrap(Shape.of(values.length), DataBuffers.of(values, false, false)); + } + + /** + * Creates an N-dimensional array of ints of the given shape. + * + *

All values are initialized to zeros. + * + * @param shape shape of the array + * @return new int N-dimensional array + * @throws IllegalArgumentException if shape is null or has unknown dimensions + */ + public static IntNdArray ofInts(Shape shape) { + return wrap(shape, DataBuffers.ofInts(shape.size())); + } + + /** + * Wraps a buffer in an int N-dimensional array of a given shape. + * + * @param shape shape of the array + * @param buffer buffer to wrap + * @return new int N-dimensional array + * @throws IllegalArgumentException if shape is null, has unknown dimensions or has size bigger + * in the buffer size + */ + public static IntNdArray wrap(Shape shape, IntDataBuffer buffer) { + return IntDenseNdArray.create(buffer, shape); + } + + // SHORT ARRAYS + + /** + * Creates short scalar (rank 0) initialized with the given value. + * + * @param value scalar value + * @return new short scalar + */ + public static ShortNdArray scalarOf(short value) { + return ofShorts(Shape.scalar()).setShort(value); + } + + /** + * Creates a short vector (rank 1) initialized with the given values. + * + *

Modifying the data of the returned vector will also impact the values in the array + * passed in parameter. + * + * @param values vector values + * @return new short vector + * @throws IllegalArgumentException if values is null + */ + public static ShortNdArray vectorOf(short... values) { + if (values == null) { + throw new IllegalArgumentException(); + } + return wrap(Shape.of(values.length), DataBuffers.of(values, false, false)); + } + + /** + * Creates an N-dimensional array of shorts of the given shape. + * + *

All values are initialized to zeros. + * + * @param shape shape of the array + * @return new short N-dimensional array + * @throws IllegalArgumentException if shape is null or has unknown dimensions + */ + public static ShortNdArray ofShorts(Shape shape) { + return wrap(shape, DataBuffers.ofShorts(shape.size())); + } + + /** + * Wraps a buffer in a short N-dimensional array of a given shape. + * + * @param shape shape of the array + * @param buffer buffer to wrap + * @return new short N-dimensional array + * @throws IllegalArgumentException if shape is null, has unknown dimensions or has size bigger + * in the buffer size + */ + public static ShortNdArray wrap(Shape shape, ShortDataBuffer buffer) { + return ShortDenseNdArray.create(buffer, shape); + } + + // FLOAT ARRAYS + + /** + * Creates float scalar (rank 0) initialized with the given value. + * + * @param value scalar value + * @return new float scalar + */ + public static FloatNdArray scalarOf(float value) { + return ofFloats(Shape.scalar()).setFloat(value); + } + + /** + * Creates a float vector (rank 1) initialized with the given values. + * + *

Modifying the data of the returned vector will also impact the values in the array + * passed in parameter. + * + * @param values vector values + * @return new float vector + * @throws IllegalArgumentException if values is null + */ + public static FloatNdArray vectorOf(float... values) { + if (values == null) { + throw new IllegalArgumentException(); + } + return wrap(Shape.of(values.length), DataBuffers.of(values, false, false)); + } + + /** + * Creates an N-dimensional array of floats of the given shape. + * + *

All values are initialized to zeros. + * + * @param shape shape of the array + * @return new float N-dimensional array + * @throws IllegalArgumentException if shape is null or has unknown dimensions + */ + public static FloatNdArray ofFloats(Shape shape) { + return wrap(shape, DataBuffers.ofFloats(shape.size())); + } + + /** + * Wraps a buffer in a float N-dimensional array of a given shape. + * + * @param shape shape of the array + * @param buffer buffer to wrap + * @return new float N-dimensional array + * @throws IllegalArgumentException if shape is null, has unknown dimensions or has size bigger + * in the buffer size + */ + public static FloatNdArray wrap(Shape shape, FloatDataBuffer buffer) { + return FloatDenseNdArray.create(buffer, shape); + } + + // DOUBLE ARRAYS + + /** + * Creates double scalar (rank 0) initialized with the given value. + * + * @param value scalar value + * @return new double scalar + */ + public static DoubleNdArray scalarOf(double value) { + return ofDoubles(Shape.scalar()).setDouble(value); + } + + /** + * Creates a double vector (rank 1) initialized with the given values. + * + *

Modifying the data of the returned vector will also impact the values in the array + * passed in parameter. + * + * @param values vector values + * @return new double vector + * @throws IllegalArgumentException if values is null + */ + public static DoubleNdArray vectorOf(double... values) { + if (values == null) { + throw new IllegalArgumentException(); + } + return wrap(Shape.of(values.length), DataBuffers.of(values, false, false)); + } + + /** + * Creates an N-dimensional array of doubles of the given shape. + * + *

All values are initialized to zeros. + * + * @param shape shape of the array + * @return new double N-dimensional array + * @throws IllegalArgumentException if shape is null or has unknown dimensions + */ + public static DoubleNdArray ofDoubles(Shape shape) { + return wrap(shape, DataBuffers.ofDoubles(shape.size())); + } + + /** + * Wraps a buffer in a double N-dimensional array of a given shape. + * + * @param shape shape of the array + * @param buffer buffer to wrap + * @return new double N-dimensional array + * @throws IllegalArgumentException if shape is null, has unknown dimensions or has size bigger + * in the buffer size + */ + public static DoubleNdArray wrap(Shape shape, DoubleDataBuffer buffer) { + return DoubleDenseNdArray.create(buffer, shape); + } + + // BOOLEAN ARRAYS + + /** + * Creates boolean scalar (rank 0) initialized with the given value. + * + * @param value scalar value + * @return new boolean scalar + */ + public static BooleanNdArray scalarOf(boolean value) { + return ofBooleans(Shape.scalar()).setBoolean(value); + } + + /** + * Creates a boolean vector (rank 1) initialized with the given values. + * + *

Modifying the data of the returned vector will also impact the values in the array + * passed in parameter. + * + * @param values vector values + * @return new boolean vector + * @throws IllegalArgumentException if values is null + */ + public static BooleanNdArray vectorOf(boolean... values) { + if (values == null) { + throw new IllegalArgumentException(); + } + return wrap(Shape.of(values.length), DataBuffers.of(values, false, false)); + } + + /** + * Creates an N-dimensional array of booleans of the given shape. + * + *

All values are initialized to zeros. + * + * @param shape shape of the array + * @return new boolean N-dimensional array + * @throws IllegalArgumentException if shape is null or has unknown dimensions + */ + public static BooleanNdArray ofBooleans(Shape shape) { + return wrap(shape, DataBuffers.ofBooleans(shape.size())); + } + + /** + * Wraps a buffer in a boolean N-dimensional array of a given shape. + * + * @param shape shape of the array + * @param buffer buffer to wrap + * @return new boolean N-dimensional array + * @throws IllegalArgumentException if shape is null, has unknown dimensions or has size bigger + * in the buffer size + */ + public static BooleanNdArray wrap(Shape shape, BooleanDataBuffer buffer) { + return BooleanDenseNdArray.create(buffer, shape); + } + + // OBJECT ARRAYS + + /** + * Creates scalar (rank 0) initialized with the given value. + * + * @param value scalar value + * @param the data type + * @return new scalar + */ + @SuppressWarnings("unchecked") + public static NdArray scalarOfObject(T value) { + if (value == null) { + throw new IllegalArgumentException(); + } + return ofObjects((Class)value.getClass(), Shape.scalar()).setObject(value); + } + + /** + * Creates a vector (rank 1) initialized with the given values. + * + *

Modifying the data of the returned vector will also impact the values in the array + * passed in parameter. + * + * @param values vector values + * @param the data type + * @return new vector + * @throws IllegalArgumentException if values is null + */ + @SafeVarargs + public static NdArray vectorOfObjects(T... values) { + if (values == null || values.length == 0) { + throw new IllegalArgumentException("Null or zero length input supplied to vectorOfObjects."); + } + return wrap(Shape.of(values.length), DataBuffers.of(values, false, false)); + } + + /** + * Creates an N-dimensional array of the given shape. + * + *

All values are initialized to zeros. + * + * @param clazz class of the data to be stored in this array + * @param shape shape of the array + * @param the data type + * @return new N-dimensional array + * @throws IllegalArgumentException if shape is null or has unknown dimensions + */ + public static NdArray ofObjects(Class clazz, Shape shape) { + return wrap(shape, DataBuffers.ofObjects(clazz, shape.size())); + } + + /** + * Wraps a buffer in an N-dimensional array of a given shape. + * + * @param shape shape of the array + * @param buffer buffer to wrap + * @param the data type + * @return new N-dimensional array + * @throws IllegalArgumentException if shape is null, has unknown dimensions or has size bigger + * in the buffer size + */ + public static NdArray wrap(Shape shape, DataBuffer buffer) { + return DenseNdArray.wrap(buffer, shape); + } +} + diff --git a/ndarray/src/main/java/org/tensorflow/ndarray/Shape.java b/ndarray/src/main/java/org/tensorflow/ndarray/Shape.java new file mode 100644 index 00000000000..85a905408c7 --- /dev/null +++ b/ndarray/src/main/java/org/tensorflow/ndarray/Shape.java @@ -0,0 +1,447 @@ +/* +Copyright 2019 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +======================================================================= +*/ + +package org.tensorflow.ndarray; + +import java.util.Arrays; + +/** + * The shape of a Tensor or {@link NdArray}. + * + *

A {@code Shape} defines sizes along its axes. It may contain an unknown size for one of the + * axes or may be totally unknown, in which case not even the number of axes is known. If the size + * of an axis is unknown, {@link Shape#UNKNOWN_SIZE} should be used as its size. + */ +public final class Shape { + + /** The size of an unknown axis or the total unknown size for an unknown Shape. */ + public static long UNKNOWN_SIZE = -1L; + + /** + * Creates a Shape representing an unknown number of dimensions. + * + * @return A Shape for which {@link Shape#isUnknown()} is true, never null. + */ + public static Shape unknown() { + return new Shape(null); + } + + /** + * Creates a Shape representing a scalar value. + * + * @return A Shape without dimensions for which {@link Shape#isScalar()} is true, never null. + */ + public static Shape scalar() { + return new Shape(new long[0]); + } + + /** + * Create a Shape representing a scalar or an N-dimensional value. + * + *

Creates a Shape representing a scalar or an N-dimensional value (N being at least 1), with + * the provided size for each dimension. A -1 indicates that the size of the corresponding + * dimension is unknown. If no sizes are provided, a Shape representing a scalar is created. For + * example: + * + * 

{@code
+   * // A 2-element vector.
+   * Shape vector = Shape.of(2);
+   *
+   * // A 2x3 matrix.
+   * Shape matrix = Shape.of(2, 3);
+   *
+   * // A matrix with 4 columns but an unknown number of rows.
+   * // This is typically used to indicate the shape of tensors that represent
+   * // a variable-sized batch of values. The Shape below might represent a
+   * // variable-sized batch of 4-element vectors.
+   * Shape batch = Shape.of(-1, 4);
+   *
+   * // A scalar. For readability, you should prefer calling Shape.scalar()
+   * Shape scalar = Shape.of()
+   * }
+ * + * @param dimensionSizes number of elements in each dimension of this shape, if any, or + * {@link Shape#UNKNOWN_SIZE} if unknown. + * @return a new shape + */ + public static Shape of(long... dimensionSizes) { + if (dimensionSizes == null || dimensionSizes.length == 0) { + return scalar(); + } + return new Shape(dimensionSizes); + } + + /** + * Returns the total number of elements a Tensor with this Shape would have. + * + *

If {@link Shape#isUnknown()} is true or {@link Shape#hasUnknownDimension()} is true, {@link + * Shape#UNKNOWN_SIZE} is returned. + * + * @return The total number of elements a Tensor with this shape would have if it can be + * calculated, else {@link Shape#UNKNOWN_SIZE}. + */ + public long size() { + if (size == null) { + size = computeSize(dimensionSizes); + } + return size; + } + + /** + * The size of the dimension with the given index. + * + *

If {@link Shape#isUnknown()} is true or the size of the dimension with the given index has + * an unknown size, {@link Shape#UNKNOWN_SIZE} is returned. + * + * @param i the index of the dimension to get the size for. If this Shape has a known number of + * dimensions, it must be < {@link Shape#numDimensions()}. The index may be negative, in which + * case the position is counted from the end of the shape. E.g.: {@code size(-1)} returns the + * size of the last dimension, {@code size(-2)} the size of the second to last dimension etc. + * @return The size of the dimension with the given index if known, {@link Shape#UNKNOWN_SIZE} + * otherwise. + */ + public long size(int i) { + if (dimensionSizes == null) { + return UNKNOWN_SIZE; + } else if (i >= 0) { + return dimensionSizes[i]; + } else { + return dimensionSizes[dimensionSizes.length + i]; + } + } + + /** + * Returns the number of dimensions of this Shape. -1 if unknown, 0 for a scalar, 1 for a vector, + * 2 for a matrix etc. + */ + public int numDimensions() { + return dimensionSizes != null ? dimensionSizes.length : -1; + } + + /** Returns whether one or more dimensions of this Shape have an unknown size. */ + public boolean hasUnknownDimension() { + if (dimensionSizes == null) { + return true; + } + for (long dimSize : dimensionSizes) { + if (dimSize == UNKNOWN_SIZE) { + return true; + } + } + return false; + } + + /** Returns whether this Shape represents a scalar. */ + public boolean isScalar() { + return dimensionSizes != null && dimensionSizes.length == 0; + } + + /** Returns whether this Shape is the shape of a vector. */ + public boolean isVector() { + return dimensionSizes != null && dimensionSizes.length == 1; + } + + /** Returns whether this Shape is the shape of a matrix */ + public boolean isMatrix() { + return dimensionSizes != null && dimensionSizes.length == 2; + } + + /** Returns whether the number of dimensions of this Shape is unknown. */ + public boolean isUnknown() { + return dimensionSizes == null; + } + + /** + * Returns a defensive copy of the this Shape's axes. Changes to the returned array to not change + * this Shape's state. Returns null if {@link Shape#isUnknown()} is true. + */ + public long[] asArray() { + if (this.dimensionSizes == null) { + return null; + } else { + return Arrays.copyOf(dimensionSizes, dimensionSizes.length); + } + } + + @Override + public int hashCode() { + return dimensionSizes != null ? Arrays.hashCode(dimensionSizes) : super.hashCode(); + } + + /** + * Equals implementation for Shapes. Two Shapes are considered equal iff: + * + *

+ *

    + *
  • the number of dimensions is defined and equal for both + *
  • the size of each dimension is defined and equal for both + *
+ * + *

If either Shape has unknown dimensions (even if they are the same in both) or if either + * shape has an unknown number of dimensions (even if both return {@code true} for {@link + * Shape#isUnknown()}), they are not considered equal! However, a shape will always equal itself, + * even if it is unknown or contains unknown dimensions. + */ + @Override + public boolean equals(Object obj) { + if (this == obj) { + return true; + } + // Shapes are equivalent if all of their dimensions are equals + if (obj instanceof Shape) { + Shape otherShape = (Shape) obj; + if (otherShape.hasUnknownDimension()) { + return false; + } + return Arrays.equals(dimensionSizes, otherShape.dimensionSizes); + } + return false; + } + + /** Succinct description of the Shape meant for debugging. */ + @Override + public String toString() { + return Arrays.toString(dimensionSizes); + } + + private Shape(long[] dimensionSizes) { + this.dimensionSizes = dimensionSizes; + } + + private final long[] dimensionSizes; + private Long size; + + /** + * Returns a 1-dimensional Shape with first dimension matching the first dimension of this Shape. + */ + public Shape head() { + return take(1); + } + + /** + * Returns an n-dimensional Shape with the dimensions matching the first n dimensions of this + * shape + * + * @param n the number of leading dimensions to get, must be <= than {@link Shape#numDimensions()} + * @return an n-dimensional Shape with the first n dimensions matching the first n dimensions of + * this Shape + */ + public Shape take(int n) { + if (n > numDimensions()) { + throw new ArrayIndexOutOfBoundsException( + "Cannot take " + n + " dimensions, shape has only " + numDimensions() + "."); + } + long[] newDimensions = new long[n]; + System.arraycopy(dimensionSizes, 0, newDimensions, 0, n); + return Shape.of(newDimensions); + } + + /** Returns a new Shape, with this Shape's first dimension removed. */ + public Shape tail() { + if (dimensionSizes.length < 2) return Shape.of(); + return Shape.of(Arrays.copyOfRange(dimensionSizes, 1, dimensionSizes.length)); + } + + /** + * Returns an n-dimensional Shape with the dimensions matching the last n dimensions of this + * Shape. + * + * @param n the number of trailing dimensions to get, must be <= than {@link + * Shape#numDimensions()} + * @return an n-dimensional shape with the dimensions matching the last n dimensions of this + * Shape, never null + */ + public Shape takeLast(int n) { + if (n > numDimensions()) { + throw new ArrayIndexOutOfBoundsException( + "Cannot take last " + n + " dimensions, shape has only " + numDimensions() + "."); + } + long[] newDimensions = new long[n]; + System.arraycopy(dimensionSizes, numDimensions() - n, newDimensions, 0, n); + return Shape.of(newDimensions); + } + + /** + * Return a {@code end - begin} dimensional shape with dimensions matching this Shape from {@code begin} to {@code end}. + * @param begin Where to start the sub-shape. + * @param end Where to end the sub-shape, exclusive. + * @return the sub-shape bounded by begin and end. + */ + public Shape subShape(int begin, int end){ + if (end > numDimensions()) { + throw new ArrayIndexOutOfBoundsException( + "End index " + end + " out of bounds: shape only has " + numDimensions() + " dimensions."); + } + if (begin < 0) { + throw new ArrayIndexOutOfBoundsException( + "Begin index " + begin + " out of bounds: cannot be less than 0."); + } + + long[] newDimensions = new long[end - begin]; + System.arraycopy(dimensionSizes, begin, newDimensions, 0, end - begin); + return Shape.of(newDimensions); + } + + /** + * Returns a new Shape, with a new first dimension added. In order for this call to succeed, + * {@link Shape#isUnknown()} must be {@code false}. + * + * @param firstDimension the dimension to prepend + * @return a new shape with the given dimension first, followed by this Shape's dimensions, never + * null + */ + public Shape prepend(long firstDimension) { + long[] newDimensions = new long[dimensionSizes.length + 1]; + newDimensions[0] = firstDimension; + System.arraycopy(dimensionSizes, 0, newDimensions, 1, dimensionSizes.length); + + return Shape.of(newDimensions); + } + + /** + * Returns a new Shape, with a new last dimension added. In order for this call to succeed, {@link + * Shape#isUnknown()} must be {@code false}. + * + * @param lastDimension the dimension to append + * @return a new Shape with this Shape's dimensions followed by the given dimension, never null + */ + public Shape append(long lastDimension) { + long[] newDimensions = new long[dimensionSizes.length + 1]; + newDimensions[newDimensions.length - 1] = lastDimension; + System.arraycopy(dimensionSizes, 0, newDimensions, 0, dimensionSizes.length); + + return Shape.of(newDimensions); + } + + /** + * Returns a new Shape, with another Shape's dimensions prepended. For both this Shape and the + * other Shape, {@link Shape#isUnknown()} must return false. E.g. {@code + * Shape.of(3,4).prepend(Shape.of(1,2)) => Shape.of(1,2,3,4) } + * + * @param other another Shape, must not be {@code null}, must not be unknown + * @return A new Shape consisting of the given Shape's dimensions followed by this Shape's + * dimensions, never null + */ + public Shape prepend(Shape other) { + long[] newDimensions = new long[other.dimensionSizes.length + dimensionSizes.length]; + System.arraycopy(other.dimensionSizes, 0, newDimensions, 0, other.dimensionSizes.length); + System.arraycopy( + dimensionSizes, 0, newDimensions, other.dimensionSizes.length, dimensionSizes.length); + return Shape.of(newDimensions); + } + + /** + * Returns a new Shape, with another Shapes' dimensions appended. For both this Shape and the + * other Shape, {@link Shape#isUnknown()} must return false. E.g. @code + * Shape.of(3,4).append(Shape.of(1,2)) => Shape.of(3,4,1,2) } + * + * @param other another Shape, must not be {@code null}, must not be unknown + * @return A new Shape consisting of this Shape's dimensions followed by the given Shape's + * dimensions + */ + public Shape append(Shape other) { + long[] newDimensions = new long[dimensionSizes.length + other.dimensionSizes.length]; + System.arraycopy(dimensionSizes, 0, newDimensions, 0, dimensionSizes.length); + System.arraycopy( + other.dimensionSizes, 0, newDimensions, dimensionSizes.length, other.dimensionSizes.length); + return Shape.of(newDimensions); + } + + private static long computeSize(long[] dimensionSizes) { + if (dimensionSizes == null) { + return UNKNOWN_SIZE; + } + long computedSize = 1L; + for (long dimensionSize : dimensionSizes) { + if (dimensionSize == UNKNOWN_SIZE) { + return UNKNOWN_SIZE; + } + computedSize *= dimensionSize; + } + return computedSize; + } + + /** + * Determines whether another shape is compatible with this one. + * + *

+ * + *

Two possibly-partially-defined shapes are compatible if there exists a fully-defined shape + * that both shapes can represent. Thus, compatibility allows the shape inference code to reason + * about partially-defined shapes. For example: + * + *

    + *
  • Shape.unknown() is compatible with all shapes. + *
  • Shape(UNKNOWN_SIZE, UNKNOWN_SIZE) is compatible with all two-dimensional + * shapes, such as Shape(32, 784), and also Shape.unknown(). It is + * not compatible with, for example, Shape(UNKNOWN_SIZE) or + * Shape(UNKNOWN_SIZE, UNKNOWN_SIZE, UNKNOWN_SIZE). + *
  • Shape(32, UNKNOWN_SIZE) is compatible with all two-dimensional shapes with + * size 32 in the 0th dimension, and also Shape(UNKNOWN_SIZE, UNKNOWN_SIZE) and + * Shape.unknown(). It is not compatible with, for example, Shape(32) + * , Shape(32, UNKNOWN_SIZE, 1) or Shape(64, UNKNOWN_SIZE). + *
  • Shape(32, 784) is compatible with itself, and also + * Shape(32, UNKNOWN_SIZE), Shape(UNKNOWN_SIZE, 784), + * Shape(UNKNOWN_SIZE, UNKNOWN_SIZE) and Shape.unknown(). It is not + * compatible with, for example, Shape(32, 1, 784) or Shape(UNKNOWN_SIZE) + * . + *
+ * + *

The compatibility relation is reflexive and symmetric, but not transitive. For example, + * Shape(32, 784) is compatible with Shape.unknown(), and + * Shape.unknown() is compatible with Shape(4, 4), but Shape(32, 784) + * is not compatible with Shape(4, 4). + * + *

Compatibility is not the same as broadcasting. Compatible shapes must have the same number + * of dimensions and for each dimension pair, one dimension has to equal the other dimensions or + * at least one of the dimensions in the pair has to be UNKNOWN_SIZE. + * + *

Broadcasting allows different dimensions, but paired dimensions have to either be equal, or + * one dimension must be 1. If one shape has less dimensions than another shape, the smaller shape + * is "stretched" with dimensions of 1. + * + * @param shape The other shape + * @return true, if the two shapes are compatible. + */ + public boolean isCompatibleWith(Shape shape) { + if (!this.isUnknown() && !shape.isUnknown()) { + if (numDimensions() != shape.numDimensions()) { + return false; + } + for (int i = 0; i < numDimensions(); i++) { + if (!isCompatible(size(i), shape.size(i))) { + return false; + } + } + } + return true; + } + + /** + * Test to see if two shape dimensions are compatible. + * + *

The dimensions are compatible if either dimension is Shape.UNKNOWN_SIZE or both + * dimensions are equal + * + * @param dim the first dimension + * @param otherDim the second dimension + * @return true, if both dimensions are compatible + */ + public static boolean isCompatible(long dim, long otherDim) { + return dim == Shape.UNKNOWN_SIZE || otherDim == Shape.UNKNOWN_SIZE || dim == otherDim; + } +} diff --git a/ndarray/src/main/java/org/tensorflow/ndarray/Shaped.java b/ndarray/src/main/java/org/tensorflow/ndarray/Shaped.java new file mode 100644 index 00000000000..fbe19d75623 --- /dev/null +++ b/ndarray/src/main/java/org/tensorflow/ndarray/Shaped.java @@ -0,0 +1,51 @@ +/* + Copyright 2020 The TensorFlow Authors. All Rights Reserved. + + Licensed under the Apache License, Version 2.0 (the "License"); + you may not use this file except in compliance with the License. + You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + + Unless required by applicable law or agreed to in writing, software + distributed under the License is distributed on an "AS IS" BASIS, + WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + See the License for the specific language governing permissions and + limitations under the License. + ======================================================================= + */ +package org.tensorflow.ndarray; + +import java.util.function.BiConsumer; +import java.util.function.Consumer; +import org.tensorflow.ndarray.buffer.DataBuffer; +import org.tensorflow.ndarray.index.Index; + +/** + * Any data container with a given {@link Shape}. + */ +public interface Shaped { + + /** + * @return the shape of this container + */ + Shape shape(); + + /** + * @return the rank of this container + */ + default int rank() { + return shape().numDimensions(); + } + + /** + * Computes and returns the total size of this container, in number of values. + * + *

For example, given a 3x3x2 matrix, the return value will be 18. + * + * @return number of values in this element + */ + default long size() { + return shape().size(); + } +} diff --git a/ndarray/src/main/java/org/tensorflow/ndarray/ShortNdArray.java b/ndarray/src/main/java/org/tensorflow/ndarray/ShortNdArray.java new file mode 100644 index 00000000000..f9335b4d5d2 --- /dev/null +++ b/ndarray/src/main/java/org/tensorflow/ndarray/ShortNdArray.java @@ -0,0 +1,108 @@ +/* + Copyright 2019 The TensorFlow Authors. All Rights Reserved. + + Licensed under the Apache License, Version 2.0 (the "License"); + you may not use this file except in compliance with the License. + You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + + Unless required by applicable law or agreed to in writing, software + distributed under the License is distributed on an "AS IS" BASIS, + WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + See the License for the specific language governing permissions and + limitations under the License. + ======================================================================= + */ +package org.tensorflow.ndarray; + +import org.tensorflow.ndarray.buffer.DataBuffer; +import org.tensorflow.ndarray.buffer.ShortDataBuffer; +import org.tensorflow.ndarray.index.Index; + +/** + * An {@link NdArray} of shorts. + */ +public interface ShortNdArray extends NdArray { + + /** + * Returns the short value of the scalar found at the given coordinates. + * + *

To access the scalar element, the number of coordinates provided must be equal to the number + * of dimensions of this array (i.e. its rank). For example: + * 

{@code
+   *  ShortNdArray matrix = NdArrays.ofShorts(shape(2, 2));  // matrix rank = 2
+   *  matrix.getShort(0, 1);  // succeeds, returns 0.0f
+   *  matrix.getShort(0);  // throws IllegalRankException
+   *
+   *  ShortNdArray scalar = matrix.get(0, 1);  // scalar rank = 0
+   *  scalar.getShort();  // succeeds, returns 0.0f
+   * }
+ * + * @param coordinates coordinates of the scalar to resolve + * @return value of that scalar + * @throws IndexOutOfBoundsException if some coordinates are outside the limits of their respective dimension + * @throws IllegalRankException if number of coordinates is not sufficient to access a scalar element + */ + short getShort(long... coordinates); + + /** + * Assigns the short value of the scalar found at the given coordinates. + * + *

To access the scalar element, the number of coordinates provided must be equal to the number + * of dimensions of this array (i.e. its rank). For example: + * 

{@code
+   *  ShortNdArray matrix = NdArrays.ofShorts(shape(2, 2));  // matrix rank = 2
+   *  matrix.setShort(10.0f, 0, 1);  // succeeds
+   *  matrix.setShort(10.0f, 0);  // throws IllegalRankException
+   *
+   *  ShortNdArray scalar = matrix.get(0, 1);  // scalar rank = 0
+   *  scalar.setShort(10.0f);  // succeeds
+   * }
+ * + * @param value value to assign + * @param coordinates coordinates of the scalar to assign + * @return this array + * @throws IndexOutOfBoundsException if some coordinates are outside the limits of their respective dimension + * @throws IllegalRankException if number of coordinates is not sufficient to access a scalar element + */ + ShortNdArray setShort(short value, long... coordinates); + + @Override + ShortNdArray slice(Index... coordinates); + + @Override + ShortNdArray get(long... coordinates); + + @Override + ShortNdArray set(NdArray src, long... coordinates); + + @Override + default Short getObject(long... coordinates) { + return getShort(coordinates); + } + + @Override + default ShortNdArray setObject(Short value, long... coordinates) { + return setShort(value, coordinates); + } + + @Override + NdArraySequence elements(int dimensionIdx); + + @Override + NdArraySequence scalars(); + + @Override + ShortNdArray copyTo(NdArray dst); + + @Override + ShortNdArray read(DataBuffer dst); + + ShortNdArray read(ShortDataBuffer dst); + + @Override + ShortNdArray write(DataBuffer src); + + ShortNdArray write(ShortDataBuffer src); +} diff --git a/ndarray/src/main/java/org/tensorflow/ndarray/StdArrays.java b/ndarray/src/main/java/org/tensorflow/ndarray/StdArrays.java new file mode 100644 index 00000000000..7d847bd1a9c --- /dev/null +++ b/ndarray/src/main/java/org/tensorflow/ndarray/StdArrays.java @@ -0,0 +1,3809 @@ +package org.tensorflow.ndarray; + +import static org.tensorflow.ndarray.NdArrays.vectorOf; + +import java.lang.reflect.Array; +import org.tensorflow.ndarray.buffer.DataBuffers; + +/** + * Utility class for working with {@link NdArray} instances mixed with standard Java arrays. + */ +public final class StdArrays { + + /** + * Copy an array of ints in a new {@link IntNdArray} + * + * @param array source array + * @return the {@code IntNdArray} copy + */ + public static IntNdArray ndCopyOf(int[] array) { + IntNdArray ndArray = NdArrays.ofInts(shapeOf(array)); + copyTo(array, ndArray); + return ndArray; + } + + /** + * Copy a 2-dimensional array of ints in a new {@link IntNdArray} + * + * @param array source array + * @return the {@code IntNdArray} copy + */ + public static IntNdArray ndCopyOf(int[][] array) { + IntNdArray ndArray = NdArrays.ofInts(shapeOf(array)); + copyTo(array, ndArray); + return ndArray; + } + + /** + * Copy a 3-dimensional array of ints in a new {@link IntNdArray} + * + * @param array source array + * @return the {@code IntNdArray} copy + */ + public static IntNdArray ndCopyOf(int[][][] array) { + IntNdArray ndArray = NdArrays.ofInts(shapeOf(array)); + copyTo(array, ndArray); + return ndArray; + } + + /** + * Copy a 4-dimensional array of ints in a new {@link IntNdArray} + * + * @param array source array + * @return the {@code IntNdArray} copy + */ + public static IntNdArray ndCopyOf(int[][][][] array) { + IntNdArray ndArray = NdArrays.ofInts(shapeOf(array)); + copyTo(array, ndArray); + return ndArray; + } + + /** + * Copy a 5-dimensional array of ints in a new {@link IntNdArray} + * + * @param array source array + * @return the {@code IntNdArray} copy + */ + public static IntNdArray ndCopyOf(int[][][][][] array) { + IntNdArray ndArray = NdArrays.ofInts(shapeOf(array)); + copyTo(array, ndArray); + return ndArray; + } + + /** + * Copy a 6-dimensional array of ints in a new {@link IntNdArray} + * + * @param array source array + * @return the {@code IntNdArray} copy + */ + public static IntNdArray ndCopyOf(int[][][][][][] array) { + IntNdArray ndArray = NdArrays.ofInts(shapeOf(array)); + copyTo(array, ndArray); + return ndArray; + } + + /** + * Copy an array of longs in a new {@link LongNdArray} + * + * @param array source array + * @return the {@code LongNdArray} copy + */ + public static LongNdArray ndCopyOf(long[] array) { + LongNdArray ndArray = NdArrays.ofLongs(shapeOf(array)); + copyTo(array, ndArray); + return ndArray; + } + + /** + * Copy a 2-dimensional array of longs in a new {@link LongNdArray} + * + * @param array source array + * @return the {@code LongNdArray} copy + */ + public static LongNdArray ndCopyOf(long[][] array) { + LongNdArray ndArray = NdArrays.ofLongs(shapeOf(array)); + copyTo(array, ndArray); + return ndArray; + } + + /** + * Copy a 3-dimensional array of longs in a new {@link LongNdArray} + * + * @param array source array + * @return the {@code LongNdArray} copy + */ + public static LongNdArray ndCopyOf(long[][][] array) { + LongNdArray ndArray = NdArrays.ofLongs(shapeOf(array)); + copyTo(array, ndArray); + return ndArray; + } + + /** + * Copy a 4-dimensional array of longs in a new {@link LongNdArray} + * + * @param array source array + * @return the {@code LongNdArray} copy + */ + public static LongNdArray ndCopyOf(long[][][][] array) { + LongNdArray ndArray = NdArrays.ofLongs(shapeOf(array)); + copyTo(array, ndArray); + return ndArray; + } + + /** + * Copy a 5-dimensional array of longs in a new {@link LongNdArray} + * + * @param array source array + * @return the {@code LongNdArray} copy + */ + public static LongNdArray ndCopyOf(long[][][][][] array) { + LongNdArray ndArray = NdArrays.ofLongs(shapeOf(array)); + copyTo(array, ndArray); + return ndArray; + } + + /** + * Copy a 6-dimensional array of longs in a new {@link LongNdArray} + * + * @param array source array + * @return the {@code LongNdArray} copy + */ + public static LongNdArray ndCopyOf(long[][][][][][] array) { + LongNdArray ndArray = NdArrays.ofLongs(shapeOf(array)); + copyTo(array, ndArray); + return ndArray; + } + + /** + * Copy an array of floats in a new {@link FloatNdArray} + * + * @param array source array + * @return the {@code FloatNdArray} copy + */ + public static FloatNdArray ndCopyOf(float[] array) { + FloatNdArray ndArray = NdArrays.ofFloats(shapeOf(array)); + copyTo(array, ndArray); + return ndArray; + } + + /** + * Copy a 2-dimensional array of floats in a new {@link FloatNdArray} + * + * @param array source array + * @return the {@code FloatNdArray} copy + */ + public static FloatNdArray ndCopyOf(float[][] array) { + FloatNdArray ndArray = NdArrays.ofFloats(shapeOf(array)); + copyTo(array, ndArray); + return ndArray; + } + + /** + * Copy a 3-dimensional array of floats in a new {@link FloatNdArray} + * + * @param array source array + * @return the {@code FloatNdArray} copy + */ + public static FloatNdArray ndCopyOf(float[][][] array) { + FloatNdArray ndArray = NdArrays.ofFloats(shapeOf(array)); + copyTo(array, ndArray); + return ndArray; + } + + /** + * Copy a 4-dimensional array of floats in a new {@link FloatNdArray} + * + * @param array source array + * @return the {@code FloatNdArray} copy + */ + public static FloatNdArray ndCopyOf(float[][][][] array) { + FloatNdArray ndArray = NdArrays.ofFloats(shapeOf(array)); + copyTo(array, ndArray); + return ndArray; + } + + /** + * Copy a 5-dimensional array of floats in a new {@link FloatNdArray} + * + * @param array source array + * @return the {@code FloatNdArray} copy + */ + public static FloatNdArray ndCopyOf(float[][][][][] array) { + FloatNdArray ndArray = NdArrays.ofFloats(shapeOf(array)); + copyTo(array, ndArray); + return ndArray; + } + + /** + * Copy a 6-dimensional array of floats in a new {@link FloatNdArray} + * + * @param array source array + * @return the {@code FloatNdArray} copy + */ + public static FloatNdArray ndCopyOf(float[][][][][][] array) { + FloatNdArray ndArray = NdArrays.ofFloats(shapeOf(array)); + copyTo(array, ndArray); + return ndArray; + } + + /** + * Copy an array of doubles in a new {@link DoubleNdArray} + * + * @param array source array + * @return the {@code DoubleNdArray} copy + */ + public static DoubleNdArray ndCopyOf(double[] array) { + DoubleNdArray ndArray = NdArrays.ofDoubles(shapeOf(array)); + copyTo(array, ndArray); + return ndArray; + } + + /** + * Copy a 2-dimensional array of doubles in a new {@link DoubleNdArray} + * + * @param array source array + * @return the {@code DoubleNdArray} copy + */ + public static DoubleNdArray ndCopyOf(double[][] array) { + DoubleNdArray ndArray = NdArrays.ofDoubles(shapeOf(array)); + copyTo(array, ndArray); + return ndArray; + } + + /** + * Copy a 3-dimensional array of doubles in a new {@link DoubleNdArray} + * + * @param array source array + * @return the {@code DoubleNdArray} copy + */ + public static DoubleNdArray ndCopyOf(double[][][] array) { + DoubleNdArray ndArray = NdArrays.ofDoubles(shapeOf(array)); + copyTo(array, ndArray); + return ndArray; + } + + /** + * Copy a 4-dimensional array of doubles in a new {@link DoubleNdArray} + * + * @param array source array + * @return the {@code DoubleNdArray} copy + */ + public static DoubleNdArray ndCopyOf(double[][][][] array) { + DoubleNdArray ndArray = NdArrays.ofDoubles(shapeOf(array)); + copyTo(array, ndArray); + return ndArray; + } + + /** + * Copy a 5-dimensional array of doubles in a new {@link DoubleNdArray} + * + * @param array source array + * @return the {@code DoubleNdArray} copy + */ + public static DoubleNdArray ndCopyOf(double[][][][][] array) { + DoubleNdArray ndArray = NdArrays.ofDoubles(shapeOf(array)); + copyTo(array, ndArray); + return ndArray; + } + + /** + * Copy a 6-dimensional array of doubles in a new {@link DoubleNdArray} + * + * @param array source array + * @return the {@code DoubleNdArray} copy + */ + public static DoubleNdArray ndCopyOf(double[][][][][][] array) { + DoubleNdArray ndArray = NdArrays.ofDoubles(shapeOf(array)); + copyTo(array, ndArray); + return ndArray; + } + + /** + * Copy an array of bytes in a new {@link ByteNdArray} + * + * @param array source array + * @return the {@code ByteNdArray} copy + */ + public static ByteNdArray ndCopyOf(byte[] array) { + ByteNdArray ndArray = NdArrays.ofBytes(shapeOf(array)); + copyTo(array, ndArray); + return ndArray; + } + + /** + * Copy a 2-dimensional array of bytes in a new {@link ByteNdArray} + * + * @param array source array + * @return the {@code ByteNdArray} copy + */ + public static ByteNdArray ndCopyOf(byte[][] array) { + ByteNdArray ndArray = NdArrays.ofBytes(shapeOf(array)); + copyTo(array, ndArray); + return ndArray; + } + + /** + * Copy a 3-dimensional array of bytes in a new {@link ByteNdArray} + * + * @param array source array + * @return the {@code ByteNdArray} copy + */ + public static ByteNdArray ndCopyOf(byte[][][] array) { + ByteNdArray ndArray = NdArrays.ofBytes(shapeOf(array)); + copyTo(array, ndArray); + return ndArray; + } + + /** + * Copy a 4-dimensional array of bytes in a new {@link ByteNdArray} + * + * @param array source array + * @return the {@code ByteNdArray} copy + */ + public static ByteNdArray ndCopyOf(byte[][][][] array) { + ByteNdArray ndArray = NdArrays.ofBytes(shapeOf(array)); + copyTo(array, ndArray); + return ndArray; + } + + /** + * Copy a 5-dimensional array of bytes in a new {@link ByteNdArray} + * + * @param array source array + * @return the {@code ByteNdArray} copy + */ + public static ByteNdArray ndCopyOf(byte[][][][][] array) { + ByteNdArray ndArray = NdArrays.ofBytes(shapeOf(array)); + copyTo(array, ndArray); + return ndArray; + } + + /** + * Copy a 6-dimensional array of bytes in a new {@link ByteNdArray} + * + * @param array source array + * @return the {@code ByteNdArray} copy + */ + public static ByteNdArray ndCopyOf(byte[][][][][][] array) { + ByteNdArray ndArray = NdArrays.ofBytes(shapeOf(array)); + copyTo(array, ndArray); + return ndArray; + } + + /** + * Copy an array of shorts in a new {@link ShortNdArray} + * + * @param array source array + * @return the {@code ShortNdArray} copy + */ + public static ShortNdArray ndCopyOf(short[] array) { + ShortNdArray ndArray = NdArrays.ofShorts(shapeOf(array)); + copyTo(array, ndArray); + return ndArray; + } + + /** + * Copy a 2-dimensional array of shorts in a new {@link ShortNdArray} + * + * @param array source array + * @return the {@code ShortNdArray} copy + */ + public static ShortNdArray ndCopyOf(short[][] array) { + ShortNdArray ndArray = NdArrays.ofShorts(shapeOf(array)); + copyTo(array, ndArray); + return ndArray; + } + + /** + * Copy a 3-dimensional array of shorts in a new {@link ShortNdArray} + * + * @param array source array + * @return the {@code ShortNdArray} copy + */ + public static ShortNdArray ndCopyOf(short[][][] array) { + ShortNdArray ndArray = NdArrays.ofShorts(shapeOf(array)); + copyTo(array, ndArray); + return ndArray; + } + + /** + * Copy a 4-dimensional array of shorts in a new {@link ShortNdArray} + * + * @param array source array + * @return the {@code ShortNdArray} copy + */ + public static ShortNdArray ndCopyOf(short[][][][] array) { + ShortNdArray ndArray = NdArrays.ofShorts(shapeOf(array)); + copyTo(array, ndArray); + return ndArray; + } + + /** + * Copy a 5-dimensional array of shorts in a new {@link ShortNdArray} + * + * @param array source array + * @return the {@code ShortNdArray} copy + */ + public static ShortNdArray ndCopyOf(short[][][][][] array) { + ShortNdArray ndArray = NdArrays.ofShorts(shapeOf(array)); + copyTo(array, ndArray); + return ndArray; + } + + /** + * Copy a 6-dimensional array of shorts in a new {@link ShortNdArray} + * + * @param array source array + * @return the {@code ShortNdArray} copy + */ + public static ShortNdArray ndCopyOf(short[][][][][][] array) { + ShortNdArray ndArray = NdArrays.ofShorts(shapeOf(array)); + copyTo(array, ndArray); + return ndArray; + } + + /** + * Copy an array of booleans in a new {@link BooleanNdArray} + * + * @param array source array + * @return the {@code BooleanNdArray} copy + */ + public static BooleanNdArray ndCopyOf(boolean[] array) { + BooleanNdArray ndArray = NdArrays.ofBooleans(shapeOf(array)); + copyTo(array, ndArray); + return ndArray; + } + + /** + * Copy a 2-dimensional array of booleans in a new {@link BooleanNdArray} + * + * @param array source array + * @return the {@code BooleanNdArray} copy + */ + public static BooleanNdArray ndCopyOf(boolean[][] array) { + BooleanNdArray ndArray = NdArrays.ofBooleans(shapeOf(array)); + copyTo(array, ndArray); + return ndArray; + } + + /** + * Copy a 3-dimensional array of booleans in a new {@link BooleanNdArray} + * + * @param array source array + * @return the {@code BooleanNdArray} copy + */ + public static BooleanNdArray ndCopyOf(boolean[][][] array) { + BooleanNdArray ndArray = NdArrays.ofBooleans(shapeOf(array)); + copyTo(array, ndArray); + return ndArray; + } + + /** + * Copy a 4-dimensional array of booleans in a new {@link BooleanNdArray} + * + * @param array source array + * @return the {@code BooleanNdArray} copy + */ + public static BooleanNdArray ndCopyOf(boolean[][][][] array) { + BooleanNdArray ndArray = NdArrays.ofBooleans(shapeOf(array)); + copyTo(array, ndArray); + return ndArray; + } + + /** + * Copy a 5-dimensional array of booleans in a new {@link BooleanNdArray} + * + * @param array source array + * @return the {@code BooleanNdArray} copy + */ + public static BooleanNdArray ndCopyOf(boolean[][][][][] array) { + BooleanNdArray ndArray = NdArrays.ofBooleans(shapeOf(array)); + copyTo(array, ndArray); + return ndArray; + } + + /** + * Copy a 6-dimensional array of booleans in a new {@link BooleanNdArray} + * + * @param array source array + * @return the {@code BooleanNdArray} copy + */ + public static BooleanNdArray ndCopyOf(boolean[][][][][][] array) { + BooleanNdArray ndArray = NdArrays.ofBooleans(shapeOf(array)); + copyTo(array, ndArray); + return ndArray; + } + + /** + * Copy an array of objects in a new {@link NdArray} + * + * @param array source array + * @param data type + * @return the {@code NdArray} copy + */ + public static NdArray ndCopyOf(T[] array) { + @SuppressWarnings("unchecked") + NdArray ndArray = NdArrays.ofObjects(componentTypeOf(array), shapeOf(array)); + copyTo(array, ndArray); + return ndArray; + } + + /** + * Copy a 2-dimensional array of objects in a new {@link NdArray} + * + * @param array source array + * @param data type + * @return the {@code NdArray} copy + */ + public static NdArray ndCopyOf(T[][] array) { + @SuppressWarnings("unchecked") + NdArrayndArray = NdArrays.ofObjects(componentTypeOf(array), shapeOf(array)); + copyTo(array, ndArray); + return ndArray; + } + + /** + * Copy a 3-dimensional array of objects in a new {@link NdArray} + * + * @param array source array + * @param data type + * @return the {@code NdArray} copy + */ + public static NdArray ndCopyOf(T[][][] array) { + @SuppressWarnings("unchecked") + NdArrayndArray = NdArrays.ofObjects(componentTypeOf(array), shapeOf(array)); + copyTo(array, ndArray); + return ndArray; + } + + /** + * Copy a 4-dimensional array of objects in a new {@link NdArray} + * + * @param array source array + * @param data type + * @return the {@code NdArray} copy + */ + public static NdArray ndCopyOf(T[][][][] array) { + @SuppressWarnings("unchecked") + NdArrayndArray = NdArrays.ofObjects(componentTypeOf(array), shapeOf(array)); + copyTo(array, ndArray); + return ndArray; + } + + /** + * Copy a 5-dimensional array of objects in a new {@link NdArray} + * + * @param array source array + * @param data type + * @return the {@code NdArray} copy + */ + public static NdArray ndCopyOf(T[][][][][] array) { + @SuppressWarnings("unchecked") + NdArrayndArray = NdArrays.ofObjects(componentTypeOf(array), shapeOf(array)); + copyTo(array, ndArray); + return ndArray; + } + + /** + * Copy a 6-dimensional array of objects in a new {@link NdArray} + * + * @param array source array + * @param data type + * @return the {@code NdArray} copy + */ + public static NdArray ndCopyOf(T[][][][][][] array) { + @SuppressWarnings("unchecked") + NdArrayndArray = NdArrays.ofObjects(componentTypeOf(array), shapeOf(array)); + copyTo(array, ndArray); + return ndArray; + } + + /** + * Copy a {@link IntNdArray} in a new 1-dimension standard array of ints + * + * @param ndArray source array + * @return the array copy + * @throws IllegalArgumentException if {@code ndArray} is not of rank-1 or has a shape that + * exceeds standard arrays limits + */ + public static int[] array1dCopyOf(IntNdArray ndArray) { + int[] dims = computeArrayDims(ndArray, 1); + int[] array = new int[dims[0]]; + copyFrom(ndArray, array); + return array; + } + + /** + * Copy a {@link IntNdArray} in a new 2-dimension standard array of ints + * + * @param ndArray source array + * @return the array copy + * @throws IllegalArgumentException if {@code ndArray} is not of rank-2 or has a shape that + * exceeds standard arrays limits + */ + public static int[][] array2dCopyOf(IntNdArray ndArray) { + int[] dims = computeArrayDims(ndArray, 2); + int[][] array = new int[dims[0]][dims[1]]; + copyFrom(ndArray, array); + return array; + } + + /** + * Copy a {@link IntNdArray} in a new 3-dimension standard array of ints + * + * @param ndArray source array + * @return the array copy + * @throws IllegalArgumentException if {@code ndArray} is not of rank-3 or has a shape that + * exceeds standard arrays limits + */ + public static int[][][] array3dCopyOf(IntNdArray ndArray) { + int[] dims = computeArrayDims(ndArray, 3); + int[][][] array = new int[dims[0]][dims[1]][dims[2]]; + copyFrom(ndArray, array); + return array; + } + + /** + * Copy a {@link IntNdArray} in a new 4-dimension standard array of ints + * + * @param ndArray source array + * @return the array copy + * @throws IllegalArgumentException if {@code ndArray} is not of rank-4 or has a shape that + * exceeds standard arrays limits + */ + public static int[][][][] array4dCopyOf(IntNdArray ndArray) { + int[] dims = computeArrayDims(ndArray, 4); + int[][][][] array = new int[dims[0]][dims[1]][dims[2]][dims[3]]; + copyFrom(ndArray, array); + return array; + } + + /** + * Copy a {@link IntNdArray} in a new 5-dimension standard array of ints + * + * @param ndArray source array + * @return the array copy + * @throws IllegalArgumentException if {@code ndArray} is not of rank-5 or has a shape that + * exceeds standard arrays limits + */ + public static int[][][][][] array5dCopyOf(IntNdArray ndArray) { + int[] dims = computeArrayDims(ndArray, 5); + int[][][][][] array = new int[dims[0]][dims[1]][dims[2]][dims[3]][dims[4]]; + copyFrom(ndArray, array); + return array; + } + + /** + * Copy a {@link IntNdArray} in a new 6-dimension standard array of ints + * + * @param ndArray source array + * @return the array copy + * @throws IllegalArgumentException if {@code ndArray} is not of rank-6 or has a shape that + * exceeds standard arrays limits + */ + public static int[][][][][][] array6dCopyOf(IntNdArray ndArray) { + int[] dims = computeArrayDims(ndArray, 6); + int[][][][][][] array = new int[dims[0]][dims[1]][dims[2]][dims[3]][dims[4]][dims[5]]; + copyFrom(ndArray, array); + return array; + } + + /** + * Copy a {@link LongNdArray} in a new 1-dimension standard array of longs + * + * @param ndArray source array + * @return the array copy + * @throws IllegalArgumentException if {@code ndArray} is not of rank-1 or has a shape that + * exceeds standard arrays limits + */ + public static long[] array1dCopyOf(LongNdArray ndArray) { + int[] dims = computeArrayDims(ndArray, 1); + long[] array = new long[dims[0]]; + copyFrom(ndArray, array); + return array; + } + + /** + * Copy a {@link LongNdArray} in a new 2-dimension standard array of longs + * + * @param ndArray source array + * @return the array copy + * @throws IllegalArgumentException if {@code ndArray} is not of rank-2 or has a shape that + * exceeds standard arrays limits + */ + public static long[][] array2dCopyOf(LongNdArray ndArray) { + int[] dims = computeArrayDims(ndArray, 2); + long[][] array = new long[dims[0]][dims[1]]; + copyFrom(ndArray, array); + return array; + } + + /** + * Copy a {@link LongNdArray} in a new 3-dimension standard array of longs + * + * @param ndArray source array + * @return the array copy + * @throws IllegalArgumentException if {@code ndArray} is not of rank-3 or has a shape that + * exceeds standard arrays limits + */ + public static long[][][] array3dCopyOf(LongNdArray ndArray) { + int[] dims = computeArrayDims(ndArray, 3); + long[][][] array = new long[dims[0]][dims[1]][dims[2]]; + copyFrom(ndArray, array); + return array; + } + + /** + * Copy a {@link LongNdArray} in a new 4-dimension standard array of longs + * + * @param ndArray source array + * @return the array copy + * @throws IllegalArgumentException if {@code ndArray} is not of rank-4 or has a shape that + * exceeds standard arrays limits + */ + public static long[][][][] array4dCopyOf(LongNdArray ndArray) { + int[] dims = computeArrayDims(ndArray, 4); + long[][][][] array = new long[dims[0]][dims[1]][dims[2]][dims[3]]; + copyFrom(ndArray, array); + return array; + } + + /** + * Copy a {@link LongNdArray} in a new 5-dimension standard array of longs + * + * @param ndArray source array + * @return the array copy + * @throws IllegalArgumentException if {@code ndArray} is not of rank-5 or has a shape that + * exceeds standard arrays limits + */ + public static long[][][][][] array5dCopyOf(LongNdArray ndArray) { + int[] dims = computeArrayDims(ndArray, 5); + long[][][][][] array = new long[dims[0]][dims[1]][dims[2]][dims[3]][dims[4]]; + copyFrom(ndArray, array); + return array; + } + + /** + * Copy a {@link LongNdArray} in a new 6-dimension standard array of longs + * + * @param ndArray source array + * @return the array copy + * @throws IllegalArgumentException if {@code ndArray} is not of rank-6 or has a shape that + * exceeds standard arrays limits + */ + public static long[][][][][][] array6dCopyOf(LongNdArray ndArray) { + int[] dims = computeArrayDims(ndArray, 6); + long[][][][][][] array = new long[dims[0]][dims[1]][dims[2]][dims[3]][dims[4]][dims[5]]; + copyFrom(ndArray, array); + return array; + } + + /** + * Copy a {@link FloatNdArray} in a new 1-dimension standard array of floats + * + * @param ndArray source array + * @return the array copy + * @throws IllegalArgumentException if {@code ndArray} is not of rank-1 or has a shape that + * exceeds standard arrays limits + */ + public static float[] array1dCopyOf(FloatNdArray ndArray) { + int[] dims = computeArrayDims(ndArray, 1); + float[] array = new float[dims[0]]; + copyFrom(ndArray, array); + return array; + } + + /** + * Copy a {@link FloatNdArray} in a new 2-dimension standard array of floats + * + * @param ndArray source array + * @return the array copy + * @throws IllegalArgumentException if {@code ndArray} is not of rank-2 or has a shape that + * exceeds standard arrays limits + */ + public static float[][] array2dCopyOf(FloatNdArray ndArray) { + int[] dims = computeArrayDims(ndArray, 2); + float[][] array = new float[dims[0]][dims[1]]; + copyFrom(ndArray, array); + return array; + } + + /** + * Copy a {@link FloatNdArray} in a new 3-dimension standard array of floats + * + * @param ndArray source array + * @return the array copy + * @throws IllegalArgumentException if {@code ndArray} is not of rank-3 or has a shape that + * exceeds standard arrays limits + */ + public static float[][][] array3dCopyOf(FloatNdArray ndArray) { + int[] dims = computeArrayDims(ndArray, 3); + float[][][] array = new float[dims[0]][dims[1]][dims[2]]; + copyFrom(ndArray, array); + return array; + } + + /** + * Copy a {@link FloatNdArray} in a new 4-dimension standard array of floats + * + * @param ndArray source array + * @return the array copy + * @throws IllegalArgumentException if {@code ndArray} is not of rank-4 or has a shape that + * exceeds standard arrays limits + */ + public static float[][][][] array4dCopyOf(FloatNdArray ndArray) { + int[] dims = computeArrayDims(ndArray, 4); + float[][][][] array = new float[dims[0]][dims[1]][dims[2]][dims[3]]; + copyFrom(ndArray, array); + return array; + } + + /** + * Copy a {@link FloatNdArray} in a new 5-dimension standard array of floats + * + * @param ndArray source array + * @return the array copy + * @throws IllegalArgumentException if {@code ndArray} is not of rank-5 or has a shape that + * exceeds standard arrays limits + */ + public static float[][][][][] array5dCopyOf(FloatNdArray ndArray) { + int[] dims = computeArrayDims(ndArray, 5); + float[][][][][] array = new float[dims[0]][dims[1]][dims[2]][dims[3]][dims[4]]; + copyFrom(ndArray, array); + return array; + } + + /** + * Copy a {@link FloatNdArray} in a new 6-dimension standard array of floats + * + * @param ndArray source array + * @return the array copy + * @throws IllegalArgumentException if {@code ndArray} is not of rank-6 or has a shape that + * exceeds standard arrays limits + */ + public static float[][][][][][] array6dCopyOf(FloatNdArray ndArray) { + int[] dims = computeArrayDims(ndArray, 6); + float[][][][][][] array = new float[dims[0]][dims[1]][dims[2]][dims[3]][dims[4]][dims[5]]; + copyFrom(ndArray, array); + return array; + } + + /** + * Copy a {@link DoubleNdArray} in a new 1-dimension standard array of doubles + * + * @param ndArray source array + * @return the array copy + * @throws IllegalArgumentException if {@code ndArray} is not of rank-1 or has a shape that + * exceeds standard arrays limits + */ + public static double[] array1dCopyOf(DoubleNdArray ndArray) { + int[] dims = computeArrayDims(ndArray, 1); + double[] array = new double[dims[0]]; + copyFrom(ndArray, array); + return array; + } + + /** + * Copy a {@link DoubleNdArray} in a new 2-dimension standard array of doubles + * + * @param ndArray source array + * @return the array copy + * @throws IllegalArgumentException if {@code ndArray} is not of rank-2 or has a shape that + * exceeds standard arrays limits + */ + public static double[][] array2dCopyOf(DoubleNdArray ndArray) { + int[] dims = computeArrayDims(ndArray, 2); + double[][] array = new double[dims[0]][dims[1]]; + copyFrom(ndArray, array); + return array; + } + + /** + * Copy a {@link DoubleNdArray} in a new 3-dimension standard array of doubles + * + * @param ndArray source array + * @return the array copy + * @throws IllegalArgumentException if {@code ndArray} is not of rank-3 or has a shape that + * exceeds standard arrays limits + */ + public static double[][][] array3dCopyOf(DoubleNdArray ndArray) { + int[] dims = computeArrayDims(ndArray, 3); + double[][][] array = new double[dims[0]][dims[1]][dims[2]]; + copyFrom(ndArray, array); + return array; + } + + /** + * Copy a {@link DoubleNdArray} in a new 4-dimension standard array of doubles + * + * @param ndArray source array + * @return the array copy + * @throws IllegalArgumentException if {@code ndArray} is not of rank-4 or has a shape that + * exceeds standard arrays limits + */ + public static double[][][][] array4dCopyOf(DoubleNdArray ndArray) { + int[] dims = computeArrayDims(ndArray, 4); + double[][][][] array = new double[dims[0]][dims[1]][dims[2]][dims[3]]; + copyFrom(ndArray, array); + return array; + } + + /** + * Copy a {@link DoubleNdArray} in a new 5-dimension standard array of doubles + * + * @param ndArray source array + * @return the array copy + * @throws IllegalArgumentException if {@code ndArray} is not of rank-5 or has a shape that + * exceeds standard arrays limits + */ + public static double[][][][][] array5dCopyOf(DoubleNdArray ndArray) { + int[] dims = computeArrayDims(ndArray, 5); + double[][][][][] array = new double[dims[0]][dims[1]][dims[2]][dims[3]][dims[4]]; + copyFrom(ndArray, array); + return array; + } + + /** + * Copy a {@link DoubleNdArray} in a new 6-dimension standard array of doubles + * + * @param ndArray source array + * @return the array copy + * @throws IllegalArgumentException if {@code ndArray} is not of rank-6 or has a shape that + * exceeds standard arrays limits + */ + public static double[][][][][][] array6dCopyOf(DoubleNdArray ndArray) { + int[] dims = computeArrayDims(ndArray, 6); + double[][][][][][] array = new double[dims[0]][dims[1]][dims[2]][dims[3]][dims[4]][dims[5]]; + copyFrom(ndArray, array); + return array; + } + + /** + * Copy a {@link ByteNdArray} in a new 1-dimension standard array of bytes + * + * @param ndArray source array + * @return the array copy + * @throws IllegalArgumentException if {@code ndArray} is not of rank-1 or has a shape that + * exceeds standard arrays limits + */ + public static byte[] array1dCopyOf(ByteNdArray ndArray) { + int[] dims = computeArrayDims(ndArray, 1); + byte[] array = new byte[dims[0]]; + copyFrom(ndArray, array); + return array; + } + + /** + * Copy a {@link ByteNdArray} in a new 2-dimension standard array of bytes + * + * @param ndArray source array + * @return the array copy + * @throws IllegalArgumentException if {@code ndArray} is not of rank-2 or has a shape that + * exceeds standard arrays limits + */ + public static byte[][] array2dCopyOf(ByteNdArray ndArray) { + int[] dims = computeArrayDims(ndArray, 2); + byte[][] array = new byte[dims[0]][dims[1]]; + copyFrom(ndArray, array); + return array; + } + + /** + * Copy a {@link ByteNdArray} in a new 3-dimension standard array of bytes + * + * @param ndArray source array + * @return the array copy + * @throws IllegalArgumentException if {@code ndArray} is not of rank-3 or has a shape that + * exceeds standard arrays limits + */ + public static byte[][][] array3dCopyOf(ByteNdArray ndArray) { + int[] dims = computeArrayDims(ndArray, 3); + byte[][][] array = new byte[dims[0]][dims[1]][dims[2]]; + copyFrom(ndArray, array); + return array; + } + + /** + * Copy a {@link ByteNdArray} in a new 4-dimension standard array of bytes + * + * @param ndArray source array + * @return the array copy + * @throws IllegalArgumentException if {@code ndArray} is not of rank-4 or has a shape that + * exceeds standard arrays limits + */ + public static byte[][][][] array4dCopyOf(ByteNdArray ndArray) { + int[] dims = computeArrayDims(ndArray, 4); + byte[][][][] array = new byte[dims[0]][dims[1]][dims[2]][dims[3]]; + copyFrom(ndArray, array); + return array; + } + + /** + * Copy a {@link ByteNdArray} in a new 5-dimension standard array of bytes + * + * @param ndArray source array + * @return the array copy + * @throws IllegalArgumentException if {@code ndArray} is not of rank-5 or has a shape that + * exceeds standard arrays limits + */ + public static byte[][][][][] array5dCopyOf(ByteNdArray ndArray) { + int[] dims = computeArrayDims(ndArray, 5); + byte[][][][][] array = new byte[dims[0]][dims[1]][dims[2]][dims[3]][dims[4]]; + copyFrom(ndArray, array); + return array; + } + + /** + * Copy a {@link ByteNdArray} in a new 6-dimension standard array of bytes + * + * @param ndArray source array + * @return the array copy + * @throws IllegalArgumentException if {@code ndArray} is not of rank-6 or has a shape that + * exceeds standard arrays limits + */ + public static byte[][][][][][] array6dCopyOf(ByteNdArray ndArray) { + int[] dims = computeArrayDims(ndArray, 6); + byte[][][][][][] array = new byte[dims[0]][dims[1]][dims[2]][dims[3]][dims[4]][dims[5]]; + copyFrom(ndArray, array); + return array; + } + + /** + * Copy a {@link ShortNdArray} in a new 1-dimension standard array of shorts + * + * @param ndArray source array + * @return the array copy + * @throws IllegalArgumentException if {@code ndArray} is not of rank-1 or has a shape that + * exceeds standard arrays limits + */ + public static short[] array1dCopyOf(ShortNdArray ndArray) { + int[] dims = computeArrayDims(ndArray, 1); + short[] array = new short[dims[0]]; + copyFrom(ndArray, array); + return array; + } + + /** + * Copy a {@link ShortNdArray} in a new 2-dimension standard array of shorts + * + * @param ndArray source array + * @return the array copy + * @throws IllegalArgumentException if {@code ndArray} is not of rank-2 or has a shape that + * exceeds standard arrays limits + */ + public static short[][] array2dCopyOf(ShortNdArray ndArray) { + int[] dims = computeArrayDims(ndArray, 2); + short[][] array = new short[dims[0]][dims[1]]; + copyFrom(ndArray, array); + return array; + } + + /** + * Copy a {@link ShortNdArray} in a new 3-dimension standard array of shorts + * + * @param ndArray source array + * @return the array copy + * @throws IllegalArgumentException if {@code ndArray} is not of rank-3 or has a shape that + * exceeds standard arrays limits + */ + public static short[][][] array3dCopyOf(ShortNdArray ndArray) { + int[] dims = computeArrayDims(ndArray, 3); + short[][][] array = new short[dims[0]][dims[1]][dims[2]]; + copyFrom(ndArray, array); + return array; + } + + /** + * Copy a {@link ShortNdArray} in a new 4-dimension standard array of shorts + * + * @param ndArray source array + * @return the array copy + * @throws IllegalArgumentException if {@code ndArray} is not of rank-4 or has a shape that + * exceeds standard arrays limits + */ + public static short[][][][] array4dCopyOf(ShortNdArray ndArray) { + int[] dims = computeArrayDims(ndArray, 4); + short[][][][] array = new short[dims[0]][dims[1]][dims[2]][dims[3]]; + copyFrom(ndArray, array); + return array; + } + + /** + * Copy a {@link ShortNdArray} in a new 5-dimension standard array of shorts + * + * @param ndArray source array + * @return the array copy + * @throws IllegalArgumentException if {@code ndArray} is not of rank-5 or has a shape that + * exceeds standard arrays limits + */ + public static short[][][][][] array5dCopyOf(ShortNdArray ndArray) { + int[] dims = computeArrayDims(ndArray, 5); + short[][][][][] array = new short[dims[0]][dims[1]][dims[2]][dims[3]][dims[4]]; + copyFrom(ndArray, array); + return array; + } + + /** + * Copy a {@link ShortNdArray} in a new 6-dimension standard array of shorts + * + * @param ndArray source array + * @return the array copy + * @throws IllegalArgumentException if {@code ndArray} is not of rank-6 or has a shape that + * exceeds standard arrays limits + */ + public static short[][][][][][] array6dCopyOf(ShortNdArray ndArray) { + int[] dims = computeArrayDims(ndArray, 6); + short[][][][][][] array = new short[dims[0]][dims[1]][dims[2]][dims[3]][dims[4]][dims[5]]; + copyFrom(ndArray, array); + return array; + } + + /** + * Copy a {@link BooleanNdArray} in a new 1-dimension standard array of booleans + * + * @param ndArray source array + * @return the array copy + * @throws IllegalArgumentException if {@code ndArray} is not of rank-1 or has a shape that + * exceeds standard arrays limits + */ + public static boolean[] array1dCopyOf(BooleanNdArray ndArray) { + int[] dims = computeArrayDims(ndArray, 1); + boolean[] array = new boolean[dims[0]]; + copyFrom(ndArray, array); + return array; + } + + /** + * Copy a {@link BooleanNdArray} in a new 2-dimension standard array of booleans + * + * @param ndArray source array + * @return the array copy + * @throws IllegalArgumentException if {@code ndArray} is not of rank-2 or has a shape that + * exceeds standard arrays limits + */ + public static boolean[][] array2dCopyOf(BooleanNdArray ndArray) { + int[] dims = computeArrayDims(ndArray, 2); + boolean[][] array = new boolean[dims[0]][dims[1]]; + copyFrom(ndArray, array); + return array; + } + + /** + * Copy a {@link BooleanNdArray} in a new 3-dimension standard array of booleans + * + * @param ndArray source array + * @return the array copy + * @throws IllegalArgumentException if {@code ndArray} is not of rank-3 or has a shape that + * exceeds standard arrays limits + */ + public static boolean[][][] array3dCopyOf(BooleanNdArray ndArray) { + int[] dims = computeArrayDims(ndArray, 3); + boolean[][][] array = new boolean[dims[0]][dims[1]][dims[2]]; + copyFrom(ndArray, array); + return array; + } + + /** + * Copy a {@link BooleanNdArray} in a new 4-dimension standard array of booleans + * + * @param ndArray source array + * @return the array copy + * @throws IllegalArgumentException if {@code ndArray} is not of rank-4 or has a shape that + * exceeds standard arrays limits + */ + public static boolean[][][][] array4dCopyOf(BooleanNdArray ndArray) { + int[] dims = computeArrayDims(ndArray, 4); + boolean[][][][] array = new boolean[dims[0]][dims[1]][dims[2]][dims[3]]; + copyFrom(ndArray, array); + return array; + } + + /** + * Copy a {@link BooleanNdArray} in a new 5-dimension standard array of booleans + * + * @param ndArray source array + * @return the array copy + * @throws IllegalArgumentException if {@code ndArray} is not of rank-5 or has a shape that + * exceeds standard arrays limits + */ + public static boolean[][][][][] array5dCopyOf(BooleanNdArray ndArray) { + int[] dims = computeArrayDims(ndArray, 5); + boolean[][][][][] array = new boolean[dims[0]][dims[1]][dims[2]][dims[3]][dims[4]]; + copyFrom(ndArray, array); + return array; + } + + /** + * Copy a {@link BooleanNdArray} in a new 6-dimension standard array of booleans + * + * @param ndArray source array + * @return the array copy + * @throws IllegalArgumentException if {@code ndArray} is not of rank-6 or has a shape that + * exceeds standard arrays limits + */ + public static boolean[][][][][][] array6dCopyOf(BooleanNdArray ndArray) { + int[] dims = computeArrayDims(ndArray, 6); + boolean[][][][][][] array = new boolean[dims[0]][dims[1]][dims[2]][dims[3]][dims[4]][dims[5]]; + copyFrom(ndArray, array); + return array; + } + + /** + * Copy a {@link NdArray NdArray<T>} in a new 1-dimension standard array of objects + * + * @param ndArray source array + * @param objectType type of object + * @param data type + * @return the array copy + * @throws IllegalArgumentException if {@code ndArray} is not of rank-1 or has a shape that + * exceeds standard arrays limits + */ + public static T[] array1dCopyOf(NdArray ndArray, Class objectType) { + int[] dims = computeArrayDims(ndArray, 1); + T[] array = (T[])Array.newInstance(objectType, dims[0]); + copyFrom(ndArray, array); + return array; + } + + /** + * Copy a {@link NdArray NdArray<T>} in a new 2-dimension standard array of objects + * + * @param ndArray source array + * @param objectType type of object + * @param data type + * @return the array copy + * @throws IllegalArgumentException if {@code ndArray} is not of rank-2 or has a shape that + * exceeds standard arrays limits + */ + public static T[][] array2dCopyOf(NdArray ndArray, Class objectType) { + int[] dims = computeArrayDims(ndArray, 2); + T[][] array = (T[][])Array.newInstance(objectType, dims[0], dims[1]); + copyFrom(ndArray, array); + return array; + } + + /** + * Copy a {@link NdArray NdArray<T>} in a new 3-dimension standard array of objects + * + * @param ndArray source array + * @param objectType type of object + * @param data type + * @return the array copy + * @throws IllegalArgumentException if {@code ndArray} is not of rank-3 or has a shape that + * exceeds standard arrays limits + */ + public static T[][][] array3dCopyOf(NdArray ndArray, Class objectType) { + int[] dims = computeArrayDims(ndArray, 3); + T[][][] array = (T[][][])Array.newInstance(objectType, dims[0], dims[1], dims[2]); + copyFrom(ndArray, array); + return array; + } + + /** + * Copy a {@link NdArray NdArray<T>} in a new 4-dimension standard array of objects + * + * @param ndArray source array + * @param objectType type of object + * @param data type + * @return the array copy + * @throws IllegalArgumentException if {@code ndArray} is not of rank-4 or has a shape that + * exceeds standard arrays limits + */ + public static T[][][][] array4dCopyOf(NdArray ndArray, Class objectType) { + int[] dims = computeArrayDims(ndArray, 4); + T[][][][] array = (T[][][][])Array.newInstance(objectType, dims[0], dims[1], dims[2], dims[3]); + copyFrom(ndArray, array); + return array; + } + + /** + * Copy a {@link NdArray NdArray<T>} in a new 5-dimension standard array of objects + * + * @param ndArray source array + * @param objectType type of object + * @param data type + * @return the array copy + * @throws IllegalArgumentException if {@code ndArray} is not of rank-5 or has a shape that + * exceeds standard arrays limits + */ + public static T[][][][][] array5dCopyOf(NdArray ndArray, Class objectType) { + int[] dims = computeArrayDims(ndArray, 5); + T[][][][][] array = + (T[][][][][])Array.newInstance(objectType, dims[0], dims[1], dims[2], dims[3], dims[4]); + copyFrom(ndArray, array); + return array; + } + + /** + * Copy a {@link NdArray NdArray<T>} in a new 6-dimension standard array of objects + * + * @param ndArray source array + * @param objectType type of object + * @param data type + * @return the array copy + * @throws IllegalArgumentException if {@code ndArray} is not of rank-6 or has a shape that + * exceeds standard arrays limits + */ + public static T[][][][][][] array6dCopyOf(NdArray ndArray, Class objectType) { + int[] dims = computeArrayDims(ndArray, 6); + T[][][][][][] array = + (T[][][][][][])Array.newInstance(objectType, dims[0], dims[1], dims[2], dims[3], dims[4], dims[5]); + copyFrom(ndArray, array); + return array; + } + + /** + * Copy an array of ints into the {@code dst} {@link NdArray} + * + * @param src source array + * @param dst destination rank-1 array + * @throws IllegalArgumentException if {@code dst} is not of rank-1 or has an incompatible shape + * with the source array + */ + public static void copyTo(int[] src, IntNdArray dst) { + NdArrays.vectorOf(src).copyTo(dst); + } + + /** + * Copy a 2-dimensional array of ints into the {@code dst} {@link NdArray} + * + * @param src source array + * @param dst destination rank-2 array + * @throws IllegalArgumentException if {@code dst} is not of rank-2 or has an incompatible shape + * with the source array + */ + public static void copyTo(int[][] src, IntNdArray dst) { + dst.elements(0).forEachIndexed((idx, e) -> + NdArrays.vectorOf(src[(int)idx[0]]).copyTo(e) + ); + } + + /** + * Copy a 3-dimensional array of ints into the {@code dst} {@link NdArray} + * + * @param src source array + * @param dst destination rank-3 array + * @throws IllegalArgumentException if {@code dst} is not of rank-3 or has an incompatible shape + * with the source array + */ + public static void copyTo(int[][][] src, IntNdArray dst) { + dst.elements(1).forEachIndexed((idx, e) -> + NdArrays.vectorOf(src[(int)idx[0]][(int)idx[1]]).copyTo(e) + ); + } + + /** + * Copy a 4-dimensional array of ints into the {@code dst} {@link NdArray} + * + * @param src source array + * @param dst destination rank-4 array + * @throws IllegalArgumentException if {@code dst} is not of rank-4 or has an incompatible shape + * with the source array + */ + public static void copyTo(int[][][][] src, IntNdArray dst) { + dst.elements(2).forEachIndexed((idx, e) -> + NdArrays.vectorOf(src[(int)idx[0]][(int)idx[1]][(int)idx[2]]).copyTo(e) + ); + } + + /** + * Copy a 5-dimensional array of ints into the {@code dst} {@link NdArray} + * + * @param src source array + * @param dst destination rank-5 array + * @throws IllegalArgumentException if {@code dst} is not of rank-5 or has an incompatible shape + * with the source array + */ + public static void copyTo(int[][][][][] src, IntNdArray dst) { + dst.elements(3).forEachIndexed((idx, e) -> + NdArrays.vectorOf(src[(int)idx[0]][(int)idx[1]][(int)idx[2]][(int)idx[3]]).copyTo(e) + ); + } + + /** + * Copy a 6-dimensional array of ints into the {@code dst} {@link NdArray} + * + * @param src source array + * @param dst destination rank-6 array + * @throws IllegalArgumentException if {@code dst} is not of rank-6 or has an incompatible shape + * with the source array + */ + public static void copyTo(int[][][][][][] src, IntNdArray dst) { + dst.elements(4).forEachIndexed((idx, e) -> + NdArrays.vectorOf(src[(int)idx[0]][(int)idx[1]][(int)idx[2]][(int)idx[3]][(int)idx[4]]).copyTo(e) + ); + } + + /** + * Copy an array of longs into the {@code dst} {@link NdArray} + * + * @param src source array + * @param dst destination rank-1 array + * @throws IllegalArgumentException if {@code dst} is not of rank-1 or has an incompatible shape + * with the source array + */ + public static void copyTo(long[] src, LongNdArray dst) { + NdArrays.vectorOf(src).copyTo(dst); + } + + /** + * Copy a 2-dimensional array of longs into the {@code dst} {@link NdArray} + * + * @param src source array + * @param dst destination rank-2 array + * @throws IllegalArgumentException if {@code dst} is not of rank-2 or has an incompatible shape + * with the source array + */ + public static void copyTo(long[][] src, LongNdArray dst) { + dst.elements(0).forEachIndexed((idx, e) -> + NdArrays.vectorOf(src[(int)idx[0]]).copyTo(e) + ); + } + + /** + * Copy a 3-dimensional array of longs into the {@code dst} {@link NdArray} + * + * @param src source array + * @param dst destination rank-3 array + * @throws IllegalArgumentException if {@code dst} is not of rank-3 or has an incompatible shape + * with the source array + */ + public static void copyTo(long[][][] src, LongNdArray dst) { + dst.elements(1).forEachIndexed((idx, e) -> + NdArrays.vectorOf(src[(int)idx[0]][(int)idx[1]]).copyTo(e) + ); + } + + /** + * Copy a 4-dimensional array of longs into the {@code dst} {@link NdArray} + * + * @param src source array + * @param dst destination rank-4 array + * @throws IllegalArgumentException if {@code dst} is not of rank-4 or has an incompatible shape + * with the source array + */ + public static void copyTo(long[][][][] src, LongNdArray dst) { + dst.elements(2).forEachIndexed((idx, e) -> + NdArrays.vectorOf(src[(int)idx[0]][(int)idx[1]][(int)idx[2]]).copyTo(e) + ); + } + + /** + * Copy a 5-dimensional array of longs into the {@code dst} {@link NdArray} + * + * @param src source array + * @param dst destination rank-5 array + * @throws IllegalArgumentException if {@code dst} is not of rank-5 or has an incompatible shape + * with the source array + */ + public static void copyTo(long[][][][][] src, LongNdArray dst) { + dst.elements(3).forEachIndexed((idx, e) -> + NdArrays.vectorOf(src[(int)idx[0]][(int)idx[1]][(int)idx[2]][(int)idx[3]]).copyTo(e) + ); + } + + /** + * Copy a 6-dimensional array of longs into the {@code dst} {@link NdArray} + * + * @param src source array + * @param dst destination rank-6 array + * @throws IllegalArgumentException if {@code dst} is not of rank-6 or has an incompatible shape + * with the source array + */ + public static void copyTo(long[][][][][][] src, LongNdArray dst) { + dst.elements(4).forEachIndexed((idx, e) -> + NdArrays.vectorOf(src[(int)idx[0]][(int)idx[1]][(int)idx[2]][(int)idx[3]][(int)idx[4]]).copyTo(e) + ); + } + + /** + * Copy an array of floats into the {@code dst} {@link NdArray} + * + * @param src source array + * @param dst destination rank-1 array + * @throws IllegalArgumentException if {@code dst} is not of rank-1 or has an incompatible shape + * with the source array + */ + public static void copyTo(float[] src, FloatNdArray dst) { + NdArrays.vectorOf(src).copyTo(dst); + } + + /** + * Copy a 2-dimensional array of floats into the {@code dst} {@link NdArray} + * + * @param src source array + * @param dst destination rank-2 array + * @throws IllegalArgumentException if {@code dst} is not of rank-2 or has an incompatible shape + * with the source array + */ + public static void copyTo(float[][] src, FloatNdArray dst) { + dst.elements(0).forEachIndexed((idx, e) -> + NdArrays.vectorOf(src[(int)idx[0]]).copyTo(e) + ); + } + + /** + * Copy a 3-dimensional array of floats into the {@code dst} {@link NdArray} + * + * @param src source array + * @param dst destination rank-3 array + * @throws IllegalArgumentException if {@code dst} is not of rank-3 or has an incompatible shape + * with the source array + */ + public static void copyTo(float[][][] src, FloatNdArray dst) { + dst.elements(1).forEachIndexed((idx, e) -> + NdArrays.vectorOf(src[(int)idx[0]][(int)idx[1]]).copyTo(e) + ); + } + + /** + * Copy a 4-dimensional array of floats into the {@code dst} {@link NdArray} + * + * @param src source array + * @param dst destination rank-4 array + * @throws IllegalArgumentException if {@code dst} is not of rank-4 or has an incompatible shape + * with the source array + */ + public static void copyTo(float[][][][] src, FloatNdArray dst) { + dst.elements(2).forEachIndexed((idx, e) -> + NdArrays.vectorOf(src[(int)idx[0]][(int)idx[1]][(int)idx[2]]).copyTo(e) + ); + } + + /** + * Copy a 5-dimensional array of floats into the {@code dst} {@link NdArray} + * + * @param src source array + * @param dst destination rank-5 array + * @throws IllegalArgumentException if {@code dst} is not of rank-5 or has an incompatible shape + * with the source array + */ + public static void copyTo(float[][][][][] src, FloatNdArray dst) { + dst.elements(3).forEachIndexed((idx, e) -> + NdArrays.vectorOf(src[(int)idx[0]][(int)idx[1]][(int)idx[2]][(int)idx[3]]).copyTo(e) + ); + } + + /** + * Copy a 6-dimensional array of floats into the {@code dst} {@link NdArray} + * + * @param src source array + * @param dst destination rank-6 array + * @throws IllegalArgumentException if {@code dst} is not of rank-6 or has an incompatible shape + * with the source array + */ + public static void copyTo(float[][][][][][] src, FloatNdArray dst) { + dst.elements(4).forEachIndexed((idx, e) -> + NdArrays.vectorOf(src[(int)idx[0]][(int)idx[1]][(int)idx[2]][(int)idx[3]][(int)idx[4]]).copyTo(e) + ); + } + + /** + * Copy an array of doubles into the {@code dst} {@link NdArray} + * + * @param src source array + * @param dst destination rank-1 array + * @throws IllegalArgumentException if {@code dst} is not of rank-1 or has an incompatible shape + * with the source array + */ + public static void copyTo(double[] src, DoubleNdArray dst) { + NdArrays.vectorOf(src).copyTo(dst); + } + + /** + * Copy a 2-dimensional array of doubles into the {@code dst} {@link NdArray} + * + * @param src source array + * @param dst destination rank-2 array + * @throws IllegalArgumentException if {@code dst} is not of rank-2 or has an incompatible shape + * with the source array + */ + public static void copyTo(double[][] src, DoubleNdArray dst) { + dst.elements(0).forEachIndexed((idx, e) -> + NdArrays.vectorOf(src[(int)idx[0]]).copyTo(e) + ); + } + + /** + * Copy a 3-dimensional array of doubles into the {@code dst} {@link NdArray} + * + * @param src source array + * @param dst destination rank-3 array + * @throws IllegalArgumentException if {@code dst} is not of rank-3 or has an incompatible shape + * with the source array + */ + public static void copyTo(double[][][] src, DoubleNdArray dst) { + dst.elements(1).forEachIndexed((idx, e) -> + NdArrays.vectorOf(src[(int)idx[0]][(int)idx[1]]).copyTo(e) + ); + } + + /** + * Copy a 4-dimensional array of doubles into the {@code dst} {@link NdArray} + * + * @param src source array + * @param dst destination rank-4 array + * @throws IllegalArgumentException if {@code dst} is not of rank-4 or has an incompatible shape + * with the source array + */ + public static void copyTo(double[][][][] src, DoubleNdArray dst) { + dst.elements(2).forEachIndexed((idx, e) -> + NdArrays.vectorOf(src[(int)idx[0]][(int)idx[1]][(int)idx[2]]).copyTo(e) + ); + } + + /** + * Copy a 5-dimensional array of doubles into the {@code dst} {@link NdArray} + * + * @param src source array + * @param dst destination rank-5 array + * @throws IllegalArgumentException if {@code dst} is not of rank-5 or has an incompatible shape + * with the source array + */ + public static void copyTo(double[][][][][] src, DoubleNdArray dst) { + dst.elements(3).forEachIndexed((idx, e) -> + NdArrays.vectorOf(src[(int)idx[0]][(int)idx[1]][(int)idx[2]][(int)idx[3]]).copyTo(e) + ); + } + + /** + * Copy a 6-dimensional array of doubles into the {@code dst} {@link NdArray} + * + * @param src source array + * @param dst destination rank-6 array + * @throws IllegalArgumentException if {@code dst} is not of rank-6 or has an incompatible shape + * with the source array + */ + public static void copyTo(double[][][][][][] src, DoubleNdArray dst) { + dst.elements(4).forEachIndexed((idx, e) -> + NdArrays.vectorOf(src[(int)idx[0]][(int)idx[1]][(int)idx[2]][(int)idx[3]][(int)idx[4]]).copyTo(e) + ); + } + + /** + * Copy an array of bytes into the {@code dst} {@link NdArray} + * + * @param src source array + * @param dst destination rank-1 array + * @throws IllegalArgumentException if {@code dst} is not of rank-1 or has an incompatible shape + * with the source array + */ + public static void copyTo(byte[] src, ByteNdArray dst) { + NdArrays.vectorOf(src).copyTo(dst); + } + + /** + * Copy a 2-dimensional array of bytes into the {@code dst} {@link NdArray} + * + * @param src source array + * @param dst destination rank-2 array + * @throws IllegalArgumentException if {@code dst} is not of rank-2 or has an incompatible shape + * with the source array + */ + public static void copyTo(byte[][] src, ByteNdArray dst) { + dst.elements(0).forEachIndexed((idx, e) -> + NdArrays.vectorOf(src[(int)idx[0]]).copyTo(e) + ); + } + + /** + * Copy a 3-dimensional array of bytes into the {@code dst} {@link NdArray} + * + * @param src source array + * @param dst destination rank-3 array + * @throws IllegalArgumentException if {@code dst} is not of rank-3 or has an incompatible shape + * with the source array + */ + public static void copyTo(byte[][][] src, ByteNdArray dst) { + dst.elements(1).forEachIndexed((idx, e) -> + NdArrays.vectorOf(src[(int)idx[0]][(int)idx[1]]).copyTo(e) + ); + } + + /** + * Copy a 4-dimensional array of bytes into the {@code dst} {@link NdArray} + * + * @param src source array + * @param dst destination rank-4 array + * @throws IllegalArgumentException if {@code dst} is not of rank-4 or has an incompatible shape + * with the source array + */ + public static void copyTo(byte[][][][] src, ByteNdArray dst) { + dst.elements(2).forEachIndexed((idx, e) -> + NdArrays.vectorOf(src[(int)idx[0]][(int)idx[1]][(int)idx[2]]).copyTo(e) + ); + } + + /** + * Copy a 5-dimensional array of bytes into the {@code dst} {@link NdArray} + * + * @param src source array + * @param dst destination rank-5 array + * @throws IllegalArgumentException if {@code dst} is not of rank-5 or has an incompatible shape + * with the source array + */ + public static void copyTo(byte[][][][][] src, ByteNdArray dst) { + dst.elements(3).forEachIndexed((idx, e) -> + NdArrays.vectorOf(src[(int)idx[0]][(int)idx[1]][(int)idx[2]][(int)idx[3]]).copyTo(e) + ); + } + + /** + * Copy a 6-dimensional array of bytes into the {@code dst} {@link NdArray} + * + * @param src source array + * @param dst destination rank-6 array + * @throws IllegalArgumentException if {@code dst} is not of rank-6 or has an incompatible shape + * with the source array + */ + public static void copyTo(byte[][][][][][] src, ByteNdArray dst) { + dst.elements(4).forEachIndexed((idx, e) -> + NdArrays.vectorOf(src[(int)idx[0]][(int)idx[1]][(int)idx[2]][(int)idx[3]][(int)idx[4]]).copyTo(e) + ); + } + + /** + * Copy an array of shorts into the {@code dst} {@link NdArray} + * + * @param src source array + * @param dst destination rank-1 array + * @throws IllegalArgumentException if {@code dst} is not of rank-1 or has an incompatible shape + * with the source array + */ + public static void copyTo(short[] src, ShortNdArray dst) { + NdArrays.vectorOf(src).copyTo(dst); + } + + /** + * Copy a 2-dimensional array of shorts into the {@code dst} {@link NdArray} + * + * @param src source array + * @param dst destination rank-2 array + * @throws IllegalArgumentException if {@code dst} is not of rank-2 or has an incompatible shape + * with the source array + */ + public static void copyTo(short[][] src, ShortNdArray dst) { + dst.elements(0).forEachIndexed((idx, e) -> + NdArrays.vectorOf(src[(int)idx[0]]).copyTo(e) + ); + } + + /** + * Copy a 3-dimensional array of shorts into the {@code dst} {@link NdArray} + * + * @param src source array + * @param dst destination rank-3 array + * @throws IllegalArgumentException if {@code dst} is not of rank-3 or has an incompatible shape + * with the source array + */ + public static void copyTo(short[][][] src, ShortNdArray dst) { + dst.elements(1).forEachIndexed((idx, e) -> + NdArrays.vectorOf(src[(int)idx[0]][(int)idx[1]]).copyTo(e) + ); + } + + /** + * Copy a 4-dimensional array of shorts into the {@code dst} {@link NdArray} + * + * @param src source array + * @param dst destination rank-4 array + * @throws IllegalArgumentException if {@code dst} is not of rank-4 or has an incompatible shape + * with the source array + */ + public static void copyTo(short[][][][] src, ShortNdArray dst) { + dst.elements(2).forEachIndexed((idx, e) -> + NdArrays.vectorOf(src[(int)idx[0]][(int)idx[1]][(int)idx[2]]).copyTo(e) + ); + } + + /** + * Copy a 5-dimensional array of shorts into the {@code dst} {@link NdArray} + * + * @param src source array + * @param dst destination rank-5 array + * @throws IllegalArgumentException if {@code dst} is not of rank-5 or has an incompatible shape + * with the source array + */ + public static void copyTo(short[][][][][] src, ShortNdArray dst) { + dst.elements(3).forEachIndexed((idx, e) -> + NdArrays.vectorOf(src[(int)idx[0]][(int)idx[1]][(int)idx[2]][(int)idx[3]]).copyTo(e) + ); + } + + /** + * Copy a 6-dimensional array of shorts into the {@code dst} {@link NdArray} + * + * @param src source array + * @param dst destination rank-6 array + * @throws IllegalArgumentException if {@code dst} is not of rank-6 or has an incompatible shape + * with the source array + */ + public static void copyTo(short[][][][][][] src, ShortNdArray dst) { + dst.elements(4).forEachIndexed((idx, e) -> + NdArrays.vectorOf(src[(int)idx[0]][(int)idx[1]][(int)idx[2]][(int)idx[3]][(int)idx[4]]).copyTo(e) + ); + } + + /** + * Copy an array of booleans into the {@code dst} {@link NdArray} + * + * @param src source array + * @param dst destination rank-1 array + * @throws IllegalArgumentException if {@code dst} is not of rank-1 or has an incompatible shape + * with the source array + */ + public static void copyTo(boolean[] src, BooleanNdArray dst) { + NdArrays.vectorOf(src).copyTo(dst); + } + + /** + * Copy a 2-dimensional array of booleans into the {@code dst} {@link NdArray} + * + * @param src source array + * @param dst destination rank-2 array + * @throws IllegalArgumentException if {@code dst} is not of rank-2 or has an incompatible shape + * with the source array + */ + public static void copyTo(boolean[][] src, BooleanNdArray dst) { + dst.elements(0).forEachIndexed((idx, e) -> + NdArrays.vectorOf(src[(int)idx[0]]).copyTo(e) + ); + } + + /** + * Copy a 3-dimensional array of booleans into the {@code dst} {@link NdArray} + * + * @param src source array + * @param dst destination rank-3 array + * @throws IllegalArgumentException if {@code dst} is not of rank-3 or has an incompatible shape + * with the source array + */ + public static void copyTo(boolean[][][] src, BooleanNdArray dst) { + dst.elements(1).forEachIndexed((idx, e) -> + NdArrays.vectorOf(src[(int)idx[0]][(int)idx[1]]).copyTo(e) + ); + } + + /** + * Copy a 4-dimensional array of booleans into the {@code dst} {@link NdArray} + * + * @param src source array + * @param dst destination rank-4 array + * @throws IllegalArgumentException if {@code dst} is not of rank-4 or has an incompatible shape + * with the source array + */ + public static void copyTo(boolean[][][][] src, BooleanNdArray dst) { + dst.elements(2).forEachIndexed((idx, e) -> + NdArrays.vectorOf(src[(int)idx[0]][(int)idx[1]][(int)idx[2]]).copyTo(e) + ); + } + + /** + * Copy a 5-dimensional array of booleans into the {@code dst} {@link NdArray} + * + * @param src source array + * @param dst destination rank-5 array + * @throws IllegalArgumentException if {@code dst} is not of rank-5 or has an incompatible shape + * with the source array + */ + public static void copyTo(boolean[][][][][] src, BooleanNdArray dst) { + dst.elements(3).forEachIndexed((idx, e) -> + NdArrays.vectorOf(src[(int)idx[0]][(int)idx[1]][(int)idx[2]][(int)idx[3]]).copyTo(e) + ); + } + + /** + * Copy a 6-dimensional array of booleans into the {@code dst} {@link NdArray} + * + * @param src source array + * @param dst destination rank-6 array + * @throws IllegalArgumentException if {@code dst} is not of rank-6 or has an incompatible shape + * with the source array + */ + public static void copyTo(boolean[][][][][][] src, BooleanNdArray dst) { + dst.elements(4).forEachIndexed((idx, e) -> + NdArrays.vectorOf(src[(int)idx[0]][(int)idx[1]][(int)idx[2]][(int)idx[3]][(int)idx[4]]).copyTo(e) + ); + } + + /** + * Copy an array of objects into the {@code dst} {@link NdArray} + * + * @param src source array + * @param dst destination rank-1 array + * @param data type + * @throws IllegalArgumentException if {@code dst} is not of rank-1 or has an incompatible shape + * with the source array + */ + public static void copyTo(T[] src, NdArray dst) { + NdArrays.vectorOfObjects(src).copyTo(dst); + } + + /** + * Copy a 2-dimensional array of objects into the {@code dst} {@link NdArray} + * + * @param src source array + * @param dst destination rank-2 array + * @param data type + * @throws IllegalArgumentException if {@code dst} is not of rank-2 or has an incompatible shape + * with the source array + */ + public static void copyTo(T[][] src, NdArray dst) { + dst.elements(0).forEachIndexed((idx, e) -> + NdArrays.vectorOfObjects(src[(int)idx[0]]).copyTo(e) + ); + } + + /** + * Copy a 3-dimensional array of objects into the {@code dst} {@link NdArray} + * + * @param src source array + * @param dst destination rank-3 array + * @param data type + * @throws IllegalArgumentException if {@code dst} is not of rank-3 or has an incompatible shape + * with the source array + */ + public static void copyTo(T[][][] src, NdArray dst) { + dst.elements(1).forEachIndexed((idx, e) -> + NdArrays.vectorOfObjects(src[(int)idx[0]][(int)idx[1]]).copyTo(e) + ); + } + + /** + * Copy a 4-dimensional array of objects into the {@code dst} {@link NdArray} + * + * @param src source array + * @param dst destination rank-4 array + * @param data type + * @throws IllegalArgumentException if {@code dst} is not of rank-4 or has an incompatible shape + * with the source array + */ + public static void copyTo(T[][][][] src, NdArray dst) { + dst.elements(2).forEachIndexed((idx, e) -> + NdArrays.vectorOfObjects(src[(int)idx[0]][(int)idx[1]][(int)idx[2]]).copyTo(e) + ); + } + + /** + * Copy a 5-dimensional array of objects into the {@code dst} {@link NdArray} + * + * @param src source array + * @param dst destination rank-5 array + * @param data type + * @throws IllegalArgumentException if {@code dst} is not of rank-5 or has an incompatible shape + * with the source array + */ + public static void copyTo(T[][][][][] src, NdArray dst) { + dst.elements(3).forEachIndexed((idx, e) -> + NdArrays.vectorOfObjects(src[(int)idx[0]][(int)idx[1]][(int)idx[2]][(int)idx[3]]).copyTo(e) + ); + } + + /** + * Copy a 6-dimensional array of objects into the {@code dst} {@link NdArray} + * + * @param src source array + * @param dst destination rank-6 array + * @param data type + * @throws IllegalArgumentException if {@code dst} is not of rank-6 or has an incompatible shape + * with the source array + */ + public static void copyTo(T[][][][][][] src, NdArray dst) { + dst.elements(4).forEachIndexed((idx, e) -> + NdArrays.vectorOfObjects(src[(int)idx[0]][(int)idx[1]][(int)idx[2]][(int)idx[3]][(int)idx[4]]).copyTo(e) + ); + } + + + /** + * Copy a {@link NdArray} to an array of ints + * + * @param src source rank-1 array + * @param dst destination array + * @throws IllegalArgumentException if {@code src} is not of rank-1 + * @throws ArrayIndexOutOfBoundsException if not all elements of {@code src} can fit it the destination array + */ + public static void copyFrom(IntNdArray src, int[] dst) { + if (src.rank() != 1) { + throw new IllegalArgumentException("Array cannot be copied from NdArray of rank " + src.rank()); + } + if (src.size() > dst.length) { + throw new ArrayIndexOutOfBoundsException(String.valueOf(src.size()) + " > " + dst.length); + } + src.read(DataBuffers.of(dst, false, false)); + } + + /** + * Copy a {@link NdArray} to a 2-dimensional array of ints + * + * @param src source rank-2 array + * @param dst destination array + * @throws IllegalArgumentException if {@code src} is not of rank-2 + * @throws ArrayIndexOutOfBoundsException if not all elements of {@code src} can fit it the destination array + */ + public static void copyFrom(IntNdArray src, int[][] dst) { + if (src.rank() != 2) { + throw new IllegalArgumentException("Array cannot be copied from NdArray of rank " + src.rank()); + } + src.elements(0).forEachIndexed((idx, e) -> + copyFrom(e, dst[(int)idx[0]]) + ); + } + + /** + * Copy a {@link NdArray} to a 3-dimensional array of ints + * + * @param src source rank-3 array + * @param dst destination array + * @throws IllegalArgumentException if {@code src} is not of rank-3 + * @throws ArrayIndexOutOfBoundsException if not all elements of {@code src} can fit it the destination array + */ + public static void copyFrom(IntNdArray src, int[][][] dst) { + if (src.rank() != 3) { + throw new IllegalArgumentException("Array cannot be copied from NdArray of rank " + src.rank()); + } + src.elements(1).forEachIndexed((idx, e) -> + copyFrom(e, dst[(int)idx[0]][(int)idx[1]]) + ); + } + + /** + * Copy a {@link NdArray} to a 4-dimensional array of ints + * + * @param src source rank-4 array + * @param dst destination array + * @throws IllegalArgumentException if {@code src} is not of rank-4 + * @throws ArrayIndexOutOfBoundsException if not all elements of {@code src} can fit it the destination array + */ + public static void copyFrom(IntNdArray src, int[][][][] dst) { + if (src.rank() != 4) { + throw new IllegalArgumentException("Array cannot be copied from NdArray of rank " + src.rank()); + } + src.elements(2).forEachIndexed((idx, e) -> + copyFrom(e, dst[(int)idx[0]][(int)idx[1]][(int)idx[2]]) + ); + } + + /** + * Copy a {@link NdArray} to a 5-dimensional array of ints + * + * @param src source rank-5 array + * @param dst destination array + * @throws IllegalArgumentException if {@code src} is not of rank-5 + * @throws ArrayIndexOutOfBoundsException if not all elements of {@code src} can fit it the destination array + */ + public static void copyFrom(IntNdArray src, int[][][][][] dst) { + if (src.rank() != 5) { + throw new IllegalArgumentException("Array cannot be copied from NdArray of rank " + src.rank()); + } + src.elements(3).forEachIndexed((idx, e) -> + copyFrom(e, dst[(int)idx[0]][(int)idx[1]][(int)idx[2]][(int)idx[3]]) + ); + } + + /** + * Copy a {@link NdArray} to a 6-dimensional array of ints + * + * @param src source rank-6 array + * @param dst destination array + * @throws IllegalArgumentException if {@code src} is not of rank-6 + * @throws ArrayIndexOutOfBoundsException if not all elements of {@code src} can fit it the destination array + */ + public static void copyFrom(IntNdArray src, int[][][][][][] dst) { + if (src.rank() != 6) { + throw new IllegalArgumentException("Array cannot be copied from NdArray of rank " + src.rank()); + } + src.elements(4).forEachIndexed((idx, e) -> + copyFrom(e, dst[(int)idx[0]][(int)idx[1]][(int)idx[2]][(int)idx[3]][(int)idx[4]]) + ); + } + + /** + * Copy a {@link NdArray} to an array of longs + * + * @param src source rank-1 array + * @param dst destination array + * @throws IllegalArgumentException if {@code src} is not of rank-1 + * @throws ArrayIndexOutOfBoundsException if not all elements of {@code src} can fit it the destination array + */ + public static void copyFrom(LongNdArray src, long[] dst) { + if (src.rank() != 1) { + throw new IllegalArgumentException("Array cannot be copied from NdArray of rank " + src.rank()); + } + if (src.size() > dst.length) { + throw new ArrayIndexOutOfBoundsException(String.valueOf(src.size()) + " > " + dst.length); + } + src.read(DataBuffers.of(dst, false, false)); + } + + /** + * Copy a {@link NdArray} to a 2-dimensional array of longs + * + * @param src source rank-2 array + * @param dst destination array + * @throws IllegalArgumentException if {@code src} is not of rank-2 + * @throws ArrayIndexOutOfBoundsException if not all elements of {@code src} can fit it the destination array + */ + public static void copyFrom(LongNdArray src, long[][] dst) { + if (src.rank() != 2) { + throw new IllegalArgumentException("Array cannot be copied from NdArray of rank " + src.rank()); + } + src.elements(0).forEachIndexed((idx, e) -> + copyFrom(e, dst[(int)idx[0]]) + ); + } + + /** + * Copy a {@link NdArray} to a 3-dimensional array of longs + * + * @param src source rank-3 array + * @param dst destination array + * @throws IllegalArgumentException if {@code src} is not of rank-3 + * @throws ArrayIndexOutOfBoundsException if not all elements of {@code src} can fit it the destination array + */ + public static void copyFrom(LongNdArray src, long[][][] dst) { + if (src.rank() != 3) { + throw new IllegalArgumentException("Array cannot be copied from NdArray of rank " + src.rank()); + } + src.elements(1).forEachIndexed((idx, e) -> + copyFrom(e, dst[(int)idx[0]][(int)idx[1]]) + ); + } + + /** + * Copy a {@link NdArray} to a 4-dimensional array of longs + * + * @param src source rank-4 array + * @param dst destination array + * @throws IllegalArgumentException if {@code src} is not of rank-4 + * @throws ArrayIndexOutOfBoundsException if not all elements of {@code src} can fit it the destination array + */ + public static void copyFrom(LongNdArray src, long[][][][] dst) { + if (src.rank() != 4) { + throw new IllegalArgumentException("Array cannot be copied from NdArray of rank " + src.rank()); + } + src.elements(2).forEachIndexed((idx, e) -> + copyFrom(e, dst[(int)idx[0]][(int)idx[1]][(int)idx[2]]) + ); + } + + /** + * Copy a {@link NdArray} to a 5-dimensional array of longs + * + * @param src source rank-5 array + * @param dst destination array + * @throws IllegalArgumentException if {@code src} is not of rank-5 + * @throws ArrayIndexOutOfBoundsException if not all elements of {@code src} can fit it the destination array + */ + public static void copyFrom(LongNdArray src, long[][][][][] dst) { + if (src.rank() != 5) { + throw new IllegalArgumentException("Array cannot be copied from NdArray of rank " + src.rank()); + } + src.elements(3).forEachIndexed((idx, e) -> + copyFrom(e, dst[(int)idx[0]][(int)idx[1]][(int)idx[2]][(int)idx[3]]) + ); + } + + /** + * Copy a {@link NdArray} to a 6-dimensional array of longs + * + * @param src source rank-6 array + * @param dst destination array + * @throws IllegalArgumentException if {@code src} is not of rank-6 + * @throws ArrayIndexOutOfBoundsException if not all elements of {@code src} can fit it the destination array + */ + public static void copyFrom(LongNdArray src, long[][][][][][] dst) { + if (src.rank() != 6) { + throw new IllegalArgumentException("Array cannot be copied from NdArray of rank " + src.rank()); + } + src.elements(4).forEachIndexed((idx, e) -> + copyFrom(e, dst[(int)idx[0]][(int)idx[1]][(int)idx[2]][(int)idx[3]][(int)idx[4]]) + ); + } + + /** + * Copy a {@link NdArray} to an array of floats + * + * @param src source rank-1 array + * @param dst destination array + * @throws IllegalArgumentException if {@code src} is not of rank-1 + * @throws ArrayIndexOutOfBoundsException if not all elements of {@code src} can fit it the destination array + */ + public static void copyFrom(FloatNdArray src, float[] dst) { + if (src.rank() != 1) { + throw new IllegalArgumentException("Array cannot be copied from NdArray of rank " + src.rank()); + } + if (src.size() > dst.length) { + throw new ArrayIndexOutOfBoundsException(String.valueOf(src.size()) + " > " + dst.length); + } + src.read(DataBuffers.of(dst, false, false)); + } + + /** + * Copy a {@link NdArray} to a 2-dimensional array of floats + * + * @param src source rank-2 array + * @param dst destination array + * @throws IllegalArgumentException if {@code src} is not of rank-2 + * @throws ArrayIndexOutOfBoundsException if not all elements of {@code src} can fit it the destination array + */ + public static void copyFrom(FloatNdArray src, float[][] dst) { + if (src.rank() != 2) { + throw new IllegalArgumentException("Array cannot be copied from NdArray of rank " + src.rank()); + } + src.elements(0).forEachIndexed((idx, e) -> + copyFrom(e, dst[(int)idx[0]]) + ); + } + + /** + * Copy a {@link NdArray} to a 3-dimensional array of floats + * + * @param src source rank-3 array + * @param dst destination array + * @throws IllegalArgumentException if {@code src} is not of rank-3 + * @throws ArrayIndexOutOfBoundsException if not all elements of {@code src} can fit it the destination array + */ + public static void copyFrom(FloatNdArray src, float[][][] dst) { + if (src.rank() != 3) { + throw new IllegalArgumentException("Array cannot be copied from NdArray of rank " + src.rank()); + } + src.elements(1).forEachIndexed((idx, e) -> + copyFrom(e, dst[(int)idx[0]][(int)idx[1]]) + ); + } + + /** + * Copy a {@link NdArray} to a 4-dimensional array of floats + * + * @param src source rank-4 array + * @param dst destination array + * @throws IllegalArgumentException if {@code src} is not of rank-4 + * @throws ArrayIndexOutOfBoundsException if not all elements of {@code src} can fit it the destination array + */ + public static void copyFrom(FloatNdArray src, float[][][][] dst) { + if (src.rank() != 4) { + throw new IllegalArgumentException("Array cannot be copied from NdArray of rank " + src.rank()); + } + src.elements(2).forEachIndexed((idx, e) -> + copyFrom(e, dst[(int)idx[0]][(int)idx[1]][(int)idx[2]]) + ); + } + + /** + * Copy a {@link NdArray} to a 5-dimensional array of floats + * + * @param src source rank-5 array + * @param dst destination array + * @throws IllegalArgumentException if {@code src} is not of rank-5 + * @throws ArrayIndexOutOfBoundsException if not all elements of {@code src} can fit it the destination array + */ + public static void copyFrom(FloatNdArray src, float[][][][][] dst) { + if (src.rank() != 5) { + throw new IllegalArgumentException("Array cannot be copied from NdArray of rank " + src.rank()); + } + src.elements(3).forEachIndexed((idx, e) -> + copyFrom(e, dst[(int)idx[0]][(int)idx[1]][(int)idx[2]][(int)idx[3]]) + ); + } + + /** + * Copy a {@link NdArray} to a 6-dimensional array of floats + * + * @param src source rank-6 array + * @param dst destination array + * @throws IllegalArgumentException if {@code src} is not of rank-6 + * @throws ArrayIndexOutOfBoundsException if not all elements of {@code src} can fit it the destination array + */ + public static void copyFrom(FloatNdArray src, float[][][][][][] dst) { + if (src.rank() != 6) { + throw new IllegalArgumentException("Array cannot be copied from NdArray of rank " + src.rank()); + } + src.elements(4).forEachIndexed((idx, e) -> + copyFrom(e, dst[(int)idx[0]][(int)idx[1]][(int)idx[2]][(int)idx[3]][(int)idx[4]]) + ); + } + + /** + * Copy a {@link NdArray} to an array of doubles + * + * @param src source rank-1 array + * @param dst destination array + * @throws IllegalArgumentException if {@code src} is not of rank-1 + * @throws ArrayIndexOutOfBoundsException if not all elements of {@code src} can fit it the destination array + */ + public static void copyFrom(DoubleNdArray src, double[] dst) { + if (src.rank() != 1) { + throw new IllegalArgumentException("Array cannot be copied from NdArray of rank " + src.rank()); + } + if (src.size() > dst.length) { + throw new ArrayIndexOutOfBoundsException(String.valueOf(src.size()) + " > " + dst.length); + } + src.read(DataBuffers.of(dst, false, false)); + } + + /** + * Copy a {@link NdArray} to a 2-dimensional array of doubles + * + * @param src source rank-2 array + * @param dst destination array + * @throws IllegalArgumentException if {@code src} is not of rank-2 + * @throws ArrayIndexOutOfBoundsException if not all elements of {@code src} can fit it the destination array + */ + public static void copyFrom(DoubleNdArray src, double[][] dst) { + if (src.rank() != 2) { + throw new IllegalArgumentException("Array cannot be copied from NdArray of rank " + src.rank()); + } + src.elements(0).forEachIndexed((idx, e) -> + copyFrom(e, dst[(int)idx[0]]) + ); + } + + /** + * Copy a {@link NdArray} to a 3-dimensional array of doubles + * + * @param src source rank-3 array + * @param dst destination array + * @throws IllegalArgumentException if {@code src} is not of rank-3 + * @throws ArrayIndexOutOfBoundsException if not all elements of {@code src} can fit it the destination array + */ + public static void copyFrom(DoubleNdArray src, double[][][] dst) { + if (src.rank() != 3) { + throw new IllegalArgumentException("Array cannot be copied from NdArray of rank " + src.rank()); + } + src.elements(1).forEachIndexed((idx, e) -> + copyFrom(e, dst[(int)idx[0]][(int)idx[1]]) + ); + } + + /** + * Copy a {@link NdArray} to a 4-dimensional array of doubles + * + * @param src source rank-4 array + * @param dst destination array + * @throws IllegalArgumentException if {@code src} is not of rank-4 + * @throws ArrayIndexOutOfBoundsException if not all elements of {@code src} can fit it the destination array + */ + public static void copyFrom(DoubleNdArray src, double[][][][] dst) { + if (src.rank() != 4) { + throw new IllegalArgumentException("Array cannot be copied from NdArray of rank " + src.rank()); + } + src.elements(2).forEachIndexed((idx, e) -> + copyFrom(e, dst[(int)idx[0]][(int)idx[1]][(int)idx[2]]) + ); + } + + /** + * Copy a {@link NdArray} to a 5-dimensional array of doubles + * + * @param src source rank-5 array + * @param dst destination array + * @throws IllegalArgumentException if {@code src} is not of rank-5 + * @throws ArrayIndexOutOfBoundsException if not all elements of {@code src} can fit it the destination array + */ + public static void copyFrom(DoubleNdArray src, double[][][][][] dst) { + if (src.rank() != 5) { + throw new IllegalArgumentException("Array cannot be copied from NdArray of rank " + src.rank()); + } + src.elements(3).forEachIndexed((idx, e) -> + copyFrom(e, dst[(int)idx[0]][(int)idx[1]][(int)idx[2]][(int)idx[3]]) + ); + } + + /** + * Copy a {@link NdArray} to a 6-dimensional array of doubles + * + * @param src source rank-6 array + * @param dst destination array + * @throws IllegalArgumentException if {@code src} is not of rank-6 + * @throws ArrayIndexOutOfBoundsException if not all elements of {@code src} can fit it the destination array + */ + public static void copyFrom(DoubleNdArray src, double[][][][][][] dst) { + if (src.rank() != 6) { + throw new IllegalArgumentException("Array cannot be copied from NdArray of rank " + src.rank()); + } + src.elements(4).forEachIndexed((idx, e) -> + copyFrom(e, dst[(int)idx[0]][(int)idx[1]][(int)idx[2]][(int)idx[3]][(int)idx[4]]) + ); + } + + /** + * Copy a {@link NdArray} to an array of bytes + * + * @param src source rank-1 array + * @param dst destination array + * @throws IllegalArgumentException if {@code src} is not of rank-1 + * @throws ArrayIndexOutOfBoundsException if not all elements of {@code src} can fit it the destination array + */ + public static void copyFrom(ByteNdArray src, byte[] dst) { + if (src.rank() != 1) { + throw new IllegalArgumentException("Array cannot be copied from NdArray of rank " + src.rank()); + } + if (src.size() > dst.length) { + throw new ArrayIndexOutOfBoundsException(String.valueOf(src.size()) + " > " + dst.length); + } + src.read(DataBuffers.of(dst, false, false)); + } + + /** + * Copy a {@link NdArray} to a 2-dimensional array of bytes + * + * @param src source rank-2 array + * @param dst destination array + * @throws IllegalArgumentException if {@code src} is not of rank-2 + * @throws ArrayIndexOutOfBoundsException if not all elements of {@code src} can fit it the destination array + */ + public static void copyFrom(ByteNdArray src, byte[][] dst) { + if (src.rank() != 2) { + throw new IllegalArgumentException("Array cannot be copied from NdArray of rank " + src.rank()); + } + src.elements(0).forEachIndexed((idx, e) -> + copyFrom(e, dst[(int)idx[0]]) + ); + } + + /** + * Copy a {@link NdArray} to a 3-dimensional array of bytes + * + * @param src source rank-3 array + * @param dst destination array + * @throws IllegalArgumentException if {@code src} is not of rank-3 + * @throws ArrayIndexOutOfBoundsException if not all elements of {@code src} can fit it the destination array + */ + public static void copyFrom(ByteNdArray src, byte[][][] dst) { + if (src.rank() != 3) { + throw new IllegalArgumentException("Array cannot be copied from NdArray of rank " + src.rank()); + } + src.elements(1).forEachIndexed((idx, e) -> + copyFrom(e, dst[(int)idx[0]][(int)idx[1]]) + ); + } + + /** + * Copy a {@link NdArray} to a 4-dimensional array of bytes + * + * @param src source rank-4 array + * @param dst destination array + * @throws IllegalArgumentException if {@code src} is not of rank-4 + * @throws ArrayIndexOutOfBoundsException if not all elements of {@code src} can fit it the destination array + */ + public static void copyFrom(ByteNdArray src, byte[][][][] dst) { + if (src.rank() != 4) { + throw new IllegalArgumentException("Array cannot be copied from NdArray of rank " + src.rank()); + } + src.elements(2).forEachIndexed((idx, e) -> + copyFrom(e, dst[(int)idx[0]][(int)idx[1]][(int)idx[2]]) + ); + } + + /** + * Copy a {@link NdArray} to a 5-dimensional array of bytes + * + * @param src source rank-5 array + * @param dst destination array + * @throws IllegalArgumentException if {@code src} is not of rank-5 + * @throws ArrayIndexOutOfBoundsException if not all elements of {@code src} can fit it the destination array + */ + public static void copyFrom(ByteNdArray src, byte[][][][][] dst) { + if (src.rank() != 5) { + throw new IllegalArgumentException("Array cannot be copied from NdArray of rank " + src.rank()); + } + src.elements(3).forEachIndexed((idx, e) -> + copyFrom(e, dst[(int)idx[0]][(int)idx[1]][(int)idx[2]][(int)idx[3]]) + ); + } + + /** + * Copy a {@link NdArray} to a 6-dimensional array of bytes + * + * @param src source rank-6 array + * @param dst destination array + * @throws IllegalArgumentException if {@code src} is not of rank-6 + * @throws ArrayIndexOutOfBoundsException if not all elements of {@code src} can fit it the destination array + */ + public static void copyFrom(ByteNdArray src, byte[][][][][][] dst) { + if (src.rank() != 6) { + throw new IllegalArgumentException("Array cannot be copied from NdArray of rank " + src.rank()); + } + src.elements(4).forEachIndexed((idx, e) -> + copyFrom(e, dst[(int)idx[0]][(int)idx[1]][(int)idx[2]][(int)idx[3]][(int)idx[4]]) + ); + } + + /** + * Copy a {@link NdArray} to an array of shorts + * + * @param src source rank-1 array + * @param dst destination array + * @throws IllegalArgumentException if {@code src} is not of rank-1 + * @throws ArrayIndexOutOfBoundsException if not all elements of {@code src} can fit it the destination array + */ + public static void copyFrom(ShortNdArray src, short[] dst) { + if (src.rank() != 1) { + throw new IllegalArgumentException("Array cannot be copied from NdArray of rank " + src.rank()); + } + if (src.size() > dst.length) { + throw new ArrayIndexOutOfBoundsException(String.valueOf(src.size()) + " > " + dst.length); + } + src.read(DataBuffers.of(dst, false, false)); + } + + /** + * Copy a {@link NdArray} to a 2-dimensional array of shorts + * + * @param src source rank-2 array + * @param dst destination array + * @throws IllegalArgumentException if {@code src} is not of rank-2 + * @throws ArrayIndexOutOfBoundsException if not all elements of {@code src} can fit it the destination array + */ + public static void copyFrom(ShortNdArray src, short[][] dst) { + if (src.rank() != 2) { + throw new IllegalArgumentException("Array cannot be copied from NdArray of rank " + src.rank()); + } + src.elements(0).forEachIndexed((idx, e) -> + copyFrom(e, dst[(int)idx[0]]) + ); + } + + /** + * Copy a {@link NdArray} to a 3-dimensional array of shorts + * + * @param src source rank-3 array + * @param dst destination array + * @throws IllegalArgumentException if {@code src} is not of rank-3 + * @throws ArrayIndexOutOfBoundsException if not all elements of {@code src} can fit it the destination array + */ + public static void copyFrom(ShortNdArray src, short[][][] dst) { + if (src.rank() != 3) { + throw new IllegalArgumentException("Array cannot be copied from NdArray of rank " + src.rank()); + } + src.elements(1).forEachIndexed((idx, e) -> + copyFrom(e, dst[(int)idx[0]][(int)idx[1]]) + ); + } + + /** + * Copy a {@link NdArray} to a 4-dimensional array of shorts + * + * @param src source rank-4 array + * @param dst destination array + * @throws IllegalArgumentException if {@code src} is not of rank-4 + * @throws ArrayIndexOutOfBoundsException if not all elements of {@code src} can fit it the destination array + */ + public static void copyFrom(ShortNdArray src, short[][][][] dst) { + if (src.rank() != 4) { + throw new IllegalArgumentException("Array cannot be copied from NdArray of rank " + src.rank()); + } + src.elements(2).forEachIndexed((idx, e) -> + copyFrom(e, dst[(int)idx[0]][(int)idx[1]][(int)idx[2]]) + ); + } + + /** + * Copy a {@link NdArray} to a 5-dimensional array of shorts + * + * @param src source rank-5 array + * @param dst destination array + * @throws IllegalArgumentException if {@code src} is not of rank-5 + * @throws ArrayIndexOutOfBoundsException if not all elements of {@code src} can fit it the destination array + */ + public static void copyFrom(ShortNdArray src, short[][][][][] dst) { + if (src.rank() != 5) { + throw new IllegalArgumentException("Array cannot be copied from NdArray of rank " + src.rank()); + } + src.elements(3).forEachIndexed((idx, e) -> + copyFrom(e, dst[(int)idx[0]][(int)idx[1]][(int)idx[2]][(int)idx[3]]) + ); + } + + /** + * Copy a {@link NdArray} to a 6-dimensional array of shorts + * + * @param src source rank-6 array + * @param dst destination array + * @throws IllegalArgumentException if {@code src} is not of rank-6 + * @throws ArrayIndexOutOfBoundsException if not all elements of {@code src} can fit it the destination array + */ + public static void copyFrom(ShortNdArray src, short[][][][][][] dst) { + if (src.rank() != 6) { + throw new IllegalArgumentException("Array cannot be copied from NdArray of rank " + src.rank()); + } + src.elements(4).forEachIndexed((idx, e) -> + copyFrom(e, dst[(int)idx[0]][(int)idx[1]][(int)idx[2]][(int)idx[3]][(int)idx[4]]) + ); + } + + /** + * Copy a {@link NdArray} to an array of booleans. + * + * @param src source rank-1 array + * @param dst destination array + * @throws IllegalArgumentException if {@code src} is not of rank-1 + * @throws ArrayIndexOutOfBoundsException if not all elements of {@code src} can fit it the destination array + */ + public static void copyFrom(BooleanNdArray src, boolean[] dst) { + if (src.rank() != 1) { + throw new IllegalArgumentException("Array cannot be copied from NdArray of rank " + src.rank()); + } + if (src.size() > dst.length) { + throw new ArrayIndexOutOfBoundsException(String.valueOf(src.size()) + " > " + dst.length); + } + src.read(DataBuffers.of(dst, false, false)); + } + + /** + * Copy a {@link NdArray} to a 2-dimensional array of booleans + * + * @param src source rank-2 array + * @param dst destination array + * @throws IllegalArgumentException if {@code src} is not of rank-2 + * @throws ArrayIndexOutOfBoundsException if not all elements of {@code src} can fit it the destination array + */ + public static void copyFrom(BooleanNdArray src, boolean[][] dst) { + if (src.rank() != 2) { + throw new IllegalArgumentException("Array cannot be copied from NdArray of rank " + src.rank()); + } + src.elements(0).forEachIndexed((idx, e) -> + copyFrom(e, dst[(int)idx[0]]) + ); + } + + /** + * Copy a {@link NdArray} to a 3-dimensional array of booleans + * + * @param src source rank-3 array + * @param dst destination array + * @throws IllegalArgumentException if {@code src} is not of rank-3 + * @throws ArrayIndexOutOfBoundsException if not all elements of {@code src} can fit it the destination array + */ + public static void copyFrom(BooleanNdArray src, boolean[][][] dst) { + if (src.rank() != 3) { + throw new IllegalArgumentException("Array cannot be copied from NdArray of rank " + src.rank()); + } + src.elements(1).forEachIndexed((idx, e) -> + copyFrom(e, dst[(int)idx[0]][(int)idx[1]]) + ); + } + + /** + * Copy a {@link NdArray} to a 4-dimensional array of booleans + * + * @param src source rank-4 array + * @param dst destination array + * @throws IllegalArgumentException if {@code src} is not of rank-4 + * @throws ArrayIndexOutOfBoundsException if not all elements of {@code src} can fit it the destination array + */ + public static void copyFrom(BooleanNdArray src, boolean[][][][] dst) { + if (src.rank() != 4) { + throw new IllegalArgumentException("Array cannot be copied from NdArray of rank " + src.rank()); + } + src.elements(2).forEachIndexed((idx, e) -> + copyFrom(e, dst[(int)idx[0]][(int)idx[1]][(int)idx[2]]) + ); + } + + /** + * Copy a {@link NdArray} to a 5-dimensional array of booleans + * + * @param src source rank-5 array + * @param dst destination array + * @throws IllegalArgumentException if {@code src} is not of rank-5 + * @throws ArrayIndexOutOfBoundsException if not all elements of {@code src} can fit it the destination array + */ + public static void copyFrom(BooleanNdArray src, boolean[][][][][] dst) { + if (src.rank() != 5) { + throw new IllegalArgumentException("Array cannot be copied from NdArray of rank " + src.rank()); + } + src.elements(3).forEachIndexed((idx, e) -> + copyFrom(e, dst[(int)idx[0]][(int)idx[1]][(int)idx[2]][(int)idx[3]]) + ); + } + + /** + * Copy a {@link NdArray} to a 6-dimensional array of booleans + * + * @param src source rank-6 array + * @param dst destination array + * @throws IllegalArgumentException if {@code src} is not of rank-6 + * @throws ArrayIndexOutOfBoundsException if not all elements of {@code src} can fit it the destination array + */ + public static void copyFrom(BooleanNdArray src, boolean[][][][][][] dst) { + if (src.rank() != 6) { + throw new IllegalArgumentException("Array cannot be copied from NdArray of rank " + src.rank()); + } + src.elements(4).forEachIndexed((idx, e) -> + copyFrom(e, dst[(int)idx[0]][(int)idx[1]][(int)idx[2]][(int)idx[3]][(int)idx[4]]) + ); + } + + /** + * Copy a {@link NdArray} to an array of objects + * + * @param src source rank-1 array + * @param dst destination array + * @param data type + * @throws IllegalArgumentException if {@code src} is not of rank-1 + * @throws ArrayIndexOutOfBoundsException if not all elements of {@code src} can fit it the destination array + */ + public static void copyFrom(NdArray src, T[] dst) { + if (src.rank() != 1) { + throw new IllegalArgumentException("Array cannot be copied from NdArray of rank " + src.rank()); + } + if (src.size() > dst.length) { + throw new ArrayIndexOutOfBoundsException(String.valueOf(src.size()) + " > " + dst.length); + } + src.read(DataBuffers.of(dst, false, false)); + } + + /** + * Copy a {@link NdArray} to a 2-dimensional array of objects + * + * @param src source rank-2 array + * @param dst destination array + * @param data type + * @throws IllegalArgumentException if {@code src} is not of rank-2 + * @throws ArrayIndexOutOfBoundsException if not all elements of {@code src} can fit it the destination array + */ + public static void copyFrom(NdArray src, T[][] dst) { + if (src.rank() != 2) { + throw new IllegalArgumentException("Array cannot be copied from NdArray of rank " + src.rank()); + } + src.elements(0).forEachIndexed((idx, e) -> + copyFrom(e, dst[(int)idx[0]]) + ); + } + + /** + * Copy a {@link NdArray} to a 3-dimensional array of objects + * + * @param src source rank-3 array + * @param dst destination array + * @param data type + * @throws IllegalArgumentException if {@code src} is not of rank-3 + * @throws ArrayIndexOutOfBoundsException if not all elements of {@code src} can fit it the destination array + */ + public static void copyFrom(NdArray src, T[][][] dst) { + if (src.rank() != 3) { + throw new IllegalArgumentException("Array cannot be copied from NdArray of rank " + src.rank()); + } + src.elements(1).forEachIndexed((idx, e) -> + copyFrom(e, dst[(int)idx[0]][(int)idx[1]]) + ); + } + + /** + * Copy a {@link NdArray} to a 4-dimensional array of objects + * + * @param src source rank-4 array + * @param dst destination array + * @param data type + * @throws IllegalArgumentException if {@code src} is not of rank-4 + * @throws ArrayIndexOutOfBoundsException if not all elements of {@code src} can fit it the destination array + */ + public static void copyFrom(NdArray src, T[][][][] dst) { + if (src.rank() != 4) { + throw new IllegalArgumentException("Array cannot be copied from NdArray of rank " + src.rank()); + } + src.elements(2).forEachIndexed((idx, e) -> + copyFrom(e, dst[(int)idx[0]][(int)idx[1]][(int)idx[2]]) + ); + } + + /** + * Copy a {@link NdArray} to a 5-dimensional array of objects + * + * @param src source rank-5 array + * @param dst destination array + * @param data type + * @throws IllegalArgumentException if {@code src} is not of rank-5 + * @throws ArrayIndexOutOfBoundsException if not all elements of {@code src} can fit it the destination array + */ + public static void copyFrom(NdArray src, T[][][][][] dst) { + if (src.rank() != 5) { + throw new IllegalArgumentException("Array cannot be copied from NdArray of rank " + src.rank()); + } + src.elements(3).forEachIndexed((idx, e) -> + copyFrom(e, dst[(int)idx[0]][(int)idx[1]][(int)idx[2]][(int)idx[3]]) + ); + } + + /** + * Copy a {@link NdArray} to a 6-dimensional array of objects + * + * @param src source rank-6 array + * @param dst destination array + * @param data type + * @throws IllegalArgumentException if {@code src} is not of rank-6 + * @throws ArrayIndexOutOfBoundsException if not all elements of {@code src} can fit it the destination array + */ + public static void copyFrom(NdArray src, T[][][][][][] dst) { + if (src.rank() != 6) { + throw new IllegalArgumentException("Array cannot be copied from NdArray of rank " + src.rank()); + } + src.elements(4).forEachIndexed((idx, e) -> + copyFrom(e, dst[(int)idx[0]][(int)idx[1]][(int)idx[2]][(int)idx[3]][(int)idx[4]]) + ); + } + + /** + * Compute the shape of an int array. + * + * @param array 1D array + * @return shape of the array + */ + public static Shape shapeOf(int[] array) { + return Shape.of(array.length); + } + + /** + * Compute the shape of a 2-dimensional int array. + * + * @param array 2D array + * @return shape of the array + */ + public static Shape shapeOf(int[][] array) { + return Shape.of(computeShape(array, new long[2])); + } + + /** + * Compute the shape of a 3-dimensional int array. + * + * @param array 3D array + * @return shape of the array + */ + public static Shape shapeOf(int[][][] array) { + return Shape.of(computeShape(array, new long[3])); + } + + /** + * Compute the shape of a 4-dimensional int array. + * + * @param array 4D array + * @return shape of the array + */ + public static Shape shapeOf(int[][][][] array) { + return Shape.of(computeShape(array, new long[4])); + } + + /** + * Compute the shape of a 5-dimensional int array. + * + * @param array 5D array + * @return shape of the array + */ + public static Shape shapeOf(int[][][][][] array) { + return Shape.of(computeShape(array, new long[5])); + } + + /** + * Compute the shape of a 6-dimensional int array. + * + * @param array 6D array + * @return shape of the array + */ + public static Shape shapeOf(int[][][][][][] array) { + return Shape.of(computeShape(array, new long[6])); + } + + /** + * Compute the shape of a long array. + * + * @param array 1D array + * @return shape of the array + */ + public static Shape shapeOf(long[] array) { + return Shape.of(array.length); + } + + /** + * Compute the shape of a 2-dimensional long array. + * + * @param array 2D array + * @return shape of the array + */ + public static Shape shapeOf(long[][] array) { + return Shape.of(computeShape(array, new long[2])); + } + + /** + * Compute the shape of a 3-dimensional long array. + * + * @param array 3D array + * @return shape of the array + */ + public static Shape shapeOf(long[][][] array) { + return Shape.of(computeShape(array, new long[3])); + } + + /** + * Compute the shape of a 4-dimensional long array. + * + * @param array 4D array + * @return shape of the array + */ + public static Shape shapeOf(long[][][][] array) { + return Shape.of(computeShape(array, new long[4])); + } + + /** + * Compute the shape of a 5-dimensional long array. + * + * @param array 5D array + * @return shape of the array + */ + public static Shape shapeOf(long[][][][][] array) { + return Shape.of(computeShape(array, new long[5])); + } + + /** + * Compute the shape of a 6-dimensional long array. + * + * @param array 6D array + * @return shape of the array + */ + public static Shape shapeOf(long[][][][][][] array) { + return Shape.of(computeShape(array, new long[6])); + } + + /** + * Compute the shape of a float array. + * + * @param array 1D array + * @return shape of the array + */ + public static Shape shapeOf(float[] array) { + return Shape.of(array.length); + } + + /** + * Compute the shape of a 2-dimensional float array. + * + * @param array 2D array + * @return shape of the array + */ + public static Shape shapeOf(float[][] array) { + return Shape.of(computeShape(array, new long[2])); + } + + /** + * Compute the shape of a 3-dimensional float array. + * + * @param array 3D array + * @return shape of the array + */ + public static Shape shapeOf(float[][][] array) { + return Shape.of(computeShape(array, new long[3])); + } + + /** + * Compute the shape of a 4-dimensional float array. + * + * @param array 4D array + * @return shape of the array + */ + public static Shape shapeOf(float[][][][] array) { + return Shape.of(computeShape(array, new long[4])); + } + + /** + * Compute the shape of a 5-dimensional float array. + * + * @param array 5D array + * @return shape of the array + */ + public static Shape shapeOf(float[][][][][] array) { + return Shape.of(computeShape(array, new long[5])); + } + + /** + * Compute the shape of a 6-dimensional float array. + * + * @param array 6D array + * @return shape of the array + */ + public static Shape shapeOf(float[][][][][][] array) { + return Shape.of(computeShape(array, new long[6])); + } + + /** + * Compute the shape of a double array. + * + * @param array 1D array + * @return shape of the array + */ + public static Shape shapeOf(double[] array) { + return Shape.of(array.length); + } + + /** + * Compute the shape of a 2-dimensional double array. + * + * @param array 2D array + * @return shape of the array + */ + public static Shape shapeOf(double[][] array) { + return Shape.of(computeShape(array, new long[2])); + } + + /** + * Compute the shape of a 3-dimensional double array. + * + * @param array 3D array + * @return shape of the array + */ + public static Shape shapeOf(double[][][] array) { + return Shape.of(computeShape(array, new long[3])); + } + + /** + * Compute the shape of a 4-dimensional double array. + * + * @param array 4D array + * @return shape of the array + */ + public static Shape shapeOf(double[][][][] array) { + return Shape.of(computeShape(array, new long[4])); + } + + /** + * Compute the shape of a 5-dimensional double array. + * + * @param array 5D array + * @return shape of the array + */ + public static Shape shapeOf(double[][][][][] array) { + return Shape.of(computeShape(array, new long[5])); + } + + /** + * Compute the shape of a 6-dimensional double array. + * + * @param array 6D array + * @return shape of the array + */ + public static Shape shapeOf(double[][][][][][] array) { + return Shape.of(computeShape(array, new long[6])); + } + + /** + * Compute the shape of a byte array. + * + * @param array 1D array + * @return shape of the array + */ + public static Shape shapeOf(byte[] array) { + return Shape.of(array.length); + } + + /** + * Compute the shape of a 2-dimensional byte array. + * + * @param array 2D array + * @return shape of the array + */ + public static Shape shapeOf(byte[][] array) { + return Shape.of(computeShape(array, new long[2])); + } + + /** + * Compute the shape of a 3-dimensional byte array. + * + * @param array 3D array + * @return shape of the array + */ + public static Shape shapeOf(byte[][][] array) { + return Shape.of(computeShape(array, new long[3])); + } + + /** + * Compute the shape of a 4-dimensional byte array. + * + * @param array 4D array + * @return shape of the array + */ + public static Shape shapeOf(byte[][][][] array) { + return Shape.of(computeShape(array, new long[4])); + } + + /** + * Compute the shape of a 5-dimensional byte array. + * + * @param array 5D array + * @return shape of the array + */ + public static Shape shapeOf(byte[][][][][] array) { + return Shape.of(computeShape(array, new long[5])); + } + + /** + * Compute the shape of a 6-dimensional byte array. + * + * @param array 6D array + * @return shape of the array + */ + public static Shape shapeOf(byte[][][][][][] array) { + return Shape.of(computeShape(array, new long[6])); + } + + /** + * Compute the shape of a short array. + * + * @param array 1D array + * @return shape of the array + */ + public static Shape shapeOf(short[] array) { + return Shape.of(array.length); + } + + /** + * Compute the shape of a 2-dimensional short array. + * + * @param array 2D array + * @return shape of the array + */ + public static Shape shapeOf(short[][] array) { + return Shape.of(computeShape(array, new long[2])); + } + + /** + * Compute the shape of a 3-dimensional short array. + * + * @param array 3D array + * @return shape of the array + */ + public static Shape shapeOf(short[][][] array) { + return Shape.of(computeShape(array, new long[3])); + } + + /** + * Compute the shape of a 4-dimensional short array. + * + * @param array 4D array + * @return shape of the array + */ + public static Shape shapeOf(short[][][][] array) { + return Shape.of(computeShape(array, new long[4])); + } + + /** + * Compute the shape of a 5-dimensional short array. + * + * @param array 5D array + * @return shape of the array + */ + public static Shape shapeOf(short[][][][][] array) { + return Shape.of(computeShape(array, new long[5])); + } + + /** + * Compute the shape of a 6-dimensional short array. + * + * @param array 6D array + * @return shape of the array + */ + public static Shape shapeOf(short[][][][][][] array) { + return Shape.of(computeShape(array, new long[6])); + } + + /** + * Compute the shape of a boolean array. + * + * @param array 1D array + * @return shape of the array + */ + public static Shape shapeOf(boolean[] array) { + return Shape.of(array.length); + } + + /** + * Compute the shape of a 2-dimensional boolean array. + * + * @param array 2D array + * @return shape of the array + */ + public static Shape shapeOf(boolean[][] array) { + return Shape.of(computeShape(array, new long[2])); + } + + /** + * Compute the shape of a 3-dimensional boolean array. + * + * @param array 3D array + * @return shape of the array + */ + public static Shape shapeOf(boolean[][][] array) { + return Shape.of(computeShape(array, new long[3])); + } + + /** + * Compute the shape of a 4-dimensional boolean array. + * + * @param array 4D array + * @return shape of the array + */ + public static Shape shapeOf(boolean[][][][] array) { + return Shape.of(computeShape(array, new long[4])); + } + + /** + * Compute the shape of a 5-dimensional boolean array. + * + * @param array 5D array + * @return shape of the array + */ + public static Shape shapeOf(boolean[][][][][] array) { + return Shape.of(computeShape(array, new long[5])); + } + + /** + * Compute the shape of a 6-dimensional boolean array. + * + * @param array 6D array + * @return shape of the array + */ + public static Shape shapeOf(boolean[][][][][][] array) { + return Shape.of(computeShape(array, new long[6])); + } + + /** + * Compute the shape of an object array. + * + * @param array 1D array + * @param data type + * @return shape of the array + */ + public static Shape shapeOf(T[] array) { + return Shape.of(array.length); + } + + /** + * Compute the shape of a 2-dimensional object array. + * + * @param array 2D array + * @param data type + * @return shape of the array + */ + public static Shape shapeOf(T[][] array) { + return Shape.of(computeShape(array, new long[2])); + } + + /** + * Compute the shape of a 3-dimensional object array. + * + * @param array 3D array + * @param data type + * @return shape of the array + */ + public static Shape shapeOf(T[][][] array) { + return Shape.of(computeShape(array, new long[3])); + } + + /** + * Compute the shape of a 4-dimensional object array. + * + * @param array 4D array + * @param data type + * @return shape of the array + */ + public static Shape shapeOf(T[][][][] array) { + return Shape.of(computeShape(array, new long[4])); + } + + /** + * Compute the shape of a 5-dimensional object array. + * + * @param array 5D array + * @param data type + * @return shape of the array + */ + public static Shape shapeOf(T[][][][][] array) { + return Shape.of(computeShape(array, new long[5])); + } + + /** + * Compute the shape of a 6-dimensional object array. + * + * @param array 6D array + * @param data type + * @return shape of the array + */ + public static Shape shapeOf(T[][][][][][] array) { + return Shape.of(computeShape(array, new long[6])); + } + + private static void dimSize(int arrayLength, long[] shape, int dimIdx) { + if (shape[dimIdx] == 0) { + shape[dimIdx] = arrayLength; + } else if (shape[dimIdx] != arrayLength) { + shape[dimIdx] = Shape.UNKNOWN_SIZE; + } + } + + private static long[] computeShape(int[][] array, long[] shape) { + if (array == null) { + throw new IllegalStateException("The array or one of its subarray is null"); + } + dimSize(array.length, shape, shape.length - 2); + for (int i = 0; i < array.length; ++i) { + if (array[i] == null) { + throw new IllegalStateException("One of the subarray is null"); + } + dimSize(array[i].length, shape, shape.length - 1); + } + return shape; + } + + private static long[] computeShape(int[][][] array, long[] shape) { + if (array == null) { + throw new IllegalStateException("The array or one of its subarray is null"); + } + dimSize(array.length, shape, shape.length - 3); + for (int i = 0; i < array.length; ++i) { + computeShape(array[i], shape); + } + return shape; + } + + private static long[] computeShape(int[][][][] array, long[] shape) { + if (array == null) { + throw new IllegalStateException("The array or one of its subarray is null"); + } + dimSize(array.length, shape, shape.length - 4); + for (int i = 0; i < array.length; ++i) { + computeShape(array[i], shape); + } + return shape; + } + + private static long[] computeShape(int[][][][][] array, long[] shape) { + if (array == null) { + throw new IllegalStateException("The array or one of its subarray is null"); + } + dimSize(array.length, shape, shape.length - 5); + for (int i = 0; i < array.length; ++i) { + computeShape(array[i], shape); + } + return shape; + } + + private static long[] computeShape(int[][][][][][] array, long[] shape) { + if (array == null) { + throw new IllegalStateException("The array or one of its subarray is null"); + } + dimSize(array.length, shape, shape.length - 6); + for (int i = 0; i < array.length; ++i) { + computeShape(array[i], shape); + } + return shape; + } + + private static long[] computeShape(long[][] array, long[] shape) { + if (array == null) { + throw new IllegalStateException("The array or one of its subarray is null"); + } + dimSize(array.length, shape, shape.length - 2); + for (int i = 0; i < array.length; ++i) { + if (array[i] == null) { + throw new IllegalStateException("One of the subarray is null"); + } + dimSize(array[i].length, shape, shape.length - 1); + } + return shape; + } + + private static long[] computeShape(long[][][] array, long[] shape) { + if (array == null) { + throw new IllegalStateException("The array or one of its subarray is null"); + } + dimSize(array.length, shape, shape.length - 3); + for (int i = 0; i < array.length; ++i) { + computeShape(array[i], shape); + } + return shape; + } + + private static long[] computeShape(long[][][][] array, long[] shape) { + if (array == null) { + throw new IllegalStateException("The array or one of its subarray is null"); + } + dimSize(array.length, shape, shape.length - 4); + for (int i = 0; i < array.length; ++i) { + computeShape(array[i], shape); + } + return shape; + } + + private static long[] computeShape(long[][][][][] array, long[] shape) { + if (array == null) { + throw new IllegalStateException("The array or one of its subarray is null"); + } + dimSize(array.length, shape, shape.length - 5); + for (int i = 0; i < array.length; ++i) { + computeShape(array[i], shape); + } + return shape; + } + + private static long[] computeShape(long[][][][][][] array, long[] shape) { + if (array == null) { + throw new IllegalStateException("The array or one of its subarray is null"); + } + dimSize(array.length, shape, shape.length - 6); + for (int i = 0; i < array.length; ++i) { + computeShape(array[i], shape); + } + return shape; + } + + private static long[] computeShape(float[][] array, long[] shape) { + if (array == null) { + throw new IllegalStateException("The array or one of its subarray is null"); + } + dimSize(array.length, shape, shape.length - 2); + for (int i = 0; i < array.length; ++i) { + if (array[i] == null) { + throw new IllegalStateException("One of the subarray is null"); + } + dimSize(array[i].length, shape, shape.length - 1); + } + return shape; + } + + private static long[] computeShape(float[][][] array, long[] shape) { + if (array == null) { + throw new IllegalStateException("The array or one of its subarray is null"); + } + dimSize(array.length, shape, shape.length - 3); + for (int i = 0; i < array.length; ++i) { + computeShape(array[i], shape); + } + return shape; + } + + private static long[] computeShape(float[][][][] array, long[] shape) { + if (array == null) { + throw new IllegalStateException("The array or one of its subarray is null"); + } + dimSize(array.length, shape, shape.length - 4); + for (int i = 0; i < array.length; ++i) { + computeShape(array[i], shape); + } + return shape; + } + + private static long[] computeShape(float[][][][][] array, long[] shape) { + if (array == null) { + throw new IllegalStateException("The array or one of its subarray is null"); + } + dimSize(array.length, shape, shape.length - 5); + for (int i = 0; i < array.length; ++i) { + computeShape(array[i], shape); + } + return shape; + } + + private static long[] computeShape(float[][][][][][] array, long[] shape) { + if (array == null) { + throw new IllegalStateException("The array or one of its subarray is null"); + } + dimSize(array.length, shape, shape.length - 6); + for (int i = 0; i < array.length; ++i) { + computeShape(array[i], shape); + } + return shape; + } + + private static long[] computeShape(double[][] array, long[] shape) { + if (array == null) { + throw new IllegalStateException("The array or one of its subarray is null"); + } + dimSize(array.length, shape, shape.length - 2); + for (int i = 0; i < array.length; ++i) { + if (array[i] == null) { + throw new IllegalStateException("One of the subarray is null"); + } + dimSize(array[i].length, shape, shape.length - 1); + } + return shape; + } + + private static long[] computeShape(double[][][] array, long[] shape) { + if (array == null) { + throw new IllegalStateException("The array or one of its subarray is null"); + } + dimSize(array.length, shape, shape.length - 3); + for (int i = 0; i < array.length; ++i) { + computeShape(array[i], shape); + } + return shape; + } + + private static long[] computeShape(double[][][][] array, long[] shape) { + if (array == null) { + throw new IllegalStateException("The array or one of its subarray is null"); + } + dimSize(array.length, shape, shape.length - 4); + for (int i = 0; i < array.length; ++i) { + computeShape(array[i], shape); + } + return shape; + } + + private static long[] computeShape(double[][][][][] array, long[] shape) { + if (array == null) { + throw new IllegalStateException("The array or one of its subarray is null"); + } + dimSize(array.length, shape, shape.length - 5); + for (int i = 0; i < array.length; ++i) { + computeShape(array[i], shape); + } + return shape; + } + + private static long[] computeShape(double[][][][][][] array, long[] shape) { + if (array == null) { + throw new IllegalStateException("The array or one of its subarray is null"); + } + dimSize(array.length, shape, shape.length - 6); + for (int i = 0; i < array.length; ++i) { + computeShape(array[i], shape); + } + return shape; + } + + private static long[] computeShape(byte[][] array, long[] shape) { + if (array == null) { + throw new IllegalStateException("The array or one of its subarray is null"); + } + dimSize(array.length, shape, shape.length - 2); + for (int i = 0; i < array.length; ++i) { + if (array[i] == null) { + throw new IllegalStateException("One of the subarray is null"); + } + dimSize(array[i].length, shape, shape.length - 1); + } + return shape; + } + + private static long[] computeShape(byte[][][] array, long[] shape) { + if (array == null) { + throw new IllegalStateException("The array or one of its subarray is null"); + } + dimSize(array.length, shape, shape.length - 3); + for (int i = 0; i < array.length; ++i) { + computeShape(array[i], shape); + } + return shape; + } + + private static long[] computeShape(byte[][][][] array, long[] shape) { + if (array == null) { + throw new IllegalStateException("The array or one of its subarray is null"); + } + dimSize(array.length, shape, shape.length - 4); + for (int i = 0; i < array.length; ++i) { + computeShape(array[i], shape); + } + return shape; + } + + private static long[] computeShape(byte[][][][][] array, long[] shape) { + if (array == null) { + throw new IllegalStateException("The array or one of its subarray is null"); + } + dimSize(array.length, shape, shape.length - 5); + for (int i = 0; i < array.length; ++i) { + computeShape(array[i], shape); + } + return shape; + } + + private static long[] computeShape(byte[][][][][][] array, long[] shape) { + if (array == null) { + throw new IllegalStateException("The array or one of its subarray is null"); + } + dimSize(array.length, shape, shape.length - 6); + for (int i = 0; i < array.length; ++i) { + computeShape(array[i], shape); + } + return shape; + } + + private static long[] computeShape(short[][] array, long[] shape) { + if (array == null) { + throw new IllegalStateException("The array or one of its subarray is null"); + } + dimSize(array.length, shape, shape.length - 2); + for (int i = 0; i < array.length; ++i) { + if (array[i] == null) { + throw new IllegalStateException("One of the subarray is null"); + } + dimSize(array[i].length, shape, shape.length - 1); + } + return shape; + } + + private static long[] computeShape(short[][][] array, long[] shape) { + if (array == null) { + throw new IllegalStateException("The array or one of its subarray is null"); + } + dimSize(array.length, shape, shape.length - 3); + for (int i = 0; i < array.length; ++i) { + computeShape(array[i], shape); + } + return shape; + } + + private static long[] computeShape(short[][][][] array, long[] shape) { + if (array == null) { + throw new IllegalStateException("The array or one of its subarray is null"); + } + dimSize(array.length, shape, shape.length - 4); + for (int i = 0; i < array.length; ++i) { + computeShape(array[i], shape); + } + return shape; + } + + private static long[] computeShape(short[][][][][] array, long[] shape) { + if (array == null) { + throw new IllegalStateException("The array or one of its subarray is null"); + } + dimSize(array.length, shape, shape.length - 5); + for (int i = 0; i < array.length; ++i) { + computeShape(array[i], shape); + } + return shape; + } + + private static long[] computeShape(short[][][][][][] array, long[] shape) { + if (array == null) { + throw new IllegalStateException("The array or one of its subarray is null"); + } + dimSize(array.length, shape, shape.length - 6); + for (int i = 0; i < array.length; ++i) { + computeShape(array[i], shape); + } + return shape; + } + + private static long[] computeShape(boolean[][] array, long[] shape) { + if (array == null) { + throw new IllegalStateException("The array or one of its subarray is null"); + } + dimSize(array.length, shape, shape.length - 2); + for (int i = 0; i < array.length; ++i) { + if (array[i] == null) { + throw new IllegalStateException("One of the subarray is null"); + } + dimSize(array[i].length, shape, shape.length - 1); + } + return shape; + } + + private static long[] computeShape(boolean[][][] array, long[] shape) { + if (array == null) { + throw new IllegalStateException("The array or one of its subarray is null"); + } + dimSize(array.length, shape, shape.length - 3); + for (int i = 0; i < array.length; ++i) { + computeShape(array[i], shape); + } + return shape; + } + + private static long[] computeShape(boolean[][][][] array, long[] shape) { + if (array == null) { + throw new IllegalStateException("The array or one of its subarray is null"); + } + dimSize(array.length, shape, shape.length - 4); + for (int i = 0; i < array.length; ++i) { + computeShape(array[i], shape); + } + return shape; + } + + private static long[] computeShape(boolean[][][][][] array, long[] shape) { + if (array == null) { + throw new IllegalStateException("The array or one of its subarray is null"); + } + dimSize(array.length, shape, shape.length - 5); + for (int i = 0; i < array.length; ++i) { + computeShape(array[i], shape); + } + return shape; + } + + private static long[] computeShape(boolean[][][][][][] array, long[] shape) { + if (array == null) { + throw new IllegalStateException("The array or one of its subarray is null"); + } + dimSize(array.length, shape, shape.length - 6); + for (int i = 0; i < array.length; ++i) { + computeShape(array[i], shape); + } + return shape; + } + + private static long[] computeShape(T[][] array, long[] shape) { + if (array == null) { + throw new IllegalStateException("The array or one of its subarray is null"); + } + dimSize(array.length, shape, shape.length - 2); + for (int i = 0; i < array.length; ++i) { + if (array[i] == null) { + throw new IllegalStateException("One of the subarray is null"); + } + dimSize(array[i].length, shape, shape.length - 1); + } + return shape; + } + + private static long[] computeShape(T[][][] array, long[] shape) { + if (array == null) { + throw new IllegalStateException("The array or one of its subarray is null"); + } + dimSize(array.length, shape, shape.length - 3); + for (int i = 0; i < array.length; ++i) { + computeShape(array[i], shape); + } + return shape; + } + + private static long[] computeShape(T[][][][] array, long[] shape) { + if (array == null) { + throw new IllegalStateException("The array or one of its subarray is null"); + } + dimSize(array.length, shape, shape.length - 4); + for (int i = 0; i < array.length; ++i) { + computeShape(array[i], shape); + } + return shape; + } + + private static long[] computeShape(T[][][][][] array, long[] shape) { + if (array == null) { + throw new IllegalStateException("The array or one of its subarray is null"); + } + dimSize(array.length, shape, shape.length - 5); + for (int i = 0; i < array.length; ++i) { + computeShape(array[i], shape); + } + return shape; + } + + private static long[] computeShape(T[][][][][][] array, long[] shape) { + if (array == null) { + throw new IllegalStateException("The array or one of its subarray is null"); + } + dimSize(array.length, shape, shape.length - 6); + for (int i = 0; i < array.length; ++i) { + computeShape(array[i], shape); + } + return shape; + } + + private static Class componentTypeOf(Object array) { + Class componentType = array.getClass().getComponentType(); + while (componentType.isArray()) { + componentType = componentType.getComponentType(); + } + return (Class)componentType; + } + + private static int[] computeArrayDims(NdArray ndArray, int expectedRank) { + Shape shape = ndArray.shape(); + if (shape.numDimensions() != expectedRank) { + throw new IllegalArgumentException("NdArray must be of rank " + expectedRank); + } + int[] arrayShape = new int[expectedRank]; + for (int i = 0; i < expectedRank; ++i) { + long dimSize = shape.size(i); + if (dimSize > Integer.MAX_VALUE) { + throw new IllegalArgumentException("Dimension " + i + " is too large to fit in a standard array (" + shape.size(i) + ")"); + } + arrayShape[i] = (int)dimSize; + } + return arrayShape; + } +} diff --git a/ndarray/src/main/java/org/tensorflow/ndarray/buffer/BooleanDataBuffer.java b/ndarray/src/main/java/org/tensorflow/ndarray/buffer/BooleanDataBuffer.java new file mode 100644 index 00000000000..73a570d4fe8 --- /dev/null +++ b/ndarray/src/main/java/org/tensorflow/ndarray/buffer/BooleanDataBuffer.java @@ -0,0 +1,165 @@ +/* + Copyright 2019 The TensorFlow Authors. All Rights Reserved. + + Licensed under the Apache License, Version 2.0 (the "License"); + you may not use this file except in compliance with the License. + You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + + Unless required by applicable law or agreed to in writing, software + distributed under the License is distributed on an "AS IS" BASIS, + WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + See the License for the specific language governing permissions and + limitations under the License. + ======================================================================= + */ +package org.tensorflow.ndarray.buffer; + +import java.nio.BufferOverflowException; +import java.nio.BufferUnderflowException; +import java.nio.ReadOnlyBufferException; + +/** + * A {@link DataBuffer} of booleans. + */ +public interface BooleanDataBuffer extends DataBuffer { + + /** + * Reads the boolean at the given index. + * + * @param index the index from which the float will be read + * @return the boolean at the given index + * @throws IndexOutOfBoundsException if index is negative or not smaller than the buffer size + */ + boolean getBoolean(long index); + + /** + * Writes the given boolean into this buffer at the given index. + * + * @param value the boolean to be written + * @param index the index at which the value will be written + * @return this buffer + * @throws IndexOutOfBoundsException if index is negative or not smaller than the buffer size + * @throws ReadOnlyBufferException if this buffer is read-only + */ + BooleanDataBuffer setBoolean(boolean value, long index); + + /** + * Bulk get method, using boolean arrays. + *

+ * This method transfers values from this buffer into the given destination array. If there are + * fewer values in the buffer than are required to satisfy the request, that is, if + * {@code dst.length > size()}, then no values are transferred and a + * BufferUnderflowException is thrown. + *

+ * Otherwise, this method copies {@code n = dst.length} values from this buffer into the given + * array. + * + * @param dst the array into which values are to be written + * @return this buffer + * @throws BufferUnderflowException if there are not enough values to copy from this buffer + */ + default BooleanDataBuffer read(boolean[] dst) { + return read(dst, 0, dst.length); + } + + /** + * Bulk get method, using boolean arrays. + *

+ * This method transfers values from this buffer into the given destination array. If there are + * fewer values in the buffer than are required to satisfy the request, that is, if + * {@code length > size()}, then no values are transferred and a + * BufferUnderflowException is thrown. + *

+ * Otherwise, this method copies {@code n = length} values from this buffer into the given array + * starting at the given offset. + * + * @param dst the array into which values are to be written + * @param offset the offset within the array of the first value to be written; must be + * non-negative and no larger than {@code dst.length} + * @param length the maximum number of values to be written to the given array; must be + * non-negative and no larger than {@code dst.length - offset} + * @return this buffer + * @throws BufferUnderflowException if there are fewer than length values remaining in this buffer + * @throws IndexOutOfBoundsException if the preconditions on the offset and length parameters do + * not hold + */ + BooleanDataBuffer read(boolean[] dst, int offset, int length); + + /** + * Bulk put method, using boolean arrays. + *

+ * This method transfers the values in the given source array into this buffer. If there are + * more values in the source array than in this buffer, that is, if + * {@code src.length > size()}, then no values are transferred and a + * BufferOverflowException is thrown. + *

+ * Otherwise, this method copies {@code n = src.length} values from the given array. + * + * @param src the source array from which values are to be read + * @return this buffer + * @throws BufferOverflowException if there is insufficient space in this buffer for the values in + * the source array + * @throws ReadOnlyBufferException if this buffer is read-only + */ + default BooleanDataBuffer write(boolean[] src) { + return write(src, 0, src.length); + } + + /** + * Bulk put method, using boolean arrays. + *

+ * This method transfers the values in the given source array into this buffer. If there are + * more values in the source array than in this buffer, that is, if + * {@code length > size()}, then no values are transferred and a + * BufferOverflowException is thrown. + *

+ * Otherwise, this method copies {@code n = length} values from the given array into this buffer, + * starting at the given offset. + * + * @param src the source array from which values are to be read + * @param offset the offset within the array of the first value to be read; must be non-negative + * and no larger than {@code src.length} + * @param length the number of values to be read from the given array; must be non-negative and no + * larger than {@code src.length - offset} + * @return this buffer + * @throws BufferOverflowException if there is insufficient space in this buffer for the values in + * the source array + * @throws IndexOutOfBoundsException if the preconditions on the offset and length parameters do + * not hold + * @throws ReadOnlyBufferException if this buffer is read-only + */ + BooleanDataBuffer write(boolean[] src, int offset, int length); + + @Override + default Boolean getObject(long index) { + return getBoolean(index); + } + + @Override + default BooleanDataBuffer setObject(Boolean value, long index) { + return setBoolean(value, index); + } + + @Override + BooleanDataBuffer copyTo(DataBuffer dst, long size); + + @Override + default BooleanDataBuffer offset(long index) { + return slice(index, size() - index); + } + + @Override + default BooleanDataBuffer narrow(long size) { + return slice(0, size); + } + + @Override + BooleanDataBuffer slice(long index, long size); + + @Override + default DataBufferWindow window(long size) { + throw new UnsupportedOperationException(); + } +} diff --git a/ndarray/src/main/java/org/tensorflow/ndarray/buffer/ByteDataBuffer.java b/ndarray/src/main/java/org/tensorflow/ndarray/buffer/ByteDataBuffer.java new file mode 100644 index 00000000000..b1cce441b13 --- /dev/null +++ b/ndarray/src/main/java/org/tensorflow/ndarray/buffer/ByteDataBuffer.java @@ -0,0 +1,232 @@ +/* + Copyright 2019 The TensorFlow Authors. All Rights Reserved. + + Licensed under the Apache License, Version 2.0 (the "License"); + you may not use this file except in compliance with the License. + You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + + Unless required by applicable law or agreed to in writing, software + distributed under the License is distributed on an "AS IS" BASIS, + WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + See the License for the specific language governing permissions and + limitations under the License. + ======================================================================= + */ +package org.tensorflow.ndarray.buffer; + +import java.nio.BufferOverflowException; +import java.nio.BufferUnderflowException; +import java.nio.ReadOnlyBufferException; + +/** + * A {@link DataBuffer} of bytes. + */ +public interface ByteDataBuffer extends DataBuffer { + + /** + * Reads the byte at the given index. + * + * @param index the index from which the float will be read + * @return the byte at the given index + * @throws IndexOutOfBoundsException if index is negative or not smaller than the buffer size + */ + byte getByte(long index); + + /** + * Writes the given byte into this buffer at the given index. + * + * @param value the byte to be written + * @param index the index at which the value will be written + * @return this buffer + * @throws IndexOutOfBoundsException if index is negative or not smaller than the buffer size + * @throws ReadOnlyBufferException if this buffer is read-only + */ + ByteDataBuffer setByte(byte value, long index); + + /** + * Bulk get method, using byte arrays. + *

+ * This method transfers values from this buffer into the given destination array. If there are + * fewer values in the buffer than are required to satisfy the request, that is, if + * {@code dst.length > size()}, then no values are transferred and a + * BufferUnderflowException is thrown. + *

+ * Otherwise, this method copies {@code n = dst.length} values from this buffer into the given + * array. + * + * @param dst the array into which values are to be written + * @return this buffer + * @throws BufferUnderflowException if there are not enough values to copy from this buffer + */ + default ByteDataBuffer read(byte[] dst) { + return read(dst, 0, dst.length); + } + + /** + * Bulk get method, using byte arrays. + *

+ * This method transfers values from this buffer into the given destination array. If there are + * fewer values in the buffer than are required to satisfy the request, that is, if + * {@code length > size()}, then no values are transferred and a + * BufferUnderflowException is thrown. + *

+ * Otherwise, this method copies {@code n = length} values from this buffer into the given array + * starting at the given offset. + * + * @param dst the array into which values are to be written + * @param offset the offset within the array of the first value to be written; must be + * non-negative and no larger than {@code dst.length} + * @param length the maximum number of values to be written to the given array; must be + * non-negative and no larger than {@code dst.length - offset} + * @return this buffer + * @throws BufferUnderflowException if there are fewer than length values remaining in this buffer + * @throws IndexOutOfBoundsException if the preconditions on the offset and length parameters do + * not hold + */ + ByteDataBuffer read(byte[] dst, int offset, int length); + + /** + * Bulk put method, using byte arrays. + *

+ * This method transfers the values in the given source array into this buffer. If there are + * more values in the source array than in this buffer, that is, if + * {@code src.length > size()}, then no values are transferred and a + * BufferOverflowException is thrown. + *

+ * Otherwise, this method copies {@code n = src.length} values from the given array. + * + * @param src the source array from which values are to be read + * @return this buffer + * @throws BufferOverflowException if there is insufficient space in this buffer for the values in + * the source array + * @throws ReadOnlyBufferException if this buffer is read-only + */ + default ByteDataBuffer write(byte[] src) { + return write(src, 0, src.length); + } + + /** + * Bulk put method, using byte arrays. + *

+ * This method transfers the values in the given source array into this buffer. If there are + * more values in the source array than in this buffer, that is, if + * {@code length > size()}, then no values are transferred and a + * BufferOverflowException is thrown. + *

+ * Otherwise, this method copies {@code n = length} values from the given array into this buffer, + * starting at the given offset. + * + * @param src the source array from which values are to be read + * @param offset the offset within the array of the first value to be read; must be non-negative + * and no larger than {@code src.length} + * @param length the number of values to be read from the given array; must be non-negative and no + * larger than {@code src.length - offset} + * @return this buffer + * @throws BufferOverflowException if there is insufficient space in this buffer for the values in + * the source array + * @throws IndexOutOfBoundsException if the preconditions on the offset and length parameters do + * not hold + * @throws ReadOnlyBufferException if this buffer is read-only + */ + ByteDataBuffer write(byte[] src, int offset, int length); + + /** + * Return this byte buffer as a buffer of ints. + * + *

The returned buffer provides a different view on the same memory as the original byte buffer, + * meaning that changing a value in one will affect the other. + * + * @return this buffer as a {@link IntDataBuffer} + * @throws IllegalStateException if this buffer cannot be converted + */ + IntDataBuffer asInts(); + + /** + * Return this byte buffer as a buffer of shorts. + * + *

The returned buffer provides a different view on the same memory as the original byte buffer, + * meaning that changing a value in one will affect the other. + * + * @return this buffer as a {@link ShortDataBuffer} + * @throws IllegalStateException if this buffer cannot be converted + */ + ShortDataBuffer asShorts(); + + /** + * Return this byte buffer as a buffer of longs. + * + *

The returned buffer provides a different view on the same memory as the original byte buffer, + * meaning that changing a value in one will affect the other. + * + * @return this buffer as a {@link LongDataBuffer} + * @throws IllegalStateException if this buffer cannot be converted + */ + LongDataBuffer asLongs(); + + /** + * Return this byte buffer as a buffer of floats. + * + *

The returned buffer provides a different view on the same memory as the original byte buffer, + * meaning that changing a value in one will affect the other. + * + * @return this buffer as a {@link FloatDataBuffer} + * @throws IllegalStateException if this buffer cannot be converted + */ + FloatDataBuffer asFloats(); + + /** + * Return this byte buffer as a buffer of doubles. + * + *

The returned buffer provides a different view on the same memory as the original byte buffer, + * meaning that changing a value in one will affect the other. + * + * @return this buffer as a {@link DoubleDataBuffer} + * @throws IllegalStateException if this buffer cannot be converted + */ + DoubleDataBuffer asDoubles(); + + /** + * Return this byte buffer as a buffer of booleans. + * + *

The returned buffer provides a different view on the same memory as the original byte buffer, + * meaning that changing a value in one will affect the other. + * + * @return this buffer as a {@link BooleanDataBuffer} + * @throws IllegalStateException if this buffer cannot be converted + */ + BooleanDataBuffer asBooleans(); + + @Override + default Byte getObject(long index) { + return getByte(index); + } + + @Override + default ByteDataBuffer setObject(Byte value, long index) { + return setByte(value, index); + } + + @Override + ByteDataBuffer copyTo(DataBuffer dst, long size); + + + @Override + default ByteDataBuffer offset(long index) { + return slice(index, size() - index); + } + + @Override + default ByteDataBuffer narrow(long size) { + return slice(0, size); + } + + @Override + ByteDataBuffer slice(long index, long size); + + @Override + default DataBufferWindow window(long size) { + throw new UnsupportedOperationException(); + } +} diff --git a/ndarray/src/main/java/org/tensorflow/ndarray/buffer/DataBuffer.java b/ndarray/src/main/java/org/tensorflow/ndarray/buffer/DataBuffer.java new file mode 100644 index 00000000000..e62ba87ce6e --- /dev/null +++ b/ndarray/src/main/java/org/tensorflow/ndarray/buffer/DataBuffer.java @@ -0,0 +1,324 @@ +/* + Copyright 2019 The TensorFlow Authors. All Rights Reserved. + + Licensed under the Apache License, Version 2.0 (the "License"); + you may not use this file except in compliance with the License. + You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + + Unless required by applicable law or agreed to in writing, software + distributed under the License is distributed on an "AS IS" BASIS, + WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + See the License for the specific language governing permissions and + limitations under the License. + ======================================================================= + */ + +package org.tensorflow.ndarray.buffer; + +import java.nio.BufferOverflowException; +import java.nio.BufferUnderflowException; +import java.nio.ReadOnlyBufferException; + +/** + * A container of data of a specific type. + * + *

Instances of {@code DataBuffer} map native or heap memory segments to a linear view that + * supports: + *

    + *
  • 64-bits indexing, allowing to work with buffer larger than 231 bytes
    • + *
  • Storage of object of any types and not only primitives
    • + *
  • Generic types allows to work directly with boxed types as well, which does not require + * explicit buffer types as with the standard JDK buffers. + *
+ * It is important to note that there is no guarantee the memory managed by a {@code DataBuffer} + * is linear, specially when dealing with non-primitive types or large buffers. + * + * @param type of data stored in this buffer + */ +public interface DataBuffer { + + /** + * Size of the buffer, in elements. + *

+ * For exemple, in case of a byte buffer, this value is equal to the number of bytes this buffer + * can hold. For an integer buffer, it is equal to the number of integers, therefore the size + * in bytes of this buffer is {@code size() * Integer.BYTES}. + * + * @return the buffer size + */ + long size(); + + /** + * Tells whether or not this buffer is backed by an accessible array. + * + * @return true if, and only if, this buffer is read-only + */ + boolean isReadOnly(); + + /** + * Reads the value at the given index. + * + * Important: Usage of this method should be limited to buffers of non-primitive types or + * when the data type is not deterministically known by the caller. In any other case, prefer + * the usage of its primitive variant which will significantly improve performances + * (e.g. {@code IntDataBuffer.getInt(idx)} + * + * @param index the index from which the float will be read + * @return the value at the given index + * @throws IndexOutOfBoundsException if index is negative or not smaller than the buffer size + */ + T getObject(long index); + + /** + * Writes the given value into this buffer at the given index. + * + * Important: Usage of this method should be limited to buffers of non-primitive types or + * when the data type is not deterministically known by the caller. In any other case, prefer + * the usage of its primitive variant which will significantly improve performances + * (e.g. {@code IntDataBuffer.setInt(idx)} + * + * @param value the value to be written + * @param index the index at which the value will be written + * @return this buffer + * @throws IndexOutOfBoundsException if index is negative or not smaller than the buffer size + * @throws ReadOnlyBufferException if this buffer is read-only + */ + DataBuffer setObject(T value, long index); + + /** + * Read the references of the objects in this buffer into the destination array. + *

+ * This method transfers values from this buffer into the given destination array. If there are + * fewer values in the buffer than are required to satisfy the request, that is, if + * {@code dst.length > size()}, then no values are transferred and a + * BufferUnderflowException is thrown. + *

+ * Otherwise, this method copies {@code n = dst.length} values from this buffer into the given + * array. + * + * @param dst the array into which values are to be written + * @return this buffer + * @throws BufferUnderflowException if there are not enough values to copy from this buffer + */ + default DataBuffer read(T[] dst) { + return read(dst, 0, dst.length); + } + + /** + * Read the references of the objects in this buffer into the destination array. + *

+ * This method transfers values from this buffer into the given destination array. If there are + * fewer values in the buffer than are required to satisfy the request, that is, if + * {@code length > size()}, then no values are transferred and a + * BufferUnderflowException is thrown. + *

+ * Otherwise, this method copies {@code n = length} values from this buffer into the given array + * starting at the given offset. + * + * @param dst the array into which values are to be written + * @param offset the offset within the array of the first value to be written; must be + * non-negative and no larger than {@code dst.length} + * @param length the maximum number of values to be written to the given array; must be + * non-negative and no larger than {@code dst.length - offset} + * @return this buffer + * @throws BufferUnderflowException if there are fewer than length values remaining in this buffer + * @throws IndexOutOfBoundsException if the preconditions on the offset and length parameters do + * not hold + */ + DataBuffer read(T[] dst, int offset, int length); + + /** + * Write the references of the objects in the source array into this buffer. + *

+ * This method transfers the values in the given source array into this buffer. If there are + * more values in the source array than in this buffer, that is, if + * {@code src.length > size()}, then no values are transferred and a + * BufferOverflowException is thrown. + *

+ * Otherwise, this method copies {@code n = src.length} values from the given array. + * + * @param src the source array from which values are to be read + * @return this buffer + * @throws BufferOverflowException if there is insufficient space in this buffer for the values in + * the source array + * @throws ReadOnlyBufferException if this buffer is read-only + */ + default DataBuffer write(T[] src) { + return write(src, 0, src.length); + } + + /** + * Bulk put method, using int arrays. + *

+ * This method transfers the values in the given source array into this buffer. If there are + * more values in the source array than in this buffer, that is, if + * {@code length > size()}, then no values are transferred and a + * BufferOverflowException is thrown. + *

+ * Otherwise, this method copies {@code n = length} values from the given array into this buffer, + * starting at the given offset. + * + * @param src the source array from which values are to be read + * @param offset the offset within the array of the first value to be read; must be non-negative + * and no larger than {@code src.length} + * @param length the number of values to be read from the given array; must be non-negative and no + * larger than {@code src.length - offset} + * @return this buffer + * @throws BufferOverflowException if there is insufficient space in this buffer for the values in + * the source array + * @throws IndexOutOfBoundsException if the preconditions on the offset and length parameters do + * not hold + * @throws ReadOnlyBufferException if this buffer is read-only + */ + DataBuffer write(T[] src, int offset, int length); + + /** + * Write the references of the objects in the source array into this buffer. + *

+ * If there are more values to copy than the destination buffer size, i.e. + * {@code size > dst.size()}, then no values are transferred and a + * BufferOverflowException is thrown. On the other hand, if there are more values to copy that + * the source buffer size, i.e. {@code > src.size()}, then a BufferUnderfloatException is thrown. + *

+ * Otherwise, this method copies {@code n = size} values from this buffer into + * the destination buffer. + * + * @param dst the destination buffer into which values are copied; must not be this buffer + * @param size number of values to copy to the destination buffer + * @return this buffer + * @throws IllegalArgumentException if the destination buffer is this buffer + * @throws ReadOnlyBufferException if the destination buffer is read-only + * @throws java.nio.BufferOverflowException if there is not enough space in destination buffer + * @throws java.nio.BufferUnderflowException if there are not enough values in the source buffer + */ + DataBuffer copyTo(DataBuffer dst, long size); + + /** + * Creates a new buffer whose content is a shared subsequence of this buffer's content, starting + * at the given index. + *

+ * The index must not be greater than this buffer size. Changes to this buffer's content will + * be visible in the new buffer and vice versa. The new buffer will be read-only if, and only if, + * this buffer is read-only. + *

+ * This call is equivalent to {@link #slice(long, long) slice(index, size() - index)} + * + * @param index index of the first value of the new buffer created, must not be greater than + * {@code size()} + * @return the new buffer + * @throws IllegalArgumentException if index do not pass validation checks + */ + default DataBuffer offset(long index) { + return slice(index, size() - index); + } + + /** + * Creates a new buffer whose content is a shared subsequence of this buffer's content, whose + * size is set to the given value. + *

+ * The new size must not be greater than this buffer size. Changes to this buffer's + * content will be visible in the new buffer and vice versa. The new buffer will be read-only if, + * and only if, this buffer is read-only. + *

+ * This call is equivalent to {@link #slice(long, long) slice(0, size)} + * + * @param size size of this new buffer + * @return the new buffer + * @throws IllegalArgumentException if index and/or size values do not pass validation checks + */ + default DataBuffer narrow(long size) { + return slice(0, size); + } + + /** + * Creates a new buffer whose content is a shared subsequence of this buffer's content, starting + * at the given index and of the given size. + *

+ * The index plus the new size must not be greater than this buffer size. Changes to this + * buffer's content will be visible in the new buffer and vice versa. The new buffer will be + * read-only if, and only if, this buffer is read-only. + * + * @param index index of the first value of the new buffer created + * @param size size of this new buffer, must not be greater than {@code size()} + * @return the new buffer + * @throws IllegalArgumentException if size value do not pass validation checks + */ + DataBuffer slice(long index, long size); + + /** + * Creates a {@link DataBufferWindow} that provides a partial view of this buffer. + * + *

The created window has a fixed size and can {@link DataBufferWindow#slide(long) "slide"} + * along this buffer to provide different views of the data without allocating a new buffer + * instance, like {@link #offset(long)} does. This improves overall performance when this + * operation is repeated frequently. For example: + * + * 

{@code
+   * IntDataBuffer bufferA = DataBuffers.ofInts(1024);
+   * // ... init buffer data
+   * IntDataBuffer bufferB = DataBuffers.ofInts(1, 2, 3, 4);
+   *
+   * // Return the index of the first occurrence of bufferB in bufferA using a sliding window
+   * DataBufferWindow windowA = bufferA.window(4);
+   * for (int i = 0; i < bufferA.size() - bufferB.size(); ++i) {
+   *     if (windowA.slideTo(i).buffer().equals(bufferB)) {
+   *         return i;
+   *     }
+   * }
+   * }
+ * + *

The returned object is stateful and is not thread-safe. + * + * @param size size of the window + * @return a new window that starts at the index 0 of this buffer + * @throws UnsupportedOperationException if this type of buffer does not support buffer windows + */ + default DataBufferWindow> window(long size) { + throw new UnsupportedOperationException(); + } + + /** + * Visits the backing storage of this buffer. + * + *

The buffer implementation is responsible of passing back a reference to the actual data + * storage to the provided visitor. The visitor does not have to handle all possible types of + * data storage and can override only methods for storage it is actually interested in. For any + * other type of storage, this call will fallback to {@link DataStorageVisitor#fallback()} so the + * visitor can execute some generic routine if needed. + * + * @param visitor visits the data storage of this buffer + * @param type of value returned by the visitor + * @return the same value returned by the visitor + */ + default R accept(DataStorageVisitor visitor) { + return visitor.fallback(); + } + + /** + * Checks equality between data buffers. + * + *

A data buffer is equal to another object if this object is another {@link DataBuffer} of the + * same size, type and the elements are equal and in the same order. For example: + * + * 

{@code
+   * IntDataBuffer buffer = DataBuffers.of(1, 2, 3);
+   *
+   * assertEquals(buffer, DataBuffers.of(1, 2, 3));  // true
+   * assertEquals(buffer, DataBuffers.ofObjects(1, 2, 3));  // true, as Integers are equal to ints
+   * assertNotEquals(buffer, DataBuffers.of(1, 2, 3, 0));  // false, different sizes
+   * assertNotEquals(buffer, DataBuffers.of(1, 3, 2));  // false, different order
+   * assertNotEquals(buffer, DataBuffers.of(1L, 2L, 3L));  // false, different types
+   * }
+ * + *

Note that the computation required to verify equality between two buffers can be expensive + * in some cases and therefore, it is recommended to not use this method in a critical path + * where performances matter. + * + * @param obj object to compare this buffer with + * @return true if this buffer is equal to the provided object + */ + @Override + boolean equals(Object obj); +} diff --git a/ndarray/src/main/java/org/tensorflow/ndarray/buffer/DataBufferWindow.java b/ndarray/src/main/java/org/tensorflow/ndarray/buffer/DataBufferWindow.java new file mode 100644 index 00000000000..85fc8c43d05 --- /dev/null +++ b/ndarray/src/main/java/org/tensorflow/ndarray/buffer/DataBufferWindow.java @@ -0,0 +1,93 @@ +package org.tensorflow.ndarray.buffer; + +/** + * A mutable container for viewing part of a {@link DataBuffer}. + * + *

Data buffer windows have a fixed size and can {@link DataBufferWindow#slide(long) "slide"} + * along a buffer to provide different views of the data without allocating a new buffer instance, + * like {@link DataBuffer#offset(long)} does. This improves overall performance when this operation + * is repeated frequently. For example: + * + * 

{@code
+ * IntDataBuffer bufferA = DataBuffers.ofInts(1024);
+ * // ... init buffer data
+ * IntDataBuffer bufferB = DataBuffers.ofInts(1, 2, 3, 4);
+ *
+ * // Return the index of the first occurrence of bufferB in bufferA using a sliding window
+ * DataBufferWindow windowA = bufferA.window(4);
+ * for (int i = 0; i < bufferA.size() - bufferB.size(); ++i) {
+ *     if (windowA.slideTo(i).buffer().equals(bufferB)) {
+ *         return i;
+ *     }
+ * }
+ * }
+ * + *

{@code DataBufferWindow} instances are stateful and not thread-safe. + * + * @param the type of buffer being viewed + */ +public interface DataBufferWindow> { + + /** + * Returns the current offset of this window in the original buffer. + */ + long offset(); + + /** + * Returns the size of this buffer window. + */ + long size(); + + /** + * Moves the window at the given position in the original buffer. + * + *

The size of the window remains the same and its offset is set to {@code index}, so that + * accessing the value of {@link #buffer()} at index {@code x} will return the value at + * {@code index + x} in the original buffer. + * + * @param index new offset for this window + * @return this instance + * @throws IndexOutOfBoundsException if the window cannot be slid because it goes beyond + * the original buffer limits + */ + DataBufferWindow slideTo(long index); + + /** + * Moves the window of {@code step} elements in the original buffer. + * + *

The size of the window remains the same and its offset is set to {@code offset() + step}. + * If {@code step} is positive, then the window will slide forward. If it is negative, it will + * slide backward. + * + * @param step value to add to the current offset of this window + * @return this instance + * @throws IndexOutOfBoundsException if the window cannot be slid because it goes beyond + * the original buffer limits + */ + DataBufferWindow slide(long step); + + /** + * Returns the buffer backing this window. + * + *

Each window instance has it's own buffer providing a view onto the original + * {@link DataBuffer}. The buffers are mutated when the window slides to different offsets. + * For example: + * + * 

{@code
+   * IntDataBuffer buffer = DataBuffers.of(0, 1, 2, 3);
+   * DataBufferWindow window = buffer.window(0, 2);
+   *
+   * IntDataBuffer windowBuffer = window.buffer();
+   * assertEquals(0, windowBuffer.getInt(0));
+   * assertEquals(1, windowBuffer.getInt(1));
+   *
+   * window.slideTo(2);
+   * assertEquals(2, windowBuffer.getInt(0));
+   * assertEquals(3, windowBuffer.getInt(1));
+   * assertSame(windowBuffer, window.buffer());
+   * }
+ * + * @return this window's buffer + */ + B buffer(); +} diff --git a/ndarray/src/main/java/org/tensorflow/ndarray/buffer/DataBuffers.java b/ndarray/src/main/java/org/tensorflow/ndarray/buffer/DataBuffers.java new file mode 100644 index 00000000000..a5feb2599d0 --- /dev/null +++ b/ndarray/src/main/java/org/tensorflow/ndarray/buffer/DataBuffers.java @@ -0,0 +1,457 @@ +/* + Copyright 2019 The TensorFlow Authors. All Rights Reserved. + + Licensed under the Apache License, Version 2.0 (the "License"); + you may not use this file except in compliance with the License. + You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + + Unless required by applicable law or agreed to in writing, software + distributed under the License is distributed on an "AS IS" BASIS, + WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + See the License for the specific language governing permissions and + limitations under the License. + ======================================================================= + */ +package org.tensorflow.ndarray.buffer; + +import java.lang.reflect.Array; +import java.nio.ByteBuffer; +import java.nio.DoubleBuffer; +import java.nio.FloatBuffer; +import java.nio.IntBuffer; +import java.nio.LongBuffer; +import java.nio.ShortBuffer; +import java.util.Arrays; +import java.util.BitSet; +import org.tensorflow.ndarray.impl.buffer.Validator; +import org.tensorflow.ndarray.impl.buffer.misc.MiscDataBufferFactory; +import org.tensorflow.ndarray.impl.buffer.nio.NioDataBufferFactory; +import org.tensorflow.ndarray.impl.buffer.raw.RawDataBufferFactory; + +/** + * Helper class for creating {@code DataBuffer} instances. + */ +public final class DataBuffers { + + /** + * Creates a buffer of bytes that can store up to {@code size} values + * + * @param size size of the buffer to allocate + * @return a new buffer + */ + public static ByteDataBuffer ofBytes(long size) { + Validator.createArgs(size, MAX_32BITS); + if (RawDataBufferFactory.canBeUsed()) { + return RawDataBufferFactory.create(new byte[(int)size], false); + } + return NioDataBufferFactory.create(ByteBuffer.allocate((int)size)); + } + + /** + * Creates a buffer of longs that can store up to {@code size} values + * + * @param size size of the buffer to allocate + * @return a new buffer + */ + public static LongDataBuffer ofLongs(long size) { + Validator.createArgs(size, MAX_32BITS); + if (RawDataBufferFactory.canBeUsed()) { + return RawDataBufferFactory.create(new long[(int)size], false); + } + return NioDataBufferFactory.create(LongBuffer.allocate((int)size)); + } + + /** + * Creates a buffer of integers that can store up to {@code size} values + * + * @param size size of the buffer to allocate + * @return a new buffer + */ + public static IntDataBuffer ofInts(long size) { + Validator.createArgs(size, MAX_32BITS); + if (RawDataBufferFactory.canBeUsed()) { + return RawDataBufferFactory.create(new int[(int)size], false); + } + return NioDataBufferFactory.create(IntBuffer.allocate((int)size)); + } + + /** + * Creates a buffer of shorts that can store up to {@code size} values + * + * @param size size of the buffer to allocate + * @return a new buffer + */ + public static ShortDataBuffer ofShorts(long size) { + Validator.createArgs(size, MAX_32BITS); + if (RawDataBufferFactory.canBeUsed()) { + return RawDataBufferFactory.create(new short[(int)size], false); + } + return NioDataBufferFactory.create(ShortBuffer.allocate((int)size)); + } + + /** + * Creates a buffer of doubles that can store up to {@code size} values + * + * @param size size of the buffer to allocate + * @return a new buffer + */ + public static DoubleDataBuffer ofDoubles(long size) { + Validator.createArgs(size, MAX_32BITS); + if (RawDataBufferFactory.canBeUsed()) { + return RawDataBufferFactory.create(new double[(int)size], false); + } + return NioDataBufferFactory.create(DoubleBuffer.allocate((int)size)); + } + + /** + * Creates a buffer of floats that can store up to {@code size} values + * + * @param size size of the buffer to allocate + * @return a new buffer + */ + public static FloatDataBuffer ofFloats(long size) { + Validator.createArgs(size, MAX_32BITS); + if (RawDataBufferFactory.canBeUsed()) { + return RawDataBufferFactory.create(new float[(int)size], false); + } + return NioDataBufferFactory.create(FloatBuffer.allocate((int)size)); + } + + /** + * Creates a buffer of booleans that can store up to {@code size} values + * + * @param size size of the buffer to allocate + * @return a new buffer + */ + public static BooleanDataBuffer ofBooleans(long size) { + Validator.createArgs(size, MAX_32BITS); + if (RawDataBufferFactory.canBeUsed()) { + return RawDataBufferFactory.create(new boolean[(int)size], false); + } + return MiscDataBufferFactory.create(new BitSet((int)size), size, false); + } + + /** + * Creates a buffer of references to objects of type {@code clazz` that can store up to `size} + * values. + * + * @param type the type of object stored in this buffer + * @param size size of the buffer to allocate + * @param data type + * @return a new buffer + */ + public static DataBuffer ofObjects(Class type, long size) { + Validator.createArgs(size, MAX_32BITS); + @SuppressWarnings("unchecked") + T[] array = (T[])Array.newInstance(type, (int)size); + return MiscDataBufferFactory.create(array, false); + } + + /** + * Create a buffer from an array of floats into a data buffer. + * + *

The returned buffer allows read and write operations and share the memory of the source + * array, which is equivalent to call {@link #of(float[], boolean, boolean) of(values, false, false}} + * + * @param values float values + * @return a new buffer + */ + public static FloatDataBuffer of(float... values) { + return of(values, false, false); + } + + /** + * Create a buffer from an array of bytes into a data buffer. + * + *

The returned buffer allows read and write operations and share the memory of the source + * array, which is equivalent to call {@link #of(byte[], boolean, boolean) of(values, false, false}} + * + * @param values byte values + * @return a new buffer + */ + public static ByteDataBuffer of(byte... values) { + return of(values, false, false); + } + + /** + * Create a buffer from an array of longs into a data buffer. + * + *

The returned buffer allows read and write operations and share the memory of the source + * array, which is equivalent to call {@link #of(long[], boolean, boolean) of(values, false, false}} + * + * @param values long values + * @return a new buffer + */ + public static LongDataBuffer of(long... values) { + return of(values, false, false); + } + + /** + * Create a buffer from an array of ints into a data buffer. + * + *

The returned buffer allows read and write operations and share the memory of the source + * array, which is equivalent to call {@link #of(int[], boolean, boolean) of(values, false, false}} + * + * @param values int values + * @return a new buffer + */ + public static IntDataBuffer of(int... values) { + return of(values, false, false); + } + + /** + * Create a buffer from an array of shorts into a data buffer. + * + *

The returned buffer allows read and write operations and share the memory of the source + * array, which is equivalent to call {@link #of(short[], boolean, boolean) of(values, false, false}} + * + * @param values short values + * @return a new buffer + */ + public static ShortDataBuffer of(short... values) { + return of(values, false, false); + } + + /** + * Create a buffer from an array of doubles into a data buffer. + * + *

The returned buffer allows read and write operations and share the memory of the source + * array, which is equivalent to call {@link #of(double[], boolean, boolean) of(array, false, false}} + * + * @param values double values + * @return a new buffer + */ + public static DoubleDataBuffer of(double... values) { + return of(values, false, false); + } + + /** + * Create a buffer from an array of booleans into a data buffer. + * + *

The returned buffer allows read and write operations and share the memory of the source + * array, which is equivalent to call {@link #of(boolean[], boolean, boolean) of(values, false, false}} + * + * @param values booleans values + * @return a new buffer + */ + public static BooleanDataBuffer of(boolean... values) { + return of(values, false, false); + } + + /** + * Create a buffer from an array of objects into a data buffer. + * + *

The returned buffer allows read and write operations and share the memory of the source + * array, which is equivalent to call {@link #of(Object[], boolean, boolean) of(values, false, false}} + * + * @param values objects values + * @param data type + * @return a new buffer + */ + @SafeVarargs + public static DataBuffer ofObjects(T... values) { + return of(values, false, false); + } + + /** + * Create a buffer from an array of floats into a data buffer. + * + * @param array array of floats + * @param readOnly true if the buffer created must be read-only + * @param makeCopy true if the array must be copied, false will wrap the provided array + * @return a new buffer + */ + public static FloatDataBuffer of(float[] array, boolean readOnly, boolean makeCopy) { + float[] bufferArray = makeCopy ? Arrays.copyOf(array, array.length) : array; + if (RawDataBufferFactory.canBeUsed()) { + return RawDataBufferFactory.create(bufferArray, readOnly); + } + FloatBuffer buf = FloatBuffer.wrap(bufferArray); + return NioDataBufferFactory.create(readOnly ? buf.asReadOnlyBuffer() : buf); + } + + /** + * Create a buffer from an array of bytes into a data buffer. + * + * @param array array of bytes + * @param readOnly true if the buffer created must be read-only + * @param makeCopy true if the array must be copied, false will wrap the provided array + * @return a new buffer + */ + public static ByteDataBuffer of(byte[] array, boolean readOnly, boolean makeCopy) { + byte[] bufferArray = makeCopy ? Arrays.copyOf(array, array.length) : array; + if (RawDataBufferFactory.canBeUsed()) { + return RawDataBufferFactory.create(bufferArray, readOnly); + } + ByteBuffer buf = ByteBuffer.wrap(bufferArray); + return NioDataBufferFactory.create(readOnly ? buf.asReadOnlyBuffer() : buf); + } + + /** + * Create a buffer from an array of longs into a data buffer. + * + * @param array array of longs + * @param readOnly true if the buffer created must be read-only + * @param makeCopy true if the array must be copied, false will wrap the provided array + * @return a new buffer + */ + public static LongDataBuffer of(long[] array, boolean readOnly, boolean makeCopy) { + long[] bufferArray = makeCopy ? Arrays.copyOf(array, array.length) : array; + if (RawDataBufferFactory.canBeUsed()) { + return RawDataBufferFactory.create(bufferArray, readOnly); + } + LongBuffer buf = LongBuffer.wrap(bufferArray); + return NioDataBufferFactory.create(readOnly ? buf.asReadOnlyBuffer() : buf); + } + + /** + * Create a buffer from an array of ints into a data buffer. + * + * @param array array of ints + * @param readOnly true if the buffer created must be read-only + * @param makeCopy true if the array must be copied, false will wrap the provided array + * @return a new buffer + */ + public static IntDataBuffer of(int[] array, boolean readOnly, boolean makeCopy) { + int[] bufferArray = makeCopy ? Arrays.copyOf(array, array.length) : array; + if (RawDataBufferFactory.canBeUsed()) { + return RawDataBufferFactory.create(bufferArray, readOnly); + } + IntBuffer buf = IntBuffer.wrap(bufferArray); + return NioDataBufferFactory.create(readOnly ? buf.asReadOnlyBuffer() : buf); + } + + /** + * Create a buffer from an array of shorts into a data buffer. + * + * @param array array of shorts + * @param readOnly true if the buffer created must be read-only + * @param makeCopy true if the array must be copied, false will wrap the provided array + * @return a new buffer + */ + public static ShortDataBuffer of(short[] array, boolean readOnly, boolean makeCopy) { + short[] bufferArray = makeCopy ? Arrays.copyOf(array, array.length) : array; + if (RawDataBufferFactory.canBeUsed()) { + return RawDataBufferFactory.create(bufferArray, readOnly); + } + ShortBuffer buf = ShortBuffer.wrap(bufferArray); + return NioDataBufferFactory.create(readOnly ? buf.asReadOnlyBuffer() : buf); + } + + /** + * Create a buffer from an array of doubles into a data buffer. + * + * @param array array of doubles + * @param readOnly true if the buffer created must be read-only + * @param makeCopy true if the array must be copied, false will wrap the provided array + * @return a new buffer + */ + public static DoubleDataBuffer of(double[] array, boolean readOnly, boolean makeCopy) { + double[] bufferArray = makeCopy ? Arrays.copyOf(array, array.length) : array; + if (RawDataBufferFactory.canBeUsed()) { + return RawDataBufferFactory.create(bufferArray, readOnly); + } + DoubleBuffer buf = DoubleBuffer.wrap(bufferArray); + return NioDataBufferFactory.create(readOnly ? buf.asReadOnlyBuffer() : buf); + } + + /** + * Create a buffer from an array of booleans into a data buffer. + * + * @param array array of booleans + * @param readOnly true if the buffer created must be read-only + * @param makeCopy true if the array must be copied, false will wrap the provided array + * @return a new buffer + */ + public static BooleanDataBuffer of(boolean[] array, boolean readOnly, boolean makeCopy) { + boolean[] bufferArray = makeCopy ? Arrays.copyOf(array, array.length) : array; + if (RawDataBufferFactory.canBeUsed()) { + return RawDataBufferFactory.create(bufferArray, readOnly); + } + return MiscDataBufferFactory.create(bufferArray, readOnly); + } + + /** + * Create a buffer from an array of objects into a data buffer. + * + * @param array array of objects + * @param readOnly true if the buffer created must be read-only + * @param makeCopy true if the array must be copied, false will wrap the provided array + * @param data type + * @return a new buffer + */ + public static DataBuffer of(T[] array, boolean readOnly, boolean makeCopy) { + T[] bufferArray = makeCopy ? Arrays.copyOf(array, array.length) : array; + return MiscDataBufferFactory.create(bufferArray, readOnly); + } + + /** + * Wraps a JDK NIO {@link ByteBuffer} into a data buffer. + * + * @param buf buffer to wrap + * @return a new buffer + */ + public static ByteDataBuffer of(ByteBuffer buf) { + return NioDataBufferFactory.create(buf.duplicate()); + } + + /** + * Wraps a JDK NIO {@link IntBuffer} into a data buffer. + * + * @param buf buffer to wrap + * @return a new buffer + */ + public static IntDataBuffer of(IntBuffer buf) { + return NioDataBufferFactory.create(buf.duplicate()); + } + + /** + * Wraps a JDK NIO {@link ShortBuffer} into a data buffer. + * + * @param buf buffer to wrap + * @return a new buffer + */ + public static ShortDataBuffer of(ShortBuffer buf) { + return NioDataBufferFactory.create(buf.duplicate()); + } + + /** + * Wraps a JDK NIO {@link LongBuffer} into a data buffer. + * + * @param buf buffer to wrap + * @return a new buffer + */ + public static LongDataBuffer of(LongBuffer buf) { + return NioDataBufferFactory.create(buf.duplicate()); + } + + /** + * Wraps a JDK NIO {@link FloatBuffer} into a data buffer. + * + * @param buf buffer to wrap + * @return a new buffer + */ + public static FloatDataBuffer of(FloatBuffer buf) { + return NioDataBufferFactory.create(buf.duplicate()); + } + + /** + * Wraps a JDK NIO {@link DoubleBuffer} into a data buffer. + * + * @param buf buffer to wrap + * @return a new buffer + */ + public static DoubleDataBuffer of(DoubleBuffer buf) { + return NioDataBufferFactory.create(buf.duplicate()); + } + + /* + * The maximum size for a buffer of this type, i.e. the maximum number of bytes it can store. + *

+ * As the maximum size may vary depending on the JVM implementation and on the platform, this + * property returns a value that is safe for most of them. + */ + static long MAX_32BITS = Integer.MAX_VALUE - 10; +} diff --git a/ndarray/src/main/java/org/tensorflow/ndarray/buffer/DataStorageVisitor.java b/ndarray/src/main/java/org/tensorflow/ndarray/buffer/DataStorageVisitor.java new file mode 100644 index 00000000000..560320cd7eb --- /dev/null +++ b/ndarray/src/main/java/org/tensorflow/ndarray/buffer/DataStorageVisitor.java @@ -0,0 +1,147 @@ +package org.tensorflow.ndarray.buffer; + +import java.nio.ByteBuffer; +import java.nio.DoubleBuffer; +import java.nio.FloatBuffer; +import java.nio.IntBuffer; +import java.nio.LongBuffer; +import java.nio.ShortBuffer; +import java.util.BitSet; + +/** + * Visit the backing storage of {@link DataBuffer} instances. + * + * @param value type returned by the visitor + */ +public interface DataStorageVisitor { + + /** + * Visit the {@link ByteBuffer} backing a given instance of a {@link DataBuffer} + * + * @param buffer underlying buffer + * @return any value + * @see DataBuffer#accept(DataStorageVisitor) + */ + default R visit(ByteBuffer buffer) { + return fallback(); + } + + /** + * Visit the {@link ShortBuffer} backing a given instance of a {@link DataBuffer} + * + * @param buffer underlying buffer + * @return any value + * @see DataBuffer#accept(DataStorageVisitor) + */ + default R visit(ShortBuffer buffer) { + return fallback(); + } + + /** + * Visit the {@link IntBuffer} backing a given instance of a {@link DataBuffer} + * + * @param buffer underlying buffer + * @return any value + * @see DataBuffer#accept(DataStorageVisitor) + */ + default R visit(IntBuffer buffer) { + return fallback(); + } + + /** + * Visit the {@link LongBuffer} backing a given instance of a {@link DataBuffer} + * + * @param buffer underlying buffer + * @return any value + * @see DataBuffer#accept(DataStorageVisitor) + */ + default R visit(LongBuffer buffer) { + return fallback(); + } + + /** + * Visit the {@link FloatBuffer} backing a given instance of a {@link DataBuffer} + * + * @param buffer underlying buffer + * @return any value + * @see DataBuffer#accept(DataStorageVisitor) + */ + default R visit(FloatBuffer buffer) { + return fallback(); + } + + /** + * Visit the {@link DoubleBuffer} backing a given instance of a {@link DataBuffer} + * + * @param buffer underlying buffer + * @return any value + * @see DataBuffer#accept(DataStorageVisitor) + */ + default R visit(DoubleBuffer buffer) { + return fallback(); + } + + /** + * Visit the boolean array backing a given instance of a {@link DataBuffer} + * + * @param array underlying array + * @param offset offset of the buffer within the array + * @param length length of the buffer within the array + * @return any value + * @see DataBuffer#accept(DataStorageVisitor) + */ + default R visit(boolean[] array, int offset, int length) { + return fallback(); + } + + /** + * Visit the bit set backing a given instance of a {@link DataBuffer} + * + * @param bitSet underlying bit set + * @param offset offset of the buffer within the bit set + * @param numBits number of bits used to represent the buffer within the bit set + * @return any value + * @see DataBuffer#accept(DataStorageVisitor) + */ + default R visit(BitSet bitSet, int offset, long numBits) { + return fallback(); + } + + /** + * Visit the object array backing a given instance of a {@link DataBuffer} + * + * @param array underlying array + * @param offset offset of the buffer within the array + * @param length length of the buffer within the array + * @return any value + * @see DataBuffer#accept(DataStorageVisitor) + */ + default R visit(Object[] array, int offset, int length) { + return fallback(); + } + + /** + * Visit the raw memory segment of a given instance of a {@link DataBuffer} + * + * @param address native address of the buffer + * @param length length of the buffer + * @param scale number of bytes required to store a single value of this buffer + * @return any value + * @see DataBuffer#accept(DataStorageVisitor) + */ + default R visit(long address, long length, long scale) { + return fallback(); + } + + /** + * Fallback method called if the visitor implementation does not support the type of backing storage + * for a given {@link DataBuffer} + * + *

The implementor of this interface must override the {@code visit} methods for type of storage + * it supports. If {@link DataBuffer#accept(DataStorageVisitor)} is called on a buffer + * using a different type of storage, the invocation will fallback to this method. + * + * @return any value + */ + R fallback(); +} diff --git a/ndarray/src/main/java/org/tensorflow/ndarray/buffer/DoubleDataBuffer.java b/ndarray/src/main/java/org/tensorflow/ndarray/buffer/DoubleDataBuffer.java new file mode 100644 index 00000000000..f2db925eb78 --- /dev/null +++ b/ndarray/src/main/java/org/tensorflow/ndarray/buffer/DoubleDataBuffer.java @@ -0,0 +1,165 @@ +/* + Copyright 2019 The TensorFlow Authors. All Rights Reserved. + + Licensed under the Apache License, Version 2.0 (the "License"); + you may not use this file except in compliance with the License. + You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + + Unless required by applicable law or agreed to in writing, software + distributed under the License is distributed on an "AS IS" BASIS, + WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + See the License for the specific language governing permissions and + limitations under the License. + ======================================================================= + */ +package org.tensorflow.ndarray.buffer; + +import java.nio.BufferOverflowException; +import java.nio.BufferUnderflowException; +import java.nio.ReadOnlyBufferException; + +/** + * A {@link DataBuffer} of doubles. + */ +public interface DoubleDataBuffer extends DataBuffer { + + /** + * Reads the double at the given index. + * + * @param index the index from which the float will be read + * @return the double at the given index + * @throws IndexOutOfBoundsException if index is negative or not smaller than the buffer size + */ + double getDouble(long index); + + /** + * Writes the given double into this buffer at the given index. + * + * @param value the double to be written + * @param index the index at which the value will be written + * @return this buffer + * @throws IndexOutOfBoundsException if index is negative or not smaller than the buffer size + * @throws ReadOnlyBufferException if this buffer is read-only + */ + DoubleDataBuffer setDouble(double value, long index); + + /** + * Bulk get method, using double arrays. + *

+ * This method transfers values from this buffer into the given destination array. If there are + * fewer values in the buffer than are required to satisfy the request, that is, if + * {@code dst.length > size()}, then no values are transferred and a + * BufferUnderflowException is thrown. + *

+ * Otherwise, this method copies {@code n = dst.length} values from this buffer into the given + * array. + * + * @param dst the array into which values are to be written + * @return this buffer + * @throws BufferUnderflowException if there are not enough values to copy from this buffer + */ + default DoubleDataBuffer read(double[] dst) { + return read(dst, 0, dst.length); + } + + /** + * Bulk get method, using double arrays. + *

+ * This method transfers values from this buffer into the given destination array. If there are + * fewer values in the buffer than are required to satisfy the request, that is, if + * {@code length > size()}, then no values are transferred and a + * BufferUnderflowException is thrown. + *

+ * Otherwise, this method copies {@code n = length} values from this buffer into the given array + * starting at the given offset. + * + * @param dst the array into which values are to be written + * @param offset the offset within the array of the first value to be written; must be + * non-negative and no larger than {@code dst.length} + * @param length the maximum number of values to be written to the given array; must be + * non-negative and no larger than {@code dst.length - offset} + * @return this buffer + * @throws BufferUnderflowException if there are fewer than length values remaining in this buffer + * @throws IndexOutOfBoundsException if the preconditions on the offset and length parameters do + * not hold + */ + DoubleDataBuffer read(double[] dst, int offset, int length); + + /** + * Bulk put method, using double arrays. + *

+ * This method transfers the values in the given source array into this buffer. If there are + * more values in the source array than in this buffer, that is, if + * {@code src.length > size()}, then no values are transferred and a + * BufferOverflowException is thrown. + *

+ * Otherwise, this method copies {@code n = src.length} values from the given array. + * + * @param src the source array from which values are to be read + * @return this buffer + * @throws BufferOverflowException if there is insufficient space in this buffer for the values in + * the source array + * @throws ReadOnlyBufferException if this buffer is read-only + */ + default DoubleDataBuffer write(double[] src) { + return write(src, 0, src.length); + } + + /** + * Bulk put method, using double arrays. + *

+ * This method transfers the values in the given source array into this buffer. If there are + * more values in the source array than in this buffer, that is, if + * {@code length > size()}, then no values are transferred and a + * BufferOverflowException is thrown. + *

+ * Otherwise, this method copies {@code n = length} values from the given array into this buffer, + * starting at the given offset. + * + * @param src the source array from which values are to be read + * @param offset the offset within the array of the first value to be read; must be non-negative + * and no larger than {@code src.length} + * @param length the number of values to be read from the given array; must be non-negative and no + * larger than {@code src.length - offset} + * @return this buffer + * @throws BufferOverflowException if there is insufficient space in this buffer for the values in + * the source array + * @throws IndexOutOfBoundsException if the preconditions on the offset and length parameters do + * not hold + * @throws ReadOnlyBufferException if this buffer is read-only + */ + DoubleDataBuffer write(double[] src, int offset, int length); + + @Override + default Double getObject(long index) { + return getDouble(index); + } + + @Override + default DoubleDataBuffer setObject(Double value, long index) { + return setDouble(value, index); + } + + @Override + DoubleDataBuffer copyTo(DataBuffer dst, long size); + + @Override + default DoubleDataBuffer offset(long index) { + return slice(index, size() - index); + } + + @Override + default DoubleDataBuffer narrow(long size) { + return slice(0, size); + } + + @Override + DoubleDataBuffer slice(long index, long size); + + @Override + default DataBufferWindow window(long size) { + throw new UnsupportedOperationException(); + } +} diff --git a/ndarray/src/main/java/org/tensorflow/ndarray/buffer/FloatDataBuffer.java b/ndarray/src/main/java/org/tensorflow/ndarray/buffer/FloatDataBuffer.java new file mode 100644 index 00000000000..4961c1b3445 --- /dev/null +++ b/ndarray/src/main/java/org/tensorflow/ndarray/buffer/FloatDataBuffer.java @@ -0,0 +1,165 @@ +/* + Copyright 2019 The TensorFlow Authors. All Rights Reserved. + + Licensed under the Apache License, Version 2.0 (the "License"); + you may not use this file except in compliance with the License. + You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + + Unless required by applicable law or agreed to in writing, software + distributed under the License is distributed on an "AS IS" BASIS, + WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + See the License for the specific language governing permissions and + limitations under the License. + ======================================================================= + */ +package org.tensorflow.ndarray.buffer; + +import java.nio.BufferOverflowException; +import java.nio.BufferUnderflowException; +import java.nio.ReadOnlyBufferException; + +/** + * A {@link DataBuffer} of floats. + */ +public interface FloatDataBuffer extends DataBuffer { + + /** + * Reads the float at the given index. + * + * @param index the index from which the float will be read + * @return the float at the given index + * @throws IndexOutOfBoundsException if index is negative or not smaller than the buffer size + */ + float getFloat(long index); + + /** + * Writes the given float into this buffer at the given index. + * + * @param value the float to be written + * @param index the index at which the value will be written + * @return this buffer + * @throws IndexOutOfBoundsException if index is negative or not smaller than the buffer size + * @throws ReadOnlyBufferException if this buffer is read-only + */ + FloatDataBuffer setFloat(float value, long index); + + /** + * Bulk get method, using float arrays. + *

+ * This method transfers values from this buffer into the given destination array. If there are + * fewer values in the buffer than are required to satisfy the request, that is, if + * {@code dst.length > size()}, then no values are transferred and a + * BufferUnderflowException is thrown. + *

+ * Otherwise, this method copies {@code n = dst.length} values from this buffer into the given + * array. + * + * @param dst the array into which values are to be written + * @return this buffer + * @throws BufferUnderflowException if there are not enough values to copy from this buffer + */ + default FloatDataBuffer read(float[] dst) { + return read(dst, 0, dst.length); + } + + /** + * Bulk get method, using float arrays. + *

+ * This method transfers values from this buffer into the given destination array. If there are + * fewer values in the buffer than are required to satisfy the request, that is, if + * {@code length > size()}, then no values are transferred and a + * BufferUnderflowException is thrown. + *

+ * Otherwise, this method copies {@code n = length} values from this buffer into the given array + * starting at the given offset. + * + * @param dst the array into which values are to be written + * @param offset the offset within the array of the first value to be written; must be + * non-negative and no larger than {@code dst.length} + * @param length the maximum number of values to be written to the given array; must be + * non-negative and no larger than {@code dst.length - offset} + * @return this buffer + * @throws BufferUnderflowException if there are fewer than length values remaining in this buffer + * @throws IndexOutOfBoundsException if the preconditions on the offset and length parameters do + * not hold + */ + FloatDataBuffer read(float[] dst, int offset, int length); + + /** + * Bulk put method, using float arrays. + *

+ * This method transfers the values in the given source array into this buffer. If there are + * more values in the source array than in this buffer, that is, if + * {@code src.length > size()}, then no values are transferred and a + * BufferOverflowException is thrown. + *

+ * Otherwise, this method copies {@code n = src.length} values from the given array. + * + * @param src the source array from which values are to be read + * @return this buffer + * @throws BufferOverflowException if there is insufficient space in this buffer for the values in + * the source array + * @throws ReadOnlyBufferException if this buffer is read-only + */ + default FloatDataBuffer write(float[] src) { + return write(src, 0, src.length); + } + + /** + * Bulk put method, using float arrays. + *

+ * This method transfers the values in the given source array into this buffer. If there are + * more values in the source array than in this buffer, that is, if + * {@code length > size()}, then no values are transferred and a + * BufferOverflowException is thrown. + *

+ * Otherwise, this method copies {@code n = length} values from the given array into this buffer, + * starting at the given offset. + * + * @param src the source array from which values are to be read + * @param offset the offset within the array of the first value to be read; must be non-negative + * and no larger than {@code src.length} + * @param length the number of values to be read from the given array; must be non-negative and no + * larger than {@code src.length - offset} + * @return this buffer + * @throws BufferOverflowException if there is insufficient space in this buffer for the values in + * the source array + * @throws IndexOutOfBoundsException if the preconditions on the offset and length parameters do + * not hold + * @throws ReadOnlyBufferException if this buffer is read-only + */ + FloatDataBuffer write(float[] src, int offset, int length); + + @Override + default Float getObject(long index) { + return getFloat(index); + } + + @Override + default FloatDataBuffer setObject(Float value, long index) { + return setFloat(value, index); + } + + @Override + FloatDataBuffer copyTo(DataBuffer dst, long size); + + @Override + default FloatDataBuffer offset(long index) { + return slice(index, size() - index); + } + + @Override + default FloatDataBuffer narrow(long size) { + return slice(0, size); + } + + @Override + FloatDataBuffer slice(long index, long size); + + @Override + default DataBufferWindow window(long size) { + throw new UnsupportedOperationException(); + } +} diff --git a/ndarray/src/main/java/org/tensorflow/ndarray/buffer/IntDataBuffer.java b/ndarray/src/main/java/org/tensorflow/ndarray/buffer/IntDataBuffer.java new file mode 100644 index 00000000000..2d660756e09 --- /dev/null +++ b/ndarray/src/main/java/org/tensorflow/ndarray/buffer/IntDataBuffer.java @@ -0,0 +1,165 @@ +/* + Copyright 2019 The TensorFlow Authors. All Rights Reserved. + + Licensed under the Apache License, Version 2.0 (the "License"); + you may not use this file except in compliance with the License. + You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + + Unless required by applicable law or agreed to in writing, software + distributed under the License is distributed on an "AS IS" BASIS, + WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + See the License for the specific language governing permissions and + limitations under the License. + ======================================================================= + */ +package org.tensorflow.ndarray.buffer; + +import java.nio.BufferOverflowException; +import java.nio.BufferUnderflowException; +import java.nio.ReadOnlyBufferException; + +/** + * A {@link DataBuffer} of ints. + */ +public interface IntDataBuffer extends DataBuffer { + + /** + * Reads the int at the given index. + * + * @param index the index from which the float will be read + * @return the int at the given index + * @throws IndexOutOfBoundsException if index is negative or not smaller than the buffer size + */ + int getInt(long index); + + /** + * Writes the given int into this buffer at the given index. + * + * @param value the int to be written + * @param index the index at which the value will be written + * @return this buffer + * @throws IndexOutOfBoundsException if index is negative or not smaller than the buffer size + * @throws ReadOnlyBufferException if this buffer is read-only + */ + IntDataBuffer setInt(int value, long index); + + /** + * Bulk get method, using int arrays. + *

+ * This method transfers values from this buffer into the given destination array. If there are + * fewer values in the buffer than are required to satisfy the request, that is, if + * {@code dst.length > size()}, then no values are transferred and a + * BufferUnderflowException is thrown. + *

+ * Otherwise, this method copies {@code n = dst.length} values from this buffer into the given + * array. + * + * @param dst the array into which values are to be written + * @return this buffer + * @throws BufferUnderflowException if there are not enough values to copy from this buffer + */ + default IntDataBuffer read(int[] dst) { + return read(dst, 0, dst.length); + } + + /** + * Bulk get method, using int arrays. + *

+ * This method transfers values from this buffer into the given destination array. If there are + * fewer values in the buffer than are required to satisfy the request, that is, if + * {@code length > size()}, then no values are transferred and a + * BufferUnderflowException is thrown. + *

+ * Otherwise, this method copies {@code n = length} values from this buffer into the given array + * starting at the given offset. + * + * @param dst the array into which values are to be written + * @param offset the offset within the array of the first value to be written; must be + * non-negative and no larger than {@code dst.length} + * @param length the maximum number of values to be written to the given array; must be + * non-negative and no larger than {@code dst.length - offset} + * @return this buffer + * @throws BufferUnderflowException if there are fewer than length values remaining in this buffer + * @throws IndexOutOfBoundsException if the preconditions on the offset and length parameters do + * not hold + */ + IntDataBuffer read(int[] dst, int offset, int length); + + /** + * Bulk put method, using int arrays. + *

+ * This method transfers the values in the given source array into this buffer. If there are + * more values in the source array than in this buffer, that is, if + * {@code src.length > size()}, then no values are transferred and a + * BufferOverflowException is thrown. + *

+ * Otherwise, this method copies {@code n = src.length} values from the given array. + * + * @param src the source array from which values are to be read + * @return this buffer + * @throws BufferOverflowException if there is insufficient space in this buffer for the values in + * the source array + * @throws ReadOnlyBufferException if this buffer is read-only + */ + default IntDataBuffer write(int[] src) { + return write(src, 0, src.length); + } + + /** + * Bulk put method, using int arrays. + *

+ * This method transfers the values in the given source array into this buffer. If there are + * more values in the source array than in this buffer, that is, if + * {@code length > size()}, then no values are transferred and a + * BufferOverflowException is thrown. + *

+ * Otherwise, this method copies {@code n = length} values from the given array into this buffer, + * starting at the given offset. + * + * @param src the source array from which values are to be read + * @param offset the offset within the array of the first value to be read; must be non-negative + * and no larger than {@code src.length} + * @param length the number of values to be read from the given array; must be non-negative and no + * larger than {@code src.length - offset} + * @return this buffer + * @throws BufferOverflowException if there is insufficient space in this buffer for the values in + * the source array + * @throws IndexOutOfBoundsException if the preconditions on the offset and length parameters do + * not hold + * @throws ReadOnlyBufferException if this buffer is read-only + */ + IntDataBuffer write(int[] src, int offset, int length); + + @Override + default Integer getObject(long index) { + return getInt(index); + } + + @Override + default IntDataBuffer setObject(Integer value, long index) { + return setInt(value, index); + } + + @Override + IntDataBuffer copyTo(DataBuffer dst, long size); + + @Override + default IntDataBuffer offset(long index) { + return slice(index, size() - index); + } + + @Override + default IntDataBuffer narrow(long size) { + return slice(0, size); + } + + @Override + IntDataBuffer slice(long index, long size); + + @Override + default DataBufferWindow window(long size) { + throw new UnsupportedOperationException(); + } +} diff --git a/ndarray/src/main/java/org/tensorflow/ndarray/buffer/LongDataBuffer.java b/ndarray/src/main/java/org/tensorflow/ndarray/buffer/LongDataBuffer.java new file mode 100644 index 00000000000..f88ae4a80b4 --- /dev/null +++ b/ndarray/src/main/java/org/tensorflow/ndarray/buffer/LongDataBuffer.java @@ -0,0 +1,165 @@ +/* + Copyright 2019 The TensorFlow Authors. All Rights Reserved. + + Licensed under the Apache License, Version 2.0 (the "License"); + you may not use this file except in compliance with the License. + You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + + Unless required by applicable law or agreed to in writing, software + distributed under the License is distributed on an "AS IS" BASIS, + WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + See the License for the specific language governing permissions and + limitations under the License. + ======================================================================= + */ +package org.tensorflow.ndarray.buffer; + +import java.nio.BufferOverflowException; +import java.nio.BufferUnderflowException; +import java.nio.ReadOnlyBufferException; + +/** + * A {@link DataBuffer} of longs. + */ +public interface LongDataBuffer extends DataBuffer { + + /** + * Reads the long at the given index. + * + * @param index the index from which the float will be read + * @return the long at the given index + * @throws IndexOutOfBoundsException if index is negative or not smaller than the buffer size + */ + long getLong(long index); + + /** + * Writes the given long into this buffer at the given index. + * + * @param value the long to be written + * @param index the index at which the value will be written + * @return this buffer + * @throws IndexOutOfBoundsException if index is negative or not smaller than the buffer size + * @throws ReadOnlyBufferException if this buffer is read-only + */ + LongDataBuffer setLong(long value, long index); + + /** + * Bulk get method, using long arrays. + *

+ * This method transfers values from this buffer into the given destination array. If there are + * fewer values in the buffer than are required to satisfy the request, that is, if + * {@code dst.length > size()}, then no values are transferred and a + * BufferUnderflowException is thrown. + *

+ * Otherwise, this method copies {@code n = dst.length} values from this buffer into the given + * array. + * + * @param dst the array into which values are to be written + * @return this buffer + * @throws BufferUnderflowException if there are not enough values to copy from this buffer + */ + default LongDataBuffer read(long[] dst) { + return read(dst, 0, dst.length); + } + + /** + * Bulk get method, using long arrays. + *

+ * This method transfers values from this buffer into the given destination array. If there are + * fewer values in the buffer than are required to satisfy the request, that is, if + * {@code length > size()}, then no values are transferred and a + * BufferUnderflowException is thrown. + *

+ * Otherwise, this method copies {@code n = length} values from this buffer into the given array + * starting at the given offset. + * + * @param dst the array into which values are to be written + * @param offset the offset within the array of the first value to be written; must be + * non-negative and no larger than {@code dst.length} + * @param length the maximum number of values to be written to the given array; must be + * non-negative and no larger than {@code dst.length - offset} + * @return this buffer + * @throws BufferUnderflowException if there are fewer than length values remaining in this buffer + * @throws IndexOutOfBoundsException if the preconditions on the offset and length parameters do + * not hold + */ + LongDataBuffer read(long[] dst, int offset, int length); + + /** + * Bulk put method, using long arrays. + *

+ * This method transfers the values in the given source array into this buffer. If there are + * more values in the source array than in this buffer, that is, if + * {@code src.length > size()}, then no values are transferred and a + * BufferOverflowException is thrown. + *

+ * Otherwise, this method copies {@code n = src.length} values from the given array. + * + * @param src the source array from which values are to be read + * @return this buffer + * @throws BufferOverflowException if there is insufficient space in this buffer for the values in + * the source array + * @throws ReadOnlyBufferException if this buffer is read-only + */ + default LongDataBuffer write(long[] src) { + return write(src, 0, src.length); + } + + /** + * Bulk put method, using long arrays. + *

+ * This method transfers the values in the given source array into this buffer. If there are + * more values in the source array than in this buffer, that is, if + * {@code length > size()}, then no values are transferred and a + * BufferOverflowException is thrown. + *

+ * Otherwise, this method copies {@code n = length} values from the given array into this buffer, + * starting at the given offset. + * + * @param src the source array from which values are to be read + * @param offset the offset within the array of the first value to be read; must be non-negative + * and no larger than {@code src.length} + * @param length the number of values to be read from the given array; must be non-negative and no + * larger than {@code src.length - offset} + * @return this buffer + * @throws BufferOverflowException if there is insufficient space in this buffer for the values in + * the source array + * @throws IndexOutOfBoundsException if the preconditions on the offset and length parameters do + * not hold + * @throws ReadOnlyBufferException if this buffer is read-only + */ + LongDataBuffer write(long[] src, int offset, int length); + + @Override + default Long getObject(long index) { + return getLong(index); + } + + @Override + default LongDataBuffer setObject(Long value, long index) { + return setLong(value, index); + } + + @Override + LongDataBuffer copyTo(DataBuffer dst, long size); + + @Override + default LongDataBuffer offset(long index) { + return slice(index, size() - index); + } + + @Override + default LongDataBuffer narrow(long size) { + return slice(0, size); + } + + @Override + LongDataBuffer slice(long index, long size); + + @Override + default DataBufferWindow window(long size) { + throw new UnsupportedOperationException(); + } +} diff --git a/ndarray/src/main/java/org/tensorflow/ndarray/buffer/ShortDataBuffer.java b/ndarray/src/main/java/org/tensorflow/ndarray/buffer/ShortDataBuffer.java new file mode 100644 index 00000000000..290e2d57619 --- /dev/null +++ b/ndarray/src/main/java/org/tensorflow/ndarray/buffer/ShortDataBuffer.java @@ -0,0 +1,165 @@ +/* + Copyright 2019 The TensorFlow Authors. All Rights Reserved. + + Licensed under the Apache License, Version 2.0 (the "License"); + you may not use this file except in compliance with the License. + You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + + Unless required by applicable law or agreed to in writing, software + distributed under the License is distributed on an "AS IS" BASIS, + WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + See the License for the specific language governing permissions and + limitations under the License. + ======================================================================= + */ +package org.tensorflow.ndarray.buffer; + +import java.nio.BufferOverflowException; +import java.nio.BufferUnderflowException; +import java.nio.ReadOnlyBufferException; + +/** + * A {@link DataBuffer} of shorts. + */ +public interface ShortDataBuffer extends DataBuffer { + + /** + * Reads the short at the given index. + * + * @param index the index from which the float will be read + * @return the short at the given index + * @throws IndexOutOfBoundsException if index is negative or not smaller than the buffer size + */ + short getShort(long index); + + /** + * Writes the given short into this buffer at the given index. + * + * @param value the short to be written + * @param index the index at which the value will be written + * @return this buffer + * @throws IndexOutOfBoundsException if index is negative or not smaller than the buffer size + * @throws ReadOnlyBufferException if this buffer is read-only + */ + ShortDataBuffer setShort(short value, long index); + + /** + * Bulk get method, using short arrays. + *

+ * This method transfers values from this buffer into the given destination array. If there are + * fewer values in the buffer than are required to satisfy the request, that is, if + * {@code dst.length > size()}, then no values are transferred and a + * BufferUnderflowException is thrown. + *

+ * Otherwise, this method copies {@code n = dst.length} values from this buffer into the given + * array. + * + * @param dst the array into which values are to be written + * @return this buffer + * @throws BufferUnderflowException if there are not enough values to copy from this buffer + */ + default ShortDataBuffer read(short[] dst) { + return read(dst, 0, dst.length); + } + + /** + * Bulk get method, using short arrays. + *

+ * This method transfers values from this buffer into the given destination array. If there are + * fewer values in the buffer than are required to satisfy the request, that is, if + * {@code length > size()}, then no values are transferred and a + * BufferUnderflowException is thrown. + *

+ * Otherwise, this method copies {@code n = length} values from this buffer into the given array + * starting at the given offset. + * + * @param dst the array into which values are to be written + * @param offset the offset within the array of the first value to be written; must be + * non-negative and no larger than {@code dst.length} + * @param length the maximum number of values to be written to the given array; must be + * non-negative and no larger than {@code dst.length - offset} + * @return this buffer + * @throws BufferUnderflowException if there are fewer than length values remaining in this buffer + * @throws IndexOutOfBoundsException if the preconditions on the offset and length parameters do + * not hold + */ + ShortDataBuffer read(short[] dst, int offset, int length); + + /** + * Bulk put method, using short arrays. + *

+ * This method transfers the values in the given source array into this buffer. If there are + * more values in the source array than in this buffer, that is, if + * {@code src.length > size()}, then no values are transferred and a + * BufferOverflowException is thrown. + *

+ * Otherwise, this method copies {@code n = src.length} values from the given array. + * + * @param src the source array from which values are to be read + * @return this buffer + * @throws BufferOverflowException if there is insufficient space in this buffer for the values in + * the source array + * @throws ReadOnlyBufferException if this buffer is read-only + */ + default ShortDataBuffer write(short[] src) { + return write(src, 0, src.length); + } + + /** + * Bulk put method, using short arrays. + *

+ * This method transfers the values in the given source array into this buffer. If there are + * more values in the source array than in this buffer, that is, if + * {@code length > size()}, then no values are transferred and a + * BufferOverflowException is thrown. + *

+ * Otherwise, this method copies {@code n = length} values from the given array into this buffer, + * starting at the given offset. + * + * @param src the source array from which values are to be read + * @param offset the offset within the array of the first value to be read; must be non-negative + * and no larger than {@code src.length} + * @param length the number of values to be read from the given array; must be non-negative and no + * larger than {@code src.length - offset} + * @return this buffer + * @throws BufferOverflowException if there is insufficient space in this buffer for the values in + * the source array + * @throws IndexOutOfBoundsException if the preconditions on the offset and length parameters do + * not hold + * @throws ReadOnlyBufferException if this buffer is read-only + */ + ShortDataBuffer write(short[] src, int offset, int length); + + @Override + default Short getObject(long index) { + return getShort(index); + } + + @Override + default ShortDataBuffer setObject(Short value, long index) { + return setShort(value, index); + } + + @Override + ShortDataBuffer copyTo(DataBuffer dst, long size); + + @Override + default ShortDataBuffer offset(long index) { + return slice(index, size() - index); + } + + @Override + default ShortDataBuffer narrow(long size) { + return slice(0, size); + } + + @Override + ShortDataBuffer slice(long index, long size); + + @Override + default DataBufferWindow window(long size) { + throw new UnsupportedOperationException(); + } +} diff --git a/ndarray/src/main/java/org/tensorflow/ndarray/buffer/layout/BooleanDataLayout.java b/ndarray/src/main/java/org/tensorflow/ndarray/buffer/layout/BooleanDataLayout.java new file mode 100644 index 00000000000..c7092c8720d --- /dev/null +++ b/ndarray/src/main/java/org/tensorflow/ndarray/buffer/layout/BooleanDataLayout.java @@ -0,0 +1,65 @@ +/* + * Copyright 2019 The TensorFlow Authors. All Rights Reserved. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + * ======================================================================= + */ +package org.tensorflow.ndarray.buffer.layout; + +import org.tensorflow.ndarray.buffer.BooleanDataBuffer; +import org.tensorflow.ndarray.buffer.DataBuffer; +import org.tensorflow.ndarray.impl.buffer.adapter.DataBufferAdapterFactory; + +/** + * A {@link DataLayout} that converts data stored in a buffer to booleans. + * + * @param type of buffer this layout can be applied to + * @see DataLayout + */ +public interface BooleanDataLayout> extends DataLayout { + + @Override + default BooleanDataBuffer applyTo(S buffer) { + return DataBufferAdapterFactory.create(buffer, this); + } + + /** + * Writes a boolean into the buffer at the given index after converting it to the buffer type. + * + * @param buffer the buffer to write to + * @param value the boolean to convert and write + * @param index index in the buffer where the converted value should be written + * @see #writeObject(DataBuffer, Boolean, long) + */ + void writeBoolean(S buffer, boolean value, long index); + + /** + * Reads {@code n = scale()} values from the buffer at the given index and return them as a boolean. + * + * @param buffer the buffer to read from + * @param index position of the buffer to read in the buffer + * @return the boolean value + * @see #readObject(DataBuffer, long) + */ + boolean readBoolean(S buffer, long index); + + @Override + default void writeObject(S buffer, Boolean value, long index) { + writeBoolean(buffer, value, index); + } + + @Override + default Boolean readObject(S buffer, long index) { + return readBoolean(buffer, index); + } +} diff --git a/ndarray/src/main/java/org/tensorflow/ndarray/buffer/layout/ByteDataLayout.java b/ndarray/src/main/java/org/tensorflow/ndarray/buffer/layout/ByteDataLayout.java new file mode 100644 index 00000000000..e4d4bf9c8cf --- /dev/null +++ b/ndarray/src/main/java/org/tensorflow/ndarray/buffer/layout/ByteDataLayout.java @@ -0,0 +1,65 @@ +/* + Copyright 2019 The TensorFlow Authors. All Rights Reserved. + + Licensed under the Apache License, Version 2.0 (the "License"); + you may not use this file except in compliance with the License. + You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + + Unless required by applicable law or agreed to in writing, software + distributed under the License is distributed on an "AS IS" BASIS, + WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + See the License for the specific language governing permissions and + limitations under the License. + ======================================================================= + */ +package org.tensorflow.ndarray.buffer.layout; + +import org.tensorflow.ndarray.buffer.ByteDataBuffer; +import org.tensorflow.ndarray.buffer.DataBuffer; +import org.tensorflow.ndarray.impl.buffer.adapter.DataBufferAdapterFactory; + +/** + * A {@link DataLayout} that converts data stored in a buffer to bytes. + * + * @param type of buffer this layout can be applied to + * @see DataLayout + */ +public interface ByteDataLayout> extends DataLayout { + + @Override + default ByteDataBuffer applyTo(S buffer) { + return DataBufferAdapterFactory.create(buffer, this); + } + + /** + * Writes a byte into the buffer at the given index after converting it to the buffer type. + * + * @param buffer the buffer to write to + * @param value the byte to convert and write + * @param index index in the buffer where the converted value should be written + * @see #writeObject(DataBuffer, Byte, long) + */ + void writeByte(S buffer, byte value, long index); + + /** + * Reads {@code n = scale()} values from the buffer at the given index and return them as a byte. + * + * @param buffer the buffer to read from + * @param index position of the buffer to read in the buffer + * @return the byte value + * @see #readObject(DataBuffer, long) + */ + byte readByte(S buffer, long index); + + @Override + default void writeObject(S buffer, Byte value, long index) { + writeByte(buffer, value, index); + } + + @Override + default Byte readObject(S buffer, long index) { + return readByte(buffer, index); + } +} diff --git a/ndarray/src/main/java/org/tensorflow/ndarray/buffer/layout/DataLayout.java b/ndarray/src/main/java/org/tensorflow/ndarray/buffer/layout/DataLayout.java new file mode 100644 index 00000000000..93cc542e07a --- /dev/null +++ b/ndarray/src/main/java/org/tensorflow/ndarray/buffer/layout/DataLayout.java @@ -0,0 +1,129 @@ +/* + * Copyright 2019 The TensorFlow Authors. All Rights Reserved. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + * ======================================================================= + */ +package org.tensorflow.ndarray.buffer.layout; + +import org.tensorflow.ndarray.buffer.DataBuffer; +import org.tensorflow.ndarray.impl.buffer.adapter.DataBufferAdapterFactory; + +/** + * Converts data stored in a buffer to a given type. + * + *

{@code DataLayout} instances are used to define a custom format for storing and reading data + * of a {@link DataBuffer}. They provide a segregation layer between the type of data stored in the + * buffer (the buffer type) and the type of data manipulated by the end user (the + * user type). + * + *

Since the conversion methods are invoked for every value that is written or read, working + * with data layouts may have a negative impact on the performances so using primitive types directly + * should be preferred whenever possible. + * + *

It is also recommended to implement immutable data layouts so they can be reapplied to multiple + * buffers without reallocating a new instance for each of them. For example: + * + * 

+ * class BigIntegerBufferAllocator {
+ *
+ *     public DataBuffer<BigInteger> allocate(long size) {
+ *         return LAYOUT.applyTo(DataBuffers.ofLongs(size * LAYOUT.scale()));  // scale is 1 by default
+ *     }
+ *
+ *     private static final DataLayout<LongDataBuffer, BigInteger> LAYOUT = new DataLayout<LongDataBuffer, BigInteger>() {
+ *
+ *         @Override
+ *         public void writeObject(LongDataBuffer buffer, BigInteger value, long index) {
+ *             buffer.setLong(value.longValue(), index);
+ *         }
+ *
+ *         @Override
+ *         public BigInteger readObject(LongDataBuffer buffer, long index) {
+ *             return BigInteger.valueOf(buffer.getLong(index));
+ *         }
+ *     };
+ * }
+ *
+ * + * @param type of buffer this layout can be applied to + * @param user data type of this layout + */ +public interface DataLayout, T> { + + /** + * Apply this layout to the provided buffer. + * + *

The returned {@link DataBuffer} instance is simply a wrapper to the original buffer and does + * not have a backing storage of his own. + * + * @param buffer the target buffer to apply this layout to + * @return a buffer with this layout + */ + default DataBuffer applyTo(S buffer) { + return DataBufferAdapterFactory.create(buffer, this); + } + + /** + * Writes a user value into the buffer at the given index after converting it to the buffer type. + * + *

It is the responsibility of the implementors of this interface to write the converted value + * to the given buffer before this call returns, using the most appropriate method. For example, + * for a layout converting a {@code BigInteger} to a single {@code long}, + * 

+   * @Override
+   * public void writeObject(LongDataBuffer buffer, BigInteger value, long index) {
+   *   buffer.setLong(value.longValue(), index);
+   * }
+   *
+ * If a single user value scales over more than one buffer values, {@code index} indicates the + * starting position of the sequence to be written to the buffer. + * + * @param buffer the buffer to write to + * @param value the value in the user type to convert and write + * @param index index in the buffer where the converted value should be written + */ + void writeObject(S buffer, T value, long index); + + /** + * Reads {@code n = scale()} values from the buffer at the given index and return them as a single + * value in the user type. + * + *

It is the responsibility of the implementors of this interface to read the value to be + * converted from the given buffer, using the most appropriate method. For example, for a layout + * that converting a single {@code long} to a {@code BigInteger}, + * 

+   * @Override
+   * public BigInteger readObject(LongDataBuffer buffer, long index) {
+   *   return BigInteger.valueOf(buffer.getLong(index));
+   * }
+   *
+ * If a single user value scales over more than one buffer values, {@code index} indicates the + * starting position of the sequence to be read from the buffer. + * + * @param buffer the buffer to read from + * @param index position of the buffer to read in the buffer + * @return the converted value + */ + T readObject(S buffer, long index); + + /** + * Indicates the number of buffer values are required to represent a single user value, default is 1. + * + *

Scale must be positive and must be an integer, meaning that a single buffer value in a buffer cannot + * be used to represent more than one user value. + */ + default int scale() { + return 1; + } +} diff --git a/ndarray/src/main/java/org/tensorflow/ndarray/buffer/layout/DataLayouts.java b/ndarray/src/main/java/org/tensorflow/ndarray/buffer/layout/DataLayouts.java new file mode 100644 index 00000000000..8f69168930c --- /dev/null +++ b/ndarray/src/main/java/org/tensorflow/ndarray/buffer/layout/DataLayouts.java @@ -0,0 +1,96 @@ +/* + * Copyright 2020 The TensorFlow Authors. All Rights Reserved. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + * ======================================================================= + */ + +package org.tensorflow.ndarray.buffer.layout; + +import java.nio.charset.Charset; +import org.tensorflow.ndarray.buffer.ByteDataBuffer; +import org.tensorflow.ndarray.buffer.DataBuffer; +import org.tensorflow.ndarray.buffer.ShortDataBuffer; +import org.tensorflow.ndarray.impl.buffer.layout.Bfloat16Layout; +import org.tensorflow.ndarray.impl.buffer.layout.BoolLayout; +import org.tensorflow.ndarray.impl.buffer.layout.Float16Layout; +import org.tensorflow.ndarray.impl.buffer.layout.StringLayout; + +/** + * Exposes {@link DataLayout} instances of data formats frequently used in linear algebra computation. + * + *

Example of usage: + * 

{@code
+ * // Storing boolean values in a ByteDataBuffer
+ * BooleanDataBuffer boolBuffer = DataLayouts.BOOL.applyTo(byteDataBuffer);
+ *
+ * // Allocating a new buffer of 256 half floats
+ * FloatDataBuffer halfBuffer = DataLayouts.FLOAT16.applyTo(DataBuffers.ofShorts(256 * DataLayouts.FLOAT16.scale());
+ * }
+ */ +public final class DataLayouts { + + /** + * Data layout for converting 16-bit bfloats to/from short values. + * + *

This format used to be specific to TensorFlow but has now been adopted more broadly in the + * machine learning field. It is optimized for fast conversion with single-precision 32-bit + * floating points by simply shifting their value and truncating the mantissa to only 7 bits. + * + *

Therefore, this is a lost of precision in the fraction part compared to the IEEE-754 + * half-precision floating point specification (see {@link #FLOAT16} but it has a larger range of + * possible values in the whole part as it preserves the 8-bit exponent and uses the same bias, + * (i.e. an absolute range above 0 of approximately [10-40, 3.39 × 1038] + * + *

Some CPUs support the bfloat16 format natively for better performances. + */ + public static final FloatDataLayout BFLOAT16 = new Bfloat16Layout(); + + /** + * Data layout for converting 16-bit half floats to/from short values. + * + *

Half floats are stored in memory accordingly to the IEEE-754 half-precision floating point + * specification, and are converted to/from 32-bit floats in the user space. + * + *

There is a potential loss of precision when converting a single float (32-bit) to a half + * float (16-bit). Absolute range of values above 0 for a half float is approximately + * [5.96 × 10-8, 6.55 × 104] and their decimal part is rounded up + * to a 10 bits mantissa. + * + *

In general, half float computation perform better on GPUs since, in general, CPUs do not + * support this format natively.

+ */ + public static final FloatDataLayout FLOAT16 = new Float16Layout(); + + /** + * Data layout for converting booleans to/from byte values. + * + *

Since there is no Java NIO boolean buffer, this layout is particularly useful for mapping + * booleans values to standard byte buffers. The conversion between a boolean and a byte requires + * explicit type casting. + */ + public static final BooleanDataLayout BOOL = new BoolLayout(); + + /** + * Creates a data layout for converting strings to/from byte sequences. + * + *

This layout requires a {@code charset} in parameter to specify how the strings must be + * encoded/decoded as byte sequences. So a new layout instance is always returned. + * + * @param charset charset to use + * @return a new string layout + */ + public static DataLayout, String> ofStrings(Charset charset) { + return StringLayout.of(charset); + } +} diff --git a/ndarray/src/main/java/org/tensorflow/ndarray/buffer/layout/DoubleDataLayout.java b/ndarray/src/main/java/org/tensorflow/ndarray/buffer/layout/DoubleDataLayout.java new file mode 100644 index 00000000000..efd1e461802 --- /dev/null +++ b/ndarray/src/main/java/org/tensorflow/ndarray/buffer/layout/DoubleDataLayout.java @@ -0,0 +1,65 @@ +/* + * Copyright 2019 The TensorFlow Authors. All Rights Reserved. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + * ======================================================================= + */ +package org.tensorflow.ndarray.buffer.layout; + +import org.tensorflow.ndarray.buffer.DataBuffer; +import org.tensorflow.ndarray.buffer.DoubleDataBuffer; +import org.tensorflow.ndarray.impl.buffer.adapter.DataBufferAdapterFactory; + +/** + * A {@link DataLayout} that converts data stored in a buffer to doubles. + * + * @param type of buffer this layout can be applied to + * @see DataLayout + */ +public interface DoubleDataLayout> extends DataLayout { + + @Override + default DoubleDataBuffer applyTo(S buffer) { + return DataBufferAdapterFactory.create(buffer, this); + } + + /** + * Writes a double into the buffer at the given index after converting it to the buffer type. + * + * @param buffer the buffer to write to + * @param value the double to convert and write + * @param index index in the buffer where the converted value should be written + * @see #writeObject(DataBuffer, Double, long) + */ + void writeDouble(S buffer, double value, long index); + + /** + * Reads {@code n = scale()} buffer values at the given index and return them as a double. + * + * @param buffer the buffer to read from + * @param index position of the buffer to read in the buffer + * @return the double value + * @see #readObject(DataBuffer, long) + */ + double readDouble(S buffer, long index); + + @Override + default void writeObject(S buffer, Double value, long index) { + writeDouble(buffer, value, index); + } + + @Override + default Double readObject(S buffer, long index) { + return readDouble(buffer, index); + } +} diff --git a/ndarray/src/main/java/org/tensorflow/ndarray/buffer/layout/FloatDataLayout.java b/ndarray/src/main/java/org/tensorflow/ndarray/buffer/layout/FloatDataLayout.java new file mode 100644 index 00000000000..a57f525d69f --- /dev/null +++ b/ndarray/src/main/java/org/tensorflow/ndarray/buffer/layout/FloatDataLayout.java @@ -0,0 +1,65 @@ +/* + * Copyright 2019 The TensorFlow Authors. All Rights Reserved. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + * ======================================================================= + */ +package org.tensorflow.ndarray.buffer.layout; + +import org.tensorflow.ndarray.buffer.DataBuffer; +import org.tensorflow.ndarray.buffer.FloatDataBuffer; +import org.tensorflow.ndarray.impl.buffer.adapter.DataBufferAdapterFactory; + +/** + * A {@link DataLayout} that converts data stored in a buffer to floats. + * + * @param type of buffer this layout can be applied to + * @see DataLayout + */ +public interface FloatDataLayout> extends DataLayout { + + @Override + default FloatDataBuffer applyTo(S buffer) { + return DataBufferAdapterFactory.create(buffer, this); + } + + /** + * Writes a float into the buffer at the given index after converting it to the buffer type. + * + * @param buffer the buffer to write to + * @param value the float to convert and write + * @param index index in the buffer where the converted value should be written + * @see #writeObject(DataBuffer, Float, long) + */ + void writeFloat(S buffer, float value, long index); + + /** + * Reads {@code n = scale()} values from the buffer at the given index and return them as a float. + * + * @param buffer the buffer to read from + * @param index position of the buffer to read in the buffer + * @return the float value + * @see #readObject(DataBuffer, long) + */ + float readFloat(S buffer, long index); + + @Override + default void writeObject(S buffer, Float value, long index) { + writeFloat(buffer, value, index); + } + + @Override + default Float readObject(S buffer, long index) { + return readFloat(buffer, index); + } +} diff --git a/ndarray/src/main/java/org/tensorflow/ndarray/buffer/layout/IntDataLayout.java b/ndarray/src/main/java/org/tensorflow/ndarray/buffer/layout/IntDataLayout.java new file mode 100644 index 00000000000..718deac9b9f --- /dev/null +++ b/ndarray/src/main/java/org/tensorflow/ndarray/buffer/layout/IntDataLayout.java @@ -0,0 +1,66 @@ +/* + * Copyright 2019 The TensorFlow Authors. All Rights Reserved. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + * ======================================================================= + */ + +package org.tensorflow.ndarray.buffer.layout; + +import org.tensorflow.ndarray.buffer.DataBuffer; +import org.tensorflow.ndarray.buffer.IntDataBuffer; +import org.tensorflow.ndarray.impl.buffer.adapter.DataBufferAdapterFactory; + +/** + * A {@link DataLayout} that converts data stored in a buffer to ints. + * + * @param type of buffer this layout can be applied to + * @see DataLayout + */ +public interface IntDataLayout> extends DataLayout { + + @Override + default IntDataBuffer applyTo(S buffer) { + return DataBufferAdapterFactory.create(buffer, this); + } + + /** + * Writes a int into the buffer at the given index after converting it to the buffer type. + * + * @param buffer the buffer to write to + * @param value the int to convert and write + * @param index index in the buffer where the converted value should be written + * @see #writeObject(DataBuffer, Integer, long) + */ + void writeInt(S buffer, int value, long index); + + /** + * Reads {@code n = scale()} values from the buffer at the given index and return them as an int. + * + * @param buffer the buffer to read from + * @param index position of the buffer to read in the buffer + * @return the int value + * @see #readObject(DataBuffer, long) + */ + int readInt(S buffer, long index); + + @Override + default void writeObject(S buffer, Integer value, long index) { + writeInt(buffer, value, index); + } + + @Override + default Integer readObject(S buffer, long index) { + return readInt(buffer, index); + } +} diff --git a/ndarray/src/main/java/org/tensorflow/ndarray/buffer/layout/LongDataLayout.java b/ndarray/src/main/java/org/tensorflow/ndarray/buffer/layout/LongDataLayout.java new file mode 100644 index 00000000000..f5c86ddd378 --- /dev/null +++ b/ndarray/src/main/java/org/tensorflow/ndarray/buffer/layout/LongDataLayout.java @@ -0,0 +1,65 @@ +/* + * Copyright 2019 The TensorFlow Authors. All Rights Reserved. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + * ======================================================================= + */ +package org.tensorflow.ndarray.buffer.layout; + +import org.tensorflow.ndarray.buffer.DataBuffer; +import org.tensorflow.ndarray.buffer.LongDataBuffer; +import org.tensorflow.ndarray.impl.buffer.adapter.DataBufferAdapterFactory; + +/** + * A {@link DataLayout} that converts data stored in a buffer to longs. + * + * @param type of buffer this layout can be applied to + * @see DataLayout + */ +public interface LongDataLayout> extends DataLayout { + + @Override + default LongDataBuffer applyTo(S buffer) { + return DataBufferAdapterFactory.create(buffer, this); + } + + /** + * Writes a long into the buffer at the given index after converting it to the buffer type. + * + * @param buffer the buffer to write to + * @param value the long to convert and write + * @param index index in the buffer where the converted value should be written + * @see #writeObject(DataBuffer, Long, long) + */ + void writeLong(S buffer, long value, long index); + + /** + * Reads {@code n = scale()} values from the buffer at the given index and return them as a long. + * + * @param buffer the buffer to read from + * @param index position of the buffer to read in the buffer + * @return the long value + * @see #readObject(DataBuffer, long) + */ + long readLong(S buffer, long index); + + @Override + default void writeObject(S buffer, Long value, long index) { + writeLong(buffer, value, index); + } + + @Override + default Long readObject(S buffer, long index) { + return readLong(buffer, index); + } +} diff --git a/ndarray/src/main/java/org/tensorflow/ndarray/buffer/layout/ShortDataLayout.java b/ndarray/src/main/java/org/tensorflow/ndarray/buffer/layout/ShortDataLayout.java new file mode 100644 index 00000000000..89c1fd0dec4 --- /dev/null +++ b/ndarray/src/main/java/org/tensorflow/ndarray/buffer/layout/ShortDataLayout.java @@ -0,0 +1,65 @@ +/* + * Copyright 2019 The TensorFlow Authors. All Rights Reserved. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + * ======================================================================= + */ +package org.tensorflow.ndarray.buffer.layout; + +import org.tensorflow.ndarray.buffer.DataBuffer; +import org.tensorflow.ndarray.buffer.ShortDataBuffer; +import org.tensorflow.ndarray.impl.buffer.adapter.DataBufferAdapterFactory; + +/** + * A {@link DataLayout} that converts data stored in a buffer to shorts. + * + * @param type of buffer this layout can be applied to + * @see DataLayout + */ +public interface ShortDataLayout> extends DataLayout { + + @Override + default ShortDataBuffer applyTo(S buffer) { + return DataBufferAdapterFactory.create(buffer, this); + } + + /** + * Writes a short into the buffer at the given index after converting it to the buffer type. + * + * @param buffer the buffer to write to + * @param value the short to convert and write + * @param index index in the buffer where the converted value should be written + * @see #writeObject(DataBuffer, Short, long) + */ + void writeShort(S buffer, short value, long index); + + /** + * Reads {@code n = scale()} buffer values at the given index and return them as a short. + * + * @param buffer the buffer to read from + * @param index position of the value to read in the buffer + * @return the short value + * @see #readObject(DataBuffer, long) + */ + short readShort(S buffer, long index); + + @Override + default void writeObject(S buffer, Short value, long index) { + writeShort(buffer, value, index); + } + + @Override + default Short readObject(S buffer, long index) { + return readShort(buffer, index); + } +} diff --git a/ndarray/src/main/java/org/tensorflow/ndarray/impl/AbstractNdArray.java b/ndarray/src/main/java/org/tensorflow/ndarray/impl/AbstractNdArray.java new file mode 100644 index 00000000000..690dedc2042 --- /dev/null +++ b/ndarray/src/main/java/org/tensorflow/ndarray/impl/AbstractNdArray.java @@ -0,0 +1,92 @@ +/* + Copyright 2019 The TensorFlow Authors. All Rights Reserved. + + Licensed under the Apache License, Version 2.0 (the "License"); + you may not use this file except in compliance with the License. + You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + + Unless required by applicable law or agreed to in writing, software + distributed under the License is distributed on an "AS IS" BASIS, + WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + See the License for the specific language governing permissions and + limitations under the License. + ======================================================================= + */ +package org.tensorflow.ndarray.impl; + +import java.util.Iterator; +import org.tensorflow.ndarray.NdArray; +import org.tensorflow.ndarray.NdArraySequence; +import org.tensorflow.ndarray.Shape; +import org.tensorflow.ndarray.impl.dimension.DimensionalSpace; + +@SuppressWarnings("unchecked") +public abstract class AbstractNdArray> implements NdArray { + + public abstract U slice(long position, DimensionalSpace dimensions); + + public DimensionalSpace dimensions() { + return dimensions; + } + + @Override + public Shape shape() { + return dimensions.shape(); + } + + @Override + public NdArraySequence scalars() { + // negative if this array is a scalar, should be handled in `elements(dimIdx)` + return (NdArraySequence)elements(shape().numDimensions() - 1); + } + + @Override + public int hashCode() { + return slowHashCode(); + } + + @Override + public boolean equals(Object obj) { + if (this == obj) { + return true; + } + if (!(obj instanceof NdArray)) { + return false; + } + return slowEquals((NdArray)obj); + } + + protected AbstractNdArray(DimensionalSpace dimensions) { + this.dimensions = dimensions; + } + + protected void slowCopyTo(NdArray array) { + scalars().forEachIndexed((coords, e) -> array.setObject(e.getObject(), coords)); + } + + protected int slowHashCode() { + final int prime = 31; + int result = 1; + for (NdArray scalar : scalars()) { + result = prime * result + scalar.getObject().hashCode(); + } + result = prime * result + shape().hashCode(); + return result; + } + + protected boolean slowEquals(NdArray array) { + if (!shape().equals(array.shape())) { // this guarantees also that we have the same number of scalar values + return false; + } + for (Iterator> thisIter = scalars().iterator(), otherIter = array.scalars().iterator(); thisIter.hasNext();) { + if (!thisIter.next().getObject().equals(otherIter.next().getObject())) { + return false; + } + } + return true; + } + + protected final DimensionalSpace dimensions; +} diff --git a/ndarray/src/main/java/org/tensorflow/ndarray/impl/Validator.java b/ndarray/src/main/java/org/tensorflow/ndarray/impl/Validator.java new file mode 100644 index 00000000000..285d09966de --- /dev/null +++ b/ndarray/src/main/java/org/tensorflow/ndarray/impl/Validator.java @@ -0,0 +1,55 @@ +/* + Copyright 2019 The TensorFlow Authors. All Rights Reserved. + + Licensed under the Apache License, Version 2.0 (the "License"); + you may not use this file except in compliance with the License. + You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + + Unless required by applicable law or agreed to in writing, software + distributed under the License is distributed on an "AS IS" BASIS, + WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + See the License for the specific language governing permissions and + limitations under the License. + ======================================================================= + */ +package org.tensorflow.ndarray.impl; + +import java.nio.BufferOverflowException; +import java.nio.BufferUnderflowException; +import org.tensorflow.ndarray.NdArray; +import org.tensorflow.ndarray.buffer.DataBuffer; + +public class Validator { + + public static void copyToNdArrayArgs(NdArray ndArray, NdArray otherNdArray) { + if (!ndArray.shape().equals(otherNdArray.shape())) { + throw new IllegalArgumentException("Can only copy to arrays of the same shape (" + + ndArray.shape() + " != " + otherNdArray.shape() + ")"); + } + } + + public static void readToBufferArgs(NdArray ndArray, DataBuffer dst) { + if (dst.size() < ndArray.size()) { + throw new BufferOverflowException(); + } + } + + public static void writeFromBufferArgs(NdArray ndArray, DataBuffer src) { + if (src.size() < ndArray.size()) { + throw new BufferUnderflowException(); + } + } + + private static void copyArrayArgs(int arrayLength, int arrayOffset) { + if (arrayOffset < 0) { + throw new IndexOutOfBoundsException("Offset must be non-negative"); + } + if (arrayOffset > arrayLength) { + throw new IndexOutOfBoundsException("Offset must be no larger than array length"); + } + } + + protected Validator() {} +} diff --git a/ndarray/src/main/java/org/tensorflow/ndarray/impl/buffer/AbstractDataBuffer.java b/ndarray/src/main/java/org/tensorflow/ndarray/impl/buffer/AbstractDataBuffer.java new file mode 100644 index 00000000000..e5103a2c17a --- /dev/null +++ b/ndarray/src/main/java/org/tensorflow/ndarray/impl/buffer/AbstractDataBuffer.java @@ -0,0 +1,182 @@ +/* + Copyright 2019 The TensorFlow Authors. All Rights Reserved. + + Licensed under the Apache License, Version 2.0 (the "License"); + you may not use this file except in compliance with the License. + You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + + Unless required by applicable law or agreed to in writing, software + distributed under the License is distributed on an "AS IS" BASIS, + WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + See the License for the specific language governing permissions and + limitations under the License. + ======================================================================= + */ +package org.tensorflow.ndarray.impl.buffer; + +import java.util.Arrays; +import java.util.HashMap; +import java.util.Map; +import java.util.Objects; +import org.tensorflow.ndarray.buffer.DataBuffer; + +public abstract class AbstractDataBuffer implements DataBuffer { + + @Override + public DataBuffer read(T[] dst, int offset, int length) { + Validator.readArgs(this, dst.length, offset, length); + for (int i = 0; i < length; ++i) { + dst[i + offset] = getObject(i); + } + return this; + } + + @Override + public DataBuffer write(T[] src, int offset, int length) { + Validator.writeArgs(this, src.length, offset, length); + for (int i = 0; i < length; ++i) { + setObject(src[i + offset], i); + } + return this; + } + + @Override + public DataBuffer copyTo(DataBuffer dst, long size) { + return slowCopyTo(dst, size); + } + + @Override + public int hashCode() { + // This hash code computation is generic to all types of data buffers and accurate but not optimized + // for performances, it needs to be improved if there is a present use case for such hash codes. + return slowHashCode(); + } + + @Override + public boolean equals(Object obj) { + if (this == obj) { + return true; + } + if (!(obj instanceof DataBuffer)) { + return false; + } + return slowEquals((DataBuffer)obj); + } + + @SuppressWarnings("unchecked") + protected > U slowCopyTo(DataBuffer dst, long size) { + Validator.copyToArgs(this, dst, size); + for (long idx = 0L; idx < size; ++idx) { + dst.setObject(getObject(idx), idx); + } + return (U)this; + } + + protected int slowHashCode() { + final int prime = 31; + int result = 1; + + // First check from the first non-null element if we are dealing with a buffer of arrays + long idx = 0L; + for (; idx < size(); ++idx) { + T o = getObject(idx); + if (o != null) { + if (o.getClass().isArray()) { + result = prime * result + arrayHashCode(idx, o.getClass()); // compute hash codes based on array elements + return result; + } + result = prime * result + o.hashCode(); + break; // continue hash code computation without array type check + } + result = prime * result; + } + while (++idx < size()) { + result = prime * result + Objects.hashCode(getObject(idx)); + } + return result; + } + + protected boolean slowEquals(DataBuffer other) { + if (other.size() != size()) { + return false; + } + long idx = 0L; + for (; idx < size(); ++idx) { + Object thisObject = getObject(idx); + if (thisObject != null) { + if (thisObject.getClass().isArray()) { + return arrayEquals(idx, thisObject.getClass(), other); + } + if (!Objects.equals(other.getObject(idx), thisObject)) { + return false; + } + break; // continue equality comparison without array type check + } + if (other.getObject(idx) != null) { + return false; + } + } + while (++idx < size()) { + if (!Objects.equals(other.getObject(idx), getObject(idx))) { + return false; + } + } + return true; + } + + private int arrayHashCode(long startIdx, Class arrayClass) { + ArrayHashCoder hashCoder = ARRAY_HASH_CODERS.getOrDefault(arrayClass, DEFAULT_ARRAY_HASH_CODER); + final int prime = 31; + int result = 1; + for (long idx = startIdx; idx < size(); ++idx) { + result = prime * result + hashCoder.hashCode(this, idx); + } + return result; + } + + private boolean arrayEquals(long startIdx, Class arrayClass, DataBuffer other) { + ArrayComparator comparator = ARRAY_COMPARATORS.getOrDefault(arrayClass, DEFAULT_ARRAY_COMPARATOR); + for (long idx = startIdx; idx < size(); ++idx) { + if (!comparator.equals(this, other, idx)) { + return false; + } + } + return true; + } + + @FunctionalInterface + private static interface ArrayHashCoder { + int hashCode(DataBuffer buffer, long index); + } + private static final Map, ArrayHashCoder> ARRAY_HASH_CODERS = new HashMap<>(); + private static final ArrayHashCoder DEFAULT_ARRAY_HASH_CODER; + + @FunctionalInterface + private static interface ArrayComparator { + boolean equals(DataBuffer buffer, DataBuffer otherBuffer, long index); + } + private static final Map, ArrayComparator> ARRAY_COMPARATORS = new HashMap<>(); + private static final ArrayComparator DEFAULT_ARRAY_COMPARATOR; + + static { + ARRAY_HASH_CODERS.put(byte[].class, (b, idx) -> Arrays.hashCode((byte[])b.getObject(idx))); + ARRAY_HASH_CODERS.put(int[].class, (b, idx) -> Arrays.hashCode((int[])b.getObject(idx))); + ARRAY_HASH_CODERS.put(short[].class, (b, idx) -> Arrays.hashCode((short[])b.getObject(idx))); + ARRAY_HASH_CODERS.put(long[].class, (b, idx) -> Arrays.hashCode((long[])b.getObject(idx))); + ARRAY_HASH_CODERS.put(float[].class, (b, idx) -> Arrays.hashCode((float[])b.getObject(idx))); + ARRAY_HASH_CODERS.put(double[].class, (b, idx) -> Arrays.hashCode((double[])b.getObject(idx))); + ARRAY_HASH_CODERS.put(boolean[].class, (b, idx) -> Arrays.hashCode((boolean[])b.getObject(idx))); + DEFAULT_ARRAY_HASH_CODER = (b, idx) -> Arrays.deepHashCode((Object[])b.getObject(idx)); + + ARRAY_COMPARATORS.put(byte[].class, (b1, b2, idx) -> Arrays.equals((byte[])b1.getObject(idx), (byte[])b2.getObject(idx))); + ARRAY_COMPARATORS.put(int[].class, (b1, b2, idx) -> Arrays.equals((int[])b1.getObject(idx), (int[])b2.getObject(idx))); + ARRAY_COMPARATORS.put(short[].class, (b1, b2, idx) -> Arrays.equals((short[])b1.getObject(idx), (short[])b2.getObject(idx))); + ARRAY_COMPARATORS.put(long[].class, (b1, b2, idx) -> Arrays.equals((long[])b1.getObject(idx), (long[])b2.getObject(idx))); + ARRAY_COMPARATORS.put(float[].class, (b1, b2, idx) -> Arrays.equals((float[])b1.getObject(idx), (float[])b2.getObject(idx))); + ARRAY_COMPARATORS.put(double[].class, (b1, b2, idx) -> Arrays.equals((double[])b1.getObject(idx), (double[])b2.getObject(idx))); + ARRAY_COMPARATORS.put(boolean[].class, (b1, b2, idx) -> Arrays.equals((boolean[])b1.getObject(idx), (boolean[])b2.getObject(idx))); + DEFAULT_ARRAY_COMPARATOR = (b1, b2, idx) -> Arrays.deepEquals((Object[])b1.getObject(idx), (Object[])b2.getObject(idx)); + } +} diff --git a/ndarray/src/main/java/org/tensorflow/ndarray/impl/buffer/AbstractDataBufferWindow.java b/ndarray/src/main/java/org/tensorflow/ndarray/impl/buffer/AbstractDataBufferWindow.java new file mode 100644 index 00000000000..55d4fd56021 --- /dev/null +++ b/ndarray/src/main/java/org/tensorflow/ndarray/impl/buffer/AbstractDataBufferWindow.java @@ -0,0 +1,48 @@ +package org.tensorflow.ndarray.impl.buffer; + +import org.tensorflow.ndarray.buffer.DataBuffer; +import org.tensorflow.ndarray.buffer.DataBufferWindow; + +public abstract class AbstractDataBufferWindow> implements DataBufferWindow { + + @Override + public final long offset() { + return offset; + } + + @Override + public final long size() { + return windowBuffer.size(); + } + + @Override + public final DataBufferWindow slideTo(long index) { + if (index < 0 || index > maxOffset) { + throw new IndexOutOfBoundsException(); + } + offset(index); + offset = index; + return this; + } + + @Override + public final DataBufferWindow slide(long step) { + return slideTo(offset + step); + } + + @Override + public final B buffer() { + return windowBuffer; + } + + protected abstract void offset(long offset); + + protected AbstractDataBufferWindow(B windowBuffer, long bufferLimit) { + this.windowBuffer = windowBuffer; + maxOffset = bufferLimit - windowBuffer.size(); + } + + private final B windowBuffer; + private final long maxOffset; + private long offset = 0; +} diff --git a/ndarray/src/main/java/org/tensorflow/ndarray/impl/buffer/Validator.java b/ndarray/src/main/java/org/tensorflow/ndarray/impl/buffer/Validator.java new file mode 100644 index 00000000000..8f18e620b90 --- /dev/null +++ b/ndarray/src/main/java/org/tensorflow/ndarray/impl/buffer/Validator.java @@ -0,0 +1,132 @@ +/* + Copyright 2019 The TensorFlow Authors. All Rights Reserved. + + Licensed under the Apache License, Version 2.0 (the "License"); + you may not use this file except in compliance with the License. + You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + + Unless required by applicable law or agreed to in writing, software + distributed under the License is distributed on an "AS IS" BASIS, + WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + See the License for the specific language governing permissions and + limitations under the License. + ======================================================================= + */ +package org.tensorflow.ndarray.impl.buffer; + +import java.nio.BufferOverflowException; +import java.nio.BufferUnderflowException; +import java.nio.ReadOnlyBufferException; +import org.tensorflow.ndarray.buffer.DataBuffer; + +public class Validator { + + public static void createArgs(long size, long maxSize) { + if (size < 0) { + throw new IllegalArgumentException("Size must be non-negative"); + } + if (size > maxSize) { + throw new IllegalArgumentException("Buffer size must be no greater than maximum size allowed (" + maxSize + ")"); + } + } + + public static void getArgs(DataBuffer buffer, long index) { + if (index < 0) { + throw new IndexOutOfBoundsException("Index must be non-negative"); + } + if (index >= buffer.size()) { + throw new IndexOutOfBoundsException("Index must be smaller than the buffer size"); + } + } + + public static void setArgs(DataBuffer buffer, long index) { + if (index < 0) { + throw new IndexOutOfBoundsException("Index must be non-negative"); + } + if (index >= buffer.size()) { + throw new IndexOutOfBoundsException("Index must be smaller than the buffer size"); + } + if (buffer.isReadOnly()) { + throw new ReadOnlyBufferException(); + } + } + + public static void copyToArgs(DataBuffer src, DataBuffer dst, long size) { + if (dst == src) { + throw new IllegalArgumentException("Source cannot be the same buffer as destination"); + } + if (size > dst.size()) { + throw new BufferOverflowException(); + } + if (size > src.size()) { + throw new BufferUnderflowException(); + } + if (dst.isReadOnly()) { + throw new ReadOnlyBufferException(); + } + } + + public static void readArgs(DataBuffer buffer, int arrayLength, int offset, int length) { + if (length > buffer.size()) { + throw new BufferUnderflowException(); + } + arrayArgs(arrayLength, offset, length); + } + + public static void writeArgs(DataBuffer buffer, int arrayLength, int offset, int length) { + if (length > buffer.size()) { + throw new BufferOverflowException(); + } + if (buffer.isReadOnly()) { + throw new ReadOnlyBufferException(); + } + arrayArgs(arrayLength, offset, length); + } + + public static void offsetArgs(DataBuffer buffer, long index) { + if (index < 0) { + throw new IllegalArgumentException("Index must be non-negative"); + } + if (index > buffer.size()) { + throw new IllegalArgumentException("Index must not exceed buffer size"); + } + } + + public static void narrowArgs(DataBuffer buffer, long size) { + if (size < 0) { + throw new IllegalArgumentException("Size must be non-negative"); + } + if (size > buffer.size()) { + throw new IllegalArgumentException("Cannot narrow a buffer of size " + buffer.size() + " to " + size); + } + } + + public static void sliceArgs(DataBuffer buffer, long index, long size) { + if (index < 0) { + throw new IllegalArgumentException("Index must be non-negative"); + } + if (size < 0) { + throw new IllegalArgumentException("Size must be non-negative"); + } + if (index + size > buffer.size()) { + throw new IllegalArgumentException("Buffer view must not exceed original buffer limits"); + } + } + + private static void arrayArgs(int arrayLength, int offset, int length) { + if (offset < 0) { + throw new IndexOutOfBoundsException("Offset must be non-negative"); + } + if (offset > arrayLength) { + throw new IndexOutOfBoundsException("Offset must be no larger than array length"); + } + if (length < 0) { + throw new IndexOutOfBoundsException("Length must be non-negative"); + } + if (length > arrayLength - offset) { + throw new IndexOutOfBoundsException("Length must be no larger than array length minus the offset"); + } + } +} diff --git a/ndarray/src/main/java/org/tensorflow/ndarray/impl/buffer/adapter/AbstractDataBufferAdapter.java b/ndarray/src/main/java/org/tensorflow/ndarray/impl/buffer/adapter/AbstractDataBufferAdapter.java new file mode 100644 index 00000000000..3c9b6df1e93 --- /dev/null +++ b/ndarray/src/main/java/org/tensorflow/ndarray/impl/buffer/adapter/AbstractDataBufferAdapter.java @@ -0,0 +1,68 @@ +/* + * Copyright 2019 The TensorFlow Authors. All Rights Reserved. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + * ======================================================================= + */ + +package org.tensorflow.ndarray.impl.buffer.adapter; + +import org.tensorflow.ndarray.impl.buffer.Validator; +import org.tensorflow.ndarray.buffer.DataBuffer; +import org.tensorflow.ndarray.impl.buffer.AbstractDataBuffer; +import org.tensorflow.ndarray.buffer.layout.DataLayout; + +@SuppressWarnings("unchecked") +abstract class AbstractDataBufferAdapter, T, U extends DataBuffer> extends AbstractDataBuffer { + + @Override + public long size() { + return size; + } + + @Override + public boolean isReadOnly() { + return buffer.isReadOnly(); + } + + @Override + public T getObject(long index) { + Validator.getArgs(this, index); + return layout.readObject(buffer, index * layout.scale()); + } + + @Override + public U setObject(T value, long index) { + Validator.setArgs(this, index); + layout.writeObject(buffer, value, index * layout.scale()); + return (U)this; + } + + AbstractDataBufferAdapter(S buffer, DataLayout layout) { + this.buffer = buffer; + this.layout = layout; + size = buffer.size() / layout.scale(); + } + + DataLayout layout() { + return layout; + } + + S buffer() { + return buffer; + } + + private final S buffer; + private final DataLayout layout; + private final long size; +} diff --git a/ndarray/src/main/java/org/tensorflow/ndarray/impl/buffer/adapter/BooleanDataBufferAdapter.java b/ndarray/src/main/java/org/tensorflow/ndarray/impl/buffer/adapter/BooleanDataBufferAdapter.java new file mode 100644 index 00000000000..40217b57d6a --- /dev/null +++ b/ndarray/src/main/java/org/tensorflow/ndarray/impl/buffer/adapter/BooleanDataBufferAdapter.java @@ -0,0 +1,116 @@ +/* + * Copyright 2019 The TensorFlow Authors. All Rights Reserved. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + * ======================================================================= + */ + +package org.tensorflow.ndarray.impl.buffer.adapter; + +import org.tensorflow.ndarray.impl.buffer.Validator; +import org.tensorflow.ndarray.buffer.BooleanDataBuffer; +import org.tensorflow.ndarray.buffer.DataBuffer; +import org.tensorflow.ndarray.buffer.layout.BooleanDataLayout; + +class BooleanDataBufferAdapter> extends AbstractDataBufferAdapter + implements BooleanDataBuffer { + + @Override + public boolean getBoolean(long index) { + Validator.getArgs(this, index); + return layout.readBoolean(buffer(), index * layout.scale()); + } + + @Override + public BooleanDataBuffer setBoolean(boolean value, long index) { + Validator.setArgs(this, index); + layout.writeBoolean(buffer(), value, index * layout.scale()); + return this; + } + + @Override + public BooleanDataBuffer read(boolean[] dst, int offset, int length) { + Validator.readArgs(this, dst.length, offset, length); + for (int i = 0, j = offset; i < length; ++i, ++j) { + dst[j] = layout.readBoolean(buffer(), i * layout.scale()); + } + return this; + } + + @Override + public BooleanDataBuffer write(boolean[] src, int offset, int length) { + Validator.writeArgs(this, src.length, offset, length); + for (int i = 0, j = offset; i < length; ++i, ++j) { + layout.writeBoolean(buffer(), src[j], i * layout.scale()); + } + return this; + } + + @Override + public BooleanDataBuffer copyTo(DataBuffer dst, long size) { + Validator.copyToArgs(this, dst, size); + if (dst instanceof BooleanDataBuffer) { + BooleanDataBuffer booleanDst = (BooleanDataBuffer)dst; + for (long idx = 0L; idx < size; ++idx) { + booleanDst.setBoolean(getBoolean(idx), idx); + } + return this; + } + return slowCopyTo(dst, size); + } + + @Override + @SuppressWarnings("unchecked") + public BooleanDataBuffer offset(long index) { + return new BooleanDataBufferAdapter<>((S)buffer().offset(index * layout.scale()), layout); + } + + @Override + @SuppressWarnings("unchecked") + public BooleanDataBuffer narrow(long size) { + return new BooleanDataBufferAdapter<>((S)buffer().narrow(size * layout.scale()), layout); + } + + @Override + @SuppressWarnings("unchecked") + public BooleanDataBuffer slice(long index, long size) { + return new BooleanDataBufferAdapter<>((S)buffer().slice(index * layout.scale(), size * layout.scale()), layout); + } + + @Override + public boolean equals(Object obj) { + if (this == obj) { + return true; + } + if (!(obj instanceof BooleanDataBuffer)) { + return super.equals(obj); + } + BooleanDataBuffer other = (BooleanDataBuffer)obj; + if (other.size() != size()) { + return false; + } + for (long idx = 0L; idx < size(); ++idx) { + if (other.getBoolean(idx) != getBoolean(idx)) { + return false; + } + } + return true; + } + + BooleanDataBufferAdapter(S buffer, BooleanDataLayout layout) { + super(buffer, layout); + this.layout = layout; + } + + private BooleanDataLayout layout; +} diff --git a/ndarray/src/main/java/org/tensorflow/ndarray/impl/buffer/adapter/ByteDataBufferAdapter.java b/ndarray/src/main/java/org/tensorflow/ndarray/impl/buffer/adapter/ByteDataBufferAdapter.java new file mode 100644 index 00000000000..c120a3ba810 --- /dev/null +++ b/ndarray/src/main/java/org/tensorflow/ndarray/impl/buffer/adapter/ByteDataBufferAdapter.java @@ -0,0 +1,135 @@ +package org.tensorflow.ndarray.impl.buffer.adapter; + +import org.tensorflow.ndarray.impl.buffer.Validator; +import org.tensorflow.ndarray.buffer.BooleanDataBuffer; +import org.tensorflow.ndarray.buffer.ByteDataBuffer; +import org.tensorflow.ndarray.buffer.DataBuffer; +import org.tensorflow.ndarray.buffer.DoubleDataBuffer; +import org.tensorflow.ndarray.buffer.FloatDataBuffer; +import org.tensorflow.ndarray.buffer.IntDataBuffer; +import org.tensorflow.ndarray.buffer.LongDataBuffer; +import org.tensorflow.ndarray.buffer.ShortDataBuffer; +import org.tensorflow.ndarray.buffer.layout.ByteDataLayout; + +class ByteDataBufferAdapter> extends AbstractDataBufferAdapter + implements ByteDataBuffer { + + @Override + public byte getByte(long index) { + Validator.getArgs(this, index); + return layout.readByte(buffer(), index * layout.scale()); + } + + @Override + public ByteDataBuffer setByte(byte value, long index) { + Validator.setArgs(this, index); + layout.writeByte(buffer(), value, index * layout.scale()); + return this; + } + + @Override + public ByteDataBuffer read(byte[] dst, int offset, int length) { + Validator.readArgs(this, dst.length, offset, length); + for (int i = 0, j = offset; i < length; ++i, ++j) { + dst[j] = layout.readByte(buffer(), i * layout.scale()); + } + return this; + } + + @Override + public ByteDataBuffer write(byte[] src, int offset, int length) { + Validator.writeArgs(this, src.length, offset, length); + for (int i = 0, j = offset; i < length; ++i, ++j) { + layout.writeByte(buffer(), src[j], i * layout.scale()); + } + return this; + } + + @Override + public ByteDataBuffer copyTo(DataBuffer dst, long size) { + Validator.copyToArgs(this, dst, size); + if (dst instanceof ByteDataBuffer) { + ByteDataBuffer byteDst = (ByteDataBuffer)dst; + for (long idx = 0L; idx < size; ++idx) { + byteDst.setByte(getByte(idx), idx); + } + return this; + } + return slowCopyTo(dst, size); + } + + @Override + public IntDataBuffer asInts() { + throw new IllegalStateException("Byte buffers with layout cannot be converted"); + } + + @Override + public ShortDataBuffer asShorts() { + throw new IllegalStateException("Byte buffers with layout cannot be converted"); + } + + @Override + public LongDataBuffer asLongs() { + throw new IllegalStateException("Byte buffers with layout cannot be converted"); + } + + @Override + public FloatDataBuffer asFloats() { + throw new IllegalStateException("Byte buffers with layout cannot be converted"); + } + + @Override + public DoubleDataBuffer asDoubles() { + throw new IllegalStateException("Byte buffers with layout cannot be converted"); + } + + @Override + public BooleanDataBuffer asBooleans() { + throw new IllegalStateException("Byte buffers with layout cannot be converted"); + } + + @Override + @SuppressWarnings("unchecked") + public ByteDataBuffer offset(long index) { + return new ByteDataBufferAdapter<>((S)buffer().offset(index * layout.scale()), layout); + } + + @Override + @SuppressWarnings("unchecked") + public ByteDataBuffer narrow(long size) { + return new ByteDataBufferAdapter<>((S)buffer().narrow(size * layout.scale()), layout); + } + + @Override + @SuppressWarnings("unchecked") + public ByteDataBuffer slice(long index, long size) { + return new ByteDataBufferAdapter<>((S)buffer().slice(index * layout.scale(), size * layout.scale()), layout); + } + + @Override + public boolean equals(Object obj) { + if (this == obj) { + return true; + } + if (!(obj instanceof ByteDataBuffer)) { + return super.equals(obj); + } + ByteDataBuffer other = (ByteDataBuffer)obj; + if (other.size() != size()) { + return false; + } + for (long idx = 0L; idx < size(); ++idx) { + if (other.getByte(idx) != getByte(idx)) { + return false; + } + } + return true; + } + + ByteDataBufferAdapter(S buffer, ByteDataLayout layout) { + super(buffer, layout); + this.layout = layout; + } + + private ByteDataLayout layout; +} diff --git a/ndarray/src/main/java/org/tensorflow/ndarray/impl/buffer/adapter/DataBufferAdapter.java b/ndarray/src/main/java/org/tensorflow/ndarray/impl/buffer/adapter/DataBufferAdapter.java new file mode 100644 index 00000000000..de20ab2cfd7 --- /dev/null +++ b/ndarray/src/main/java/org/tensorflow/ndarray/impl/buffer/adapter/DataBufferAdapter.java @@ -0,0 +1,47 @@ +/* + * Copyright 2019 The TensorFlow Authors. All Rights Reserved. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + * ======================================================================= + */ + +package org.tensorflow.ndarray.impl.buffer.adapter; + +import org.tensorflow.ndarray.buffer.DataBuffer; +import org.tensorflow.ndarray.buffer.layout.DataLayout; + +@SuppressWarnings("unchecked") +class DataBufferAdapter, T> extends AbstractDataBufferAdapter> { + + @Override + @SuppressWarnings("unchecked") + public DataBuffer offset(long index) { + return new DataBufferAdapter<>((S)buffer().offset(index * layout().scale()), layout()); + } + + @Override + @SuppressWarnings("unchecked") + public DataBuffer narrow(long size) { + return new DataBufferAdapter<>((S)buffer().narrow(size * layout().scale()), layout()); + } + + @Override + @SuppressWarnings("unchecked") + public DataBuffer slice(long index, long size) { + return new DataBufferAdapter<>((S)buffer().slice(index * layout().scale(), size * layout().scale()), layout()); + } + + DataBufferAdapter(S buffer, DataLayout layout) { + super(buffer, layout); + } +} diff --git a/ndarray/src/main/java/org/tensorflow/ndarray/impl/buffer/adapter/DataBufferAdapterFactory.java b/ndarray/src/main/java/org/tensorflow/ndarray/impl/buffer/adapter/DataBufferAdapterFactory.java new file mode 100644 index 00000000000..468051e1b46 --- /dev/null +++ b/ndarray/src/main/java/org/tensorflow/ndarray/impl/buffer/adapter/DataBufferAdapterFactory.java @@ -0,0 +1,142 @@ +/* + * Copyright 2019 The TensorFlow Authors. All Rights Reserved. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + * ======================================================================= + */ + +package org.tensorflow.ndarray.impl.buffer.adapter; + +import org.tensorflow.ndarray.buffer.BooleanDataBuffer; +import org.tensorflow.ndarray.buffer.ByteDataBuffer; +import org.tensorflow.ndarray.buffer.DataBuffer; +import org.tensorflow.ndarray.buffer.DoubleDataBuffer; +import org.tensorflow.ndarray.buffer.FloatDataBuffer; +import org.tensorflow.ndarray.buffer.IntDataBuffer; +import org.tensorflow.ndarray.buffer.LongDataBuffer; +import org.tensorflow.ndarray.buffer.ShortDataBuffer; +import org.tensorflow.ndarray.buffer.layout.BooleanDataLayout; +import org.tensorflow.ndarray.buffer.layout.ByteDataLayout; +import org.tensorflow.ndarray.buffer.layout.DataLayout; +import org.tensorflow.ndarray.buffer.layout.DoubleDataLayout; +import org.tensorflow.ndarray.buffer.layout.FloatDataLayout; +import org.tensorflow.ndarray.buffer.layout.IntDataLayout; +import org.tensorflow.ndarray.buffer.layout.LongDataLayout; +import org.tensorflow.ndarray.buffer.layout.ShortDataLayout; + +/** + * Factory of data buffer adapters. + * + *

Data buffer adapters are used to apply a {@link DataLayout} to a buffer. Conceptually, they act + * as a proxy that intercept each I/O call and perform the required type conversions after/before + * delegating the task to the underlying buffer. + */ +public class DataBufferAdapterFactory { + + /** + * Creates an adapter that applies a byte data layout to the given buffer. + * + * @param buffer the delegate buffer + * @param layout layout to apply + * @param the type of the buffer + * @return buffer adapter + */ + public static > ByteDataBuffer create(S buffer, ByteDataLayout layout) { + return new ByteDataBufferAdapter<>(buffer, layout); + } + + /** + * Creates an adapter that applies a boolean data layout to the given buffer. + * + * @param buffer the delegate buffer + * @param layout layout to apply + * @param the type of the buffer + * @return buffer adapter + */ + public static > BooleanDataBuffer create(S buffer, BooleanDataLayout layout) { + return new BooleanDataBufferAdapter<>(buffer, layout); + } + + /** + * Creates an adapter that applies a double data layout to the given buffer. + * + * @param buffer the delegate buffer + * @param layout layout to apply + * @param the type of the buffer + * @return buffer adapter + */ + public static > DoubleDataBuffer create(S buffer, DoubleDataLayout layout) { + return new DoubleDataBufferAdapter<>(buffer, layout); + } + + /** + * Creates an adapter that applies a float data layout to the given buffer. + * + * @param buffer the delegate buffer + * @param layout layout to apply + * @param the type of the buffer + * @return buffer adapter + */ + public static > FloatDataBuffer create(S buffer, FloatDataLayout layout) { + return new FloatDataBufferAdapter<>(buffer, layout); + } + + /** + * Creates an adapter that applies a integer data layout to the given buffer. + * + * @param buffer the delegate buffer + * @param layout layout to apply + * @param the type of the buffer + * @return buffer adapter + */ + public static > IntDataBuffer create(S buffer, IntDataLayout layout) { + return new IntDataBufferAdapter<>(buffer, layout); + } + + /** + * Creates an adapter that applies a long data layout to the given buffer. + * + * @param buffer the delegate buffer + * @param layout layout to apply + * @param the type of the buffer + * @return buffer adapter + */ + public static > LongDataBuffer create(S buffer, LongDataLayout layout) { + return new LongDataBufferAdapter<>(buffer, layout); + } + + /** + * Creates an adapter that applies a short data layout to the given buffer. + * + * @param buffer the delegate buffer + * @param layout layout to apply + * @param the type of the buffer + * @return buffer adapter + */ + public static > ShortDataBuffer create(S buffer, ShortDataLayout layout) { + return new ShortDataBufferAdapter<>(buffer, layout); + } + + /** + * Creates an adapter that applies a data layout to the given buffer. + * + * @param buffer the delegate buffer + * @param layout layout to apply + * @param the type of the buffer + * @param the type of data returned by the layout + * @return buffer adapter + */ + public static , T> DataBuffer create(S buffer, DataLayout layout) { + return new DataBufferAdapter<>(buffer, layout); + } +} diff --git a/ndarray/src/main/java/org/tensorflow/ndarray/impl/buffer/adapter/DoubleDataBufferAdapter.java b/ndarray/src/main/java/org/tensorflow/ndarray/impl/buffer/adapter/DoubleDataBufferAdapter.java new file mode 100644 index 00000000000..253acbac269 --- /dev/null +++ b/ndarray/src/main/java/org/tensorflow/ndarray/impl/buffer/adapter/DoubleDataBufferAdapter.java @@ -0,0 +1,116 @@ +/* + * Copyright 2019 The TensorFlow Authors. All Rights Reserved. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + * ======================================================================= + */ + +package org.tensorflow.ndarray.impl.buffer.adapter; + +import org.tensorflow.ndarray.impl.buffer.Validator; +import org.tensorflow.ndarray.buffer.DataBuffer; +import org.tensorflow.ndarray.buffer.DoubleDataBuffer; +import org.tensorflow.ndarray.buffer.layout.DoubleDataLayout; + +class DoubleDataBufferAdapter> extends AbstractDataBufferAdapter + implements DoubleDataBuffer { + + @Override + public double getDouble(long index) { + Validator.getArgs(this, index); + return layout.readDouble(buffer(), index * layout.scale()); + } + + @Override + public DoubleDataBuffer setDouble(double value, long index) { + Validator.setArgs(this, index); + layout.writeDouble(buffer(), value, index * layout.scale()); + return this; + } + + @Override + public DoubleDataBuffer read(double[] dst, int offset, int length) { + Validator.readArgs(this, dst.length, offset, length); + for (int i = 0, j = offset; i < length; ++i, ++j) { + dst[j] = layout.readDouble(buffer(), i * layout.scale()); + } + return this; + } + + @Override + public DoubleDataBuffer write(double[] src, int offset, int length) { + Validator.writeArgs(this, src.length, offset, length); + for (int i = 0, j = offset; i < length; ++i, ++j) { + layout.writeDouble(buffer(), src[j], i * layout.scale()); + } + return this; + } + + @Override + public DoubleDataBuffer copyTo(DataBuffer dst, long size) { + Validator.copyToArgs(this, dst, size); + if (dst instanceof DoubleDataBuffer) { + DoubleDataBuffer doubleDst = (DoubleDataBuffer)dst; + for (long idx = 0L; idx < size; ++idx) { + doubleDst.setDouble(getDouble(idx), idx); + } + return this; + } + return slowCopyTo(dst, size); + } + + @Override + @SuppressWarnings("unchecked") + public DoubleDataBuffer offset(long index) { + return new DoubleDataBufferAdapter<>((S)buffer().offset(index * layout.scale()), layout); + } + + @Override + @SuppressWarnings("unchecked") + public DoubleDataBuffer narrow(long size) { + return new DoubleDataBufferAdapter<>((S)buffer().narrow(size * layout.scale()), layout); + } + + @Override + @SuppressWarnings("unchecked") + public DoubleDataBuffer slice(long index, long size) { + return new DoubleDataBufferAdapter<>((S)buffer().slice(index * layout.scale(), size * layout.scale()), layout); + } + + @Override + public boolean equals(Object obj) { + if (this == obj) { + return true; + } + if (!(obj instanceof DoubleDataBuffer)) { + return super.equals(obj); + } + DoubleDataBuffer other = (DoubleDataBuffer)obj; + if (other.size() != size()) { + return false; + } + for (long idx = 0L; idx < size(); ++idx) { + if (other.getDouble(idx) != getDouble(idx)) { + return false; + } + } + return true; + } + + DoubleDataBufferAdapter(S buffer, DoubleDataLayout layout) { + super(buffer, layout); + this.layout = layout; + } + + private DoubleDataLayout layout; +} diff --git a/ndarray/src/main/java/org/tensorflow/ndarray/impl/buffer/adapter/FloatDataBufferAdapter.java b/ndarray/src/main/java/org/tensorflow/ndarray/impl/buffer/adapter/FloatDataBufferAdapter.java new file mode 100644 index 00000000000..69928bf0ccb --- /dev/null +++ b/ndarray/src/main/java/org/tensorflow/ndarray/impl/buffer/adapter/FloatDataBufferAdapter.java @@ -0,0 +1,116 @@ +/* + * Copyright 2019 The TensorFlow Authors. All Rights Reserved. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + * ======================================================================= + */ + +package org.tensorflow.ndarray.impl.buffer.adapter; + +import org.tensorflow.ndarray.impl.buffer.Validator; +import org.tensorflow.ndarray.buffer.DataBuffer; +import org.tensorflow.ndarray.buffer.FloatDataBuffer; +import org.tensorflow.ndarray.buffer.layout.FloatDataLayout; + +class FloatDataBufferAdapter> extends AbstractDataBufferAdapter + implements FloatDataBuffer { + + @Override + public float getFloat(long index) { + Validator.getArgs(this, index); + return layout.readFloat(buffer(), index * layout.scale()); + } + + @Override + public FloatDataBuffer setFloat(float value, long index) { + Validator.setArgs(this, index); + layout.writeFloat(buffer(), value, index * layout.scale()); + return this; + } + + @Override + public FloatDataBuffer read(float[] dst, int offset, int length) { + Validator.readArgs(this, dst.length, offset, length); + for (int i = 0, j = offset; i < length; ++i, ++j) { + dst[j] = layout.readFloat(buffer(), i * layout.scale()); + } + return this; + } + + @Override + public FloatDataBuffer write(float[] src, int offset, int length) { + Validator.writeArgs(this, src.length, offset, length); + for (int i = 0, j = offset; i < length; ++i, ++j) { + layout.writeFloat(buffer(), src[j], i * layout.scale()); + } + return this; + } + + @Override + public FloatDataBuffer copyTo(DataBuffer dst, long size) { + Validator.copyToArgs(this, dst, size); + if (dst instanceof FloatDataBuffer) { + FloatDataBuffer floatDst = (FloatDataBuffer)dst; + for (long idx = 0L; idx < size; ++idx) { + floatDst.setFloat(getFloat(idx), idx); + } + return this; + } + return slowCopyTo(dst, size); + } + + @Override + @SuppressWarnings("unchecked") + public FloatDataBuffer offset(long index) { + return new FloatDataBufferAdapter<>((S)buffer().offset(index * layout.scale()), layout); + } + + @Override + @SuppressWarnings("unchecked") + public FloatDataBuffer narrow(long size) { + return new FloatDataBufferAdapter<>((S)buffer().narrow(size * layout.scale()), layout); + } + + @Override + @SuppressWarnings("unchecked") + public FloatDataBuffer slice(long index, long size) { + return new FloatDataBufferAdapter<>((S)buffer().slice(index * layout.scale(), size * layout.scale()), layout); + } + + @Override + public boolean equals(Object obj) { + if (this == obj) { + return true; + } + if (!(obj instanceof FloatDataBuffer)) { + return super.equals(obj); + } + FloatDataBuffer other = (FloatDataBuffer)obj; + if (other.size() != size()) { + return false; + } + for (long idx = 0L; idx < size(); ++idx) { + if (other.getFloat(idx) != getFloat(idx)) { + return false; + } + } + return true; + } + + FloatDataBufferAdapter(S buffer, FloatDataLayout layout) { + super(buffer, layout); + this.layout = layout; + } + + private FloatDataLayout layout; +} diff --git a/ndarray/src/main/java/org/tensorflow/ndarray/impl/buffer/adapter/IntDataBufferAdapter.java b/ndarray/src/main/java/org/tensorflow/ndarray/impl/buffer/adapter/IntDataBufferAdapter.java new file mode 100644 index 00000000000..052b63fe0f3 --- /dev/null +++ b/ndarray/src/main/java/org/tensorflow/ndarray/impl/buffer/adapter/IntDataBufferAdapter.java @@ -0,0 +1,116 @@ +/* + * Copyright 2019 The TensorFlow Authors. All Rights Reserved. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + * ======================================================================= + */ + +package org.tensorflow.ndarray.impl.buffer.adapter; + +import org.tensorflow.ndarray.impl.buffer.Validator; +import org.tensorflow.ndarray.buffer.DataBuffer; +import org.tensorflow.ndarray.buffer.IntDataBuffer; +import org.tensorflow.ndarray.buffer.layout.IntDataLayout; + +class IntDataBufferAdapter> extends AbstractDataBufferAdapter + implements IntDataBuffer { + + @Override + public int getInt(long index) { + Validator.getArgs(this, index); + return layout.readInt(buffer(), index * layout.scale()); + } + + @Override + public IntDataBuffer setInt(int value, long index) { + Validator.setArgs(this, index); + layout.writeInt(buffer(), value, index * layout.scale()); + return this; + } + + @Override + public IntDataBuffer read(int[] dst, int offset, int length) { + Validator.readArgs(this, dst.length, offset, length); + for (int i = 0, j = offset; i < length; ++i, ++j) { + dst[j] = layout.readInt(buffer(), i * layout.scale()); + } + return this; + } + + @Override + public IntDataBuffer write(int[] src, int offset, int length) { + Validator.writeArgs(this, src.length, offset, length); + for (int i = 0, j = offset; i < length; ++i, ++j) { + layout.writeInt(buffer(), src[j], i * layout.scale()); + } + return this; + } + + @Override + public IntDataBuffer copyTo(DataBuffer dst, long size) { + Validator.copyToArgs(this, dst, size); + if (dst instanceof IntDataBuffer) { + IntDataBuffer intDst = (IntDataBuffer)dst; + for (long idx = 0L; idx < size; ++idx) { + intDst.setInt(getInt(idx), idx); + } + return this; + } + return slowCopyTo(dst, size); + } + + @Override + @SuppressWarnings("unchecked") + public IntDataBuffer offset(long index) { + return new IntDataBufferAdapter<>((S)buffer().offset(index * layout.scale()), layout); + } + + @Override + @SuppressWarnings("unchecked") + public IntDataBuffer narrow(long size) { + return new IntDataBufferAdapter<>((S)buffer().narrow(size * layout.scale()), layout); + } + + @Override + @SuppressWarnings("unchecked") + public IntDataBuffer slice(long index, long size) { + return new IntDataBufferAdapter<>((S)buffer().slice(index * layout.scale(), size * layout.scale()), layout); + } + + @Override + public boolean equals(Object obj) { + if (this == obj) { + return true; + } + if (!(obj instanceof IntDataBuffer)) { + return super.equals(obj); + } + IntDataBuffer other = (IntDataBuffer)obj; + if (other.size() != size()) { + return false; + } + for (long idx = 0L; idx < size(); ++idx) { + if (other.getInt(idx) != getInt(idx)) { + return false; + } + } + return true; + } + + IntDataBufferAdapter(S buffer, IntDataLayout layout) { + super(buffer, layout); + this.layout = layout; + } + + private IntDataLayout layout; +} diff --git a/ndarray/src/main/java/org/tensorflow/ndarray/impl/buffer/adapter/LongDataBufferAdapter.java b/ndarray/src/main/java/org/tensorflow/ndarray/impl/buffer/adapter/LongDataBufferAdapter.java new file mode 100644 index 00000000000..aea154d4b9f --- /dev/null +++ b/ndarray/src/main/java/org/tensorflow/ndarray/impl/buffer/adapter/LongDataBufferAdapter.java @@ -0,0 +1,116 @@ +/* + * Copyright 2019 The TensorFlow Authors. All Rights Reserved. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + * ======================================================================= + */ + +package org.tensorflow.ndarray.impl.buffer.adapter; + +import org.tensorflow.ndarray.impl.buffer.Validator; +import org.tensorflow.ndarray.buffer.DataBuffer; +import org.tensorflow.ndarray.buffer.LongDataBuffer; +import org.tensorflow.ndarray.buffer.layout.LongDataLayout; + +class LongDataBufferAdapter> extends AbstractDataBufferAdapter + implements LongDataBuffer { + + @Override + public long getLong(long index) { + Validator.getArgs(this, index); + return layout.readLong(buffer(), index * layout.scale()); + } + + @Override + public LongDataBuffer setLong(long value, long index) { + Validator.setArgs(this, index); + layout.writeLong(buffer(), value, index * layout.scale()); + return this; + } + + @Override + public LongDataBuffer read(long[] dst, int offset, int length) { + Validator.readArgs(this, dst.length, offset, length); + for (int i = 0, j = offset; i < length; ++i, ++j) { + dst[j] = layout.readLong(buffer(), i * layout.scale()); + } + return this; + } + + @Override + public LongDataBuffer write(long[] src, int offset, int length) { + Validator.writeArgs(this, src.length, offset, length); + for (int i = 0, j = offset; i < length; ++i, ++j) { + layout.writeLong(buffer(), src[j], i * layout.scale()); + } + return this; + } + + @Override + public LongDataBuffer copyTo(DataBuffer dst, long size) { + Validator.copyToArgs(this, dst, size); + if (dst instanceof LongDataBuffer) { + LongDataBuffer longDst = (LongDataBuffer)dst; + for (long idx = 0L; idx < size; ++idx) { + longDst.setLong(getLong(idx), idx); + } + return this; + } + return slowCopyTo(dst, size); + } + + @Override + @SuppressWarnings("unchecked") + public LongDataBuffer offset(long index) { + return new LongDataBufferAdapter<>((S)buffer().offset(index * layout.scale()), layout); + } + + @Override + @SuppressWarnings("unchecked") + public LongDataBuffer narrow(long size) { + return new LongDataBufferAdapter<>((S)buffer().narrow(size * layout.scale()), layout); + } + + @Override + @SuppressWarnings("unchecked") + public LongDataBuffer slice(long index, long size) { + return new LongDataBufferAdapter<>((S)buffer().slice(index * layout.scale(), size * layout.scale()), layout); + } + + @Override + public boolean equals(Object obj) { + if (this == obj) { + return true; + } + if (!(obj instanceof LongDataBuffer)) { + return super.equals(obj); + } + LongDataBuffer other = (LongDataBuffer)obj; + if (other.size() != size()) { + return false; + } + for (long idx = 0L; idx < size(); ++idx) { + if (other.getLong(idx) != getLong(idx)) { + return false; + } + } + return true; + } + + LongDataBufferAdapter(S buffer, LongDataLayout layout) { + super(buffer, layout); + this.layout = layout; + } + + private LongDataLayout layout; +} diff --git a/ndarray/src/main/java/org/tensorflow/ndarray/impl/buffer/adapter/ShortDataBufferAdapter.java b/ndarray/src/main/java/org/tensorflow/ndarray/impl/buffer/adapter/ShortDataBufferAdapter.java new file mode 100644 index 00000000000..6b6b1bb24b7 --- /dev/null +++ b/ndarray/src/main/java/org/tensorflow/ndarray/impl/buffer/adapter/ShortDataBufferAdapter.java @@ -0,0 +1,116 @@ +/* + * Copyright 2019 The TensorFlow Authors. All Rights Reserved. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + * ======================================================================= + */ + +package org.tensorflow.ndarray.impl.buffer.adapter; + +import org.tensorflow.ndarray.impl.buffer.Validator; +import org.tensorflow.ndarray.buffer.DataBuffer; +import org.tensorflow.ndarray.buffer.ShortDataBuffer; +import org.tensorflow.ndarray.buffer.layout.ShortDataLayout; + +class ShortDataBufferAdapter> extends AbstractDataBufferAdapter + implements ShortDataBuffer { + + @Override + public short getShort(long index) { + Validator.getArgs(this, index); + return layout.readShort(buffer(), index * layout.scale()); + } + + @Override + public ShortDataBuffer setShort(short value, long index) { + Validator.setArgs(this, index); + layout.writeShort(buffer(), value, index * layout.scale()); + return this; + } + + @Override + public ShortDataBuffer read(short[] dst, int offset, int length) { + Validator.readArgs(this, dst.length, offset, length); + for (int i = 0, j = offset; i < length; ++i, ++j) { + dst[j] = layout.readShort(buffer(), i * layout.scale()); + } + return this; + } + + @Override + public ShortDataBuffer write(short[] src, int offset, int length) { + Validator.writeArgs(this, src.length, offset, length); + for (int i = 0, j = offset; i < length; ++i, ++j) { + layout.writeShort(buffer(), src[j], i * layout.scale()); + } + return this; + } + + @Override + public ShortDataBuffer copyTo(DataBuffer dst, long size) { + Validator.copyToArgs(this, dst, size); + if (dst instanceof ShortDataBuffer) { + ShortDataBuffer shortDst = (ShortDataBuffer)dst; + for (long idx = 0L; idx < size; ++idx) { + shortDst.setShort(getShort(idx), idx); + } + return this; + } + return slowCopyTo(dst, size); + } + + @Override + @SuppressWarnings("unchecked") + public ShortDataBuffer offset(long index) { + return new ShortDataBufferAdapter<>((S)buffer().offset(index * layout.scale()), layout); + } + + @Override + @SuppressWarnings("unchecked") + public ShortDataBuffer narrow(long size) { + return new ShortDataBufferAdapter<>((S)buffer().narrow(size * layout.scale()), layout); + } + + @Override + @SuppressWarnings("unchecked") + public ShortDataBuffer slice(long index, long size) { + return new ShortDataBufferAdapter<>((S)buffer().slice(index * layout.scale(), size * layout.scale()), layout); + } + + @Override + public boolean equals(Object obj) { + if (this == obj) { + return true; + } + if (!(obj instanceof ShortDataBuffer)) { + return super.equals(obj); + } + ShortDataBuffer other = (ShortDataBuffer)obj; + if (other.size() != size()) { + return false; + } + for (long idx = 0L; idx < size(); ++idx) { + if (other.getShort(idx) != getShort(idx)) { + return false; + } + } + return true; + } + + ShortDataBufferAdapter(S buffer, ShortDataLayout layout) { + super(buffer, layout); + this.layout = layout; + } + + private ShortDataLayout layout; +} diff --git a/ndarray/src/main/java/org/tensorflow/ndarray/impl/buffer/layout/Bfloat16Layout.java b/ndarray/src/main/java/org/tensorflow/ndarray/impl/buffer/layout/Bfloat16Layout.java new file mode 100644 index 00000000000..3f8171088cb --- /dev/null +++ b/ndarray/src/main/java/org/tensorflow/ndarray/impl/buffer/layout/Bfloat16Layout.java @@ -0,0 +1,54 @@ +/* + * Copyright 2020 The TensorFlow Authors. All Rights Reserved. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + * ======================================================================= + */ + +package org.tensorflow.ndarray.impl.buffer.layout; + +import org.tensorflow.ndarray.buffer.ShortDataBuffer; +import org.tensorflow.ndarray.buffer.layout.FloatDataLayout; + +/** + * Data layout that converts 32-bit floats from/to 16-bit, truncating their mantissa to 7 bits but + * preserving the 8-bit exponent with the same bias. + */ +public final class Bfloat16Layout implements FloatDataLayout { + + @Override + public void writeFloat(ShortDataBuffer buffer, float value, long index) { + buffer.setShort(float32to16(value), index); + } + + @Override + public float readFloat(ShortDataBuffer buffer, long index) { + return float16to32(buffer.getShort(index)); + } + + // + // FLOAT 32-bit to/from BFLOAT 16-bit conversions + // + // We simply shift the value from 32-bit to 16-bit and vice-versa. NaN special case is ignored. + // + + // VisibleForTesting + static short float32to16(float f32) { + return (short)(Float.floatToIntBits(f32) >>> 16); + } + + // Visible for testing + static float float16to32(short i16) { + return Float.intBitsToFloat((int)i16 << 16); + } +} diff --git a/ndarray/src/main/java/org/tensorflow/ndarray/impl/buffer/layout/BoolLayout.java b/ndarray/src/main/java/org/tensorflow/ndarray/impl/buffer/layout/BoolLayout.java new file mode 100644 index 00000000000..0358d60d662 --- /dev/null +++ b/ndarray/src/main/java/org/tensorflow/ndarray/impl/buffer/layout/BoolLayout.java @@ -0,0 +1,47 @@ +/* + * Copyright 2020 The TensorFlow Authors. All Rights Reserved. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + * ======================================================================= + */ + +package org.tensorflow.ndarray.impl.buffer.layout; + +import org.tensorflow.ndarray.buffer.ByteDataBuffer; +import org.tensorflow.ndarray.buffer.layout.BooleanDataLayout; + +/** + * Data layout that converts booleans from/to bytes. + */ +public final class BoolLayout implements BooleanDataLayout { + + @Override + public void writeBoolean(ByteDataBuffer buffer, boolean value, long index) { + buffer.setByte(booleanToByte(value), index); + } + + @Override + public boolean readBoolean(ByteDataBuffer buffer, long index) { + return byteToBoolean(buffer.getByte(index)); + } + + // Visible for testing + static byte booleanToByte(boolean b) { + return (byte)(b ? 0x1 : 0x0); + } + + // Visible for testing + static boolean byteToBoolean(byte b) { + return b != 0x0; + } +} diff --git a/ndarray/src/main/java/org/tensorflow/ndarray/impl/buffer/layout/Float16Layout.java b/ndarray/src/main/java/org/tensorflow/ndarray/impl/buffer/layout/Float16Layout.java new file mode 100644 index 00000000000..b19744bbd13 --- /dev/null +++ b/ndarray/src/main/java/org/tensorflow/ndarray/impl/buffer/layout/Float16Layout.java @@ -0,0 +1,119 @@ +/* + * Copyright 2020 The TensorFlow Authors. All Rights Reserved. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + * ======================================================================= + */ + +package org.tensorflow.ndarray.impl.buffer.layout; + +import org.tensorflow.ndarray.buffer.ShortDataBuffer; +import org.tensorflow.ndarray.buffer.layout.FloatDataLayout; + +/** + * Data layout that converts 32-bit floats from/to 16-bit, accordingly to the IEEE-754 half-precision + * floating point specification. + */ +public final class Float16Layout implements FloatDataLayout { + + @Override + public void writeFloat(ShortDataBuffer buffer, float value, long index) { + buffer.setShort(float32to16(value), index); + } + + @Override + public float readFloat(ShortDataBuffer buffer, long index) { + return float16to32(buffer.getShort(index)); + } + + // + // FLOAT 32-bit to/from 16-bit conversions + // + // The following conversion algorithms are issued from the C++ implementation found in the + // Eigen library used by TensorFlow native library. + // See https://eigen.tuxfamily.org/dox-devel/Half_8h_source.html for more details. + // + + // VisibleForTesting + static short float32to16(float f32) { + int i16; + int i32 = Float.floatToIntBits(f32); + short sign16 = (short) ((i32 >>> 16) & 0x8000); + i32 &= 0x7FFFFFFF; // remove sign + + if (i32 >= (E32BIAS + E16MAX + 1) << E32SHIFT) { + // float32 value is higher than float16 max value (max16 -> 2^15 * 2 -> 2^16) + // - if float32 value is higher than infinite (i.e. s32 > 0), then it is NaN and should also + // be NaN in float16 (0x7e00) + // - else, float16 value is forced to infinite (0x7c00) + i16 = i32 > E32MASK ? 0x7E00 : 0x7C00; + + } else if (i32 < (E32BIAS + E16MIN) << E32SHIFT){ + // float32 abs value is smaller than float16 min abs value (min16 = 2^-14), could also be 0 + // - apply magic number to align significand 10 bits at the bottom on the float and subtract bias + i16 = Float.floatToIntBits(Float.intBitsToFloat(i32) + MAGIC_32_16_FLOAT) - MAGIC_32_16; + + } else { + // float32 value can be rounded up to a normalized float16 value (i.e. exp32 = [113(-14), 142(15)]) + // - rebase exponent to float16 + // - round up significand to the 13nd bit if s16 is even, on the 12nd bit if it is odd + int round = 0xFFF + ((i32 >>> 13) & 0x1); + i16 = (i32 + ((E16BIAS - E32BIAS) << E32SHIFT) + round) >>> 13; + } + return (short)(i16 | sign16); + } + + // Visible for testing + static float float16to32(short i16) { + int i32 = (i16 & 0x7FFF) << (S32BITS - S16BITS); // remove sign and align in float32 + i32 += (E32BIAS - E16BIAS) << E32SHIFT; // rebase exponent to float32 + + // Handle float16 exponent special cases + switch (i16 & E16MASK) { + case E16MASK: + // float16 value is infinite or NaN + // - adjust float32 exponent one more time + i32 += (E32BIAS - E16BIAS) << E32SHIFT; + break; + case 0x0: + // float16 value is zero or subnormal + // - adjust float32 exponent + // - renormalize using magic number + i32 = Float.floatToIntBits(Float.intBitsToFloat(i32 + (1 << E32SHIFT)) - MAGIC_16_32_FLOAT); + break; + default: + break; + } + return Float.intBitsToFloat(i32 | ((i16 & 0x8000) << 16)); // reapply sign + } + + // float32 format + private static final int E32SHIFT = 23; // position of the exponent in float32 + private static final int E32MASK = 0xFF << E32SHIFT; // mask for float32 exponent (== Infinity) + private static final int E32BIAS = 127; // exponent bias for float32 + private static final int S32BITS = 23; // number of bits in float32 significand + + // float16 format + private static final int E16SHIFT = 10; // position of the exponent in float16 + private static final int E16MASK = 0x1F << E16SHIFT; // mask for float16 exponent (== Infinity) + private static final int E16BIAS = 15; // exponent bias for float16 + private static final int E16MAX = 15; // max value for float16 exponent + private static final int E16MIN = -14; // min value for float16 exponent + private static final int S16BITS = 10; // number of bits in float16 significand + + // magic numbers used when converting denormalized values + private static final int MAGIC_32_16 = ((E32BIAS - E16BIAS) + (S32BITS - S16BITS) + 1) << E32SHIFT; + private static final float MAGIC_32_16_FLOAT = Float.intBitsToFloat(MAGIC_32_16); + private static final int MAGIC_16_32 = (E32BIAS - E16BIAS + 1) << E32SHIFT; + private static final float MAGIC_16_32_FLOAT = Float.intBitsToFloat(MAGIC_16_32); +} diff --git a/ndarray/src/main/java/org/tensorflow/ndarray/impl/buffer/layout/StringLayout.java b/ndarray/src/main/java/org/tensorflow/ndarray/impl/buffer/layout/StringLayout.java new file mode 100644 index 00000000000..51576c0100b --- /dev/null +++ b/ndarray/src/main/java/org/tensorflow/ndarray/impl/buffer/layout/StringLayout.java @@ -0,0 +1,48 @@ +/* + * Copyright 2020 The TensorFlow Authors. All Rights Reserved. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + * ======================================================================= + */ + +package org.tensorflow.ndarray.impl.buffer.layout; + +import java.nio.charset.Charset; +import org.tensorflow.ndarray.buffer.DataBuffer; +import org.tensorflow.ndarray.buffer.layout.DataLayout; + +/** + * Data layout that converts a String to/from a sequence of bytes applying a given charset. + */ +public final class StringLayout implements DataLayout, String> { + + public static StringLayout of(Charset charset) { + return new StringLayout(charset); + } + + @Override + public void writeObject(DataBuffer buffer, String value, long index) { + buffer.setObject(value.getBytes(charset), index); + } + + @Override + public String readObject(DataBuffer buffer, long index) { + return new String(buffer.getObject(index), charset); + } + + private StringLayout(Charset charset) { + this.charset = charset; + } + + private final Charset charset; +} diff --git a/ndarray/src/main/java/org/tensorflow/ndarray/impl/buffer/misc/ArrayDataBuffer.java b/ndarray/src/main/java/org/tensorflow/ndarray/impl/buffer/misc/ArrayDataBuffer.java new file mode 100644 index 00000000000..676e291357a --- /dev/null +++ b/ndarray/src/main/java/org/tensorflow/ndarray/impl/buffer/misc/ArrayDataBuffer.java @@ -0,0 +1,126 @@ +/* + Copyright 2019 The TensorFlow Authors. All Rights Reserved. + + Licensed under the Apache License, Version 2.0 (the "License"); + you may not use this file except in compliance with the License. + You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + + Unless required by applicable law or agreed to in writing, software + distributed under the License is distributed on an "AS IS" BASIS, + WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + See the License for the specific language governing permissions and + limitations under the License. + ======================================================================= + */ +package org.tensorflow.ndarray.impl.buffer.misc; + +import java.util.Arrays; +import org.tensorflow.ndarray.impl.buffer.AbstractDataBuffer; +import org.tensorflow.ndarray.impl.buffer.Validator; +import org.tensorflow.ndarray.buffer.DataBuffer; +import org.tensorflow.ndarray.buffer.DataStorageVisitor; + +class ArrayDataBuffer extends AbstractDataBuffer { + + @Override + public long size() { + return length; + } + + @Override + public boolean isReadOnly() { + return readOnly; + } + + @Override + public T getObject(long index) { + Validator.getArgs(this, index); + return values[(int)index + offset]; + } + + @Override + public DataBuffer setObject(T value, long index) { + Validator.setArgs(this, index); + values[(int)index + offset] = value; + return this; + } + + @Override + public DataBuffer copyTo(DataBuffer dst, long size) { + Validator.copyToArgs(this, dst, size); + return dst.accept(new DataStorageVisitor>() { + + @Override + public DataBuffer visit(Object[] array, int arrayOffset, int arrayLength) { + System.arraycopy(values, offset, array, arrayOffset, (int)size); + return ArrayDataBuffer.this; + } + + @Override + public DataBuffer fallback() { + for (int idx = 0; idx < size; ++idx) { + dst.setObject(values[idx + offset], idx); + } + return ArrayDataBuffer.this; + } + }); + } + + @Override + public DataBuffer slice(long index, long size) { + Validator.sliceArgs(this, index, size); + return new ArrayDataBuffer<>(values, readOnly, offset + (int)index, (int)size); + } + + @Override + public R accept(DataStorageVisitor visitor) { + return visitor.visit(values, offset, length); + } + + @Override + public boolean equals(Object obj) { + if (this == obj) { + return true; + } + if (!(obj instanceof DataBuffer)) { + return false; + } + DataBuffer other = (DataBuffer)obj; + if (size() != other.size()) { + return false; + } + return other.accept(new DataStorageVisitor() { + + @Override + public Boolean visit(Object[] array, int arrayOffset, int arrayLength) { + if (offset == 0 && values.length == length && arrayOffset == 0 && array.length == arrayLength) { + return Arrays.deepEquals(array, values); + } + return slowEquals(other); + } + + @Override + public Boolean fallback() { + return slowEquals(other); + } + }); + } + + ArrayDataBuffer(T[] values, boolean readOnly) { + this(values, readOnly, 0, values.length); + } + + private ArrayDataBuffer(T[] values, boolean readOnly, int offset, int length) { + this.values = values; + this.readOnly = readOnly; + this.offset = offset; + this.length = length; + } + + private final T[] values; + private final boolean readOnly; + private final int offset; + private final int length; +} diff --git a/ndarray/src/main/java/org/tensorflow/ndarray/impl/buffer/misc/BitSetDataBuffer.java b/ndarray/src/main/java/org/tensorflow/ndarray/impl/buffer/misc/BitSetDataBuffer.java new file mode 100644 index 00000000000..5b5ec15294b --- /dev/null +++ b/ndarray/src/main/java/org/tensorflow/ndarray/impl/buffer/misc/BitSetDataBuffer.java @@ -0,0 +1,183 @@ +/* + * Copyright 2019 The TensorFlow Authors. All Rights Reserved. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + * ======================================================================= + */ + +package org.tensorflow.ndarray.impl.buffer.misc; + +import java.util.BitSet; +import org.tensorflow.ndarray.impl.buffer.AbstractDataBuffer; +import org.tensorflow.ndarray.impl.buffer.Validator; +import org.tensorflow.ndarray.buffer.BooleanDataBuffer; +import org.tensorflow.ndarray.buffer.DataBuffer; +import org.tensorflow.ndarray.buffer.DataStorageVisitor; + +class BitSetDataBuffer extends AbstractDataBuffer implements BooleanDataBuffer { + + @Override + public long size() { + return numBits; + } + + @Override + public boolean isReadOnly() { + return readOnly; + } + + @Override + public boolean getBoolean(long index) { + Validator.getArgs(this, index); + return bitSet.get((int)index + offset); + } + + @Override + public BooleanDataBuffer setBoolean(boolean value, long index) { + Validator.setArgs(this, index); + bitSet.set((int)index + offset, value); + return this; + } + + @Override + public BooleanDataBuffer read(boolean[] dst, int offset, int length) { + Validator.readArgs(this, dst.length, offset, length); + for (int i = this.offset, j = offset; i < this.offset + length; ++i, ++j) { + dst[j] = bitSet.get(i); + } + return this; + } + + @Override + public BooleanDataBuffer write(boolean[] src, int offset, int length) { + Validator.readArgs(this, src.length, offset, length); + for (int i = this.offset, j = offset; i < this.offset + length; ++i, ++j) { + bitSet.set(i, src[j]); + } + return this; + } + + @Override + public BooleanDataBuffer copyTo(DataBuffer dst, long size) { + Validator.copyToArgs(this, dst, size); + return dst.accept(new DataStorageVisitor() { + + @Override + public BooleanDataBuffer visit(boolean[] array, int arrayOffset, int arrayLength) { + for (int idx = 0; idx < size; ++idx) { + array[idx + arrayOffset] = bitSet.get(idx + offset); + } + return BitSetDataBuffer.this; + } + + @Override + public BooleanDataBuffer visit(BitSet dstBitSet, int dstOffset, long numBits) { + for (int idx = 0; idx < size; ++idx) { + dstBitSet.set(idx + dstOffset, bitSet.get(idx + offset)); + } + return BitSetDataBuffer.this; + } + + @Override + public BooleanDataBuffer fallback() { + if (dst instanceof BooleanDataBuffer) { + BooleanDataBuffer booleanDst = (BooleanDataBuffer)dst; + for (int idx = 0; idx < size; ++idx) { + booleanDst.setBoolean(bitSet.get(idx + offset), idx); + } + } else { + for (int idx = 0; idx < size; ++idx) { + dst.setObject(bitSet.get(idx + offset), idx); + } + } + return BitSetDataBuffer.this; + } + }); + } + + @Override + public BooleanDataBuffer slice(long index, long size) { + Validator.sliceArgs(this, index, size); + return new BitSetDataBuffer(bitSet, size, readOnly, offset + (int)index); + } + + @Override + public R accept(DataStorageVisitor visitor) { + return visitor.visit(bitSet, offset, numBits); + } + + @Override + public boolean equals(Object obj) { + if (this == obj) { + return true; + } + if (!(obj instanceof BooleanDataBuffer)) { + return super.equals(obj); + } + BooleanDataBuffer other = (BooleanDataBuffer)obj; + if (size() != other.size()) { + return false; + } + return other.accept(new DataStorageVisitor() { + + @Override + public Boolean visit(boolean[] array, int arrayOffset, int length) { + for (int idx = 0; idx < size(); ++idx) { + if (array[idx + arrayOffset] != bitSet.get(idx + offset)) { + return false; + } + } + return true; + } + + @Override + public Boolean visit(BitSet otherBitSet, int otherOffset, long otherNumBits) { + if (offset == 0 && otherOffset == 0 && numBits == otherNumBits) { + return bitSet.equals(otherBitSet); + } + for (int idx = 0; idx < size(); ++idx) { + if (otherBitSet.get(idx + otherOffset) != bitSet.get(idx + offset)) { + return false; + } + } + return true; + } + + @Override + public Boolean fallback() { + for (int idx = 0; idx < size(); ++idx) { + if (other.getBoolean(idx) != bitSet.get(idx + offset)) { + return false; + } + } + return true; + } + }); + } + + BitSetDataBuffer(BitSet bitSet, long numBits, boolean readOnly) { + this(bitSet, numBits, readOnly, 0); + } + + private BitSetDataBuffer(BitSet bitSet, long numBits, boolean readOnly, int offset) { + this.bitSet = bitSet; + this.numBits = numBits; + this.readOnly = readOnly; + this.offset = offset; + } + + private final BitSet bitSet; + private final long numBits; + private final boolean readOnly; + private final int offset; +} diff --git a/ndarray/src/main/java/org/tensorflow/ndarray/impl/buffer/misc/BooleanArrayDataBuffer.java b/ndarray/src/main/java/org/tensorflow/ndarray/impl/buffer/misc/BooleanArrayDataBuffer.java new file mode 100644 index 00000000000..f8d033519ec --- /dev/null +++ b/ndarray/src/main/java/org/tensorflow/ndarray/impl/buffer/misc/BooleanArrayDataBuffer.java @@ -0,0 +1,176 @@ +/* + Copyright 2019 The TensorFlow Authors. All Rights Reserved. + + Licensed under the Apache License, Version 2.0 (the "License"); + you may not use this file except in compliance with the License. + You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + + Unless required by applicable law or agreed to in writing, software + distributed under the License is distributed on an "AS IS" BASIS, + WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + See the License for the specific language governing permissions and + limitations under the License. + ======================================================================= + */ +package org.tensorflow.ndarray.impl.buffer.misc; + +import java.util.Arrays; +import java.util.BitSet; +import org.tensorflow.ndarray.impl.buffer.AbstractDataBuffer; +import org.tensorflow.ndarray.impl.buffer.Validator; +import org.tensorflow.ndarray.buffer.BooleanDataBuffer; +import org.tensorflow.ndarray.buffer.DataBuffer; +import org.tensorflow.ndarray.buffer.DataStorageVisitor; + +class BooleanArrayDataBuffer extends AbstractDataBuffer implements + BooleanDataBuffer { + + @Override + public long size() { + return length; + } + + @Override + public boolean isReadOnly() { + return readOnly; + } + + @Override + public boolean getBoolean(long index) { + Validator.getArgs(this, index); + return values[(int)index + offset]; + } + + @Override + public BooleanDataBuffer setBoolean(boolean value, long index) { + Validator.setArgs(this, index); + values[(int)index + offset] = value; + return this; + } + + @Override + public BooleanDataBuffer read(boolean[] dst, int offset, int length) { + System.arraycopy(values, this.offset, dst, offset, length); + return this; + } + + @Override + public BooleanDataBuffer write(boolean[] src, int offset, int length) { + System.arraycopy(src, offset, values, this.offset, length); + return null; + } + + @Override + public BooleanDataBuffer copyTo(DataBuffer dst, long size) { + Validator.copyToArgs(this, dst, size); + return dst.accept(new DataStorageVisitor() { + + @Override + public BooleanDataBuffer visit(boolean[] array, int arrayOffset, int arrayLength) { + System.arraycopy(values, offset, array, arrayOffset, (int)size); + return BooleanArrayDataBuffer.this; + } + + @Override + public BooleanDataBuffer visit(BitSet bitSet, int bitSetOffset, long numBits) { + for (int idx = 0; idx < size; ++idx) { + bitSet.set(idx + bitSetOffset, values[idx + offset]); + } + return BooleanArrayDataBuffer.this; + } + + @Override + public BooleanDataBuffer fallback() { + if (dst instanceof BooleanDataBuffer) { + BooleanDataBuffer booleanDst = (BooleanDataBuffer)dst; + for (int idx = 0; idx < size; ++idx) { + booleanDst.setBoolean(values[idx + offset], idx); + } + } else { + for (int idx = 0; idx < size; ++idx) { + dst.setObject(values[idx + offset], idx); + } + } + return BooleanArrayDataBuffer.this; + } + }); + } + + @Override + public BooleanDataBuffer slice(long index, long size) { + Validator.sliceArgs(this, index, size); + return new BooleanArrayDataBuffer(values, readOnly, offset + (int)index, (int)size); + } + + @Override + public R accept(DataStorageVisitor visitor) { + return visitor.visit(values, offset, length); + } + + @Override + public boolean equals(Object obj) { + if (this == obj) { + return true; + } + if (!(obj instanceof BooleanDataBuffer)) { + return super.equals(obj); + } + BooleanDataBuffer other = (BooleanDataBuffer)obj; + if (size() != other.size()) { + return false; + } + return other.accept(new DataStorageVisitor() { + + @Override + public Boolean visit(boolean[] array, int arrayOffset, int arrayLength) { + if (offset == 0 && values.length == length && arrayOffset == 0 && array.length == arrayLength) { + return Arrays.equals(array, values); + } + for (int idx = 0; idx < size(); ++idx) { + if (array[idx + arrayOffset] != values[idx + offset]) { + return false; + } + } + return true; + } + + @Override + public Boolean visit(BitSet bitSet, int bitSetOffset, long numBits) { + for (int idx = 0; idx < size(); ++idx) { + if (bitSet.get(idx + bitSetOffset) != values[idx + offset]) { + return false; + } + } + return true; + } + + @Override + public Boolean fallback() { + for (int idx = 0; idx < size(); ++idx) { + if (other.getBoolean(idx) != values[idx + offset]) { + return false; + } + } + return true; + } + }); + } + + BooleanArrayDataBuffer(boolean[] values, boolean readOnly) { + this(values, readOnly, 0, values.length); + } + + private BooleanArrayDataBuffer(boolean[] values, boolean readOnly, int offset, int length) { + this.values = values; + this.readOnly = readOnly; + this.offset = offset; + this.length = length; + } + + private final boolean[] values; + private final boolean readOnly; + private final int offset; + private final int length; +} diff --git a/ndarray/src/main/java/org/tensorflow/ndarray/impl/buffer/misc/MiscDataBufferFactory.java b/ndarray/src/main/java/org/tensorflow/ndarray/impl/buffer/misc/MiscDataBufferFactory.java new file mode 100644 index 00000000000..84cfce6bc66 --- /dev/null +++ b/ndarray/src/main/java/org/tensorflow/ndarray/impl/buffer/misc/MiscDataBufferFactory.java @@ -0,0 +1,40 @@ +/* + * Copyright 2019 The TensorFlow Authors. All Rights Reserved. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + * ======================================================================= + */ + +package org.tensorflow.ndarray.impl.buffer.misc; + +import java.util.BitSet; +import org.tensorflow.ndarray.buffer.BooleanDataBuffer; +import org.tensorflow.ndarray.buffer.DataBuffer; + +/** + * Factory of miscellaneous data buffers + */ +public class MiscDataBufferFactory { + + public static BooleanDataBuffer create(BitSet bitSet, long numBits, boolean readOnly) { + return new BitSetDataBuffer(bitSet, numBits, readOnly); + } + + public static BooleanDataBuffer create(boolean[] array, boolean readOnly) { + return new BooleanArrayDataBuffer(array, readOnly); + } + + public static DataBuffer create(T[] array, boolean readOnly) { + return new ArrayDataBuffer<>(array, readOnly); + } +} diff --git a/ndarray/src/main/java/org/tensorflow/ndarray/impl/buffer/nio/AbstractNioDataBuffer.java b/ndarray/src/main/java/org/tensorflow/ndarray/impl/buffer/nio/AbstractNioDataBuffer.java new file mode 100644 index 00000000000..82bc981ad46 --- /dev/null +++ b/ndarray/src/main/java/org/tensorflow/ndarray/impl/buffer/nio/AbstractNioDataBuffer.java @@ -0,0 +1,41 @@ +/* + Copyright 2019 The TensorFlow Authors. All Rights Reserved. + + Licensed under the Apache License, Version 2.0 (the "License"); + you may not use this file except in compliance with the License. + You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + + Unless required by applicable law or agreed to in writing, software + distributed under the License is distributed on an "AS IS" BASIS, + WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + See the License for the specific language governing permissions and + limitations under the License. + ======================================================================= + */ + +package org.tensorflow.ndarray.impl.buffer.nio; + +import java.nio.Buffer; +import org.tensorflow.ndarray.impl.buffer.AbstractDataBuffer; + +/** + * Base class for all JDK-based data buffers. + * + * @param type of elements (or values) stored in this buffer + */ +abstract class AbstractNioDataBuffer extends AbstractDataBuffer { + + @Override + public long size() { + return buf().capacity(); + } + + @Override + public boolean isReadOnly() { + return buf().isReadOnly(); + } + + abstract Buffer buf(); +} diff --git a/ndarray/src/main/java/org/tensorflow/ndarray/impl/buffer/nio/ByteNioDataBuffer.java b/ndarray/src/main/java/org/tensorflow/ndarray/impl/buffer/nio/ByteNioDataBuffer.java new file mode 100644 index 00000000000..5ede97cef78 --- /dev/null +++ b/ndarray/src/main/java/org/tensorflow/ndarray/impl/buffer/nio/ByteNioDataBuffer.java @@ -0,0 +1,185 @@ +/* + Copyright 2019 The TensorFlow Authors. All Rights Reserved. + + Licensed under the Apache License, Version 2.0 (the "License"); + you may not use this file except in compliance with the License. + You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + + Unless required by applicable law or agreed to in writing, software + distributed under the License is distributed on an "AS IS" BASIS, + WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + See the License for the specific language governing permissions and + limitations under the License. + ======================================================================= + */ + +package org.tensorflow.ndarray.impl.buffer.nio; + +import java.nio.ByteBuffer; +import org.tensorflow.ndarray.impl.buffer.Validator; +import org.tensorflow.ndarray.impl.buffer.adapter.DataBufferAdapterFactory; +import org.tensorflow.ndarray.buffer.BooleanDataBuffer; +import org.tensorflow.ndarray.buffer.ByteDataBuffer; +import org.tensorflow.ndarray.buffer.DataBuffer; +import org.tensorflow.ndarray.buffer.DataStorageVisitor; +import org.tensorflow.ndarray.buffer.DoubleDataBuffer; +import org.tensorflow.ndarray.buffer.FloatDataBuffer; +import org.tensorflow.ndarray.buffer.IntDataBuffer; +import org.tensorflow.ndarray.buffer.LongDataBuffer; +import org.tensorflow.ndarray.buffer.ShortDataBuffer; +import org.tensorflow.ndarray.buffer.layout.DataLayouts; + +/** + * A buffer of bytes using a JDK {@link ByteBuffer} for storage. + */ +final class ByteNioDataBuffer extends AbstractNioDataBuffer + implements ByteDataBuffer { + + @Override + public byte getByte(long index) { + return buf.get((int)index); + } + + @Override + public ByteDataBuffer setByte(byte value, long index) { + buf.put((int)index, value); + return this; + } + + @Override + public ByteDataBuffer read(byte[] dst, int offset, int length) { + buf.duplicate().get(dst, offset, length); + return this; + } + + @Override + public ByteDataBuffer write(byte[] src, int offset, int length) { + buf.duplicate().put(src, offset, length); + return this; + } + + @Override + public ByteDataBuffer copyTo(DataBuffer dst, long size) { + Validator.copyToArgs(this, dst, size); + return dst.accept(new DataStorageVisitor() { + + @Override + public ByteDataBuffer visit(ByteBuffer buffer) { + buffer.duplicate().put((ByteBuffer)buf.duplicate().limit((int)size)); + return ByteNioDataBuffer.this; + } + + @Override + public ByteDataBuffer fallback() { + if (dst instanceof ByteDataBuffer) { + ByteDataBuffer byteDst = (ByteDataBuffer)dst; + for (long idx = 0L; idx < size; ++idx) { + byteDst.setByte(getByte(idx), idx); + } + return ByteNioDataBuffer.this; + } + return slowCopyTo(dst, size); + } + }); + } + + @Override + public IntDataBuffer asInts() { + return new IntNioDataBuffer(buf.asIntBuffer()); + } + + @Override + public ShortDataBuffer asShorts() { + return new ShortNioDataBuffer(buf.asShortBuffer()); + } + + @Override + public LongDataBuffer asLongs() { + return new LongNioDataBuffer(buf.asLongBuffer()); + } + + @Override + public FloatDataBuffer asFloats() { + return new FloatNioDataBuffer(buf.asFloatBuffer()); + } + + @Override + public DoubleDataBuffer asDoubles() { + return new DoubleNioDataBuffer(buf.asDoubleBuffer()); + } + + @Override + public BooleanDataBuffer asBooleans() { + return DataBufferAdapterFactory.create(this, DataLayouts.BOOL); + } + + @Override + public ByteDataBuffer offset(long index) { + Validator.offsetArgs(this, index); + return new ByteNioDataBuffer(((ByteBuffer)buf.duplicate().position((int)index)).slice()); + } + + @Override + public ByteDataBuffer narrow(long size) { + Validator.narrowArgs(this, size); + return new ByteNioDataBuffer(((ByteBuffer)buf.duplicate().limit((int)size)).slice()); + } + + @Override + public ByteDataBuffer slice(long index, long size) { + Validator.sliceArgs(this, index, size); + ByteBuffer sliceBuf = buf.duplicate(); + sliceBuf.position((int)index); + sliceBuf.limit((int)index + (int)size); + return new ByteNioDataBuffer(sliceBuf.slice()); + } + + @Override + public R accept(DataStorageVisitor visitor) { + return visitor.visit(buf); + } + + @Override + public boolean equals(Object obj) { + if (this == obj) { + return true; + } + if (!(obj instanceof ByteDataBuffer)) { + return super.equals(obj); + } + ByteDataBuffer other = (ByteDataBuffer)obj; + if (size() != other.size()) { + return false; + } + return other.accept(new DataStorageVisitor() { + + @Override + public Boolean visit(ByteBuffer buffer) { + return buf.equals(buffer); + } + + @Override + public Boolean fallback() { + for (int idx = 0; idx < size(); ++idx) { + if (other.getByte(idx) != getByte(idx)) { + return false; + } + } + return true; + } + }); + } + + @Override + ByteBuffer buf() { + return buf; + } + + ByteNioDataBuffer(ByteBuffer buf) { + this.buf = buf; + } + + private ByteBuffer buf; +} diff --git a/ndarray/src/main/java/org/tensorflow/ndarray/impl/buffer/nio/DoubleNioDataBuffer.java b/ndarray/src/main/java/org/tensorflow/ndarray/impl/buffer/nio/DoubleNioDataBuffer.java new file mode 100644 index 00000000000..bddc5db1e3f --- /dev/null +++ b/ndarray/src/main/java/org/tensorflow/ndarray/impl/buffer/nio/DoubleNioDataBuffer.java @@ -0,0 +1,147 @@ +/* + Copyright 2019 The TensorFlow Authors. All Rights Reserved. + + Licensed under the Apache License, Version 2.0 (the "License"); + you may not use this file except in compliance with the License. + You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + + Unless required by applicable law or agreed to in writing, software + distributed under the License is distributed on an "AS IS" BASIS, + WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + See the License for the specific language governing permissions and + limitations under the License. + ======================================================================= + */ + +package org.tensorflow.ndarray.impl.buffer.nio; + +import java.nio.DoubleBuffer; +import org.tensorflow.ndarray.impl.buffer.Validator; +import org.tensorflow.ndarray.buffer.DataBuffer; +import org.tensorflow.ndarray.buffer.DataStorageVisitor; +import org.tensorflow.ndarray.buffer.DoubleDataBuffer; + +/** + * A buffer of bytes using a JDK {@link DoubleBuffer} for storage. + */ +final class DoubleNioDataBuffer extends AbstractNioDataBuffer + implements DoubleDataBuffer { + + @Override + public double getDouble(long index) { + return buf.get((int)index); + } + + @Override + public DoubleDataBuffer setDouble(double value, long index) { + buf.put((int)index, value); + return this; + } + + @Override + public DoubleDataBuffer read(double[] dst, int offset, int length) { + buf.duplicate().get(dst, offset, length); + return this; + } + + @Override + public DoubleDataBuffer write(double[] src, int offset, int length) { + buf.duplicate().put(src, offset, length); + return this; + } + + @Override + public DoubleDataBuffer copyTo(DataBuffer dst, long size) { + Validator.copyToArgs(this, dst, size); + return dst.accept(new DataStorageVisitor() { + + @Override + public DoubleDataBuffer visit(DoubleBuffer buffer) { + buffer.duplicate().put((DoubleBuffer)buf.duplicate().limit((int)size)); + return DoubleNioDataBuffer.this; + } + + @Override + public DoubleDataBuffer fallback() { + if (dst instanceof DoubleDataBuffer) { + DoubleDataBuffer doubleDst = (DoubleDataBuffer)dst; + for (long idx = 0L; idx < size; ++idx) { + doubleDst.setDouble(getDouble(idx), idx); + } + return DoubleNioDataBuffer.this; + } + return slowCopyTo(dst, size); + } + }); + } + + @Override + public DoubleDataBuffer offset(long index) { + Validator.offsetArgs(this, index); + return new DoubleNioDataBuffer(((DoubleBuffer)buf.duplicate().position((int)index)).slice()); + } + + @Override + public DoubleDataBuffer narrow(long size) { + Validator.narrowArgs(this, size); + return new DoubleNioDataBuffer(((DoubleBuffer)buf.duplicate().limit((int)size)).slice()); + } + + @Override + public DoubleDataBuffer slice(long index, long size) { + Validator.sliceArgs(this, index, size); + DoubleBuffer sliceBuf = buf.duplicate(); + sliceBuf.position((int)index); + sliceBuf.limit((int)index + (int)size); + return new DoubleNioDataBuffer(sliceBuf.slice()); + } + + @Override + public R accept(DataStorageVisitor visitor) { + return visitor.visit(buf); + } + + @Override + public boolean equals(Object obj) { + if (this == obj) { + return true; + } + if (!(obj instanceof DoubleDataBuffer)) { + return super.equals(obj); + } + DoubleDataBuffer other = (DoubleDataBuffer)obj; + if (size() != other.size()) { + return false; + } + return other.accept(new DataStorageVisitor() { + + @Override + public Boolean visit(DoubleBuffer buffer) { + return buf.equals(buffer); + } + + @Override + public Boolean fallback() { + for (int idx = 0; idx < size(); ++idx) { + if (other.getDouble(idx) != getDouble(idx)) { + return false; + } + } + return true; + } + }); + } + + @Override + DoubleBuffer buf() { + return buf; + } + + DoubleNioDataBuffer(DoubleBuffer buf) { + this.buf = buf; + } + + private DoubleBuffer buf; +} diff --git a/ndarray/src/main/java/org/tensorflow/ndarray/impl/buffer/nio/FloatNioDataBuffer.java b/ndarray/src/main/java/org/tensorflow/ndarray/impl/buffer/nio/FloatNioDataBuffer.java new file mode 100644 index 00000000000..06a9a31b56a --- /dev/null +++ b/ndarray/src/main/java/org/tensorflow/ndarray/impl/buffer/nio/FloatNioDataBuffer.java @@ -0,0 +1,147 @@ +/* + Copyright 2019 The TensorFlow Authors. All Rights Reserved. + + Licensed under the Apache License, Version 2.0 (the "License"); + you may not use this file except in compliance with the License. + You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + + Unless required by applicable law or agreed to in writing, software + distributed under the License is distributed on an "AS IS" BASIS, + WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + See the License for the specific language governing permissions and + limitations under the License. + ======================================================================= + */ + +package org.tensorflow.ndarray.impl.buffer.nio; + +import java.nio.FloatBuffer; +import org.tensorflow.ndarray.impl.buffer.Validator; +import org.tensorflow.ndarray.buffer.DataBuffer; +import org.tensorflow.ndarray.buffer.DataStorageVisitor; +import org.tensorflow.ndarray.buffer.FloatDataBuffer; + +/** + * A buffer of bytes using a JDK {@link FloatBuffer} for storage. + */ +final class FloatNioDataBuffer extends AbstractNioDataBuffer + implements FloatDataBuffer { + + @Override + public float getFloat(long index) { + return buf.get((int)index); + } + + @Override + public FloatDataBuffer setFloat(float value, long index) { + buf.put((int)index, value); + return this; + } + + @Override + public FloatDataBuffer read(float[] dst, int offset, int length) { + buf.duplicate().get(dst, offset, length); + return this; + } + + @Override + public FloatDataBuffer write(float[] src, int offset, int length) { + buf.duplicate().put(src, offset, length); + return this; + } + + @Override + public FloatDataBuffer copyTo(DataBuffer dst, long size) { + Validator.copyToArgs(this, dst, size); + return dst.accept(new DataStorageVisitor() { + + @Override + public FloatDataBuffer visit(FloatBuffer buffer) { + buffer.duplicate().put((FloatBuffer)buf.duplicate().limit((int)size)); + return FloatNioDataBuffer.this; + } + + @Override + public FloatDataBuffer fallback() { + if (dst instanceof FloatDataBuffer) { + FloatDataBuffer floatDst = (FloatDataBuffer)dst; + for (long idx = 0L; idx < size; ++idx) { + floatDst.setFloat(getFloat(idx), idx); + } + return FloatNioDataBuffer.this; + } + return slowCopyTo(dst, size); + } + }); + } + + @Override + public FloatDataBuffer offset(long index) { + Validator.offsetArgs(this, index); + return new FloatNioDataBuffer(((FloatBuffer)buf.duplicate().position((int)index)).slice()); + } + + @Override + public FloatDataBuffer narrow(long size) { + Validator.narrowArgs(this, size); + return new FloatNioDataBuffer(((FloatBuffer)buf.duplicate().limit((int)size)).slice()); + } + + @Override + public FloatDataBuffer slice(long index, long size) { + Validator.sliceArgs(this, index, size); + FloatBuffer sliceBuf = buf.duplicate(); + sliceBuf.position((int)index); + sliceBuf.limit((int)index + (int)size); + return new FloatNioDataBuffer(sliceBuf.slice()); + } + + @Override + public R accept(DataStorageVisitor visitor) { + return visitor.visit(buf); + } + + @Override + public boolean equals(Object obj) { + if (this == obj) { + return true; + } + if (!(obj instanceof FloatDataBuffer)) { + return super.equals(obj); + } + FloatDataBuffer other = (FloatDataBuffer)obj; + if (size() != other.size()) { + return false; + } + return other.accept(new DataStorageVisitor() { + + @Override + public Boolean visit(FloatBuffer buffer) { + return buf.equals(buffer); + } + + @Override + public Boolean fallback() { + for (int idx = 0; idx < size(); ++idx) { + if (other.getFloat(idx) != getFloat(idx)) { + return false; + } + } + return true; + } + }); + } + + @Override + FloatBuffer buf() { + return buf; + } + + FloatNioDataBuffer(FloatBuffer buf) { + this.buf = buf; + } + + private FloatBuffer buf; +} diff --git a/ndarray/src/main/java/org/tensorflow/ndarray/impl/buffer/nio/IntNioDataBuffer.java b/ndarray/src/main/java/org/tensorflow/ndarray/impl/buffer/nio/IntNioDataBuffer.java new file mode 100644 index 00000000000..cea729e86a7 --- /dev/null +++ b/ndarray/src/main/java/org/tensorflow/ndarray/impl/buffer/nio/IntNioDataBuffer.java @@ -0,0 +1,147 @@ +/* + Copyright 2019 The TensorFlow Authors. All Rights Reserved. + + Licensed under the Apache License, Version 2.0 (the "License"); + you may not use this file except in compliance with the License. + You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + + Unless required by applicable law or agreed to in writing, software + distributed under the License is distributed on an "AS IS" BASIS, + WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + See the License for the specific language governing permissions and + limitations under the License. + ======================================================================= + */ + +package org.tensorflow.ndarray.impl.buffer.nio; + +import java.nio.IntBuffer; +import org.tensorflow.ndarray.impl.buffer.Validator; +import org.tensorflow.ndarray.buffer.DataBuffer; +import org.tensorflow.ndarray.buffer.DataStorageVisitor; +import org.tensorflow.ndarray.buffer.IntDataBuffer; + +/** + * A buffer of bytes using a JDK {@link IntBuffer} for storage. + */ +final class IntNioDataBuffer extends AbstractNioDataBuffer + implements IntDataBuffer { + + @Override + public int getInt(long index) { + return buf.get((int)index); + } + + @Override + public IntDataBuffer setInt(int value, long index) { + buf.put((int)index, value); + return this; + } + + @Override + public IntDataBuffer read(int[] dst, int offset, int length) { + buf.duplicate().get(dst, offset, length); + return this; + } + + @Override + public IntDataBuffer write(int[] src, int offset, int length) { + buf.duplicate().put(src, offset, length); + return this; + } + + @Override + public IntDataBuffer copyTo(DataBuffer dst, long size) { + Validator.copyToArgs(this, dst, size); + return dst.accept(new DataStorageVisitor() { + + @Override + public IntDataBuffer visit(IntBuffer buffer) { + buffer.duplicate().put((IntBuffer)buf.duplicate().limit((int)size)); + return IntNioDataBuffer.this; + } + + @Override + public IntDataBuffer fallback() { + if (dst instanceof IntDataBuffer) { + IntDataBuffer intDst = (IntDataBuffer)dst; + for (long idx = 0L; idx < size; ++idx) { + intDst.setInt(getInt(idx), idx); + } + return IntNioDataBuffer.this; + } + return slowCopyTo(dst, size); + } + }); + } + + @Override + public IntDataBuffer offset(long index) { + Validator.offsetArgs(this, index); + return new IntNioDataBuffer(((IntBuffer)buf.duplicate().position((int)index)).slice()); + } + + @Override + public IntDataBuffer narrow(long size) { + Validator.narrowArgs(this, size); + return new IntNioDataBuffer(((IntBuffer)buf.duplicate().limit((int)size)).slice()); + } + + @Override + public IntDataBuffer slice(long index, long size) { + Validator.sliceArgs(this, index, size); + IntBuffer sliceBuf = buf.duplicate(); + sliceBuf.position((int)index); + sliceBuf.limit((int)index + (int)size); + return new IntNioDataBuffer(sliceBuf.slice()); + } + + @Override + public R accept(DataStorageVisitor visitor) { + return visitor.visit(buf); + } + + @Override + public boolean equals(Object obj) { + if (this == obj) { + return true; + } + if (!(obj instanceof IntDataBuffer)) { + return super.equals(obj); + } + IntDataBuffer other = (IntDataBuffer)obj; + if (size() != other.size()) { + return false; + } + return other.accept(new DataStorageVisitor() { + + @Override + public Boolean visit(IntBuffer buffer) { + return buf.equals(buffer); + } + + @Override + public Boolean fallback() { + for (int idx = 0; idx < size(); ++idx) { + if (other.getInt(idx) != getInt(idx)) { + return false; + } + } + return true; + } + }); + } + + @Override + IntBuffer buf() { + return buf; + } + + IntNioDataBuffer(IntBuffer buf) { + this.buf = buf; + } + + private IntBuffer buf; +} diff --git a/ndarray/src/main/java/org/tensorflow/ndarray/impl/buffer/nio/LongNioDataBuffer.java b/ndarray/src/main/java/org/tensorflow/ndarray/impl/buffer/nio/LongNioDataBuffer.java new file mode 100644 index 00000000000..7231ee7d408 --- /dev/null +++ b/ndarray/src/main/java/org/tensorflow/ndarray/impl/buffer/nio/LongNioDataBuffer.java @@ -0,0 +1,147 @@ +/* + Copyright 2019 The TensorFlow Authors. All Rights Reserved. + + Licensed under the Apache License, Version 2.0 (the "License"); + you may not use this file except in compliance with the License. + You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + + Unless required by applicable law or agreed to in writing, software + distributed under the License is distributed on an "AS IS" BASIS, + WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + See the License for the specific language governing permissions and + limitations under the License. + ======================================================================= + */ + +package org.tensorflow.ndarray.impl.buffer.nio; + +import java.nio.LongBuffer; +import org.tensorflow.ndarray.impl.buffer.Validator; +import org.tensorflow.ndarray.buffer.DataBuffer; +import org.tensorflow.ndarray.buffer.DataStorageVisitor; +import org.tensorflow.ndarray.buffer.LongDataBuffer; + +/** + * A buffer of bytes using a JDK {@link LongBuffer} for storage. + */ +final class LongNioDataBuffer extends AbstractNioDataBuffer + implements LongDataBuffer { + + @Override + public long getLong(long index) { + return buf.get((int)index); + } + + @Override + public LongDataBuffer setLong(long value, long index) { + buf.put((int)index, value); + return this; + } + + @Override + public LongDataBuffer read(long[] dst, int offset, int length) { + buf.duplicate().get(dst, offset, length); + return this; + } + + @Override + public LongDataBuffer write(long[] src, int offset, int length) { + buf.duplicate().put(src, offset, length); + return this; + } + + @Override + public LongDataBuffer copyTo(DataBuffer dst, long size) { + Validator.copyToArgs(this, dst, size); + return dst.accept(new DataStorageVisitor() { + + @Override + public LongDataBuffer visit(LongBuffer buffer) { + buffer.duplicate().put((LongBuffer)buf.duplicate().limit((int)size)); + return LongNioDataBuffer.this; + } + + @Override + public LongDataBuffer fallback() { + if (dst instanceof LongDataBuffer) { + LongDataBuffer longDst = (LongDataBuffer)dst; + for (long idx = 0L; idx < size; ++idx) { + longDst.setLong(getLong(idx), idx); + } + return LongNioDataBuffer.this; + } + return slowCopyTo(dst, size); + } + }); + } + + @Override + public LongDataBuffer offset(long index) { + Validator.offsetArgs(this, index); + return new LongNioDataBuffer(((LongBuffer)buf.duplicate().position((int)index)).slice()); + } + + @Override + public LongDataBuffer narrow(long size) { + Validator.narrowArgs(this, size); + return new LongNioDataBuffer(((LongBuffer)buf.duplicate().limit((int)size)).slice()); + } + + @Override + public LongDataBuffer slice(long index, long size) { + Validator.sliceArgs(this, index, size); + LongBuffer sliceBuf = buf.duplicate(); + sliceBuf.position((int)index); + sliceBuf.limit((int)index + (int)size); + return new LongNioDataBuffer(sliceBuf.slice()); + } + + @Override + public R accept(DataStorageVisitor visitor) { + return visitor.visit(buf); + } + + @Override + public boolean equals(Object obj) { + if (this == obj) { + return true; + } + if (!(obj instanceof LongDataBuffer)) { + return super.equals(obj); + } + LongDataBuffer other = (LongDataBuffer)obj; + if (size() != other.size()) { + return false; + } + return other.accept(new DataStorageVisitor() { + + @Override + public Boolean visit(LongBuffer buffer) { + return buf.equals(buffer); + } + + @Override + public Boolean fallback() { + for (int idx = 0; idx < size(); ++idx) { + if (other.getLong(idx) != getLong(idx)) { + return false; + } + } + return true; + } + }); + } + + @Override + LongBuffer buf() { + return buf; + } + + LongNioDataBuffer(LongBuffer buf) { + this.buf = buf; + } + + private LongBuffer buf; +} diff --git a/ndarray/src/main/java/org/tensorflow/ndarray/impl/buffer/nio/NioDataBufferFactory.java b/ndarray/src/main/java/org/tensorflow/ndarray/impl/buffer/nio/NioDataBufferFactory.java new file mode 100644 index 00000000000..4e84fc9bc17 --- /dev/null +++ b/ndarray/src/main/java/org/tensorflow/ndarray/impl/buffer/nio/NioDataBufferFactory.java @@ -0,0 +1,61 @@ +/* + * Copyright 2019 The TensorFlow Authors. All Rights Reserved. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + * ======================================================================= + */ + +package org.tensorflow.ndarray.impl.buffer.nio; + +import java.nio.ByteBuffer; +import java.nio.DoubleBuffer; +import java.nio.FloatBuffer; +import java.nio.IntBuffer; +import java.nio.LongBuffer; +import java.nio.ShortBuffer; +import org.tensorflow.ndarray.buffer.ByteDataBuffer; +import org.tensorflow.ndarray.buffer.DoubleDataBuffer; +import org.tensorflow.ndarray.buffer.FloatDataBuffer; +import org.tensorflow.ndarray.buffer.IntDataBuffer; +import org.tensorflow.ndarray.buffer.LongDataBuffer; +import org.tensorflow.ndarray.buffer.ShortDataBuffer; + +/** + * Factory of JDK NIO-based data buffers + */ +public class NioDataBufferFactory { + + public static ByteDataBuffer create(ByteBuffer buffer) { + return new ByteNioDataBuffer(buffer); + } + + public static DoubleDataBuffer create(DoubleBuffer buffer) { + return new DoubleNioDataBuffer(buffer); + } + + public static FloatDataBuffer create(FloatBuffer buffer) { + return new FloatNioDataBuffer(buffer); + } + + public static IntDataBuffer create(IntBuffer buffer) { + return new IntNioDataBuffer(buffer); + } + + public static LongDataBuffer create(LongBuffer buffer) { + return new LongNioDataBuffer(buffer); + } + + public static ShortDataBuffer create(ShortBuffer buffer) { + return new ShortNioDataBuffer(buffer); + } +} diff --git a/ndarray/src/main/java/org/tensorflow/ndarray/impl/buffer/nio/ShortNioDataBuffer.java b/ndarray/src/main/java/org/tensorflow/ndarray/impl/buffer/nio/ShortNioDataBuffer.java new file mode 100644 index 00000000000..776faa103c2 --- /dev/null +++ b/ndarray/src/main/java/org/tensorflow/ndarray/impl/buffer/nio/ShortNioDataBuffer.java @@ -0,0 +1,147 @@ +/* + Copyright 2019 The TensorFlow Authors. All Rights Reserved. + + Licensed under the Apache License, Version 2.0 (the "License"); + you may not use this file except in compliance with the License. + You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + + Unless required by applicable law or agreed to in writing, software + distributed under the License is distributed on an "AS IS" BASIS, + WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + See the License for the specific language governing permissions and + limitations under the License. + ======================================================================= + */ + +package org.tensorflow.ndarray.impl.buffer.nio; + +import java.nio.ShortBuffer; +import org.tensorflow.ndarray.impl.buffer.Validator; +import org.tensorflow.ndarray.buffer.DataBuffer; +import org.tensorflow.ndarray.buffer.DataStorageVisitor; +import org.tensorflow.ndarray.buffer.ShortDataBuffer; + +/** + * A buffer of bytes using a JDK {@link ShortBuffer} for storage. + */ +final class ShortNioDataBuffer extends AbstractNioDataBuffer + implements ShortDataBuffer { + + @Override + public short getShort(long index) { + return buf.get((int)index); + } + + @Override + public ShortDataBuffer setShort(short value, long index) { + buf.put((int)index, value); + return this; + } + + @Override + public ShortDataBuffer read(short[] dst, int offset, int length) { + buf.duplicate().get(dst, offset, length); + return this; + } + + @Override + public ShortDataBuffer write(short[] src, int offset, int length) { + buf.duplicate().put(src, offset, length); + return this; + } + + @Override + public ShortDataBuffer copyTo(DataBuffer dst, long size) { + Validator.copyToArgs(this, dst, size); + return dst.accept(new DataStorageVisitor() { + + @Override + public ShortDataBuffer visit(ShortBuffer buffer) { + buffer.duplicate().put((ShortBuffer)buf.duplicate().limit((int)size)); + return ShortNioDataBuffer.this; + } + + @Override + public ShortDataBuffer fallback() { + if (dst instanceof ShortDataBuffer) { + ShortDataBuffer shortDst = (ShortDataBuffer)dst; + for (long idx = 0L; idx < size; ++idx) { + shortDst.setShort(getShort(idx), idx); + } + return ShortNioDataBuffer.this; + } + return slowCopyTo(dst, size); + } + }); + } + + @Override + public ShortDataBuffer offset(long index) { + Validator.offsetArgs(this, index); + return new ShortNioDataBuffer(((ShortBuffer)buf.duplicate().position((int)index)).slice()); + } + + @Override + public ShortDataBuffer narrow(long size) { + Validator.narrowArgs(this, size); + return new ShortNioDataBuffer(((ShortBuffer)buf.duplicate().limit((int)size)).slice()); + } + + @Override + public ShortDataBuffer slice(long index, long size) { + Validator.sliceArgs(this, index, size); + ShortBuffer sliceBuf = buf.duplicate(); + sliceBuf.position((int)index); + sliceBuf.limit((int)index + (int)size); + return new ShortNioDataBuffer(sliceBuf.slice()); + } + + @Override + public R accept(DataStorageVisitor visitor) { + return visitor.visit(buf); + } + + @Override + public boolean equals(Object obj) { + if (this == obj) { + return true; + } + if (!(obj instanceof ShortDataBuffer)) { + return super.equals(obj); + } + ShortDataBuffer other = (ShortDataBuffer)obj; + if (size() != other.size()) { + return false; + } + return other.accept(new DataStorageVisitor() { + + @Override + public Boolean visit(ShortBuffer buffer) { + return buf.equals(buffer); + } + + @Override + public Boolean fallback() { + for (int idx = 0; idx < size(); ++idx) { + if (other.getShort(idx) != getShort(idx)) { + return false; + } + } + return true; + } + }); + } + + @Override + ShortBuffer buf() { + return buf; + } + + ShortNioDataBuffer(ShortBuffer buf) { + this.buf = buf; + } + + private ShortBuffer buf; +} diff --git a/ndarray/src/main/java/org/tensorflow/ndarray/impl/buffer/raw/AbstractRawDataBuffer.java b/ndarray/src/main/java/org/tensorflow/ndarray/impl/buffer/raw/AbstractRawDataBuffer.java new file mode 100644 index 00000000000..c6050385b77 --- /dev/null +++ b/ndarray/src/main/java/org/tensorflow/ndarray/impl/buffer/raw/AbstractRawDataBuffer.java @@ -0,0 +1,94 @@ +/* + * Copyright 2019 The TensorFlow Authors. All Rights Reserved. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + * ======================================================================= + */ + +package org.tensorflow.ndarray.impl.buffer.raw; + +import org.tensorflow.ndarray.impl.buffer.AbstractDataBuffer; +import org.tensorflow.ndarray.impl.buffer.Validator; +import org.tensorflow.ndarray.buffer.DataBuffer; +import org.tensorflow.ndarray.buffer.DataBufferWindow; + +@SuppressWarnings("unchecked") +abstract class AbstractRawDataBuffer> extends AbstractDataBuffer { + + public long size() { + return memory.size(); + } + + @Override + public boolean isReadOnly() { + return readOnly; + } + + public B read(Object dst, int dstLength) { + Validator.readArgs(this, dstLength, 0, dstLength); + memory.copyTo(UnsafeMemoryHandle.fromArray(dst, dstLength), dstLength); + return (B)this; + } + + public B read(Object dst, int dstLength, int offset, int length) { + Validator.readArgs(this, dstLength, offset, length); + memory.copyTo(UnsafeMemoryHandle.fromArray(dst, dstLength).offset(offset), length); + return (B)this; + } + + public B write(Object src, int srcLength) { + Validator.writeArgs(this, srcLength, 0, srcLength); + UnsafeMemoryHandle.fromArray(src, srcLength).copyTo(memory, srcLength); + return (B)this; + } + + public B write(Object src, int srcLength, int offset, int length) { + Validator.writeArgs(this, srcLength, offset, length); + UnsafeMemoryHandle.fromArray(src, srcLength).offset(offset).copyTo(memory, length); + return (B)this; + } + + @Override + public B copyTo(DataBuffer dst, long size) { + Validator.copyToArgs(this, dst, size); + if (dst instanceof AbstractRawDataBuffer) { + AbstractRawDataBuffer unsafeDst = (AbstractRawDataBuffer)dst; + memory.copyTo(unsafeDst.memory, size); + } else { + super.copyTo(dst, size); + } + return (B)this; + } + + @Override + public B slice(long index, long size) { + Validator.sliceArgs(this, index, size); + return instantiate(memory.slice(index, size)); + } + + @Override + public DataBufferWindow window(long size) { + B windowBuffer = instantiate(memory.slice(0, size)); + return new RawDataBufferWindow<>((AbstractRawDataBuffer)windowBuffer, size()); + } + + protected final UnsafeMemoryHandle memory; + protected final boolean readOnly; + + protected abstract B instantiate(UnsafeMemoryHandle region); + + AbstractRawDataBuffer(UnsafeMemoryHandle memory, boolean readOnly) { + this.memory = memory; + this.readOnly = readOnly; + } +} diff --git a/ndarray/src/main/java/org/tensorflow/ndarray/impl/buffer/raw/BooleanRawDataBuffer.java b/ndarray/src/main/java/org/tensorflow/ndarray/impl/buffer/raw/BooleanRawDataBuffer.java new file mode 100644 index 00000000000..e7e825ea505 --- /dev/null +++ b/ndarray/src/main/java/org/tensorflow/ndarray/impl/buffer/raw/BooleanRawDataBuffer.java @@ -0,0 +1,146 @@ +/* + * Copyright 2019 The TensorFlow Authors. All Rights Reserved. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + * ======================================================================= + */ + +package org.tensorflow.ndarray.impl.buffer.raw; + +import java.util.Arrays; +import org.tensorflow.ndarray.impl.buffer.Validator; +import org.tensorflow.ndarray.buffer.BooleanDataBuffer; +import org.tensorflow.ndarray.buffer.DataBuffer; +import org.tensorflow.ndarray.buffer.DataStorageVisitor; + +final class BooleanRawDataBuffer extends AbstractRawDataBuffer + implements BooleanDataBuffer { + + @Override + public boolean getBoolean(long index) { + Validator.getArgs(this, index); + return memory.getBoolean(index); + } + + @Override + public BooleanDataBuffer setBoolean(boolean value, long index) { + Validator.setArgs(this, index); + memory.setBoolean(value, index); + return this; + } + + @Override + public BooleanDataBuffer read(boolean[] dst) { + return read(dst, dst.length); + } + + @Override + public BooleanDataBuffer read(boolean[] dst, int offset, int length) { + return read(dst, dst.length, offset, length); + } + + @Override + public BooleanDataBuffer write(boolean[] src) { + return write(src, src.length); + } + + @Override + public BooleanDataBuffer write(boolean[] src, int offset, int length) { + return write(src, src.length, offset, length); + } + + @Override + public BooleanDataBuffer copyTo(DataBuffer dst, long size) { + Validator.copyToArgs(this, dst, size); + return dst.accept(new DataStorageVisitor() { + + @Override + public BooleanDataBuffer visit(boolean[] array, int offset, int length) { + memory.copyTo(UnsafeMemoryHandle.fromArray(array, offset, length), size); + return BooleanRawDataBuffer.this; + } + + @Override + public BooleanDataBuffer visit(long address, long length, long scale) { + memory.copyTo(UnsafeMemoryHandle.fromAddress(address, length, scale), size); + return BooleanRawDataBuffer.this; + } + + @Override + public BooleanDataBuffer fallback() { + if (dst instanceof BooleanDataBuffer) { + BooleanDataBuffer booleanDst = (BooleanDataBuffer)dst; + for (long idx = 0L; idx < size; ++idx) { + booleanDst.setBoolean(getBoolean(idx), idx); + } + return BooleanRawDataBuffer.this; + } + return slowCopyTo(dst, size); + } + }); + } + + @Override + public R accept(DataStorageVisitor visitor) { + if (memory.isArray()) { + return visitor.visit((boolean[])memory.object, memory.arrayOffset(boolean[].class), (int)memory.size()); + } + return visitor.visit(memory.byteOffset, memory.byteSize, memory.scale); + } + + @Override + public boolean equals(Object obj) { + if (this == obj) { + return true; + } + if (!(obj instanceof BooleanDataBuffer)) { + return super.equals(obj); + } + BooleanDataBuffer other = (BooleanDataBuffer)obj; + if (size() != other.size()) { + return false; + } + return other.accept(new DataStorageVisitor() { + + @Override + public Boolean visit(boolean[] array, int offset, int length) { + if (memory.isArray() && memory.arrayOffset(boolean[].class) == 0 && offset == 0) { + boolean[] thisArray = memory.array(); + if (thisArray.length == array.length) { + return Arrays.equals(thisArray, array); + } + } + return fallback(); + } + + @Override + public Boolean fallback() { + for (long idx = 0L; idx < size(); ++idx) { + if (other.getBoolean(idx) != getBoolean(idx)) { + return false; + } + } + return true; + } + }); + } + + @Override + protected BooleanDataBuffer instantiate(UnsafeMemoryHandle memory) { + return new BooleanRawDataBuffer(memory, readOnly); + } + + BooleanRawDataBuffer(UnsafeMemoryHandle memory, boolean readOnly) { + super(memory, readOnly); + } +} diff --git a/ndarray/src/main/java/org/tensorflow/ndarray/impl/buffer/raw/ByteRawDataBuffer.java b/ndarray/src/main/java/org/tensorflow/ndarray/impl/buffer/raw/ByteRawDataBuffer.java new file mode 100644 index 00000000000..b4b490e98ed --- /dev/null +++ b/ndarray/src/main/java/org/tensorflow/ndarray/impl/buffer/raw/ByteRawDataBuffer.java @@ -0,0 +1,185 @@ +/* + * Copyright 2019 The TensorFlow Authors. All Rights Reserved. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + * ======================================================================= + */ + +package org.tensorflow.ndarray.impl.buffer.raw; + +import java.nio.ByteBuffer; +import org.tensorflow.ndarray.impl.buffer.Validator; +import org.tensorflow.ndarray.buffer.BooleanDataBuffer; +import org.tensorflow.ndarray.buffer.ByteDataBuffer; +import org.tensorflow.ndarray.buffer.DataBuffer; +import org.tensorflow.ndarray.buffer.DataStorageVisitor; +import org.tensorflow.ndarray.buffer.DoubleDataBuffer; +import org.tensorflow.ndarray.buffer.FloatDataBuffer; +import org.tensorflow.ndarray.buffer.IntDataBuffer; +import org.tensorflow.ndarray.buffer.LongDataBuffer; +import org.tensorflow.ndarray.buffer.ShortDataBuffer; + +final class ByteRawDataBuffer extends AbstractRawDataBuffer + implements ByteDataBuffer { + + @Override + public byte getByte(long index) { + Validator.getArgs(this, index); + return memory.getByte(index); + } + + @Override + public ByteDataBuffer setByte(byte value, long index) { + Validator.setArgs(this, index); + memory.setByte(value, index); + return this; + } + + @Override + public ByteDataBuffer read(byte[] dst) { + return read(dst, dst.length); + } + + @Override + public ByteDataBuffer read(byte[] dst, int offset, int length) { + return read(dst, dst.length, offset, length); + } + + @Override + public ByteDataBuffer write(byte[] src) { + return write(src, src.length); + } + + @Override + public ByteDataBuffer write(byte[] src, int offset, int length) { + return write(src, src.length, offset, length); + } + + @Override + public ByteDataBuffer copyTo(DataBuffer dst, long size) { + Validator.copyToArgs(this, dst, size); + return dst.accept(new DataStorageVisitor() { + + @Override + public ByteDataBuffer visit(ByteBuffer buffer) { + if (buffer.hasArray()) { + memory.copyTo(UnsafeMemoryHandle.fromArray(buffer.array(), buffer.position(), buffer.limit()), size); + } else if (memory.isArray()) { + buffer.put(memory.toArrayByteBuffer()); + } else { + slowCopyTo(dst, size); + } + return ByteRawDataBuffer.this; + } + + @Override + public ByteDataBuffer visit(long address, long length, long scale) { + memory.copyTo(UnsafeMemoryHandle.fromAddress(address, length, scale), size); + return ByteRawDataBuffer.this; + } + + @Override + public ByteDataBuffer fallback() { + if (dst instanceof ByteDataBuffer) { + ByteDataBuffer byteDst = (ByteDataBuffer)dst; + for (long idx = 0L; idx < size; ++idx) { + byteDst.setByte(getByte(idx), idx); + } + return ByteRawDataBuffer.this; + } + return slowCopyTo(dst, size); + } + }); + } + + @Override + public IntDataBuffer asInts() { + return new IntRawDataBuffer(memory.rescale(Integer.BYTES), readOnly); + } + + @Override + public ShortDataBuffer asShorts() { + return new ShortRawDataBuffer(memory.rescale(Short.BYTES), readOnly); + } + + @Override + public LongDataBuffer asLongs() { + return new LongRawDataBuffer(memory.rescale(Long.BYTES), readOnly); + } + + @Override + public FloatDataBuffer asFloats() { + return new FloatRawDataBuffer(memory.rescale(Float.BYTES), readOnly); + } + + @Override + public DoubleDataBuffer asDoubles() { + return new DoubleRawDataBuffer(memory.rescale(Double.BYTES), readOnly); + } + + @Override + public BooleanDataBuffer asBooleans() { + return new BooleanRawDataBuffer(memory.rescale(Byte.BYTES), readOnly); + } + + @Override + public R accept(DataStorageVisitor visitor) { + if (memory.isArray()) { + return visitor.visit(memory.toArrayByteBuffer()); + } + return visitor.visit(memory.byteOffset, memory.byteSize, memory.scale); + } + + @Override + public boolean equals(Object obj) { + if (this == obj) { + return true; + } + if (!(obj instanceof ByteDataBuffer)) { + return super.equals(obj); + } + ByteDataBuffer other = (ByteDataBuffer)obj; + if (size() != other.size()) { + return false; + } + return other.accept(new DataStorageVisitor() { + + @Override + public Boolean visit(ByteBuffer buffer) { + if (memory.isArray()) { + return buffer.equals(memory.toArrayByteBuffer()); + } + return fallback(); + } + + @Override + public Boolean fallback() { + for (long idx = 0L; idx < size(); ++idx) { + if (other.getByte(idx) != getByte(idx)) { + return false; + } + } + return true; + } + }); + } + + @Override + protected ByteDataBuffer instantiate(UnsafeMemoryHandle memory) { + return new ByteRawDataBuffer(memory, readOnly); + } + + ByteRawDataBuffer(UnsafeMemoryHandle memory, boolean readOnly) { + super(memory, readOnly); + } +} diff --git a/ndarray/src/main/java/org/tensorflow/ndarray/impl/buffer/raw/DoubleRawDataBuffer.java b/ndarray/src/main/java/org/tensorflow/ndarray/impl/buffer/raw/DoubleRawDataBuffer.java new file mode 100644 index 00000000000..680d9565184 --- /dev/null +++ b/ndarray/src/main/java/org/tensorflow/ndarray/impl/buffer/raw/DoubleRawDataBuffer.java @@ -0,0 +1,149 @@ +/* + * Copyright 2019 The TensorFlow Authors. All Rights Reserved. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + * ======================================================================= + */ + +package org.tensorflow.ndarray.impl.buffer.raw; + +import java.nio.DoubleBuffer; +import org.tensorflow.ndarray.impl.buffer.Validator; +import org.tensorflow.ndarray.buffer.DataBuffer; +import org.tensorflow.ndarray.buffer.DataStorageVisitor; +import org.tensorflow.ndarray.buffer.DoubleDataBuffer; + +final class DoubleRawDataBuffer extends AbstractRawDataBuffer + implements DoubleDataBuffer { + + @Override + public double getDouble(long index) { + Validator.getArgs(this, index); + return memory.getDouble(index); + } + + @Override + public DoubleDataBuffer setDouble(double value, long index) { + Validator.setArgs(this, index); + memory.setDouble(value, index); + return this; + } + + @Override + public DoubleDataBuffer read(double[] dst) { + return read(dst, dst.length); + } + + @Override + public DoubleDataBuffer read(double[] dst, int offset, int length) { + return read(dst, dst.length, offset, length); + } + + @Override + public DoubleDataBuffer write(double[] src) { + return write(src, src.length); + } + + @Override + public DoubleDataBuffer write(double[] src, int offset, int length) { + return write(src, src.length, offset, length); + } + + @Override + public DoubleDataBuffer copyTo(DataBuffer dst, long size) { + Validator.copyToArgs(this, dst, size); + return dst.accept(new DataStorageVisitor() { + + @Override + public DoubleDataBuffer visit(DoubleBuffer buffer) { + if (buffer.hasArray()) { + memory.copyTo(UnsafeMemoryHandle.fromArray(buffer.array(), buffer.position(), buffer.limit()), size); + } else if (memory.isArray()) { + buffer.put(memory.toArrayDoubleBuffer()); + } else { + slowCopyTo(dst, size); + } + return DoubleRawDataBuffer.this; + } + + @Override + public DoubleDataBuffer visit(long address, long length, long scale) { + memory.copyTo(UnsafeMemoryHandle.fromAddress(address, length, scale), size); + return DoubleRawDataBuffer.this; + } + + @Override + public DoubleDataBuffer fallback() { + if (dst instanceof DoubleDataBuffer) { + DoubleDataBuffer doubleDst = (DoubleDataBuffer)dst; + for (long idx = 0L; idx < size; ++idx) { + doubleDst.setDouble(getDouble(idx), idx); + } + return DoubleRawDataBuffer.this; + } + return slowCopyTo(dst, size); + } + }); + } + + @Override + public R accept(DataStorageVisitor visitor) { + if (memory.isArray()) { + return visitor.visit(memory.toArrayDoubleBuffer()); + } + return visitor.visit(memory.byteOffset, memory.byteSize, memory.scale); + } + + @Override + public boolean equals(Object obj) { + if (this == obj) { + return true; + } + if (!(obj instanceof DoubleDataBuffer)) { + return super.equals(obj); + } + DoubleDataBuffer other = (DoubleDataBuffer)obj; + if (size() != other.size()) { + return false; + } + return other.accept(new DataStorageVisitor() { + + @Override + public Boolean visit(DoubleBuffer buffer) { + if (memory.isArray()) { + return buffer.equals(memory.toArrayDoubleBuffer()); + } + return fallback(); + } + + @Override + public Boolean fallback() { + for (long idx = 0L; idx < size(); ++idx) { + if (other.getDouble(idx) != getDouble(idx)) { + return false; + } + } + return true; + } + }); + } + + @Override + protected DoubleDataBuffer instantiate(UnsafeMemoryHandle memory) { + return new DoubleRawDataBuffer(memory, readOnly); + } + + DoubleRawDataBuffer(UnsafeMemoryHandle memory, boolean readOnly) { + super(memory, readOnly); + } +} diff --git a/ndarray/src/main/java/org/tensorflow/ndarray/impl/buffer/raw/FloatRawDataBuffer.java b/ndarray/src/main/java/org/tensorflow/ndarray/impl/buffer/raw/FloatRawDataBuffer.java new file mode 100644 index 00000000000..43bd370a88b --- /dev/null +++ b/ndarray/src/main/java/org/tensorflow/ndarray/impl/buffer/raw/FloatRawDataBuffer.java @@ -0,0 +1,150 @@ +/* + * Copyright 2019 The TensorFlow Authors. All Rights Reserved. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + * ======================================================================= + */ + +package org.tensorflow.ndarray.impl.buffer.raw; + +import java.nio.FloatBuffer; + +import org.tensorflow.ndarray.impl.buffer.Validator; +import org.tensorflow.ndarray.buffer.DataBuffer; +import org.tensorflow.ndarray.buffer.DataStorageVisitor; +import org.tensorflow.ndarray.buffer.FloatDataBuffer; + +final class FloatRawDataBuffer extends AbstractRawDataBuffer + implements FloatDataBuffer { + + @Override + public float getFloat(long index) { + Validator.getArgs(this, index); + return memory.getFloat(index); + } + + @Override + public FloatDataBuffer setFloat(float value, long index) { + Validator.setArgs(this, index); + memory.setFloat(value, index); + return this; + } + + @Override + public FloatDataBuffer read(float[] dst) { + return read(dst, dst.length); + } + + @Override + public FloatDataBuffer read(float[] dst, int offset, int length) { + return read(dst, dst.length, offset, length); + } + + @Override + public FloatDataBuffer write(float[] src) { + return write(src, src.length); + } + + @Override + public FloatDataBuffer write(float[] src, int offset, int length) { + return write(src, src.length, offset, length); + } + + @Override + public FloatDataBuffer copyTo(DataBuffer dst, long size) { + Validator.copyToArgs(this, dst, size); + return dst.accept(new DataStorageVisitor() { + + @Override + public FloatDataBuffer visit(FloatBuffer buffer) { + if (buffer.hasArray()) { + memory.copyTo(UnsafeMemoryHandle.fromArray(buffer.array(), buffer.position(), buffer.limit()), size); + } else if (memory.isArray()) { + buffer.put(memory.toArrayFloatBuffer()); + } else { + slowCopyTo(dst, size); + } + return FloatRawDataBuffer.this; + } + + @Override + public FloatDataBuffer visit(long address, long length, long scale) { + memory.copyTo(UnsafeMemoryHandle.fromAddress(address, length, scale), size); + return FloatRawDataBuffer.this; + } + + @Override + public FloatDataBuffer fallback() { + if (dst instanceof FloatDataBuffer) { + FloatDataBuffer floatDst = (FloatDataBuffer)dst; + for (long idx = 0L; idx < size; ++idx) { + floatDst.setFloat(getFloat(idx), idx); + } + return FloatRawDataBuffer.this; + } + return slowCopyTo(dst, size); + } + }); + } + + @Override + public R accept(DataStorageVisitor visitor) { + if (memory.isArray()) { + return visitor.visit(memory.toArrayFloatBuffer()); + } + return visitor.visit(memory.byteOffset, memory.byteSize, memory.scale); + } + + @Override + public boolean equals(Object obj) { + if (this == obj) { + return true; + } + if (!(obj instanceof FloatDataBuffer)) { + return super.equals(obj); + } + FloatDataBuffer other = (FloatDataBuffer)obj; + if (size() != other.size()) { + return false; + } + return other.accept(new DataStorageVisitor() { + + @Override + public Boolean visit(FloatBuffer buffer) { + if (memory.isArray()) { + return buffer.equals(memory.toArrayFloatBuffer()); + } + return fallback(); + } + + @Override + public Boolean fallback() { + for (long idx = 0L; idx < size(); ++idx) { + if (other.getFloat(idx) != getFloat(idx)) { + return false; + } + } + return true; + } + }); + } + + @Override + protected FloatDataBuffer instantiate(UnsafeMemoryHandle memory) { + return new FloatRawDataBuffer(memory, readOnly); + } + + FloatRawDataBuffer(UnsafeMemoryHandle memory, boolean readOnly) { + super(memory, readOnly); + } +} diff --git a/ndarray/src/main/java/org/tensorflow/ndarray/impl/buffer/raw/IntRawDataBuffer.java b/ndarray/src/main/java/org/tensorflow/ndarray/impl/buffer/raw/IntRawDataBuffer.java new file mode 100644 index 00000000000..1c905e2756d --- /dev/null +++ b/ndarray/src/main/java/org/tensorflow/ndarray/impl/buffer/raw/IntRawDataBuffer.java @@ -0,0 +1,149 @@ +/* + * Copyright 2019 The TensorFlow Authors. All Rights Reserved. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + * ======================================================================= + */ + +package org.tensorflow.ndarray.impl.buffer.raw; + +import java.nio.IntBuffer; +import org.tensorflow.ndarray.impl.buffer.Validator; +import org.tensorflow.ndarray.buffer.DataBuffer; +import org.tensorflow.ndarray.buffer.DataStorageVisitor; +import org.tensorflow.ndarray.buffer.IntDataBuffer; + +final class IntRawDataBuffer extends AbstractRawDataBuffer + implements IntDataBuffer { + + @Override + public int getInt(long index) { + Validator.getArgs(this, index); + return memory.getInt(index); + } + + @Override + public IntDataBuffer setInt(int value, long index) { + Validator.setArgs(this, index); + memory.setInt(value, index); + return this; + } + + @Override + public IntDataBuffer read(int[] dst) { + return read(dst, dst.length); + } + + @Override + public IntDataBuffer read(int[] dst, int offset, int length) { + return read(dst, dst.length, offset, length); + } + + @Override + public IntDataBuffer write(int[] src) { + return write(src, src.length); + } + + @Override + public IntDataBuffer write(int[] src, int offset, int length) { + return write(src, src.length, offset, length); + } + + @Override + public IntDataBuffer copyTo(DataBuffer dst, long size) { + Validator.copyToArgs(this, dst, size); + return dst.accept(new DataStorageVisitor() { + + @Override + public IntDataBuffer visit(IntBuffer buffer) { + if (buffer.hasArray()) { + memory.copyTo(UnsafeMemoryHandle.fromArray(buffer.array(), buffer.position(), buffer.limit()), size); + } else if (memory.isArray()) { + buffer.put(memory.toArrayIntBuffer()); + } else { + slowCopyTo(dst, size); + } + return IntRawDataBuffer.this; + } + + @Override + public IntDataBuffer visit(long address, long length, long scale) { + memory.copyTo(UnsafeMemoryHandle.fromAddress(address, length, scale), size); + return IntRawDataBuffer.this; + } + + @Override + public IntDataBuffer fallback() { + if (dst instanceof IntDataBuffer) { + IntDataBuffer intDst = (IntDataBuffer)dst; + for (long idx = 0L; idx < size; ++idx) { + intDst.setInt(getInt(idx), idx); + } + return IntRawDataBuffer.this; + } + return slowCopyTo(dst, size); + } + }); + } + + @Override + public R accept(DataStorageVisitor visitor) { + if (memory.isArray()) { + return visitor.visit(memory.toArrayIntBuffer()); + } + return visitor.visit(memory.byteOffset, memory.byteSize, memory.scale); + } + + @Override + public boolean equals(Object obj) { + if (this == obj) { + return true; + } + if (!(obj instanceof IntDataBuffer)) { + return super.equals(obj); + } + IntDataBuffer other = (IntDataBuffer)obj; + if (size() != other.size()) { + return false; + } + return other.accept(new DataStorageVisitor() { + + @Override + public Boolean visit(IntBuffer buffer) { + if (memory.isArray()) { + return buffer.equals(memory.toArrayIntBuffer()); + } + return fallback(); + } + + @Override + public Boolean fallback() { + for (long idx = 0L; idx < size(); ++idx) { + if (other.getInt(idx) != getInt(idx)) { + return false; + } + } + return true; + } + }); + } + + @Override + protected IntDataBuffer instantiate(UnsafeMemoryHandle memory) { + return new IntRawDataBuffer(memory, readOnly); + } + + IntRawDataBuffer(UnsafeMemoryHandle memory, boolean readOnly) { + super(memory, readOnly); + } +} diff --git a/ndarray/src/main/java/org/tensorflow/ndarray/impl/buffer/raw/LongRawDataBuffer.java b/ndarray/src/main/java/org/tensorflow/ndarray/impl/buffer/raw/LongRawDataBuffer.java new file mode 100644 index 00000000000..724868fe81f --- /dev/null +++ b/ndarray/src/main/java/org/tensorflow/ndarray/impl/buffer/raw/LongRawDataBuffer.java @@ -0,0 +1,149 @@ +/* + * Copyright 2019 The TensorFlow Authors. All Rights Reserved. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + * ======================================================================= + */ + +package org.tensorflow.ndarray.impl.buffer.raw; + +import java.nio.LongBuffer; +import org.tensorflow.ndarray.impl.buffer.Validator; +import org.tensorflow.ndarray.buffer.DataBuffer; +import org.tensorflow.ndarray.buffer.DataStorageVisitor; +import org.tensorflow.ndarray.buffer.LongDataBuffer; + +final class LongRawDataBuffer extends AbstractRawDataBuffer + implements LongDataBuffer { + + @Override + public long getLong(long index) { + Validator.getArgs(this, index); + return memory.getLong(index); + } + + @Override + public LongDataBuffer setLong(long value, long index) { + Validator.setArgs(this, index); + memory.setLong(value, index); + return this; + } + + @Override + public LongDataBuffer read(long[] dst) { + return read(dst, dst.length); + } + + @Override + public LongDataBuffer read(long[] dst, int offset, int length) { + return read(dst, dst.length, offset, length); + } + + @Override + public LongDataBuffer write(long[] src) { + return write(src, src.length); + } + + @Override + public LongDataBuffer write(long[] src, int offset, int length) { + return write(src, src.length, offset, length); + } + + @Override + public LongDataBuffer copyTo(DataBuffer dst, long size) { + Validator.copyToArgs(this, dst, size); + return dst.accept(new DataStorageVisitor() { + + @Override + public LongDataBuffer visit(LongBuffer buffer) { + if (buffer.hasArray()) { + memory.copyTo(UnsafeMemoryHandle.fromArray(buffer.array(), buffer.position(), buffer.limit()), size); + } else if (memory.isArray()) { + buffer.put(memory.toArrayLongBuffer()); + } else { + slowCopyTo(dst, size); + } + return LongRawDataBuffer.this; + } + + @Override + public LongDataBuffer visit(long address, long length, long scale) { + memory.copyTo(UnsafeMemoryHandle.fromAddress(address, length, scale), size); + return LongRawDataBuffer.this; + } + + @Override + public LongDataBuffer fallback() { + if (dst instanceof LongDataBuffer) { + LongDataBuffer longDst = (LongDataBuffer)dst; + for (long idx = 0L; idx < size; ++idx) { + longDst.setLong(getLong(idx), idx); + } + return LongRawDataBuffer.this; + } + return slowCopyTo(dst, size); + } + }); + } + + @Override + public R accept(DataStorageVisitor visitor) { + if (memory.isArray()) { + return visitor.visit(memory.toArrayLongBuffer()); + } + return visitor.visit(memory.byteOffset, memory.byteSize, memory.scale); + } + + @Override + public boolean equals(Object obj) { + if (this == obj) { + return true; + } + if (!(obj instanceof LongDataBuffer)) { + return super.equals(obj); + } + LongDataBuffer other = (LongDataBuffer)obj; + if (size() != other.size()) { + return false; + } + return other.accept(new DataStorageVisitor() { + + @Override + public Boolean visit(LongBuffer buffer) { + if (memory.isArray()) { + return buffer.equals(memory.toArrayLongBuffer()); + } + return fallback(); + } + + @Override + public Boolean fallback() { + for (long idx = 0L; idx < size(); ++idx) { + if (other.getLong(idx) != getLong(idx)) { + return false; + } + } + return true; + } + }); + } + + @Override + protected LongDataBuffer instantiate(UnsafeMemoryHandle memory) { + return new LongRawDataBuffer(memory, readOnly); + } + + LongRawDataBuffer(UnsafeMemoryHandle memory, boolean readOnly) { + super(memory, readOnly); + } +} diff --git a/ndarray/src/main/java/org/tensorflow/ndarray/impl/buffer/raw/RawDataBufferFactory.java b/ndarray/src/main/java/org/tensorflow/ndarray/impl/buffer/raw/RawDataBufferFactory.java new file mode 100644 index 00000000000..7253b239af2 --- /dev/null +++ b/ndarray/src/main/java/org/tensorflow/ndarray/impl/buffer/raw/RawDataBufferFactory.java @@ -0,0 +1,148 @@ +/* + * Copyright 2019 The TensorFlow Authors. All Rights Reserved. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + * ======================================================================= + */ + +package org.tensorflow.ndarray.impl.buffer.raw; + +import org.tensorflow.ndarray.impl.buffer.Validator; +import org.tensorflow.ndarray.buffer.BooleanDataBuffer; +import org.tensorflow.ndarray.buffer.ByteDataBuffer; +import org.tensorflow.ndarray.buffer.DoubleDataBuffer; +import org.tensorflow.ndarray.buffer.FloatDataBuffer; +import org.tensorflow.ndarray.buffer.IntDataBuffer; +import org.tensorflow.ndarray.buffer.LongDataBuffer; +import org.tensorflow.ndarray.buffer.ShortDataBuffer; + +/** + * Factory of raw data buffers + */ +public class RawDataBufferFactory { + + public static boolean canBeUsed() { + return UnsafeReference.isAvailable(); + } + + public static BooleanDataBuffer create(boolean[] array, boolean readOnly) { + return new BooleanRawDataBuffer(UnsafeMemoryHandle.fromArray(array, array.length), readOnly); + } + + public static ByteDataBuffer create(byte[] array, boolean readOnly) { + if (!canBeUsed()) { + throw new IllegalStateException("Raw data buffers are not available"); + } + return new ByteRawDataBuffer(UnsafeMemoryHandle.fromArray(array, array.length), readOnly); + } + + public static DoubleDataBuffer create(double[] array, boolean readOnly) { + if (!canBeUsed()) { + throw new IllegalStateException("Raw data buffers are not available"); + } + return new DoubleRawDataBuffer(UnsafeMemoryHandle.fromArray(array, array.length), readOnly); + } + + public static FloatDataBuffer create(float[] array, boolean readOnly) { + if (!canBeUsed()) { + throw new IllegalStateException("Raw data buffers are not available"); + } + return new FloatRawDataBuffer(UnsafeMemoryHandle.fromArray(array, array.length), readOnly); + } + + public static IntDataBuffer create(int[] array, boolean readOnly) { + if (!canBeUsed()) { + throw new IllegalStateException("Raw data buffers are not available"); + } + return new IntRawDataBuffer(UnsafeMemoryHandle.fromArray(array, array.length), readOnly); + } + + public static LongDataBuffer create(long[] array, boolean readOnly) { + if (!canBeUsed()) { + throw new IllegalStateException("Raw data buffers are not available"); + } + return new LongRawDataBuffer(UnsafeMemoryHandle.fromArray(array, array.length), readOnly); + } + + public static ShortDataBuffer create(short[] array, boolean readOnly) { + if (!canBeUsed()) { + throw new IllegalStateException("Raw data buffers are not available"); + } + return new ShortRawDataBuffer(UnsafeMemoryHandle.fromArray(array, array.length), readOnly); + } + + protected static BooleanDataBuffer mapNativeBooleans(long address, long size, boolean readOnly) { + if (!canBeUsed()) { + throw new IllegalStateException("Raw data buffers are not available"); + } + Validator.createArgs(size, MAX_64BITS); + return new BooleanRawDataBuffer(UnsafeMemoryHandle.fromAddress(address, size, Byte.BYTES), readOnly); + } + + protected static ByteDataBuffer mapNativeBytes(long address, long size, boolean readOnly) { + if (!canBeUsed()) { + throw new IllegalStateException("Raw data buffers are not available"); + } + Validator.createArgs(size, MAX_64BITS); + return new ByteRawDataBuffer(UnsafeMemoryHandle.fromAddress(address, size, Byte.BYTES), readOnly); + } + + protected static DoubleDataBuffer mapNativeDoubles(long address, long size, boolean readOnly) { + if (!canBeUsed()) { + throw new IllegalStateException("Raw data buffers are not available"); + } + Validator.createArgs(size, MAX_64BITS); + return new DoubleRawDataBuffer(UnsafeMemoryHandle.fromAddress(address, size, Double.BYTES), readOnly); + } + + protected static FloatDataBuffer mapNativeFloats(long address, long size, boolean readOnly) { + if (!canBeUsed()) { + throw new IllegalStateException("Raw data buffers are not available"); + } + Validator.createArgs(size, MAX_64BITS); + return new FloatRawDataBuffer(UnsafeMemoryHandle.fromAddress(address, size, Float.BYTES), readOnly); + } + + protected static IntDataBuffer mapNativeInts(long address, long size, boolean readOnly) { + if (!canBeUsed()) { + throw new IllegalStateException("Raw data buffers are not available"); + } + Validator.createArgs(size, MAX_64BITS); + return new IntRawDataBuffer(UnsafeMemoryHandle.fromAddress(address, size, Integer.BYTES), readOnly); + } + + protected static LongDataBuffer mapNativeLongs(long address, long size, boolean readOnly) { + if (!canBeUsed()) { + throw new IllegalStateException("Raw data buffers are not available"); + } + Validator.createArgs(size, MAX_64BITS); + return new LongRawDataBuffer(UnsafeMemoryHandle.fromAddress(address, size, Long.BYTES), readOnly); + } + + protected static ShortDataBuffer mapNativeShorts(long address, long size, boolean readOnly) { + if (!canBeUsed()) { + throw new IllegalStateException("Raw data buffers are not available"); + } + Validator.createArgs(size, MAX_64BITS); + return new ShortRawDataBuffer(UnsafeMemoryHandle.fromAddress(address, size, Short.BYTES), readOnly); + } + + /* + * The maximum size for a buffer of this type, i.e. the maximum number of bytes it can store. + *

+ * As the maximum size may vary depending on the JVM implementation and on the platform, this + * property returns a value that is safe for most of them. + */ + static long MAX_32BITS = Integer.MAX_VALUE - 10; + static long MAX_64BITS = Long.MAX_VALUE - 10; +} diff --git a/ndarray/src/main/java/org/tensorflow/ndarray/impl/buffer/raw/RawDataBufferWindow.java b/ndarray/src/main/java/org/tensorflow/ndarray/impl/buffer/raw/RawDataBufferWindow.java new file mode 100644 index 00000000000..dc18a6caa6e --- /dev/null +++ b/ndarray/src/main/java/org/tensorflow/ndarray/impl/buffer/raw/RawDataBufferWindow.java @@ -0,0 +1,19 @@ +package org.tensorflow.ndarray.impl.buffer.raw; + +import org.tensorflow.ndarray.buffer.DataBuffer; +import org.tensorflow.ndarray.impl.buffer.AbstractDataBufferWindow; + +final class RawDataBufferWindow> extends AbstractDataBufferWindow { + + @Override + public void offset(long offset) { + windowMemory.rebase(offset); + } + + > RawDataBufferWindow(R windowBuffer, long bufferLimit) { + super((B)windowBuffer, bufferLimit); + this.windowMemory = windowBuffer.memory; + } + + private final UnsafeMemoryHandle windowMemory; +} diff --git a/ndarray/src/main/java/org/tensorflow/ndarray/impl/buffer/raw/ShortRawDataBuffer.java b/ndarray/src/main/java/org/tensorflow/ndarray/impl/buffer/raw/ShortRawDataBuffer.java new file mode 100644 index 00000000000..80f9c289852 --- /dev/null +++ b/ndarray/src/main/java/org/tensorflow/ndarray/impl/buffer/raw/ShortRawDataBuffer.java @@ -0,0 +1,149 @@ +/* + * Copyright 2019 The TensorFlow Authors. All Rights Reserved. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + * ======================================================================= + */ + +package org.tensorflow.ndarray.impl.buffer.raw; + +import java.nio.ShortBuffer; +import org.tensorflow.ndarray.impl.buffer.Validator; +import org.tensorflow.ndarray.buffer.DataBuffer; +import org.tensorflow.ndarray.buffer.DataStorageVisitor; +import org.tensorflow.ndarray.buffer.ShortDataBuffer; + +final class ShortRawDataBuffer extends AbstractRawDataBuffer + implements ShortDataBuffer { + + @Override + public short getShort(long index) { + Validator.getArgs(this, index); + return memory.getShort(index); + } + + @Override + public ShortDataBuffer setShort(short value, long index) { + Validator.setArgs(this, index); + memory.setShort(value, index); + return this; + } + + @Override + public ShortDataBuffer read(short[] dst) { + return read(dst, dst.length); + } + + @Override + public ShortDataBuffer read(short[] dst, int offset, int length) { + return read(dst, dst.length, offset, length); + } + + @Override + public ShortDataBuffer write(short[] src) { + return write(src, src.length); + } + + @Override + public ShortDataBuffer write(short[] src, int offset, int length) { + return write(src, src.length, offset, length); + } + + @Override + public ShortDataBuffer copyTo(DataBuffer dst, long size) { + Validator.copyToArgs(this, dst, size); + return dst.accept(new DataStorageVisitor() { + + @Override + public ShortDataBuffer visit(ShortBuffer buffer) { + if (buffer.hasArray()) { + memory.copyTo(UnsafeMemoryHandle.fromArray(buffer.array(), buffer.position(), buffer.limit()), size); + } else if (memory.isArray()) { + buffer.put(memory.toArrayShortBuffer()); + } else { + slowCopyTo(dst, size); + } + return ShortRawDataBuffer.this; + } + + @Override + public ShortDataBuffer visit(long address, long length, long scale) { + memory.copyTo(UnsafeMemoryHandle.fromAddress(address, length, scale), size); + return ShortRawDataBuffer.this; + } + + @Override + public ShortDataBuffer fallback() { + if (dst instanceof ShortDataBuffer) { + ShortDataBuffer shortDst = (ShortDataBuffer)dst; + for (long idx = 0L; idx < size; ++idx) { + shortDst.setShort(getShort(idx), idx); + } + return ShortRawDataBuffer.this; + } + return slowCopyTo(dst, size); + } + }); + } + + @Override + public R accept(DataStorageVisitor visitor) { + if (memory.isArray()) { + return visitor.visit(memory.toArrayShortBuffer()); + } + return visitor.visit(memory.byteOffset, memory.byteSize, memory.scale); + } + + @Override + public boolean equals(Object obj) { + if (this == obj) { + return true; + } + if (!(obj instanceof ShortDataBuffer)) { + return super.equals(obj); + } + ShortDataBuffer other = (ShortDataBuffer)obj; + if (size() != other.size()) { + return false; + } + return other.accept(new DataStorageVisitor() { + + @Override + public Boolean visit(ShortBuffer buffer) { + if (memory.isArray()) { + return buffer.equals(memory.toArrayShortBuffer()); + } + return fallback(); + } + + @Override + public Boolean fallback() { + for (long idx = 0L; idx < size(); ++idx) { + if (other.getShort(idx) != getShort(idx)) { + return false; + } + } + return true; + } + }); + } + + @Override + protected ShortDataBuffer instantiate(UnsafeMemoryHandle memory) { + return new ShortRawDataBuffer(memory, readOnly); + } + + ShortRawDataBuffer(UnsafeMemoryHandle memory, boolean readOnly) { + super(memory, readOnly); + } +} diff --git a/ndarray/src/main/java/org/tensorflow/ndarray/impl/buffer/raw/UnsafeMemoryHandle.java b/ndarray/src/main/java/org/tensorflow/ndarray/impl/buffer/raw/UnsafeMemoryHandle.java new file mode 100644 index 00000000000..bd61e53a128 --- /dev/null +++ b/ndarray/src/main/java/org/tensorflow/ndarray/impl/buffer/raw/UnsafeMemoryHandle.java @@ -0,0 +1,195 @@ +/* + * Copyright 2019 The TensorFlow Authors. All Rights Reserved. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + * ======================================================================= + */ + +package org.tensorflow.ndarray.impl.buffer.raw; + +import java.nio.ByteBuffer; +import java.nio.DoubleBuffer; +import java.nio.FloatBuffer; +import java.nio.IntBuffer; +import java.nio.LongBuffer; +import java.nio.ShortBuffer; + +final class UnsafeMemoryHandle { + + static UnsafeMemoryHandle fromArray(Object array, int length) { + return fromArray(array, 0, length); + } + + static UnsafeMemoryHandle fromArray(Object array, int arrayOffset, int length) { + long scale = UnsafeReference.UNSAFE.arrayIndexScale(array.getClass()); + int baseOffset = UnsafeReference.UNSAFE.arrayBaseOffset(array.getClass()); + return new UnsafeMemoryHandle(array, baseOffset + (arrayOffset * scale), length * scale, scale); + } + + static UnsafeMemoryHandle fromAddress(long address, long byteSize, long scale) { + return new UnsafeMemoryHandle(address, byteSize, scale); + } + + long size() { + return size; + } + + byte getByte(long index) { + return UnsafeReference.UNSAFE.getByte(object, align(index)); + } + + void setByte(byte value, long index) { + UnsafeReference.UNSAFE.putByte(object, align(index), value); + } + + boolean getBoolean(long index) { + return UnsafeReference.UNSAFE.getBoolean(object, align(index)); + } + + void setBoolean(boolean value, long index) { + UnsafeReference.UNSAFE.putBoolean(object, align(index), value); + } + + short getShort(long index) { + return UnsafeReference.UNSAFE.getShort(object, align(index)); + } + + void setShort(short value, long index) { + UnsafeReference.UNSAFE.putShort(object, align(index), value); + } + + int getInt(long index) { + return UnsafeReference.UNSAFE.getInt(object, align(index)); + } + + void setInt(int value, long index) { + UnsafeReference.UNSAFE.putInt(object, align(index), value); + } + + float getFloat(long index) { + return UnsafeReference.UNSAFE.getFloat(object, align(index)); + } + + void setFloat(float value, long index) { + UnsafeReference.UNSAFE.putFloat(object, align(index), value); + } + + double getDouble(long index) { + return UnsafeReference.UNSAFE.getDouble(object, align(index)); + } + + void setDouble(double value, long index) { + UnsafeReference.UNSAFE.putDouble(object, align(index), value); + } + + long getLong(long index) { + return UnsafeReference.UNSAFE.getLong(object, align(index)); + } + + void setLong(long value, long index) { + UnsafeReference.UNSAFE.putLong(object, align(index), value); + } + + void copyTo(UnsafeMemoryHandle memory, long length) { + UnsafeReference.UNSAFE.copyMemory(object, byteOffset, memory.object, memory.byteOffset, length * scale); + } + + UnsafeMemoryHandle offset(long index) { + long offset = scale(index); + return new UnsafeMemoryHandle(object, this.byteOffset + offset, byteSize - offset, scale); + } + + UnsafeMemoryHandle narrow(long size) { + return new UnsafeMemoryHandle(object, byteOffset, scale(size), scale); + } + + UnsafeMemoryHandle slice(long index, long size) { + return new UnsafeMemoryHandle(object, this.byteOffset + scale(index), scale(size), scale); + } + + UnsafeMemoryHandle rescale(long scale) { + if (object != null) { + throw new IllegalStateException("Raw heap memory cannot be rescaled"); + } + return new UnsafeMemoryHandle(null, byteOffset, byteSize, scale); + } + + void rebase(long index) { + byteOffset = baseOffset + scale(index); + } + + boolean isArray() { + return object != null; + } + + @SuppressWarnings("unchecked") + A array() { + return (A)object; + } + + int arrayOffset(Class arrayClass) { + return (int)((byteOffset - UnsafeReference.UNSAFE.arrayBaseOffset(arrayClass)) / scale); + } + + ByteBuffer toArrayByteBuffer() { + return ByteBuffer.wrap((byte[])object, (int) byteOffset - UnsafeReference.UNSAFE.arrayBaseOffset(byte[].class), (int)size); + } + + ShortBuffer toArrayShortBuffer() { + return ShortBuffer.wrap((short[])object, (int)((byteOffset - UnsafeReference.UNSAFE.arrayBaseOffset(short[].class)) / scale), (int)size); + } + + IntBuffer toArrayIntBuffer() { + return IntBuffer.wrap((int[])object, (int)((byteOffset - UnsafeReference.UNSAFE.arrayBaseOffset(int[].class)) / scale), (int)size); + } + + LongBuffer toArrayLongBuffer() { + return LongBuffer.wrap((long[])object, (int)((byteOffset - UnsafeReference.UNSAFE.arrayBaseOffset(long[].class)) / scale), (int)size); + } + + FloatBuffer toArrayFloatBuffer() { + return FloatBuffer.wrap((float[])object, (int)((byteOffset - UnsafeReference.UNSAFE.arrayBaseOffset(float[].class)) / scale), (int)size); + } + + DoubleBuffer toArrayDoubleBuffer() { + return DoubleBuffer.wrap((double[])object, (int)((byteOffset - UnsafeReference.UNSAFE.arrayBaseOffset(double[].class)) / scale), (int)size); + } + + final Object object; + final long baseOffset; + long byteOffset; + final long byteSize; + final long scale; + final long size; + + private UnsafeMemoryHandle(Object object, long baseOffset, long byteSize, long scale) { + this.object = object; + this.baseOffset = baseOffset; + byteOffset = baseOffset; + this.byteSize = byteSize; + this.scale = scale; + size = byteSize / scale; + } + + private UnsafeMemoryHandle(long address, long byteSize, long scale) { + this(null, address, byteSize, scale); + } + + private long align(long index) { + return byteOffset + index * scale; + } + + private long scale(long value) { + return value * scale; + } +} diff --git a/ndarray/src/main/java/org/tensorflow/ndarray/impl/buffer/raw/UnsafeReference.java b/ndarray/src/main/java/org/tensorflow/ndarray/impl/buffer/raw/UnsafeReference.java new file mode 100644 index 00000000000..7b95eac7349 --- /dev/null +++ b/ndarray/src/main/java/org/tensorflow/ndarray/impl/buffer/raw/UnsafeReference.java @@ -0,0 +1,64 @@ +/* + * Copyright 2019 The TensorFlow Authors. All Rights Reserved. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + * ======================================================================= + */ + +package org.tensorflow.ndarray.impl.buffer.raw; + +import java.lang.reflect.Field; +import sun.misc.Unsafe; + +final class UnsafeReference { + + static boolean isAvailable() { + return UNSAFE != null; + } + + static final Unsafe UNSAFE; + + static { + Unsafe unsafe = null; + try { + Class clazz = Class.forName("sun.misc.Unsafe"); + Field theUnsafe = clazz.getDeclaredField("theUnsafe"); + theUnsafe.setAccessible(true); + Object instance = theUnsafe.get(null); + if (instance.getClass() == clazz) { + // Validate that this Unsafe instance exposes all methods we need + clazz.getDeclaredMethod("getByte", Object.class, long.class); + clazz.getDeclaredMethod("putByte", Object.class, long.class, byte.class); + clazz.getDeclaredMethod("getShort", Object.class, long.class); + clazz.getDeclaredMethod("putShort", Object.class, long.class, short.class); + clazz.getDeclaredMethod("getInt", Object.class, long.class); + clazz.getDeclaredMethod("putInt", Object.class, long.class, int.class); + clazz.getDeclaredMethod("getLong", Object.class, long.class); + clazz.getDeclaredMethod("putLong", Object.class, long.class, long.class); + clazz.getDeclaredMethod("getFloat", Object.class, long.class); + clazz.getDeclaredMethod("putFloat", Object.class, long.class, float.class); + clazz.getDeclaredMethod("getDouble", Object.class, long.class); + clazz.getDeclaredMethod("putDouble", Object.class, long.class, double.class); + clazz.getDeclaredMethod("getBoolean", Object.class, long.class); + clazz.getDeclaredMethod("putBoolean", Object.class, long.class, boolean.class); + clazz.getDeclaredMethod("copyMemory", Object.class, long.class, Object.class, long.class, long.class); + clazz.getDeclaredMethod("arrayBaseOffset", Class.class); + clazz.getDeclaredMethod("arrayIndexScale", Class.class); + unsafe = (Unsafe) instance; + } + } catch (ClassNotFoundException | NoSuchMethodException | NoSuchFieldException | SecurityException | IllegalAccessException | ClassCastException ex) { + // Do nothing, keep unsafe as null + } + UNSAFE = unsafe; + } +} diff --git a/ndarray/src/main/java/org/tensorflow/ndarray/impl/dense/AbstractDenseNdArray.java b/ndarray/src/main/java/org/tensorflow/ndarray/impl/dense/AbstractDenseNdArray.java new file mode 100644 index 00000000000..0497095116e --- /dev/null +++ b/ndarray/src/main/java/org/tensorflow/ndarray/impl/dense/AbstractDenseNdArray.java @@ -0,0 +1,163 @@ +/* + Copyright 2019 The TensorFlow Authors. All Rights Reserved. + + Licensed under the Apache License, Version 2.0 (the "License"); + you may not use this file except in compliance with the License. + You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + + Unless required by applicable law or agreed to in writing, software + distributed under the License is distributed on an "AS IS" BASIS, + WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + See the License for the specific language governing permissions and + limitations under the License. + ======================================================================= + */ +package org.tensorflow.ndarray.impl.dense; + +import org.tensorflow.ndarray.NdArray; +import org.tensorflow.ndarray.NdArraySequence; +import org.tensorflow.ndarray.impl.AbstractNdArray; +import org.tensorflow.ndarray.impl.dimension.RelativeDimensionalSpace; +import org.tensorflow.ndarray.impl.sequence.FastElementSequence; +import org.tensorflow.ndarray.index.Index; +import org.tensorflow.ndarray.buffer.DataBuffer; +import org.tensorflow.ndarray.buffer.DataBufferWindow; +import org.tensorflow.ndarray.impl.dimension.DimensionalSpace; +import org.tensorflow.ndarray.impl.sequence.SlicingElementSequence; +import org.tensorflow.ndarray.impl.sequence.SingleElementSequence; + +@SuppressWarnings("unchecked") +public abstract class AbstractDenseNdArray> extends AbstractNdArray { + + @Override + public NdArraySequence elements(int dimensionIdx) { + if (dimensionIdx >= shape().numDimensions()) { + throw new IllegalArgumentException("Cannot iterate elements in dimension '" + dimensionIdx + + "' of array with shape " + shape()); + } + if (rank() == 0 && dimensionIdx < 0) { + return new SingleElementSequence<>(this); + } + DimensionalSpace elemDims = dimensions().from(dimensionIdx + 1); + try { + DataBufferWindow> elemWindow = buffer().window(elemDims.physicalSize()); + U element = instantiate(elemWindow.buffer(), elemDims); + return new FastElementSequence(this, dimensionIdx, element, elemWindow); + } catch (UnsupportedOperationException e) { + // If buffer windows are not supported, fallback to slicing (and slower) sequence + return new SlicingElementSequence<>(this, dimensionIdx, elemDims); + } + } + + @Override + public U slice(long position, DimensionalSpace sliceDimensions) { + DataBuffer sliceBuffer = buffer().slice(position, sliceDimensions.physicalSize()); + return instantiate(sliceBuffer, sliceDimensions); + } + + @Override + public U slice(Index... indices) { + if (indices == null) { + throw new IllegalArgumentException("Slicing requires at least one index"); + } + RelativeDimensionalSpace sliceDimensions = dimensions().mapTo(indices); + return slice(sliceDimensions.position(), sliceDimensions); + } + + @Override + public U get(long... coords) { + return slice(positionOf(coords, false), dimensions().from(coords.length)); + } + + @Override + public T getObject(long... coords) { + return buffer().getObject(positionOf(coords, true)); + } + + @Override + public U set(NdArray src, long... coordinates) { + src.copyTo((coordinates == null || coordinates.length == 0) ? this : get(coordinates)); + return (U)this; + } + + @Override + public U setObject(T value, long... coords) { + buffer().setObject(value, positionOf(coords, true)); + return (U)this; + } + + @Override + public U read(DataBuffer dst) { + Validator.readToBufferArgs(this, dst); + DataTransfer.execute(buffer(), dimensions(), dst, DataTransfer::ofValue); + return (U)this; + } + + @Override + public U write(DataBuffer src) { + Validator.writeFromBufferArgs(this, src); + DataTransfer.execute(src, buffer(), dimensions(), DataTransfer::ofValue); + return (U)this; + } + + @Override + public int hashCode() { + if (dimensions().isSegmented()) { + return slowHashCode(); + } + final int prime = 31; + int result = 1; + result = prime * result + buffer().hashCode(); + result = prime * result + shape().hashCode(); + return result; + } + + @Override + public boolean equals(Object obj) { + if (this == obj) { + return true; + } + if (!(obj instanceof AbstractDenseNdArray)) { + return super.equals(obj); + } + AbstractDenseNdArray other = (AbstractDenseNdArray)obj; + if (dimensions().isSegmented() || other.dimensions().isSegmented()) { + return slowEquals(other); + } + if (!shape().equals(other.shape())) { + return false; + } + return buffer().equals(other.buffer()); + } + + protected AbstractDenseNdArray(DimensionalSpace dimensions) { + super(dimensions); + } + + abstract protected DataBuffer buffer(); + + abstract U instantiate(DataBuffer buffer, DimensionalSpace dimensions); + + long positionOf(long[] coords, boolean isValue) { + if (coords == null || coords.length == 0) { + return 0; + } + Validator.coordinates(dimensions, coords, isValue); + return dimensions.positionOf(coords); + } + + @Override + protected void slowCopyTo(NdArray array) { + if (array instanceof AbstractDenseNdArray) { + AbstractDenseNdArray dst = (AbstractDenseNdArray)array; + long offset = 0L; + for (NdArray s : scalars()) { + dst.buffer().setObject(s.getObject(), offset++); + } + } else { + super.slowCopyTo(array); + } + } +} diff --git a/ndarray/src/main/java/org/tensorflow/ndarray/impl/dense/BooleanDenseNdArray.java b/ndarray/src/main/java/org/tensorflow/ndarray/impl/dense/BooleanDenseNdArray.java new file mode 100644 index 00000000000..0764146f962 --- /dev/null +++ b/ndarray/src/main/java/org/tensorflow/ndarray/impl/dense/BooleanDenseNdArray.java @@ -0,0 +1,91 @@ +/* + Copyright 2019 The TensorFlow Authors. All Rights Reserved. + + Licensed under the Apache License, Version 2.0 (the "License"); + you may not use this file except in compliance with the License. + You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + + Unless required by applicable law or agreed to in writing, software + distributed under the License is distributed on an "AS IS" BASIS, + WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + See the License for the specific language governing permissions and + limitations under the License. + ======================================================================= + */ +package org.tensorflow.ndarray.impl.dense; + +import org.tensorflow.ndarray.BooleanNdArray; +import org.tensorflow.ndarray.NdArray; +import org.tensorflow.ndarray.Shape; +import org.tensorflow.ndarray.buffer.BooleanDataBuffer; +import org.tensorflow.ndarray.buffer.DataBuffer; +import org.tensorflow.ndarray.impl.dimension.DimensionalSpace; + +public class BooleanDenseNdArray extends AbstractDenseNdArray + implements BooleanNdArray { + + public static BooleanNdArray create(BooleanDataBuffer buffer, Shape shape) { + Validator.denseShape(buffer, shape); + return new BooleanDenseNdArray(buffer, shape); + } + + @Override + public boolean getBoolean(long... indices) { + return buffer.getBoolean(positionOf(indices, true)); + } + + @Override + public BooleanNdArray setBoolean(boolean value, long... indices) { + buffer.setBoolean(value, positionOf(indices, true)); + return this; + } + + @Override + public BooleanNdArray copyTo(NdArray dst) { + Validator.copyToNdArrayArgs(this, dst); + if (dst instanceof BooleanDenseNdArray) { + BooleanDenseNdArray booleanDst = (BooleanDenseNdArray)dst; + DataTransfer.execute(buffer, dimensions(), booleanDst.buffer, booleanDst.dimensions(), DataTransfer::ofBoolean); + } else { + slowCopyTo(dst); + } + return this; + } + + @Override + public BooleanNdArray read(BooleanDataBuffer dst) { + Validator.readToBufferArgs(this, dst); + DataTransfer.execute(buffer, dimensions(), dst, DataTransfer::ofBoolean); + return this; + } + + @Override + public BooleanNdArray write(BooleanDataBuffer src) { + Validator.writeFromBufferArgs(this, src); + DataTransfer.execute(src, buffer, dimensions(), DataTransfer::ofBoolean); + return this; + } + + protected BooleanDenseNdArray(BooleanDataBuffer buffer, Shape shape) { + this(buffer, DimensionalSpace.create(shape)); + } + + @Override + BooleanDenseNdArray instantiate(DataBuffer buffer, DimensionalSpace dimensions) { + return new BooleanDenseNdArray((BooleanDataBuffer)buffer, dimensions); + } + + @Override + protected BooleanDataBuffer buffer() { + return buffer; + } + + private final BooleanDataBuffer buffer; + + private BooleanDenseNdArray(BooleanDataBuffer buffer, DimensionalSpace dimensions) { + super(dimensions); + this.buffer = buffer; + } +} \ No newline at end of file diff --git a/ndarray/src/main/java/org/tensorflow/ndarray/impl/dense/ByteDenseNdArray.java b/ndarray/src/main/java/org/tensorflow/ndarray/impl/dense/ByteDenseNdArray.java new file mode 100644 index 00000000000..172432b5939 --- /dev/null +++ b/ndarray/src/main/java/org/tensorflow/ndarray/impl/dense/ByteDenseNdArray.java @@ -0,0 +1,91 @@ +/* + Copyright 2019 The TensorFlow Authors. All Rights Reserved. + + Licensed under the Apache License, Version 2.0 (the "License"); + you may not use this file except in compliance with the License. + You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + + Unless required by applicable law or agreed to in writing, software + distributed under the License is distributed on an "AS IS" BASIS, + WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + See the License for the specific language governing permissions and + limitations under the License. + ======================================================================= + */ +package org.tensorflow.ndarray.impl.dense; + +import org.tensorflow.ndarray.ByteNdArray; +import org.tensorflow.ndarray.NdArray; +import org.tensorflow.ndarray.Shape; +import org.tensorflow.ndarray.buffer.ByteDataBuffer; +import org.tensorflow.ndarray.buffer.DataBuffer; +import org.tensorflow.ndarray.impl.dimension.DimensionalSpace; + +public class ByteDenseNdArray extends AbstractDenseNdArray + implements ByteNdArray { + + public static ByteNdArray create(ByteDataBuffer buffer, Shape shape) { + Validator.denseShape(buffer, shape); + return new ByteDenseNdArray(buffer, shape); + } + + @Override + public byte getByte(long... indices) { + return buffer.getByte(positionOf(indices, true)); + } + + @Override + public ByteNdArray setByte(byte value, long... indices) { + buffer.setByte(value, positionOf(indices, true)); + return this; + } + + @Override + public ByteNdArray copyTo(NdArray dst) { + Validator.copyToNdArrayArgs(this, dst); + if (dst instanceof ByteDenseNdArray) { + ByteDenseNdArray byteDst = (ByteDenseNdArray)dst; + DataTransfer.execute(buffer, dimensions(), byteDst.buffer, byteDst.dimensions(), DataTransfer::ofByte); + } else { + slowCopyTo(dst); + } + return this; + } + + @Override + public ByteNdArray read(ByteDataBuffer dst) { + Validator.readToBufferArgs(this, dst); + DataTransfer.execute(buffer, dimensions(), dst, DataTransfer::ofByte); + return this; + } + + @Override + public ByteNdArray write(ByteDataBuffer src) { + Validator.writeFromBufferArgs(this, src); + DataTransfer.execute(src, buffer, dimensions(), DataTransfer::ofByte); + return this; + } + + protected ByteDenseNdArray(ByteDataBuffer buffer, Shape shape) { + this(buffer, DimensionalSpace.create(shape)); + } + + @Override + ByteDenseNdArray instantiate(DataBuffer buffer, DimensionalSpace dimensions) { + return new ByteDenseNdArray((ByteDataBuffer)buffer, dimensions); + } + + @Override + protected ByteDataBuffer buffer() { + return buffer; + } + + private final ByteDataBuffer buffer; + + private ByteDenseNdArray(ByteDataBuffer buffer, DimensionalSpace dimensions) { + super(dimensions); + this.buffer = buffer; + } +} \ No newline at end of file diff --git a/ndarray/src/main/java/org/tensorflow/ndarray/impl/dense/DataTransfer.java b/ndarray/src/main/java/org/tensorflow/ndarray/impl/dense/DataTransfer.java new file mode 100644 index 00000000000..d3afa223231 --- /dev/null +++ b/ndarray/src/main/java/org/tensorflow/ndarray/impl/dense/DataTransfer.java @@ -0,0 +1,136 @@ +/* + * Copyright 2019 The TensorFlow Authors. All Rights Reserved. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + * ======================================================================= + */ + +package org.tensorflow.ndarray.impl.dense; + +import org.tensorflow.ndarray.buffer.BooleanDataBuffer; +import org.tensorflow.ndarray.buffer.ByteDataBuffer; +import org.tensorflow.ndarray.buffer.DataBuffer; +import org.tensorflow.ndarray.buffer.DoubleDataBuffer; +import org.tensorflow.ndarray.buffer.FloatDataBuffer; +import org.tensorflow.ndarray.buffer.IntDataBuffer; +import org.tensorflow.ndarray.buffer.LongDataBuffer; +import org.tensorflow.ndarray.buffer.ShortDataBuffer; +import org.tensorflow.ndarray.impl.dimension.DimensionalSpace; +import org.tensorflow.ndarray.impl.sequence.PositionIterator; + +final class DataTransfer { + + @FunctionalInterface + interface OfValue> { + void copy(B srcBuffer, long srcIndex, B dstBuffer, long dstIndex); + } + + static > void ofValue(B srcBuf, long srcIdx, B dstBuf, long dstIdx) { + dstBuf.setObject(srcBuf.getObject(srcIdx), dstIdx); + } + + static void ofByte(ByteDataBuffer srcBuf, long srcIdx, ByteDataBuffer dstBuf, long dstIdx) { + dstBuf.setByte(srcBuf.getByte(srcIdx), dstIdx); + } + + static void ofInt(IntDataBuffer srcBuf, long srcIdx, IntDataBuffer dstBuf, long dstIdx) { + dstBuf.setInt(srcBuf.getInt(srcIdx), dstIdx); + } + + static void ofLong(LongDataBuffer srcBuf, long srcIdx, LongDataBuffer dstBuf, long dstIdx) { + dstBuf.setLong(srcBuf.getLong(srcIdx), dstIdx); + } + + static void ofDouble(DoubleDataBuffer srcBuf, long srcIdx, DoubleDataBuffer dstBuf, long dstIdx) { + dstBuf.setDouble(srcBuf.getDouble(srcIdx), dstIdx); + } + + static void ofFloat(FloatDataBuffer srcBuf, long srcIdx, FloatDataBuffer dstBuf, long dstIdx) { + dstBuf.setFloat(srcBuf.getFloat(srcIdx), dstIdx); + } + + static void ofShort(ShortDataBuffer srcBuf, long srcIdx, ShortDataBuffer dstBuf, long dstIdx) { + dstBuf.setShort(srcBuf.getShort(srcIdx), dstIdx); + } + + static void ofBoolean(BooleanDataBuffer srcBuf, long srcIdx, BooleanDataBuffer dstBuf, long dstIdx) { + dstBuf.setBoolean(srcBuf.getBoolean(srcIdx), dstIdx); + } + + static > void execute(B srcBuffer, DimensionalSpace srcDimensions, B dstBuffer, DimensionalSpace dstDimensions, OfValue valueTransfer) { + if (srcDimensions.isSegmented() || dstDimensions.isSegmented()) { + int segmentationIdx = Math.max(srcDimensions.segmentationIdx(), dstDimensions.segmentationIdx()); + copyByElement( + srcBuffer, + PositionIterator.create(srcDimensions, segmentationIdx), + dstBuffer, + PositionIterator.create(dstDimensions, segmentationIdx), + srcDimensions.get(segmentationIdx).elementSize(), + valueTransfer + ); + } else { + srcBuffer.copyTo(dstBuffer, srcDimensions.physicalSize()); + } + } + + static > void execute(B srcBuffer, B dstBuffer, DimensionalSpace dstDimensions, OfValue valueTransfer) { + if (dstDimensions.isSegmented()) { + long elementSize = dstDimensions.get(dstDimensions.segmentationIdx()).elementSize(); + copyByElement( + srcBuffer, + PositionIterator.sequence(elementSize, srcBuffer.size()), + dstBuffer, + PositionIterator.create(dstDimensions, dstDimensions.segmentationIdx()), + elementSize, + valueTransfer + ); + } else { + srcBuffer.copyTo(dstBuffer, dstDimensions.physicalSize()); + } + } + + static > void execute(B srcBuffer, DimensionalSpace srcDimensions, B dstBuffer, OfValue valueTransfer) { + if (srcDimensions.isSegmented()) { + long elementSize = srcDimensions.get(srcDimensions.segmentationIdx()).elementSize(); + copyByElement( + srcBuffer, + PositionIterator.create(srcDimensions, srcDimensions.segmentationIdx()), + dstBuffer, + PositionIterator.sequence(elementSize, dstBuffer.size()), + elementSize, + valueTransfer + ); + } else { + srcBuffer.copyTo(dstBuffer, srcDimensions.physicalSize()); + } + } + + private static > void copyByElement( + B srcBuffer, + PositionIterator srcIterator, + B dstBuffer, + PositionIterator dstIterator, + long elementSize, + OfValue valueTransfer + ) { + if (elementSize == 1) { + while (srcIterator.hasNext()) { + valueTransfer.copy(srcBuffer, srcIterator.nextLong(), dstBuffer, dstIterator.nextLong()); + } + } else { + while (srcIterator.hasNext()) { + srcBuffer.offset(srcIterator.nextLong()).copyTo(dstBuffer.offset(dstIterator.nextLong()), elementSize); + } + } + } +} diff --git a/ndarray/src/main/java/org/tensorflow/ndarray/impl/dense/DenseNdArray.java b/ndarray/src/main/java/org/tensorflow/ndarray/impl/dense/DenseNdArray.java new file mode 100644 index 00000000000..819d95de2fc --- /dev/null +++ b/ndarray/src/main/java/org/tensorflow/ndarray/impl/dense/DenseNdArray.java @@ -0,0 +1,63 @@ +/* + Copyright 2019 The TensorFlow Authors. All Rights Reserved. + + Licensed under the Apache License, Version 2.0 (the "License"); + you may not use this file except in compliance with the License. + You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + + Unless required by applicable law or agreed to in writing, software + distributed under the License is distributed on an "AS IS" BASIS, + WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + See the License for the specific language governing permissions and + limitations under the License. + ======================================================================= + */ +package org.tensorflow.ndarray.impl.dense; + +import org.tensorflow.ndarray.Shape; +import org.tensorflow.ndarray.buffer.DataBuffer; +import org.tensorflow.ndarray.NdArray; +import org.tensorflow.ndarray.impl.dimension.DimensionalSpace; + +public class DenseNdArray extends AbstractDenseNdArray> { + + public static NdArray wrap(DataBuffer buffer, Shape shape) { + Validator.denseShape(buffer, shape); + return new DenseNdArray<>(buffer, shape); + } + + @Override + public NdArray copyTo(NdArray dst) { + Validator.copyToNdArrayArgs(this, dst); + if (dst instanceof DenseNdArray) { + DenseNdArray denseDst = (DenseNdArray)dst; + DataTransfer.execute(buffer, dimensions(), denseDst.buffer, denseDst.dimensions(), DataTransfer::ofValue); + } else { + slowCopyTo(dst); + } + return this; + } + + protected DenseNdArray(DataBuffer buffer, Shape shape) { + this(buffer, DimensionalSpace.create(shape)); + } + + @Override + DenseNdArray instantiate(DataBuffer buffer, DimensionalSpace dimensions) { + return new DenseNdArray<>(buffer, dimensions); + } + + @Override + protected DataBuffer buffer() { + return buffer; + } + + private final DataBuffer buffer; + + private DenseNdArray(DataBuffer buffer, DimensionalSpace dimensions) { + super(dimensions); + this.buffer = buffer; + } +} \ No newline at end of file diff --git a/ndarray/src/main/java/org/tensorflow/ndarray/impl/dense/DoubleDenseNdArray.java b/ndarray/src/main/java/org/tensorflow/ndarray/impl/dense/DoubleDenseNdArray.java new file mode 100644 index 00000000000..f54b8d0347a --- /dev/null +++ b/ndarray/src/main/java/org/tensorflow/ndarray/impl/dense/DoubleDenseNdArray.java @@ -0,0 +1,91 @@ +/* + Copyright 2019 The TensorFlow Authors. All Rights Reserved. + + Licensed under the Apache License, Version 2.0 (the "License"); + you may not use this file except in compliance with the License. + You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + + Unless required by applicable law or agreed to in writing, software + distributed under the License is distributed on an "AS IS" BASIS, + WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + See the License for the specific language governing permissions and + limitations under the License. + ======================================================================= + */ +package org.tensorflow.ndarray.impl.dense; + +import org.tensorflow.ndarray.DoubleNdArray; +import org.tensorflow.ndarray.NdArray; +import org.tensorflow.ndarray.Shape; +import org.tensorflow.ndarray.buffer.DataBuffer; +import org.tensorflow.ndarray.buffer.DoubleDataBuffer; +import org.tensorflow.ndarray.impl.dimension.DimensionalSpace; + +public class DoubleDenseNdArray extends AbstractDenseNdArray + implements DoubleNdArray { + + public static DoubleNdArray create(DoubleDataBuffer buffer, Shape shape) { + Validator.denseShape(buffer, shape); + return new DoubleDenseNdArray(buffer, shape); + } + + @Override + public double getDouble(long... indices) { + return buffer.getDouble(positionOf(indices, true)); + } + + @Override + public DoubleNdArray setDouble(double value, long... indices) { + buffer.setDouble(value, positionOf(indices, true)); + return this; + } + + @Override + public DoubleNdArray copyTo(NdArray dst) { + Validator.copyToNdArrayArgs(this, dst); + if (dst instanceof DoubleDenseNdArray) { + DoubleDenseNdArray doubleDst = (DoubleDenseNdArray)dst; + DataTransfer.execute(buffer, dimensions(), doubleDst.buffer, doubleDst.dimensions(), DataTransfer::ofDouble); + } else { + slowCopyTo(dst); + } + return this; + } + + @Override + public DoubleNdArray read(DoubleDataBuffer dst) { + Validator.readToBufferArgs(this, dst); + DataTransfer.execute(buffer, dimensions(), dst, DataTransfer::ofDouble); + return this; + } + + @Override + public DoubleNdArray write(DoubleDataBuffer src) { + Validator.writeFromBufferArgs(this, src); + DataTransfer.execute(src, buffer, dimensions(), DataTransfer::ofDouble); + return this; + } + + protected DoubleDenseNdArray(DoubleDataBuffer buffer, Shape shape) { + this(buffer, DimensionalSpace.create(shape)); + } + + @Override + DoubleDenseNdArray instantiate(DataBuffer buffer, DimensionalSpace dimensions) { + return new DoubleDenseNdArray((DoubleDataBuffer)buffer, dimensions); + } + + @Override + protected DoubleDataBuffer buffer() { + return buffer; + } + + private final DoubleDataBuffer buffer; + + private DoubleDenseNdArray(DoubleDataBuffer buffer, DimensionalSpace dimensions) { + super(dimensions); + this.buffer = buffer; + } +} \ No newline at end of file diff --git a/ndarray/src/main/java/org/tensorflow/ndarray/impl/dense/FloatDenseNdArray.java b/ndarray/src/main/java/org/tensorflow/ndarray/impl/dense/FloatDenseNdArray.java new file mode 100644 index 00000000000..196b5ef8b11 --- /dev/null +++ b/ndarray/src/main/java/org/tensorflow/ndarray/impl/dense/FloatDenseNdArray.java @@ -0,0 +1,91 @@ +/* + Copyright 2019 The TensorFlow Authors. All Rights Reserved. + + Licensed under the Apache License, Version 2.0 (the "License"); + you may not use this file except in compliance with the License. + You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + + Unless required by applicable law or agreed to in writing, software + distributed under the License is distributed on an "AS IS" BASIS, + WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + See the License for the specific language governing permissions and + limitations under the License. + ======================================================================= + */ +package org.tensorflow.ndarray.impl.dense; + +import org.tensorflow.ndarray.FloatNdArray; +import org.tensorflow.ndarray.NdArray; +import org.tensorflow.ndarray.Shape; +import org.tensorflow.ndarray.buffer.DataBuffer; +import org.tensorflow.ndarray.buffer.FloatDataBuffer; +import org.tensorflow.ndarray.impl.dimension.DimensionalSpace; + +public class FloatDenseNdArray extends AbstractDenseNdArray + implements FloatNdArray { + + public static FloatNdArray create(FloatDataBuffer buffer, Shape shape) { + Validator.denseShape(buffer, shape); + return new FloatDenseNdArray(buffer, shape); + } + + @Override + public float getFloat(long... indices) { + return buffer.getFloat(positionOf(indices, true)); + } + + @Override + public FloatNdArray setFloat(float value, long... indices) { + buffer.setFloat(value, positionOf(indices, true)); + return this; + } + + @Override + public FloatNdArray copyTo(NdArray dst) { + Validator.copyToNdArrayArgs(this, dst); + if (dst instanceof FloatDenseNdArray) { + FloatDenseNdArray floatDst = (FloatDenseNdArray)dst; + DataTransfer.execute(buffer, dimensions(), floatDst.buffer, floatDst.dimensions(), DataTransfer::ofFloat); + } else { + slowCopyTo(dst); + } + return this; + } + + @Override + public FloatNdArray read(FloatDataBuffer dst) { + Validator.readToBufferArgs(this, dst); + DataTransfer.execute(buffer, dimensions(), dst, DataTransfer::ofFloat); + return this; + } + + @Override + public FloatNdArray write(FloatDataBuffer src) { + Validator.writeFromBufferArgs(this, src); + DataTransfer.execute(src, buffer, dimensions(), DataTransfer::ofFloat); + return this; + } + + protected FloatDenseNdArray(FloatDataBuffer buffer, Shape shape) { + this(buffer, DimensionalSpace.create(shape)); + } + + @Override + FloatDenseNdArray instantiate(DataBuffer buffer, DimensionalSpace dimensions) { + return new FloatDenseNdArray((FloatDataBuffer) buffer, dimensions); + } + + @Override + public FloatDataBuffer buffer() { + return buffer; + } + + private final FloatDataBuffer buffer; + + private FloatDenseNdArray(FloatDataBuffer buffer, DimensionalSpace dimensions) { + super(dimensions); + this.buffer = buffer; + } +} diff --git a/ndarray/src/main/java/org/tensorflow/ndarray/impl/dense/IntDenseNdArray.java b/ndarray/src/main/java/org/tensorflow/ndarray/impl/dense/IntDenseNdArray.java new file mode 100644 index 00000000000..a7af498dd6f --- /dev/null +++ b/ndarray/src/main/java/org/tensorflow/ndarray/impl/dense/IntDenseNdArray.java @@ -0,0 +1,91 @@ +/* + Copyright 2019 The TensorFlow Authors. All Rights Reserved. + + Licensed under the Apache License, Version 2.0 (the "License"); + you may not use this file except in compliance with the License. + You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + + Unless required by applicable law or agreed to in writing, software + distributed under the License is distributed on an "AS IS" BASIS, + WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + See the License for the specific language governing permissions and + limitations under the License. + ======================================================================= + */ +package org.tensorflow.ndarray.impl.dense; + +import org.tensorflow.ndarray.Shape; +import org.tensorflow.ndarray.buffer.DataBuffer; +import org.tensorflow.ndarray.buffer.IntDataBuffer; +import org.tensorflow.ndarray.IntNdArray; +import org.tensorflow.ndarray.NdArray; +import org.tensorflow.ndarray.impl.dimension.DimensionalSpace; + +public class IntDenseNdArray extends AbstractDenseNdArray + implements IntNdArray { + + public static IntNdArray create(IntDataBuffer buffer, Shape shape) { + Validator.denseShape(buffer, shape); + return new IntDenseNdArray(buffer, shape); + } + + @Override + public int getInt(long... indices) { + return buffer.getInt(positionOf(indices, true)); + } + + @Override + public IntNdArray setInt(int value, long... indices) { + buffer.setInt(value, positionOf(indices, true)); + return this; + } + + @Override + public IntNdArray copyTo(NdArray dst) { + Validator.copyToNdArrayArgs(this, dst); + if (dst instanceof IntDenseNdArray) { + IntDenseNdArray intDst = (IntDenseNdArray)dst; + DataTransfer.execute(buffer, dimensions(), intDst.buffer, intDst.dimensions(), DataTransfer::ofInt); + } else { + slowCopyTo(dst); + } + return this; + } + + @Override + public IntNdArray read(IntDataBuffer dst) { + Validator.readToBufferArgs(this, dst); + DataTransfer.execute(buffer, dimensions(), dst, DataTransfer::ofInt); + return this; + } + + @Override + public IntNdArray write(IntDataBuffer src) { + Validator.writeFromBufferArgs(this, src); + DataTransfer.execute(src, buffer, dimensions(), DataTransfer::ofInt); + return this; + } + + protected IntDenseNdArray(IntDataBuffer buffer, Shape shape) { + this(buffer, DimensionalSpace.create(shape)); + } + + @Override + IntDenseNdArray instantiate(DataBuffer buffer, DimensionalSpace dimensions) { + return new IntDenseNdArray((IntDataBuffer)buffer, dimensions); + } + + @Override + protected IntDataBuffer buffer() { + return buffer; + } + + private final IntDataBuffer buffer; + + private IntDenseNdArray(IntDataBuffer buffer, DimensionalSpace dimensions) { + super(dimensions); + this.buffer = buffer; + } +} \ No newline at end of file diff --git a/ndarray/src/main/java/org/tensorflow/ndarray/impl/dense/LongDenseNdArray.java b/ndarray/src/main/java/org/tensorflow/ndarray/impl/dense/LongDenseNdArray.java new file mode 100644 index 00000000000..cd56dadfb2b --- /dev/null +++ b/ndarray/src/main/java/org/tensorflow/ndarray/impl/dense/LongDenseNdArray.java @@ -0,0 +1,91 @@ +/* + Copyright 2019 The TensorFlow Authors. All Rights Reserved. + + Licensed under the Apache License, Version 2.0 (the "License"); + you may not use this file except in compliance with the License. + You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + + Unless required by applicable law or agreed to in writing, software + distributed under the License is distributed on an "AS IS" BASIS, + WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + See the License for the specific language governing permissions and + limitations under the License. + ======================================================================= + */ +package org.tensorflow.ndarray.impl.dense; + +import org.tensorflow.ndarray.LongNdArray; +import org.tensorflow.ndarray.NdArray; +import org.tensorflow.ndarray.Shape; +import org.tensorflow.ndarray.buffer.DataBuffer; +import org.tensorflow.ndarray.buffer.LongDataBuffer; +import org.tensorflow.ndarray.impl.dimension.DimensionalSpace; + +public class LongDenseNdArray extends AbstractDenseNdArray + implements LongNdArray { + + public static LongNdArray create(LongDataBuffer buffer, Shape shape) { + Validator.denseShape(buffer, shape); + return new LongDenseNdArray(buffer, shape); + } + + @Override + public long getLong(long... indices) { + return buffer.getLong(positionOf(indices, true)); + } + + @Override + public LongNdArray setLong(long value, long... indices) { + buffer.setLong(value, positionOf(indices, true)); + return this; + } + + @Override + public LongNdArray copyTo(NdArray dst) { + Validator.copyToNdArrayArgs(this, dst); + if (dst instanceof LongDenseNdArray) { + LongDenseNdArray longDst = (LongDenseNdArray)dst; + DataTransfer.execute(buffer, dimensions(), longDst.buffer, longDst.dimensions(), DataTransfer::ofLong); + } else { + slowCopyTo(dst); + } + return this; + } + + @Override + public LongNdArray read(LongDataBuffer dst) { + Validator.readToBufferArgs(this, dst); + DataTransfer.execute(buffer, dimensions(), dst, DataTransfer::ofLong); + return this; + } + + @Override + public LongNdArray write(LongDataBuffer src) { + Validator.writeFromBufferArgs(this, src); + DataTransfer.execute(src, buffer, dimensions(), DataTransfer::ofLong); + return this; + } + + protected LongDenseNdArray(LongDataBuffer buffer, Shape shape) { + this(buffer, DimensionalSpace.create(shape)); + } + + @Override + LongDenseNdArray instantiate(DataBuffer buffer, DimensionalSpace dimensions) { + return new LongDenseNdArray((LongDataBuffer)buffer, dimensions); + } + + @Override + protected LongDataBuffer buffer() { + return buffer; + } + + private final LongDataBuffer buffer; + + private LongDenseNdArray(LongDataBuffer buffer, DimensionalSpace dimensions) { + super(dimensions); + this.buffer = buffer; + } +} \ No newline at end of file diff --git a/ndarray/src/main/java/org/tensorflow/ndarray/impl/dense/ShortDenseNdArray.java b/ndarray/src/main/java/org/tensorflow/ndarray/impl/dense/ShortDenseNdArray.java new file mode 100644 index 00000000000..291c01ac8e1 --- /dev/null +++ b/ndarray/src/main/java/org/tensorflow/ndarray/impl/dense/ShortDenseNdArray.java @@ -0,0 +1,91 @@ +/* + Copyright 2019 The TensorFlow Authors. All Rights Reserved. + + Licensed under the Apache License, Version 2.0 (the "License"); + you may not use this file except in compliance with the License. + You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + + Unless required by applicable law or agreed to in writing, software + distributed under the License is distributed on an "AS IS" BASIS, + WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + See the License for the specific language governing permissions and + limitations under the License. + ======================================================================= + */ +package org.tensorflow.ndarray.impl.dense; + +import org.tensorflow.ndarray.NdArray; +import org.tensorflow.ndarray.ShortNdArray; +import org.tensorflow.ndarray.Shape; +import org.tensorflow.ndarray.buffer.DataBuffer; +import org.tensorflow.ndarray.buffer.ShortDataBuffer; +import org.tensorflow.ndarray.impl.dimension.DimensionalSpace; + +public class ShortDenseNdArray extends AbstractDenseNdArray + implements ShortNdArray { + + public static ShortNdArray create(ShortDataBuffer buffer, Shape shape) { + Validator.denseShape(buffer, shape); + return new ShortDenseNdArray(buffer, shape); + } + + @Override + public short getShort(long... indices) { + return buffer.getShort(positionOf(indices, true)); + } + + @Override + public ShortNdArray setShort(short value, long... indices) { + buffer.setShort(value, positionOf(indices, true)); + return this; + } + + @Override + public ShortNdArray copyTo(NdArray dst) { + Validator.copyToNdArrayArgs(this, dst); + if (dst instanceof ShortDenseNdArray) { + ShortDenseNdArray shortDst = (ShortDenseNdArray)dst; + DataTransfer.execute(buffer, dimensions(), shortDst.buffer, shortDst.dimensions(), DataTransfer::ofShort); + } else { + slowCopyTo(dst); + } + return this; + } + + @Override + public ShortNdArray read(ShortDataBuffer dst) { + Validator.readToBufferArgs(this, dst); + DataTransfer.execute(buffer, dimensions(), dst, DataTransfer::ofShort); + return this; + } + + @Override + public ShortNdArray write(ShortDataBuffer src) { + Validator.writeFromBufferArgs(this, src); + DataTransfer.execute(src, buffer, dimensions(), DataTransfer::ofShort); + return this; + } + + protected ShortDenseNdArray(ShortDataBuffer buffer, Shape shape) { + this(buffer, DimensionalSpace.create(shape)); + } + + @Override + ShortDenseNdArray instantiate(DataBuffer buffer, DimensionalSpace dimensions) { + return new ShortDenseNdArray((ShortDataBuffer)buffer, dimensions); + } + + @Override + protected ShortDataBuffer buffer() { + return buffer; + } + + private final ShortDataBuffer buffer; + + private ShortDenseNdArray(ShortDataBuffer buffer, DimensionalSpace dimensions) { + super(dimensions); + this.buffer = buffer; + } +} \ No newline at end of file diff --git a/ndarray/src/main/java/org/tensorflow/ndarray/impl/dense/Validator.java b/ndarray/src/main/java/org/tensorflow/ndarray/impl/dense/Validator.java new file mode 100644 index 00000000000..b0bcae252fb --- /dev/null +++ b/ndarray/src/main/java/org/tensorflow/ndarray/impl/dense/Validator.java @@ -0,0 +1,49 @@ +/* + Copyright 2019 The TensorFlow Authors. All Rights Reserved. + + Licensed under the Apache License, Version 2.0 (the "License"); + you may not use this file except in compliance with the License. + You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + + Unless required by applicable law or agreed to in writing, software + distributed under the License is distributed on an "AS IS" BASIS, + WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + See the License for the specific language governing permissions and + limitations under the License. + ======================================================================= + */ +package org.tensorflow.ndarray.impl.dense; + +import org.tensorflow.ndarray.IllegalRankException; +import org.tensorflow.ndarray.Shape; +import org.tensorflow.ndarray.buffer.DataBuffer; +import org.tensorflow.ndarray.impl.dimension.DimensionalSpace; + +final class Validator extends org.tensorflow.ndarray.impl.Validator { + + static void coordinates(DimensionalSpace dimensions, long[] coords, + boolean isValue) { + if (coords.length > dimensions.numDimensions()) { + throw new IndexOutOfBoundsException(); + } + if (isValue && coords.length != dimensions.numDimensions()) { + throw new IllegalRankException("Not a scalar value"); + } + } + + static void denseShape(DataBuffer buffer, Shape shape) { + if (shape == null) { + throw new IllegalArgumentException("Shape cannot be null"); + } + if (shape.hasUnknownDimension()) { + throw new IllegalArgumentException("Dense arrays cannot have unknown dimension(s)"); + } + if (buffer.size() < shape.size()) { + throw new IllegalArgumentException("Buffer size is smaller than the shape size"); + }; + } + + private Validator() {} +} diff --git a/ndarray/src/main/java/org/tensorflow/ndarray/impl/dimension/AbstractDimension.java b/ndarray/src/main/java/org/tensorflow/ndarray/impl/dimension/AbstractDimension.java new file mode 100644 index 00000000000..f2a0fb05b6e --- /dev/null +++ b/ndarray/src/main/java/org/tensorflow/ndarray/impl/dimension/AbstractDimension.java @@ -0,0 +1,45 @@ +/* + Copyright 2019 The TensorFlow Authors. All Rights Reserved. + + Licensed under the Apache License, Version 2.0 (the "License"); + you may not use this file except in compliance with the License. + You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + + Unless required by applicable law or agreed to in writing, software + distributed under the License is distributed on an "AS IS" BASIS, + WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + See the License for the specific language governing permissions and + limitations under the License. + ======================================================================= + */ +package org.tensorflow.ndarray.impl.dimension; + +abstract class AbstractDimension implements Dimension { + + /** + * Dimensions are known to be equal if they have the same number of elements + */ + @Override + public int hashCode() { + final int prime = 17; + long numElements = numElements(); + return 31 * prime + (int)(numElements ^ (numElements >>> 32)); + } + + /** + * Dimensions are known to be equal if they have the same number of elements + */ + @Override + public boolean equals(Object obj) { + if (this == obj) { + return true; + } + if (obj instanceof Dimension) { + Dimension otherDimension = (Dimension) obj; + return numElements() == otherDimension.numElements(); + } + return false; + } +} diff --git a/ndarray/src/main/java/org/tensorflow/ndarray/impl/dimension/Axis.java b/ndarray/src/main/java/org/tensorflow/ndarray/impl/dimension/Axis.java new file mode 100644 index 00000000000..91973c752a1 --- /dev/null +++ b/ndarray/src/main/java/org/tensorflow/ndarray/impl/dimension/Axis.java @@ -0,0 +1,61 @@ +/* + Copyright 2019 The TensorFlow Authors. All Rights Reserved. + + Licensed under the Apache License, Version 2.0 (the "License"); + you may not use this file except in compliance with the License. + You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + + Unless required by applicable law or agreed to in writing, software + distributed under the License is distributed on an "AS IS" BASIS, + WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + See the License for the specific language governing permissions and + limitations under the License. + ======================================================================= + */ +package org.tensorflow.ndarray.impl.dimension; + +final class Axis extends AbstractDimension { + + @Override + public long numElements() { + return numElements; + } + + @Override + public long positionOf(long coord) { + if (coord >= numElements) { + throw new IndexOutOfBoundsException(); + } + return elementSize * coord; + } + + @Override + public boolean isSegmented() { + return false; // all axis are continuous + } + + @Override + public long elementSize() { + return elementSize; + } + + @Override + public long physicalSize() { + return elementSize * numElements; + } + + @Override + public String toString() { + return String.valueOf(numElements); + } + + Axis(long numElements, long elementSize) { + this.numElements = numElements; + this.elementSize = elementSize; + } + + private final long numElements; + private final long elementSize; +} diff --git a/ndarray/src/main/java/org/tensorflow/ndarray/impl/dimension/Dimension.java b/ndarray/src/main/java/org/tensorflow/ndarray/impl/dimension/Dimension.java new file mode 100644 index 00000000000..cbae2d897a8 --- /dev/null +++ b/ndarray/src/main/java/org/tensorflow/ndarray/impl/dimension/Dimension.java @@ -0,0 +1,36 @@ +/* + Copyright 2019 The TensorFlow Authors. All Rights Reserved. + + Licensed under the Apache License, Version 2.0 (the "License"); + you may not use this file except in compliance with the License. + You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + + Unless required by applicable law or agreed to in writing, software + distributed under the License is distributed on an "AS IS" BASIS, + WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + See the License for the specific language governing permissions and + limitations under the License. + ======================================================================= + */ +package org.tensorflow.ndarray.impl.dimension; + +import org.tensorflow.ndarray.index.Index; + +public interface Dimension { + + default Dimension withIndex(Index index) { + return new IndexedDimension(index, this); + } + + long numElements(); + + long elementSize(); + + long physicalSize(); + + long positionOf(long coord); + + boolean isSegmented(); +} diff --git a/ndarray/src/main/java/org/tensorflow/ndarray/impl/dimension/DimensionalSpace.java b/ndarray/src/main/java/org/tensorflow/ndarray/impl/dimension/DimensionalSpace.java new file mode 100644 index 00000000000..7d0f0222bbe --- /dev/null +++ b/ndarray/src/main/java/org/tensorflow/ndarray/impl/dimension/DimensionalSpace.java @@ -0,0 +1,224 @@ +/* + Copyright 2019 The TensorFlow Authors. All Rights Reserved. + + Licensed under the Apache License, Version 2.0 (the "License"); + you may not use this file except in compliance with the License. + You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + + Unless required by applicable law or agreed to in writing, software + distributed under the License is distributed on an "AS IS" BASIS, + WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + See the License for the specific language governing permissions and + limitations under the License. + ======================================================================= + */ + +package org.tensorflow.ndarray.impl.dimension; + +import java.util.Arrays; +import org.tensorflow.ndarray.Shape; +import org.tensorflow.ndarray.index.Index; + +public class DimensionalSpace { + + public static DimensionalSpace create(Shape shape) { + Dimension[] dimensions = new Dimension[shape.numDimensions()]; + + // Start from the last dimension, where all elements are continuous + for (int i = dimensions.length - 1, elementSize = 1; i >= 0; --i) { + dimensions[i] = new Axis(shape.size(i), elementSize); + elementSize *= dimensions[i].numElements(); + } + return new DimensionalSpace(dimensions, shape); + } + + public RelativeDimensionalSpace mapTo(Index[] indices) { + if (dimensions == null) { + throw new ArrayIndexOutOfBoundsException(); + } + int dimIdx = 0; + int indexIdx = 0; + int newDimIdx = 0; + int segmentationIdx = -1; + long initialOffset = 0; + + int newAxes = 0; + boolean seenEllipsis = false; + for (Index idx : indices) { + if (idx.isNewAxis()) { + newAxes += 1; + } + if (idx.isEllipsis()) { + if (seenEllipsis) { + throw new IllegalArgumentException("Only one ellipsis allowed"); + } else { + seenEllipsis = true; + } + } + } + int newLength = dimensions.length + newAxes; + + Dimension[] newDimensions = new Dimension[newLength]; + while (indexIdx < indices.length) { + + if (indices[indexIdx].isPoint()) { + // When an index targets a single point in a given dimension, calculate the offset of this + // point and cumulate the offset of any subsequent point as well + long offset = 0; + do { + offset += indices[indexIdx].mapCoordinate(0, dimensions[dimIdx]); + dimIdx++; + } while (++indexIdx < indices.length && indices[indexIdx].isPoint()); + + // If this is the first index, then the offset is the position of the whole dimension + // space within the original one. If not, then we apply the offset to the last vectorial + // dimension + if (newDimIdx == 0) { + initialOffset = offset; + } else { + long reducedSize = dimensions[dimIdx - 1].elementSize(); + newDimensions[newDimIdx - 1] = new ReducedDimension(newDimensions[newDimIdx - 1], offset, reducedSize); + segmentationIdx = newDimIdx - 1; + } + + } else if (indices[indexIdx].isNewAxis()) { + long newSize; + if (dimIdx == 0) { + // includes everything. Should really include future reduction (at()) but that doesn't seem to cause issues + // elsewhere + newSize = dimensions[0].numElements() * dimensions[0].elementSize(); + } else { + newSize = dimensions[dimIdx - 1].elementSize(); + } + + newDimensions[newDimIdx] = new Axis(1, newSize); + segmentationIdx = newDimIdx; // is this correct? + ++newDimIdx; + ++indexIdx; + } else if (indices[indexIdx].isEllipsis()) { + int remainingDimensions = dimensions.length - dimIdx; + int requiredDimensions = 0; + for (int i = indexIdx + 1; i < indices.length; i++) { + if (!indices[i].isNewAxis()) { + requiredDimensions++; + } + } + // while the number of dimensions left < the number of indices that consume axes + while (remainingDimensions > requiredDimensions) { + Dimension dim = dimensions[dimIdx++]; + if (dim.isSegmented()) { + segmentationIdx = newDimIdx; + } + newDimensions[newDimIdx++] = dim; + remainingDimensions--; + } + indexIdx++; + } else { + // Map any other index to the appropriate dimension of this space + Dimension newDimension = indices[indexIdx].apply(dimensions[dimIdx++]); + newDimensions[newDimIdx] = newDimension; + if (newDimension.isSegmented()) { + segmentationIdx = newDimIdx; + } + ++newDimIdx; + ++indexIdx; + } + } + + // When the number of indices provided is smaller than the number of dimensions in this space, + // we copy the remaining dimensions directly to the new space as well. + for (; dimIdx < dimensions.length; ++dimIdx, ++newDimIdx) { + Dimension dim = dimensions[dimIdx]; + newDimensions[newDimIdx] = dim; + if (dim.isSegmented()) { + segmentationIdx = newDimIdx; + } + } + return new RelativeDimensionalSpace(Arrays.copyOf(newDimensions, newDimIdx), segmentationIdx, initialOffset); + } + + public DimensionalSpace from(int dimensionStart) { + if (dimensionStart > dimensions.length) { + throw new IndexOutOfBoundsException(); + } + Dimension[] newDimensions = Arrays.copyOfRange(dimensions, dimensionStart, dimensions.length); + if (segmentationIdx > dimensionStart) { + return new DimensionalSpace(newDimensions, segmentationIdx - dimensionStart); + } + return new DimensionalSpace(newDimensions); + } + + public Shape shape() { + if (shape == null) { + shape = toShape(dimensions); + } + return shape; + } + + public int numDimensions() { + return dimensions.length; + } + + public long numElements(int i) { + return dimensions[i].numElements(); + } + + public long physicalSize() { + return dimensions.length > 0 ? dimensions[0].physicalSize() : 1; // dimensions.length == 0 for scalars + } + + public Dimension get(int i) { + return dimensions[i]; + } + + public boolean isSegmented() { + return segmentationIdx >= 0; + } + + public int segmentationIdx() { + return segmentationIdx; + } + + public long positionOf(long[] coords) { + long position = 0L; + for (int i = 0; i < coords.length; ++i) { + position += dimensions[i].positionOf(coords[i]); + } + return position; + } + + /** Succinct description of the shape meant for debugging. */ + @Override + public String toString() { + return Arrays.toString(dimensions); + } + + DimensionalSpace(Dimension[] dimensions, int segmentationIdx) { + this.dimensions = dimensions; + this.segmentationIdx = segmentationIdx; + } + + private DimensionalSpace(Dimension[] dimensions) { + this(dimensions, -1); + } + + private DimensionalSpace(Dimension[] dimensions, Shape shape) { + this(dimensions); + this.shape = shape; + } + + private final Dimension[] dimensions; + private final int segmentationIdx; + private Shape shape; + + private static Shape toShape(Dimension[] dimensions) { + long[] shapeDimSizes = new long[dimensions.length]; + int i = 0; + for (Dimension dimension : dimensions) { + shapeDimSizes[i++] = dimension.numElements(); + } + return Shape.of(shapeDimSizes); + } +} diff --git a/ndarray/src/main/java/org/tensorflow/ndarray/impl/dimension/IndexedDimension.java b/ndarray/src/main/java/org/tensorflow/ndarray/impl/dimension/IndexedDimension.java new file mode 100644 index 00000000000..2b609bc3535 --- /dev/null +++ b/ndarray/src/main/java/org/tensorflow/ndarray/impl/dimension/IndexedDimension.java @@ -0,0 +1,69 @@ +/* + Copyright 2019 The TensorFlow Authors. All Rights Reserved. + + Licensed under the Apache License, Version 2.0 (the "License"); + you may not use this file except in compliance with the License. + You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + + Unless required by applicable law or agreed to in writing, software + distributed under the License is distributed on an "AS IS" BASIS, + WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + See the License for the specific language governing permissions and + limitations under the License. + ======================================================================= + */ +package org.tensorflow.ndarray.impl.dimension; + +import org.tensorflow.ndarray.index.Index; + +final class IndexedDimension extends AbstractDimension { + + @Override + public long numElements() { + return numElements; + } + + @Override + public long positionOf(long coord) { + if (coord >= numElements()) { + throw new IndexOutOfBoundsException(); + } + return originalDimension.positionOf(index.mapCoordinate(coord, originalDimension)); + } + + @Override + public boolean isSegmented() { + // TODO (karllessard) for now we consider all indexed dimensions as segmented but might depend + // on the actual index + return true; + } + + @Override + public long elementSize() { + return originalDimension.elementSize(); // indices do not change the size of an inner element + } + + @Override + public long physicalSize() { + // TODO (karllessard) we consider this dimension takes the same amount of memory that the + // original one but might depend on the actual index + return originalDimension.physicalSize(); + } + + @Override + public String toString() { + return String.valueOf(numElements()); + } + + IndexedDimension(Index index, Dimension originalDimension) { + this.index = index; + this.originalDimension = originalDimension; + this.numElements = index.numElements(originalDimension); + } + + private final Index index; + private final Dimension originalDimension; + private final long numElements; +} diff --git a/ndarray/src/main/java/org/tensorflow/ndarray/impl/dimension/ReducedDimension.java b/ndarray/src/main/java/org/tensorflow/ndarray/impl/dimension/ReducedDimension.java new file mode 100644 index 00000000000..4b5cb1adcf8 --- /dev/null +++ b/ndarray/src/main/java/org/tensorflow/ndarray/impl/dimension/ReducedDimension.java @@ -0,0 +1,62 @@ +/* + Copyright 2019 The TensorFlow Authors. All Rights Reserved. + + Licensed under the Apache License, Version 2.0 (the "License"); + you may not use this file except in compliance with the License. + You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + + Unless required by applicable law or agreed to in writing, software + distributed under the License is distributed on an "AS IS" BASIS, + WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + See the License for the specific language governing permissions and + limitations under the License. + ======================================================================= + */ +package org.tensorflow.ndarray.impl.dimension; + +final class ReducedDimension extends AbstractDimension { + + @Override + public long numElements() { + return originalDimension.numElements(); + } + + @Override + public long positionOf(long coord) { + return originalDimension.positionOf(coord) + offset; + } + + @Override + public boolean isSegmented() { + return true; + } + + @Override + public long elementSize() { + return elementSize; + } + + @Override + public long physicalSize() { + // We simplify the computation by assuming that a reduced dimension takes the same amount of + // memory than the original one + return originalDimension.physicalSize(); + } + + @Override + public String toString() { + return String.valueOf(numElements()); + } + + ReducedDimension(Dimension originalDimension, long offset, long elementSize) { + this.originalDimension = originalDimension; + this.offset = offset; + this.elementSize = elementSize; + } + + private final Dimension originalDimension; + private final long offset; + private final long elementSize; +} diff --git a/ndarray/src/main/java/org/tensorflow/ndarray/impl/dimension/RelativeDimensionalSpace.java b/ndarray/src/main/java/org/tensorflow/ndarray/impl/dimension/RelativeDimensionalSpace.java new file mode 100644 index 00000000000..4259bbcf76e --- /dev/null +++ b/ndarray/src/main/java/org/tensorflow/ndarray/impl/dimension/RelativeDimensionalSpace.java @@ -0,0 +1,32 @@ +/* + Copyright 2019 The TensorFlow Authors. All Rights Reserved. + + Licensed under the Apache License, Version 2.0 (the "License"); + you may not use this file except in compliance with the License. + You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + + Unless required by applicable law or agreed to in writing, software + distributed under the License is distributed on an "AS IS" BASIS, + WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + See the License for the specific language governing permissions and + limitations under the License. + ======================================================================= + */ + +package org.tensorflow.ndarray.impl.dimension; + +public class RelativeDimensionalSpace extends DimensionalSpace { + + public long position() { + return position; + } + + RelativeDimensionalSpace(Dimension[] dimensions, int segmentationIdx, long position) { + super(dimensions, segmentationIdx); + this.position = position; + } + + private long position; +} diff --git a/ndarray/src/main/java/org/tensorflow/ndarray/impl/sequence/CoordinatesIncrementor.java b/ndarray/src/main/java/org/tensorflow/ndarray/impl/sequence/CoordinatesIncrementor.java new file mode 100644 index 00000000000..8c9c9f86f4c --- /dev/null +++ b/ndarray/src/main/java/org/tensorflow/ndarray/impl/sequence/CoordinatesIncrementor.java @@ -0,0 +1,38 @@ +/* + * Copyright 2020 The TensorFlow Authors. All Rights Reserved. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + * ======================================================================= + */ + +package org.tensorflow.ndarray.impl.sequence; + +final class CoordinatesIncrementor { + + boolean increment() { + for (int i = coords.length - 1; i >= 0; --i) { + if ((coords[i] = (coords[i] + 1) % shape[i]) > 0) { + return true; + } + } + return false; + } + + CoordinatesIncrementor(long[] shape, int dimensionIdx) { + this.shape = shape; + this.coords = new long[dimensionIdx + 1]; + } + + final long[] shape; + final long[] coords; +} diff --git a/ndarray/src/main/java/org/tensorflow/ndarray/impl/sequence/FastElementSequence.java b/ndarray/src/main/java/org/tensorflow/ndarray/impl/sequence/FastElementSequence.java new file mode 100644 index 00000000000..92cebeb2338 --- /dev/null +++ b/ndarray/src/main/java/org/tensorflow/ndarray/impl/sequence/FastElementSequence.java @@ -0,0 +1,81 @@ +/* + * Copyright 2020 The TensorFlow Authors. All Rights Reserved. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + * ======================================================================= + */ + +package org.tensorflow.ndarray.impl.sequence; + +import java.util.Iterator; +import java.util.function.BiConsumer; + +import org.tensorflow.ndarray.NdArray; +import org.tensorflow.ndarray.NdArraySequence; +import org.tensorflow.ndarray.buffer.DataBufferWindow; +import org.tensorflow.ndarray.impl.AbstractNdArray; + +/** + * A sequence recycling the same {@code NdArray} instance when iterating its elements + * + * @param Type of the elements + * @param Type of the {@code NdArray} with this sequence + */ +public final class FastElementSequence> implements NdArraySequence { + + public FastElementSequence(AbstractNdArray ndArray, int dimensionIdx, U element, DataBufferWindow elementWindow) { + this.ndArray = ndArray; + this.dimensionIdx = dimensionIdx; + this.element = element; + this.elementWindow = elementWindow; + } + + @Override + public Iterator iterator() { + return new SequenceIterator(); + } + + @Override + public void forEachIndexed(BiConsumer consumer) { + PositionIterator.createIndexed(ndArray.dimensions(), dimensionIdx).forEachIndexed((long[] coords, long position) -> { + elementWindow.slideTo(position); + consumer.accept(coords, element); + }); + } + + @Override + public NdArraySequence asSlices() { + return new SlicingElementSequence(ndArray, dimensionIdx); + } + + private class SequenceIterator implements Iterator { + + @Override + public boolean hasNext() { + return positionIterator.hasNext(); + } + + @Override + public U next() { + elementWindow.slideTo(positionIterator.nextLong()); + return element; + } + + private final PositionIterator positionIterator = PositionIterator.create(ndArray.dimensions(), dimensionIdx); + } + + private final AbstractNdArray ndArray; + private final int dimensionIdx; + private final U element; + private final DataBufferWindow elementWindow; +} diff --git a/ndarray/src/main/java/org/tensorflow/ndarray/impl/sequence/IndexedPositionIterator.java b/ndarray/src/main/java/org/tensorflow/ndarray/impl/sequence/IndexedPositionIterator.java new file mode 100644 index 00000000000..30ece1599b6 --- /dev/null +++ b/ndarray/src/main/java/org/tensorflow/ndarray/impl/sequence/IndexedPositionIterator.java @@ -0,0 +1,28 @@ +/* + * Copyright 2019 The TensorFlow Authors. All Rights Reserved. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + * ======================================================================= + */ + +package org.tensorflow.ndarray.impl.sequence; + +public interface IndexedPositionIterator extends PositionIterator { + + @FunctionalInterface + interface CoordsLongConsumer { + void consume(long[] coords, long position); + } + + void forEachIndexed(CoordsLongConsumer consumer); +} diff --git a/ndarray/src/main/java/org/tensorflow/ndarray/impl/sequence/IndexedSequentialPositionIterator.java b/ndarray/src/main/java/org/tensorflow/ndarray/impl/sequence/IndexedSequentialPositionIterator.java new file mode 100644 index 00000000000..80b3de681bd --- /dev/null +++ b/ndarray/src/main/java/org/tensorflow/ndarray/impl/sequence/IndexedSequentialPositionIterator.java @@ -0,0 +1,51 @@ +/* + * Copyright 2019 The TensorFlow Authors. All Rights Reserved. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + * ======================================================================= + */ + +package org.tensorflow.ndarray.impl.sequence; + +import org.tensorflow.ndarray.impl.dimension.DimensionalSpace; + +class IndexedSequentialPositionIterator extends SequentialPositionIterator implements IndexedPositionIterator { + + @Override + public void forEachIndexed(CoordsLongConsumer consumer) { + while (hasNext()) { + consumer.consume(coords, nextLong()); + incrementCoords(); + } + } + + private void incrementCoords() { + for (int i = coords.length - 1; i >= 0; --i) { + if (coords[i] < shape[i] - 1) { + coords[i] += 1L; + return; + } + coords[i] = 0L; + } + } + + IndexedSequentialPositionIterator(DimensionalSpace dimensions, int dimensionIdx) { + super(dimensions, dimensionIdx); + this.shape = dimensions.shape().asArray(); + this.coords = new long[dimensionIdx + 1]; + //this.coordsIncrementor = new CoordinatesIncrementor(dimensions.shape().asArray(), dimensionIdx); + } + + private final long[] shape; + private final long[] coords; +} diff --git a/ndarray/src/main/java/org/tensorflow/ndarray/impl/sequence/NdPositionIterator.java b/ndarray/src/main/java/org/tensorflow/ndarray/impl/sequence/NdPositionIterator.java new file mode 100644 index 00000000000..789474c58ae --- /dev/null +++ b/ndarray/src/main/java/org/tensorflow/ndarray/impl/sequence/NdPositionIterator.java @@ -0,0 +1,70 @@ +/* + * Copyright 2019 The TensorFlow Authors. All Rights Reserved. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + * ======================================================================= + */ + +package org.tensorflow.ndarray.impl.sequence; + +import java.util.NoSuchElementException; +import org.tensorflow.ndarray.impl.dimension.DimensionalSpace; + +class NdPositionIterator implements IndexedPositionIterator { + + @Override + public boolean hasNext() { + return coords != null; + } + + @Override + public long nextLong() { + if (!hasNext()) { + throw new NoSuchElementException(); + } + long position = dimensions.positionOf(coords); + increment(); + return position; + } + + @Override + public void forEachIndexed(CoordsLongConsumer consumer) { + while (hasNext()) { + consumer.consume(coords, dimensions.positionOf(coords)); + increment(); + } + } + + private void increment() { + if (!increment(coords, dimensions)) { + coords = null; + } + } + + static boolean increment(long[] coords, DimensionalSpace dimensions) { + for (int i = coords.length - 1; i >= 0; --i) { + if ((coords[i] = (coords[i] + 1) % dimensions.get(i).numElements()) > 0) { + return true; + } + } + return false; + } + + NdPositionIterator(DimensionalSpace dimensions, int dimensionIdx) { + this.dimensions = dimensions; + this.coords = new long[dimensionIdx + 1]; + } + + private final DimensionalSpace dimensions; + private long[] coords; +} diff --git a/ndarray/src/main/java/org/tensorflow/ndarray/impl/sequence/PositionIterator.java b/ndarray/src/main/java/org/tensorflow/ndarray/impl/sequence/PositionIterator.java new file mode 100644 index 00000000000..83ed940563c --- /dev/null +++ b/ndarray/src/main/java/org/tensorflow/ndarray/impl/sequence/PositionIterator.java @@ -0,0 +1,42 @@ +/* + * Copyright 2019 The TensorFlow Authors. All Rights Reserved. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + * ======================================================================= + */ + +package org.tensorflow.ndarray.impl.sequence; + +import java.util.PrimitiveIterator; +import org.tensorflow.ndarray.impl.dimension.DimensionalSpace; + +public interface PositionIterator extends PrimitiveIterator.OfLong { + + static PositionIterator create(DimensionalSpace dimensions, int dimensionIdx) { + if (dimensions.isSegmented()) { + return new NdPositionIterator(dimensions, dimensionIdx); + } + return new SequentialPositionIterator(dimensions, dimensionIdx); + } + + static IndexedPositionIterator createIndexed(DimensionalSpace dimensions, int dimensionIdx) { + if (dimensions.isSegmented()) { + return new NdPositionIterator(dimensions, dimensionIdx); + } + return new IndexedSequentialPositionIterator(dimensions, dimensionIdx); + } + + static PositionIterator sequence(long stride, long end) { + return new SequentialPositionIterator(stride, end); + } +} diff --git a/ndarray/src/main/java/org/tensorflow/ndarray/impl/sequence/SequentialPositionIterator.java b/ndarray/src/main/java/org/tensorflow/ndarray/impl/sequence/SequentialPositionIterator.java new file mode 100644 index 00000000000..65c6fc966cc --- /dev/null +++ b/ndarray/src/main/java/org/tensorflow/ndarray/impl/sequence/SequentialPositionIterator.java @@ -0,0 +1,55 @@ +/* + * Copyright 2019 The TensorFlow Authors. All Rights Reserved. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + * ======================================================================= + */ + +package org.tensorflow.ndarray.impl.sequence; + +import java.util.NoSuchElementException; +import org.tensorflow.ndarray.impl.dimension.DimensionalSpace; + +class SequentialPositionIterator implements PositionIterator { + + @Override + public boolean hasNext() { + return index < end; + } + + @Override + public long nextLong() { + if (!hasNext()) { + throw new NoSuchElementException(); + } + return stride * index++; + } + + SequentialPositionIterator(DimensionalSpace dimensions, int dimensionIdx) { + long size = 1; + for (int i = 0; i <= dimensionIdx; ++i) { + size *= dimensions.get(i).numElements(); + } + this.stride = dimensions.get(dimensionIdx).elementSize(); + this.end = size; + } + + SequentialPositionIterator(long stride, long end) { + this.stride = stride; + this.end = end; + } + + private final long stride; + private final long end; + private long index; +} diff --git a/ndarray/src/main/java/org/tensorflow/ndarray/impl/sequence/SingleElementSequence.java b/ndarray/src/main/java/org/tensorflow/ndarray/impl/sequence/SingleElementSequence.java new file mode 100644 index 00000000000..59525bf486b --- /dev/null +++ b/ndarray/src/main/java/org/tensorflow/ndarray/impl/sequence/SingleElementSequence.java @@ -0,0 +1,71 @@ +/* + * Copyright 2019 The TensorFlow Authors. All Rights Reserved. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + * ======================================================================= + */ + +package org.tensorflow.ndarray.impl.sequence; + +import java.util.Iterator; +import java.util.function.BiConsumer; +import org.tensorflow.ndarray.IllegalRankException; +import org.tensorflow.ndarray.NdArray; +import org.tensorflow.ndarray.NdArraySequence; +import org.tensorflow.ndarray.impl.AbstractNdArray; + +/** + * A sequence of one single element + * + * @param Type of the element + * @param Type of the {@code NdArray} with this sequence + */ +public final class SingleElementSequence> implements NdArraySequence { + + public SingleElementSequence(AbstractNdArray ndArray) { + this.ndArray = ndArray; + } + + @Override + public Iterator iterator() { + return new Iterator() { + + @Override + public boolean hasNext() { + return element != null; + } + + @Override + public U next() { + U ret = element; + element = null; + return ret; + } + + @SuppressWarnings("unchecked") + private U element = (U)ndArray; + }; + } + + @Override + public NdArraySequence asSlices() { + return this; // no need to slice, as there are only one element + } + + @Override + public void forEachIndexed(BiConsumer consumer) { + throw new IllegalRankException("Single element has no coordinates to iterate on, use forEach()"); + } + + private final AbstractNdArray ndArray; +} diff --git a/ndarray/src/main/java/org/tensorflow/ndarray/impl/sequence/SlicingElementSequence.java b/ndarray/src/main/java/org/tensorflow/ndarray/impl/sequence/SlicingElementSequence.java new file mode 100644 index 00000000000..6fe8398ea70 --- /dev/null +++ b/ndarray/src/main/java/org/tensorflow/ndarray/impl/sequence/SlicingElementSequence.java @@ -0,0 +1,77 @@ +/* + * Copyright 2019 The TensorFlow Authors. All Rights Reserved. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + * ======================================================================= + */ + +package org.tensorflow.ndarray.impl.sequence; + +import java.util.Iterator; +import java.util.function.BiConsumer; +import org.tensorflow.ndarray.NdArray; +import org.tensorflow.ndarray.NdArraySequence; +import org.tensorflow.ndarray.impl.AbstractNdArray; +import org.tensorflow.ndarray.impl.dimension.DimensionalSpace; + +/** + * A sequence creating a new {@code NdArray} instance (slice) for each element of an iteration + * + * @param Type of the element + * @param Type of the {@code NdArray} with this sequence + */ +public final class SlicingElementSequence> implements NdArraySequence { + + public SlicingElementSequence(AbstractNdArray ndArray, int dimensionIdx) { + this(ndArray, dimensionIdx, ndArray.dimensions().from(dimensionIdx + 1)); + } + + public SlicingElementSequence(AbstractNdArray ndArray, int dimensionIdx, DimensionalSpace elementDimensions) { + this.ndArray = ndArray; + this.dimensionIdx = dimensionIdx; + this.elementDimensions = elementDimensions; + } + + @Override + public Iterator iterator() { + PositionIterator positionIterator = PositionIterator.create(ndArray.dimensions(), dimensionIdx); + return new Iterator() { + + @Override + public boolean hasNext() { + return positionIterator.hasNext(); + } + + @Override + public U next() { + return ndArray.slice(positionIterator.next(), elementDimensions); + } + }; + } + + @Override + public void forEachIndexed(BiConsumer consumer) { + PositionIterator.createIndexed(ndArray.dimensions(), dimensionIdx).forEachIndexed((long[] coords, long position) -> + consumer.accept(coords, ndArray.slice(position, elementDimensions)) + ); + } + + @Override + public NdArraySequence asSlices() { + return this; + } + + private final AbstractNdArray ndArray; + private final int dimensionIdx; + private final DimensionalSpace elementDimensions; +} diff --git a/ndarray/src/main/java/org/tensorflow/ndarray/index/All.java b/ndarray/src/main/java/org/tensorflow/ndarray/index/All.java new file mode 100644 index 00000000000..9d3139f3248 --- /dev/null +++ b/ndarray/src/main/java/org/tensorflow/ndarray/index/All.java @@ -0,0 +1,57 @@ +/* + Copyright 2019 The TensorFlow Authors. All Rights Reserved. + + Licensed under the Apache License, Version 2.0 (the "License"); + you may not use this file except in compliance with the License. + You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + + Unless required by applicable law or agreed to in writing, software + distributed under the License is distributed on an "AS IS" BASIS, + WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + See the License for the specific language governing permissions and + limitations under the License. + ======================================================================= + */ +package org.tensorflow.ndarray.index; + +import org.tensorflow.ndarray.impl.dimension.Dimension; + +final class All implements Index { + + static final All INSTANCE = new All(); + + @Override + public long numElements(Dimension dim) { + return dim.numElements(); + } + + @Override + public long mapCoordinate(long coordinate, Dimension dim) { + return coordinate; + } + + @Override + public Dimension apply(Dimension dim) { + return dim; + } + + private All() { + } + + @Override + public boolean beginMask() { + return true; + } + + @Override + public boolean endMask() { + return true; + } + + @Override + public String toString() { + return All.class.getSimpleName() + "()"; + } +} diff --git a/ndarray/src/main/java/org/tensorflow/ndarray/index/At.java b/ndarray/src/main/java/org/tensorflow/ndarray/index/At.java new file mode 100644 index 00000000000..31ce021ddc8 --- /dev/null +++ b/ndarray/src/main/java/org/tensorflow/ndarray/index/At.java @@ -0,0 +1,74 @@ +/* + Copyright 2019 The TensorFlow Authors. All Rights Reserved. + + Licensed under the Apache License, Version 2.0 (the "License"); + you may not use this file except in compliance with the License. + You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + + Unless required by applicable law or agreed to in writing, software + distributed under the License is distributed on an "AS IS" BASIS, + WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + See the License for the specific language governing permissions and + limitations under the License. + ======================================================================= + */ +package org.tensorflow.ndarray.index; + +import java.util.StringJoiner; +import org.tensorflow.ndarray.impl.dimension.Dimension; + +final class At implements Index { + + @Override + public long numElements(Dimension dim) { + return 1; + } + + @Override + public long mapCoordinate(long coordinate, Dimension dim) { + long coord = this.coord >= 0 ? this.coord : dim.numElements() + this.coord; + return dim.positionOf(coord); + } + + @Override + public Dimension apply(Dimension dim) { + if (!keepDim) { + throw new UnsupportedOperationException("Should be handled in DimensionalSpace."); + } + + return dim.withIndex(this); + } + + @Override + public boolean isPoint() { + return !keepDim; + } + + At(long coord, boolean keepDim) { + this.coord = coord; + this.keepDim = keepDim; + } + + private final long coord; + private final boolean keepDim; + + @Override + public long begin() { + return coord; + } + + @Override + public long end() { + return coord + 1; + } + + @Override + public String toString() { + return new StringJoiner(", ", At.class.getSimpleName() + "(", ")") + .add("coord=" + coord) + .add("keepDim=" + keepDim) + .toString(); + } +} diff --git a/ndarray/src/main/java/org/tensorflow/ndarray/index/Ellipsis.java b/ndarray/src/main/java/org/tensorflow/ndarray/index/Ellipsis.java new file mode 100644 index 00000000000..d4085735df2 --- /dev/null +++ b/ndarray/src/main/java/org/tensorflow/ndarray/index/Ellipsis.java @@ -0,0 +1,48 @@ +/* + Copyright 2020 The TensorFlow Authors. All Rights Reserved. + + Licensed under the Apache License, Version 2.0 (the "License"); + you may not use this file except in compliance with the License. + You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + + Unless required by applicable law or agreed to in writing, software + distributed under the License is distributed on an "AS IS" BASIS, + WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + See the License for the specific language governing permissions and + limitations under the License. + ============================================================================== + */ +package org.tensorflow.ndarray.index; + +import org.tensorflow.ndarray.impl.dimension.Dimension; + +final class Ellipsis implements Index { + + static final Ellipsis INSTANCE = new Ellipsis(); + + private Ellipsis() { + + } + + @Override + public long numElements(Dimension dim) { + throw new UnsupportedOperationException("Should be handled in DimensionalSpace."); + } + + @Override + public long mapCoordinate(long coordinate, Dimension dim) { + throw new UnsupportedOperationException("Should be handled in DimensionalSpace."); + } + + @Override + public boolean isEllipsis() { + return true; + } + + @Override + public String toString() { + return Ellipsis.class.getSimpleName() + "()"; + } +} diff --git a/ndarray/src/main/java/org/tensorflow/ndarray/index/Hyperslab.java b/ndarray/src/main/java/org/tensorflow/ndarray/index/Hyperslab.java new file mode 100644 index 00000000000..55c4e510748 --- /dev/null +++ b/ndarray/src/main/java/org/tensorflow/ndarray/index/Hyperslab.java @@ -0,0 +1,90 @@ +/* + * Copyright 2020 Matteo Di Giovinazzo. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package org.tensorflow.ndarray.index; + +import java.util.StringJoiner; +import org.tensorflow.ndarray.impl.dimension.Dimension; + +/** + * A hyperslab is a rectangular pattern defined by four arrays. + * + * The {@code start} defines the origin of the hyperslab in the original coordinates. + * The {@code stride} is the number of elements to increment between selected elements. + * A stride of '1' is every element, a stride of '2' is every second element, etc. + * The default stride is 1. + * The {@code count} is the number of elements in the hyperslab selection. + * When the stride is 1, the selection is a hyper rectangle with a corner at {@code start} + * and size {@code count[0]} by {@code count[1]} by ... + * When stride is greater than one, the hyperslab bounded by start and the corners + * defined by {@code stride[n] * count[n]}. + * The {@code block} is a count on the number of repetitions of the hyperslab. + * The default block size is '1', which is one hyperslab. A block of 2 would be + * two hyperslabs in that dimension, with the second starting at {@code start[n]+ (count[n] * stride[n]) + 1}. + * + * @see https://portal.hdfgroup.org/display/HDF5/Reading+From+or+Writing+To+a+Subset+of+a+Dataset + * @see https://portal.hdfgroup.org/display/HDF5/H5S_SELECT_HYPERSLAB + * @see https://support.hdfgroup.org/HDF5/doc1.6/UG/12_Dataspaces.html + * @author Matteo Di Giovinazzo + */ +final class Hyperslab implements Index { + + @Override + public long numElements(Dimension dimension) { + return count * block; + } + + @Override + public long mapCoordinate(long coordinate, Dimension dimension) { + return start + stride * (coordinate / block) + (coordinate % block); + } + + @Override + public Dimension apply(Dimension dim) { + return dim.withIndex(this); + } + + @Override + public boolean isPoint() { + return false; + } + + Hyperslab(long start, long stride, long count, long block) { + this.start = start; + this.stride = stride; + this.count = count; + this.block = block; + } + + private final long start; + private final long stride; + private final long count; + private final long block; + + @Override + public String toString() { + return new StringJoiner(", ", Hyperslab.class.getSimpleName() + "Hyperslab(", ")") + .add("start=" + start) + .add("stride=" + stride) + .add("count=" + count) + .add("block=" + block) + .toString(); + } + + @Override + public boolean isStridedSlicingCompliant() { + return false; + } +} diff --git a/ndarray/src/main/java/org/tensorflow/ndarray/index/Index.java b/ndarray/src/main/java/org/tensorflow/ndarray/index/Index.java new file mode 100644 index 00000000000..617ca4d474b --- /dev/null +++ b/ndarray/src/main/java/org/tensorflow/ndarray/index/Index.java @@ -0,0 +1,131 @@ +/* + Copyright 2019 The TensorFlow Authors. All Rights Reserved. + + Licensed under the Apache License, Version 2.0 (the "License"); + you may not use this file except in compliance with the License. + You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + + Unless required by applicable law or agreed to in writing, software + distributed under the License is distributed on an "AS IS" BASIS, + WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + See the License for the specific language governing permissions and + limitations under the License. + ======================================================================= + */ +package org.tensorflow.ndarray.index; + +import org.tensorflow.ndarray.NdArray; +import org.tensorflow.ndarray.impl.dimension.Dimension; + +/** + * An index used for slicing a view out of an N-dimensional array. + * + *

A slice, i.e. a reduced view, of an N-dimensional array is obtain by calling + * {@link NdArray#slice(Index...)}, given a list of indices that select which elements on a given dimension should be + * included/excluded from that view. + */ +public interface Index { + + /** + * Returns the number of elements that can be retrieved using this index on the given dimension. + * + *

An index that maps one-by-one all elements of the dimensions will return a value + * equal to {@code dim.numElements()}, while an index that only maps a subset of these will return a smaller value. + * + * @param dim the indexed dimension + * @return number of elements accessible + */ + long numElements(Dimension dim); + + /** + * Transforms an element coordinate to a new coordinate by applying this index to the given dimension. + * + *

For example, if the coordinate is 0 and this index flips the {@code n} elements on this + * dimension, then the returned value will be {@code n-1}. + * + * @param coordinate coordinate to transform + * @param dim dimension the indexed dimension + * @return transformed coordinate + */ + long mapCoordinate(long coordinate, Dimension dim); + + /** + * Applies this index to the given dimension. + * + *

When accessing the elements from the returned dimension, this index will automatically + * apply and may transform the original position. + * + * @param dim dimension to apply this index to + * @return an indexed dimension + */ + default Dimension apply(Dimension dim) { + return dim.withIndex(this); + } + + /** + * Returns true if this index is a single point, reducing the number of dimensions by one + */ + default boolean isPoint() { + return false; + } + + /** + * Returns true if this index is a new axis, adding a dimension of size 1 + */ + default boolean isNewAxis() { + return false; + } + + /** + * Returns true if this index is an ellipsis, expanding to take as many dimensions as possible (and applying all() to + * them) + */ + default boolean isEllipsis() { + return false; + } + + /** + * Get whether the Index supports strided slice style indexing (using start, end, stride, and flags, i.e. TensorFlow's). + */ + default boolean isStridedSlicingCompliant() { + return true; + } + + /** + * Get the start of the index, for strided slice style indexing. + */ + default long begin() { + return 0; + } + + /** + * Get the end of the index, strided slice style indexing. + */ + default long end() { + return 0; + } + + /** + * Get the stride of the index, for strided slice style indexing. + */ + default long stride() { + return 1; + } + + /** + * Get whether the Index should start at the beginning of the dimension, for strided slice style indexing. + */ + default boolean beginMask() { + return false; + } + + /** + * Get whether the Index should end at the beginning of the dimension, for strided slice style indexing. + */ + default boolean endMask() { + return false; + } +} diff --git a/ndarray/src/main/java/org/tensorflow/ndarray/index/Indices.java b/ndarray/src/main/java/org/tensorflow/ndarray/index/Indices.java new file mode 100644 index 00000000000..346ab705595 --- /dev/null +++ b/ndarray/src/main/java/org/tensorflow/ndarray/index/Indices.java @@ -0,0 +1,363 @@ +/* + Copyright 2019 The TensorFlow Authors. All Rights Reserved. + + Licensed under the Apache License, Version 2.0 (the "License"); + you may not use this file except in compliance with the License. + You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + + Unless required by applicable law or agreed to in writing, software + distributed under the License is distributed on an "AS IS" BASIS, + WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + See the License for the specific language governing permissions and + limitations under the License. + ======================================================================= + */ +package org.tensorflow.ndarray.index; + +import org.tensorflow.ndarray.IllegalRankException; +import org.tensorflow.ndarray.NdArray; +import org.tensorflow.ndarray.NdArrays; +import org.tensorflow.ndarray.Shape; +import org.tensorflow.ndarray.buffer.DataBuffers; + +/** + * Helper class for instantiating {@link Index} objects. + */ +public final class Indices { + + /** + * A coordinate that selects a specific element on a given dimension. + * + *

When this index is applied to a given dimension, the dimension is resolved as a + * single element and therefore is excluded from the computation of the rank. + * + *

For example, given a 3D matrix on the axis [x, y, z], if + * {@code matrix.slice(all(), at(0), at(0)}, then the rank of the returned slice is 1 and its number of elements is + * {@code x.numElements()} + * + * @param coord coordinate of the element on the indexed axis + * @return index + */ + public static Index at(long coord) { + return new At(coord, false); + } + + /** + * A coordinate that selects a specific element on a given dimension. + * + *

This is equivalent to call {@link #at(long)} but where the value of the coordinate is + * provided by an N-dimensional array. + * + * @param coord scalar indicating the coordinate of the element on the indexed axis + * @return index + * @throws IllegalRankException if {@code coord} is not a scalar (rank 0) + */ + public static Index at(NdArray coord) { + if (coord.rank() > 0) { + throw new IllegalRankException("Only scalars are accepted as a value index"); + } + return new At(coord.getObject().longValue(), false); + } + + /** + * A coordinate that selects a specific element on a given dimension. + * + *

When this index is applied to a given dimension, the dimension is resolved as a + * single element and therefore, if {@code keepDim} is false, is excluded from the computation of the rank. If {@code} + * keepDim is true, the dimension is collapsed down to one element. + * + *

For example, given a 3D matrix on the axis [x, y, z], if + * {@code matrix.slice(all(), at(0), at(0)}, then the rank of the returned slice is 1 and its number of elements is + * {@code x.numElements()} + * + * @param coord coordinate of the element on the indexed axis + * @param keepDim whether to remove the dimension. + * @return index + */ + public static Index at(long coord, boolean keepDim) { + return new At(coord, keepDim); + } + + /** + * A coordinate that selects a specific element on a given dimension. + * + *

This is equivalent to call {@link #at(long, boolean)} but where the value of the coordinate is + * provided by an N-dimensional array. + *

+ * If {@code} keepDim is true, the dimension is collapsed down to one element instead of being removed. + * + * @param coord scalar indicating the coordinate of the element on the indexed axis + * @param keepDim whether to remove the dimension. + * @return index + * @throws IllegalRankException if {@code coord} is not a scalar (rank 0) + */ + public static Index at(NdArray coord, boolean keepDim) { + if (coord.rank() > 0) { + throw new IllegalRankException("Only scalars are accepted as a value index"); + } + return new At(coord.getObject().longValue(), keepDim); + } + + /** + * An index that returns all elements of a dimension in the original order. + * + *

Applying this index to a given dimension will return the original dimension + * directly. + * + *

For example, given a vector with {@code n} elements, {@code all()} returns + * x0, x1, ..., xn-1 + * + * @return index + */ + public static Index all() { + return All.INSTANCE; + } + + /** + * An index that returns only specific elements on a given dimension. + * + *

For example, given a vector with {@code n} elements on the {@code x} axis, and {@code n > 10}, + * {@code seq(8, 0, 3)} returns x8, x0, x3 + * + * @param coords coordinates of the elements in the sequence + * @return index + */ + public static Index seq(long... coords) { + if (coords == null) { + throw new IllegalArgumentException(); + } + return new Sequence(NdArrays.wrap(Shape.of(coords.length), DataBuffers.of(coords, true, false))); + } + + /** + * An index that returns only specific elements on a given dimension. + * + *

This is equivalent to {@link #seq(long...)} but where the coordinates of the elements in + * the sequence are provided by an N-dimensional array. + * + * @param coords vector of coordinates of the elements in the sequence + * @return index + * @throws IllegalRankException if {@code coords} is not a vector (rank 1) + */ + public static Index seq(NdArray coords) { + if (coords.rank() != 1) { + throw new IllegalRankException("Only vectors are accepted as an element index"); + } + return new Sequence(coords); + } + + /** + * An index that returns only elements found at an even position in the original dimension. + * + *

For example, given a vector with {@code n} elements on the {@code x} axis, and n is even, + * {@code even()} returns x0, x2, ..., xn-2 + * + * @return index + */ + public static Index even() { + return step(2); + } + + /** + * An index that returns only elements found at an odd position in the original dimension. + * + *

For example, given a vector with {@code n} elements on the {@code x} axis, and n is even, + * {@code odd()} returns x1, x3, ..., xn-1 + * + * @return index + */ + public static Index odd() { + return sliceFrom(1, 2); + } + + /** + * An index that skips a fixed amount of coordinates between each values returned. + * + *

For example, given a vector with {@code n} elements on the {@code x} axis, + * {@code step(k)} returns x0, xk, xk*2, ... + * + * @param stride the number of elements between each steps + * @return index + */ + public static Index step(long stride) { + return new Step(stride); + } + + /** + * An index that returns only elements on a given dimension starting at a specific coordinate. + * + *

For example, given a vector with {@code n} elements on the {@code x} axis, and {@code n > k}, + * {@code from(k)} returns xk, xk+1, ..., xn-1 + * + * @param start coordinate of the first element of the sequence + * @return index + */ + public static Index sliceFrom(long start) { + return sliceFrom(start, 1); + } + + /** + * An index that returns only elements on a given dimension starting at a specific coordinate, using the given + * stride. + * + *

For example, given a vector with {@code n} elements on the {@code x} axis, and {@code n > k}, + * {@code from(k)} returns xk, xk+1, ..., xn-1 + * + * @param start coordinate of the first element of the sequence + * @param stride the stride to use + * @return index + * @see #slice(long, long, long) + */ + public static Index sliceFrom(long start, long stride) { + return new SliceFrom(start, stride); + } + + /** + * An index that returns only elements on a given dimension up to a specific coordinate. + * + *

For example, given a vector with {@code n} elements on the {@code x} axis, and {@code n > k}, + * {@code to(k)} returns x0, x1, ..., xk + * + * @param end coordinate of the last element of the sequence (exclusive) + * @return index + */ + public static Index sliceTo(long end) { + return sliceTo(end, 1); + } + + /** + * An index that returns only elements on a given dimension up to a specific coordinate, using the given stride. + * + *

For example, given a vector with {@code n} elements on the {@code x} axis, and {@code n > k}, + * {@code to(k)} returns x0, x1, ..., xk + * + * @param end coordinate of the last element of the sequence (exclusive) + * @param stride the stride to use + * @return index + * @see #slice(long, long, long) + */ + public static Index sliceTo(long end, long stride) { + return new SliceTo(end, stride); + } + + /** + * An index that returns only elements on a given dimension between two coordinates. + * + *

For example, given a vector with {@code n} elements on the {@code x} axis, and {@code n > k > j}, + * {@code range(j, k)} returns xj, xj+1, ..., xk + * + * @param start coordinate of the first element of the sequence + * @param end coordinate of the last element of the sequence (exclusive) + * @return index + */ + public static Index range(long start, long end) { + return slice(start, end); + } + + /** + * An index that returns only elements on a given dimension between two coordinates. + * + *

For example, given a vector with {@code n} elements on the {@code x} axis, and {@code n > k > j}, + * {@code range(j, k)} returns xj, xj+1, ..., xk + * + * @return index + */ + public static Index flip() { + return slice(null, null, -1); + } + + /** + * An index that returns elements according to an hyperslab defined by {@code start}, {@code stride}, {@code count}, + * {@code block}. See {@link Hyperslab}. + * + * @param start Starting location for the hyperslab. + * @param stride The number of elements to separate each element or block to be selected. + * @param count The number of elements or blocks to select along the dimension. + * @param block The size of the block selected from the dimension. + * @return index + */ + public static Index hyperslab(long start, long stride, long count, long block) { + return new Hyperslab(start, stride, count, block); + } + + /** + * An index that inserts a new dimension of size 1 into the resulting array. + * + * @return index + */ + public static Index newAxis() { + return NewAxis.INSTANCE; + } + + /** + * An index that expands to fill all available source dimensions. Works the same as Python's {@code ...}. + * + * @return index + */ + public static Index ellipsis() { + return Ellipsis.INSTANCE; + } + + /** + * An index that returns elements between {@code start} and {@code end}. If {@code start} or {@code end} is {@code + * null}, starts or ends at the beginning or the end, respectively. + *

+ * Analogous to Python's {@code :} slice syntax. + * + * @return index + */ + public static Index slice(long start, long end) { + return slice(start, end, 1); + } + + /** + * An index that returns every {@code stride}-th element between {@code start} and {@code end}. If {@code start} or + * {@code end} is {@code null}, starts or ends at the beginning or the end, respectively. + *

+ * Analogous to Python's {@code :} slice syntax. + * + * @return index + */ + public static Index slice(long start, long end, long stride) { + return new Slice(start, end, stride); + } + + /** + * An index that returns elements between {@code start} and {@code end}. If {@code start} or {@code end} is {@code + * null}, starts or ends at the beginning or the end, respectively. + *

+ * Analogous to Python's {@code :} slice syntax. + * + * @return index + */ + public static Index slice(Long start, Long end) { + return slice(start, end, 1); + } + + /** + * An index that returns every {@code stride}-th element between {@code start} and {@code end}. If {@code start} or + * {@code end} is {@code null}, starts or ends at the beginning or the end, respectively. + *

+ * Analogous to Python's {@code :} slice syntax. + * + * @return index + */ + public static Index slice(Long start, Long end, long stride) { + if (start == null && end == null) { + if (stride == 1) { + return Indices.all(); + } else { + return Indices.step(stride); + } + } else if (start == null) { + return Indices.sliceTo(end, stride); + } else if (end == null) { + return Indices.sliceFrom(start, stride); + } + + return slice(start.longValue(), end.longValue(), stride); + } + +} diff --git a/ndarray/src/main/java/org/tensorflow/ndarray/index/NewAxis.java b/ndarray/src/main/java/org/tensorflow/ndarray/index/NewAxis.java new file mode 100644 index 00000000000..a68b1ed9ad1 --- /dev/null +++ b/ndarray/src/main/java/org/tensorflow/ndarray/index/NewAxis.java @@ -0,0 +1,53 @@ +/* + Copyright 2020 The TensorFlow Authors. All Rights Reserved. + + Licensed under the Apache License, Version 2.0 (the "License"); + you may not use this file except in compliance with the License. + You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + + Unless required by applicable law or agreed to in writing, software + distributed under the License is distributed on an "AS IS" BASIS, + WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + See the License for the specific language governing permissions and + limitations under the License. + ============================================================================== + */ +package org.tensorflow.ndarray.index; + +import org.tensorflow.ndarray.impl.dimension.Dimension; + +final class NewAxis implements Index { + + static final NewAxis INSTANCE = new NewAxis(); + + private NewAxis() { + + } + + @Override + public long numElements(Dimension dim) { + return 1; + } + + @Override + public long mapCoordinate(long coordinate, Dimension dim) { + return coordinate; + } + + @Override + public Dimension apply(Dimension dim) { + throw new IllegalStateException(); + } + + @Override + public boolean isNewAxis() { + return true; + } + + @Override + public String toString() { + return NewAxis.class.getSimpleName() + "()"; + } +} diff --git a/ndarray/src/main/java/org/tensorflow/ndarray/index/Sequence.java b/ndarray/src/main/java/org/tensorflow/ndarray/index/Sequence.java new file mode 100644 index 00000000000..5b93e434e54 --- /dev/null +++ b/ndarray/src/main/java/org/tensorflow/ndarray/index/Sequence.java @@ -0,0 +1,52 @@ +/* + Copyright 2019 The TensorFlow Authors. All Rights Reserved. + + Licensed under the Apache License, Version 2.0 (the "License"); + you may not use this file except in compliance with the License. + You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + + Unless required by applicable law or agreed to in writing, software + distributed under the License is distributed on an "AS IS" BASIS, + WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + See the License for the specific language governing permissions and + limitations under the License. + ======================================================================= + */ +package org.tensorflow.ndarray.index; + +import java.util.StringJoiner; +import org.tensorflow.ndarray.NdArray; +import org.tensorflow.ndarray.impl.dimension.Dimension; + +final class Sequence implements Index { + + @Override + public long numElements(Dimension dim) { + return coords.size(); + } + + @Override + public long mapCoordinate(long coordinate, Dimension dim) { + return coords.getObject(coordinate).longValue(); + } + + Sequence(NdArray coords) { + this.coords = coords; + } + + private final NdArray coords; + + @Override + public String toString() { + return new StringJoiner(", ", Sequence.class.getSimpleName() + "(", ")") + .add("coords=" + coords) + .toString(); + } + + @Override + public boolean isStridedSlicingCompliant() { + return false; + } +} diff --git a/ndarray/src/main/java/org/tensorflow/ndarray/index/Slice.java b/ndarray/src/main/java/org/tensorflow/ndarray/index/Slice.java new file mode 100644 index 00000000000..1be4368261c --- /dev/null +++ b/ndarray/src/main/java/org/tensorflow/ndarray/index/Slice.java @@ -0,0 +1,89 @@ +/* + Copyright 2020 The TensorFlow Authors. All Rights Reserved. + + Licensed under the Apache License, Version 2.0 (the "License"); + you may not use this file except in compliance with the License. + You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + + Unless required by applicable law or agreed to in writing, software + distributed under the License is distributed on an "AS IS" BASIS, + WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + See the License for the specific language governing permissions and + limitations under the License. + ============================================================================== + */ +package org.tensorflow.ndarray.index; + +import java.util.StringJoiner; +import org.tensorflow.ndarray.impl.dimension.Dimension; + +final class Slice implements Index { + + Slice(long start, long end, long stride) { + this.start = start; + this.end = end; + this.stride = stride; + + if (stride == 0) { + throw new IllegalArgumentException("Can not have a stride of 0"); + } + } + + @Override + public long numElements(Dimension dim) { + long length = end(dim) - start(dim); + + return (length / stride) + (length % stride != 0 ? 1 : 0); + } + + @Override + public long mapCoordinate(long coordinate, Dimension dim) { + return start(dim) + stride * coordinate; + } + + @Override + public long begin() { + return start; + } + + @Override + public long end() { + return end; + } + + @Override + public long stride() { + return stride; + } + + @Override + public String toString() { + return new StringJoiner(", ", Slice.class.getSimpleName() + "(", ")") + .add("start=" + start) + .add("end=" + end) + .add("stride=" + stride) + .toString(); + } + + private long start(Dimension dim) { + if (start < 0) { + return dim.numElements() + start; + } + + return start; + } + + private long end(Dimension dim) { + if (end < 0) { + return dim.numElements() + end; + } else { + return end; + } + } + + private final long start; + private final long end; + private final long stride; +} diff --git a/ndarray/src/main/java/org/tensorflow/ndarray/index/SliceFrom.java b/ndarray/src/main/java/org/tensorflow/ndarray/index/SliceFrom.java new file mode 100644 index 00000000000..c968a325cf7 --- /dev/null +++ b/ndarray/src/main/java/org/tensorflow/ndarray/index/SliceFrom.java @@ -0,0 +1,86 @@ +/* + Copyright 2020 The TensorFlow Authors. All Rights Reserved. + + Licensed under the Apache License, Version 2.0 (the "License"); + you may not use this file except in compliance with the License. + You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + + Unless required by applicable law or agreed to in writing, software + distributed under the License is distributed on an "AS IS" BASIS, + WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + See the License for the specific language governing permissions and + limitations under the License. + ============================================================================== + */ +package org.tensorflow.ndarray.index; + +import java.util.StringJoiner; +import org.tensorflow.ndarray.impl.dimension.Dimension; + +final class SliceFrom implements Index { + + SliceFrom(long start, long stride) { + this.start = start; + this.stride = stride; + + if (stride == 0) { + throw new IllegalArgumentException("Can not have a stride of 0"); + } + } + + @Override + public long numElements(Dimension dim) { + long length = end(dim) - start(dim); + + return (length / stride) + (length % stride != 0 ? 1 : 0); + } + + @Override + public long mapCoordinate(long coordinate, Dimension dim) { + return start(dim) + stride * coordinate; + } + + @Override + public long begin() { + return start; + } + + @Override + public boolean endMask() { + return true; + } + + @Override + public long stride() { + return stride; + } + + @Override + public String toString() { + return new StringJoiner(", ", SliceFrom.class.getSimpleName() + "(", ")") + .add("start=" + start) + .add("stride=" + stride) + .toString(); + } + + private long start(Dimension dim) { + if (start < 0) { + return dim.numElements() + start; + } + + return start; + } + + private long end(Dimension dim) { + if (stride > 0) { + return dim.numElements(); + } else { + return -1; // it's exclusive + } + } + + private final long start; + private final long stride; +} diff --git a/ndarray/src/main/java/org/tensorflow/ndarray/index/SliceTo.java b/ndarray/src/main/java/org/tensorflow/ndarray/index/SliceTo.java new file mode 100644 index 00000000000..761d1d52a3a --- /dev/null +++ b/ndarray/src/main/java/org/tensorflow/ndarray/index/SliceTo.java @@ -0,0 +1,86 @@ +/* + Copyright 2020 The TensorFlow Authors. All Rights Reserved. + + Licensed under the Apache License, Version 2.0 (the "License"); + you may not use this file except in compliance with the License. + You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + + Unless required by applicable law or agreed to in writing, software + distributed under the License is distributed on an "AS IS" BASIS, + WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + See the License for the specific language governing permissions and + limitations under the License. + ============================================================================== + */ +package org.tensorflow.ndarray.index; + +import java.util.StringJoiner; +import org.tensorflow.ndarray.impl.dimension.Dimension; + +final class SliceTo implements Index { + + SliceTo(long end, long stride) { + this.end = end; + this.stride = stride; + + if (stride == 0) { + throw new IllegalArgumentException("Can not have a stride of 0"); + } + } + + @Override + public long numElements(Dimension dim) { + long length = end(dim) - start(dim); + + return (length / stride) + (length % stride != 0 ? 1 : 0); + } + + @Override + public long mapCoordinate(long coordinate, Dimension dim) { + return start(dim) + stride * coordinate; + } + + @Override + public long end() { + return end; + } + + @Override + public boolean beginMask() { + return true; + } + + @Override + public long stride() { + return stride; + } + + @Override + public String toString() { + return new StringJoiner(", ", SliceTo.class.getSimpleName() + "(", ")") + .add("end=" + end) + .add("stride=" + stride) + .toString(); + } + + private long start(Dimension dim) { + if (stride > 0) { + return 0; + } + + return dim.numElements() - 1; // it's inclusive + } + + private long end(Dimension dim) { + if (end < 0) { + return dim.numElements() + end; + } else { + return end; + } + } + + private final long end; + private final long stride; +} diff --git a/ndarray/src/main/java/org/tensorflow/ndarray/index/Step.java b/ndarray/src/main/java/org/tensorflow/ndarray/index/Step.java new file mode 100644 index 00000000000..c9a21c507b6 --- /dev/null +++ b/ndarray/src/main/java/org/tensorflow/ndarray/index/Step.java @@ -0,0 +1,83 @@ +/* + Copyright 2020 The TensorFlow Authors. All Rights Reserved. + + Licensed under the Apache License, Version 2.0 (the "License"); + you may not use this file except in compliance with the License. + You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + + Unless required by applicable law or agreed to in writing, software + distributed under the License is distributed on an "AS IS" BASIS, + WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + See the License for the specific language governing permissions and + limitations under the License. + ============================================================================== + */ +package org.tensorflow.ndarray.index; + +import java.util.StringJoiner; +import org.tensorflow.ndarray.impl.dimension.Dimension; + +final class Step implements Index { + + Step(long stride) { + this.stride = stride; + + if (stride == 0) { + throw new IllegalArgumentException("Can not have a stride of 0"); + } + } + + @Override + public long numElements(Dimension dim) { + long length = end(dim) - start(dim); + + return (length / stride) + (length % stride != 0 ? 1 : 0); + } + + @Override + public long mapCoordinate(long coordinate, Dimension dim) { + return start(dim) + stride * coordinate; + } + + @Override + public boolean beginMask() { + return true; + } + + @Override + public boolean endMask() { + return true; + } + + @Override + public long stride() { + return stride; + } + + @Override + public String toString() { + return new StringJoiner(", ", Step.class.getSimpleName() + "(", ")") + .add("stride=" + stride) + .toString(); + } + + private long start(Dimension dim) { + if (stride > 0) { + return 0; + } + + return dim.numElements() - 1; // it's inclusive + } + + private long end(Dimension dim) { + if (stride > 0) { + return dim.numElements(); + } else { + return -1; // it's exclusive + } + } + + private final long stride; +} diff --git a/ndarray/src/test/java/org/tensorflow/ndarray/BooleanNdArrayTestBase.java b/ndarray/src/test/java/org/tensorflow/ndarray/BooleanNdArrayTestBase.java new file mode 100644 index 00000000000..6426ff5a1c2 --- /dev/null +++ b/ndarray/src/test/java/org/tensorflow/ndarray/BooleanNdArrayTestBase.java @@ -0,0 +1,57 @@ +/* + Copyright 2019 The TensorFlow Authors. All Rights Reserved. + + Licensed under the Apache License, Version 2.0 (the "License"); + you may not use this file except in compliance with the License. + You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + + Unless required by applicable law or agreed to in writing, software + distributed under the License is distributed on an "AS IS" BASIS, + WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + See the License for the specific language governing permissions and + limitations under the License. + ======================================================================= + */ +package org.tensorflow.ndarray; + +import static org.junit.jupiter.api.Assertions.assertTrue; +import static org.junit.jupiter.api.Assertions.assertFalse; +import static org.tensorflow.ndarray.NdArrays.vectorOf; + +import org.junit.jupiter.api.Test; + +public abstract class BooleanNdArrayTestBase extends NdArrayTestBase { + + @Override + protected abstract BooleanNdArray allocate(Shape shape); + + @Override + protected Boolean valueOf(Long val) { + return val > 0; + } + + @Test + public void iteratePrimitiveElements() { + BooleanNdArray matrix3d = allocate(Shape.of(5, 4, 5)); + + matrix3d.scalars().forEachIndexed((coords, scalar) -> + scalar.setBoolean(coords[2] > 0) + ); + + assertFalse(matrix3d.getBoolean(0, 0, 0)); + assertTrue(matrix3d.getBoolean(0, 0, 1)); + assertTrue(matrix3d.getBoolean(0, 0, 4)); + assertTrue(matrix3d.getBoolean(0, 1, 2)); + + matrix3d.elements(1).forEach(vector -> + vector.set(vectorOf(true, false, true, false, true)) + ); + + assertTrue(matrix3d.getBoolean(0, 0, 0)); + assertFalse(matrix3d.getBoolean(0, 0, 1)); + assertTrue(matrix3d.getBoolean(0, 0, 4)); + assertTrue(matrix3d.getBoolean(0, 1, 2)); + } +} diff --git a/ndarray/src/test/java/org/tensorflow/ndarray/ByteNdArrayTestBase.java b/ndarray/src/test/java/org/tensorflow/ndarray/ByteNdArrayTestBase.java new file mode 100644 index 00000000000..407efffda94 --- /dev/null +++ b/ndarray/src/test/java/org/tensorflow/ndarray/ByteNdArrayTestBase.java @@ -0,0 +1,55 @@ +/* + Copyright 2019 The TensorFlow Authors. All Rights Reserved. + + Licensed under the Apache License, Version 2.0 (the "License"); + you may not use this file except in compliance with the License. + You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + + Unless required by applicable law or agreed to in writing, software + distributed under the License is distributed on an "AS IS" BASIS, + WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + See the License for the specific language governing permissions and + limitations under the License. + ======================================================================= + */ +package org.tensorflow.ndarray; + +import static org.junit.jupiter.api.Assertions.assertEquals; + +import org.junit.jupiter.api.Test; + +public abstract class ByteNdArrayTestBase extends NdArrayTestBase { + + @Override + protected abstract ByteNdArray allocate(Shape shape); + + @Override + protected Byte valueOf(Long val) { + return val.byteValue(); + } + + @Test + public void iteratePrimitiveElements() { + ByteNdArray matrix3d = allocate(Shape.of(5, 4, 5)); + + matrix3d.scalars().forEachIndexed((coords, scalar) -> + scalar.setByte((byte)coords[2]) + ); + + assertEquals(0, matrix3d.getByte(0, 0, 0)); + assertEquals(1, matrix3d.getByte(0, 0, 1)); + assertEquals(4, matrix3d.getByte(0, 0, 4)); + assertEquals(2, matrix3d.getByte(0, 1, 2)); + + matrix3d.elements(1).forEach(vector -> + vector.set(NdArrays.vectorOf((byte)5, (byte)6, (byte)7, (byte)8, (byte)9)) + ); + + assertEquals(5, matrix3d.getByte(0, 0, 0)); + assertEquals(6, matrix3d.getByte(0, 0, 1)); + assertEquals(9, matrix3d.getByte(0, 0, 4)); + assertEquals(7, matrix3d.getByte(0, 1, 2)); + } +} diff --git a/ndarray/src/test/java/org/tensorflow/ndarray/DoubleNdArrayTestBase.java b/ndarray/src/test/java/org/tensorflow/ndarray/DoubleNdArrayTestBase.java new file mode 100644 index 00000000000..d4f98e2caa0 --- /dev/null +++ b/ndarray/src/test/java/org/tensorflow/ndarray/DoubleNdArrayTestBase.java @@ -0,0 +1,55 @@ +/* + Copyright 2019 The TensorFlow Authors. All Rights Reserved. + + Licensed under the Apache License, Version 2.0 (the "License"); + you may not use this file except in compliance with the License. + You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + + Unless required by applicable law or agreed to in writing, software + distributed under the License is distributed on an "AS IS" BASIS, + WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + See the License for the specific language governing permissions and + limitations under the License. + ======================================================================= + */ +package org.tensorflow.ndarray; + +import static org.junit.jupiter.api.Assertions.assertEquals; + +import org.junit.jupiter.api.Test; + +public abstract class DoubleNdArrayTestBase extends NdArrayTestBase { + + @Override + protected abstract DoubleNdArray allocate(Shape shape); + + @Override + protected Double valueOf(Long val) { + return val.doubleValue(); + } + + @Test + public void iteratePrimitiveElements() { + DoubleNdArray matrix3d = allocate(Shape.of(5, 4, 5)); + + matrix3d.scalars().forEachIndexed((coords, scalar) -> + scalar.setDouble((double)coords[2]) + ); + + assertEquals(0.0, matrix3d.getDouble(0, 0, 0), 0.0); + assertEquals(1.0, matrix3d.getDouble(0, 0, 1), 0.0); + assertEquals(4.0, matrix3d.getDouble(0, 0, 4), 0.0); + assertEquals(2.0, matrix3d.getDouble(0, 1, 2), 0.0); + + matrix3d.elements(1).forEach(vector -> + vector.set(NdArrays.vectorOf(5.0, 6.0, 7.0, 8.0, 9.0)) + ); + + assertEquals(5, matrix3d.getDouble(0, 0, 0), 0.0); + assertEquals(6, matrix3d.getDouble(0, 0, 1), 0.0); + assertEquals(9, matrix3d.getDouble(0, 0, 4), 0.0); + assertEquals(7, matrix3d.getDouble(0, 1, 2), 0.0); + } +} diff --git a/ndarray/src/test/java/org/tensorflow/ndarray/FloatNdArrayTestBase.java b/ndarray/src/test/java/org/tensorflow/ndarray/FloatNdArrayTestBase.java new file mode 100644 index 00000000000..55f05ae3de1 --- /dev/null +++ b/ndarray/src/test/java/org/tensorflow/ndarray/FloatNdArrayTestBase.java @@ -0,0 +1,55 @@ +/* + Copyright 2019 The TensorFlow Authors. All Rights Reserved. + + Licensed under the Apache License, Version 2.0 (the "License"); + you may not use this file except in compliance with the License. + You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + + Unless required by applicable law or agreed to in writing, software + distributed under the License is distributed on an "AS IS" BASIS, + WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + See the License for the specific language governing permissions and + limitations under the License. + ======================================================================= + */ +package org.tensorflow.ndarray; + +import static org.junit.jupiter.api.Assertions.assertEquals; + +import org.junit.jupiter.api.Test; + +public abstract class FloatNdArrayTestBase extends NdArrayTestBase { + + @Override + protected abstract FloatNdArray allocate(Shape shape); + + @Override + protected Float valueOf(Long val) { + return val.floatValue(); + } + + @Test + public void iteratePrimitiveElements() { + FloatNdArray matrix3d = allocate(Shape.of(5, 4, 5)); + + matrix3d.scalars().forEachIndexed((coords, scalar) -> + scalar.setFloat((float)coords[2]) + ); + + assertEquals(0.0f, matrix3d.getFloat(0, 0, 0), 0.0f); + assertEquals(1.0f, matrix3d.getFloat(0, 0, 1), 0.0f); + assertEquals(4.0f, matrix3d.getFloat(0, 0, 4), 0.0f); + assertEquals(2.0f, matrix3d.getFloat(0, 1, 2), 0.0f); + + matrix3d.elements(1).forEach(vector -> + vector.set(NdArrays.vectorOf(5.0f, 6.0f, 7.0f, 8.0f, 9.0f)) + ); + + assertEquals(5, matrix3d.getFloat(0, 0, 0), 0.0f); + assertEquals(6, matrix3d.getFloat(0, 0, 1), 0.0f); + assertEquals(9, matrix3d.getFloat(0, 0, 4), 0.0f); + assertEquals(7, matrix3d.getFloat(0, 1, 2), 0.0f); + } +} diff --git a/ndarray/src/test/java/org/tensorflow/ndarray/IndexTest.java b/ndarray/src/test/java/org/tensorflow/ndarray/IndexTest.java new file mode 100644 index 00000000000..6f92dab9b99 --- /dev/null +++ b/ndarray/src/test/java/org/tensorflow/ndarray/IndexTest.java @@ -0,0 +1,205 @@ +/* + Copyright 2020 The TensorFlow Authors. All Rights Reserved. + + Licensed under the Apache License, Version 2.0 (the "License"); + you may not use this file except in compliance with the License. + You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + + Unless required by applicable law or agreed to in writing, software + distributed under the License is distributed on an "AS IS" BASIS, + WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + See the License for the specific language governing permissions and + limitations under the License. + ============================================================================== + */ +package org.tensorflow.ndarray; + +import static org.junit.jupiter.api.Assertions.assertEquals; +import static org.junit.jupiter.api.Assertions.assertTrue; + +import org.junit.jupiter.api.Test; +import org.tensorflow.ndarray.index.Indices; + +public class IndexTest { + @Test + public void testNullConversions(){ + assertTrue(Indices.slice(null, 0L).beginMask(), + "Passed null for slice start but didn't set begin mask"); + + assertTrue(Indices.slice(null, 0L).beginMask(), + "Passed null for slice start but didn't set begin mask"); + + assertTrue(Indices.slice(null, null).beginMask(), + "Passed null for slice start but didn't set begin mask"); + + assertTrue(Indices.slice(0L, null).endMask(), + "Passed null for slice end but didn't set end mask"); + + assertTrue(Indices.slice(0L, null).endMask(), + "Passed null for slice end but didn't set end mask"); + + assertTrue(Indices.slice(null, null).endMask(), + "Passed null for slice end but didn't set end mask"); + } + + @Test + public void testNewaxis(){ + IntNdArray matrix3d = NdArrays.ofInts(Shape.of(5, 4, 5)); + + matrix3d.scalars().forEachIndexed((coords, scalar) -> + scalar.setInt((int)coords[2]) + ); + + IntNdArray slice1 = matrix3d.slice(Indices.all(), Indices.all(), Indices.all(), Indices.newAxis()); + + assertEquals(Shape.of(5, 4, 5, 1), slice1.shape()); + assertEquals(0, slice1.getInt(0, 0, 0, 0)); + assertEquals(1, slice1.getInt(0, 0, 1, 0)); + assertEquals(4, slice1.getInt(0, 0, 4, 0)); + assertEquals(2, slice1.getInt(0, 1, 2, 0)); + + IntNdArray slice2 = matrix3d.slice(Indices.all(), Indices.all(), Indices.newAxis(), Indices.all()); + + assertEquals(Shape.of(5, 4, 1, 5), slice2.shape()); + assertEquals(0, slice2.getInt(0, 0, 0, 0)); + assertEquals(1, slice2.getInt(0, 0, 0, 1)); + assertEquals(4, slice2.getInt(0, 0, 0, 4)); + assertEquals(2, slice2.getInt(0, 1, 0, 2)); + + IntNdArray slice3 = matrix3d.slice(Indices.all(), Indices.newAxis(), Indices.all(), Indices.all()); + + assertEquals(Shape.of(5, 1, 4, 5), slice3.shape()); + assertEquals(0, slice3.getInt(0, 0, 0, 0)); + assertEquals(1, slice3.getInt(0, 0, 0, 1)); + assertEquals(4, slice3.getInt(0, 0, 0, 4)); + assertEquals(2, slice3.getInt(0, 0, 1, 2)); + + IntNdArray slice4 = matrix3d.slice(Indices.newAxis(), Indices.all(), Indices.all(), Indices.all()); + + assertEquals(Shape.of(1, 5, 4, 5), slice4.shape()); + assertEquals(0, slice4.getInt(0, 0, 0, 0)); + assertEquals(1, slice4.getInt(0, 0, 0, 1)); + assertEquals(4, slice4.getInt(0, 0, 0, 4)); + assertEquals(2, slice4.getInt(0, 0, 1, 2)); + + } + + @Test + public void testEllipsis(){ + IntNdArray matrix3d = NdArrays.ofInts(Shape.of(5, 4, 5)); + + matrix3d.scalars().forEachIndexed((coords, scalar) -> + scalar.setInt((int)coords[2]) + ); + + assertEquals( + matrix3d.slice(Indices.all(), Indices.all(), Indices.at(0)), + matrix3d.slice(Indices.ellipsis(), Indices.at(0)) + ); + + assertEquals( + matrix3d.slice(Indices.at(0), Indices.all(), Indices.all()), + matrix3d.slice(Indices.at(0), Indices.ellipsis()) + ); + + assertEquals( + matrix3d.slice(Indices.at(0), Indices.all(), Indices.at(0)), + matrix3d.slice(Indices.at(0), Indices.ellipsis(), Indices.at(0)) + ); + + // newaxis interacts specially with ellipsis (since it doesn't consume a dimension), test this + + assertEquals( + matrix3d.slice(Indices.all(), Indices.all(), Indices.newAxis(), Indices.at(0)), + matrix3d.slice(Indices.ellipsis(), Indices.newAxis(), Indices.at(0)) + ); + + assertEquals( + matrix3d.slice(Indices.newAxis(), Indices.all(), Indices.all(), Indices.at(0)), + matrix3d.slice(Indices.newAxis(), Indices.ellipsis(), Indices.at(0)) + ); + + assertEquals( + matrix3d.slice(Indices.all(), Indices.all(), Indices.at(0), Indices.newAxis()), + matrix3d.slice(Indices.ellipsis(), Indices.at(0), Indices.newAxis()) + ); + } + + @Test + public void testSlice(){ + IntNdArray matrix3d = NdArrays.ofInts(Shape.of(5, 4, 5)); + + matrix3d.scalars().forEachIndexed((coords, scalar) -> + scalar.setInt((int)coords[2]) + ); + + IntNdArray slice1 = matrix3d.slice(Indices.all(), Indices.sliceTo(3), Indices.all()); + + assertEquals(Shape.of(5, 3, 5), slice1.shape()); + assertEquals(0, slice1.getInt(0, 0, 0)); + assertEquals(1, slice1.getInt(0, 0, 1)); + assertEquals(2, slice1.getInt(0, 1, 2)); + + IntNdArray slice2 = matrix3d.slice(Indices.all(), Indices.all(), Indices.slice(1, 4)); + + assertEquals(Shape.of(5, 4, 3), slice2.shape()); + assertEquals(1, slice2.getInt(0, 0, 0)); + assertEquals(3, slice2.getInt(0, 0, 2)); + assertEquals(2, slice2.getInt(0, 1, 1)); + + assertEquals(slice2, matrix3d.slice(Indices.all(), Indices.all(), Indices.slice(1, -1))); + + assertEquals(slice2, matrix3d.slice(Indices.all(), Indices.all(), Indices.slice(-4, -1))); + + assertEquals(Shape.of(5, 4, 0), matrix3d.slice(Indices.all(), Indices.all(), Indices.slice(1, 4, -2)).shape()); + + IntNdArray slice3 = matrix3d.slice(Indices.all(), Indices.all(), Indices.slice(4, 1, -2)); + + assertEquals(Shape.of(5, 4, 2), slice3.shape()); + assertEquals(4, slice3.getInt(0, 0, 0)); + assertEquals(2, slice3.getInt(0, 1, 1)); + + assertEquals(slice3, matrix3d.slice(Indices.all(), Indices.all(), Indices.slice(-1, 1, -2))); + + assertEquals(slice3, matrix3d.slice(Indices.all(), Indices.all(), Indices.slice(-1, -4, -2))); + + IntNdArray slice4 = matrix3d.slice(Indices.all(), Indices.all(), Indices.slice(null, null, -1)); + + assertEquals(Shape.of(5, 4, 5), slice4.shape()); + assertEquals(4, slice4.getInt(0, 0, 0)); + assertEquals(3, slice4.getInt(0, 0, 1)); + assertEquals(2, slice4.getInt(0, 1, 2)); + } + + @Test + public void testAt(){ + IntNdArray matrix3d = NdArrays.ofInts(Shape.of(5, 4, 5)); + + matrix3d.scalars().forEachIndexed((coords, scalar) -> + scalar.setInt((int)coords[2]) + ); + + IntNdArray slice1 = matrix3d.slice(Indices.all(), Indices.all(), Indices.at(0)); + + assertEquals(Shape.of(5, 4), slice1.shape()); + assertEquals(0, slice1.getInt(0, 0)); + + IntNdArray slice2 = matrix3d.slice(Indices.all(), Indices.all(), Indices.at(3)); + + assertEquals(Shape.of(5, 4), slice2.shape()); + assertEquals(3, slice2.getInt(0, 0)); + + IntNdArray slice3 = matrix3d.slice(Indices.all(), Indices.all(), Indices.at(-3)); + + assertEquals(Shape.of(5, 4), slice3.shape()); + assertEquals(2, slice3.getInt(0, 0)); + + IntNdArray slice4 = matrix3d.slice(Indices.all(), Indices.all(), Indices.at(-3, true)); + + assertEquals(Shape.of(5, 4, 1), slice4.shape()); + assertEquals(2, slice4.getInt(0, 0, 0)); + } + +} diff --git a/ndarray/src/test/java/org/tensorflow/ndarray/IntNdArrayTestBase.java b/ndarray/src/test/java/org/tensorflow/ndarray/IntNdArrayTestBase.java new file mode 100644 index 00000000000..1a3c7cb1a12 --- /dev/null +++ b/ndarray/src/test/java/org/tensorflow/ndarray/IntNdArrayTestBase.java @@ -0,0 +1,55 @@ +/* + Copyright 2019 The TensorFlow Authors. All Rights Reserved. + + Licensed under the Apache License, Version 2.0 (the "License"); + you may not use this file except in compliance with the License. + You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + + Unless required by applicable law or agreed to in writing, software + distributed under the License is distributed on an "AS IS" BASIS, + WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + See the License for the specific language governing permissions and + limitations under the License. + ======================================================================= + */ +package org.tensorflow.ndarray; + +import static org.junit.jupiter.api.Assertions.assertEquals; + +import org.junit.jupiter.api.Test; + +public abstract class IntNdArrayTestBase extends NdArrayTestBase { + + @Override + protected abstract IntNdArray allocate(Shape shape); + + @Override + protected Integer valueOf(Long val) { + return val.intValue(); + } + + @Test + public void iteratePrimitiveElements() { + IntNdArray matrix3d = allocate(Shape.of(5, 4, 5)); + + matrix3d.scalars().forEachIndexed((coords, scalar) -> + scalar.setInt((int)coords[2]) + ); + + assertEquals(0, matrix3d.getInt(0, 0, 0)); + assertEquals(1, matrix3d.getInt(0, 0, 1)); + assertEquals(4, matrix3d.getInt(0, 0, 4)); + assertEquals(2, matrix3d.getInt(0, 1, 2)); + + matrix3d.elements(1).forEach(vector -> + vector.set(NdArrays.vectorOf(5, 6, 7, 8, 9)) + ); + + assertEquals(5, matrix3d.getInt(0, 0, 0)); + assertEquals(6, matrix3d.getInt(0, 0, 1)); + assertEquals(9, matrix3d.getInt(0, 0, 4)); + assertEquals(7, matrix3d.getInt(0, 1, 2)); + } +} diff --git a/ndarray/src/test/java/org/tensorflow/ndarray/LongNdArrayTestBase.java b/ndarray/src/test/java/org/tensorflow/ndarray/LongNdArrayTestBase.java new file mode 100644 index 00000000000..b91c19d6557 --- /dev/null +++ b/ndarray/src/test/java/org/tensorflow/ndarray/LongNdArrayTestBase.java @@ -0,0 +1,55 @@ +/* + Copyright 2019 The TensorFlow Authors. All Rights Reserved. + + Licensed under the Apache License, Version 2.0 (the "License"); + you may not use this file except in compliance with the License. + You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + + Unless required by applicable law or agreed to in writing, software + distributed under the License is distributed on an "AS IS" BASIS, + WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + See the License for the specific language governing permissions and + limitations under the License. + ======================================================================= + */ +package org.tensorflow.ndarray; + +import static org.junit.jupiter.api.Assertions.assertEquals; + +import org.junit.jupiter.api.Test; + +public abstract class LongNdArrayTestBase extends NdArrayTestBase { + + @Override + protected abstract LongNdArray allocate(Shape shape); + + @Override + protected Long valueOf(Long val) { + return val; + } + + @Test + public void iteratePrimitiveElements() { + LongNdArray matrix3d = allocate(Shape.of(5, 4, 5)); + + matrix3d.scalars().forEachIndexed((coords, scalar) -> + scalar.setLong(coords[2]) + ); + + assertEquals(0, matrix3d.getLong(0, 0, 0)); + assertEquals(1, matrix3d.getLong(0, 0, 1)); + assertEquals(4, matrix3d.getLong(0, 0, 4)); + assertEquals(2, matrix3d.getLong(0, 1, 2)); + + matrix3d.elements(1).forEach(vector -> + vector.set(NdArrays.vectorOf(5L, 6L, 7L, 8L, 9L)) + ); + + assertEquals(5, matrix3d.getLong(0, 0, 0)); + assertEquals(6, matrix3d.getLong(0, 0, 1)); + assertEquals(9, matrix3d.getLong(0, 0, 4)); + assertEquals(7, matrix3d.getLong(0, 1, 2)); + } +} diff --git a/ndarray/src/test/java/org/tensorflow/ndarray/NdArrayTestBase.java b/ndarray/src/test/java/org/tensorflow/ndarray/NdArrayTestBase.java new file mode 100644 index 00000000000..26ac533daa8 --- /dev/null +++ b/ndarray/src/test/java/org/tensorflow/ndarray/NdArrayTestBase.java @@ -0,0 +1,338 @@ +/* + Copyright 2019 The TensorFlow Authors. All Rights Reserved. + + Licensed under the Apache License, Version 2.0 (the "License"); + you may not use this file except in compliance with the License. + You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + + Unless required by applicable law or agreed to in writing, software + distributed under the License is distributed on an "AS IS" BASIS, + WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + See the License for the specific language governing permissions and + limitations under the License. + ======================================================================= + */ +package org.tensorflow.ndarray; + +import static org.junit.jupiter.api.Assertions.assertEquals; +import static org.junit.jupiter.api.Assertions.assertNotEquals; +import static org.junit.jupiter.api.Assertions.fail; +import static org.tensorflow.ndarray.NdArrays.vectorOfObjects; +import static org.tensorflow.ndarray.index.Indices.all; +import static org.tensorflow.ndarray.index.Indices.at; +import static org.tensorflow.ndarray.index.Indices.even; +import static org.tensorflow.ndarray.index.Indices.flip; +import static org.tensorflow.ndarray.index.Indices.sliceFrom; +import static org.tensorflow.ndarray.index.Indices.odd; +import static org.tensorflow.ndarray.index.Indices.range; +import static org.tensorflow.ndarray.index.Indices.seq; +import static org.tensorflow.ndarray.index.Indices.sliceTo; + +import java.nio.BufferOverflowException; +import java.nio.BufferUnderflowException; +import org.junit.jupiter.api.Test; +import org.tensorflow.ndarray.buffer.DataBuffer; + +public abstract class NdArrayTestBase { + + protected abstract NdArray allocate(Shape shape); + + protected abstract DataBuffer allocateBuffer(long size); + + protected abstract T valueOf(Long val); + + protected T zeroOrNull() { + return valueOf(0L); + } + + @Test + public void shapeAndSizes() { + Shape scalarShape = Shape.scalar(); + NdArray scalar = allocate(scalarShape); + assertEquals(scalarShape, scalar.shape()); + assertEquals(0, scalar.rank()); + assertEquals(scalarShape, Shape.of()); + + Shape vectorShape = Shape.of(10); + NdArray vector = allocate(vectorShape); + assertEquals(vectorShape, vector.shape()); + assertEquals(1, vector.rank()); + } + + @Test + public void setAndGetValues() { + NdArray matrix = allocate(Shape.of(5, 4)); + assertEquals(zeroOrNull(), matrix.getObject(3, 3)); + + matrix.setObject(valueOf(10L), 3, 3); + assertEquals(valueOf(10L), matrix.getObject(3, 3)); + try { + matrix.setObject(valueOf(10L), 3, 4); + fail(); + } catch (IndexOutOfBoundsException e) { + // as expected + } + try { + matrix.setObject(valueOf(10L), -1, 3); + fail(); + } catch (IndexOutOfBoundsException e) { + // as expected + } + try { + matrix.getObject(3); + fail(); + } catch (IllegalRankException e) { + // as expected + } + try { + matrix.setObject(valueOf(10L), 3); + fail(); + } catch (IllegalRankException e) { + // as expected + } + + NdArray matrix2 = allocate(Shape.of(3, 2)) + .set(vectorOfObjects(valueOf(1L), valueOf(2L)), 0) + .set(vectorOfObjects(valueOf(3L), valueOf(4L)), 1) + .setObject(valueOf(5L), 2, 0) + .setObject(valueOf(6L), 2, 1); + + assertEquals(valueOf(1L), matrix2.getObject(0, 0)); + assertEquals(valueOf(2L), matrix2.getObject(0, 1)); + assertEquals(valueOf(3L), matrix2.getObject(1, 0)); + assertEquals(valueOf(4L), matrix2.getObject(1, 1)); + assertEquals(valueOf(5L), matrix2.getObject(2, 0)); + assertEquals(valueOf(6L), matrix2.getObject(2, 1)); + } + + @Test + public void iterateElements() { + NdArray matrix3d = allocate(Shape.of(5, 4, 5)); + + matrix3d.scalars().forEachIndexed((coords, scalar) -> { + scalar.setObject(valueOf(coords[2])); + }); + + assertEquals(valueOf(0L), matrix3d.getObject(0, 0, 0)); + assertEquals(valueOf(1L), matrix3d.getObject(0, 0, 1)); + assertEquals(valueOf(4L), matrix3d.getObject(0, 0, 4)); + assertEquals(valueOf(2L), matrix3d.getObject(0, 1, 2)); + + matrix3d.elements(1).forEach(vector -> { + vector.set(vectorOfObjects(valueOf(5L), valueOf(6L), valueOf(7L), valueOf(8L), valueOf(9L))); + }); + + assertEquals(valueOf(5L), matrix3d.getObject(0, 0, 0)); + assertEquals(valueOf(6L), matrix3d.getObject(0, 0, 1)); + assertEquals(valueOf(9L), matrix3d.getObject(0, 0, 4)); + assertEquals(valueOf(7L), matrix3d.getObject(0, 1, 2)); + + long value = 0L; + for (NdArray matrix : matrix3d.elements(0)) { + assertEquals(2L, matrix.shape().numDimensions()); + assertEquals(4L, matrix.shape().size(0)); + assertEquals(5L, matrix.shape().size(1)); + + for (NdArray vector : matrix.elements(0)) { + assertEquals(1L, vector.shape().numDimensions()) ; + assertEquals(5L, vector.shape().size(0)); + + for (NdArray scalar : vector.scalars()) { + assertEquals(0L, scalar.shape().numDimensions()) ; + scalar.setObject(valueOf(value++)); + try { + scalar.elements(0); + fail(); + } catch (IllegalArgumentException e) { + // as expected + } + } + } + } + assertEquals(valueOf(0L), matrix3d.getObject(0, 0, 0)); + assertEquals(valueOf(5L), matrix3d.getObject(0, 1, 0)); + assertEquals(valueOf(9L), matrix3d.getObject(0, 1, 4)); + assertEquals(valueOf(20L), matrix3d.getObject(1, 0, 0)); + assertEquals(valueOf(25L), matrix3d.getObject(1, 1, 0)); + assertEquals(valueOf(99L), matrix3d.getObject(4, 3, 4)); + } + + @Test + public void slices() { + NdArray matrix3d = allocate(Shape.of(5, 4, 5)); + + T val100 = valueOf(100L); + matrix3d.setObject(val100, 1, 0, 0); + T val101 = valueOf(101L); + matrix3d.setObject(val101, 1, 0, 1); + + // Vector (1,0,*) + NdArray vector10X = matrix3d.get(1, 0); + assertEquals(Shape.of(5), vector10X.shape()); + assertEquals(val100, vector10X.getObject(0)); + assertEquals(val101, vector10X.getObject(1)); + + T val102 = valueOf(102L); + vector10X.setObject(val102, 2); + assertEquals(val102, vector10X.getObject(2)); + assertEquals(val102, matrix3d.getObject(1, 0, 2)); + + // Vector (*,0,0) + NdArray vectorX00 = matrix3d.slice(all(), at(0), at(0)); + assertEquals(Shape.of(5), vectorX00.shape()); + assertEquals(val100, vectorX00.getObject(1)); + T val200 = valueOf(200L); + vectorX00.setObject(val200, 2); + assertEquals(val200, vectorX00.getObject(2)); + assertEquals(val200, matrix3d.getObject(2, 0, 0)); + + // Vector (1,0,[2,0]) + NdArray vector10_20 = matrix3d.slice(at(1), at(0), seq(2, 0)); + assertEquals(vector10_20.shape(), Shape.of(2)); + assertEquals(val102, vector10_20.getObject(0)); + assertEquals(val100, vector10_20.getObject(1)); + + // Vector (1,0,[even]) + NdArray vector10_even = matrix3d.slice(at(1), at(0), even()); + assertEquals(vector10_even.shape(), Shape.of(3)); + assertEquals(val100, vector10_even.getObject(0)); + assertEquals(val102, vector10_even.getObject(1)); + + // Vector ([odd]) from vector (1,0,[even]) + NdArray vector10_even_odd = vector10_even.slice(odd()); + assertEquals(vector10_even_odd.shape(), Shape.of(1)); + assertEquals(val102, vector10_even_odd.getObject(0)); + + // Vector (1,0,[flip]) + NdArray vector10_flip = matrix3d.slice(at(1), at(0), flip()); + assertEquals(vector10_flip.shape(), Shape.of(5)); + assertEquals(val100, vector10_flip.getObject(4)); + assertEquals(val101, vector10_flip.getObject(3)); + + // Vector (1,0,[from 1]) from vector (1,0,*) + NdArray vector10_1toX = vector10X.slice(sliceFrom(1)); + assertEquals(vector10_1toX.shape(), Shape.of(4)); + assertEquals(val101, vector10_1toX.getObject(0)); + assertEquals(val102, vector10_1toX.getObject(1)); + + // Vector (1,0,[to 1]) from vector (1,0,*) + NdArray vector10_Xto1 = vector10X.slice(sliceTo(2)); + assertEquals(vector10_Xto1.shape(), Shape.of(2)); + assertEquals(val100, vector10_Xto1.getObject(0)); + assertEquals(val101, vector10_Xto1.getObject(1)); + + // Vector (1,0,[1 to 3]) + NdArray vector10_1to3 = matrix3d.slice(at(1), at(0), range(1, 3)); + assertEquals(vector10_1to3.shape(), Shape.of(2)); + assertEquals(val101, vector10_1to3.getObject(0)); + assertEquals(val102, vector10_1to3.getObject(1)); + + // Scalar (1,0,0) from vector (1,0,*) + NdArray scalar100 = vector10X.get(0); + assertEquals(Shape.of(), scalar100.shape()); + assertEquals(val100, scalar100.getObject()); + + // Slice scalar (1,0,z) + LongNdArray z = NdArrays.scalarOf(2L); + NdArray scalar102 = matrix3d.slice(at(1), at(0), at(z)); + assertEquals(scalar102.shape(), Shape.of()); + assertEquals(val102, scalar102.getObject()); + + // Slicing the 3D matrix so we only keep the first element of the second dimension + NdArray matrix_X0Z = matrix3d.slice(all(), at(0)); + assertEquals(2, matrix_X0Z.rank()); + assertEquals(Shape.of(5, 5), matrix_X0Z.shape()); + assertEquals(val100, matrix_X0Z.getObject(1, 0)); + assertEquals(val101, matrix_X0Z.getObject(1, 1)); + assertEquals(val200, matrix_X0Z.getObject(2, 0)); + } + + @Test + public void writeAndReadWithBuffers() { + DataBuffer buffer = allocateBuffer(15L); + for (long val = 0L; val < buffer.size(); ++val) { + buffer.setObject(valueOf(val), val); + } + NdArray matrix = allocate(Shape.of(3, 5)); + matrix.write(buffer); + assertEquals(valueOf(0L), matrix.getObject(0, 0)); + assertEquals(valueOf(4L), matrix.getObject(0, 4)); + assertEquals(valueOf(5L), matrix.getObject(1, 0)); + assertEquals(valueOf(10L), matrix.getObject(2, 0)); + assertEquals(valueOf(14L), matrix.getObject(2, 4)); + + matrix.setObject(valueOf(100L), 1, 0); + matrix.read(buffer); + assertEquals(valueOf(0L), buffer.getObject(0)); + assertEquals(valueOf(4L), buffer.getObject(4)); + assertEquals(valueOf(100L), buffer.getObject(5)); + assertEquals(valueOf(10L), buffer.getObject(10)); + assertEquals(valueOf(14L), buffer.getObject(14)); + + try { + matrix.write(buffer.narrow(10)); + fail(); + } catch (BufferUnderflowException e) { + // as expected + } + try { + matrix.read(buffer.narrow(10)); + fail(); + } catch (BufferOverflowException e) { + // as expected + } + } + + @Test + public void ndArrayCopies() { + NdArray matrixA = allocate(Shape.of(3, 5)); + + long value = 0L; + for (NdArray s : matrixA.scalars()) { + s.setObject(valueOf(value++)); + } + NdArray matrixB = allocate(Shape.of(3, 5)).setObject(valueOf(100L), 1, 0); + matrixA.copyTo(matrixB); + assertEquals(valueOf(0L), matrixB.getObject(0, 0)); + assertEquals(valueOf(4L), matrixB.getObject(0, 4)); + assertEquals(valueOf(5L), matrixB.getObject(1, 0)); + assertEquals(valueOf(10L), matrixB.getObject(2, 0)); + assertEquals(valueOf(14L), matrixB.getObject(2, 4)); + + NdArray matrixC = allocate(Shape.of(3, 4)); + try { + matrixA.copyTo(matrixC); + fail(); + } catch (IllegalArgumentException e) { + // as expected + } + } + + @Test + public void equalsAndHashCode() { + NdArray array1 = allocate(Shape.of(2, 2)); + NdArray array2 = allocate(Shape.of(2, 2)); + NdArray array3 = allocate(Shape.of(2, 2)); + NdArray array4 = allocate(Shape.of(1, 2, 2)); + + @SuppressWarnings("unchecked") + T[][][] values = (T[][][])(new Object[][][] { + { { valueOf(0L), valueOf(1L) }, { valueOf(2L), valueOf(0L) } } + }); + + StdArrays.copyTo(values[0], array1); + StdArrays.copyTo(values[0], array2); + StdArrays.copyTo(values[0], array3); + array3.setObject(valueOf(0L), 0, 1); + StdArrays.copyTo(values, array4); + + assertEquals(array1, array2); + assertEquals(array1.hashCode(), array2.hashCode()); + assertNotEquals(array1, array3); + assertNotEquals(array1.hashCode(), array3.hashCode()); + assertNotEquals(array1, array4); + assertNotEquals(array1.hashCode(), array4.hashCode()); + } +} diff --git a/ndarray/src/test/java/org/tensorflow/ndarray/ShapeTest.java b/ndarray/src/test/java/org/tensorflow/ndarray/ShapeTest.java new file mode 100644 index 00000000000..d2e3e432a2c --- /dev/null +++ b/ndarray/src/test/java/org/tensorflow/ndarray/ShapeTest.java @@ -0,0 +1,170 @@ +/* +Copyright 2019 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +======================================================================= +*/ +package org.tensorflow.ndarray; + +import org.junit.jupiter.api.Test; + +import static org.junit.jupiter.api.Assertions.*; + +public class ShapeTest { + + @Test + public void allKnownDimensions() { + Shape shape = Shape.of(5, 4, 5); + assertEquals(3, shape.numDimensions()); + assertEquals(5, shape.size(0)); + assertEquals(4, shape.size(1)); + assertEquals(5, shape.size(2)); + assertEquals(100, shape.size()); + assertArrayEquals(new long[] {5, 4, 5}, shape.asArray()); + try { + shape.size(3); + fail(); + } catch (IndexOutOfBoundsException e) { + // as expected + } + assertEquals(5, shape.size(-1)); + assertEquals(4, shape.size(-2)); + assertEquals(5, shape.size(-3)); + try { + shape.size(-4); + fail(); + } catch (IndexOutOfBoundsException e) { + // as expected + } + assertFalse(shape.isUnknown()); + assertFalse(shape.hasUnknownDimension()); + assertFalse(shape.isScalar()); + } + + @Test + public void hashCodeEquals() { + Shape shape1 = Shape.of(5, 4, 5); + Shape shape2 = Shape.of(5, 4, 5); + Shape shape3 = Shape.of(5, 4, 5, 6); + Shape shape4 = Shape.of(5, 4, 1); + + assertEquals(shape1, shape2); + assertEquals(shape1.hashCode(), shape2.hashCode()); + assertNotEquals(shape1, shape3); + assertNotEquals(shape1.hashCode(), shape3.hashCode()); + assertNotEquals(shape1, shape4); + assertNotEquals(shape1.hashCode(), shape4.hashCode()); + + Shape scalar1 = Shape.of(); + Shape scalar2 = Shape.of(); + assertEquals(scalar1, scalar2); + assertNotEquals(scalar1, shape1); + + Shape unknown1 = Shape.of(-1, 4, 5); + Shape unknown2 = Shape.of(-1, 4, 5); + assertNotEquals(unknown1, unknown2); + assertNotEquals(unknown1, shape1); + assertEquals(unknown1, unknown1); + + Shape sizeUnknown1 = Shape.unknown(); + Shape sizeUnknown2 = Shape.unknown(); + assertNotEquals(sizeUnknown1, sizeUnknown2); + assertEquals(sizeUnknown1, sizeUnknown1); + } + + @Test + public void testShapeModification() { + Shape one = Shape.of(2, 4, 6, 8); + assertEquals(one.head(), Shape.of(2)); + assertEquals(one.tail(), Shape.of(4, 6, 8)); + + Shape two = Shape.of(5); + assertEquals(two.head(), two); + assertEquals(two.tail(), Shape.of()); + + try { + Shape.of().head(); + fail(); + } catch (IndexOutOfBoundsException e) { + // as expected + } + + assertEquals(Shape.of().tail(), Shape.of()); + + Shape three = Shape.of(2, 4, 6); + assertEquals(three.prepend(5), Shape.of(5, 2, 4, 6)); + + assertEquals(Shape.of(5, 2, 4, 6), two.append(three)); + assertEquals(Shape.of(2, 4, 6, 5), two.prepend(three)); + assertEquals(Shape.of(1, 2, 3, 4), Shape.of(1, 2).append(Shape.of(3, 4))); + assertEquals(Shape.of(1, 2, 3, 4), Shape.of(1, 2, 3).append(4)); + assertEquals(Shape.of(1, 2, 3, 4), Shape.of(1, 2, 3, 4).append(Shape.scalar())); + assertEquals(Shape.of(3, 4, 1, 2), Shape.of(1, 2).prepend(Shape.of(3, 4))); + assertEquals(Shape.of(4, 6), three.takeLast(2)); + assertEquals(Shape.scalar(), three.takeLast(0)); + assertEquals(Shape.of(2, 4), three.take(2)); + assertEquals(Shape.scalar(), three.take(0)); + + try { + Shape.unknown().append(Shape.of(1, 2)); + fail(); + } catch (NullPointerException e) { + // as expected + } + + try { + Shape.unknown().prepend(Shape.of(1, 2)); + fail(); + } catch (NullPointerException e) { + // as expected + } + + // changing the values of the array returned by asArray should not mutate the shape + long[] internalShape = one.asArray(); + assertNotNull(internalShape); + internalShape[0] = 42L; + assertEquals(2L, one.size(0)); + } + + @Test + public void testShapeCompatible() { + Shape a = Shape.unknown(); + Shape b = Shape.of(2, 2); + assertTrue(a.isCompatibleWith(b)); + assertTrue(b.isCompatibleWith(a)); + + a = Shape.of(2, 2); + assertTrue(a.isCompatibleWith(b)); + assertTrue(b.isCompatibleWith(a)); + + a = Shape.of(2, -1); + assertTrue(a.isCompatibleWith(b)); + assertTrue(b.isCompatibleWith(a)); + + a = Shape.of(-1, 2); + assertTrue(a.isCompatibleWith(b)); + assertTrue(b.isCompatibleWith(a)); + + a = Shape.of(-1, -1); + assertTrue(a.isCompatibleWith(b)); + assertTrue(b.isCompatibleWith(a)); + + a = Shape.of(1, 2); + assertFalse(a.isCompatibleWith(b)); + assertFalse(b.isCompatibleWith(a)); + + a = Shape.of(1, 2, 3); + assertFalse(a.isCompatibleWith(b)); + assertFalse(b.isCompatibleWith(a)); + } +} diff --git a/ndarray/src/test/java/org/tensorflow/ndarray/ShortNdArrayTestBase.java b/ndarray/src/test/java/org/tensorflow/ndarray/ShortNdArrayTestBase.java new file mode 100644 index 00000000000..f9043fec4f5 --- /dev/null +++ b/ndarray/src/test/java/org/tensorflow/ndarray/ShortNdArrayTestBase.java @@ -0,0 +1,55 @@ +/* + Copyright 2019 The TensorFlow Authors. All Rights Reserved. + + Licensed under the Apache License, Version 2.0 (the "License"); + you may not use this file except in compliance with the License. + You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + + Unless required by applicable law or agreed to in writing, software + distributed under the License is distributed on an "AS IS" BASIS, + WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + See the License for the specific language governing permissions and + limitations under the License. + ======================================================================= + */ +package org.tensorflow.ndarray; + +import static org.junit.jupiter.api.Assertions.assertEquals; + +import org.junit.jupiter.api.Test; + +public abstract class ShortNdArrayTestBase extends NdArrayTestBase { + + @Override + protected abstract ShortNdArray allocate(Shape shape); + + @Override + protected Short valueOf(Long val) { + return val.shortValue(); + } + + @Test + public void iteratePrimitiveElements() { + ShortNdArray matrix3d = allocate(Shape.of(5, 4, 5)); + + matrix3d.scalars().forEachIndexed((coords, scalar) -> + scalar.setShort((short)coords[2]) + ); + + assertEquals(0, matrix3d.getShort(0, 0, 0)); + assertEquals(1, matrix3d.getShort(0, 0, 1)); + assertEquals(4, matrix3d.getShort(0, 0, 4)); + assertEquals(2, matrix3d.getShort(0, 1, 2)); + + matrix3d.elements(1).forEach(vector -> + vector.set(NdArrays.vectorOf((short)5, (short)6, (short)7, (short)8, (short)9)) + ); + + assertEquals(5, matrix3d.getShort(0, 0, 0)); + assertEquals(6, matrix3d.getShort(0, 0, 1)); + assertEquals(9, matrix3d.getShort(0, 0, 4)); + assertEquals(7, matrix3d.getShort(0, 1, 2)); + } +} diff --git a/ndarray/src/test/java/org/tensorflow/ndarray/StdArraysTest.java b/ndarray/src/test/java/org/tensorflow/ndarray/StdArraysTest.java new file mode 100644 index 00000000000..b7b41564c33 --- /dev/null +++ b/ndarray/src/test/java/org/tensorflow/ndarray/StdArraysTest.java @@ -0,0 +1,211 @@ +package org.tensorflow.ndarray; + +import static org.junit.jupiter.api.Assertions.assertArrayEquals; +import static org.junit.jupiter.api.Assertions.assertEquals; +import static org.junit.jupiter.api.Assertions.assertNull; +import static org.junit.jupiter.api.Assertions.fail; + +import org.junit.jupiter.api.Test; + +public class StdArraysTest { + + @Test + public void vectors() { + IntNdArray vector = NdArrays.ofInts(Shape.of(2)); + + StdArrays.copyTo(new int[] {1, 2}, vector); + assertEquals(1, vector.getInt(0)); + assertEquals(2, vector.getInt(1)); + + try { + StdArrays.copyTo(new int[] {1, 2, 3}, vector); + fail(); + } catch (IllegalArgumentException e) { + // as expected + } + try { + StdArrays.copyTo(new int[] {1, 2}, NdArrays.ofInts(Shape.of(4))); + fail(); + } catch (IllegalArgumentException e) { + // as expected + } + try { + StdArrays.copyTo(new int[] {1, 2}, NdArrays.ofInts(Shape.of(2, 2))); + fail(); + } catch (IllegalArgumentException e) { + // as expected + } + + int[] array = StdArrays.array1dCopyOf(vector); + assertEquals(1, array[0]); + assertEquals(2, array[1]); + + array = new int[3]; + StdArrays.copyFrom(vector, array); + assertEquals(1, array[0]); + assertEquals(2, array[1]); + assertEquals(0, array[2]); + + try { + StdArrays.copyFrom(vector, new int[1]); + fail(); + } catch (ArrayIndexOutOfBoundsException e) { + // as expected + } + try { + StdArrays.copyFrom(vector, new int[1][2]); + fail(); + } catch (IllegalArgumentException e) { + // as expected + } + try { + StdArrays.copyFrom(vector, new int[2][2][2]); + fail(); + } catch (IllegalArgumentException e) { + // as expected + } + } + + @Test + public void matrices() { + IntNdArray matrix = NdArrays.ofInts(Shape.of(2, 2)); + + StdArrays.copyTo(new int[][] { + {1, 2}, + {3, 4} + }, matrix); + assertEquals(1, matrix.getInt(0, 0)); + assertEquals(2, matrix.getInt(0, 1)); + assertEquals(3, matrix.getInt(1, 0)); + assertEquals(4, matrix.getInt(1, 1)); + try { + StdArrays.copyTo(new int[][] {{1, 2, 3}, {4, 5, 6}}, matrix); + fail(); + } catch (IllegalArgumentException e) { + // as expected + } + try { + StdArrays.copyTo(new int[][] {{1, 2}, {3, 4}}, NdArrays.ofInts(Shape.of(3, 3))); + fail(); + } catch (IllegalArgumentException e) { + // as expected + } + try { + StdArrays.copyTo(new int[][] {{1, 2}, {3, 4}}, NdArrays.ofInts(Shape.of(2, 2, 1))); + fail(); + } catch (IllegalArgumentException e) { + // as expected + } + + int[][] array = StdArrays.array2dCopyOf(matrix); + assertEquals(1, array[0][0]); + assertEquals(2, array[0][1]); + assertEquals(3, array[1][0]); + assertEquals(4, array[1][1]); + + array = new int[3][3]; + StdArrays.copyFrom(matrix, array); + assertArrayEquals(new int[] { 1, 2, 0 }, array[0]); + assertArrayEquals(new int[] { 3, 4, 0 }, array[1]); + assertArrayEquals(new int[] { 0, 0, 0 }, array[2]); + + try { + StdArrays.copyFrom(matrix, new int[1][2]); + fail(); + } catch (ArrayIndexOutOfBoundsException e) { + // as expected + } + try { + StdArrays.copyFrom(matrix, new int[2][1]); + fail(); + } catch (ArrayIndexOutOfBoundsException e) { + // as expected + } + try { + StdArrays.copyFrom(matrix, new int[2]); + fail(); + } catch (IllegalArgumentException e) { + // as expected + } + try { + StdArrays.copyFrom(matrix, new int[1][2][2]); + fail(); + } catch (IllegalArgumentException e) { + // as expected + } + try { + StdArrays.copyFrom(matrix, new int[2][2][2]); + fail(); + } catch (IllegalArgumentException e) { + // as expected + } + } + + @Test + public void objectMatrix() { + NdArray matrix = StdArrays.ndCopyOf(new String[][] {{"ab", "bc"}, {"cd", "de"}}); + assertEquals(NdArrays.vectorOfObjects("ab", "bc"), matrix.get(0)); + assertEquals(NdArrays.vectorOfObjects("cd", "de"), matrix.get(1)); + + String[][] array = StdArrays.array2dCopyOf(matrix, String.class); + assertEquals("ab", array[0][0]); + assertEquals("bc", array[0][1]); + assertEquals("cd", array[1][0]); + assertEquals("de", array[1][1]); + + array = new String[2][3]; + StdArrays.copyFrom(matrix, array); + assertEquals("ab", array[0][0]); + assertEquals("bc", array[0][1]); + assertNull(array[0][2]); + assertEquals("cd", array[1][0]); + assertEquals("de", array[1][1]); + assertNull(array[1][2]); + } + + @Test + public void cannotInitDenseMatrixWithRaggedArray() { + IntNdArray matrix = NdArrays.ofInts(Shape.of(2, 2)); + try { + StdArrays.copyTo(new int[][]{ + {1, 2}, + {3} + }, matrix); + fail(); + } catch (IllegalArgumentException e) { + // as expected + } + } + + @Test + public void computeShapeDense3DMatrix() { + Shape shape = StdArrays.shapeOf(new int[][][] { + { + {1, 2, 3}, {4, 5, 6} + }, + { + {1, 2, 3}, {4, 5, 6} + } + }); + assertArrayEquals(new long[] {2, 2, 3}, shape.asArray()); + } + + @Test + public void shapeOfRagged3DMatrix() { + Shape shape = StdArrays.shapeOf(new int[][][] { + { + {1, 2, 3}, {4, 5, 6}, {7, 8, 9} + }, + { + {1, 2, 3}, {4, 5, 6} + } + }); + assertArrayEquals(new long[] {2, Shape.UNKNOWN_SIZE, 3}, shape.asArray()); + } + + @Test + public void shapeOfEmptyArray() { + Shape shape = StdArrays.shapeOf(new int[2][2][3]); + assertArrayEquals(new long[] {2, 2, 3}, shape.asArray()); + } +} diff --git a/ndarray/src/test/java/org/tensorflow/ndarray/benchmark/NdArrayBenchmark.java b/ndarray/src/test/java/org/tensorflow/ndarray/benchmark/NdArrayBenchmark.java new file mode 100644 index 00000000000..fb7022bc830 --- /dev/null +++ b/ndarray/src/test/java/org/tensorflow/ndarray/benchmark/NdArrayBenchmark.java @@ -0,0 +1,163 @@ +/* + Copyright 2019 The TensorFlow Authors. All Rights Reserved. + + Licensed under the Apache License, Version 2.0 (the "License"); + you may not use this file except in compliance with the License. + You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + + Unless required by applicable law or agreed to in writing, software + distributed under the License is distributed on an "AS IS" BASIS, + WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + See the License for the specific language governing permissions and + limitations under the License. + ======================================================================= + */ +package org.tensorflow.ndarray.benchmark; + +import static org.tensorflow.ndarray.index.Indices.all; +import static org.tensorflow.ndarray.index.Indices.at; + +import java.awt.image.BufferedImage; +import java.awt.image.Raster; +import java.io.IOException; +import javax.imageio.ImageIO; +import org.openjdk.jmh.annotations.Benchmark; +import org.openjdk.jmh.annotations.BenchmarkMode; +import org.openjdk.jmh.annotations.Fork; +import org.openjdk.jmh.annotations.Measurement; +import org.openjdk.jmh.annotations.Mode; +import org.openjdk.jmh.annotations.Scope; +import org.openjdk.jmh.annotations.Setup; +import org.openjdk.jmh.annotations.State; +import org.openjdk.jmh.annotations.Warmup; +import org.openjdk.jmh.runner.RunnerException; +import org.tensorflow.ndarray.Shape; +import org.tensorflow.ndarray.FloatNdArray; +import org.tensorflow.ndarray.NdArrays; +import org.tensorflow.ndarray.StdArrays; + +@Fork(value = 1, jvmArgs = {"-Xms4G", "-Xmx4G"}) +@BenchmarkMode(Mode.AverageTime) +@Warmup(iterations = 3) +@Measurement(iterations = 5) +@State(Scope.Benchmark) +public class NdArrayBenchmark { + + public static void main(String[] args) throws IOException, RunnerException { + org.openjdk.jmh.Main.main(args); + } + + @Setup + public void setUp() throws IOException { + BufferedImage image = ImageIO.read(getClass().getClassLoader().getResourceAsStream(TEST_IMAGE)); + + int numPixels = image.getWidth() * image.getHeight(); + pixels = NdArrays.ofFloats(Shape.of(numPixels, 3)); + channels = NdArrays.ofFloats(Shape.of(3, numPixels)); + + Raster imageData = image.getData(); + float[] pixel = new float[3]; + for (int y = 0, pixelIdx = 0; y < image.getHeight(); ++y) { + for (int x = 0; x < image.getWidth(); ++x, ++pixelIdx) { + imageData.getPixel(x, y, pixel); + StdArrays.copyTo(pixel, pixels.get(pixelIdx)); + StdArrays.copyTo(pixel, channels.slice(all(), at(pixelIdx))); + } + } + batches = NdArrays.ofFloats(Shape.of(BATCH_SIZE, 3, numPixels)); + firstBatch = batches.get(0); + } + + @Benchmark + @Measurement(batchSize = 2049 * 1537) + public void getElementAtIndex() { + pixels.get(0); + } + + @Benchmark + @Measurement(batchSize = 2049 * 1537) + public void slicing() { + batches.slice(at(0), all(), at(0)); + } + + @Benchmark + public void readingAllPixelsChannelsBySequence() { + pixels.scalars().forEach(pixel -> pixel.getFloat()); + } + + @Benchmark + public void readingAllPixelsChannelsBySequenceSlices() { + pixels.scalars().asSlices().forEach(pixel -> pixel.getFloat()); + } + + @Benchmark + @Measurement(batchSize = 100) + public void readingAllPixelsChannelsByIndex() { + long[] shape = pixels.shape().asArray(); + for (int i = 0; i < shape[0]; ++i) { + for (int j = 0; j < shape[1]; ++j) { + pixels.getFloat(i, j); + } + } + } + + @Benchmark + @Measurement(batchSize = BATCH_SIZE) + public void writeFirstBatchChannels() { + firstBatch.set(channels); + } + + @Benchmark + public void writeAllBatchChannels() { + batches.elements(0).forEach(batch -> + batch.set(channels) + ); + } + + @Benchmark + @Measurement(batchSize = 2049 * 1537) + public void writeOnePixelBySlicing() { + batches.slice(at(0), all(), at(0)).set(pixels.get(0)); + } + + @Benchmark + public void writeAllPixelsBySlicing() { + batches.elements(0).forEach(batch -> + pixels.elements(0).forEachIndexed((coords, pixel) -> + batch.slice(all(), at(coords[0])).set(pixel) + ) + ); + } + + @Benchmark + @Measurement(batchSize = 2049 * 1537) + public void writeOnePixelsByIndex() { + batches + .setFloat(pixels.getFloat(0, 0), 0, 0, 0) + .setFloat(pixels.getFloat(0, 1), 0, 1, 0) + .setFloat(pixels.getFloat(0, 2), 0, 2, 0); + } + + @Benchmark + public void writeAllPixelsByIndex() { + batches.elements(0).forEach(batch -> + pixels.elements(0).forEachIndexed((coords, pixel) -> { + long pixelIndex = coords[0]; + batch + .setFloat(pixel.getFloat(0), 0, pixelIndex) + .setFloat(pixel.getFloat(1), 1, pixelIndex) + .setFloat(pixel.getFloat(2), 2, pixelIndex); + }) + ); + } + + private static final String TEST_IMAGE = "castle.jpg"; + private static final int BATCH_SIZE = 60; + + private FloatNdArray pixels; + private FloatNdArray channels; + private FloatNdArray batches; + private FloatNdArray firstBatch; +} diff --git a/ndarray/src/test/java/org/tensorflow/ndarray/buffer/BooleanDataBufferTestBase.java b/ndarray/src/test/java/org/tensorflow/ndarray/buffer/BooleanDataBufferTestBase.java new file mode 100644 index 00000000000..3f6df8aa1ce --- /dev/null +++ b/ndarray/src/test/java/org/tensorflow/ndarray/buffer/BooleanDataBufferTestBase.java @@ -0,0 +1,142 @@ +/* + Copyright 2019 The TensorFlow Authors. All Rights Reserved. + + Licensed under the Apache License, Version 2.0 (the "License"); + you may not use this file except in compliance with the License. + You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + + Unless required by applicable law or agreed to in writing, software + distributed under the License is distributed on an "AS IS" BASIS, + WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + See the License for the specific language governing permissions and + limitations under the License. + ======================================================================= + */ +package org.tensorflow.ndarray.buffer; + +import static org.junit.jupiter.api.Assertions.assertTrue; +import static org.junit.jupiter.api.Assertions.assertArrayEquals; +import static org.junit.jupiter.api.Assertions.assertEquals; +import static org.junit.jupiter.api.Assertions.assertFalse; +import static org.junit.jupiter.api.Assertions.assertNotEquals; + +import java.util.Arrays; +import java.util.BitSet; +import org.junit.jupiter.api.Test; +import org.tensorflow.ndarray.impl.buffer.misc.MiscDataBufferFactory; + +public abstract class BooleanDataBufferTestBase extends DataBufferTestBase { + + @Override + protected abstract BooleanDataBuffer allocate(long size); + + @Override + protected Boolean valueOf(Long val) { + return val != 0; + } + + @Test + public void writeAndReadFromArray() { + BooleanDataBuffer buffer = allocate(10L); + boolean[] values = new boolean[]{true, false, false, true, false}; + + buffer.write(values); + assertTrue(buffer.getObject(0)); + assertFalse(buffer.getObject(1)); + + buffer.offset(5).write(values); + assertTrue(buffer.getObject(5)); + + boolean[] read = new boolean[5]; + buffer.read(read); + assertArrayEquals(values, read); + + buffer.write(values, 2, 3); + assertFalse(buffer.getObject(0)); + assertTrue(buffer.getObject(1)); + assertFalse(buffer.getObject(2)); + + Arrays.fill(read, false); + buffer.read(read, 1, 2); + assertFalse(read[0]); + assertFalse(read[1]); + assertTrue(read[2]); + assertFalse(read[3]); + } + + @Test + public void equalWithBitSetBuffer() { + BitSet bitSet1 = BitSet.valueOf(new byte[] { 0x01, 0x01 }); + BooleanDataBuffer bitSet1Buffer = MiscDataBufferFactory.create(bitSet1, 12, true); + + BitSet bitSet2 = BitSet.valueOf(new byte[] { 0x11, 0x01 }); + BooleanDataBuffer bitSet2Buffer = MiscDataBufferFactory.create(bitSet2, 12, true); + + BooleanDataBuffer buffer = allocate(12) + .setBoolean(true, 0) + .setBoolean(true, 8); + + assertTrue(bitSet1Buffer.equals(buffer)); + assertTrue(buffer.equals(bitSet1Buffer)); + assertEquals(bitSet1Buffer.hashCode(), buffer.hashCode()); + + assertFalse(bitSet2Buffer.equals(buffer)); + assertFalse(buffer.equals(bitSet2Buffer)); + assertNotEquals(bitSet2Buffer.hashCode(), buffer.hashCode()); + } + + @Test + public void equalWithBooleanArrayBuffer() { + boolean[] array1 = new boolean[] { false, false, false, true, true, false }; + BooleanDataBuffer array1Buffer = MiscDataBufferFactory.create(array1, true); + + boolean[] array2 = new boolean[] { false, false, false, true, true, true }; + BooleanDataBuffer array2Buffer = MiscDataBufferFactory.create(array2, true); + + BooleanDataBuffer buffer = allocate(6) + .setBoolean(true, 3) + .setBoolean(true, 4); + + assertTrue(array1Buffer.equals(buffer)); + assertTrue(buffer.equals(array1Buffer)); + assertEquals(array1Buffer.hashCode(), buffer.hashCode()); + + assertFalse(array2Buffer.equals(buffer)); + assertFalse(buffer.equals(array2Buffer)); + assertNotEquals(array2Buffer.hashCode(), buffer.hashCode()); + } + + @Test + public void equalWithBooleanObjectBuffer() { + Boolean[] array1 = new Boolean[] { false, false, false, true, true, false }; + DataBuffer array1Buffer = MiscDataBufferFactory.create(array1, true); + + boolean[] array2 = new boolean[] { false, false, false, true, true, true }; + DataBuffer array2Buffer = MiscDataBufferFactory.create(array2, true); + + BooleanDataBuffer buffer = allocate(6) + .setBoolean(true, 3) + .setBoolean(true, 4); + + assertTrue(array1Buffer.equals(buffer)); + assertTrue(buffer.equals(array1Buffer)); + assertEquals(array1Buffer.hashCode(), buffer.hashCode()); + + assertFalse(array2Buffer.equals(buffer)); + assertFalse(buffer.equals(array2Buffer)); + assertNotEquals(array2Buffer.hashCode(), buffer.hashCode()); + } + + @Test + public void notEqualWithOtherTypes() { + BooleanDataBuffer buffer = allocate(2) + .setBoolean(false, 0) + .setBoolean(true, 1); + ByteDataBuffer byteBuffer = DataBuffers.of((byte)0, (byte)1); + + assertFalse(buffer.equals(byteBuffer)); + assertFalse(byteBuffer.equals(buffer)); + } +} diff --git a/ndarray/src/test/java/org/tensorflow/ndarray/buffer/ByteDataBufferTestBase.java b/ndarray/src/test/java/org/tensorflow/ndarray/buffer/ByteDataBufferTestBase.java new file mode 100644 index 00000000000..777368466f5 --- /dev/null +++ b/ndarray/src/test/java/org/tensorflow/ndarray/buffer/ByteDataBufferTestBase.java @@ -0,0 +1,145 @@ +/* + Copyright 2019 The TensorFlow Authors. All Rights Reserved. + + Licensed under the Apache License, Version 2.0 (the "License"); + you may not use this file except in compliance with the License. + You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + + Unless required by applicable law or agreed to in writing, software + distributed under the License is distributed on an "AS IS" BASIS, + WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + See the License for the specific language governing permissions and + limitations under the License. + ======================================================================= + */ +package org.tensorflow.ndarray.buffer; + +import static org.junit.jupiter.api.Assertions.assertArrayEquals; +import static org.junit.jupiter.api.Assertions.assertEquals; +import static org.junit.jupiter.api.Assertions.assertFalse; +import static org.junit.jupiter.api.Assertions.assertNotEquals; +import static org.junit.jupiter.api.Assertions.assertTrue; + +import java.nio.ByteBuffer; +import java.util.Arrays; +import org.junit.jupiter.api.Test; +import org.tensorflow.ndarray.impl.buffer.misc.MiscDataBufferFactory; +import org.tensorflow.ndarray.impl.buffer.nio.NioDataBufferFactory; +import org.tensorflow.ndarray.impl.buffer.raw.RawDataBufferFactory; + +public abstract class ByteDataBufferTestBase extends DataBufferTestBase { + + @Override + protected abstract ByteDataBuffer allocate(long size); + + @Override + protected Byte valueOf(Long val) { + return val.byteValue(); + } + + @Test + public void writeAndReadFromArray() { + ByteDataBuffer buffer = allocate(10L); + byte[] oneToFive = new byte[]{ 1, 2, 3, 4, 5 }; + + buffer.write(oneToFive); + assertEquals(2, buffer.getByte(1)); + + buffer.offset(5).write(oneToFive); + assertEquals(2, buffer.getByte(1)); + assertEquals(2, buffer.getByte(6)); + + byte[] read = new byte[5]; + buffer.read(read); + assertArrayEquals(oneToFive, read); + + buffer.write(oneToFive, 2, 2); + assertEquals(3, buffer.getByte(0)); + assertEquals(4, buffer.getByte(1)); + assertEquals(3, buffer.getByte(2)); + + Arrays.fill(read, valueOf(0L)); + buffer.read(read, 1, 2); + assertEquals(0, read[0]); + assertEquals(3, read[1]); + assertEquals(4, read[2]); + assertEquals(0, read[3]); + } + + @Test + public void equalWithByteNioBuffer() { + ByteDataBuffer nioBuffer1 = NioDataBufferFactory.create(ByteBuffer.wrap(new byte[] { 0x01, 0x10 })); + ByteDataBuffer nioBuffer2 = NioDataBufferFactory.create(ByteBuffer.wrap(new byte[] { 0x01, 0x11 })); + + ByteDataBuffer buffer = allocate(2) + .setByte((byte)0x01, 0) + .setByte((byte)0x10, 1); + + assertTrue(nioBuffer1.equals(buffer)); + assertTrue(buffer.equals(nioBuffer1)); + assertEquals(nioBuffer1.hashCode(), buffer.hashCode()); + + assertFalse(nioBuffer2.equals(buffer)); + assertFalse(buffer.equals(nioBuffer2)); + assertNotEquals(nioBuffer2.hashCode(), buffer.hashCode()); + } + + @Test + public void equalWithByteRawBuffer() { + ByteDataBuffer rawBuffer1 = RawDataBufferFactory.create(new byte[] { 0x01, 0x10 }, true); + ByteDataBuffer rawBuffer2 = RawDataBufferFactory.create(new byte[] { 0x01, 0x11 }, true); + + ByteDataBuffer buffer = allocate(2) + .setByte((byte)0x01, 0) + .setByte((byte)0x10, 1); + + assertTrue(rawBuffer1.equals(buffer)); + assertTrue(buffer.equals(rawBuffer1)); + assertEquals(rawBuffer1.hashCode(), buffer.hashCode()); + + assertFalse(rawBuffer2.equals(buffer)); + assertFalse(buffer.equals(rawBuffer2)); + assertNotEquals(rawBuffer2.hashCode(), buffer.hashCode()); + } + + @Test + public void equalWithByteObjectBuffer() { + DataBuffer objBuffer1 = MiscDataBufferFactory.create(new Byte[] { 0x01, 0x10 }, true); + DataBuffer objBuffer2 = MiscDataBufferFactory.create(new Byte[] { 0x01, 0x11 }, true); + + ByteDataBuffer buffer = allocate(2) + .setByte((byte)0x01, 0) + .setByte((byte)0x10, 1); + + assertTrue(objBuffer1.equals(buffer)); + assertTrue(buffer.equals(objBuffer1)); + assertEquals(objBuffer1.hashCode(), buffer.hashCode()); + + assertFalse(objBuffer2.equals(buffer)); + assertFalse(buffer.equals(objBuffer2)); + assertNotEquals(objBuffer2.hashCode(), buffer.hashCode()); + } + + @Test + public void notEqualWithOtherTypes() { + ByteDataBuffer buffer = allocate(2) + .setByte((byte)1, 0) + .setByte((byte)16, 1); + LongDataBuffer longBuffer = DataBuffers.of(1L, 16L); + + assertFalse(buffer.equals(longBuffer)); + assertFalse(longBuffer.equals(buffer)); + + try { + IntDataBuffer intBuffer = buffer.asInts(); + + assertFalse(buffer.equals(intBuffer)); + assertFalse(intBuffer.equals(buffer)); + + } catch (IllegalStateException e) { + // some byte buffers cannot be converted to ints, ignore the test in that case + } + } +} diff --git a/ndarray/src/test/java/org/tensorflow/ndarray/buffer/DataBufferTestBase.java b/ndarray/src/test/java/org/tensorflow/ndarray/buffer/DataBufferTestBase.java new file mode 100644 index 00000000000..9a023915735 --- /dev/null +++ b/ndarray/src/test/java/org/tensorflow/ndarray/buffer/DataBufferTestBase.java @@ -0,0 +1,293 @@ +/* + Copyright 2019 The TensorFlow Authors. All Rights Reserved. + + Licensed under the Apache License, Version 2.0 (the "License"); + you may not use this file except in compliance with the License. + You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + + Unless required by applicable law or agreed to in writing, software + distributed under the License is distributed on an "AS IS" BASIS, + WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + See the License for the specific language governing permissions and + limitations under the License. + ======================================================================= + */ +package org.tensorflow.ndarray.buffer; + +import static org.junit.jupiter.api.Assertions.assertEquals; +import static org.junit.jupiter.api.Assertions.assertFalse; +import static org.junit.jupiter.api.Assertions.assertNotEquals; +import static org.junit.jupiter.api.Assertions.assertSame; +import static org.junit.jupiter.api.Assertions.assertTrue; +import static org.junit.jupiter.api.Assertions.fail; + +import java.nio.BufferOverflowException; +import java.nio.BufferUnderflowException; +import org.junit.jupiter.api.Test; +import org.tensorflow.ndarray.buffer.DataBuffer; +import org.tensorflow.ndarray.buffer.DataBufferWindow; +import org.tensorflow.ndarray.buffer.DataBuffers; + +public abstract class DataBufferTestBase { + + protected final boolean enableLargeBufferTests = System.getProperty("testLargeBuffers") != null; + + protected long maxSize() { + return DataBuffers.MAX_32BITS; + } + + protected abstract DataBuffer allocate(long size); + + protected abstract T valueOf(Long val); + + @Test + public void bufferSize() { + DataBuffer buffer = allocate(10L); + assertEquals(10L, buffer.size()); + + buffer = allocate(0L); + assertEquals(0L, buffer.size()); + + if (enableLargeBufferTests) { + buffer = allocate(maxSize()); + assertEquals(maxSize(), buffer.size()); + } + } + + @Test + public void offsetNarrowAndSlice() { + DataBuffer buffer = allocate(10L).setObject(valueOf(1L), 5); // 0, 0, 0, 0, 0, 1, 0, 0, 0, 0 + assertEquals(10L, buffer.size()); + assertEquals(valueOf(1L), buffer.getObject(5)); + + DataBuffer subBuffer = buffer.slice(2, 6); // 0, 0, 0, 1, 0, 0 + assertEquals(6L, subBuffer.size()); + assertEquals(valueOf(1L), subBuffer.getObject(3)); + + subBuffer = subBuffer.offset(2L); // 0, 1, 0, 0 + assertEquals(4L, subBuffer.size()); + assertEquals(valueOf(1L), subBuffer.getObject(1)); + + subBuffer = subBuffer.narrow(2L); // 0, 1 + assertEquals(2L, subBuffer.size()); + assertEquals(valueOf(1L), subBuffer.getObject(1)); + try { + subBuffer.getObject(2); + fail(); + } catch (IndexOutOfBoundsException e) { + //as expected + } + try { + buffer.slice(2, 12); + fail(); + } catch (IllegalArgumentException e) { + // as expected + } + try { + buffer.slice(-1, 3); + fail(); + } catch (IllegalArgumentException e) { + // as expected + } + try { + buffer.slice(2, -1); + fail(); + } catch (IllegalArgumentException e) { + // as expected + } + try { + buffer.offset(-1L); + fail(); + } catch (IllegalArgumentException e) { + // as expected + } + try { + buffer.offset(11L); + fail(); + } catch (IllegalArgumentException e) { + // as expected + } + try { + buffer.narrow(-1L); + fail(); + } catch (IllegalArgumentException e) { + // as expected + } + try { + buffer.narrow(11L); + fail(); + } catch (IllegalArgumentException e) { + // as expected + } + } + + @Test + public void putAndGet() { + DataBuffer buffer = allocate(10L); + + buffer.setObject(valueOf(5L), 5L); + assertEquals(valueOf(5L), buffer.getObject(5L)); + try { + buffer.setObject(valueOf(10L), 10L); + fail(); + } catch (IndexOutOfBoundsException e) { + // as expected + } + try { + buffer.getObject(10L); + fail(); + } catch (IndexOutOfBoundsException e) { + // as expected + } + try { + buffer.setObject(valueOf(-1L), -1L); + fail(); + } catch (IndexOutOfBoundsException e) { + // as expected + } + try { + buffer.getObject(-1L); + fail(); + } catch (IndexOutOfBoundsException e) { + // as expected + } + } + + @Test + public void copyToBuffer() { + DataBuffer srcBuffer = allocate(25L); + srcBuffer.setObject(valueOf(5L), 5L); + srcBuffer.setObject(valueOf(10L), 10L); + srcBuffer.setObject(valueOf(15L), 15L); + srcBuffer.setObject(valueOf(20L), 20L); + try { + srcBuffer.copyTo(srcBuffer, srcBuffer.size()); + fail(); + } catch (IllegalArgumentException e) { + // as expected + } + DataBuffer dstBuffer = allocate(30L); + srcBuffer.copyTo(dstBuffer, srcBuffer.size()); + assertEquals(valueOf(5L), dstBuffer.getObject(5L)); + try { + srcBuffer.copyTo(dstBuffer, dstBuffer.size()); + fail(); + } catch (BufferUnderflowException e) { + // as expected + } + try { + dstBuffer.copyTo(srcBuffer, dstBuffer.size()); + fail(); + } catch (BufferOverflowException e) { + // as expected + } + } + + @Test + public void createFromVarargs() { + DataBuffer buffer = DataBuffers.ofObjects(valueOf(1L), valueOf(2L), valueOf(3L)); + assertEquals(3, buffer.size()); + assertEquals(valueOf(1L), buffer.getObject(0)); + assertEquals(valueOf(2L), buffer.getObject(1)); + assertEquals(valueOf(3L), buffer.getObject(2)); + } + + @Test + public void equalWithObjectBuffer() { + DataBuffer buffer1 = allocate(2) + .setObject(valueOf(0L), 0) + .setObject(valueOf(1L), 1); + DataBuffer buffer2 = allocate(2) + .setObject(valueOf(0L), 0) + .setObject(valueOf(1L), 1); + DataBuffer buffer3 = allocate(2) + .setObject(valueOf(1L), 0) + .setObject(valueOf(0L), 1); + DataBuffer buffer4 = allocate(1) + .setObject(valueOf(0L), 0); + DataBuffer buffer5 = allocate(3) + .setObject(valueOf(0L), 0) + .setObject(valueOf(1L), 1) + .setObject(valueOf(2L), 2); + + assertTrue(buffer1.equals(buffer2)); + assertTrue(buffer2.equals(buffer1)); + assertEquals(buffer1.hashCode(), buffer1.hashCode()); + assertEquals(buffer1.hashCode(), buffer2.hashCode()); + + assertFalse(buffer3.equals(buffer1)); + assertFalse(buffer1.equals(buffer3)); + assertNotEquals(buffer3.hashCode(), buffer1.hashCode()); + + assertFalse(buffer4.equals(buffer1)); + assertFalse(buffer1.equals(buffer4)); + assertNotEquals(buffer4.hashCode(), buffer1.hashCode()); + + assertFalse(buffer5.equals(buffer1)); + assertFalse(buffer1.equals(buffer5)); + assertNotEquals(buffer5.hashCode(), buffer1.hashCode()); + } + + @Test + public void bufferWindow() { + DataBuffer buffer = allocate(20); + DataBufferWindow> bufferWindow; + try { + bufferWindow = buffer.window(4); + } catch (UnsupportedOperationException e) { + return; // skip test if this buffer does not support windows + } + assertEquals(0, bufferWindow.offset()); + assertEquals(4, bufferWindow.size()); + assertEquals(4, bufferWindow.buffer().size()); + + for (long i = 0; i < buffer.size(); ++i) { + buffer.setObject(valueOf(i), i); + } + assertEquals(valueOf(2L), bufferWindow.buffer().getObject(2)); + DataBuffer windowBuffer = bufferWindow.buffer(); + + bufferWindow.slide(10); + assertEquals(10, bufferWindow.offset()); + assertEquals(4, bufferWindow.size()); + assertEquals(valueOf(12L), bufferWindow.buffer().getObject(2)); + assertSame(windowBuffer, bufferWindow.buffer()); + + bufferWindow.slide(-2); + assertEquals(8, bufferWindow.offset()); + assertEquals(4, bufferWindow.size()); + assertEquals(valueOf(10L), bufferWindow.buffer().getObject(2)); + + bufferWindow.slideTo(16); + assertEquals(16, bufferWindow.offset()); + assertEquals(4, bufferWindow.size()); + assertEquals(valueOf(18L), bufferWindow.buffer().getObject(2)); + + try { + bufferWindow.slide(1); + fail(); + } catch (IndexOutOfBoundsException e) { + // as expected + } + try { + bufferWindow.slide(-17); + fail(); + } catch (IndexOutOfBoundsException e) { + // as expected + } + try { + bufferWindow.slideTo(-1); + fail(); + } catch (IndexOutOfBoundsException e) { + // as expected + } + try { + bufferWindow.slideTo(17); + fail(); + } catch (IndexOutOfBoundsException e) { + // as expected + } + } +} diff --git a/ndarray/src/test/java/org/tensorflow/ndarray/buffer/DoubleDataBufferTestBase.java b/ndarray/src/test/java/org/tensorflow/ndarray/buffer/DoubleDataBufferTestBase.java new file mode 100644 index 00000000000..4dee064968c --- /dev/null +++ b/ndarray/src/test/java/org/tensorflow/ndarray/buffer/DoubleDataBufferTestBase.java @@ -0,0 +1,135 @@ +/* + Copyright 2019 The TensorFlow Authors. All Rights Reserved. + + Licensed under the Apache License, Version 2.0 (the "License"); + you may not use this file except in compliance with the License. + You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + + Unless required by applicable law or agreed to in writing, software + distributed under the License is distributed on an "AS IS" BASIS, + WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + See the License for the specific language governing permissions and + limitations under the License. + ======================================================================= + */ +package org.tensorflow.ndarray.buffer; + +import static org.junit.jupiter.api.Assertions.assertArrayEquals; +import static org.junit.jupiter.api.Assertions.assertEquals; +import static org.junit.jupiter.api.Assertions.assertFalse; +import static org.junit.jupiter.api.Assertions.assertNotEquals; +import static org.junit.jupiter.api.Assertions.assertTrue; + +import java.nio.DoubleBuffer; +import java.util.Arrays; +import org.junit.jupiter.api.Test; +import org.tensorflow.ndarray.impl.buffer.misc.MiscDataBufferFactory; +import org.tensorflow.ndarray.impl.buffer.nio.NioDataBufferFactory; +import org.tensorflow.ndarray.impl.buffer.raw.RawDataBufferFactory; + +public abstract class DoubleDataBufferTestBase extends DataBufferTestBase { + + @Override + protected abstract DoubleDataBuffer allocate(long size); + + @Override + protected Double valueOf(Long val) { + return val.doubleValue(); + } + + @Test + public void writeAndReadFromArray() { + DoubleDataBuffer buffer = allocate(10L); + double[] oneToFive = new double[]{ 1.0, 2.0, 3.0, 4.0, 5.0 }; + + buffer.write(oneToFive); + assertEquals(2.0, buffer.getDouble(1), 0.0); + + buffer.offset(5).write(oneToFive); + assertEquals(2.0, buffer.getDouble(1), 0.0); + assertEquals(2.0, buffer.getDouble(6), 0.0); + + double[] read = new double[5]; + buffer.read(read); + assertArrayEquals(oneToFive, read, 0.0); + + buffer.write(oneToFive, 2, 2); + assertEquals(3.0, buffer.getDouble(0), 0.0); + assertEquals(4.0, buffer.getDouble(1), 0.0); + assertEquals(3.0, buffer.getDouble(2), 0.0); + + Arrays.fill(read, valueOf(0L)); + buffer.read(read, 1, 2); + assertEquals(0.0, read[0], 0.0); + assertEquals(3.0, read[1], 0.0); + assertEquals(4.0, read[2], 0.0); + assertEquals(0.0, read[3], 0.0); + } + + @Test + public void equalWithDoubleNioBuffer() { + DoubleDataBuffer nioBuffer1 = NioDataBufferFactory.create(DoubleBuffer.wrap(new double[] { 1.0, 16.0 })); + DoubleDataBuffer nioBuffer2 = NioDataBufferFactory.create(DoubleBuffer.wrap(new double[] { 1.0, 25.0 })); + + DoubleDataBuffer buffer = allocate(2) + .setDouble(1.0, 0) + .setDouble(16.0, 1); + + assertTrue(nioBuffer1.equals(buffer)); + assertTrue(buffer.equals(nioBuffer1)); + assertEquals(nioBuffer1.hashCode(), buffer.hashCode()); + + assertFalse(nioBuffer2.equals(buffer)); + assertFalse(buffer.equals(nioBuffer2)); + assertNotEquals(nioBuffer2.hashCode(), buffer.hashCode()); + } + + @Test + public void equalWithDoubleRawBuffer() { + DoubleDataBuffer rawBuffer1 = RawDataBufferFactory.create(new double[] { 1.0, 16.0 }, true); + DoubleDataBuffer rawBuffer2 = RawDataBufferFactory.create(new double[] { 1.0, 25.0 }, true); + + DoubleDataBuffer buffer = allocate(2) + .setDouble(1.0, 0) + .setDouble(16.0, 1); + + assertTrue(rawBuffer1.equals(buffer)); + assertTrue(buffer.equals(rawBuffer1)); + assertEquals(rawBuffer1.hashCode(), buffer.hashCode()); + + assertFalse(rawBuffer2.equals(buffer)); + assertFalse(buffer.equals(rawBuffer2)); + assertNotEquals(rawBuffer2.hashCode(), buffer.hashCode()); + } + + @Test + public void equalWithDoubleObjectBuffer() { + DataBuffer objBuffer1 = MiscDataBufferFactory.create(new Double[] { 1.0, 16.0 }, true); + DataBuffer objBuffer2 = MiscDataBufferFactory.create(new Double[] { 1.0, 25.0 }, true); + + DoubleDataBuffer buffer = allocate(2) + .setDouble(1.0, 0) + .setDouble(16.0, 1); + + assertTrue(objBuffer1.equals(buffer)); + assertTrue(buffer.equals(objBuffer1)); + assertEquals(objBuffer1.hashCode(), buffer.hashCode()); + + assertFalse(objBuffer2.equals(buffer)); + assertFalse(buffer.equals(objBuffer2)); + assertNotEquals(objBuffer2.hashCode(), buffer.hashCode()); + } + + @Test + public void notEqualWithOtherTypes() { + DoubleDataBuffer buffer = allocate(2) + .setDouble(1.0, 0) + .setDouble(16.0, 1); + FloatDataBuffer floatBuffer = DataBuffers.of(1.0f, 16.0f); + + assertFalse(buffer.equals(floatBuffer)); + assertFalse(floatBuffer.equals(buffer)); + } +} diff --git a/ndarray/src/test/java/org/tensorflow/ndarray/buffer/FloatDataBufferTestBase.java b/ndarray/src/test/java/org/tensorflow/ndarray/buffer/FloatDataBufferTestBase.java new file mode 100644 index 00000000000..49c4f15b808 --- /dev/null +++ b/ndarray/src/test/java/org/tensorflow/ndarray/buffer/FloatDataBufferTestBase.java @@ -0,0 +1,135 @@ +/* + Copyright 2019 The TensorFlow Authors. All Rights Reserved. + + Licensed under the Apache License, Version 2.0 (the "License"); + you may not use this file except in compliance with the License. + You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + + Unless required by applicable law or agreed to in writing, software + distributed under the License is distributed on an "AS IS" BASIS, + WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + See the License for the specific language governing permissions and + limitations under the License. + ======================================================================= + */ +package org.tensorflow.ndarray.buffer; + +import static org.junit.jupiter.api.Assertions.assertArrayEquals; +import static org.junit.jupiter.api.Assertions.assertEquals; +import static org.junit.jupiter.api.Assertions.assertFalse; +import static org.junit.jupiter.api.Assertions.assertNotEquals; +import static org.junit.jupiter.api.Assertions.assertTrue; + +import java.nio.FloatBuffer; +import java.util.Arrays; +import org.junit.jupiter.api.Test; +import org.tensorflow.ndarray.impl.buffer.misc.MiscDataBufferFactory; +import org.tensorflow.ndarray.impl.buffer.nio.NioDataBufferFactory; +import org.tensorflow.ndarray.impl.buffer.raw.RawDataBufferFactory; + +public abstract class FloatDataBufferTestBase extends DataBufferTestBase { + + @Override + protected abstract FloatDataBuffer allocate(long size); + + @Override + protected Float valueOf(Long val) { + return val.floatValue(); + } + + @Test + public void writeAndReadFromArray() { + FloatDataBuffer buffer = allocate(10L); + float[] oneToFive = new float[]{ 1.0f, 2.0f, 3.0f, 4.0f, 5.0f }; + + buffer.write(oneToFive); + assertEquals(2.0f, buffer.getFloat(1), 0.0f); + + buffer.offset(5).write(oneToFive); + assertEquals(2.0f, buffer.getFloat(1), 0.0f); + assertEquals(2.0f, buffer.getFloat(6), 0.0f); + + float[] read = new float[5]; + buffer.read(read); + assertArrayEquals(oneToFive, read, 0.0f); + + buffer.write(oneToFive, 2, 2); + assertEquals(3.0f, buffer.getFloat(0), 0.0f); + assertEquals(4.0f, buffer.getFloat(1), 0.0f); + assertEquals(3.0f, buffer.getFloat(2), 0.0f); + + Arrays.fill(read, valueOf(0L)); + buffer.read(read, 1, 2); + assertEquals(0.0f, read[0], 0.0f); + assertEquals(3.0f, read[1], 0.0f); + assertEquals(4.0f, read[2], 0.0f); + assertEquals(0.0f, read[3], 0.0f); + } + + @Test + public void equalWithFloatNioBuffer() { + FloatDataBuffer nioBuffer1 = NioDataBufferFactory.create(FloatBuffer.wrap(new float[] { 1.0f, 16.0f })); + FloatDataBuffer nioBuffer2 = NioDataBufferFactory.create(FloatBuffer.wrap(new float[] { 1.0f, 25.0f })); + + FloatDataBuffer buffer = allocate(2) + .setFloat(1.0f, 0) + .setFloat(16.0f, 1); + + assertTrue(nioBuffer1.equals(buffer)); + assertTrue(buffer.equals(nioBuffer1)); + assertEquals(nioBuffer1.hashCode(), buffer.hashCode()); + + assertFalse(nioBuffer2.equals(buffer)); + assertFalse(buffer.equals(nioBuffer2)); + assertNotEquals(nioBuffer2.hashCode(), buffer.hashCode()); + } + + @Test + public void equalWithFloatRawBuffer() { + FloatDataBuffer rawBuffer1 = RawDataBufferFactory.create(new float[] { 1.0f, 16.0f }, true); + FloatDataBuffer rawBuffer2 = RawDataBufferFactory.create(new float[] { 1.0f, 25.0f }, true); + + FloatDataBuffer buffer = allocate(2) + .setFloat(1.0f, 0) + .setFloat(16.0f, 1); + + assertTrue(rawBuffer1.equals(buffer)); + assertTrue(buffer.equals(rawBuffer1)); + assertEquals(rawBuffer1.hashCode(), buffer.hashCode()); + + assertFalse(rawBuffer2.equals(buffer)); + assertFalse(buffer.equals(rawBuffer2)); + assertNotEquals(rawBuffer2.hashCode(), buffer.hashCode()); + } + + @Test + public void equalWithFloatObjectBuffer() { + DataBuffer objBuffer1 = MiscDataBufferFactory.create(new Float[] { 1.0f, 16.0f }, true); + DataBuffer objBuffer2 = MiscDataBufferFactory.create(new Float[] { 1.0f, 25.0f }, true); + + FloatDataBuffer buffer = allocate(2) + .setFloat(1.0f, 0) + .setFloat(16.0f, 1); + + assertTrue(objBuffer1.equals(buffer)); + assertTrue(buffer.equals(objBuffer1)); + assertEquals(objBuffer1.hashCode(), buffer.hashCode()); + + assertFalse(objBuffer2.equals(buffer)); + assertFalse(buffer.equals(objBuffer2)); + assertNotEquals(objBuffer2.hashCode(), buffer.hashCode()); + } + + @Test + public void notEqualWithOtherTypes() { + FloatDataBuffer buffer = allocate(2) + .setFloat(1.0f, 0) + .setFloat(16.0f, 1); + DoubleDataBuffer doubleBuffer = DataBuffers.of(1.0, 16.0); + + assertFalse(buffer.equals(doubleBuffer)); + assertFalse(doubleBuffer.equals(buffer)); + } +} diff --git a/ndarray/src/test/java/org/tensorflow/ndarray/buffer/IntDataBufferTestBase.java b/ndarray/src/test/java/org/tensorflow/ndarray/buffer/IntDataBufferTestBase.java new file mode 100644 index 00000000000..f3642e88ef8 --- /dev/null +++ b/ndarray/src/test/java/org/tensorflow/ndarray/buffer/IntDataBufferTestBase.java @@ -0,0 +1,135 @@ +/* + Copyright 2019 The TensorFlow Authors. All Rights Reserved. + + Licensed under the Apache License, Version 2.0 (the "License"); + you may not use this file except in compliance with the License. + You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + + Unless required by applicable law or agreed to in writing, software + distributed under the License is distributed on an "AS IS" BASIS, + WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + See the License for the specific language governing permissions and + limitations under the License. + ======================================================================= + */ +package org.tensorflow.ndarray.buffer; + +import static org.junit.jupiter.api.Assertions.assertArrayEquals; +import static org.junit.jupiter.api.Assertions.assertEquals; +import static org.junit.jupiter.api.Assertions.assertFalse; +import static org.junit.jupiter.api.Assertions.assertNotEquals; +import static org.junit.jupiter.api.Assertions.assertTrue; + +import java.nio.IntBuffer; +import java.util.Arrays; +import org.junit.jupiter.api.Test; +import org.tensorflow.ndarray.impl.buffer.misc.MiscDataBufferFactory; +import org.tensorflow.ndarray.impl.buffer.nio.NioDataBufferFactory; +import org.tensorflow.ndarray.impl.buffer.raw.RawDataBufferFactory; + +public abstract class IntDataBufferTestBase extends DataBufferTestBase { + + @Override + protected abstract IntDataBuffer allocate(long size); + + @Override + protected Integer valueOf(Long val) { + return val.intValue(); + } + + @Test + public void writeAndReadFromArray() { + IntDataBuffer buffer = allocate(10L); + int[] oneToFive = new int[]{ 1, 2, 3, 4, 5 }; + + buffer.write(oneToFive); + assertEquals(2, buffer.getInt(1)); + + buffer.offset(5).write(oneToFive); + assertEquals(2, buffer.getInt(1)); + assertEquals(2, buffer.getInt(6)); + + int[] read = new int[5]; + buffer.read(read); + assertArrayEquals(oneToFive, read); + + buffer.write(oneToFive, 2, 2); + assertEquals(3, buffer.getInt(0)); + assertEquals(4, buffer.getInt(1)); + assertEquals(3, buffer.getInt(2)); + + Arrays.fill(read, valueOf(0L)); + buffer.read(read, 1, 2); + assertEquals(0, read[0]); + assertEquals(3, read[1]); + assertEquals(4, read[2]); + assertEquals(0, read[3]); + } + + @Test + public void equalWithIntNioBuffer() { + IntDataBuffer nioBuffer1 = NioDataBufferFactory.create(IntBuffer.wrap(new int[] { 1, 16 })); + IntDataBuffer nioBuffer2 = NioDataBufferFactory.create(IntBuffer.wrap(new int[] { 1, 25 })); + + IntDataBuffer buffer = allocate(2) + .setInt(1, 0) + .setInt(16, 1); + + assertTrue(nioBuffer1.equals(buffer)); + assertTrue(buffer.equals(nioBuffer1)); + assertEquals(nioBuffer1.hashCode(), buffer.hashCode()); + + assertFalse(nioBuffer2.equals(buffer)); + assertFalse(buffer.equals(nioBuffer2)); + assertNotEquals(nioBuffer2.hashCode(), buffer.hashCode()); + } + + @Test + public void equalWithIntRawBuffer() { + IntDataBuffer rawBuffer1 = RawDataBufferFactory.create(new int[] { 1, 16 }, true); + IntDataBuffer rawBuffer2 = RawDataBufferFactory.create(new int[] { 1, 25 }, true); + + IntDataBuffer buffer = allocate(2) + .setInt(1, 0) + .setInt(16, 1); + + assertTrue(rawBuffer1.equals(buffer)); + assertTrue(buffer.equals(rawBuffer1)); + assertEquals(rawBuffer1.hashCode(), buffer.hashCode()); + + assertFalse(rawBuffer2.equals(buffer)); + assertFalse(buffer.equals(rawBuffer2)); + assertNotEquals(rawBuffer2.hashCode(), buffer.hashCode()); + } + + @Test + public void equalWithIntObjectBuffer() { + DataBuffer objBuffer1 = MiscDataBufferFactory.create(new Integer[] { 1, 16 }, true); + DataBuffer objBuffer2 = MiscDataBufferFactory.create(new Integer[] { 1, 25 }, true); + + IntDataBuffer buffer = allocate(2) + .setInt(1, 0) + .setInt(16, 1); + + assertTrue(objBuffer1.equals(buffer)); + assertTrue(buffer.equals(objBuffer1)); + assertEquals(objBuffer1.hashCode(), buffer.hashCode()); + + assertFalse(objBuffer2.equals(buffer)); + assertFalse(buffer.equals(objBuffer2)); + assertNotEquals(objBuffer2.hashCode(), buffer.hashCode()); + } + + @Test + public void notEqualWithOtherTypes() { + IntDataBuffer buffer = allocate(2) + .setInt(1, 0) + .setInt(16, 1); + LongDataBuffer longBuffer = DataBuffers.of(1L, 16L); + + assertFalse(buffer.equals(longBuffer)); + assertFalse(longBuffer.equals(buffer)); + } +} diff --git a/ndarray/src/test/java/org/tensorflow/ndarray/buffer/LongDataBufferTestBase.java b/ndarray/src/test/java/org/tensorflow/ndarray/buffer/LongDataBufferTestBase.java new file mode 100644 index 00000000000..e0d8b1b4539 --- /dev/null +++ b/ndarray/src/test/java/org/tensorflow/ndarray/buffer/LongDataBufferTestBase.java @@ -0,0 +1,135 @@ +/* + Copyright 2019 The TensorFlow Authors. All Rights Reserved. + + Licensed under the Apache License, Version 2.0 (the "License"); + you may not use this file except in compliance with the License. + You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + + Unless required by applicable law or agreed to in writing, software + distributed under the License is distributed on an "AS IS" BASIS, + WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + See the License for the specific language governing permissions and + limitations under the License. + ======================================================================= + */ +package org.tensorflow.ndarray.buffer; + +import static org.junit.jupiter.api.Assertions.assertArrayEquals; +import static org.junit.jupiter.api.Assertions.assertEquals; +import static org.junit.jupiter.api.Assertions.assertFalse; +import static org.junit.jupiter.api.Assertions.assertNotEquals; +import static org.junit.jupiter.api.Assertions.assertTrue; + +import java.nio.LongBuffer; +import java.util.Arrays; +import org.junit.jupiter.api.Test; +import org.tensorflow.ndarray.impl.buffer.misc.MiscDataBufferFactory; +import org.tensorflow.ndarray.impl.buffer.nio.NioDataBufferFactory; +import org.tensorflow.ndarray.impl.buffer.raw.RawDataBufferFactory; + +public abstract class LongDataBufferTestBase extends DataBufferTestBase { + + @Override + protected abstract LongDataBuffer allocate(long size); + + @Override + protected Long valueOf(Long val) { + return val; + } + + @Test + public void writeAndReadFromArray() { + LongDataBuffer buffer = allocate(10L); + long[] oneToFive = new long[]{ 1L, 2L, 3L, 4L, 5L }; + + buffer.write(oneToFive); + assertEquals(2, buffer.getLong(1)); + + buffer.offset(5).write(oneToFive); + assertEquals(2L, buffer.getLong(1)); + assertEquals(2L, buffer.getLong(6)); + + long[] read = new long[5]; + buffer.read(read); + assertArrayEquals(oneToFive, read); + + buffer.write(oneToFive, 2, 2); + assertEquals(3L, buffer.getLong(0)); + assertEquals(4L, buffer.getLong(1)); + assertEquals(3L, buffer.getLong(2)); + + Arrays.fill(read, valueOf(0L)); + buffer.read(read, 1, 2); + assertEquals(0L, read[0]); + assertEquals(3L, read[1]); + assertEquals(4L, read[2]); + assertEquals(0L, read[3]); + } + + @Test + public void equalWithLongNioBuffer() { + LongDataBuffer nioBuffer1 = NioDataBufferFactory.create(LongBuffer.wrap(new long[] { 1, 16 })); + LongDataBuffer nioBuffer2 = NioDataBufferFactory.create(LongBuffer.wrap(new long[] { 1, 25 })); + + LongDataBuffer buffer = allocate(2) + .setLong(1, 0) + .setLong(16, 1); + + assertTrue(nioBuffer1.equals(buffer)); + assertTrue(buffer.equals(nioBuffer1)); + assertEquals(nioBuffer1.hashCode(), buffer.hashCode()); + + assertFalse(nioBuffer2.equals(buffer)); + assertFalse(buffer.equals(nioBuffer2)); + assertNotEquals(nioBuffer2.hashCode(), buffer.hashCode()); + } + + @Test + public void equalWithLongRawBuffer() { + LongDataBuffer rawBuffer1 = RawDataBufferFactory.create(new long[] { 1, 16 }, true); + LongDataBuffer rawBuffer2 = RawDataBufferFactory.create(new long[] { 1, 25 }, true); + + LongDataBuffer buffer = allocate(2) + .setLong(1, 0) + .setLong(16, 1); + + assertTrue(rawBuffer1.equals(buffer)); + assertTrue(buffer.equals(rawBuffer1)); + assertEquals(rawBuffer1.hashCode(), buffer.hashCode()); + + assertFalse(rawBuffer2.equals(buffer)); + assertFalse(buffer.equals(rawBuffer2)); + assertNotEquals(rawBuffer2.hashCode(), buffer.hashCode()); + } + + @Test + public void equalWithLongObjectBuffer() { + DataBuffer objBuffer1 = MiscDataBufferFactory.create(new Long[] { 1L, 16L }, true); + DataBuffer objBuffer2 = MiscDataBufferFactory.create(new Long[] { 1L, 25L }, true); + + LongDataBuffer buffer = allocate(2) + .setLong(1, 0) + .setLong(16, 1); + + assertTrue(objBuffer1.equals(buffer)); + assertTrue(buffer.equals(objBuffer1)); + assertEquals(objBuffer1.hashCode(), buffer.hashCode()); + + assertFalse(objBuffer2.equals(buffer)); + assertFalse(buffer.equals(objBuffer2)); + assertNotEquals(objBuffer2.hashCode(), buffer.hashCode()); + } + + @Test + public void notEqualWithOtherTypes() { + LongDataBuffer buffer = allocate(2) + .setLong(1L, 0) + .setLong(16L, 1); + IntDataBuffer intBuffer = DataBuffers.of(1, 16); + + assertFalse(buffer.equals(intBuffer)); + assertFalse(intBuffer.equals(buffer)); + } +} diff --git a/ndarray/src/test/java/org/tensorflow/ndarray/buffer/ShortDataBufferTestBase.java b/ndarray/src/test/java/org/tensorflow/ndarray/buffer/ShortDataBufferTestBase.java new file mode 100644 index 00000000000..f3269e85a8f --- /dev/null +++ b/ndarray/src/test/java/org/tensorflow/ndarray/buffer/ShortDataBufferTestBase.java @@ -0,0 +1,135 @@ +/* + Copyright 2019 The TensorFlow Authors. All Rights Reserved. + + Licensed under the Apache License, Version 2.0 (the "License"); + you may not use this file except in compliance with the License. + You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + + Unless required by applicable law or agreed to in writing, software + distributed under the License is distributed on an "AS IS" BASIS, + WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + See the License for the specific language governing permissions and + limitations under the License. + ======================================================================= + */ +package org.tensorflow.ndarray.buffer; + +import static org.junit.jupiter.api.Assertions.assertArrayEquals; +import static org.junit.jupiter.api.Assertions.assertEquals; +import static org.junit.jupiter.api.Assertions.assertFalse; +import static org.junit.jupiter.api.Assertions.assertNotEquals; +import static org.junit.jupiter.api.Assertions.assertTrue; + +import java.nio.ShortBuffer; +import java.util.Arrays; +import org.junit.jupiter.api.Test; +import org.tensorflow.ndarray.impl.buffer.misc.MiscDataBufferFactory; +import org.tensorflow.ndarray.impl.buffer.nio.NioDataBufferFactory; +import org.tensorflow.ndarray.impl.buffer.raw.RawDataBufferFactory; + +public abstract class ShortDataBufferTestBase extends DataBufferTestBase { + + @Override + protected abstract ShortDataBuffer allocate(long size); + + @Override + protected Short valueOf(Long val) { + return val.shortValue(); + } + + @Test + public void writeAndReadFromArray() { + ShortDataBuffer buffer = allocate(10L); + short[] oneToFive = new short[]{ 1, 2, 3, 4, 5 }; + + buffer.write(oneToFive); + assertEquals(2, buffer.getShort(1)); + + buffer.offset(5).write(oneToFive); + assertEquals(2, buffer.getShort(1), 0); + assertEquals(2, buffer.getShort(6), 0); + + short[] read = new short[5]; + buffer.read(read); + assertArrayEquals(oneToFive, read); + + buffer.write(oneToFive, 2, 2); + assertEquals(3, buffer.getShort(0)); + assertEquals(4, buffer.getShort(1)); + assertEquals(3, buffer.getShort(2)); + + Arrays.fill(read, valueOf(0L)); + buffer.read(read, 1, 2); + assertEquals(0, read[0]); + assertEquals(3, read[1]); + assertEquals(4, read[2]); + assertEquals(0, read[3]); + } + + @Test + public void equalWithShortNioBuffer() { + ShortDataBuffer nioBuffer1 = NioDataBufferFactory.create(ShortBuffer.wrap(new short[] { 1, 16 })); + ShortDataBuffer nioBuffer2 = NioDataBufferFactory.create(ShortBuffer.wrap(new short[] { 1, 25 })); + + ShortDataBuffer buffer = allocate(2) + .setShort((short)1, 0) + .setShort((short)16, 1); + + assertTrue(nioBuffer1.equals(buffer)); + assertTrue(buffer.equals(nioBuffer1)); + assertEquals(nioBuffer1.hashCode(), buffer.hashCode()); + + assertFalse(nioBuffer2.equals(buffer)); + assertFalse(buffer.equals(nioBuffer2)); + assertNotEquals(nioBuffer2.hashCode(), buffer.hashCode()); + } + + @Test + public void equalWithShortRawBuffer() { + ShortDataBuffer rawBuffer1 = RawDataBufferFactory.create(new short[] { 1, 16 }, true); + ShortDataBuffer rawBuffer2 = RawDataBufferFactory.create(new short[] { 1, 25 }, true); + + ShortDataBuffer buffer = allocate(2) + .setShort((short)1, 0) + .setShort((short)16, 1); + + assertTrue(rawBuffer1.equals(buffer)); + assertTrue(buffer.equals(rawBuffer1)); + assertEquals(rawBuffer1.hashCode(), buffer.hashCode()); + + assertFalse(rawBuffer2.equals(buffer)); + assertFalse(buffer.equals(rawBuffer2)); + assertNotEquals(rawBuffer2.hashCode(), buffer.hashCode()); + } + + @Test + public void equalWithShortObjectBuffer() { + DataBuffer objBuffer1 = MiscDataBufferFactory.create(new Short[] { 1, 16 }, true); + DataBuffer objBuffer2 = MiscDataBufferFactory.create(new Short[] { 1, 25 }, true); + + ShortDataBuffer buffer = allocate(2) + .setShort((short)1, 0) + .setShort((short)16, 1); + + assertTrue(objBuffer1.equals(buffer)); + assertTrue(buffer.equals(objBuffer1)); + assertEquals(objBuffer1.hashCode(), buffer.hashCode()); + + assertFalse(objBuffer2.equals(buffer)); + assertFalse(buffer.equals(objBuffer2)); + assertNotEquals(objBuffer2.hashCode(), buffer.hashCode()); + } + + @Test + public void notEqualWithOtherTypes() { + ShortDataBuffer buffer = allocate(2) + .setShort((short)1, 0) + .setShort((short)16, 1); + LongDataBuffer longBuffer = DataBuffers.of(1L, 16L); + + assertFalse(buffer.equals(longBuffer)); + assertFalse(longBuffer.equals(buffer)); + } +} diff --git a/ndarray/src/test/java/org/tensorflow/ndarray/impl/buffer/adapter/BigIntegerDataBufferAdapterTest.java b/ndarray/src/test/java/org/tensorflow/ndarray/impl/buffer/adapter/BigIntegerDataBufferAdapterTest.java new file mode 100644 index 00000000000..4bb86fe3f33 --- /dev/null +++ b/ndarray/src/test/java/org/tensorflow/ndarray/impl/buffer/adapter/BigIntegerDataBufferAdapterTest.java @@ -0,0 +1,67 @@ +/* + * Copyright 2019 The TensorFlow Authors. All Rights Reserved. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + * ======================================================================= + */ + +package org.tensorflow.ndarray.impl.buffer.adapter; + +import java.math.BigInteger; +import org.tensorflow.ndarray.buffer.ByteDataBuffer; +import org.tensorflow.ndarray.buffer.DataBuffer; +import org.tensorflow.ndarray.buffer.DataBufferTestBase; +import org.tensorflow.ndarray.buffer.DataBuffers; +import org.tensorflow.ndarray.buffer.layout.DataLayout; + +public class BigIntegerDataBufferAdapterTest extends DataBufferTestBase { + + @Override + protected DataBuffer allocate(long size) { + return LAYOUT.applyTo(DataBuffers.ofBytes(size * LAYOUT.scale())); + } + + @Override + protected long maxSize() { + return super.maxSize() / 3; + } + + @Override + protected BigInteger valueOf(Long val) { + return BigInteger.valueOf(val); + } + + private static DataLayout LAYOUT = new DataLayout() { + + @Override + public void writeObject(ByteDataBuffer buffer, BigInteger value, long index) { + byte[] bytes = value.toByteArray(); + buffer.setByte(bytes.length > 2 ? bytes[2] : 0, index); + buffer.setByte(bytes.length > 1 ? bytes[1] : 0, index + 1); + buffer.setByte(bytes[0], index + 2); + } + + @Override + public BigInteger readObject(ByteDataBuffer buffer, long index) { + byte byte2 = buffer.getByte(index); + byte byte1 = buffer.getByte(index + 1); + byte byte0 = buffer.getByte(index + 2); + return new BigInteger(new byte[] { byte2, byte1, byte0 }); + } + + @Override + public int scale() { + return 3; + } + }; +} diff --git a/ndarray/src/test/java/org/tensorflow/ndarray/impl/buffer/adapter/BooleanDataBufferAdapterTest.java b/ndarray/src/test/java/org/tensorflow/ndarray/impl/buffer/adapter/BooleanDataBufferAdapterTest.java new file mode 100644 index 00000000000..a15e8f388a8 --- /dev/null +++ b/ndarray/src/test/java/org/tensorflow/ndarray/impl/buffer/adapter/BooleanDataBufferAdapterTest.java @@ -0,0 +1,45 @@ +/* + * Copyright 2019 The TensorFlow Authors. All Rights Reserved. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + * ======================================================================= + */ + +package org.tensorflow.ndarray.impl.buffer.adapter; + +import org.tensorflow.ndarray.buffer.BooleanDataBuffer; +import org.tensorflow.ndarray.buffer.BooleanDataBufferTestBase; +import org.tensorflow.ndarray.buffer.ByteDataBuffer; +import org.tensorflow.ndarray.buffer.DataBuffers; +import org.tensorflow.ndarray.buffer.layout.BooleanDataLayout; + +public class BooleanDataBufferAdapterTest extends BooleanDataBufferTestBase { + + @Override + protected BooleanDataBuffer allocate(long size) { + return LAYOUT.applyTo(DataBuffers.ofBytes(size * LAYOUT.scale())); + } + + private static BooleanDataLayout LAYOUT = new BooleanDataLayout() { + + @Override + public void writeBoolean(ByteDataBuffer buffer, boolean value, long index) { + buffer.setByte((byte)(value ? 1 : 0), index); + } + + @Override + public boolean readBoolean(ByteDataBuffer buffer, long index) { + return buffer.getByte(index) > 0; + } + }; +} diff --git a/ndarray/src/test/java/org/tensorflow/ndarray/impl/buffer/adapter/ByteDataBufferAdapterTest.java b/ndarray/src/test/java/org/tensorflow/ndarray/impl/buffer/adapter/ByteDataBufferAdapterTest.java new file mode 100644 index 00000000000..8a6287601f5 --- /dev/null +++ b/ndarray/src/test/java/org/tensorflow/ndarray/impl/buffer/adapter/ByteDataBufferAdapterTest.java @@ -0,0 +1,27 @@ +package org.tensorflow.ndarray.impl.buffer.adapter; + +import org.tensorflow.ndarray.buffer.ByteDataBuffer; +import org.tensorflow.ndarray.buffer.ByteDataBufferTestBase; +import org.tensorflow.ndarray.buffer.DataBuffers; +import org.tensorflow.ndarray.buffer.ShortDataBuffer; +import org.tensorflow.ndarray.buffer.layout.ByteDataLayout; + +public class ByteDataBufferAdapterTest extends ByteDataBufferTestBase { + + public ByteDataBuffer allocate(long size) { + return LAYOUT.applyTo(DataBuffers.ofShorts(size * LAYOUT.scale())); + } + + private static ByteDataLayout LAYOUT = new ByteDataLayout() { + + @Override + public void writeByte(ShortDataBuffer buffer, byte value, long index) { + buffer.setShort(value, index); + } + + @Override + public byte readByte(ShortDataBuffer buffer, long index) { + return (byte)buffer.getShort(index); + } + }; +} diff --git a/ndarray/src/test/java/org/tensorflow/ndarray/impl/buffer/adapter/DoubleDataBufferAdapterTest.java b/ndarray/src/test/java/org/tensorflow/ndarray/impl/buffer/adapter/DoubleDataBufferAdapterTest.java new file mode 100644 index 00000000000..8dfee1182b1 --- /dev/null +++ b/ndarray/src/test/java/org/tensorflow/ndarray/impl/buffer/adapter/DoubleDataBufferAdapterTest.java @@ -0,0 +1,61 @@ +/* + * Copyright 2019 The TensorFlow Authors. All Rights Reserved. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + * ======================================================================= + */ + +package org.tensorflow.ndarray.impl.buffer.adapter; + +import org.tensorflow.ndarray.buffer.ByteDataBuffer; +import org.tensorflow.ndarray.buffer.DataBuffers; +import org.tensorflow.ndarray.buffer.DoubleDataBuffer; +import org.tensorflow.ndarray.buffer.DoubleDataBufferTestBase; +import org.tensorflow.ndarray.buffer.layout.DoubleDataLayout; + +public class DoubleDataBufferAdapterTest extends DoubleDataBufferTestBase { + + @Override + protected DoubleDataBuffer allocate(long size) { + return LAYOUT.applyTo(DataBuffers.ofBytes(size * LAYOUT.scale())); + } + + @Override + protected long maxSize() { + return super.maxSize() / 3; + } + + private static DoubleDataLayout LAYOUT = new DoubleDataLayout() { + + @Override + public void writeDouble(ByteDataBuffer buffer, double value, long index) { + long bits = Double.doubleToLongBits(value); + buffer.setByte((byte)((bits >> 56) & 0xFF), index); + buffer.setByte((byte)((bits >> 48) & 0xFF), index + 1); + buffer.setByte((byte)((bits >> 40) & 0xFF), index + 2); + } + + @Override + public double readDouble(ByteDataBuffer buffer, long index) { + long byte7 = buffer.getByte(index); + long byte6 = buffer.getByte(index + 1); + long byte5 = buffer.getByte(index + 2); + return Double.longBitsToDouble(((byte7 & 0xFF) << 56) | ((byte6 & 0xFF) << 48) | ((byte5 & 0xFF) << 40)); + } + + @Override + public int scale() { + return 3; + } + }; +} diff --git a/ndarray/src/test/java/org/tensorflow/ndarray/impl/buffer/adapter/FloatDataBufferAdapterTest.java b/ndarray/src/test/java/org/tensorflow/ndarray/impl/buffer/adapter/FloatDataBufferAdapterTest.java new file mode 100644 index 00000000000..82b8ee947dd --- /dev/null +++ b/ndarray/src/test/java/org/tensorflow/ndarray/impl/buffer/adapter/FloatDataBufferAdapterTest.java @@ -0,0 +1,52 @@ +/* + * Copyright 2019 The TensorFlow Authors. All Rights Reserved. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + * ======================================================================= + */ + +package org.tensorflow.ndarray.impl.buffer.adapter; + +import org.tensorflow.ndarray.buffer.DataBuffers; +import org.tensorflow.ndarray.buffer.FloatDataBuffer; +import org.tensorflow.ndarray.buffer.FloatDataBufferTestBase; +import org.tensorflow.ndarray.buffer.ShortDataBuffer; +import org.tensorflow.ndarray.buffer.layout.FloatDataLayout; + +public class FloatDataBufferAdapterTest extends FloatDataBufferTestBase { + + @Override + public FloatDataBuffer allocate(long size) { + return LAYOUT.applyTo(DataBuffers.ofShorts(size * LAYOUT.scale())); + } + + @Override + protected long maxSize() { + return super.maxSize() / 2; + } + + private static FloatDataLayout LAYOUT = new FloatDataLayout() { + + @Override + public void writeFloat(ShortDataBuffer buffer, float value, long index) { + int bits = Float.floatToIntBits(value); + buffer.setShort((short)(bits >> 16), index); + } + + @Override + public float readFloat(ShortDataBuffer buffer, long index) { + int i = buffer.getShort(index); + return Float.intBitsToFloat(i << 16); + } + }; +} diff --git a/ndarray/src/test/java/org/tensorflow/ndarray/impl/buffer/adapter/IntDataBufferAdapterTest.java b/ndarray/src/test/java/org/tensorflow/ndarray/impl/buffer/adapter/IntDataBufferAdapterTest.java new file mode 100644 index 00000000000..9c00f92b00d --- /dev/null +++ b/ndarray/src/test/java/org/tensorflow/ndarray/impl/buffer/adapter/IntDataBufferAdapterTest.java @@ -0,0 +1,51 @@ +/* + * Copyright 2019 The TensorFlow Authors. All Rights Reserved. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + * ======================================================================= + */ + +package org.tensorflow.ndarray.impl.buffer.adapter; + +import org.tensorflow.ndarray.buffer.DataBuffers; +import org.tensorflow.ndarray.buffer.IntDataBuffer; +import org.tensorflow.ndarray.buffer.IntDataBufferTestBase; +import org.tensorflow.ndarray.buffer.ShortDataBuffer; +import org.tensorflow.ndarray.buffer.layout.IntDataLayout; + +public class IntDataBufferAdapterTest extends IntDataBufferTestBase { + + @Override + protected IntDataBuffer allocate(long size) { + return LAYOUT.applyTo(DataBuffers.ofShorts(size * LAYOUT.scale())); + } + + @Override + protected long maxSize() { + return super.maxSize() / 2; + } + + private static IntDataLayout LAYOUT = new IntDataLayout() { + + @Override + public void writeInt(ShortDataBuffer buffer, int value, long index) { + buffer.setShort((short)(((value & 0x80000000) >> 16) | (value & 0x7FFF)), index); + } + + @Override + public int readInt(ShortDataBuffer buffer, long index) { + int i = buffer.getShort(index); + return ((i & 0x8000) << 16) | ((i & 0x7FFF)); + } + }; +} diff --git a/ndarray/src/test/java/org/tensorflow/ndarray/impl/buffer/adapter/LongDataBufferAdapterTest.java b/ndarray/src/test/java/org/tensorflow/ndarray/impl/buffer/adapter/LongDataBufferAdapterTest.java new file mode 100644 index 00000000000..40bc4c55b3e --- /dev/null +++ b/ndarray/src/test/java/org/tensorflow/ndarray/impl/buffer/adapter/LongDataBufferAdapterTest.java @@ -0,0 +1,60 @@ +/* + * Copyright 2019 The TensorFlow Authors. All Rights Reserved. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + * ======================================================================= + */ + +package org.tensorflow.ndarray.impl.buffer.adapter; + +import org.tensorflow.ndarray.buffer.ByteDataBuffer; +import org.tensorflow.ndarray.buffer.DataBuffers; +import org.tensorflow.ndarray.buffer.LongDataBuffer; +import org.tensorflow.ndarray.buffer.LongDataBufferTestBase; +import org.tensorflow.ndarray.buffer.layout.LongDataLayout; + +public class LongDataBufferAdapterTest extends LongDataBufferTestBase { + + @Override + protected LongDataBuffer allocate(long size) { + return LAYOUT.applyTo(DataBuffers.ofBytes(size * LAYOUT.scale())); + } + + @Override + protected long maxSize() { + return super.maxSize() / 3; + } + + private static LongDataLayout LAYOUT = new LongDataLayout() { + + @Override + public void writeLong(ByteDataBuffer buffer, long value, long index) { + buffer.setByte((byte)(((value >> 56) & 0x80) | ((value >> 16) & 0x7F)), index); + buffer.setByte((byte)((value >> 8) & 0xFF), index + 1); + buffer.setByte((byte)(value & 0xFF), index + 2); + } + + @Override + public long readLong(ByteDataBuffer buffer, long index) { + long msb = buffer.getByte(index); + long midb = buffer.getByte(index + 1); + long lsb = buffer.getByte(index + 2); + return ((msb & 0x80) << 56) | ((msb & 0x7F) << 16) | ((midb & 0xFF) << 8) | (lsb & 0xFF); + } + + @Override + public int scale() { + return 3; + } + }; +} diff --git a/ndarray/src/test/java/org/tensorflow/ndarray/impl/buffer/adapter/ShortDataBufferAdapterTest.java b/ndarray/src/test/java/org/tensorflow/ndarray/impl/buffer/adapter/ShortDataBufferAdapterTest.java new file mode 100644 index 00000000000..3c11d3a46ad --- /dev/null +++ b/ndarray/src/test/java/org/tensorflow/ndarray/impl/buffer/adapter/ShortDataBufferAdapterTest.java @@ -0,0 +1,45 @@ +/* + * Copyright 2019 The TensorFlow Authors. All Rights Reserved. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + * ======================================================================= + */ + +package org.tensorflow.ndarray.impl.buffer.adapter; + +import org.tensorflow.ndarray.buffer.ByteDataBuffer; +import org.tensorflow.ndarray.buffer.DataBuffers; +import org.tensorflow.ndarray.buffer.ShortDataBuffer; +import org.tensorflow.ndarray.buffer.ShortDataBufferTestBase; +import org.tensorflow.ndarray.buffer.layout.ShortDataLayout; + +public class ShortDataBufferAdapterTest extends ShortDataBufferTestBase { + + public ShortDataBuffer allocate(long size) { + return LAYOUT.applyTo(DataBuffers.ofBytes(size * LAYOUT.scale())); + } + + private static ShortDataLayout LAYOUT = new ShortDataLayout() { + + @Override + public void writeShort(ByteDataBuffer buffer, short value, long index) { + buffer.setByte((byte)(((value & 0x8000) >> 8) | (value & 0x7F)), index); + } + + @Override + public short readShort(ByteDataBuffer buffer, long index) { + int b = buffer.getByte(index); + return (short)(((b & 0x80) << 8) | (b & 0x7F)); + } + }; +} diff --git a/ndarray/src/test/java/org/tensorflow/ndarray/impl/buffer/layout/Bfloat16LayoutTest.java b/ndarray/src/test/java/org/tensorflow/ndarray/impl/buffer/layout/Bfloat16LayoutTest.java new file mode 100644 index 00000000000..48ddeb1c56e --- /dev/null +++ b/ndarray/src/test/java/org/tensorflow/ndarray/impl/buffer/layout/Bfloat16LayoutTest.java @@ -0,0 +1,84 @@ +/* + * Copyright 2020 The TensorFlow Authors. All Rights Reserved. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + * ======================================================================= + */ + +package org.tensorflow.ndarray.impl.buffer.layout; + +import static org.junit.jupiter.api.Assertions.assertEquals; + +import org.junit.jupiter.api.Test; + +public class Bfloat16LayoutTest { + + @Test + public void testFloat32to16() { + + // Zero and subnormals + assertEquals((short)0x0000, Bfloat16Layout.float32to16(0.0f)); + assertEquals((short)0x8000, Bfloat16Layout.float32to16(-0.0f)); + assertEquals((short)0x0001, Bfloat16Layout.float32to16(1e-40f)); + assertEquals((short)0xC000, Bfloat16Layout.float32to16(-2.0f)); + assertEquals((short)0x0000, Bfloat16Layout.float32to16(4.59e-41f)); + + // Infinite and NaN + assertEquals((short)0x7F80, Bfloat16Layout.float32to16(Float.POSITIVE_INFINITY)); + assertEquals((short)0xFF80, Bfloat16Layout.float32to16(Float.NEGATIVE_INFINITY)); + assertEquals((short)0x7FC0, Bfloat16Layout.float32to16(Float.NaN)); + assertEquals((short)0x7FC0, Bfloat16Layout.float32to16(Float.intBitsToFloat(0xFFFFFFFF))); + + // Normalized + assertEquals((short)0x3F80, Bfloat16Layout.float32to16(1.0f)); + assertEquals((short)0xBF80, Bfloat16Layout.float32to16(-1.0f)); + assertEquals((short)0x42C8, Bfloat16Layout.float32to16(100.0f)); + assertEquals((short)0xC2CA, Bfloat16Layout.float32to16(-101.0f)); + assertEquals((short)0x3F8F, Bfloat16Layout.float32to16(1.1171875f)); + assertEquals((short)0x4800, Bfloat16Layout.float32to16(131072f)); + assertEquals((short)0x7F7F, Bfloat16Layout.float32to16(3.3895314e38f)); + assertEquals((short)0xFF7F, Bfloat16Layout.float32to16(-3.3895314e38f)); + + // Rounding up + assertEquals((short)0x3FCF, Bfloat16Layout.float32to16(1.6191406f)); // 1.6171875 + assertEquals((short)0x4780, Bfloat16Layout.float32to16(65600.0f)); // 65536.0 + } + + @Test + public void testFloat16to32() { + + // Zero and subnormals + assertEquals(0.0f, Bfloat16Layout.float16to32((short)0x0000), 0); + assertEquals(-0.0f, Bfloat16Layout.float16to32((short)0x8000), 0); + assertEquals(9.18355E-41f, Bfloat16Layout.float16to32((short)0x0001), 1e-8f); + assertEquals(-9.403955E-38, Bfloat16Layout.float16to32((short)0x8200), 1e-8f); + + // Infinite and NaN + assertEquals(Float.POSITIVE_INFINITY, Bfloat16Layout.float16to32((short)0x7F80), 0); + assertEquals(Float.NEGATIVE_INFINITY, Bfloat16Layout.float16to32((short)0xFF80), 0); + assertEquals(Float.NaN, Bfloat16Layout.float16to32((short)0x7FC0), 0); + assertEquals(Float.intBitsToFloat(0xFFFFFFFF), Bfloat16Layout.float16to32((short)0x7FC0), 0); + + // Normalized + assertEquals(1.0f, Bfloat16Layout.float16to32((short)0x3F80), 0); + assertEquals(-1.0f, Bfloat16Layout.float16to32((short)0xBF80), 0); + assertEquals(100.0f, Bfloat16Layout.float16to32((short)0x42C8), 0); + assertEquals(-101.0f, Bfloat16Layout.float16to32((short)0xC2CA), 0); + assertEquals(1.1171875f, Bfloat16Layout.float16to32((short)0x3F8F), 0); + assertEquals(131072f, Bfloat16Layout.float16to32((short)0x4800), 0); + assertEquals(3.3895314e38f, Bfloat16Layout.float16to32((short)0x7F7F), 0); + assertEquals(-3.3895314e38f, Bfloat16Layout.float16to32((short)0xFF7F), 0); + assertEquals(1.6171875f, Bfloat16Layout.float16to32((short)0x3FCF), 0); + assertEquals(65536.0, Bfloat16Layout.float16to32((short)0x4780), 0); + } +} diff --git a/ndarray/src/test/java/org/tensorflow/ndarray/impl/buffer/layout/BoolLayoutTest.java b/ndarray/src/test/java/org/tensorflow/ndarray/impl/buffer/layout/BoolLayoutTest.java new file mode 100644 index 00000000000..6ba903cadec --- /dev/null +++ b/ndarray/src/test/java/org/tensorflow/ndarray/impl/buffer/layout/BoolLayoutTest.java @@ -0,0 +1,42 @@ +/* + * Copyright 2020 The TensorFlow Authors. All Rights Reserved. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + * ======================================================================= + */ + +package org.tensorflow.ndarray.impl.buffer.layout; + +import static org.junit.jupiter.api.Assertions.assertEquals; +import static org.junit.jupiter.api.Assertions.assertFalse; +import static org.junit.jupiter.api.Assertions.assertTrue; + +import org.junit.jupiter.api.Test; + +public class BoolLayoutTest { + + @Test + public void booleanToByteTest() { + assertEquals((byte)1, BoolLayout.booleanToByte(true)); + assertEquals((byte)0, BoolLayout.booleanToByte(false)); + } + + @Test + public void byteToBooleanTest() { + assertTrue(BoolLayout.byteToBoolean((byte)1)); + assertTrue(BoolLayout.byteToBoolean((byte)127)); + assertTrue(BoolLayout.byteToBoolean((byte)-128)); + assertTrue(BoolLayout.byteToBoolean((byte)255)); + assertFalse(BoolLayout.byteToBoolean((byte)0)); + } +} diff --git a/ndarray/src/test/java/org/tensorflow/ndarray/impl/buffer/layout/Float16LayoutTest.java b/ndarray/src/test/java/org/tensorflow/ndarray/impl/buffer/layout/Float16LayoutTest.java new file mode 100644 index 00000000000..7bc430ac4ba --- /dev/null +++ b/ndarray/src/test/java/org/tensorflow/ndarray/impl/buffer/layout/Float16LayoutTest.java @@ -0,0 +1,90 @@ +/* + * Copyright 2020 The TensorFlow Authors. All Rights Reserved. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + * ======================================================================= + */ + +package org.tensorflow.ndarray.impl.buffer.layout; + +import static org.junit.jupiter.api.Assertions.assertEquals; + +import org.junit.jupiter.api.Test; + +public class Float16LayoutTest { + + @Test + public void testFloat32to16() { + + // Zero and subnormals + assertEquals((short)0x0000, Float16Layout.float32to16(0.0f)); + assertEquals((short)0x8000, Float16Layout.float32to16(-0.0f)); + assertEquals((short)0x0001, Float16Layout.float32to16(6e-8f)); + assertEquals((short)0x8200, Float16Layout.float32to16(-3.052e-5f)); + assertEquals((short)0x0000, Float16Layout.float32to16(6e-9f)); + + // Infinite and NaN + assertEquals((short)0x7C00, Float16Layout.float32to16(Float.POSITIVE_INFINITY)); + assertEquals((short)0xFC00, Float16Layout.float32to16(Float.NEGATIVE_INFINITY)); + assertEquals((short)0x7C00, Float16Layout.float32to16(65520.0f)); + assertEquals((short)0x7C00, Float16Layout.float32to16(165536.0f)); + assertEquals((short)0xFC00, Float16Layout.float32to16(-65520.0f)); + assertEquals((short)0x7E00, Float16Layout.float32to16(Float.NaN)); + assertEquals((short)0x7E00, Float16Layout.float32to16(Float.intBitsToFloat(0xFFFFFFFF))); + + // Normalized + assertEquals((short)0x7BFF, Float16Layout.float32to16(65519.0f)); + assertEquals((short)0x3C00, Float16Layout.float32to16(1.0f)); + assertEquals((short)0xBC00, Float16Layout.float32to16(-1.0f)); + assertEquals((short)0x5640, Float16Layout.float32to16(100.0f)); + assertEquals((short)0xD650, Float16Layout.float32to16(-101.0f)); + assertEquals((short)0x3C7E, Float16Layout.float32to16(1.123f)); + + // Rounding up + assertEquals((short)0x3C7E, Float16Layout.float32to16(1.1235f)); // 1.123 + assertEquals((short)0x3C7F, Float16Layout.float32to16(1.1236f)); // 1.124 + assertEquals((short)0x4000, Float16Layout.float32to16(2.0009f)); // 2.0 + assertEquals((short)0x4001, Float16Layout.float32to16(2.001f)); // 2.002 + assertEquals((short)0x5C00, Float16Layout.float32to16(256.125f)); // 256.0 + assertEquals((short)0x5C01, Float16Layout.float32to16(256.126f)); // 256.3 + assertEquals((short)0x5C01, Float16Layout.float32to16(256.30f)); // 256.3 + assertEquals((short)0x5C01, Float16Layout.float32to16(256.374f)); // 256.3 + assertEquals((short)0x5C02, Float16Layout.float32to16(256.375f)); // 256.5 + assertEquals((short)0x5C02, Float16Layout.float32to16(256.51f)); // 256.5 + } + + @Test + public void testFloat16to32() { + + // Zero and subnormals + assertEquals(0.0f, Float16Layout.float16to32((short)0x0000), 0); + assertEquals(-0.0f, Float16Layout.float16to32((short)0x8000), 0); + assertEquals(6e-8f, Float16Layout.float16to32((short)0x0001), 1e-8f); + assertEquals(-3.052e-5f, Float16Layout.float16to32((short)0x8200), 1e-8f); + + // Infinite and NaN + assertEquals(Float.POSITIVE_INFINITY, Float16Layout.float16to32((short)0x7C00), 0); + assertEquals(Float.NEGATIVE_INFINITY, Float16Layout.float16to32((short)0xFC00), 0); + assertEquals(Float.NaN, Float16Layout.float16to32((short)0x7E00), 0); + assertEquals(Float.intBitsToFloat(0xFFFFFFFF), Float16Layout.float16to32((short)0x7E00), 0); + + // Normalized + assertEquals(1.0f, Float16Layout.float16to32((short)0x3C00), 1e-1f); + assertEquals(-1.0f, Float16Layout.float16to32((short)0xBC00), 1e-1f); + assertEquals(100.0f, Float16Layout.float16to32((short)0x5640), 1e-1f); + assertEquals(-101.0f, Float16Layout.float16to32((short)0xD650), 1e-1f); + assertEquals(1.123f, Float16Layout.float16to32((short)0x3C7E), 1e-3f); + assertEquals(1.123f, Float16Layout.float16to32((short)0x3C7E), 1e-3f); + assertEquals(-62.34f, Float16Layout.float16to32((short)0xD3CB), 1e-2f); + } +} diff --git a/ndarray/src/test/java/org/tensorflow/ndarray/impl/buffer/misc/ArrayDataBufferTest.java b/ndarray/src/test/java/org/tensorflow/ndarray/impl/buffer/misc/ArrayDataBufferTest.java new file mode 100644 index 00000000000..1c43a3e3638 --- /dev/null +++ b/ndarray/src/test/java/org/tensorflow/ndarray/impl/buffer/misc/ArrayDataBufferTest.java @@ -0,0 +1,265 @@ +/* + Copyright 2019 The TensorFlow Authors. All Rights Reserved. + + Licensed under the Apache License, Version 2.0 (the "License"); + you may not use this file except in compliance with the License. + You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + + Unless required by applicable law or agreed to in writing, software + distributed under the License is distributed on an "AS IS" BASIS, + WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + See the License for the specific language governing permissions and + limitations under the License. + ======================================================================= + */ +package org.tensorflow.ndarray.impl.buffer.misc; + +import static org.junit.jupiter.api.Assertions.assertEquals; +import static org.junit.jupiter.api.Assertions.assertFalse; +import static org.junit.jupiter.api.Assertions.assertNotEquals; +import static org.junit.jupiter.api.Assertions.assertTrue; + +import java.math.BigDecimal; +import org.junit.jupiter.api.Test; +import org.tensorflow.ndarray.buffer.DataBuffer; +import org.tensorflow.ndarray.buffer.DataBufferTestBase; + +public class ArrayDataBufferTest extends DataBufferTestBase { + + @Override + protected DataBuffer allocate(long size) { + return new ArrayDataBuffer<>(new BigDecimal[(int)size], false); + } + + @Override + protected BigDecimal valueOf(Long val) { + return BigDecimal.valueOf(val); + } + + @Test + public void byteArrayBufferEquals() { + DataBuffer buffer1 = new ArrayDataBuffer<>(new byte[][] { { 0x01 }, { 0x03 } }, true); + DataBuffer buffer2 = new ArrayDataBuffer<>(new byte[][] { { 0x01 }, { 0x03 } }, true); + DataBuffer buffer3 = new ArrayDataBuffer<>(new byte[][] { { 0x02 }, { 0x03 } }, true); + DataBuffer buffer4 = new ArrayDataBuffer<>(new byte[][][] { { { 0x01 } }, { { 0x03 } } }, true); + DataBuffer buffer5 = new ArrayDataBuffer<>(new byte[][][] { { { 0x01 } }, { { 0x03 } } }, true); + + assertTrue(buffer1.equals(buffer2)); + assertTrue(buffer2.equals(buffer1)); + assertEquals(buffer1.hashCode(), buffer2.hashCode()); + + assertFalse(buffer1.equals(buffer3)); + assertFalse(buffer3.equals(buffer1)); + assertNotEquals(buffer1.hashCode(), buffer3.hashCode()); + + assertFalse(buffer1.equals(buffer4)); + assertFalse(buffer4.equals(buffer1)); + assertNotEquals(buffer1.hashCode(), buffer4.hashCode()); + + assertTrue(buffer4.equals(buffer5)); + assertTrue(buffer4.equals(buffer5)); + assertEquals(buffer4.hashCode(), buffer5.hashCode()); + } + + @Test + public void intArrayBufferEquals() { + DataBuffer buffer1 = new ArrayDataBuffer<>(new int[][] { { 10 }, { 30 } }, true); + DataBuffer buffer2 = new ArrayDataBuffer<>(new int[][] { { 10 }, { 30 } }, true); + DataBuffer buffer3 = new ArrayDataBuffer<>(new int[][] { { 20 }, { 30 } }, true); + DataBuffer buffer4 = new ArrayDataBuffer<>(new int[][][] { { { 10 } }, { { 30 } } }, true); + DataBuffer buffer5 = new ArrayDataBuffer<>(new int[][][] { { { 10 } }, { { 30 } } }, true); + + assertTrue(buffer1.equals(buffer2)); + assertTrue(buffer2.equals(buffer1)); + assertEquals(buffer1.hashCode(), buffer2.hashCode()); + + assertFalse(buffer1.equals(buffer3)); + assertFalse(buffer3.equals(buffer1)); + assertNotEquals(buffer1.hashCode(), buffer3.hashCode()); + + assertFalse(buffer1.equals(buffer4)); + assertFalse(buffer4.equals(buffer1)); + assertNotEquals(buffer1.hashCode(), buffer4.hashCode()); + + assertTrue(buffer4.equals(buffer5)); + assertTrue(buffer4.equals(buffer5)); + assertEquals(buffer4.hashCode(), buffer5.hashCode()); + } + + @Test + public void shortArrayBufferEquals() { + DataBuffer buffer1 = new ArrayDataBuffer<>(new short[][] { { 10 }, { 30 } }, true); + DataBuffer buffer2 = new ArrayDataBuffer<>(new short[][] { { 10 }, { 30 } }, true); + DataBuffer buffer3 = new ArrayDataBuffer<>(new short[][] { { 20 }, { 30 } }, true); + DataBuffer buffer4 = new ArrayDataBuffer<>(new short[][][] { { { 10 } }, { { 30 } } }, true); + DataBuffer buffer5 = new ArrayDataBuffer<>(new short[][][] { { { 10 } }, { { 30 } } }, true); + + assertTrue(buffer1.equals(buffer2)); + assertTrue(buffer2.equals(buffer1)); + assertEquals(buffer1.hashCode(), buffer2.hashCode()); + + assertFalse(buffer1.equals(buffer3)); + assertFalse(buffer3.equals(buffer1)); + assertNotEquals(buffer1.hashCode(), buffer3.hashCode()); + + assertFalse(buffer1.equals(buffer4)); + assertFalse(buffer4.equals(buffer1)); + assertNotEquals(buffer1.hashCode(), buffer4.hashCode()); + + assertTrue(buffer4.equals(buffer5)); + assertTrue(buffer4.equals(buffer5)); + assertEquals(buffer4.hashCode(), buffer5.hashCode()); + } + + @Test + public void longArrayBufferEquals() { + DataBuffer buffer1 = new ArrayDataBuffer<>(new long[][] { { 10 }, { 30 } }, true); + DataBuffer buffer2 = new ArrayDataBuffer<>(new long[][] { { 10 }, { 30 } }, true); + DataBuffer buffer3 = new ArrayDataBuffer<>(new long[][] { { 20 }, { 30 } }, true); + DataBuffer buffer4 = new ArrayDataBuffer<>(new long[][][] { { { 10 } }, { { 30 } } }, true); + DataBuffer buffer5 = new ArrayDataBuffer<>(new long[][][] { { { 10 } }, { { 30 } } }, true); + + assertTrue(buffer1.equals(buffer2)); + assertTrue(buffer2.equals(buffer1)); + assertEquals(buffer1.hashCode(), buffer2.hashCode()); + + assertFalse(buffer1.equals(buffer3)); + assertFalse(buffer3.equals(buffer1)); + assertNotEquals(buffer1.hashCode(), buffer3.hashCode()); + + assertFalse(buffer1.equals(buffer4)); + assertFalse(buffer4.equals(buffer1)); + assertNotEquals(buffer1.hashCode(), buffer4.hashCode()); + + assertTrue(buffer4.equals(buffer5)); + assertTrue(buffer4.equals(buffer5)); + assertEquals(buffer4.hashCode(), buffer5.hashCode()); + } + + @Test + public void floatArrayBufferEquals() { + DataBuffer buffer1 = new ArrayDataBuffer<>(new float[][] { { 10 }, { 30 } }, true); + DataBuffer buffer2 = new ArrayDataBuffer<>(new float[][] { { 10 }, { 30 } }, true); + DataBuffer buffer3 = new ArrayDataBuffer<>(new float[][] { { 20 }, { 30 } }, true); + DataBuffer buffer4 = new ArrayDataBuffer<>(new float[][][] { { { 10 } }, { { 30 } } }, true); + DataBuffer buffer5 = new ArrayDataBuffer<>(new float[][][] { { { 10 } }, { { 30 } } }, true); + + assertTrue(buffer1.equals(buffer2)); + assertTrue(buffer2.equals(buffer1)); + assertEquals(buffer1.hashCode(), buffer2.hashCode()); + + assertFalse(buffer1.equals(buffer3)); + assertFalse(buffer3.equals(buffer1)); + assertNotEquals(buffer1.hashCode(), buffer3.hashCode()); + + assertFalse(buffer1.equals(buffer4)); + assertFalse(buffer4.equals(buffer1)); + assertNotEquals(buffer1.hashCode(), buffer4.hashCode()); + + assertTrue(buffer4.equals(buffer5)); + assertTrue(buffer4.equals(buffer5)); + assertEquals(buffer4.hashCode(), buffer5.hashCode()); + } + + @Test + public void doubleArrayBufferEquals() { + DataBuffer buffer1 = new ArrayDataBuffer<>(new double[][] { { 10 }, { 30 } }, true); + DataBuffer buffer2 = new ArrayDataBuffer<>(new double[][] { { 10 }, { 30 } }, true); + DataBuffer buffer3 = new ArrayDataBuffer<>(new double[][] { { 20 }, { 30 } }, true); + DataBuffer buffer4 = new ArrayDataBuffer<>(new double[][][] { { { 10 } }, { { 30 } } }, true); + DataBuffer buffer5 = new ArrayDataBuffer<>(new double[][][] { { { 10 } }, { { 30 } } }, true); + + assertTrue(buffer1.equals(buffer2)); + assertTrue(buffer2.equals(buffer1)); + assertEquals(buffer1.hashCode(), buffer2.hashCode()); + + assertFalse(buffer1.equals(buffer3)); + assertFalse(buffer3.equals(buffer1)); + assertNotEquals(buffer1.hashCode(), buffer3.hashCode()); + + assertFalse(buffer1.equals(buffer4)); + assertFalse(buffer4.equals(buffer1)); + assertNotEquals(buffer1.hashCode(), buffer4.hashCode()); + + assertTrue(buffer4.equals(buffer5)); + assertTrue(buffer4.equals(buffer5)); + assertEquals(buffer4.hashCode(), buffer5.hashCode()); + } + + @Test + public void booleanArrayBufferEquals() { + DataBuffer buffer1 = new ArrayDataBuffer<>(new boolean[][] { { true }, { false } }, true); + DataBuffer buffer2 = new ArrayDataBuffer<>(new boolean[][] { { true }, { false} }, true); + DataBuffer buffer3 = new ArrayDataBuffer<>(new boolean[][] { { false }, { false } }, true); + DataBuffer buffer4 = new ArrayDataBuffer<>(new boolean[][][] { { { true } }, { { false } } }, true); + DataBuffer buffer5 = new ArrayDataBuffer<>(new boolean[][][] { { { true } }, { { false } } }, true); + + assertTrue(buffer1.equals(buffer2)); + assertTrue(buffer2.equals(buffer1)); + assertEquals(buffer1.hashCode(), buffer2.hashCode()); + + assertFalse(buffer1.equals(buffer3)); + assertFalse(buffer3.equals(buffer1)); + assertNotEquals(buffer1.hashCode(), buffer3.hashCode()); + + assertFalse(buffer1.equals(buffer4)); + assertFalse(buffer4.equals(buffer1)); + assertNotEquals(buffer1.hashCode(), buffer4.hashCode()); + + assertTrue(buffer4.equals(buffer5)); + assertTrue(buffer4.equals(buffer5)); + assertEquals(buffer4.hashCode(), buffer5.hashCode()); + } + + @Test + public void objectArrayBufferEquals() { + DataBuffer buffer1 = new ArrayDataBuffer<>(new String[][] { { "10" }, { "30" } }, true); + DataBuffer buffer2 = new ArrayDataBuffer<>(new String[][] { { "10" }, { "30" } }, true); + DataBuffer buffer3 = new ArrayDataBuffer<>(new String[][] { { "20" }, { "30" } }, true); + DataBuffer buffer4 = new ArrayDataBuffer<>(new String[][][] { { { "10" } }, { { "30" } } }, true); + DataBuffer buffer5 = new ArrayDataBuffer<>(new String[][][] { { { "10" } }, { { "30" } } }, true); + + assertTrue(buffer1.equals(buffer2)); + assertTrue(buffer2.equals(buffer1)); + assertEquals(buffer1.hashCode(), buffer2.hashCode()); + + assertFalse(buffer1.equals(buffer3)); + assertFalse(buffer3.equals(buffer1)); + assertNotEquals(buffer1.hashCode(), buffer3.hashCode()); + + assertFalse(buffer1.equals(buffer4)); + assertFalse(buffer4.equals(buffer1)); + assertNotEquals(buffer1.hashCode(), buffer4.hashCode()); + + assertTrue(buffer4.equals(buffer5)); + assertTrue(buffer4.equals(buffer5)); + assertEquals(buffer4.hashCode(), buffer5.hashCode()); + } + + @Test + public void nullableObjectArrayBufferEquals() { + DataBuffer buffer1 = new ArrayDataBuffer<>(new String[][] { null, { "30" } }, true); + DataBuffer buffer2 = new ArrayDataBuffer<>(new String[][] { null, { "30" } }, true); + DataBuffer buffer3 = new ArrayDataBuffer<>(new String[][] { { "20" }, { "30" } }, true); + DataBuffer buffer4 = new ArrayDataBuffer<>(new String[][][] { { { "10" } }, null }, true); + DataBuffer buffer5 = new ArrayDataBuffer<>(new String[][][] { { { "10" } }, null }, true); + + assertTrue(buffer1.equals(buffer2)); + assertTrue(buffer2.equals(buffer1)); + assertEquals(buffer1.hashCode(), buffer2.hashCode()); + + assertFalse(buffer1.equals(buffer3)); + assertFalse(buffer3.equals(buffer1)); + assertNotEquals(buffer1.hashCode(), buffer3.hashCode()); + + assertFalse(buffer1.equals(buffer4)); + assertFalse(buffer4.equals(buffer1)); + assertNotEquals(buffer1.hashCode(), buffer4.hashCode()); + + assertTrue(buffer4.equals(buffer5)); + assertTrue(buffer4.equals(buffer5)); + assertEquals(buffer4.hashCode(), buffer5.hashCode()); + } +} diff --git a/ndarray/src/test/java/org/tensorflow/ndarray/impl/buffer/misc/BitSetDataBufferTest.java b/ndarray/src/test/java/org/tensorflow/ndarray/impl/buffer/misc/BitSetDataBufferTest.java new file mode 100644 index 00000000000..ec5c513869a --- /dev/null +++ b/ndarray/src/test/java/org/tensorflow/ndarray/impl/buffer/misc/BitSetDataBufferTest.java @@ -0,0 +1,34 @@ +/* + Copyright 2019 The TensorFlow Authors. All Rights Reserved. + + Licensed under the Apache License, Version 2.0 (the "License"); + you may not use this file except in compliance with the License. + You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + + Unless required by applicable law or agreed to in writing, software + distributed under the License is distributed on an "AS IS" BASIS, + WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + See the License for the specific language governing permissions and + limitations under the License. + ======================================================================= + */ +package org.tensorflow.ndarray.impl.buffer.misc; + +import java.util.BitSet; +import org.tensorflow.ndarray.buffer.BooleanDataBuffer; +import org.tensorflow.ndarray.buffer.BooleanDataBufferTestBase; + +public class BitSetDataBufferTest extends BooleanDataBufferTestBase { + + @Override + protected BooleanDataBuffer allocate(long size) { + return new BitSetDataBuffer(new BitSet((int)size), size, false); + } + + @Override + protected Boolean valueOf(Long val) { + return val != 0; + } +} diff --git a/ndarray/src/test/java/org/tensorflow/ndarray/impl/buffer/misc/StringArrayDataBufferTest.java b/ndarray/src/test/java/org/tensorflow/ndarray/impl/buffer/misc/StringArrayDataBufferTest.java new file mode 100644 index 00000000000..3e9c3c0cdbf --- /dev/null +++ b/ndarray/src/test/java/org/tensorflow/ndarray/impl/buffer/misc/StringArrayDataBufferTest.java @@ -0,0 +1,33 @@ +/* + Copyright 2019 The TensorFlow Authors. All Rights Reserved. + + Licensed under the Apache License, Version 2.0 (the "License"); + you may not use this file except in compliance with the License. + You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + + Unless required by applicable law or agreed to in writing, software + distributed under the License is distributed on an "AS IS" BASIS, + WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + See the License for the specific language governing permissions and + limitations under the License. + ======================================================================= + */ +package org.tensorflow.ndarray.impl.buffer.misc; + +import org.tensorflow.ndarray.buffer.DataBuffer; +import org.tensorflow.ndarray.buffer.DataBufferTestBase; + +public class StringArrayDataBufferTest extends DataBufferTestBase { + + @Override + protected DataBuffer allocate(long size) { + return new ArrayDataBuffer<>(new String[(int)size], false); + } + + @Override + protected String valueOf(Long val) { + return val.toString(); + } +} diff --git a/ndarray/src/test/java/org/tensorflow/ndarray/impl/buffer/nio/ByteNioDataBufferTest.java b/ndarray/src/test/java/org/tensorflow/ndarray/impl/buffer/nio/ByteNioDataBufferTest.java new file mode 100644 index 00000000000..28ff5a6c104 --- /dev/null +++ b/ndarray/src/test/java/org/tensorflow/ndarray/impl/buffer/nio/ByteNioDataBufferTest.java @@ -0,0 +1,29 @@ +/* + Copyright 2019 The TensorFlow Authors. All Rights Reserved. + + Licensed under the Apache License, Version 2.0 (the "License"); + you may not use this file except in compliance with the License. + You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + + Unless required by applicable law or agreed to in writing, software + distributed under the License is distributed on an "AS IS" BASIS, + WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + See the License for the specific language governing permissions and + limitations under the License. + ======================================================================= + */ +package org.tensorflow.ndarray.impl.buffer.nio; + +import java.nio.ByteBuffer; +import org.tensorflow.ndarray.buffer.ByteDataBuffer; +import org.tensorflow.ndarray.buffer.ByteDataBufferTestBase; + +public class ByteNioDataBufferTest extends ByteDataBufferTestBase { + + @Override + protected ByteDataBuffer allocate(long size) { + return new ByteNioDataBuffer(ByteBuffer.allocate((int)size)); + } +} diff --git a/ndarray/src/test/java/org/tensorflow/ndarray/impl/buffer/nio/DoubleNioDataBufferTest.java b/ndarray/src/test/java/org/tensorflow/ndarray/impl/buffer/nio/DoubleNioDataBufferTest.java new file mode 100644 index 00000000000..7a4d39dce94 --- /dev/null +++ b/ndarray/src/test/java/org/tensorflow/ndarray/impl/buffer/nio/DoubleNioDataBufferTest.java @@ -0,0 +1,29 @@ +/* + Copyright 2019 The TensorFlow Authors. All Rights Reserved. + + Licensed under the Apache License, Version 2.0 (the "License"); + you may not use this file except in compliance with the License. + You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + + Unless required by applicable law or agreed to in writing, software + distributed under the License is distributed on an "AS IS" BASIS, + WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + See the License for the specific language governing permissions and + limitations under the License. + ======================================================================= + */ +package org.tensorflow.ndarray.impl.buffer.nio; + +import java.nio.DoubleBuffer; +import org.tensorflow.ndarray.buffer.DoubleDataBuffer; +import org.tensorflow.ndarray.buffer.DoubleDataBufferTestBase; + +public class DoubleNioDataBufferTest extends DoubleDataBufferTestBase { + + @Override + protected DoubleDataBuffer allocate(long size) { + return new DoubleNioDataBuffer(DoubleBuffer.allocate((int)size)); + } +} diff --git a/ndarray/src/test/java/org/tensorflow/ndarray/impl/buffer/nio/FloatNioDataBufferTest.java b/ndarray/src/test/java/org/tensorflow/ndarray/impl/buffer/nio/FloatNioDataBufferTest.java new file mode 100644 index 00000000000..08089e76ad8 --- /dev/null +++ b/ndarray/src/test/java/org/tensorflow/ndarray/impl/buffer/nio/FloatNioDataBufferTest.java @@ -0,0 +1,29 @@ +/* + Copyright 2019 The TensorFlow Authors. All Rights Reserved. + + Licensed under the Apache License, Version 2.0 (the "License"); + you may not use this file except in compliance with the License. + You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + + Unless required by applicable law or agreed to in writing, software + distributed under the License is distributed on an "AS IS" BASIS, + WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + See the License for the specific language governing permissions and + limitations under the License. + ======================================================================= + */ +package org.tensorflow.ndarray.impl.buffer.nio; + +import java.nio.FloatBuffer; +import org.tensorflow.ndarray.buffer.FloatDataBuffer; +import org.tensorflow.ndarray.buffer.FloatDataBufferTestBase; + +public class FloatNioDataBufferTest extends FloatDataBufferTestBase { + + @Override + protected FloatDataBuffer allocate(long size) { + return new FloatNioDataBuffer(FloatBuffer.allocate((int)size)); + } +} diff --git a/ndarray/src/test/java/org/tensorflow/ndarray/impl/buffer/nio/IntNioDataBufferTest.java b/ndarray/src/test/java/org/tensorflow/ndarray/impl/buffer/nio/IntNioDataBufferTest.java new file mode 100644 index 00000000000..00a993e42ed --- /dev/null +++ b/ndarray/src/test/java/org/tensorflow/ndarray/impl/buffer/nio/IntNioDataBufferTest.java @@ -0,0 +1,29 @@ +/* + Copyright 2019 The TensorFlow Authors. All Rights Reserved. + + Licensed under the Apache License, Version 2.0 (the "License"); + you may not use this file except in compliance with the License. + You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + + Unless required by applicable law or agreed to in writing, software + distributed under the License is distributed on an "AS IS" BASIS, + WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + See the License for the specific language governing permissions and + limitations under the License. + ======================================================================= + */ +package org.tensorflow.ndarray.impl.buffer.nio; + +import java.nio.IntBuffer; +import org.tensorflow.ndarray.buffer.IntDataBuffer; +import org.tensorflow.ndarray.buffer.IntDataBufferTestBase; + +public class IntNioDataBufferTest extends IntDataBufferTestBase { + + @Override + protected IntDataBuffer allocate(long size) { + return new IntNioDataBuffer(IntBuffer.allocate((int)size)); + } +} diff --git a/ndarray/src/test/java/org/tensorflow/ndarray/impl/buffer/nio/LongNioDataBufferTest.java b/ndarray/src/test/java/org/tensorflow/ndarray/impl/buffer/nio/LongNioDataBufferTest.java new file mode 100644 index 00000000000..5922d2b922c --- /dev/null +++ b/ndarray/src/test/java/org/tensorflow/ndarray/impl/buffer/nio/LongNioDataBufferTest.java @@ -0,0 +1,29 @@ +/* + Copyright 2019 The TensorFlow Authors. All Rights Reserved. + + Licensed under the Apache License, Version 2.0 (the "License"); + you may not use this file except in compliance with the License. + You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + + Unless required by applicable law or agreed to in writing, software + distributed under the License is distributed on an "AS IS" BASIS, + WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + See the License for the specific language governing permissions and + limitations under the License. + ======================================================================= + */ +package org.tensorflow.ndarray.impl.buffer.nio; + +import java.nio.LongBuffer; +import org.tensorflow.ndarray.buffer.LongDataBuffer; +import org.tensorflow.ndarray.buffer.LongDataBufferTestBase; + +public class LongNioDataBufferTest extends LongDataBufferTestBase { + + @Override + protected LongDataBuffer allocate(long size) { + return new LongNioDataBuffer(LongBuffer.allocate((int)size)); + } +} diff --git a/ndarray/src/test/java/org/tensorflow/ndarray/impl/buffer/nio/ShortNioDataBufferTest.java b/ndarray/src/test/java/org/tensorflow/ndarray/impl/buffer/nio/ShortNioDataBufferTest.java new file mode 100644 index 00000000000..c76191fbcf1 --- /dev/null +++ b/ndarray/src/test/java/org/tensorflow/ndarray/impl/buffer/nio/ShortNioDataBufferTest.java @@ -0,0 +1,29 @@ +/* + Copyright 2019 The TensorFlow Authors. All Rights Reserved. + + Licensed under the Apache License, Version 2.0 (the "License"); + you may not use this file except in compliance with the License. + You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + + Unless required by applicable law or agreed to in writing, software + distributed under the License is distributed on an "AS IS" BASIS, + WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + See the License for the specific language governing permissions and + limitations under the License. + ======================================================================= + */ +package org.tensorflow.ndarray.impl.buffer.nio; + +import java.nio.ShortBuffer; +import org.tensorflow.ndarray.buffer.ShortDataBuffer; +import org.tensorflow.ndarray.buffer.ShortDataBufferTestBase; + +public class ShortNioDataBufferTest extends ShortDataBufferTestBase { + + @Override + protected ShortDataBuffer allocate(long size) { + return new ShortNioDataBuffer(ShortBuffer.allocate((int)size)); + } +} diff --git a/ndarray/src/test/java/org/tensorflow/ndarray/impl/buffer/raw/BooleanRawDataBufferTest.java b/ndarray/src/test/java/org/tensorflow/ndarray/impl/buffer/raw/BooleanRawDataBufferTest.java new file mode 100644 index 00000000000..1f09d76055d --- /dev/null +++ b/ndarray/src/test/java/org/tensorflow/ndarray/impl/buffer/raw/BooleanRawDataBufferTest.java @@ -0,0 +1,28 @@ +/* + Copyright 2019 The TensorFlow Authors. All Rights Reserved. + + Licensed under the Apache License, Version 2.0 (the "License"); + you may not use this file except in compliance with the License. + You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + + Unless required by applicable law or agreed to in writing, software + distributed under the License is distributed on an "AS IS" BASIS, + WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + See the License for the specific language governing permissions and + limitations under the License. + ======================================================================= + */ +package org.tensorflow.ndarray.impl.buffer.raw; + +import org.tensorflow.ndarray.buffer.BooleanDataBuffer; +import org.tensorflow.ndarray.buffer.BooleanDataBufferTestBase; + +public class BooleanRawDataBufferTest extends BooleanDataBufferTestBase { + + @Override + protected BooleanDataBuffer allocate(long size) { + return new BooleanRawDataBuffer(UnsafeMemoryHandle.fromArray(new boolean[(int)size], (int)size), false); + } +} diff --git a/ndarray/src/test/java/org/tensorflow/ndarray/impl/buffer/raw/ByteRawDataBufferTest.java b/ndarray/src/test/java/org/tensorflow/ndarray/impl/buffer/raw/ByteRawDataBufferTest.java new file mode 100644 index 00000000000..4a415aff49f --- /dev/null +++ b/ndarray/src/test/java/org/tensorflow/ndarray/impl/buffer/raw/ByteRawDataBufferTest.java @@ -0,0 +1,28 @@ +/* + Copyright 2019 The TensorFlow Authors. All Rights Reserved. + + Licensed under the Apache License, Version 2.0 (the "License"); + you may not use this file except in compliance with the License. + You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + + Unless required by applicable law or agreed to in writing, software + distributed under the License is distributed on an "AS IS" BASIS, + WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + See the License for the specific language governing permissions and + limitations under the License. + ======================================================================= + */ +package org.tensorflow.ndarray.impl.buffer.raw; + +import org.tensorflow.ndarray.buffer.ByteDataBuffer; +import org.tensorflow.ndarray.buffer.ByteDataBufferTestBase; + +public class ByteRawDataBufferTest extends ByteDataBufferTestBase { + + @Override + protected ByteDataBuffer allocate(long size) { + return new ByteRawDataBuffer(UnsafeMemoryHandle.fromArray(new byte[(int)size], (int)size), false); + } +} diff --git a/ndarray/src/test/java/org/tensorflow/ndarray/impl/buffer/raw/DoubleRawDataBufferTest.java b/ndarray/src/test/java/org/tensorflow/ndarray/impl/buffer/raw/DoubleRawDataBufferTest.java new file mode 100644 index 00000000000..df845092dd1 --- /dev/null +++ b/ndarray/src/test/java/org/tensorflow/ndarray/impl/buffer/raw/DoubleRawDataBufferTest.java @@ -0,0 +1,28 @@ +/* + Copyright 2019 The TensorFlow Authors. All Rights Reserved. + + Licensed under the Apache License, Version 2.0 (the "License"); + you may not use this file except in compliance with the License. + You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + + Unless required by applicable law or agreed to in writing, software + distributed under the License is distributed on an "AS IS" BASIS, + WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + See the License for the specific language governing permissions and + limitations under the License. + ======================================================================= + */ +package org.tensorflow.ndarray.impl.buffer.raw; + +import org.tensorflow.ndarray.buffer.DoubleDataBuffer; +import org.tensorflow.ndarray.buffer.DoubleDataBufferTestBase; + +public class DoubleRawDataBufferTest extends DoubleDataBufferTestBase { + + @Override + protected DoubleDataBuffer allocate(long size) { + return new DoubleRawDataBuffer(UnsafeMemoryHandle.fromArray(new double[(int)size], (int)size), false); + } +} diff --git a/ndarray/src/test/java/org/tensorflow/ndarray/impl/buffer/raw/FloatRawDataBufferTest.java b/ndarray/src/test/java/org/tensorflow/ndarray/impl/buffer/raw/FloatRawDataBufferTest.java new file mode 100644 index 00000000000..bc453d79f37 --- /dev/null +++ b/ndarray/src/test/java/org/tensorflow/ndarray/impl/buffer/raw/FloatRawDataBufferTest.java @@ -0,0 +1,28 @@ +/* + Copyright 2019 The TensorFlow Authors. All Rights Reserved. + + Licensed under the Apache License, Version 2.0 (the "License"); + you may not use this file except in compliance with the License. + You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + + Unless required by applicable law or agreed to in writing, software + distributed under the License is distributed on an "AS IS" BASIS, + WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + See the License for the specific language governing permissions and + limitations under the License. + ======================================================================= + */ +package org.tensorflow.ndarray.impl.buffer.raw; + +import org.tensorflow.ndarray.buffer.FloatDataBuffer; +import org.tensorflow.ndarray.buffer.FloatDataBufferTestBase; + +public class FloatRawDataBufferTest extends FloatDataBufferTestBase { + + @Override + protected FloatDataBuffer allocate(long size) { + return new FloatRawDataBuffer(UnsafeMemoryHandle.fromArray(new float[(int)size], (int)size), false); + } +} diff --git a/ndarray/src/test/java/org/tensorflow/ndarray/impl/buffer/raw/IntRawDataBufferTest.java b/ndarray/src/test/java/org/tensorflow/ndarray/impl/buffer/raw/IntRawDataBufferTest.java new file mode 100644 index 00000000000..1142f19131d --- /dev/null +++ b/ndarray/src/test/java/org/tensorflow/ndarray/impl/buffer/raw/IntRawDataBufferTest.java @@ -0,0 +1,28 @@ +/* + Copyright 2019 The TensorFlow Authors. All Rights Reserved. + + Licensed under the Apache License, Version 2.0 (the "License"); + you may not use this file except in compliance with the License. + You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + + Unless required by applicable law or agreed to in writing, software + distributed under the License is distributed on an "AS IS" BASIS, + WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + See the License for the specific language governing permissions and + limitations under the License. + ======================================================================= + */ +package org.tensorflow.ndarray.impl.buffer.raw; + +import org.tensorflow.ndarray.buffer.IntDataBuffer; +import org.tensorflow.ndarray.buffer.IntDataBufferTestBase; + +public class IntRawDataBufferTest extends IntDataBufferTestBase { + + @Override + protected IntDataBuffer allocate(long size) { + return new IntRawDataBuffer(UnsafeMemoryHandle.fromArray(new int[(int)size], (int)size), false); + } +} diff --git a/ndarray/src/test/java/org/tensorflow/ndarray/impl/buffer/raw/LongRawDataBufferTest.java b/ndarray/src/test/java/org/tensorflow/ndarray/impl/buffer/raw/LongRawDataBufferTest.java new file mode 100644 index 00000000000..af86d64a414 --- /dev/null +++ b/ndarray/src/test/java/org/tensorflow/ndarray/impl/buffer/raw/LongRawDataBufferTest.java @@ -0,0 +1,28 @@ +/* + Copyright 2019 The TensorFlow Authors. All Rights Reserved. + + Licensed under the Apache License, Version 2.0 (the "License"); + you may not use this file except in compliance with the License. + You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + + Unless required by applicable law or agreed to in writing, software + distributed under the License is distributed on an "AS IS" BASIS, + WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + See the License for the specific language governing permissions and + limitations under the License. + ======================================================================= + */ +package org.tensorflow.ndarray.impl.buffer.raw; + +import org.tensorflow.ndarray.buffer.LongDataBuffer; +import org.tensorflow.ndarray.buffer.LongDataBufferTestBase; + +public class LongRawDataBufferTest extends LongDataBufferTestBase { + + @Override + protected LongDataBuffer allocate(long size) { + return new LongRawDataBuffer(UnsafeMemoryHandle.fromArray(new long[(int)size], (int)size), false); + } +} diff --git a/ndarray/src/test/java/org/tensorflow/ndarray/impl/buffer/raw/ShortRawDataBufferTest.java b/ndarray/src/test/java/org/tensorflow/ndarray/impl/buffer/raw/ShortRawDataBufferTest.java new file mode 100644 index 00000000000..1ce1f25391b --- /dev/null +++ b/ndarray/src/test/java/org/tensorflow/ndarray/impl/buffer/raw/ShortRawDataBufferTest.java @@ -0,0 +1,28 @@ +/* + Copyright 2019 The TensorFlow Authors. All Rights Reserved. + + Licensed under the Apache License, Version 2.0 (the "License"); + you may not use this file except in compliance with the License. + You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + + Unless required by applicable law or agreed to in writing, software + distributed under the License is distributed on an "AS IS" BASIS, + WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + See the License for the specific language governing permissions and + limitations under the License. + ======================================================================= + */ +package org.tensorflow.ndarray.impl.buffer.raw; + +import org.tensorflow.ndarray.buffer.ShortDataBuffer; +import org.tensorflow.ndarray.buffer.ShortDataBufferTestBase; + +public class ShortRawDataBufferTest extends ShortDataBufferTestBase { + + @Override + protected ShortDataBuffer allocate(long size) { + return new ShortRawDataBuffer(UnsafeMemoryHandle.fromArray(new short[(int)size], (int)size), false); + } +} diff --git a/ndarray/src/test/java/org/tensorflow/ndarray/impl/dense/BooleanDenseNdArrayTest.java b/ndarray/src/test/java/org/tensorflow/ndarray/impl/dense/BooleanDenseNdArrayTest.java new file mode 100644 index 00000000000..36540104eb7 --- /dev/null +++ b/ndarray/src/test/java/org/tensorflow/ndarray/impl/dense/BooleanDenseNdArrayTest.java @@ -0,0 +1,35 @@ +/* + Copyright 2019 The TensorFlow Authors. All Rights Reserved. + + Licensed under the Apache License, Version 2.0 (the "License"); + you may not use this file except in compliance with the License. + You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + + Unless required by applicable law or agreed to in writing, software + distributed under the License is distributed on an "AS IS" BASIS, + WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + See the License for the specific language governing permissions and + limitations under the License. + ======================================================================= + */ +package org.tensorflow.ndarray.impl.dense; + +import org.tensorflow.ndarray.Shape; +import org.tensorflow.ndarray.buffer.DataBuffer; +import org.tensorflow.ndarray.buffer.DataBuffers; +import org.tensorflow.ndarray.BooleanNdArray; +import org.tensorflow.ndarray.BooleanNdArrayTestBase; +import org.tensorflow.ndarray.NdArrays; + +public class BooleanDenseNdArrayTest extends BooleanNdArrayTestBase { + + @Override protected BooleanNdArray allocate(Shape shape) { + return NdArrays.ofBooleans(shape); + } + + @Override protected DataBuffer allocateBuffer(long size) { + return DataBuffers.ofBooleans(size); + } +} diff --git a/ndarray/src/test/java/org/tensorflow/ndarray/impl/dense/ByteDenseNdArrayTest.java b/ndarray/src/test/java/org/tensorflow/ndarray/impl/dense/ByteDenseNdArrayTest.java new file mode 100644 index 00000000000..2e5d1939bc3 --- /dev/null +++ b/ndarray/src/test/java/org/tensorflow/ndarray/impl/dense/ByteDenseNdArrayTest.java @@ -0,0 +1,35 @@ +/* + Copyright 2019 The TensorFlow Authors. All Rights Reserved. + + Licensed under the Apache License, Version 2.0 (the "License"); + you may not use this file except in compliance with the License. + You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + + Unless required by applicable law or agreed to in writing, software + distributed under the License is distributed on an "AS IS" BASIS, + WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + See the License for the specific language governing permissions and + limitations under the License. + ======================================================================= + */ +package org.tensorflow.ndarray.impl.dense; + +import org.tensorflow.ndarray.Shape; +import org.tensorflow.ndarray.buffer.DataBuffer; +import org.tensorflow.ndarray.buffer.DataBuffers; +import org.tensorflow.ndarray.ByteNdArray; +import org.tensorflow.ndarray.ByteNdArrayTestBase; +import org.tensorflow.ndarray.NdArrays; + +public class ByteDenseNdArrayTest extends ByteNdArrayTestBase { + + @Override protected ByteNdArray allocate(Shape shape) { + return NdArrays.ofBytes(shape); + } + + @Override protected DataBuffer allocateBuffer(long size) { + return DataBuffers.ofBytes(size); + } +} diff --git a/ndarray/src/test/java/org/tensorflow/ndarray/impl/dense/DenseNdArrayTest.java b/ndarray/src/test/java/org/tensorflow/ndarray/impl/dense/DenseNdArrayTest.java new file mode 100644 index 00000000000..375f7643875 --- /dev/null +++ b/ndarray/src/test/java/org/tensorflow/ndarray/impl/dense/DenseNdArrayTest.java @@ -0,0 +1,51 @@ +package org.tensorflow.ndarray.impl.dense; + +import static org.junit.jupiter.api.Assertions.assertFalse; +import static org.junit.jupiter.api.Assertions.assertTrue; + +import org.junit.jupiter.api.Test; +import org.tensorflow.ndarray.Shape; +import org.tensorflow.ndarray.IntNdArray; +import org.tensorflow.ndarray.NdArrays; +import org.tensorflow.ndarray.StdArrays; +import org.tensorflow.ndarray.index.Indices; + +public class DenseNdArrayTest { + + @Test + public void arrayEquals() { + IntNdArray array = NdArrays.ofInts(Shape.of(2, 2)) + .set(NdArrays.vectorOf(1, 2), 0) + .set(NdArrays.vectorOf(3, 4), 1); + + assertTrue(array.equals(StdArrays.ndCopyOf(new int[][] {{1, 2}, {3, 4}}))); + assertTrue(array.equals(StdArrays.ndCopyOf(new Integer[][] {{1, 2}, {3, 4}}))); + assertFalse(array.equals(NdArrays.vectorOf(1, 2, 3, 4))); + assertFalse(array.equals(StdArrays.ndCopyOf(new int[][] {{3, 4}, {1, 2}}))); + assertFalse(array.equals(StdArrays.ndCopyOf(new long[][] {{1L, 2L}, {3L, 4L}}))); + } + + @Test + public void equalsAndHashCodeOnSlices() { + IntNdArray vector1 = NdArrays.vectorOf(3, 4); + IntNdArray vector2 = NdArrays.vectorOf(1, 2, 3, 4); + IntNdArray matrix1 = StdArrays.ndCopyOf(new int[][] {{1, 2}, {3, 4}}); + IntNdArray matrix2 = StdArrays.ndCopyOf(new int[][] {{1, 0, 2, 0}, {3, 0, 4, 0}}); + IntNdArray matrix3d1 = StdArrays.ndCopyOf(new int[][][] { + {{1, 2}, {3, 4}}, + {{5, 6}, {7, 8}} + }); + IntNdArray matrix3d2 = StdArrays.ndCopyOf(new int[][][] { + {{1, 2}, {4, 5}}, + {{3, 4}, {6, 7}} + }); + + assertTrue(vector1.equals(vector2.slice(Indices.sliceFrom(2)))); + assertTrue(vector1.equals(matrix1.get(1))); + assertTrue(vector1.equals(matrix2.get(1).slice(Indices.even()))); + assertTrue(matrix1.equals(matrix2.slice(Indices.all(), Indices.even()))); + assertTrue(matrix3d1.get(0).equals(matrix1)); + assertFalse(matrix3d1.get(0).equals(vector2)); + assertTrue(matrix1.equals(matrix3d2.slice(Indices.all(), Indices.at(0)))); + } +} diff --git a/ndarray/src/test/java/org/tensorflow/ndarray/impl/dense/DoubleDenseNdArrayTest.java b/ndarray/src/test/java/org/tensorflow/ndarray/impl/dense/DoubleDenseNdArrayTest.java new file mode 100644 index 00000000000..9810a744c50 --- /dev/null +++ b/ndarray/src/test/java/org/tensorflow/ndarray/impl/dense/DoubleDenseNdArrayTest.java @@ -0,0 +1,35 @@ +/* + Copyright 2019 The TensorFlow Authors. All Rights Reserved. + + Licensed under the Apache License, Version 2.0 (the "License"); + you may not use this file except in compliance with the License. + You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + + Unless required by applicable law or agreed to in writing, software + distributed under the License is distributed on an "AS IS" BASIS, + WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + See the License for the specific language governing permissions and + limitations under the License. + ======================================================================= + */ +package org.tensorflow.ndarray.impl.dense; + +import org.tensorflow.ndarray.Shape; +import org.tensorflow.ndarray.buffer.DataBuffer; +import org.tensorflow.ndarray.buffer.DataBuffers; +import org.tensorflow.ndarray.DoubleNdArray; +import org.tensorflow.ndarray.DoubleNdArrayTestBase; +import org.tensorflow.ndarray.NdArrays; + +public class DoubleDenseNdArrayTest extends DoubleNdArrayTestBase { + + @Override protected DoubleNdArray allocate(Shape shape) { + return NdArrays.ofDoubles(shape); + } + + @Override protected DataBuffer allocateBuffer(long size) { + return DataBuffers.ofDoubles(size); + } +} diff --git a/ndarray/src/test/java/org/tensorflow/ndarray/impl/dense/FloatDenseNdArrayTest.java b/ndarray/src/test/java/org/tensorflow/ndarray/impl/dense/FloatDenseNdArrayTest.java new file mode 100644 index 00000000000..efee2bf2cb8 --- /dev/null +++ b/ndarray/src/test/java/org/tensorflow/ndarray/impl/dense/FloatDenseNdArrayTest.java @@ -0,0 +1,35 @@ +/* + Copyright 2019 The TensorFlow Authors. All Rights Reserved. + + Licensed under the Apache License, Version 2.0 (the "License"); + you may not use this file except in compliance with the License. + You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + + Unless required by applicable law or agreed to in writing, software + distributed under the License is distributed on an "AS IS" BASIS, + WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + See the License for the specific language governing permissions and + limitations under the License. + ======================================================================= + */ +package org.tensorflow.ndarray.impl.dense; + +import org.tensorflow.ndarray.Shape; +import org.tensorflow.ndarray.buffer.DataBuffer; +import org.tensorflow.ndarray.buffer.DataBuffers; +import org.tensorflow.ndarray.FloatNdArray; +import org.tensorflow.ndarray.FloatNdArrayTestBase; +import org.tensorflow.ndarray.NdArrays; + +public class FloatDenseNdArrayTest extends FloatNdArrayTestBase { + + @Override protected FloatNdArray allocate(Shape shape) { + return NdArrays.ofFloats(shape); + } + + @Override protected DataBuffer allocateBuffer(long size) { + return DataBuffers.ofFloats(size); + } +} diff --git a/ndarray/src/test/java/org/tensorflow/ndarray/impl/dense/IntDenseNdArrayTest.java b/ndarray/src/test/java/org/tensorflow/ndarray/impl/dense/IntDenseNdArrayTest.java new file mode 100644 index 00000000000..712f6f44333 --- /dev/null +++ b/ndarray/src/test/java/org/tensorflow/ndarray/impl/dense/IntDenseNdArrayTest.java @@ -0,0 +1,35 @@ +/* + Copyright 2019 The TensorFlow Authors. All Rights Reserved. + + Licensed under the Apache License, Version 2.0 (the "License"); + you may not use this file except in compliance with the License. + You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + + Unless required by applicable law or agreed to in writing, software + distributed under the License is distributed on an "AS IS" BASIS, + WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + See the License for the specific language governing permissions and + limitations under the License. + ======================================================================= + */ +package org.tensorflow.ndarray.impl.dense; + +import org.tensorflow.ndarray.Shape; +import org.tensorflow.ndarray.buffer.DataBuffer; +import org.tensorflow.ndarray.buffer.DataBuffers; +import org.tensorflow.ndarray.IntNdArray; +import org.tensorflow.ndarray.IntNdArrayTestBase; +import org.tensorflow.ndarray.NdArrays; + +public class IntDenseNdArrayTest extends IntNdArrayTestBase { + + @Override protected IntNdArray allocate(Shape shape) { + return NdArrays.ofInts(shape); + } + + @Override protected DataBuffer allocateBuffer(long size) { + return DataBuffers.ofInts(size); + } +} diff --git a/ndarray/src/test/java/org/tensorflow/ndarray/impl/dense/LongDenseNdArrayTest.java b/ndarray/src/test/java/org/tensorflow/ndarray/impl/dense/LongDenseNdArrayTest.java new file mode 100644 index 00000000000..346e3845080 --- /dev/null +++ b/ndarray/src/test/java/org/tensorflow/ndarray/impl/dense/LongDenseNdArrayTest.java @@ -0,0 +1,35 @@ +/* + Copyright 2019 The TensorFlow Authors. All Rights Reserved. + + Licensed under the Apache License, Version 2.0 (the "License"); + you may not use this file except in compliance with the License. + You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + + Unless required by applicable law or agreed to in writing, software + distributed under the License is distributed on an "AS IS" BASIS, + WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + See the License for the specific language governing permissions and + limitations under the License. + ======================================================================= + */ +package org.tensorflow.ndarray.impl.dense; + +import org.tensorflow.ndarray.Shape; +import org.tensorflow.ndarray.buffer.DataBuffer; +import org.tensorflow.ndarray.buffer.DataBuffers; +import org.tensorflow.ndarray.LongNdArray; +import org.tensorflow.ndarray.LongNdArrayTestBase; +import org.tensorflow.ndarray.NdArrays; + +public class LongDenseNdArrayTest extends LongNdArrayTestBase { + + @Override protected LongNdArray allocate(Shape shape) { + return NdArrays.ofLongs(shape); + } + + @Override protected DataBuffer allocateBuffer(long size) { + return DataBuffers.ofLongs(size); + } +} diff --git a/ndarray/src/test/java/org/tensorflow/ndarray/impl/dense/ShortDenseNdArrayTest.java b/ndarray/src/test/java/org/tensorflow/ndarray/impl/dense/ShortDenseNdArrayTest.java new file mode 100644 index 00000000000..6f845c7c65d --- /dev/null +++ b/ndarray/src/test/java/org/tensorflow/ndarray/impl/dense/ShortDenseNdArrayTest.java @@ -0,0 +1,35 @@ +/* + Copyright 2019 The TensorFlow Authors. All Rights Reserved. + + Licensed under the Apache License, Version 2.0 (the "License"); + you may not use this file except in compliance with the License. + You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + + Unless required by applicable law or agreed to in writing, software + distributed under the License is distributed on an "AS IS" BASIS, + WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + See the License for the specific language governing permissions and + limitations under the License. + ======================================================================= + */ +package org.tensorflow.ndarray.impl.dense; + +import org.tensorflow.ndarray.Shape; +import org.tensorflow.ndarray.buffer.DataBuffer; +import org.tensorflow.ndarray.buffer.DataBuffers; +import org.tensorflow.ndarray.NdArrays; +import org.tensorflow.ndarray.ShortNdArray; +import org.tensorflow.ndarray.ShortNdArrayTestBase; + +public class ShortDenseNdArrayTest extends ShortNdArrayTestBase { + + @Override protected ShortNdArray allocate(Shape shape) { + return NdArrays.ofShorts(shape); + } + + @Override protected DataBuffer allocateBuffer(long size) { + return DataBuffers.ofShorts(size); + } +} diff --git a/ndarray/src/test/java/org/tensorflow/ndarray/impl/dense/StringDenseNdArrayTest.java b/ndarray/src/test/java/org/tensorflow/ndarray/impl/dense/StringDenseNdArrayTest.java new file mode 100644 index 00000000000..5afc1420ab2 --- /dev/null +++ b/ndarray/src/test/java/org/tensorflow/ndarray/impl/dense/StringDenseNdArrayTest.java @@ -0,0 +1,43 @@ +/* + Copyright 2019 The TensorFlow Authors. All Rights Reserved. + + Licensed under the Apache License, Version 2.0 (the "License"); + you may not use this file except in compliance with the License. + You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + + Unless required by applicable law or agreed to in writing, software + distributed under the License is distributed on an "AS IS" BASIS, + WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + See the License for the specific language governing permissions and + limitations under the License. + ======================================================================= + */ +package org.tensorflow.ndarray.impl.dense; + +import org.tensorflow.ndarray.Shape; +import org.tensorflow.ndarray.buffer.DataBuffer; +import org.tensorflow.ndarray.buffer.DataBuffers; +import org.tensorflow.ndarray.NdArray; +import org.tensorflow.ndarray.NdArrayTestBase; +import org.tensorflow.ndarray.NdArrays; + +public class StringDenseNdArrayTest extends NdArrayTestBase { + + @Override protected NdArray allocate(Shape shape) { + return NdArrays.ofObjects(String.class, shape); + } + + @Override protected DataBuffer allocateBuffer(long size) { + return DataBuffers.ofObjects(String.class, size); + } + + @Override protected String valueOf(Long val) { + return val.toString(); + } + + protected String zeroOrNull() { + return null; + } +} diff --git a/ndarray/src/test/java/org/tensorflow/ndarray/impl/sequence/ElementSequenceTest.java b/ndarray/src/test/java/org/tensorflow/ndarray/impl/sequence/ElementSequenceTest.java new file mode 100644 index 00000000000..bad78404e9b --- /dev/null +++ b/ndarray/src/test/java/org/tensorflow/ndarray/impl/sequence/ElementSequenceTest.java @@ -0,0 +1,145 @@ +/* + * Copyright 2019 The TensorFlow Authors. All Rights Reserved. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + * ======================================================================= + */ + +package org.tensorflow.ndarray.impl.sequence; + +import static org.junit.jupiter.api.Assertions.assertArrayEquals; +import static org.junit.jupiter.api.Assertions.assertEquals; +import static org.junit.jupiter.api.Assertions.fail; + +import java.util.ArrayList; +import java.util.Arrays; +import java.util.List; +import org.junit.jupiter.api.Test; +import org.tensorflow.ndarray.Shape; +import org.tensorflow.ndarray.buffer.DataBufferWindow; +import org.tensorflow.ndarray.buffer.DataBuffers; +import org.tensorflow.ndarray.buffer.IntDataBuffer; +import org.tensorflow.ndarray.IntNdArray; +import org.tensorflow.ndarray.NdArraySequence; +import org.tensorflow.ndarray.NdArrays; +import org.tensorflow.ndarray.impl.AbstractNdArray; + +public class ElementSequenceTest { + + @Test + public void iterateVectorsWithIndex() { + IntNdArray array = NdArrays.ofInts(Shape.of(2, 3, 2)); + + NdArraySequence sequence = new SlicingElementSequence( + (AbstractNdArray)array, 1); + List coords = new ArrayList<>((int)array.shape().size()); + sequence.forEachIndexed((c, e) -> coords.add(Arrays.copyOf(c, c.length))); + + assertEquals(6, coords.size()); + assertArrayEquals(new long[] {0, 0}, coords.get(0)); + assertArrayEquals(new long[] {0, 1}, coords.get(1)); + assertArrayEquals(new long[] {0, 2}, coords.get(2)); + assertArrayEquals(new long[] {1, 0}, coords.get(3)); + assertArrayEquals(new long[] {1, 1}, coords.get(4)); + assertArrayEquals(new long[] {1, 2}, coords.get(5)); + } + + @Test + public void iterateScalarsWithIndex() { + IntNdArray array = NdArrays.ofInts(Shape.of(2, 3, 2)); + + NdArraySequence cursor = new SlicingElementSequence( + (AbstractNdArray)array, 2); + List coords = new ArrayList<>((int)array.shape().size()); + cursor.forEachIndexed((c, e) -> coords.add(Arrays.copyOf(c, c.length))); + + assertEquals(12, coords.size()); + assertArrayEquals(new long[] {0, 0, 0}, coords.get(0)); + assertArrayEquals(new long[] {0, 0, 1}, coords.get(1)); + assertArrayEquals(new long[] {0, 1, 0}, coords.get(2)); + assertArrayEquals(new long[] {0, 1, 1}, coords.get(3)); + assertArrayEquals(new long[] {0, 2, 0}, coords.get(4)); + assertArrayEquals(new long[] {0, 2, 1}, coords.get(5)); + assertArrayEquals(new long[] {1, 0, 0}, coords.get(6)); + assertArrayEquals(new long[] {1, 0, 1}, coords.get(7)); + assertArrayEquals(new long[] {1, 1, 0}, coords.get(8)); + assertArrayEquals(new long[] {1, 1, 1}, coords.get(9)); + assertArrayEquals(new long[] {1, 2, 0}, coords.get(10)); + assertArrayEquals(new long[] {1, 2, 1}, coords.get(11)); + } + + @Test + public void slicingElementSequenceReturnsUniqueInstances() { + IntNdArray array = NdArrays.ofInts(Shape.of(2, 3, 2)); + NdArraySequence sequence = new SlicingElementSequence( + (AbstractNdArray) array, 1); + List elements = new ArrayList<>(); + sequence.forEach(e -> { + elements.forEach(tmp -> { + if (tmp == e) { + fail(); + } + }); + elements.add(e); + }); + } + + @Test + public void fastElementSequenceReturnsSameInstance() { + IntNdArray array = NdArrays.ofInts(Shape.of(2, 3, 2)); + IntNdArray element = array.get(0); + NdArraySequence sequence = new FastElementSequence( + (AbstractNdArray) array, 1, element, mockDataBufferWindow(2)); + sequence.forEach(e -> { + if (e != element) { + fail(); + } + }); + } + + private DataBufferWindow mockDataBufferWindow(long size) { + return new DataBufferWindow() { + + @Override + public long offset() { + return offset; + } + + @Override + public long size() { + return size; + } + + @Override + public DataBufferWindow slideTo(long index) { + offset = index; + return this; + } + + @Override + public DataBufferWindow slide(long step) { + offset += step; + return this; + } + + @Override + public IntDataBuffer buffer() { + return buffer; + } + + private long offset; + private final long size = 2; + private final IntDataBuffer buffer = DataBuffers.ofInts(2); + }; + } +} diff --git a/ndarray/src/test/resources/COPYRIGHT.txt b/ndarray/src/test/resources/COPYRIGHT.txt new file mode 100644 index 00000000000..5e7bd50bb48 --- /dev/null +++ b/ndarray/src/test/resources/COPYRIGHT.txt @@ -0,0 +1 @@ +All images in this folder and its subfolders are free of any copyright. \ No newline at end of file diff --git a/ndarray/src/test/resources/castle.jpg b/ndarray/src/test/resources/castle.jpg new 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From 2fe76c0f4ddec7b00ca9c202030cb25fe915433d Mon Sep 17 00:00:00 2001 From: Ryan Nett Date: Tue, 18 May 2021 16:07:36 -0700 Subject: [PATCH 04/63] Kotlin friendly names (Shape.get) --- .../java/org/tensorflow/ndarray/Shape.java | 75 ++++++++++++++++--- .../org/tensorflow/ndarray/StdArrays.java | 4 +- .../impl/dimension/DimensionalSpace.java | 6 +- .../tensorflow/ndarray/NdArrayTestBase.java | 18 ++--- .../org/tensorflow/ndarray/ShapeTest.java | 30 +++++--- 5 files changed, 96 insertions(+), 37 deletions(-) diff --git a/ndarray/src/main/java/org/tensorflow/ndarray/Shape.java b/ndarray/src/main/java/org/tensorflow/ndarray/Shape.java index 85a905408c7..d8bb2bb5c38 100644 --- a/ndarray/src/main/java/org/tensorflow/ndarray/Shape.java +++ b/ndarray/src/main/java/org/tensorflow/ndarray/Shape.java @@ -17,7 +17,9 @@ package org.tensorflow.ndarray; +import java.util.ArrayList; import java.util.Arrays; +import java.util.List; /** * The shape of a Tensor or {@link NdArray}. @@ -74,8 +76,8 @@ public static Shape scalar() { * Shape scalar = Shape.of() * } * - * @param dimensionSizes number of elements in each dimension of this shape, if any, or - * {@link Shape#UNKNOWN_SIZE} if unknown. + * @param dimensionSizes number of elements in each dimension of this shape, if any, or {@link + * Shape#UNKNOWN_SIZE} if unknown. * @return a new shape */ public static Shape of(long... dimensionSizes) { @@ -108,13 +110,34 @@ public long size() { * an unknown size, {@link Shape#UNKNOWN_SIZE} is returned. * * @param i the index of the dimension to get the size for. If this Shape has a known number of - * dimensions, it must be < {@link Shape#numDimensions()}. The index may be negative, in which - * case the position is counted from the end of the shape. E.g.: {@code size(-1)} returns the - * size of the last dimension, {@code size(-2)} the size of the second to last dimension etc. + * dimensions, it must be < {@link Shape#numDimensions()}. The index may be negative, in + * which case the position is counted from the end of the shape. E.g.: {@code size(-1)} + * returns the size of the last dimension, {@code size(-2)} the size of the second to last + * dimension etc. * @return The size of the dimension with the given index if known, {@link Shape#UNKNOWN_SIZE} * otherwise. + * @deprecated Renamed to {@link #get(int)}. */ - public long size(int i) { + @Deprecated + public long size(int i){ + return get(i); + } + + /** + * The size of the dimension with the given index. + * + *

If {@link Shape#isUnknown()} is true or the size of the dimension with the given index has + * an unknown size, {@link Shape#UNKNOWN_SIZE} is returned. + * + * @param i the index of the dimension to get the size for. If this Shape has a known number of + * dimensions, it must be < {@link Shape#numDimensions()}. The index may be negative, in + * which case the position is counted from the end of the shape. E.g.: {@code size(-1)} + * returns the size of the last dimension, {@code size(-2)} the size of the second to last + * dimension etc. + * @return The size of the dimension with the given index if known, {@link Shape#UNKNOWN_SIZE} + * otherwise. + */ + public long get(int i) { if (dimensionSizes == null) { return UNKNOWN_SIZE; } else if (i >= 0) { @@ -177,6 +200,24 @@ public long[] asArray() { } } + /** + * Returns a defensive copy of the this Shape's axes. Changes to the returned list do not change + * this Shape's state. Returns null if {@link Shape#isUnknown()} is true. + */ + public List toListOrNull() { + long[] array = asArray(); + if (array == null) { + return null; + } + + List list = new ArrayList<>(array.length); + for (long l : array) { + list.add(l); + } + + return list; + } + @Override public int hashCode() { return dimensionSizes != null ? Arrays.hashCode(dimensionSizes) : super.hashCode(); @@ -186,6 +227,7 @@ public int hashCode() { * Equals implementation for Shapes. Two Shapes are considered equal iff: * *

+ * *

* - * @param data type for {@code output} output * @param inputs List of 1 or 2 Tensors. * @param equation String describing the Einstein Summation operation; in the format of np.einsum. * @param data type for {@code Einsum} output and operands @@ -531,7 +513,6 @@ public Einsum einsum(Iterable> inputs, String eq * {@code axis}. If {@code keep_dims} is true, the reduced dimensions are * retained with length 1. * - * @param data type for {@code output} output * @param input The tensor to reduce. * @param axis The dimensions to reduce. Must be in the range * {@code [-rank(input), rank(input))}. @@ -554,7 +535,6 @@ public EuclideanNorm euclideanNorm(Operand input, * may detect the condition and raise an exception or it may simply return a * garbage result. * - * @param data type for {@code output} output * @param input Shape is {@code [..., M, M]}. * @param options carries optional attribute values * @param data type for {@code MatrixInverse} output and operands @@ -632,7 +612,6 @@ public LoadAndRemapMatrix loadAndRemapMatrix(Operand ckptPath, * is the {@code LU} decomposition of the input and {@code P} is the corresponding * permutation matrix. * - * @param data type for {@code sign} output * @param input Shape is {@code [N, M, M]}. * @param data type for {@code LogMatrixDeterminant} output and operands * @return a new instance of LogMatrixDeterminant @@ -657,8 +636,6 @@ public LogMatrixDeterminant logMatrixDeterminant(Operand * and {@code M-1}, inclusive. If P_mat denotes the permutation matrix corresponding to * P, then the L, U and P satisfies P_mat * input = L * U. * - * @param data type for {@code lu} output - * @param data type for {@code p} output * @param input A tensor of shape {@code [..., M, M]} whose inner-most 2 dimensions form matrices of * size {@code [M, M]}. * @param data type for {@code Lu} output and operands @@ -684,8 +661,6 @@ public Lu lu(Operand input) { * and {@code M-1}, inclusive. If P_mat denotes the permutation matrix corresponding to * P, then the L, U and P satisfies P_mat * input = L * U. * - * @param data type for {@code lu} output - * @param data type for {@code p} output * @param input A tensor of shape {@code [..., M, M]} whose inner-most 2 dimensions form matrices of * size {@code [M, M]}. * @param outputIdxType The value of the outputIdxType attribute @@ -707,7 +682,6 @@ public Lu lu(Operand input, *

Note: The default kernel implementation for MatMul on GPUs uses * cublas. * - * @param data type for {@code product} output * @param a The a value * @param b The b value * @param options carries optional attribute values @@ -801,7 +775,6 @@ public MatMul matMul(Operand a, Operand b, MatMul.Opt * [9, 2]] * * - * @param data type for {@code output} output * @param diagonal Rank {@code r}, where {@code r >= 1} * @param k Diagonal offset(s). Positive value means superdiagonal, 0 refers to the main * diagonal, and negative value means subdiagonals. {@code k} can be a single integer @@ -886,7 +859,6 @@ public MatrixDiag matrixDiag(Operand diagonal, Operand * - * @param data type for {@code diagonal} output * @param input Rank {@code r} tensor where {@code r >= 2}. * @param k Diagonal offset(s). Positive value means superdiagonal, 0 refers to the main * diagonal, and negative value means subdiagonals. {@code k} can be a single integer @@ -995,7 +967,6 @@ public MatrixDiagPart matrixDiagPart(Operand input, Oper * * * - * @param data type for {@code diagonal} output * @param input Rank {@code r} tensor where {@code r >= 2}. * @param k Diagonal offset(s). Positive value means superdiagonal, 0 refers to the main * diagonal, and negative value means subdiagonals. {@code k} can be a single integer @@ -1123,7 +1094,6 @@ public MatrixDiagPartV3 matrixDiagPartV3(Operand input, * * * - * @param data type for {@code output} output * @param diagonal Rank {@code r}, where {@code r >= 1} * @param k Diagonal offset(s). Positive value means superdiagonal, 0 refers to the main * diagonal, and negative value means subdiagonals. {@code k} can be a single integer @@ -1150,7 +1120,6 @@ public MatrixDiagV3 matrixDiagV3(Operand diagonal, Opera /** * Deprecated, use python implementation tf.linalg.matrix_exponential. * - * @param data type for {@code output} output * @param input The input value * @param data type for {@code MatrixExponential} output and operands * @return a new instance of MatrixExponential @@ -1173,7 +1142,6 @@ public MatrixExponential matrixExponential(Operand input * form square matrices. The output is a tensor of the same shape as the input * containing the exponential for all input submatrices {@code [..., :, :]}. * - * @param data type for {@code output} output * @param input Shape is {@code [..., M, M]}. * @param data type for {@code MatrixLogarithm} output and operands * @return a new instance of MatrixLogarithm @@ -1281,7 +1249,6 @@ public MatrixLogarithm matrixLogarithm(Operand input) { * * * - * @param data type for {@code output} output * @param input Rank {@code r+1}, where {@code r >= 1}. * @param diagonal Rank {@code r} when {@code k} is an integer or {@code k[0] == k[1]}. Otherwise, it has rank {@code r+1}. * {@code k >= 1}. @@ -1331,7 +1298,6 @@ public MatrixSetDiag matrixSetDiag(Operand input, Operan * typically 6-7 times slower than the fast path. If {@code fast} is {@code False} then * {@code l2_regularizer} is ignored. * - * @param data type for {@code output} output * @param matrix Shape is {@code [..., M, N]}. * @param rhs Shape is {@code [..., M, K]}. * @param l2Regularizer Scalar tensor. @@ -1362,7 +1328,6 @@ public MatrixSolveLs matrixSolveLs(Operand matrix, Opera * q_full, r_full = qr(a, full_matrices=True) * * - * @param data type for {@code q} output * @param input A tensor of shape {@code [..., M, N]} whose inner-most 2 dimensions * form matrices of size {@code [M, N]}. Let {@code P} be the minimum of {@code M} and {@code N}. * @param options carries optional attribute values @@ -1380,7 +1345,6 @@ public Qr qr(Operand input, Qr.Options... options) { * outer dimension of {@code b} (after being transposed if {@code transposed_b} is * non-zero). * - * @param data type for {@code out} output * @param a Must be a two-dimensional tensor. * @param b Must be a two-dimensional tensor. * @param minA The float value that the lowest quantized {@code a} value represents. @@ -1411,7 +1375,6 @@ public QuantizedMatMul quantizedMatMul * non-zero). Then do broadcast add operation with bias values on the matrix * multiplication result. The bias size must match inner dimension of {@code b}. * - * @param data type for {@code out} output * @param a A matrix to be multiplied. Must be a two-dimensional tensor of type {@code quint8}. * @param b A matrix to be multiplied and must be a two-dimensional tensor of type {@code qint8}. * @param bias A 1D bias tensor with size matching inner dimension of {@code b} (after being @@ -1442,7 +1405,6 @@ public QuantizedMatMulWithBias quantizedMatMulWithBias( * multiplication result. The bias size must match inner dimension of {@code b}. Then do * relu activation to get non-negative result. * - * @param data type for {@code out} output * @param a A matrix to be multiplied. Must be a two-dimensional tensor of type {@code quint8}. * @param b A matrix to be multiplied and must be a two-dimensional tensor of type {@code qint8}. * @param bias A 1D bias tensor with size matching with inner dimension of {@code b} (after being @@ -1474,7 +1436,6 @@ public QuantizedMatMulWithBiasAndRelu quantizedMatMulWith * relu activation to get non-negative result. Then do requantize operation to get * final uint8 result. * - * @param data type for {@code out} output * @param a A matrix to be multiplied. Must be a two-dimensional tensor of type {@code quint8}. * @param b A matrix to be multiplied and must be a two-dimensional tensor of type {@code qint8}. * @param bias A 1D bias tensor with size matching with inner dimension of {@code b} (after being @@ -1512,7 +1473,6 @@ public QuantizedMatMulWithBiasAndReluAndRequantize quanti * e = self_adjoint_eig(a, compute_v=False) * * - * @param data type for {@code e} output * @param input {@code Tensor} input of shape {@code [N, N]}. * @param options carries optional attribute values * @param data type for {@code SelfAdjointEigV2} output and operands @@ -1532,7 +1492,6 @@ public SelfAdjointEig selfAdjointEig(Operand input, * If {@code adjoint} is {@code True} then each output matrix satisfies * {@code adjoint(matrix[..., :, :]) * output[..., :, :] = rhs[..., :, :]}. * - * @param data type for {@code output} output * @param matrix Shape is {@code [..., M, M]}. * @param rhs Shape is {@code [..., M, K]}. * @param options carries optional attribute values @@ -1559,7 +1518,6 @@ public Solve solve(Operand matrix, Operand rhs, * form square matrices. The output is a tensor of the same shape as the input * containing the matrix square root for all input submatrices {@code [..., :, :]}. * - * @param data type for {@code output} output * @param input Shape is {@code [..., M, M]}. * @param data type for {@code MatrixSquareRoot} output and operands * @return a new instance of Sqrtm @@ -1581,7 +1539,6 @@ public Sqrtm sqrtm(Operand input) { * s, _, _ = svd(a, compute_uv=False) * * - * @param data type for {@code s} output * @param input A tensor of shape {@code [..., M, N]} whose inner-most 2 dimensions * form matrices of size {@code [M, N]}. Let {@code P} be the minimum of {@code M} and {@code N}. * @param options carries optional attribute values @@ -1608,7 +1565,6 @@ public Svd svd(Operand input, Svd.Options... options) { * [0, 0, 0, 4]] * * - * @param data type for {@code output} output * @param diagonal Rank k tensor where k is at most 1. * @param data type for {@code Diag} output and operands * @return a new instance of TensorDiag @@ -1634,7 +1590,6 @@ public TensorDiag tensorDiag(Operand diagonal) { * tf.diag_part(input) ==> [1, 2, 3, 4] * * - * @param data type for {@code diagonal} output * @param input Rank k tensor where k is even and not zero. * @param data type for {@code DiagPart} output and operands * @return a new instance of TensorDiagPart @@ -1648,7 +1603,6 @@ public TensorDiagPart tensorDiagPart(Operand input) { * The output {@code y} has the same rank as {@code x}. The shapes of {@code x} and {@code y} satisfy: * {@code y.shape[i] == x.shape[perm[i]] for i in [0, 1, ..., rank(x) - 1]} * - * @param data type for {@code y} output * @param x The x value * @param perm The perm value * @param data type for {@code Transpose} output and operands @@ -1703,7 +1657,6 @@ public Transpose transpose(Operand x, Operand * - * @param data type for {@code output} output * @param matrix Shape is {@code [..., M, M]}. * @param rhs Shape is {@code [..., M, K]}. * @param options carries optional attribute values @@ -1719,7 +1672,6 @@ public TriangularSolve triangularSolve(Operand matrix, O * Calculate product with tridiagonal matrix. * Calculates product of two matrices, where left matrix is a tridiagonal matrix. * - * @param data type for {@code output} output * @param superdiag Tensor of shape {@code [..., 1, M]}, representing superdiagonals of * tri-diagonal matrices to the left of multiplication. Last element is ignored. * @param maindiag Tensor of shape {@code [..., 1, M]}, representing main diagonals of tri-diagonal @@ -1746,7 +1698,6 @@ public TridiagonalMatMul tridiagonalMatMul(Operand super * library is used: https://docs.nvidia.com/cuda/cusparse/index.html#gtsv * Partial pivoting is not yet supported by XLA backends. * - * @param data type for {@code output} output * @param diagonals Tensor of shape {@code [..., 3, M]} whose innermost 2 dimensions represent the * tridiagonal matrices with three rows being the superdiagonal, diagonals, and * subdiagonals, in order. The last element of the superdiagonal and the first diff --git a/tensorflow-core/tensorflow-core-api/src/gen/annotations/org/tensorflow/op/LinalgSparseOps.java b/tensorflow-core/tensorflow-core-api/src/gen/annotations/org/tensorflow/op/LinalgSparseOps.java index ed8c4fdbb90..7210249ba1f 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/annotations/org/tensorflow/op/LinalgSparseOps.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/annotations/org/tensorflow/op/LinalgSparseOps.java @@ -59,7 +59,6 @@ public final class LinalgSparseOps { * This op is meant only for debugging / testing, and its interface is not expected * to be stable. * - * @param data type for {@code values} output * @param csrSparseMatrix A batched CSRSparseMatrix. * @param index The index in {@code csr_sparse_matrix}'s batch. * @param type The value of the type attribute @@ -74,7 +73,6 @@ public CSRSparseMatrixComponents cSRSparseMatrixComponents( /** * Convert a (possibly batched) CSRSparseMatrix to dense. * - * @param data type for {@code dense_output} output * @param sparseInput A batched CSRSparseMatrix. * @param type The value of the type attribute * @param data type for {@code CSRSparseMatrixToDense} output and operands @@ -88,7 +86,6 @@ public CSRSparseMatrixToDense cSRSparseMatrixToDense( /** * Converts a (possibly batched) CSRSparesMatrix to a SparseTensor. * - * @param data type for {@code values} output * @param sparseMatrix A (possibly batched) CSRSparseMatrix. * @param type The value of the type attribute * @param data type for {@code CSRSparseMatrixToSparseTensor} output and operands @@ -152,7 +149,6 @@ public SparseMatrixAdd sparseMatrixAdd(Operand * - * @param data type for {@code output} output * @param a A CSRSparseMatrix. * @param b A dense tensor. * @param options carries optional attribute values diff --git a/tensorflow-core/tensorflow-core-api/src/gen/annotations/org/tensorflow/op/MathOps.java b/tensorflow-core/tensorflow-core-api/src/gen/annotations/org/tensorflow/op/MathOps.java index ee2e3a46c27..d3dcfc686ad 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/annotations/org/tensorflow/op/MathOps.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/annotations/org/tensorflow/op/MathOps.java @@ -168,7 +168,6 @@ public final class MathOps { * value of each element in {@code x}. For example, if x is an input element and y is * an output element, this operation computes \(y = |x|\). * - * @param data type for {@code y} output * @param x The x value * @param data type for {@code Abs} output and operands * @return a new instance of Abs @@ -186,7 +185,6 @@ public Abs abs(Operand x) { *

Unlike the original {@code accumulate_n}, {@code accumulate_n_v2} is differentiable. *

Returns a {@code Tensor} of same shape and type as the elements of {@code inputs}. * - * @param data type for {@code sum} output * @param inputs A list of {@code Tensor} objects, each with same shape and type. * @param shape Shape of elements of {@code inputs}. * @param data type for {@code AccumulateNV2} output and operands @@ -201,7 +199,6 @@ public AccumulateN accumulateN(Iterable> inputs, * Provided an input tensor, the {@code tf.math.acos} operation returns the inverse cosine of each element of the tensor. If {@code y = tf.math.cos(x)} then, {@code x = tf.math.acos(y)}. *

Input range is {@code [-1, 1]} and the output has a range of {@code [0, pi]}. * - * @param data type for {@code y} output * @param x The x value * @param data type for {@code Acos} output and operands * @return a new instance of Acos @@ -219,7 +216,6 @@ public Acos acos(Operand x) { * tf.math.acosh(x) ==> [nan nan 0. 0.62236255 5.9914584 9.903487 inf] * * - * @param data type for {@code y} output * @param x The x value * @param data type for {@code Acosh} output and operands * @return a new instance of Acosh @@ -235,7 +231,6 @@ public Acosh acosh(Operand x) { *

Given two input tensors, the {@code tf.add} operation computes the sum for every element in the tensor. *

Both input and output have a range {@code (-inf, inf)}. * - * @param data type for {@code z} output * @param x The x value * @param y The y value * @param data type for {@code Add} output and operands @@ -253,7 +248,6 @@ public Add add(Operand x, Operand y) { * tf.math.add_n(x) ==> 26 * * - * @param data type for {@code sum} output * @param inputs The inputs value * @param data type for {@code AddN} output and operands * @return a new instance of AddN @@ -278,7 +272,6 @@ public AddN addN(Iterable> inputs) { * Equivalent to np.angle. *
{@literal @}end_compatibility * - * @param data type for {@code output} output * @param input The input value * @return a new instance of Angle, with default output types */ @@ -302,7 +295,6 @@ public Angle angle(Operand input) { * Equivalent to np.angle. *
{@literal @}end_compatibility * - * @param data type for {@code output} output * @param input The input value * @param Tout The value of the Tout attribute * @param data type for {@code Angle} output and operands @@ -339,7 +331,6 @@ public ApproximateEqual approximateEqual(Operand x, Operand * # here a[4] = 166.32 which is the largest element of a across axis 0 * * - * @param data type for {@code output} output * @param input The input value * @param dimension int16, int32 or int64, must be in the range {@code [-rank(input), rank(input))}. * Describes which dimension of the input Tensor to reduce across. For vectors, @@ -364,7 +355,6 @@ public ArgMax argMax(Operand input, * # here a[4] = 166.32 which is the largest element of a across axis 0 * * - * @param data type for {@code output} output * @param input The input value * @param dimension int16, int32 or int64, must be in the range {@code [-rank(input), rank(input))}. * Describes which dimension of the input Tensor to reduce across. For vectors, @@ -391,7 +381,6 @@ public ArgMax argMax(Operand input, * # here a[0] = 1 which is the smallest element of a across axis 0 * * - * @param data type for {@code output} output * @param input The input value * @param dimension int32 or int64, must be in the range {@code [-rank(input), rank(input))}. * Describes which dimension of the input Tensor to reduce across. For vectors, @@ -416,7 +405,6 @@ public ArgMin argMin(Operand input, * # here a[0] = 1 which is the smallest element of a across axis 0 * * - * @param data type for {@code output} output * @param input The input value * @param dimension int32 or int64, must be in the range {@code [-rank(input), rank(input))}. * Describes which dimension of the input Tensor to reduce across. For vectors, @@ -445,7 +433,6 @@ public ArgMin argMin(Operand input, * tf.math.asin(y) # [1.047, 0.785] = x * * - * @param data type for {@code y} output * @param x The x value * @param data type for {@code Asin} output and operands * @return a new instance of Asin @@ -464,7 +451,6 @@ public Asin asin(Operand x) { * tf.math.asinh(x) ==> [-inf -1.4436355 -0.4812118 0.8813736 1.0159732 5.991471 9.903487 inf] * * - * @param data type for {@code y} output * @param x The x value * @param data type for {@code Asinh} output and operands * @return a new instance of Asinh @@ -488,7 +474,6 @@ public Asinh asinh(Operand x) { * tf.math.atan(y) # [1.047, 0.785] = x * * - * @param data type for {@code y} output * @param x The x value * @param data type for {@code Atan} output and operands * @return a new instance of Atan @@ -516,7 +501,6 @@ public Atan atan(Operand x) { * * * - * @param data type for {@code z} output * @param y The y value * @param x The x value * @param data type for {@code Atan2} output and operands @@ -538,7 +522,6 @@ public Atan2 atan2(Operand y, Operand x) { * tf.math.atanh(x) ==> [nan -inf -0.54930615 inf 0. 0.54930615 nan nan] * * - * @param data type for {@code y} output * @param x The x value * @param data type for {@code Atanh} output and operands * @return a new instance of Atanh @@ -550,7 +533,6 @@ public Atanh atanh(Operand x) { /** * The BesselI0 operation * - * @param data type for {@code y} output * @param x The x value * @param data type for {@code BesselI0} output and operands * @return a new instance of BesselI0 @@ -562,7 +544,6 @@ public BesselI0 besselI0(Operand x) { /** * The BesselI0e operation * - * @param data type for {@code y} output * @param x The x value * @param data type for {@code BesselI0e} output and operands * @return a new instance of BesselI0e @@ -574,7 +555,6 @@ public BesselI0e besselI0e(Operand x) { /** * The BesselI1 operation * - * @param data type for {@code y} output * @param x The x value * @param data type for {@code BesselI1} output and operands * @return a new instance of BesselI1 @@ -586,7 +566,6 @@ public BesselI1 besselI1(Operand x) { /** * The BesselI1e operation * - * @param data type for {@code y} output * @param x The x value * @param data type for {@code BesselI1e} output and operands * @return a new instance of BesselI1e @@ -604,7 +583,6 @@ public BesselI1e besselI1e(Operand x) { *

is the incomplete beta function and \(B(a, b)\) is the complete * beta function. * - * @param data type for {@code z} output * @param a The a value * @param b The b value * @param x The x value @@ -624,7 +602,6 @@ public Betainc betainc(Operand a, Operand b, Operan * {@code i}. *

Values in {@code arr} outside of the range [0, size) are ignored. * - * @param data type for {@code bins} output * @param arr int32 {@code Tensor}. * @param sizeOutput non-negative int32 scalar {@code Tensor}. * @param weights is an int32, int64, float32, or float64 {@code Tensor} with the same @@ -641,7 +618,6 @@ public Bincount bincount(Operand arr, Operand data type for {@code y} output * @param x The x value * @param data type for {@code Ceil} output and operands * @return a new instance of Ceil @@ -667,7 +643,6 @@ public Ceil ceil(Operand x) { * * * - * @param data type for {@code y} output * @param x The x value * @return a new instance of ComplexAbs, with default output types */ @@ -692,7 +667,6 @@ public ComplexAbs complexAbs(Operand x) { * * * - * @param data type for {@code y} output * @param x The x value * @param Tout The value of the Tout attribute * @param data type for {@code ComplexAbs} output and operands @@ -715,7 +689,6 @@ public ComplexAbs complexAbs(Operand x, * tf.conj(input) ==> [-2.25 - 4.75j, 3.25 - 5.75j] * * - * @param data type for {@code output} output * @param input The input value * @param data type for {@code Conj} output and operands * @return a new instance of Conj @@ -735,7 +708,6 @@ public Conj conj(Operand input) { * tf.math.cos(x) ==> [nan -0.91113025 0.87758255 0.5403023 0.36235774 0.48718765 -0.95215535 nan] * * - * @param data type for {@code y} output * @param x The x value * @param data type for {@code Cos} output and operands * @return a new instance of Cos @@ -754,7 +726,6 @@ public Cos cos(Operand x) { * tf.math.cosh(x) ==> [inf 4.0515420e+03 1.1276259e+00 1.5430807e+00 1.8106556e+00 3.7621956e+00 1.1013233e+04 inf] * * - * @param data type for {@code y} output * @param x The x value * @param data type for {@code Cosh} output and operands * @return a new instance of Cosh @@ -786,7 +757,6 @@ public Cosh cosh(Operand x) { * tf.cumprod([a, b, c], exclusive=True, reverse=True) # => [b * c, c, 1] * * - * @param data type for {@code out} output * @param x A {@code Tensor}. Must be one of the following types: {@code float32}, {@code float64}, * {@code int64}, {@code int32}, {@code uint8}, {@code uint16}, {@code int16}, {@code int8}, {@code complex64}, * {@code complex128}, {@code qint8}, {@code quint8}, {@code qint32}, {@code half}. @@ -824,7 +794,6 @@ public Cumprod cumprod(Operand x, Operand * - * @param data type for {@code out} output * @param x A {@code Tensor}. Must be one of the following types: {@code float32}, {@code float64}, * {@code int64}, {@code int32}, {@code uint8}, {@code uint16}, {@code int16}, {@code int8}, {@code complex64}, * {@code complex128}, {@code qint8}, {@code quint8}, {@code qint32}, {@code half}. @@ -858,7 +827,6 @@ public Cumsum cumsum(Operand x, OperandBy setting the {@code reverse} kwarg to {@code True}, the cumulative log-sum-exp is performed in the * opposite direction. * - * @param data type for {@code out} output * @param x A {@code Tensor}. Must be one of the following types: {@code float16}, {@code float32}, {@code float64}. * @param axis A {@code Tensor} of type {@code int32} (default: 0). Must be in the range * {@code [-rank(x), rank(x))}. @@ -880,7 +848,6 @@ public CumulativeLogsumexp cumulativeLogsumexp(Operand * {@code i}. *

NOTE: {@code math.FloorMod} supports broadcasting. More about broadcasting * here * - * @param data type for {@code z} output * @param x The x value * @param y The y value * @param data type for {@code FloorMod} output and operands @@ -1168,7 +1124,6 @@ public GreaterEqual greaterEqual(Operand x, Operand y) *

Note, above {@code Q(a, x)} ({@code Igammac}) is the upper regularized complete * Gamma function. * - * @param data type for {@code z} output * @param a The a value * @param x The x value * @param data type for {@code Igamma} output and operands @@ -1181,7 +1136,6 @@ public Igamma igamma(Operand a, Operand x) { /** * Computes the gradient of {@code igamma(a, x)} wrt {@code a}. * - * @param data type for {@code z} output * @param a The a value * @param x The x value * @param data type for {@code IgammaGradA} output and operands @@ -1201,7 +1155,6 @@ public IgammaGradA igammaGradA(Operand a, Operand x *

Note, above {@code P(a, x)} ({@code Igamma}) is the lower regularized complete * Gamma function. * - * @param data type for {@code z} output * @param a The a value * @param x The x value * @param data type for {@code Igammac} output and operands @@ -1223,7 +1176,6 @@ public Igammac igammac(Operand a, Operand x) { * tf.imag(input) ==> [4.75, 5.75] * * - * @param data type for {@code output} output * @param input The input value * @return a new instance of Imag, with default output types */ @@ -1243,7 +1195,6 @@ public Imag imag(Operand input) { * tf.imag(input) ==> [4.75, 5.75] * * - * @param data type for {@code output} output * @param input The input value * @param Tout The value of the Tout attribute * @param data type for {@code Imag} output and operands @@ -1267,7 +1218,6 @@ public Imag imag(Operand input, Class * invert_permutation(x) ==> [2, 4, 3, 0, 1] * * - * @param data type for {@code y} output * @param x 1-D. * @param data type for {@code InvertPermutation} output and operands * @return a new instance of InvertPermutation @@ -1388,7 +1338,6 @@ public LessEqual lessEqual(Operand x, Operand y) { * tf.math.lgamma(x) ==> [inf, 0.5723649, 0., 2.4537368, inf, -4.6477685] * * - * @param data type for {@code y} output * @param x The x value * @param data type for {@code Lgamma} output and operands * @return a new instance of Lgamma @@ -1406,7 +1355,6 @@ public Lgamma lgamma(Operand x) { * tf.math.log(x) ==> [-inf, -0.6931472, 0. , 1.609438] * * - * @param data type for {@code y} output * @param x The x value * @param data type for {@code Log} output and operands * @return a new instance of Log @@ -1424,7 +1372,6 @@ public Log log(Operand x) { * tf.math.log1p(x) ==> [0., 0.4054651, 0.6931472, 1.7917595] * * - * @param data type for {@code y} output * @param x The x value * @param data type for {@code Log1p} output and operands * @return a new instance of Log1p @@ -1474,7 +1421,6 @@ public LogicalOr logicalOr(Operand x, Operand y) { * NOTE: {@code math.Maximum} supports broadcasting. More about broadcasting * here * - * @param data type for {@code z} output * @param x The x value * @param y The y value * @param data type for {@code Maximum} output and operands @@ -1491,7 +1437,6 @@ public Maximum maximum(Operand x, Operand y) { * {@code axis}. If {@code keep_dims} is true, the reduced dimensions are * retained with length 1. * - * @param data type for {@code output} output * @param input The tensor to reduce. * @param axis The dimensions to reduce. Must be in the range * {@code [-rank(input), rank(input))}. @@ -1509,7 +1454,6 @@ public Mean mean(Operand input, OperandNOTE: {@code math.Minimum} supports broadcasting. More about broadcasting * here * - * @param data type for {@code z} output * @param x The x value * @param y The y value * @param data type for {@code Minimum} output and operands @@ -1526,7 +1470,6 @@ public Minimum minimum(Operand x, Operand y) { *

NOTE: {@code math.Mod} supports broadcasting. More about broadcasting * here * - * @param data type for {@code z} output * @param x The x value * @param y The y value * @param data type for {@code Mod} output and operands @@ -1541,7 +1484,6 @@ public Mod mod(Operand x, Operand y) { * NOTE: {@code math.Mul} supports broadcasting. More about broadcasting * here * - * @param data type for {@code z} output * @param x The x value * @param y The y value * @param data type for {@code Mul} output and operands @@ -1556,7 +1498,6 @@ public Mul mul(Operand x, Operand y) { * NOTE: {@code math.MulNoNan} supports broadcasting. More about broadcasting * here * - * @param data type for {@code z} output * @param x The x value * @param y The y value * @param data type for {@code MulNoNan} output and operands @@ -1569,7 +1510,6 @@ public MulNoNan mulNoNan(Operand x, Operand y) { /** * The Ndtri operation * - * @param data type for {@code y} output * @param x The x value * @param data type for {@code Ndtri} output and operands * @return a new instance of Ndtri @@ -1582,7 +1522,6 @@ public Ndtri ndtri(Operand x) { * Computes numerical negative value element-wise. * I.e., \(y = -x\). * - * @param data type for {@code y} output * @param x The x value * @param data type for {@code Neg} output and operands * @return a new instance of Neg @@ -1599,7 +1538,6 @@ public Neg neg(Operand x) { * Equivalent to C++ std::nextafter function. *
{@literal @}end_compatibility * - * @param data type for {@code output} output * @param x1 The x1 value * @param x2 The x2 value * @param data type for {@code NextAfter} output and operands @@ -1632,7 +1570,6 @@ public NotEqual notEqual(Operand x, Operand y, *

where \(\psi(x)\) is the digamma function. * The polygamma function is defined only for non-negative integer orders \a\. * - * @param data type for {@code z} output * @param a The a value * @param x The x value * @param data type for {@code Polygamma} output and operands @@ -1667,7 +1604,6 @@ public PopulationCount populationCount(Operand x) { * tf.pow(x, y) ==> [[256, 65536], [9, 27]] * * - * @param data type for {@code z} output * @param x The x value * @param y The y value * @param data type for {@code Pow} output and operands @@ -1680,7 +1616,6 @@ public Pow pow(Operand x, Operand y) { /** * Returns x + y element-wise, working on quantized buffers. * - * @param data type for {@code z} output * @param x The x value * @param y The y value * @param minX The float value that the lowest quantized {@code x} value represents. @@ -1700,7 +1635,6 @@ public QuantizedAdd quantizedAdd(Operand data type for {@code z} output * @param x The x value * @param y The y value * @param minX The float value that the lowest quantized {@code x} value represents. @@ -1729,7 +1663,6 @@ public QuantizedMul quantizedMul(Operand * - * @param data type for {@code output} output * @param input The input value * @return a new instance of Real, with default output types */ @@ -1749,7 +1682,6 @@ public Real real(Operand input) { * tf.real(input) ==> [-2.25, 3.25] * * - * @param data type for {@code output} output * @param input The input value * @param Tout The value of the Tout attribute * @param data type for {@code Real} output and operands @@ -1765,7 +1697,6 @@ public Real real(Operand input, Class *

NOTE: {@code Div} supports broadcasting. More about broadcasting * here * - * @param data type for {@code z} output * @param x The x value * @param y The y value * @param data type for {@code RealDiv} output and operands @@ -1779,7 +1710,6 @@ public RealDiv realDiv(Operand x, Operand y) { * Computes the reciprocal of x element-wise. * I.e., \(y = 1 / x\). * - * @param data type for {@code y} output * @param x The x value * @param data type for {@code Reciprocal} output and operands * @return a new instance of Reciprocal @@ -1793,7 +1723,6 @@ public Reciprocal reciprocal(Operand x) { * Specifically, {@code grad = -dy * y*y}, where {@code y = 1/x}, and {@code dy} * is the corresponding input gradient. * - * @param data type for {@code z} output * @param y The y value * @param dy The dy value * @param data type for {@code ReciprocalGrad} output and operands @@ -1822,7 +1751,6 @@ public RequantizationRangePerChannel requantizationRangePerChannel( /** * Requantizes input with min and max values known per channel. * - * @param data type for {@code output} output * @param input The original input tensor. * @param inputMin The minimum value of the input tensor * @param inputMax The maximum value of the input tensor. @@ -1850,7 +1778,6 @@ public RequantizePerChannel requantizePerChannel( * rint([-1.7, -1.5, -0.2, 0.2, 1.5, 1.7, 2.0]) ==> [-2., -2., -0., 0., 2., 2., 2.] * * - * @param data type for {@code y} output * @param x The x value * @param data type for {@code Rint} output and operands * @return a new instance of Rint @@ -1864,7 +1791,6 @@ public Rint rint(Operand x) { * Rounds half to even. Also known as bankers rounding. If you want to round * according to the current system rounding mode use std::cint. * - * @param data type for {@code y} output * @param x The x value * @param data type for {@code Round} output and operands * @return a new instance of Round @@ -1877,7 +1803,6 @@ public Round round(Operand x) { * Computes reciprocal of square root of x element-wise. * I.e., \(y = 1 / \sqrt{x}\). * - * @param data type for {@code y} output * @param x The x value * @param data type for {@code Rsqrt} output and operands * @return a new instance of Rsqrt @@ -1891,7 +1816,6 @@ public Rsqrt rsqrt(Operand x) { * Specifically, {@code grad = dy * -0.5 * y^3}, where {@code y = rsqrt(x)}, and {@code dy} * is the corresponding input gradient. * - * @param data type for {@code z} output * @param y The y value * @param dy The dy value * @param data type for {@code RsqrtGrad} output and operands @@ -1942,7 +1866,6 @@ public RsqrtGrad rsqrtGrad(Operand y, Operand dy) { * * * - * @param data type for {@code output} output * @param data The data value * @param segmentIds A 1-D tensor whose size is equal to the size of {@code data}'s * first dimension. Values should be sorted and can be repeated. @@ -1989,7 +1912,6 @@ public SegmentMax segmentMax(Operand data, * * * - * @param data type for {@code output} output * @param data The data value * @param segmentIds A 1-D tensor whose size is equal to the size of {@code data}'s * first dimension. Values should be sorted and can be repeated. @@ -2044,7 +1966,6 @@ public SegmentMean segmentMean(Operand data, * * * - * @param data type for {@code output} output * @param data The data value * @param segmentIds A 1-D tensor whose size is equal to the size of {@code data}'s * first dimension. Values should be sorted and can be repeated. @@ -2093,7 +2014,6 @@ public SegmentMin segmentMin(Operand data, * * * - * @param data type for {@code output} output * @param data The data value * @param segmentIds A 1-D tensor whose size is equal to the size of {@code data}'s * first dimension. Values should be sorted and can be repeated. @@ -2119,9 +2039,7 @@ public SegmentProd segmentProd(Operand data, * that {@code segment_ids[j] == i}. *

If the sum is empty for a given segment ID {@code i}, {@code output[i] = 0}. *

Note that this op is currently only supported with jit_compile=True. - * * - * @param data type for {@code output} output * @param data The data value * @param segmentIds A 1-D tensor whose size is equal to the size of {@code data}'s * first dimension. Values should be sorted and can be repeated. @@ -2141,7 +2059,6 @@ public SegmentSum segmentSum(Operand data, * Computes sigmoid of {@code x} element-wise. * Specifically, {@code y = 1 / (1 + exp(-x))}. * - * @param data type for {@code y} output * @param x The x value * @param data type for {@code Sigmoid} output and operands * @return a new instance of Sigmoid @@ -2155,7 +2072,6 @@ public Sigmoid sigmoid(Operand x) { * Specifically, {@code grad = dy * y * (1 - y)}, where {@code y = sigmoid(x)}, and * {@code dy} is the corresponding input gradient. * - * @param data type for {@code z} output * @param y The y value * @param dy The dy value * @param data type for {@code SigmoidGrad} output and operands @@ -2179,7 +2095,6 @@ public SigmoidGrad sigmoidGrad(Operand y, Operand dy) * * * - * @param data type for {@code y} output * @param x The x value * @param data type for {@code Sign} output and operands * @return a new instance of Sign @@ -2198,7 +2113,6 @@ public Sign sign(Operand x) { * tf.math.sin(x) ==> [nan -0.4121185 -0.47942555 0.84147096 0.9320391 -0.87329733 -0.54402107 nan] * * - * @param data type for {@code y} output * @param x The x value * @param data type for {@code Sin} output and operands * @return a new instance of Sin @@ -2217,7 +2131,6 @@ public Sin sin(Operand x) { * tf.math.sinh(x) ==> [-inf -4.0515420e+03 -5.2109528e-01 1.1752012e+00 1.5094614e+00 3.6268604e+00 1.1013232e+04 inf] * * - * @param data type for {@code y} output * @param x The x value * @param data type for {@code Sinh} output and operands * @return a new instance of Sinh @@ -2231,7 +2144,6 @@ public Sinh sinh(Operand x) { * Creates a Sobol sequence with {@code num_results} samples. Each sample has dimension * {@code dim}. Skips the first {@code skip} samples. * - * @param data type for {@code samples} output * @param dim Positive scalar {@code Tensor} representing each sample's dimension. * @param numResults Positive scalar {@code Tensor} of dtype int32. The number of Sobol points to return * in the output. @@ -2249,7 +2161,6 @@ public SobolSample sobolSample(Operand dim, Operand nu * Creates a Sobol sequence with {@code num_results} samples. Each sample has dimension * {@code dim}. Skips the first {@code skip} samples. * - * @param data type for {@code samples} output * @param dim Positive scalar {@code Tensor} representing each sample's dimension. * @param numResults Positive scalar {@code Tensor} of dtype int32. The number of Sobol points to return * in the output. @@ -2267,7 +2178,6 @@ public SobolSample sobolSample(Operand dim, /** * The Softplus operation * - * @param data type for {@code activations} output * @param features The features value * @param data type for {@code Softplus} output and operands * @return a new instance of Softplus @@ -2279,7 +2189,6 @@ public Softplus softplus(Operand features) { /** * Computes softplus gradients for a softplus operation. * - * @param data type for {@code backprops} output * @param gradients The backpropagated gradients to the corresponding softplus operation. * @param features The features passed as input to the corresponding softplus operation. * @param data type for {@code SoftplusGrad} output and operands @@ -2294,7 +2203,6 @@ public SoftplusGrad softplusGrad(Operand gradients, * Computes square root of x element-wise. * I.e., \(y = \sqrt{x} = x^{1/2}\). * - * @param data type for {@code y} output * @param x The x value * @param data type for {@code Sqrt} output and operands * @return a new instance of Sqrt @@ -2308,7 +2216,6 @@ public Sqrt sqrt(Operand x) { * Specifically, {@code grad = dy * 0.5 / y}, where {@code y = sqrt(x)}, and {@code dy} * is the corresponding input gradient. * - * @param data type for {@code z} output * @param y The y value * @param dy The dy value * @param data type for {@code SqrtGrad} output and operands @@ -2322,7 +2229,6 @@ public SqrtGrad sqrtGrad(Operand y, Operand dy) { * Computes square of x element-wise. * I.e., \(y = x * x = x^2\). * - * @param data type for {@code y} output * @param x The x value * @param data type for {@code Square} output and operands * @return a new instance of Square @@ -2336,7 +2242,6 @@ public Square square(Operand x) { * NOTE: {@code math.SquaredDifference} supports broadcasting. More about broadcasting * here * - * @param data type for {@code z} output * @param x The x value * @param y The y value * @param data type for {@code SquaredDifference} output and operands @@ -2351,7 +2256,6 @@ public SquaredDifference squaredDifference(Operand x, Op * NOTE: {@code math.Sub} supports broadcasting. More about broadcasting * here * - * @param data type for {@code z} output * @param x The x value * @param y The y value * @param data type for {@code Sub} output and operands @@ -2372,7 +2276,6 @@ public Sub sub(Operand x, Operand y) { * tf.math.tan(x) ==> [nan 0.45231566 -0.5463025 1.5574077 2.572152 -1.7925274 0.32097113 nan] * * - * @param data type for {@code y} output * @param x The x value * @param data type for {@code Tan} output and operands * @return a new instance of Tan @@ -2398,7 +2301,6 @@ public Tan tan(Operand x) { * * * - * @param data type for {@code y} output * @param x The x value * @param data type for {@code Tanh} output and operands * @return a new instance of Tanh @@ -2412,7 +2314,6 @@ public Tanh tanh(Operand x) { * Specifically, {@code grad = dy * (1 - y*y)}, where {@code y = tanh(x)}, and {@code dy} * is the corresponding input gradient. * - * @param data type for {@code z} output * @param y The y value * @param dy The dy value * @param data type for {@code TanhGrad} output and operands @@ -2431,7 +2332,6 @@ public TanhGrad tanhGrad(Operand y, Operand dy) { *

NOTE: {@code math.TruncateDiv} supports broadcasting. More about broadcasting * here * - * @param data type for {@code z} output * @param x The x value * @param y The y value * @param data type for {@code TruncateDiv} output and operands @@ -2447,7 +2347,6 @@ public TruncateDiv truncateDiv(Operand x, Operand y) *

NOTE: {@code math.TruncateMod} supports broadcasting. More about broadcasting * here * - * @param data type for {@code z} output * @param x The x value * @param y The y value * @param data type for {@code TruncateMod} output and operands @@ -2475,7 +2374,6 @@ public TruncateMod truncateMod(Operand x, Operand y * if {@code operand.quantization_axis} >= 0 and {@code output.quantization_axis} >= 0, * {@code operand.dims} - {@code operand.quantization_axis} must be equal to {@code output.dims} - {@code output.quantization_axis}. * - * @param data type for {@code output} output * @param lhs Must be a quantized tensor. * @param rhs Must be a quantized tensor. * @param lhsScales The float value(s) used as scale factors when quantizing the original data that {@code lhs} represents. @@ -2547,7 +2445,6 @@ public UniformQuantizedAdd uniformQuantizedAdd(Operand * * * - * @param data type for {@code output} output * @param data The data value * @param segmentIds A tensor whose shape is a prefix of {@code data.shape}. * The values must be less than {@code num_segments}. @@ -2594,7 +2491,6 @@ public UnsortedSegmentMax unsortedSegmentMax(Operand d * out-of-bound indices or outputting a tensor with a 0 stored in the first * dimension of its shape if {@code num_segments} is 0. * - * @param data type for {@code output} output * @param data The data value * @param segmentIds A tensor whose shape is a prefix of {@code data.shape}. * The values must be less than {@code num_segments}. @@ -2640,7 +2536,6 @@ public UnsortedSegmentMin unsortedSegmentMin(Operand d * out-of-bound indices or outputting a tensor with a 0 stored in the first * dimension of its shape if {@code num_segments} is 0. * - * @param data type for {@code output} output * @param data The data value * @param segmentIds A tensor whose shape is a prefix of {@code data.shape}. * The values must be less than {@code num_segments}. @@ -2689,7 +2584,6 @@ public UnsortedSegmentProd unsortedSegmentProd(Operand d * * * - * @param data type for {@code output} output * @param data The data value * @param segmentIds A tensor whose shape is a prefix of {@code data.shape}. * The values must be less than {@code num_segments}. @@ -2707,7 +2601,6 @@ public UnsortedSegmentSum unsortedSegmentSum(Operand dat /** * Returns 0 if x == 0, and x / y otherwise, elementwise. * - * @param data type for {@code z} output * @param x The x value * @param y The y value * @param data type for {@code Xdivy} output and operands @@ -2720,7 +2613,6 @@ public Xdivy xdivy(Operand x, Operand y) { /** * Returns 0 if x == 0, and x * log1p(y) otherwise, elementwise. * - * @param data type for {@code z} output * @param x The x value * @param y The y value * @param data type for {@code Xlog1py} output and operands @@ -2733,7 +2625,6 @@ public Xlog1py xlog1py(Operand x, Operand y) { /** * Returns 0 if x == 0, and x * log(y) otherwise, elementwise. * - * @param data type for {@code z} output * @param x The x value * @param y The y value * @param data type for {@code Xlogy} output and operands @@ -2748,7 +2639,6 @@ public Xlogy xlogy(Operand x, Operand y) { * The Hurwitz zeta function is defined as: *

\(\zeta(x, q) = \sum_{n=0}^{\infty} (q + n)^{-x}\) * - * @param data type for {@code z} output * @param x The x value * @param q The q value * @param data type for {@code Zeta} output and operands diff --git a/tensorflow-core/tensorflow-core-api/src/gen/annotations/org/tensorflow/op/MathSpecialOps.java b/tensorflow-core/tensorflow-core-api/src/gen/annotations/org/tensorflow/op/MathSpecialOps.java index 05af5fe921d..e486615af1b 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/annotations/org/tensorflow/op/MathSpecialOps.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/annotations/org/tensorflow/op/MathSpecialOps.java @@ -51,7 +51,6 @@ public final class MathSpecialOps { /** * The BesselJ0 operation * - * @param data type for {@code y} output * @param x The x value * @param data type for {@code BesselJ0} output and operands * @return a new instance of BesselJ0 @@ -63,7 +62,6 @@ public BesselJ0 besselJ0(Operand x) { /** * The BesselJ1 operation * - * @param data type for {@code y} output * @param x The x value * @param data type for {@code BesselJ1} output and operands * @return a new instance of BesselJ1 @@ -75,7 +73,6 @@ public BesselJ1 besselJ1(Operand x) { /** * The BesselK0 operation * - * @param data type for {@code y} output * @param x The x value * @param data type for {@code BesselK0} output and operands * @return a new instance of BesselK0 @@ -87,7 +84,6 @@ public BesselK0 besselK0(Operand x) { /** * The BesselK0e operation * - * @param data type for {@code y} output * @param x The x value * @param data type for {@code BesselK0e} output and operands * @return a new instance of BesselK0e @@ -99,7 +95,6 @@ public BesselK0e besselK0e(Operand x) { /** * The BesselK1 operation * - * @param data type for {@code y} output * @param x The x value * @param data type for {@code BesselK1} output and operands * @return a new instance of BesselK1 @@ -111,7 +106,6 @@ public BesselK1 besselK1(Operand x) { /** * The BesselK1e operation * - * @param data type for {@code y} output * @param x The x value * @param data type for {@code BesselK1e} output and operands * @return a new instance of BesselK1e @@ -123,7 +117,6 @@ public BesselK1e besselK1e(Operand x) { /** * The BesselY0 operation * - * @param data type for {@code y} output * @param x The x value * @param data type for {@code BesselY0} output and operands * @return a new instance of BesselY0 @@ -135,7 +128,6 @@ public BesselY0 besselY0(Operand x) { /** * The BesselY1 operation * - * @param data type for {@code y} output * @param x The x value * @param data type for {@code BesselY1} output and operands * @return a new instance of BesselY1 @@ -147,7 +139,6 @@ public BesselY1 besselY1(Operand x) { /** * The Dawsn operation * - * @param data type for {@code y} output * @param x The x value * @param data type for {@code Dawsn} output and operands * @return a new instance of Dawsn @@ -159,7 +150,6 @@ public Dawsn dawsn(Operand x) { /** * The Expint operation * - * @param data type for {@code y} output * @param x The x value * @param data type for {@code Expint} output and operands * @return a new instance of Expint @@ -171,7 +161,6 @@ public Expint expint(Operand x) { /** * The FresnelCos operation * - * @param data type for {@code y} output * @param x The x value * @param data type for {@code FresnelCos} output and operands * @return a new instance of FresnelCos @@ -183,7 +172,6 @@ public FresnelCos fresnelCos(Operand x) { /** * The FresnelSin operation * - * @param data type for {@code y} output * @param x The x value * @param data type for {@code FresnelSin} output and operands * @return a new instance of FresnelSin @@ -195,7 +183,6 @@ public FresnelSin fresnelSin(Operand x) { /** * The Spence operation * - * @param data type for {@code y} output * @param x The x value * @param data type for {@code Spence} output and operands * @return a new instance of Spence diff --git a/tensorflow-core/tensorflow-core-api/src/gen/annotations/org/tensorflow/op/NnOps.java b/tensorflow-core/tensorflow-core-api/src/gen/annotations/org/tensorflow/op/NnOps.java index 2e20b52b946..9859a308562 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/annotations/org/tensorflow/op/NnOps.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/annotations/org/tensorflow/op/NnOps.java @@ -155,7 +155,6 @@ public final class NnOps { * Each entry in {@code output} is the mean of the corresponding size {@code ksize} * window in {@code value}. * - * @param data type for {@code output} output * @param value 4-D with shape {@code [batch, height, width, channels]}. * @param ksize The size of the sliding window for each dimension of {@code value}. * @param strides The stride of the sliding window for each dimension of {@code value}. @@ -174,7 +173,6 @@ public AvgPool avgPool(Operand value, List ksize * Each entry in {@code output} is the mean of the corresponding size {@code ksize} window in * {@code value}. * - * @param data type for {@code output} output * @param input Shape {@code [batch, depth, rows, cols, channels]} tensor to pool over. * @param ksize 1-D tensor of length 5. The size of the window for each dimension of * the input tensor. Must have {@code ksize[0] = ksize[4] = 1}. @@ -193,7 +191,6 @@ public AvgPool3d avgPool3d(Operand input, List k /** * Computes gradients of average pooling function. * - * @param data type for {@code output} output * @param origInputShape The original input dimensions. * @param grad Output backprop of shape {@code [batch, depth, rows, cols, channels]}. * @param ksize 1-D tensor of length 5. The size of the window for each dimension of @@ -214,7 +211,6 @@ public AvgPool3dGrad avgPool3dGrad(Operand origIn /** * Computes gradients of the average pooling function. * - * @param data type for {@code output} output * @param origInputShape 1-D. Shape of the original input to {@code avg_pool}. * @param grad 4-D with shape {@code [batch, height, width, channels]}. Gradients w.r.t. * the output of {@code avg_pool}. @@ -235,7 +231,6 @@ public AvgPoolGrad avgPoolGrad(Operand origInputS * Batch normalization. * This op is deprecated. Prefer {@code tf.nn.batch_normalization}. * - * @param data type for {@code result} output * @param t A 4D input Tensor. * @param m A 1D mean Tensor with size matching the last dimension of t. * This is the first output from tf.nn.moments, @@ -264,7 +259,6 @@ public BatchNormWithGlobalNormalization batchNormWithGlobal * Gradients for batch normalization. * This op is deprecated. See {@code tf.nn.batch_normalization}. * - * @param data type for {@code dx} output * @param t A 4D input Tensor. * @param m A 1D mean Tensor with size matching the last dimension of t. * This is the first output from tf.nn.moments, @@ -293,7 +287,6 @@ public BatchNormWithGlobalNormalizationGrad batchNormWithGl * This is a special case of {@code tf.add} where {@code bias} is restricted to be 1-D. * Broadcasting is supported, so {@code value} may have any number of dimensions. * - * @param data type for {@code output} output * @param value Any number of dimensions. * @param bias 1-D with size the last dimension of {@code value}. * @param options carries optional attribute values @@ -311,7 +304,6 @@ public BiasAdd biasAdd(Operand value, Operand bias, * For NHWC data format, the feature dimension is the last. For NCHW data format, * the feature dimension is the third-to-last. * - * @param data type for {@code output} output * @param outBackprop Any number of dimensions. * @param options carries optional attribute values * @param data type for {@code BiasAddGrad} output and operands @@ -345,7 +337,6 @@ public BiasAddGrad biasAddGrad(Operand outBackprop, * all gate-related outputs should be reordered. * * - * @param data type for {@code i} output * @param seqLenMax Maximum time length actually used by this input. Outputs are padded * with zeros beyond this length. * @param x The sequence input to the LSTM, shape (timelen, batch_size, num_inputs). @@ -370,7 +361,6 @@ public BlockLSTM blockLSTM(Operand seqLenMax, Ope * Computes the LSTM cell backward propagation for the entire time sequence. * This implementation is to be used in conjunction of BlockLSTMV2. * - * @param data type for {@code x_grad} output * @param seqLenMax Maximum time length actually used by this input. Outputs are padded * with zeros beyond this length. * @param x The sequence input to the LSTM, shape (timelen, batch_size, num_inputs). @@ -445,7 +435,6 @@ public ComputeAccidentalHits computeAccidentalHits(Operand trueClasses, * General function for computing a N-D convolution. It is required that * {@code 1 <= N <= 3}. * - * @param data type for {@code output} output * @param input Tensor of type T and shape {@code batch_shape + spatial_shape + [in_channels]} in the * case that {@code channels_last_format = true} or shape * {@code batch_shape + [in_channels] + spatial_shape} if {@code channels_last_format = false}. @@ -490,7 +479,6 @@ public Conv conv(Operand input, Operand filter, Lis *

Must have {@code strides[0] = strides[3] = 1}. For the most common case of the same * horizontal and vertices strides, {@code strides = [1, stride, stride, 1]}. * - * @param data type for {@code output} output * @param input A 4-D tensor. The dimension order is interpreted according to the value * of {@code data_format}, see below for details. * @param filter A 4-D tensor of shape @@ -511,7 +499,6 @@ public Conv2d conv2d(Operand input, Operand filter, /** * Computes the gradients of convolution with respect to the filter. * - * @param data type for {@code output} output * @param input 4-D with shape {@code [batch, in_height, in_width, in_channels]}. * @param filterSizes An integer vector representing the tensor shape of {@code filter}, * where {@code filter} is a 4-D @@ -535,7 +522,6 @@ public Conv2dBackpropFilter conv2dBackpropFilter(Operand< /** * Computes the gradients of convolution with respect to the input. * - * @param data type for {@code output} output * @param inputSizes An integer vector representing the shape of {@code input}, * where {@code input} is a 4-D {@code [batch, height, width, channels]} tensor. * @param filter 4-D with shape @@ -563,7 +549,6 @@ public Conv2dBackpropInput conv2dBackpropInput(OperandOur Conv3D implements a form of cross-correlation. * - * @param data type for {@code output} output * @param input Shape {@code [batch, in_depth, in_height, in_width, in_channels]}. * @param filter Shape {@code [filter_depth, filter_height, filter_width, in_channels, out_channels]}. {@code in_channels} must match between {@code input} and {@code filter}. * @param strides 1-D tensor of length 5. The stride of the sliding window for each @@ -581,7 +566,6 @@ public Conv3d conv3d(Operand input, Operand filter, /** * Computes the gradients of 3-D convolution with respect to the filter. * - * @param data type for {@code output} output * @param input Shape {@code [batch, depth, rows, cols, in_channels]}. * @param filterSizes An integer vector representing the tensor shape of {@code filter}, * where {@code filter} is a 5-D @@ -604,7 +588,6 @@ public Conv3dBackpropFilter conv3dBackpropFilter(Operand< /** * Computes the gradients of 3-D convolution with respect to the input. * - * @param data type for {@code output} output * @param inputSizes An integer vector representing the tensor shape of {@code input}, * where {@code input} is a 5-D * {@code [batch, depth, rows, cols, in_channels]} tensor. @@ -632,7 +615,6 @@ public Conv3dBackpropInput conv3dBackpropInput( * "A B" is returned if merge_repeated = True but "A B B B B" is * returned if merge_repeated = False. * - * @param data type for {@code log_probability} output * @param inputs 3-D, shape: {@code (max_time x batch_size x num_classes)}, the logits. * @param sequenceLength A vector containing sequence lengths, size {@code (batch)}. * @param beamWidth A scalar >= 0 (beam search beam width). @@ -658,7 +640,6 @@ public CtcBeamSearchDecoder ctcBeamSearchDecoder(Operand< * time and batch corresponds to the blank, index {@code (num_classes - 1)}, no new * element is emitted. * - * @param data type for {@code log_probability} output * @param inputs 3-D, shape: {@code (max_time x batch_size x num_classes)}, the logits. * @param sequenceLength A vector containing sequence lengths, size {@code (batch_size)}. * @param options carries optional attribute values @@ -675,7 +656,6 @@ public CtcGreedyDecoder ctcGreedyDecoder(Operand input * the gradient. This class performs the softmax operation for you, so inputs * should be e.g. linear projections of outputs by an LSTM. * - * @param data type for {@code loss} output * @param inputs 3-D, shape: {@code (max_time x batch_size x num_classes)}, the logits. * @param labelsIndices The indices of a {@code SparseTensor}. * {@code labels_indices(i, :) == [b, t]} means {@code labels_values(i)} stores the id for @@ -730,7 +710,6 @@ public CtcLoss ctcLoss(Operand inputs, Operand * reserve_space: An opaque tensor that can be used in backprop calculation. It * is only produced if is_training is true. * - * @param data type for {@code output} output * @param input The input value * @param inputH The inputH value * @param inputC The inputC value @@ -795,7 +774,6 @@ public CudnnRNN cudnnRNN(Operand input, Operand inp * params_backprop: The backprop to the params buffer in the forward pass. Has the * same shape as params. * - * @param data type for {@code input_backprop} output * @param input The input value * @param inputH The inputH value * @param inputC The inputC value @@ -852,7 +830,6 @@ public CudnnRNNBackprop cudnnRNNBackprop(Operand input * num_proj: The output dimensionality for the projection matrices. If None or 0, * no projection is performed. * - * @param data type for {@code params} output * @param numLayers The numLayers value * @param numUnits The numUnits value * @param inputSize The inputSize value @@ -900,7 +877,6 @@ public CudnnRNNCanonicalToParams cudnnRNNCanonicalToParam * num_proj: The output dimensionality for the projection matrices. If None or 0, * no projection is performed. * - * @param data type for {@code weights} output * @param numLayers The numLayers value * @param numUnits The numUnits value * @param inputSize The inputSize value @@ -941,7 +917,6 @@ public CudnnRNNParamsToCanonical cudnnRNNParamsToCanonica * CudnnRNNParamsBiases to save and restore them in a way that is compatible * across different runs. * - * @param data type for {@code params_size} output * @param numLayers The numLayers value * @param numUnits The numUnits value * @param inputSize The inputSize value @@ -962,7 +937,6 @@ public CudnnRnnParamsSize cudnnRnnPara * Returns the dimension index in the destination data format given the one in * the source data format. * - * @param data type for {@code y} output * @param x A Tensor with each element as a dimension index in source data format. * Must be in the range [-4, 4). * @param options carries optional attribute values @@ -1006,7 +980,6 @@ public DataFormatDimMap dataFormatDimMap(Operand x, * [1, 2] * * - * @param data type for {@code y} output * @param x Tensor of rank 1 or 2 in source data format. * @param options carries optional attribute values * @param data type for {@code DataFormatVecPermute} output and operands @@ -1094,7 +1067,6 @@ public DataFormatVecPermute dataFormatVecPermute(Operand< * * * - * @param data type for {@code output} output * @param input The input value * @param blockSize The size of the spatial block, same as in Space2Depth. * @param options carries optional attribute values @@ -1125,7 +1097,6 @@ public DepthToSpace depthToSpace(Operand input, Long blo *

Must have {@code strides[0] = strides[3] = 1}. For the most common case of the same * horizontal and vertices strides, {@code strides = [1, stride, stride, 1]}. * - * @param data type for {@code output} output * @param input The input value * @param filter The filter value * @param strides 1-D of length 4. The stride of the sliding window for each dimension @@ -1144,7 +1115,6 @@ public DepthwiseConv2dNative depthwiseConv2dNative(Operan /** * Computes the gradients of depthwise convolution with respect to the filter. * - * @param data type for {@code output} output * @param input 4-D with shape based on {@code data_format}. For example, if * {@code data_format} is 'NHWC' then {@code input} is a 4-D {@code [batch, in_height, in_width, in_channels]} tensor. * @param filterSizes An integer vector representing the tensor shape of {@code filter}, @@ -1170,7 +1140,6 @@ public DepthwiseConv2dNativeBackpropFilter depthwiseConv2 /** * Computes the gradients of depthwise convolution with respect to the input. * - * @param data type for {@code output} output * @param inputSizes An integer vector representing the shape of {@code input}, based * on {@code data_format}. For example, if {@code data_format} is 'NHWC' then * {@code input} is a 4-D {@code [batch, height, width, channels]} tensor. @@ -1217,7 +1186,6 @@ public DepthwiseConv2dNativeBackpropInput depthwiseConv2d *

Note on duality: The dilation of {@code input} by the {@code filter} is equal to the * negation of the erosion of {@code -input} by the reflected {@code filter}. * - * @param data type for {@code output} output * @param input 4-D with shape {@code [batch, in_height, in_width, depth]}. * @param filter 3-D with shape {@code [filter_height, filter_width, depth]}. * @param strides The stride of the sliding window for each dimension of the input @@ -1236,7 +1204,6 @@ public Dilation2d dilation2d(Operand input, Operand /** * Computes the gradient of morphological 2-D dilation with respect to the filter. * - * @param data type for {@code filter_backprop} output * @param input 4-D with shape {@code [batch, in_height, in_width, depth]}. * @param filter 3-D with shape {@code [filter_height, filter_width, depth]}. * @param outBackprop 4-D with shape {@code [batch, out_height, out_width, depth]}. @@ -1257,7 +1224,6 @@ public Dilation2dBackpropFilter dilation2dBackpropFilter( /** * Computes the gradient of morphological 2-D dilation with respect to the input. * - * @param data type for {@code in_backprop} output * @param input 4-D with shape {@code [batch, in_height, in_width, depth]}. * @param filter 3-D with shape {@code [filter_height, filter_width, depth]}. * @param outBackprop 4-D with shape {@code [batch, out_height, out_width, depth]}. @@ -1298,7 +1264,6 @@ public Dilation2dBackpropInput dilation2dBackpropInput(Op *

See Fast and Accurate Deep Network Learning by Exponential Linear Units (ELUs) * * - * @param data type for {@code activations} output * @param features The features value * @param data type for {@code Elu} output and operands * @return a new instance of Elu @@ -1310,7 +1275,6 @@ public Elu elu(Operand features) { /** * Computes gradients for the exponential linear (Elu) operation. * - * @param data type for {@code backprops} output * @param gradients The backpropagated gradients to the corresponding Elu operation. * @param outputs The outputs of the corresponding Elu operation. * @param data type for {@code EluGrad} output and operands @@ -1358,7 +1322,6 @@ public FixedUnigramCandidateSampler fixedUnigramCandidateSampler(Operand * generated, a mean operation is performed instead of a max operation in each * pooling region. * - * @param data type for {@code output} output * @param value 4-D with shape {@code [batch, height, width, channels]}. * @param poolingRatio Pooling ratio for each dimension of {@code value}, currently only * supports row and col dimension and should be >= 1.0. For example, a valid @@ -1383,7 +1346,6 @@ public FractionalAvgPool fractionalAvgPool(Operand val * just need to know the shape of original input tensor, instead of the whole * tensor. * - * @param data type for {@code output} output * @param origInputTensorShape Original input tensor shape for {@code fractional_avg_pool} * @param outBackprop 4-D with shape {@code [batch, height, width, channels]}. Gradients * w.r.t. the output of {@code fractional_avg_pool}. @@ -1431,7 +1393,6 @@ public FractionalAvgPoolGrad fractionalAvgPoolGrad( *

For more details on fractional max pooling, see this paper: * Benjamin Graham, Fractional Max-Pooling * - * @param data type for {@code output} output * @param value 4-D with shape {@code [batch, height, width, channels]}. * @param poolingRatio Pooling ratio for each dimension of {@code value}, currently only * supports row and col dimension and should be >= 1.0. For example, a valid @@ -1451,7 +1412,6 @@ public FractionalMaxPool fractionalMaxPool(Operand val /** * Computes gradient of the FractionalMaxPool function. * - * @param data type for {@code output} output * @param origInput Original input for {@code fractional_max_pool} * @param origOutput Original output for {@code fractional_max_pool} * @param outBackprop 4-D with shape {@code [batch, height, width, channels]}. Gradients @@ -1475,8 +1435,6 @@ public FractionalMaxPoolGrad fractionalMaxPoolGrad(Operan * Note that the size of 4D Tensors are defined by either "NHWC" or "NCHW". * The size of 1D Tensors matches the dimension C of the 4D Tensors. * - * @param data type for {@code y} output - * @param data type for {@code batch_mean} output * @param x A 4D Tensor for input data. * @param scale A 1D Tensor for scaling factor, to scale the normalized x. * @param offset A 1D Tensor for offset, to shift to the normalized x. @@ -1500,8 +1458,6 @@ public FusedBatchNorm fusedBatchNor * Note that the size of 4D Tensors are defined by either "NHWC" or "NCHW". * The size of 1D Tensors matches the dimension C of the 4D Tensors. * - * @param data type for {@code x_backprop} output - * @param data type for {@code scale_backprop} output * @param yBackprop A 4D Tensor for the gradient with respect to y. * @param x A 4D Tensor for input data. * @param scale A 1D Tensor for scaling factor, to scale the normalized x. @@ -1542,7 +1498,6 @@ public FusedBatchNormGrad fusedBatc * will block if multiple versions are being run in parallel. This is because this * operator is primarily an optimization to minimize memory usage. * - * @param data type for {@code output} output * @param input 4-D with shape {@code [batch, in_height, in_width, in_channels]}. * @param paddings A two-column matrix specifying the padding sizes. The number of * rows must be the same as the rank of {@code input}. @@ -1574,7 +1529,6 @@ public FusedPadConv2d fusedPadConv2d(Operand input, * will block if multiple versions are being run in parallel. This is because this * operator is primarily an optimization to minimize memory usage. * - * @param data type for {@code output} output * @param input 4-D with shape {@code [batch, in_height, in_width, in_channels]}. * @param sizeOutput A 1-D int32 Tensor of 2 elements: {@code new_height, new_width}. The * new size for the images. @@ -1637,7 +1591,6 @@ public FusedResizeAndPadConv2d fusedResizeAndPadConv2d(Op * h = (1-u) \circ c + u \circ h_prev * * - * @param data type for {@code r} output * @param x The x value * @param hPrev The hPrev value * @param wRu The wRu value @@ -1728,7 +1681,6 @@ public GRUBlockCell gRUBlockCell(Operand x, Operand * d_b_c = sum of d_c_bar along axis = 0 * * - * @param data type for {@code d_x} output * @param x The x value * @param hPrev The hPrev value * @param wRu The wRu value @@ -1778,7 +1730,6 @@ public InTopK inTopK(Operand predictions, Operand< * Specifically, {@code grad = -dy * y*y}, where {@code y = 1/x}, and {@code dy} * is the corresponding input gradient. * - * @param data type for {@code z} output * @param y The y value * @param dy The dy value * @param data type for {@code InvGrad} output and operands @@ -1791,7 +1742,6 @@ public InvGrad invGrad(Operand y, Operand dy) { /** * Solves a batch of isotonic regression problems. * - * @param data type for {@code output} output * @param input A (batch_size, dim)-tensor holding a batch of inputs. * @return a new instance of IsotonicRegression, with default output types */ @@ -1802,7 +1752,6 @@ public IsotonicRegression isotonicRegression(Operand data type for {@code output} output * @param input A (batch_size, dim)-tensor holding a batch of inputs. * @param outputDtype Dtype of output. * @param data type for {@code IsotonicRegression} output and operands @@ -1820,7 +1769,6 @@ public IsotonicRegression isotonicRegression( * output = sum(t ** 2) / 2 * * - * @param data type for {@code output} output * @param t Typically 2-D, but may have any dimensions. * @param data type for {@code L2Loss} output and operands * @return a new instance of L2Loss @@ -1854,7 +1802,6 @@ public L2Loss l2Loss(Operand t) { * h = co .* o * * - * @param data type for {@code i} output * @param x The input to the LSTM cell, shape (batch_size, num_inputs). * @param csPrev Value of the cell state at previous time step. * @param hPrev Output of the previous cell at previous time step. @@ -1877,7 +1824,6 @@ public LSTMBlockCell lSTMBlockCell(Operand x, Operand< * Computes the LSTM cell backward propagation for 1 timestep. * This implementation is to be used in conjunction of LSTMBlockCell. * - * @param data type for {@code cs_prev_grad} output * @param x The input to the LSTM cell, shape (batch_size, num_inputs). * @param csPrev The previous cell state. * @param hPrev The previous h state. @@ -1908,7 +1854,6 @@ public LSTMBlockCellGrad lSTMBlockCellGrad(Operand x, /** * Computes rectified linear: {@code max(features, features * alpha)}. * - * @param data type for {@code activations} output * @param features The features value * @param options carries optional attribute values * @param data type for {@code LeakyRelu} output and operands @@ -1960,7 +1905,6 @@ public LearnedUnigramCandidateSampler learnedUnigramCandidateSampler(OperandFor details, see Krizhevsky et al., ImageNet classification with deep * convolutional neural networks (NIPS 2012) . * - * @param data type for {@code output} output * @param input 4-D. * @param options carries optional attribute values * @param data type for {@code LRN} output and operands @@ -1974,7 +1918,6 @@ public LocalResponseNormalization localResponseNormalizat /** * Gradients for Local Response Normalization. * - * @param data type for {@code output} output * @param inputGrads 4-D with shape {@code [batch, height, width, channels]}. * @param inputImage 4-D with shape {@code [batch, height, width, channels]}. * @param outputImage 4-D with shape {@code [batch, height, width, channels]}. @@ -1995,7 +1938,6 @@ public LocalResponseNormalizationGrad localResponseNormal * logsoftmax[i, j] = logits[i, j] - log(sum(exp(logits[i]))) * * - * @param data type for {@code logsoftmax} output * @param logits 2-D with shape {@code [batch_size, num_classes]}. * @param data type for {@code LogSoftmax} output and operands * @return a new instance of LogSoftmax @@ -2007,7 +1949,6 @@ public LogSoftmax logSoftmax(Operand logits) { /** * Performs max pooling on the input. * - * @param data type for {@code output} output * @param input 4-D input to pool over. * @param ksize The size of the window for each dimension of the input tensor. * @param strides The stride of the sliding window for each dimension of the @@ -2025,7 +1966,6 @@ public MaxPool maxPool(Operand input, Operand /** * Performs 3D max pooling on the input. * - * @param data type for {@code output} output * @param input Shape {@code [batch, depth, rows, cols, channels]} tensor to pool over. * @param ksize 1-D tensor of length 5. The size of the window for each dimension of * the input tensor. Must have {@code ksize[0] = ksize[4] = 1}. @@ -2044,7 +1984,6 @@ public MaxPool3d maxPool3d(Operand input, List k /** * Computes gradients of 3D max pooling function. * - * @param data type for {@code output} output * @param origInput The original input tensor. * @param origOutput The original output tensor. * @param grad Output backprop of shape {@code [batch, depth, rows, cols, channels]}. @@ -2067,7 +2006,6 @@ public MaxPool3dGrad maxPool3dGrad(Ope /** * Computes second-order gradients of the maxpooling function. * - * @param data type for {@code output} output * @param origInput The original input tensor. * @param origOutput The original output tensor. * @param grad Output backprop of shape {@code [batch, depth, rows, cols, channels]}. @@ -2089,7 +2027,6 @@ public MaxPool3dGradGrad maxPool3dGradGrad(Operand ori /** * Computes gradients of the maxpooling function. * - * @param data type for {@code output} output * @param origInput The original input tensor. * @param origOutput The original output tensor. * @param grad 4-D. Gradients w.r.t. the output of {@code max_pool}. @@ -2110,7 +2047,6 @@ public MaxPoolGrad maxPoolGrad(Operand origInput, Oper /** * Computes second-order gradients of the maxpooling function. * - * @param data type for {@code output} output * @param origInput The original input tensor. * @param origOutput The original output tensor. * @param grad 4-D. Gradients of gradients w.r.t. the input of {@code max_pool}. @@ -2131,7 +2067,6 @@ public MaxPoolGradGrad maxPoolGradGrad(Operand origInp /** * Computes second-order gradients of the maxpooling function. * - * @param data type for {@code output} output * @param input The original input. * @param grad 4-D with shape {@code [batch, height, width, channels]}. Gradients w.r.t. the * input of {@code max_pool}. @@ -2153,7 +2088,6 @@ public MaxPoolGradGradWithArgmax maxPoolGradGradWithArgma /** * Computes gradients of the maxpooling function. * - * @param data type for {@code output} output * @param input The original input. * @param grad 4-D with shape {@code [batch, height, width, channels]}. Gradients w.r.t. the * output of {@code max_pool}. @@ -2183,8 +2117,6 @@ public MaxPoolGradWithArgmax maxPoolGradWithArgmax(Operan * (either negative or too large). This is a bug, but fixing it is difficult to do * in a safe backwards compatible way, especially due to flattening. * - * @param data type for {@code output} output - * @param data type for {@code argmax} output * @param input 4-D with shape {@code [batch, height, width, channels]}. Input to pool over. * @param ksize The size of the window for each dimension of the input tensor. * @param strides The stride of the sliding window for each dimension of the @@ -2210,8 +2142,6 @@ public MaxPoolWithArgmax maxPoolWithArgmax(Operan * (either negative or too large). This is a bug, but fixing it is difficult to do * in a safe backwards compatible way, especially due to flattening. * - * @param data type for {@code output} output - * @param data type for {@code argmax} output * @param input 4-D with shape {@code [batch, height, width, channels]}. Input to pool over. * @param ksize The size of the window for each dimension of the input tensor. * @param strides The stride of the sliding window for each dimension of the @@ -2239,7 +2169,6 @@ public MaxPoolWithArgmax maxPoolWit * values.shape = input.shape[:-1] * * - * @param data type for {@code values} output * @param input 1-D or higher with last dimension at least {@code n+1}. * @param n 0-D. Position of sorted vector to select along the last dimension (along * each row for matrices). Valid range of n is {@code [0, input.shape[:-1])} @@ -2255,7 +2184,6 @@ public NthElement nthElement(Operand input, Operand data type for {@code output} output * @param input 4-D with shape {@code [batch, height, width, channels]}. * @param minInput The float value that the lowest quantized input value represents. * @param maxInput The float value that the highest quantized input value represents. @@ -2278,7 +2206,6 @@ public QuantizedAvgPool quantizedAvgPool(Operand input * This op is deprecated and will be removed in the future. Prefer * {@code tf.nn.batch_normalization}. * - * @param data type for {@code result} output * @param t A 4D input Tensor. * @param tMin The value represented by the lowest quantized input. * @param tMax The value represented by the highest quantized input. @@ -2322,7 +2249,6 @@ public QuantizedBatchNormWithGlobalNormal * Adds Tensor 'bias' to Tensor 'input' for Quantized types. * Broadcasts the values of bias on dimensions 0..N-2 of 'input'. * - * @param data type for {@code output} output * @param input The input value * @param bias A 1D bias Tensor with size matching the last dimension of 'input'. * @param minInput The float value that the lowest quantized input value represents. @@ -2342,7 +2268,6 @@ public QuantizedBiasAdd quantizedBiasAdd(Operand data type for {@code output} output * @param input The input value * @param filter The filter value * @param minInput The minInput value @@ -2367,7 +2292,6 @@ public QuantizedConv2DAndRelu quantizedConv2DAndRelu( /** * The QuantizedConv2DAndReluAndRequantize operation * - * @param data type for {@code output} output * @param input The input value * @param filter The filter value * @param minInput The minInput value @@ -2395,7 +2319,6 @@ public QuantizedConv2DAndReluAndRequantize quantizedConv2 /** * The QuantizedConv2DAndRequantize operation * - * @param data type for {@code output} output * @param input The input value * @param filter The filter value * @param minInput The minInput value @@ -2423,7 +2346,6 @@ public QuantizedConv2DAndRequantize quantizedConv2DAndReq /** * Computes QuantizedConv2D per channel. * - * @param data type for {@code output} output * @param input The original input tensor. * @param filter The original filter tensor. * @param minInput The minimum value of the input tensor @@ -2448,7 +2370,6 @@ public QuantizedConv2DPerChannel quantizedConv2DPerChanne /** * The QuantizedConv2DWithBias operation * - * @param data type for {@code output} output * @param input The input value * @param filter The filter value * @param bias The bias value @@ -2474,7 +2395,6 @@ public QuantizedConv2DWithBias quantizedConv2DWithBias( /** * The QuantizedConv2DWithBiasAndRelu operation * - * @param data type for {@code output} output * @param input The input value * @param filter The filter value * @param bias The bias value @@ -2500,7 +2420,6 @@ public QuantizedConv2DWithBiasAndRelu quantizedConv2DWith /** * The QuantizedConv2DWithBiasAndReluAndRequantize operation * - * @param data type for {@code output} output * @param input The input value * @param filter The filter value * @param bias The bias value @@ -2529,7 +2448,6 @@ public QuantizedConv2DWithBiasAndReluAndRequantize quanti /** * The QuantizedConv2DWithBiasAndRequantize operation * - * @param data type for {@code output} output * @param input The input value * @param filter The filter value * @param bias The bias value @@ -2558,7 +2476,6 @@ public QuantizedConv2DWithBiasAndRequantize quantizedConv /** * The QuantizedConv2DWithBiasSignedSumAndReluAndRequantize operation * - * @param data type for {@code output} output * @param input The input value * @param filter The filter value * @param bias The bias value @@ -2592,7 +2509,6 @@ public QuantizedConv2DWithBiasSignedSumAndReluAndRequantize< /** * The QuantizedConv2DWithBiasSumAndRelu operation * - * @param data type for {@code output} output * @param input The input value * @param filter The filter value * @param bias The bias value @@ -2619,7 +2535,6 @@ public QuantizedConv2DWithBiasSumAndRelu quantizedConv2DW /** * The QuantizedConv2DWithBiasSumAndReluAndRequantize operation * - * @param data type for {@code output} output * @param input The input value * @param filter The filter value * @param bias The bias value @@ -2657,7 +2572,6 @@ public QuantizedConv2DWithBiasSumAndReluAndRequantize qua * This means that you can only interpret the quantized output in the same way, by * taking the returned minimum and maximum values into account. * - * @param data type for {@code output} output * @param input The input value * @param filter filter's input_depth dimension must match input's depth dimensions. * @param minInput The float value that the lowest quantized input value represents. @@ -2682,7 +2596,6 @@ public QuantizedConv2d quantizedConv2d(Operand data type for {@code output} output * @param input The original input tensor. * @param filter The original filter tensor. * @param minInput The float value that the minimum quantized input value represents. @@ -2707,7 +2620,6 @@ public QuantizedDepthwiseConv2D quantizedDepthwiseConv2D( /** * Computes quantized depthwise Conv2D with Bias. * - * @param data type for {@code output} output * @param input The original input tensor. * @param filter The original filter tensor. * @param bias The original bias tensor. @@ -2733,7 +2645,6 @@ public QuantizedDepthwiseConv2DWithBias quantizedDepthwis /** * Computes quantized depthwise Conv2D with Bias and Relu. * - * @param data type for {@code output} output * @param input The original input tensor. * @param filter The original filter tensor. * @param bias The original bias tensor. @@ -2759,7 +2670,6 @@ public QuantizedDepthwiseConv2DWithBiasAndRelu quantizedD /** * Computes quantized depthwise Conv2D with Bias, Relu and Requantize. * - * @param data type for {@code output} output * @param input The original input tensor. * @param filter The original filter tensor. * @param bias The original bias tensor. @@ -2788,7 +2698,6 @@ public QuantizedDepthwiseConv2DWithBiasAndReluAndRequantize< /** * Quantized Instance normalization. * - * @param data type for {@code y} output * @param x A 4D input Tensor. * @param xMin The value represented by the lowest quantized input. * @param xMax The value represented by the highest quantized input. @@ -2804,7 +2713,6 @@ public QuantizedInstanceNorm quantizedInstanceNorm(Operan /** * Produces the max pool of the input tensor for quantized types. * - * @param data type for {@code output} output * @param input The 4D (batch x rows x cols x depth) Tensor to MaxReduce over. * @param minInput The float value that the lowest quantized input value represents. * @param maxInput The float value that the highest quantized input value represents. @@ -2825,7 +2733,6 @@ public QuantizedMaxPool quantizedMaxPool(Operand input /** * Computes Quantized Rectified Linear: {@code max(features, 0)} * - * @param data type for {@code activations} output * @param features The features value * @param minFeatures The float value that the lowest quantized value represents. * @param maxFeatures The float value that the highest quantized value represents. @@ -2841,7 +2748,6 @@ public QuantizedRelu quantizedRelu(Operand data type for {@code activations} output * @param features The features value * @param minFeatures The float value that the lowest quantized value represents. * @param maxFeatures The float value that the highest quantized value represents. @@ -2857,7 +2763,6 @@ public QuantizedRelu6 quantizedRelu6(Operand data type for {@code activations} output * @param features The features value * @param maxValue The maxValue value * @param minFeatures The float value that the lowest quantized value represents. @@ -2885,7 +2790,6 @@ public QuantizedReluX quantizedReluX(Operand * * - * @param data type for {@code activations} output * @param features The features value * @param data type for {@code Relu} output and operands * @return a new instance of Relu @@ -2897,7 +2801,6 @@ public Relu relu(Operand features) { /** * Computes rectified linear 6: {@code min(max(features, 0), 6)}. * - * @param data type for {@code activations} output * @param features The features value * @param data type for {@code Relu6} output and operands * @return a new instance of Relu6 @@ -2909,7 +2812,6 @@ public Relu6 relu6(Operand features) { /** * Computes rectified linear 6 gradients for a Relu6 operation. * - * @param data type for {@code backprops} output * @param gradients The backpropagated gradients to the corresponding Relu6 operation. * @param features The features passed as input to the corresponding Relu6 operation, or * its output; using either one produces the same result. @@ -2923,7 +2825,6 @@ public Relu6Grad relu6Grad(Operand gradients, Operand< /** * Computes rectified linear gradients for a Relu operation. * - * @param data type for {@code backprops} output * @param gradients The backpropagated gradients to the corresponding Relu operation. * @param features The features passed as input to the corresponding Relu operation, OR * the outputs of that operation (both work equivalently). @@ -2942,7 +2843,6 @@ public ReluGrad reluGrad(Operand gradients, Operand * For correct dropout, use {@code tf.contrib.nn.alpha_dropout}. *

See Self-Normalizing Neural Networks * - * @param data type for {@code activations} output * @param features The features value * @param data type for {@code Selu} output and operands * @return a new instance of Selu @@ -2954,7 +2854,6 @@ public Selu selu(Operand features) { /** * Computes gradients for the scaled exponential linear (Selu) operation. * - * @param data type for {@code backprops} output * @param gradients The backpropagated gradients to the corresponding Selu operation. * @param outputs The outputs of the corresponding Selu operation. * @param data type for {@code SeluGrad} output and operands @@ -2971,7 +2870,6 @@ public SeluGrad seluGrad(Operand gradients, Operand * $$softmax[i, j] = exp(logits[i, j]) / sum_j(exp(logits[i, j]))$$ * * - * @param data type for {@code softmax} output * @param logits 2-D with shape {@code [batch_size, num_classes]}. * @param data type for {@code Softmax} output and operands * @return a new instance of Softmax @@ -2984,7 +2882,6 @@ public Softmax softmax(Operand logits) { * Computes softmax cross entropy cost and gradients to backpropagate. * Inputs are the logits, not probabilities. * - * @param data type for {@code loss} output * @param features batch_size x num_classes matrix * @param labels batch_size x num_classes matrix * The caller must ensure that each batch of labels represents a valid @@ -3000,7 +2897,6 @@ public SoftmaxCrossEntropyWithLogits softmaxCrossEntropyW /** * Computes softsign: {@code features / (abs(features) + 1)}. * - * @param data type for {@code activations} output * @param features The features value * @param data type for {@code Softsign} output and operands * @return a new instance of Softsign @@ -3012,7 +2908,6 @@ public Softsign softsign(Operand features) { /** * Computes softsign gradients for a softsign operation. * - * @param data type for {@code backprops} output * @param gradients The backpropagated gradients to the corresponding softsign operation. * @param features The features passed as input to the corresponding softsign operation. * @param data type for {@code SoftsignGrad} output and operands @@ -3090,7 +2985,6 @@ public SoftsignGrad softsignGrad(Operand gradients, *

Among others, this operation is useful for reducing atrous convolution into * regular convolution. * - * @param data type for {@code output} output * @param input 4-D with shape {@code [batch, height, width, depth]}. * @param paddings 2-D tensor of non-negative integers with shape {@code [2, 2]}. It specifies * the padding of the input with zeros across the spatial dimensions as follows: @@ -3182,7 +3076,6 @@ public SpaceToBatch spaceToBatch(Operand input, * [13, 14, 15, 16]]]] * * - * @param data type for {@code output} output * @param input The input value * @param blockSize The size of the spatial block. * @param options carries optional attribute values @@ -3202,7 +3095,6 @@ public SpaceToDepth spaceToDepth(Operand input, Long blo * given row. *

Inputs are the logits, not probabilities. * - * @param data type for {@code loss} output * @param features batch_size x num_classes matrix * @param labels batch_size vector with values in [0, num_classes). * This is the label for the given minibatch entry. @@ -3226,8 +3118,6 @@ public SparseSoftmaxCrossEntropyWithLogits sparseSoftmaxC * *

If two elements are equal, the lower-index element appears first. * - * @param data type for {@code values} output - * @param data type for {@code indices} output * @param input 1-D or higher with last dimension at least {@code k}. * @param k 0-D. Number of top elements to look for along the last dimension (along each * row for matrices). @@ -3252,8 +3142,6 @@ public TopK topK(Operand input, Operand *

If two elements are equal, the lower-index element appears first. * - * @param data type for {@code values} output - * @param data type for {@code indices} output * @param input 1-D or higher with last dimension at least {@code k}. * @param k 0-D. Number of top elements to look for along the last dimension (along each * row for matrices). @@ -3287,7 +3175,6 @@ public TopK topK(Operand input, *

{@code output} is also quantized, using the same formula. * If {@code rhs} is per-tensor quantized, {@code output} must be also per-tensor quantized. * - * @param data type for {@code output} output * @param lhs Must be a quantized tensor, rank >= 3. * @param rhs Must be a quantized tensor, same rank as {@code lhs}. * @param lhsScales The float value(s) used as scale factors when quantizing the original data that {@code lhs} represents. @@ -3358,7 +3245,6 @@ public UniformQuantizedConvolution uni *

{@code rhs} must be quantized Tensor, where its data value is quantized using the formula: * quantized_data = clip(original_data / scale + zero_point, quantization_min_val, quantization_max_val). * - * @param data type for {@code output} output * @param lhs Must be a non-quantized Tensor of {@code Tlhs}, rank >= 3. * @param rhs Must be a quantized Tensor of {@code Trhs}, same rank as {@code lhs}. * @param rhsScales The float value(s) used as scale factors when quantizing the original data that {@code rhs} represents. diff --git a/tensorflow-core/tensorflow-core-api/src/gen/annotations/org/tensorflow/op/Ops.java b/tensorflow-core/tensorflow-core-api/src/gen/annotations/org/tensorflow/op/Ops.java index 93a6a3eb05c..8483a4efb61 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/annotations/org/tensorflow/op/Ops.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/annotations/org/tensorflow/op/Ops.java @@ -77,6 +77,7 @@ import org.tensorflow.op.core.BroadcastTo; import org.tensorflow.op.core.Bucketize; import org.tensorflow.op.core.Case; +import org.tensorflow.op.core.CheckPinned; import org.tensorflow.op.core.ClipByValue; import org.tensorflow.op.core.CompositeTensorVariantFromComponents; import org.tensorflow.op.core.CompositeTensorVariantToComponents; @@ -407,10 +408,10 @@ public final class Ops { public final CollectiveOps collective; - public final AudioOps audio; - public final DistributeOps distribute; + public final AudioOps audio; + public final SignalOps signal; public final TrainOps train; @@ -419,10 +420,10 @@ public final class Ops { public final SummaryOps summary; - public final ImageOps image; - public final RaggedOps ragged; + public final ImageOps image; + public final ShapeOps shape; public final IoOps io; @@ -450,14 +451,14 @@ public final class Ops { bitwise = new BitwiseOps(this); debugging = new DebuggingOps(this); collective = new CollectiveOps(this); - audio = new AudioOps(this); distribute = new DistributeOps(this); + audio = new AudioOps(this); signal = new SignalOps(this); train = new TrainOps(this); quantization = new QuantizationOps(this); summary = new SummaryOps(this); - image = new ImageOps(this); ragged = new RaggedOps(this); + image = new ImageOps(this); shape = new ShapeOps(this); io = new IoOps(this); dtypes = new DtypesOps(this); @@ -618,7 +619,6 @@ public Any any(Operand input, Operand axis, Any.Option * See https://arxiv.org/abs/2206.14286 for the algorithm details. * This op is only optimized on TPU currently. * - * @param data type for {@code values} output * @param input Array to search. Must be at least 1-D of the floating type * @param k Specifies the number of min/max-k. * @param options carries optional attribute values @@ -732,7 +732,6 @@ public AssertThat assertThat(Operand condition, Iterable> data * This operation outputs "ref" after the assignment is done. * This makes it easier to chain operations that need to use the reset value. * - * @param data type for {@code output_ref} output * @param ref Should be from a {@code Variable} node. May be uninitialized. * @param value The value to be assigned to the variable. * @param options carries optional attribute values @@ -749,7 +748,6 @@ public Assign assign(Operand ref, Operand value, * This operation outputs "ref" after the update is done. * This makes it easier to chain operations that need to use the reset value. * - * @param data type for {@code output_ref} output * @param ref Should be from a {@code Variable} node. * @param value The value to be added to the variable. * @param options carries optional attribute values @@ -780,7 +778,6 @@ public AssignAddVariableOp assignAddVariableOp(Operand resource * This operation outputs "ref" after the update is done. * This makes it easier to chain operations that need to use the reset value. * - * @param data type for {@code output_ref} output * @param ref Should be from a {@code Variable} node. * @param value The value to be subtracted to the variable. * @param options carries optional attribute values @@ -1027,7 +1024,6 @@ public BatchFunction batchFunction(Iterable> inTensors, * dimension are moved in spatial blocks to the {@code height} and {@code width} dimensions, * followed by cropping along the {@code height} and {@code width} dimensions. * - * @param data type for {@code output} output * @param input 4-D tensor with shape * {@code [batch*block_size*block_size, height_pad/block_size, width_pad/block_size, depth]}. Note that the batch size of the input tensor must be divisible by * {@code block_size * block_size}. @@ -1055,7 +1051,6 @@ public BatchToSpace batchToSpace(Operand input, * optionally cropped according to {@code crops} to produce the output. This is the * reverse of SpaceToBatch. See below for a precise description. * - * @param data type for {@code output} output * @param input N-D with shape {@code input_shape = [batch] + spatial_shape + remaining_shape}, * where spatial_shape has M dimensions. * @param blockShape 1-D with shape {@code [M]}, all values must be >= 1. @@ -1221,7 +1216,6 @@ public BatchToSpaceNd batchToSpaceNd(Operand input, * buffer is made on BE machines when types are of different sizes in order to get * the same casting results as on LE machines. * - * @param data type for {@code output} output * @param input The input value * @param type The value of the type attribute * @param data type for {@code Bitcast} output and operands @@ -1292,7 +1286,6 @@ public Operand booleanMaskUpdate(Operand tensor, Operand * Given {@code s0} and {@code s1}, tensors that represent shapes, compute {@code r0}, the * broadcasted shape. {@code s0}, {@code s1} and {@code r0} are all integer vectors. * - * @param data type for {@code r0} output * @param s0 The s0 value * @param s1 The s1 value * @param data type for {@code BroadcastArgs} output and operands @@ -1307,7 +1300,6 @@ public BroadcastDynamicShape broadcastDynamicShape(Operan * Return the reduction indices for computing gradients of s0 op s1 with broadcast. * This is typically used by gradient computations for a broadcasting operation. * - * @param data type for {@code r0} output * @param s0 The s0 value * @param s1 The s1 value * @param data type for {@code BroadcastGradientArgs} output and operands @@ -1357,7 +1349,6 @@ public BroadcastGradientArgs broadcastGradientArgs(Operan * shape. (In a graph context, {@code broadcast_to} might be fused to * subsequent operation and then be optimized away, however.) * - * @param data type for {@code output} output * @param input A Tensor to broadcast. * @param shape An 1-D {@code int} Tensor. The shape of the desired output. * @param data type for {@code BroadcastTo} output and operands @@ -1451,6 +1442,22 @@ public Case caseOp(Operand branchIndex, Iterable> input, return Case.create(scope, branchIndex, input, Tout, branches, options); } + /** + * Checks whether a tensor is located in host memory pinned for GPU. + * When run: + *

    + *
  • Reports an {@code InvalidArgument} error if {@code tensor} is not in pinned memory.
    • + *
  • Reports a {@code FailedPrecondition} error if not built with CUDA.
    • + *
+ * + * @param tensor The tensor value + * @param data type for {@code CheckPinned} output and operands + * @return a new instance of CheckPinned + */ + public CheckPinned checkPinned(Operand tensor) { + return CheckPinned.create(scope, tensor); + } + /** * Clips tensor values to a specified min and max. * Given a tensor {@code t}, this operation returns a tensor of the same type and @@ -1458,7 +1465,6 @@ public Case caseOp(Operand branchIndex, Iterable> input, * Any values less than {@code clip_value_min} are set to {@code clip_value_min}. Any values * greater than {@code clip_value_max} are set to {@code clip_value_max}. * - * @param data type for {@code output} output * @param t A {@code Tensor}. * @param clipValueMin A 0-D (scalar) {@code Tensor}, or a {@code Tensor} with the same shape * as {@code t}. The minimum value to clip by. @@ -1508,7 +1514,6 @@ public CompositeTensorVariantToComponents compositeTensorVariantToComponents( /** * Concatenates tensors along one dimension. * - * @param data type for {@code output} output * @param values List of {@code N} Tensors to concatenate. Their ranks and types must match, * and their sizes must match in all dimensions except {@code concat_dim}. * @param axis 0-D. The dimension along which to concatenate. Must be in the @@ -1531,14 +1536,13 @@ public Concat concat(Iterable> values, * y = [2, 3, 7] * z = [2, 9, 7] * offsets = concat_offset(1, [x, y, z]) - * [list(off.numpy()) for off in offsets] + * [[a.item() for a in list(off.numpy())] for off in offsets] * [[0, 0, 0], [0, 2, 0], [0, 5, 0]] * * * *

This is typically used by gradient computations for a concat operation. * - * @param data type for {@code offset} output * @param concatDim The dimension along which to concatenate. * @param shape The {@code N} int32 or int64 vectors representing shape of tensors being concatenated. * @param data type for {@code ConcatOffset} output and operands @@ -2262,11 +2266,7 @@ public Constant constant(Class type, Shape shape, ByteDa /** * Create a constant by making an immutable copy of {@code tensor}. {@code tensor} may be closed - * afterwards without issue. - * - *

Note: this endpoint cannot be simply called {@code constant} since it will conflict with - * other endpoints accepting an NdArray in parameter {e.g. {@link #tensorOf(Scope, - * FloatNdArray)}}. + * afterward without issue. * * @param tensor a Tensor holding the constant value * @return a constant of the same data type as `tensor` @@ -2318,7 +2318,6 @@ public ControlTrigger controlTrigger() { /** * The CopyToMesh operation * - * @param data type for {@code output} output * @param input The input value * @param mesh The value of the mesh attribute * @param data type for {@code CopyToMesh} output and operands @@ -2331,7 +2330,6 @@ public CopyToMesh copyToMesh(Operand input, String mesh) /** * The CopyToMeshGrad operation * - * @param data type for {@code output} output * @param input The input value * @param forwardInput The forwardInput value * @param data type for {@code CopyToMeshGrad} output and operands @@ -2345,7 +2343,6 @@ public CopyToMeshGrad copyToMeshGrad(Operand input, /** * Increments 'ref' until it reaches 'limit'. * - * @param data type for {@code output} output * @param ref Should be from a scalar {@code Variable} node. * @param limit If incrementing ref would bring it above limit, instead generates an * 'OutOfRange' error. @@ -2440,7 +2437,6 @@ public DecodeProto decodeProto(Operand bytes, String messageType, /** * Makes a copy of {@code x}. * - * @param data type for {@code y} output * @param x The source tensor of type {@code T}. * @param data type for {@code DeepCopy} output and operands * @return a new instance of DeepCopy @@ -2482,7 +2478,6 @@ public DestroyResourceOp destroyResourceOp(Operand resource, * using control dependencies. *

Outputs the final value of the tensor pointed to by 'ref'. * - * @param data type for {@code value} output * @param ref A reference to the temporary variable tensor. * @param varName Name of the temporary variable, usually the name of the matching * 'TemporaryVariable' op. @@ -2560,7 +2555,6 @@ public DummyMemoryCache dummyMemoryCache() { * * * - * @param data type for {@code outputs} output * @param data The data value * @param partitions Any shape. Indices in the range {@code [0, num_partitions)}. * @param numPartitions The number of partitions to output. @@ -2628,7 +2622,6 @@ public DynamicPartition dynamicPartition(Operand data, * * * - * @param data type for {@code merged} output * @param indices The indices value * @param data The data value * @param data type for {@code DynamicStitch} output and operands @@ -2672,7 +2665,6 @@ public EditDistance editDistance(Operand hypothesisInd * Creates a tensor with the given shape. *

This operation creates a tensor of {@code shape} and {@code dtype}. * - * @param data type for {@code output} output * @param shape 1-D. Represents the shape of the output tensor. * @param dtype The value of the dtype attribute * @param options carries optional attribute values @@ -2778,7 +2770,6 @@ public EncodeProto encodeProto(Operand sizes, Iterable> value * Raises an error if the input tensor's shape does not match the specified shape. * Returns the input tensor otherwise. * - * @param data type for {@code output} output * @param input A tensor, whose shape is to be validated. * @param shape The expected (possibly partially specified) shape of the input tensor. * @param data type for {@code EnsureShape} output and operands @@ -2796,7 +2787,6 @@ public EnsureShape ensureShape(Operand input, Shape shap * it may be changed in the child frame. At most {@code parallel_iterations} iterations * are run in parallel in the child frame. * - * @param data type for {@code output} output * @param data The tensor to be made available to the child frame. * @param frameName The name of the child frame. * @param options carries optional attribute values @@ -2812,7 +2802,6 @@ public Enter enter(Operand data, String frameName, * Exits the current frame to its parent frame. * Exit makes its input {@code data} available to the parent frame. * - * @param data type for {@code output} output * @param data The tensor to be made available to the parent frame. * @param data type for {@code Exit} output and operands * @return a new instance of Exit @@ -2847,7 +2836,6 @@ public Exit exit(Operand data) { *

This operation is related to {@code squeeze()}, which removes dimensions of * size 1. * - * @param data type for {@code output} output * @param input The input value * @param axis 0-D (scalar). Specifies the dimension index at which to * expand the shape of {@code input}. Must be in the range @@ -2863,7 +2851,6 @@ public ExpandDims expandDims(Operand input, /** * Extract {@code patches} from {@code input} and put them in the {@code "depth"} output dimension. 3D extension of {@code extract_image_patches}. * - * @param data type for {@code patches} output * @param input 5-D Tensor with shape {@code [batch, in_planes, in_rows, in_cols, depth]}. * @param ksizes The size of the sliding window for each dimension of {@code input}. * @param strides 1-D of length 5. How far the centers of two consecutive patches are in @@ -2888,7 +2875,6 @@ public ExtractVolumePatches extractVolumePatches(Operand< * function input) or guaranteed not to be used (e.g. if mirroring an * intermediate output needed for the gradient computation of the other branch). * - * @param data type for {@code output} output * @param dtype The type of the output. * @param shape 

    *  The purported shape of the output. This is only used for shape inference;
@@ -2934,7 +2920,6 @@ public FileSystemSetConfiguration fileSystemSetConfiguration(Operand sc
    *  based on other runtime Tensors, unlike {@code tf.constant}.
    *  
    *
-   * @param  data type for {@code output} output
    * @param dims 1-D. Represents the shape of the output tensor.
    * @param value 0-D (scalar). Value to fill the returned tensor.
    *  
{@literal @}compatibility(numpy)

@@ -3028,9 +3013,11 @@ public For forOp(Operand start, Operand limit, Operand d
    *  
Note that on CPU, if an out of bound index is found, an error is returned.
    *  On GPU, if an out of bound index is found, a 0 is stored in the
    *  corresponding output value.
+   *  
Note that on TPU, if any dimension of {@code params} is of size 0 then the output will
+   *  be the expected shape filled with zeros. On CPU and GPU an error will be
+   *  returned.
    *  
See also {@code tf.batch_gather} and {@code tf.gather_nd}.
    *
-   * @param  data type for {@code output} output
    * @param params The tensor from which to gather values. Must be at least rank
    *  {@code axis + 1}.
    * @param indices Index tensor. Must be in range {@code [0, params.shape[axis])}.
@@ -3068,9 +3055,17 @@ public  Gather gather(Operand params, Operand
    *  indices.shape[:-1] + params.shape[indices.shape[-1]:]
    *
- *

Note that on CPU, if an out of bound index is found, an error is returned. - * On GPU, if an out of bound index is found, a 0 is stored in the - * corresponding output value. + *

If {@code indices} contains any out-of-bound indices, depending on + * {@code bad_indices_policy}, the op will either return an error or ignore the + * out-of-bound indices. {@code bad_indices_policy} can be one of the following values: + *

    + *
  1. "" or "DEFAULT": raises on CPU and ignore on GPU. This is because + * historically on CPU and GPU we handle errors in different ways, and for + * backward compatibility we keep the default behavior.
    2. + *
  2. "ERROR": raises error; GPU does not support this value.
    3. + *
  3. "IGNORE": ignore error and set the corresponding output to 0; + * supported on both CPU and GPU.
    4. + *
*

Some examples below. *

Simple indexing into a matrix: * 

@@ -3137,15 +3132,15 @@ public  Gather gather(Operand params, Operand
    *  
See also {@code tf.gather} and {@code tf.batch_gather}.
    *
-   * @param  data type for {@code output} output
    * @param params The tensor from which to gather values.
    * @param indices Index tensor.
+   * @param options carries optional attribute values
    * @param  data type for {@code GatherNd} output and operands
    * @return a new instance of GatherNd
    */
   public  GatherNd gatherNd(Operand params,
-      Operand indices) {
-    return GatherNd.create(scope, params, indices);
+      Operand indices, GatherNd.Options... options) {
+    return GatherNd.create(scope, params, indices, options);
   }
 
   /**
@@ -3185,7 +3180,6 @@ public GetSessionHandle getSessionHandle(Operand value) {
   /**
    * Get the value of the tensor specified by its handle.
    *
-   * @param  data type for {@code value} output
    * @param handle The handle for a tensor stored in the session state.
    * @param dtype The type of the output value.
    * @param  data type for {@code GetSessionTensor} output and operands
@@ -3250,7 +3244,6 @@ public Gradients gradients(Iterable> y, IterableReturns the input tensor without modification.
    *
-   * @param  data type for {@code output} output
    * @param input The input value
    * @param  data type for {@code GuaranteeConst} output and operands
    * @return a new instance of GuaranteeConst
@@ -3294,7 +3287,6 @@ public  HashTable hashTable(Class keyDtype,
    *    sess.run(hist) => [2, 1, 1, 0, 2]
    *
* - * @param data type for {@code out} output * @param values Numeric {@code Tensor}. * @param valueRange Shape [2] {@code Tensor} of same {@code dtype} as {@code values}. * values <= value_range[0] will be mapped to hist[0], @@ -3325,7 +3317,6 @@ public HistogramFixedWidth histogramFixedWidth(Opera * sess.run(hist) => [2, 1, 1, 0, 2] * * - * @param data type for {@code out} output * @param values Numeric {@code Tensor}. * @param valueRange Shape [2] {@code Tensor} of same {@code dtype} as {@code values}. * values <= value_range[0] will be mapped to hist[0], @@ -3344,7 +3335,6 @@ public HistogramFixedWidth histogramFi /** * Returns a constant tensor on the host. Only for writing C++ tests. * - * @param data type for {@code output} output * @param value Attr {@code value} is the tensor to return. * @param dtype The value of the dtype attribute * @param data type for {@code HostConst} output and operands @@ -3357,7 +3347,6 @@ public HostConst hostConst(Tensor value, Class dtype) { /** * Return a tensor with the same shape and contents as the input tensor or value. * - * @param data type for {@code output} output * @param input The input value * @param data type for {@code Identity} output and operands * @return a new instance of Identity @@ -3425,7 +3414,6 @@ public If ifOp(Operand cond, Iterable> input, * Returns immutable tensor from memory region. * The current implementation memmaps the tensor from a file. * - * @param data type for {@code tensor} output * @param dtype Type of the returned tensor. * @param shape Shape of the returned tensor. * @param memoryRegionName Name of readonly memory region used by the tensor, see @@ -3485,7 +3473,6 @@ public InitializeTableFromTextFile initializeTableFromTextFile( * Computes y = x; y[i, :] += v; return y. * * - * @param data type for {@code y} output * @param x A {@code Tensor} of type T. * @param i A vector. Indices into the left-most dimension of {@code x}. * @param v A {@code Tensor} of type T. Same dimension sizes as x except the first dimension, which must be the same as i's size. @@ -3503,7 +3490,6 @@ public InplaceAdd inplaceAdd(Operand x, Operand * Computes y = x; y[i, :] -= v; return y. * * - * @param data type for {@code y} output * @param x A {@code Tensor} of type T. * @param i A vector. Indices into the left-most dimension of {@code x}. * @param v A {@code Tensor} of type T. Same dimension sizes as x except the first dimension, which must be the same as i's size. @@ -3520,7 +3506,6 @@ public InplaceSub inplaceSub(Operand x, Operand *

Originally this function is mutative however for compilation we make this * operation create / operate on a copy of {@code x}. * - * @param data type for {@code y} output * @param x A tensor of type {@code T}. * @param i A vector. Indices into the left-most dimension of {@code x}. * @param v A {@code Tensor} of type T. Same dimension sizes as x except the first dimension, which must be the same as i's size. @@ -3578,7 +3563,6 @@ public KthOrderStatistic kthOrderStatistic(Operand input, Long k) { * tf.linspace(10.0, 12.0, 3, name="linspace") => [ 10.0 11.0 12.0] * * - * @param data type for {@code output} output * @param start 0-D tensor. First entry in the range. * @param stop 0-D tensor. Last entry in the range. * @param num 0-D tensor. Number of values to generate. @@ -3593,8 +3577,6 @@ public LinSpace linSpace(Operand start, Operand sto /** * Outputs all keys and values in the table. * - * @param data type for {@code keys} output - * @param data type for {@code values} output * @param tableHandle Handle to the table. * @param Tkeys The value of the Tkeys attribute * @param Tvalues The value of the Tvalues attribute @@ -3614,7 +3596,6 @@ public LookupTableExport lookupTableExp *

The scalar {@code default_value} is the value output for keys not present in the * table. It must also be of the same type as the table values. * - * @param data type for {@code values} output * @param tableHandle Handle to the table. * @param keys Any shape. Keys to look up. * @param defaultValue The defaultValue value @@ -3708,7 +3689,6 @@ public LoopCond loopCond(Operand input) { *

result == [[1, 2, 2], * [0, 1, 5]] * - * @param data type for {@code output} output * @param sortedInputs 2-D Tensor where each row is ordered. * @param values 2-D Tensor with the same numbers of rows as {@code sorted_search_values}. Contains * the values that will be searched for in {@code sorted_search_values}. @@ -3736,7 +3716,6 @@ public LowerBound lowerBound(Operand sortedInputs, *

result == [[1, 2, 2], * [0, 1, 5]] * - * @param data type for {@code output} output * @param sortedInputs 2-D Tensor where each row is ordered. * @param values 2-D Tensor with the same numbers of rows as {@code sorted_search_values}. Contains * the values that will be searched for in {@code sorted_search_values}. @@ -3901,7 +3880,6 @@ public MapUnstageNoKey mapUnstageNoKey(Operand indices, * {@code axis}. If {@code keep_dims} is true, the reduced dimensions are * retained with length 1. * - * @param data type for {@code output} output * @param input The tensor to reduce. * @param axis The dimensions to reduce. Must be in the range * {@code [-rank(input), rank(input))}. @@ -3921,7 +3899,6 @@ public Max max(Operand input, Operand{@code Merge} forwards the first tensor to become available to {@code output}, and sets * {@code value_index} to its index in {@code inputs}. * - * @param data type for {@code output} output * @param inputs The input tensors, exactly one of which will become available. * @param data type for {@code Merge} output and operands * @return a new instance of Merge @@ -3937,7 +3914,6 @@ public Merge merge(Iterable> inputs) { * {@code axis}. If {@code keep_dims} is true, the reduced dimensions are * retained with length 1. * - * @param data type for {@code output} output * @param input The tensor to reduce. * @param axis The dimensions to reduce. Must be in the range * {@code [-rank(input), rank(input))}. @@ -3974,7 +3950,6 @@ public Min min(Operand input, Operand * - * @param data type for {@code output} output * @param input The input tensor to be padded. * @param paddings A two-column matrix specifying the padding sizes. The number of * rows must be the same as the rank of {@code input}. @@ -4008,7 +3983,6 @@ public MirrorPad mirrorPad(Operand input, * [11, 28]] * * - * @param data type for {@code output} output * @param input The input tensor to be folded. * @param paddings A two-column matrix specifying the padding sizes. The number of * rows must be the same as the rank of {@code input}. @@ -4187,7 +4161,6 @@ public MutexLock mutexLock(Operand mutex) { * num_devices: The number of devices participating in this reduction. * shared_name: Identifier that shared between ops of the same reduction. * - * @param data type for {@code data} output * @deprecated use {@link org.tensorflow.op.distribute.NcclAllReduce} instead * @param input The input value * @param reduction The value of the reduction attribute @@ -4211,7 +4184,6 @@ public NcclAllReduce ncclAllReduce(Operand input, Stri * output: The same as input. * shape: The shape of the input tensor. * - * @param data type for {@code output} output * @deprecated use {@link org.tensorflow.op.distribute.NcclBroadcast} instead * @param input The input value * @param shape The value of the shape attribute @@ -4232,7 +4204,6 @@ public NcclBroadcast ncclBroadcast(Operand input, Shap * data: the value of the reduction across all {@code num_devices} devices. * reduction: the reduction operation to perform. * - * @param data type for {@code data} output * @deprecated use {@link org.tensorflow.op.distribute.NcclReduce} instead * @param input The input value * @param reduction The value of the reduction attribute @@ -4248,7 +4219,6 @@ public NcclReduce ncclReduce(Iterable> input, /** * Makes its input available to the next iteration. * - * @param data type for {@code output} output * @param data The tensor to be made available to the next iteration. * @param data type for {@code NextIteration} output and operands * @return a new instance of NextIteration @@ -4343,7 +4313,6 @@ public NoOp noOp() { * ] * * - * @param data type for {@code output} output * @param indices A tensor of indices. * @param depth A scalar defining the depth of the one hot dimension. * @param onValue A scalar defining the value to fill in output when {@code indices[j] = i}. @@ -4372,7 +4341,6 @@ public Ones ones(Operand dims, Class /** * Returns a tensor of ones with the same shape and type as x. * - * @param data type for {@code y} output * @param x a tensor of type T. * @param data type for {@code OnesLike} output and operands * @return a new instance of OnesLike @@ -4506,7 +4474,6 @@ public OrderedMapUnstageNoKey orderedMapUnstageNoKey(Operand indices, * [0, 0, 0, 0, 0, 0]] * * - * @param data type for {@code output} output * @param input The input value * @param paddings The paddings value * @param constantValues The constantValues value @@ -4534,7 +4501,6 @@ public Pad pad(Operand input, Operand * will copy pieces of the input into the output as they become available, in * some situations this can provide a performance benefit. * - * @param data type for {@code output} output * @param values Tensors to be concatenated. All must have size 1 in the first dimension * and same shape. * @param shape the final shape of the result; should be equal to the shapes of any input @@ -4602,7 +4568,6 @@ public ParallelConcat parallelConcat(Iterable> v * * * - * @param data type for {@code merged} output * @param indices The indices value * @param data The data value * @param data type for {@code ParallelDynamicStitch} output and operands @@ -4641,7 +4606,6 @@ public PartitionedCall partitionedCall(Iterable> args, * intended as a way to represent a value that will always be fed, and to * provide attrs that enable the fed value to be checked at runtime. * - * @param data type for {@code output} output * @param dtype The type of elements in the tensor. * @param options carries optional attribute values * @param data type for {@code Placeholder} output and operands @@ -4655,7 +4619,6 @@ public Placeholder placeholder(Class dtype, /** * A placeholder op that passes through {@code input} when its output is not fed. * - * @param data type for {@code output} output * @param input The default value to produce when {@code output} is not fed. * @param shape The (possibly partial) shape of the tensor. * @param data type for {@code PlaceholderWithDefault} output and operands @@ -4685,7 +4648,6 @@ public Print print(Operand input, Print.Options... options) { * {@code axis}. If {@code keep_dims} is true, the reduced dimensions are * retained with length 1. * - * @param data type for {@code output} output * @param input The tensor to reduce. * @param axis The dimensions to reduce. Must be in the range * {@code [-rank(input), rank(input))}. @@ -4701,7 +4663,6 @@ public Prod prod(Operand input, Operand data type for {@code output} output * @param tensor The tensor value * @param shape Defines the shape of the output tensor. * @param inputMin The minimum value of the input. @@ -4721,7 +4682,6 @@ public QuantizedReshape quantizedReshape(Operand tensor, * first dimension must match. *

The outputs are deterministic. * - * @param data type for {@code output} output * @param index A scalar tensor or a vector of dtype {@code dtype}. The index (or indices) to be shuffled. Must be within [0, max_index]. * @param seed A tensor of dtype {@code Tseed} and shape [3] or [n, 3]. The random seed. * @param maxIndex A scalar tensor or vector of dtype {@code dtype}. The upper bound(s) of the interval (inclusive). @@ -4746,7 +4706,6 @@ public RandomIndexShuffle randomIndexShuffle(Operand i * tf.range(start, limit, delta) ==> [3, 6, 9, 12, 15] * * - * @param data type for {@code output} output * @param start 0-D (scalar). First entry in the sequence. * @param limit 0-D (scalar). Upper limit of sequence, exclusive. * @param delta 0-D (scalar). Optional. Default is 1. Number that increments {@code start}. @@ -4785,7 +4744,6 @@ public Rank rank(Operand input) { * influenced by any of the writes which depend directly or indirectly on this * operation. * - * @param data type for {@code value} output * @param resource handle to the resource in which to store the variable. * @param dtype the dtype of the value. * @param data type for {@code ReadVariableOp} output and operands @@ -4799,7 +4757,6 @@ public ReadVariableOp readVariableOp(Operand data type for {@code tensor} output * @param tensorType The value of the tensorType attribute * @param tensorName The name of the tensor to receive. * @param sendDevice The name of the device sending the tensor. @@ -4857,7 +4814,6 @@ public ReduceAny reduceAny(Operand input, Operand axis * {@code axis}. If {@code keep_dims} is true, the reduced dimensions are * retained with length 1. * - * @param data type for {@code output} output * @param input The tensor to reduce. * @param axis The dimensions to reduce. Must be in the range * {@code [-rank(input), rank(input))}. @@ -4877,7 +4833,6 @@ public ReduceMax reduceMax(Operand input, * {@code axis}. If {@code keep_dims} is true, the reduced dimensions are * retained with length 1. * - * @param data type for {@code output} output * @param input The tensor to reduce. * @param axis The dimensions to reduce. Must be in the range * {@code [-rank(input), rank(input))}. @@ -4897,7 +4852,6 @@ public ReduceMin reduceMin(Operand input, * {@code axis}. If {@code keep_dims} is true, the reduced dimensions are * retained with length 1. * - * @param data type for {@code output} output * @param input The tensor to reduce. * @param axis The dimensions to reduce. Must be in the range * {@code [-rank(input), rank(input))}. @@ -4917,7 +4871,6 @@ public ReduceProd reduceProd(Operand input, * {@code axis}. If {@code keep_dims} is true, the reduced dimensions are * retained with length 1. * - * @param data type for {@code output} output * @param input The tensor to reduce. * @param axis The dimensions to reduce. Must be in the range * {@code [-rank(input), rank(input))}. @@ -4937,7 +4890,6 @@ public ReduceSum reduceSum(Operand input, Operand data type for {@code output} output * @param data The tensor to be made available to the child frame. * @param frameName The name of the child frame. * @param options carries optional attribute values @@ -4953,7 +4905,6 @@ public RefEnter refEnter(Operand data, String frameName, * Exits the current frame to its parent frame. * Exit makes its input {@code data} available to the parent frame. * - * @param data type for {@code output} output * @param data The tensor to be made available to the parent frame. * @param data type for {@code RefExit} output and operands * @return a new instance of RefExit @@ -4965,7 +4916,6 @@ public RefExit refExit(Operand data) { /** * Return the same ref tensor as the input ref tensor. * - * @param data type for {@code output} output * @param input The input value * @param data type for {@code RefIdentity} output and operands * @return a new instance of RefIdentity @@ -4981,7 +4931,6 @@ public RefIdentity refIdentity(Operand input) { *

{@code Merge} forwards the first tensor for become available to {@code output}, and sets * {@code value_index} to its index in {@code inputs}. * - * @param data type for {@code output} output * @param inputs The input tensors, exactly one of which will become available. * @param data type for {@code RefMerge} output and operands * @return a new instance of RefMerge @@ -4993,7 +4942,6 @@ public RefMerge refMerge(Iterable> inputs) { /** * Makes its input available to the next iteration. * - * @param data type for {@code output} output * @param data The tensor to be made available to the next iteration. * @param data type for {@code RefNextIteration} output and operands * @return a new instance of RefNextIteration @@ -5005,7 +4953,6 @@ public RefNextIteration refNextIteration(Operand data) { /** * Forwards the {@code index}th element of {@code inputs} to {@code output}. * - * @param data type for {@code output} output * @param index A scalar that determines the input that gets selected. * @param inputs A list of ref tensors, one of which will be forwarded to {@code output}. * @param data type for {@code RefSelect} output and operands @@ -5022,7 +4969,6 @@ public RefSelect refSelect(Operand index, * the data goes to {@code output_false}. *

See also {@code Switch} and {@code Merge}. * - * @param data type for {@code output_false} output * @param data The ref tensor to be forwarded to the appropriate output. * @param pred A scalar that specifies which output port will receive data. * @param data type for {@code RefSwitch} output and operands @@ -5035,7 +4981,6 @@ public RefSwitch refSwitch(Operand data, Operand /** * The Relayout operation * - * @param data type for {@code output} output * @param input The input value * @param layout The value of the layout attribute * @param data type for {@code Relayout} output and operands @@ -5048,7 +4993,6 @@ public Relayout relayout(Operand input, String layout) { /** * The RelayoutLike operation * - * @param data type for {@code output} output * @param input The input value * @param layoutInput The layoutInput value * @param data type for {@code RelayoutLike} output and operands @@ -5130,7 +5074,6 @@ public RemoteCall remoteCall(Operand target, Iterable> args, * reshape(t, []) ==> 7 * * - * @param data type for {@code output} output * @param tensor The tensor value * @param shape Defines the shape of the output tensor. * @param data type for {@code Reshape} output and operands @@ -5143,7 +5086,6 @@ public Reshape reshape(Operand tensor, Operand data type for {@code output} output * @param resource Should be from a scalar {@code Variable} node. * @param limit If incrementing ref would bring it above limit, instead generates an * 'OutOfRange' error. @@ -5171,7 +5113,6 @@ public ResourceCountUpTo resourceCountUpTo( * output[i, ..., j, :, ... :] = params[indices[i, ..., j], :, ..., :] * * - * @param data type for {@code output} output * @param resource The resource value * @param indices The indices value * @param dtype The value of the dtype attribute @@ -5187,7 +5128,6 @@ public ResourceGather resourceGather(Operand data type for {@code output} output * @param resource The resource value * @param indices The indices value * @param dtype The value of the dtype attribute @@ -5633,7 +5573,6 @@ public ResourceStridedSliceAssign resourceStridedSliceAssign * [12, 13, 14, 15]]]] * * - * @param data type for {@code output} output * @param tensor Up to 8-D. * @param axis 1-D. The indices of the dimensions to reverse. Must be in the range * {@code [-rank(tensor), rank(tensor))}. @@ -5695,7 +5634,6 @@ public Reverse reverse(Operand tensor, Operand * - * @param data type for {@code output} output * @param input The input to reverse. * @param seqLengths 1-D with length {@code input.dims(batch_dim)} and * {@code max(seq_lengths) <= input.dims(seq_dim)} @@ -5730,7 +5668,6 @@ public ReverseSequence reverseSequence(Operand input, * roll(t, shift=[2, -3], axis=[1, 1]) ==> [[1, 2, 3, 4, 0], [6, 7, 8, 9, 5]] * * - * @param data type for {@code output} output * @param input The input value * @param shift Dimension must be 0-D or 1-D. {@code shift[i]} specifies the number of places by which * elements are shifted positively (towards larger indices) along the dimension @@ -5770,7 +5707,6 @@ public Roll roll(Operand input, Operand * * - * @param data type for {@code output_ref} output * @param ref Should be from a {@code Variable} node. * @param indices A tensor of indices into the first dimension of {@code ref}. * @param updates A tensor of updated values to add to {@code ref}. @@ -5802,7 +5738,6 @@ public ScatterAdd scatterAdd(Operand ref, * the same location, their contributions divide. *

Requires {@code updates.shape = indices.shape + ref.shape[1:]} or {@code updates.shape = []}. * - * @param data type for {@code output_ref} output * @param ref Should be from a {@code Variable} node. * @param indices A tensor of indices into the first dimension of {@code ref}. * @param updates A tensor of values that {@code ref} is divided by. @@ -5837,7 +5772,6 @@ public ScatterDiv scatterDiv(Operand ref, * * * - * @param data type for {@code output_ref} output * @param ref Should be from a {@code Variable} node. * @param indices A tensor of indices into the first dimension of {@code ref}. * @param updates A tensor of updated values to reduce into {@code ref}. @@ -5872,7 +5806,6 @@ public ScatterMax scatterMax(Operand ref, * * * - * @param data type for {@code output_ref} output * @param ref Should be from a {@code Variable} node. * @param indices A tensor of indices into the first dimension of {@code ref}. * @param updates A tensor of updated values to reduce into {@code ref}. @@ -5904,7 +5837,6 @@ public ScatterMin scatterMin(Operand ref, * the same location, their contributions multiply. *

Requires {@code updates.shape = indices.shape + ref.shape[1:]} or {@code updates.shape = []}. * - * @param data type for {@code output_ref} output * @param ref Should be from a {@code Variable} node. * @param indices A tensor of indices into the first dimension of {@code ref}. * @param updates A tensor of updated values to multiply to {@code ref}. @@ -5990,20 +5922,28 @@ public ScatterMul scatterMul(Operand ref, * [[0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0]], * [[5, 5, 5, 5], [6, 6, 6, 6], [7, 7, 7, 7], [8, 8, 8, 8]]] * - *

Note that on CPU, if an out of bound index is found, an error is returned. - * On GPU, if an out of bound index is found, the index is ignored. + *

If {@code indices} contains any out-of-bound indices, depending on + * {@code bad_indices_policy}, the op will either return an error or ignore the + * out-of-bound indices. {@code bad_indices_policy} can be one of the following values: + *

    + *
  1. "" or "DEFAULT": raises on CPU and ignore on GPU. This is because + * historically on CPU and GPU we handle errors in different ways, and for + * backward compatibility we keep the default behavior.
    2. + *
  2. "ERROR": raises error; GPU does not support this value.
    3. + *
  3. "IGNORE": ignore the bad indices; supported on both CPU and GPU.
    4. + *
* - * @param data type for {@code output} output * @param indices Tensor of indices. * @param updates Values to scatter into the output tensor. * @param shape 1-D. The shape of the output tensor. + * @param options carries optional attribute values * @param data type for {@code ScatterNd} output and operands * @param data type for {@code ScatterNd} output and operands * @return a new instance of ScatterNd */ public ScatterNd scatterNd(Operand indices, - Operand updates, Operand shape) { - return ScatterNd.create(scope, indices, updates, shape); + Operand updates, Operand shape, ScatterNd.Options... options) { + return ScatterNd.create(scope, indices, updates, shape, options); } /** @@ -6035,7 +5975,6 @@ public ScatterNd scatterNd(Operand in *

See {@code tf.scatter_nd} for more details about how to make updates to * slices. * - * @param data type for {@code output_ref} output * @param ref A mutable Tensor. Should be from a Variable node. * @param indices A Tensor. Must be one of the following types: int32, int64. * A tensor of indices into ref. @@ -6053,7 +5992,6 @@ public ScatterNdAdd scatterNdAdd(Operand ref, /** * Computes element-wise maximum. * - * @param data type for {@code output_ref} output * @param ref A mutable Tensor. Should be from a Variable node. * @param indices A Tensor. Must be one of the following types: int32, int64. * A tensor of indices into ref. @@ -6071,7 +6009,6 @@ public ScatterNdMax scatterNdMax(Operand ref, /** * Computes element-wise minimum. * - * @param data type for {@code output_ref} output * @param ref A mutable Tensor. Should be from a Variable node. * @param indices A Tensor. Must be one of the following types: int32, int64. * A tensor of indices into ref. @@ -6116,18 +6053,19 @@ public ScatterNdMin scatterNdMin(Operand ref, * *

See {@code tf.scatter_nd} for more details about how to make updates to slices. * - * @param data type for {@code output} output * @param input A Tensor. * @param indices A Tensor. Must be one of the following types: {@code int32}, {@code int64}. * A tensor of indices into {@code input}. * @param updates A Tensor. Must have the same type as ref. A tensor of updated values * to add to {@code input}. + * @param options carries optional attribute values * @param data type for {@code ScatterNdNonAliasingAdd} output and operands * @return a new instance of ScatterNdNonAliasingAdd */ public ScatterNdNonAliasingAdd scatterNdNonAliasingAdd(Operand input, - Operand indices, Operand updates) { - return ScatterNdNonAliasingAdd.create(scope, input, indices, updates); + Operand indices, Operand updates, + ScatterNdNonAliasingAdd.Options... options) { + return ScatterNdNonAliasingAdd.create(scope, input, indices, updates, options); } /** @@ -6160,7 +6098,6 @@ public ScatterNdNonAliasingAdd scatterNdNonAliasingAdd(Oper *

See {@code tf.scatter_nd} for more details about how to make updates to * slices. * - * @param data type for {@code output_ref} output * @param ref A mutable Tensor. Should be from a Variable node. * @param indices A Tensor. Must be one of the following types: int32, int64. * A tensor of indices into ref. @@ -6204,7 +6141,6 @@ public ScatterNdSub scatterNdSub(Operand ref, * slices. *

See also {@code tf.scatter_update} and {@code tf.batch_scatter_update}. * - * @param data type for {@code output_ref} output * @param ref A mutable Tensor. Should be from a Variable node. * @param indices A Tensor. Must be one of the following types: int32, int64. * A tensor of indices into ref. @@ -6240,7 +6176,6 @@ public ScatterNdUpdate scatterNdUpdate(Operand ref, * * * - * @param data type for {@code output_ref} output * @param ref Should be from a {@code Variable} node. * @param indices A tensor of indices into the first dimension of {@code ref}. * @param updates A tensor of updated values to subtract from {@code ref}. @@ -6277,7 +6212,6 @@ public ScatterSub scatterSub(Operand ref, * *

See also {@code tf.batch_scatter_update} and {@code tf.scatter_nd_update}. * - * @param data type for {@code output_ref} output * @param ref Should be from a {@code Variable} node. * @param indices A tensor of indices into the first dimension of {@code ref}. * @param updates A tensor of updated values to store in {@code ref}. @@ -6293,7 +6227,6 @@ public ScatterUpdate scatterUpdate(Operand ref, /** * The SelectV2 operation * - * @param data type for {@code output} output * @param condition The condition value * @param t The t value * @param e The e value @@ -6339,8 +6272,6 @@ public Send send(Operand tensor, String tensorName, String send * idx ==> [1, 3, 5] * * - * @param data type for {@code out} output - * @param data type for {@code idx} output * @param x 1-D. Values to keep. * @param y 1-D. Values to remove. * @param data type for {@code ListDiff} output and operands @@ -6369,8 +6300,6 @@ public SetDiff1d setDiff1d(Operand x, Operand * idx ==> [1, 3, 5] * * - * @param data type for {@code out} output - * @param data type for {@code idx} output * @param x 1-D. Values to keep. * @param y 1-D. Values to remove. * @param outIdx The value of the outIdx attribute @@ -6412,7 +6341,6 @@ public SetSize setSize(Operand setIndices, Operand setV * shape(t) ==> [2, 2, 3] * * - * @param data type for {@code output} output * @param input The input value * @return a new instance of Shape, with default output types */ @@ -6429,7 +6357,6 @@ public org.tensorflow.op.core.Shape shape(Operand input * shape(t) ==> [2, 2, 3] * * - * @param data type for {@code output} output * @param input The input value * @param outType The value of the outType attribute * @param data type for {@code Shape} output and operands @@ -6444,7 +6371,6 @@ public org.tensorflow.op.core.Shape shape(Operand data type for {@code output} output * @param input The input value * @return a new instance of ShapeN, with default output types */ @@ -6456,7 +6382,6 @@ public ShapeN shapeN(Iterable> input) { * Returns shape of tensors. * This operation returns N 1-D integer tensors representing shape of {@code input[i]s}. * - * @param data type for {@code output} output * @param input The input value * @param outType The value of the outType attribute * @param data type for {@code ShapeN} output and operands @@ -6477,7 +6402,6 @@ public ShapeN shapeN(Iterable> i * size(t) ==> 12 * * - * @param data type for {@code output} output * @param input The input value * @return a new instance of Size, with default output types */ @@ -6495,7 +6419,6 @@ public Size size(Operand input) { * size(t) ==> 12 * * - * @param data type for {@code output} output * @param input The input value * @param outType The value of the outType attribute * @param data type for {@code Size} output and operands @@ -6525,7 +6448,6 @@ public Skipgram skipgram(String filename, Long batchSize, Skipgram.Options... op *

Requirements: * 0 <= begin[i] <= begin[i] + size[i] <= Di for i in [0, n) * - * @param data type for {@code output} output * @param input The input value * @param begin begin[i] specifies the offset into the 'i'th dimension of * 'input' to slice from. @@ -6545,7 +6467,6 @@ public Slice slice(Operand input, Ope /** * Returns a copy of the input tensor. * - * @param data type for {@code output} output * @param input The input value * @param data type for {@code Snapshot} output and operands * @return a new instance of Snapshot @@ -6653,7 +6574,6 @@ public Snapshot snapshot(Operand input) { *

Among others, this operation is useful for reducing atrous convolution into * regular convolution. * - * @param data type for {@code output} output * @param input N-D with shape {@code input_shape = [batch] + spatial_shape + remaining_shape}, * where spatial_shape has {@code M} dimensions. * @param blockShape 1-D with shape {@code [M]}, all values must be >= 1. @@ -6672,7 +6592,6 @@ public SpaceToBatchNd spaceToBatchNd(Operand input, /** * Splits a tensor into {@code num_split} tensors along one dimension. * - * @param data type for {@code output} output * @param axis 0-D. The dimension along which to split. Must be in the range * {@code [-rank(value), rank(value))}. * @param value The tensor to split. @@ -6688,7 +6607,6 @@ public Split split(Operand axis, Operand value, /** * Splits a tensor into {@code num_split} tensors along one dimension. * - * @param data type for {@code output} output * @param value The tensor to split. * @param sizeSplits list containing the sizes of each output tensor along the split * dimension. Must sum to the dimension of value along split_dim. @@ -6721,7 +6639,6 @@ public SplitV splitV(Operand value, Operand * - * @param data type for {@code output} output * @param input The {@code input} to squeeze. * @param options carries optional attribute values * @param data type for {@code Squeeze} output and operands @@ -6749,7 +6666,6 @@ public Squeeze squeeze(Operand input, Squeeze.Options... * *

This is the opposite of {@code unpack}. * - * @param data type for {@code output} output * @param values Must be of same shape and type. * @param options carries optional attribute values * @param data type for {@code Pack} output and operands @@ -6787,7 +6703,6 @@ public StackCreate stackCreate(Operand maxSize, Class< /** * Pop the element at the top of the stack. * - * @param data type for {@code elem} output * @param handle The handle to a stack. * @param elemType The type of the elem that is popped. * @param data type for {@code StackPopV2} output and operands @@ -6801,7 +6716,6 @@ public StackPop stackPop(Operand handle, /** * Push an element onto the stack. * - * @param data type for {@code output} output * @param handle The handle to a stack. * @param elem The tensor to be pushed onto the stack. * @param options carries optional attribute values @@ -7083,7 +6997,6 @@ public StatelessWhile statelessWhile(Iterable> input, ConcreteFunctio * The values are cast with a deterministic pseudo-random tensor from a uniform distribution generated from user given key, counter, algorithm. Values will saturate if out of the specified integer type range, and will become zero if inputs are NaN. *

The outputs are a deterministic function of {@code input}, {@code key}, {@code counter}, {@code alg}. * - * @param data type for {@code output} output * @param input The operand to stochastically cast to int. * @param key Key for the counter-based RNG algorithm (shape uint64[1]). * @param counter Initial counter for the counter-based RNG algorithm (shape uint64[2] or uint64[1] depending on the algorithm). If a larger vector is given, only the needed portion on the left (i.e. [:N]) will be used. @@ -7151,7 +7064,6 @@ public StochasticCastToInt stochasticCastToInt( * example generation process. * * - * @param data type for {@code output} output * @param input The input value * @param data type for {@code StopGradient} output and operands * @return a new instance of StopGradient @@ -7169,16 +7081,17 @@ public StopGradient stopGradient(Operand input) { * equal to `n`, but this need not be the case. Each range specification entry can be one of the * following: * - *

- An ellipsis (...) using {@link Indices#ellipsis()}. Ellipses are used to imply zero or - * more dimensions of full-dimension selection. For example, {@code stridedSlice(foo, - * Indices.ellipsis()} is the identity slice. + *

- An ellipsis (...) using {@link org.tensorflow.ndarray.index.Indices#ellipsis()}. Ellipses + * are used to imply zero or more dimensions of full-dimension selection. For example, {@code + * stridedSlice(foo, Indices.ellipsis()} is the identity slice. * - *

- A new axis using {@link Indices#newAxis()}. This is used to insert a new shape=1 - * dimension. For example, `{@code stridedSlice(foo, Indices.newAxis())} where {@code foo} is - * shape {@code (3, 4)} produces a {@code (1, 3, 4)} tensor. + *

- A new axis using {@link org.tensorflow.ndarray.index.Indices#newAxis()}. This is used to + * insert a new shape=1 dimension. For example, `{@code stridedSlice(foo, Indices.newAxis())} + * where {@code foo} is shape {@code (3, 4)} produces a {@code (1, 3, 4)} tensor. * - *

- A range {@code begin:end:stride} using {@link Indices#slice(Long, Long, long)} - * Index.slice()} or {@link Indices#all()}. This is used to specify how much to choose from a + *

- A range {@code begin:end:stride} using {@link + * org.tensorflow.ndarray.index.Indices#slice(Long, Long, long)} Index.slice()} or {@link + * org.tensorflow.ndarray.index.Indices#all()}. This is used to specify how much to choose from a * given dimension. {@code stride} can be any integer but 0. {@code begin} is an integer which * represents the index of the first value to select while {@code end} represents the index of the * last value to select (exclusive). Begin and end can be null, in which case the index begins or @@ -7195,10 +7108,11 @@ public StopGradient stopGradient(Operand input) { * elements). For example {@code foo = [1,2,3,4]; stridedSlice(foo, Indices.slice(-2, null, -1)} * is {@code [4,3]}. * - *

- A single index using {@link Indices#at(long)}. This is used to keep only elements that - * have a given index. For example ({@code stridedSlice(foo, Indices.at(2))} on a shape {@code - * (5,6)} tensor produces a shape {@code (6,)} tensor. The dimension can be kept with size one - * using {@link Indices#at(long, boolean)}. + *

- A single index using {@link org.tensorflow.ndarray.index.Indices#at(long)}. This is used + * to keep only elements that have a given index. For example ({@code stridedSlice(foo, + * Indices.at(2))} on a shape {@code (5,6)} tensor produces a shape {@code (6,)} tensor. The + * dimension can be kept with size one using {@link org.tensorflow.ndarray.index.Indices#at(long, + * boolean)}. * *

These semantics generally follow NumPy's indexing semantics, which can be found here: https://numpy.org/doc/stable/reference/arrays.indexing.html @@ -7206,9 +7120,9 @@ public StopGradient stopGradient(Operand input) { *

Requirements: `0 != strides[i] for i in [0, m)` Only one ellipsis. * * @param data type for {@code output()} output - * @param indices The indices to slice. See {@link Indices}. + * @param indices The indices to slice. See {@link org.tensorflow.ndarray.index.Indices}. * @return a new instance of StridedSlice - * @see Indices + * @see org.tensorflow.ndarray.index.Indices */ public StridedSlice stridedSlice(Operand input, Index... indices) { return StridedSliceHelper.stridedSlice(scope, input, indices); @@ -7314,7 +7228,6 @@ public StridedSlice stridedSlice(Operand input, Index... * {@code 0 != strides[i] for i in [0, m)} * {@code ellipsis_mask must be a power of two (only one ellipsis)} * - * @param data type for {@code output} output * @param input The input value * @param begin {@code begin[k]} specifies the offset into the {@code k}th range specification. * The exact dimension this corresponds to will be determined by context. @@ -7351,9 +7264,10 @@ public StridedSlice stridedSlice(Operand * @param data type for {@code outputRef()} output * @param ref the tensor to assign to. * @param value the value to assign. - * @param indices The indices to slice. See {@link Indices}. + * @param indices The indices to slice. See {@link org.tensorflow.ndarray.index.Indices}. * @return a new instance of StridedSliceAssign - * @see org.tensorflow.op.Ops#stridedSlice(Operand, Index...) + * @see org.tensorflow.op.Ops#stridedSlice(org.tensorflow.Operand, + * org.tensorflow.ndarray.index.Index...) */ public StridedSliceAssign stridedSliceAssign(Operand ref, Operand value, Index... indices) { @@ -7368,7 +7282,6 @@ public StridedSliceAssign stridedSliceAssign(Operand ref *

NOTE this op currently does not support broadcasting and so {@code value}'s * shape must be exactly the shape produced by the slice of {@code ref}. * - * @param data type for {@code output_ref} output * @param ref The ref value * @param begin The begin value * @param end The end value @@ -7395,7 +7308,6 @@ public StridedSliceAssign stridedSliceAs * {@code dy} is the input gradient to be propagated and {@code shape} is the * shape of {@code StridedSlice}'s {@code input}. * - * @param data type for {@code output} output * @param shape The shape value * @param begin The begin value * @param end The end value @@ -7419,7 +7331,6 @@ public StridedSliceGrad stridedSliceGrad * {@code axis}. If {@code keep_dims} is true, the reduced dimensions are * retained with length 1. * - * @param data type for {@code output} output * @param input The tensor to reduce. * @param axis The dimensions to reduce. Must be in the range * {@code [-rank(input), rank(input))}. @@ -7438,7 +7349,6 @@ public Sum sum(Operand input, Operand * the data goes to {@code output_false}. *

See also {@code RefSwitch} and {@code Merge}. * - * @param data type for {@code output_false} output * @param data The tensor to be forwarded to the appropriate output. * @param pred A scalar that specifies which output port will receive data. * @param data type for {@code Switch} output and operands @@ -7473,7 +7383,6 @@ public SyncDevice syncDevice() { * var = state_ops.assign_add(var, [[6.0, 7.0]]) * final = state_ops._destroy_temporary_variable(var, var_name=var_name) * - * @param data type for {@code ref} output * @param shape The shape of the variable tensor. * @param dtype The type of elements in the variable tensor. * @param options carries optional attribute values @@ -7524,7 +7433,6 @@ public TensorArrayClose tensorArrayClose(Operand handle) { * *

All elements must have the same shape (excepting the first dimension). * - * @param data type for {@code value} output * @param handle The handle to a TensorArray. * @param flowIn A float scalar that enforces proper chaining of operations. * @param dtype The type of the elem that is returned. @@ -7541,7 +7449,6 @@ public TensorArrayConcat tensorArrayConcat(Operand data type for {@code value} output * @param handle The handle to a TensorArray. * @param indices The locations in the TensorArray from which to read tensor elements. * @param flowIn A float scalar that enforces proper chaining of operations. @@ -7622,7 +7529,6 @@ public TensorArrayGradWithShape tensorArrayGradWithShape(Operand data type for {@code value} output * @param handle The handle value * @param flowIn The flowIn value * @param dtype The value of the dtype attribute @@ -7638,7 +7544,6 @@ public TensorArrayPack tensorArrayPack(Operand han /** * Read an element from the TensorArray into output {@code value}. * - * @param data type for {@code value} output * @param handle The handle to a TensorArray. * @param index The index value * @param flowIn A float scalar that enforces proper chaining of operations. @@ -7750,7 +7655,6 @@ public TensorArrayWrite tensorArrayWrite(Operand handle, Operan * tensor: The concated result. * lengths: Output tensor containing sizes of the 0th dimension of tensors in the list, used for computing the gradient. * - * @param data type for {@code tensor} output * @param inputHandle The inputHandle value * @param elementShape The elementShape value * @param leadingDims The leadingDims value @@ -7783,7 +7687,6 @@ public TensorListConcatLists tensorListConcatLists( * input_handle: the list * element_shape: the shape of elements of the list * - * @param data type for {@code element_shape} output * @param inputHandle The inputHandle value * @param shapeType The value of the shapeType attribute * @param data type for {@code TensorListElementShape} output and operands @@ -7817,7 +7720,6 @@ public TensorListFromTensor tensorListFromTensor(Operand tensor * indices: The indices used to index into the list. * values: The tensor. * - * @param data type for {@code values} output * @param inputHandle The inputHandle value * @param indices The indices value * @param elementShape The elementShape value @@ -7837,7 +7739,6 @@ public TensorListGather tensorListGather( * index: the position in the list from which an element will be retrieved * item: the element at that position * - * @param data type for {@code item} output * @param inputHandle The inputHandle value * @param index The index value * @param elementShape The elementShape value @@ -7871,7 +7772,6 @@ public TensorListLength tensorListLength(Operand inputHandle) { * element_dtype: the type of elements in the list * element_shape: the shape of the output tensor * - * @param data type for {@code tensor} output * @param inputHandle The inputHandle value * @param elementShape The elementShape value * @param elementDtype The value of the elementDtype attribute @@ -8033,7 +7933,6 @@ public TensorListSplit tensorListSplit(Operand tensor, * tensor: the gathered result * num_elements: optional. If not -1, the number of elements in the list. * - * @param data type for {@code tensor} output * @param inputHandle The inputHandle value * @param elementShape The elementShape value * @param elementDtype The value of the elementDtype attribute @@ -8101,7 +8000,6 @@ public TensorMapInsert tensorMapInsert(Operand inputHandle, * key: the key to be looked up * value: the value found from the given key * - * @param data type for {@code value} output * @param inputHandle The inputHandle value * @param key The key value * @param valueDtype The value of the valueDtype attribute @@ -8130,7 +8028,6 @@ public TensorMapSize tensorMapSize(Operand inputHandle) { * input_handle: the input map * keys: the returned Tensor of all keys in the map * - * @param data type for {@code keys} output * @param inputHandle The inputHandle value * @param keyDtype The value of the keyDtype attribute * @param data type for {@code TensorMapStackKeys} output and operands @@ -8201,19 +8098,28 @@ public TensorMapStackKeys tensorMapStackKeys( * * * - *

Note: on CPU, if an out of bound index is found, an error is returned. - * On GPU, if an out of bound index is found, the index is ignored. + *

If {@code indices} contains any out-of-bound indices, depending on + * {@code bad_indices_policy}, the op will either return an error or ignore the + * out-of-bound indices. {@code bad_indices_policy} can be one of the following values: + *

    + *
  1. "" or "DEFAULT": raises on CPU and ignore on GPU. This is because + * historically on CPU and GPU we handle errors in different ways, and for + * backward compatibility we keep the default behavior.
    2. + *
  2. "ERROR": raises error; GPU does not support this value.
    3. + *
  3. "IGNORE": ignore the bad indices; supported on both CPU and GPU.
    4. + *
* - * @param data type for {@code output} output * @param tensor Tensor to copy/update. * @param indices Index tensor. * @param updates Updates to scatter into output. + * @param options carries optional attribute values * @param data type for {@code TensorScatterAdd} output and operands * @return a new instance of TensorScatterNdAdd */ public TensorScatterNdAdd tensorScatterNdAdd(Operand tensor, - Operand indices, Operand updates) { - return TensorScatterNdAdd.create(scope, tensor, indices, updates); + Operand indices, Operand updates, + TensorScatterNdAdd.Options... options) { + return TensorScatterNdAdd.create(scope, tensor, indices, updates, options); } /** @@ -8233,31 +8139,33 @@ public TensorScatterNdAdd tensorScatterNdAdd(Operand ten * *

Refer to {@code tf.tensor_scatter_nd_update} for more details. * - * @param data type for {@code output} output * @param tensor Tensor to update. * @param indices Index tensor. * @param updates Updates to scatter into output. + * @param options carries optional attribute values * @param data type for {@code TensorScatterMax} output and operands * @return a new instance of TensorScatterNdMax */ public TensorScatterNdMax tensorScatterNdMax(Operand tensor, - Operand indices, Operand updates) { - return TensorScatterNdMax.create(scope, tensor, indices, updates); + Operand indices, Operand updates, + TensorScatterNdMax.Options... options) { + return TensorScatterNdMax.create(scope, tensor, indices, updates, options); } /** * The TensorScatterMin operation * - * @param data type for {@code output} output * @param tensor Tensor to update. * @param indices Index tensor. * @param updates Updates to scatter into output. + * @param options carries optional attribute values * @param data type for {@code TensorScatterMin} output and operands * @return a new instance of TensorScatterNdMin */ public TensorScatterNdMin tensorScatterNdMin(Operand tensor, - Operand indices, Operand updates) { - return TensorScatterNdMin.create(scope, tensor, indices, updates); + Operand indices, Operand updates, + TensorScatterNdMin.Options... options) { + return TensorScatterNdMin.create(scope, tensor, indices, updates, options); } /** @@ -8318,16 +8226,17 @@ public TensorScatterNdMin tensorScatterNdMin(Operand ten *

Note that on CPU, if an out of bound index is found, an error is returned. * On GPU, if an out of bound index is found, the index is ignored. * - * @param data type for {@code output} output * @param tensor Tensor to copy/update. * @param indices Index tensor. * @param updates Updates to scatter into output. + * @param options carries optional attribute values * @param data type for {@code TensorScatterSub} output and operands * @return a new instance of TensorScatterNdSub */ public TensorScatterNdSub tensorScatterNdSub(Operand tensor, - Operand indices, Operand updates) { - return TensorScatterNdSub.create(scope, tensor, indices, updates); + Operand indices, Operand updates, + TensorScatterNdSub.Options... options) { + return TensorScatterNdSub.create(scope, tensor, indices, updates, options); } /** @@ -8338,7 +8247,6 @@ public TensorScatterNdSub tensorScatterNdSub(Operand ten * scattered onto an existing tensor (as opposed to a zero-tensor). If the memory * for the existing tensor cannot be re-used, a copy is made and updated. *

If {@code indices} contains duplicates, then we pick the last update for the index. - *

If an out of bound index is found on CPU, an error is returned. *

WARNING: There are some GPU specific semantics for this operation. *

    *
  • If an out of bound index is found, the index is ignored.
    • @@ -8360,18 +8268,29 @@ public TensorScatterNdSub tensorScatterNdSub(Operand ten * 
        *  indices.shape[:-1] + tensor.shape[indices.shape[-1]:]
    *
    + *

    If {@code indices} contains any out-of-bound indices, depending on + * {@code bad_indices_policy}, the op will either return an error or ignore the + * out-of-bound indices. {@code bad_indices_policy} can be one of the following values: + *

      + *
    1. "" or "DEFAULT": raises on CPU and ignore on GPU. This is because + * historically on CPU and GPU we handle errors in different ways, and for + * backward compatibility we keep the default behavior.
      2. + *
    2. "ERROR": raises error; GPU does not support this value.
      3. + *
    3. "IGNORE": ignore the bad indices; supported on both CPU and GPU.
      4. + *
    *

    For usage examples see the python tf.tensor_scatter_nd_update {@link org.tensorflow.op.Ops#tensorScatterNdUpdate} function * - * @param data type for {@code output} output * @param tensor Tensor to copy/update. * @param indices Index tensor. * @param updates Updates to scatter into output. + * @param options carries optional attribute values * @param data type for {@code TensorScatterUpdate} output and operands * @return a new instance of TensorScatterNdUpdate */ public TensorScatterNdUpdate tensorScatterNdUpdate(Operand tensor, - Operand indices, Operand updates) { - return TensorScatterNdUpdate.create(scope, tensor, indices, updates); + Operand indices, Operand updates, + TensorScatterNdUpdate.Options... options) { + return TensorScatterNdUpdate.create(scope, tensor, indices, updates, options); } /** @@ -8382,7 +8301,6 @@ public TensorScatterNdUpdate tensorScatterNdUpdate(Operand< *

    NOTE this op currently does not support broadcasting and so {@code value}'s shape * must be exactly the shape produced by the slice of {@code input}. * - * @param data type for {@code output} output * @param input The input value * @param begin The begin value * @param end The end value @@ -8433,7 +8351,6 @@ public TensorStridedSliceUpdate tensorSt * * * - * @param data type for {@code output} output * @param input Can be of any rank. * @param multiples 1-D. Length must be the same as the number of dimensions in {@code input} * @param data type for {@code Tile} output and operands @@ -8522,7 +8439,6 @@ public TopKWithUnique topKWithUnique(Operand input, Long k) { * assumed to possibly belong to the same batch. If left empty, the op name will * be used as the shared name. * - * @param data type for {@code unbatched_tensor} output * @param batchedTensor The batchedTensor value * @param batchIndex The batchIndex value * @param id The id value @@ -8552,7 +8468,6 @@ public Unbatch unbatch(Operand batchedTensor, Operand data type for {@code batched_grad} output * @param originalInput The originalInput value * @param batchIndex The batchIndex value * @param grad The grad value @@ -8573,7 +8488,6 @@ public UnbatchGrad unbatchGrad(Operand originalInput, * If quantization_axis is -1 (per-tensor quantized), the entire operand is clipped using scalar min, max. * Otherwise (per-channel quantized), the clipping is also done per-channel. * - * @param data type for {@code output} output * @param operand Must be a Tensor of T. * @param min The min value(s) to clip operand. Must be a Tensor of T. * Must be a scalar Tensor if quantization_axis is -1 (per-tensor quantization), otherwise 1D Tensor of size (operand.dim_size(quantization_axis),) (per-axis quantization). @@ -8635,8 +8549,6 @@ public UniformQuantizedClipByValue uniformQuantizedClipBy * idx ==> [0, 1, 1] * * - * @param data type for {@code y} output - * @param data type for {@code idx} output * @param x A {@code Tensor}. * @param axis A {@code Tensor} of type {@code int32} (default: None). The axis of the Tensor to * find the unique elements. @@ -8686,8 +8598,6 @@ public Unique unique(Operand x, Operand * - * @param data type for {@code y} output - * @param data type for {@code idx} output * @param x A {@code Tensor}. * @param axis A {@code Tensor} of type {@code int32} (default: None). The axis of the Tensor to * find the unique elements. @@ -8744,8 +8654,6 @@ public Unique unique(Operand x, * count ==> [1, 2] * * - * @param data type for {@code y} output - * @param data type for {@code idx} output * @param x A {@code Tensor}. * @param axis A {@code Tensor} of type {@code int32} (default: None). The axis of the Tensor to * find the unique elements. @@ -8800,8 +8708,6 @@ public UniqueWithCounts uniqueWithCounts(Operand * count ==> [1, 2] * * - * @param data type for {@code y} output - * @param data type for {@code idx} output * @param x A {@code Tensor}. * @param axis A {@code Tensor} of type {@code int32} (default: None). The axis of the Tensor to * find the unique elements. @@ -8835,7 +8741,6 @@ public UniqueWithCounts uniqueWithCou * Equivalent to np.unravel_index *
    {@literal @}end_compatibility * - * @param data type for {@code output} output * @param indices An 0-D or 1-D {@code int} Tensor whose elements are indices into the * flattened version of an array of dimensions dims. * @param dims An 1-D {@code int} Tensor. The shape of the array to use for unraveling @@ -8859,7 +8764,6 @@ public UnravelIndex unravelIndex(Operand indices, Oper * Etc. *

    This is the opposite of {@code pack}. * - * @param data type for {@code output} output * @param value 1-D or higher, with {@code axis} dimension size equal to {@code num}. * @param num The value of the num attribute * @param options carries optional attribute values @@ -8900,7 +8804,6 @@ public Unstage unstage(List> dtypes, Unstage.Options... o *

    result == [[1, 2, 4], * [0, 2, 5]] * - * @param data type for {@code output} output * @param sortedInputs 2-D Tensor where each row is ordered. * @param values 2-D Tensor with the same numbers of rows as {@code sorted_search_values}. Contains * the values that will be searched for in {@code sorted_search_values}. @@ -8928,7 +8831,6 @@ public UpperBound upperBound(Operand sortedInputs, *

    result == [[1, 2, 4], * [0, 2, 5]] * - * @param data type for {@code output} output * @param sortedInputs 2-D Tensor where each row is ordered. * @param values 2-D Tensor with the same numbers of rows as {@code sorted_search_values}. Contains * the values that will be searched for in {@code sorted_search_values}. @@ -8988,7 +8890,6 @@ public Variable variable(Operand init, Variable.Options. * TODO(zhifengc/mrry): Adds a pointer to a more detail document * about sharing states in tensorflow. * - * @param data type for {@code ref} output * @param shape The shape of the variable tensor. * @param dtype The type of elements in the variable tensor. * @param options carries optional attribute values @@ -9009,7 +8910,6 @@ public Variable variable(Shape shape, Class dtype, * shape(t) ==> [2, 2, 3] * * - * @param data type for {@code output} output * @param input The input value * @return a new instance of VariableShape, with default output types */ @@ -9026,7 +8926,6 @@ public VariableShape variableShape(Operand input) { * shape(t) ==> [2, 2, 3] * * - * @param data type for {@code output} output * @param input The input value * @param outType The value of the outType attribute * @param data type for {@code VariableShape} output and operands @@ -9147,7 +9046,6 @@ public Zeros zeros(Operand dims, Class data type for {@code y} output * @param x a tensor of type T. * @param data type for {@code ZerosLike} output and operands * @return a new instance of ZerosLike diff --git a/tensorflow-core/tensorflow-core-api/src/gen/annotations/org/tensorflow/op/QuantizationOps.java b/tensorflow-core/tensorflow-core-api/src/gen/annotations/org/tensorflow/op/QuantizationOps.java index 99f3648ea27..8bd174ba427 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/annotations/org/tensorflow/op/QuantizationOps.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/annotations/org/tensorflow/op/QuantizationOps.java @@ -107,7 +107,6 @@ public final class QuantizationOps { * max_range / max_expected_T); * * - * @param data type for {@code output} output * @param input The input value * @param minRange The minimum scalar value possibly produced for the input. * @param maxRange The maximum scalar value possibly produced for the input. @@ -165,7 +164,6 @@ public Dequantize dequantize(Operand input, * max_range / max_expected_T); * * - * @param data type for {@code output} output * @param input The input value * @param minRange The minimum scalar value possibly produced for the input. * @param maxRange The maximum scalar value possibly produced for the input. @@ -275,6 +273,28 @@ public FakeQuantWithMinMaxArgsGradient fakeQuantWithMinMaxArgsGradient( *

*

This operation has a gradient and thus allows for training {@code min} and {@code max} * values. + *

+ *
+ *
+ *

constant_input = tf.constant([[1.2, -0.3, 0.7], [2.1, 0.5, -1.0]], dtype=tf.float32) + *

min_val = -0.5 + * max_val = 0.8 + * num_bits = 8 + * narrow_range = False #False:for the quantization range [0; 2^num_bits - 1] + *

quantized_data = tf.quantization.fake_quant_with_min_max_vars( + * ... inputs=constant_input, min=min_val, max=max_val, num_bits=num_bits, narrow_range=narrow_range + * ... ) + *

print("Input:\n", constant_input.numpy()) + * Input: + * [[ 1.2 -0.3 0.7] + * [ 2.1 0.5 -1. ]] + * print("Output:\n", quantized_data.numpy()) + * Output: + * [[ 0.8003921 -0.3007843 0.6984313] + * [ 0.8003921 0.4996078 -0.4996078]] + *

+ *
+ *
* * @param inputs The inputs value * @param min The min value @@ -456,7 +476,6 @@ public FakeQuantWithMinMaxVarsPerChannelGradient fakeQuantWithMinMaxVarsPerChann * The legacy default value for this is 0.01, but it is strongly suggested to * set it to 0 for new uses. * - * @param data type for {@code output} output * @param input The input value * @param minRange The minimum value of the quantization range. This value may be adjusted by the * op depending on other parameters. The adjusted value is written to {@code output_min}. @@ -482,7 +501,6 @@ public Quantize quantize(Operand input, * This is almost identical to QuantizeAndDequantizeV2, except that num_bits is a * tensor, so its value can change during training. * - * @param data type for {@code output} output * @param input The input value * @param inputMin The inputMin value * @param inputMax The inputMax value @@ -502,7 +520,6 @@ public QuantizeAndDequantize quantizeAndDequantize(Operan * This is almost identical to QuantizeAndDequantizeV2, except that num_bits is a * tensor, so its value can change during training. * - * @param data type for {@code output} output * @param input The input value * @param inputMin The inputMin value * @param inputMax The inputMax value @@ -522,7 +539,6 @@ public QuantizeAndDequantizeV3 quantizeAndDequantizeV3(Op * This is almost identical to QuantizeAndDequantizeV2, except that it returns a * gradient of 1 for inputs that are within the quantization range, or 0 otherwise. * - * @param data type for {@code output} output * @param input Tensor to quantize and then dequantize. * @param inputMin If {@code range_given == True}, this specifies the minimum input value that needs to * be represented, otherwise it is determined from the min value of the {@code input} @@ -544,7 +560,6 @@ public QuantizeAndDequantizeV4 quantizeAndDequantizeV4(Op * Returns a gradient of 1 for inputs that are within the quantization range, * or 0 otherwise. * - * @param data type for {@code input_backprop} output * @param gradients The gradients value * @param input The input value * @param inputMin The inputMin value @@ -581,7 +596,6 @@ public QuantizeAndDequantizeV4Grad quantizeAndDequantizeV * that output into this operator, we can reduce it from 32 bits down to 8 with * minimal loss of accuracy. * - * @param data type for {@code output} output * @param input The input value * @param inputMin The float value that the minimum quantized input value represents. * @param inputMax The float value that the maximum quantized input value represents. @@ -598,7 +612,6 @@ public QuantizeDownAndShrinkRange quantizeDownAndShrinkRa /** * Concatenates quantized tensors along one dimension. * - * @param data type for {@code output} output * @param concatDim 0-D. The dimension along which to concatenate. Must be in the * range [0, rank(values)). * @param values The {@code N} Tensors to concatenate. Their ranks and types must match, @@ -617,7 +630,6 @@ public QuantizedConcat quantizedConcat(Operand conc /** * The QuantizedMatMulWithBiasAndDequantize operation * - * @param data type for {@code out} output * @param a The a value * @param b The b value * @param bias The bias value @@ -644,7 +656,6 @@ public QuantizedMatMulWithBiasAndDequantize quantizedMatM /** * The QuantizedMatMulWithBiasAndRequantize operation * - * @param data type for {@code out} output * @param a The a value * @param b The b value * @param bias The bias value @@ -694,7 +705,6 @@ public RequantizationRange requantizationRange(Operand input, * {@code input_max} is 1.0f, and we are dealing with {@code quint16} quantized data, then a 0 * value in the 16-bit data should be interpreted as -1.0f, and a 65535 means 1.0f. * - * @param data type for {@code output} output * @param input The input value * @param inputMin The float value that the minimum quantized input value represents. * @param inputMax The float value that the maximum quantized input value represents. @@ -715,7 +725,6 @@ public Requantize requantize(Operand i * Given quantized {@code input} which was quantized using {@code scales} and {@code zero_points}, performs dequantization using the formula: * dequantized_data = (quantized_data - zero_point) * scale. * - * @param data type for {@code output} output * @param input Must be a Tensor of Tin. * @param scales The float value(s) used as scale(s) when quantizing original data that input represents. * Must be a scalar Tensor if quantization_axis is -1 (per-tensor quantization), otherwise 1D Tensor of size (input.dim_size(quantization_axis),) (per-axis quantization). @@ -746,7 +755,6 @@ public UniformDequantize uniformDequantize( * Given {@code input}, {@code scales} and {@code zero_points}, performs quantization using the formula: * quantized_data = floor(input_data * (1.0f / scale) + 0.5f) + zero_point * - * @param data type for {@code output} output * @param input Must be a Tensor of Tin. * @param scales The float value(s) to use as scale(s) to quantize {@code input}. * Must be a scalar Tensor if quantization_axis is -1 (per-tensor quantization), otherwise 1D Tensor of size (input.dim_size(quantization_axis),) (per-axis quantization). @@ -780,7 +788,6 @@ public UniformQuantize uniformQuantize(Operand data type for {@code output} output * @param lhs Must be a 2D Tensor of Tin. * @param rhs Must be a 2D Tensor of Tin. * @param lhsScales The float value(s) used as scale when quantizing original data that lhs represents. @@ -833,7 +840,6 @@ public UniformQuantizedDot uniformQuan * {@code rhs} must be quantized Tensor, where its data value is quantized using the formula: * quantized_data = clip(original_data / scale + zero_point, quantization_min_val, quantization_max_val). * - * @param data type for {@code output} output * @param lhs Must be a 2D Tensor of Tlhs. * @param rhs Must be a 2D Tensor of Trhs. * @param rhsScales The float value(s) used as scale when quantizing original data that rhs represents. @@ -873,7 +879,6 @@ public UniformQuantizedDotHybrid uniformQuantizedDotHybri * i.e. At least one among input_quantization_axis and output_quantization_axis must be -1, or two must be equal. * * - * @param data type for {@code output} output * @param input Must be a Tensor of Tin. * @param inputScales The float value(s) used as scale(s) when quantizing original data that {@code input} represents. * Must be a scalar Tensor if quantization_axis is -1 (per-tensor quantization), otherwise 1D Tensor of size (input.dim_size(quantization_axis),) (per-axis quantization). diff --git a/tensorflow-core/tensorflow-core-api/src/gen/annotations/org/tensorflow/op/RaggedOps.java b/tensorflow-core/tensorflow-core-api/src/gen/annotations/org/tensorflow/op/RaggedOps.java index 83bf63f461f..43b18f0cf57 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/annotations/org/tensorflow/op/RaggedOps.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/annotations/org/tensorflow/op/RaggedOps.java @@ -60,7 +60,6 @@ public final class RaggedOps { * {@code i}. *

Values in {@code arr} outside of the range [0, size) are ignored. * - * @param data type for {@code output} output * @param splits 1D int64 {@code Tensor}. * @param values 2D int {@code Tensor}. * @param sizeOutput non-negative int scalar {@code Tensor}. @@ -82,7 +81,6 @@ public RaggedBincount raggedBincount( * Performs sparse-output bin counting for a ragged tensor input. * Counts the number of times each value occurs in the input. * - * @param data type for {@code output_values} output * @param splits Tensor containing the row splits of the ragged tensor to count. * @param values Tensor containing values of the sparse tensor to count. * @param weights A Tensor of the same shape as indices containing per-index weight values. @@ -102,8 +100,6 @@ public RaggedCountSparseOutput raggedCountSparseOutput( * Generates a feature cross from a list of tensors, and returns it as a * RaggedTensor. See {@code tf.ragged.cross} for more details. * - * @param data type for {@code output_values} output - * @param data type for {@code output_row_splits} output * @param raggedValues The values tensor for each RaggedTensor input. * @param raggedRowSplits The row_splits tensor for each RaggedTensor input. * @param sparseIndices The indices tensor for each SparseTensor input. @@ -135,7 +131,6 @@ public RaggedCross raggedCross( /** * The RaggedFillEmptyRows operation * - * @param data type for {@code output_values} output * @param valueRowids The valueRowids value * @param values The values value * @param nrows The nrows value @@ -151,7 +146,6 @@ public RaggedFillEmptyRows raggedFillEmptyRows(Operand data type for {@code d_values} output * @param reverseIndexMap The reverseIndexMap value * @param gradValues The gradValues value * @param data type for {@code RaggedFillEmptyRowsGrad} output and operands @@ -183,8 +177,6 @@ public RaggedFillEmptyRowsGrad raggedFillEmptyRowsGrad( *

(Note: This c++ op is used to implement the higher-level python * {@code tf.ragged.gather} op, which also supports ragged indices.) * - * @param data type for {@code output_nested_splits} output - * @param data type for {@code output_dense_values} output * @param paramsNestedSplits The {@code nested_row_splits} tensors that define the row-partitioning for the * {@code params} RaggedTensor input. * @param paramsDenseValues The {@code flat_values} for the {@code params} RaggedTensor. There was a terminology change @@ -221,8 +213,6 @@ public RaggedGather raggedGather( * The vector inputs must all have the same size. Scalar inputs are broadcast * to match the size of the vector inputs. * - * @param data type for {@code rt_nested_splits} output - * @param data type for {@code rt_dense_values} output * @param starts The starts of each range. * @param limits The limits of each range. * @param deltas The deltas of each range. @@ -250,8 +240,6 @@ public RaggedRange raggedRange(Operand starts, * The vector inputs must all have the same size. Scalar inputs are broadcast * to match the size of the vector inputs. * - * @param data type for {@code rt_nested_splits} output - * @param data type for {@code rt_dense_values} output * @param starts The starts of each range. * @param limits The limits of each range. * @param deltas The deltas of each range. @@ -279,8 +267,6 @@ public RaggedRange raggedRange(Oper * inferred as {@code output_ragged_rank} - {@code rank(encoded_ragged)}. See * {@code RaggedTensorToVariant} for the corresponding encoding logic. * - * @param data type for {@code output_nested_splits} output - * @param data type for {@code output_dense_values} output * @param encodedRagged A {@code variant} Tensor containing encoded {@code RaggedTensor}s. * @param inputRaggedRank The ragged rank of each encoded {@code RaggedTensor} component in the input. If set to * -1, this is inferred as {@code output_ragged_rank} - {@code rank(encoded_ragged)} @@ -310,8 +296,6 @@ public RaggedTensorFromVariant raggedTensorFromVari * inferred as {@code output_ragged_rank} - {@code rank(encoded_ragged)}. See * {@code RaggedTensorToVariant} for the corresponding encoding logic. * - * @param data type for {@code output_nested_splits} output - * @param data type for {@code output_dense_values} output * @param encodedRagged A {@code variant} Tensor containing encoded {@code RaggedTensor}s. * @param inputRaggedRank The ragged rank of each encoded {@code RaggedTensor} component in the input. If set to * -1, this is inferred as {@code output_ragged_rank} - {@code rank(encoded_ragged)} @@ -335,7 +319,6 @@ public RaggedTensorFromVariant ragged * output=SparseTensor(indices=sparse_indices, values=sparse_values, * dense_shape=sparse_dense_shape) * - * @param data type for {@code sparse_values} output * @param rtNestedSplits The {@code row_splits} for the {@code RaggedTensor}. * @param rtDenseValues The {@code flat_values} for the {@code RaggedTensor}. * @param data type for {@code RaggedTensorToSparse} output and operands @@ -365,7 +348,6 @@ public RaggedTensorToSparse raggedTensorToSparse( * is preceded by "FIRST_DIM_SIZE". * * - * @param data type for {@code result} output * @param shape The desired shape of the output tensor. If left unspecified (empty), * the minimal shape required to contain all the elements in the ragged tensor * (the natural shape) will be used. If some dimensions are left unspecified, then @@ -438,7 +420,6 @@ public RaggedTensorToVariant raggedTensorToVariant( * the outer row-splits and the shape of the dense-values that were provided as * inputs to the RaggedTensorToVariant op. * - * @param data type for {@code dense_values_grad} output * @param encodedRaggedGrad A {@code variant} Tensor containing encoded {@code RaggedTensor} gradients. * @param rowSplits Outermost row-splits that were used as input to the RaggedTensorToVariant op. * @param denseValuesShape Shape of the dense_values that was used as an input to the diff --git a/tensorflow-core/tensorflow-core-api/src/gen/annotations/org/tensorflow/op/RandomExperimentalOps.java b/tensorflow-core/tensorflow-core-api/src/gen/annotations/org/tensorflow/op/RandomExperimentalOps.java index 09a2b385b6f..34d3585f270 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/annotations/org/tensorflow/op/RandomExperimentalOps.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/annotations/org/tensorflow/op/RandomExperimentalOps.java @@ -49,7 +49,6 @@ public final class RandomExperimentalOps { * *

The outputs are a deterministic function of {@code value}, {@code key}, {@code counter} and {@code alg}. * - * @param data type for {@code output} output * @param value The tensor to be shuffled. * @param key Key for the counter-based RNG algorithm (shape uint64[1]). * @param counter Initial counter for the counter-based RNG algorithm (shape uint64[2] or uint64[1] depending on the algorithm). If a larger vector is given, only the needed portion on the left (i.e. [:N]) will be used. diff --git a/tensorflow-core/tensorflow-core-api/src/gen/annotations/org/tensorflow/op/RandomOps.java b/tensorflow-core/tensorflow-core-api/src/gen/annotations/org/tensorflow/op/RandomOps.java index 3c62a3b57a1..c5ff9a489a0 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/annotations/org/tensorflow/op/RandomOps.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/annotations/org/tensorflow/op/RandomOps.java @@ -203,7 +203,6 @@ public LogUniformCandidateSampler logUniformCandidateSampler(Operand tru /** * Draws samples from a multinomial distribution. * - * @param data type for {@code output} output * @param logits 2-D Tensor with shape {@code [batch_size, num_classes]}. Each slice {@code [i, :]} * represents the unnormalized log probabilities for all classes. * @param numSamples 0-D. Number of independent samples to draw for each row slice. @@ -218,7 +217,6 @@ public Multinomial multinomial(Operand logits, /** * Draws samples from a multinomial distribution. * - * @param data type for {@code output} output * @param logits 2-D Tensor with shape {@code [batch_size, num_classes]}. Each slice {@code [i, :]} * represents the unnormalized log probabilities for all classes. * @param numSamples 0-D. Number of independent samples to draw for each row slice. @@ -236,7 +234,6 @@ public Multinomial multinomial(Operand * Non-deterministically generates some integers. * This op may use some OS-provided source of non-determinism (e.g. an RNG), so each execution will give different results. * - * @param data type for {@code output} output * @param shape The shape of the output tensor. * @return a new instance of NonDeterministicInts, with default output types */ @@ -248,7 +245,6 @@ public NonDeterministicInts nonDeterministicInts(Operand data type for {@code output} output * @param shape The shape of the output tensor. * @param dtype The type of the output. * @param data type for {@code NonDeterministicInts} output and operands @@ -264,7 +260,6 @@ public NonDeterministicInts nonDeterministicInts( * scalar which applies to the entire output, or a vector of length shape[0] which * stores the parameters for each batch. * - * @param data type for {@code output} output * @param shape The shape of the output tensor. Batches are indexed by the 0th dimension. * @param means The mean parameter of each batch. * @param stdevs The standard deviation parameter of each batch. Must be greater than 0. @@ -287,7 +282,6 @@ public ParameterizedTruncatedNormal parameterizedTruncate * transformation-rejection from pairs of uniform and normal random variables. * See http://dl.acm.org/citation.cfm?id=358414 * - * @param data type for {@code output} output * @param shape 1-D integer tensor. Shape of independent samples to draw from each * distribution described by the shape parameters given in alpha. * @param alpha A tensor in which each scalar is a "shape" parameter describing the @@ -304,7 +298,6 @@ public RandomGamma randomGamma(Operand /** * Computes the derivative of a Gamma random sample w.r.t. {@code alpha}. * - * @param data type for {@code output} output * @param alpha The alpha value * @param sample The sample value * @param data type for {@code RandomGammaGrad} output and operands @@ -326,7 +319,6 @@ public RandomGammaGrad randomGammaGrad(Operand alpha, * See Donald E. Knuth (1969). Seminumerical Algorithms. The Art of Computer * Programming, Volume 2. Addison Wesley * - * @param data type for {@code output} output * @param shape 1-D integer tensor. Shape of independent samples to draw from each * distribution described by the shape parameters given in rate. * @param rate A tensor in which each scalar is a "rate" parameter describing the @@ -350,7 +342,6 @@ public RandomPoisson randomPoisson(Operand shape, * See Donald E. Knuth (1969). Seminumerical Algorithms. The Art of Computer * Programming, Volume 2. Addison Wesley * - * @param data type for {@code output} output * @param shape 1-D integer tensor. Shape of independent samples to draw from each * distribution described by the shape parameters given in rate. * @param rate A tensor in which each scalar is a "rate" parameter describing the @@ -376,7 +367,6 @@ public RandomPoisson randomPoisson(Operand * - * @param data type for {@code output} output * @param value The tensor to be shuffled. * @param options carries optional attribute values * @param data type for {@code RandomShuffle} output and operands @@ -391,7 +381,6 @@ public RandomShuffle randomShuffle(Operand value, * Outputs random values from a normal distribution. * The generated values will have mean 0 and standard deviation 1. * - * @param data type for {@code output} output * @param shape The shape of the output tensor. * @param dtype The type of the output. * @param options carries optional attribute values @@ -408,7 +397,6 @@ public RandomStandardNormal randomStandardNormal( * The generated values follow a uniform distribution in the range {@code [0, 1)}. The * lower bound 0 is included in the range, while the upper bound 1 is excluded. * - * @param data type for {@code output} output * @param shape The shape of the output tensor. * @param dtype The type of the output. * @param options carries optional attribute values @@ -429,7 +417,6 @@ public RandomUniform randomUniform(Operand data type for {@code output} output * @param shape The shape of the output tensor. * @param minval 0-D. Inclusive lower bound on the generated integers. * @param maxval 0-D. Exclusive upper bound on the generated integers. @@ -491,7 +478,6 @@ public RngSkip rngSkip(Operand resource, Operand algori /** * The StatefulRandomBinomial operation * - * @param data type for {@code output} output * @param resource The resource value * @param algorithm The algorithm value * @param shape The shape value @@ -509,7 +495,6 @@ public StatefulRandomBinomial statefulRandomBinomial /** * The StatefulRandomBinomial operation * - * @param data type for {@code output} output * @param resource The resource value * @param algorithm The algorithm value * @param shape The shape value @@ -530,7 +515,6 @@ public StatefulRandomBinomial stateful * Outputs random values from a normal distribution. * The generated values will have mean 0 and standard deviation 1. * - * @param data type for {@code output} output * @param resource The handle of the resource variable that stores the state of the RNG. * @param algorithm The RNG algorithm. * @param shape The shape of the output tensor. @@ -545,7 +529,6 @@ public StatefulStandardNormal statefulStandardNormal(Operand data type for {@code output} output * @param resource The handle of the resource variable that stores the state of the RNG. * @param algorithm The RNG algorithm. * @param shape The shape of the output tensor. @@ -565,7 +548,6 @@ public StatefulStandardNormal statefulStandardNormal( * deviation 1, except that values whose magnitude is more than 2 standard * deviations from the mean are dropped and re-picked. * - * @param data type for {@code output} output * @param resource The handle of the resource variable that stores the state of the RNG. * @param algorithm The RNG algorithm. * @param shape The shape of the output tensor. @@ -583,7 +565,6 @@ public StatefulTruncatedNormal statefulTruncatedNormal( * deviation 1, except that values whose magnitude is more than 2 standard * deviations from the mean are dropped and re-picked. * - * @param data type for {@code output} output * @param resource The handle of the resource variable that stores the state of the RNG. * @param algorithm The RNG algorithm. * @param shape The shape of the output tensor. @@ -602,7 +583,6 @@ public StatefulTruncatedNormal statefulTruncatedNormal( * The generated values follow a uniform distribution in the range {@code [0, 1)}. The * lower bound 0 is included in the range, while the upper bound 1 is excluded. * - * @param data type for {@code output} output * @param resource The handle of the resource variable that stores the state of the RNG. * @param algorithm The RNG algorithm. * @param shape The shape of the output tensor. @@ -618,7 +598,6 @@ public StatefulUniform statefulUniform(Operand resour * The generated values follow a uniform distribution in the range {@code [0, 1)}. The * lower bound 0 is included in the range, while the upper bound 1 is excluded. * - * @param data type for {@code output} output * @param resource The handle of the resource variable that stores the state of the RNG. * @param algorithm The RNG algorithm. * @param shape The shape of the output tensor. @@ -635,7 +614,6 @@ public StatefulUniform statefulUniform(Operand data type for {@code output} output * @param resource The handle of the resource variable that stores the state of the RNG. * @param algorithm The RNG algorithm. * @param shape The shape of the output tensor. @@ -658,7 +636,6 @@ public StatefulUniformFullInt statefulUniformFullInt( * power of two. The bias is small for values of {@code maxval - minval} significantly * smaller than the range of the output (either {@code 2^32} or {@code 2^64}). * - * @param data type for {@code output} output * @param resource The handle of the resource variable that stores the state of the RNG. * @param algorithm The RNG algorithm. * @param shape The shape of the output tensor. @@ -676,7 +653,6 @@ public StatefulUniformInt statefulUniformInt( /** * Draws samples from a multinomial distribution. * - * @param data type for {@code output} output * @param logits 2-D Tensor with shape {@code [batch_size, num_classes]}. Each slice {@code [i, :]} * represents the unnormalized log probabilities for all classes. * @param numSamples 0-D. Number of independent samples to draw for each row slice. @@ -691,7 +667,6 @@ public StatelessMultinomial statelessMultinomial(Operand data type for {@code output} output * @param logits 2-D Tensor with shape {@code [batch_size, num_classes]}. Each slice {@code [i, :]} * represents the unnormalized log probabilities for all classes. * @param numSamples 0-D. Number of independent samples to draw for each row slice. @@ -709,7 +684,6 @@ public StatelessMultinomial statelessMultinomial( /** * The StatelessParameterizedTruncatedNormal operation * - * @param data type for {@code output} output * @param shape The shape of the output tensor. * @param seed 2 seeds (shape [2]). * @param means The mean parameter of each batch. @@ -731,7 +705,6 @@ public StatelessParameterizedTruncatedNormal statelessPar * Outputs random values from a binomial distribution. *

The outputs are a deterministic function of {@code shape}, {@code seed}, {@code counts}, and {@code probs}. * - * @param data type for {@code output} output * @param shape The shape of the output tensor. * @param seed 2 seeds (shape [2]). * @param counts The counts of the binomial distribution. Must be broadcastable with {@code probs}, @@ -752,7 +725,6 @@ public StatelessRandomBinomial statelessRandomBinomi * Outputs random values from a binomial distribution. *

The outputs are a deterministic function of {@code shape}, {@code seed}, {@code counts}, and {@code probs}. * - * @param data type for {@code output} output * @param shape The shape of the output tensor. * @param seed 2 seeds (shape [2]). * @param counts The counts of the binomial distribution. Must be broadcastable with {@code probs}, @@ -775,7 +747,6 @@ public StatelessRandomBinomial statele * Outputs random values from a gamma distribution. *

The outputs are a deterministic function of the inputs. * - * @param data type for {@code output} output * @param shape The shape of the output tensor. * @param key Key for the counter-based RNG algorithm (shape uint64[1]). * @param counter Initial counter for the counter-based RNG algorithm (shape uint64[2] or uint64[1] depending on the algorithm). If a larger vector is given, only the needed portion on the left (i.e. [:N]) will be used. @@ -830,7 +801,6 @@ public StatelessRandomGetKeyCounterAlg statelessRandomGetKeyCounterAlg( * The generated values will have mean 0 and standard deviation 1. *

The outputs are a deterministic function of {@code shape} and {@code seed}. * - * @param data type for {@code output} output * @param shape The shape of the output tensor. * @param seed 2 seeds (shape [2]). * @return a new instance of StatelessRandomNormal, with default output types @@ -845,7 +815,6 @@ public StatelessRandomNormal statelessRandomNormal(OperandThe outputs are a deterministic function of {@code shape} and {@code seed}. * - * @param data type for {@code output} output * @param shape The shape of the output tensor. * @param seed 2 seeds (shape [2]). * @param dtype The type of the output. @@ -862,7 +831,6 @@ public StatelessRandomNormal statelessRandomNormal( * The generated values will have mean 0 and standard deviation 1. *

The outputs are a deterministic function of {@code shape}, {@code key}, {@code counter} and {@code alg}. * - * @param data type for {@code output} output * @param shape The shape of the output tensor. * @param key Key for the counter-based RNG algorithm (shape uint64[1]). * @param counter Initial counter for the counter-based RNG algorithm (shape uint64[2] or uint64[1] depending on the algorithm). If a larger vector is given, only the needed portion on the left (i.e. [:N]) will be used. @@ -879,7 +847,6 @@ public StatelessRandomNormalV2 statelessRandomNormalV2(OperandThe outputs are a deterministic function of {@code shape}, {@code key}, {@code counter} and {@code alg}. * - * @param data type for {@code output} output * @param shape The shape of the output tensor. * @param key Key for the counter-based RNG algorithm (shape uint64[1]). * @param counter Initial counter for the counter-based RNG algorithm (shape uint64[2] or uint64[1] depending on the algorithm). If a larger vector is given, only the needed portion on the left (i.e. [:N]) will be used. @@ -899,7 +866,6 @@ public StatelessRandomNormalV2 statelessRandomNormalV2( * Outputs random values from a Poisson distribution. *

The outputs are a deterministic function of {@code shape}, {@code seed}, and {@code lam}. * - * @param data type for {@code output} output * @param shape The shape of the output tensor. * @param seed 2 seeds (shape [2]). * @param lam The rate of the Poisson distribution. Shape must match the rightmost dimensions @@ -920,7 +886,6 @@ public StatelessRandomPoisson statelessRandomPoisson( * lower bound 0 is included in the range, while the upper bound 1 is excluded. *

The outputs are a deterministic function of {@code shape} and {@code seed}. * - * @param data type for {@code output} output * @param shape The shape of the output tensor. * @param seed 2 seeds (shape [2]). * @return a new instance of StatelessRandomUniform, with default output types @@ -936,7 +901,6 @@ public StatelessRandomUniform statelessRandomUniform(OperandThe outputs are a deterministic function of {@code shape} and {@code seed}. * - * @param data type for {@code output} output * @param shape The shape of the output tensor. * @param seed 2 seeds (shape [2]). * @param dtype The type of the output. @@ -953,7 +917,6 @@ public StatelessRandomUniform statelessRandomUniform( * The generated values are uniform integers covering the whole range of {@code dtype}. *

The outputs are a deterministic function of {@code shape} and {@code seed}. * - * @param data type for {@code output} output * @param shape The shape of the output tensor. * @param seed 2 seeds (shape [2]). * @param dtype The type of the output. @@ -970,7 +933,6 @@ public StatelessRandomUniformFullInt statelessRandomUnifo * The generated values are uniform integers covering the whole range of {@code dtype}. *

The outputs are a deterministic function of {@code shape}, {@code key}, {@code counter} and {@code alg}. * - * @param data type for {@code output} output * @param shape The shape of the output tensor. * @param key Key for the counter-based RNG algorithm (shape uint64[1]). * @param counter Initial counter for the counter-based RNG algorithm (shape uint64[2] or uint64[1] depending on the algorithm). If a larger vector is given, only the needed portion on the left (i.e. [:N]) will be used. @@ -990,7 +952,6 @@ public StatelessRandomUniformFullIntV2 statelessRandomUni * The generated values follow a uniform distribution in the range {@code [minval, maxval)}. *

The outputs are a deterministic function of {@code shape}, {@code seed}, {@code minval}, and {@code maxval}. * - * @param data type for {@code output} output * @param shape The shape of the output tensor. * @param seed 2 seeds (shape [2]). * @param minval Minimum value (inclusive, scalar). @@ -1009,7 +970,6 @@ public StatelessRandomUniformInt statelessRandomUniformIn * The generated values follow a uniform distribution in the range {@code [minval, maxval)}. *

The outputs are a deterministic function of {@code shape}, {@code key}, {@code counter}, {@code alg}, {@code minval} and {@code maxval}. * - * @param data type for {@code output} output * @param shape The shape of the output tensor. * @param key Key for the counter-based RNG algorithm (shape uint64[1]). * @param counter Initial counter for the counter-based RNG algorithm (shape uint64[2] or uint64[1] depending on the algorithm). If a larger vector is given, only the needed portion on the left (i.e. [:N]) will be used. @@ -1031,7 +991,6 @@ public StatelessRandomUniformIntV2 statelessRandomUniform * lower bound 0 is included in the range, while the upper bound 1 is excluded. *

The outputs are a deterministic function of {@code shape}, {@code key}, {@code counter} and {@code alg}. * - * @param data type for {@code output} output * @param shape The shape of the output tensor. * @param key Key for the counter-based RNG algorithm (shape uint64[1]). * @param counter Initial counter for the counter-based RNG algorithm (shape uint64[2] or uint64[1] depending on the algorithm). If a larger vector is given, only the needed portion on the left (i.e. [:N]) will be used. @@ -1050,7 +1009,6 @@ public StatelessRandomUniformV2 statelessRandomUniformV2( * lower bound 0 is included in the range, while the upper bound 1 is excluded. *

The outputs are a deterministic function of {@code shape}, {@code key}, {@code counter} and {@code alg}. * - * @param data type for {@code output} output * @param shape The shape of the output tensor. * @param key Key for the counter-based RNG algorithm (shape uint64[1]). * @param counter Initial counter for the counter-based RNG algorithm (shape uint64[2] or uint64[1] depending on the algorithm). If a larger vector is given, only the needed portion on the left (i.e. [:N]) will be used. @@ -1072,7 +1030,6 @@ public StatelessRandomUniformV2 statelessRandomUniformV2( * deviations from the mean are dropped and re-picked. *

The outputs are a deterministic function of {@code shape} and {@code seed}. * - * @param data type for {@code output} output * @param shape The shape of the output tensor. * @param seed 2 seeds (shape [2]). * @return a new instance of StatelessTruncatedNormal, with default output types @@ -1089,7 +1046,6 @@ public StatelessTruncatedNormal statelessTruncatedNormal( * deviations from the mean are dropped and re-picked. *

The outputs are a deterministic function of {@code shape} and {@code seed}. * - * @param data type for {@code output} output * @param shape The shape of the output tensor. * @param seed 2 seeds (shape [2]). * @param dtype The type of the output. @@ -1108,7 +1064,6 @@ public StatelessTruncatedNormal statelessTruncatedNormal( * deviations from the mean are dropped and re-picked. *

The outputs are a deterministic function of {@code shape}, {@code key}, {@code counter} and {@code alg}. * - * @param data type for {@code output} output * @param shape The shape of the output tensor. * @param key Key for the counter-based RNG algorithm (shape uint64[1]). * @param counter Initial counter for the counter-based RNG algorithm (shape uint64[2] or uint64[1] depending on the algorithm). If a larger vector is given, only the needed portion on the left (i.e. [:N]) will be used. @@ -1128,7 +1083,6 @@ public StatelessTruncatedNormalV2 statelessTruncatedNormalV2( * deviations from the mean are dropped and re-picked. *

The outputs are a deterministic function of {@code shape}, {@code key}, {@code counter} and {@code alg}. * - * @param data type for {@code output} output * @param shape The shape of the output tensor. * @param key Key for the counter-based RNG algorithm (shape uint64[1]). * @param counter Initial counter for the counter-based RNG algorithm (shape uint64[2] or uint64[1] depending on the algorithm). If a larger vector is given, only the needed portion on the left (i.e. [:N]) will be used. @@ -1176,7 +1130,6 @@ public ThreadUnsafeUnigramCandidateSampler threadUnsafeUnigramCandidateSampler( * deviation 1, except that values whose magnitude is more than 2 standard * deviations from the mean are dropped and re-picked. * - * @param data type for {@code output} output * @param shape The shape of the output tensor. * @param dtype The type of the output. * @param options carries optional attribute values diff --git a/tensorflow-core/tensorflow-core-api/src/gen/annotations/org/tensorflow/op/ShapeOps.java b/tensorflow-core/tensorflow-core-api/src/gen/annotations/org/tensorflow/op/ShapeOps.java index c9cdae676a4..68cb802f86d 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/annotations/org/tensorflow/op/ShapeOps.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/annotations/org/tensorflow/op/ShapeOps.java @@ -388,7 +388,8 @@ public Operand tail(Shape shape, Class type) { * shape. * * @param shape the TensorFlow shape - * @param n the number of leading dimensions to get, must be <= than the shape's numDimensions() + * @param n the number of leading dimensions to get, must be less than or equal to the shape's + * numDimensions() * @return a 1-dimensional operand with the dimensions matching the first n dimensions of the * shape */ @@ -401,7 +402,8 @@ public Operand take(Shape shape, Operand n) { * shape. * * @param shape the TensorFlow shape - * @param n the number of leading dimensions to get, must be <= than the shape's numDimensions() + * @param n the number of leading dimensions to get, must be less than or equal to the shape's + * numDimensions() * @param type the shape datatype. * @param the shape datatype. * @return a 1-dimensional operand with the dimensions matching * the first n dimensions of the @@ -416,7 +418,8 @@ public Operand take(Shape shape, Operand n, Class Operand takeLast(Shape shape, Operand * shape. * * @param shape the TensorFlow shape - * @param n the number of leading dimensions to get, must be <= than the shape's numDimensions() + * @param n the number of leading dimensions to get, must be less than or equal to the shape's + * numDimensions() * @param type the shape datatype. * @param the shape datatype. * @return a 1-dimensional operand containing the dimensions matching the last n dimensions of the diff --git a/tensorflow-core/tensorflow-core-api/src/gen/annotations/org/tensorflow/op/SignalOps.java b/tensorflow-core/tensorflow-core-api/src/gen/annotations/org/tensorflow/op/SignalOps.java index 33e2cd4d920..ac5703c264a 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/annotations/org/tensorflow/op/SignalOps.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/annotations/org/tensorflow/op/SignalOps.java @@ -125,7 +125,6 @@ public BatchIfft3d batchIfft3d(Operand input) { * Computes the 1-dimensional discrete Fourier transform over the inner-most * dimension of {@code input}. * - * @param data type for {@code output} output * @param input A complex tensor. * @param data type for {@code FFT} output and operands * @return a new instance of Fft @@ -139,7 +138,6 @@ public Fft fft(Operand input) { * Computes the 2-dimensional discrete Fourier transform over the inner-most * 2 dimensions of {@code input}. * - * @param data type for {@code output} output * @param input A complex tensor. * @param data type for {@code FFT2D} output and operands * @return a new instance of Fft2d @@ -153,7 +151,6 @@ public Fft2d fft2d(Operand input) { * Computes the 3-dimensional discrete Fourier transform over the inner-most 3 * dimensions of {@code input}. * - * @param data type for {@code output} output * @param input A complex tensor. * @param data type for {@code FFT3D} output and operands * @return a new instance of Fft3d @@ -173,7 +170,6 @@ public Fft3d fft3d(Operand input) { *

Axes mean the dimensions to perform the transform on. Default is to perform on * all axes. * - * @param data type for {@code output} output * @param input A complex tensor. * @param fftLength An int32 tensor. The FFT length for each dimension. * @param axes An int32 tensor with a same shape as fft_length. Axes to perform the transform. @@ -190,7 +186,6 @@ public FftNd fftNd(Operand input, Operand fftLen * Computes the inverse 1-dimensional discrete Fourier transform over the * inner-most dimension of {@code input}. * - * @param data type for {@code output} output * @param input A complex tensor. * @param data type for {@code IFFT} output and operands * @return a new instance of Ifft @@ -204,7 +199,6 @@ public Ifft ifft(Operand input) { * Computes the inverse 2-dimensional discrete Fourier transform over the * inner-most 2 dimensions of {@code input}. * - * @param data type for {@code output} output * @param input A complex tensor. * @param data type for {@code IFFT2D} output and operands * @return a new instance of Ifft2d @@ -218,7 +212,6 @@ public Ifft2d ifft2d(Operand input) { * Computes the inverse 3-dimensional discrete Fourier transform over the * inner-most 3 dimensions of {@code input}. * - * @param data type for {@code output} output * @param input A complex tensor. * @param data type for {@code IFFT3D} output and operands * @return a new instance of Ifft3d @@ -238,7 +231,6 @@ public Ifft3d ifft3d(Operand input) { *

Axes mean the dimensions to perform the transform on. Default is to perform on * all axes. * - * @param data type for {@code output} output * @param input A complex tensor. * @param fftLength An int32 tensor. The FFT length for each dimension. * @param axes An int32 tensor with a same shape as fft_length. Axes to perform the transform. @@ -264,7 +256,6 @@ public IfftNd ifftNd(Operand input, Operand fftL * than the corresponding dimension of {@code input}, the dimension is cropped. If it is * larger, the dimension is padded with zeros. * - * @param data type for {@code output} output * @param input A complex tensor. * @param fftLength An int32 tensor of shape [1]. The FFT length. * @return a new instance of Irfft, with default output types @@ -287,7 +278,6 @@ public Irfft irfft(Operand input, Operand fft * than the corresponding dimension of {@code input}, the dimension is cropped. If it is * larger, the dimension is padded with zeros. * - * @param data type for {@code output} output * @param input A complex tensor. * @param fftLength An int32 tensor of shape [1]. The FFT length. * @param Treal The value of the Treal attribute @@ -314,7 +304,6 @@ public Irfft irfft(Operand input, * corresponding dimension of {@code input}, the dimension is cropped. If it is larger, * the dimension is padded with zeros. * - * @param data type for {@code output} output * @param input A complex tensor. * @param fftLength An int32 tensor of shape [2]. The FFT length for each dimension. * @return a new instance of Irfft2d, with default output types @@ -338,7 +327,6 @@ public Irfft2d irfft2d(Operand input, Operand * corresponding dimension of {@code input}, the dimension is cropped. If it is larger, * the dimension is padded with zeros. * - * @param data type for {@code output} output * @param input A complex tensor. * @param fftLength An int32 tensor of shape [2]. The FFT length for each dimension. * @param Treal The value of the Treal attribute @@ -365,7 +353,6 @@ public Irfft2d irfft2d(Operand input, * corresponding dimension of {@code input}, the dimension is cropped. If it is larger, * the dimension is padded with zeros. * - * @param data type for {@code output} output * @param input A complex tensor. * @param fftLength An int32 tensor of shape [3]. The FFT length for each dimension. * @return a new instance of Irfft3d, with default output types @@ -389,7 +376,6 @@ public Irfft3d irfft3d(Operand input, Operand * corresponding dimension of {@code input}, the dimension is cropped. If it is larger, * the dimension is padded with zeros. * - * @param data type for {@code output} output * @param input A complex tensor. * @param fftLength An int32 tensor of shape [3]. The FFT length for each dimension. * @param Treal The value of the Treal attribute @@ -413,7 +399,6 @@ public Irfft3d irfft3d(Operand input, *

Axes mean the dimensions to perform the transform on. Default is to perform on * all axes. * - * @param data type for {@code output} output * @param input A complex tensor. * @param fftLength An int32 tensor. The FFT length for each dimension. * @param axes An int32 tensor with a same shape as fft_length. Axes to perform the transform. @@ -436,7 +421,6 @@ public IrfftNd irfftNd(Operand input, Operand *

Axes mean the dimensions to perform the transform on. Default is to perform on * all axes. * - * @param data type for {@code output} output * @param input A complex tensor. * @param fftLength An int32 tensor. The FFT length for each dimension. * @param axes An int32 tensor with a same shape as fft_length. Axes to perform the transform. @@ -460,7 +444,6 @@ public IrfftNd irfftNd(Operand input, * corresponding dimension of {@code input}, the dimension is cropped. If it is larger, * the dimension is padded with zeros. * - * @param data type for {@code output} output * @param input A float32 tensor. * @param fftLength An int32 tensor of shape [1]. The FFT length. * @param Tcomplex The value of the Tcomplex attribute @@ -484,7 +467,6 @@ public Rfft rfft(Operand input, Operand< * corresponding dimension of {@code input}, the dimension is cropped. If it is larger, * the dimension is padded with zeros. * - * @param data type for {@code output} output * @param input A float32 tensor. * @param fftLength An int32 tensor of shape [2]. The FFT length for each dimension. * @param Tcomplex The value of the Tcomplex attribute @@ -508,7 +490,6 @@ public Rfft2d rfft2d(Operand input, * corresponding dimension of {@code input}, the dimension is cropped. If it is larger, * the dimension is padded with zeros. * - * @param data type for {@code output} output * @param input A float32 tensor. * @param fftLength An int32 tensor of shape [3]. The FFT length for each dimension. * @param Tcomplex The value of the Tcomplex attribute @@ -532,7 +513,6 @@ public Rfft3d rfft3d(Operand input, *

Axes mean the dimensions to perform the transform on. Default is to perform on * all axes. * - * @param data type for {@code output} output * @param input A complex tensor. * @param fftLength An int32 tensor. The FFT length for each dimension. * @param axes An int32 tensor with a same shape as fft_length. Axes to perform the transform. diff --git a/tensorflow-core/tensorflow-core-api/src/gen/annotations/org/tensorflow/op/SparseOps.java b/tensorflow-core/tensorflow-core-api/src/gen/annotations/org/tensorflow/op/SparseOps.java index 91726a9a693..f6f83acce58 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/annotations/org/tensorflow/op/SparseOps.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/annotations/org/tensorflow/op/SparseOps.java @@ -17,14 +17,18 @@ // package org.tensorflow.op; +import java.util.List; import org.tensorflow.Operand; import org.tensorflow.ndarray.Shape; import org.tensorflow.op.sparse.AddManySparseToTensorsMap; import org.tensorflow.op.sparse.AddSparseToTensorsMap; +import org.tensorflow.op.sparse.ConvertToListOfSparseCoreCooTensors; +import org.tensorflow.op.sparse.ConvertToSparseCoreCsrWrappedCooTensor; import org.tensorflow.op.sparse.DenseCountSparseOutput; import org.tensorflow.op.sparse.DenseToDenseSetOperation; import org.tensorflow.op.sparse.DenseToSparseSetOperation; import org.tensorflow.op.sparse.DeserializeSparse; +import org.tensorflow.op.sparse.GetStatsFromListOfSparseCoreCooTensors; import org.tensorflow.op.sparse.SparseAccumulatorApplyGradient; import org.tensorflow.op.sparse.SparseAccumulatorTakeGradient; import org.tensorflow.op.sparse.SparseAdd; @@ -68,6 +72,7 @@ import org.tensorflow.op.sparse.SparseToSparseSetOperation; import org.tensorflow.op.sparse.TakeManySparseFromTensorsMap; import org.tensorflow.types.TBool; +import org.tensorflow.types.TFloat32; import org.tensorflow.types.TInt32; import org.tensorflow.types.TInt64; import org.tensorflow.types.TString; @@ -151,11 +156,60 @@ public AddSparseToTensorsMap addSparseToTensorsMap(Operand sparseIndices return AddSparseToTensorsMap.create(scope, sparseIndices, sparseValues, sparseShape, options); } + /** + * The ConvertToListOfSparseCoreCooTensors operation + * + * @param indicesOrRowSplits The indicesOrRowSplits value + * @param values The values value + * @param weights The weights value + * @param sampleCount The value of the sampleCount attribute + * @param numScPerChip The value of the numScPerChip attribute + * @param rowOffset The value of the rowOffset attribute + * @param colOffset The value of the colOffset attribute + * @param colShift The value of the colShift attribute + * @param numScShards The value of the numScShards attribute + * @param stackedTableSampleCount The value of the stackedTableSampleCount attribute + * @param combiner The value of the combiner attribute + * @return a new instance of ConvertToListOfSparseCoreCooTensors + */ + public ConvertToListOfSparseCoreCooTensors convertToListOfSparseCoreCooTensors( + Operand indicesOrRowSplits, Operand values, Operand weights, + Long sampleCount, Long numScPerChip, Long rowOffset, Long colOffset, Long colShift, + Long numScShards, Long stackedTableSampleCount, String combiner) { + return ConvertToListOfSparseCoreCooTensors.create(scope, indicesOrRowSplits, values, weights, sampleCount, numScPerChip, rowOffset, colOffset, colShift, numScShards, stackedTableSampleCount, combiner); + } + + /** + * The ConvertToSparseCoreCsrWrappedCooTensor operation + * + * @param sortedRowIdsList The sortedRowIdsList value + * @param sortedColIdsList The sortedColIdsList value + * @param sortedGainsList The sortedGainsList value + * @param idCountsList The idCountsList value + * @param splits The splits value + * @param sampleCountPerSc The value of the sampleCountPerSc attribute + * @param numReplica The value of the numReplica attribute + * @param maxMinibatchesPerSc The value of the maxMinibatchesPerSc attribute + * @param maxIdsPerChipPerSample The value of the maxIdsPerChipPerSample attribute + * @param tableVocabSize The value of the tableVocabSize attribute + * @param featureWidth The value of the featureWidth attribute + * @param tableName The value of the tableName attribute + * @param allowIdDropping The value of the allowIdDropping attribute + * @return a new instance of ConvertToSparseCoreCsrWrappedCooTensor + */ + public ConvertToSparseCoreCsrWrappedCooTensor convertToSparseCoreCsrWrappedCooTensor( + Iterable> sortedRowIdsList, Iterable> sortedColIdsList, + Iterable> sortedGainsList, Iterable> idCountsList, + Operand splits, Long sampleCountPerSc, Long numReplica, Long maxMinibatchesPerSc, + Long maxIdsPerChipPerSample, Long tableVocabSize, Long featureWidth, String tableName, + Boolean allowIdDropping) { + return ConvertToSparseCoreCsrWrappedCooTensor.create(scope, sortedRowIdsList, sortedColIdsList, sortedGainsList, idCountsList, splits, sampleCountPerSc, numReplica, maxMinibatchesPerSc, maxIdsPerChipPerSample, tableVocabSize, featureWidth, tableName, allowIdDropping); + } + /** * Performs sparse-output bin counting for a tf.tensor input. * Counts the number of times each value occurs in the input. * - * @param data type for {@code output_values} output * @param values Tensor containing data to count. * @param weights A Tensor of the same shape as indices containing per-index weight values. May * also be the empty tensor if no weights are used. @@ -179,7 +233,6 @@ public DenseCountSparseOutput denseCountSparseOutput( * dimension contains the result of {@code set_operation} applied to the corresponding * {@code [0...n-1]} dimension of {@code set}. * - * @param data type for {@code result_values} output * @param set1 {@code Tensor} with rank {@code n}. 1st {@code n-1} dimensions must be the same as {@code set2}. * Dimension {@code n} contains values in a set, duplicates are allowed but ignored. * @param set2 {@code Tensor} with rank {@code n}. 1st {@code n-1} dimensions must be the same as {@code set1}. @@ -209,7 +262,6 @@ public DenseToDenseSetOperation denseToDenseSetOperation(Op * dimension contains the result of {@code set_operation} applied to the corresponding * {@code [0...n-1]} dimension of {@code set}. * - * @param data type for {@code result_values} output * @param set1 {@code Tensor} with rank {@code n}. 1st {@code n-1} dimensions must be the same as {@code set2}. * Dimension {@code n} contains values in a set, duplicates are allowed but ignored. * @param set2Indices 2D {@code Tensor}, indices of a {@code SparseTensor}. Must be in row-major @@ -272,7 +324,6 @@ public DenseToSparseSetOperation denseToSparseSetOperation( * shape = [2 50] * * - * @param data type for {@code sparse_values} output * @param serializedSparse The serialized {@code SparseTensor} objects. The last dimension * must have 3 columns. * @param dtype The {@code dtype} of the serialized {@code SparseTensor} objects. @@ -284,6 +335,29 @@ public DeserializeSparse deserializeSparse( return DeserializeSparse.create(scope, serializedSparse, dtype); } + /** + * The GetStatsFromListOfSparseCoreCooTensors operation + * + * @param rowIdsList The rowIdsList value + * @param colIdsList The colIdsList value + * @param gainsList The gainsList value + * @param sampleCountList The value of the sampleCountList attribute + * @param colOffsetList The value of the colOffsetList attribute + * @param numReplica The value of the numReplica attribute + * @param tableVocabSize The value of the tableVocabSize attribute + * @param featureWidth The value of the featureWidth attribute + * @param numScPerChip The value of the numScPerChip attribute + * @param tableName The value of the tableName attribute + * @return a new instance of GetStatsFromListOfSparseCoreCooTensors + */ + public GetStatsFromListOfSparseCoreCooTensors getStatsFromListOfSparseCoreCooTensors( + Iterable> rowIdsList, Iterable> colIdsList, + Iterable> gainsList, List sampleCountList, List colOffsetList, + Long numReplica, Long tableVocabSize, Long featureWidth, Long numScPerChip, + String tableName) { + return GetStatsFromListOfSparseCoreCooTensors.create(scope, rowIdsList, colIdsList, gainsList, sampleCountList, colOffsetList, numReplica, tableVocabSize, featureWidth, numScPerChip, tableName); + } + /** * Applies a sparse gradient to a given accumulator. * Does not add if local_step is smaller than the accumulator's @@ -317,7 +391,6 @@ public SparseAccumulatorApplyGradient sparseAccumulatorApplyGradient(Operand data type for {@code values} output * @param handle The handle to a SparseConditionalAccumulator. * @param numRequired Number of gradients required before we return an aggregate. * @param dtype The data type of accumulated gradients. Needs to correspond to the type @@ -344,7 +417,6 @@ public SparseAccumulatorTakeGradient sparseAccumulatorTakeG * only for a positive value. *

In the following shapes, {@code nnz} is the count after taking {@code thresh} into account. * - * @param data type for {@code sum_values} output * @param aIndices 2-D. The {@code indices} of the first {@code SparseTensor}, size {@code [nnz, ndims]} Matrix. * @param aValues 1-D. The {@code values} of the first {@code SparseTensor}, size {@code [nnz]} Vector. * @param aShape 1-D. The {@code shape} of the first {@code SparseTensor}, size {@code [ndims]} Vector. @@ -369,7 +441,6 @@ public SparseAdd sparseAdd(Operand aIndices, Operan * non-empty values of the sum, and outputs the gradients w.r.t. the non-empty * values of A and B. * - * @param data type for {@code a_val_grad} output * @param backpropValGrad 1-D with shape {@code [nnz(sum)]}. The gradient with respect to * the non-empty values of the sum. * @param aIndices 2-D. The {@code indices} of the {@code SparseTensor} A, size {@code [nnz(A), ndims]}. @@ -393,7 +464,6 @@ public SparseAddGrad sparseAddGrad(Operand backpropValGr * {@code i}. *

Values in {@code arr} outside of the range [0, size) are ignored. * - * @param data type for {@code output} output * @param indices 2D int64 {@code Tensor}. * @param values 1D int {@code Tensor}. * @param denseShape 1D int64 {@code Tensor}. @@ -452,7 +522,6 @@ public SparseBincount sparseBincount( * [b c ] [ ] [b c ] * * - * @param data type for {@code output_values} output * @param indices 2-D. Indices of each input {@code SparseTensor}. * @param values 1-D. Non-empty values of each {@code SparseTensor}. * @param shapes 1-D. Shapes of each {@code SparseTensor}. @@ -490,7 +559,6 @@ public SparseConditionalAccumulator sparseConditionalAccumulat * Performs sparse-output bin counting for a sparse tensor input. * Counts the number of times each value occurs in the input. * - * @param data type for {@code output_values} output * @param indices Tensor containing the indices of the sparse tensor to count. * @param values Tensor containing values of the sparse tensor to count. * @param denseShape Tensor containing the dense shape of the sparse tensor to count. @@ -624,7 +692,6 @@ public SparseCrossHashed sparseCrossHashed(Iterable> indices, * indices and shape, but possibly with different non-zero values. The output of * this Op is the resultant non-zero values. * - * @param data type for {@code output} output * @param spIndices 2-D. {@code N x R} matrix with the indices of non-empty values in a * SparseTensor, possibly not in canonical ordering. * @param spValues 1-D. {@code N} non-empty values corresponding to {@code sp_indices}. @@ -643,7 +710,6 @@ public SparseDenseCwiseAdd sparseDenseCwiseAdd(OperandLimitation: this Op only broadcasts the dense side to the sparse side, but not * the other direction. * - * @param data type for {@code output} output * @param spIndices 2-D. {@code N x R} matrix with the indices of non-empty values in a * SparseTensor, possibly not in canonical ordering. * @param spValues 1-D. {@code N} non-empty values corresponding to {@code sp_indices}. @@ -665,7 +731,6 @@ public SparseDenseCwiseDiv sparseDenseCwiseDiv(OperandLimitation: this Op only broadcasts the dense side to the sparse side, but not * the other direction. * - * @param data type for {@code output} output * @param spIndices 2-D. {@code N x R} matrix with the indices of non-empty values in a * SparseTensor, possibly not in canonical ordering. * @param spValues 1-D. {@code N} non-empty values corresponding to {@code sp_indices}. @@ -716,7 +781,6 @@ public SparseDenseCwiseMul sparseDenseCwiseMul(Operand * - * @param data type for {@code output_values} output * @param indices 2-D. the indices of the sparse tensor. * @param values 1-D. the values of the sparse tensor. * @param denseShape 1-D. the shape of the sparse tensor. @@ -741,7 +805,6 @@ public SparseFillEmptyRows sparseFillEmptyRows(Operand data type for {@code d_values} output * @param reverseIndexMap 1-D. The reverse index map from SparseFillEmptyRows. * @param gradValues 1-D. The gradients from backprop. * @param data type for {@code SparseFillEmptyRowsGrad} output and operands @@ -786,7 +849,6 @@ public SparseMatMul sparseMatMul(Operand a, Operand data type for {@code output} output * @param inputIndices 2-D. {@code N x R} matrix with the indices of non-empty values in a * SparseTensor, possibly not in canonical ordering. * @param inputValues 1-D. {@code N} non-empty values corresponding to {@code input_indices}. @@ -815,7 +877,6 @@ public SparseReduceMax sparseReduceMax(Operand in * with a single element is returned. Additionally, the axes can be negative, * which are interpreted according to the indexing rules in Python. * - * @param data type for {@code output_values} output * @param inputIndices 2-D. {@code N x R} matrix with the indices of non-empty values in a * SparseTensor, possibly not in canonical ordering. * @param inputValues 1-D. {@code N} non-empty values corresponding to {@code input_indices}. @@ -844,7 +905,6 @@ public SparseReduceMaxSparse sparseReduceMaxSparse( * with a single element is returned. Additionally, the axes can be negative, * which are interpreted according to the indexing rules in Python. * - * @param data type for {@code output} output * @param inputIndices 2-D. {@code N x R} matrix with the indices of non-empty values in a * SparseTensor, possibly not in canonical ordering. * @param inputValues 1-D. {@code N} non-empty values corresponding to {@code input_indices}. @@ -873,7 +933,6 @@ public SparseReduceSum sparseReduceSum(Operand inpu * with a single element is returned. Additionally, the axes can be negative, * which are interpreted according to the indexing rules in Python. * - * @param data type for {@code output_values} output * @param inputIndices 2-D. {@code N x R} matrix with the indices of non-empty values in a * SparseTensor, possibly not in canonical ordering. * @param inputValues 1-D. {@code N} non-empty values corresponding to {@code input_indices}. @@ -898,7 +957,6 @@ public SparseReduceSumSparse sparseReduceSumSparse( *

If the tensor has rank {@code R} and {@code N} non-empty values, {@code input_indices} has * shape {@code [N, R]}, input_values has length {@code N}, and input_shape has length {@code R}. * - * @param data type for {@code output_values} output * @param inputIndices 2-D. {@code N x R} matrix with the indices of non-empty values in a * SparseTensor, possibly not in canonical ordering. * @param inputValues 1-D. {@code N} non-empty values corresponding to {@code input_indices}. @@ -943,7 +1001,6 @@ public SparseReshape sparseReshape(Operand inputIndices, Operand *

Like {@code SegmentMean}, but {@code segment_ids} can have rank less than {@code data}'s first * dimension, selecting a subset of dimension 0, specified by {@code indices}. * - * @param data type for {@code output} output * @param data The data value * @param indices A 1-D tensor. Has same rank as {@code segment_ids}. * @param segmentIds A 1-D tensor. Values should be sorted and can be repeated. @@ -963,8 +1020,6 @@ public SparseSegmentMean sparseSegmentMean(Operand dat * value is the number of unique indexes in "indices". Also returns vector * "sorted_unique_indices" containing the corresponding indexes from "indices". * - * @param data type for {@code output} output - * @param data type for {@code sorted_unique_indices} output * @param grad gradient propagated to the SparseSegmentMean op. * @param indices indices passed to the corresponding SparseSegmentMean op. * @param segmentIds segment_ids passed to the corresponding SparseSegmentMean op. @@ -987,7 +1042,6 @@ public SparseSegmentMeanGrad sparse * the section on segmentation * for an explanation of segments. * - * @param data type for {@code output} output * @param data The data value * @param indices A 1-D tensor. Has same rank as {@code segment_ids}. * @param segmentIds A 1-D tensor. Values should be sorted and can be repeated. @@ -1007,7 +1061,6 @@ public SparseSegmentMeanWithNumSegments sparseSegmentMean * N is the size of the segment being reduced. *

See {@code tf.sparse.segment_sum} for usage examples. * - * @param data type for {@code output} output * @param data The data value * @param indices A 1-D tensor. Has same rank as {@code segment_ids}. * @param segmentIds A 1-D tensor. Values should be sorted and can be repeated. @@ -1027,8 +1080,6 @@ public SparseSegmentSqrtN sparseSegmentSqrtN(Operand d * value is the number of unique indexes in "indices". Also returns vector * "sorted_unique_indices" containing the corresponding indexes from "indices". * - * @param data type for {@code output} output - * @param data type for {@code sorted_unique_indices} output * @param grad gradient propagated to the SparseSegmentSqrtN op. * @param indices indices passed to the corresponding SparseSegmentSqrtN op. * @param segmentIds segment_ids passed to the corresponding SparseSegmentSqrtN op. @@ -1052,7 +1103,6 @@ public SparseSegmentSqrtNGrad spars * the section on segmentation * for an explanation of segments. * - * @param data type for {@code output} output * @param data The data value * @param indices A 1-D tensor. Has same rank as {@code segment_ids}. * @param segmentIds A 1-D tensor. Values should be sorted and can be repeated. @@ -1097,7 +1147,6 @@ public SparseSegmentSqrtNWithNumSegments sparseSegmentSqr * tf.segment_sum(c, tf.constant([0, 0, 1])) * * - * @param data type for {@code output} output * @param data The data value * @param indices A 1-D tensor. Has same rank as {@code segment_ids}. * @param segmentIds A 1-D tensor. Values should be sorted and can be repeated. @@ -1117,8 +1166,6 @@ public SparseSegmentSum sparseSegmentSum(Operand data, * value is the number of unique indexes in "indices". Also returns vector * "sorted_unique_indices" containing the corresponding indexes from "indices". * - * @param data type for {@code output} output - * @param data type for {@code sorted_unique_indices} output * @param grad gradient propagated to the SparseSegmentSum op. * @param indices indices passed to the corresponding SparseSegmentSum op. * @param segmentIds segment_ids passed to the corresponding SparseSegmentSum op. @@ -1160,7 +1207,6 @@ public SparseSegmentSumGrad sparseS * # [ 0 0 0 0]] * * - * @param data type for {@code output} output * @param data The data value * @param indices A 1-D tensor. Has same rank as {@code segment_ids}. * @param segmentIds A 1-D tensor. Values should be sorted and can be repeated. @@ -1194,7 +1240,6 @@ public SparseSegmentSumWithNumSegments sparseSegmentSumWi * [ ] * * - * @param data type for {@code output_values} output * @param indices 2-D tensor represents the indices of the sparse tensor. * @param values 1-D tensor represents the values of the sparse tensor. * @param shape 1-D. tensor represents the shape of the sparse tensor. @@ -1216,7 +1261,6 @@ public SparseSlice sparseSlice(Operand indices, Ope * the sliced {@code SparseTensor}, and outputs the gradients w.r.t. * the non-empty values of input {@code SparseTensor}. * - * @param data type for {@code val_grad} output * @param backpropValGrad 1-D. The gradient with respect to * the non-empty values of the sliced {@code SparseTensor}. * @param inputIndices 2-D. The {@code indices} of the input {@code SparseTensor}. @@ -1245,7 +1289,6 @@ public SparseSliceGrad sparseSliceGrad(Operand backpropV *

Hence, the {@code SparseTensor} result has exactly the same non-zero indices and * shape. * - * @param data type for {@code output} output * @param spIndices 2-D. {@code NNZ x R} matrix with the indices of non-empty values in a * SparseTensor, in canonical ordering. * @param spValues 1-D. {@code NNZ} non-empty values corresponding to {@code sp_indices}. @@ -1262,7 +1305,6 @@ public SparseSoftmax sparseSoftmax(Operand spIndi * Returns the element-wise max of two SparseTensors. * Assumes the two SparseTensors have the same shape, i.e., no broadcasting. * - * @param data type for {@code output_values} output * @param aIndices 2-D. {@code N x R} matrix with the indices of non-empty values in a * SparseTensor, in the canonical lexicographic ordering. * @param aValues 1-D. {@code N} non-empty values corresponding to {@code a_indices}. @@ -1283,7 +1325,6 @@ public SparseSparseMaximum sparseSparseMaximum(Operand data type for {@code output_values} output * @param aIndices 2-D. {@code N x R} matrix with the indices of non-empty values in a * SparseTensor, in the canonical lexicographic ordering. * @param aValues 1-D. {@code N} non-empty values corresponding to {@code a_indices}. @@ -1321,7 +1362,6 @@ public SparseSparseMinimum sparseSparseMinimum(Operand * - * @param data type for {@code output_values} output * @param splitDim 0-D. The dimension along which to split. Must be in the range * {@code [0, rank(shape))}. * @param indices 2-D tensor represents the indices of the sparse tensor. @@ -1342,7 +1382,6 @@ public SparseSplit sparseSplit(Operand splitDim, * Adds up a {@code SparseTensor} and a dense {@code Tensor}, producing a dense {@code Tensor}. * This Op does not require {@code a_indices} be sorted in standard lexicographic order. * - * @param data type for {@code output} output * @param aIndices 2-D. The {@code indices} of the {@code SparseTensor}, with shape {@code [nnz, ndims]}. * @param aValues 1-D. The {@code values} of the {@code SparseTensor}, with shape {@code [nnz]}. * @param aShape 1-D. The {@code shape} of the {@code SparseTensor}, with shape {@code [ndims]}. @@ -1367,7 +1406,6 @@ public SparseTensorDenseAdd sparseTensor * A should be sorted in order of increasing dimension 1 (i.e., "column major" * order instead of "row major" order). * - * @param data type for {@code product} output * @param aIndices 2-D. The {@code indices} of the {@code SparseTensor}, size {@code [nnz, 2]} Matrix. * @param aValues 1-D. The {@code values} of the {@code SparseTensor}, size {@code [nnz]} Vector. * @param aShape 1-D. The {@code shape} of the {@code SparseTensor}, size {@code [2]} Vector. @@ -1401,7 +1439,6 @@ public SparseTensorDenseMatMul sparseTensorDenseMatMul( * contain any repeats. If {@code validate_indices} is true, these properties * are checked during execution. * - * @param data type for {@code dense} output * @param sparseIndices 0-D, 1-D, or 2-D. {@code sparse_indices[i]} contains the complete * index where {@code sparse_values[i]} will be placed. * @param outputShape 1-D. Shape of the dense output tensor. @@ -1441,7 +1478,6 @@ public SparseToDense sparseToDense( * dimension contains the result of {@code set_operation} applied to the corresponding * {@code [0...n-1]} dimension of {@code set}. * - * @param data type for {@code result_values} output * @param set1Indices 2D {@code Tensor}, indices of a {@code SparseTensor}. Must be in row-major * order. * @param set1Values 1D {@code Tensor}, values of a {@code SparseTensor}. Must be in row-major @@ -1511,7 +1547,6 @@ public SparseToSparseSetOperation sparseToSparseSetOperatio * shape = [2 50] * * - * @param data type for {@code sparse_values} output * @param sparseHandles 1-D, The {@code N} serialized {@code SparseTensor} objects. * Shape: {@code [N]}. * @param dtype The {@code dtype} of the {@code SparseTensor} objects stored in the diff --git a/tensorflow-core/tensorflow-core-api/src/gen/annotations/org/tensorflow/op/StringsOps.java b/tensorflow-core/tensorflow-core-api/src/gen/annotations/org/tensorflow/op/StringsOps.java index b7d38d58553..56a82c2dbf6 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/annotations/org/tensorflow/op/StringsOps.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/annotations/org/tensorflow/op/StringsOps.java @@ -260,7 +260,6 @@ public StringLength stringLength(Operand input, StringLength.Options... * strings and outputs a ragged tensor with 1 ragged dimension containing ngrams * of that string, joined along the innermost axis. * - * @param data type for {@code ngrams_splits} output * @param data The values tensor of the ragged string tensor to make ngrams out of. Must be a * 1D string tensor. * @param dataSplits The splits tensor of the ragged string tensor to make ngrams out of. @@ -510,7 +509,6 @@ public ToHashBucketStrong toHashBucketStrong(Operand input, Long numBuc * * * - * @param data type for {@code output} output * @param stringTensor The stringTensor value * @return a new instance of ToNumber, with default output types */ @@ -533,7 +531,6 @@ public ToNumber toNumber(Operand stringTensor) { * * * - * @param data type for {@code output} output * @param stringTensor The stringTensor value * @param outType The numeric type to interpret each string in {@code string_tensor} as. * @param data type for {@code StringToNumber} output and operands @@ -559,7 +556,6 @@ public ToNumber toNumber(Operand stringTensor, C * string (in row-major order). * * - * @param data type for {@code row_splits} output * @param input The text to be decoded. Can have any shape. Note that the output is flattened * to a vector of char values. * @param inputEncoding Text encoding of the input strings. This is any of the encodings supported @@ -588,7 +584,6 @@ public UnicodeDecode unicodeDecode(Operand input, String inputE * string (in row-major order). * * - * @param data type for {@code row_splits} output * @param input The text to be decoded. Can have any shape. Note that the output is flattened * to a vector of char values. * @param inputEncoding Text encoding of the input strings. This is any of the encodings supported @@ -623,7 +618,6 @@ public UnicodeDecode unicodeDecode(Operand input * string (in row-major order). * * - * @param data type for {@code row_splits} output * @param input The text to be decoded. Can have any shape. Note that the output is flattened * to a vector of char values. * @param inputEncoding Text encoding of the input strings. This is any of the encodings supported @@ -656,7 +650,6 @@ public UnicodeDecodeWithOffsets unicodeDecodeWithOffsets(Operand * * - * @param data type for {@code row_splits} output * @param input The text to be decoded. Can have any shape. Note that the output is flattened * to a vector of char values. * @param inputEncoding Text encoding of the input strings. This is any of the encodings supported diff --git a/tensorflow-core/tensorflow-core-api/src/gen/annotations/org/tensorflow/op/TpuOps.java b/tensorflow-core/tensorflow-core-api/src/gen/annotations/org/tensorflow/op/TpuOps.java index 59a9f973858..f6ea8e12178 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/annotations/org/tensorflow/op/TpuOps.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/annotations/org/tensorflow/op/TpuOps.java @@ -26,8 +26,6 @@ import org.tensorflow.op.tpu.CompilationResult; import org.tensorflow.op.tpu.Compile; import org.tensorflow.op.tpu.CompileSucceededAssert; -import org.tensorflow.op.tpu.ComputeDedupDataSize; -import org.tensorflow.op.tpu.ComputeDedupDataTupleMask; import org.tensorflow.op.tpu.ConfigureAndInitializeGlobalTPU; import org.tensorflow.op.tpu.ConfigureDistributedTPU; import org.tensorflow.op.tpu.ConfigureTPUEmbedding; @@ -52,6 +50,7 @@ import org.tensorflow.op.tpu.FinalizeTPUEmbedding; import org.tensorflow.op.tpu.GetMinibatchSplitsWithPhysicalReplica; import org.tensorflow.op.tpu.GetMinibatchesInCsrWithPhysicalReplica; +import org.tensorflow.op.tpu.GetTpuTaskId; import org.tensorflow.op.tpu.GlobalIterId; import org.tensorflow.op.tpu.InfeedDequeue; import org.tensorflow.op.tpu.InfeedDequeueTuple; @@ -158,7 +157,6 @@ public final class TpuOps { *

replica 0's output: {@code [[A], [C]]} * replica 1's output: {@code [[B], [D]]} * - * @param data type for {@code output} output * @param input The local input to the sum. * @param groupAssignment An int32 tensor with shape * [num_groups, num_replicas_per_group]. {@code group_assignment[i]} represents the @@ -242,32 +240,6 @@ public CompileSucceededAssert compileSucceededAssert(Operand compilatio return CompileSucceededAssert.create(scope, compilationStatus); } - /** - * An op computes the size of the deduplication data from embedding core and returns the updated config. - * This op is to compute size of the deduplication data so to provide this - * information to the op that computes the tuple mask of deduplication data can - * have static output shape. - * - * @param config Serialized TPUEmbeddingConfiguration proto. - * @return a new instance of ComputeDedupDataSize - */ - public ComputeDedupDataSize computeDedupDataSize(String config) { - return ComputeDedupDataSize.create(scope, config); - } - - /** - * An op computes tuple mask of deduplication data from embedding core. - * The deduplication data receiving from embedding core is a Tensor with - * type=DT_VARIANT. The tensor itself is an XLA nested tuple, whose elements are - * rank 1 tensors. This op is to represents types and length of these elements. - * - * @param config Serialized TPUEmbeddingConfiguration proto. - * @return a new instance of ComputeDedupDataTupleMask - */ - public ComputeDedupDataTupleMask computeDedupDataTupleMask(String config) { - return ComputeDedupDataTupleMask.create(scope, config); - } - /** * An op that sets up the centralized structures for a distributed TPU system. * @@ -365,7 +337,6 @@ public ConvertToCooTensor convertToCooTensor(Operand indicesOrRowSplits, * and {@code B, D, F, H} as group 1. Thus we get the outputs: * {@code [A+C+E+G, B+D+F+H, A+C+E+G, B+D+F+H, A+C+E+G, B+D+F+H, A+C+E+G, B+D+F+H]}. * - * @param data type for {@code output} output * @param input The local input to the sum. * @param groupAssignment An int32 tensor with shape * [num_groups, num_replicas_per_group]. {@code group_assignment[i]} represents the @@ -789,6 +760,16 @@ public GetMinibatchesInCsrWithPhysicalReplica getMinibatchesInCsrWithPhysicalRep return GetMinibatchesInCsrWithPhysicalReplica.create(scope, programKey, rowIds, colIds, gains, splits, idCounts, sampleCount, numReplica, maxMinibatchesPerSc, maxIdsPerChipPerSample, tableVocabSize, featureWidth, numScPerChip, tableName, miniBatchInCsr); } + /** + * An op returns the TPU task ID from TPU topology. + * This op is to return the TPU task ID from TPU topology. + * + * @return a new instance of GetTpuTaskId + */ + public GetTpuTaskId getTpuTaskId() { + return GetTpuTaskId.create(scope); + } + /** * The GlobalIterId operation * @@ -801,7 +782,6 @@ public GlobalIterId globalIterId() { /** * A placeholder op for a value that will be fed into the computation. * - * @param data type for {@code output} output * @param dtype The type of elements in the tensor. * @param shape The shape of the tensor. * @param data type for {@code InfeedDequeue} output and operands @@ -1252,7 +1232,6 @@ public OrdinalSelector ordinalSelector() { * Retrieves a single tensor from the computation outfeed. * This operation will block indefinitely until data is available. * - * @param data type for {@code output} output * @param dtype The type of elements in the tensor. * @param shape The shape of the tensor. * @param options carries optional attribute values @@ -1302,7 +1281,6 @@ public OutfeedDequeueTupleV2 outfeedDequeueTupleV2(Operand deviceOrdinal * tensor allowing dynamic outfeed. * This operation will block indefinitely until data is available. * - * @param data type for {@code output} output * @param deviceOrdinal An int scalar tensor, representing the TPU device to use. This should be -1 when * the Op is running on a TPU device, and >= 0 when the Op is running on the CPU * device. @@ -1355,7 +1333,6 @@ public PartitionedCall partitionedCall(Iterable> args, Operand data type for {@code output} output * @param inputs A list of partitioned inputs which must have the same shape. * @param partitionDims A list of integers describing how each dimension is partitioned. Emptiness * indicates the inputs are replicated. @@ -1372,7 +1349,6 @@ public PartitionedInput partitionedInput(Iterable data type for {@code output} output * @param inputs A tensor which represents the full shape of partitioned tensors. * @param numSplits The value of the numSplits attribute * @param partitionDims A list of integers describing how each dimension is partitioned. Emptiness @@ -1454,7 +1430,6 @@ public ReplicateMetadata replicateMetadata(Long numReplicas, * *

The above computation has a replicated input of two replicas. * - * @param data type for {@code output} output * @param inputs The inputs value * @param options carries optional attribute values * @param data type for {@code TPUReplicatedInput} output and operands @@ -1476,7 +1451,6 @@ public ReplicatedInput replicatedInput(Iterable> * *

The above computation has a replicated output of two replicas. * - * @param data type for {@code outputs} output * @param input The input value * @param numReplicas The value of the numReplicas attribute * @param data type for {@code TPUReplicatedOutput} output and operands @@ -1784,8 +1758,6 @@ public ShutdownTPUSystem shutdownTPUSystem() { * values. This op is to split these values into two groups for two types, and * construct each group as one tensor to return. * - * @param data type for {@code integer_tensor} output - * @param data type for {@code float_tensor} output * @param input An XLA tuple including integer and float elements as deduplication data tuple. * @param integerType integer_tensor type. Allowed types: int32, int64, uint32, uint64. * @param floatType float_tensor type. Allowed types: half, bfloat16, float. @@ -1913,7 +1885,6 @@ public TPUReplicateMetadata tPUReplicateMetadata(Long numReplicas, * *

The above computation has a replicated input of two replicas. * - * @param data type for {@code output} output * @deprecated use {@link org.tensorflow.op.tpu.ReplicatedInput} instead * @param inputs The inputs value * @param options carries optional attribute values @@ -1937,7 +1908,6 @@ public TPUReplicatedInput tPUReplicatedInput(Iterable *

The above computation has a replicated output of two replicas. * - * @param data type for {@code outputs} output * @deprecated use {@link org.tensorflow.op.tpu.ReplicatedOutput} instead * @param input The input value * @param numReplicas The value of the numReplicas attribute diff --git a/tensorflow-core/tensorflow-core-api/src/gen/annotations/org/tensorflow/op/TrainOps.java b/tensorflow-core/tensorflow-core-api/src/gen/annotations/org/tensorflow/op/TrainOps.java index 0442b896828..3ee5b8de813 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/annotations/org/tensorflow/op/TrainOps.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/annotations/org/tensorflow/op/TrainOps.java @@ -166,7 +166,6 @@ public AccumulatorSetGlobalStep accumulatorSetGlobalStep(Operand handle * the accumulated gradients. Also automatically increments the recorded * global_step in the accumulator by 1, and resets the aggregate to 0. * - * @param data type for {@code average} output * @param handle The handle to an accumulator. * @param numRequired Number of gradients required before we return an aggregate. * @param dtype The data type of accumulated gradients. Needs to correspond to the type @@ -185,7 +184,6 @@ public AccumulatorTakeGradient accumulatorTakeGradient( * v_t <- max(beta2 * v_{t-1}, abs(g)) * variable <- variable - learning_rate / (1 - beta1^t) * m_t / (v_t + epsilon) * - * @param data type for {@code out} output * @param var Should be from a Variable(). * @param m Should be from a Variable(). * @param v Should be from a Variable(). @@ -212,7 +210,6 @@ public ApplyAdaMax applyAdaMax(Operand var, Operand m * update_accum = rho() * update_accum + (1 - rho()) * update.square(); * var -= update; * - * @param data type for {@code out} output * @param var Should be from a Variable(). * @param accum Should be from a Variable(). * @param accumUpdate Should be from a Variable(). @@ -235,7 +232,6 @@ public ApplyAdadelta applyAdadelta(Operand var, Operand< * accum += grad * grad * var -= lr * grad * (1 / sqrt(accum)) * - * @param data type for {@code out} output * @param var Should be from a Variable(). * @param accum Should be from a Variable(). * @param lr Scaling factor. Must be a scalar. @@ -252,7 +248,6 @@ public ApplyAdagrad applyAdagrad(Operand var, Operand /** * Update '*var' according to the proximal adagrad scheme. * - * @param data type for {@code out} output * @param var Should be from a Variable(). * @param gradientAccumulator Should be from a Variable(). * @param gradientSquaredAccumulator Should be from a Variable(). @@ -277,7 +272,6 @@ public ApplyAdagradDa applyAdagradDa(Operand var, * accum += grad * grad * var -= lr * grad * (1 / sqrt(accum)) * - * @param data type for {@code out} output * @param var Should be from a Variable(). * @param accum Should be from a Variable(). * @param lr Scaling factor. Must be a scalar. @@ -299,7 +293,6 @@ public ApplyAdagradV2 applyAdagradV2(Operand var, Operan * $$v_t := \beta_2 \cdot v_{t-1} + (1 - \beta_2) \cdot g^2$$ * $$\text{var} := \begin{cases} \text{var} - (m_t \beta_1 + g \cdot (1 - \beta_1))\cdot\text{lr}_t/(\sqrt{v_t} + \epsilon), &\text{if use_nesterov}\\ \text{var} - m_t \cdot \text{lr}_t /(\sqrt{v_t} + \epsilon), &\text{otherwise} \end{cases}$$ * - * @param data type for {@code out} output * @param var Should be from a Variable(). * @param m Should be from a Variable(). * @param v Should be from a Variable(). @@ -326,7 +319,6 @@ public ApplyAdam applyAdam(Operand var, Operand m, Op * update <- (alpha + sign_decay * sign(g) *sign(m)) * g * variable <- variable - lr_t * update * - * @param data type for {@code out} output * @param var Should be from a Variable(). * @param m Should be from a Variable(). * @param lr Scaling factor. Must be a scalar. @@ -361,7 +353,6 @@ public ApplyAddSign applyAddSign(Operand var, Operand * mom <- momentum * mom_{t-1} + lr * grad / sqrt(ms - mg * mg + epsilon) * var <- var - mom * - * @param data type for {@code out} output * @param var Should be from a Variable(). * @param mg Should be from a Variable(). * @param ms Should be from a Variable(). @@ -392,7 +383,6 @@ public ApplyCenteredRmsProp applyCenteredRmsProp(Operand * var = (sign(linear) * l1 - linear) / quadratic if |linear| > l1 else 0.0 * accum = accum_new * - * @param data type for {@code out} output * @param var Should be from a Variable(). * @param accum Should be from a Variable(). * @param linear Should be from a Variable(). @@ -415,7 +405,6 @@ public ApplyFtrl applyFtrl(Operand var, Operand accum /** * Update '*var' by subtracting 'alpha' * 'delta' from it. * - * @param data type for {@code out} output * @param var Should be from a Variable(). * @param alpha Scaling factor. Must be a scalar. * @param delta The change. @@ -434,7 +423,6 @@ public ApplyGradientDescent applyGradientDescent(Operand *

accum = accum * momentum + grad * var -= lr * accum * - * @param data type for {@code out} output * @param var Should be from a Variable(). * @param accum Should be from a Variable(). * @param lr Scaling factor. Must be a scalar. @@ -455,7 +443,6 @@ public ApplyMomentum applyMomentum(Operand var, Operand< * update <- exp(logbase * sign_decay * sign(g) * sign(m_t)) * g * variable <- variable - lr_t * update * - * @param data type for {@code out} output * @param var Should be from a Variable(). * @param m Should be from a Variable(). * @param lr Scaling factor. Must be a scalar. @@ -479,7 +466,6 @@ public ApplyPowerSign applyPowerSign(Operand var, Operan * prox_v = var - lr * grad * (1 / sqrt(accum)) * var = sign(prox_v)/(1+lrl2) * max{|prox_v|-lrl1,0} * - * @param data type for {@code out} output * @param var Should be from a Variable(). * @param accum Should be from a Variable(). * @param lr Scaling factor. Must be a scalar. @@ -501,7 +487,6 @@ public ApplyProximalAdagrad applyProximalAdagrad(Operand * prox_v = var - alpha * delta * var = sign(prox_v)/(1+alphal2) * max{|prox_v|-alphal1,0} * - * @param data type for {@code out} output * @param var Should be from a Variable(). * @param alpha Scaling factor. Must be a scalar. * @param l1 L1 regularization. Must be a scalar. @@ -528,7 +513,6 @@ public ApplyProximalGradientDescent applyProximalGradientDe * mom <- momentum * mom_{t-1} + lr * grad / sqrt(ms + epsilon) * var <- var - mom * - * @param data type for {@code out} output * @param var Should be from a Variable(). * @param ms Should be from a Variable(). * @param mom Should be from a Variable(). @@ -570,7 +554,6 @@ public ApplyRmsProp applyRmsProp(Operand var, Operand * about broadcasting * here . * - * @param data type for {@code output} output * @param x 2-D or higher with shape {@code [..., r_x, c_x]}. * @param y 2-D or higher with shape {@code [..., r_y, c_y]}. * @param Tout If not spcified, Tout is the same type to input type. @@ -717,7 +700,6 @@ public NegTrain negTrain(Operand wIn, Operand wOut, Operand< * op exists to prevent subtle bugs from silently returning unimplemented * gradients in some corner cases. * - * @param data type for {@code output} output * @param input any tensor. * @param options carries optional attribute values * @param data type for {@code PreventGradient} output and operands @@ -776,7 +758,6 @@ public ResourceAccumulatorSetGlobalStep resourceAccumulatorSetGlobalStep( * the accumulated gradients. Also automatically increments the recorded * global_step in the accumulator by 1, and resets the aggregate to 0. * - * @param data type for {@code average} output * @param handle The handle to an accumulator. * @param numRequired Number of gradients required before we return an aggregate. * @param dtype The data type of accumulated gradients. Needs to correspond to the type @@ -1535,7 +1516,6 @@ public Restore restore(Operand prefix, Operand tensorNames, *

The {@code shape_and_slice} input has the same format as the * elements of the {@code shapes_and_slices} input of the {@code SaveSlices} op. * - * @param data type for {@code tensor} output * @param filePattern Must have a single element. The pattern of the files from * which we read the tensor. * @param tensorName Must have a single element. The name of the tensor to be @@ -1687,7 +1667,6 @@ public SdcaShrinkL1 sdcaShrinkL1(Iterable> weights, Float l1, /** * var: Should be from a Variable(). * - * @param data type for {@code out} output * @param var The var value * @param accum Should be from a Variable(). * @param accumUpdate : Should be from a Variable(). @@ -1712,7 +1691,6 @@ public SparseApplyAdadelta sparseApplyAdadelta(Operand v * $$accum += grad * grad$$ * $$var -= lr * grad * (1 / sqrt(accum))$$ * - * @param data type for {@code out} output * @param var Should be from a Variable(). * @param accum Should be from a Variable(). * @param lr Learning rate. Must be a scalar. @@ -1732,7 +1710,6 @@ public SparseApplyAdagrad sparseApplyAdagrad(Operand var /** * Update entries in '*var' and '*accum' according to the proximal adagrad scheme. * - * @param data type for {@code out} output * @param var Should be from a Variable(). * @param gradientAccumulator Should be from a Variable(). * @param gradientSquaredAccumulator Should be from a Variable(). @@ -1769,7 +1746,6 @@ public SparseApplyAdagradDa sparseApplyAdagradDa(Operand * $$mom <- momentum * mom_{t-1} + lr * grad / sqrt(ms + epsilon)$$ * $$var <- var - mom$$ * - * @param data type for {@code out} output * @param var Should be from a Variable(). * @param mg Should be from a Variable(). * @param ms Should be from a Variable(). @@ -1802,7 +1778,6 @@ public SparseApplyCenteredRmsProp sparseApplyCenteredRmsPro * var = (sign(linear) * l1 - linear) / quadratic if |linear| > l1 else 0.0 * accum = accum_new * - * @param data type for {@code out} output * @param var Should be from a Variable(). * @param accum Should be from a Variable(). * @param linear Should be from a Variable(). @@ -1831,7 +1806,6 @@ public SparseApplyFtrl sparseApplyFtrl(Operand var, Oper *

$$accum = accum * momentum + grad$$ * $$var -= lr * accum$$ * - * @param data type for {@code out} output * @param var Should be from a Variable(). * @param accum Should be from a Variable(). * @param lr Learning rate. Must be a scalar. @@ -1856,7 +1830,6 @@ public SparseApplyMomentum sparseApplyMomentum(Operand v * $$prox_v -= lr * grad * (1 / sqrt(accum))$$ * $$var = sign(prox_v)/(1+lrl2) * max{|prox_v|-lrl1,0}$$ * - * @param data type for {@code out} output * @param var Should be from a Variable(). * @param accum Should be from a Variable(). * @param lr Learning rate. Must be a scalar. @@ -1880,7 +1853,6 @@ public SparseApplyProximalAdagrad sparseApplyProximalAdagra * $$prox_v = var - alpha * grad$$ * $$var = sign(prox_v)/(1+alphal2) * max{|prox_v|-alphal1,0}$$ * - * @param data type for {@code out} output * @param var Should be from a Variable(). * @param alpha Scaling factor. Must be a scalar. * @param l1 L1 regularization. Must be a scalar. @@ -1908,7 +1880,6 @@ public SparseApplyProximalGradientDescent sparseApplyProxim * $$mom <- momentum * mom_{t-1} + lr * grad / sqrt(ms + epsilon)$$ * $$var <- var - mom$$ * - * @param data type for {@code out} output * @param var Should be from a Variable(). * @param ms Should be from a Variable(). * @param mom Should be from a Variable(). @@ -1960,7 +1931,6 @@ public SymbolicGradient symbolicGradient(Iterable> input, * along each dimension, {@code train.TileGrad} takes in {@code multiples} and aggregates * each repeated tile of {@code input} into {@code output}. * - * @param data type for {@code output} output * @param input The input value * @param multiples The multiples value * @param data type for {@code TileGrad} output and operands diff --git a/tensorflow-core/tensorflow-core-api/src/gen/annotations/org/tensorflow/op/XlaOps.java b/tensorflow-core/tensorflow-core-api/src/gen/annotations/org/tensorflow/op/XlaOps.java index 75f9104ce4b..22a2ef5ae85 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/annotations/org/tensorflow/op/XlaOps.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/annotations/org/tensorflow/op/XlaOps.java @@ -27,9 +27,6 @@ import org.tensorflow.op.xla.SplitND; import org.tensorflow.op.xla.XlaHostCompute; import org.tensorflow.op.xla.XlaRecvFromHost; -import org.tensorflow.op.xla.XlaRecvTPUEmbeddingActivations; -import org.tensorflow.op.xla.XlaRecvTPUEmbeddingDeduplicationData; -import org.tensorflow.op.xla.XlaSendTPUEmbeddingGradients; import org.tensorflow.op.xla.XlaSendToHost; import org.tensorflow.op.xla.XlaSparseCoreAdagrad; import org.tensorflow.op.xla.XlaSparseCoreAdagradMomentum; @@ -95,18 +92,8 @@ public final class XlaOps { * * * @param resource Resource variable for concatenated input tensors across all dimensions. - * } - * in_arg { - * name: "inputs" - * description: <<END - * Input tensor slices in row-major order to merge across all dimensions. All + * @param inputs Input tensor slices in row-major order to merge across all dimensions. All * inputs must have the same shape. - * } - * out_arg { - * name: "output" - * description: <<END - * Output tensor formed from merging input slices based on num_concats defined. - * @param inputs The inputs value * @param numConcats Number of ways to merge per dimension. * @param options carries optional attribute values * @return a new instance of AssignVariableConcatND @@ -149,14 +136,8 @@ public AssignVariableConcatND assignVariableConcatND(Operand re * [8, 9, 10]] * * - * @param data type for {@code output} output * @param inputs Input tensor slices in row-major order to merge across all dimensions. All * inputs must have the same shape. - * } - * out_arg { - * name: "output" - * description: <<END - * Output tensor formed from merging input slices based on num_concats defined. * @param numConcats Number of ways to merge per dimension. * @param options carries optional attribute values * @param data type for {@code XlaConcatND} output and operands @@ -199,13 +180,7 @@ public ConcatND concatND(Iterable> inputs, List< * [0, 0]] * * - * @param data type for {@code outputs} output * @param resource Resource variable of input tensor to split across all dimensions. - * } - * out_arg { - * name: "outputs" - * description: <<END - * Output slices based on input and num_splits defined, in row-major order. * @param T The value of the T attribute * @param N The value of the N attribute * @param numSplits Number of ways to split per dimension. Shape dimensions must be evenly @@ -252,13 +227,7 @@ public ReadVariableSplitND readVariableSplitND( * [0, 0]] * * - * @param data type for {@code outputs} output * @param input Input tensor to split across all dimensions. - * } - * out_arg { - * name: "outputs" - * description: <<END - * Output slices based on input and num_splits defined, in row-major order. * @param N The value of the N attribute * @param numSplits Number of ways to split per dimension. Shape dimensions must be evenly * divisible. @@ -298,7 +267,6 @@ public XlaHostCompute xlaHostCompute(Iterable> inputs, * shape: shape for output. * key: A unique identifier for this region used to match up host transfers. * - * @param data type for {@code output} output * @param Toutput The value of the Toutput attribute * @param shape The value of the shape attribute * @param key The value of the key attribute @@ -310,76 +278,6 @@ public XlaRecvFromHost xlaRecvFromHost(Class Toutput, Sh return XlaRecvFromHost.create(scope, Toutput, shape, key); } - /** - * An op that receives embedding activations on the TPU. - * The TPU system performs the embedding lookups and aggregations. The results of - * these aggregations are visible to the Tensorflow Graph as the outputs of a - * XlaRecvTPUEmbeddingActivations Op. This op returns a list containing one - * Tensor of activations per table specified in the model. - * - * @param deduplicationData A Tensor with type=DT_VARIANT containing the deduplication - * data. The tensor is an XLA nested tuple containing N elements (where N is - * the ratio of the number of embedding to tensor cores per TPU chip). Each - * element of the nested tuple is a tuple of rank 1 tensors. Each tensor either - * contains indices (DT_UINT32) for embedding lookup on the TensorCore or - * weights (DT_FLOAT) to apply to the output of the embedding lookup operation. - * @param numTables The number of output activation tensors. If feature descriptor is - * present in the tpu embedding config, it is equal to the number of features - * otherwise equal to number of embedding tables in the model. - * @param config Serialized TPUEmbeddingConfiguration proto. - * @return a new instance of XlaRecvTPUEmbeddingActivations - */ - public XlaRecvTPUEmbeddingActivations xlaRecvTPUEmbeddingActivations( - Operand deduplicationData, Long numTables, String config) { - return XlaRecvTPUEmbeddingActivations.create(scope, deduplicationData, numTables, config); - } - - /** - * Receives deduplication data (indices and weights) from the embedding core. - * The deduplication data is a Tensor with type=DT_VARIANT. The tensor itself is an - * XLA nested tuple containing N elements (where N is the ratio of the number of - * embedding to tensor cores per TPU chip). Each element of the nested tuple is a - * tuple of rank 1 tensors. Each tensor either contains indices (DT_UINT32) for - * embedding lookup on the TensorCore or weights (DT_FLOAT) to apply to the output - * of the embedding lookup operation. - * - * @param config Serialized TPUEmbeddingConfiguration proto. - * @return a new instance of XlaRecvTPUEmbeddingDeduplicationData - */ - public XlaRecvTPUEmbeddingDeduplicationData xlaRecvTPUEmbeddingDeduplicationData(String config) { - return XlaRecvTPUEmbeddingDeduplicationData.create(scope, config); - } - - /** - * An op that performs gradient updates of embedding tables. - * The gradients argument is a TensorList having the same length and shapes as the - * return value of XlaRecvTPUEmbeddingActivations, but contains gradients of the - * model's loss with respect to the embedding activations. The embedding tables are - * updated from these gradients via the optimizer specified in the - * TPUEmbeddingConfiguration proto given to tpu.initialize_system. - * - * @param gradients A TensorList of gradients with which to update embedding tables. - * @param learningRates A TensorList of learning rates used for updating the embedding - * tables via the optimizer. The length of the TensorList must be equal to the - * number of dynamic learning rate tags specified in the - * TPUEmbeddingConfiguration proto. - * @param deduplicationData A Tensor with type=DT_VARIANT containing the deduplication - * data. The tensor is an XLA nested tuple containing N elements (where N is - * the ratio of the number of embedding to tensor cores per TPU chip). Each - * element of the nested tuple is a tuple of rank 1 tensors. Each tensor either - * contains indices (DT_UINT32) for embedding lookup on the TensorCore or - * weights (DT_FLOAT) to apply to the output of the embedding lookup operation. - * @param config Serialized TPUEmbeddingConfiguration proto. - * @param options carries optional attribute values - * @return a new instance of XlaSendTPUEmbeddingGradients - */ - public XlaSendTPUEmbeddingGradients xlaSendTPUEmbeddingGradients( - Iterable> gradients, Iterable> learningRates, - Operand deduplicationData, String config, - XlaSendTPUEmbeddingGradients.Options... options) { - return XlaSendTPUEmbeddingGradients.create(scope, gradients, learningRates, deduplicationData, config, options); - } - /** * An op to send a tensor to the host. * input: the tensor that will be sent to the host. diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/bitwise/BitwiseAnd.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/bitwise/BitwiseAnd.java index 34789dce80c..7fea36a03b3 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/bitwise/BitwiseAnd.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/bitwise/BitwiseAnd.java @@ -52,8 +52,6 @@ * res = bitwise_ops.bitwise_and(lhs, rhs) * tf.assert_equal(tf.cast(res, tf.float32), exp) # TRUE * - * - * @param data type for {@code z} output */ @OpMetadata( opType = BitwiseAnd.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/bitwise/BitwiseOr.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/bitwise/BitwiseOr.java index afa384f6e38..1e57451698b 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/bitwise/BitwiseOr.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/bitwise/BitwiseOr.java @@ -52,8 +52,6 @@ * res = bitwise_ops.bitwise_or(lhs, rhs) * tf.assert_equal(tf.cast(res, tf.float32), exp) # TRUE * - * - * @param data type for {@code z} output */ @OpMetadata( opType = BitwiseOr.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/bitwise/BitwiseXor.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/bitwise/BitwiseXor.java index dc26dc145aa..52953422482 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/bitwise/BitwiseXor.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/bitwise/BitwiseXor.java @@ -52,8 +52,6 @@ * res = bitwise_ops.bitwise_xor(lhs, rhs) * tf.assert_equal(tf.cast(res, tf.float32), exp) # TRUE * - * - * @param data type for {@code z} output */ @OpMetadata( opType = BitwiseXor.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/bitwise/Invert.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/bitwise/Invert.java index a2d9a985bae..8dcb5a72de7 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/bitwise/Invert.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/bitwise/Invert.java @@ -73,8 +73,6 @@ * expected = tf.constant([dtype.max - x for x in inputs], dtype=tf.float32) * tf.assert_equal(tf.cast(inverted, tf.float32), tf.cast(expected, tf.float32)) * - * - * @param data type for {@code y} output */ @OpMetadata( opType = Invert.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/bitwise/LeftShift.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/bitwise/LeftShift.java index 5874dc12979..ccf41c473f8 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/bitwise/LeftShift.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/bitwise/LeftShift.java @@ -63,8 +63,6 @@ * bitwise_ops.left_shift(lhs, rhs) * # <tf.Tensor: shape=(4,), dtype=int8, numpy=array([ -2, 64, 101, 32], dtype=int8)> * - * - * @param data type for {@code z} output */ @OpMetadata( opType = LeftShift.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/bitwise/RightShift.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/bitwise/RightShift.java index 22c95c81136..6c1407b9d19 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/bitwise/RightShift.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/bitwise/RightShift.java @@ -65,8 +65,6 @@ * bitwise_ops.right_shift(lhs, rhs) * # <tf.Tensor: shape=(4,), dtype=int8, numpy=array([ -2, 64, 101, 32], dtype=int8)> * - * - * @param data type for {@code z} output */ @OpMetadata( opType = RightShift.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/collective/CollectiveAllToAll.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/collective/CollectiveAllToAll.java index 99ccff79289..9c513486b9b 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/collective/CollectiveAllToAll.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/collective/CollectiveAllToAll.java @@ -37,8 +37,6 @@ /** * Mutually exchanges multiple tensors of identical type and shape. - * - * @param data type for {@code data} output */ @OpMetadata( opType = CollectiveAllToAll.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/collective/CollectiveBcastRecv.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/collective/CollectiveBcastRecv.java index 332b5dcf9ab..a66995e4d4e 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/collective/CollectiveBcastRecv.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/collective/CollectiveBcastRecv.java @@ -38,8 +38,6 @@ /** * Receives a tensor value broadcast from another device. - * - * @param data type for {@code data} output */ @OpMetadata( opType = CollectiveBcastRecv.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/collective/CollectiveBcastSend.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/collective/CollectiveBcastSend.java index ee495b56951..df7a315413f 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/collective/CollectiveBcastSend.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/collective/CollectiveBcastSend.java @@ -36,8 +36,6 @@ /** * Broadcasts a tensor value to one or more other devices. - * - * @param data type for {@code data} output */ @OpMetadata( opType = CollectiveBcastSend.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/collective/CollectiveGather.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/collective/CollectiveGather.java index d3997e8743f..57a2b134ff6 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/collective/CollectiveGather.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/collective/CollectiveGather.java @@ -41,8 +41,6 @@ * {@code is_stateless} means each op does not need control dependencies to other * collective ops. In this case, keys that are unique at runtime * (e.g. {@code instance_key}) should be used to distinguish collective groups. - * - * @param data type for {@code data} output */ @OpMetadata( opType = CollectiveGather.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/collective/CollectivePermute.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/collective/CollectivePermute.java index 9fd029facf3..380a949a664 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/collective/CollectivePermute.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/collective/CollectivePermute.java @@ -40,8 +40,6 @@ *

For example, suppose there are 4 TPU instances: {@code [A, B, C, D]}. Passing * source_target_pairs={@code [[0,1],[1,2],[2,3],[3,0]]} gets the outputs: * {@code [D, A, B, C]}. - * - * @param data type for {@code output} output */ @OpMetadata( opType = CollectivePermute.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/collective/CollectiveReduce.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/collective/CollectiveReduce.java index 7eab3bb0f17..8f6c26778e1 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/collective/CollectiveReduce.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/collective/CollectiveReduce.java @@ -37,8 +37,6 @@ /** * Mutually reduces multiple tensors of identical type and shape. - * - * @param data type for {@code data} output */ @OpMetadata( opType = CollectiveReduce.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/collective/CollectiveReduceScatter.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/collective/CollectiveReduceScatter.java index 5ab06edf273..8b89dbaf183 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/collective/CollectiveReduceScatter.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/collective/CollectiveReduceScatter.java @@ -41,8 +41,6 @@ * {@code is_stateless} means each op does not need control dependencies to other * collective ops. In this case, keys that are unique at runtime * (e.g. {@code instance_key}) should be used to distinguish collective groups. - * - * @param data type for {@code data} output */ @OpMetadata( opType = CollectiveReduceScatter.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/ApproxTopK.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/ApproxTopK.java index 1daca9f077e..48f4f94315b 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/ApproxTopK.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/ApproxTopK.java @@ -38,8 +38,6 @@ * Returns min/max k values and their indices of the input operand in an approximate manner. * See https://arxiv.org/abs/2206.14286 for the algorithm details. * This op is only optimized on TPU currently. - * - * @param data type for {@code values} output */ @OpMetadata( opType = ApproxTopK.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/Assign.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/Assign.java index a8001c6103a..e49f3eafacc 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/Assign.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/Assign.java @@ -37,8 +37,6 @@ * Update 'ref' by assigning 'value' to it. * This operation outputs "ref" after the assignment is done. * This makes it easier to chain operations that need to use the reset value. - * - * @param data type for {@code output_ref} output */ @OpMetadata( opType = Assign.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/AssignAdd.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/AssignAdd.java index 2b6f78046ca..848231d569a 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/AssignAdd.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/AssignAdd.java @@ -37,8 +37,6 @@ * Update 'ref' by adding 'value' to it. * This operation outputs "ref" after the update is done. * This makes it easier to chain operations that need to use the reset value. - * - * @param data type for {@code output_ref} output */ @OpMetadata( opType = AssignAdd.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/AssignSub.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/AssignSub.java index 162fc069e92..cc96d634945 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/AssignSub.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/AssignSub.java @@ -37,8 +37,6 @@ * Update 'ref' by subtracting 'value' from it. * This operation outputs "ref" after the update is done. * This makes it easier to chain operations that need to use the reset value. - * - * @param data type for {@code output_ref} output */ @OpMetadata( opType = AssignSub.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/BatchFunction.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/BatchFunction.java index d544fdeca5a..577f213f47d 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/BatchFunction.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/BatchFunction.java @@ -163,6 +163,12 @@ public static BatchFunction create(Scope scope, Iterable> inTensors, if (opts.lowPriorityMaxEnqueuedBatches != null) { opBuilder.setAttr("low_priority_max_enqueued_batches", opts.lowPriorityMaxEnqueuedBatches); } + if (opts.mixedPriorityPolicy != null) { + opBuilder.setAttr("mixed_priority_policy", opts.mixedPriorityPolicy); + } + if (opts.batchPaddingPolicy != null) { + opBuilder.setAttr("batch_padding_policy", opts.batchPaddingPolicy); + } if (opts.enableLargeBatchSplitting != null) { opBuilder.setAttr("enable_large_batch_splitting", opts.enableLargeBatchSplitting); } @@ -291,6 +297,26 @@ public static Options lowPriorityMaxEnqueuedBatches(Long lowPriorityMaxEnqueuedB return new Options().lowPriorityMaxEnqueuedBatches(lowPriorityMaxEnqueuedBatches); } + /** + * Sets the mixedPriorityPolicy option. + * + * @param mixedPriorityPolicy the mixedPriorityPolicy option + * @return this Options instance. + */ + public static Options mixedPriorityPolicy(String mixedPriorityPolicy) { + return new Options().mixedPriorityPolicy(mixedPriorityPolicy); + } + + /** + * Sets the batchPaddingPolicy option. + * + * @param batchPaddingPolicy the batchPaddingPolicy option + * @return this Options instance. + */ + public static Options batchPaddingPolicy(String batchPaddingPolicy) { + return new Options().batchPaddingPolicy(batchPaddingPolicy); + } + /** * Sets the enableLargeBatchSplitting option. * @@ -339,6 +365,10 @@ public static class Options { private Long lowPriorityMaxEnqueuedBatches; + private String mixedPriorityPolicy; + + private String batchPaddingPolicy; + private Boolean enableLargeBatchSplitting; private Options() { @@ -475,6 +505,28 @@ public Options lowPriorityMaxEnqueuedBatches(Long lowPriorityMaxEnqueuedBatches) return this; } + /** + * Sets the mixedPriorityPolicy option. + * + * @param mixedPriorityPolicy the mixedPriorityPolicy option + * @return this Options instance. + */ + public Options mixedPriorityPolicy(String mixedPriorityPolicy) { + this.mixedPriorityPolicy = mixedPriorityPolicy; + return this; + } + + /** + * Sets the batchPaddingPolicy option. + * + * @param batchPaddingPolicy the batchPaddingPolicy option + * @return this Options instance. + */ + public Options batchPaddingPolicy(String batchPaddingPolicy) { + this.batchPaddingPolicy = batchPaddingPolicy; + return this; + } + /** * Sets the enableLargeBatchSplitting option. * @@ -571,6 +623,16 @@ public static class Inputs extends RawOpInputs { */ public final long lowPriorityMaxEnqueuedBatches; + /** + * The mixedPriorityPolicy attribute + */ + public final String mixedPriorityPolicy; + + /** + * The batchPaddingPolicy attribute + */ + public final String batchPaddingPolicy; + /** * the types of tensors to be batched. */ @@ -593,7 +655,7 @@ public static class Inputs extends RawOpInputs { public final boolean enableLargeBatchSplitting; public Inputs(GraphOperation op) { - super(new BatchFunction(op), op, Arrays.asList("num_batch_threads", "max_batch_size", "batch_timeout_micros", "max_enqueued_batches", "allowed_batch_sizes", "container", "shared_name", "batching_queue", "low_priority_max_batch_size", "low_priority_batch_timeout_micros", "low_priority_allowed_batch_sizes", "low_priority_max_enqueued_batches", "Tin", "Tcaptured", "Tout", "enable_large_batch_splitting")); + super(new BatchFunction(op), op, Arrays.asList("num_batch_threads", "max_batch_size", "batch_timeout_micros", "max_enqueued_batches", "allowed_batch_sizes", "container", "shared_name", "batching_queue", "low_priority_max_batch_size", "low_priority_batch_timeout_micros", "low_priority_allowed_batch_sizes", "low_priority_max_enqueued_batches", "mixed_priority_policy", "batch_padding_policy", "Tin", "Tcaptured", "Tout", "enable_large_batch_splitting")); int inputIndex = 0; int inTensorsLength = op.inputListLength("in_tensors"); inTensors = Arrays.asList((Operand[]) op.inputList(inputIndex, inTensorsLength)); @@ -613,6 +675,8 @@ public Inputs(GraphOperation op) { lowPriorityBatchTimeoutMicros = op.attributes().getAttrInt("low_priority_batch_timeout_micros"); lowPriorityAllowedBatchSizes = op.attributes().getAttrIntList("low_priority_allowed_batch_sizes"); lowPriorityMaxEnqueuedBatches = op.attributes().getAttrInt("low_priority_max_enqueued_batches"); + mixedPriorityPolicy = op.attributes().getAttrString("mixed_priority_policy"); + batchPaddingPolicy = op.attributes().getAttrString("batch_padding_policy"); Tin = op.attributes().getAttrTypeList("Tin"); Tcaptured = op.attributes().getAttrTypeList("Tcaptured"); Tout = op.attributes().getAttrTypeList("Tout"); diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/BatchToSpace.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/BatchToSpace.java index 889bd521e0d..09fa1d49bcb 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/BatchToSpace.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/BatchToSpace.java @@ -42,8 +42,6 @@ * this op outputs a copy of the input tensor where values from the {@code batch} * dimension are moved in spatial blocks to the {@code height} and {@code width} dimensions, * followed by cropping along the {@code height} and {@code width} dimensions. - * - * @param data type for {@code output} output */ @OpMetadata( opType = BatchToSpace.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/BatchToSpaceNd.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/BatchToSpaceNd.java index c7cf592d517..65a98188342 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/BatchToSpaceNd.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/BatchToSpaceNd.java @@ -42,8 +42,6 @@ * the input. The spatial dimensions of this intermediate result are then * optionally cropped according to {@code crops} to produce the output. This is the * reverse of SpaceToBatch. See below for a precise description. - * - * @param data type for {@code output} output */ @OpMetadata( opType = BatchToSpaceNd.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/Bitcast.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/Bitcast.java index c1bd2421b15..82a2a99d295 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/Bitcast.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/Bitcast.java @@ -96,8 +96,6 @@ * endian orderings will give different results. A copy from input buffer to output * buffer is made on BE machines when types are of different sizes in order to get * the same casting results as on LE machines. - * - * @param data type for {@code output} output */ @OpMetadata( opType = Bitcast.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/BroadcastDynamicShape.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/BroadcastDynamicShape.java index 96cfa009842..165e7e12b9a 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/BroadcastDynamicShape.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/BroadcastDynamicShape.java @@ -37,8 +37,6 @@ * Return the shape of s0 op s1 with broadcast. * Given {@code s0} and {@code s1}, tensors that represent shapes, compute {@code r0}, the * broadcasted shape. {@code s0}, {@code s1} and {@code r0} are all integer vectors. - * - * @param data type for {@code r0} output */ @OpMetadata( opType = BroadcastDynamicShape.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/BroadcastGradientArgs.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/BroadcastGradientArgs.java index fe9cf0e7039..f29d66c8de6 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/BroadcastGradientArgs.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/BroadcastGradientArgs.java @@ -36,8 +36,6 @@ /** * Return the reduction indices for computing gradients of s0 op s1 with broadcast. * This is typically used by gradient computations for a broadcasting operation. - * - * @param data type for {@code r0} output */ @OpMetadata( opType = BroadcastGradientArgs.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/BroadcastTo.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/BroadcastTo.java index d9ada9ae323..f27247cd37a 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/BroadcastTo.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/BroadcastTo.java @@ -72,8 +72,6 @@ * The newly-created tensor takes the full memory of the broadcasted * shape. (In a graph context, {@code broadcast_to} might be fused to * subsequent operation and then be optimized away, however.) - * - * @param data type for {@code output} output */ @OpMetadata( opType = BroadcastTo.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/CheckPinned.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/CheckPinned.java new file mode 100644 index 00000000000..2708bcad2bf --- /dev/null +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/CheckPinned.java @@ -0,0 +1,115 @@ +/* Copyright 2018-2023 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +=======================================================================*/ + +// This class has been generated, DO NOT EDIT! + +package org.tensorflow.op.core; + +import java.util.Arrays; +import org.tensorflow.GraphOperation; +import org.tensorflow.Operand; +import org.tensorflow.Operation; +import org.tensorflow.OperationBuilder; +import org.tensorflow.Output; +import org.tensorflow.op.RawOp; +import org.tensorflow.op.RawOpInputs; +import org.tensorflow.op.Scope; +import org.tensorflow.op.annotation.Endpoint; +import org.tensorflow.op.annotation.OpInputsMetadata; +import org.tensorflow.op.annotation.OpMetadata; +import org.tensorflow.op.annotation.Operator; +import org.tensorflow.proto.DataType; +import org.tensorflow.types.family.TType; + +/** + * Checks whether a tensor is located in host memory pinned for GPU. + * When run: + *

    + *
  • Reports an {@code InvalidArgument} error if {@code tensor} is not in pinned memory.
    • + *
  • Reports a {@code FailedPrecondition} error if not built with CUDA.
    • + *
+ */ +@OpMetadata( + opType = CheckPinned.OP_NAME, + inputsClass = CheckPinned.Inputs.class +) +@Operator +public final class CheckPinned extends RawOp implements Operand { + /** + * The name of this op, as known by TensorFlow core engine + */ + public static final String OP_NAME = "CheckPinned"; + + private Output output; + + public CheckPinned(Operation operation) { + super(operation, OP_NAME); + int outputIdx = 0; + output = operation.output(outputIdx++); + } + + /** + * Factory method to create a class wrapping a new CheckPinned operation. + * + * @param scope current scope + * @param tensor The tensor value + * @param data type for {@code CheckPinned} output and operands + * @return a new instance of CheckPinned + */ + @Endpoint( + describeByClass = true + ) + public static CheckPinned create(Scope scope, Operand tensor) { + OperationBuilder opBuilder = scope.opBuilder(OP_NAME, "CheckPinned"); + opBuilder.addInput(tensor.asOutput()); + return new CheckPinned<>(opBuilder.build()); + } + + /** + * Gets output. + * + * @return output. + */ + public Output output() { + return output; + } + + @Override + public Output asOutput() { + return output; + } + + @OpInputsMetadata( + outputsClass = CheckPinned.class + ) + public static class Inputs extends RawOpInputs> { + /** + * The tensor input + */ + public final Operand tensor; + + /** + * The T attribute + */ + public final DataType T; + + public Inputs(GraphOperation op) { + super(new CheckPinned<>(op), op, Arrays.asList("T")); + int inputIndex = 0; + tensor = (Operand) op.input(inputIndex++); + T = op.attributes().getAttrType("T"); + } + } +} diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/ClipByValue.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/ClipByValue.java index 4477b0d4924..2ae7185a7e5 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/ClipByValue.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/ClipByValue.java @@ -39,8 +39,6 @@ * shape as {@code t} with its values clipped to {@code clip_value_min} and {@code clip_value_max}. * Any values less than {@code clip_value_min} are set to {@code clip_value_min}. Any values * greater than {@code clip_value_max} are set to {@code clip_value_max}. - * - * @param data type for {@code output} output */ @OpMetadata( opType = ClipByValue.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/Concat.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/Concat.java index 894b3a574be..cf3b735f4be 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/Concat.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/Concat.java @@ -37,8 +37,6 @@ /** * Concatenates tensors along one dimension. - * - * @param data type for {@code output} output */ @OpMetadata( opType = Concat.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/ConcatOffset.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/ConcatOffset.java index df14b30a11b..9b9a8d813c3 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/ConcatOffset.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/ConcatOffset.java @@ -47,14 +47,12 @@ * y = [2, 3, 7] * z = [2, 9, 7] * offsets = concat_offset(1, [x, y, z]) - * [list(off.numpy()) for off in offsets] + * [[a.item() for a in list(off.numpy())] for off in offsets] * [[0, 0, 0], [0, 2, 0], [0, 5, 0]] * * * *

This is typically used by gradient computations for a concat operation. - * - * @param data type for {@code offset} output */ @OpMetadata( opType = ConcatOffset.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/Copy.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/Copy.java index 9b55fac9069..a04de48877b 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/Copy.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/Copy.java @@ -42,8 +42,6 @@ * deep-copying. See the documentation of Debug* ops for more details. *

Unlike the CopyHost Op, this op does not have HostMemory constraint on its * input or output. - * - * @param data type for {@code output} output */ @OpMetadata( opType = Copy.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/CopyHost.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/CopyHost.java index 59af18c8b33..055c9d878bf 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/CopyHost.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/CopyHost.java @@ -40,8 +40,6 @@ * gRPC gating status, the output will simply forward the input tensor without * deep-copying. See the documentation of Debug* ops for more details. *

Unlike the Copy Op, this op has HostMemory constraint on its input or output. - * - * @param data type for {@code output} output */ @OpMetadata( opType = CopyHost.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/CopyToMesh.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/CopyToMesh.java index f83d6c6ad61..166d4613d54 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/CopyToMesh.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/CopyToMesh.java @@ -35,8 +35,6 @@ /** * The CopyToMesh operation - * - * @param data type for {@code output} output */ @OpMetadata( opType = CopyToMesh.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/CopyToMeshGrad.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/CopyToMeshGrad.java index fa3467cd849..095d5b5d7ce 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/CopyToMeshGrad.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/CopyToMeshGrad.java @@ -35,8 +35,6 @@ /** * The CopyToMeshGrad operation - * - * @param data type for {@code output} output */ @OpMetadata( opType = CopyToMeshGrad.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/CountUpTo.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/CountUpTo.java index 7a81a4419e6..0f404fa1419 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/CountUpTo.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/CountUpTo.java @@ -35,8 +35,6 @@ /** * Increments 'ref' until it reaches 'limit'. - * - * @param data type for {@code output} output */ @OpMetadata( opType = CountUpTo.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/DeepCopy.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/DeepCopy.java index ca15dbb9a55..f0b9b3927a8 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/DeepCopy.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/DeepCopy.java @@ -35,8 +35,6 @@ /** * Makes a copy of {@code x}. - * - * @param data type for {@code y} output */ @OpMetadata( opType = DeepCopy.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/DestroyTemporaryVariable.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/DestroyTemporaryVariable.java index cc8f2bafb2f..876a1e46ee5 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/DestroyTemporaryVariable.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/DestroyTemporaryVariable.java @@ -41,8 +41,6 @@ * This is typically achieved by chaining the ref through each assign op, or by * using control dependencies. *

Outputs the final value of the tensor pointed to by 'ref'. - * - * @param data type for {@code value} output */ @OpMetadata( opType = DestroyTemporaryVariable.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/DynamicPartition.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/DynamicPartition.java index b851e0cccdf..d7d7bf7c328 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/DynamicPartition.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/DynamicPartition.java @@ -78,8 +78,6 @@ * * * - * - * @param data type for {@code outputs} output */ @OpMetadata( opType = DynamicPartition.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/DynamicStitch.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/DynamicStitch.java index 9aba2968627..d160ab8255c 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/DynamicStitch.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/DynamicStitch.java @@ -90,8 +90,6 @@ *

* *
- * - * @param data type for {@code merged} output */ @OpMetadata( opType = DynamicStitch.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/Empty.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/Empty.java index 02c76780ba2..6f7d74d94e5 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/Empty.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/Empty.java @@ -38,8 +38,6 @@ /** * Creates a tensor with the given shape. *

This operation creates a tensor of {@code shape} and {@code dtype}. - * - * @param data type for {@code output} output */ @OpMetadata( opType = Empty.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/EnsureShape.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/EnsureShape.java index 131285dc0e6..bbada3714ac 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/EnsureShape.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/EnsureShape.java @@ -38,8 +38,6 @@ * Ensures that the tensor's shape matches the expected shape. * Raises an error if the input tensor's shape does not match the specified shape. * Returns the input tensor otherwise. - * - * @param data type for {@code output} output */ @OpMetadata( opType = EnsureShape.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/Enter.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/Enter.java index baed3b18053..309e5700eb1 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/Enter.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/Enter.java @@ -40,8 +40,6 @@ * {@code is_constant} is true, {@code output} is a constant in the child frame; otherwise * it may be changed in the child frame. At most {@code parallel_iterations} iterations * are run in parallel in the child frame. - * - * @param data type for {@code output} output */ @OpMetadata( opType = Enter.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/Exit.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/Exit.java index c1535016b59..8dea6a66fe6 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/Exit.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/Exit.java @@ -36,8 +36,6 @@ /** * Exits the current frame to its parent frame. * Exit makes its input {@code data} available to the parent frame. - * - * @param data type for {@code output} output */ @OpMetadata( opType = Exit.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/ExpandDims.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/ExpandDims.java index bf17427d228..0f0e030b71d 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/ExpandDims.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/ExpandDims.java @@ -59,8 +59,6 @@ *

{@code -1-input.dims() <= dim <= input.dims()} *

This operation is related to {@code squeeze()}, which removes dimensions of * size 1. - * - * @param data type for {@code output} output */ @OpMetadata( opType = ExpandDims.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/ExtractVolumePatches.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/ExtractVolumePatches.java index 12afb6060b3..350c416e235 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/ExtractVolumePatches.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/ExtractVolumePatches.java @@ -36,8 +36,6 @@ /** * Extract {@code patches} from {@code input} and put them in the {@code "depth"} output dimension. 3D extension of {@code extract_image_patches}. - * - * @param data type for {@code patches} output */ @OpMetadata( opType = ExtractVolumePatches.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/FakeParam.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/FakeParam.java index ee07de5268d..79e63958dda 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/FakeParam.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/FakeParam.java @@ -40,8 +40,6 @@ * valid output when run, so must either be removed (e.g. replaced with a * function input) or guaranteed not to be used (e.g. if mirroring an * intermediate output needed for the gradient computation of the other branch). - * - * @param data type for {@code output} output */ @OpMetadata( opType = FakeParam.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/Fill.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/Fill.java index 5ba5931795e..8634981f57c 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/Fill.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/Fill.java @@ -53,8 +53,6 @@ *

  • Because {@code tf.fill} evaluates at graph runtime, it supports dynamic shapes * based on other runtime Tensors, unlike {@code tf.constant}.
    • * - * - * @param data type for {@code output} output */ @OpMetadata( opType = Fill.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/Gather.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/Gather.java index 1b1e3f888ee..43e09807b66 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/Gather.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/Gather.java @@ -57,9 +57,10 @@ *

    Note that on CPU, if an out of bound index is found, an error is returned. * On GPU, if an out of bound index is found, a 0 is stored in the * corresponding output value. + *

    Note that on TPU, if any dimension of {@code params} is of size 0 then the output will + * be the expected shape filled with zeros. On CPU and GPU an error will be + * returned. *

    See also {@code tf.batch_gather} and {@code tf.gather_nd}. - * - * @param data type for {@code output} output */ @OpMetadata( opType = Gather.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/GatherNd.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/GatherNd.java index b1a05118129..755bf4e7905 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/GatherNd.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/GatherNd.java @@ -57,9 +57,17 @@ * 

      * indices.shape[:-1] + params.shape[indices.shape[-1]:]
  *
    - *

    Note that on CPU, if an out of bound index is found, an error is returned. - * On GPU, if an out of bound index is found, a 0 is stored in the - * corresponding output value. + *

    If {@code indices} contains any out-of-bound indices, depending on + * {@code bad_indices_policy}, the op will either return an error or ignore the + * out-of-bound indices. {@code bad_indices_policy} can be one of the following values: + *

      + *
    1. "" or "DEFAULT": raises on CPU and ignore on GPU. This is because + * historically on CPU and GPU we handle errors in different ways, and for + * backward compatibility we keep the default behavior.
      2. + *
    2. "ERROR": raises error; GPU does not support this value.
      3. + *
    3. "IGNORE": ignore error and set the corresponding output to 0; + * supported on both CPU and GPU.
      4. + *
    *

    Some examples below. *

    Simple indexing into a matrix: * 

    @@ -125,8 +133,6 @@
  *     output = [['b0', 'b1'], ['d0', 'c1']]
  *
    *

    See also {@code tf.gather} and {@code tf.batch_gather}. - * - * @param data type for {@code output} output */ @OpMetadata( opType = GatherNd.OP_NAME, @@ -153,6 +159,7 @@ public GatherNd(Operation operation) { * @param scope current scope * @param params The tensor from which to gather values. * @param indices Index tensor. + * @param options carries optional attribute values * @param data type for {@code GatherNd} output and operands * @return a new instance of GatherNd */ @@ -160,13 +167,30 @@ public GatherNd(Operation operation) { describeByClass = true ) public static GatherNd create(Scope scope, Operand params, - Operand indices) { + Operand indices, Options... options) { OperationBuilder opBuilder = scope.opBuilder(OP_NAME, "GatherNd"); opBuilder.addInput(params.asOutput()); opBuilder.addInput(indices.asOutput()); + if (options != null) { + for (Options opts : options) { + if (opts.badIndicesPolicy != null) { + opBuilder.setAttr("bad_indices_policy", opts.badIndicesPolicy); + } + } + } return new GatherNd<>(opBuilder.build()); } + /** + * Sets the badIndicesPolicy option. + * + * @param badIndicesPolicy the badIndicesPolicy option + * @return this Options instance. + */ + public static Options badIndicesPolicy(String badIndicesPolicy) { + return new Options().badIndicesPolicy(badIndicesPolicy); + } + /** * Gets output. * Values from {@code params} gathered from indices given by {@code indices}, with @@ -182,6 +206,27 @@ public Output asOutput() { return output; } + /** + * Optional attributes for {@link org.tensorflow.op.core.GatherNd} + */ + public static class Options { + private String badIndicesPolicy; + + private Options() { + } + + /** + * Sets the badIndicesPolicy option. + * + * @param badIndicesPolicy the badIndicesPolicy option + * @return this Options instance. + */ + public Options badIndicesPolicy(String badIndicesPolicy) { + this.badIndicesPolicy = badIndicesPolicy; + return this; + } + } + @OpInputsMetadata( outputsClass = GatherNd.class ) @@ -206,13 +251,19 @@ public static class Inputs extends RawOpInputs> { */ public final DataType Tindices; + /** + * The badIndicesPolicy attribute + */ + public final String badIndicesPolicy; + public Inputs(GraphOperation op) { - super(new GatherNd<>(op), op, Arrays.asList("Tparams", "Tindices")); + super(new GatherNd<>(op), op, Arrays.asList("Tparams", "Tindices", "bad_indices_policy")); int inputIndex = 0; params = (Operand) op.input(inputIndex++); indices = (Operand) op.input(inputIndex++); Tparams = op.attributes().getAttrType("Tparams"); Tindices = op.attributes().getAttrType("Tindices"); + badIndicesPolicy = op.attributes().getAttrString("bad_indices_policy"); } } } diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/GetSessionTensor.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/GetSessionTensor.java index a2445004e6d..0cccfb42045 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/GetSessionTensor.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/GetSessionTensor.java @@ -37,8 +37,6 @@ /** * Get the value of the tensor specified by its handle. - * - * @param data type for {@code value} output */ @OpMetadata( opType = GetSessionTensor.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/GuaranteeConst.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/GuaranteeConst.java index 8839f77471f..c4235de8ff2 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/GuaranteeConst.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/GuaranteeConst.java @@ -39,8 +39,6 @@ *

    Only accepts value typed tensors as inputs and rejects resource variable handles * as input. *

    Returns the input tensor without modification. - * - * @param data type for {@code output} output */ @OpMetadata( opType = GuaranteeConst.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/HistogramFixedWidth.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/HistogramFixedWidth.java index 0846ac056c0..782cfc69f05 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/HistogramFixedWidth.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/HistogramFixedWidth.java @@ -51,8 +51,6 @@ * variables.global_variables_initializer().run() * sess.run(hist) => [2, 1, 1, 0, 2] * - * - * @param data type for {@code out} output */ @OpMetadata( opType = HistogramFixedWidth.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/HostConst.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/HostConst.java index 8aa7bf2e13c..82f5ef8f295 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/HostConst.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/HostConst.java @@ -37,8 +37,6 @@ /** * Returns a constant tensor on the host. Only for writing C++ tests. - * - * @param data type for {@code output} output */ @OpMetadata( opType = HostConst.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/Identity.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/Identity.java index 12c84344373..d0729ab93da 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/Identity.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/Identity.java @@ -35,8 +35,6 @@ /** * Return a tensor with the same shape and contents as the input tensor or value. - * - * @param data type for {@code output} output */ @OpMetadata( opType = Identity.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/ImmutableConst.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/ImmutableConst.java index 47cbe749ee9..12d647268ba 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/ImmutableConst.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/ImmutableConst.java @@ -38,8 +38,6 @@ /** * Returns immutable tensor from memory region. * The current implementation memmaps the tensor from a file. - * - * @param data type for {@code tensor} output */ @OpMetadata( opType = ImmutableConst.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/InplaceAdd.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/InplaceAdd.java index c42388fc55c..78f37851589 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/InplaceAdd.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/InplaceAdd.java @@ -39,8 +39,6 @@ * 

      * Computes y = x; y[i, :] += v; return y.
  *
    - * - * @param data type for {@code y} output */ @OpMetadata( opType = InplaceAdd.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/InplaceSub.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/InplaceSub.java index a39bf6d741b..31d0287aab2 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/InplaceSub.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/InplaceSub.java @@ -40,8 +40,6 @@ * * Computes y = x; y[i, :] -= v; return y. * - * - * @param data type for {@code y} output */ @OpMetadata( opType = InplaceSub.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/InplaceUpdate.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/InplaceUpdate.java index 8aecb6edf8c..d34e0f15011 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/InplaceUpdate.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/InplaceUpdate.java @@ -39,8 +39,6 @@ * Computes {@code x[i, :] = v; return x}. *

    Originally this function is mutative however for compilation we make this * operation create / operate on a copy of {@code x}. - * - * @param data type for {@code y} output */ @OpMetadata( opType = InplaceUpdate.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/LinSpace.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/LinSpace.java index 3473ddf487e..317eb054e29 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/LinSpace.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/LinSpace.java @@ -42,8 +42,6 @@ * 

      * tf.linspace(10.0, 12.0, 3, name="linspace") => [ 10.0  11.0  12.0]
  *
    - * - * @param data type for {@code output} output */ @OpMetadata( opType = LinSpace.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/LookupTableExport.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/LookupTableExport.java index 7406671423c..7546b26f8f4 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/LookupTableExport.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/LookupTableExport.java @@ -36,10 +36,6 @@ /** * Outputs all keys and values in the table. - * - * @param data type for {@code keys} output - * - * @param data type for {@code values} output */ @OpMetadata( opType = LookupTableExport.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/LookupTableFind.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/LookupTableFind.java index b097f2ee81d..1155c94662f 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/LookupTableFind.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/LookupTableFind.java @@ -39,8 +39,6 @@ * The output {@code values} is of the type of the table values. *

    The scalar {@code default_value} is the value output for keys not present in the * table. It must also be of the same type as the table values. - * - * @param data type for {@code values} output */ @OpMetadata( opType = LookupTableFind.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/LowerBound.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/LowerBound.java index 8cf633e2d7f..2a4b761a8fd 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/LowerBound.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/LowerBound.java @@ -51,8 +51,6 @@ *

    result = LowerBound(sorted_sequence, values) *

    result == [[1, 2, 2], * [0, 1, 5]] - * - * @param data type for {@code output} output */ @OpMetadata( opType = LowerBound.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/Max.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/Max.java index fb03ee5c942..04c4f1481d3 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/Max.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/Max.java @@ -39,8 +39,6 @@ * {@code keep_dims} is true, the rank of the tensor is reduced by 1 for each entry in * {@code axis}. If {@code keep_dims} is true, the reduced dimensions are * retained with length 1. - * - * @param data type for {@code output} output */ @OpMetadata( opType = Max.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/Merge.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/Merge.java index 7e4c77434b9..f5a189c9c58 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/Merge.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/Merge.java @@ -41,8 +41,6 @@ * It is usually combined with {@code Switch} to implement branching. *

    {@code Merge} forwards the first tensor to become available to {@code output}, and sets * {@code value_index} to its index in {@code inputs}. - * - * @param data type for {@code output} output */ @OpMetadata( opType = Merge.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/Min.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/Min.java index f3db8fedac0..89ac31b5854 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/Min.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/Min.java @@ -39,8 +39,6 @@ * {@code keep_dims} is true, the rank of the tensor is reduced by 1 for each entry in * {@code axis}. If {@code keep_dims} is true, the reduced dimensions are * retained with length 1. - * - * @param data type for {@code output} output */ @OpMetadata( opType = Min.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/MirrorPad.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/MirrorPad.java index e63036ec117..751bec8fd66 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/MirrorPad.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/MirrorPad.java @@ -57,8 +57,6 @@ * [5, 4, 4, 5, 6, 6, 5] * [5, 4, 4, 5, 6, 6, 5]] * - * - * @param data type for {@code output} output */ @OpMetadata( opType = MirrorPad.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/MirrorPadGrad.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/MirrorPadGrad.java index 64235a34e0a..d1286e4bd89 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/MirrorPadGrad.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/MirrorPadGrad.java @@ -50,8 +50,6 @@ * pad(t, paddings) ==> [[ 1, 5] * [11, 28]] * - * - * @param data type for {@code output} output */ @OpMetadata( opType = MirrorPadGrad.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/NcclAllReduce.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/NcclAllReduce.java index d49045a1bad..5e8f5709b65 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/NcclAllReduce.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/NcclAllReduce.java @@ -46,8 +46,6 @@ * num_devices: The number of devices participating in this reduction. * shared_name: Identifier that shared between ops of the same reduction. * - * @param data type for {@code data} output - * * @deprecated use {@link org.tensorflow.op.distribute.NcclAllReduce} instead */ @OpMetadata( diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/NcclBroadcast.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/NcclBroadcast.java index 4d5c2d771de..5e6c2a583ef 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/NcclBroadcast.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/NcclBroadcast.java @@ -43,8 +43,6 @@ * output: The same as input. * shape: The shape of the input tensor. * - * @param data type for {@code output} output - * * @deprecated use {@link org.tensorflow.op.distribute.NcclBroadcast} instead */ @OpMetadata( diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/NcclReduce.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/NcclReduce.java index 8b050aba7e3..cd3dea3af6f 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/NcclReduce.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/NcclReduce.java @@ -43,8 +43,6 @@ * data: the value of the reduction across all {@code num_devices} devices. * reduction: the reduction operation to perform. * - * @param data type for {@code data} output - * * @deprecated use {@link org.tensorflow.op.distribute.NcclReduce} instead */ @OpMetadata( diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/NextIteration.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/NextIteration.java index 33e50ce1b5d..1f0f73c672f 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/NextIteration.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/NextIteration.java @@ -35,8 +35,6 @@ /** * Makes its input available to the next iteration. - * - * @param data type for {@code output} output */ @OpMetadata( opType = NextIteration.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/OneHot.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/OneHot.java index 09f55f7eaff..8ed3c25bd8e 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/OneHot.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/OneHot.java @@ -111,8 +111,6 @@ * [0.0, 0.0, 0.0] // one_hot(-1) * ] * - * - * @param data type for {@code output} output */ @OpMetadata( opType = OneHot.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/OnesLike.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/OnesLike.java index b69df0d0952..51178e062f4 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/OnesLike.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/OnesLike.java @@ -35,8 +35,6 @@ /** * Returns a tensor of ones with the same shape and type as x. - * - * @param data type for {@code y} output */ @OpMetadata( opType = OnesLike.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/Pad.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/Pad.java index d80e87f0f2d..60ddbcf6817 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/Pad.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/Pad.java @@ -56,8 +56,6 @@ * [0, 0, 2, 2, 0, 0] * [0, 0, 0, 0, 0, 0]] * - * - * @param data type for {@code output} output */ @OpMetadata( opType = Pad.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/ParallelConcat.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/ParallelConcat.java index c5cbde1618c..b12c3b896aa 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/ParallelConcat.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/ParallelConcat.java @@ -50,8 +50,6 @@ * that the input shapes be known during graph construction. Parallel concat * will copy pieces of the input into the output as they become available, in * some situations this can provide a performance benefit. - * - * @param data type for {@code output} output */ @OpMetadata( opType = ParallelConcat.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/ParallelDynamicStitch.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/ParallelDynamicStitch.java index a23c3d135a8..c9fd16880ca 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/ParallelDynamicStitch.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/ParallelDynamicStitch.java @@ -89,8 +89,6 @@ *

    * *
    - * - * @param data type for {@code merged} output */ @OpMetadata( opType = ParallelDynamicStitch.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/Placeholder.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/Placeholder.java index 634500dcfc0..f4c450973da 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/Placeholder.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/Placeholder.java @@ -40,8 +40,6 @@ * N.B. This operation will fail with an error if it is executed. It is * intended as a way to represent a value that will always be fed, and to * provide attrs that enable the fed value to be checked at runtime. - * - * @param data type for {@code output} output */ @OpMetadata( opType = Placeholder.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/PlaceholderWithDefault.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/PlaceholderWithDefault.java index 9604ea0a92a..202d4cc476c 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/PlaceholderWithDefault.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/PlaceholderWithDefault.java @@ -36,8 +36,6 @@ /** * A placeholder op that passes through {@code input} when its output is not fed. - * - * @param data type for {@code output} output */ @OpMetadata( opType = PlaceholderWithDefault.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/Prod.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/Prod.java index 71c7f986eb6..3f1c696a0bc 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/Prod.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/Prod.java @@ -40,8 +40,6 @@ * {@code keep_dims} is true, the rank of the tensor is reduced by 1 for each entry in * {@code axis}. If {@code keep_dims} is true, the reduced dimensions are * retained with length 1. - * - * @param data type for {@code output} output */ @OpMetadata( opType = Prod.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/QuantizedReshape.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/QuantizedReshape.java index 6e92b83bf89..84816c6893f 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/QuantizedReshape.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/QuantizedReshape.java @@ -37,8 +37,6 @@ /** * Reshapes a quantized tensor as per the Reshape op. - * - * @param data type for {@code output} output */ @OpMetadata( opType = QuantizedReshape.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/RandomIndexShuffle.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/RandomIndexShuffle.java index 68cd7f9f0eb..76538abf9cb 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/RandomIndexShuffle.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/RandomIndexShuffle.java @@ -39,8 +39,6 @@ *

    If multiple inputs are vectors (matrix in case of seed) then the size of the * first dimension must match. *

    The outputs are deterministic. - * - * @param data type for {@code output} output */ @OpMetadata( opType = RandomIndexShuffle.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/Range.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/Range.java index 0699bd59b09..702214095a5 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/Range.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/Range.java @@ -44,8 +44,6 @@ * # 'delta' is 3 * tf.range(start, limit, delta) ==> [3, 6, 9, 12, 15] * - * - * @param data type for {@code output} output */ @OpMetadata( opType = Range.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/ReadVariableOp.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/ReadVariableOp.java index 236991942ee..f57c2781c3f 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/ReadVariableOp.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/ReadVariableOp.java @@ -41,8 +41,6 @@ * writes on which this operation depends directly or indirectly, and to not be * influenced by any of the writes which depend directly or indirectly on this * operation. - * - * @param data type for {@code value} output */ @OpMetadata( opType = ReadVariableOp.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/Recv.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/Recv.java index 1853328543d..5b3caab37b8 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/Recv.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/Recv.java @@ -36,8 +36,6 @@ /** * Receives the named tensor from send_device on recv_device. - * - * @param data type for {@code tensor} output */ @OpMetadata( opType = Recv.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/ReduceMax.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/ReduceMax.java index 529841fd5fa..dca6c6a5ffc 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/ReduceMax.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/ReduceMax.java @@ -39,8 +39,6 @@ * {@code keep_dims} is true, the rank of the tensor is reduced by 1 for each entry in * {@code axis}. If {@code keep_dims} is true, the reduced dimensions are * retained with length 1. - * - * @param data type for {@code output} output */ @OpMetadata( opType = ReduceMax.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/ReduceMin.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/ReduceMin.java index f349357096b..a7e544cfaab 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/ReduceMin.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/ReduceMin.java @@ -39,8 +39,6 @@ * {@code keep_dims} is true, the rank of the tensor is reduced by 1 for each entry in * {@code axis}. If {@code keep_dims} is true, the reduced dimensions are * retained with length 1. - * - * @param data type for {@code output} output */ @OpMetadata( opType = ReduceMin.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/ReduceProd.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/ReduceProd.java index 49008ad1a36..3dc53ad9c58 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/ReduceProd.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/ReduceProd.java @@ -40,8 +40,6 @@ * {@code keep_dims} is true, the rank of the tensor is reduced by 1 for each entry in * {@code axis}. If {@code keep_dims} is true, the reduced dimensions are * retained with length 1. - * - * @param data type for {@code output} output */ @OpMetadata( opType = ReduceProd.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/ReduceSum.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/ReduceSum.java index 05851e60764..bbe161f9210 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/ReduceSum.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/ReduceSum.java @@ -40,8 +40,6 @@ * {@code keep_dims} is true, the rank of the tensor is reduced by 1 for each entry in * {@code axis}. If {@code keep_dims} is true, the reduced dimensions are * retained with length 1. - * - * @param data type for {@code output} output */ @OpMetadata( opType = ReduceSum.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/RefEnter.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/RefEnter.java index 888c0ee977b..218092a2563 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/RefEnter.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/RefEnter.java @@ -39,8 +39,6 @@ * {@code is_constant} is true, {@code output} is a constant in the child frame; otherwise * it may be changed in the child frame. At most {@code parallel_iterations} iterations * are run in parallel in the child frame. - * - * @param data type for {@code output} output */ @OpMetadata( opType = RefEnter.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/RefExit.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/RefExit.java index c23ff2d03d7..9a840da2c3d 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/RefExit.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/RefExit.java @@ -36,8 +36,6 @@ /** * Exits the current frame to its parent frame. * Exit makes its input {@code data} available to the parent frame. - * - * @param data type for {@code output} output */ @OpMetadata( opType = RefExit.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/RefIdentity.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/RefIdentity.java index 53d515be8e1..c3bb004b548 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/RefIdentity.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/RefIdentity.java @@ -35,8 +35,6 @@ /** * Return the same ref tensor as the input ref tensor. - * - * @param data type for {@code output} output */ @OpMetadata( opType = RefIdentity.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/RefMerge.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/RefMerge.java index 9354cb2847b..4baf6cc6260 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/RefMerge.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/RefMerge.java @@ -41,8 +41,6 @@ * It is usually combined with {@code Switch} to implement branching. *

    {@code Merge} forwards the first tensor for become available to {@code output}, and sets * {@code value_index} to its index in {@code inputs}. - * - * @param data type for {@code output} output */ @OpMetadata( opType = RefMerge.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/RefNextIteration.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/RefNextIteration.java index 5c7f1d2c4b7..ef647c70cd6 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/RefNextIteration.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/RefNextIteration.java @@ -35,8 +35,6 @@ /** * Makes its input available to the next iteration. - * - * @param data type for {@code output} output */ @OpMetadata( opType = RefNextIteration.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/RefSelect.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/RefSelect.java index 02c6ddc8e2f..d7ffa33956e 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/RefSelect.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/RefSelect.java @@ -37,8 +37,6 @@ /** * Forwards the {@code index}th element of {@code inputs} to {@code output}. - * - * @param data type for {@code output} output */ @OpMetadata( opType = RefSelect.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/RefSwitch.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/RefSwitch.java index 04a2d4811ab..2e97b2bbcad 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/RefSwitch.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/RefSwitch.java @@ -39,8 +39,6 @@ * If {@code pred} is true, the {@code data} input is forwarded to {@code output_true}. Otherwise, * the data goes to {@code output_false}. *

    See also {@code Switch} and {@code Merge}. - * - * @param data type for {@code output_false} output */ @OpMetadata( opType = RefSwitch.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/Relayout.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/Relayout.java index 959987e6200..503d3cfe42a 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/Relayout.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/Relayout.java @@ -35,8 +35,6 @@ /** * The Relayout operation - * - * @param data type for {@code output} output */ @OpMetadata( opType = Relayout.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/RelayoutLike.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/RelayoutLike.java index 7fd8a91fb8b..499cb8d6c72 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/RelayoutLike.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/RelayoutLike.java @@ -35,8 +35,6 @@ /** * The RelayoutLike operation - * - * @param data type for {@code output} output */ @OpMetadata( opType = RelayoutLike.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/Reshape.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/Reshape.java index 4b1ce466a7d..54c0aba057e 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/Reshape.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/Reshape.java @@ -90,8 +90,6 @@ * # shape `[]` reshapes to a scalar * reshape(t, []) ==> 7 * - * - * @param data type for {@code output} output */ @OpMetadata( opType = Reshape.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/ResourceCountUpTo.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/ResourceCountUpTo.java index f8e5cf5abef..0ca0faa179e 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/ResourceCountUpTo.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/ResourceCountUpTo.java @@ -37,8 +37,6 @@ /** * Increments variable pointed to by 'resource' until it reaches 'limit'. - * - * @param data type for {@code output} output */ @OpMetadata( opType = ResourceCountUpTo.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/ResourceGather.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/ResourceGather.java index 5dff2d95dc2..c458bacea4c 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/ResourceGather.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/ResourceGather.java @@ -49,8 +49,6 @@ * # Higher rank indices * output[i, ..., j, :, ... :] = params[indices[i, ..., j], :, ..., :] * - * - * @param data type for {@code output} output */ @OpMetadata( opType = ResourceGather.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/ResourceGatherNd.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/ResourceGatherNd.java index 1a86a282ab9..f9c6b72b544 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/ResourceGatherNd.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/ResourceGatherNd.java @@ -37,8 +37,6 @@ /** * The ResourceGatherNd operation - * - * @param data type for {@code output} output */ @OpMetadata( opType = ResourceGatherNd.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/ResourceScatterNdAdd.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/ResourceScatterNdAdd.java index 4c1d9d3820c..ee6c1cf7d61 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/ResourceScatterNdAdd.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/ResourceScatterNdAdd.java @@ -103,6 +103,9 @@ public static ResourceScatterNdAdd create(Scope scope, Operand if (opts.useLocking != null) { opBuilder.setAttr("use_locking", opts.useLocking); } + if (opts.badIndicesPolicy != null) { + opBuilder.setAttr("bad_indices_policy", opts.badIndicesPolicy); + } } } return new ResourceScatterNdAdd(opBuilder.build()); @@ -120,12 +123,24 @@ public static Options useLocking(Boolean useLocking) { return new Options().useLocking(useLocking); } + /** + * Sets the badIndicesPolicy option. + * + * @param badIndicesPolicy the badIndicesPolicy option + * @return this Options instance. + */ + public static Options badIndicesPolicy(String badIndicesPolicy) { + return new Options().badIndicesPolicy(badIndicesPolicy); + } + /** * Optional attributes for {@link org.tensorflow.op.core.ResourceScatterNdAdd} */ public static class Options { private Boolean useLocking; + private String badIndicesPolicy; + private Options() { } @@ -141,6 +156,17 @@ public Options useLocking(Boolean useLocking) { this.useLocking = useLocking; return this; } + + /** + * Sets the badIndicesPolicy option. + * + * @param badIndicesPolicy the badIndicesPolicy option + * @return this Options instance. + */ + public Options badIndicesPolicy(String badIndicesPolicy) { + this.badIndicesPolicy = badIndicesPolicy; + return this; + } } @OpInputsMetadata( @@ -181,8 +207,13 @@ public static class Inputs extends RawOpInputs { */ public final boolean useLocking; + /** + * The badIndicesPolicy attribute + */ + public final String badIndicesPolicy; + public Inputs(GraphOperation op) { - super(new ResourceScatterNdAdd(op), op, Arrays.asList("T", "Tindices", "use_locking")); + super(new ResourceScatterNdAdd(op), op, Arrays.asList("T", "Tindices", "use_locking", "bad_indices_policy")); int inputIndex = 0; ref = (Operand) op.input(inputIndex++); indices = (Operand) op.input(inputIndex++); @@ -190,6 +221,7 @@ public Inputs(GraphOperation op) { T = op.attributes().getAttrType("T"); Tindices = op.attributes().getAttrType("Tindices"); useLocking = op.attributes().getAttrBool("use_locking"); + badIndicesPolicy = op.attributes().getAttrString("bad_indices_policy"); } } } diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/ResourceScatterNdMax.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/ResourceScatterNdMax.java index 193d4c7dfda..379843a67c7 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/ResourceScatterNdMax.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/ResourceScatterNdMax.java @@ -77,6 +77,9 @@ public static ResourceScatterNdMax create(Scope scope, Operand if (opts.useLocking != null) { opBuilder.setAttr("use_locking", opts.useLocking); } + if (opts.badIndicesPolicy != null) { + opBuilder.setAttr("bad_indices_policy", opts.badIndicesPolicy); + } } } return new ResourceScatterNdMax(opBuilder.build()); @@ -94,12 +97,24 @@ public static Options useLocking(Boolean useLocking) { return new Options().useLocking(useLocking); } + /** + * Sets the badIndicesPolicy option. + * + * @param badIndicesPolicy the badIndicesPolicy option + * @return this Options instance. + */ + public static Options badIndicesPolicy(String badIndicesPolicy) { + return new Options().badIndicesPolicy(badIndicesPolicy); + } + /** * Optional attributes for {@link org.tensorflow.op.core.ResourceScatterNdMax} */ public static class Options { private Boolean useLocking; + private String badIndicesPolicy; + private Options() { } @@ -115,6 +130,17 @@ public Options useLocking(Boolean useLocking) { this.useLocking = useLocking; return this; } + + /** + * Sets the badIndicesPolicy option. + * + * @param badIndicesPolicy the badIndicesPolicy option + * @return this Options instance. + */ + public Options badIndicesPolicy(String badIndicesPolicy) { + this.badIndicesPolicy = badIndicesPolicy; + return this; + } } @OpInputsMetadata( @@ -155,8 +181,13 @@ public static class Inputs extends RawOpInputs { */ public final boolean useLocking; + /** + * The badIndicesPolicy attribute + */ + public final String badIndicesPolicy; + public Inputs(GraphOperation op) { - super(new ResourceScatterNdMax(op), op, Arrays.asList("T", "Tindices", "use_locking")); + super(new ResourceScatterNdMax(op), op, Arrays.asList("T", "Tindices", "use_locking", "bad_indices_policy")); int inputIndex = 0; ref = (Operand) op.input(inputIndex++); indices = (Operand) op.input(inputIndex++); @@ -164,6 +195,7 @@ public Inputs(GraphOperation op) { T = op.attributes().getAttrType("T"); Tindices = op.attributes().getAttrType("Tindices"); useLocking = op.attributes().getAttrBool("use_locking"); + badIndicesPolicy = op.attributes().getAttrString("bad_indices_policy"); } } } diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/ResourceScatterNdMin.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/ResourceScatterNdMin.java index 9a1023916fd..ba46417abba 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/ResourceScatterNdMin.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/ResourceScatterNdMin.java @@ -77,6 +77,9 @@ public static ResourceScatterNdMin create(Scope scope, Operand if (opts.useLocking != null) { opBuilder.setAttr("use_locking", opts.useLocking); } + if (opts.badIndicesPolicy != null) { + opBuilder.setAttr("bad_indices_policy", opts.badIndicesPolicy); + } } } return new ResourceScatterNdMin(opBuilder.build()); @@ -94,12 +97,24 @@ public static Options useLocking(Boolean useLocking) { return new Options().useLocking(useLocking); } + /** + * Sets the badIndicesPolicy option. + * + * @param badIndicesPolicy the badIndicesPolicy option + * @return this Options instance. + */ + public static Options badIndicesPolicy(String badIndicesPolicy) { + return new Options().badIndicesPolicy(badIndicesPolicy); + } + /** * Optional attributes for {@link org.tensorflow.op.core.ResourceScatterNdMin} */ public static class Options { private Boolean useLocking; + private String badIndicesPolicy; + private Options() { } @@ -115,6 +130,17 @@ public Options useLocking(Boolean useLocking) { this.useLocking = useLocking; return this; } + + /** + * Sets the badIndicesPolicy option. + * + * @param badIndicesPolicy the badIndicesPolicy option + * @return this Options instance. + */ + public Options badIndicesPolicy(String badIndicesPolicy) { + this.badIndicesPolicy = badIndicesPolicy; + return this; + } } @OpInputsMetadata( @@ -155,8 +181,13 @@ public static class Inputs extends RawOpInputs { */ public final boolean useLocking; + /** + * The badIndicesPolicy attribute + */ + public final String badIndicesPolicy; + public Inputs(GraphOperation op) { - super(new ResourceScatterNdMin(op), op, Arrays.asList("T", "Tindices", "use_locking")); + super(new ResourceScatterNdMin(op), op, Arrays.asList("T", "Tindices", "use_locking", "bad_indices_policy")); int inputIndex = 0; ref = (Operand) op.input(inputIndex++); indices = (Operand) op.input(inputIndex++); @@ -164,6 +195,7 @@ public Inputs(GraphOperation op) { T = op.attributes().getAttrType("T"); Tindices = op.attributes().getAttrType("Tindices"); useLocking = op.attributes().getAttrBool("use_locking"); + badIndicesPolicy = op.attributes().getAttrString("bad_indices_policy"); } } } diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/ResourceScatterNdSub.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/ResourceScatterNdSub.java index 4c321416231..f39e42e742b 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/ResourceScatterNdSub.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/ResourceScatterNdSub.java @@ -103,6 +103,9 @@ public static ResourceScatterNdSub create(Scope scope, Operand if (opts.useLocking != null) { opBuilder.setAttr("use_locking", opts.useLocking); } + if (opts.badIndicesPolicy != null) { + opBuilder.setAttr("bad_indices_policy", opts.badIndicesPolicy); + } } } return new ResourceScatterNdSub(opBuilder.build()); @@ -120,12 +123,24 @@ public static Options useLocking(Boolean useLocking) { return new Options().useLocking(useLocking); } + /** + * Sets the badIndicesPolicy option. + * + * @param badIndicesPolicy the badIndicesPolicy option + * @return this Options instance. + */ + public static Options badIndicesPolicy(String badIndicesPolicy) { + return new Options().badIndicesPolicy(badIndicesPolicy); + } + /** * Optional attributes for {@link org.tensorflow.op.core.ResourceScatterNdSub} */ public static class Options { private Boolean useLocking; + private String badIndicesPolicy; + private Options() { } @@ -141,6 +156,17 @@ public Options useLocking(Boolean useLocking) { this.useLocking = useLocking; return this; } + + /** + * Sets the badIndicesPolicy option. + * + * @param badIndicesPolicy the badIndicesPolicy option + * @return this Options instance. + */ + public Options badIndicesPolicy(String badIndicesPolicy) { + this.badIndicesPolicy = badIndicesPolicy; + return this; + } } @OpInputsMetadata( @@ -181,8 +207,13 @@ public static class Inputs extends RawOpInputs { */ public final boolean useLocking; + /** + * The badIndicesPolicy attribute + */ + public final String badIndicesPolicy; + public Inputs(GraphOperation op) { - super(new ResourceScatterNdSub(op), op, Arrays.asList("T", "Tindices", "use_locking")); + super(new ResourceScatterNdSub(op), op, Arrays.asList("T", "Tindices", "use_locking", "bad_indices_policy")); int inputIndex = 0; ref = (Operand) op.input(inputIndex++); indices = (Operand) op.input(inputIndex++); @@ -190,6 +221,7 @@ public Inputs(GraphOperation op) { T = op.attributes().getAttrType("T"); Tindices = op.attributes().getAttrType("Tindices"); useLocking = op.attributes().getAttrBool("use_locking"); + badIndicesPolicy = op.attributes().getAttrString("bad_indices_policy"); } } } diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/ResourceScatterNdUpdate.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/ResourceScatterNdUpdate.java index 1a21fa30916..588d923c05a 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/ResourceScatterNdUpdate.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/ResourceScatterNdUpdate.java @@ -104,6 +104,9 @@ public static ResourceScatterNdUpdate create(Scope scope, Operand { */ public final boolean useLocking; + /** + * The badIndicesPolicy attribute + */ + public final String badIndicesPolicy; + public Inputs(GraphOperation op) { - super(new ResourceScatterNdUpdate(op), op, Arrays.asList("T", "Tindices", "use_locking")); + super(new ResourceScatterNdUpdate(op), op, Arrays.asList("T", "Tindices", "use_locking", "bad_indices_policy")); int inputIndex = 0; ref = (Operand) op.input(inputIndex++); indices = (Operand) op.input(inputIndex++); @@ -191,6 +222,7 @@ public Inputs(GraphOperation op) { T = op.attributes().getAttrType("T"); Tindices = op.attributes().getAttrType("Tindices"); useLocking = op.attributes().getAttrBool("use_locking"); + badIndicesPolicy = op.attributes().getAttrString("bad_indices_policy"); } } } diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/Reverse.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/Reverse.java index 65a6ac9ab0c..711b7148209 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/Reverse.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/Reverse.java @@ -76,8 +76,6 @@ * [16, 17, 18, 19], * [12, 13, 14, 15]]]] * - * - * @param data type for {@code output} output */ @OpMetadata( opType = Reverse.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/ReverseSequence.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/ReverseSequence.java index b7eb3fb25a2..e18f16874f0 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/ReverseSequence.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/ReverseSequence.java @@ -84,8 +84,6 @@ * output[3:, :, 2, :, ...] = input[3:, :, 2, :, ...] * output[2:, :, 3, :, ...] = input[2:, :, 3, :, ...] * - * - * @param data type for {@code output} output */ @OpMetadata( opType = ReverseSequence.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/Roll.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/Roll.java index a2f04750d53..e190730b970 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/Roll.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/Roll.java @@ -54,8 +54,6 @@ * # 't' is [[0, 1, 2, 3, 4], [5, 6, 7, 8, 9]] * roll(t, shift=[2, -3], axis=[1, 1]) ==> [[1, 2, 3, 4, 0], [6, 7, 8, 9, 5]] * - * - * @param data type for {@code output} output */ @OpMetadata( opType = Roll.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/ScatterAdd.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/ScatterAdd.java index bc66b56b3d1..9f0bc6a526f 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/ScatterAdd.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/ScatterAdd.java @@ -55,8 +55,6 @@ *

    * *
    - * - * @param data type for {@code output_ref} output */ @OpMetadata( opType = ScatterAdd.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/ScatterDiv.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/ScatterDiv.java index 083f4de2a81..902d11400e5 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/ScatterDiv.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/ScatterDiv.java @@ -52,8 +52,6 @@ *

    Duplicate entries are handled correctly: if multiple {@code indices} reference * the same location, their contributions divide. *

    Requires {@code updates.shape = indices.shape + ref.shape[1:]} or {@code updates.shape = []}. - * - * @param data type for {@code output_ref} output */ @OpMetadata( opType = ScatterDiv.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/ScatterMax.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/ScatterMax.java index 162556fb11c..9b761e52419 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/ScatterMax.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/ScatterMax.java @@ -54,8 +54,6 @@ *

    * *
    - * - * @param data type for {@code output_ref} output */ @OpMetadata( opType = ScatterMax.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/ScatterMin.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/ScatterMin.java index 4264f92bc7e..7f725ad19d0 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/ScatterMin.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/ScatterMin.java @@ -54,8 +54,6 @@ *
    * *
    - * - * @param data type for {@code output_ref} output */ @OpMetadata( opType = ScatterMin.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/ScatterMul.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/ScatterMul.java index 7fb20e9d36e..ae8bbca9670 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/ScatterMul.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/ScatterMul.java @@ -52,8 +52,6 @@ *

    Duplicate entries are handled correctly: if multiple {@code indices} reference * the same location, their contributions multiply. *

    Requires {@code updates.shape = indices.shape + ref.shape[1:]} or {@code updates.shape = []}. - * - * @param data type for {@code output_ref} output */ @OpMetadata( opType = ScatterMul.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/ScatterNd.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/ScatterNd.java index 34487ebf9d7..ad6bcd00a16 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/ScatterNd.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/ScatterNd.java @@ -107,10 +107,16 @@ * [[0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0]], * [[5, 5, 5, 5], [6, 6, 6, 6], [7, 7, 7, 7], [8, 8, 8, 8]]] * - *

    Note that on CPU, if an out of bound index is found, an error is returned. - * On GPU, if an out of bound index is found, the index is ignored. - * - * @param data type for {@code output} output + *

    If {@code indices} contains any out-of-bound indices, depending on + * {@code bad_indices_policy}, the op will either return an error or ignore the + * out-of-bound indices. {@code bad_indices_policy} can be one of the following values: + *

      + *
    1. "" or "DEFAULT": raises on CPU and ignore on GPU. This is because + * historically on CPU and GPU we handle errors in different ways, and for + * backward compatibility we keep the default behavior.
      2. + *
    2. "ERROR": raises error; GPU does not support this value.
      3. + *
    3. "IGNORE": ignore the bad indices; supported on both CPU and GPU.
      4. + *
    */ @OpMetadata( opType = ScatterNd.OP_NAME, @@ -138,6 +144,7 @@ public ScatterNd(Operation operation) { * @param indices Tensor of indices. * @param updates Values to scatter into the output tensor. * @param shape 1-D. The shape of the output tensor. + * @param options carries optional attribute values * @param data type for {@code ScatterNd} output and operands * @param data type for {@code ScatterNd} output and operands * @return a new instance of ScatterNd @@ -146,14 +153,31 @@ public ScatterNd(Operation operation) { describeByClass = true ) public static ScatterNd create(Scope scope, - Operand indices, Operand updates, Operand shape) { + Operand indices, Operand updates, Operand shape, Options... options) { OperationBuilder opBuilder = scope.opBuilder(OP_NAME, "ScatterNd"); opBuilder.addInput(indices.asOutput()); opBuilder.addInput(updates.asOutput()); opBuilder.addInput(shape.asOutput()); + if (options != null) { + for (Options opts : options) { + if (opts.badIndicesPolicy != null) { + opBuilder.setAttr("bad_indices_policy", opts.badIndicesPolicy); + } + } + } return new ScatterNd<>(opBuilder.build()); } + /** + * Sets the badIndicesPolicy option. + * + * @param badIndicesPolicy the badIndicesPolicy option + * @return this Options instance. + */ + public static Options badIndicesPolicy(String badIndicesPolicy) { + return new Options().badIndicesPolicy(badIndicesPolicy); + } + /** * Gets output. * A new tensor with the given shape and updates applied according @@ -169,6 +193,27 @@ public Output asOutput() { return output; } + /** + * Optional attributes for {@link org.tensorflow.op.core.ScatterNd} + */ + public static class Options { + private String badIndicesPolicy; + + private Options() { + } + + /** + * Sets the badIndicesPolicy option. + * + * @param badIndicesPolicy the badIndicesPolicy option + * @return this Options instance. + */ + public Options badIndicesPolicy(String badIndicesPolicy) { + this.badIndicesPolicy = badIndicesPolicy; + return this; + } + } + @OpInputsMetadata( outputsClass = ScatterNd.class ) @@ -198,14 +243,20 @@ public static class Inputs extends RawOpInpu */ public final DataType Tindices; + /** + * The badIndicesPolicy attribute + */ + public final String badIndicesPolicy; + public Inputs(GraphOperation op) { - super(new ScatterNd<>(op), op, Arrays.asList("T", "Tindices")); + super(new ScatterNd<>(op), op, Arrays.asList("T", "Tindices", "bad_indices_policy")); int inputIndex = 0; indices = (Operand) op.input(inputIndex++); updates = (Operand) op.input(inputIndex++); shape = (Operand) op.input(inputIndex++); T = op.attributes().getAttrType("T"); Tindices = op.attributes().getAttrType("Tindices"); + badIndicesPolicy = op.attributes().getAttrString("bad_indices_policy"); } } } diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/ScatterNdAdd.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/ScatterNdAdd.java index aef9eed4a32..257dce25682 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/ScatterNdAdd.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/ScatterNdAdd.java @@ -62,8 +62,6 @@ * *

    See {@code tf.scatter_nd} for more details about how to make updates to * slices. - * - * @param data type for {@code output_ref} output */ @OpMetadata( opType = ScatterNdAdd.OP_NAME, @@ -111,6 +109,9 @@ public static ScatterNdAdd create(Scope scope, Operand r if (opts.useLocking != null) { opBuilder.setAttr("use_locking", opts.useLocking); } + if (opts.badIndicesPolicy != null) { + opBuilder.setAttr("bad_indices_policy", opts.badIndicesPolicy); + } } } return new ScatterNdAdd<>(opBuilder.build()); @@ -128,6 +129,16 @@ public static Options useLocking(Boolean useLocking) { return new Options().useLocking(useLocking); } + /** + * Sets the badIndicesPolicy option. + * + * @param badIndicesPolicy the badIndicesPolicy option + * @return this Options instance. + */ + public static Options badIndicesPolicy(String badIndicesPolicy) { + return new Options().badIndicesPolicy(badIndicesPolicy); + } + /** * Gets outputRef. * Same as ref. Returned as a convenience for operations that want @@ -149,6 +160,8 @@ public Output asOutput() { public static class Options { private Boolean useLocking; + private String badIndicesPolicy; + private Options() { } @@ -164,6 +177,17 @@ public Options useLocking(Boolean useLocking) { this.useLocking = useLocking; return this; } + + /** + * Sets the badIndicesPolicy option. + * + * @param badIndicesPolicy the badIndicesPolicy option + * @return this Options instance. + */ + public Options badIndicesPolicy(String badIndicesPolicy) { + this.badIndicesPolicy = badIndicesPolicy; + return this; + } } @OpInputsMetadata( @@ -204,8 +228,13 @@ public static class Inputs extends RawOpInputs> */ public final boolean useLocking; + /** + * The badIndicesPolicy attribute + */ + public final String badIndicesPolicy; + public Inputs(GraphOperation op) { - super(new ScatterNdAdd<>(op), op, Arrays.asList("T", "Tindices", "use_locking")); + super(new ScatterNdAdd<>(op), op, Arrays.asList("T", "Tindices", "use_locking", "bad_indices_policy")); int inputIndex = 0; ref = (Operand) op.input(inputIndex++); indices = (Operand) op.input(inputIndex++); @@ -213,6 +242,7 @@ public Inputs(GraphOperation op) { T = op.attributes().getAttrType("T"); Tindices = op.attributes().getAttrType("Tindices"); useLocking = op.attributes().getAttrBool("use_locking"); + badIndicesPolicy = op.attributes().getAttrString("bad_indices_policy"); } } } diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/ScatterNdMax.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/ScatterNdMax.java index 0adccafee2a..a7ebdf162d6 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/ScatterNdMax.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/ScatterNdMax.java @@ -36,8 +36,6 @@ /** * Computes element-wise maximum. - * - * @param data type for {@code output_ref} output */ @OpMetadata( opType = ScatterNdMax.OP_NAME, @@ -85,6 +83,9 @@ public static ScatterNdMax create(Scope scope, Operand r if (opts.useLocking != null) { opBuilder.setAttr("use_locking", opts.useLocking); } + if (opts.badIndicesPolicy != null) { + opBuilder.setAttr("bad_indices_policy", opts.badIndicesPolicy); + } } } return new ScatterNdMax<>(opBuilder.build()); @@ -102,6 +103,16 @@ public static Options useLocking(Boolean useLocking) { return new Options().useLocking(useLocking); } + /** + * Sets the badIndicesPolicy option. + * + * @param badIndicesPolicy the badIndicesPolicy option + * @return this Options instance. + */ + public static Options badIndicesPolicy(String badIndicesPolicy) { + return new Options().badIndicesPolicy(badIndicesPolicy); + } + /** * Gets outputRef. * Same as ref. Returned as a convenience for operations that want @@ -123,6 +134,8 @@ public Output asOutput() { public static class Options { private Boolean useLocking; + private String badIndicesPolicy; + private Options() { } @@ -138,6 +151,17 @@ public Options useLocking(Boolean useLocking) { this.useLocking = useLocking; return this; } + + /** + * Sets the badIndicesPolicy option. + * + * @param badIndicesPolicy the badIndicesPolicy option + * @return this Options instance. + */ + public Options badIndicesPolicy(String badIndicesPolicy) { + this.badIndicesPolicy = badIndicesPolicy; + return this; + } } @OpInputsMetadata( @@ -178,8 +202,13 @@ public static class Inputs extends RawOpInputs> */ public final boolean useLocking; + /** + * The badIndicesPolicy attribute + */ + public final String badIndicesPolicy; + public Inputs(GraphOperation op) { - super(new ScatterNdMax<>(op), op, Arrays.asList("T", "Tindices", "use_locking")); + super(new ScatterNdMax<>(op), op, Arrays.asList("T", "Tindices", "use_locking", "bad_indices_policy")); int inputIndex = 0; ref = (Operand) op.input(inputIndex++); indices = (Operand) op.input(inputIndex++); @@ -187,6 +216,7 @@ public Inputs(GraphOperation op) { T = op.attributes().getAttrType("T"); Tindices = op.attributes().getAttrType("Tindices"); useLocking = op.attributes().getAttrBool("use_locking"); + badIndicesPolicy = op.attributes().getAttrString("bad_indices_policy"); } } } diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/ScatterNdMin.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/ScatterNdMin.java index d2780381fcb..3ade02671ed 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/ScatterNdMin.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/ScatterNdMin.java @@ -36,8 +36,6 @@ /** * Computes element-wise minimum. - * - * @param data type for {@code output_ref} output */ @OpMetadata( opType = ScatterNdMin.OP_NAME, @@ -85,6 +83,9 @@ public static ScatterNdMin create(Scope scope, Operand r if (opts.useLocking != null) { opBuilder.setAttr("use_locking", opts.useLocking); } + if (opts.badIndicesPolicy != null) { + opBuilder.setAttr("bad_indices_policy", opts.badIndicesPolicy); + } } } return new ScatterNdMin<>(opBuilder.build()); @@ -102,6 +103,16 @@ public static Options useLocking(Boolean useLocking) { return new Options().useLocking(useLocking); } + /** + * Sets the badIndicesPolicy option. + * + * @param badIndicesPolicy the badIndicesPolicy option + * @return this Options instance. + */ + public static Options badIndicesPolicy(String badIndicesPolicy) { + return new Options().badIndicesPolicy(badIndicesPolicy); + } + /** * Gets outputRef. * Same as ref. Returned as a convenience for operations that want @@ -123,6 +134,8 @@ public Output asOutput() { public static class Options { private Boolean useLocking; + private String badIndicesPolicy; + private Options() { } @@ -138,6 +151,17 @@ public Options useLocking(Boolean useLocking) { this.useLocking = useLocking; return this; } + + /** + * Sets the badIndicesPolicy option. + * + * @param badIndicesPolicy the badIndicesPolicy option + * @return this Options instance. + */ + public Options badIndicesPolicy(String badIndicesPolicy) { + this.badIndicesPolicy = badIndicesPolicy; + return this; + } } @OpInputsMetadata( @@ -178,8 +202,13 @@ public static class Inputs extends RawOpInputs> */ public final boolean useLocking; + /** + * The badIndicesPolicy attribute + */ + public final String badIndicesPolicy; + public Inputs(GraphOperation op) { - super(new ScatterNdMin<>(op), op, Arrays.asList("T", "Tindices", "use_locking")); + super(new ScatterNdMin<>(op), op, Arrays.asList("T", "Tindices", "use_locking", "bad_indices_policy")); int inputIndex = 0; ref = (Operand) op.input(inputIndex++); indices = (Operand) op.input(inputIndex++); @@ -187,6 +216,7 @@ public Inputs(GraphOperation op) { T = op.attributes().getAttrType("T"); Tindices = op.attributes().getAttrType("Tindices"); useLocking = op.attributes().getAttrBool("use_locking"); + badIndicesPolicy = op.attributes().getAttrString("bad_indices_policy"); } } } diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/ScatterNdNonAliasingAdd.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/ScatterNdNonAliasingAdd.java index 4d29ef748d8..c152dadc35e 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/ScatterNdNonAliasingAdd.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/ScatterNdNonAliasingAdd.java @@ -63,8 +63,6 @@ * [1, 13, 3, 14, 14, 6, 7, 20] * *

    See {@code tf.scatter_nd} for more details about how to make updates to slices. - * - * @param data type for {@code output} output */ @OpMetadata( opType = ScatterNdNonAliasingAdd.OP_NAME, @@ -94,6 +92,7 @@ public ScatterNdNonAliasingAdd(Operation operation) { * A tensor of indices into {@code input}. * @param updates A Tensor. Must have the same type as ref. A tensor of updated values * to add to {@code input}. + * @param options carries optional attribute values * @param data type for {@code ScatterNdNonAliasingAdd} output and operands * @return a new instance of ScatterNdNonAliasingAdd */ @@ -101,14 +100,31 @@ public ScatterNdNonAliasingAdd(Operation operation) { describeByClass = true ) public static ScatterNdNonAliasingAdd create(Scope scope, Operand input, - Operand indices, Operand updates) { + Operand indices, Operand updates, Options... options) { OperationBuilder opBuilder = scope.opBuilder(OP_NAME, "ScatterNdNonAliasingAdd"); opBuilder.addInput(input.asOutput()); opBuilder.addInput(indices.asOutput()); opBuilder.addInput(updates.asOutput()); + if (options != null) { + for (Options opts : options) { + if (opts.badIndicesPolicy != null) { + opBuilder.setAttr("bad_indices_policy", opts.badIndicesPolicy); + } + } + } return new ScatterNdNonAliasingAdd<>(opBuilder.build()); } + /** + * Sets the badIndicesPolicy option. + * + * @param badIndicesPolicy the badIndicesPolicy option + * @return this Options instance. + */ + public static Options badIndicesPolicy(String badIndicesPolicy) { + return new Options().badIndicesPolicy(badIndicesPolicy); + } + /** * Gets output. * A {@code Tensor} with the same shape as {@code input}, containing values of {@code input} @@ -124,6 +140,27 @@ public Output asOutput() { return output; } + /** + * Optional attributes for {@link org.tensorflow.op.core.ScatterNdNonAliasingAdd} + */ + public static class Options { + private String badIndicesPolicy; + + private Options() { + } + + /** + * Sets the badIndicesPolicy option. + * + * @param badIndicesPolicy the badIndicesPolicy option + * @return this Options instance. + */ + public Options badIndicesPolicy(String badIndicesPolicy) { + this.badIndicesPolicy = badIndicesPolicy; + return this; + } + } + @OpInputsMetadata( outputsClass = ScatterNdNonAliasingAdd.class ) @@ -155,14 +192,20 @@ public static class Inputs extends RawOpInputs(op), op, Arrays.asList("T", "Tindices")); + super(new ScatterNdNonAliasingAdd<>(op), op, Arrays.asList("T", "Tindices", "bad_indices_policy")); int inputIndex = 0; input = (Operand) op.input(inputIndex++); indices = (Operand) op.input(inputIndex++); updates = (Operand) op.input(inputIndex++); T = op.attributes().getAttrType("T"); Tindices = op.attributes().getAttrType("Tindices"); + badIndicesPolicy = op.attributes().getAttrString("bad_indices_policy"); } } } diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/ScatterNdSub.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/ScatterNdSub.java index b2018d27511..21654611e88 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/ScatterNdSub.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/ScatterNdSub.java @@ -63,8 +63,6 @@ * *

    See {@code tf.scatter_nd} for more details about how to make updates to * slices. - * - * @param data type for {@code output_ref} output */ @OpMetadata( opType = ScatterNdSub.OP_NAME, @@ -112,6 +110,9 @@ public static ScatterNdSub create(Scope scope, Operand r if (opts.useLocking != null) { opBuilder.setAttr("use_locking", opts.useLocking); } + if (opts.badIndicesPolicy != null) { + opBuilder.setAttr("bad_indices_policy", opts.badIndicesPolicy); + } } } return new ScatterNdSub<>(opBuilder.build()); @@ -129,6 +130,16 @@ public static Options useLocking(Boolean useLocking) { return new Options().useLocking(useLocking); } + /** + * Sets the badIndicesPolicy option. + * + * @param badIndicesPolicy the badIndicesPolicy option + * @return this Options instance. + */ + public static Options badIndicesPolicy(String badIndicesPolicy) { + return new Options().badIndicesPolicy(badIndicesPolicy); + } + /** * Gets outputRef. * Same as ref. Returned as a convenience for operations that want @@ -150,6 +161,8 @@ public Output asOutput() { public static class Options { private Boolean useLocking; + private String badIndicesPolicy; + private Options() { } @@ -165,6 +178,17 @@ public Options useLocking(Boolean useLocking) { this.useLocking = useLocking; return this; } + + /** + * Sets the badIndicesPolicy option. + * + * @param badIndicesPolicy the badIndicesPolicy option + * @return this Options instance. + */ + public Options badIndicesPolicy(String badIndicesPolicy) { + this.badIndicesPolicy = badIndicesPolicy; + return this; + } } @OpInputsMetadata( @@ -205,8 +229,13 @@ public static class Inputs extends RawOpInputs> */ public final boolean useLocking; + /** + * The badIndicesPolicy attribute + */ + public final String badIndicesPolicy; + public Inputs(GraphOperation op) { - super(new ScatterNdSub<>(op), op, Arrays.asList("T", "Tindices", "use_locking")); + super(new ScatterNdSub<>(op), op, Arrays.asList("T", "Tindices", "use_locking", "bad_indices_policy")); int inputIndex = 0; ref = (Operand) op.input(inputIndex++); indices = (Operand) op.input(inputIndex++); @@ -214,6 +243,7 @@ public Inputs(GraphOperation op) { T = op.attributes().getAttrType("T"); Tindices = op.attributes().getAttrType("Tindices"); useLocking = op.attributes().getAttrBool("use_locking"); + badIndicesPolicy = op.attributes().getAttrString("bad_indices_policy"); } } } diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/ScatterNdUpdate.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/ScatterNdUpdate.java index 56427f20fac..5bf1e30fe35 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/ScatterNdUpdate.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/ScatterNdUpdate.java @@ -62,8 +62,6 @@ *

    See {@code tf.scatter_nd} for more details about how to make updates to * slices. *

    See also {@code tf.scatter_update} and {@code tf.batch_scatter_update}. - * - * @param data type for {@code output_ref} output */ @OpMetadata( opType = ScatterNdUpdate.OP_NAME, @@ -111,6 +109,9 @@ public static ScatterNdUpdate create(Scope scope, Operand(opBuilder.build()); @@ -128,6 +129,16 @@ public static Options useLocking(Boolean useLocking) { return new Options().useLocking(useLocking); } + /** + * Sets the badIndicesPolicy option. + * + * @param badIndicesPolicy the badIndicesPolicy option + * @return this Options instance. + */ + public static Options badIndicesPolicy(String badIndicesPolicy) { + return new Options().badIndicesPolicy(badIndicesPolicy); + } + /** * Gets outputRef. * Same as ref. Returned as a convenience for operations that want to @@ -149,6 +160,8 @@ public Output asOutput() { public static class Options { private Boolean useLocking; + private String badIndicesPolicy; + private Options() { } @@ -164,6 +177,17 @@ public Options useLocking(Boolean useLocking) { this.useLocking = useLocking; return this; } + + /** + * Sets the badIndicesPolicy option. + * + * @param badIndicesPolicy the badIndicesPolicy option + * @return this Options instance. + */ + public Options badIndicesPolicy(String badIndicesPolicy) { + this.badIndicesPolicy = badIndicesPolicy; + return this; + } } @OpInputsMetadata( @@ -204,8 +228,13 @@ public static class Inputs extends RawOpInputs(op), op, Arrays.asList("T", "Tindices", "use_locking")); + super(new ScatterNdUpdate<>(op), op, Arrays.asList("T", "Tindices", "use_locking", "bad_indices_policy")); int inputIndex = 0; ref = (Operand) op.input(inputIndex++); indices = (Operand) op.input(inputIndex++); @@ -213,6 +242,7 @@ public Inputs(GraphOperation op) { T = op.attributes().getAttrType("T"); Tindices = op.attributes().getAttrType("Tindices"); useLocking = op.attributes().getAttrBool("use_locking"); + badIndicesPolicy = op.attributes().getAttrString("bad_indices_policy"); } } } diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/ScatterSub.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/ScatterSub.java index 06d274ff356..4686a81470f 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/ScatterSub.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/ScatterSub.java @@ -54,8 +54,6 @@ *

    * *
    - * - * @param data type for {@code output_ref} output */ @OpMetadata( opType = ScatterSub.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/ScatterUpdate.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/ScatterUpdate.java index 711cbf7485f..60e22039589 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/ScatterUpdate.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/ScatterUpdate.java @@ -57,8 +57,6 @@ * * *

    See also {@code tf.batch_scatter_update} and {@code tf.scatter_nd_update}. - * - * @param data type for {@code output_ref} output */ @OpMetadata( opType = ScatterUpdate.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/Select.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/Select.java index 71caff86d14..c88ea468f39 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/Select.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/Select.java @@ -36,8 +36,6 @@ /** * The SelectV2 operation - * - * @param data type for {@code output} output */ @OpMetadata( opType = Select.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/SetDiff1d.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/SetDiff1d.java index 61af8e762a2..562b2088b93 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/SetDiff1d.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/SetDiff1d.java @@ -54,10 +54,6 @@ * out ==> [2, 4, 6] * idx ==> [1, 3, 5] * - * - * @param data type for {@code out} output - * - * @param data type for {@code idx} output */ @OpMetadata( opType = SetDiff1d.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/Shape.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/Shape.java index 4f9f9115847..2f7592fbc03 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/Shape.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/Shape.java @@ -44,8 +44,6 @@ * # 't' is [[[1, 1, 1], [2, 2, 2]], [[3, 3, 3], [4, 4, 4]]] * shape(t) ==> [2, 2, 3] * - * - * @param data type for {@code output} output */ @OpMetadata( opType = Shape.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/ShapeN.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/ShapeN.java index b56a39452d5..b53a00a1a82 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/ShapeN.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/ShapeN.java @@ -41,8 +41,6 @@ /** * Returns shape of tensors. * This operation returns N 1-D integer tensors representing shape of {@code input[i]s}. - * - * @param data type for {@code output} output */ @OpMetadata( opType = ShapeN.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/Size.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/Size.java index 1ad02bc0f9b..2be90850900 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/Size.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/Size.java @@ -45,8 +45,6 @@ * # 't' is [[[1, 1,, 1], [2, 2, 2]], [[3, 3, 3], [4, 4, 4]]]] * size(t) ==> 12 * - * - * @param data type for {@code output} output */ @OpMetadata( opType = Size.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/Slice.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/Slice.java index b53cae539a0..37a168fb6f7 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/Slice.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/Slice.java @@ -41,8 +41,6 @@ * 'begin'. *

    Requirements: * 0 <= begin[i] <= begin[i] + size[i] <= Di for i in [0, n) - * - * @param data type for {@code output} output */ @OpMetadata( opType = Slice.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/Snapshot.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/Snapshot.java index d8b1ed563d9..bafca31221f 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/Snapshot.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/Snapshot.java @@ -35,8 +35,6 @@ /** * Returns a copy of the input tensor. - * - * @param data type for {@code output} output */ @OpMetadata( opType = Snapshot.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/SpaceToBatchNd.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/SpaceToBatchNd.java index d56e6ef8709..2a366e46641 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/SpaceToBatchNd.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/SpaceToBatchNd.java @@ -132,8 +132,6 @@ * *

    Among others, this operation is useful for reducing atrous convolution into * regular convolution. - * - * @param data type for {@code output} output */ @OpMetadata( opType = SpaceToBatchNd.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/Split.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/Split.java index f6a01ed1950..dc4fad88677 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/Split.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/Split.java @@ -38,8 +38,6 @@ /** * Splits a tensor into {@code num_split} tensors along one dimension. - * - * @param data type for {@code output} output */ @OpMetadata( opType = Split.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/SplitV.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/SplitV.java index 8d1beb3fc5b..cc0525e9645 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/SplitV.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/SplitV.java @@ -39,8 +39,6 @@ /** * Splits a tensor into {@code num_split} tensors along one dimension. - * - * @param data type for {@code output} output */ @OpMetadata( opType = SplitV.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/Squeeze.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/Squeeze.java index 3ccc9dff638..52155b47d43 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/Squeeze.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/Squeeze.java @@ -50,8 +50,6 @@ * # 't' is a tensor of shape [1, 2, 1, 3, 1, 1] * shape(squeeze(t, [2, 4])) ==> [1, 2, 3, 1] * - * - * @param data type for {@code output} output */ @OpMetadata( opType = Squeeze.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/Stack.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/Stack.java index 0022997321a..976a86955b3 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/Stack.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/Stack.java @@ -51,8 +51,6 @@ * pack([x, y, z], axis=1) => [[1, 2, 3], [4, 5, 6]] * *

    This is the opposite of {@code unpack}. - * - * @param data type for {@code output} output */ @OpMetadata( opType = Stack.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/StackPop.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/StackPop.java index a6a3021ce14..502cfcc8c06 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/StackPop.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/StackPop.java @@ -36,8 +36,6 @@ /** * Pop the element at the top of the stack. - * - * @param data type for {@code elem} output */ @OpMetadata( opType = StackPop.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/StackPush.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/StackPush.java index c43aa1de30e..f9f05ff1912 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/StackPush.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/StackPush.java @@ -35,8 +35,6 @@ /** * Push an element onto the stack. - * - * @param data type for {@code output} output */ @OpMetadata( opType = StackPush.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/StochasticCastToInt.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/StochasticCastToInt.java index 29da2cb9a53..a06a2c8017d 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/StochasticCastToInt.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/StochasticCastToInt.java @@ -40,8 +40,6 @@ * Stochastically cast a given tensor from floats to ints. * The values are cast with a deterministic pseudo-random tensor from a uniform distribution generated from user given key, counter, algorithm. Values will saturate if out of the specified integer type range, and will become zero if inputs are NaN. *

    The outputs are a deterministic function of {@code input}, {@code key}, {@code counter}, {@code alg}. - * - * @param data type for {@code output} output */ @OpMetadata( opType = StochasticCastToInt.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/StopGradient.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/StopGradient.java index c2086cb3e92..fb486c42253 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/StopGradient.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/StopGradient.java @@ -85,8 +85,6 @@ *

  • Adversarial training, where no backprop should happen through the adversarial * example generation process.
    • * - * - * @param data type for {@code output} output */ @OpMetadata( opType = StopGradient.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/StridedSlice.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/StridedSlice.java index 6b8953f7995..ec55dae1c24 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/StridedSlice.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/StridedSlice.java @@ -133,8 +133,6 @@ *

    Requirements: * {@code 0 != strides[i] for i in [0, m)} * {@code ellipsis_mask must be a power of two (only one ellipsis)} - * - * @param data type for {@code output} output */ @OpMetadata( opType = StridedSlice.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/StridedSliceAssign.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/StridedSliceAssign.java index b2ab8d606e2..2911a675905 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/StridedSliceAssign.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/StridedSliceAssign.java @@ -41,8 +41,6 @@ * {@code begin}, {@code end}, {@code strides}, etc. work exactly as in {@code StridedSlice}. *

    NOTE this op currently does not support broadcasting and so {@code value}'s * shape must be exactly the shape produced by the slice of {@code ref}. - * - * @param data type for {@code output_ref} output */ @OpMetadata( opType = StridedSliceAssign.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/StridedSliceGrad.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/StridedSliceGrad.java index 2a234c9ab7a..fcd7518dd87 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/StridedSliceGrad.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/StridedSliceGrad.java @@ -43,8 +43,6 @@ *

    Arguments are the same as StridedSliceGrad with the exception that * {@code dy} is the input gradient to be propagated and {@code shape} is the * shape of {@code StridedSlice}'s {@code input}. - * - * @param data type for {@code output} output */ @OpMetadata( opType = StridedSliceGrad.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/Sum.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/Sum.java index 15957ea2189..abcdb1ee9ef 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/Sum.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/Sum.java @@ -40,8 +40,6 @@ * {@code keep_dims} is true, the rank of the tensor is reduced by 1 for each entry in * {@code axis}. If {@code keep_dims} is true, the reduced dimensions are * retained with length 1. - * - * @param data type for {@code output} output */ @OpMetadata( opType = Sum.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/SwitchCond.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/SwitchCond.java index c6a8f810467..c6842c9ab87 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/SwitchCond.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/SwitchCond.java @@ -39,8 +39,6 @@ * If {@code pred} is true, the {@code data} input is forwarded to {@code output_true}. Otherwise, * the data goes to {@code output_false}. *

    See also {@code RefSwitch} and {@code Merge}. - * - * @param data type for {@code output_false} output */ @OpMetadata( opType = SwitchCond.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/TemporaryVariable.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/TemporaryVariable.java index 3e8c8a70ec8..d66021bb728 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/TemporaryVariable.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/TemporaryVariable.java @@ -48,8 +48,6 @@ * var = state_ops.assign(var, [[4.0, 5.0]]) * var = state_ops.assign_add(var, [[6.0, 7.0]]) * final = state_ops._destroy_temporary_variable(var, var_name=var_name) - * - * @param data type for {@code ref} output */ @OpMetadata( opType = TemporaryVariable.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/TensorArrayConcat.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/TensorArrayConcat.java index b3dbc08ef3e..75ba48a0102 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/TensorArrayConcat.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/TensorArrayConcat.java @@ -48,8 +48,6 @@ * (n0 + n1 + ... + n(T-1) x d0 x d1 x ...) * *

    All elements must have the same shape (excepting the first dimension). - * - * @param data type for {@code value} output */ @OpMetadata( opType = TensorArrayConcat.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/TensorArrayGather.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/TensorArrayGather.java index 0f7fd351089..60d8b437b00 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/TensorArrayGather.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/TensorArrayGather.java @@ -40,8 +40,6 @@ /** * Gather specific elements from the TensorArray into output {@code value}. * All elements selected by {@code indices} must have the same shape. - * - * @param data type for {@code value} output */ @OpMetadata( opType = TensorArrayGather.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/TensorArrayPack.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/TensorArrayPack.java index 6e52e6ef906..d1cf5c89e65 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/TensorArrayPack.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/TensorArrayPack.java @@ -39,8 +39,6 @@ /** * The TensorArrayPack operation - * - * @param data type for {@code value} output */ @OpMetadata( opType = TensorArrayPack.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/TensorArrayRead.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/TensorArrayRead.java index 6765205c463..f5a0aa073a7 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/TensorArrayRead.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/TensorArrayRead.java @@ -38,8 +38,6 @@ /** * Read an element from the TensorArray into output {@code value}. - * - * @param data type for {@code value} output */ @OpMetadata( opType = TensorArrayRead.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/TensorListConcat.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/TensorListConcat.java index 664783a09c5..70ef65f9314 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/TensorListConcat.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/TensorListConcat.java @@ -48,8 +48,6 @@ * is not already set. * tensor: The concated result. * lengths: Output tensor containing sizes of the 0th dimension of tensors in the list, used for computing the gradient. - * - * @param data type for {@code tensor} output */ @OpMetadata( opType = TensorListConcat.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/TensorListElementShape.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/TensorListElementShape.java index d955a6a636d..6190f9c1c01 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/TensorListElementShape.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/TensorListElementShape.java @@ -39,8 +39,6 @@ * The shape of the elements of the given list, as a tensor. * input_handle: the list * element_shape: the shape of elements of the list - * - * @param data type for {@code element_shape} output */ @OpMetadata( opType = TensorListElementShape.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/TensorListGather.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/TensorListGather.java index 27a627b4759..ac725c72b97 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/TensorListGather.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/TensorListGather.java @@ -42,8 +42,6 @@ *

    input_handle: The input tensor list. * indices: The indices used to index into the list. * values: The tensor. - * - * @param data type for {@code values} output */ @OpMetadata( opType = TensorListGather.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/TensorListGetItem.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/TensorListGetItem.java index 1ea76d2101e..244704b5754 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/TensorListGetItem.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/TensorListGetItem.java @@ -40,8 +40,6 @@ * input_handle: the list * index: the position in the list from which an element will be retrieved * item: the element at that position - * - * @param data type for {@code item} output */ @OpMetadata( opType = TensorListGetItem.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/TensorListPopBack.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/TensorListPopBack.java index ee7a5cde1c9..af805e71f9b 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/TensorListPopBack.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/TensorListPopBack.java @@ -42,8 +42,6 @@ * tensor: the withdrawn last element of the list * element_dtype: the type of elements in the list * element_shape: the shape of the output tensor - * - * @param data type for {@code tensor} output */ @OpMetadata( opType = TensorListPopBack.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/TensorListStack.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/TensorListStack.java index fec4f942658..2d058b8e00d 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/TensorListStack.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/TensorListStack.java @@ -41,8 +41,6 @@ *

    input_handle: the input list * tensor: the gathered result * num_elements: optional. If not -1, the number of elements in the list. - * - * @param data type for {@code tensor} output */ @OpMetadata( opType = TensorListStack.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/TensorMapLookup.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/TensorMapLookup.java index dccdc1ee996..a3e8b54e888 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/TensorMapLookup.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/TensorMapLookup.java @@ -39,8 +39,6 @@ * input_handle: the input map * key: the key to be looked up * value: the value found from the given key - * - * @param data type for {@code value} output */ @OpMetadata( opType = TensorMapLookup.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/TensorMapStackKeys.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/TensorMapStackKeys.java index b2a217c98e6..8942b2f9f8b 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/TensorMapStackKeys.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/TensorMapStackKeys.java @@ -38,8 +38,6 @@ * Returns a Tensor stack of all keys in a tensor map. * input_handle: the input map * keys: the returned Tensor of all keys in the map - * - * @param data type for {@code keys} output */ @OpMetadata( opType = TensorMapStackKeys.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/TensorScatterNdAdd.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/TensorScatterNdAdd.java index a72a1defde1..77d1dd111d5 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/TensorScatterNdAdd.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/TensorScatterNdAdd.java @@ -94,10 +94,16 @@ * * * - *

    Note: on CPU, if an out of bound index is found, an error is returned. - * On GPU, if an out of bound index is found, the index is ignored. - * - * @param data type for {@code output} output + *

    If {@code indices} contains any out-of-bound indices, depending on + * {@code bad_indices_policy}, the op will either return an error or ignore the + * out-of-bound indices. {@code bad_indices_policy} can be one of the following values: + *

      + *
    1. "" or "DEFAULT": raises on CPU and ignore on GPU. This is because + * historically on CPU and GPU we handle errors in different ways, and for + * backward compatibility we keep the default behavior.
      2. + *
    2. "ERROR": raises error; GPU does not support this value.
      3. + *
    3. "IGNORE": ignore the bad indices; supported on both CPU and GPU.
      4. + *
    */ @OpMetadata( opType = TensorScatterNdAdd.OP_NAME, @@ -125,6 +131,7 @@ public TensorScatterNdAdd(Operation operation) { * @param tensor Tensor to copy/update. * @param indices Index tensor. * @param updates Updates to scatter into output. + * @param options carries optional attribute values * @param data type for {@code TensorScatterAdd} output and operands * @return a new instance of TensorScatterNdAdd */ @@ -132,14 +139,31 @@ public TensorScatterNdAdd(Operation operation) { describeByClass = true ) public static TensorScatterNdAdd create(Scope scope, Operand tensor, - Operand indices, Operand updates) { + Operand indices, Operand updates, Options... options) { OperationBuilder opBuilder = scope.opBuilder(OP_NAME, "TensorScatterNdAdd"); opBuilder.addInput(tensor.asOutput()); opBuilder.addInput(indices.asOutput()); opBuilder.addInput(updates.asOutput()); + if (options != null) { + for (Options opts : options) { + if (opts.badIndicesPolicy != null) { + opBuilder.setAttr("bad_indices_policy", opts.badIndicesPolicy); + } + } + } return new TensorScatterNdAdd<>(opBuilder.build()); } + /** + * Sets the badIndicesPolicy option. + * + * @param badIndicesPolicy the badIndicesPolicy option + * @return this Options instance. + */ + public static Options badIndicesPolicy(String badIndicesPolicy) { + return new Options().badIndicesPolicy(badIndicesPolicy); + } + /** * Gets output. * A new tensor copied from tensor and updates added according to the indices. @@ -154,6 +178,27 @@ public Output asOutput() { return output; } + /** + * Optional attributes for {@link org.tensorflow.op.core.TensorScatterNdAdd} + */ + public static class Options { + private String badIndicesPolicy; + + private Options() { + } + + /** + * Sets the badIndicesPolicy option. + * + * @param badIndicesPolicy the badIndicesPolicy option + * @return this Options instance. + */ + public Options badIndicesPolicy(String badIndicesPolicy) { + this.badIndicesPolicy = badIndicesPolicy; + return this; + } + } + @OpInputsMetadata( outputsClass = TensorScatterNdAdd.class ) @@ -183,14 +228,20 @@ public static class Inputs extends RawOpInputs(op), op, Arrays.asList("T", "Tindices")); + super(new TensorScatterNdAdd<>(op), op, Arrays.asList("T", "Tindices", "bad_indices_policy")); int inputIndex = 0; tensor = (Operand) op.input(inputIndex++); indices = (Operand) op.input(inputIndex++); updates = (Operand) op.input(inputIndex++); T = op.attributes().getAttrType("T"); Tindices = op.attributes().getAttrType("Tindices"); + badIndicesPolicy = op.attributes().getAttrString("bad_indices_policy"); } } } diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/TensorScatterNdMax.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/TensorScatterNdMax.java index ceddda24a20..cbf9b2dd471 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/TensorScatterNdMax.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/TensorScatterNdMax.java @@ -50,8 +50,6 @@ * * *

    Refer to {@code tf.tensor_scatter_nd_update} for more details. - * - * @param data type for {@code output} output */ @OpMetadata( opType = TensorScatterNdMax.OP_NAME, @@ -79,6 +77,7 @@ public TensorScatterNdMax(Operation operation) { * @param tensor Tensor to update. * @param indices Index tensor. * @param updates Updates to scatter into output. + * @param options carries optional attribute values * @param data type for {@code TensorScatterMax} output and operands * @return a new instance of TensorScatterNdMax */ @@ -86,14 +85,31 @@ public TensorScatterNdMax(Operation operation) { describeByClass = true ) public static TensorScatterNdMax create(Scope scope, Operand tensor, - Operand indices, Operand updates) { + Operand indices, Operand updates, Options... options) { OperationBuilder opBuilder = scope.opBuilder(OP_NAME, "TensorScatterNdMax"); opBuilder.addInput(tensor.asOutput()); opBuilder.addInput(indices.asOutput()); opBuilder.addInput(updates.asOutput()); + if (options != null) { + for (Options opts : options) { + if (opts.badIndicesPolicy != null) { + opBuilder.setAttr("bad_indices_policy", opts.badIndicesPolicy); + } + } + } return new TensorScatterNdMax<>(opBuilder.build()); } + /** + * Sets the badIndicesPolicy option. + * + * @param badIndicesPolicy the badIndicesPolicy option + * @return this Options instance. + */ + public static Options badIndicesPolicy(String badIndicesPolicy) { + return new Options().badIndicesPolicy(badIndicesPolicy); + } + /** * Gets output. * A new tensor copied from tensor whose values are element-wise maximum between tensor and updates according to the indices. @@ -108,6 +124,27 @@ public Output asOutput() { return output; } + /** + * Optional attributes for {@link org.tensorflow.op.core.TensorScatterNdMax} + */ + public static class Options { + private String badIndicesPolicy; + + private Options() { + } + + /** + * Sets the badIndicesPolicy option. + * + * @param badIndicesPolicy the badIndicesPolicy option + * @return this Options instance. + */ + public Options badIndicesPolicy(String badIndicesPolicy) { + this.badIndicesPolicy = badIndicesPolicy; + return this; + } + } + @OpInputsMetadata( outputsClass = TensorScatterNdMax.class ) @@ -137,14 +174,20 @@ public static class Inputs extends RawOpInputs(op), op, Arrays.asList("T", "Tindices")); + super(new TensorScatterNdMax<>(op), op, Arrays.asList("T", "Tindices", "bad_indices_policy")); int inputIndex = 0; tensor = (Operand) op.input(inputIndex++); indices = (Operand) op.input(inputIndex++); updates = (Operand) op.input(inputIndex++); T = op.attributes().getAttrType("T"); Tindices = op.attributes().getAttrType("Tindices"); + badIndicesPolicy = op.attributes().getAttrString("bad_indices_policy"); } } } diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/TensorScatterNdMin.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/TensorScatterNdMin.java index b6da07b4c31..7db99c551d1 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/TensorScatterNdMin.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/TensorScatterNdMin.java @@ -36,8 +36,6 @@ /** * The TensorScatterMin operation - * - * @param data type for {@code output} output */ @OpMetadata( opType = TensorScatterNdMin.OP_NAME, @@ -65,6 +63,7 @@ public TensorScatterNdMin(Operation operation) { * @param tensor Tensor to update. * @param indices Index tensor. * @param updates Updates to scatter into output. + * @param options carries optional attribute values * @param data type for {@code TensorScatterMin} output and operands * @return a new instance of TensorScatterNdMin */ @@ -72,14 +71,31 @@ public TensorScatterNdMin(Operation operation) { describeByClass = true ) public static TensorScatterNdMin create(Scope scope, Operand tensor, - Operand indices, Operand updates) { + Operand indices, Operand updates, Options... options) { OperationBuilder opBuilder = scope.opBuilder(OP_NAME, "TensorScatterNdMin"); opBuilder.addInput(tensor.asOutput()); opBuilder.addInput(indices.asOutput()); opBuilder.addInput(updates.asOutput()); + if (options != null) { + for (Options opts : options) { + if (opts.badIndicesPolicy != null) { + opBuilder.setAttr("bad_indices_policy", opts.badIndicesPolicy); + } + } + } return new TensorScatterNdMin<>(opBuilder.build()); } + /** + * Sets the badIndicesPolicy option. + * + * @param badIndicesPolicy the badIndicesPolicy option + * @return this Options instance. + */ + public static Options badIndicesPolicy(String badIndicesPolicy) { + return new Options().badIndicesPolicy(badIndicesPolicy); + } + /** * Gets output. * A new tensor copied from tensor whose values are element-wise minimum between tensor and updates according to the indices. @@ -94,6 +110,27 @@ public Output asOutput() { return output; } + /** + * Optional attributes for {@link org.tensorflow.op.core.TensorScatterNdMin} + */ + public static class Options { + private String badIndicesPolicy; + + private Options() { + } + + /** + * Sets the badIndicesPolicy option. + * + * @param badIndicesPolicy the badIndicesPolicy option + * @return this Options instance. + */ + public Options badIndicesPolicy(String badIndicesPolicy) { + this.badIndicesPolicy = badIndicesPolicy; + return this; + } + } + @OpInputsMetadata( outputsClass = TensorScatterNdMin.class ) @@ -123,14 +160,20 @@ public static class Inputs extends RawOpInputs(op), op, Arrays.asList("T", "Tindices")); + super(new TensorScatterNdMin<>(op), op, Arrays.asList("T", "Tindices", "bad_indices_policy")); int inputIndex = 0; tensor = (Operand) op.input(inputIndex++); indices = (Operand) op.input(inputIndex++); updates = (Operand) op.input(inputIndex++); T = op.attributes().getAttrType("T"); Tindices = op.attributes().getAttrType("Tindices"); + badIndicesPolicy = op.attributes().getAttrString("bad_indices_policy"); } } } diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/TensorScatterNdSub.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/TensorScatterNdSub.java index 3623707e77e..095e0428962 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/TensorScatterNdSub.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/TensorScatterNdSub.java @@ -91,8 +91,6 @@ * *

    Note that on CPU, if an out of bound index is found, an error is returned. * On GPU, if an out of bound index is found, the index is ignored. - * - * @param data type for {@code output} output */ @OpMetadata( opType = TensorScatterNdSub.OP_NAME, @@ -120,6 +118,7 @@ public TensorScatterNdSub(Operation operation) { * @param tensor Tensor to copy/update. * @param indices Index tensor. * @param updates Updates to scatter into output. + * @param options carries optional attribute values * @param data type for {@code TensorScatterSub} output and operands * @return a new instance of TensorScatterNdSub */ @@ -127,14 +126,31 @@ public TensorScatterNdSub(Operation operation) { describeByClass = true ) public static TensorScatterNdSub create(Scope scope, Operand tensor, - Operand indices, Operand updates) { + Operand indices, Operand updates, Options... options) { OperationBuilder opBuilder = scope.opBuilder(OP_NAME, "TensorScatterNdSub"); opBuilder.addInput(tensor.asOutput()); opBuilder.addInput(indices.asOutput()); opBuilder.addInput(updates.asOutput()); + if (options != null) { + for (Options opts : options) { + if (opts.badIndicesPolicy != null) { + opBuilder.setAttr("bad_indices_policy", opts.badIndicesPolicy); + } + } + } return new TensorScatterNdSub<>(opBuilder.build()); } + /** + * Sets the badIndicesPolicy option. + * + * @param badIndicesPolicy the badIndicesPolicy option + * @return this Options instance. + */ + public static Options badIndicesPolicy(String badIndicesPolicy) { + return new Options().badIndicesPolicy(badIndicesPolicy); + } + /** * Gets output. * A new tensor copied from tensor and updates subtracted according to the indices. @@ -149,6 +165,27 @@ public Output asOutput() { return output; } + /** + * Optional attributes for {@link org.tensorflow.op.core.TensorScatterNdSub} + */ + public static class Options { + private String badIndicesPolicy; + + private Options() { + } + + /** + * Sets the badIndicesPolicy option. + * + * @param badIndicesPolicy the badIndicesPolicy option + * @return this Options instance. + */ + public Options badIndicesPolicy(String badIndicesPolicy) { + this.badIndicesPolicy = badIndicesPolicy; + return this; + } + } + @OpInputsMetadata( outputsClass = TensorScatterNdSub.class ) @@ -178,14 +215,20 @@ public static class Inputs extends RawOpInputs(op), op, Arrays.asList("T", "Tindices")); + super(new TensorScatterNdSub<>(op), op, Arrays.asList("T", "Tindices", "bad_indices_policy")); int inputIndex = 0; tensor = (Operand) op.input(inputIndex++); indices = (Operand) op.input(inputIndex++); updates = (Operand) op.input(inputIndex++); T = op.attributes().getAttrType("T"); Tindices = op.attributes().getAttrType("Tindices"); + badIndicesPolicy = op.attributes().getAttrString("bad_indices_policy"); } } } diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/TensorScatterNdUpdate.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/TensorScatterNdUpdate.java index 3c53fca7eab..96323c0db29 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/TensorScatterNdUpdate.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/TensorScatterNdUpdate.java @@ -42,7 +42,6 @@ * scattered onto an existing tensor (as opposed to a zero-tensor). If the memory * for the existing tensor cannot be re-used, a copy is made and updated. *

    If {@code indices} contains duplicates, then we pick the last update for the index. - *

    If an out of bound index is found on CPU, an error is returned. *

    WARNING: There are some GPU specific semantics for this operation. *

      *
    • If an out of bound index is found, the index is ignored.
      • @@ -64,9 +63,17 @@ * 
        * indices.shape[:-1] + tensor.shape[indices.shape[-1]:]
  *
      + *

      If {@code indices} contains any out-of-bound indices, depending on + * {@code bad_indices_policy}, the op will either return an error or ignore the + * out-of-bound indices. {@code bad_indices_policy} can be one of the following values: + *

        + *
      1. "" or "DEFAULT": raises on CPU and ignore on GPU. This is because + * historically on CPU and GPU we handle errors in different ways, and for + * backward compatibility we keep the default behavior.
        2. + *
      2. "ERROR": raises error; GPU does not support this value.
        3. + *
      3. "IGNORE": ignore the bad indices; supported on both CPU and GPU.
        4. + *
      *

      For usage examples see the python tf.tensor_scatter_nd_update {@link org.tensorflow.op.Ops#tensorScatterNdUpdate} function - * - * @param data type for {@code output} output */ @OpMetadata( opType = TensorScatterNdUpdate.OP_NAME, @@ -94,6 +101,7 @@ public TensorScatterNdUpdate(Operation operation) { * @param tensor Tensor to copy/update. * @param indices Index tensor. * @param updates Updates to scatter into output. + * @param options carries optional attribute values * @param data type for {@code TensorScatterUpdate} output and operands * @return a new instance of TensorScatterNdUpdate */ @@ -101,14 +109,31 @@ public TensorScatterNdUpdate(Operation operation) { describeByClass = true ) public static TensorScatterNdUpdate create(Scope scope, Operand tensor, - Operand indices, Operand updates) { + Operand indices, Operand updates, Options... options) { OperationBuilder opBuilder = scope.opBuilder(OP_NAME, "TensorScatterNdUpdate"); opBuilder.addInput(tensor.asOutput()); opBuilder.addInput(indices.asOutput()); opBuilder.addInput(updates.asOutput()); + if (options != null) { + for (Options opts : options) { + if (opts.badIndicesPolicy != null) { + opBuilder.setAttr("bad_indices_policy", opts.badIndicesPolicy); + } + } + } return new TensorScatterNdUpdate<>(opBuilder.build()); } + /** + * Sets the badIndicesPolicy option. + * + * @param badIndicesPolicy the badIndicesPolicy option + * @return this Options instance. + */ + public static Options badIndicesPolicy(String badIndicesPolicy) { + return new Options().badIndicesPolicy(badIndicesPolicy); + } + /** * Gets output. * A new tensor with the given shape and updates applied according @@ -124,6 +149,27 @@ public Output asOutput() { return output; } + /** + * Optional attributes for {@link org.tensorflow.op.core.TensorScatterNdUpdate} + */ + public static class Options { + private String badIndicesPolicy; + + private Options() { + } + + /** + * Sets the badIndicesPolicy option. + * + * @param badIndicesPolicy the badIndicesPolicy option + * @return this Options instance. + */ + public Options badIndicesPolicy(String badIndicesPolicy) { + this.badIndicesPolicy = badIndicesPolicy; + return this; + } + } + @OpInputsMetadata( outputsClass = TensorScatterNdUpdate.class ) @@ -153,14 +199,20 @@ public static class Inputs extends RawOpInputs(op), op, Arrays.asList("T", "Tindices")); + super(new TensorScatterNdUpdate<>(op), op, Arrays.asList("T", "Tindices", "bad_indices_policy")); int inputIndex = 0; tensor = (Operand) op.input(inputIndex++); indices = (Operand) op.input(inputIndex++); updates = (Operand) op.input(inputIndex++); T = op.attributes().getAttrType("T"); Tindices = op.attributes().getAttrType("Tindices"); + badIndicesPolicy = op.attributes().getAttrString("bad_indices_policy"); } } } diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/TensorStridedSliceUpdate.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/TensorStridedSliceUpdate.java index 23b2d386a05..de80c141d72 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/TensorStridedSliceUpdate.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/TensorStridedSliceUpdate.java @@ -41,8 +41,6 @@ * {@code strides} etc. work exactly as in {@code StridedSlice}. *

      NOTE this op currently does not support broadcasting and so {@code value}'s shape * must be exactly the shape produced by the slice of {@code input}. - * - * @param data type for {@code output} output */ @OpMetadata( opType = TensorStridedSliceUpdate.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/Tile.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/Tile.java index c9a58b9158c..7339fdbb3de 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/Tile.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/Tile.java @@ -67,8 +67,6 @@ * * * - * - * @param data type for {@code output} output */ @OpMetadata( opType = Tile.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/Unbatch.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/Unbatch.java index a49747c48ca..fa4c04f3c27 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/Unbatch.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/Unbatch.java @@ -53,8 +53,6 @@ * shared_name: Instances of Unbatch with the same container and shared_name are * assumed to possibly belong to the same batch. If left empty, the op name will * be used as the shared name. - * - * @param data type for {@code unbatched_tensor} output */ @OpMetadata( opType = Unbatch.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/UnbatchGrad.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/UnbatchGrad.java index 912e08c3a6b..25418f3986f 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/UnbatchGrad.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/UnbatchGrad.java @@ -49,8 +49,6 @@ * shared_name: Instances of UnbatchGrad with the same container and shared_name * are assumed to possibly belong to the same batch. If left empty, the op name * will be used as the shared name. - * - * @param data type for {@code batched_grad} output */ @OpMetadata( opType = UnbatchGrad.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/UniformQuantizedClipByValue.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/UniformQuantizedClipByValue.java index ca3c5dfdd14..f1a4eb739d1 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/UniformQuantizedClipByValue.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/UniformQuantizedClipByValue.java @@ -40,8 +40,6 @@ * Given quantized {@code operand} which was quantized using {@code scales} and {@code zero_points}, performs clip by value using {@code min} and {@code max} values. * If quantization_axis is -1 (per-tensor quantized), the entire operand is clipped using scalar min, max. * Otherwise (per-channel quantized), the clipping is also done per-channel. - * - * @param data type for {@code output} output */ @OpMetadata( opType = UniformQuantizedClipByValue.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/Unique.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/Unique.java index c4324a9f324..4d17cf9f141 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/Unique.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/Unique.java @@ -74,10 +74,6 @@ * [2, 0]] * idx ==> [0, 1, 1] * - * - * @param data type for {@code y} output - * - * @param data type for {@code idx} output */ @OpMetadata( opType = Unique.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/UniqueWithCounts.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/UniqueWithCounts.java index 80a1804887f..8046082f95b 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/UniqueWithCounts.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/UniqueWithCounts.java @@ -78,10 +78,6 @@ * idx ==> [0, 1, 1] * count ==> [1, 2] * - * - * @param data type for {@code y} output - * - * @param data type for {@code idx} output */ @OpMetadata( opType = UniqueWithCounts.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/UnravelIndex.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/UnravelIndex.java index 5393635bc69..ec7c8f8c6e9 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/UnravelIndex.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/UnravelIndex.java @@ -52,8 +52,6 @@ *

      {@literal @}compatibility(numpy)
      * Equivalent to np.unravel_index *
      {@literal @}end_compatibility - * - * @param data type for {@code output} output */ @OpMetadata( opType = UnravelIndex.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/Unstack.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/Unstack.java index fd20a76940d..64c8de23911 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/Unstack.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/Unstack.java @@ -46,8 +46,6 @@ * and each tensor in {@code output} will have shape {@code (A, C, D)}. * Etc. *

      This is the opposite of {@code pack}. - * - * @param data type for {@code output} output */ @OpMetadata( opType = Unstack.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/UpperBound.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/UpperBound.java index d5e939ffde6..78e45391c8a 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/UpperBound.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/UpperBound.java @@ -51,8 +51,6 @@ *

      result = UpperBound(sorted_sequence, values) *

      result == [[1, 2, 4], * [0, 2, 5]] - * - * @param data type for {@code output} output */ @OpMetadata( opType = UpperBound.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/Variable.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/Variable.java index a0febf9c223..d8b09bfddde 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/Variable.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/Variable.java @@ -40,8 +40,6 @@ * Outputs a ref to the tensor state so it may be read or modified. * TODO(zhifengc/mrry): Adds a pointer to a more detail document * about sharing states in tensorflow. - * - * @param data type for {@code ref} output */ @OpMetadata( opType = Variable.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/VariableShape.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/VariableShape.java index 3f94b9efbd6..abfd8d7c504 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/VariableShape.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/VariableShape.java @@ -44,8 +44,6 @@ * # 't' is [[[1, 1, 1], [2, 2, 2]], [[3, 3, 3], [4, 4, 4]]] * shape(t) ==> [2, 2, 3] * - * - * @param data type for {@code output} output */ @OpMetadata( opType = VariableShape.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/ZerosLike.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/ZerosLike.java index 792a37d112c..497cf5128b8 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/ZerosLike.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/core/ZerosLike.java @@ -35,8 +35,6 @@ /** * Returns a tensor of zeros with the same shape and type as x. - * - * @param data type for {@code y} output */ @OpMetadata( opType = ZerosLike.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/data/GlobalShuffleDataset.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/data/GlobalShuffleDataset.java new file mode 100644 index 00000000000..19ec4cd2e96 --- /dev/null +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/data/GlobalShuffleDataset.java @@ -0,0 +1,230 @@ +/* Copyright 2018-2023 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +=======================================================================*/ + +// This class has been generated, DO NOT EDIT! + +package org.tensorflow.op.data; + +import java.util.Arrays; +import java.util.List; +import org.tensorflow.GraphOperation; +import org.tensorflow.Operand; +import org.tensorflow.Operation; +import org.tensorflow.OperationBuilder; +import org.tensorflow.Output; +import org.tensorflow.ndarray.Shape; +import org.tensorflow.op.Operands; +import org.tensorflow.op.RawOp; +import org.tensorflow.op.RawOpInputs; +import org.tensorflow.op.Scope; +import org.tensorflow.op.annotation.Endpoint; +import org.tensorflow.op.annotation.OpInputsMetadata; +import org.tensorflow.op.annotation.OpMetadata; +import org.tensorflow.proto.DataType; +import org.tensorflow.types.TInt64; +import org.tensorflow.types.family.TType; + +/** + * The GlobalShuffleDataset operation + */ +@OpMetadata( + opType = GlobalShuffleDataset.OP_NAME, + inputsClass = GlobalShuffleDataset.Inputs.class +) +public final class GlobalShuffleDataset extends RawOp implements Operand { + /** + * The name of this op, as known by TensorFlow core engine + */ + public static final String OP_NAME = "GlobalShuffleDataset"; + + private Output handle; + + @SuppressWarnings("unchecked") + public GlobalShuffleDataset(Operation operation) { + super(operation, OP_NAME); + int outputIdx = 0; + handle = operation.output(outputIdx++); + } + + /** + * Factory method to create a class wrapping a new GlobalShuffleDataset operation. + * + * @param scope current scope + * @param inputDataset The inputDataset value + * @param seed The seed value + * @param seed2 The seed2 value + * @param seedGenerator The seedGenerator value + * @param outputTypes The value of the outputTypes attribute + * @param outputShapes The value of the outputShapes attribute + * @param options carries optional attribute values + * @return a new instance of GlobalShuffleDataset + */ + @Endpoint( + describeByClass = true + ) + public static GlobalShuffleDataset create(Scope scope, Operand inputDataset, + Operand seed, Operand seed2, Operand seedGenerator, + List> outputTypes, List outputShapes, Options... options) { + OperationBuilder opBuilder = scope.opBuilder(OP_NAME, "GlobalShuffleDataset"); + opBuilder.addInput(inputDataset.asOutput()); + opBuilder.addInput(seed.asOutput()); + opBuilder.addInput(seed2.asOutput()); + opBuilder.addInput(seedGenerator.asOutput()); + opBuilder.setAttr("output_types", Operands.toDataTypes(outputTypes)); + Shape[] outputShapesArray = new Shape[outputShapes.size()]; + for (int i = 0 ; i < outputShapesArray.length ; i++) { + outputShapesArray[i] = outputShapes.get(i); + } + opBuilder.setAttr("output_shapes", outputShapesArray); + if (options != null) { + for (Options opts : options) { + if (opts.reshuffleEachIteration != null) { + opBuilder.setAttr("reshuffle_each_iteration", opts.reshuffleEachIteration); + } + if (opts.metadata != null) { + opBuilder.setAttr("metadata", opts.metadata); + } + } + } + return new GlobalShuffleDataset(opBuilder.build()); + } + + /** + * Sets the reshuffleEachIteration option. + * + * @param reshuffleEachIteration the reshuffleEachIteration option + * @return this Options instance. + */ + public static Options reshuffleEachIteration(Boolean reshuffleEachIteration) { + return new Options().reshuffleEachIteration(reshuffleEachIteration); + } + + /** + * Sets the metadata option. + * + * @param metadata the metadata option + * @return this Options instance. + */ + public static Options metadata(String metadata) { + return new Options().metadata(metadata); + } + + /** + * Gets handle. + * + * @return handle. + */ + public Output handle() { + return handle; + } + + @Override + @SuppressWarnings("unchecked") + public Output asOutput() { + return (Output) handle; + } + + /** + * Optional attributes for {@link org.tensorflow.op.data.GlobalShuffleDataset} + */ + public static class Options { + private Boolean reshuffleEachIteration; + + private String metadata; + + private Options() { + } + + /** + * Sets the reshuffleEachIteration option. + * + * @param reshuffleEachIteration the reshuffleEachIteration option + * @return this Options instance. + */ + public Options reshuffleEachIteration(Boolean reshuffleEachIteration) { + this.reshuffleEachIteration = reshuffleEachIteration; + return this; + } + + /** + * Sets the metadata option. + * + * @param metadata the metadata option + * @return this Options instance. + */ + public Options metadata(String metadata) { + this.metadata = metadata; + return this; + } + } + + @OpInputsMetadata( + outputsClass = GlobalShuffleDataset.class + ) + public static class Inputs extends RawOpInputs { + /** + * The inputDataset input + */ + public final Operand inputDataset; + + /** + * The seed input + */ + public final Operand seed; + + /** + * The seed2 input + */ + public final Operand seed2; + + /** + * The seedGenerator input + */ + public final Operand seedGenerator; + + /** + * The reshuffleEachIteration attribute + */ + public final boolean reshuffleEachIteration; + + /** + * The outputTypes attribute + */ + public final DataType[] outputTypes; + + /** + * The outputShapes attribute + */ + public final Shape[] outputShapes; + + /** + * The metadata attribute + */ + public final String metadata; + + public Inputs(GraphOperation op) { + super(new GlobalShuffleDataset(op), op, Arrays.asList("reshuffle_each_iteration", "output_types", "output_shapes", "metadata")); + int inputIndex = 0; + inputDataset = (Operand) op.input(inputIndex++); + seed = (Operand) op.input(inputIndex++); + seed2 = (Operand) op.input(inputIndex++); + seedGenerator = (Operand) op.input(inputIndex++); + reshuffleEachIteration = op.attributes().getAttrBool("reshuffle_each_iteration"); + outputTypes = op.attributes().getAttrTypeList("output_types"); + outputShapes = op.attributes().getAttrShapeList("output_shapes"); + metadata = op.attributes().getAttrString("metadata"); + } + } +} diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/data/IndexFlatMapDataset.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/data/IndexFlatMapDataset.java new file mode 100644 index 00000000000..b5d3f116ad5 --- /dev/null +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/data/IndexFlatMapDataset.java @@ -0,0 +1,224 @@ +/* Copyright 2018-2023 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +=======================================================================*/ + +// This class has been generated, DO NOT EDIT! + +package org.tensorflow.op.data; + +import java.util.Arrays; +import java.util.List; +import org.tensorflow.ConcreteFunction; +import org.tensorflow.GraphOperation; +import org.tensorflow.Operand; +import org.tensorflow.Operation; +import org.tensorflow.OperationBuilder; +import org.tensorflow.Output; +import org.tensorflow.ndarray.Shape; +import org.tensorflow.op.Operands; +import org.tensorflow.op.RawOp; +import org.tensorflow.op.RawOpInputs; +import org.tensorflow.op.Scope; +import org.tensorflow.op.annotation.Endpoint; +import org.tensorflow.op.annotation.OpInputsMetadata; +import org.tensorflow.op.annotation.OpMetadata; +import org.tensorflow.op.annotation.Operator; +import org.tensorflow.proto.DataType; +import org.tensorflow.types.TInt64; +import org.tensorflow.types.family.TType; + +/** + * The IndexFlatMapDataset operation + */ +@OpMetadata( + opType = IndexFlatMapDataset.OP_NAME, + inputsClass = IndexFlatMapDataset.Inputs.class +) +@Operator( + group = "data" +) +public final class IndexFlatMapDataset extends RawOp implements Operand { + /** + * The name of this op, as known by TensorFlow core engine + */ + public static final String OP_NAME = "IndexFlatMapDataset"; + + private Output handle; + + @SuppressWarnings("unchecked") + public IndexFlatMapDataset(Operation operation) { + super(operation, OP_NAME); + int outputIdx = 0; + handle = operation.output(outputIdx++); + } + + /** + * Factory method to create a class wrapping a new IndexFlatMapDataset operation. + * + * @param scope current scope + * @param inputDataset The inputDataset value + * @param mapFuncOtherArgs The mapFuncOtherArgs value + * @param indexMapFuncOtherArgs The indexMapFuncOtherArgs value + * @param outputCardinality The outputCardinality value + * @param mapFunc The value of the mapFunc attribute + * @param indexMapFunc The value of the indexMapFunc attribute + * @param outputTypes The value of the outputTypes attribute + * @param outputShapes The value of the outputShapes attribute + * @param options carries optional attribute values + * @return a new instance of IndexFlatMapDataset + */ + @Endpoint( + describeByClass = true + ) + public static IndexFlatMapDataset create(Scope scope, Operand inputDataset, + Iterable> mapFuncOtherArgs, Iterable> indexMapFuncOtherArgs, + Operand outputCardinality, ConcreteFunction mapFunc, ConcreteFunction indexMapFunc, + List> outputTypes, List outputShapes, Options... options) { + OperationBuilder opBuilder = scope.opBuilder(OP_NAME, "IndexFlatMapDataset"); + opBuilder.addInput(inputDataset.asOutput()); + opBuilder.addInputList(Operands.asOutputs(mapFuncOtherArgs)); + opBuilder.addInputList(Operands.asOutputs(indexMapFuncOtherArgs)); + opBuilder.addInput(outputCardinality.asOutput()); + opBuilder.setAttr("map_func", mapFunc); + opBuilder.setAttr("index_map_func", indexMapFunc); + opBuilder.setAttr("output_types", Operands.toDataTypes(outputTypes)); + Shape[] outputShapesArray = new Shape[outputShapes.size()]; + for (int i = 0 ; i < outputShapesArray.length ; i++) { + outputShapesArray[i] = outputShapes.get(i); + } + opBuilder.setAttr("output_shapes", outputShapesArray); + if (options != null) { + for (Options opts : options) { + if (opts.metadata != null) { + opBuilder.setAttr("metadata", opts.metadata); + } + } + } + return new IndexFlatMapDataset(opBuilder.build()); + } + + /** + * Sets the metadata option. + * + * @param metadata the metadata option + * @return this Options instance. + */ + public static Options metadata(String metadata) { + return new Options().metadata(metadata); + } + + /** + * Gets handle. + * + * @return handle. + */ + public Output handle() { + return handle; + } + + @Override + @SuppressWarnings("unchecked") + public Output asOutput() { + return (Output) handle; + } + + /** + * Optional attributes for {@link org.tensorflow.op.data.IndexFlatMapDataset} + */ + public static class Options { + private String metadata; + + private Options() { + } + + /** + * Sets the metadata option. + * + * @param metadata the metadata option + * @return this Options instance. + */ + public Options metadata(String metadata) { + this.metadata = metadata; + return this; + } + } + + @OpInputsMetadata( + outputsClass = IndexFlatMapDataset.class + ) + public static class Inputs extends RawOpInputs { + /** + * The inputDataset input + */ + public final Operand inputDataset; + + /** + * The mapFuncOtherArgs input + */ + public final Iterable> mapFuncOtherArgs; + + /** + * The indexMapFuncOtherArgs input + */ + public final Iterable> indexMapFuncOtherArgs; + + /** + * The outputCardinality input + */ + public final Operand outputCardinality; + + /** + * The TmapFuncArgs attribute + */ + public final DataType[] TmapFuncArgs; + + /** + * The TindexMapFuncArgs attribute + */ + public final DataType[] TindexMapFuncArgs; + + /** + * The outputTypes attribute + */ + public final DataType[] outputTypes; + + /** + * The outputShapes attribute + */ + public final Shape[] outputShapes; + + /** + * The metadata attribute + */ + public final String metadata; + + public Inputs(GraphOperation op) { + super(new IndexFlatMapDataset(op), op, Arrays.asList("Tmap_func_args", "Tindex_map_func_args", "output_types", "output_shapes", "metadata")); + int inputIndex = 0; + inputDataset = (Operand) op.input(inputIndex++); + int mapFuncOtherArgsLength = op.inputListLength("map_func_other_args"); + mapFuncOtherArgs = Arrays.asList((Operand[]) op.inputList(inputIndex, mapFuncOtherArgsLength)); + inputIndex += mapFuncOtherArgsLength; + int indexMapFuncOtherArgsLength = op.inputListLength("index_map_func_other_args"); + indexMapFuncOtherArgs = Arrays.asList((Operand[]) op.inputList(inputIndex, indexMapFuncOtherArgsLength)); + inputIndex += indexMapFuncOtherArgsLength; + outputCardinality = (Operand) op.input(inputIndex++); + TmapFuncArgs = op.attributes().getAttrTypeList("Tmap_func_args"); + TindexMapFuncArgs = op.attributes().getAttrTypeList("Tindex_map_func_args"); + outputTypes = op.attributes().getAttrTypeList("output_types"); + outputShapes = op.attributes().getAttrShapeList("output_shapes"); + metadata = op.attributes().getAttrString("metadata"); + } + } +} diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/data/IteratorGetModelProto.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/data/IteratorGetModelProto.java new file mode 100644 index 00000000000..1ad0de4c183 --- /dev/null +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/data/IteratorGetModelProto.java @@ -0,0 +1,102 @@ +/* Copyright 2018-2023 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +=======================================================================*/ + +// This class has been generated, DO NOT EDIT! + +package org.tensorflow.op.data; + +import java.util.Arrays; +import org.tensorflow.GraphOperation; +import org.tensorflow.Operand; +import org.tensorflow.Operation; +import org.tensorflow.OperationBuilder; +import org.tensorflow.Output; +import org.tensorflow.op.RawOp; +import org.tensorflow.op.RawOpInputs; +import org.tensorflow.op.Scope; +import org.tensorflow.op.annotation.Endpoint; +import org.tensorflow.op.annotation.OpInputsMetadata; +import org.tensorflow.op.annotation.OpMetadata; +import org.tensorflow.types.TString; +import org.tensorflow.types.family.TType; + +/** + * Returns the serialized model proto of an iterator resource. + * Returns the serialized model proto of an iterator resource. + */ +@OpMetadata( + opType = IteratorGetModelProto.OP_NAME, + inputsClass = IteratorGetModelProto.Inputs.class +) +public final class IteratorGetModelProto extends RawOp implements Operand { + /** + * The name of this op, as known by TensorFlow core engine + */ + public static final String OP_NAME = "IteratorGetModelProto"; + + private Output modelProto; + + public IteratorGetModelProto(Operation operation) { + super(operation, OP_NAME); + int outputIdx = 0; + modelProto = operation.output(outputIdx++); + } + + /** + * Factory method to create a class wrapping a new IteratorGetModelProto operation. + * + * @param scope current scope + * @param iterator An resource from an dataset iterator. + * @return a new instance of IteratorGetModelProto + */ + @Endpoint( + describeByClass = true + ) + public static IteratorGetModelProto create(Scope scope, Operand iterator) { + OperationBuilder opBuilder = scope.opBuilder(OP_NAME, "IteratorGetModelProto"); + opBuilder.addInput(iterator.asOutput()); + return new IteratorGetModelProto(opBuilder.build()); + } + + /** + * Gets modelProto. + * A serialized model proto. + * @return modelProto. + */ + public Output modelProto() { + return modelProto; + } + + @Override + public Output asOutput() { + return modelProto; + } + + @OpInputsMetadata( + outputsClass = IteratorGetModelProto.class + ) + public static class Inputs extends RawOpInputs { + /** + * An resource from an dataset iterator. + */ + public final Operand iterator; + + public Inputs(GraphOperation op) { + super(new IteratorGetModelProto(op), op, Arrays.asList()); + int inputIndex = 0; + iterator = (Operand) op.input(inputIndex++); + } + } +} diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/data/LeakyReluGrad.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/data/LeakyReluGrad.java index a42cc0f51d2..131903f2fc1 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/data/LeakyReluGrad.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/data/LeakyReluGrad.java @@ -35,8 +35,6 @@ /** * Computes rectified linear gradients for a LeakyRelu operation. - * - * @param data type for {@code backprops} output */ @OpMetadata( opType = LeakyReluGrad.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/data/MapDataset.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/data/MapDataset.java index 6e8ca298f38..4b6e7355a51 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/data/MapDataset.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/data/MapDataset.java @@ -98,6 +98,9 @@ public static MapDataset create(Scope scope, Operand inputDatas if (opts.preserveCardinality != null) { opBuilder.setAttr("preserve_cardinality", opts.preserveCardinality); } + if (opts.forceSynchronous != null) { + opBuilder.setAttr("force_synchronous", opts.forceSynchronous); + } if (opts.metadata != null) { opBuilder.setAttr("metadata", opts.metadata); } @@ -126,6 +129,16 @@ public static Options preserveCardinality(Boolean preserveCardinality) { return new Options().preserveCardinality(preserveCardinality); } + /** + * Sets the forceSynchronous option. + * + * @param forceSynchronous the forceSynchronous option + * @return this Options instance. + */ + public static Options forceSynchronous(Boolean forceSynchronous) { + return new Options().forceSynchronous(forceSynchronous); + } + /** * Sets the metadata option. * @@ -159,6 +172,8 @@ public static class Options { private Boolean preserveCardinality; + private Boolean forceSynchronous; + private String metadata; private Options() { @@ -186,6 +201,17 @@ public Options preserveCardinality(Boolean preserveCardinality) { return this; } + /** + * Sets the forceSynchronous option. + * + * @param forceSynchronous the forceSynchronous option + * @return this Options instance. + */ + public Options forceSynchronous(Boolean forceSynchronous) { + this.forceSynchronous = forceSynchronous; + return this; + } + /** * Sets the metadata option. * @@ -237,13 +263,18 @@ public static class Inputs extends RawOpInputs { */ public final boolean preserveCardinality; + /** + * The forceSynchronous attribute + */ + public final boolean forceSynchronous; + /** * The metadata attribute */ public final String metadata; public Inputs(GraphOperation op) { - super(new MapDataset(op), op, Arrays.asList("Targuments", "output_types", "output_shapes", "use_inter_op_parallelism", "preserve_cardinality", "metadata")); + super(new MapDataset(op), op, Arrays.asList("Targuments", "output_types", "output_shapes", "use_inter_op_parallelism", "preserve_cardinality", "force_synchronous", "metadata")); int inputIndex = 0; inputDataset = (Operand) op.input(inputIndex++); int otherArgumentsLength = op.inputListLength("other_arguments"); @@ -254,6 +285,7 @@ public Inputs(GraphOperation op) { outputShapes = op.attributes().getAttrShapeList("output_shapes"); useInterOpParallelism = op.attributes().getAttrBool("use_inter_op_parallelism"); preserveCardinality = op.attributes().getAttrBool("preserve_cardinality"); + forceSynchronous = op.attributes().getAttrBool("force_synchronous"); metadata = op.attributes().getAttrString("metadata"); } } diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/data/ParallelMapDataset.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/data/ParallelMapDataset.java index 68e97058b5c..6b783929411 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/data/ParallelMapDataset.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/data/ParallelMapDataset.java @@ -107,6 +107,9 @@ public static ParallelMapDataset create(Scope scope, Operand in if (opts.preserveCardinality != null) { opBuilder.setAttr("preserve_cardinality", opts.preserveCardinality); } + if (opts.useUnboundedThreadpool != null) { + opBuilder.setAttr("use_unbounded_threadpool", opts.useUnboundedThreadpool); + } if (opts.metadata != null) { opBuilder.setAttr("metadata", opts.metadata); } @@ -145,6 +148,16 @@ public static Options preserveCardinality(Boolean preserveCardinality) { return new Options().preserveCardinality(preserveCardinality); } + /** + * Sets the useUnboundedThreadpool option. + * + * @param useUnboundedThreadpool the useUnboundedThreadpool option + * @return this Options instance. + */ + public static Options useUnboundedThreadpool(Boolean useUnboundedThreadpool) { + return new Options().useUnboundedThreadpool(useUnboundedThreadpool); + } + /** * Sets the metadata option. * @@ -180,6 +193,8 @@ public static class Options { private Boolean preserveCardinality; + private Boolean useUnboundedThreadpool; + private String metadata; private Options() { @@ -218,6 +233,17 @@ public Options preserveCardinality(Boolean preserveCardinality) { return this; } + /** + * Sets the useUnboundedThreadpool option. + * + * @param useUnboundedThreadpool the useUnboundedThreadpool option + * @return this Options instance. + */ + public Options useUnboundedThreadpool(Boolean useUnboundedThreadpool) { + this.useUnboundedThreadpool = useUnboundedThreadpool; + return this; + } + /** * Sets the metadata option. * @@ -280,13 +306,18 @@ public static class Inputs extends RawOpInputs { */ public final boolean preserveCardinality; + /** + * The useUnboundedThreadpool attribute + */ + public final boolean useUnboundedThreadpool; + /** * The metadata attribute */ public final String metadata; public Inputs(GraphOperation op) { - super(new ParallelMapDataset(op), op, Arrays.asList("Targuments", "output_types", "output_shapes", "use_inter_op_parallelism", "deterministic", "preserve_cardinality", "metadata")); + super(new ParallelMapDataset(op), op, Arrays.asList("Targuments", "output_types", "output_shapes", "use_inter_op_parallelism", "deterministic", "preserve_cardinality", "use_unbounded_threadpool", "metadata")); int inputIndex = 0; inputDataset = (Operand) op.input(inputIndex++); int otherArgumentsLength = op.inputListLength("other_arguments"); @@ -299,6 +330,7 @@ public Inputs(GraphOperation op) { useInterOpParallelism = op.attributes().getAttrBool("use_inter_op_parallelism"); deterministic = op.attributes().getAttrString("deterministic"); preserveCardinality = op.attributes().getAttrBool("preserve_cardinality"); + useUnboundedThreadpool = op.attributes().getAttrBool("use_unbounded_threadpool"); metadata = op.attributes().getAttrString("metadata"); } } diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/data/WeightedFlatMapDataset.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/data/WeightedFlatMapDataset.java new file mode 100644 index 00000000000..2f97c1e168c --- /dev/null +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/data/WeightedFlatMapDataset.java @@ -0,0 +1,186 @@ +/* Copyright 2018-2023 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +=======================================================================*/ + +// This class has been generated, DO NOT EDIT! + +package org.tensorflow.op.data; + +import java.util.Arrays; +import java.util.List; +import org.tensorflow.GraphOperation; +import org.tensorflow.Operand; +import org.tensorflow.Operation; +import org.tensorflow.OperationBuilder; +import org.tensorflow.Output; +import org.tensorflow.ndarray.Shape; +import org.tensorflow.op.Operands; +import org.tensorflow.op.RawOp; +import org.tensorflow.op.RawOpInputs; +import org.tensorflow.op.Scope; +import org.tensorflow.op.annotation.Endpoint; +import org.tensorflow.op.annotation.OpInputsMetadata; +import org.tensorflow.op.annotation.OpMetadata; +import org.tensorflow.proto.DataType; +import org.tensorflow.types.TFloat64; +import org.tensorflow.types.family.TType; + +/** + * The WeightedFlatMapDataset operation + */ +@OpMetadata( + opType = WeightedFlatMapDataset.OP_NAME, + inputsClass = WeightedFlatMapDataset.Inputs.class +) +public final class WeightedFlatMapDataset extends RawOp implements Operand { + /** + * The name of this op, as known by TensorFlow core engine + */ + public static final String OP_NAME = "WeightedFlatMapDataset"; + + private Output handle; + + @SuppressWarnings("unchecked") + public WeightedFlatMapDataset(Operation operation) { + super(operation, OP_NAME); + int outputIdx = 0; + handle = operation.output(outputIdx++); + } + + /** + * Factory method to create a class wrapping a new WeightedFlatMapDataset operation. + * + * @param scope current scope + * @param inputDatasets The inputDatasets value + * @param weights The weights value + * @param outputTypes The value of the outputTypes attribute + * @param outputShapes The value of the outputShapes attribute + * @param options carries optional attribute values + * @return a new instance of WeightedFlatMapDataset + */ + @Endpoint( + describeByClass = true + ) + public static WeightedFlatMapDataset create(Scope scope, + Iterable> inputDatasets, Iterable> weights, + List> outputTypes, List outputShapes, Options... options) { + OperationBuilder opBuilder = scope.opBuilder(OP_NAME, "WeightedFlatMapDataset"); + opBuilder.addInputList(Operands.asOutputs(inputDatasets)); + opBuilder.addInputList(Operands.asOutputs(weights)); + opBuilder.setAttr("output_types", Operands.toDataTypes(outputTypes)); + Shape[] outputShapesArray = new Shape[outputShapes.size()]; + for (int i = 0 ; i < outputShapesArray.length ; i++) { + outputShapesArray[i] = outputShapes.get(i); + } + opBuilder.setAttr("output_shapes", outputShapesArray); + if (options != null) { + for (Options opts : options) { + if (opts.metadata != null) { + opBuilder.setAttr("metadata", opts.metadata); + } + } + } + return new WeightedFlatMapDataset(opBuilder.build()); + } + + /** + * Sets the metadata option. + * + * @param metadata the metadata option + * @return this Options instance. + */ + public static Options metadata(String metadata) { + return new Options().metadata(metadata); + } + + /** + * Gets handle. + * + * @return handle. + */ + public Output handle() { + return handle; + } + + @Override + @SuppressWarnings("unchecked") + public Output asOutput() { + return (Output) handle; + } + + /** + * Optional attributes for {@link org.tensorflow.op.data.WeightedFlatMapDataset} + */ + public static class Options { + private String metadata; + + private Options() { + } + + /** + * Sets the metadata option. + * + * @param metadata the metadata option + * @return this Options instance. + */ + public Options metadata(String metadata) { + this.metadata = metadata; + return this; + } + } + + @OpInputsMetadata( + outputsClass = WeightedFlatMapDataset.class + ) + public static class Inputs extends RawOpInputs { + /** + * The inputDatasets input + */ + public final Iterable> inputDatasets; + + /** + * The weights input + */ + public final Iterable> weights; + + /** + * The outputTypes attribute + */ + public final DataType[] outputTypes; + + /** + * The outputShapes attribute + */ + public final Shape[] outputShapes; + + /** + * The metadata attribute + */ + public final String metadata; + + public Inputs(GraphOperation op) { + super(new WeightedFlatMapDataset(op), op, Arrays.asList("output_types", "output_shapes", "metadata")); + int inputIndex = 0; + int inputDatasetsLength = op.inputListLength("input_datasets"); + inputDatasets = Arrays.asList((Operand[]) op.inputList(inputIndex, inputDatasetsLength)); + inputIndex += inputDatasetsLength; + int weightsLength = op.inputListLength("weights"); + weights = Arrays.asList((Operand[]) op.inputList(inputIndex, weightsLength)); + inputIndex += weightsLength; + outputTypes = op.attributes().getAttrTypeList("output_types"); + outputShapes = op.attributes().getAttrShapeList("output_shapes"); + metadata = op.attributes().getAttrString("metadata"); + } + } +} diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/data/experimental/MapDataset.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/data/experimental/MapDataset.java index f02bba6e46a..7c8cfafc8f4 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/data/experimental/MapDataset.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/data/experimental/MapDataset.java @@ -98,6 +98,9 @@ public static MapDataset create(Scope scope, Operand inputDatas if (opts.preserveCardinality != null) { opBuilder.setAttr("preserve_cardinality", opts.preserveCardinality); } + if (opts.forceSynchronous != null) { + opBuilder.setAttr("force_synchronous", opts.forceSynchronous); + } } } return new MapDataset(opBuilder.build()); @@ -123,6 +126,16 @@ public static Options preserveCardinality(Boolean preserveCardinality) { return new Options().preserveCardinality(preserveCardinality); } + /** + * Sets the forceSynchronous option. + * + * @param forceSynchronous the forceSynchronous option + * @return this Options instance. + */ + public static Options forceSynchronous(Boolean forceSynchronous) { + return new Options().forceSynchronous(forceSynchronous); + } + /** * Gets handle. * @@ -146,6 +159,8 @@ public static class Options { private Boolean preserveCardinality; + private Boolean forceSynchronous; + private Options() { } @@ -170,6 +185,17 @@ public Options preserveCardinality(Boolean preserveCardinality) { this.preserveCardinality = preserveCardinality; return this; } + + /** + * Sets the forceSynchronous option. + * + * @param forceSynchronous the forceSynchronous option + * @return this Options instance. + */ + public Options forceSynchronous(Boolean forceSynchronous) { + this.forceSynchronous = forceSynchronous; + return this; + } } @OpInputsMetadata( @@ -211,8 +237,13 @@ public static class Inputs extends RawOpInputs { */ public final boolean preserveCardinality; + /** + * The forceSynchronous attribute + */ + public final boolean forceSynchronous; + public Inputs(GraphOperation op) { - super(new MapDataset(op), op, Arrays.asList("Targuments", "output_types", "output_shapes", "use_inter_op_parallelism", "preserve_cardinality")); + super(new MapDataset(op), op, Arrays.asList("Targuments", "output_types", "output_shapes", "use_inter_op_parallelism", "preserve_cardinality", "force_synchronous")); int inputIndex = 0; inputDataset = (Operand) op.input(inputIndex++); int otherArgumentsLength = op.inputListLength("other_arguments"); @@ -223,6 +254,7 @@ public Inputs(GraphOperation op) { outputShapes = op.attributes().getAttrShapeList("output_shapes"); useInterOpParallelism = op.attributes().getAttrBool("use_inter_op_parallelism"); preserveCardinality = op.attributes().getAttrBool("preserve_cardinality"); + forceSynchronous = op.attributes().getAttrBool("force_synchronous"); } } } diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/debugging/CheckNumerics.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/debugging/CheckNumerics.java index d1aae3e74ad..86215fa9a9c 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/debugging/CheckNumerics.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/debugging/CheckNumerics.java @@ -39,8 +39,6 @@ * that are not a number (NaN) or infinity (Inf). Otherwise, returns the input * tensor. Unlike CheckNumerics (V1), CheckNumericsV2 distinguishes -Inf and +Inf * in the errors it throws. - * - * @param data type for {@code output} output */ @OpMetadata( opType = CheckNumerics.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/debugging/DebugGradientIdentity.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/debugging/DebugGradientIdentity.java index 37f2fec7d91..776a971ef27 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/debugging/DebugGradientIdentity.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/debugging/DebugGradientIdentity.java @@ -37,8 +37,6 @@ * This op is hidden from public in Python. It is used by TensorFlow Debugger to * register gradient tensors for gradient debugging. * This op operates on non-reference-type tensors. - * - * @param data type for {@code output} output */ @OpMetadata( opType = DebugGradientIdentity.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/debugging/DebugGradientRefIdentity.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/debugging/DebugGradientRefIdentity.java index 5071299a66a..76a9e9029ca 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/debugging/DebugGradientRefIdentity.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/debugging/DebugGradientRefIdentity.java @@ -37,8 +37,6 @@ * This op is hidden from public in Python. It is used by TensorFlow Debugger to * register gradient tensors for gradient debugging. * This op operates on reference-type tensors. - * - * @param data type for {@code output} output */ @OpMetadata( opType = DebugGradientRefIdentity.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/debugging/DebugIdentity.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/debugging/DebugIdentity.java index 63c7105e3c8..10edd71d4b1 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/debugging/DebugIdentity.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/debugging/DebugIdentity.java @@ -36,8 +36,6 @@ /** * Provides an identity mapping of the non-Ref type input tensor for debugging. * Provides an identity mapping of the non-Ref type input tensor for debugging. - * - * @param data type for {@code output} output */ @OpMetadata( opType = DebugIdentity.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/debugging/DebugNumericsSummary.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/debugging/DebugNumericsSummary.java index ec63e9da708..4ff0f11c7bc 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/debugging/DebugNumericsSummary.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/debugging/DebugNumericsSummary.java @@ -40,8 +40,6 @@ * Computes a numeric summary of the input tensor. The shape of the output * depends on the tensor_debug_mode attribute. * This op is used internally by TensorFlow Debugger (tfdbg) v2. - * - * @param data type for {@code output} output */ @OpMetadata( opType = DebugNumericsSummary.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/distribute/NcclAllReduce.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/distribute/NcclAllReduce.java index c5416746198..7cc17dd9d36 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/distribute/NcclAllReduce.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/distribute/NcclAllReduce.java @@ -45,8 +45,6 @@ * reduction: the reduction operation to perform. * num_devices: The number of devices participating in this reduction. * shared_name: Identifier that shared between ops of the same reduction. - * - * @param data type for {@code data} output */ @OpMetadata( opType = NcclAllReduce.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/distribute/NcclBroadcast.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/distribute/NcclBroadcast.java index 3824d6a10dd..41a2050e44f 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/distribute/NcclBroadcast.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/distribute/NcclBroadcast.java @@ -42,8 +42,6 @@ *

      input: The input to the broadcast. * output: The same as input. * shape: The shape of the input tensor. - * - * @param data type for {@code output} output */ @OpMetadata( opType = NcclBroadcast.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/distribute/NcclReduce.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/distribute/NcclReduce.java index 2a80593be6c..8fcf62bf4cc 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/distribute/NcclReduce.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/distribute/NcclReduce.java @@ -42,8 +42,6 @@ *

      input: The input to the reduction. * data: the value of the reduction across all {@code num_devices} devices. * reduction: the reduction operation to perform. - * - * @param data type for {@code data} output */ @OpMetadata( opType = NcclReduce.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/dtypes/Cast.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/dtypes/Cast.java index 806ad99e2ea..af516490d88 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/dtypes/Cast.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/dtypes/Cast.java @@ -36,8 +36,6 @@ /** * Cast x of type SrcT to y of DstT. - * - * @param data type for {@code y} output */ @OpMetadata( opType = Cast.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/dtypes/Complex.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/dtypes/Complex.java index 6b0a717157c..0da2678549f 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/dtypes/Complex.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/dtypes/Complex.java @@ -48,8 +48,6 @@ * # tensor `imag` is [4.75, 5.75] * tf.complex(real, imag) ==> [[2.25 + 4.75j], [3.25 + 5.75j]] * - * - * @param data type for {@code out} output */ @OpMetadata( opType = Complex.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/image/AdjustContrast.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/image/AdjustContrast.java index 0a6a141c036..123c74afd50 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/image/AdjustContrast.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/image/AdjustContrast.java @@ -43,8 +43,6 @@ *

      For each channel, the Op first computes the mean of the image pixels in the * channel and then adjusts each component of each pixel to * {@code (x - mean) * contrast_factor + mean}. - * - * @param data type for {@code output} output */ @OpMetadata( opType = AdjustContrast.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/image/AdjustHue.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/image/AdjustHue.java index 45fe50175c4..b0001085638 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/image/AdjustHue.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/image/AdjustHue.java @@ -41,8 +41,6 @@ *

      The input image is considered in the RGB colorspace. Conceptually, the RGB * colors are first mapped into HSV. A delta is then applied all the hue values, * and then remapped back to RGB colorspace. - * - * @param data type for {@code output} output */ @OpMetadata( opType = AdjustHue.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/image/AdjustSaturation.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/image/AdjustSaturation.java index a7fea42d8fb..5f0c063dc1d 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/image/AdjustSaturation.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/image/AdjustSaturation.java @@ -41,8 +41,6 @@ *

      The input image is considered in the RGB colorspace. Conceptually, the RGB * colors are first mapped into HSV. A scale is then applied all the saturation * values, and then remapped back to RGB colorspace. - * - * @param data type for {@code output} output */ @OpMetadata( opType = AdjustSaturation.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/image/CropAndResizeGradImage.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/image/CropAndResizeGradImage.java index 59e98a3252d..e639b0f2cb7 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/image/CropAndResizeGradImage.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/image/CropAndResizeGradImage.java @@ -38,8 +38,6 @@ /** * Computes the gradient of the crop_and_resize op wrt the input image tensor. - * - * @param data type for {@code output} output */ @OpMetadata( opType = CropAndResizeGradImage.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/image/DecodeImage.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/image/DecodeImage.java index ae91e89973a..a5c7ee7845e 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/image/DecodeImage.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/image/DecodeImage.java @@ -53,8 +53,6 @@ * unoccupied areas (in the first frame) with zeros (black). For frames after the * first frame that does not occupy the entire canvas, it uses the previous * frame to fill the unoccupied areas. - * - * @param data type for {@code image} output */ @OpMetadata( opType = DecodeImage.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/image/DecodePng.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/image/DecodePng.java index db44c3b3146..dd6384caf7c 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/image/DecodePng.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/image/DecodePng.java @@ -51,8 +51,6 @@ * of color channels. *

      This op also supports decoding JPEGs and non-animated GIFs since the interface * is the same, though it is cleaner to use {@code tf.io.decode_image}. - * - * @param data type for {@code image} output */ @OpMetadata( opType = DecodePng.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/image/DrawBoundingBoxes.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/image/DrawBoundingBoxes.java index 8033cecb4c9..56c64a5e50c 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/image/DrawBoundingBoxes.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/image/DrawBoundingBoxes.java @@ -45,8 +45,6 @@ * box is {@code [0.1, 0.2, 0.5, 0.9]}, the upper-left and bottom-right coordinates of * the bounding box will be {@code (40, 10)} to {@code (100, 50)} (in (x,y) coordinates). *

      Parts of the bounding box may fall outside the image. - * - * @param data type for {@code output} output */ @OpMetadata( opType = DrawBoundingBoxes.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/image/ExtractImagePatches.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/image/ExtractImagePatches.java index 69492ac2873..54395a44acc 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/image/ExtractImagePatches.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/image/ExtractImagePatches.java @@ -36,8 +36,6 @@ /** * Extract {@code patches} from {@code images} and put them in the "depth" output dimension. - * - * @param data type for {@code patches} output */ @OpMetadata( opType = ExtractImagePatches.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/image/ExtractJpegShape.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/image/ExtractJpegShape.java index 368fe5cfd02..4ca887e7e72 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/image/ExtractJpegShape.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/image/ExtractJpegShape.java @@ -39,8 +39,6 @@ /** * Extract the shape information of a JPEG-encoded image. * This op only parses the image header, so it is much faster than DecodeJpeg. - * - * @param data type for {@code image_shape} output */ @OpMetadata( opType = ExtractJpegShape.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/image/HsvToRgb.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/image/HsvToRgb.java index 6e32b95ca11..abd3d53d884 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/image/HsvToRgb.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/image/HsvToRgb.java @@ -39,8 +39,6 @@ * value of the pixels. The output is only well defined if the value in {@code images} * are in {@code [0,1]}. *

      See {@code rgb_to_hsv} for a description of the HSV encoding. - * - * @param data type for {@code output} output */ @OpMetadata( opType = HsvToRgb.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/image/ImageProjectiveTransformV2.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/image/ImageProjectiveTransformV2.java index 572b3e59d16..cef590ad519 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/image/ImageProjectiveTransformV2.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/image/ImageProjectiveTransformV2.java @@ -42,8 +42,6 @@ * {@code (x', y') = ((a0 x + a1 y + a2) / k, (b0 x + b1 y + b2) / k)}, where * {@code k = c0 x + c1 y + 1}. If the transformed point lays outside of the input * image, the output pixel is set to 0. - * - * @param data type for {@code transformed_images} output */ @OpMetadata( opType = ImageProjectiveTransformV2.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/image/ImageProjectiveTransformV3.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/image/ImageProjectiveTransformV3.java index 2c448fc9397..59f06c2b982 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/image/ImageProjectiveTransformV3.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/image/ImageProjectiveTransformV3.java @@ -42,8 +42,6 @@ * {@code (x', y') = ((a0 x + a1 y + a2) / k, (b0 x + b1 y + b2) / k)}, where * {@code k = c0 x + c1 y + 1}. If the transformed point lays outside of the input * image, the output pixel is set to fill_value. - * - * @param data type for {@code transformed_images} output */ @OpMetadata( opType = ImageProjectiveTransformV3.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/image/NonMaxSuppression.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/image/NonMaxSuppression.java index 65c6f7f7f2a..f682bfd1f5a 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/image/NonMaxSuppression.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/image/NonMaxSuppression.java @@ -58,8 +58,6 @@ * of other overlapping boxes instead of directly causing them to be pruned. * To enable this Soft-NMS mode, set the {@code soft_nms_sigma} parameter to be * larger than 0. - * - * @param data type for {@code selected_scores} output */ @OpMetadata( opType = NonMaxSuppression.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/image/QuantizedResizeBilinear.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/image/QuantizedResizeBilinear.java index def6ca5246e..94b4e077416 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/image/QuantizedResizeBilinear.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/image/QuantizedResizeBilinear.java @@ -38,8 +38,6 @@ /** * Resize quantized {@code images} to {@code size} using quantized bilinear interpolation. * Input images and output images must be quantized types. - * - * @param data type for {@code resized_images} output */ @OpMetadata( opType = QuantizedResizeBilinear.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/image/RandomCrop.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/image/RandomCrop.java index 966401d271c..063b7b8f529 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/image/RandomCrop.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/image/RandomCrop.java @@ -41,8 +41,6 @@ *

      This Op picks a random location in {@code image} and crops a {@code height} by {@code width} * rectangle from that location. The random location is picked so the cropped * area will fit inside the original image. - * - * @param data type for {@code output} output */ @OpMetadata( opType = RandomCrop.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/image/ResizeBicubicGrad.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/image/ResizeBicubicGrad.java index 16d5af61802..c04fe6d13e0 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/image/ResizeBicubicGrad.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/image/ResizeBicubicGrad.java @@ -36,8 +36,6 @@ /** * Computes the gradient of bicubic interpolation. - * - * @param data type for {@code output} output */ @OpMetadata( opType = ResizeBicubicGrad.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/image/ResizeBilinearGrad.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/image/ResizeBilinearGrad.java index dbd172bfbf2..166d6b46de6 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/image/ResizeBilinearGrad.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/image/ResizeBilinearGrad.java @@ -36,8 +36,6 @@ /** * Computes the gradient of bilinear interpolation. - * - * @param data type for {@code output} output */ @OpMetadata( opType = ResizeBilinearGrad.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/image/ResizeNearestNeighbor.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/image/ResizeNearestNeighbor.java index 1fc40174782..355ac564de1 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/image/ResizeNearestNeighbor.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/image/ResizeNearestNeighbor.java @@ -36,8 +36,6 @@ /** * Resize {@code images} to {@code size} using nearest neighbor interpolation. - * - * @param data type for {@code resized_images} output */ @OpMetadata( opType = ResizeNearestNeighbor.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/image/ResizeNearestNeighborGrad.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/image/ResizeNearestNeighborGrad.java index 485aa4ba63b..36df9e12b2d 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/image/ResizeNearestNeighborGrad.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/image/ResizeNearestNeighborGrad.java @@ -36,8 +36,6 @@ /** * Computes the gradient of nearest neighbor interpolation. - * - * @param data type for {@code output} output */ @OpMetadata( opType = ResizeNearestNeighborGrad.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/image/RgbToHsv.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/image/RgbToHsv.java index 3709f0bd4f7..be3c84d9b66 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/image/RgbToHsv.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/image/RgbToHsv.java @@ -56,8 +56,6 @@ * * * - * - * @param data type for {@code output} output */ @OpMetadata( opType = RgbToHsv.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/image/SampleDistortedBoundingBox.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/image/SampleDistortedBoundingBox.java index 152f96ce75f..a7378278309 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/image/SampleDistortedBoundingBox.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/image/SampleDistortedBoundingBox.java @@ -70,8 +70,6 @@ * {@code use_image_if_no_bounding_boxes = true} will assume there is a single implicit * bounding box covering the whole image. If {@code use_image_if_no_bounding_boxes} is * false and no bounding boxes are supplied, an error is raised. - * - * @param data type for {@code begin} output */ @OpMetadata( opType = SampleDistortedBoundingBox.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/image/ScaleAndTranslateGrad.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/image/ScaleAndTranslateGrad.java index 55dae2a4ae8..1749d046b37 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/image/ScaleAndTranslateGrad.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/image/ScaleAndTranslateGrad.java @@ -36,8 +36,6 @@ /** * The ScaleAndTranslateGrad operation - * - * @param data type for {@code output} output */ @OpMetadata( opType = ScaleAndTranslateGrad.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/image/StatelessSampleDistortedBoundingBox.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/image/StatelessSampleDistortedBoundingBox.java index ac9dfdfe74d..31c4de5388d 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/image/StatelessSampleDistortedBoundingBox.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/image/StatelessSampleDistortedBoundingBox.java @@ -95,8 +95,6 @@ * {@code use_image_if_no_bounding_boxes = true} will assume there is a single implicit * bounding box covering the whole image. If {@code use_image_if_no_bounding_boxes} is * false and no bounding boxes are supplied, an error is raised. - * - * @param data type for {@code begin} output */ @OpMetadata( opType = StatelessSampleDistortedBoundingBox.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/io/DecodePaddedRaw.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/io/DecodePaddedRaw.java index 0ef81b9eff2..07eac6679d4 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/io/DecodePaddedRaw.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/io/DecodePaddedRaw.java @@ -38,8 +38,6 @@ /** * Reinterpret the bytes of a string as a vector of numbers. - * - * @param data type for {@code output} output */ @OpMetadata( opType = DecodePaddedRaw.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/io/DecodeRaw.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/io/DecodeRaw.java index 068d203c2b0..217c843796f 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/io/DecodeRaw.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/io/DecodeRaw.java @@ -37,8 +37,6 @@ /** * Reinterpret the bytes of a string as a vector of numbers. - * - * @param data type for {@code output} output */ @OpMetadata( opType = DecodeRaw.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/io/DeserializeManySparse.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/io/DeserializeManySparse.java index 1ff234ea6b6..9704bd78d15 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/io/DeserializeManySparse.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/io/DeserializeManySparse.java @@ -77,8 +77,6 @@ * values = [1, 2, 3, 4, 5] * shape = [2 50] * - * - * @param data type for {@code sparse_values} output */ @OpMetadata( opType = DeserializeManySparse.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/io/ParseTensor.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/io/ParseTensor.java index 66a64b13c0b..039ff1546f0 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/io/ParseTensor.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/io/ParseTensor.java @@ -37,8 +37,6 @@ /** * Transforms a serialized tensorflow.TensorProto proto into a Tensor. - * - * @param data type for {@code output} output */ @OpMetadata( opType = ParseTensor.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/io/SerializeManySparse.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/io/SerializeManySparse.java index b0e447608f3..70f9327d112 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/io/SerializeManySparse.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/io/SerializeManySparse.java @@ -44,8 +44,6 @@ * {@code SparseTensor} objects going into each row of {@code serialized_sparse} will have * rank {@code R-1}. *

      The minibatch size {@code N} is extracted from {@code sparse_shape[0]}. - * - * @param data type for {@code serialized_sparse} output */ @OpMetadata( opType = SerializeManySparse.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/io/SerializeSparse.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/io/SerializeSparse.java index 2f450dcf3bd..b0c2b5935bf 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/io/SerializeSparse.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/io/SerializeSparse.java @@ -38,8 +38,6 @@ /** * Serialize a {@code SparseTensor} into a {@code [3]} {@code Tensor} object. - * - * @param data type for {@code serialized_sparse} output */ @OpMetadata( opType = SerializeSparse.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/linalg/BandPart.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/linalg/BandPart.java index 34f179ed2b0..a521e77b040 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/linalg/BandPart.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/linalg/BandPart.java @@ -65,8 +65,6 @@ * tf.linalg.band_part(input, -1, 0) ==> Lower triangular part. * tf.linalg.band_part(input, 0, 0) ==> Diagonal. * - * - * @param data type for {@code band} output */ @OpMetadata( opType = BandPart.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/linalg/BandedTriangularSolve.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/linalg/BandedTriangularSolve.java index 9dc6dba4348..532d4fe148b 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/linalg/BandedTriangularSolve.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/linalg/BandedTriangularSolve.java @@ -35,8 +35,6 @@ /** * The BandedTriangularSolve operation - * - * @param data type for {@code output} output */ @OpMetadata( opType = BandedTriangularSolve.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/linalg/BatchCholesky.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/linalg/BatchCholesky.java index 0016839b211..b43cf15b48e 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/linalg/BatchCholesky.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/linalg/BatchCholesky.java @@ -35,8 +35,6 @@ /** * The BatchCholesky operation - * - * @param data type for {@code output} output */ @OpMetadata( opType = BatchCholesky.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/linalg/BatchCholeskyGrad.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/linalg/BatchCholeskyGrad.java index d9ce332f7e2..5e917e740b8 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/linalg/BatchCholeskyGrad.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/linalg/BatchCholeskyGrad.java @@ -35,8 +35,6 @@ /** * The BatchCholeskyGrad operation - * - * @param data type for {@code output} output */ @OpMetadata( opType = BatchCholeskyGrad.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/linalg/BatchMatrixBandPart.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/linalg/BatchMatrixBandPart.java index 55e8a0d6a75..99cb57ff97f 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/linalg/BatchMatrixBandPart.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/linalg/BatchMatrixBandPart.java @@ -36,8 +36,6 @@ /** * The BatchMatrixBandPart operation - * - * @param data type for {@code band} output */ @OpMetadata( opType = BatchMatrixBandPart.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/linalg/BatchMatrixDeterminant.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/linalg/BatchMatrixDeterminant.java index c50a706e073..7f1bd32a749 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/linalg/BatchMatrixDeterminant.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/linalg/BatchMatrixDeterminant.java @@ -35,8 +35,6 @@ /** * The BatchMatrixDeterminant operation - * - * @param data type for {@code output} output */ @OpMetadata( opType = BatchMatrixDeterminant.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/linalg/BatchMatrixDiag.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/linalg/BatchMatrixDiag.java index bba3cae6292..edc731b1f36 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/linalg/BatchMatrixDiag.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/linalg/BatchMatrixDiag.java @@ -35,8 +35,6 @@ /** * The BatchMatrixDiag operation - * - * @param data type for {@code output} output */ @OpMetadata( opType = BatchMatrixDiag.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/linalg/BatchMatrixDiagPart.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/linalg/BatchMatrixDiagPart.java index 63e7e0e3026..ac379b960aa 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/linalg/BatchMatrixDiagPart.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/linalg/BatchMatrixDiagPart.java @@ -35,8 +35,6 @@ /** * The BatchMatrixDiagPart operation - * - * @param data type for {@code diagonal} output */ @OpMetadata( opType = BatchMatrixDiagPart.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/linalg/BatchMatrixInverse.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/linalg/BatchMatrixInverse.java index 081dab67e8b..009deec3658 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/linalg/BatchMatrixInverse.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/linalg/BatchMatrixInverse.java @@ -35,8 +35,10 @@ /** * The BatchMatrixInverse operation - * - * @param data type for {@code output} output + * DEPRECATED: This operation is deprecated and will be removed in a future version. + * Use tf.linalg.inv instead. + *

      Computes the inverse of one or more square invertible matrices or their + * adjoints (conjugate transposes). */ @OpMetadata( opType = BatchMatrixInverse.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/linalg/BatchMatrixSetDiag.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/linalg/BatchMatrixSetDiag.java index 67a97a485c0..eaea0c7db31 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/linalg/BatchMatrixSetDiag.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/linalg/BatchMatrixSetDiag.java @@ -35,8 +35,6 @@ /** * The BatchMatrixSetDiag operation - * - * @param data type for {@code output} output */ @OpMetadata( opType = BatchMatrixSetDiag.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/linalg/BatchMatrixSolve.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/linalg/BatchMatrixSolve.java index dc65bb1dce1..5b6749c53e4 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/linalg/BatchMatrixSolve.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/linalg/BatchMatrixSolve.java @@ -35,8 +35,6 @@ /** * The BatchMatrixSolve operation - * - * @param data type for {@code output} output */ @OpMetadata( opType = BatchMatrixSolve.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/linalg/BatchMatrixSolveLs.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/linalg/BatchMatrixSolveLs.java index 801c5262946..7cb6714696f 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/linalg/BatchMatrixSolveLs.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/linalg/BatchMatrixSolveLs.java @@ -36,8 +36,6 @@ /** * The BatchMatrixSolveLs operation - * - * @param data type for {@code output} output */ @OpMetadata( opType = BatchMatrixSolveLs.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/linalg/BatchMatrixTriangularSolve.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/linalg/BatchMatrixTriangularSolve.java index ae63e405dd7..d7b326bae21 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/linalg/BatchMatrixTriangularSolve.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/linalg/BatchMatrixTriangularSolve.java @@ -35,8 +35,6 @@ /** * The BatchMatrixTriangularSolve operation - * - * @param data type for {@code output} output */ @OpMetadata( opType = BatchMatrixTriangularSolve.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/linalg/BatchSelfAdjointEig.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/linalg/BatchSelfAdjointEig.java index 1d6588ac785..637625bd5db 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/linalg/BatchSelfAdjointEig.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/linalg/BatchSelfAdjointEig.java @@ -35,8 +35,6 @@ /** * The BatchSelfAdjointEigV2 operation - * - * @param data type for {@code e} output */ @OpMetadata( opType = BatchSelfAdjointEig.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/linalg/BatchSvd.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/linalg/BatchSvd.java index cf723ceeedc..a2411601e63 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/linalg/BatchSvd.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/linalg/BatchSvd.java @@ -35,8 +35,6 @@ /** * The BatchSvd operation - * - * @param data type for {@code s} output */ @OpMetadata( opType = BatchSvd.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/linalg/Cholesky.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/linalg/Cholesky.java index 294a41889da..ef6d0ca1a3d 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/linalg/Cholesky.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/linalg/Cholesky.java @@ -45,8 +45,6 @@ *

      Note: The gradient computation on GPU is faster for large matrices but * not for large batch dimensions when the submatrices are small. In this * case it might be faster to use the CPU. - * - * @param data type for {@code output} output */ @OpMetadata( opType = Cholesky.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/linalg/CholeskyGrad.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/linalg/CholeskyGrad.java index f2529b61318..ce7975bbb29 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/linalg/CholeskyGrad.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/linalg/CholeskyGrad.java @@ -37,8 +37,6 @@ * Computes the reverse mode backpropagated gradient of the Cholesky algorithm. * For an explanation see "Differentiation of the Cholesky algorithm" by * Iain Murray http://arxiv.org/abs/1602.07527. - * - * @param data type for {@code output} output */ @OpMetadata( opType = CholeskyGrad.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/linalg/ConjugateTranspose.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/linalg/ConjugateTranspose.java index e14f2e71ef9..561e4fecbf1 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/linalg/ConjugateTranspose.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/linalg/ConjugateTranspose.java @@ -39,8 +39,6 @@ * The output {@code y} has the same rank as {@code x}. The shapes of {@code x} and {@code y} satisfy: * {@code y.shape[i] == x.shape[perm[i]] for i in [0, 1, ..., rank(x) - 1]} * {@code y[i,j,k,...,s,t,u] == conj(x[perm[i], perm[j], perm[k],...,perm[s], perm[t], perm[u]])} - * - * @param data type for {@code y} output */ @OpMetadata( opType = ConjugateTranspose.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/linalg/Cross.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/linalg/Cross.java index 68ee2a65439..5c942c1e41b 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/linalg/Cross.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/linalg/Cross.java @@ -38,8 +38,6 @@ * {@code a} and {@code b} must be the same shape; they can either be simple 3-element vectors, * or any shape where the innermost dimension is 3. In the latter case, each pair * of corresponding 3-element vectors is cross-multiplied independently. - * - * @param data type for {@code product} output */ @OpMetadata( opType = Cross.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/linalg/Det.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/linalg/Det.java index 62aafcde736..d63118c9f73 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/linalg/Det.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/linalg/Det.java @@ -38,8 +38,6 @@ * The input is a tensor of shape {@code [..., M, M]} whose inner-most 2 dimensions * form square matrices. The output is a tensor containing the determinants * for all input submatrices {@code [..., :, :]}. - * - * @param data type for {@code output} output */ @OpMetadata( opType = Det.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/linalg/Eig.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/linalg/Eig.java index 783950dfde9..3276bbb78fe 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/linalg/Eig.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/linalg/Eig.java @@ -46,8 +46,6 @@ * e, v = eig(a) * e = eig(a, compute_v=False) * - * - * @param data type for {@code e} output */ @OpMetadata( opType = Eig.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/linalg/Einsum.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/linalg/Einsum.java index 51d3eeb3fa6..5b57bad8aa4 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/linalg/Einsum.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/linalg/Einsum.java @@ -99,8 +99,6 @@ * supported by {@code numpy.einsum}. *
      {@literal @}end_compatibility *

    - * - * @param data type for {@code output} output */ @OpMetadata( opType = Einsum.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/linalg/EuclideanNorm.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/linalg/EuclideanNorm.java index ab6f58f4885..f544381e1a7 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/linalg/EuclideanNorm.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/linalg/EuclideanNorm.java @@ -40,8 +40,6 @@ * {@code keep_dims} is true, the rank of the tensor is reduced by 1 for each entry in * {@code axis}. If {@code keep_dims} is true, the reduced dimensions are * retained with length 1. - * - * @param data type for {@code output} output */ @OpMetadata( opType = EuclideanNorm.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/linalg/Inv.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/linalg/Inv.java index 6b02bc2a059..93338f1df07 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/linalg/Inv.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/linalg/Inv.java @@ -42,8 +42,6 @@ *

    If a matrix is not invertible there is no guarantee what the op does. It * may detect the condition and raise an exception or it may simply return a * garbage result. - * - * @param data type for {@code output} output */ @OpMetadata( opType = Inv.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/linalg/LogMatrixDeterminant.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/linalg/LogMatrixDeterminant.java index 298e01306db..a144ac2d31c 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/linalg/LogMatrixDeterminant.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/linalg/LogMatrixDeterminant.java @@ -43,8 +43,6 @@ * The {@code log_abs_determinant} is computed as {@code det(P)*sum(log(diag(LU)))} where {@code LU} * is the {@code LU} decomposition of the input and {@code P} is the corresponding * permutation matrix. - * - * @param data type for {@code sign} output */ @OpMetadata( opType = LogMatrixDeterminant.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/linalg/Lu.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/linalg/Lu.java index 480ed23e696..9063fab1875 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/linalg/Lu.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/linalg/Lu.java @@ -51,10 +51,6 @@ *

    P represents a permutation matrix encoded as a list of indices each between {@code 0} * and {@code M-1}, inclusive. If P_mat denotes the permutation matrix corresponding to * P, then the L, U and P satisfies P_mat * input = L * U. - * - * @param data type for {@code lu} output - * - * @param data type for {@code p} output */ @OpMetadata( opType = Lu.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/linalg/MatMul.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/linalg/MatMul.java index a592a65396a..c817cbc9037 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/linalg/MatMul.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/linalg/MatMul.java @@ -41,8 +41,6 @@ * true). *

    Note: The default kernel implementation for MatMul on GPUs uses * cublas. - * - * @param data type for {@code product} output */ @OpMetadata( opType = MatMul.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/linalg/MatrixDiag.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/linalg/MatrixDiag.java index 0a292c9d1b1..5241708f71a 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/linalg/MatrixDiag.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/linalg/MatrixDiag.java @@ -116,8 +116,6 @@ * [1, 9], * [9, 2]] * - * - * @param data type for {@code output} output */ @OpMetadata( opType = MatrixDiag.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/linalg/MatrixDiagPart.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/linalg/MatrixDiagPart.java index 084c946193e..a818b134cbe 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/linalg/MatrixDiagPart.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/linalg/MatrixDiagPart.java @@ -96,8 +96,6 @@ * [3, 4, 9], * [4, 3, 8]]] * - * - * @param data type for {@code diagonal} output */ @OpMetadata( opType = MatrixDiagPart.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/linalg/MatrixDiagPartV3.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/linalg/MatrixDiagPartV3.java index d4794ab7571..c6ecab46bab 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/linalg/MatrixDiagPartV3.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/linalg/MatrixDiagPartV3.java @@ -126,8 +126,6 @@ * [4, 3, 8]]] * * - * - * @param data type for {@code diagonal} output */ @OpMetadata( opType = MatrixDiagPartV3.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/linalg/MatrixDiagV3.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/linalg/MatrixDiagV3.java index 943b92e2c95..67b5b3b74b0 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/linalg/MatrixDiagV3.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/linalg/MatrixDiagV3.java @@ -144,8 +144,6 @@ * [9, 2]] * * - * - * @param data type for {@code output} output */ @OpMetadata( opType = MatrixDiagV3.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/linalg/MatrixExponential.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/linalg/MatrixExponential.java index 961f57037f4..9332cd02b3e 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/linalg/MatrixExponential.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/linalg/MatrixExponential.java @@ -35,8 +35,6 @@ /** * Deprecated, use python implementation tf.linalg.matrix_exponential. - * - * @param data type for {@code output} output */ @OpMetadata( opType = MatrixExponential.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/linalg/MatrixLogarithm.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/linalg/MatrixLogarithm.java index b3876d3a572..f1529a1c264 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/linalg/MatrixLogarithm.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/linalg/MatrixLogarithm.java @@ -46,8 +46,6 @@ *

    The input is a tensor of shape {@code [..., M, M]} whose inner-most 2 dimensions * form square matrices. The output is a tensor of the same shape as the input * containing the exponential for all input submatrices {@code [..., :, :]}. - * - * @param data type for {@code output} output */ @OpMetadata( opType = MatrixLogarithm.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/linalg/MatrixSetDiag.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/linalg/MatrixSetDiag.java index 0ae2c206569..1ec3a1444f5 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/linalg/MatrixSetDiag.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/linalg/MatrixSetDiag.java @@ -132,8 +132,6 @@ * [7, 4, 2, 4]]] * * - * - * @param data type for {@code output} output */ @OpMetadata( opType = MatrixSetDiag.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/linalg/MatrixSolveLs.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/linalg/MatrixSolveLs.java index 3b340034827..d0601c6ee57 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/linalg/MatrixSolveLs.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/linalg/MatrixSolveLs.java @@ -66,8 +66,6 @@ * least-squares solution, even when \(A\) is rank deficient. This path is * typically 6-7 times slower than the fast path. If {@code fast} is {@code False} then * {@code l2_regularizer} is ignored. - * - * @param data type for {@code output} output */ @OpMetadata( opType = MatrixSolveLs.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/linalg/Qr.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/linalg/Qr.java index 9e73edaf6b8..037f024d04b 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/linalg/Qr.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/linalg/Qr.java @@ -47,8 +47,6 @@ * q, r = qr(a) * q_full, r_full = qr(a, full_matrices=True) * - * - * @param data type for {@code q} output */ @OpMetadata( opType = Qr.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/linalg/QuantizedMatMul.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/linalg/QuantizedMatMul.java index 93ca4112092..d3136668a39 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/linalg/QuantizedMatMul.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/linalg/QuantizedMatMul.java @@ -41,8 +41,6 @@ * {@code a} (after being transposed if {@code transpose_a} is non-zero) must match the * outer dimension of {@code b} (after being transposed if {@code transposed_b} is * non-zero). - * - * @param data type for {@code out} output */ @OpMetadata( opType = QuantizedMatMul.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/linalg/QuantizedMatMulWithBias.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/linalg/QuantizedMatMulWithBias.java index 4ff470d2594..0cc43361bf4 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/linalg/QuantizedMatMulWithBias.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/linalg/QuantizedMatMulWithBias.java @@ -43,8 +43,6 @@ * match the outer dimension of {@code b} (after being transposed if {@code transposed_b} is * non-zero). Then do broadcast add operation with bias values on the matrix * multiplication result. The bias size must match inner dimension of {@code b}. - * - * @param data type for {@code out} output */ @OpMetadata( opType = QuantizedMatMulWithBias.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/linalg/QuantizedMatMulWithBiasAndRelu.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/linalg/QuantizedMatMulWithBiasAndRelu.java index ad1182c50de..eee116597b9 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/linalg/QuantizedMatMulWithBiasAndRelu.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/linalg/QuantizedMatMulWithBiasAndRelu.java @@ -44,8 +44,6 @@ * non-zero). Then do broadcast add operation with bias values on the matrix * multiplication result. The bias size must match inner dimension of {@code b}. Then do * relu activation to get non-negative result. - * - * @param data type for {@code out} output */ @OpMetadata( opType = QuantizedMatMulWithBiasAndRelu.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/linalg/QuantizedMatMulWithBiasAndReluAndRequantize.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/linalg/QuantizedMatMulWithBiasAndReluAndRequantize.java index 91eefc72f1b..82bdde439f1 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/linalg/QuantizedMatMulWithBiasAndReluAndRequantize.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/linalg/QuantizedMatMulWithBiasAndReluAndRequantize.java @@ -45,8 +45,6 @@ * multiplication result. The bias size must match inner dimension of {@code b}. Then do * relu activation to get non-negative result. Then do requantize operation to get * final uint8 result. - * - * @param data type for {@code out} output */ @OpMetadata( opType = QuantizedMatMulWithBiasAndReluAndRequantize.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/linalg/SelfAdjointEig.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/linalg/SelfAdjointEig.java index 2d64ddb4dda..75c06a99f2a 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/linalg/SelfAdjointEig.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/linalg/SelfAdjointEig.java @@ -45,8 +45,6 @@ * e, v = self_adjoint_eig(a) * e = self_adjoint_eig(a, compute_v=False) * - * - * @param data type for {@code e} output */ @OpMetadata( opType = SelfAdjointEig.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/linalg/Solve.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/linalg/Solve.java index a0f41eda3f5..d1057183227 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/linalg/Solve.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/linalg/Solve.java @@ -41,8 +41,6 @@ * satisfies {@code matrix[..., :, :] * output[..., :, :] = rhs[..., :, :]}. * If {@code adjoint} is {@code True} then each output matrix satisfies * {@code adjoint(matrix[..., :, :]) * output[..., :, :] = rhs[..., :, :]}. - * - * @param data type for {@code output} output */ @OpMetadata( opType = Solve.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/linalg/Sqrtm.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/linalg/Sqrtm.java index 224688c8e1d..cf48c52605a 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/linalg/Sqrtm.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/linalg/Sqrtm.java @@ -47,8 +47,6 @@ *

    The input is a tensor of shape {@code [..., M, M]} whose inner-most 2 dimensions * form square matrices. The output is a tensor of the same shape as the input * containing the matrix square root for all input submatrices {@code [..., :, :]}. - * - * @param data type for {@code output} output */ @OpMetadata( opType = Sqrtm.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/linalg/Svd.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/linalg/Svd.java index b17b01cf88e..b11eafdccfc 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/linalg/Svd.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/linalg/Svd.java @@ -45,8 +45,6 @@ * s, u, v = svd(a) * s, _, _ = svd(a, compute_uv=False) * - * - * @param data type for {@code s} output */ @OpMetadata( opType = Svd.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/linalg/TensorDiag.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/linalg/TensorDiag.java index 6292194a118..69ee9258392 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/linalg/TensorDiag.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/linalg/TensorDiag.java @@ -48,8 +48,6 @@ * [0, 0, 3, 0] * [0, 0, 0, 4]] * - * - * @param data type for {@code output} output */ @OpMetadata( opType = TensorDiag.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/linalg/TensorDiagPart.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/linalg/TensorDiagPart.java index ae21a73b071..838a036f84b 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/linalg/TensorDiagPart.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/linalg/TensorDiagPart.java @@ -49,8 +49,6 @@ * * tf.diag_part(input) ==> [1, 2, 3, 4] * - * - * @param data type for {@code diagonal} output */ @OpMetadata( opType = TensorDiagPart.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/linalg/Transpose.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/linalg/Transpose.java index 65f22dfe32b..712576c0989 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/linalg/Transpose.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/linalg/Transpose.java @@ -38,8 +38,6 @@ * Shuffle dimensions of x according to a permutation. * The output {@code y} has the same rank as {@code x}. The shapes of {@code x} and {@code y} satisfy: * {@code y.shape[i] == x.shape[perm[i]] for i in [0, 1, ..., rank(x) - 1]} - * - * @param data type for {@code y} output */ @OpMetadata( opType = Transpose.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/linalg/TriangularSolve.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/linalg/TriangularSolve.java index 891f4e1f608..026fbfb70bf 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/linalg/TriangularSolve.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/linalg/TriangularSolve.java @@ -77,8 +77,6 @@ * # [4. ], * # [1.9999999]], dtype=float32)> * - * - * @param data type for {@code output} output */ @OpMetadata( opType = TriangularSolve.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/linalg/TridiagonalMatMul.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/linalg/TridiagonalMatMul.java index bd69ed483e4..a6122dabc83 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/linalg/TridiagonalMatMul.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/linalg/TridiagonalMatMul.java @@ -36,8 +36,6 @@ /** * Calculate product with tridiagonal matrix. * Calculates product of two matrices, where left matrix is a tridiagonal matrix. - * - * @param data type for {@code output} output */ @OpMetadata( opType = TridiagonalMatMul.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/linalg/TridiagonalSolve.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/linalg/TridiagonalSolve.java index 57c0864ef7d..6b0a890d12e 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/linalg/TridiagonalSolve.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/linalg/TridiagonalSolve.java @@ -42,8 +42,6 @@ * pivoting, depending on {@code partial_pivoting} attribute. On GPU, Nvidia's cuSPARSE * library is used: https://docs.nvidia.com/cuda/cusparse/index.html#gtsv * Partial pivoting is not yet supported by XLA backends. - * - * @param data type for {@code output} output */ @OpMetadata( opType = TridiagonalSolve.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/linalg/sparse/CSRSparseMatrixComponents.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/linalg/sparse/CSRSparseMatrixComponents.java index 27d77557bfb..7fd47c7c6f5 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/linalg/sparse/CSRSparseMatrixComponents.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/linalg/sparse/CSRSparseMatrixComponents.java @@ -39,8 +39,6 @@ * Reads out the CSR components at batch {@code index}. * This op is meant only for debugging / testing, and its interface is not expected * to be stable. - * - * @param data type for {@code values} output */ @OpMetadata( opType = CSRSparseMatrixComponents.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/linalg/sparse/CSRSparseMatrixToDense.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/linalg/sparse/CSRSparseMatrixToDense.java index 51bed06f6ba..97fb87d7250 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/linalg/sparse/CSRSparseMatrixToDense.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/linalg/sparse/CSRSparseMatrixToDense.java @@ -36,8 +36,6 @@ /** * Convert a (possibly batched) CSRSparseMatrix to dense. - * - * @param data type for {@code dense_output} output */ @OpMetadata( opType = CSRSparseMatrixToDense.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/linalg/sparse/CSRSparseMatrixToSparseTensor.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/linalg/sparse/CSRSparseMatrixToSparseTensor.java index 5c111887894..ad365783cea 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/linalg/sparse/CSRSparseMatrixToSparseTensor.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/linalg/sparse/CSRSparseMatrixToSparseTensor.java @@ -37,8 +37,6 @@ /** * Converts a (possibly batched) CSRSparesMatrix to a SparseTensor. - * - * @param data type for {@code values} output */ @OpMetadata( opType = CSRSparseMatrixToSparseTensor.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/linalg/sparse/SparseMatrixMatMul.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/linalg/sparse/SparseMatrixMatMul.java index 2de2e93ec3b..5d9ed9bbbf2 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/linalg/sparse/SparseMatrixMatMul.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/linalg/sparse/SparseMatrixMatMul.java @@ -56,8 +56,6 @@ * C = conjugate(transpose(A . B)) = conjugate(transpose(B)) . * conjugate(transpose(A)) * - * - * @param data type for {@code output} output */ @OpMetadata( opType = SparseMatrixMatMul.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/math/Abs.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/math/Abs.java index ef53c5f5693..0f4ee840704 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/math/Abs.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/math/Abs.java @@ -38,8 +38,6 @@ * Given a tensor {@code x}, this operation returns a tensor containing the absolute * value of each element in {@code x}. For example, if x is an input element and y is * an output element, this operation computes \(y = |x|\). - * - * @param data type for {@code y} output */ @OpMetadata( opType = Abs.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/math/AccumulateN.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/math/AccumulateN.java index 61d1df63943..3a0e466e8cd 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/math/AccumulateN.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/math/AccumulateN.java @@ -43,8 +43,6 @@ * storage is proportional to the output size rather than the inputs size. *

    Unlike the original {@code accumulate_n}, {@code accumulate_n_v2} is differentiable. *

    Returns a {@code Tensor} of same shape and type as the elements of {@code inputs}. - * - * @param data type for {@code sum} output */ @OpMetadata( opType = AccumulateN.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/math/Acos.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/math/Acos.java index 078326e1891..915e5b98b63 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/math/Acos.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/math/Acos.java @@ -37,8 +37,6 @@ * Computes acos of x element-wise. * Provided an input tensor, the {@code tf.math.acos} operation returns the inverse cosine of each element of the tensor. If {@code y = tf.math.cos(x)} then, {@code x = tf.math.acos(y)}. *

    Input range is {@code [-1, 1]} and the output has a range of {@code [0, pi]}. - * - * @param data type for {@code y} output */ @OpMetadata( opType = Acos.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/math/Acosh.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/math/Acosh.java index 60edbd7880f..8ade37b1990 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/math/Acosh.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/math/Acosh.java @@ -41,8 +41,6 @@ * x = tf.constant([-2, -0.5, 1, 1.2, 200, 10000, float("inf")]) * tf.math.acosh(x) ==> [nan nan 0. 0.62236255 5.9914584 9.903487 inf] * - * - * @param data type for {@code y} output */ @OpMetadata( opType = Acosh.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/math/Add.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/math/Add.java index 4f32acd9ee1..61db4d2e4ec 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/math/Add.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/math/Add.java @@ -39,8 +39,6 @@ * here *

    Given two input tensors, the {@code tf.add} operation computes the sum for every element in the tensor. *

    Both input and output have a range {@code (-inf, inf)}. - * - * @param data type for {@code z} output */ @OpMetadata( opType = Add.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/math/AddN.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/math/AddN.java index 6cd47212eef..f2ef9209796 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/math/AddN.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/math/AddN.java @@ -41,8 +41,6 @@ * x = [9, 7, 10] * tf.math.add_n(x) ==> 26 * - * - * @param data type for {@code sum} output */ @OpMetadata( opType = AddN.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/math/Angle.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/math/Angle.java index a9c7814636f..6ad1ff84bba 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/math/Angle.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/math/Angle.java @@ -51,8 +51,6 @@ *

    {@literal @}compatibility(numpy)
    * Equivalent to np.angle. *
    {@literal @}end_compatibility - * - * @param data type for {@code output} output */ @OpMetadata( opType = Angle.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/math/ArgMax.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/math/ArgMax.java index 5a7b5adec69..c222f3d54d5 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/math/ArgMax.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/math/ArgMax.java @@ -48,8 +48,6 @@ * # c = 4 * # here a[4] = 166.32 which is the largest element of a across axis 0 * - * - * @param data type for {@code output} output */ @OpMetadata( opType = ArgMax.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/math/ArgMin.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/math/ArgMin.java index ff138655b1f..41aa45a10ad 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/math/ArgMin.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/math/ArgMin.java @@ -48,8 +48,6 @@ * # c = 0 * # here a[0] = 1 which is the smallest element of a across axis 0 * - * - * @param data type for {@code output} output */ @OpMetadata( opType = ArgMin.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/math/Asin.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/math/Asin.java index 050107db969..810aeb5fa3b 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/math/Asin.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/math/Asin.java @@ -47,8 +47,6 @@ * * tf.math.asin(y) # [1.047, 0.785] = x * - * - * @param data type for {@code y} output */ @OpMetadata( opType = Asin.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/math/Asinh.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/math/Asinh.java index d4170db292a..918518f2b82 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/math/Asinh.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/math/Asinh.java @@ -42,8 +42,6 @@ * x = tf.constant([-float("inf"), -2, -0.5, 1, 1.2, 200, 10000, float("inf")]) * tf.math.asinh(x) ==> [-inf -1.4436355 -0.4812118 0.8813736 1.0159732 5.991471 9.903487 inf] * - * - * @param data type for {@code y} output */ @OpMetadata( opType = Asinh.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/math/Atan.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/math/Atan.java index aab73783c10..8979ab75d9e 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/math/Atan.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/math/Atan.java @@ -47,8 +47,6 @@ * * tf.math.atan(y) # [1.047, 0.785] = x * - * - * @param data type for {@code y} output */ @OpMetadata( opType = Atan.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/math/Atan2.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/math/Atan2.java index dfff4a48676..2d566d3cc22 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/math/Atan2.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/math/Atan2.java @@ -51,8 +51,6 @@ * * * - * - * @param data type for {@code z} output */ @OpMetadata( opType = Atan2.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/math/Atanh.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/math/Atanh.java index ea5729193bf..c4dd0f1ead2 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/math/Atanh.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/math/Atanh.java @@ -44,8 +44,6 @@ * x = tf.constant([-float("inf"), -1, -0.5, 1, 0, 0.5, 10, float("inf")]) * tf.math.atanh(x) ==> [nan -inf -0.54930615 inf 0. 0.54930615 nan nan] * - * - * @param data type for {@code y} output */ @OpMetadata( opType = Atanh.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/math/BesselI0.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/math/BesselI0.java index d3782706f20..945d2107a39 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/math/BesselI0.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/math/BesselI0.java @@ -35,8 +35,6 @@ /** * The BesselI0 operation - * - * @param data type for {@code y} output */ @OpMetadata( opType = BesselI0.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/math/BesselI0e.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/math/BesselI0e.java index eec8b3281a3..7e27d3e4263 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/math/BesselI0e.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/math/BesselI0e.java @@ -35,8 +35,6 @@ /** * The BesselI0e operation - * - * @param data type for {@code y} output */ @OpMetadata( opType = BesselI0e.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/math/BesselI1.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/math/BesselI1.java index bb59dc19f5c..28304567e86 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/math/BesselI1.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/math/BesselI1.java @@ -35,8 +35,6 @@ /** * The BesselI1 operation - * - * @param data type for {@code y} output */ @OpMetadata( opType = BesselI1.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/math/BesselI1e.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/math/BesselI1e.java index fe929e32eb1..df3b3f937e8 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/math/BesselI1e.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/math/BesselI1e.java @@ -35,8 +35,6 @@ /** * The BesselI1e operation - * - * @param data type for {@code y} output */ @OpMetadata( opType = BesselI1e.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/math/Betainc.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/math/Betainc.java index f7b9904c100..1a895c89f00 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/math/Betainc.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/math/Betainc.java @@ -41,8 +41,6 @@ *

    \(B(x; a, b) = \int_0^x t^{a-1} (1 - t)^{b-1} dt\) *

    is the incomplete beta function and \(B(a, b)\) is the complete * beta function. - * - * @param data type for {@code z} output */ @OpMetadata( opType = Betainc.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/math/Bincount.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/math/Bincount.java index 6e78f0799fc..463dc277eae 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/math/Bincount.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/math/Bincount.java @@ -42,8 +42,6 @@ * the value in {@code weights} at each index where the corresponding value in {@code arr} is * {@code i}. *

    Values in {@code arr} outside of the range [0, size) are ignored. - * - * @param data type for {@code bins} output */ @OpMetadata( opType = Bincount.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/math/Ceil.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/math/Ceil.java index 3db46461d7c..1a69b94a8e4 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/math/Ceil.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/math/Ceil.java @@ -35,8 +35,6 @@ /** * Returns element-wise smallest integer not less than x. - * - * @param data type for {@code y} output */ @OpMetadata( opType = Ceil.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/math/ComplexAbs.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/math/ComplexAbs.java index 798b2a9cb1a..9461d599888 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/math/ComplexAbs.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/math/ComplexAbs.java @@ -52,8 +52,6 @@ * * * - * - * @param data type for {@code y} output */ @OpMetadata( opType = ComplexAbs.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/math/Conj.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/math/Conj.java index 266da810658..d46b7f2ae5b 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/math/Conj.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/math/Conj.java @@ -45,8 +45,6 @@ * # tensor 'input' is [-2.25 + 4.75j, 3.25 + 5.75j] * tf.conj(input) ==> [-2.25 - 4.75j, 3.25 - 5.75j] * - * - * @param data type for {@code output} output */ @OpMetadata( opType = Conj.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/math/Cos.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/math/Cos.java index 0ab0152ff02..b6b5b9595c5 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/math/Cos.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/math/Cos.java @@ -43,8 +43,6 @@ * x = tf.constant([-float("inf"), -9, -0.5, 1, 1.2, 200, 10000, float("inf")]) * tf.math.cos(x) ==> [nan -0.91113025 0.87758255 0.5403023 0.36235774 0.48718765 -0.95215535 nan] * - * - * @param data type for {@code y} output */ @OpMetadata( opType = Cos.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/math/Cosh.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/math/Cosh.java index 76a98abe533..391d2efd7ab 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/math/Cosh.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/math/Cosh.java @@ -42,8 +42,6 @@ * x = tf.constant([-float("inf"), -9, -0.5, 1, 1.2, 2, 10, float("inf")]) * tf.math.cosh(x) ==> [inf 4.0515420e+03 1.1276259e+00 1.5430807e+00 1.8106556e+00 3.7621956e+00 1.1013233e+04 inf] * - * - * @param data type for {@code y} output */ @OpMetadata( opType = Cosh.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/math/Cumprod.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/math/Cumprod.java index 3e901959c5d..90bdcdc0038 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/math/Cumprod.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/math/Cumprod.java @@ -56,8 +56,6 @@ * 

      * tf.cumprod([a, b, c], exclusive=True, reverse=True)  # => [b * c, c, 1]
  *
    - * - * @param data type for {@code out} output */ @OpMetadata( opType = Cumprod.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/math/Cumsum.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/math/Cumsum.java index 12b3346db25..ff8dca235c9 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/math/Cumsum.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/math/Cumsum.java @@ -56,8 +56,6 @@ * 
      * tf.cumsum([a, b, c], exclusive=True, reverse=True)  # => [b + c, c, 0]
  *
    - * - * @param data type for {@code out} output */ @OpMetadata( opType = Cumsum.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/math/CumulativeLogsumexp.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/math/CumulativeLogsumexp.java index 52595f56eea..f7367703a41 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/math/CumulativeLogsumexp.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/math/CumulativeLogsumexp.java @@ -51,8 +51,6 @@ * floating point type is used instead. *

    By setting the {@code reverse} kwarg to {@code True}, the cumulative log-sum-exp is performed in the * opposite direction. - * - * @param data type for {@code out} output */ @OpMetadata( opType = CumulativeLogsumexp.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/math/DenseBincount.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/math/DenseBincount.java index ff9a38ba24d..808be372c5f 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/math/DenseBincount.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/math/DenseBincount.java @@ -41,8 +41,6 @@ * the value in {@code weights} at each index where the corresponding value in {@code arr} is * {@code i}. *

    Values in {@code arr} outside of the range [0, size) are ignored. - * - * @param data type for {@code output} output */ @OpMetadata( opType = DenseBincount.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/math/Digamma.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/math/Digamma.java index 37117f4e1b8..3a48d548bd4 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/math/Digamma.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/math/Digamma.java @@ -36,8 +36,6 @@ /** * Computes Psi, the derivative of Lgamma (the log of the absolute value of * {@code Gamma(x)}), element-wise. - * - * @param data type for {@code y} output */ @OpMetadata( opType = Digamma.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/math/Div.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/math/Div.java index 62a15f37da7..8ad37113d3f 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/math/Div.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/math/Div.java @@ -37,8 +37,6 @@ * Returns x / y element-wise. * NOTE: {@code math.Div} supports broadcasting. More about broadcasting * here - * - * @param data type for {@code z} output */ @OpMetadata( opType = Div.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/math/DivNoNan.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/math/DivNoNan.java index bb098cfdf14..43047bad3c6 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/math/DivNoNan.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/math/DivNoNan.java @@ -37,8 +37,6 @@ * Returns 0 if the denominator is zero. * NOTE: {@code math.DivNoNan} supports broadcasting. More about broadcasting * here - * - * @param data type for {@code z} output */ @OpMetadata( opType = DivNoNan.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/math/Erf.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/math/Erf.java index 1e2046e2892..ef607d7778b 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/math/Erf.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/math/Erf.java @@ -35,8 +35,6 @@ /** * Computes the Gauss error function of {@code x} element-wise. In statistics, for non-negative values of $x$, the error function has the following interpretation: for a random variable $Y$ that is normally distributed with mean 0 and variance $1/\sqrt{2}$, $erf(x)$ is the probability that $Y$ falls in the range $[−x, x]$. - * - * @param data type for {@code y} output */ @OpMetadata( opType = Erf.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/math/Erfc.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/math/Erfc.java index b8d11327b94..25fdbcd648c 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/math/Erfc.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/math/Erfc.java @@ -35,8 +35,6 @@ /** * Computes the complementary error function of {@code x} element-wise. - * - * @param data type for {@code y} output */ @OpMetadata( opType = Erfc.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/math/Exp.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/math/Exp.java index 1a5c7456b51..fe1d6ed1515 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/math/Exp.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/math/Exp.java @@ -56,8 +56,6 @@ * x = tf.constant(1 + 1j) * tf.math.exp(x) ==> 1.4686939399158851+2.2873552871788423j * - * - * @param data type for {@code y} output */ @OpMetadata( opType = Exp.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/math/Expm1.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/math/Expm1.java index a6f8f64ab43..b9c80edf84b 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/math/Expm1.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/math/Expm1.java @@ -47,8 +47,6 @@ * x = tf.constant(1 + 1j) * tf.math.expm1(x) ==> (0.46869393991588515+2.2873552871788423j) * - * - * @param data type for {@code y} output */ @OpMetadata( opType = Expm1.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/math/Floor.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/math/Floor.java index bb9dbc4aa32..27ed6af66ac 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/math/Floor.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/math/Floor.java @@ -35,8 +35,6 @@ /** * Returns element-wise largest integer not greater than x. - * - * @param data type for {@code y} output */ @OpMetadata( opType = Floor.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/math/FloorDiv.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/math/FloorDiv.java index 47887e1a4dd..61d57ac8c4f 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/math/FloorDiv.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/math/FloorDiv.java @@ -37,8 +37,6 @@ * Returns x // y element-wise. * NOTE: {@code math.FloorDiv} supports broadcasting. More about broadcasting * here - * - * @param data type for {@code z} output */ @OpMetadata( opType = FloorDiv.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/math/FloorMod.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/math/FloorMod.java index 58c90f87123..b41e5d112b2 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/math/FloorMod.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/math/FloorMod.java @@ -40,8 +40,6 @@ * {@code floor(x / y) * y + floormod(x, y) = x}, regardless of the signs of x and y. *

    NOTE: {@code math.FloorMod} supports broadcasting. More about broadcasting * here - * - * @param data type for {@code z} output */ @OpMetadata( opType = FloorMod.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/math/Igamma.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/math/Igamma.java index 4f116ba6e63..224c434af9f 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/math/Igamma.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/math/Igamma.java @@ -42,8 +42,6 @@ *

    is the lower incomplete Gamma function. *

    Note, above {@code Q(a, x)} ({@code Igammac}) is the upper regularized complete * Gamma function. - * - * @param data type for {@code z} output */ @OpMetadata( opType = Igamma.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/math/IgammaGradA.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/math/IgammaGradA.java index f9e7aced432..a3c6c4f20ad 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/math/IgammaGradA.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/math/IgammaGradA.java @@ -35,8 +35,6 @@ /** * Computes the gradient of {@code igamma(a, x)} wrt {@code a}. - * - * @param data type for {@code z} output */ @OpMetadata( opType = IgammaGradA.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/math/Igammac.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/math/Igammac.java index 1cc0549ad00..80f2545ce69 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/math/Igammac.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/math/Igammac.java @@ -42,8 +42,6 @@ *

    is the upper incomplete Gamma function. *

    Note, above {@code P(a, x)} ({@code Igamma}) is the lower regularized complete * Gamma function. - * - * @param data type for {@code z} output */ @OpMetadata( opType = Igammac.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/math/Imag.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/math/Imag.java index fe04cd17336..509de2b8c7b 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/math/Imag.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/math/Imag.java @@ -47,8 +47,6 @@ * # tensor 'input' is [-2.25 + 4.75j, 3.25 + 5.75j] * tf.imag(input) ==> [4.75, 5.75] * - * - * @param data type for {@code output} output */ @OpMetadata( opType = Imag.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/math/InvertPermutation.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/math/InvertPermutation.java index 3035d46e60c..a466109898c 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/math/InvertPermutation.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/math/InvertPermutation.java @@ -46,8 +46,6 @@ * # tensor `x` is [3, 4, 0, 2, 1] * invert_permutation(x) ==> [2, 4, 3, 0, 1] * - * - * @param data type for {@code y} output */ @OpMetadata( opType = InvertPermutation.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/math/Lgamma.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/math/Lgamma.java index d8c6b4889a2..4c5aea1de84 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/math/Lgamma.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/math/Lgamma.java @@ -42,8 +42,6 @@ * x = tf.constant([0, 0.5, 1, 4.5, -4, -5.6]) * tf.math.lgamma(x) ==> [inf, 0.5723649, 0., 2.4537368, inf, -4.6477685] * - * - * @param data type for {@code y} output */ @OpMetadata( opType = Lgamma.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/math/Log.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/math/Log.java index 32ee589536a..911ab61ff0c 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/math/Log.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/math/Log.java @@ -41,8 +41,6 @@ * x = tf.constant([0, 0.5, 1, 5]) * tf.math.log(x) ==> [-inf, -0.6931472, 0. , 1.609438] * - * - * @param data type for {@code y} output */ @OpMetadata( opType = Log.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/math/Log1p.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/math/Log1p.java index f280d8b0062..05fe31ad376 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/math/Log1p.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/math/Log1p.java @@ -41,8 +41,6 @@ * x = tf.constant([0, 0.5, 1, 5]) * tf.math.log1p(x) ==> [0., 0.4054651, 0.6931472, 1.7917595] * - * - * @param data type for {@code y} output */ @OpMetadata( opType = Log1p.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/math/Maximum.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/math/Maximum.java index c46c8c6e384..0c864b79f5e 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/math/Maximum.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/math/Maximum.java @@ -37,8 +37,6 @@ * Returns the max of x and y (i.e. x > y ? x : y) element-wise. * NOTE: {@code math.Maximum} supports broadcasting. More about broadcasting * here - * - * @param data type for {@code z} output */ @OpMetadata( opType = Maximum.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/math/Mean.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/math/Mean.java index 94de9fc5bd4..9018aa2bd6d 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/math/Mean.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/math/Mean.java @@ -40,8 +40,6 @@ * {@code keep_dims} is true, the rank of the tensor is reduced by 1 for each entry in * {@code axis}. If {@code keep_dims} is true, the reduced dimensions are * retained with length 1. - * - * @param data type for {@code output} output */ @OpMetadata( opType = Mean.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/math/Minimum.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/math/Minimum.java index 588bcb3328b..b516ee5c302 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/math/Minimum.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/math/Minimum.java @@ -37,8 +37,6 @@ * Returns the min of x and y (i.e. x < y ? x : y) element-wise. * NOTE: {@code math.Minimum} supports broadcasting. More about broadcasting * here - * - * @param data type for {@code z} output */ @OpMetadata( opType = Minimum.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/math/Mod.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/math/Mod.java index d318de97c9c..60ccc32e855 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/math/Mod.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/math/Mod.java @@ -39,8 +39,6 @@ * {@code tf.truncatediv(x, y) * y + truncate_mod(x, y) = x}. *

    NOTE: {@code math.Mod} supports broadcasting. More about broadcasting * here - * - * @param data type for {@code z} output */ @OpMetadata( opType = Mod.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/math/Mul.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/math/Mul.java index d7466085ada..d18a48a6472 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/math/Mul.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/math/Mul.java @@ -37,8 +37,6 @@ * Returns x * y element-wise. * NOTE: {@code math.Mul} supports broadcasting. More about broadcasting * here - * - * @param data type for {@code z} output */ @OpMetadata( opType = Mul.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/math/MulNoNan.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/math/MulNoNan.java index 85429b70ca1..7e85f94c31d 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/math/MulNoNan.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/math/MulNoNan.java @@ -37,8 +37,6 @@ * Returns x * y element-wise. Returns zero if y is zero, even if x if infinite or NaN. * NOTE: {@code math.MulNoNan} supports broadcasting. More about broadcasting * here - * - * @param data type for {@code z} output */ @OpMetadata( opType = MulNoNan.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/math/Ndtri.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/math/Ndtri.java index 37d1ffb8fc9..2c9b4f4719f 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/math/Ndtri.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/math/Ndtri.java @@ -35,8 +35,6 @@ /** * The Ndtri operation - * - * @param data type for {@code y} output */ @OpMetadata( opType = Ndtri.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/math/Neg.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/math/Neg.java index e0ec5783144..e11b274470a 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/math/Neg.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/math/Neg.java @@ -36,8 +36,6 @@ /** * Computes numerical negative value element-wise. * I.e., \(y = -x\). - * - * @param data type for {@code y} output */ @OpMetadata( opType = Neg.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/math/NextAfter.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/math/NextAfter.java index 45ff3a179ca..fef32810db3 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/math/NextAfter.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/math/NextAfter.java @@ -40,8 +40,6 @@ *

    {@literal @}compatibility(cpp)
    * Equivalent to C++ std::nextafter function. *
    {@literal @}end_compatibility - * - * @param data type for {@code output} output */ @OpMetadata( opType = NextAfter.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/math/Polygamma.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/math/Polygamma.java index b2fb442489b..f391fef2335 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/math/Polygamma.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/math/Polygamma.java @@ -39,8 +39,6 @@ *

    \(\psi^{(a)}(x) = \frac{d^a}{dx^a} \psi(x)\) *

    where \(\psi(x)\) is the digamma function. * The polygamma function is defined only for non-negative integer orders \a\. - * - * @param data type for {@code z} output */ @OpMetadata( opType = Polygamma.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/math/Pow.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/math/Pow.java index f50532e8d62..3a8f8acbb7a 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/math/Pow.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/math/Pow.java @@ -42,8 +42,6 @@ * # tensor 'y' is [[8, 16], [2, 3]] * tf.pow(x, y) ==> [[256, 65536], [9, 27]] * - * - * @param data type for {@code z} output */ @OpMetadata( opType = Pow.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/math/QuantizedAdd.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/math/QuantizedAdd.java index ad59711dca9..cf02c4ad713 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/math/QuantizedAdd.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/math/QuantizedAdd.java @@ -37,8 +37,6 @@ /** * Returns x + y element-wise, working on quantized buffers. - * - * @param data type for {@code z} output */ @OpMetadata( opType = QuantizedAdd.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/math/QuantizedMul.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/math/QuantizedMul.java index 6b5c3d05579..b9f1e5b062c 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/math/QuantizedMul.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/math/QuantizedMul.java @@ -37,8 +37,6 @@ /** * Returns x * y element-wise, working on quantized buffers. - * - * @param data type for {@code z} output */ @OpMetadata( opType = QuantizedMul.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/math/Real.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/math/Real.java index 6217269b474..c85e0d73861 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/math/Real.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/math/Real.java @@ -47,8 +47,6 @@ * # tensor 'input' is [-2.25 + 4.75j, 3.25 + 5.75j] * tf.real(input) ==> [-2.25, 3.25] * - * - * @param data type for {@code output} output */ @OpMetadata( opType = Real.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/math/RealDiv.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/math/RealDiv.java index c1aceba76d3..fb2e7e77d33 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/math/RealDiv.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/math/RealDiv.java @@ -38,8 +38,6 @@ * If {@code x} and {@code y} are reals, this will return the floating-point division. *

    NOTE: {@code Div} supports broadcasting. More about broadcasting * here - * - * @param data type for {@code z} output */ @OpMetadata( opType = RealDiv.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/math/Reciprocal.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/math/Reciprocal.java index 97ae15f6015..c0e6b9c573a 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/math/Reciprocal.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/math/Reciprocal.java @@ -36,8 +36,6 @@ /** * Computes the reciprocal of x element-wise. * I.e., \(y = 1 / x\). - * - * @param data type for {@code y} output */ @OpMetadata( opType = Reciprocal.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/math/ReciprocalGrad.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/math/ReciprocalGrad.java index 13b7b7592ab..9d1c672629f 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/math/ReciprocalGrad.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/math/ReciprocalGrad.java @@ -37,8 +37,6 @@ * Computes the gradient for the inverse of {@code x} wrt its input. * Specifically, {@code grad = -dy * y*y}, where {@code y = 1/x}, and {@code dy} * is the corresponding input gradient. - * - * @param data type for {@code z} output */ @OpMetadata( opType = ReciprocalGrad.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/math/RequantizePerChannel.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/math/RequantizePerChannel.java index c2a71d1d594..f6dcf220ade 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/math/RequantizePerChannel.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/math/RequantizePerChannel.java @@ -37,8 +37,6 @@ /** * Requantizes input with min and max values known per channel. - * - * @param data type for {@code output} output */ @OpMetadata( opType = RequantizePerChannel.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/math/Rint.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/math/Rint.java index 716bc8be07b..62a48d4ecd0 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/math/Rint.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/math/Rint.java @@ -43,8 +43,6 @@ * rint(0.5000001) ==> 1.0 * rint([-1.7, -1.5, -0.2, 0.2, 1.5, 1.7, 2.0]) ==> [-2., -2., -0., 0., 2., 2., 2.] * - * - * @param data type for {@code y} output */ @OpMetadata( opType = Rint.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/math/Round.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/math/Round.java index d8a5aff3d2d..0e7441efeb1 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/math/Round.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/math/Round.java @@ -37,8 +37,6 @@ * Rounds the values of a tensor to the nearest integer, element-wise. * Rounds half to even. Also known as bankers rounding. If you want to round * according to the current system rounding mode use std::cint. - * - * @param data type for {@code y} output */ @OpMetadata( opType = Round.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/math/Rsqrt.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/math/Rsqrt.java index 12ce75ef035..3d438f10f12 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/math/Rsqrt.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/math/Rsqrt.java @@ -36,8 +36,6 @@ /** * Computes reciprocal of square root of x element-wise. * I.e., \(y = 1 / \sqrt{x}\). - * - * @param data type for {@code y} output */ @OpMetadata( opType = Rsqrt.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/math/RsqrtGrad.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/math/RsqrtGrad.java index f92da40a82b..90fc4892083 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/math/RsqrtGrad.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/math/RsqrtGrad.java @@ -37,8 +37,6 @@ * Computes the gradient for the rsqrt of {@code x} wrt its input. * Specifically, {@code grad = dy * -0.5 * y^3}, where {@code y = rsqrt(x)}, and {@code dy} * is the corresponding input gradient. - * - * @param data type for {@code z} output */ @OpMetadata( opType = RsqrtGrad.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/math/SegmentMax.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/math/SegmentMax.java index 1939c7a4d3a..44ec468eaf4 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/math/SegmentMax.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/math/SegmentMax.java @@ -73,8 +73,6 @@ * * * - * - * @param data type for {@code output} output */ @OpMetadata( opType = SegmentMax.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/math/SegmentMean.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/math/SegmentMean.java index 7d0e2af1606..2e69b2bb8b5 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/math/SegmentMean.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/math/SegmentMean.java @@ -64,8 +64,6 @@ * * * - * - * @param data type for {@code output} output */ @OpMetadata( opType = SegmentMean.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/math/SegmentMin.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/math/SegmentMin.java index cb5a312d3ff..9dce52fceed 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/math/SegmentMin.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/math/SegmentMin.java @@ -73,8 +73,6 @@ * * * - * - * @param data type for {@code output} output */ @OpMetadata( opType = SegmentMin.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/math/SegmentProd.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/math/SegmentProd.java index 87738a1ac3a..77fd53d92a0 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/math/SegmentProd.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/math/SegmentProd.java @@ -66,8 +66,6 @@ * * * - * - * @param data type for {@code output} output */ @OpMetadata( opType = SegmentProd.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/math/SegmentSum.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/math/SegmentSum.java index 578d159e289..c47c3acd24f 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/math/SegmentSum.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/math/SegmentSum.java @@ -44,9 +44,6 @@ * that {@code segment_ids[j] == i}. *

    If the sum is empty for a given segment ID {@code i}, {@code output[i] = 0}. *

    Note that this op is currently only supported with jit_compile=True. - * - * - * @param data type for {@code output} output */ @OpMetadata( opType = SegmentSum.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/math/Sigmoid.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/math/Sigmoid.java index bd93a0303eb..8e71006a2c0 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/math/Sigmoid.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/math/Sigmoid.java @@ -36,8 +36,6 @@ /** * Computes sigmoid of {@code x} element-wise. * Specifically, {@code y = 1 / (1 + exp(-x))}. - * - * @param data type for {@code y} output */ @OpMetadata( opType = Sigmoid.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/math/SigmoidGrad.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/math/SigmoidGrad.java index 8f4b7cfe45c..a85b754cc61 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/math/SigmoidGrad.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/math/SigmoidGrad.java @@ -37,8 +37,6 @@ * Computes the gradient of the sigmoid of {@code x} wrt its input. * Specifically, {@code grad = dy * y * (1 - y)}, where {@code y = sigmoid(x)}, and * {@code dy} is the corresponding input gradient. - * - * @param data type for {@code z} output */ @OpMetadata( opType = SigmoidGrad.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/math/Sign.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/math/Sign.java index 15f5e07b597..ee9d2d65154 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/math/Sign.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/math/Sign.java @@ -46,8 +46,6 @@ * * * - * - * @param data type for {@code y} output */ @OpMetadata( opType = Sign.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/math/Sin.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/math/Sin.java index 06269cb6278..1a13ada1838 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/math/Sin.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/math/Sin.java @@ -42,8 +42,6 @@ * x = tf.constant([-float("inf"), -9, -0.5, 1, 1.2, 200, 10, float("inf")]) * tf.math.sin(x) ==> [nan -0.4121185 -0.47942555 0.84147096 0.9320391 -0.87329733 -0.54402107 nan] * - * - * @param data type for {@code y} output */ @OpMetadata( opType = Sin.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/math/Sinh.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/math/Sinh.java index 9e1a692df76..b4af201ab99 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/math/Sinh.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/math/Sinh.java @@ -42,8 +42,6 @@ * x = tf.constant([-float("inf"), -9, -0.5, 1, 1.2, 2, 10, float("inf")]) * tf.math.sinh(x) ==> [-inf -4.0515420e+03 -5.2109528e-01 1.1752012e+00 1.5094614e+00 3.6268604e+00 1.1013232e+04 inf] * - * - * @param data type for {@code y} output */ @OpMetadata( opType = Sinh.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/math/SobolSample.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/math/SobolSample.java index 95f33401f0b..5989ca78f57 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/math/SobolSample.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/math/SobolSample.java @@ -40,8 +40,6 @@ * Generates points from the Sobol sequence. * Creates a Sobol sequence with {@code num_results} samples. Each sample has dimension * {@code dim}. Skips the first {@code skip} samples. - * - * @param data type for {@code samples} output */ @OpMetadata( opType = SobolSample.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/math/Softplus.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/math/Softplus.java index aa80f8d0840..cdb0aea4f9f 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/math/Softplus.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/math/Softplus.java @@ -35,8 +35,6 @@ /** * The Softplus operation - * - * @param data type for {@code activations} output */ @OpMetadata( opType = Softplus.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/math/SoftplusGrad.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/math/SoftplusGrad.java index 5a8445dad45..3f2901810ce 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/math/SoftplusGrad.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/math/SoftplusGrad.java @@ -35,8 +35,6 @@ /** * Computes softplus gradients for a softplus operation. - * - * @param data type for {@code backprops} output */ @OpMetadata( opType = SoftplusGrad.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/math/Sqrt.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/math/Sqrt.java index ac6cd68b529..8c6edfc6e89 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/math/Sqrt.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/math/Sqrt.java @@ -36,8 +36,6 @@ /** * Computes square root of x element-wise. * I.e., \(y = \sqrt{x} = x^{1/2}\). - * - * @param data type for {@code y} output */ @OpMetadata( opType = Sqrt.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/math/SqrtGrad.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/math/SqrtGrad.java index 451143c16e4..eed0209152b 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/math/SqrtGrad.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/math/SqrtGrad.java @@ -37,8 +37,6 @@ * Computes the gradient for the sqrt of {@code x} wrt its input. * Specifically, {@code grad = dy * 0.5 / y}, where {@code y = sqrt(x)}, and {@code dy} * is the corresponding input gradient. - * - * @param data type for {@code z} output */ @OpMetadata( opType = SqrtGrad.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/math/Square.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/math/Square.java index d5811d17c2a..2952af307d2 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/math/Square.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/math/Square.java @@ -36,8 +36,6 @@ /** * Computes square of x element-wise. * I.e., \(y = x * x = x^2\). - * - * @param data type for {@code y} output */ @OpMetadata( opType = Square.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/math/SquaredDifference.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/math/SquaredDifference.java index 2af6fe083e3..4d880a79baa 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/math/SquaredDifference.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/math/SquaredDifference.java @@ -37,8 +37,6 @@ * Returns conj(x - y)(x - y) element-wise. * NOTE: {@code math.SquaredDifference} supports broadcasting. More about broadcasting * here - * - * @param data type for {@code z} output */ @OpMetadata( opType = SquaredDifference.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/math/Sub.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/math/Sub.java index 6313555f9f1..b48b311d80e 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/math/Sub.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/math/Sub.java @@ -37,8 +37,6 @@ * Returns x - y element-wise. * NOTE: {@code math.Sub} supports broadcasting. More about broadcasting * here - * - * @param data type for {@code z} output */ @OpMetadata( opType = Sub.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/math/Tan.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/math/Tan.java index 566b7d2b03f..c1073f8a5bb 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/math/Tan.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/math/Tan.java @@ -43,8 +43,6 @@ * x = tf.constant([-float("inf"), -9, -0.5, 1, 1.2, 200, 10000, float("inf")]) * tf.math.tan(x) ==> [nan 0.45231566 -0.5463025 1.5574077 2.572152 -1.7925274 0.32097113 nan] * - * - * @param data type for {@code y} output */ @OpMetadata( opType = Tan.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/math/Tanh.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/math/Tanh.java index ee24b4085df..706a8d90cd0 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/math/Tanh.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/math/Tanh.java @@ -49,8 +49,6 @@ * * * - * - * @param data type for {@code y} output */ @OpMetadata( opType = Tanh.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/math/TanhGrad.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/math/TanhGrad.java index c638f78b3fe..273adcf20a6 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/math/TanhGrad.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/math/TanhGrad.java @@ -37,8 +37,6 @@ * Computes the gradient for the tanh of {@code x} wrt its input. * Specifically, {@code grad = dy * (1 - y*y)}, where {@code y = tanh(x)}, and {@code dy} * is the corresponding input gradient. - * - * @param data type for {@code z} output */ @OpMetadata( opType = TanhGrad.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/math/TruncateDiv.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/math/TruncateDiv.java index 377eb5848d8..7857bd6221b 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/math/TruncateDiv.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/math/TruncateDiv.java @@ -41,8 +41,6 @@ * Python Semantics. *

    NOTE: {@code math.TruncateDiv} supports broadcasting. More about broadcasting * here - * - * @param data type for {@code z} output */ @OpMetadata( opType = TruncateDiv.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/math/TruncateMod.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/math/TruncateMod.java index e80c75e5709..bd7a41fafd2 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/math/TruncateMod.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/math/TruncateMod.java @@ -38,8 +38,6 @@ * the result here is consistent with a truncating divide. E.g. {@code truncate(x / y) * y + truncate_mod(x, y) = x}. *

    NOTE: {@code math.TruncateMod} supports broadcasting. More about broadcasting * here - * - * @param data type for {@code z} output */ @OpMetadata( opType = TruncateMod.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/math/UniformQuantizedAdd.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/math/UniformQuantizedAdd.java index 535d432dcca..312c712b44e 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/math/UniformQuantizedAdd.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/math/UniformQuantizedAdd.java @@ -52,8 +52,6 @@ * i.e. For both operands {@code lhs} and {@code rhs}, * if {@code operand.quantization_axis} >= 0 and {@code output.quantization_axis} >= 0, * {@code operand.dims} - {@code operand.quantization_axis} must be equal to {@code output.dims} - {@code output.quantization_axis}. - * - * @param data type for {@code output} output */ @OpMetadata( opType = UniformQuantizedAdd.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/math/UnsortedSegmentMax.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/math/UnsortedSegmentMax.java index 50d32494e80..27888d7f1f5 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/math/UnsortedSegmentMax.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/math/UnsortedSegmentMax.java @@ -67,8 +67,6 @@ * * * - * - * @param data type for {@code output} output */ @OpMetadata( opType = UnsortedSegmentMax.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/math/UnsortedSegmentMin.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/math/UnsortedSegmentMin.java index db83daaead7..af919665a56 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/math/UnsortedSegmentMin.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/math/UnsortedSegmentMin.java @@ -64,8 +64,6 @@ * result in safe but unspecified behavior, which may include ignoring * out-of-bound indices or outputting a tensor with a 0 stored in the first * dimension of its shape if {@code num_segments} is 0. - * - * @param data type for {@code output} output */ @OpMetadata( opType = UnsortedSegmentMin.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/math/UnsortedSegmentProd.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/math/UnsortedSegmentProd.java index a36c653ef2a..fd3f76bc1e7 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/math/UnsortedSegmentProd.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/math/UnsortedSegmentProd.java @@ -64,8 +64,6 @@ * result in safe but unspecified behavior, which may include ignoring * out-of-bound indices or outputting a tensor with a 0 stored in the first * dimension of its shape if {@code num_segments} is 0. - * - * @param data type for {@code output} output */ @OpMetadata( opType = UnsortedSegmentProd.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/math/UnsortedSegmentSum.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/math/UnsortedSegmentSum.java index 14c0bef2293..af4dd57e39f 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/math/UnsortedSegmentSum.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/math/UnsortedSegmentSum.java @@ -67,8 +67,6 @@ * * * - * - * @param data type for {@code output} output */ @OpMetadata( opType = UnsortedSegmentSum.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/math/Xdivy.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/math/Xdivy.java index 8be3546a9f0..0ba35ba8a83 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/math/Xdivy.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/math/Xdivy.java @@ -35,8 +35,6 @@ /** * Returns 0 if x == 0, and x / y otherwise, elementwise. - * - * @param data type for {@code z} output */ @OpMetadata( opType = Xdivy.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/math/Xlog1py.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/math/Xlog1py.java index b798c8ef598..c6e6184bed0 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/math/Xlog1py.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/math/Xlog1py.java @@ -35,8 +35,6 @@ /** * Returns 0 if x == 0, and x * log1p(y) otherwise, elementwise. - * - * @param data type for {@code z} output */ @OpMetadata( opType = Xlog1py.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/math/Xlogy.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/math/Xlogy.java index b4ad543093f..e27ef9a210c 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/math/Xlogy.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/math/Xlogy.java @@ -35,8 +35,6 @@ /** * Returns 0 if x == 0, and x * log(y) otherwise, elementwise. - * - * @param data type for {@code z} output */ @OpMetadata( opType = Xlogy.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/math/Zeta.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/math/Zeta.java index 887fb1af711..593507c4340 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/math/Zeta.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/math/Zeta.java @@ -37,8 +37,6 @@ * Compute the Hurwitz zeta function \(\zeta(x, q)\). * The Hurwitz zeta function is defined as: *

    \(\zeta(x, q) = \sum_{n=0}^{\infty} (q + n)^{-x}\) - * - * @param data type for {@code z} output */ @OpMetadata( opType = Zeta.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/math/erfinv.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/math/erfinv.java index a4b68423646..a208c49973f 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/math/erfinv.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/math/erfinv.java @@ -35,8 +35,6 @@ /** * The Erfinv operation - * - * @param data type for {@code y} output */ @OpMetadata( opType = erfinv.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/math/special/BesselJ0.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/math/special/BesselJ0.java index 6ef1d289c7d..839ca6179b3 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/math/special/BesselJ0.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/math/special/BesselJ0.java @@ -35,8 +35,6 @@ /** * The BesselJ0 operation - * - * @param data type for {@code y} output */ @OpMetadata( opType = BesselJ0.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/math/special/BesselJ1.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/math/special/BesselJ1.java index 5e7718f4144..6e125a29821 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/math/special/BesselJ1.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/math/special/BesselJ1.java @@ -35,8 +35,6 @@ /** * The BesselJ1 operation - * - * @param data type for {@code y} output */ @OpMetadata( opType = BesselJ1.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/math/special/BesselK0.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/math/special/BesselK0.java index 338b5759a10..8ec9f528212 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/math/special/BesselK0.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/math/special/BesselK0.java @@ -35,8 +35,6 @@ /** * The BesselK0 operation - * - * @param data type for {@code y} output */ @OpMetadata( opType = BesselK0.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/math/special/BesselK0e.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/math/special/BesselK0e.java index f2a01b68ba8..69d5995c59d 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/math/special/BesselK0e.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/math/special/BesselK0e.java @@ -35,8 +35,6 @@ /** * The BesselK0e operation - * - * @param data type for {@code y} output */ @OpMetadata( opType = BesselK0e.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/math/special/BesselK1.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/math/special/BesselK1.java index 8143c8107d5..f26b95a8c53 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/math/special/BesselK1.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/math/special/BesselK1.java @@ -35,8 +35,6 @@ /** * The BesselK1 operation - * - * @param data type for {@code y} output */ @OpMetadata( opType = BesselK1.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/math/special/BesselK1e.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/math/special/BesselK1e.java index 08ea2073dab..995eaccd9dd 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/math/special/BesselK1e.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/math/special/BesselK1e.java @@ -35,8 +35,6 @@ /** * The BesselK1e operation - * - * @param data type for {@code y} output */ @OpMetadata( opType = BesselK1e.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/math/special/BesselY0.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/math/special/BesselY0.java index c82e15022db..1beae63d61f 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/math/special/BesselY0.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/math/special/BesselY0.java @@ -35,8 +35,6 @@ /** * The BesselY0 operation - * - * @param data type for {@code y} output */ @OpMetadata( opType = BesselY0.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/math/special/BesselY1.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/math/special/BesselY1.java index 5b86f1987e3..3985dee42d0 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/math/special/BesselY1.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/math/special/BesselY1.java @@ -35,8 +35,6 @@ /** * The BesselY1 operation - * - * @param data type for {@code y} output */ @OpMetadata( opType = BesselY1.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/math/special/Dawsn.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/math/special/Dawsn.java index 045ffc0d94c..e34e0376249 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/math/special/Dawsn.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/math/special/Dawsn.java @@ -35,8 +35,6 @@ /** * The Dawsn operation - * - * @param data type for {@code y} output */ @OpMetadata( opType = Dawsn.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/math/special/Expint.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/math/special/Expint.java index bcdff92cb07..9b61e0fcb90 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/math/special/Expint.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/math/special/Expint.java @@ -35,8 +35,6 @@ /** * The Expint operation - * - * @param data type for {@code y} output */ @OpMetadata( opType = Expint.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/math/special/FresnelCos.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/math/special/FresnelCos.java index 790daad9115..dffb6bda0f0 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/math/special/FresnelCos.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/math/special/FresnelCos.java @@ -35,8 +35,6 @@ /** * The FresnelCos operation - * - * @param data type for {@code y} output */ @OpMetadata( opType = FresnelCos.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/math/special/FresnelSin.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/math/special/FresnelSin.java index a148cb42bff..23e7e1d4bbd 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/math/special/FresnelSin.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/math/special/FresnelSin.java @@ -35,8 +35,6 @@ /** * The FresnelSin operation - * - * @param data type for {@code y} output */ @OpMetadata( opType = FresnelSin.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/math/special/Spence.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/math/special/Spence.java index 7835a2fca79..0a012a3be6c 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/math/special/Spence.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/math/special/Spence.java @@ -35,8 +35,6 @@ /** * The Spence operation - * - * @param data type for {@code y} output */ @OpMetadata( opType = Spence.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/nn/AvgPool.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/nn/AvgPool.java index aa583ae8174..3d6355679c8 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/nn/AvgPool.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/nn/AvgPool.java @@ -38,8 +38,6 @@ * Performs average pooling on the input. * Each entry in {@code output} is the mean of the corresponding size {@code ksize} * window in {@code value}. - * - * @param data type for {@code output} output */ @OpMetadata( opType = AvgPool.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/nn/AvgPool3d.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/nn/AvgPool3d.java index b7b61a50351..5f5410d91d5 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/nn/AvgPool3d.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/nn/AvgPool3d.java @@ -38,8 +38,6 @@ * Performs 3D average pooling on the input. * Each entry in {@code output} is the mean of the corresponding size {@code ksize} window in * {@code value}. - * - * @param data type for {@code output} output */ @OpMetadata( opType = AvgPool3d.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/nn/AvgPool3dGrad.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/nn/AvgPool3dGrad.java index 6acc17b69ae..4b41a0338b3 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/nn/AvgPool3dGrad.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/nn/AvgPool3dGrad.java @@ -37,8 +37,6 @@ /** * Computes gradients of average pooling function. - * - * @param data type for {@code output} output */ @OpMetadata( opType = AvgPool3dGrad.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/nn/AvgPoolGrad.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/nn/AvgPoolGrad.java index 74acc456c92..9a2c1511bba 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/nn/AvgPoolGrad.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/nn/AvgPoolGrad.java @@ -37,8 +37,6 @@ /** * Computes gradients of the average pooling function. - * - * @param data type for {@code output} output */ @OpMetadata( opType = AvgPoolGrad.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/nn/BatchNormWithGlobalNormalization.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/nn/BatchNormWithGlobalNormalization.java index deaec7bdd3d..ef7ead8115e 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/nn/BatchNormWithGlobalNormalization.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/nn/BatchNormWithGlobalNormalization.java @@ -36,8 +36,6 @@ /** * Batch normalization. * This op is deprecated. Prefer {@code tf.nn.batch_normalization}. - * - * @param data type for {@code result} output */ @OpMetadata( opType = BatchNormWithGlobalNormalization.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/nn/BatchNormWithGlobalNormalizationGrad.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/nn/BatchNormWithGlobalNormalizationGrad.java index f75aebb0e4c..03e84d778c4 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/nn/BatchNormWithGlobalNormalizationGrad.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/nn/BatchNormWithGlobalNormalizationGrad.java @@ -36,8 +36,6 @@ /** * Gradients for batch normalization. * This op is deprecated. See {@code tf.nn.batch_normalization}. - * - * @param data type for {@code dx} output */ @OpMetadata( opType = BatchNormWithGlobalNormalizationGrad.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/nn/BiasAdd.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/nn/BiasAdd.java index c228699e9cb..5f826546b07 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/nn/BiasAdd.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/nn/BiasAdd.java @@ -37,8 +37,6 @@ * Adds {@code bias} to {@code value}. * This is a special case of {@code tf.add} where {@code bias} is restricted to be 1-D. * Broadcasting is supported, so {@code value} may have any number of dimensions. - * - * @param data type for {@code output} output */ @OpMetadata( opType = BiasAdd.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/nn/BiasAddGrad.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/nn/BiasAddGrad.java index 01c90a2fd49..33c2829c271 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/nn/BiasAddGrad.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/nn/BiasAddGrad.java @@ -38,8 +38,6 @@ * It accumulates all the values from out_backprop into the feature dimension. * For NHWC data format, the feature dimension is the last. For NCHW data format, * the feature dimension is the third-to-last. - * - * @param data type for {@code output} output */ @OpMetadata( opType = BiasAddGrad.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/nn/BlockLSTM.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/nn/BlockLSTM.java index 3363a371d20..ef303c35efc 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/nn/BlockLSTM.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/nn/BlockLSTM.java @@ -56,8 +56,6 @@ * this op uses IFCO. So in order for the following snippet to be equivalent * all gate-related outputs should be reordered. * - * - * @param data type for {@code i} output */ @OpMetadata( opType = BlockLSTM.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/nn/BlockLSTMGrad.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/nn/BlockLSTMGrad.java index 2684ae60017..85bc08f38b6 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/nn/BlockLSTMGrad.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/nn/BlockLSTMGrad.java @@ -37,8 +37,6 @@ /** * Computes the LSTM cell backward propagation for the entire time sequence. * This implementation is to be used in conjunction of BlockLSTMV2. - * - * @param data type for {@code x_grad} output */ @OpMetadata( opType = BlockLSTMGrad.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/nn/Conv.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/nn/Conv.java index 7e352a1ff76..096c8a3719f 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/nn/Conv.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/nn/Conv.java @@ -38,8 +38,6 @@ * Computes a N-D convolution given (N+1+batch_dims)-D {@code input} and (N+2)-D {@code filter} tensors. * General function for computing a N-D convolution. It is required that * {@code 1 <= N <= 3}. - * - * @param data type for {@code output} output */ @OpMetadata( opType = Conv.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/nn/Conv2d.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/nn/Conv2d.java index 9fef633fefd..6d7eb6e004e 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/nn/Conv2d.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/nn/Conv2d.java @@ -56,8 +56,6 @@ * *

    Must have {@code strides[0] = strides[3] = 1}. For the most common case of the same * horizontal and vertices strides, {@code strides = [1, stride, stride, 1]}. - * - * @param data type for {@code output} output */ @OpMetadata( opType = Conv2d.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/nn/Conv2dBackpropFilter.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/nn/Conv2dBackpropFilter.java index 9d09ebaa1df..2d5af50d5e6 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/nn/Conv2dBackpropFilter.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/nn/Conv2dBackpropFilter.java @@ -37,8 +37,6 @@ /** * Computes the gradients of convolution with respect to the filter. - * - * @param data type for {@code output} output */ @OpMetadata( opType = Conv2dBackpropFilter.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/nn/Conv2dBackpropFilterV2.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/nn/Conv2dBackpropFilterV2.java index 901d2a50f72..1b8a95c8728 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/nn/Conv2dBackpropFilterV2.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/nn/Conv2dBackpropFilterV2.java @@ -35,8 +35,6 @@ /** * Computes the gradients of convolution with respect to the filter. - * - * @param data type for {@code output} output */ @OpMetadata( opType = Conv2dBackpropFilterV2.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/nn/Conv2dBackpropInput.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/nn/Conv2dBackpropInput.java index 9e44c7170cb..fc0f5f296e1 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/nn/Conv2dBackpropInput.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/nn/Conv2dBackpropInput.java @@ -37,8 +37,6 @@ /** * Computes the gradients of convolution with respect to the input. - * - * @param data type for {@code output} output */ @OpMetadata( opType = Conv2dBackpropInput.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/nn/Conv2dBackpropInputV2.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/nn/Conv2dBackpropInputV2.java index 1fa123e14b2..04941640016 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/nn/Conv2dBackpropInputV2.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/nn/Conv2dBackpropInputV2.java @@ -35,8 +35,6 @@ /** * Computes the gradients of convolution with respect to the input. - * - * @param data type for {@code output} output */ @OpMetadata( opType = Conv2dBackpropInputV2.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/nn/Conv3d.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/nn/Conv3d.java index 5d3d0925894..7de4f93716d 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/nn/Conv3d.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/nn/Conv3d.java @@ -40,8 +40,6 @@ * two waveforms as a function of a time-lag applied to one of them. This * is also known as a sliding dot product or sliding inner-product. *

    Our Conv3D implements a form of cross-correlation. - * - * @param data type for {@code output} output */ @OpMetadata( opType = Conv3d.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/nn/Conv3dBackpropFilter.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/nn/Conv3dBackpropFilter.java index 2cc01b0dfe0..79970ac4d15 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/nn/Conv3dBackpropFilter.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/nn/Conv3dBackpropFilter.java @@ -37,8 +37,6 @@ /** * Computes the gradients of 3-D convolution with respect to the filter. - * - * @param data type for {@code output} output */ @OpMetadata( opType = Conv3dBackpropFilter.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/nn/Conv3dBackpropInput.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/nn/Conv3dBackpropInput.java index 651f027ac42..d60306ab96d 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/nn/Conv3dBackpropInput.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/nn/Conv3dBackpropInput.java @@ -36,8 +36,6 @@ /** * Computes the gradients of 3-D convolution with respect to the input. - * - * @param data type for {@code output} output */ @OpMetadata( opType = Conv3dBackpropInput.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/nn/CtcBeamSearchDecoder.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/nn/CtcBeamSearchDecoder.java index 59cde61eb54..f270607bb50 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/nn/CtcBeamSearchDecoder.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/nn/CtcBeamSearchDecoder.java @@ -43,8 +43,6 @@ * the first of these is emitted. That is, when the top path is "A B B B B", * "A B" is returned if merge_repeated = True but "A B B B B" is * returned if merge_repeated = False. - * - * @param data type for {@code log_probability} output */ @OpMetadata( opType = CtcBeamSearchDecoder.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/nn/CtcGreedyDecoder.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/nn/CtcGreedyDecoder.java index de01c874c33..688f60ab28e 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/nn/CtcGreedyDecoder.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/nn/CtcGreedyDecoder.java @@ -45,8 +45,6 @@ *

    Regardless of the value of merge_repeated, if the maximum index of a given * time and batch corresponds to the blank, index {@code (num_classes - 1)}, no new * element is emitted. - * - * @param data type for {@code log_probability} output */ @OpMetadata( opType = CtcGreedyDecoder.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/nn/CtcLoss.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/nn/CtcLoss.java index d2dd09549fa..8369dae6c75 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/nn/CtcLoss.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/nn/CtcLoss.java @@ -39,8 +39,6 @@ * Calculates the CTC Loss (log probability) for each batch entry. Also calculates * the gradient. This class performs the softmax operation for you, so inputs * should be e.g. linear projections of outputs by an LSTM. - * - * @param data type for {@code loss} output */ @OpMetadata( opType = CtcLoss.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/nn/CudnnRNN.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/nn/CudnnRNN.java index 0525df86f45..8845090aa6e 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/nn/CudnnRNN.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/nn/CudnnRNN.java @@ -73,8 +73,6 @@ * major. * reserve_space: An opaque tensor that can be used in backprop calculation. It * is only produced if is_training is true. - * - * @param data type for {@code output} output */ @OpMetadata( opType = CudnnRNN.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/nn/CudnnRNNBackprop.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/nn/CudnnRNNBackprop.java index d76dd629918..a1e09f597ac 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/nn/CudnnRNNBackprop.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/nn/CudnnRNNBackprop.java @@ -83,8 +83,6 @@ * shape as input_c. * params_backprop: The backprop to the params buffer in the forward pass. Has the * same shape as params. - * - * @param data type for {@code input_backprop} output */ @OpMetadata( opType = CudnnRNNBackprop.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/nn/CudnnRNNCanonicalToParams.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/nn/CudnnRNNCanonicalToParams.java index a513cf67d66..0c38a68a23e 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/nn/CudnnRNNCanonicalToParams.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/nn/CudnnRNNCanonicalToParams.java @@ -65,8 +65,6 @@ * seed2: the 2nd part of a seed to initialize dropout. * num_proj: The output dimensionality for the projection matrices. If None or 0, * no projection is performed. - * - * @param data type for {@code params} output */ @OpMetadata( opType = CudnnRNNCanonicalToParams.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/nn/CudnnRNNParamsToCanonical.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/nn/CudnnRNNParamsToCanonical.java index 6a1e55f34e2..b85a3568412 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/nn/CudnnRNNParamsToCanonical.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/nn/CudnnRNNParamsToCanonical.java @@ -65,8 +65,6 @@ * seed2: the 2nd part of a seed to initialize dropout. * num_proj: The output dimensionality for the projection matrices. If None or 0, * no projection is performed. - * - * @param data type for {@code weights} output */ @OpMetadata( opType = CudnnRNNParamsToCanonical.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/nn/CudnnRnnParamsSize.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/nn/CudnnRnnParamsSize.java index 051c792e878..1dbc4d48ad8 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/nn/CudnnRnnParamsSize.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/nn/CudnnRnnParamsSize.java @@ -57,8 +57,6 @@ * compatible across GPUs. Please use CudnnRNNParamsWeights and * CudnnRNNParamsBiases to save and restore them in a way that is compatible * across different runs. - * - * @param data type for {@code params_size} output */ @OpMetadata( opType = CudnnRnnParamsSize.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/nn/DataFormatDimMap.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/nn/DataFormatDimMap.java index 3376ad9ed6e..6e83cd0c867 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/nn/DataFormatDimMap.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/nn/DataFormatDimMap.java @@ -36,8 +36,6 @@ /** * Returns the dimension index in the destination data format given the one in * the source data format. - * - * @param data type for {@code y} output */ @OpMetadata( opType = DataFormatDimMap.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/nn/DataFormatVecPermute.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/nn/DataFormatVecPermute.java index e02890a40ce..f719f7cc7ce 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/nn/DataFormatVecPermute.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/nn/DataFormatVecPermute.java @@ -64,8 +64,6 @@ * 

      * [1, 2]
  *
    - * - * @param data type for {@code y} output */ @OpMetadata( opType = DataFormatVecPermute.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/nn/DepthToSpace.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/nn/DepthToSpace.java index cceb78d27d1..2f1880cda02 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/nn/DepthToSpace.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/nn/DepthToSpace.java @@ -109,8 +109,6 @@ * [ [11], [12], [15], [16]]]] * * - * - * @param data type for {@code output} output */ @OpMetadata( opType = DepthToSpace.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/nn/DepthwiseConv2dNative.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/nn/DepthwiseConv2dNative.java index e3f7f02ac33..93a0b744513 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/nn/DepthwiseConv2dNative.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/nn/DepthwiseConv2dNative.java @@ -52,8 +52,6 @@ * *

    Must have {@code strides[0] = strides[3] = 1}. For the most common case of the same * horizontal and vertices strides, {@code strides = [1, stride, stride, 1]}. - * - * @param data type for {@code output} output */ @OpMetadata( opType = DepthwiseConv2dNative.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/nn/DepthwiseConv2dNativeBackpropFilter.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/nn/DepthwiseConv2dNativeBackpropFilter.java index 6c55468131b..66eb190debf 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/nn/DepthwiseConv2dNativeBackpropFilter.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/nn/DepthwiseConv2dNativeBackpropFilter.java @@ -37,8 +37,6 @@ /** * Computes the gradients of depthwise convolution with respect to the filter. - * - * @param data type for {@code output} output */ @OpMetadata( opType = DepthwiseConv2dNativeBackpropFilter.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/nn/DepthwiseConv2dNativeBackpropInput.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/nn/DepthwiseConv2dNativeBackpropInput.java index 0f1a70bb566..287b29abba1 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/nn/DepthwiseConv2dNativeBackpropInput.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/nn/DepthwiseConv2dNativeBackpropInput.java @@ -37,8 +37,6 @@ /** * Computes the gradients of depthwise convolution with respect to the input. - * - * @param data type for {@code output} output */ @OpMetadata( opType = DepthwiseConv2dNativeBackpropInput.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/nn/Dilation2d.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/nn/Dilation2d.java index f213e685ab6..019c786873c 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/nn/Dilation2d.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/nn/Dilation2d.java @@ -57,8 +57,6 @@ * kernel size and contains all zeros. *

    Note on duality: The dilation of {@code input} by the {@code filter} is equal to the * negation of the erosion of {@code -input} by the reflected {@code filter}. - * - * @param data type for {@code output} output */ @OpMetadata( opType = Dilation2d.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/nn/Dilation2dBackpropFilter.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/nn/Dilation2dBackpropFilter.java index 93381ee22cf..cae841aee0d 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/nn/Dilation2dBackpropFilter.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/nn/Dilation2dBackpropFilter.java @@ -36,8 +36,6 @@ /** * Computes the gradient of morphological 2-D dilation with respect to the filter. - * - * @param data type for {@code filter_backprop} output */ @OpMetadata( opType = Dilation2dBackpropFilter.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/nn/Dilation2dBackpropInput.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/nn/Dilation2dBackpropInput.java index 7747bc57c64..8204785ae02 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/nn/Dilation2dBackpropInput.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/nn/Dilation2dBackpropInput.java @@ -36,8 +36,6 @@ /** * Computes the gradient of morphological 2-D dilation with respect to the input. - * - * @param data type for {@code in_backprop} output */ @OpMetadata( opType = Dilation2dBackpropInput.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/nn/Elu.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/nn/Elu.java index 6119dd0dec2..253baee2601 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/nn/Elu.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/nn/Elu.java @@ -55,8 +55,6 @@ * *

    See Fast and Accurate Deep Network Learning by Exponential Linear Units (ELUs) * - * - * @param data type for {@code activations} output */ @OpMetadata( opType = Elu.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/nn/EluGrad.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/nn/EluGrad.java index 2df99ce5c8f..4d32b6d365f 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/nn/EluGrad.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/nn/EluGrad.java @@ -35,8 +35,6 @@ /** * Computes gradients for the exponential linear (Elu) operation. - * - * @param data type for {@code backprops} output */ @OpMetadata( opType = EluGrad.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/nn/FractionalAvgPool.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/nn/FractionalAvgPool.java index 04cfd0e3cd9..bb525aac295 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/nn/FractionalAvgPool.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/nn/FractionalAvgPool.java @@ -41,8 +41,6 @@ * region generation step. The only difference is that after pooling regions are * generated, a mean operation is performed instead of a max operation in each * pooling region. - * - * @param data type for {@code output} output */ @OpMetadata( opType = FractionalAvgPool.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/nn/FractionalAvgPoolGrad.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/nn/FractionalAvgPoolGrad.java index 71b1e624c55..eee42886ab1 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/nn/FractionalAvgPoolGrad.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/nn/FractionalAvgPoolGrad.java @@ -41,8 +41,6 @@ * out_backprop to those indices that form the same pooling cell. Therefore, we * just need to know the shape of original input tensor, instead of the whole * tensor. - * - * @param data type for {@code output} output */ @OpMetadata( opType = FractionalAvgPoolGrad.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/nn/FractionalMaxPool.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/nn/FractionalMaxPool.java index d4c2cb5cf15..08bcbd1a63d 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/nn/FractionalMaxPool.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/nn/FractionalMaxPool.java @@ -63,8 +63,6 @@ * *

    For more details on fractional max pooling, see this paper: * Benjamin Graham, Fractional Max-Pooling - * - * @param data type for {@code output} output */ @OpMetadata( opType = FractionalMaxPool.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/nn/FractionalMaxPoolGrad.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/nn/FractionalMaxPoolGrad.java index 432d6bbfdb7..d44e062ccf7 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/nn/FractionalMaxPoolGrad.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/nn/FractionalMaxPoolGrad.java @@ -36,8 +36,6 @@ /** * Computes gradient of the FractionalMaxPool function. - * - * @param data type for {@code output} output */ @OpMetadata( opType = FractionalMaxPoolGrad.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/nn/FusedBatchNorm.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/nn/FusedBatchNorm.java index 41e62263399..f5cede8855e 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/nn/FusedBatchNorm.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/nn/FusedBatchNorm.java @@ -37,10 +37,6 @@ * Batch normalization. * Note that the size of 4D Tensors are defined by either "NHWC" or "NCHW". * The size of 1D Tensors matches the dimension C of the 4D Tensors. - * - * @param data type for {@code y} output - * - * @param data type for {@code batch_mean} output */ @OpMetadata( opType = FusedBatchNorm.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/nn/FusedBatchNormGrad.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/nn/FusedBatchNormGrad.java index efc751554d2..985249a19fe 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/nn/FusedBatchNormGrad.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/nn/FusedBatchNormGrad.java @@ -38,10 +38,6 @@ * Gradient for batch normalization. * Note that the size of 4D Tensors are defined by either "NHWC" or "NCHW". * The size of 1D Tensors matches the dimension C of the 4D Tensors. - * - * @param data type for {@code x_backprop} output - * - * @param data type for {@code scale_backprop} output */ @OpMetadata( opType = FusedBatchNormGrad.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/nn/FusedPadConv2d.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/nn/FusedPadConv2d.java index 1a11cf9c722..336419f92ad 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/nn/FusedPadConv2d.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/nn/FusedPadConv2d.java @@ -48,8 +48,6 @@ * Internally this op uses a single per-graph scratch buffer, which means that it * will block if multiple versions are being run in parallel. This is because this * operator is primarily an optimization to minimize memory usage. - * - * @param data type for {@code output} output */ @OpMetadata( opType = FusedPadConv2d.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/nn/FusedResizeAndPadConv2d.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/nn/FusedResizeAndPadConv2d.java index 69b33a7ffee..8491feba1d7 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/nn/FusedResizeAndPadConv2d.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/nn/FusedResizeAndPadConv2d.java @@ -47,8 +47,6 @@ * Internally this op uses a single per-graph scratch buffer, which means that it * will block if multiple versions are being run in parallel. This is because this * operator is primarily an optimization to minimize memory usage. - * - * @param data type for {@code output} output */ @OpMetadata( opType = FusedResizeAndPadConv2d.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/nn/GRUBlockCell.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/nn/GRUBlockCell.java index 413c9db45cf..0db7843bced 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/nn/GRUBlockCell.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/nn/GRUBlockCell.java @@ -73,8 +73,6 @@ * * h = (1-u) \circ c + u \circ h_prev * - * - * @param data type for {@code r} output */ @OpMetadata( opType = GRUBlockCell.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/nn/GRUBlockCellGrad.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/nn/GRUBlockCellGrad.java index 108aa910427..7379a2790ba 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/nn/GRUBlockCellGrad.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/nn/GRUBlockCellGrad.java @@ -108,8 +108,6 @@ * * d_b_c = sum of d_c_bar along axis = 0 * - * - * @param data type for {@code d_x} output */ @OpMetadata( opType = GRUBlockCellGrad.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/nn/InvGrad.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/nn/InvGrad.java index 37f66b92878..5f178f53e50 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/nn/InvGrad.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/nn/InvGrad.java @@ -37,8 +37,6 @@ * Computes the gradient for the inverse of {@code x} wrt its input. * Specifically, {@code grad = -dy * y*y}, where {@code y = 1/x}, and {@code dy} * is the corresponding input gradient. - * - * @param data type for {@code z} output */ @OpMetadata( opType = InvGrad.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/nn/IsotonicRegression.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/nn/IsotonicRegression.java index 8936770d8b7..ecd511253e8 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/nn/IsotonicRegression.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/nn/IsotonicRegression.java @@ -38,8 +38,6 @@ /** * Solves a batch of isotonic regression problems. - * - * @param data type for {@code output} output */ @OpMetadata( opType = IsotonicRegression.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/nn/L2Loss.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/nn/L2Loss.java index e3b179e440c..9cc952c05cb 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/nn/L2Loss.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/nn/L2Loss.java @@ -39,8 +39,6 @@ * 

      * output = sum(t ** 2) / 2
  *
    - * - * @param data type for {@code output} output */ @OpMetadata( opType = L2Loss.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/nn/LSTMBlockCell.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/nn/LSTMBlockCell.java index 12d4402e70f..5b1e38d3fbe 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/nn/LSTMBlockCell.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/nn/LSTMBlockCell.java @@ -57,8 +57,6 @@ * co = tanh(cs) * h = co .* o * - * - * @param data type for {@code i} output */ @OpMetadata( opType = LSTMBlockCell.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/nn/LSTMBlockCellGrad.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/nn/LSTMBlockCellGrad.java index e22e2241718..931e4bf2381 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/nn/LSTMBlockCellGrad.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/nn/LSTMBlockCellGrad.java @@ -36,8 +36,6 @@ /** * Computes the LSTM cell backward propagation for 1 timestep. * This implementation is to be used in conjunction of LSTMBlockCell. - * - * @param data type for {@code cs_prev_grad} output */ @OpMetadata( opType = LSTMBlockCellGrad.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/nn/LeakyRelu.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/nn/LeakyRelu.java index 022b81f82da..a0f088f9a03 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/nn/LeakyRelu.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/nn/LeakyRelu.java @@ -35,8 +35,6 @@ /** * Computes rectified linear: {@code max(features, features * alpha)}. - * - * @param data type for {@code activations} output */ @OpMetadata( opType = LeakyRelu.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/nn/LocalResponseNormalization.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/nn/LocalResponseNormalization.java index f0bb2b5017b..17c1e5c0d04 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/nn/LocalResponseNormalization.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/nn/LocalResponseNormalization.java @@ -46,8 +46,6 @@ * *

    For details, see Krizhevsky et al., ImageNet classification with deep * convolutional neural networks (NIPS 2012) . - * - * @param data type for {@code output} output */ @OpMetadata( opType = LocalResponseNormalization.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/nn/LocalResponseNormalizationGrad.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/nn/LocalResponseNormalizationGrad.java index 041837b7871..c0b795094aa 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/nn/LocalResponseNormalizationGrad.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/nn/LocalResponseNormalizationGrad.java @@ -35,8 +35,6 @@ /** * Gradients for Local Response Normalization. - * - * @param data type for {@code output} output */ @OpMetadata( opType = LocalResponseNormalizationGrad.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/nn/LogSoftmax.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/nn/LogSoftmax.java index 1f9ee440140..1e19b56c19f 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/nn/LogSoftmax.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/nn/LogSoftmax.java @@ -39,8 +39,6 @@ * 

      * logsoftmax[i, j] = logits[i, j] - log(sum(exp(logits[i])))
  *
    - * - * @param data type for {@code logsoftmax} output */ @OpMetadata( opType = LogSoftmax.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/nn/MaxPool.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/nn/MaxPool.java index 427b3c92bb2..75b432b8ba3 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/nn/MaxPool.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/nn/MaxPool.java @@ -36,8 +36,6 @@ /** * Performs max pooling on the input. - * - * @param data type for {@code output} output */ @OpMetadata( opType = MaxPool.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/nn/MaxPool3d.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/nn/MaxPool3d.java index d9cace3d967..d701189d5e1 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/nn/MaxPool3d.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/nn/MaxPool3d.java @@ -36,8 +36,6 @@ /** * Performs 3D max pooling on the input. - * - * @param data type for {@code output} output */ @OpMetadata( opType = MaxPool3d.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/nn/MaxPool3dGrad.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/nn/MaxPool3dGrad.java index 6ac95b8a978..932399be80b 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/nn/MaxPool3dGrad.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/nn/MaxPool3dGrad.java @@ -36,8 +36,6 @@ /** * Computes gradients of 3D max pooling function. - * - * @param data type for {@code output} output */ @OpMetadata( opType = MaxPool3dGrad.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/nn/MaxPool3dGradGrad.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/nn/MaxPool3dGradGrad.java index 5efa05dec89..74dbc598b35 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/nn/MaxPool3dGradGrad.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/nn/MaxPool3dGradGrad.java @@ -36,8 +36,6 @@ /** * Computes second-order gradients of the maxpooling function. - * - * @param data type for {@code output} output */ @OpMetadata( opType = MaxPool3dGradGrad.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/nn/MaxPoolGrad.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/nn/MaxPoolGrad.java index 214b0b0d31c..a329757270c 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/nn/MaxPoolGrad.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/nn/MaxPoolGrad.java @@ -36,8 +36,6 @@ /** * Computes gradients of the maxpooling function. - * - * @param data type for {@code output} output */ @OpMetadata( opType = MaxPoolGrad.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/nn/MaxPoolGradGrad.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/nn/MaxPoolGradGrad.java index a33ba6642b8..0b0f0f616b7 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/nn/MaxPoolGradGrad.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/nn/MaxPoolGradGrad.java @@ -36,8 +36,6 @@ /** * Computes second-order gradients of the maxpooling function. - * - * @param data type for {@code output} output */ @OpMetadata( opType = MaxPoolGradGrad.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/nn/MaxPoolGradGradWithArgmax.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/nn/MaxPoolGradGradWithArgmax.java index 35f1ffeb6dd..9dedc6014b8 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/nn/MaxPoolGradGradWithArgmax.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/nn/MaxPoolGradGradWithArgmax.java @@ -36,8 +36,6 @@ /** * Computes second-order gradients of the maxpooling function. - * - * @param data type for {@code output} output */ @OpMetadata( opType = MaxPoolGradGradWithArgmax.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/nn/MaxPoolGradWithArgmax.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/nn/MaxPoolGradWithArgmax.java index 0edd2ca5adc..60d7e7de94c 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/nn/MaxPoolGradWithArgmax.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/nn/MaxPoolGradWithArgmax.java @@ -36,8 +36,6 @@ /** * Computes gradients of the maxpooling function. - * - * @param data type for {@code output} output */ @OpMetadata( opType = MaxPoolGradWithArgmax.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/nn/MaxPoolWithArgmax.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/nn/MaxPoolWithArgmax.java index bcfba861e1e..bd19af1b703 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/nn/MaxPoolWithArgmax.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/nn/MaxPoolWithArgmax.java @@ -46,10 +46,6 @@ * even if padding is involved and the mathematically correct answer is outside * (either negative or too large). This is a bug, but fixing it is difficult to do * in a safe backwards compatible way, especially due to flattening. - * - * @param data type for {@code output} output - * - * @param data type for {@code argmax} output */ @OpMetadata( opType = MaxPoolWithArgmax.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/nn/NthElement.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/nn/NthElement.java index 383dbfc3b22..57754316380 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/nn/NthElement.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/nn/NthElement.java @@ -43,8 +43,6 @@ * 
      * values.shape = input.shape[:-1]
  *
    - * - * @param data type for {@code values} output */ @OpMetadata( opType = NthElement.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/nn/QuantizedAvgPool.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/nn/QuantizedAvgPool.java index 2e27d649947..8987fcd7d55 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/nn/QuantizedAvgPool.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/nn/QuantizedAvgPool.java @@ -37,8 +37,6 @@ /** * Produces the average pool of the input tensor for quantized types. - * - * @param data type for {@code output} output */ @OpMetadata( opType = QuantizedAvgPool.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/nn/QuantizedBatchNormWithGlobalNormalization.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/nn/QuantizedBatchNormWithGlobalNormalization.java index 0b9e3b27b55..7f22995509c 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/nn/QuantizedBatchNormWithGlobalNormalization.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/nn/QuantizedBatchNormWithGlobalNormalization.java @@ -39,8 +39,6 @@ * Quantized Batch normalization. * This op is deprecated and will be removed in the future. Prefer * {@code tf.nn.batch_normalization}. - * - * @param data type for {@code result} output */ @OpMetadata( opType = QuantizedBatchNormWithGlobalNormalization.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/nn/QuantizedBiasAdd.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/nn/QuantizedBiasAdd.java index c95300fa493..744eb1397eb 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/nn/QuantizedBiasAdd.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/nn/QuantizedBiasAdd.java @@ -38,8 +38,6 @@ /** * Adds Tensor 'bias' to Tensor 'input' for Quantized types. * Broadcasts the values of bias on dimensions 0..N-2 of 'input'. - * - * @param data type for {@code output} output */ @OpMetadata( opType = QuantizedBiasAdd.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/nn/QuantizedConv2DAndRelu.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/nn/QuantizedConv2DAndRelu.java index 4594e0401cc..9226b7b697e 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/nn/QuantizedConv2DAndRelu.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/nn/QuantizedConv2DAndRelu.java @@ -38,8 +38,6 @@ /** * The QuantizedConv2DAndRelu operation - * - * @param data type for {@code output} output */ @OpMetadata( opType = QuantizedConv2DAndRelu.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/nn/QuantizedConv2DAndReluAndRequantize.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/nn/QuantizedConv2DAndReluAndRequantize.java index 0104cbf9908..f02eba09012 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/nn/QuantizedConv2DAndReluAndRequantize.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/nn/QuantizedConv2DAndReluAndRequantize.java @@ -38,8 +38,6 @@ /** * The QuantizedConv2DAndReluAndRequantize operation - * - * @param data type for {@code output} output */ @OpMetadata( opType = QuantizedConv2DAndReluAndRequantize.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/nn/QuantizedConv2DAndRequantize.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/nn/QuantizedConv2DAndRequantize.java index 5fe5999adab..66344508160 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/nn/QuantizedConv2DAndRequantize.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/nn/QuantizedConv2DAndRequantize.java @@ -38,8 +38,6 @@ /** * The QuantizedConv2DAndRequantize operation - * - * @param data type for {@code output} output */ @OpMetadata( opType = QuantizedConv2DAndRequantize.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/nn/QuantizedConv2DPerChannel.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/nn/QuantizedConv2DPerChannel.java index 134449aba91..bfd108c34d3 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/nn/QuantizedConv2DPerChannel.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/nn/QuantizedConv2DPerChannel.java @@ -38,8 +38,6 @@ /** * Computes QuantizedConv2D per channel. - * - * @param data type for {@code output} output */ @OpMetadata( opType = QuantizedConv2DPerChannel.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/nn/QuantizedConv2DWithBias.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/nn/QuantizedConv2DWithBias.java index 27f5343c6ff..fe5566ac7e9 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/nn/QuantizedConv2DWithBias.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/nn/QuantizedConv2DWithBias.java @@ -38,8 +38,6 @@ /** * The QuantizedConv2DWithBias operation - * - * @param data type for {@code output} output */ @OpMetadata( opType = QuantizedConv2DWithBias.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/nn/QuantizedConv2DWithBiasAndRelu.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/nn/QuantizedConv2DWithBiasAndRelu.java index 61c9bb31b45..ff7d157a846 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/nn/QuantizedConv2DWithBiasAndRelu.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/nn/QuantizedConv2DWithBiasAndRelu.java @@ -38,8 +38,6 @@ /** * The QuantizedConv2DWithBiasAndRelu operation - * - * @param data type for {@code output} output */ @OpMetadata( opType = QuantizedConv2DWithBiasAndRelu.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/nn/QuantizedConv2DWithBiasAndReluAndRequantize.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/nn/QuantizedConv2DWithBiasAndReluAndRequantize.java index 081b8ac3863..b68080cc72c 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/nn/QuantizedConv2DWithBiasAndReluAndRequantize.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/nn/QuantizedConv2DWithBiasAndReluAndRequantize.java @@ -38,8 +38,6 @@ /** * The QuantizedConv2DWithBiasAndReluAndRequantize operation - * - * @param data type for {@code output} output */ @OpMetadata( opType = QuantizedConv2DWithBiasAndReluAndRequantize.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/nn/QuantizedConv2DWithBiasAndRequantize.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/nn/QuantizedConv2DWithBiasAndRequantize.java index 21f4eef7826..5301017e666 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/nn/QuantizedConv2DWithBiasAndRequantize.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/nn/QuantizedConv2DWithBiasAndRequantize.java @@ -38,8 +38,6 @@ /** * The QuantizedConv2DWithBiasAndRequantize operation - * - * @param data type for {@code output} output */ @OpMetadata( opType = QuantizedConv2DWithBiasAndRequantize.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/nn/QuantizedConv2DWithBiasSignedSumAndReluAndRequantize.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/nn/QuantizedConv2DWithBiasSignedSumAndReluAndRequantize.java index afdd8b87219..687e41485d4 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/nn/QuantizedConv2DWithBiasSignedSumAndReluAndRequantize.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/nn/QuantizedConv2DWithBiasSignedSumAndReluAndRequantize.java @@ -38,8 +38,6 @@ /** * The QuantizedConv2DWithBiasSignedSumAndReluAndRequantize operation - * - * @param data type for {@code output} output */ @OpMetadata( opType = QuantizedConv2DWithBiasSignedSumAndReluAndRequantize.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/nn/QuantizedConv2DWithBiasSumAndRelu.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/nn/QuantizedConv2DWithBiasSumAndRelu.java index d92782f88bb..34ceb6e7898 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/nn/QuantizedConv2DWithBiasSumAndRelu.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/nn/QuantizedConv2DWithBiasSumAndRelu.java @@ -38,8 +38,6 @@ /** * The QuantizedConv2DWithBiasSumAndRelu operation - * - * @param data type for {@code output} output */ @OpMetadata( opType = QuantizedConv2DWithBiasSumAndRelu.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/nn/QuantizedConv2DWithBiasSumAndReluAndRequantize.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/nn/QuantizedConv2DWithBiasSumAndReluAndRequantize.java index 0d9c4fab0f6..021873d6885 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/nn/QuantizedConv2DWithBiasSumAndReluAndRequantize.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/nn/QuantizedConv2DWithBiasSumAndReluAndRequantize.java @@ -38,8 +38,6 @@ /** * The QuantizedConv2DWithBiasSumAndReluAndRequantize operation - * - * @param data type for {@code output} output */ @OpMetadata( opType = QuantizedConv2DWithBiasSumAndReluAndRequantize.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/nn/QuantizedConv2d.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/nn/QuantizedConv2d.java index 88482fc869f..77d21ba9794 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/nn/QuantizedConv2d.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/nn/QuantizedConv2d.java @@ -42,8 +42,6 @@ * number of the associated minimum, and the highest represents the maximum. * This means that you can only interpret the quantized output in the same way, by * taking the returned minimum and maximum values into account. - * - * @param data type for {@code output} output */ @OpMetadata( opType = QuantizedConv2d.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/nn/QuantizedDepthwiseConv2D.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/nn/QuantizedDepthwiseConv2D.java index 19c05799f1f..3281b31698b 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/nn/QuantizedDepthwiseConv2D.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/nn/QuantizedDepthwiseConv2D.java @@ -38,8 +38,6 @@ /** * Computes quantized depthwise Conv2D. - * - * @param data type for {@code output} output */ @OpMetadata( opType = QuantizedDepthwiseConv2D.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/nn/QuantizedDepthwiseConv2DWithBias.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/nn/QuantizedDepthwiseConv2DWithBias.java index 9414fd9e015..70314ace0b0 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/nn/QuantizedDepthwiseConv2DWithBias.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/nn/QuantizedDepthwiseConv2DWithBias.java @@ -38,8 +38,6 @@ /** * Computes quantized depthwise Conv2D with Bias. - * - * @param data type for {@code output} output */ @OpMetadata( opType = QuantizedDepthwiseConv2DWithBias.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/nn/QuantizedDepthwiseConv2DWithBiasAndRelu.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/nn/QuantizedDepthwiseConv2DWithBiasAndRelu.java index c8d6a30445b..76b0917f709 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/nn/QuantizedDepthwiseConv2DWithBiasAndRelu.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/nn/QuantizedDepthwiseConv2DWithBiasAndRelu.java @@ -38,8 +38,6 @@ /** * Computes quantized depthwise Conv2D with Bias and Relu. - * - * @param data type for {@code output} output */ @OpMetadata( opType = QuantizedDepthwiseConv2DWithBiasAndRelu.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/nn/QuantizedDepthwiseConv2DWithBiasAndReluAndRequantize.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/nn/QuantizedDepthwiseConv2DWithBiasAndReluAndRequantize.java index b23311716d2..55dfdecdb39 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/nn/QuantizedDepthwiseConv2DWithBiasAndReluAndRequantize.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/nn/QuantizedDepthwiseConv2DWithBiasAndReluAndRequantize.java @@ -38,8 +38,6 @@ /** * Computes quantized depthwise Conv2D with Bias, Relu and Requantize. - * - * @param data type for {@code output} output */ @OpMetadata( opType = QuantizedDepthwiseConv2DWithBiasAndReluAndRequantize.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/nn/QuantizedInstanceNorm.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/nn/QuantizedInstanceNorm.java index 54bd27c1705..48aedde6806 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/nn/QuantizedInstanceNorm.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/nn/QuantizedInstanceNorm.java @@ -36,8 +36,6 @@ /** * Quantized Instance normalization. - * - * @param data type for {@code y} output */ @OpMetadata( opType = QuantizedInstanceNorm.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/nn/QuantizedMaxPool.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/nn/QuantizedMaxPool.java index b1323bb3b42..e57d4e945b4 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/nn/QuantizedMaxPool.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/nn/QuantizedMaxPool.java @@ -37,8 +37,6 @@ /** * Produces the max pool of the input tensor for quantized types. - * - * @param data type for {@code output} output */ @OpMetadata( opType = QuantizedMaxPool.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/nn/QuantizedRelu.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/nn/QuantizedRelu.java index b80e07346d9..ad55085ab6f 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/nn/QuantizedRelu.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/nn/QuantizedRelu.java @@ -37,8 +37,6 @@ /** * Computes Quantized Rectified Linear: {@code max(features, 0)} - * - * @param data type for {@code activations} output */ @OpMetadata( opType = QuantizedRelu.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/nn/QuantizedRelu6.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/nn/QuantizedRelu6.java index d820e51188a..2b2f21a6b45 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/nn/QuantizedRelu6.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/nn/QuantizedRelu6.java @@ -37,8 +37,6 @@ /** * Computes Quantized Rectified Linear 6: {@code min(max(features, 0), 6)} - * - * @param data type for {@code activations} output */ @OpMetadata( opType = QuantizedRelu6.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/nn/QuantizedReluX.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/nn/QuantizedReluX.java index 577df61b8dd..41daae389b6 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/nn/QuantizedReluX.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/nn/QuantizedReluX.java @@ -37,8 +37,6 @@ /** * Computes Quantized Rectified Linear X: {@code min(max(features, 0), max_value)} - * - * @param data type for {@code activations} output */ @OpMetadata( opType = QuantizedReluX.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/nn/Relu.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/nn/Relu.java index 218fee4f3d2..126eb0c4c56 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/nn/Relu.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/nn/Relu.java @@ -45,8 +45,6 @@ * * * - * - * @param data type for {@code activations} output */ @OpMetadata( opType = Relu.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/nn/Relu6.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/nn/Relu6.java index 19de03d7f8e..5500229b21c 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/nn/Relu6.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/nn/Relu6.java @@ -35,8 +35,6 @@ /** * Computes rectified linear 6: {@code min(max(features, 0), 6)}. - * - * @param data type for {@code activations} output */ @OpMetadata( opType = Relu6.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/nn/Relu6Grad.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/nn/Relu6Grad.java index 48ec9cb7037..9af8b816d87 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/nn/Relu6Grad.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/nn/Relu6Grad.java @@ -35,8 +35,6 @@ /** * Computes rectified linear 6 gradients for a Relu6 operation. - * - * @param data type for {@code backprops} output */ @OpMetadata( opType = Relu6Grad.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/nn/ReluGrad.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/nn/ReluGrad.java index 5e7103853f3..b15132dd583 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/nn/ReluGrad.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/nn/ReluGrad.java @@ -35,8 +35,6 @@ /** * Computes rectified linear gradients for a Relu operation. - * - * @param data type for {@code backprops} output */ @OpMetadata( opType = ReluGrad.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/nn/Selu.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/nn/Selu.java index d382a2f5a75..33d504105ec 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/nn/Selu.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/nn/Selu.java @@ -40,8 +40,6 @@ * {@code initializer = tf.variance_scaling_initializer(factor=1.0, mode='FAN_IN')}. * For correct dropout, use {@code tf.contrib.nn.alpha_dropout}. *

    See Self-Normalizing Neural Networks - * - * @param data type for {@code activations} output */ @OpMetadata( opType = Selu.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/nn/SeluGrad.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/nn/SeluGrad.java index 7a2e0656275..bd2d2203f69 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/nn/SeluGrad.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/nn/SeluGrad.java @@ -35,8 +35,6 @@ /** * Computes gradients for the scaled exponential linear (Selu) operation. - * - * @param data type for {@code backprops} output */ @OpMetadata( opType = SeluGrad.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/nn/Softmax.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/nn/Softmax.java index 36ef20f21fd..dd6b9ecb2b5 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/nn/Softmax.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/nn/Softmax.java @@ -39,8 +39,6 @@ * 

      * $$softmax[i, j] = exp(logits[i, j]) / sum_j(exp(logits[i, j]))$$
  *
    - * - * @param data type for {@code softmax} output */ @OpMetadata( opType = Softmax.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/nn/SoftmaxCrossEntropyWithLogits.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/nn/SoftmaxCrossEntropyWithLogits.java index 9a17188c048..a7836f24051 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/nn/SoftmaxCrossEntropyWithLogits.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/nn/SoftmaxCrossEntropyWithLogits.java @@ -36,8 +36,6 @@ /** * Computes softmax cross entropy cost and gradients to backpropagate. * Inputs are the logits, not probabilities. - * - * @param data type for {@code loss} output */ @OpMetadata( opType = SoftmaxCrossEntropyWithLogits.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/nn/Softsign.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/nn/Softsign.java index 1345a1ffd11..1144c4c21be 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/nn/Softsign.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/nn/Softsign.java @@ -35,8 +35,6 @@ /** * Computes softsign: {@code features / (abs(features) + 1)}. - * - * @param data type for {@code activations} output */ @OpMetadata( opType = Softsign.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/nn/SoftsignGrad.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/nn/SoftsignGrad.java index b16c933ffe0..3ebe407b08e 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/nn/SoftsignGrad.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/nn/SoftsignGrad.java @@ -35,8 +35,6 @@ /** * Computes softsign gradients for a softsign operation. - * - * @param data type for {@code backprops} output */ @OpMetadata( opType = SoftsignGrad.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/nn/SpaceToBatch.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/nn/SpaceToBatch.java index 050a12e7f98..e35f65ee574 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/nn/SpaceToBatch.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/nn/SpaceToBatch.java @@ -100,8 +100,6 @@ * *

    Among others, this operation is useful for reducing atrous convolution into * regular convolution. - * - * @param data type for {@code output} output */ @OpMetadata( opType = SpaceToBatch.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/nn/SpaceToDepth.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/nn/SpaceToDepth.java index 18449c4627c..aaaddf55663 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/nn/SpaceToDepth.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/nn/SpaceToDepth.java @@ -103,8 +103,6 @@ * [[9, 10, 11, 12], * [13, 14, 15, 16]]]] * - * - * @param data type for {@code output} output */ @OpMetadata( opType = SpaceToDepth.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/nn/SparseSoftmaxCrossEntropyWithLogits.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/nn/SparseSoftmaxCrossEntropyWithLogits.java index 043587de9b5..1b7c99a694e 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/nn/SparseSoftmaxCrossEntropyWithLogits.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/nn/SparseSoftmaxCrossEntropyWithLogits.java @@ -40,8 +40,6 @@ * of features. This label is considered to have probability 1.0 for the * given row. *

    Inputs are the logits, not probabilities. - * - * @param data type for {@code loss} output */ @OpMetadata( opType = SparseSoftmaxCrossEntropyWithLogits.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/nn/TopK.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/nn/TopK.java index b752c40666b..189f0434054 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/nn/TopK.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/nn/TopK.java @@ -46,10 +46,6 @@ * values.shape = indices.shape = input.shape[:-1] + [k] * *

    If two elements are equal, the lower-index element appears first. - * - * @param data type for {@code values} output - * - * @param data type for {@code indices} output */ @OpMetadata( opType = TopK.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/nn/UniformQuantizedConvolution.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/nn/UniformQuantizedConvolution.java index 9b4715c3a21..124c2b062f0 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/nn/UniformQuantizedConvolution.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/nn/UniformQuantizedConvolution.java @@ -55,8 +55,6 @@ * *

    {@code output} is also quantized, using the same formula. * If {@code rhs} is per-tensor quantized, {@code output} must be also per-tensor quantized. - * - * @param data type for {@code output} output */ @OpMetadata( opType = UniformQuantizedConvolution.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/nn/UniformQuantizedConvolutionHybrid.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/nn/UniformQuantizedConvolutionHybrid.java index 02b51c0dfe4..8510272759e 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/nn/UniformQuantizedConvolutionHybrid.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/nn/UniformQuantizedConvolutionHybrid.java @@ -55,8 +55,6 @@ * *

    {@code rhs} must be quantized Tensor, where its data value is quantized using the formula: * quantized_data = clip(original_data / scale + zero_point, quantization_min_val, quantization_max_val). - * - * @param data type for {@code output} output */ @OpMetadata( opType = UniformQuantizedConvolutionHybrid.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/quantization/Dequantize.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/quantization/Dequantize.java index 743b6c81d93..a062ee1db29 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/quantization/Dequantize.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/quantization/Dequantize.java @@ -80,8 +80,6 @@ * : std::max(min_range / min_expected_T, * max_range / max_expected_T); * - * - * @param data type for {@code output} output */ @OpMetadata( opType = Dequantize.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/quantization/FakeQuantWithMinMaxArgsGradient.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/quantization/FakeQuantWithMinMaxArgsGradient.java index 4f3b11ce977..87007c73d6b 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/quantization/FakeQuantWithMinMaxArgsGradient.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/quantization/FakeQuantWithMinMaxArgsGradient.java @@ -136,6 +136,38 @@ public static Options narrowRange(Boolean narrowRange) { * Gets backprops. * Backpropagated gradients below the FakeQuantWithMinMaxArgs operation: * {@code gradients * (inputs >= min && inputs <= max)}. + * 

    +   * import tensorflow as tf
+   *
+   * # Define some sample data
+   * gradients = tf.random.uniform((2, 3), minval=-5.0, maxval=5.0, dtype=tf.float32)
+   * inputs = tf.random.uniform((2, 3), minval=-10.0, maxval=10.0, dtype=tf.float32)
+   *
+   * # Define quantization parameters (adjust as needed)
+   * min_val = -2.0
+   * max_val = 8.0
+   * num_bits = 4  # Number of bits for quantization
+   *
+   * # Calculate gradients for fake quantization with specified parameters
+   * output_gradients = tf.quantization.fake_quant_with_min_max_args_gradient(
+   *     gradients=gradients, inputs=inputs, min=min_val, max=max_val, num_bits=num_bits, narrow_range = False, name=None
+   * )
+   *
+   * # Print the original gradients and the gradients after the fake-quant operation
+   * print("Original Gradients:")
+   * print(gradients)
+   * print("\nGradients after Fake-Quantization:")
+   * print(output_gradients)
+   *
+   *
    + *

    #Original Gradients: + * #tf.Tensor( + * #[[ 1.242547 3.217492 3.568469 ] + * #[-0.55371046 0.23130894 2.608243 ]], shape=(2, 3), dtype=float32) + *

    #Gradients after Fake-Quantization: + * #tf.Tensor( + * #[[ 0. 3.217492 3.568469 ] + * [-0.55371046 0.23130894 2.608243 ]], shape=(2, 3), dtype=float32)
    * @return backprops. */ public Output backprops() { diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/quantization/FakeQuantWithMinMaxVars.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/quantization/FakeQuantWithMinMaxVars.java index faa38bb0585..e78b22ca6d5 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/quantization/FakeQuantWithMinMaxVars.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/quantization/FakeQuantWithMinMaxVars.java @@ -57,6 +57,28 @@ * *

    This operation has a gradient and thus allows for training {@code min} and {@code max} * values. + *

    + *
    + *
    + *

    constant_input = tf.constant([[1.2, -0.3, 0.7], [2.1, 0.5, -1.0]], dtype=tf.float32) + *

    min_val = -0.5 + * max_val = 0.8 + * num_bits = 8 + * narrow_range = False #False:for the quantization range [0; 2^num_bits - 1] + *

    quantized_data = tf.quantization.fake_quant_with_min_max_vars( + * ... inputs=constant_input, min=min_val, max=max_val, num_bits=num_bits, narrow_range=narrow_range + * ... ) + *

    print("Input:\n", constant_input.numpy()) + * Input: + * [[ 1.2 -0.3 0.7] + * [ 2.1 0.5 -1. ]] + * print("Output:\n", quantized_data.numpy()) + * Output: + * [[ 0.8003921 -0.3007843 0.6984313] + * [ 0.8003921 0.4996078 -0.4996078]] + *

    + *
    + *
    */ @OpMetadata( opType = FakeQuantWithMinMaxVars.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/quantization/Quantize.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/quantization/Quantize.java index a6a5df07a8a..ed34d301ec7 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/quantization/Quantize.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/quantization/Quantize.java @@ -128,8 +128,6 @@ *

    Ensures the minimum quantization range is at least this value. * The legacy default value for this is 0.01, but it is strongly suggested to * set it to 0 for new uses. - * - * @param data type for {@code output} output */ @OpMetadata( opType = Quantize.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/quantization/QuantizeAndDequantize.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/quantization/QuantizeAndDequantize.java index b6552257828..eeb9f05536c 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/quantization/QuantizeAndDequantize.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/quantization/QuantizeAndDequantize.java @@ -38,8 +38,6 @@ * Quantizes then dequantizes a tensor. * This is almost identical to QuantizeAndDequantizeV2, except that num_bits is a * tensor, so its value can change during training. - * - * @param data type for {@code output} output */ @OpMetadata( opType = QuantizeAndDequantize.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/quantization/QuantizeAndDequantizeV3.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/quantization/QuantizeAndDequantizeV3.java index a715ecdb8e5..e1de6cd2ab7 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/quantization/QuantizeAndDequantizeV3.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/quantization/QuantizeAndDequantizeV3.java @@ -38,8 +38,6 @@ * Quantizes then dequantizes a tensor. * This is almost identical to QuantizeAndDequantizeV2, except that num_bits is a * tensor, so its value can change during training. - * - * @param data type for {@code output} output */ @OpMetadata( opType = QuantizeAndDequantizeV3.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/quantization/QuantizeAndDequantizeV4.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/quantization/QuantizeAndDequantizeV4.java index 75b47a7f0f9..7de2e59c64b 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/quantization/QuantizeAndDequantizeV4.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/quantization/QuantizeAndDequantizeV4.java @@ -37,8 +37,6 @@ * Quantizes then dequantizes a tensor. * This is almost identical to QuantizeAndDequantizeV2, except that it returns a * gradient of 1 for inputs that are within the quantization range, or 0 otherwise. - * - * @param data type for {@code output} output */ @OpMetadata( opType = QuantizeAndDequantizeV4.OP_NAME, @@ -114,7 +112,7 @@ public static QuantizeAndDequantizeV4 create(Scope scope, * Sets the signedInput option. * * @param signedInput Whether the quantization is signed or unsigned. (actually this parameter should - * have been called {@code signed_output}</b>) + * have been called {@code signed_output}) * @return this Options instance. */ public static Options signedInput(Boolean signedInput) { @@ -218,7 +216,7 @@ private Options() { * Sets the signedInput option. * * @param signedInput Whether the quantization is signed or unsigned. (actually this parameter should - * have been called {@code signed_output}</b>) + * have been called {@code signed_output}) * @return this Options instance. */ public Options signedInput(Boolean signedInput) { @@ -317,7 +315,7 @@ public static class Inputs extends RawOpInputs{@code signed_output}</b>) + * have been called {@code signed_output}) */ public final boolean signedInput; diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/quantization/QuantizeAndDequantizeV4Grad.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/quantization/QuantizeAndDequantizeV4Grad.java index d2d9d9e6035..65cf77c43ca 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/quantization/QuantizeAndDequantizeV4Grad.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/quantization/QuantizeAndDequantizeV4Grad.java @@ -37,8 +37,6 @@ * Returns the gradient of {@code QuantizeAndDequantizeV4}. * Returns a gradient of 1 for inputs that are within the quantization range, * or 0 otherwise. - * - * @param data type for {@code input_backprop} output */ @OpMetadata( opType = QuantizeAndDequantizeV4Grad.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/quantization/QuantizeDownAndShrinkRange.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/quantization/QuantizeDownAndShrinkRange.java index d8aee82efb2..77aaa257758 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/quantization/QuantizeDownAndShrinkRange.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/quantization/QuantizeDownAndShrinkRange.java @@ -56,8 +56,6 @@ * input values that only uses a small fraction of the possible range. By feeding * that output into this operator, we can reduce it from 32 bits down to 8 with * minimal loss of accuracy. - * - * @param data type for {@code output} output */ @OpMetadata( opType = QuantizeDownAndShrinkRange.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/quantization/QuantizedConcat.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/quantization/QuantizedConcat.java index cae65990d35..a52e49b8080 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/quantization/QuantizedConcat.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/quantization/QuantizedConcat.java @@ -38,8 +38,6 @@ /** * Concatenates quantized tensors along one dimension. - * - * @param data type for {@code output} output */ @OpMetadata( opType = QuantizedConcat.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/quantization/QuantizedMatMulWithBiasAndDequantize.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/quantization/QuantizedMatMulWithBiasAndDequantize.java index 69827ccd019..c03a82caf5c 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/quantization/QuantizedMatMulWithBiasAndDequantize.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/quantization/QuantizedMatMulWithBiasAndDequantize.java @@ -37,8 +37,6 @@ /** * The QuantizedMatMulWithBiasAndDequantize operation - * - * @param data type for {@code out} output */ @OpMetadata( opType = QuantizedMatMulWithBiasAndDequantize.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/quantization/QuantizedMatMulWithBiasAndRequantize.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/quantization/QuantizedMatMulWithBiasAndRequantize.java index cd48b07ac48..b848d068a15 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/quantization/QuantizedMatMulWithBiasAndRequantize.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/quantization/QuantizedMatMulWithBiasAndRequantize.java @@ -37,8 +37,6 @@ /** * The QuantizedMatMulWithBiasAndRequantize operation - * - * @param data type for {@code out} output */ @OpMetadata( opType = QuantizedMatMulWithBiasAndRequantize.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/quantization/Requantize.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/quantization/Requantize.java index 48bfa78ab74..0ebd2ce0e3a 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/quantization/Requantize.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/quantization/Requantize.java @@ -43,8 +43,6 @@ * interpretation of the {@code input} data. For example, if {@code input_min} is -1.0f and * {@code input_max} is 1.0f, and we are dealing with {@code quint16} quantized data, then a 0 * value in the 16-bit data should be interpreted as -1.0f, and a 65535 means 1.0f. - * - * @param data type for {@code output} output */ @OpMetadata( opType = Requantize.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/quantization/UniformDequantize.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/quantization/UniformDequantize.java index 97dad1321da..8f5d44bf663 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/quantization/UniformDequantize.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/quantization/UniformDequantize.java @@ -40,8 +40,6 @@ * Perform dequantization on the quantized Tensor {@code input}. * Given quantized {@code input} which was quantized using {@code scales} and {@code zero_points}, performs dequantization using the formula: * dequantized_data = (quantized_data - zero_point) * scale. - * - * @param data type for {@code output} output */ @OpMetadata( opType = UniformDequantize.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/quantization/UniformQuantize.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/quantization/UniformQuantize.java index 43fed90b7cc..390ceb83d8a 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/quantization/UniformQuantize.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/quantization/UniformQuantize.java @@ -40,8 +40,6 @@ * Perform quantization on Tensor {@code input}. * Given {@code input}, {@code scales} and {@code zero_points}, performs quantization using the formula: * quantized_data = floor(input_data * (1.0f / scale) + 0.5f) + zero_point - * - * @param data type for {@code output} output */ @OpMetadata( opType = UniformQuantize.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/quantization/UniformQuantizedDot.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/quantization/UniformQuantizedDot.java index 16768a99b22..eff33c22ce7 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/quantization/UniformQuantizedDot.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/quantization/UniformQuantizedDot.java @@ -44,8 +44,6 @@ * quantized_data = clip(original_data / scale + zero_point, quantization_min_val, quantization_max_val). * {@code output} is also quantized, using the same formula. * If {@code rhs} is per-tensor quantized, {@code output} must be also per-tensor quantized. - * - * @param data type for {@code output} output */ @OpMetadata( opType = UniformQuantizedDot.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/quantization/UniformQuantizedDotHybrid.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/quantization/UniformQuantizedDotHybrid.java index ed8c67f9a53..1f30f7a1a4c 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/quantization/UniformQuantizedDotHybrid.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/quantization/UniformQuantizedDotHybrid.java @@ -43,8 +43,6 @@ * {@code lhs} and {@code rhs} must be 2D Tensors and the lhs.dim_size(1) must match rhs.dim_size(0). * {@code rhs} must be quantized Tensor, where its data value is quantized using the formula: * quantized_data = clip(original_data / scale + zero_point, quantization_min_val, quantization_max_val). - * - * @param data type for {@code output} output */ @OpMetadata( opType = UniformQuantizedDotHybrid.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/quantization/UniformRequantize.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/quantization/UniformRequantize.java index 85f81e8f202..eb4c511b567 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/quantization/UniformRequantize.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/quantization/UniformRequantize.java @@ -52,8 +52,6 @@ *

  • per-axis -> per-axis where input_quantization_axis equals output_quantization_axis. * i.e. At least one among input_quantization_axis and output_quantization_axis must be -1, or two must be equal.
    • * - * - * @param data type for {@code output} output */ @OpMetadata( opType = UniformRequantize.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/ragged/RaggedBincount.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/ragged/RaggedBincount.java index 2607b8e0fcf..0aadded3990 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/ragged/RaggedBincount.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/ragged/RaggedBincount.java @@ -42,8 +42,6 @@ * the value in {@code weights} at each index where the corresponding value in {@code arr} is * {@code i}. *

    Values in {@code arr} outside of the range [0, size) are ignored. - * - * @param data type for {@code output} output */ @OpMetadata( opType = RaggedBincount.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/ragged/RaggedCountSparseOutput.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/ragged/RaggedCountSparseOutput.java index 1e654d1665b..720919e6873 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/ragged/RaggedCountSparseOutput.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/ragged/RaggedCountSparseOutput.java @@ -37,8 +37,6 @@ /** * Performs sparse-output bin counting for a ragged tensor input. * Counts the number of times each value occurs in the input. - * - * @param data type for {@code output_values} output */ @OpMetadata( opType = RaggedCountSparseOutput.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/ragged/RaggedCross.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/ragged/RaggedCross.java index 1d5cc361a5f..3b356804b4f 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/ragged/RaggedCross.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/ragged/RaggedCross.java @@ -39,10 +39,6 @@ /** * Generates a feature cross from a list of tensors, and returns it as a * RaggedTensor. See {@code tf.ragged.cross} for more details. - * - * @param data type for {@code output_values} output - * - * @param data type for {@code output_row_splits} output */ @OpMetadata( opType = RaggedCross.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/ragged/RaggedFillEmptyRows.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/ragged/RaggedFillEmptyRows.java index 5f1b9cf66ec..d8414fd1ae3 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/ragged/RaggedFillEmptyRows.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/ragged/RaggedFillEmptyRows.java @@ -37,8 +37,6 @@ /** * The RaggedFillEmptyRows operation - * - * @param data type for {@code output_values} output */ @OpMetadata( opType = RaggedFillEmptyRows.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/ragged/RaggedFillEmptyRowsGrad.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/ragged/RaggedFillEmptyRowsGrad.java index 9ea15d1320a..314e4a689af 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/ragged/RaggedFillEmptyRowsGrad.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/ragged/RaggedFillEmptyRowsGrad.java @@ -36,8 +36,6 @@ /** * The RaggedFillEmptyRowsGrad operation - * - * @param data type for {@code d_values} output */ @OpMetadata( opType = RaggedFillEmptyRowsGrad.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/ragged/RaggedGather.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/ragged/RaggedGather.java index 059c102f6ed..3c71b9987c4 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/ragged/RaggedGather.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/ragged/RaggedGather.java @@ -56,10 +56,6 @@ * *

    (Note: This c++ op is used to implement the higher-level python * {@code tf.ragged.gather} op, which also supports ragged indices.) - * - * @param data type for {@code output_nested_splits} output - * - * @param data type for {@code output_dense_values} output */ @OpMetadata( opType = RaggedGather.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/ragged/RaggedRange.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/ragged/RaggedRange.java index 52d8d2d66b9..39a6487398e 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/ragged/RaggedRange.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/ragged/RaggedRange.java @@ -50,10 +50,6 @@ *

    The input tensors {@code starts}, {@code limits}, and {@code deltas} may be scalars or vectors. * The vector inputs must all have the same size. Scalar inputs are broadcast * to match the size of the vector inputs. - * - * @param data type for {@code rt_nested_splits} output - * - * @param data type for {@code rt_dense_values} output */ @OpMetadata( opType = RaggedRange.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/ragged/RaggedTensorFromVariant.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/ragged/RaggedTensorFromVariant.java index 9223acdcd39..5e9e6cae9a7 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/ragged/RaggedTensorFromVariant.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/ragged/RaggedTensorFromVariant.java @@ -50,10 +50,6 @@ * values of the decoded {@code RaggedTensor}. If {@code input_ragged_rank} is -1, then it is * inferred as {@code output_ragged_rank} - {@code rank(encoded_ragged)}. See * {@code RaggedTensorToVariant} for the corresponding encoding logic. - * - * @param data type for {@code output_nested_splits} output - * - * @param data type for {@code output_dense_values} output */ @OpMetadata( opType = RaggedTensorFromVariant.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/ragged/RaggedTensorToSparse.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/ragged/RaggedTensorToSparse.java index 510cab39924..e765d995332 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/ragged/RaggedTensorToSparse.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/ragged/RaggedTensorToSparse.java @@ -41,8 +41,6 @@ * input=ragged.from_nested_row_splits(rt_dense_values, rt_nested_splits) * output=SparseTensor(indices=sparse_indices, values=sparse_values, * dense_shape=sparse_dense_shape) - * - * @param data type for {@code sparse_values} output */ @OpMetadata( opType = RaggedTensorToSparse.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/ragged/RaggedTensorToTensor.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/ragged/RaggedTensorToTensor.java index 127c85e9f72..1bbb93a9327 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/ragged/RaggedTensorToTensor.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/ragged/RaggedTensorToTensor.java @@ -54,8 +54,6 @@ *

  • "FIRST_DIM_SIZE": if value_rowids is used for the first dimension, then it * is preceded by "FIRST_DIM_SIZE".
    • * - * - * @param data type for {@code result} output */ @OpMetadata( opType = RaggedTensorToTensor.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/ragged/RaggedTensorToVariantGradient.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/ragged/RaggedTensorToVariantGradient.java index d8e57336a0e..ca254cd1cf5 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/ragged/RaggedTensorToVariantGradient.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/ragged/RaggedTensorToVariantGradient.java @@ -42,8 +42,6 @@ * op, given the variant-encoded ragged gradients of the outputs, along with * the outer row-splits and the shape of the dense-values that were provided as * inputs to the RaggedTensorToVariant op. - * - * @param data type for {@code dense_values_grad} output */ @OpMetadata( opType = RaggedTensorToVariantGradient.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/random/Multinomial.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/random/Multinomial.java index 6412651e6ac..a213609fca6 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/random/Multinomial.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/random/Multinomial.java @@ -38,8 +38,6 @@ /** * Draws samples from a multinomial distribution. - * - * @param data type for {@code output} output */ @OpMetadata( opType = Multinomial.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/random/NonDeterministicInts.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/random/NonDeterministicInts.java index 6008cd03718..83f81ee6c51 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/random/NonDeterministicInts.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/random/NonDeterministicInts.java @@ -38,8 +38,6 @@ /** * Non-deterministically generates some integers. * This op may use some OS-provided source of non-determinism (e.g. an RNG), so each execution will give different results. - * - * @param data type for {@code output} output */ @OpMetadata( opType = NonDeterministicInts.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/random/ParameterizedTruncatedNormal.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/random/ParameterizedTruncatedNormal.java index e2a12f2a3c9..4bc87b4da51 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/random/ParameterizedTruncatedNormal.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/random/ParameterizedTruncatedNormal.java @@ -37,8 +37,6 @@ * Outputs random values from a normal distribution. The parameters may each be a * scalar which applies to the entire output, or a vector of length shape[0] which * stores the parameters for each batch. - * - * @param data type for {@code output} output */ @OpMetadata( opType = ParameterizedTruncatedNormal.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/random/RandomGamma.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/random/RandomGamma.java index 5558b534e66..cc1a0ab9ba6 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/random/RandomGamma.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/random/RandomGamma.java @@ -38,8 +38,6 @@ * This op uses the algorithm by Marsaglia et al. to acquire samples via * transformation-rejection from pairs of uniform and normal random variables. * See http://dl.acm.org/citation.cfm?id=358414 - * - * @param data type for {@code output} output */ @OpMetadata( opType = RandomGamma.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/random/RandomGammaGrad.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/random/RandomGammaGrad.java index 7baaab08ee4..4ab62242717 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/random/RandomGammaGrad.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/random/RandomGammaGrad.java @@ -35,8 +35,6 @@ /** * Computes the derivative of a Gamma random sample w.r.t. {@code alpha}. - * - * @param data type for {@code output} output */ @OpMetadata( opType = RandomGammaGrad.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/random/RandomPoisson.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/random/RandomPoisson.java index d26081bd288..3e5fc40fc2f 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/random/RandomPoisson.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/random/RandomPoisson.java @@ -45,8 +45,6 @@ * random variables. * See Donald E. Knuth (1969). Seminumerical Algorithms. The Art of Computer * Programming, Volume 2. Addison Wesley - * - * @param data type for {@code output} output */ @OpMetadata( opType = RandomPoisson.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/random/RandomShuffle.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/random/RandomShuffle.java index 8c52e218fc8..517900e7df1 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/random/RandomShuffle.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/random/RandomShuffle.java @@ -43,8 +43,6 @@ * [3, 4], ==> [1, 2], * [5, 6]] [3, 4]] * - * - * @param data type for {@code output} output */ @OpMetadata( opType = RandomShuffle.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/random/RandomStandardNormal.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/random/RandomStandardNormal.java index 3addc74b9bb..322fe10883c 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/random/RandomStandardNormal.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/random/RandomStandardNormal.java @@ -37,8 +37,6 @@ /** * Outputs random values from a normal distribution. * The generated values will have mean 0 and standard deviation 1. - * - * @param data type for {@code output} output */ @OpMetadata( opType = RandomStandardNormal.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/random/RandomUniform.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/random/RandomUniform.java index 74487b121aa..5940994392c 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/random/RandomUniform.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/random/RandomUniform.java @@ -38,8 +38,6 @@ * Outputs random values from a uniform distribution. * The generated values follow a uniform distribution in the range {@code [0, 1)}. The * lower bound 0 is included in the range, while the upper bound 1 is excluded. - * - * @param data type for {@code output} output */ @OpMetadata( opType = RandomUniform.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/random/RandomUniformInt.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/random/RandomUniformInt.java index 243fd44c671..6eba6a6c8b8 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/random/RandomUniformInt.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/random/RandomUniformInt.java @@ -41,8 +41,6 @@ *

    The random integers are slightly biased unless {@code maxval - minval} is an exact * power of two. The bias is small for values of {@code maxval - minval} significantly * smaller than the range of the output (either {@code 2^32} or {@code 2^64}). - * - * @param data type for {@code output} output */ @OpMetadata( opType = RandomUniformInt.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/random/StatefulRandomBinomial.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/random/StatefulRandomBinomial.java index fc03e7feddb..67bc6bf1167 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/random/StatefulRandomBinomial.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/random/StatefulRandomBinomial.java @@ -38,8 +38,6 @@ /** * The StatefulRandomBinomial operation - * - * @param data type for {@code output} output */ @OpMetadata( opType = StatefulRandomBinomial.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/random/StatefulStandardNormal.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/random/StatefulStandardNormal.java index 8330a9f4b49..ff905308114 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/random/StatefulStandardNormal.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/random/StatefulStandardNormal.java @@ -39,8 +39,6 @@ /** * Outputs random values from a normal distribution. * The generated values will have mean 0 and standard deviation 1. - * - * @param data type for {@code output} output */ @OpMetadata( opType = StatefulStandardNormal.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/random/StatefulTruncatedNormal.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/random/StatefulTruncatedNormal.java index e623baabf5c..409dff36de6 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/random/StatefulTruncatedNormal.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/random/StatefulTruncatedNormal.java @@ -41,8 +41,6 @@ * The generated values follow a normal distribution with mean 0 and standard * deviation 1, except that values whose magnitude is more than 2 standard * deviations from the mean are dropped and re-picked. - * - * @param data type for {@code output} output */ @OpMetadata( opType = StatefulTruncatedNormal.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/random/StatefulUniform.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/random/StatefulUniform.java index a0e85b0458f..65f86463b06 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/random/StatefulUniform.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/random/StatefulUniform.java @@ -40,8 +40,6 @@ * Outputs random values from a uniform distribution. * The generated values follow a uniform distribution in the range {@code [0, 1)}. The * lower bound 0 is included in the range, while the upper bound 1 is excluded. - * - * @param data type for {@code output} output */ @OpMetadata( opType = StatefulUniform.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/random/StatefulUniformFullInt.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/random/StatefulUniformFullInt.java index a43b26418ea..80f425ff575 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/random/StatefulUniformFullInt.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/random/StatefulUniformFullInt.java @@ -38,8 +38,6 @@ /** * Outputs random integers from a uniform distribution. * The generated values are uniform integers covering the whole range of {@code dtype}. - * - * @param data type for {@code output} output */ @OpMetadata( opType = StatefulUniformFullInt.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/random/StatefulUniformInt.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/random/StatefulUniformInt.java index 154f3bd2841..d2854aea992 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/random/StatefulUniformInt.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/random/StatefulUniformInt.java @@ -42,8 +42,6 @@ *

    The random integers are slightly biased unless {@code maxval - minval} is an exact * power of two. The bias is small for values of {@code maxval - minval} significantly * smaller than the range of the output (either {@code 2^32} or {@code 2^64}). - * - * @param data type for {@code output} output */ @OpMetadata( opType = StatefulUniformInt.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/random/StatelessMultinomial.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/random/StatelessMultinomial.java index 1c306047fd5..45a902b2da8 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/random/StatelessMultinomial.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/random/StatelessMultinomial.java @@ -38,8 +38,6 @@ /** * Draws samples from a multinomial distribution. - * - * @param data type for {@code output} output */ @OpMetadata( opType = StatelessMultinomial.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/random/StatelessParameterizedTruncatedNormal.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/random/StatelessParameterizedTruncatedNormal.java index b10e961aab2..64f85682701 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/random/StatelessParameterizedTruncatedNormal.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/random/StatelessParameterizedTruncatedNormal.java @@ -35,8 +35,6 @@ /** * The StatelessParameterizedTruncatedNormal operation - * - * @param data type for {@code output} output */ @OpMetadata( opType = StatelessParameterizedTruncatedNormal.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/random/StatelessRandomBinomial.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/random/StatelessRandomBinomial.java index 71a3cb24cf9..ebd295592eb 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/random/StatelessRandomBinomial.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/random/StatelessRandomBinomial.java @@ -39,8 +39,6 @@ * Outputs deterministic pseudorandom random numbers from a binomial distribution. * Outputs random values from a binomial distribution. *

    The outputs are a deterministic function of {@code shape}, {@code seed}, {@code counts}, and {@code probs}. - * - * @param data type for {@code output} output */ @OpMetadata( opType = StatelessRandomBinomial.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/random/StatelessRandomGamma.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/random/StatelessRandomGamma.java index e57dfcf90f6..69bd0d03ddd 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/random/StatelessRandomGamma.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/random/StatelessRandomGamma.java @@ -39,8 +39,6 @@ * Outputs deterministic pseudorandom random numbers from a gamma distribution. * Outputs random values from a gamma distribution. *

    The outputs are a deterministic function of the inputs. - * - * @param data type for {@code output} output */ @OpMetadata( opType = StatelessRandomGamma.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/random/StatelessRandomNormal.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/random/StatelessRandomNormal.java index 7081e980beb..bf0fa718d0e 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/random/StatelessRandomNormal.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/random/StatelessRandomNormal.java @@ -39,8 +39,6 @@ * Outputs deterministic pseudorandom values from a normal distribution. * The generated values will have mean 0 and standard deviation 1. *

    The outputs are a deterministic function of {@code shape} and {@code seed}. - * - * @param data type for {@code output} output */ @OpMetadata( opType = StatelessRandomNormal.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/random/StatelessRandomNormalV2.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/random/StatelessRandomNormalV2.java index b1e9dcb4439..ef4f9aafee6 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/random/StatelessRandomNormalV2.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/random/StatelessRandomNormalV2.java @@ -41,8 +41,6 @@ * Outputs deterministic pseudorandom values from a normal distribution. * The generated values will have mean 0 and standard deviation 1. *

    The outputs are a deterministic function of {@code shape}, {@code key}, {@code counter} and {@code alg}. - * - * @param data type for {@code output} output */ @OpMetadata( opType = StatelessRandomNormalV2.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/random/StatelessRandomPoisson.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/random/StatelessRandomPoisson.java index 3a55731c32d..c617e49f652 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/random/StatelessRandomPoisson.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/random/StatelessRandomPoisson.java @@ -38,8 +38,6 @@ * Outputs deterministic pseudorandom random numbers from a Poisson distribution. * Outputs random values from a Poisson distribution. *

    The outputs are a deterministic function of {@code shape}, {@code seed}, and {@code lam}. - * - * @param data type for {@code output} output */ @OpMetadata( opType = StatelessRandomPoisson.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/random/StatelessRandomUniform.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/random/StatelessRandomUniform.java index 6e18ceffb6f..86c24f1e171 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/random/StatelessRandomUniform.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/random/StatelessRandomUniform.java @@ -40,8 +40,6 @@ * The generated values follow a uniform distribution in the range {@code [0, 1)}. The * lower bound 0 is included in the range, while the upper bound 1 is excluded. *

    The outputs are a deterministic function of {@code shape} and {@code seed}. - * - * @param data type for {@code output} output */ @OpMetadata( opType = StatelessRandomUniform.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/random/StatelessRandomUniformFullInt.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/random/StatelessRandomUniformFullInt.java index ef2bf5e7884..41e703d9ddf 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/random/StatelessRandomUniformFullInt.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/random/StatelessRandomUniformFullInt.java @@ -38,8 +38,6 @@ * Outputs deterministic pseudorandom random integers from a uniform distribution. * The generated values are uniform integers covering the whole range of {@code dtype}. *

    The outputs are a deterministic function of {@code shape} and {@code seed}. - * - * @param data type for {@code output} output */ @OpMetadata( opType = StatelessRandomUniformFullInt.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/random/StatelessRandomUniformFullIntV2.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/random/StatelessRandomUniformFullIntV2.java index 50fb67d6fe1..7a910d86feb 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/random/StatelessRandomUniformFullIntV2.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/random/StatelessRandomUniformFullIntV2.java @@ -40,8 +40,6 @@ * Outputs deterministic pseudorandom random integers from a uniform distribution. * The generated values are uniform integers covering the whole range of {@code dtype}. *

    The outputs are a deterministic function of {@code shape}, {@code key}, {@code counter} and {@code alg}. - * - * @param data type for {@code output} output */ @OpMetadata( opType = StatelessRandomUniformFullIntV2.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/random/StatelessRandomUniformInt.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/random/StatelessRandomUniformInt.java index 8bce8bc129e..5c792f75e51 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/random/StatelessRandomUniformInt.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/random/StatelessRandomUniformInt.java @@ -37,8 +37,6 @@ * Outputs deterministic pseudorandom random integers from a uniform distribution. * The generated values follow a uniform distribution in the range {@code [minval, maxval)}. *

    The outputs are a deterministic function of {@code shape}, {@code seed}, {@code minval}, and {@code maxval}. - * - * @param data type for {@code output} output */ @OpMetadata( opType = StatelessRandomUniformInt.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/random/StatelessRandomUniformIntV2.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/random/StatelessRandomUniformIntV2.java index aa3e3d0de83..ae538d14050 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/random/StatelessRandomUniformIntV2.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/random/StatelessRandomUniformIntV2.java @@ -39,8 +39,6 @@ * Outputs deterministic pseudorandom random integers from a uniform distribution. * The generated values follow a uniform distribution in the range {@code [minval, maxval)}. *

    The outputs are a deterministic function of {@code shape}, {@code key}, {@code counter}, {@code alg}, {@code minval} and {@code maxval}. - * - * @param data type for {@code output} output */ @OpMetadata( opType = StatelessRandomUniformIntV2.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/random/StatelessRandomUniformV2.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/random/StatelessRandomUniformV2.java index 8b0e106cb95..86bb5202639 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/random/StatelessRandomUniformV2.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/random/StatelessRandomUniformV2.java @@ -42,8 +42,6 @@ * The generated values follow a uniform distribution in the range {@code [0, 1)}. The * lower bound 0 is included in the range, while the upper bound 1 is excluded. *

    The outputs are a deterministic function of {@code shape}, {@code key}, {@code counter} and {@code alg}. - * - * @param data type for {@code output} output */ @OpMetadata( opType = StatelessRandomUniformV2.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/random/StatelessTruncatedNormal.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/random/StatelessTruncatedNormal.java index 2ddedee0436..83c4ebdab9c 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/random/StatelessTruncatedNormal.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/random/StatelessTruncatedNormal.java @@ -41,8 +41,6 @@ * deviation 1, except that values whose magnitude is more than 2 standard * deviations from the mean are dropped and re-picked. *

    The outputs are a deterministic function of {@code shape} and {@code seed}. - * - * @param data type for {@code output} output */ @OpMetadata( opType = StatelessTruncatedNormal.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/random/StatelessTruncatedNormalV2.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/random/StatelessTruncatedNormalV2.java index 6505cd06561..ae8b00ae1df 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/random/StatelessTruncatedNormalV2.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/random/StatelessTruncatedNormalV2.java @@ -43,8 +43,6 @@ * deviation 1, except that values whose magnitude is more than 2 standard * deviations from the mean are dropped and re-picked. *

    The outputs are a deterministic function of {@code shape}, {@code key}, {@code counter} and {@code alg}. - * - * @param data type for {@code output} output */ @OpMetadata( opType = StatelessTruncatedNormalV2.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/random/TruncatedNormal.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/random/TruncatedNormal.java index ee3e12c25e3..36fbe8a2a05 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/random/TruncatedNormal.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/random/TruncatedNormal.java @@ -39,8 +39,6 @@ * The generated values follow a normal distribution with mean 0 and standard * deviation 1, except that values whose magnitude is more than 2 standard * deviations from the mean are dropped and re-picked. - * - * @param data type for {@code output} output */ @OpMetadata( opType = TruncatedNormal.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/random/experimental/StatelessShuffle.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/random/experimental/StatelessShuffle.java index 5100d0ef8c6..dc17294084b 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/random/experimental/StatelessShuffle.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/random/experimental/StatelessShuffle.java @@ -45,8 +45,6 @@ * [5, 6]] [3, 4]] * *

    The outputs are a deterministic function of {@code value}, {@code key}, {@code counter} and {@code alg}. - * - * @param data type for {@code output} output */ @OpMetadata( opType = StatelessShuffle.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/signal/Fft.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/signal/Fft.java index 42ef1e6bdf9..220c72d1723 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/signal/Fft.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/signal/Fft.java @@ -37,8 +37,6 @@ * Fast Fourier transform. * Computes the 1-dimensional discrete Fourier transform over the inner-most * dimension of {@code input}. - * - * @param data type for {@code output} output */ @OpMetadata( opType = Fft.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/signal/Fft2d.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/signal/Fft2d.java index 118d2db63e0..4f78086027b 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/signal/Fft2d.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/signal/Fft2d.java @@ -37,8 +37,6 @@ * 2D fast Fourier transform. * Computes the 2-dimensional discrete Fourier transform over the inner-most * 2 dimensions of {@code input}. - * - * @param data type for {@code output} output */ @OpMetadata( opType = Fft2d.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/signal/Fft3d.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/signal/Fft3d.java index 6195de0eae8..7f5478e228a 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/signal/Fft3d.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/signal/Fft3d.java @@ -37,8 +37,6 @@ * 3D fast Fourier transform. * Computes the 3-dimensional discrete Fourier transform over the inner-most 3 * dimensions of {@code input}. - * - * @param data type for {@code output} output */ @OpMetadata( opType = Fft3d.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/signal/FftNd.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/signal/FftNd.java index b7f4268150c..8f530229379 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/signal/FftNd.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/signal/FftNd.java @@ -44,8 +44,6 @@ * is not given, the default shape(input) is used. *

    Axes mean the dimensions to perform the transform on. Default is to perform on * all axes. - * - * @param data type for {@code output} output */ @OpMetadata( opType = FftNd.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/signal/Ifft.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/signal/Ifft.java index 3a313a6f23e..6b1f6fa6d8c 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/signal/Ifft.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/signal/Ifft.java @@ -37,8 +37,6 @@ * Inverse fast Fourier transform. * Computes the inverse 1-dimensional discrete Fourier transform over the * inner-most dimension of {@code input}. - * - * @param data type for {@code output} output */ @OpMetadata( opType = Ifft.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/signal/Ifft2d.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/signal/Ifft2d.java index ad0902bf3a1..2c4c19b2ead 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/signal/Ifft2d.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/signal/Ifft2d.java @@ -37,8 +37,6 @@ * Inverse 2D fast Fourier transform. * Computes the inverse 2-dimensional discrete Fourier transform over the * inner-most 2 dimensions of {@code input}. - * - * @param data type for {@code output} output */ @OpMetadata( opType = Ifft2d.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/signal/Ifft3d.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/signal/Ifft3d.java index 82251ed232c..efcb06fafcd 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/signal/Ifft3d.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/signal/Ifft3d.java @@ -37,8 +37,6 @@ * Inverse 3D fast Fourier transform. * Computes the inverse 3-dimensional discrete Fourier transform over the * inner-most 3 dimensions of {@code input}. - * - * @param data type for {@code output} output */ @OpMetadata( opType = Ifft3d.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/signal/IfftNd.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/signal/IfftNd.java index 82855d2bab4..181e3756015 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/signal/IfftNd.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/signal/IfftNd.java @@ -44,8 +44,6 @@ * is not given, the default shape(input) is used. *

    Axes mean the dimensions to perform the transform on. Default is to perform on * all axes. - * - * @param data type for {@code output} output */ @OpMetadata( opType = IfftNd.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/signal/Irfft.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/signal/Irfft.java index ecf2703b6e8..50f6daef0a0 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/signal/Irfft.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/signal/Irfft.java @@ -50,8 +50,6 @@ *

    Along the axis {@code signal.Irfft} is computed on, if {@code fft_length / 2 + 1} is smaller * than the corresponding dimension of {@code input}, the dimension is cropped. If it is * larger, the dimension is padded with zeros. - * - * @param data type for {@code output} output */ @OpMetadata( opType = Irfft.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/signal/Irfft2d.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/signal/Irfft2d.java index 8a448fd2a52..01214bfec41 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/signal/Irfft2d.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/signal/Irfft2d.java @@ -51,8 +51,6 @@ * {@code fft_length / 2 + 1} for the inner-most dimension) is smaller than the * corresponding dimension of {@code input}, the dimension is cropped. If it is larger, * the dimension is padded with zeros. - * - * @param data type for {@code output} output */ @OpMetadata( opType = Irfft2d.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/signal/Irfft3d.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/signal/Irfft3d.java index a336791cb83..c83389668b4 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/signal/Irfft3d.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/signal/Irfft3d.java @@ -51,8 +51,6 @@ * {@code fft_length / 2 + 1} for the inner-most dimension) is smaller than the * corresponding dimension of {@code input}, the dimension is cropped. If it is larger, * the dimension is padded with zeros. - * - * @param data type for {@code output} output */ @OpMetadata( opType = Irfft3d.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/signal/IrfftNd.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/signal/IrfftNd.java index 93006aea156..5e83c9f4dc3 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/signal/IrfftNd.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/signal/IrfftNd.java @@ -48,8 +48,6 @@ * is not given, the default shape(input) is used. *

    Axes mean the dimensions to perform the transform on. Default is to perform on * all axes. - * - * @param data type for {@code output} output */ @OpMetadata( opType = IrfftNd.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/signal/Rfft.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/signal/Rfft.java index f5c14f6eec7..c4d7b74e39a 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/signal/Rfft.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/signal/Rfft.java @@ -46,8 +46,6 @@ *

    Along the axis {@code signal.Rfft} is computed on, if {@code fft_length} is smaller than the * corresponding dimension of {@code input}, the dimension is cropped. If it is larger, * the dimension is padded with zeros. - * - * @param data type for {@code output} output */ @OpMetadata( opType = Rfft.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/signal/Rfft2d.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/signal/Rfft2d.java index 6587b7378c1..314d16f4eec 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/signal/Rfft2d.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/signal/Rfft2d.java @@ -47,8 +47,6 @@ *

    Along each axis {@code signal.Rfft2d} is computed on, if {@code fft_length} is smaller than the * corresponding dimension of {@code input}, the dimension is cropped. If it is larger, * the dimension is padded with zeros. - * - * @param data type for {@code output} output */ @OpMetadata( opType = Rfft2d.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/signal/Rfft3d.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/signal/Rfft3d.java index 35746c0f93b..282c4b7386e 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/signal/Rfft3d.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/signal/Rfft3d.java @@ -47,8 +47,6 @@ *

    Along each axis {@code signal.Rfft3d} is computed on, if {@code fft_length} is smaller than the * corresponding dimension of {@code input}, the dimension is cropped. If it is larger, * the dimension is padded with zeros. - * - * @param data type for {@code output} output */ @OpMetadata( opType = Rfft3d.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/signal/RfftNd.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/signal/RfftNd.java index 85e48957ee4..17bf1368600 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/signal/RfftNd.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/signal/RfftNd.java @@ -47,8 +47,6 @@ * is not given, the default shape(input) is used. *

    Axes mean the dimensions to perform the transform on. Default is to perform on * all axes. - * - * @param data type for {@code output} output */ @OpMetadata( opType = RfftNd.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/sparse/ConvertToListOfSparseCoreCooTensors.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/sparse/ConvertToListOfSparseCoreCooTensors.java new file mode 100644 index 00000000000..7ed71c4c316 --- /dev/null +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/sparse/ConvertToListOfSparseCoreCooTensors.java @@ -0,0 +1,209 @@ +/* Copyright 2018-2023 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +=======================================================================*/ + +// This class has been generated, DO NOT EDIT! + +package org.tensorflow.op.sparse; + +import java.util.Arrays; +import java.util.List; +import org.tensorflow.GraphOperation; +import org.tensorflow.Operand; +import org.tensorflow.Operation; +import org.tensorflow.OperationBuilder; +import org.tensorflow.Output; +import org.tensorflow.op.RawOp; +import org.tensorflow.op.RawOpInputs; +import org.tensorflow.op.Scope; +import org.tensorflow.op.annotation.Endpoint; +import org.tensorflow.op.annotation.OpInputsMetadata; +import org.tensorflow.op.annotation.OpMetadata; +import org.tensorflow.op.annotation.Operator; +import org.tensorflow.types.TFloat32; +import org.tensorflow.types.TInt32; + +/** + * The ConvertToListOfSparseCoreCooTensors operation + */ +@OpMetadata( + opType = ConvertToListOfSparseCoreCooTensors.OP_NAME, + inputsClass = ConvertToListOfSparseCoreCooTensors.Inputs.class +) +@Operator( + group = "sparse" +) +public final class ConvertToListOfSparseCoreCooTensors extends RawOp { + /** + * The name of this op, as known by TensorFlow core engine + */ + public static final String OP_NAME = "ConvertToListOfSparseCoreCooTensors"; + + private List> rowIdsList; + + private List> colIdsList; + + private List> gainsList; + + @SuppressWarnings("unchecked") + public ConvertToListOfSparseCoreCooTensors(Operation operation) { + super(operation, OP_NAME); + int outputIdx = 0; + int rowIdsListLength = operation.outputListLength("row_ids_list"); + rowIdsList = Arrays.asList((Output[]) operation.outputList(outputIdx, rowIdsListLength)); + outputIdx += rowIdsListLength; + int colIdsListLength = operation.outputListLength("col_ids_list"); + colIdsList = Arrays.asList((Output[]) operation.outputList(outputIdx, colIdsListLength)); + outputIdx += colIdsListLength; + int gainsListLength = operation.outputListLength("gains_list"); + gainsList = Arrays.asList((Output[]) operation.outputList(outputIdx, gainsListLength)); + outputIdx += gainsListLength; + } + + /** + * Factory method to create a class wrapping a new ConvertToListOfSparseCoreCooTensors operation. + * + * @param scope current scope + * @param indicesOrRowSplits The indicesOrRowSplits value + * @param values The values value + * @param weights The weights value + * @param sampleCount The value of the sampleCount attribute + * @param numScPerChip The value of the numScPerChip attribute + * @param rowOffset The value of the rowOffset attribute + * @param colOffset The value of the colOffset attribute + * @param colShift The value of the colShift attribute + * @param numScShards The value of the numScShards attribute + * @param stackedTableSampleCount The value of the stackedTableSampleCount attribute + * @param combiner The value of the combiner attribute + * @return a new instance of ConvertToListOfSparseCoreCooTensors + */ + @Endpoint( + describeByClass = true + ) + public static ConvertToListOfSparseCoreCooTensors create(Scope scope, + Operand indicesOrRowSplits, Operand values, Operand weights, + Long sampleCount, Long numScPerChip, Long rowOffset, Long colOffset, Long colShift, + Long numScShards, Long stackedTableSampleCount, String combiner) { + OperationBuilder opBuilder = scope.opBuilder(OP_NAME, "ConvertToListOfSparseCoreCooTensors"); + opBuilder.addInput(indicesOrRowSplits.asOutput()); + opBuilder.addInput(values.asOutput()); + opBuilder.addInput(weights.asOutput()); + opBuilder.setAttr("sample_count", sampleCount); + opBuilder.setAttr("num_sc_per_chip", numScPerChip); + opBuilder.setAttr("row_offset", rowOffset); + opBuilder.setAttr("col_offset", colOffset); + opBuilder.setAttr("col_shift", colShift); + opBuilder.setAttr("num_sc_shards", numScShards); + opBuilder.setAttr("stacked_table_sample_count", stackedTableSampleCount); + opBuilder.setAttr("combiner", combiner); + return new ConvertToListOfSparseCoreCooTensors(opBuilder.build()); + } + + /** + * Gets rowIdsList. + * + * @return rowIdsList. + */ + public List> rowIdsList() { + return rowIdsList; + } + + /** + * Gets colIdsList. + * + * @return colIdsList. + */ + public List> colIdsList() { + return colIdsList; + } + + /** + * Gets gainsList. + * + * @return gainsList. + */ + public List> gainsList() { + return gainsList; + } + + @OpInputsMetadata( + outputsClass = ConvertToListOfSparseCoreCooTensors.class + ) + public static class Inputs extends RawOpInputs { + /** + * The indicesOrRowSplits input + */ + public final Operand indicesOrRowSplits; + + /** + * The values input + */ + public final Operand values; + + /** + * The weights input + */ + public final Operand weights; + + /** + * The sampleCount attribute + */ + public final long sampleCount; + + /** + * The rowOffset attribute + */ + public final long rowOffset; + + /** + * The colOffset attribute + */ + public final long colOffset; + + /** + * The colShift attribute + */ + public final long colShift; + + /** + * The numScShards attribute + */ + public final long numScShards; + + /** + * The stackedTableSampleCount attribute + */ + public final long stackedTableSampleCount; + + /** + * The combiner attribute + */ + public final String combiner; + + public Inputs(GraphOperation op) { + super(new ConvertToListOfSparseCoreCooTensors(op), op, Arrays.asList("sample_count", "row_offset", "col_offset", "col_shift", "num_sc_shards", "stacked_table_sample_count", "combiner")); + int inputIndex = 0; + indicesOrRowSplits = (Operand) op.input(inputIndex++); + values = (Operand) op.input(inputIndex++); + weights = (Operand) op.input(inputIndex++); + sampleCount = op.attributes().getAttrInt("sample_count"); + rowOffset = op.attributes().getAttrInt("row_offset"); + colOffset = op.attributes().getAttrInt("col_offset"); + colShift = op.attributes().getAttrInt("col_shift"); + numScShards = op.attributes().getAttrInt("num_sc_shards"); + stackedTableSampleCount = op.attributes().getAttrInt("stacked_table_sample_count"); + combiner = op.attributes().getAttrString("combiner"); + } + } +} diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/sparse/ConvertToSparseCoreCsrWrappedCooTensor.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/sparse/ConvertToSparseCoreCsrWrappedCooTensor.java new file mode 100644 index 00000000000..6590a927699 --- /dev/null +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/sparse/ConvertToSparseCoreCsrWrappedCooTensor.java @@ -0,0 +1,283 @@ +/* Copyright 2018-2023 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +=======================================================================*/ + +// This class has been generated, DO NOT EDIT! + +package org.tensorflow.op.sparse; + +import java.util.Arrays; +import org.tensorflow.GraphOperation; +import org.tensorflow.Operand; +import org.tensorflow.Operation; +import org.tensorflow.OperationBuilder; +import org.tensorflow.Output; +import org.tensorflow.op.Operands; +import org.tensorflow.op.RawOp; +import org.tensorflow.op.RawOpInputs; +import org.tensorflow.op.Scope; +import org.tensorflow.op.annotation.Endpoint; +import org.tensorflow.op.annotation.OpInputsMetadata; +import org.tensorflow.op.annotation.OpMetadata; +import org.tensorflow.op.annotation.Operator; +import org.tensorflow.types.TFloat32; +import org.tensorflow.types.TInt32; +import org.tensorflow.types.TInt64; + +/** + * The ConvertToSparseCoreCsrWrappedCooTensor operation + */ +@OpMetadata( + opType = ConvertToSparseCoreCsrWrappedCooTensor.OP_NAME, + inputsClass = ConvertToSparseCoreCsrWrappedCooTensor.Inputs.class +) +@Operator( + group = "sparse" +) +public final class ConvertToSparseCoreCsrWrappedCooTensor extends RawOp { + /** + * The name of this op, as known by TensorFlow core engine + */ + public static final String OP_NAME = "ConvertToSparseCoreCsrWrappedCooTensor"; + + private Output rowPointers; + + private Output sortedSampleIds; + + private Output sortedTokenIds; + + private Output sortedGains; + + private Output rowPointersUnpaddedSize; + + private Output idsUnpaddedSize; + + private Output numMinibatchesPerSc; + + public ConvertToSparseCoreCsrWrappedCooTensor(Operation operation) { + super(operation, OP_NAME); + int outputIdx = 0; + rowPointers = operation.output(outputIdx++); + sortedSampleIds = operation.output(outputIdx++); + sortedTokenIds = operation.output(outputIdx++); + sortedGains = operation.output(outputIdx++); + rowPointersUnpaddedSize = operation.output(outputIdx++); + idsUnpaddedSize = operation.output(outputIdx++); + numMinibatchesPerSc = operation.output(outputIdx++); + } + + /** + * Factory method to create a class wrapping a new ConvertToSparseCoreCsrWrappedCooTensor operation. + * + * @param scope current scope + * @param sortedRowIdsList The sortedRowIdsList value + * @param sortedColIdsList The sortedColIdsList value + * @param sortedGainsList The sortedGainsList value + * @param idCountsList The idCountsList value + * @param splits The splits value + * @param sampleCountPerSc The value of the sampleCountPerSc attribute + * @param numReplica The value of the numReplica attribute + * @param maxMinibatchesPerSc The value of the maxMinibatchesPerSc attribute + * @param maxIdsPerChipPerSample The value of the maxIdsPerChipPerSample attribute + * @param tableVocabSize The value of the tableVocabSize attribute + * @param featureWidth The value of the featureWidth attribute + * @param tableName The value of the tableName attribute + * @param allowIdDropping The value of the allowIdDropping attribute + * @return a new instance of ConvertToSparseCoreCsrWrappedCooTensor + */ + @Endpoint( + describeByClass = true + ) + public static ConvertToSparseCoreCsrWrappedCooTensor create(Scope scope, + Iterable> sortedRowIdsList, Iterable> sortedColIdsList, + Iterable> sortedGainsList, Iterable> idCountsList, + Operand splits, Long sampleCountPerSc, Long numReplica, Long maxMinibatchesPerSc, + Long maxIdsPerChipPerSample, Long tableVocabSize, Long featureWidth, String tableName, + Boolean allowIdDropping) { + OperationBuilder opBuilder = scope.opBuilder(OP_NAME, "ConvertToSparseCoreCsrWrappedCooTensor"); + opBuilder.addInputList(Operands.asOutputs(sortedRowIdsList)); + opBuilder.addInputList(Operands.asOutputs(sortedColIdsList)); + opBuilder.addInputList(Operands.asOutputs(sortedGainsList)); + opBuilder.addInputList(Operands.asOutputs(idCountsList)); + opBuilder.addInput(splits.asOutput()); + opBuilder.setAttr("sample_count_per_sc", sampleCountPerSc); + opBuilder.setAttr("num_replica", numReplica); + opBuilder.setAttr("max_minibatches_per_sc", maxMinibatchesPerSc); + opBuilder.setAttr("max_ids_per_chip_per_sample", maxIdsPerChipPerSample); + opBuilder.setAttr("table_vocab_size", tableVocabSize); + opBuilder.setAttr("feature_width", featureWidth); + opBuilder.setAttr("table_name", tableName); + opBuilder.setAttr("allow_id_dropping", allowIdDropping); + return new ConvertToSparseCoreCsrWrappedCooTensor(opBuilder.build()); + } + + /** + * Gets rowPointers. + * + * @return rowPointers. + */ + public Output rowPointers() { + return rowPointers; + } + + /** + * Gets sortedSampleIds. + * + * @return sortedSampleIds. + */ + public Output sortedSampleIds() { + return sortedSampleIds; + } + + /** + * Gets sortedTokenIds. + * + * @return sortedTokenIds. + */ + public Output sortedTokenIds() { + return sortedTokenIds; + } + + /** + * Gets sortedGains. + * + * @return sortedGains. + */ + public Output sortedGains() { + return sortedGains; + } + + /** + * Gets rowPointersUnpaddedSize. + * + * @return rowPointersUnpaddedSize. + */ + public Output rowPointersUnpaddedSize() { + return rowPointersUnpaddedSize; + } + + /** + * Gets idsUnpaddedSize. + * + * @return idsUnpaddedSize. + */ + public Output idsUnpaddedSize() { + return idsUnpaddedSize; + } + + /** + * Gets numMinibatchesPerSc. + * + * @return numMinibatchesPerSc. + */ + public Output numMinibatchesPerSc() { + return numMinibatchesPerSc; + } + + @OpInputsMetadata( + outputsClass = ConvertToSparseCoreCsrWrappedCooTensor.class + ) + public static class Inputs extends RawOpInputs { + /** + * The sortedRowIdsList input + */ + public final Iterable> sortedRowIdsList; + + /** + * The sortedColIdsList input + */ + public final Iterable> sortedColIdsList; + + /** + * The sortedGainsList input + */ + public final Iterable> sortedGainsList; + + /** + * The idCountsList input + */ + public final Iterable> idCountsList; + + /** + * The splits input + */ + public final Operand splits; + + /** + * The sampleCountPerSc attribute + */ + public final long sampleCountPerSc; + + /** + * The numReplica attribute + */ + public final long numReplica; + + /** + * The maxMinibatchesPerSc attribute + */ + public final long maxMinibatchesPerSc; + + /** + * The maxIdsPerChipPerSample attribute + */ + public final long maxIdsPerChipPerSample; + + /** + * The tableVocabSize attribute + */ + public final long tableVocabSize; + + /** + * The featureWidth attribute + */ + public final long featureWidth; + + /** + * The tableName attribute + */ + public final String tableName; + + /** + * The allowIdDropping attribute + */ + public final boolean allowIdDropping; + + public Inputs(GraphOperation op) { + super(new ConvertToSparseCoreCsrWrappedCooTensor(op), op, Arrays.asList("sample_count_per_sc", "num_replica", "max_minibatches_per_sc", "max_ids_per_chip_per_sample", "table_vocab_size", "feature_width", "table_name", "allow_id_dropping")); + int inputIndex = 0; + int sortedRowIdsListLength = op.inputListLength("sorted_row_ids_list"); + sortedRowIdsList = Arrays.asList((Operand[]) op.inputList(inputIndex, sortedRowIdsListLength)); + inputIndex += sortedRowIdsListLength; + int sortedColIdsListLength = op.inputListLength("sorted_col_ids_list"); + sortedColIdsList = Arrays.asList((Operand[]) op.inputList(inputIndex, sortedColIdsListLength)); + inputIndex += sortedColIdsListLength; + int sortedGainsListLength = op.inputListLength("sorted_gains_list"); + sortedGainsList = Arrays.asList((Operand[]) op.inputList(inputIndex, sortedGainsListLength)); + inputIndex += sortedGainsListLength; + int idCountsListLength = op.inputListLength("id_counts_list"); + idCountsList = Arrays.asList((Operand[]) op.inputList(inputIndex, idCountsListLength)); + inputIndex += idCountsListLength; + splits = (Operand) op.input(inputIndex++); + sampleCountPerSc = op.attributes().getAttrInt("sample_count_per_sc"); + numReplica = op.attributes().getAttrInt("num_replica"); + maxMinibatchesPerSc = op.attributes().getAttrInt("max_minibatches_per_sc"); + maxIdsPerChipPerSample = op.attributes().getAttrInt("max_ids_per_chip_per_sample"); + tableVocabSize = op.attributes().getAttrInt("table_vocab_size"); + featureWidth = op.attributes().getAttrInt("feature_width"); + tableName = op.attributes().getAttrString("table_name"); + allowIdDropping = op.attributes().getAttrBool("allow_id_dropping"); + } + } +} diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/sparse/DenseCountSparseOutput.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/sparse/DenseCountSparseOutput.java index 49d78c0517c..5cf78a2a0a6 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/sparse/DenseCountSparseOutput.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/sparse/DenseCountSparseOutput.java @@ -37,8 +37,6 @@ /** * Performs sparse-output bin counting for a tf.tensor input. * Counts the number of times each value occurs in the input. - * - * @param data type for {@code output_values} output */ @OpMetadata( opType = DenseCountSparseOutput.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/sparse/DenseToDenseSetOperation.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/sparse/DenseToDenseSetOperation.java index 2ea6aa671d1..546adba1a9d 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/sparse/DenseToDenseSetOperation.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/sparse/DenseToDenseSetOperation.java @@ -42,8 +42,6 @@ * has rank {@code n} and the same 1st {@code n-1} dimensions as {@code set1} and {@code set2}. The {@code nth} * dimension contains the result of {@code set_operation} applied to the corresponding * {@code [0...n-1]} dimension of {@code set}. - * - * @param data type for {@code result_values} output */ @OpMetadata( opType = DenseToDenseSetOperation.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/sparse/DenseToSparseSetOperation.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/sparse/DenseToSparseSetOperation.java index bb75893bfd4..1b8cbcaee50 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/sparse/DenseToSparseSetOperation.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/sparse/DenseToSparseSetOperation.java @@ -48,8 +48,6 @@ * has rank {@code n} and the same 1st {@code n-1} dimensions as {@code set1} and {@code set2}. The {@code nth} * dimension contains the result of {@code set_operation} applied to the corresponding * {@code [0...n-1]} dimension of {@code set}. - * - * @param data type for {@code result_values} output */ @OpMetadata( opType = DenseToSparseSetOperation.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/sparse/DeserializeSparse.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/sparse/DeserializeSparse.java index 697249eca81..ba0c51f9a1e 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/sparse/DeserializeSparse.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/sparse/DeserializeSparse.java @@ -76,8 +76,6 @@ * values = [1, 2, 3, 4, 5] * shape = [2 50] * - * - * @param data type for {@code sparse_values} output */ @OpMetadata( opType = DeserializeSparse.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/sparse/GetStatsFromListOfSparseCoreCooTensors.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/sparse/GetStatsFromListOfSparseCoreCooTensors.java new file mode 100644 index 00000000000..51f5c33d66b --- /dev/null +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/sparse/GetStatsFromListOfSparseCoreCooTensors.java @@ -0,0 +1,204 @@ +/* Copyright 2018-2023 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +=======================================================================*/ + +// This class has been generated, DO NOT EDIT! + +package org.tensorflow.op.sparse; + +import java.util.Arrays; +import java.util.List; +import org.tensorflow.GraphOperation; +import org.tensorflow.Operand; +import org.tensorflow.Operation; +import org.tensorflow.OperationBuilder; +import org.tensorflow.Output; +import org.tensorflow.op.Operands; +import org.tensorflow.op.RawOp; +import org.tensorflow.op.RawOpInputs; +import org.tensorflow.op.Scope; +import org.tensorflow.op.annotation.Endpoint; +import org.tensorflow.op.annotation.OpInputsMetadata; +import org.tensorflow.op.annotation.OpMetadata; +import org.tensorflow.op.annotation.Operator; +import org.tensorflow.types.TFloat32; +import org.tensorflow.types.TInt32; + +/** + * The GetStatsFromListOfSparseCoreCooTensors operation + */ +@OpMetadata( + opType = GetStatsFromListOfSparseCoreCooTensors.OP_NAME, + inputsClass = GetStatsFromListOfSparseCoreCooTensors.Inputs.class +) +@Operator( + group = "sparse" +) +public final class GetStatsFromListOfSparseCoreCooTensors extends RawOp { + /** + * The name of this op, as known by TensorFlow core engine + */ + public static final String OP_NAME = "GetStatsFromListOfSparseCoreCooTensors"; + + private Output maxIdsPerSparseCore; + + private Output maxUniqueIdsPerSparseCore; + + public GetStatsFromListOfSparseCoreCooTensors(Operation operation) { + super(operation, OP_NAME); + int outputIdx = 0; + maxIdsPerSparseCore = operation.output(outputIdx++); + maxUniqueIdsPerSparseCore = operation.output(outputIdx++); + } + + /** + * Factory method to create a class wrapping a new GetStatsFromListOfSparseCoreCooTensors operation. + * + * @param scope current scope + * @param rowIdsList The rowIdsList value + * @param colIdsList The colIdsList value + * @param gainsList The gainsList value + * @param sampleCountList The value of the sampleCountList attribute + * @param colOffsetList The value of the colOffsetList attribute + * @param numReplica The value of the numReplica attribute + * @param tableVocabSize The value of the tableVocabSize attribute + * @param featureWidth The value of the featureWidth attribute + * @param numScPerChip The value of the numScPerChip attribute + * @param tableName The value of the tableName attribute + * @return a new instance of GetStatsFromListOfSparseCoreCooTensors + */ + @Endpoint( + describeByClass = true + ) + public static GetStatsFromListOfSparseCoreCooTensors create(Scope scope, + Iterable> rowIdsList, Iterable> colIdsList, + Iterable> gainsList, List sampleCountList, List colOffsetList, + Long numReplica, Long tableVocabSize, Long featureWidth, Long numScPerChip, + String tableName) { + OperationBuilder opBuilder = scope.opBuilder(OP_NAME, "GetStatsFromListOfSparseCoreCooTensors"); + opBuilder.addInputList(Operands.asOutputs(rowIdsList)); + opBuilder.addInputList(Operands.asOutputs(colIdsList)); + opBuilder.addInputList(Operands.asOutputs(gainsList)); + long[] sampleCountListArray = new long[sampleCountList.size()]; + for (int i = 0 ; i < sampleCountListArray.length ; i++) { + sampleCountListArray[i] = sampleCountList.get(i); + } + opBuilder.setAttr("sample_count_list", sampleCountListArray); + long[] colOffsetListArray = new long[colOffsetList.size()]; + for (int i = 0 ; i < colOffsetListArray.length ; i++) { + colOffsetListArray[i] = colOffsetList.get(i); + } + opBuilder.setAttr("col_offset_list", colOffsetListArray); + opBuilder.setAttr("num_replica", numReplica); + opBuilder.setAttr("table_vocab_size", tableVocabSize); + opBuilder.setAttr("feature_width", featureWidth); + opBuilder.setAttr("num_sc_per_chip", numScPerChip); + opBuilder.setAttr("table_name", tableName); + return new GetStatsFromListOfSparseCoreCooTensors(opBuilder.build()); + } + + /** + * Gets maxIdsPerSparseCore. + * + * @return maxIdsPerSparseCore. + */ + public Output maxIdsPerSparseCore() { + return maxIdsPerSparseCore; + } + + /** + * Gets maxUniqueIdsPerSparseCore. + * + * @return maxUniqueIdsPerSparseCore. + */ + public Output maxUniqueIdsPerSparseCore() { + return maxUniqueIdsPerSparseCore; + } + + @OpInputsMetadata( + outputsClass = GetStatsFromListOfSparseCoreCooTensors.class + ) + public static class Inputs extends RawOpInputs { + /** + * The rowIdsList input + */ + public final Iterable> rowIdsList; + + /** + * The colIdsList input + */ + public final Iterable> colIdsList; + + /** + * The gainsList input + */ + public final Iterable> gainsList; + + /** + * The sampleCountList attribute + */ + public final long[] sampleCountList; + + /** + * The colOffsetList attribute + */ + public final long[] colOffsetList; + + /** + * The numReplica attribute + */ + public final long numReplica; + + /** + * The tableVocabSize attribute + */ + public final long tableVocabSize; + + /** + * The featureWidth attribute + */ + public final long featureWidth; + + /** + * The numScPerChip attribute + */ + public final long numScPerChip; + + /** + * The tableName attribute + */ + public final String tableName; + + public Inputs(GraphOperation op) { + super(new GetStatsFromListOfSparseCoreCooTensors(op), op, Arrays.asList("sample_count_list", "col_offset_list", "num_replica", "table_vocab_size", "feature_width", "num_sc_per_chip", "table_name")); + int inputIndex = 0; + int rowIdsListLength = op.inputListLength("row_ids_list"); + rowIdsList = Arrays.asList((Operand[]) op.inputList(inputIndex, rowIdsListLength)); + inputIndex += rowIdsListLength; + int colIdsListLength = op.inputListLength("col_ids_list"); + colIdsList = Arrays.asList((Operand[]) op.inputList(inputIndex, colIdsListLength)); + inputIndex += colIdsListLength; + int gainsListLength = op.inputListLength("gains_list"); + gainsList = Arrays.asList((Operand[]) op.inputList(inputIndex, gainsListLength)); + inputIndex += gainsListLength; + sampleCountList = op.attributes().getAttrIntList("sample_count_list"); + colOffsetList = op.attributes().getAttrIntList("col_offset_list"); + numReplica = op.attributes().getAttrInt("num_replica"); + tableVocabSize = op.attributes().getAttrInt("table_vocab_size"); + featureWidth = op.attributes().getAttrInt("feature_width"); + numScPerChip = op.attributes().getAttrInt("num_sc_per_chip"); + tableName = op.attributes().getAttrString("table_name"); + } + } +} diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/sparse/SortListOfSparseCoreCooTensors.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/sparse/SortListOfSparseCoreCooTensors.java new file mode 100644 index 00000000000..fb26033cfd2 --- /dev/null +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/sparse/SortListOfSparseCoreCooTensors.java @@ -0,0 +1,240 @@ +/* Copyright 2018-2023 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +=======================================================================*/ + +// This class has been generated, DO NOT EDIT! + +package org.tensorflow.op.sparse; + +import java.util.Arrays; +import java.util.List; +import org.tensorflow.GraphOperation; +import org.tensorflow.Operand; +import org.tensorflow.Operation; +import org.tensorflow.OperationBuilder; +import org.tensorflow.Output; +import org.tensorflow.op.Operands; +import org.tensorflow.op.RawOp; +import org.tensorflow.op.RawOpInputs; +import org.tensorflow.op.Scope; +import org.tensorflow.op.annotation.Endpoint; +import org.tensorflow.op.annotation.OpInputsMetadata; +import org.tensorflow.op.annotation.OpMetadata; +import org.tensorflow.types.TFloat32; +import org.tensorflow.types.TInt32; + +/** + * The SortListOfSparseCoreCooTensors operation + */ +@OpMetadata( + opType = SortListOfSparseCoreCooTensors.OP_NAME, + inputsClass = SortListOfSparseCoreCooTensors.Inputs.class +) +public final class SortListOfSparseCoreCooTensors extends RawOp { + /** + * The name of this op, as known by TensorFlow core engine + */ + public static final String OP_NAME = "SortListOfSparseCoreCooTensors"; + + private Output sortedRowIds; + + private Output sortedColIds; + + private Output sortedGains; + + private Output idCounts; + + public SortListOfSparseCoreCooTensors(Operation operation) { + super(operation, OP_NAME); + int outputIdx = 0; + sortedRowIds = operation.output(outputIdx++); + sortedColIds = operation.output(outputIdx++); + sortedGains = operation.output(outputIdx++); + idCounts = operation.output(outputIdx++); + } + + /** + * Factory method to create a class wrapping a new SortListOfSparseCoreCooTensors operation. + * + * @param scope current scope + * @param rowIdsList The rowIdsList value + * @param colIdsList The colIdsList value + * @param gainsList The gainsList value + * @param sampleCountList The value of the sampleCountList attribute + * @param colOffsetList The value of the colOffsetList attribute + * @param numReplica The value of the numReplica attribute + * @param tableVocabSize The value of the tableVocabSize attribute + * @param featureWidth The value of the featureWidth attribute + * @param numScPerChip The value of the numScPerChip attribute + * @param maxIdsPerSparseCore The value of the maxIdsPerSparseCore attribute + * @param maxUniqueIdsPerSparseCore The value of the maxUniqueIdsPerSparseCore attribute + * @param tableName The value of the tableName attribute + * @return a new instance of SortListOfSparseCoreCooTensors + */ + @Endpoint( + describeByClass = true + ) + public static SortListOfSparseCoreCooTensors create(Scope scope, + Iterable> rowIdsList, Iterable> colIdsList, + Iterable> gainsList, List sampleCountList, List colOffsetList, + Long numReplica, Long tableVocabSize, Long featureWidth, Long numScPerChip, + Long maxIdsPerSparseCore, Long maxUniqueIdsPerSparseCore, String tableName) { + OperationBuilder opBuilder = scope.opBuilder(OP_NAME, "SortListOfSparseCoreCooTensors"); + opBuilder.addInputList(Operands.asOutputs(rowIdsList)); + opBuilder.addInputList(Operands.asOutputs(colIdsList)); + opBuilder.addInputList(Operands.asOutputs(gainsList)); + long[] sampleCountListArray = new long[sampleCountList.size()]; + for (int i = 0 ; i < sampleCountListArray.length ; i++) { + sampleCountListArray[i] = sampleCountList.get(i); + } + opBuilder.setAttr("sample_count_list", sampleCountListArray); + long[] colOffsetListArray = new long[colOffsetList.size()]; + for (int i = 0 ; i < colOffsetListArray.length ; i++) { + colOffsetListArray[i] = colOffsetList.get(i); + } + opBuilder.setAttr("col_offset_list", colOffsetListArray); + opBuilder.setAttr("num_replica", numReplica); + opBuilder.setAttr("table_vocab_size", tableVocabSize); + opBuilder.setAttr("feature_width", featureWidth); + opBuilder.setAttr("num_sc_per_chip", numScPerChip); + opBuilder.setAttr("max_ids_per_sparse_core", maxIdsPerSparseCore); + opBuilder.setAttr("max_unique_ids_per_sparse_core", maxUniqueIdsPerSparseCore); + opBuilder.setAttr("table_name", tableName); + return new SortListOfSparseCoreCooTensors(opBuilder.build()); + } + + /** + * Gets sortedRowIds. + * + * @return sortedRowIds. + */ + public Output sortedRowIds() { + return sortedRowIds; + } + + /** + * Gets sortedColIds. + * + * @return sortedColIds. + */ + public Output sortedColIds() { + return sortedColIds; + } + + /** + * Gets sortedGains. + * + * @return sortedGains. + */ + public Output sortedGains() { + return sortedGains; + } + + /** + * Gets idCounts. + * + * @return idCounts. + */ + public Output idCounts() { + return idCounts; + } + + @OpInputsMetadata( + outputsClass = SortListOfSparseCoreCooTensors.class + ) + public static class Inputs extends RawOpInputs { + /** + * The rowIdsList input + */ + public final Iterable> rowIdsList; + + /** + * The colIdsList input + */ + public final Iterable> colIdsList; + + /** + * The gainsList input + */ + public final Iterable> gainsList; + + /** + * The sampleCountList attribute + */ + public final long[] sampleCountList; + + /** + * The colOffsetList attribute + */ + public final long[] colOffsetList; + + /** + * The numReplica attribute + */ + public final long numReplica; + + /** + * The tableVocabSize attribute + */ + public final long tableVocabSize; + + /** + * The featureWidth attribute + */ + public final long featureWidth; + + /** + * The numScPerChip attribute + */ + public final long numScPerChip; + + /** + * The maxIdsPerSparseCore attribute + */ + public final long maxIdsPerSparseCore; + + /** + * The maxUniqueIdsPerSparseCore attribute + */ + public final long maxUniqueIdsPerSparseCore; + + /** + * The tableName attribute + */ + public final String tableName; + + public Inputs(GraphOperation op) { + super(new SortListOfSparseCoreCooTensors(op), op, Arrays.asList("sample_count_list", "col_offset_list", "num_replica", "table_vocab_size", "feature_width", "num_sc_per_chip", "max_ids_per_sparse_core", "max_unique_ids_per_sparse_core", "table_name")); + int inputIndex = 0; + int rowIdsListLength = op.inputListLength("row_ids_list"); + rowIdsList = Arrays.asList((Operand[]) op.inputList(inputIndex, rowIdsListLength)); + inputIndex += rowIdsListLength; + int colIdsListLength = op.inputListLength("col_ids_list"); + colIdsList = Arrays.asList((Operand[]) op.inputList(inputIndex, colIdsListLength)); + inputIndex += colIdsListLength; + int gainsListLength = op.inputListLength("gains_list"); + gainsList = Arrays.asList((Operand[]) op.inputList(inputIndex, gainsListLength)); + inputIndex += gainsListLength; + sampleCountList = op.attributes().getAttrIntList("sample_count_list"); + colOffsetList = op.attributes().getAttrIntList("col_offset_list"); + numReplica = op.attributes().getAttrInt("num_replica"); + tableVocabSize = op.attributes().getAttrInt("table_vocab_size"); + featureWidth = op.attributes().getAttrInt("feature_width"); + numScPerChip = op.attributes().getAttrInt("num_sc_per_chip"); + maxIdsPerSparseCore = op.attributes().getAttrInt("max_ids_per_sparse_core"); + maxUniqueIdsPerSparseCore = op.attributes().getAttrInt("max_unique_ids_per_sparse_core"); + tableName = op.attributes().getAttrString("table_name"); + } + } +} diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/sparse/SparseAccumulatorTakeGradient.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/sparse/SparseAccumulatorTakeGradient.java index aeb639d2d6e..fb8a868349d 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/sparse/SparseAccumulatorTakeGradient.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/sparse/SparseAccumulatorTakeGradient.java @@ -45,8 +45,6 @@ * average of the accumulated gradients. Also automatically increments * the recorded global_step in the accumulator by 1, and resets the * aggregate to 0. - * - * @param data type for {@code values} output */ @OpMetadata( opType = SparseAccumulatorTakeGradient.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/sparse/SparseAdd.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/sparse/SparseAdd.java index 1591773a20c..88ef61b78a1 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/sparse/SparseAdd.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/sparse/SparseAdd.java @@ -48,8 +48,6 @@ * {@code thresh == 0} (default) means everything is kept and actual thresholding happens * only for a positive value. *

    In the following shapes, {@code nnz} is the count after taking {@code thresh} into account. - * - * @param data type for {@code sum_values} output */ @OpMetadata( opType = SparseAdd.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/sparse/SparseAddGrad.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/sparse/SparseAddGrad.java index 7d6c0923f4f..8a844c96eff 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/sparse/SparseAddGrad.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/sparse/SparseAddGrad.java @@ -40,8 +40,6 @@ * as {@code SparseTensor} objects. This op takes in the upstream gradient w.r.t. * non-empty values of the sum, and outputs the gradients w.r.t. the non-empty * values of A and B. - * - * @param data type for {@code a_val_grad} output */ @OpMetadata( opType = SparseAddGrad.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/sparse/SparseBincount.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/sparse/SparseBincount.java index b7414e4ab54..9eca1295d45 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/sparse/SparseBincount.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/sparse/SparseBincount.java @@ -42,8 +42,6 @@ * the value in {@code weights} at each index where the corresponding value in {@code arr} is * {@code i}. *

    Values in {@code arr} outside of the range [0, size) are ignored. - * - * @param data type for {@code output} output */ @OpMetadata( opType = SparseBincount.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/sparse/SparseConcat.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/sparse/SparseConcat.java index 6d53b3a723b..016f010647b 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/sparse/SparseConcat.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/sparse/SparseConcat.java @@ -74,8 +74,6 @@ * [ a] concat [ d e ] = [ a d e ] * [b c ] [ ] [b c ] * - * - * @param data type for {@code output_values} output */ @OpMetadata( opType = SparseConcat.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/sparse/SparseCountSparseOutput.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/sparse/SparseCountSparseOutput.java index c3983444bd3..4c59b4e2774 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/sparse/SparseCountSparseOutput.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/sparse/SparseCountSparseOutput.java @@ -37,8 +37,6 @@ /** * Performs sparse-output bin counting for a sparse tensor input. * Counts the number of times each value occurs in the input. - * - * @param data type for {@code output_values} output */ @OpMetadata( opType = SparseCountSparseOutput.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/sparse/SparseDenseCwiseAdd.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/sparse/SparseDenseCwiseAdd.java index 261d292d3b0..10ac8721d98 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/sparse/SparseDenseCwiseAdd.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/sparse/SparseDenseCwiseAdd.java @@ -43,8 +43,6 @@ *

    By these rules, the result is a logical SparseTensor with exactly the same * indices and shape, but possibly with different non-zero values. The output of * this Op is the resultant non-zero values. - * - * @param data type for {@code output} output */ @OpMetadata( opType = SparseDenseCwiseAdd.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/sparse/SparseDenseCwiseDiv.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/sparse/SparseDenseCwiseDiv.java index e0b56d6827c..724997892b1 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/sparse/SparseDenseCwiseDiv.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/sparse/SparseDenseCwiseDiv.java @@ -38,8 +38,6 @@ * Component-wise divides a SparseTensor by a dense Tensor. * Limitation: this Op only broadcasts the dense side to the sparse side, but not * the other direction. - * - * @param data type for {@code output} output */ @OpMetadata( opType = SparseDenseCwiseDiv.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/sparse/SparseDenseCwiseMul.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/sparse/SparseDenseCwiseMul.java index 3fb7a03c683..fe8386f0838 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/sparse/SparseDenseCwiseMul.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/sparse/SparseDenseCwiseMul.java @@ -41,8 +41,6 @@ * contents of the dense tensor (even if it's +/-INF and that INF*0 == NaN). *

    Limitation: this Op only broadcasts the dense side to the sparse side, but not * the other direction. - * - * @param data type for {@code output} output */ @OpMetadata( opType = SparseDenseCwiseMul.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/sparse/SparseFillEmptyRows.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/sparse/SparseFillEmptyRows.java index 989fda03492..ef0d2f85afa 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/sparse/SparseFillEmptyRows.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/sparse/SparseFillEmptyRows.java @@ -71,8 +71,6 @@ * 

      * reverse_index_map[j] = out_j s.t. indices[j, :] == output_indices[out_j, :]
  *
    - * - * @param data type for {@code output_values} output */ @OpMetadata( opType = SparseFillEmptyRows.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/sparse/SparseFillEmptyRowsGrad.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/sparse/SparseFillEmptyRowsGrad.java index 21d4e2f099f..3b1c80bb5b1 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/sparse/SparseFillEmptyRowsGrad.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/sparse/SparseFillEmptyRowsGrad.java @@ -43,8 +43,6 @@ *

    d_values[j] = grad_values[reverse_index_map[j]] * d_default_value = sum_{k : 0 .. N_full - 1} ( * grad_values[k] * 1{k not in reverse_index_map}) - * - * @param data type for {@code d_values} output */ @OpMetadata( opType = SparseFillEmptyRowsGrad.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/sparse/SparseReduceMax.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/sparse/SparseReduceMax.java index 1e48a53ea82..256695f0acd 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/sparse/SparseReduceMax.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/sparse/SparseReduceMax.java @@ -47,8 +47,6 @@ *

    If {@code reduction_axes} has no entries, all dimensions are reduced, and a tensor * with a single element is returned. Additionally, the axes can be negative, * which are interpreted according to the indexing rules in Python. - * - * @param data type for {@code output} output */ @OpMetadata( opType = SparseReduceMax.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/sparse/SparseReduceMaxSparse.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/sparse/SparseReduceMaxSparse.java index 8f337f0c19e..b0a65daea67 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/sparse/SparseReduceMaxSparse.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/sparse/SparseReduceMaxSparse.java @@ -47,8 +47,6 @@ *

    If {@code reduction_axes} has no entries, all dimensions are reduced, and a tensor * with a single element is returned. Additionally, the axes can be negative, * which are interpreted according to the indexing rules in Python. - * - * @param data type for {@code output_values} output */ @OpMetadata( opType = SparseReduceMaxSparse.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/sparse/SparseReduceSum.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/sparse/SparseReduceSum.java index 26e0ecbfc45..3589487bece 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/sparse/SparseReduceSum.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/sparse/SparseReduceSum.java @@ -47,8 +47,6 @@ *

    If {@code reduction_axes} has no entries, all dimensions are reduced, and a tensor * with a single element is returned. Additionally, the axes can be negative, * which are interpreted according to the indexing rules in Python. - * - * @param data type for {@code output} output */ @OpMetadata( opType = SparseReduceSum.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/sparse/SparseReduceSumSparse.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/sparse/SparseReduceSumSparse.java index bb434694ccf..ef58eac0af1 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/sparse/SparseReduceSumSparse.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/sparse/SparseReduceSumSparse.java @@ -47,8 +47,6 @@ *

    If {@code reduction_axes} has no entries, all dimensions are reduced, and a tensor * with a single element is returned. Additionally, the axes can be negative, * which are interpreted according to the indexing rules in Python. - * - * @param data type for {@code output_values} output */ @OpMetadata( opType = SparseReduceSumSparse.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/sparse/SparseReorder.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/sparse/SparseReorder.java index 9e963285d77..4e2883435f9 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/sparse/SparseReorder.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/sparse/SparseReorder.java @@ -42,8 +42,6 @@ *

    Reordering does not affect the shape of the SparseTensor. *

    If the tensor has rank {@code R} and {@code N} non-empty values, {@code input_indices} has * shape {@code [N, R]}, input_values has length {@code N}, and input_shape has length {@code R}. - * - * @param data type for {@code output_values} output */ @OpMetadata( opType = SparseReorder.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/sparse/SparseSegmentMean.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/sparse/SparseSegmentMean.java index c1899b2fbf6..4703ba10fca 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/sparse/SparseSegmentMean.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/sparse/SparseSegmentMean.java @@ -38,8 +38,6 @@ * See {@code tf.sparse.segment_sum} for usage examples. *

    Like {@code SegmentMean}, but {@code segment_ids} can have rank less than {@code data}'s first * dimension, selecting a subset of dimension 0, specified by {@code indices}. - * - * @param data type for {@code output} output */ @OpMetadata( opType = SparseSegmentMean.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/sparse/SparseSegmentMeanGrad.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/sparse/SparseSegmentMeanGrad.java index 50f29512a23..9da8038eee9 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/sparse/SparseSegmentMeanGrad.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/sparse/SparseSegmentMeanGrad.java @@ -39,10 +39,6 @@ * Returns tensor "output" with same shape as grad, except for dimension 0 whose * value is the number of unique indexes in "indices". Also returns vector * "sorted_unique_indices" containing the corresponding indexes from "indices". - * - * @param data type for {@code output} output - * - * @param data type for {@code sorted_unique_indices} output */ @OpMetadata( opType = SparseSegmentMeanGrad.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/sparse/SparseSegmentMeanWithNumSegments.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/sparse/SparseSegmentMeanWithNumSegments.java index d1c0e07c099..99cf33231a5 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/sparse/SparseSegmentMeanWithNumSegments.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/sparse/SparseSegmentMeanWithNumSegments.java @@ -40,8 +40,6 @@ *

    Read * the section on segmentation * for an explanation of segments. - * - * @param data type for {@code output} output */ @OpMetadata( opType = SparseSegmentMeanWithNumSegments.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/sparse/SparseSegmentSqrtN.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/sparse/SparseSegmentSqrtN.java index ee0dc4238fc..5e299d7d124 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/sparse/SparseSegmentSqrtN.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/sparse/SparseSegmentSqrtN.java @@ -37,8 +37,6 @@ * Computes the sum along sparse segments of a tensor divided by the sqrt of N. * N is the size of the segment being reduced. *

    See {@code tf.sparse.segment_sum} for usage examples. - * - * @param data type for {@code output} output */ @OpMetadata( opType = SparseSegmentSqrtN.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/sparse/SparseSegmentSqrtNGrad.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/sparse/SparseSegmentSqrtNGrad.java index 075cbacbcfb..b458c7daff9 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/sparse/SparseSegmentSqrtNGrad.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/sparse/SparseSegmentSqrtNGrad.java @@ -39,10 +39,6 @@ * Returns tensor "output" with same shape as grad, except for dimension 0 whose * value is the number of unique indexes in "indices". Also returns vector * "sorted_unique_indices" containing the corresponding indexes from "indices". - * - * @param data type for {@code output} output - * - * @param data type for {@code sorted_unique_indices} output */ @OpMetadata( opType = SparseSegmentSqrtNGrad.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/sparse/SparseSegmentSqrtNWithNumSegments.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/sparse/SparseSegmentSqrtNWithNumSegments.java index 84ccc501312..146dd696d6e 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/sparse/SparseSegmentSqrtNWithNumSegments.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/sparse/SparseSegmentSqrtNWithNumSegments.java @@ -41,8 +41,6 @@ *

    Read * the section on segmentation * for an explanation of segments. - * - * @param data type for {@code output} output */ @OpMetadata( opType = SparseSegmentSqrtNWithNumSegments.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/sparse/SparseSegmentSum.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/sparse/SparseSegmentSum.java index cf2ce2c9851..2f28386d05c 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/sparse/SparseSegmentSum.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/sparse/SparseSegmentSum.java @@ -61,8 +61,6 @@ * # Which is equivalent to: * tf.segment_sum(c, tf.constant([0, 0, 1])) * - * - * @param data type for {@code output} output */ @OpMetadata( opType = SparseSegmentSum.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/sparse/SparseSegmentSumGrad.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/sparse/SparseSegmentSumGrad.java index 71b8f92448e..1372d6f7089 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/sparse/SparseSegmentSumGrad.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/sparse/SparseSegmentSumGrad.java @@ -39,10 +39,6 @@ * Returns tensor "output" with same shape as grad, except for dimension 0 whose * value is the number of unique indexes in "indices". Also returns vector * "sorted_unique_indices" containing the corresponding indexes from "indices". - * - * @param data type for {@code output} output - * - * @param data type for {@code sorted_unique_indices} output */ @OpMetadata( opType = SparseSegmentSumGrad.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/sparse/SparseSegmentSumWithNumSegments.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/sparse/SparseSegmentSumWithNumSegments.java index 4c44377244d..88b577afec1 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/sparse/SparseSegmentSumWithNumSegments.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/sparse/SparseSegmentSumWithNumSegments.java @@ -59,8 +59,6 @@ * # [-1 -2 -3 -4] * # [ 0 0 0 0]] * - * - * @param data type for {@code output} output */ @OpMetadata( opType = SparseSegmentSumWithNumSegments.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/sparse/SparseSlice.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/sparse/SparseSlice.java index 58c794dfb2f..a3718f1a7e0 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/sparse/SparseSlice.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/sparse/SparseSlice.java @@ -52,8 +52,6 @@ * [ d e ] * [ ] * - * - * @param data type for {@code output_values} output */ @OpMetadata( opType = SparseSlice.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/sparse/SparseSliceGrad.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/sparse/SparseSliceGrad.java index 4cfa41a7e45..969ef935dc7 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/sparse/SparseSliceGrad.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/sparse/SparseSliceGrad.java @@ -39,8 +39,6 @@ * This op takes in the upstream gradient w.r.t. non-empty values of * the sliced {@code SparseTensor}, and outputs the gradients w.r.t. * the non-empty values of input {@code SparseTensor}. - * - * @param data type for {@code val_grad} output */ @OpMetadata( opType = SparseSliceGrad.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/sparse/SparseSoftmax.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/sparse/SparseSoftmax.java index be61533da26..43cd85b5a9f 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/sparse/SparseSoftmax.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/sparse/SparseSoftmax.java @@ -48,8 +48,6 @@ * (3) Renormalizes the remaining elements. *

    Hence, the {@code SparseTensor} result has exactly the same non-zero indices and * shape. - * - * @param data type for {@code output} output */ @OpMetadata( opType = SparseSoftmax.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/sparse/SparseSparseMaximum.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/sparse/SparseSparseMaximum.java index 22a1d407274..80b44623ca8 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/sparse/SparseSparseMaximum.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/sparse/SparseSparseMaximum.java @@ -37,8 +37,6 @@ /** * Returns the element-wise max of two SparseTensors. * Assumes the two SparseTensors have the same shape, i.e., no broadcasting. - * - * @param data type for {@code output_values} output */ @OpMetadata( opType = SparseSparseMaximum.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/sparse/SparseSparseMinimum.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/sparse/SparseSparseMinimum.java index 8dd8978c627..ecbc022d09d 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/sparse/SparseSparseMinimum.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/sparse/SparseSparseMinimum.java @@ -37,8 +37,6 @@ /** * Returns the element-wise min of two SparseTensors. * Assumes the two SparseTensors have the same shape, i.e., no broadcasting. - * - * @param data type for {@code output_values} output */ @OpMetadata( opType = SparseSparseMinimum.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/sparse/SparseSplit.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/sparse/SparseSplit.java index a09e9ff9d38..da66d34d134 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/sparse/SparseSplit.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/sparse/SparseSplit.java @@ -55,8 +55,6 @@ * [ d e ] * [ ] * - * - * @param data type for {@code output_values} output */ @OpMetadata( opType = SparseSplit.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/sparse/SparseTensorDenseAdd.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/sparse/SparseTensorDenseAdd.java index c153cf68776..7f73769030b 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/sparse/SparseTensorDenseAdd.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/sparse/SparseTensorDenseAdd.java @@ -37,8 +37,6 @@ /** * Adds up a {@code SparseTensor} and a dense {@code Tensor}, producing a dense {@code Tensor}. * This Op does not require {@code a_indices} be sorted in standard lexicographic order. - * - * @param data type for {@code output} output */ @OpMetadata( opType = SparseTensorDenseAdd.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/sparse/SparseTensorDenseMatMul.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/sparse/SparseTensorDenseMatMul.java index 346c9297596..0425354268c 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/sparse/SparseTensorDenseMatMul.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/sparse/SparseTensorDenseMatMul.java @@ -45,8 +45,6 @@ * if adjoint_a == true: * A should be sorted in order of increasing dimension 1 (i.e., "column major" * order instead of "row major" order). - * - * @param data type for {@code product} output */ @OpMetadata( opType = SparseTensorDenseMatMul.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/sparse/SparseToDense.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/sparse/SparseToDense.java index 95c8f189d48..448a7c4ec83 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/sparse/SparseToDense.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/sparse/SparseToDense.java @@ -52,8 +52,6 @@ *

    Indices should be sorted in lexicographic order, and indices must not * contain any repeats. If {@code validate_indices} is true, these properties * are checked during execution. - * - * @param data type for {@code dense} output */ @OpMetadata( opType = SparseToDense.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/sparse/SparseToSparseSetOperation.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/sparse/SparseToSparseSetOperation.java index 8a71016a669..e658f88abb7 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/sparse/SparseToSparseSetOperation.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/sparse/SparseToSparseSetOperation.java @@ -54,8 +54,6 @@ * has rank {@code n} and the same 1st {@code n-1} dimensions as {@code set1} and {@code set2}. The {@code nth} * dimension contains the result of {@code set_operation} applied to the corresponding * {@code [0...n-1]} dimension of {@code set}. - * - * @param data type for {@code result_values} output */ @OpMetadata( opType = SparseToSparseSetOperation.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/sparse/TakeManySparseFromTensorsMap.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/sparse/TakeManySparseFromTensorsMap.java index e72ec904466..2c6293f402d 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/sparse/TakeManySparseFromTensorsMap.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/sparse/TakeManySparseFromTensorsMap.java @@ -77,8 +77,6 @@ * values = [1, 2, 3, 4, 5] * shape = [2 50] * - * - * @param data type for {@code sparse_values} output */ @OpMetadata( opType = TakeManySparseFromTensorsMap.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/strings/StringNGrams.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/strings/StringNGrams.java index 6f5739989d0..c04fa6cd987 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/strings/StringNGrams.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/strings/StringNGrams.java @@ -40,8 +40,6 @@ * This op accepts a ragged tensor with 1 ragged dimension containing only * strings and outputs a ragged tensor with 1 ragged dimension containing ngrams * of that string, joined along the innermost axis. - * - * @param data type for {@code ngrams_splits} output */ @OpMetadata( opType = StringNGrams.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/strings/ToNumber.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/strings/ToNumber.java index e4564334bf1..74e4816ed43 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/strings/ToNumber.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/strings/ToNumber.java @@ -50,8 +50,6 @@ * * * - * - * @param data type for {@code output} output */ @OpMetadata( opType = ToNumber.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/strings/UnicodeDecode.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/strings/UnicodeDecode.java index bffb35e17e0..40624c66adf 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/strings/UnicodeDecode.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/strings/UnicodeDecode.java @@ -52,8 +52,6 @@ *

  • {@code row_splits[i+1] - row_splits[i]} is the number of characters in the {@code i}th * string (in row-major order).
    • * - * - * @param data type for {@code row_splits} output */ @OpMetadata( opType = UnicodeDecode.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/strings/UnicodeDecodeWithOffsets.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/strings/UnicodeDecodeWithOffsets.java index 690789b6843..5989e8e7106 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/strings/UnicodeDecodeWithOffsets.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/strings/UnicodeDecodeWithOffsets.java @@ -56,8 +56,6 @@ *
  • {@code row_splits[i+1] - row_splits[i]} is the number of characters in the {@code i}th * string (in row-major order).
    • * - * - * @param data type for {@code row_splits} output */ @OpMetadata( opType = UnicodeDecodeWithOffsets.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/tpu/AllToAll.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/tpu/AllToAll.java index dfe6664886c..3bd1592cbc7 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/tpu/AllToAll.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/tpu/AllToAll.java @@ -49,8 +49,6 @@ * split_count=2 *

    replica 0's output: {@code [[A], [C]]} * replica 1's output: {@code [[B], [D]]} - * - * @param data type for {@code output} output */ @OpMetadata( opType = AllToAll.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/tpu/ComputeDedupDataSize.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/tpu/ComputeDedupDataSize.java index 4305affd43e..6ff27567e92 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/tpu/ComputeDedupDataSize.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/tpu/ComputeDedupDataSize.java @@ -29,7 +29,6 @@ import org.tensorflow.op.annotation.Endpoint; import org.tensorflow.op.annotation.OpInputsMetadata; import org.tensorflow.op.annotation.OpMetadata; -import org.tensorflow.op.annotation.Operator; import org.tensorflow.types.TInt32; /** @@ -42,14 +41,11 @@ opType = ComputeDedupDataSize.OP_NAME, inputsClass = ComputeDedupDataSize.Inputs.class ) -@Operator( - group = "tpu" -) public final class ComputeDedupDataSize extends RawOp implements Operand { /** * The name of this op, as known by TensorFlow core engine */ - public static final String OP_NAME = "ComputeDedupDataSize"; + public static final String OP_NAME = "ComputeDedupDataSizeV2"; private Output numElements; @@ -60,18 +56,25 @@ public ComputeDedupDataSize(Operation operation) { } /** - * Factory method to create a class wrapping a new ComputeDedupDataSize operation. + * Factory method to create a class wrapping a new ComputeDedupDataSizeV2 operation. * * @param scope current scope * @param config Serialized TPUEmbeddingConfiguration proto. + * @param embeddingPartitions Serialized EmbeddingPartitionsProto proto. + * @param hbmBuffersConfig Serialized HbmBuffersConfig proto. + * @param tpuTopology Serialized TpuTopologyArgsProto proto. * @return a new instance of ComputeDedupDataSize */ @Endpoint( describeByClass = true ) - public static ComputeDedupDataSize create(Scope scope, String config) { + public static ComputeDedupDataSize create(Scope scope, String config, String embeddingPartitions, + String hbmBuffersConfig, String tpuTopology) { OperationBuilder opBuilder = scope.opBuilder(OP_NAME, "ComputeDedupDataSize"); opBuilder.setAttr("config", config); + opBuilder.setAttr("embedding_partitions", embeddingPartitions); + opBuilder.setAttr("hbm_buffers_config", hbmBuffersConfig); + opBuilder.setAttr("tpu_topology", tpuTopology); return new ComputeDedupDataSize(opBuilder.build()); } @@ -98,10 +101,28 @@ public static class Inputs extends RawOpInputs { */ public final String config; + /** + * Serialized EmbeddingPartitionsProto proto. + */ + public final String embeddingPartitions; + + /** + * Serialized HbmBuffersConfig proto. + */ + public final String hbmBuffersConfig; + + /** + * Serialized TpuTopologyArgsProto proto. + */ + public final String tpuTopology; + public Inputs(GraphOperation op) { - super(new ComputeDedupDataSize(op), op, Arrays.asList("config")); + super(new ComputeDedupDataSize(op), op, Arrays.asList("config", "embedding_partitions", "hbm_buffers_config", "tpu_topology")); int inputIndex = 0; config = op.attributes().getAttrString("config"); + embeddingPartitions = op.attributes().getAttrString("embedding_partitions"); + hbmBuffersConfig = op.attributes().getAttrString("hbm_buffers_config"); + tpuTopology = op.attributes().getAttrString("tpu_topology"); } } } diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/tpu/ComputeDedupDataTupleMask.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/tpu/ComputeDedupDataTupleMask.java index 95078aebabc..1160a8536a2 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/tpu/ComputeDedupDataTupleMask.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/tpu/ComputeDedupDataTupleMask.java @@ -29,7 +29,6 @@ import org.tensorflow.op.annotation.Endpoint; import org.tensorflow.op.annotation.OpInputsMetadata; import org.tensorflow.op.annotation.OpMetadata; -import org.tensorflow.op.annotation.Operator; import org.tensorflow.types.TInt32; /** @@ -42,14 +41,11 @@ opType = ComputeDedupDataTupleMask.OP_NAME, inputsClass = ComputeDedupDataTupleMask.Inputs.class ) -@Operator( - group = "tpu" -) public final class ComputeDedupDataTupleMask extends RawOp implements Operand { /** * The name of this op, as known by TensorFlow core engine */ - public static final String OP_NAME = "ComputeDedupDataTupleMask"; + public static final String OP_NAME = "ComputeDedupDataTupleMaskV2"; private Output outputShape; @@ -60,18 +56,25 @@ public ComputeDedupDataTupleMask(Operation operation) { } /** - * Factory method to create a class wrapping a new ComputeDedupDataTupleMask operation. + * Factory method to create a class wrapping a new ComputeDedupDataTupleMaskV2 operation. * * @param scope current scope * @param config Serialized TPUEmbeddingConfiguration proto. + * @param embeddingPartitions Serialized EmbeddingPartitionsProto proto. + * @param hbmBuffersConfig Serialized HbmBuffersConfig proto. + * @param tpuTopology Serialized TpuTopologyArgsProto proto. * @return a new instance of ComputeDedupDataTupleMask */ @Endpoint( describeByClass = true ) - public static ComputeDedupDataTupleMask create(Scope scope, String config) { + public static ComputeDedupDataTupleMask create(Scope scope, String config, + String embeddingPartitions, String hbmBuffersConfig, String tpuTopology) { OperationBuilder opBuilder = scope.opBuilder(OP_NAME, "ComputeDedupDataTupleMask"); opBuilder.setAttr("config", config); + opBuilder.setAttr("embedding_partitions", embeddingPartitions); + opBuilder.setAttr("hbm_buffers_config", hbmBuffersConfig); + opBuilder.setAttr("tpu_topology", tpuTopology); return new ComputeDedupDataTupleMask(opBuilder.build()); } @@ -103,10 +106,28 @@ public static class Inputs extends RawOpInputs { */ public final String config; + /** + * Serialized EmbeddingPartitionsProto proto. + */ + public final String embeddingPartitions; + + /** + * Serialized HbmBuffersConfig proto. + */ + public final String hbmBuffersConfig; + + /** + * Serialized TpuTopologyArgsProto proto. + */ + public final String tpuTopology; + public Inputs(GraphOperation op) { - super(new ComputeDedupDataTupleMask(op), op, Arrays.asList("config")); + super(new ComputeDedupDataTupleMask(op), op, Arrays.asList("config", "embedding_partitions", "hbm_buffers_config", "tpu_topology")); int inputIndex = 0; config = op.attributes().getAttrString("config"); + embeddingPartitions = op.attributes().getAttrString("embedding_partitions"); + hbmBuffersConfig = op.attributes().getAttrString("hbm_buffers_config"); + tpuTopology = op.attributes().getAttrString("tpu_topology"); } } } diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/tpu/CrossReplicaSum.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/tpu/CrossReplicaSum.java index c56e985eafb..15e942cac31 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/tpu/CrossReplicaSum.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/tpu/CrossReplicaSum.java @@ -41,8 +41,6 @@ * Passing group_assignment={@code [[0,2,4,6],[1,3,5,7]]} sets {@code A, C, E, G} as group 0, * and {@code B, D, F, H} as group 1. Thus we get the outputs: * {@code [A+C+E+G, B+D+F+H, A+C+E+G, B+D+F+H, A+C+E+G, B+D+F+H, A+C+E+G, B+D+F+H]}. - * - * @param data type for {@code output} output */ @OpMetadata( opType = CrossReplicaSum.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/tpu/FinalizeTPUEmbedding.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/tpu/FinalizeTPUEmbedding.java index 6ce405ea522..db44a52d05b 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/tpu/FinalizeTPUEmbedding.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/tpu/FinalizeTPUEmbedding.java @@ -22,6 +22,7 @@ import org.tensorflow.Operand; import org.tensorflow.Operation; import org.tensorflow.OperationBuilder; +import org.tensorflow.Output; import org.tensorflow.op.RawOp; import org.tensorflow.op.RawOpInputs; import org.tensorflow.op.Scope; @@ -45,14 +46,21 @@ public final class FinalizeTPUEmbedding extends RawOp { /** * The name of this op, as known by TensorFlow core engine */ - public static final String OP_NAME = "FinalizeTPUEmbedding"; + public static final String OP_NAME = "FinalizeTPUEmbeddingV2"; + + private Output embeddingPartitions; + + private Output hbmBuffersConfig; public FinalizeTPUEmbedding(Operation operation) { super(operation, OP_NAME); + int outputIdx = 0; + embeddingPartitions = operation.output(outputIdx++); + hbmBuffersConfig = operation.output(outputIdx++); } /** - * Factory method to create a class wrapping a new FinalizeTPUEmbedding operation. + * Factory method to create a class wrapping a new FinalizeTPUEmbeddingV2 operation. * * @param scope current scope * @param commonConfig A string-encoded common configuration proto containing metadata @@ -73,6 +81,26 @@ public static FinalizeTPUEmbedding create(Scope scope, Operand commonCo return new FinalizeTPUEmbedding(opBuilder.build()); } + /** + * Gets embeddingPartitions. + * A string-encoded embedding partitions proto describing how embedding tables are + * partitioned along their feature and ID. + * @return embeddingPartitions. + */ + public Output embeddingPartitions() { + return embeddingPartitions; + } + + /** + * Gets hbmBuffersConfig. + * A string-encoded HBM buffers config proto specifies where HBM buffers are + * located. + * @return hbmBuffersConfig. + */ + public Output hbmBuffersConfig() { + return hbmBuffersConfig; + } + @OpInputsMetadata( outputsClass = FinalizeTPUEmbedding.class ) diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/tpu/GetTpuTaskId.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/tpu/GetTpuTaskId.java new file mode 100644 index 00000000000..c4eb00be8dd --- /dev/null +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/tpu/GetTpuTaskId.java @@ -0,0 +1,97 @@ +/* Copyright 2018-2023 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +=======================================================================*/ + +// This class has been generated, DO NOT EDIT! + +package org.tensorflow.op.tpu; + +import java.util.Arrays; +import org.tensorflow.GraphOperation; +import org.tensorflow.Operand; +import org.tensorflow.Operation; +import org.tensorflow.OperationBuilder; +import org.tensorflow.Output; +import org.tensorflow.op.RawOp; +import org.tensorflow.op.RawOpInputs; +import org.tensorflow.op.Scope; +import org.tensorflow.op.annotation.Endpoint; +import org.tensorflow.op.annotation.OpInputsMetadata; +import org.tensorflow.op.annotation.OpMetadata; +import org.tensorflow.op.annotation.Operator; +import org.tensorflow.types.TInt32; + +/** + * An op returns the TPU task ID from TPU topology. + * This op is to return the TPU task ID from TPU topology. + */ +@OpMetadata( + opType = GetTpuTaskId.OP_NAME, + inputsClass = GetTpuTaskId.Inputs.class +) +@Operator( + group = "tpu" +) +public final class GetTpuTaskId extends RawOp implements Operand { + /** + * The name of this op, as known by TensorFlow core engine + */ + public static final String OP_NAME = "GetTpuTaskId"; + + private Output tpuTaskId; + + public GetTpuTaskId(Operation operation) { + super(operation, OP_NAME); + int outputIdx = 0; + tpuTaskId = operation.output(outputIdx++); + } + + /** + * Factory method to create a class wrapping a new GetTpuTaskId operation. + * + * @param scope current scope + * @return a new instance of GetTpuTaskId + */ + @Endpoint( + describeByClass = true + ) + public static GetTpuTaskId create(Scope scope) { + OperationBuilder opBuilder = scope.opBuilder(OP_NAME, "GetTpuTaskId"); + return new GetTpuTaskId(opBuilder.build()); + } + + /** + * Gets tpuTaskId. + * The TPU task ID from TPU topology. + * @return tpuTaskId. + */ + public Output tpuTaskId() { + return tpuTaskId; + } + + @Override + public Output asOutput() { + return tpuTaskId; + } + + @OpInputsMetadata( + outputsClass = GetTpuTaskId.class + ) + public static class Inputs extends RawOpInputs { + public Inputs(GraphOperation op) { + super(new GetTpuTaskId(op), op, Arrays.asList()); + int inputIndex = 0; + } + } +} diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/tpu/InfeedDequeue.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/tpu/InfeedDequeue.java index 20e200e26af..2f2d689a23a 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/tpu/InfeedDequeue.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/tpu/InfeedDequeue.java @@ -37,8 +37,6 @@ /** * A placeholder op for a value that will be fed into the computation. - * - * @param data type for {@code output} output */ @OpMetadata( opType = InfeedDequeue.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/tpu/OutfeedDequeue.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/tpu/OutfeedDequeue.java index 27a9edc8214..f2043c5047c 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/tpu/OutfeedDequeue.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/tpu/OutfeedDequeue.java @@ -38,8 +38,6 @@ /** * Retrieves a single tensor from the computation outfeed. * This operation will block indefinitely until data is available. - * - * @param data type for {@code output} output */ @OpMetadata( opType = OutfeedDequeue.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/tpu/OutfeedDequeueV2.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/tpu/OutfeedDequeueV2.java index 481f916e86a..dc0d6a3649a 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/tpu/OutfeedDequeueV2.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/tpu/OutfeedDequeueV2.java @@ -40,8 +40,6 @@ * Retrieves a single tensor from the computation outfeed. Device ordinal is a * tensor allowing dynamic outfeed. * This operation will block indefinitely until data is available. - * - * @param data type for {@code output} output */ @OpMetadata( opType = OutfeedDequeueV2.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/tpu/PartitionedInput.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/tpu/PartitionedInput.java index be69029e573..89d11541c1b 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/tpu/PartitionedInput.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/tpu/PartitionedInput.java @@ -37,8 +37,6 @@ /** * An op that groups a list of partitioned inputs together. Supports ND sharding. - * - * @param data type for {@code output} output */ @OpMetadata( opType = PartitionedInput.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/tpu/PartitionedOutput.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/tpu/PartitionedOutput.java index a49b96f066d..b69bdea9a7b 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/tpu/PartitionedOutput.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/tpu/PartitionedOutput.java @@ -38,8 +38,6 @@ /** * An op that demultiplexes a tensor to be sharded by XLA to a list of partitioned * outputs outside the XLA computation. Supports ND sharding. - * - * @param data type for {@code output} output */ @OpMetadata( opType = PartitionedOutput.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/tpu/ReplicatedInput.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/tpu/ReplicatedInput.java index 37c057fc375..5f5ae14be0e 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/tpu/ReplicatedInput.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/tpu/ReplicatedInput.java @@ -46,8 +46,6 @@ * %computation = "tf.Computation"(%replicated_input) * *

    The above computation has a replicated input of two replicas. - * - * @param data type for {@code output} output */ @OpMetadata( opType = ReplicatedInput.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/tpu/ReplicatedOutput.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/tpu/ReplicatedOutput.java index fcc447fb932..6daab9ae1a2 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/tpu/ReplicatedOutput.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/tpu/ReplicatedOutput.java @@ -45,8 +45,6 @@ * %replicated_output:2 = "tf.TPUReplicatedOutput"(%computation) * *

    The above computation has a replicated output of two replicas. - * - * @param data type for {@code outputs} output */ @OpMetadata( opType = ReplicatedOutput.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/tpu/SplitDedupData.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/tpu/SplitDedupData.java index ad72b480077..8e8d4537dff 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/tpu/SplitDedupData.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/tpu/SplitDedupData.java @@ -41,10 +41,6 @@ * Deduplication data is an XLA tuple, which consists of integer and floating point * values. This op is to split these values into two groups for two types, and * construct each group as one tensor to return. - * - * @param data type for {@code integer_tensor} output - * - * @param data type for {@code float_tensor} output */ @OpMetadata( opType = SplitDedupData.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/tpu/TPUReplicatedInput.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/tpu/TPUReplicatedInput.java index 1816bb842df..80ac7e3ea03 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/tpu/TPUReplicatedInput.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/tpu/TPUReplicatedInput.java @@ -47,8 +47,6 @@ * *

    The above computation has a replicated input of two replicas. * - * @param data type for {@code output} output - * * @deprecated use {@link org.tensorflow.op.tpu.ReplicatedInput} instead */ @OpMetadata( diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/tpu/TPUReplicatedOutput.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/tpu/TPUReplicatedOutput.java index ea53c36f109..dcc1b12b2b8 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/tpu/TPUReplicatedOutput.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/tpu/TPUReplicatedOutput.java @@ -46,8 +46,6 @@ * *

    The above computation has a replicated output of two replicas. * - * @param data type for {@code outputs} output - * * @deprecated use {@link org.tensorflow.op.tpu.ReplicatedOutput} instead */ @OpMetadata( diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/tpu/UpdateTaskIdAndGlobalCoreArray.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/tpu/UpdateTaskIdAndGlobalCoreArray.java new file mode 100644 index 00000000000..1a0fb866178 --- /dev/null +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/tpu/UpdateTaskIdAndGlobalCoreArray.java @@ -0,0 +1,86 @@ +/* Copyright 2018-2023 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +=======================================================================*/ + +// This class has been generated, DO NOT EDIT! + +package org.tensorflow.op.tpu; + +import java.util.Arrays; +import org.tensorflow.GraphOperation; +import org.tensorflow.Operand; +import org.tensorflow.Operation; +import org.tensorflow.OperationBuilder; +import org.tensorflow.op.Operands; +import org.tensorflow.op.RawOp; +import org.tensorflow.op.RawOpInputs; +import org.tensorflow.op.Scope; +import org.tensorflow.op.annotation.Endpoint; +import org.tensorflow.op.annotation.OpInputsMetadata; +import org.tensorflow.op.annotation.OpMetadata; +import org.tensorflow.types.TInt32; + +/** + * An op to update the task ID and global core array. + * This op is to update the task ID and global core array. + */ +@OpMetadata( + opType = UpdateTaskIdAndGlobalCoreArray.OP_NAME, + inputsClass = UpdateTaskIdAndGlobalCoreArray.Inputs.class +) +public final class UpdateTaskIdAndGlobalCoreArray extends RawOp { + /** + * The name of this op, as known by TensorFlow core engine + */ + public static final String OP_NAME = "UpdateTaskIdAndGlobalCoreArray"; + + public UpdateTaskIdAndGlobalCoreArray(Operation operation) { + super(operation, OP_NAME); + } + + /** + * Factory method to create a class wrapping a new UpdateTaskIdAndGlobalCoreArray operation. + * + * @param scope current scope + * @param tpuTaskIdToShardId An array of int32 that maps TPU task ID to shard ID. + * @return a new instance of UpdateTaskIdAndGlobalCoreArray + */ + @Endpoint( + describeByClass = true + ) + public static UpdateTaskIdAndGlobalCoreArray create(Scope scope, + Iterable> tpuTaskIdToShardId) { + OperationBuilder opBuilder = scope.opBuilder(OP_NAME, "UpdateTaskIdAndGlobalCoreArray"); + opBuilder.addInputList(Operands.asOutputs(tpuTaskIdToShardId)); + return new UpdateTaskIdAndGlobalCoreArray(opBuilder.build()); + } + + @OpInputsMetadata( + outputsClass = UpdateTaskIdAndGlobalCoreArray.class + ) + public static class Inputs extends RawOpInputs { + /** + * An array of int32 that maps TPU task ID to shard ID. + */ + public final Iterable> tpuTaskIdToShardId; + + public Inputs(GraphOperation op) { + super(new UpdateTaskIdAndGlobalCoreArray(op), op, Arrays.asList()); + int inputIndex = 0; + int tpuTaskIdToShardIdLength = op.inputListLength("tpu_task_id_to_shard_id"); + tpuTaskIdToShardId = Arrays.asList((Operand[]) op.inputList(inputIndex, tpuTaskIdToShardIdLength)); + inputIndex += tpuTaskIdToShardIdLength; + } + } +} diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/train/AccumulatorTakeGradient.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/train/AccumulatorTakeGradient.java index a2d152ab93e..e7c94866732 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/train/AccumulatorTakeGradient.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/train/AccumulatorTakeGradient.java @@ -43,8 +43,6 @@ * aggregated more than num_required gradients, it returns the average of * the accumulated gradients. Also automatically increments the recorded * global_step in the accumulator by 1, and resets the aggregate to 0. - * - * @param data type for {@code average} output */ @OpMetadata( opType = AccumulatorTakeGradient.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/train/ApplyAdaMax.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/train/ApplyAdaMax.java index 5a6b4fa2871..0bdb47444ad 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/train/ApplyAdaMax.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/train/ApplyAdaMax.java @@ -38,8 +38,6 @@ * m_t <- beta1 * m_{t-1} + (1 - beta1) * g * v_t <- max(beta2 * v_{t-1}, abs(g)) * variable <- variable - learning_rate / (1 - beta1^t) * m_t / (v_t + epsilon) - * - * @param data type for {@code out} output */ @OpMetadata( opType = ApplyAdaMax.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/train/ApplyAdadelta.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/train/ApplyAdadelta.java index be5bdc297ea..7d53245fe2a 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/train/ApplyAdadelta.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/train/ApplyAdadelta.java @@ -39,8 +39,6 @@ * update = (update_accum + epsilon).sqrt() * (accum + epsilon()).rsqrt() * grad; * update_accum = rho() * update_accum + (1 - rho()) * update.square(); * var -= update; - * - * @param data type for {@code out} output */ @OpMetadata( opType = ApplyAdadelta.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/train/ApplyAdagrad.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/train/ApplyAdagrad.java index 9a717cb0daf..0d243bfce4b 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/train/ApplyAdagrad.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/train/ApplyAdagrad.java @@ -37,8 +37,6 @@ * Update '*var' according to the adagrad scheme. * accum += grad * grad * var -= lr * grad * (1 / sqrt(accum)) - * - * @param data type for {@code out} output */ @OpMetadata( opType = ApplyAdagrad.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/train/ApplyAdagradDa.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/train/ApplyAdagradDa.java index b1577260bf8..a2769eae2e7 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/train/ApplyAdagradDa.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/train/ApplyAdagradDa.java @@ -36,8 +36,6 @@ /** * Update '*var' according to the proximal adagrad scheme. - * - * @param data type for {@code out} output */ @OpMetadata( opType = ApplyAdagradDa.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/train/ApplyAdagradV2.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/train/ApplyAdagradV2.java index 6766d80538e..22d0edd340e 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/train/ApplyAdagradV2.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/train/ApplyAdagradV2.java @@ -37,8 +37,6 @@ * Update '*var' according to the adagrad scheme. * accum += grad * grad * var -= lr * grad * (1 / sqrt(accum)) - * - * @param data type for {@code out} output */ @OpMetadata( opType = ApplyAdagradV2.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/train/ApplyAdam.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/train/ApplyAdam.java index 91dbb1d72f6..8dbd525dc98 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/train/ApplyAdam.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/train/ApplyAdam.java @@ -39,8 +39,6 @@ * $$m_t := \beta_1 \cdot m{t-1} + (1 - \beta_1) \cdot g$$ * $$v_t := \beta_2 \cdot v_{t-1} + (1 - \beta_2) \cdot g^2$$ * $$\text{var} := \begin{cases} \text{var} - (m_t \beta_1 + g \cdot (1 - \beta_1))\cdot\text{lr}_t/(\sqrt{v_t} + \epsilon), &\text{if use_nesterov}\\ \text{var} - m_t \cdot \text{lr}_t /(\sqrt{v_t} + \epsilon), &\text{otherwise} \end{cases}$$ - * - * @param data type for {@code out} output */ @OpMetadata( opType = ApplyAdam.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/train/ApplyAddSign.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/train/ApplyAddSign.java index 434802b1590..69127231eb1 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/train/ApplyAddSign.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/train/ApplyAddSign.java @@ -38,8 +38,6 @@ * m_t <- beta1 * m_{t-1} + (1 - beta1) * g * update <- (alpha + sign_decay * sign(g) *sign(m)) * g * variable <- variable - lr_t * update - * - * @param data type for {@code out} output */ @OpMetadata( opType = ApplyAddSign.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/train/ApplyCenteredRmsProp.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/train/ApplyCenteredRmsProp.java index 46f9975e74a..f7801bf277e 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/train/ApplyCenteredRmsProp.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/train/ApplyCenteredRmsProp.java @@ -49,8 +49,6 @@ * ms <- rho * ms_{t-1} + (1-rho) * grad * grad * mom <- momentum * mom_{t-1} + lr * grad / sqrt(ms - mg * mg + epsilon) * var <- var - mom - * - * @param data type for {@code out} output */ @OpMetadata( opType = ApplyCenteredRmsProp.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/train/ApplyFtrl.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/train/ApplyFtrl.java index c14505600ef..cd010677d47 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/train/ApplyFtrl.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/train/ApplyFtrl.java @@ -42,8 +42,6 @@ * quadratic = 1.0 / (accum_new^(lr_power) * lr) + 2 * l2 * var = (sign(linear) * l1 - linear) / quadratic if |linear| > l1 else 0.0 * accum = accum_new - * - * @param data type for {@code out} output */ @OpMetadata( opType = ApplyFtrl.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/train/ApplyGradientDescent.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/train/ApplyGradientDescent.java index f7c93955d6b..5ebb7b31330 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/train/ApplyGradientDescent.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/train/ApplyGradientDescent.java @@ -35,8 +35,6 @@ /** * Update '*var' by subtracting 'alpha' * 'delta' from it. - * - * @param data type for {@code out} output */ @OpMetadata( opType = ApplyGradientDescent.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/train/ApplyMomentum.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/train/ApplyMomentum.java index fc82fa94853..1aa402b6783 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/train/ApplyMomentum.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/train/ApplyMomentum.java @@ -38,8 +38,6 @@ * Set use_nesterov = True if you want to use Nesterov momentum. *

    accum = accum * momentum + grad * var -= lr * accum - * - * @param data type for {@code out} output */ @OpMetadata( opType = ApplyMomentum.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/train/ApplyPowerSign.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/train/ApplyPowerSign.java index dad41ae5e50..f298f853be2 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/train/ApplyPowerSign.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/train/ApplyPowerSign.java @@ -38,8 +38,6 @@ * m_t <- beta1 * m_{t-1} + (1 - beta1) * g * update <- exp(logbase * sign_decay * sign(g) * sign(m_t)) * g * variable <- variable - lr_t * update - * - * @param data type for {@code out} output */ @OpMetadata( opType = ApplyPowerSign.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/train/ApplyProximalAdagrad.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/train/ApplyProximalAdagrad.java index 8f2c0b1d0b2..a095146963b 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/train/ApplyProximalAdagrad.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/train/ApplyProximalAdagrad.java @@ -38,8 +38,6 @@ * accum += grad * grad * prox_v = var - lr * grad * (1 / sqrt(accum)) * var = sign(prox_v)/(1+lrl2) * max{|prox_v|-lrl1,0} - * - * @param data type for {@code out} output */ @OpMetadata( opType = ApplyProximalAdagrad.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/train/ApplyProximalGradientDescent.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/train/ApplyProximalGradientDescent.java index 488faf4d559..ffd6ee70e68 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/train/ApplyProximalGradientDescent.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/train/ApplyProximalGradientDescent.java @@ -37,8 +37,6 @@ * Update '*var' as FOBOS algorithm with fixed learning rate. * prox_v = var - alpha * delta * var = sign(prox_v)/(1+alphal2) * max{|prox_v|-alphal1,0} - * - * @param data type for {@code out} output */ @OpMetadata( opType = ApplyProximalGradientDescent.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/train/ApplyRmsProp.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/train/ApplyRmsProp.java index 539fa33e176..fcfeb5b895a 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/train/ApplyRmsProp.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/train/ApplyRmsProp.java @@ -43,8 +43,6 @@ *

    ms <- rho * ms_{t-1} + (1-rho) * grad * grad * mom <- momentum * mom_{t-1} + lr * grad / sqrt(ms + epsilon) * var <- var - mom - * - * @param data type for {@code out} output */ @OpMetadata( opType = ApplyRmsProp.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/train/BatchMatMul.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/train/BatchMatMul.java index 14fdcd8d781..17560573705 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/train/BatchMatMul.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/train/BatchMatMul.java @@ -56,8 +56,6 @@ *

    NOTE: {@code train.BatchMatMul} supports broadcasting in the batch dimensions. More * about broadcasting * here . - * - * @param data type for {@code output} output */ @OpMetadata( opType = BatchMatMul.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/train/PreventGradient.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/train/PreventGradient.java index a7181e6cb0b..c98b11d0050 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/train/PreventGradient.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/train/PreventGradient.java @@ -41,8 +41,6 @@ * because no gradient must ever be registered for this function. This * op exists to prevent subtle bugs from silently returning unimplemented * gradients in some corner cases. - * - * @param data type for {@code output} output */ @OpMetadata( opType = PreventGradient.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/train/ResourceAccumulatorTakeGradient.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/train/ResourceAccumulatorTakeGradient.java index 4b7a918f597..843ecae89f1 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/train/ResourceAccumulatorTakeGradient.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/train/ResourceAccumulatorTakeGradient.java @@ -42,8 +42,6 @@ * aggregated more than num_required gradients, it returns the average of * the accumulated gradients. Also automatically increments the recorded * global_step in the accumulator by 1, and resets the aggregate to 0. - * - * @param data type for {@code average} output */ @OpMetadata( opType = ResourceAccumulatorTakeGradient.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/train/RestoreSlice.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/train/RestoreSlice.java index b0faba2454c..a33a34b3179 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/train/RestoreSlice.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/train/RestoreSlice.java @@ -42,8 +42,6 @@ * larger tensor and the slice that the restored tensor covers. *

    The {@code shape_and_slice} input has the same format as the * elements of the {@code shapes_and_slices} input of the {@code SaveSlices} op. - * - * @param data type for {@code tensor} output */ @OpMetadata( opType = RestoreSlice.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/train/SparseApplyAdadelta.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/train/SparseApplyAdadelta.java index c68618fecc1..8b12e83f51f 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/train/SparseApplyAdadelta.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/train/SparseApplyAdadelta.java @@ -36,8 +36,6 @@ /** * var: Should be from a Variable(). - * - * @param data type for {@code out} output */ @OpMetadata( opType = SparseApplyAdadelta.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/train/SparseApplyAdagrad.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/train/SparseApplyAdagrad.java index a75507dde54..fbda4c582a0 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/train/SparseApplyAdagrad.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/train/SparseApplyAdagrad.java @@ -39,8 +39,6 @@ * That is for rows we have grad for, we update var and accum as follows: * $$accum += grad * grad$$ * $$var -= lr * grad * (1 / sqrt(accum))$$ - * - * @param data type for {@code out} output */ @OpMetadata( opType = SparseApplyAdagrad.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/train/SparseApplyAdagradDa.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/train/SparseApplyAdagradDa.java index cdd24328bb6..33cdae176f0 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/train/SparseApplyAdagradDa.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/train/SparseApplyAdagradDa.java @@ -37,8 +37,6 @@ /** * Update entries in '*var' and '*accum' according to the proximal adagrad scheme. - * - * @param data type for {@code out} output */ @OpMetadata( opType = SparseApplyAdagradDa.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/train/SparseApplyCenteredRmsProp.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/train/SparseApplyCenteredRmsProp.java index 731ecdd88a7..cfbf01b8044 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/train/SparseApplyCenteredRmsProp.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/train/SparseApplyCenteredRmsProp.java @@ -49,8 +49,6 @@ *

    $$ms <- rho * ms_{t-1} + (1-rho) * grad * grad$$ * $$mom <- momentum * mom_{t-1} + lr * grad / sqrt(ms + epsilon)$$ * $$var <- var - mom$$ - * - * @param data type for {@code out} output */ @OpMetadata( opType = SparseApplyCenteredRmsProp.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/train/SparseApplyFtrl.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/train/SparseApplyFtrl.java index 8c609b198bd..72cce364480 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/train/SparseApplyFtrl.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/train/SparseApplyFtrl.java @@ -44,8 +44,6 @@ * quadratic = 1.0 / (accum_new^(lr_power) * lr) + 2 * l2 * var = (sign(linear) * l1 - linear) / quadratic if |linear| > l1 else 0.0 * accum = accum_new - * - * @param data type for {@code out} output */ @OpMetadata( opType = SparseApplyFtrl.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/train/SparseApplyMomentum.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/train/SparseApplyMomentum.java index 2e22789fd23..d2ae83d8c17 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/train/SparseApplyMomentum.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/train/SparseApplyMomentum.java @@ -40,8 +40,6 @@ *

    That is for rows we have grad for, we update var and accum as follows: *

    $$accum = accum * momentum + grad$$ * $$var -= lr * accum$$ - * - * @param data type for {@code out} output */ @OpMetadata( opType = SparseApplyMomentum.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/train/SparseApplyProximalAdagrad.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/train/SparseApplyProximalAdagrad.java index 68ca59089e1..70b28897f24 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/train/SparseApplyProximalAdagrad.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/train/SparseApplyProximalAdagrad.java @@ -41,8 +41,6 @@ * $$prox_v = var$$ * $$prox_v -= lr * grad * (1 / sqrt(accum))$$ * $$var = sign(prox_v)/(1+lrl2) * max{|prox_v|-lrl1,0}$$ - * - * @param data type for {@code out} output */ @OpMetadata( opType = SparseApplyProximalAdagrad.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/train/SparseApplyProximalGradientDescent.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/train/SparseApplyProximalGradientDescent.java index 08b098f80ca..3da972089e7 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/train/SparseApplyProximalGradientDescent.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/train/SparseApplyProximalGradientDescent.java @@ -39,8 +39,6 @@ * That is for rows we have grad for, we update var as follows: * $$prox_v = var - alpha * grad$$ * $$var = sign(prox_v)/(1+alphal2) * max{|prox_v|-alphal1,0}$$ - * - * @param data type for {@code out} output */ @OpMetadata( opType = SparseApplyProximalGradientDescent.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/train/SparseApplyRmsProp.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/train/SparseApplyRmsProp.java index a648dc04b08..3c642ebcf81 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/train/SparseApplyRmsProp.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/train/SparseApplyRmsProp.java @@ -44,8 +44,6 @@ *

    $$ms <- rho * ms_{t-1} + (1-rho) * grad * grad$$ * $$mom <- momentum * mom_{t-1} + lr * grad / sqrt(ms + epsilon)$$ * $$var <- var - mom$$ - * - * @param data type for {@code out} output */ @OpMetadata( opType = SparseApplyRmsProp.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/train/TileGrad.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/train/TileGrad.java index bdc5c23fc46..9e1b7e0fbb4 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/train/TileGrad.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/train/TileGrad.java @@ -39,8 +39,6 @@ * Since {@code Tile} takes an input and repeats the input {@code multiples} times * along each dimension, {@code train.TileGrad} takes in {@code multiples} and aggregates * each repeated tile of {@code input} into {@code output}. - * - * @param data type for {@code output} output */ @OpMetadata( opType = TileGrad.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/xla/AssignVariableConcatND.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/xla/AssignVariableConcatND.java index 51f000b2687..c58943ff50d 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/xla/AssignVariableConcatND.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/xla/AssignVariableConcatND.java @@ -88,18 +88,8 @@ public AssignVariableConcatND(Operation operation) { * * @param scope current scope * @param resource Resource variable for concatenated input tensors across all dimensions. - * } - * in_arg { - * name: "inputs" - * description: <<END - * Input tensor slices in row-major order to merge across all dimensions. All + * @param inputs Input tensor slices in row-major order to merge across all dimensions. All * inputs must have the same shape. - * } - * out_arg { - * name: "output" - * description: <<END - * Output tensor formed from merging input slices based on num_concats defined. - * @param inputs The inputs value * @param numConcats Number of ways to merge per dimension. * @param options carries optional attribute values * @return a new instance of AssignVariableConcatND @@ -197,22 +187,12 @@ public Options paddings(Long... paddings) { public static class Inputs extends RawOpInputs { /** * Resource variable for concatenated input tensors across all dimensions. - * } - * in_arg { - * name: "inputs" - * description: <<END - * Input tensor slices in row-major order to merge across all dimensions. All - * inputs must have the same shape. - * } - * out_arg { - * name: "output" - * description: <<END - * Output tensor formed from merging input slices based on num_concats defined. */ public final Operand resource; /** - * The inputs input + * Input tensor slices in row-major order to merge across all dimensions. All + * inputs must have the same shape. */ public final Iterable> inputs; diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/xla/ConcatND.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/xla/ConcatND.java index 7e55c95e679..5749305af89 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/xla/ConcatND.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/xla/ConcatND.java @@ -66,8 +66,6 @@ * [4, 5, 6], * [8, 9, 10]] * - * - * @param data type for {@code output} output */ @OpMetadata( opType = ConcatND.OP_NAME, @@ -96,11 +94,6 @@ public ConcatND(Operation operation) { * @param scope current scope * @param inputs Input tensor slices in row-major order to merge across all dimensions. All * inputs must have the same shape. - * } - * out_arg { - * name: "output" - * description: <<END - * Output tensor formed from merging input slices based on num_concats defined. * @param numConcats Number of ways to merge per dimension. * @param options carries optional attribute values * @param data type for {@code XlaConcatND} output and operands @@ -158,7 +151,7 @@ public static Options paddings(Long... paddings) { /** * Gets output. - * + * Output tensor formed from merging input slices based on num_concats defined. * @return output. */ public Output output() { @@ -213,11 +206,6 @@ public static class Inputs extends RawOpInputs> { /** * Input tensor slices in row-major order to merge across all dimensions. All * inputs must have the same shape. - * } - * out_arg { - * name: "output" - * description: <<END - * Output tensor formed from merging input slices based on num_concats defined. */ public final Iterable> inputs; diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/xla/ReadVariableSplitND.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/xla/ReadVariableSplitND.java index 666103dd273..9788f2927f0 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/xla/ReadVariableSplitND.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/xla/ReadVariableSplitND.java @@ -67,8 +67,6 @@ * [[8, 0], * [0, 0]] * - * - * @param data type for {@code outputs} output */ @OpMetadata( opType = ReadVariableSplitND.OP_NAME, @@ -99,11 +97,6 @@ public ReadVariableSplitND(Operation operation) { * * @param scope current scope * @param resource Resource variable of input tensor to split across all dimensions. - * } - * out_arg { - * name: "outputs" - * description: <<END - * Output slices based on input and num_splits defined, in row-major order. * @param T The value of the T attribute * @param N The value of the N attribute * @param numSplits Number of ways to split per dimension. Shape dimensions must be evenly @@ -165,7 +158,7 @@ public static Options paddings(Long... paddings) { /** * Gets outputs. - * + * Output slices based on input and num_splits defined, in row-major order. * @return outputs. */ public List> outputs() { @@ -218,11 +211,6 @@ public Options paddings(Long... paddings) { public static class Inputs extends RawOpInputs> { /** * Resource variable of input tensor to split across all dimensions. - * } - * out_arg { - * name: "outputs" - * description: <<END - * Output slices based on input and num_splits defined, in row-major order. */ public final Operand resource; diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/xla/SplitND.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/xla/SplitND.java index 6bf5656f68c..299b2f95437 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/xla/SplitND.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/xla/SplitND.java @@ -66,8 +66,6 @@ * [[8, 0], * [0, 0]] * - * - * @param data type for {@code outputs} output */ @OpMetadata( opType = SplitND.OP_NAME, @@ -98,11 +96,6 @@ public SplitND(Operation operation) { * * @param scope current scope * @param input Input tensor to split across all dimensions. - * } - * out_arg { - * name: "outputs" - * description: <<END - * Output slices based on input and num_splits defined, in row-major order. * @param N The value of the N attribute * @param numSplits Number of ways to split per dimension. Shape dimensions must be evenly * divisible. @@ -161,7 +154,7 @@ public static Options paddings(Long... paddings) { /** * Gets outputs. - * + * Output slices based on input and num_splits defined, in row-major order. * @return outputs. */ public List> outputs() { @@ -214,11 +207,6 @@ public Options paddings(Long... paddings) { public static class Inputs extends RawOpInputs> { /** * Input tensor to split across all dimensions. - * } - * out_arg { - * name: "outputs" - * description: <<END - * Output slices based on input and num_splits defined, in row-major order. */ public final Operand input; diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/xla/XlaRecvFromHost.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/xla/XlaRecvFromHost.java index c8d5507a673..b05f7199f7a 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/xla/XlaRecvFromHost.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/xla/XlaRecvFromHost.java @@ -41,8 +41,6 @@ * Toutput: element type for output. * shape: shape for output. * key: A unique identifier for this region used to match up host transfers. - * - * @param data type for {@code output} output */ @OpMetadata( opType = XlaRecvFromHost.OP_NAME, diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/xla/XlaRecvTPUEmbeddingActivations.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/xla/XlaRecvTPUEmbeddingActivations.java index 1af06a9de56..b3499a237f0 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/xla/XlaRecvTPUEmbeddingActivations.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/xla/XlaRecvTPUEmbeddingActivations.java @@ -31,7 +31,6 @@ import org.tensorflow.op.annotation.Endpoint; import org.tensorflow.op.annotation.OpInputsMetadata; import org.tensorflow.op.annotation.OpMetadata; -import org.tensorflow.op.annotation.Operator; import org.tensorflow.types.TFloat32; import org.tensorflow.types.family.TType; @@ -46,14 +45,11 @@ opType = XlaRecvTPUEmbeddingActivations.OP_NAME, inputsClass = XlaRecvTPUEmbeddingActivations.Inputs.class ) -@Operator( - group = "xla" -) public final class XlaRecvTPUEmbeddingActivations extends RawOp implements Iterable> { /** * The name of this op, as known by TensorFlow core engine */ - public static final String OP_NAME = "XlaRecvTPUEmbeddingActivations"; + public static final String OP_NAME = "XlaRecvTPUEmbeddingActivationsV2"; private List> outputs; @@ -67,7 +63,7 @@ public XlaRecvTPUEmbeddingActivations(Operation operation) { } /** - * Factory method to create a class wrapping a new XlaRecvTPUEmbeddingActivations operation. + * Factory method to create a class wrapping a new XlaRecvTPUEmbeddingActivationsV2 operation. * * @param scope current scope * @param deduplicationData A Tensor with type=DT_VARIANT containing the deduplication @@ -80,17 +76,24 @@ public XlaRecvTPUEmbeddingActivations(Operation operation) { * present in the tpu embedding config, it is equal to the number of features * otherwise equal to number of embedding tables in the model. * @param config Serialized TPUEmbeddingConfiguration proto. + * @param embeddingPartitions Serialized EmbeddingPartitionsProto proto. + * @param hbmBuffersConfig Serialized HbmBuffersConfig proto. + * @param tpuTopology Serialized TpuTopologyArgsProto proto. * @return a new instance of XlaRecvTPUEmbeddingActivations */ @Endpoint( describeByClass = true ) public static XlaRecvTPUEmbeddingActivations create(Scope scope, - Operand deduplicationData, Long numTables, String config) { + Operand deduplicationData, Long numTables, String config, + String embeddingPartitions, String hbmBuffersConfig, String tpuTopology) { OperationBuilder opBuilder = scope.opBuilder(OP_NAME, "XlaRecvTPUEmbeddingActivations"); opBuilder.addInput(deduplicationData.asOutput()); opBuilder.setAttr("num_tables", numTables); opBuilder.setAttr("config", config); + opBuilder.setAttr("embedding_partitions", embeddingPartitions); + opBuilder.setAttr("hbm_buffers_config", hbmBuffersConfig); + opBuilder.setAttr("tpu_topology", tpuTopology); return new XlaRecvTPUEmbeddingActivations(opBuilder.build()); } @@ -129,11 +132,29 @@ public static class Inputs extends RawOpInputs { */ public final String config; + /** + * Serialized EmbeddingPartitionsProto proto. + */ + public final String embeddingPartitions; + + /** + * Serialized HbmBuffersConfig proto. + */ + public final String hbmBuffersConfig; + + /** + * Serialized TpuTopologyArgsProto proto. + */ + public final String tpuTopology; + public Inputs(GraphOperation op) { - super(new XlaRecvTPUEmbeddingActivations(op), op, Arrays.asList("config")); + super(new XlaRecvTPUEmbeddingActivations(op), op, Arrays.asList("config", "embedding_partitions", "hbm_buffers_config", "tpu_topology")); int inputIndex = 0; deduplicationData = (Operand) op.input(inputIndex++); config = op.attributes().getAttrString("config"); + embeddingPartitions = op.attributes().getAttrString("embedding_partitions"); + hbmBuffersConfig = op.attributes().getAttrString("hbm_buffers_config"); + tpuTopology = op.attributes().getAttrString("tpu_topology"); } } } diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/xla/XlaRecvTPUEmbeddingDeduplicationData.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/xla/XlaRecvTPUEmbeddingDeduplicationData.java index abf76b9a0ad..a0c18fb338c 100644 --- a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/xla/XlaRecvTPUEmbeddingDeduplicationData.java +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/xla/XlaRecvTPUEmbeddingDeduplicationData.java @@ -29,7 +29,6 @@ import org.tensorflow.op.annotation.Endpoint; import org.tensorflow.op.annotation.OpInputsMetadata; import org.tensorflow.op.annotation.OpMetadata; -import org.tensorflow.op.annotation.Operator; import org.tensorflow.types.family.TType; /** @@ -45,14 +44,11 @@ opType = XlaRecvTPUEmbeddingDeduplicationData.OP_NAME, inputsClass = XlaRecvTPUEmbeddingDeduplicationData.Inputs.class ) -@Operator( - group = "xla" -) public final class XlaRecvTPUEmbeddingDeduplicationData extends RawOp implements Operand { /** * The name of this op, as known by TensorFlow core engine */ - public static final String OP_NAME = "XlaRecvTPUEmbeddingDeduplicationData"; + public static final String OP_NAME = "XlaRecvTPUEmbeddingDeduplicationDataV2"; private Output output; @@ -64,18 +60,25 @@ public XlaRecvTPUEmbeddingDeduplicationData(Operation operation) { } /** - * Factory method to create a class wrapping a new XlaRecvTPUEmbeddingDeduplicationData operation. + * Factory method to create a class wrapping a new XlaRecvTPUEmbeddingDeduplicationDataV2 operation. * * @param scope current scope * @param config Serialized TPUEmbeddingConfiguration proto. + * @param embeddingPartitions Serialized EmbeddingPartitionsProto proto. + * @param hbmBuffersConfig Serialized HbmBuffersConfig proto. + * @param tpuTopology Serialized TpuTopologyArgsProto proto. * @return a new instance of XlaRecvTPUEmbeddingDeduplicationData */ @Endpoint( describeByClass = true ) - public static XlaRecvTPUEmbeddingDeduplicationData create(Scope scope, String config) { + public static XlaRecvTPUEmbeddingDeduplicationData create(Scope scope, String config, + String embeddingPartitions, String hbmBuffersConfig, String tpuTopology) { OperationBuilder opBuilder = scope.opBuilder(OP_NAME, "XlaRecvTPUEmbeddingDeduplicationData"); opBuilder.setAttr("config", config); + opBuilder.setAttr("embedding_partitions", embeddingPartitions); + opBuilder.setAttr("hbm_buffers_config", hbmBuffersConfig); + opBuilder.setAttr("tpu_topology", tpuTopology); return new XlaRecvTPUEmbeddingDeduplicationData(opBuilder.build()); } @@ -103,10 +106,28 @@ public static class Inputs extends RawOpInputs> gradients, Iterable> learningRates, - Operand deduplicationData, String config, Options... options) { + Operand deduplicationData, String config, String embeddingPartitions, + String hbmBuffersConfig, String tpuTopology, Options... options) { OperationBuilder opBuilder = scope.opBuilder(OP_NAME, "XlaSendTPUEmbeddingGradients"); opBuilder.addInputList(Operands.asOutputs(gradients)); opBuilder.addInputList(Operands.asOutputs(learningRates)); opBuilder.addInput(deduplicationData.asOutput()); opBuilder.setAttr("config", config); + opBuilder.setAttr("embedding_partitions", embeddingPartitions); + opBuilder.setAttr("hbm_buffers_config", hbmBuffersConfig); + opBuilder.setAttr("tpu_topology", tpuTopology); if (options != null) { for (Options opts : options) { if (opts.NumLearningRateTags != null) { @@ -161,8 +164,23 @@ public static class Inputs extends RawOpInputs { */ public final String config; + /** + * Serialized EmbeddingPartitionsProto proto. + */ + public final String embeddingPartitions; + + /** + * Serialized HbmBuffersConfig proto. + */ + public final String hbmBuffersConfig; + + /** + * Serialized TpuTopologyArgsProto proto. + */ + public final String tpuTopology; + public Inputs(GraphOperation op) { - super(new XlaSendTPUEmbeddingGradients(op), op, Arrays.asList("config")); + super(new XlaSendTPUEmbeddingGradients(op), op, Arrays.asList("config", "embedding_partitions", "hbm_buffers_config", "tpu_topology")); int inputIndex = 0; int gradientsLength = op.inputListLength("gradients"); gradients = Arrays.asList((Operand[]) op.inputList(inputIndex, gradientsLength)); @@ -172,6 +190,9 @@ public Inputs(GraphOperation op) { inputIndex += learningRatesLength; deduplicationData = (Operand) op.input(inputIndex++); config = op.attributes().getAttrString("config"); + embeddingPartitions = op.attributes().getAttrString("embedding_partitions"); + hbmBuffersConfig = op.attributes().getAttrString("hbm_buffers_config"); + tpuTopology = op.attributes().getAttrString("tpu_topology"); } } } diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/xla/XlaSparseDenseMatmulGradWithAdagradAndStaticBufferSize.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/xla/XlaSparseDenseMatmulGradWithAdagradAndStaticBufferSize.java new file mode 100644 index 00000000000..f8ab65a6ee1 --- /dev/null +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/xla/XlaSparseDenseMatmulGradWithAdagradAndStaticBufferSize.java @@ -0,0 +1,279 @@ +/* Copyright 2018-2023 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +=======================================================================*/ + +// This class has been generated, DO NOT EDIT! + +package org.tensorflow.op.xla; + +import java.util.Arrays; +import org.tensorflow.GraphOperation; +import org.tensorflow.Operand; +import org.tensorflow.Operation; +import org.tensorflow.OperationBuilder; +import org.tensorflow.Output; +import org.tensorflow.op.RawOp; +import org.tensorflow.op.RawOpInputs; +import org.tensorflow.op.Scope; +import org.tensorflow.op.annotation.Endpoint; +import org.tensorflow.op.annotation.OpInputsMetadata; +import org.tensorflow.op.annotation.OpMetadata; +import org.tensorflow.types.TFloat32; +import org.tensorflow.types.TInt32; + +/** + * The XlaSparseDenseMatmulGradWithAdagradAndStaticBufferSize operation + */ +@OpMetadata( + opType = XlaSparseDenseMatmulGradWithAdagradAndStaticBufferSize.OP_NAME, + inputsClass = XlaSparseDenseMatmulGradWithAdagradAndStaticBufferSize.Inputs.class +) +public final class XlaSparseDenseMatmulGradWithAdagradAndStaticBufferSize extends RawOp { + /** + * The name of this op, as known by TensorFlow core engine + */ + public static final String OP_NAME = "XlaSparseDenseMatmulGradWithAdagradAndStaticBufferSize"; + + private Output updatedEmbeddingTable; + + private Output updatedAccumulator; + + public XlaSparseDenseMatmulGradWithAdagradAndStaticBufferSize(Operation operation) { + super(operation, OP_NAME); + int outputIdx = 0; + updatedEmbeddingTable = operation.output(outputIdx++); + updatedAccumulator = operation.output(outputIdx++); + } + + /** + * Factory method to create a class wrapping a new XlaSparseDenseMatmulGradWithAdagradAndStaticBufferSize operation. + * + * @param scope current scope + * @param rowPointers The rowPointers value + * @param sortedSampleIds The sortedSampleIds value + * @param sortedTokenIds The sortedTokenIds value + * @param sortedGains The sortedGains value + * @param activationGradients The activationGradients value + * @param learningRate The learningRate value + * @param embeddingTable The embeddingTable value + * @param accumulator The accumulator value + * @param numMinibatchesPerPhysicalSparseCore The numMinibatchesPerPhysicalSparseCore value + * @param maxIdsPerSparseCore The value of the maxIdsPerSparseCore attribute + * @param maxUniqueIdsPerSparseCore The value of the maxUniqueIdsPerSparseCore attribute + * @param tableName The value of the tableName attribute + * @param options carries optional attribute values + * @return a new instance of XlaSparseDenseMatmulGradWithAdagradAndStaticBufferSize + */ + @Endpoint( + describeByClass = true + ) + public static XlaSparseDenseMatmulGradWithAdagradAndStaticBufferSize create(Scope scope, + Operand rowPointers, Operand sortedSampleIds, Operand sortedTokenIds, + Operand sortedGains, Operand activationGradients, + Operand learningRate, Operand embeddingTable, + Operand accumulator, Operand numMinibatchesPerPhysicalSparseCore, + Long maxIdsPerSparseCore, Long maxUniqueIdsPerSparseCore, String tableName, + Options... options) { + OperationBuilder opBuilder = scope.opBuilder(OP_NAME, "XlaSparseDenseMatmulGradWithAdagradAndStaticBufferSize"); + opBuilder.addInput(rowPointers.asOutput()); + opBuilder.addInput(sortedSampleIds.asOutput()); + opBuilder.addInput(sortedTokenIds.asOutput()); + opBuilder.addInput(sortedGains.asOutput()); + opBuilder.addInput(activationGradients.asOutput()); + opBuilder.addInput(learningRate.asOutput()); + opBuilder.addInput(embeddingTable.asOutput()); + opBuilder.addInput(accumulator.asOutput()); + opBuilder.addInput(numMinibatchesPerPhysicalSparseCore.asOutput()); + opBuilder.setAttr("max_ids_per_sparse_core", maxIdsPerSparseCore); + opBuilder.setAttr("max_unique_ids_per_sparse_core", maxUniqueIdsPerSparseCore); + opBuilder.setAttr("table_name", tableName); + if (options != null) { + for (Options opts : options) { + if (opts.clipWeightMin != null) { + opBuilder.setAttr("clip_weight_min", opts.clipWeightMin); + } + if (opts.clipWeightMax != null) { + opBuilder.setAttr("clip_weight_max", opts.clipWeightMax); + } + } + } + return new XlaSparseDenseMatmulGradWithAdagradAndStaticBufferSize(opBuilder.build()); + } + + /** + * Sets the clipWeightMin option. + * + * @param clipWeightMin the clipWeightMin option + * @return this Options instance. + */ + public static Options clipWeightMin(Float clipWeightMin) { + return new Options().clipWeightMin(clipWeightMin); + } + + /** + * Sets the clipWeightMax option. + * + * @param clipWeightMax the clipWeightMax option + * @return this Options instance. + */ + public static Options clipWeightMax(Float clipWeightMax) { + return new Options().clipWeightMax(clipWeightMax); + } + + /** + * Gets updatedEmbeddingTable. + * + * @return updatedEmbeddingTable. + */ + public Output updatedEmbeddingTable() { + return updatedEmbeddingTable; + } + + /** + * Gets updatedAccumulator. + * + * @return updatedAccumulator. + */ + public Output updatedAccumulator() { + return updatedAccumulator; + } + + /** + * Optional attributes for {@link org.tensorflow.op.xla.XlaSparseDenseMatmulGradWithAdagradAndStaticBufferSize} + */ + public static class Options { + private Float clipWeightMin; + + private Float clipWeightMax; + + private Options() { + } + + /** + * Sets the clipWeightMin option. + * + * @param clipWeightMin the clipWeightMin option + * @return this Options instance. + */ + public Options clipWeightMin(Float clipWeightMin) { + this.clipWeightMin = clipWeightMin; + return this; + } + + /** + * Sets the clipWeightMax option. + * + * @param clipWeightMax the clipWeightMax option + * @return this Options instance. + */ + public Options clipWeightMax(Float clipWeightMax) { + this.clipWeightMax = clipWeightMax; + return this; + } + } + + @OpInputsMetadata( + outputsClass = XlaSparseDenseMatmulGradWithAdagradAndStaticBufferSize.class + ) + public static class Inputs extends RawOpInputs { + /** + * The rowPointers input + */ + public final Operand rowPointers; + + /** + * The sortedSampleIds input + */ + public final Operand sortedSampleIds; + + /** + * The sortedTokenIds input + */ + public final Operand sortedTokenIds; + + /** + * The sortedGains input + */ + public final Operand sortedGains; + + /** + * The activationGradients input + */ + public final Operand activationGradients; + + /** + * The learningRate input + */ + public final Operand learningRate; + + /** + * The embeddingTable input + */ + public final Operand embeddingTable; + + /** + * The accumulator input + */ + public final Operand accumulator; + + /** + * The numMinibatchesPerPhysicalSparseCore input + */ + public final Operand numMinibatchesPerPhysicalSparseCore; + + /** + * The clipWeightMin attribute + */ + public final float clipWeightMin; + + /** + * The clipWeightMax attribute + */ + public final float clipWeightMax; + + /** + * The maxIdsPerSparseCore attribute + */ + public final long maxIdsPerSparseCore; + + /** + * The maxUniqueIdsPerSparseCore attribute + */ + public final long maxUniqueIdsPerSparseCore; + + /** + * The tableName attribute + */ + public final String tableName; + + public Inputs(GraphOperation op) { + super(new XlaSparseDenseMatmulGradWithAdagradAndStaticBufferSize(op), op, Arrays.asList("clip_weight_min", "clip_weight_max", "max_ids_per_sparse_core", "max_unique_ids_per_sparse_core", "table_name")); + int inputIndex = 0; + rowPointers = (Operand) op.input(inputIndex++); + sortedSampleIds = (Operand) op.input(inputIndex++); + sortedTokenIds = (Operand) op.input(inputIndex++); + sortedGains = (Operand) op.input(inputIndex++); + activationGradients = (Operand) op.input(inputIndex++); + learningRate = (Operand) op.input(inputIndex++); + embeddingTable = (Operand) op.input(inputIndex++); + accumulator = (Operand) op.input(inputIndex++); + numMinibatchesPerPhysicalSparseCore = (Operand) op.input(inputIndex++); + clipWeightMin = op.attributes().getAttrFloat("clip_weight_min"); + clipWeightMax = op.attributes().getAttrFloat("clip_weight_max"); + maxIdsPerSparseCore = op.attributes().getAttrInt("max_ids_per_sparse_core"); + maxUniqueIdsPerSparseCore = op.attributes().getAttrInt("max_unique_ids_per_sparse_core"); + tableName = op.attributes().getAttrString("table_name"); + } + } +} diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/xla/XlaSparseDenseMatmulGradWithAdagradMomentumAndStaticBufferSize.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/xla/XlaSparseDenseMatmulGradWithAdagradMomentumAndStaticBufferSize.java new file mode 100644 index 00000000000..e007641d72a --- /dev/null +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/xla/XlaSparseDenseMatmulGradWithAdagradMomentumAndStaticBufferSize.java @@ -0,0 +1,340 @@ +/* Copyright 2018-2023 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +=======================================================================*/ + +// This class has been generated, DO NOT EDIT! + +package org.tensorflow.op.xla; + +import java.util.Arrays; +import org.tensorflow.GraphOperation; +import org.tensorflow.Operand; +import org.tensorflow.Operation; +import org.tensorflow.OperationBuilder; +import org.tensorflow.Output; +import org.tensorflow.op.RawOp; +import org.tensorflow.op.RawOpInputs; +import org.tensorflow.op.Scope; +import org.tensorflow.op.annotation.Endpoint; +import org.tensorflow.op.annotation.OpInputsMetadata; +import org.tensorflow.op.annotation.OpMetadata; +import org.tensorflow.types.TFloat32; +import org.tensorflow.types.TInt32; + +/** + * The XlaSparseDenseMatmulGradWithAdagradMomentumAndStaticBufferSize operation + */ +@OpMetadata( + opType = XlaSparseDenseMatmulGradWithAdagradMomentumAndStaticBufferSize.OP_NAME, + inputsClass = XlaSparseDenseMatmulGradWithAdagradMomentumAndStaticBufferSize.Inputs.class +) +public final class XlaSparseDenseMatmulGradWithAdagradMomentumAndStaticBufferSize extends RawOp { + /** + * The name of this op, as known by TensorFlow core engine + */ + public static final String OP_NAME = "XlaSparseDenseMatmulGradWithAdagradMomentumAndStaticBufferSize"; + + private Output updatedEmbeddingTable; + + private Output updatedAccumulator; + + private Output updatedMomenta; + + public XlaSparseDenseMatmulGradWithAdagradMomentumAndStaticBufferSize(Operation operation) { + super(operation, OP_NAME); + int outputIdx = 0; + updatedEmbeddingTable = operation.output(outputIdx++); + updatedAccumulator = operation.output(outputIdx++); + updatedMomenta = operation.output(outputIdx++); + } + + /** + * Factory method to create a class wrapping a new XlaSparseDenseMatmulGradWithAdagradMomentumAndStaticBufferSize operation. + * + * @param scope current scope + * @param rowPointers The rowPointers value + * @param sortedSampleIds The sortedSampleIds value + * @param sortedTokenIds The sortedTokenIds value + * @param sortedGains The sortedGains value + * @param activationGradients The activationGradients value + * @param learningRate The learningRate value + * @param embeddingTable The embeddingTable value + * @param accumulator The accumulator value + * @param momenta The momenta value + * @param numMinibatchesPerPhysicalSparseCore The numMinibatchesPerPhysicalSparseCore value + * @param useNesterov The value of the useNesterov attribute + * @param exponent The value of the exponent attribute + * @param beta1 The value of the beta1 attribute + * @param beta2 The value of the beta2 attribute + * @param epsilon The value of the epsilon attribute + * @param maxIdsPerSparseCore The value of the maxIdsPerSparseCore attribute + * @param maxUniqueIdsPerSparseCore The value of the maxUniqueIdsPerSparseCore attribute + * @param tableName The value of the tableName attribute + * @param options carries optional attribute values + * @return a new instance of XlaSparseDenseMatmulGradWithAdagradMomentumAndStaticBufferSize + */ + @Endpoint( + describeByClass = true + ) + public static XlaSparseDenseMatmulGradWithAdagradMomentumAndStaticBufferSize create(Scope scope, + Operand rowPointers, Operand sortedSampleIds, Operand sortedTokenIds, + Operand sortedGains, Operand activationGradients, + Operand learningRate, Operand embeddingTable, + Operand accumulator, Operand momenta, + Operand numMinibatchesPerPhysicalSparseCore, Boolean useNesterov, Float exponent, + Float beta1, Float beta2, Float epsilon, Long maxIdsPerSparseCore, + Long maxUniqueIdsPerSparseCore, String tableName, Options... options) { + OperationBuilder opBuilder = scope.opBuilder(OP_NAME, "XlaSparseDenseMatmulGradWithAdagradMomentumAndStaticBufferSize"); + opBuilder.addInput(rowPointers.asOutput()); + opBuilder.addInput(sortedSampleIds.asOutput()); + opBuilder.addInput(sortedTokenIds.asOutput()); + opBuilder.addInput(sortedGains.asOutput()); + opBuilder.addInput(activationGradients.asOutput()); + opBuilder.addInput(learningRate.asOutput()); + opBuilder.addInput(embeddingTable.asOutput()); + opBuilder.addInput(accumulator.asOutput()); + opBuilder.addInput(momenta.asOutput()); + opBuilder.addInput(numMinibatchesPerPhysicalSparseCore.asOutput()); + opBuilder.setAttr("use_nesterov", useNesterov); + opBuilder.setAttr("exponent", exponent); + opBuilder.setAttr("beta1", beta1); + opBuilder.setAttr("beta2", beta2); + opBuilder.setAttr("epsilon", epsilon); + opBuilder.setAttr("max_ids_per_sparse_core", maxIdsPerSparseCore); + opBuilder.setAttr("max_unique_ids_per_sparse_core", maxUniqueIdsPerSparseCore); + opBuilder.setAttr("table_name", tableName); + if (options != null) { + for (Options opts : options) { + if (opts.clipWeightMin != null) { + opBuilder.setAttr("clip_weight_min", opts.clipWeightMin); + } + if (opts.clipWeightMax != null) { + opBuilder.setAttr("clip_weight_max", opts.clipWeightMax); + } + } + } + return new XlaSparseDenseMatmulGradWithAdagradMomentumAndStaticBufferSize(opBuilder.build()); + } + + /** + * Sets the clipWeightMin option. + * + * @param clipWeightMin the clipWeightMin option + * @return this Options instance. + */ + public static Options clipWeightMin(Float clipWeightMin) { + return new Options().clipWeightMin(clipWeightMin); + } + + /** + * Sets the clipWeightMax option. + * + * @param clipWeightMax the clipWeightMax option + * @return this Options instance. + */ + public static Options clipWeightMax(Float clipWeightMax) { + return new Options().clipWeightMax(clipWeightMax); + } + + /** + * Gets updatedEmbeddingTable. + * + * @return updatedEmbeddingTable. + */ + public Output updatedEmbeddingTable() { + return updatedEmbeddingTable; + } + + /** + * Gets updatedAccumulator. + * + * @return updatedAccumulator. + */ + public Output updatedAccumulator() { + return updatedAccumulator; + } + + /** + * Gets updatedMomenta. + * + * @return updatedMomenta. + */ + public Output updatedMomenta() { + return updatedMomenta; + } + + /** + * Optional attributes for {@link org.tensorflow.op.xla.XlaSparseDenseMatmulGradWithAdagradMomentumAndStaticBufferSize} + */ + public static class Options { + private Float clipWeightMin; + + private Float clipWeightMax; + + private Options() { + } + + /** + * Sets the clipWeightMin option. + * + * @param clipWeightMin the clipWeightMin option + * @return this Options instance. + */ + public Options clipWeightMin(Float clipWeightMin) { + this.clipWeightMin = clipWeightMin; + return this; + } + + /** + * Sets the clipWeightMax option. + * + * @param clipWeightMax the clipWeightMax option + * @return this Options instance. + */ + public Options clipWeightMax(Float clipWeightMax) { + this.clipWeightMax = clipWeightMax; + return this; + } + } + + @OpInputsMetadata( + outputsClass = XlaSparseDenseMatmulGradWithAdagradMomentumAndStaticBufferSize.class + ) + public static class Inputs extends RawOpInputs { + /** + * The rowPointers input + */ + public final Operand rowPointers; + + /** + * The sortedSampleIds input + */ + public final Operand sortedSampleIds; + + /** + * The sortedTokenIds input + */ + public final Operand sortedTokenIds; + + /** + * The sortedGains input + */ + public final Operand sortedGains; + + /** + * The activationGradients input + */ + public final Operand activationGradients; + + /** + * The learningRate input + */ + public final Operand learningRate; + + /** + * The embeddingTable input + */ + public final Operand embeddingTable; + + /** + * The accumulator input + */ + public final Operand accumulator; + + /** + * The momenta input + */ + public final Operand momenta; + + /** + * The numMinibatchesPerPhysicalSparseCore input + */ + public final Operand numMinibatchesPerPhysicalSparseCore; + + /** + * The useNesterov attribute + */ + public final boolean useNesterov; + + /** + * The exponent attribute + */ + public final float exponent; + + /** + * The beta1 attribute + */ + public final float beta1; + + /** + * The beta2 attribute + */ + public final float beta2; + + /** + * The epsilon attribute + */ + public final float epsilon; + + /** + * The clipWeightMin attribute + */ + public final float clipWeightMin; + + /** + * The clipWeightMax attribute + */ + public final float clipWeightMax; + + /** + * The maxIdsPerSparseCore attribute + */ + public final long maxIdsPerSparseCore; + + /** + * The maxUniqueIdsPerSparseCore attribute + */ + public final long maxUniqueIdsPerSparseCore; + + /** + * The tableName attribute + */ + public final String tableName; + + public Inputs(GraphOperation op) { + super(new XlaSparseDenseMatmulGradWithAdagradMomentumAndStaticBufferSize(op), op, Arrays.asList("use_nesterov", "exponent", "beta1", "beta2", "epsilon", "clip_weight_min", "clip_weight_max", "max_ids_per_sparse_core", "max_unique_ids_per_sparse_core", "table_name")); + int inputIndex = 0; + rowPointers = (Operand) op.input(inputIndex++); + sortedSampleIds = (Operand) op.input(inputIndex++); + sortedTokenIds = (Operand) op.input(inputIndex++); + sortedGains = (Operand) op.input(inputIndex++); + activationGradients = (Operand) op.input(inputIndex++); + learningRate = (Operand) op.input(inputIndex++); + embeddingTable = (Operand) op.input(inputIndex++); + accumulator = (Operand) op.input(inputIndex++); + momenta = (Operand) op.input(inputIndex++); + numMinibatchesPerPhysicalSparseCore = (Operand) op.input(inputIndex++); + useNesterov = op.attributes().getAttrBool("use_nesterov"); + exponent = op.attributes().getAttrFloat("exponent"); + beta1 = op.attributes().getAttrFloat("beta1"); + beta2 = op.attributes().getAttrFloat("beta2"); + epsilon = op.attributes().getAttrFloat("epsilon"); + clipWeightMin = op.attributes().getAttrFloat("clip_weight_min"); + clipWeightMax = op.attributes().getAttrFloat("clip_weight_max"); + maxIdsPerSparseCore = op.attributes().getAttrInt("max_ids_per_sparse_core"); + maxUniqueIdsPerSparseCore = op.attributes().getAttrInt("max_unique_ids_per_sparse_core"); + tableName = op.attributes().getAttrString("table_name"); + } + } +} diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/xla/XlaSparseDenseMatmulGradWithAdamAndStaticBufferSize.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/xla/XlaSparseDenseMatmulGradWithAdamAndStaticBufferSize.java new file mode 100644 index 00000000000..fe875c67f69 --- /dev/null +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/xla/XlaSparseDenseMatmulGradWithAdamAndStaticBufferSize.java @@ -0,0 +1,331 @@ +/* Copyright 2018-2023 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +=======================================================================*/ + +// This class has been generated, DO NOT EDIT! + +package org.tensorflow.op.xla; + +import java.util.Arrays; +import org.tensorflow.GraphOperation; +import org.tensorflow.Operand; +import org.tensorflow.Operation; +import org.tensorflow.OperationBuilder; +import org.tensorflow.Output; +import org.tensorflow.op.RawOp; +import org.tensorflow.op.RawOpInputs; +import org.tensorflow.op.Scope; +import org.tensorflow.op.annotation.Endpoint; +import org.tensorflow.op.annotation.OpInputsMetadata; +import org.tensorflow.op.annotation.OpMetadata; +import org.tensorflow.types.TFloat32; +import org.tensorflow.types.TInt32; + +/** + * The XlaSparseDenseMatmulGradWithAdamAndStaticBufferSize operation + */ +@OpMetadata( + opType = XlaSparseDenseMatmulGradWithAdamAndStaticBufferSize.OP_NAME, + inputsClass = XlaSparseDenseMatmulGradWithAdamAndStaticBufferSize.Inputs.class +) +public final class XlaSparseDenseMatmulGradWithAdamAndStaticBufferSize extends RawOp { + /** + * The name of this op, as known by TensorFlow core engine + */ + public static final String OP_NAME = "XlaSparseDenseMatmulGradWithAdamAndStaticBufferSize"; + + private Output updatedEmbeddingTable; + + private Output updatedMomenta; + + private Output updatedVelocity; + + public XlaSparseDenseMatmulGradWithAdamAndStaticBufferSize(Operation operation) { + super(operation, OP_NAME); + int outputIdx = 0; + updatedEmbeddingTable = operation.output(outputIdx++); + updatedMomenta = operation.output(outputIdx++); + updatedVelocity = operation.output(outputIdx++); + } + + /** + * Factory method to create a class wrapping a new XlaSparseDenseMatmulGradWithAdamAndStaticBufferSize operation. + * + * @param scope current scope + * @param rowPointers The rowPointers value + * @param sortedSampleIds The sortedSampleIds value + * @param sortedTokenIds The sortedTokenIds value + * @param sortedGains The sortedGains value + * @param activationGradients The activationGradients value + * @param learningRate The learningRate value + * @param embeddingTable The embeddingTable value + * @param momenta The momenta value + * @param velocity The velocity value + * @param numMinibatchesPerPhysicalSparseCore The numMinibatchesPerPhysicalSparseCore value + * @param useSumInsideSqrt The value of the useSumInsideSqrt attribute + * @param beta1 The value of the beta1 attribute + * @param beta2 The value of the beta2 attribute + * @param epsilon The value of the epsilon attribute + * @param maxIdsPerSparseCore The value of the maxIdsPerSparseCore attribute + * @param maxUniqueIdsPerSparseCore The value of the maxUniqueIdsPerSparseCore attribute + * @param tableName The value of the tableName attribute + * @param options carries optional attribute values + * @return a new instance of XlaSparseDenseMatmulGradWithAdamAndStaticBufferSize + */ + @Endpoint( + describeByClass = true + ) + public static XlaSparseDenseMatmulGradWithAdamAndStaticBufferSize create(Scope scope, + Operand rowPointers, Operand sortedSampleIds, Operand sortedTokenIds, + Operand sortedGains, Operand activationGradients, + Operand learningRate, Operand embeddingTable, Operand momenta, + Operand velocity, Operand numMinibatchesPerPhysicalSparseCore, + Boolean useSumInsideSqrt, Float beta1, Float beta2, Float epsilon, Long maxIdsPerSparseCore, + Long maxUniqueIdsPerSparseCore, String tableName, Options... options) { + OperationBuilder opBuilder = scope.opBuilder(OP_NAME, "XlaSparseDenseMatmulGradWithAdamAndStaticBufferSize"); + opBuilder.addInput(rowPointers.asOutput()); + opBuilder.addInput(sortedSampleIds.asOutput()); + opBuilder.addInput(sortedTokenIds.asOutput()); + opBuilder.addInput(sortedGains.asOutput()); + opBuilder.addInput(activationGradients.asOutput()); + opBuilder.addInput(learningRate.asOutput()); + opBuilder.addInput(embeddingTable.asOutput()); + opBuilder.addInput(momenta.asOutput()); + opBuilder.addInput(velocity.asOutput()); + opBuilder.addInput(numMinibatchesPerPhysicalSparseCore.asOutput()); + opBuilder.setAttr("use_sum_inside_sqrt", useSumInsideSqrt); + opBuilder.setAttr("beta1", beta1); + opBuilder.setAttr("beta2", beta2); + opBuilder.setAttr("epsilon", epsilon); + opBuilder.setAttr("max_ids_per_sparse_core", maxIdsPerSparseCore); + opBuilder.setAttr("max_unique_ids_per_sparse_core", maxUniqueIdsPerSparseCore); + opBuilder.setAttr("table_name", tableName); + if (options != null) { + for (Options opts : options) { + if (opts.clipWeightMin != null) { + opBuilder.setAttr("clip_weight_min", opts.clipWeightMin); + } + if (opts.clipWeightMax != null) { + opBuilder.setAttr("clip_weight_max", opts.clipWeightMax); + } + } + } + return new XlaSparseDenseMatmulGradWithAdamAndStaticBufferSize(opBuilder.build()); + } + + /** + * Sets the clipWeightMin option. + * + * @param clipWeightMin the clipWeightMin option + * @return this Options instance. + */ + public static Options clipWeightMin(Float clipWeightMin) { + return new Options().clipWeightMin(clipWeightMin); + } + + /** + * Sets the clipWeightMax option. + * + * @param clipWeightMax the clipWeightMax option + * @return this Options instance. + */ + public static Options clipWeightMax(Float clipWeightMax) { + return new Options().clipWeightMax(clipWeightMax); + } + + /** + * Gets updatedEmbeddingTable. + * + * @return updatedEmbeddingTable. + */ + public Output updatedEmbeddingTable() { + return updatedEmbeddingTable; + } + + /** + * Gets updatedMomenta. + * + * @return updatedMomenta. + */ + public Output updatedMomenta() { + return updatedMomenta; + } + + /** + * Gets updatedVelocity. + * + * @return updatedVelocity. + */ + public Output updatedVelocity() { + return updatedVelocity; + } + + /** + * Optional attributes for {@link org.tensorflow.op.xla.XlaSparseDenseMatmulGradWithAdamAndStaticBufferSize} + */ + public static class Options { + private Float clipWeightMin; + + private Float clipWeightMax; + + private Options() { + } + + /** + * Sets the clipWeightMin option. + * + * @param clipWeightMin the clipWeightMin option + * @return this Options instance. + */ + public Options clipWeightMin(Float clipWeightMin) { + this.clipWeightMin = clipWeightMin; + return this; + } + + /** + * Sets the clipWeightMax option. + * + * @param clipWeightMax the clipWeightMax option + * @return this Options instance. + */ + public Options clipWeightMax(Float clipWeightMax) { + this.clipWeightMax = clipWeightMax; + return this; + } + } + + @OpInputsMetadata( + outputsClass = XlaSparseDenseMatmulGradWithAdamAndStaticBufferSize.class + ) + public static class Inputs extends RawOpInputs { + /** + * The rowPointers input + */ + public final Operand rowPointers; + + /** + * The sortedSampleIds input + */ + public final Operand sortedSampleIds; + + /** + * The sortedTokenIds input + */ + public final Operand sortedTokenIds; + + /** + * The sortedGains input + */ + public final Operand sortedGains; + + /** + * The activationGradients input + */ + public final Operand activationGradients; + + /** + * The learningRate input + */ + public final Operand learningRate; + + /** + * The embeddingTable input + */ + public final Operand embeddingTable; + + /** + * The momenta input + */ + public final Operand momenta; + + /** + * The velocity input + */ + public final Operand velocity; + + /** + * The numMinibatchesPerPhysicalSparseCore input + */ + public final Operand numMinibatchesPerPhysicalSparseCore; + + /** + * The useSumInsideSqrt attribute + */ + public final boolean useSumInsideSqrt; + + /** + * The beta1 attribute + */ + public final float beta1; + + /** + * The beta2 attribute + */ + public final float beta2; + + /** + * The epsilon attribute + */ + public final float epsilon; + + /** + * The clipWeightMin attribute + */ + public final float clipWeightMin; + + /** + * The clipWeightMax attribute + */ + public final float clipWeightMax; + + /** + * The maxIdsPerSparseCore attribute + */ + public final long maxIdsPerSparseCore; + + /** + * The maxUniqueIdsPerSparseCore attribute + */ + public final long maxUniqueIdsPerSparseCore; + + /** + * The tableName attribute + */ + public final String tableName; + + public Inputs(GraphOperation op) { + super(new XlaSparseDenseMatmulGradWithAdamAndStaticBufferSize(op), op, Arrays.asList("use_sum_inside_sqrt", "beta1", "beta2", "epsilon", "clip_weight_min", "clip_weight_max", "max_ids_per_sparse_core", "max_unique_ids_per_sparse_core", "table_name")); + int inputIndex = 0; + rowPointers = (Operand) op.input(inputIndex++); + sortedSampleIds = (Operand) op.input(inputIndex++); + sortedTokenIds = (Operand) op.input(inputIndex++); + sortedGains = (Operand) op.input(inputIndex++); + activationGradients = (Operand) op.input(inputIndex++); + learningRate = (Operand) op.input(inputIndex++); + embeddingTable = (Operand) op.input(inputIndex++); + momenta = (Operand) op.input(inputIndex++); + velocity = (Operand) op.input(inputIndex++); + numMinibatchesPerPhysicalSparseCore = (Operand) op.input(inputIndex++); + useSumInsideSqrt = op.attributes().getAttrBool("use_sum_inside_sqrt"); + beta1 = op.attributes().getAttrFloat("beta1"); + beta2 = op.attributes().getAttrFloat("beta2"); + epsilon = op.attributes().getAttrFloat("epsilon"); + clipWeightMin = op.attributes().getAttrFloat("clip_weight_min"); + clipWeightMax = op.attributes().getAttrFloat("clip_weight_max"); + maxIdsPerSparseCore = op.attributes().getAttrInt("max_ids_per_sparse_core"); + maxUniqueIdsPerSparseCore = op.attributes().getAttrInt("max_unique_ids_per_sparse_core"); + tableName = op.attributes().getAttrString("table_name"); + } + } +} diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/xla/XlaSparseDenseMatmulGradWithCsrInput.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/xla/XlaSparseDenseMatmulGradWithCsrInput.java new file mode 100644 index 00000000000..7ac92263e93 --- /dev/null +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/xla/XlaSparseDenseMatmulGradWithCsrInput.java @@ -0,0 +1,184 @@ +/* Copyright 2018-2023 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +=======================================================================*/ + +// This class has been generated, DO NOT EDIT! + +package org.tensorflow.op.xla; + +import java.util.Arrays; +import java.util.Iterator; +import java.util.List; +import org.tensorflow.ConcreteFunction; +import org.tensorflow.GraphOperation; +import org.tensorflow.Operand; +import org.tensorflow.Operation; +import org.tensorflow.OperationBuilder; +import org.tensorflow.Output; +import org.tensorflow.op.Operands; +import org.tensorflow.op.RawOp; +import org.tensorflow.op.RawOpInputs; +import org.tensorflow.op.Scope; +import org.tensorflow.op.annotation.Endpoint; +import org.tensorflow.op.annotation.OpInputsMetadata; +import org.tensorflow.op.annotation.OpMetadata; +import org.tensorflow.types.TFloat32; +import org.tensorflow.types.TInt32; + +/** + * The XlaSparseDenseMatmulGradWithCsrInput operation + */ +@OpMetadata( + opType = XlaSparseDenseMatmulGradWithCsrInput.OP_NAME, + inputsClass = XlaSparseDenseMatmulGradWithCsrInput.Inputs.class +) +public final class XlaSparseDenseMatmulGradWithCsrInput extends RawOp implements Iterable> { + /** + * The name of this op, as known by TensorFlow core engine + */ + public static final String OP_NAME = "XlaSparseDenseMatmulGradWithCsrInput"; + + private List> updatedTables; + + @SuppressWarnings("unchecked") + public XlaSparseDenseMatmulGradWithCsrInput(Operation operation) { + super(operation, OP_NAME); + int outputIdx = 0; + int updatedTablesLength = operation.outputListLength("updated_tables"); + updatedTables = Arrays.asList((Output[]) operation.outputList(outputIdx, updatedTablesLength)); + outputIdx += updatedTablesLength; + } + + /** + * Factory method to create a class wrapping a new XlaSparseDenseMatmulGradWithCsrInput operation. + * + * @param scope current scope + * @param rowPointers The rowPointers value + * @param sortedSampleIds The sortedSampleIds value + * @param sortedTokenIds The sortedTokenIds value + * @param sortedGains The sortedGains value + * @param activationGradients The activationGradients value + * @param tables The tables value + * @param hyperparameters The hyperparameters value + * @param numMinibatchesPerPhysicalSparseCore The numMinibatchesPerPhysicalSparseCore value + * @param customComputation The value of the customComputation attribute + * @param tableName The value of the tableName attribute + * @return a new instance of XlaSparseDenseMatmulGradWithCsrInput + */ + @Endpoint( + describeByClass = true + ) + public static XlaSparseDenseMatmulGradWithCsrInput create(Scope scope, + Operand rowPointers, Operand sortedSampleIds, Operand sortedTokenIds, + Operand sortedGains, Operand activationGradients, + Iterable> tables, Iterable> hyperparameters, + Operand numMinibatchesPerPhysicalSparseCore, ConcreteFunction customComputation, + String tableName) { + OperationBuilder opBuilder = scope.opBuilder(OP_NAME, "XlaSparseDenseMatmulGradWithCsrInput"); + opBuilder.addInput(rowPointers.asOutput()); + opBuilder.addInput(sortedSampleIds.asOutput()); + opBuilder.addInput(sortedTokenIds.asOutput()); + opBuilder.addInput(sortedGains.asOutput()); + opBuilder.addInput(activationGradients.asOutput()); + opBuilder.addInputList(Operands.asOutputs(tables)); + opBuilder.addInputList(Operands.asOutputs(hyperparameters)); + opBuilder.addInput(numMinibatchesPerPhysicalSparseCore.asOutput()); + opBuilder.setAttr("custom_computation", customComputation); + opBuilder.setAttr("table_name", tableName); + return new XlaSparseDenseMatmulGradWithCsrInput(opBuilder.build()); + } + + /** + * Gets updatedTables. + * + * @return updatedTables. + */ + public List> updatedTables() { + return updatedTables; + } + + @Override + @SuppressWarnings({"rawtypes", "unchecked"}) + public Iterator> iterator() { + return (Iterator) updatedTables.iterator(); + } + + @OpInputsMetadata( + outputsClass = XlaSparseDenseMatmulGradWithCsrInput.class + ) + public static class Inputs extends RawOpInputs { + /** + * The rowPointers input + */ + public final Operand rowPointers; + + /** + * The sortedSampleIds input + */ + public final Operand sortedSampleIds; + + /** + * The sortedTokenIds input + */ + public final Operand sortedTokenIds; + + /** + * The sortedGains input + */ + public final Operand sortedGains; + + /** + * The activationGradients input + */ + public final Operand activationGradients; + + /** + * The tables input + */ + public final Iterable> tables; + + /** + * The hyperparameters input + */ + public final Iterable> hyperparameters; + + /** + * The numMinibatchesPerPhysicalSparseCore input + */ + public final Operand numMinibatchesPerPhysicalSparseCore; + + /** + * The tableName attribute + */ + public final String tableName; + + public Inputs(GraphOperation op) { + super(new XlaSparseDenseMatmulGradWithCsrInput(op), op, Arrays.asList("table_name")); + int inputIndex = 0; + rowPointers = (Operand) op.input(inputIndex++); + sortedSampleIds = (Operand) op.input(inputIndex++); + sortedTokenIds = (Operand) op.input(inputIndex++); + sortedGains = (Operand) op.input(inputIndex++); + activationGradients = (Operand) op.input(inputIndex++); + int tablesLength = op.inputListLength("tables"); + tables = Arrays.asList((Operand[]) op.inputList(inputIndex, tablesLength)); + inputIndex += tablesLength; + int hyperparametersLength = op.inputListLength("hyperparameters"); + hyperparameters = Arrays.asList((Operand[]) op.inputList(inputIndex, hyperparametersLength)); + inputIndex += hyperparametersLength; + numMinibatchesPerPhysicalSparseCore = (Operand) op.input(inputIndex++); + tableName = op.attributes().getAttrString("table_name"); + } + } +} diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/xla/XlaSparseDenseMatmulGradWithFtrlAndStaticBufferSize.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/xla/XlaSparseDenseMatmulGradWithFtrlAndStaticBufferSize.java new file mode 100644 index 00000000000..7bfa0c2cc45 --- /dev/null +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/xla/XlaSparseDenseMatmulGradWithFtrlAndStaticBufferSize.java @@ -0,0 +1,341 @@ +/* Copyright 2018-2023 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +=======================================================================*/ + +// This class has been generated, DO NOT EDIT! + +package org.tensorflow.op.xla; + +import java.util.Arrays; +import org.tensorflow.GraphOperation; +import org.tensorflow.Operand; +import org.tensorflow.Operation; +import org.tensorflow.OperationBuilder; +import org.tensorflow.Output; +import org.tensorflow.op.RawOp; +import org.tensorflow.op.RawOpInputs; +import org.tensorflow.op.Scope; +import org.tensorflow.op.annotation.Endpoint; +import org.tensorflow.op.annotation.OpInputsMetadata; +import org.tensorflow.op.annotation.OpMetadata; +import org.tensorflow.types.TFloat32; +import org.tensorflow.types.TInt32; + +/** + * The XlaSparseDenseMatmulGradWithFtrlAndStaticBufferSize operation + */ +@OpMetadata( + opType = XlaSparseDenseMatmulGradWithFtrlAndStaticBufferSize.OP_NAME, + inputsClass = XlaSparseDenseMatmulGradWithFtrlAndStaticBufferSize.Inputs.class +) +public final class XlaSparseDenseMatmulGradWithFtrlAndStaticBufferSize extends RawOp { + /** + * The name of this op, as known by TensorFlow core engine + */ + public static final String OP_NAME = "XlaSparseDenseMatmulGradWithFtrlAndStaticBufferSize"; + + private Output updatedEmbeddingTable; + + private Output updatedAccumulator; + + private Output updatedLinear; + + public XlaSparseDenseMatmulGradWithFtrlAndStaticBufferSize(Operation operation) { + super(operation, OP_NAME); + int outputIdx = 0; + updatedEmbeddingTable = operation.output(outputIdx++); + updatedAccumulator = operation.output(outputIdx++); + updatedLinear = operation.output(outputIdx++); + } + + /** + * Factory method to create a class wrapping a new XlaSparseDenseMatmulGradWithFtrlAndStaticBufferSize operation. + * + * @param scope current scope + * @param rowPointers The rowPointers value + * @param sortedSampleIds The sortedSampleIds value + * @param sortedTokenIds The sortedTokenIds value + * @param sortedGains The sortedGains value + * @param activationGradients The activationGradients value + * @param learningRate The learningRate value + * @param embeddingTable The embeddingTable value + * @param accumulator The accumulator value + * @param linear The linear value + * @param numMinibatchesPerPhysicalSparseCore The numMinibatchesPerPhysicalSparseCore value + * @param multiplyLinearByLearningRate The value of the multiplyLinearByLearningRate attribute + * @param beta The value of the beta attribute + * @param learningRatePower The value of the learningRatePower attribute + * @param l1RegularizationStrength The value of the l1RegularizationStrength attribute + * @param l2RegularizationStrength The value of the l2RegularizationStrength attribute + * @param maxIdsPerSparseCore The value of the maxIdsPerSparseCore attribute + * @param maxUniqueIdsPerSparseCore The value of the maxUniqueIdsPerSparseCore attribute + * @param tableName The value of the tableName attribute + * @param options carries optional attribute values + * @return a new instance of XlaSparseDenseMatmulGradWithFtrlAndStaticBufferSize + */ + @Endpoint( + describeByClass = true + ) + public static XlaSparseDenseMatmulGradWithFtrlAndStaticBufferSize create(Scope scope, + Operand rowPointers, Operand sortedSampleIds, Operand sortedTokenIds, + Operand sortedGains, Operand activationGradients, + Operand learningRate, Operand embeddingTable, + Operand accumulator, Operand linear, + Operand numMinibatchesPerPhysicalSparseCore, Boolean multiplyLinearByLearningRate, + Float beta, Float learningRatePower, Float l1RegularizationStrength, + Float l2RegularizationStrength, Long maxIdsPerSparseCore, Long maxUniqueIdsPerSparseCore, + String tableName, Options... options) { + OperationBuilder opBuilder = scope.opBuilder(OP_NAME, "XlaSparseDenseMatmulGradWithFtrlAndStaticBufferSize"); + opBuilder.addInput(rowPointers.asOutput()); + opBuilder.addInput(sortedSampleIds.asOutput()); + opBuilder.addInput(sortedTokenIds.asOutput()); + opBuilder.addInput(sortedGains.asOutput()); + opBuilder.addInput(activationGradients.asOutput()); + opBuilder.addInput(learningRate.asOutput()); + opBuilder.addInput(embeddingTable.asOutput()); + opBuilder.addInput(accumulator.asOutput()); + opBuilder.addInput(linear.asOutput()); + opBuilder.addInput(numMinibatchesPerPhysicalSparseCore.asOutput()); + opBuilder.setAttr("multiply_linear_by_learning_rate", multiplyLinearByLearningRate); + opBuilder.setAttr("beta", beta); + opBuilder.setAttr("learning_rate_power", learningRatePower); + opBuilder.setAttr("l1_regularization_strength", l1RegularizationStrength); + opBuilder.setAttr("l2_regularization_strength", l2RegularizationStrength); + opBuilder.setAttr("max_ids_per_sparse_core", maxIdsPerSparseCore); + opBuilder.setAttr("max_unique_ids_per_sparse_core", maxUniqueIdsPerSparseCore); + opBuilder.setAttr("table_name", tableName); + if (options != null) { + for (Options opts : options) { + if (opts.clipWeightMin != null) { + opBuilder.setAttr("clip_weight_min", opts.clipWeightMin); + } + if (opts.clipWeightMax != null) { + opBuilder.setAttr("clip_weight_max", opts.clipWeightMax); + } + } + } + return new XlaSparseDenseMatmulGradWithFtrlAndStaticBufferSize(opBuilder.build()); + } + + /** + * Sets the clipWeightMin option. + * + * @param clipWeightMin the clipWeightMin option + * @return this Options instance. + */ + public static Options clipWeightMin(Float clipWeightMin) { + return new Options().clipWeightMin(clipWeightMin); + } + + /** + * Sets the clipWeightMax option. + * + * @param clipWeightMax the clipWeightMax option + * @return this Options instance. + */ + public static Options clipWeightMax(Float clipWeightMax) { + return new Options().clipWeightMax(clipWeightMax); + } + + /** + * Gets updatedEmbeddingTable. + * + * @return updatedEmbeddingTable. + */ + public Output updatedEmbeddingTable() { + return updatedEmbeddingTable; + } + + /** + * Gets updatedAccumulator. + * + * @return updatedAccumulator. + */ + public Output updatedAccumulator() { + return updatedAccumulator; + } + + /** + * Gets updatedLinear. + * + * @return updatedLinear. + */ + public Output updatedLinear() { + return updatedLinear; + } + + /** + * Optional attributes for {@link org.tensorflow.op.xla.XlaSparseDenseMatmulGradWithFtrlAndStaticBufferSize} + */ + public static class Options { + private Float clipWeightMin; + + private Float clipWeightMax; + + private Options() { + } + + /** + * Sets the clipWeightMin option. + * + * @param clipWeightMin the clipWeightMin option + * @return this Options instance. + */ + public Options clipWeightMin(Float clipWeightMin) { + this.clipWeightMin = clipWeightMin; + return this; + } + + /** + * Sets the clipWeightMax option. + * + * @param clipWeightMax the clipWeightMax option + * @return this Options instance. + */ + public Options clipWeightMax(Float clipWeightMax) { + this.clipWeightMax = clipWeightMax; + return this; + } + } + + @OpInputsMetadata( + outputsClass = XlaSparseDenseMatmulGradWithFtrlAndStaticBufferSize.class + ) + public static class Inputs extends RawOpInputs { + /** + * The rowPointers input + */ + public final Operand rowPointers; + + /** + * The sortedSampleIds input + */ + public final Operand sortedSampleIds; + + /** + * The sortedTokenIds input + */ + public final Operand sortedTokenIds; + + /** + * The sortedGains input + */ + public final Operand sortedGains; + + /** + * The activationGradients input + */ + public final Operand activationGradients; + + /** + * The learningRate input + */ + public final Operand learningRate; + + /** + * The embeddingTable input + */ + public final Operand embeddingTable; + + /** + * The accumulator input + */ + public final Operand accumulator; + + /** + * The linear input + */ + public final Operand linear; + + /** + * The numMinibatchesPerPhysicalSparseCore input + */ + public final Operand numMinibatchesPerPhysicalSparseCore; + + /** + * The multiplyLinearByLearningRate attribute + */ + public final boolean multiplyLinearByLearningRate; + + /** + * The beta attribute + */ + public final float beta; + + /** + * The learningRatePower attribute + */ + public final float learningRatePower; + + /** + * The l1RegularizationStrength attribute + */ + public final float l1RegularizationStrength; + + /** + * The l2RegularizationStrength attribute + */ + public final float l2RegularizationStrength; + + /** + * The clipWeightMin attribute + */ + public final float clipWeightMin; + + /** + * The clipWeightMax attribute + */ + public final float clipWeightMax; + + /** + * The maxIdsPerSparseCore attribute + */ + public final long maxIdsPerSparseCore; + + /** + * The maxUniqueIdsPerSparseCore attribute + */ + public final long maxUniqueIdsPerSparseCore; + + /** + * The tableName attribute + */ + public final String tableName; + + public Inputs(GraphOperation op) { + super(new XlaSparseDenseMatmulGradWithFtrlAndStaticBufferSize(op), op, Arrays.asList("multiply_linear_by_learning_rate", "beta", "learning_rate_power", "l1_regularization_strength", "l2_regularization_strength", "clip_weight_min", "clip_weight_max", "max_ids_per_sparse_core", "max_unique_ids_per_sparse_core", "table_name")); + int inputIndex = 0; + rowPointers = (Operand) op.input(inputIndex++); + sortedSampleIds = (Operand) op.input(inputIndex++); + sortedTokenIds = (Operand) op.input(inputIndex++); + sortedGains = (Operand) op.input(inputIndex++); + activationGradients = (Operand) op.input(inputIndex++); + learningRate = (Operand) op.input(inputIndex++); + embeddingTable = (Operand) op.input(inputIndex++); + accumulator = (Operand) op.input(inputIndex++); + linear = (Operand) op.input(inputIndex++); + numMinibatchesPerPhysicalSparseCore = (Operand) op.input(inputIndex++); + multiplyLinearByLearningRate = op.attributes().getAttrBool("multiply_linear_by_learning_rate"); + beta = op.attributes().getAttrFloat("beta"); + learningRatePower = op.attributes().getAttrFloat("learning_rate_power"); + l1RegularizationStrength = op.attributes().getAttrFloat("l1_regularization_strength"); + l2RegularizationStrength = op.attributes().getAttrFloat("l2_regularization_strength"); + clipWeightMin = op.attributes().getAttrFloat("clip_weight_min"); + clipWeightMax = op.attributes().getAttrFloat("clip_weight_max"); + maxIdsPerSparseCore = op.attributes().getAttrInt("max_ids_per_sparse_core"); + maxUniqueIdsPerSparseCore = op.attributes().getAttrInt("max_unique_ids_per_sparse_core"); + tableName = op.attributes().getAttrString("table_name"); + } + } +} diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/xla/XlaSparseDenseMatmulGradWithSgdAndStaticBufferSize.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/xla/XlaSparseDenseMatmulGradWithSgdAndStaticBufferSize.java new file mode 100644 index 00000000000..65c059d2821 --- /dev/null +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/xla/XlaSparseDenseMatmulGradWithSgdAndStaticBufferSize.java @@ -0,0 +1,263 @@ +/* Copyright 2018-2023 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +=======================================================================*/ + +// This class has been generated, DO NOT EDIT! + +package org.tensorflow.op.xla; + +import java.util.Arrays; +import org.tensorflow.GraphOperation; +import org.tensorflow.Operand; +import org.tensorflow.Operation; +import org.tensorflow.OperationBuilder; +import org.tensorflow.Output; +import org.tensorflow.op.RawOp; +import org.tensorflow.op.RawOpInputs; +import org.tensorflow.op.Scope; +import org.tensorflow.op.annotation.Endpoint; +import org.tensorflow.op.annotation.OpInputsMetadata; +import org.tensorflow.op.annotation.OpMetadata; +import org.tensorflow.types.TFloat32; +import org.tensorflow.types.TInt32; + +/** + * The XlaSparseDenseMatmulGradWithSgdAndStaticBufferSize operation + */ +@OpMetadata( + opType = XlaSparseDenseMatmulGradWithSgdAndStaticBufferSize.OP_NAME, + inputsClass = XlaSparseDenseMatmulGradWithSgdAndStaticBufferSize.Inputs.class +) +public final class XlaSparseDenseMatmulGradWithSgdAndStaticBufferSize extends RawOp implements Operand { + /** + * The name of this op, as known by TensorFlow core engine + */ + public static final String OP_NAME = "XlaSparseDenseMatmulGradWithSgdAndStaticBufferSize"; + + private Output updatedEmbeddingTable; + + public XlaSparseDenseMatmulGradWithSgdAndStaticBufferSize(Operation operation) { + super(operation, OP_NAME); + int outputIdx = 0; + updatedEmbeddingTable = operation.output(outputIdx++); + } + + /** + * Factory method to create a class wrapping a new XlaSparseDenseMatmulGradWithSgdAndStaticBufferSize operation. + * + * @param scope current scope + * @param rowPointers The rowPointers value + * @param sortedSampleIds The sortedSampleIds value + * @param sortedTokenIds The sortedTokenIds value + * @param sortedGains The sortedGains value + * @param activationGradients The activationGradients value + * @param learningRate The learningRate value + * @param embeddingTable The embeddingTable value + * @param numMinibatchesPerPhysicalSparseCore The numMinibatchesPerPhysicalSparseCore value + * @param maxIdsPerSparseCore The value of the maxIdsPerSparseCore attribute + * @param maxUniqueIdsPerSparseCore The value of the maxUniqueIdsPerSparseCore attribute + * @param tableName The value of the tableName attribute + * @param options carries optional attribute values + * @return a new instance of XlaSparseDenseMatmulGradWithSgdAndStaticBufferSize + */ + @Endpoint( + describeByClass = true + ) + public static XlaSparseDenseMatmulGradWithSgdAndStaticBufferSize create(Scope scope, + Operand rowPointers, Operand sortedSampleIds, Operand sortedTokenIds, + Operand sortedGains, Operand activationGradients, + Operand learningRate, Operand embeddingTable, + Operand numMinibatchesPerPhysicalSparseCore, Long maxIdsPerSparseCore, + Long maxUniqueIdsPerSparseCore, String tableName, Options... options) { + OperationBuilder opBuilder = scope.opBuilder(OP_NAME, "XlaSparseDenseMatmulGradWithSgdAndStaticBufferSize"); + opBuilder.addInput(rowPointers.asOutput()); + opBuilder.addInput(sortedSampleIds.asOutput()); + opBuilder.addInput(sortedTokenIds.asOutput()); + opBuilder.addInput(sortedGains.asOutput()); + opBuilder.addInput(activationGradients.asOutput()); + opBuilder.addInput(learningRate.asOutput()); + opBuilder.addInput(embeddingTable.asOutput()); + opBuilder.addInput(numMinibatchesPerPhysicalSparseCore.asOutput()); + opBuilder.setAttr("max_ids_per_sparse_core", maxIdsPerSparseCore); + opBuilder.setAttr("max_unique_ids_per_sparse_core", maxUniqueIdsPerSparseCore); + opBuilder.setAttr("table_name", tableName); + if (options != null) { + for (Options opts : options) { + if (opts.clipWeightMin != null) { + opBuilder.setAttr("clip_weight_min", opts.clipWeightMin); + } + if (opts.clipWeightMax != null) { + opBuilder.setAttr("clip_weight_max", opts.clipWeightMax); + } + } + } + return new XlaSparseDenseMatmulGradWithSgdAndStaticBufferSize(opBuilder.build()); + } + + /** + * Sets the clipWeightMin option. + * + * @param clipWeightMin the clipWeightMin option + * @return this Options instance. + */ + public static Options clipWeightMin(Float clipWeightMin) { + return new Options().clipWeightMin(clipWeightMin); + } + + /** + * Sets the clipWeightMax option. + * + * @param clipWeightMax the clipWeightMax option + * @return this Options instance. + */ + public static Options clipWeightMax(Float clipWeightMax) { + return new Options().clipWeightMax(clipWeightMax); + } + + /** + * Gets updatedEmbeddingTable. + * + * @return updatedEmbeddingTable. + */ + public Output updatedEmbeddingTable() { + return updatedEmbeddingTable; + } + + @Override + public Output asOutput() { + return updatedEmbeddingTable; + } + + /** + * Optional attributes for {@link org.tensorflow.op.xla.XlaSparseDenseMatmulGradWithSgdAndStaticBufferSize} + */ + public static class Options { + private Float clipWeightMin; + + private Float clipWeightMax; + + private Options() { + } + + /** + * Sets the clipWeightMin option. + * + * @param clipWeightMin the clipWeightMin option + * @return this Options instance. + */ + public Options clipWeightMin(Float clipWeightMin) { + this.clipWeightMin = clipWeightMin; + return this; + } + + /** + * Sets the clipWeightMax option. + * + * @param clipWeightMax the clipWeightMax option + * @return this Options instance. + */ + public Options clipWeightMax(Float clipWeightMax) { + this.clipWeightMax = clipWeightMax; + return this; + } + } + + @OpInputsMetadata( + outputsClass = XlaSparseDenseMatmulGradWithSgdAndStaticBufferSize.class + ) + public static class Inputs extends RawOpInputs { + /** + * The rowPointers input + */ + public final Operand rowPointers; + + /** + * The sortedSampleIds input + */ + public final Operand sortedSampleIds; + + /** + * The sortedTokenIds input + */ + public final Operand sortedTokenIds; + + /** + * The sortedGains input + */ + public final Operand sortedGains; + + /** + * The activationGradients input + */ + public final Operand activationGradients; + + /** + * The learningRate input + */ + public final Operand learningRate; + + /** + * The embeddingTable input + */ + public final Operand embeddingTable; + + /** + * The numMinibatchesPerPhysicalSparseCore input + */ + public final Operand numMinibatchesPerPhysicalSparseCore; + + /** + * The clipWeightMin attribute + */ + public final float clipWeightMin; + + /** + * The clipWeightMax attribute + */ + public final float clipWeightMax; + + /** + * The maxIdsPerSparseCore attribute + */ + public final long maxIdsPerSparseCore; + + /** + * The maxUniqueIdsPerSparseCore attribute + */ + public final long maxUniqueIdsPerSparseCore; + + /** + * The tableName attribute + */ + public final String tableName; + + public Inputs(GraphOperation op) { + super(new XlaSparseDenseMatmulGradWithSgdAndStaticBufferSize(op), op, Arrays.asList("clip_weight_min", "clip_weight_max", "max_ids_per_sparse_core", "max_unique_ids_per_sparse_core", "table_name")); + int inputIndex = 0; + rowPointers = (Operand) op.input(inputIndex++); + sortedSampleIds = (Operand) op.input(inputIndex++); + sortedTokenIds = (Operand) op.input(inputIndex++); + sortedGains = (Operand) op.input(inputIndex++); + activationGradients = (Operand) op.input(inputIndex++); + learningRate = (Operand) op.input(inputIndex++); + embeddingTable = (Operand) op.input(inputIndex++); + numMinibatchesPerPhysicalSparseCore = (Operand) op.input(inputIndex++); + clipWeightMin = op.attributes().getAttrFloat("clip_weight_min"); + clipWeightMax = op.attributes().getAttrFloat("clip_weight_max"); + maxIdsPerSparseCore = op.attributes().getAttrInt("max_ids_per_sparse_core"); + maxUniqueIdsPerSparseCore = op.attributes().getAttrInt("max_unique_ids_per_sparse_core"); + tableName = op.attributes().getAttrString("table_name"); + } + } +} diff --git a/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/xla/XlaSparseDenseMatmulWithStaticBufferSize.java b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/xla/XlaSparseDenseMatmulWithStaticBufferSize.java new file mode 100644 index 00000000000..268a9b0fc4b --- /dev/null +++ b/tensorflow-core/tensorflow-core-api/src/gen/java/org/tensorflow/op/xla/XlaSparseDenseMatmulWithStaticBufferSize.java @@ -0,0 +1,202 @@ +/* Copyright 2018-2023 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +=======================================================================*/ + +// This class has been generated, DO NOT EDIT! + +package org.tensorflow.op.xla; + +import java.util.Arrays; +import org.tensorflow.GraphOperation; +import org.tensorflow.Operand; +import org.tensorflow.Operation; +import org.tensorflow.OperationBuilder; +import org.tensorflow.Output; +import org.tensorflow.op.RawOp; +import org.tensorflow.op.RawOpInputs; +import org.tensorflow.op.Scope; +import org.tensorflow.op.annotation.Endpoint; +import org.tensorflow.op.annotation.OpInputsMetadata; +import org.tensorflow.op.annotation.OpMetadata; +import org.tensorflow.types.TFloat32; +import org.tensorflow.types.TInt32; + +/** + * The XlaSparseDenseMatmulWithStaticBufferSize operation + */ +@OpMetadata( + opType = XlaSparseDenseMatmulWithStaticBufferSize.OP_NAME, + inputsClass = XlaSparseDenseMatmulWithStaticBufferSize.Inputs.class +) +public final class XlaSparseDenseMatmulWithStaticBufferSize extends RawOp implements Operand { + /** + * The name of this op, as known by TensorFlow core engine + */ + public static final String OP_NAME = "XlaSparseDenseMatmulWithStaticBufferSize"; + + private Output activations; + + public XlaSparseDenseMatmulWithStaticBufferSize(Operation operation) { + super(operation, OP_NAME); + int outputIdx = 0; + activations = operation.output(outputIdx++); + } + + /** + * Factory method to create a class wrapping a new XlaSparseDenseMatmulWithStaticBufferSize operation. + * + * @param scope current scope + * @param rowPointers The rowPointers value + * @param sortedSampleIds The sortedSampleIds value + * @param sortedTokenIds The sortedTokenIds value + * @param sortedGains The sortedGains value + * @param embeddingTable The embeddingTable value + * @param numMinibatchesPerPhysicalSparseCore The numMinibatchesPerPhysicalSparseCore value + * @param inputSize The value of the inputSize attribute + * @param quantizationConfigLow The value of the quantizationConfigLow attribute + * @param quantizationConfigHigh The value of the quantizationConfigHigh attribute + * @param quantizationConfigNumBuckets The value of the quantizationConfigNumBuckets attribute + * @param maxIdsPerSparseCore The value of the maxIdsPerSparseCore attribute + * @param maxUniqueIdsPerSparseCore The value of the maxUniqueIdsPerSparseCore attribute + * @param tableName The value of the tableName attribute + * @return a new instance of XlaSparseDenseMatmulWithStaticBufferSize + */ + @Endpoint( + describeByClass = true + ) + public static XlaSparseDenseMatmulWithStaticBufferSize create(Scope scope, + Operand rowPointers, Operand sortedSampleIds, Operand sortedTokenIds, + Operand sortedGains, Operand embeddingTable, + Operand numMinibatchesPerPhysicalSparseCore, Long inputSize, + Float quantizationConfigLow, Float quantizationConfigHigh, Long quantizationConfigNumBuckets, + Long maxIdsPerSparseCore, Long maxUniqueIdsPerSparseCore, String tableName) { + OperationBuilder opBuilder = scope.opBuilder(OP_NAME, "XlaSparseDenseMatmulWithStaticBufferSize"); + opBuilder.addInput(rowPointers.asOutput()); + opBuilder.addInput(sortedSampleIds.asOutput()); + opBuilder.addInput(sortedTokenIds.asOutput()); + opBuilder.addInput(sortedGains.asOutput()); + opBuilder.addInput(embeddingTable.asOutput()); + opBuilder.addInput(numMinibatchesPerPhysicalSparseCore.asOutput()); + opBuilder.setAttr("input_size", inputSize); + opBuilder.setAttr("quantization_config_low", quantizationConfigLow); + opBuilder.setAttr("quantization_config_high", quantizationConfigHigh); + opBuilder.setAttr("quantization_config_num_buckets", quantizationConfigNumBuckets); + opBuilder.setAttr("max_ids_per_sparse_core", maxIdsPerSparseCore); + opBuilder.setAttr("max_unique_ids_per_sparse_core", maxUniqueIdsPerSparseCore); + opBuilder.setAttr("table_name", tableName); + return new XlaSparseDenseMatmulWithStaticBufferSize(opBuilder.build()); + } + + /** + * Gets activations. + * + * @return activations. + */ + public Output activations() { + return activations; + } + + @Override + public Output asOutput() { + return activations; + } + + @OpInputsMetadata( + outputsClass = XlaSparseDenseMatmulWithStaticBufferSize.class + ) + public static class Inputs extends RawOpInputs { + /** + * The rowPointers input + */ + public final Operand rowPointers; + + /** + * The sortedSampleIds input + */ + public final Operand sortedSampleIds; + + /** + * The sortedTokenIds input + */ + public final Operand sortedTokenIds; + + /** + * The sortedGains input + */ + public final Operand sortedGains; + + /** + * The embeddingTable input + */ + public final Operand embeddingTable; + + /** + * The numMinibatchesPerPhysicalSparseCore input + */ + public final Operand numMinibatchesPerPhysicalSparseCore; + + /** + * The inputSize attribute + */ + public final long inputSize; + + /** + * The quantizationConfigLow attribute + */ + public final float quantizationConfigLow; + + /** + * The quantizationConfigHigh attribute + */ + public final float quantizationConfigHigh; + + /** + * The quantizationConfigNumBuckets attribute + */ + public final long quantizationConfigNumBuckets; + + /** + * The maxIdsPerSparseCore attribute + */ + public final long maxIdsPerSparseCore; + + /** + * The maxUniqueIdsPerSparseCore attribute + */ + public final long maxUniqueIdsPerSparseCore; + + /** + * The tableName attribute + */ + public final String tableName; + + public Inputs(GraphOperation op) { + super(new XlaSparseDenseMatmulWithStaticBufferSize(op), op, Arrays.asList("input_size", "quantization_config_low", "quantization_config_high", "quantization_config_num_buckets", "max_ids_per_sparse_core", "max_unique_ids_per_sparse_core", "table_name")); + int inputIndex = 0; + rowPointers = (Operand) op.input(inputIndex++); + sortedSampleIds = (Operand) op.input(inputIndex++); + sortedTokenIds = (Operand) op.input(inputIndex++); + sortedGains = (Operand) op.input(inputIndex++); + embeddingTable = (Operand) op.input(inputIndex++); + numMinibatchesPerPhysicalSparseCore = (Operand) op.input(inputIndex++); + inputSize = op.attributes().getAttrInt("input_size"); + quantizationConfigLow = op.attributes().getAttrFloat("quantization_config_low"); + quantizationConfigHigh = op.attributes().getAttrFloat("quantization_config_high"); + quantizationConfigNumBuckets = op.attributes().getAttrInt("quantization_config_num_buckets"); + maxIdsPerSparseCore = op.attributes().getAttrInt("max_ids_per_sparse_core"); + maxUniqueIdsPerSparseCore = op.attributes().getAttrInt("max_unique_ids_per_sparse_core"); + tableName = op.attributes().getAttrString("table_name"); + } + } +} diff --git a/tensorflow-core/tensorflow-core-generator/pom.xml b/tensorflow-core/tensorflow-core-generator/pom.xml index a90d85a1d4b..923bd52b4e6 100644 --- a/tensorflow-core/tensorflow-core-generator/pom.xml +++ b/tensorflow-core/tensorflow-core-generator/pom.xml @@ -5,7 +5,7 @@ org.tensorflow tensorflow-core - 1.0.0-SNAPSHOT + 1.1.0-SNAPSHOT tensorflow-core-generator jar diff --git a/tensorflow-core/tensorflow-core-native/BUILD b/tensorflow-core/tensorflow-core-native/BUILD index e8d01ef0a72..b3b1a2cfcd7 100644 --- a/tensorflow-core/tensorflow-core-native/BUILD +++ b/tensorflow-core/tensorflow-core-native/BUILD @@ -4,6 +4,8 @@ java_proto_library( name = "java_proto_gen_sources", deps = [ "@org_tensorflow//tensorflow/core:protos_all", + "@local_xla//xla/tsl/protobuf:bfc_memory_map_proto", + "@local_xla//xla/tsl/protobuf:test_log_proto", "@local_tsl//tsl/protobuf:protos_all" ] ) diff --git a/tensorflow-core/tensorflow-core-native/WORKSPACE b/tensorflow-core/tensorflow-core-native/WORKSPACE index 1a745b13613..ad2c74508ad 100644 --- a/tensorflow-core/tensorflow-core-native/WORKSPACE +++ b/tensorflow-core/tensorflow-core-native/WORKSPACE @@ -14,96 +14,74 @@ http_archive( patch_tool = "patch", patch_args = ["-p1"], patch_cmds = [ - "find tensorflow third_party/xla/third_party/tsl -name \\*.proto | xargs sed -i.bak '/^option java_package/d'", - "find tensorflow third_party/xla/third_party/tsl -name \\*.proto | xargs sed -i.bak 's/^package tensorflow\\([^;]*\\).*$/package tensorflow\\1;\\noption java_package = \"org.tensorflow.proto\\1\";/'", + "find tensorflow third_party/xla/third_party/tsl third_party/xla/xla/tsl -name \\*.proto | xargs sed -i.bak '/^option java_package/d'", + "find tensorflow third_party/xla/third_party/tsl third_party/xla/xla/tsl -name \\*.proto | xargs sed -i.bak 's/^package tensorflow\\([^;]*\\).*$/package tensorflow\\1;\\noption java_package = \"org.tensorflow.proto\\1\";/'", ], urls = [ - "https://github.com/tensorflow/tensorflow/archive/refs/tags/v2.16.2.tar.gz", + "https://github.com/tensorflow/tensorflow/archive/refs/tags/v2.18.0.tar.gz", ], - sha256 = "023849bf253080cb1e4f09386f5eb900492da2288274086ed6cfecd6d99da9eb", - strip_prefix = "tensorflow-2.16.2" + sha256 = "d7876f4bb0235cac60eb6316392a7c48676729860da1ab659fb440379ad5186d", + strip_prefix = "tensorflow-2.18.0" ) ##### Copy content of tensorflow/WORKSPACE here (make sure to change references of default package "//" to "@org_tensorflow//") +# buildifier: disable=load-on-top + # We must initialize hermetic python first. load("@bazel_tools//tools/build_defs/repo:http.bzl", "http_archive") http_archive( - name = "bazel_skylib", - sha256 = "74d544d96f4a5bb630d465ca8bbcfe231e3594e5aae57e1edbf17a6eb3ca2506", - urls = [ - "https://storage.googleapis.com/mirror.tensorflow.org/github.com/bazelbuild/bazel-skylib/releases/download/1.3.0/bazel-skylib-1.3.0.tar.gz", - "https://github.com/bazelbuild/bazel-skylib/releases/download/1.3.0/bazel-skylib-1.3.0.tar.gz", - ], + name = "rules_java", + sha256 = "c73336802d0b4882e40770666ad055212df4ea62cfa6edf9cb0f9d29828a0934", + url = "https://github.com/bazelbuild/rules_java/releases/download/5.3.5/rules_java-5.3.5.tar.gz", ) -http_archive( - name = "rules_python", - sha256 = "9d04041ac92a0985e344235f5d946f71ac543f1b1565f2cdbc9a2aaee8adf55b", - strip_prefix = "rules_python-0.26.0", - url = "https://github.com/bazelbuild/rules_python/releases/download/0.26.0/rules_python-0.26.0.tar.gz", -) +# Initialize the TensorFlow repository and all dependencies. +# +# The cascade of load() statements and tf_workspace?() calls works around the +# restriction that load() statements need to be at the top of .bzl files. +# E.g. we can not retrieve a new repository with http_archive and then load() +# a macro from that repository in the same file. +load("@org_tensorflow//tensorflow:workspace3.bzl", "tf_workspace3") -load("@rules_python//python:repositories.bzl", "py_repositories") +tf_workspace3() -py_repositories() +# Initialize hermetic Python +load("@local_xla//third_party/py:python_init_rules.bzl", "python_init_rules") -load("@rules_python//python:repositories.bzl", "python_register_toolchains") -load( - "@org_tensorflow//tensorflow/tools/toolchains/python:python_repo.bzl", - "python_repository", -) - -python_repository(name = "python_version_repo") +python_init_rules() -load("@python_version_repo//:py_version.bzl", "TF_PYTHON_VERSION") +load("@local_xla//third_party/py:python_init_repositories.bzl", "python_init_repositories") -python_register_toolchains( - name = "python", - ignore_root_user_error = True, - python_version = TF_PYTHON_VERSION, +python_init_repositories( + default_python_version = "system", + local_wheel_dist_folder = "dist", + local_wheel_inclusion_list = [ + "tensorflow*", + "tf_nightly*", + ], + local_wheel_workspaces = ["//:WORKSPACE"], + requirements = { + "3.9": "@org_tensorflow//:requirements_lock_3_9.txt", + "3.10": "@org_tensorflow//:requirements_lock_3_10.txt", + "3.11": "@org_tensorflow//:requirements_lock_3_11.txt", + "3.12": "@org_tensorflow//:requirements_lock_3_12.txt", + }, ) -load("@python//:defs.bzl", "interpreter") -load("@rules_python//python:pip.bzl", "package_annotation", "pip_parse") +load("@local_xla//third_party/py:python_init_toolchains.bzl", "python_init_toolchains") -NUMPY_ANNOTATIONS = { - "numpy": package_annotation( - additive_build_content = """\ -filegroup( - name = "includes", - srcs = glob(["site-packages/numpy/core/include/**/*.h"]), -) -cc_library( - name = "numpy_headers", - hdrs = [":includes"], - strip_include_prefix="site-packages/numpy/core/include/", -) -""", - ), -} +python_init_toolchains() -#pip_parse( -# name = "pypi", -# annotations = NUMPY_ANNOTATIONS, -# python_interpreter_target = interpreter, -# requirements = "//:requirements_lock_" + HERMETIC_PYTHON_VERSION.replace(".", "_") + ".txt", -#) +load("@local_xla//third_party/py:python_init_pip.bzl", "python_init_pip") -#load("@pypi//:requirements.bzl", "install_deps") +python_init_pip() -#install_deps() +load("@pypi//:requirements.bzl", "install_deps") -# Initialize the TensorFlow repository and all dependencies. -# -# The cascade of load() statements and tf_workspace?() calls works around the -# restriction that load() statements need to be at the top of .bzl files. -# E.g. we can not retrieve a new repository with http_archive and then load() -# a macro from that repository in the same file. -load("@org_tensorflow//tensorflow:workspace3.bzl", "tf_workspace3") - -tf_workspace3() +install_deps() +# End hermetic Python initialization load("@org_tensorflow//tensorflow:workspace2.bzl", "tf_workspace2") @@ -115,4 +93,51 @@ tf_workspace1() load("@org_tensorflow//tensorflow:workspace0.bzl", "tf_workspace0") -tf_workspace0() \ No newline at end of file +tf_workspace0() + +load( + "@local_tsl//third_party/gpus/cuda/hermetic:cuda_json_init_repository.bzl", + "cuda_json_init_repository", +) + +cuda_json_init_repository() + +load( + "@cuda_redist_json//:distributions.bzl", + "CUDA_REDISTRIBUTIONS", + "CUDNN_REDISTRIBUTIONS", +) +load( + "@local_tsl//third_party/gpus/cuda/hermetic:cuda_redist_init_repositories.bzl", + "cuda_redist_init_repositories", + "cudnn_redist_init_repository", +) + +cuda_redist_init_repositories( + cuda_redistributions = CUDA_REDISTRIBUTIONS, +) + +cudnn_redist_init_repository( + cudnn_redistributions = CUDNN_REDISTRIBUTIONS, +) + +load( + "@local_tsl//third_party/gpus/cuda/hermetic:cuda_configure.bzl", + "cuda_configure", +) + +cuda_configure(name = "local_config_cuda") + +load( + "@local_tsl//third_party/nccl/hermetic:nccl_redist_init_repository.bzl", + "nccl_redist_init_repository", +) + +nccl_redist_init_repository() + +load( + "@local_tsl//third_party/nccl/hermetic:nccl_configure.bzl", + "nccl_configure", +) + +nccl_configure(name = "local_config_nccl") \ No newline at end of file diff --git a/tensorflow-core/tensorflow-core-native/pom.xml b/tensorflow-core/tensorflow-core-native/pom.xml index 23682984a4a..3814864439a 100644 --- a/tensorflow-core/tensorflow-core-native/pom.xml +++ b/tensorflow-core/tensorflow-core-native/pom.xml @@ -6,7 +6,7 @@ org.tensorflow tensorflow-core - 1.0.0-SNAPSHOT + 1.1.0-SNAPSHOT tensorflow-core-native jar @@ -113,12 +113,6 @@ ${project.version} ${javacpp.platform.linux-x86_64}-gpu - - ${project.groupId} - ${project.artifactId} - ${project.version} - ${javacpp.platform.macosx-x86_64} - ${project.groupId} ${project.artifactId} @@ -167,10 +161,6 @@ ${project.build.directory}/${project.artifactId}-${project.version}-${javacpp.platform.macosx-arm64}.jar ${javacpp.platform.macosx-arm64} - - ${project.build.directory}/${project.artifactId}-${project.version}-${javacpp.platform.macosx-x86_64}.jar - ${javacpp.platform.macosx-x86_64} - ${project.build.directory}/${project.artifactId}-${project.version}-${javacpp.platform.windows-x86_64}.jar ${javacpp.platform.windows-x86_64} @@ -329,6 +319,7 @@ ${project.basedir}/bazel-${project.artifactId}/external/org_tensorflow/ ${project.basedir}/bazel-${project.artifactId}/external/local_tsl/ + ${project.basedir}/bazel-${project.artifactId}/external/local_xla/ ${project.basedir}/bazel-bin/external/org_tensorflow/ ${project.basedir}/bazel-${project.artifactId}/external/com_google_absl/ ${project.basedir}/bazel-${project.artifactId}/external/eigen_archive/ diff --git a/tensorflow-core/tensorflow-core-native/scripts/bazel_generate.sh b/tensorflow-core/tensorflow-core-native/scripts/bazel_generate.sh index 9d9941d1cf8..ab0fd0ec6c1 100755 --- a/tensorflow-core/tensorflow-core-native/scripts/bazel_generate.sh +++ b/tensorflow-core/tensorflow-core-native/scripts/bazel_generate.sh @@ -24,7 +24,7 @@ cp -f $TENSORFLOW_SRCS/core/ops/ops.pbtxt $GEN_RESOURCE_DIR/org/tensorflow cp -rf $TENSORFLOW_SRCS/core/api_def/base_api $GEN_RESOURCE_DIR/org/tensorflow/ # Copy generated Java protos from source jars -echo "Extracting TF/TSL proto Java sources" +echo "Extracting TF/TSL/XLA proto Java sources" cd $GEN_SRCS_DIR -find $TENSORFLOW_BIN $BAZEL_BIN/external/local_tsl/tsl -name \*-speed-src.jar -exec jar xf {} \; +find $TENSORFLOW_BIN $BAZEL_BIN/external/local_tsl/tsl $BAZEL_BIN/external/local_xla/xla -name \*-speed-src.jar -exec jar xf {} \; rm -rf META-INF diff --git a/tensorflow-core/tensorflow-core-native/scripts/dist_download.sh b/tensorflow-core/tensorflow-core-native/scripts/dist_download.sh index 7727faefaed..acf28b9391d 100755 --- a/tensorflow-core/tensorflow-core-native/scripts/dist_download.sh +++ b/tensorflow-core/tensorflow-core-native/scripts/dist_download.sh @@ -5,23 +5,20 @@ DOWNLOAD_FOLDER="$1" case ${PLATFORM:-} in 'linux-x86_64') - WHEEL_URL='https://files.pythonhosted.org/packages/c6/d9/f2ff325194b8e8acb6b69f303c838b0486f41b8028ec42261f2eb037a031/tensorflow_cpu-2.16.2-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl' + WHEEL_URL='https://files.pythonhosted.org/packages/aa/1d/032a9d40762895e51cad06f382135c14d16487a0ad9dcc65aae5bd89c968/tensorflow_cpu-2.18.0-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl' ;; 'linux-x86_64-gpu') - WHEEL_URL='https://files.pythonhosted.org/packages/43/dd/8f03331107b76e63313d2089ddfbd13f15e51fb8ed73517cdd0ab3341928/tensorflow-2.16.2-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl' + WHEEL_URL='https://files.pythonhosted.org/packages/84/76/c55967ac9968ddaede25a4dce37aba37e9030656f02c12676151ce1b6f22/tensorflow-2.18.0-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl' ;; - 'macosx-x86_64') - WHEEL_URL='https://files.pythonhosted.org/packages/6d/69/9999c2d9e8a3b08dfcfc7e9259a05fb1da5f700936091d2eb4a7985c2776/tensorflow-2.16.2-cp311-cp311-macosx_10_15_x86_64.whl' + 'linux-arm64') + WHEEL_URL='https://files.pythonhosted.org/packages/56/e4/55aaac2b15af4dad079e5af329a79d961e5206589d0e02b1e8da221472ed/tensorflow-2.18.0-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl' ;; 'macosx-arm64') - WHEEL_URL='https://files.pythonhosted.org/packages/9d/72/6f09443493b9df2fd8a9585c9af4d9453762906a8e5735a8a5efa6e3d1e3/tensorflow-2.16.2-cp311-cp311-macosx_12_0_arm64.whl' + WHEEL_URL='https://files.pythonhosted.org/packages/26/08/556c4159675c1a30e077ec2a942eeeb81b457cc35c247a5b4a59a1274f05/tensorflow-2.18.0-cp311-cp311-macosx_12_0_arm64.whl' ;; 'windows-x86_64') - WHEEL_URL='https://files.pythonhosted.org/packages/46/87/c3e4e9fe7c630f38a6984afdd1d4ed531ef9c74dc66b86f46f6bdd89d608/tensorflow_intel-2.16.2-cp311-cp311-win_amd64.whl' - CLIB_URL='https://storage.googleapis.com/tensorflow/versions/2.16.2/libtensorflow-cpu-windows-x86_64.zip' - ;; - 'linux-arm64') - WHEEL_URL='https://files.pythonhosted.org/packages/b5/01/c03e98c8e97d151d9ce075fae210f838832eb53d8aa55669d384cb72925b/tensorflow-2.16.2-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl' + WHEEL_URL='https://files.pythonhosted.org/packages/76/ad/fa6c508a15ff79cb5409294c293388e0999b7d480f84b65e4287277434fe/tensorflow_intel-2.18.0-cp311-cp311-win_amd64.whl' + CLIB_URL='https://storage.googleapis.com/tensorflow/versions/2.18.0/libtensorflow-cpu-windows-x86_64.zip' ;; *) echo "TensorFlow distribution for ${PLATFORM} is not supported for download" @@ -55,7 +52,8 @@ if [[ "$PLATFORM" =~ "linux" ]]; then ln -fs libtensorflow_cc.so.2 libtensorflow_cc.so ln -fs libtensorflow_framework.so.2 libtensorflow_framework.so if [[ "$PLATFORM" == "linux-arm64" ]]; then - ln -fs libomp-e9212f90.so.5 libomp-e9212f90.so + cp ../tensorflow.libs/libomp-6196b3b5.so.5 libomp-6196b3b5.so.5 + ln -fs libomp-6196b3b5.so.5 libomp-6196b3b5.so fi elif [[ "$PLATFORM" =~ "macosx" ]]; then ln -fs libtensorflow_cc.2.dylib libtensorflow_cc.dylib diff --git a/tensorflow-core/tensorflow-core-native/src/gen/java/org/tensorflow/internal/c_api/global/tensorflow.java b/tensorflow-core/tensorflow-core-native/src/gen/java/org/tensorflow/internal/c_api/global/tensorflow.java index 2a80e6bb86d..8a6ef604683 100644 --- a/tensorflow-core/tensorflow-core-native/src/gen/java/org/tensorflow/internal/c_api/global/tensorflow.java +++ b/tensorflow-core/tensorflow-core-native/src/gen/java/org/tensorflow/internal/c_api/global/tensorflow.java @@ -1,4 +1,4 @@ -// Targeted by JavaCPP version 1.5.10: DO NOT EDIT THIS FILE +// Targeted by JavaCPP version 1.5.12-SNAPSHOT: DO NOT EDIT THIS FILE package org.tensorflow.internal.c_api.global; @@ -274,7 +274,7 @@ public static native void TF_TString_Copy(TF_TString dst, String src, // #endif // TENSORFLOW_TSL_PLATFORM_CTSTRING_H_ -// Parsed from tsl/c/tsl_status.h +// Parsed from xla/tsl/c/tsl_status.h /* Copyright 2019 The TensorFlow Authors. All Rights Reserved. @@ -291,8 +291,8 @@ public static native void TF_TString_Copy(TF_TString dst, String src, limitations under the License. ==============================================================================*/ -// #ifndef TENSORFLOW_TSL_C_TSL_STATUS_H_ -// #define TENSORFLOW_TSL_C_TSL_STATUS_H_ +// #ifndef XLA_TSL_C_TSL_STATUS_H_ +// #define XLA_TSL_C_TSL_STATUS_H_ // #ifdef __cplusplus // #endif @@ -351,7 +351,7 @@ public static native void TF_TString_Copy(TF_TString dst, String src, // #ifdef __cplusplus /* end extern "C" */ // #endif -// #endif // TENSORFLOW_TSL_C_TSL_STATUS_H_ +// #endif // XLA_TSL_C_TSL_STATUS_H_ // Parsed from tensorflow/c/c_api_macros.h @@ -507,7 +507,7 @@ public static native void TF_TString_Copy(TF_TString dst, String src, // #define TENSORFLOW_C_TF_STATUS_H_ // #include "tensorflow/c/c_api_macros.h" -// #include "tsl/c/tsl_status.h" +// #include "xla/tsl/c/tsl_status.h" // #ifdef __cplusplus // Targeting ../TF_Status.java @@ -689,6 +689,13 @@ public static native TF_Tensor TF_NewTensor( Deallocator_Pointer_long_Pointer deallocator, Pointer deallocator_arg); +// Returns the alignment, in bytes, required for allocating aligned tensors. +// +// This can be used in combination with TF_NewTensor to manually manage +// memory while ensuring the resulting tensors satisfy TensorFlow's +// memory alignment preferences. +public static native @Cast("size_t") long TF_TensorDefaultAlignment(); + // Allocate and return a new Tensor. // // This function is an alternative to TF_NewTensor and should be used when @@ -4367,7 +4374,8 @@ public static native void TFE_ContextUpdateServerDefWithTimeout( // This API is for experimental usage and may be subject to change. public static native void TFE_ContextSetServerDefWithTimeout( TFE_Context ctx, int keep_alive_secs, @Const Pointer proto, @Cast("size_t") long proto_len, - @Cast("int64_t") long init_timeout_in_ms, TF_Status status); + @Cast("int64_t") long init_timeout_in_ms, TF_Status status, + @Cast("bool") boolean clear_existing_contexts); // Set server def with retries and timeout. This is helpful for fault-tolerant // initial connection in high-preemption environments, such as @@ -4375,7 +4383,8 @@ public static native void TFE_ContextSetServerDefWithTimeout( // This API is for experimental usage and may be subject to change. public static native void TFE_ContextSetServerDefWithTimeoutAndRetries( TFE_Context ctx, int keep_alive_secs, @Const Pointer proto, @Cast("size_t") long proto_len, - @Cast("int64_t") long init_timeout_in_ms, int retries, TF_Status status); + @Cast("int64_t") long init_timeout_in_ms, int retries, TF_Status status, + @Cast("bool") boolean clear_existing_contexts); // Checks whether a remote worker is alive or not. This will return true even if // the context doesn't exist on the remote worker. diff --git a/tensorflow-core/tensorflow-core-native/src/gen/java/org/tensorflow/proto/AvailableDeviceInfo.java b/tensorflow-core/tensorflow-core-native/src/gen/java/org/tensorflow/proto/AvailableDeviceInfo.java index e71192c47b2..50aa7d93009 100644 --- a/tensorflow-core/tensorflow-core-native/src/gen/java/org/tensorflow/proto/AvailableDeviceInfo.java +++ b/tensorflow-core/tensorflow-core-native/src/gen/java/org/tensorflow/proto/AvailableDeviceInfo.java @@ -1,5 +1,5 @@ // Generated by the protocol buffer compiler. DO NOT EDIT! -// source: tsl/protobuf/test_log.proto +// source: xla/tsl/protobuf/test_log.proto package org.tensorflow.proto; diff --git a/tensorflow-core/tensorflow-core-native/src/gen/java/org/tensorflow/proto/AvailableDeviceInfoOrBuilder.java b/tensorflow-core/tensorflow-core-native/src/gen/java/org/tensorflow/proto/AvailableDeviceInfoOrBuilder.java index ef9f13504d3..c35a7c6a745 100644 --- a/tensorflow-core/tensorflow-core-native/src/gen/java/org/tensorflow/proto/AvailableDeviceInfoOrBuilder.java +++ b/tensorflow-core/tensorflow-core-native/src/gen/java/org/tensorflow/proto/AvailableDeviceInfoOrBuilder.java @@ -1,5 +1,5 @@ // Generated by the protocol buffer compiler. DO NOT EDIT! -// source: tsl/protobuf/test_log.proto +// source: xla/tsl/protobuf/test_log.proto package org.tensorflow.proto; diff --git a/tensorflow-core/tensorflow-core-native/src/gen/java/org/tensorflow/proto/BenchmarkEntries.java b/tensorflow-core/tensorflow-core-native/src/gen/java/org/tensorflow/proto/BenchmarkEntries.java index b3ed52d11c0..73be037bfe8 100644 --- a/tensorflow-core/tensorflow-core-native/src/gen/java/org/tensorflow/proto/BenchmarkEntries.java +++ b/tensorflow-core/tensorflow-core-native/src/gen/java/org/tensorflow/proto/BenchmarkEntries.java @@ -1,5 +1,5 @@ // Generated by the protocol buffer compiler. DO NOT EDIT! -// source: tsl/protobuf/test_log.proto +// source: xla/tsl/protobuf/test_log.proto package org.tensorflow.proto; diff --git a/tensorflow-core/tensorflow-core-native/src/gen/java/org/tensorflow/proto/BenchmarkEntriesOrBuilder.java b/tensorflow-core/tensorflow-core-native/src/gen/java/org/tensorflow/proto/BenchmarkEntriesOrBuilder.java index b99b30bf045..de029d1d399 100644 --- a/tensorflow-core/tensorflow-core-native/src/gen/java/org/tensorflow/proto/BenchmarkEntriesOrBuilder.java +++ b/tensorflow-core/tensorflow-core-native/src/gen/java/org/tensorflow/proto/BenchmarkEntriesOrBuilder.java @@ -1,5 +1,5 @@ // Generated by the protocol buffer compiler. DO NOT EDIT! -// source: tsl/protobuf/test_log.proto +// source: xla/tsl/protobuf/test_log.proto package org.tensorflow.proto; diff --git a/tensorflow-core/tensorflow-core-native/src/gen/java/org/tensorflow/proto/BenchmarkEntry.java b/tensorflow-core/tensorflow-core-native/src/gen/java/org/tensorflow/proto/BenchmarkEntry.java index 0c470285827..efe111640d5 100644 --- a/tensorflow-core/tensorflow-core-native/src/gen/java/org/tensorflow/proto/BenchmarkEntry.java +++ b/tensorflow-core/tensorflow-core-native/src/gen/java/org/tensorflow/proto/BenchmarkEntry.java @@ -1,5 +1,5 @@ // Generated by the protocol buffer compiler. DO NOT EDIT! -// source: tsl/protobuf/test_log.proto +// source: xla/tsl/protobuf/test_log.proto package org.tensorflow.proto; diff --git a/tensorflow-core/tensorflow-core-native/src/gen/java/org/tensorflow/proto/BenchmarkEntryOrBuilder.java b/tensorflow-core/tensorflow-core-native/src/gen/java/org/tensorflow/proto/BenchmarkEntryOrBuilder.java index 476aae9ca10..fba00ccb7f1 100644 --- a/tensorflow-core/tensorflow-core-native/src/gen/java/org/tensorflow/proto/BenchmarkEntryOrBuilder.java +++ b/tensorflow-core/tensorflow-core-native/src/gen/java/org/tensorflow/proto/BenchmarkEntryOrBuilder.java @@ -1,5 +1,5 @@ // Generated by the protocol buffer compiler. DO NOT EDIT! -// source: tsl/protobuf/test_log.proto +// source: xla/tsl/protobuf/test_log.proto package org.tensorflow.proto; diff --git a/tensorflow-core/tensorflow-core-native/src/gen/java/org/tensorflow/proto/BfcMemoryMap.java b/tensorflow-core/tensorflow-core-native/src/gen/java/org/tensorflow/proto/BfcMemoryMap.java index fad0c98b837..e894298881d 100644 --- a/tensorflow-core/tensorflow-core-native/src/gen/java/org/tensorflow/proto/BfcMemoryMap.java +++ b/tensorflow-core/tensorflow-core-native/src/gen/java/org/tensorflow/proto/BfcMemoryMap.java @@ -1,5 +1,5 @@ // Generated by the protocol buffer compiler. DO NOT EDIT! -// source: tsl/protobuf/bfc_memory_map.proto +// source: xla/tsl/protobuf/bfc_memory_map.proto package org.tensorflow.proto; @@ -5091,28 +5091,28 @@ public org.tensorflow.proto.BfcMemoryMap.MemoryDump getDefaultInstanceForType() descriptor; static { java.lang.String[] descriptorData = { - "\n!tsl/protobuf/bfc_memory_map.proto\022\nten" + - "sorflow\"\222\001\n\021MemAllocatorStats\022\022\n\nnum_all" + - "ocs\030\001 \001(\003\022\024\n\014bytes_in_use\030\002 \001(\003\022\031\n\021peak_" + - "bytes_in_use\030\003 \001(\003\022\032\n\022largest_alloc_size" + - "\030\004 \001(\003\022\034\n\024fragmentation_metric\030\005 \001(\002\"\256\001\n" + - "\010MemChunk\022\017\n\007address\030\001 \001(\004\022\014\n\004size\030\002 \001(\003" + - "\022\026\n\016requested_size\030\003 \001(\003\022\013\n\003bin\030\004 \001(\005\022\017\n" + - "\007op_name\030\005 \001(\t\022\026\n\016freed_at_count\030\006 \001(\004\022\024" + - "\n\014action_count\030\007 \001(\004\022\016\n\006in_use\030\010 \001(\010\022\017\n\007" + - "step_id\030\t \001(\004\"\213\001\n\nBinSummary\022\013\n\003bin\030\001 \001(" + - "\005\022\032\n\022total_bytes_in_use\030\002 \001(\003\022\032\n\022total_b" + - "ytes_in_bin\030\003 \001(\003\022\033\n\023total_chunks_in_use" + - "\030\004 \001(\003\022\033\n\023total_chunks_in_bin\030\005 \001(\003\".\n\010S" + - "napShot\022\024\n\014action_count\030\001 \001(\004\022\014\n\004size\030\002 " + - "\001(\003\"\315\001\n\nMemoryDump\022\026\n\016allocator_name\030\001 \001" + - "(\t\022+\n\013bin_summary\030\002 \003(\0132\026.tensorflow.Bin" + - "Summary\022#\n\005chunk\030\003 \003(\0132\024.tensorflow.MemC" + - "hunk\022\'\n\tsnap_shot\030\004 \003(\0132\024.tensorflow.Sna" + - "pShot\022,\n\005stats\030\005 \001(\0132\035.tensorflow.MemAll" + - "ocatorStatsBV\n\024org.tensorflow.protoZ>git" + - "hub.com/google/tsl/tsl/go/protobuf/for_c" + - "ore_protos_go_protob\006proto3" + "\n%xla/tsl/protobuf/bfc_memory_map.proto\022" + + "\ntensorflow\"\222\001\n\021MemAllocatorStats\022\022\n\nnum" + + "_allocs\030\001 \001(\003\022\024\n\014bytes_in_use\030\002 \001(\003\022\031\n\021p" + + "eak_bytes_in_use\030\003 \001(\003\022\032\n\022largest_alloc_" + + "size\030\004 \001(\003\022\034\n\024fragmentation_metric\030\005 \001(\002" + + "\"\256\001\n\010MemChunk\022\017\n\007address\030\001 \001(\004\022\014\n\004size\030\002" + + " \001(\003\022\026\n\016requested_size\030\003 \001(\003\022\013\n\003bin\030\004 \001(" + + "\005\022\017\n\007op_name\030\005 \001(\t\022\026\n\016freed_at_count\030\006 \001" + + "(\004\022\024\n\014action_count\030\007 \001(\004\022\016\n\006in_use\030\010 \001(\010" + + "\022\017\n\007step_id\030\t \001(\004\"\213\001\n\nBinSummary\022\013\n\003bin\030" + + "\001 \001(\005\022\032\n\022total_bytes_in_use\030\002 \001(\003\022\032\n\022tot" + + "al_bytes_in_bin\030\003 \001(\003\022\033\n\023total_chunks_in" + + "_use\030\004 \001(\003\022\033\n\023total_chunks_in_bin\030\005 \001(\003\"" + + ".\n\010SnapShot\022\024\n\014action_count\030\001 \001(\004\022\014\n\004siz" + + "e\030\002 \001(\003\"\315\001\n\nMemoryDump\022\026\n\016allocator_name" + + "\030\001 \001(\t\022+\n\013bin_summary\030\002 \003(\0132\026.tensorflow" + + ".BinSummary\022#\n\005chunk\030\003 \003(\0132\024.tensorflow." + + "MemChunk\022\'\n\tsnap_shot\030\004 \003(\0132\024.tensorflow" + + ".SnapShot\022,\n\005stats\030\005 \001(\0132\035.tensorflow.Me" + + "mAllocatorStatsBV\n\024org.tensorflow.protoZ" + + ">github.com/google/tsl/tsl/go/protobuf/f" + + "or_core_protos_go_protob\006proto3" }; descriptor = com.google.protobuf.Descriptors.FileDescriptor .internalBuildGeneratedFileFrom(descriptorData, diff --git a/tensorflow-core/tensorflow-core-native/src/gen/java/org/tensorflow/proto/BuildConfiguration.java b/tensorflow-core/tensorflow-core-native/src/gen/java/org/tensorflow/proto/BuildConfiguration.java index 8e3f0c9e7b5..19b464ffb52 100644 --- a/tensorflow-core/tensorflow-core-native/src/gen/java/org/tensorflow/proto/BuildConfiguration.java +++ b/tensorflow-core/tensorflow-core-native/src/gen/java/org/tensorflow/proto/BuildConfiguration.java @@ -1,5 +1,5 @@ // Generated by the protocol buffer compiler. DO NOT EDIT! -// source: tsl/protobuf/test_log.proto +// source: xla/tsl/protobuf/test_log.proto package org.tensorflow.proto; diff --git a/tensorflow-core/tensorflow-core-native/src/gen/java/org/tensorflow/proto/BuildConfigurationOrBuilder.java b/tensorflow-core/tensorflow-core-native/src/gen/java/org/tensorflow/proto/BuildConfigurationOrBuilder.java index 0f4bc0c0740..112534dc95a 100644 --- a/tensorflow-core/tensorflow-core-native/src/gen/java/org/tensorflow/proto/BuildConfigurationOrBuilder.java +++ b/tensorflow-core/tensorflow-core-native/src/gen/java/org/tensorflow/proto/BuildConfigurationOrBuilder.java @@ -1,5 +1,5 @@ // Generated by the protocol buffer compiler. DO NOT EDIT! -// source: tsl/protobuf/test_log.proto +// source: xla/tsl/protobuf/test_log.proto package org.tensorflow.proto; diff --git a/tensorflow-core/tensorflow-core-native/src/gen/java/org/tensorflow/proto/CPUInfo.java b/tensorflow-core/tensorflow-core-native/src/gen/java/org/tensorflow/proto/CPUInfo.java index 906c5e01a83..3816e55e459 100644 --- a/tensorflow-core/tensorflow-core-native/src/gen/java/org/tensorflow/proto/CPUInfo.java +++ b/tensorflow-core/tensorflow-core-native/src/gen/java/org/tensorflow/proto/CPUInfo.java @@ -1,5 +1,5 @@ // Generated by the protocol buffer compiler. DO NOT EDIT! -// source: tsl/protobuf/test_log.proto +// source: xla/tsl/protobuf/test_log.proto package org.tensorflow.proto; diff --git a/tensorflow-core/tensorflow-core-native/src/gen/java/org/tensorflow/proto/CPUInfoOrBuilder.java b/tensorflow-core/tensorflow-core-native/src/gen/java/org/tensorflow/proto/CPUInfoOrBuilder.java index de66bb23d57..9ede760853d 100644 --- a/tensorflow-core/tensorflow-core-native/src/gen/java/org/tensorflow/proto/CPUInfoOrBuilder.java +++ b/tensorflow-core/tensorflow-core-native/src/gen/java/org/tensorflow/proto/CPUInfoOrBuilder.java @@ -1,5 +1,5 @@ // Generated by the protocol buffer compiler. DO NOT EDIT! -// source: tsl/protobuf/test_log.proto +// source: xla/tsl/protobuf/test_log.proto package org.tensorflow.proto; diff --git a/tensorflow-core/tensorflow-core-native/src/gen/java/org/tensorflow/proto/CommitId.java b/tensorflow-core/tensorflow-core-native/src/gen/java/org/tensorflow/proto/CommitId.java index 3fdd1c804b6..9f6ad5f08bc 100644 --- a/tensorflow-core/tensorflow-core-native/src/gen/java/org/tensorflow/proto/CommitId.java +++ b/tensorflow-core/tensorflow-core-native/src/gen/java/org/tensorflow/proto/CommitId.java @@ -1,5 +1,5 @@ // Generated by the protocol buffer compiler. DO NOT EDIT! -// source: tsl/protobuf/test_log.proto +// source: xla/tsl/protobuf/test_log.proto package org.tensorflow.proto; diff --git a/tensorflow-core/tensorflow-core-native/src/gen/java/org/tensorflow/proto/CommitIdOrBuilder.java b/tensorflow-core/tensorflow-core-native/src/gen/java/org/tensorflow/proto/CommitIdOrBuilder.java index 1b124825e66..cb78f3bd9d2 100644 --- a/tensorflow-core/tensorflow-core-native/src/gen/java/org/tensorflow/proto/CommitIdOrBuilder.java +++ b/tensorflow-core/tensorflow-core-native/src/gen/java/org/tensorflow/proto/CommitIdOrBuilder.java @@ -1,5 +1,5 @@ // Generated by the protocol buffer compiler. DO NOT EDIT! -// source: tsl/protobuf/test_log.proto +// source: xla/tsl/protobuf/test_log.proto package org.tensorflow.proto; diff --git a/tensorflow-core/tensorflow-core-native/src/gen/java/org/tensorflow/proto/ConfigProto.java b/tensorflow-core/tensorflow-core-native/src/gen/java/org/tensorflow/proto/ConfigProto.java index 04d15e4a308..5dcca1ed5f7 100644 --- a/tensorflow-core/tensorflow-core-native/src/gen/java/org/tensorflow/proto/ConfigProto.java +++ b/tensorflow-core/tensorflow-core-native/src/gen/java/org/tensorflow/proto/ConfigProto.java @@ -354,6 +354,17 @@ public interface ExperimentalOrBuilder extends */ boolean getEnableMultiHost(); + /** + * 

    +     * If true, use ifrt as the backend for TFRT. This is only used when
+     * `use_tfrt` is true.
+     *
    + * + * bool tfrt_use_ifrt = 32; + * @return The tfrtUseIfrt. + */ + boolean getTfrtUseIfrt(); + /** * 
          * Port for the Pathways server. Ignored if enable_multi_host=false.
@@ -1101,6 +1112,22 @@ public boolean getEnableMultiHost() {
       return enableMultiHost_;
     }
 
+    public static final int TFRT_USE_IFRT_FIELD_NUMBER = 32;
+    private boolean tfrtUseIfrt_;
+    /**
+     * 
    +     * If true, use ifrt as the backend for TFRT. This is only used when
+     * `use_tfrt` is true.
+     * 
    
+     *
+     * bool tfrt_use_ifrt = 32;
+     * @return The tfrtUseIfrt.
+     */
+    @java.lang.Override
+    public boolean getTfrtUseIfrt() {
+      return tfrtUseIfrt_;
+    }
+
     public static final int BACKEND_SERVER_PORT_FIELD_NUMBER = 28;
     private int backendServerPort_;
     /**
@@ -1369,6 +1396,9 @@ public void writeTo(com.google.protobuf.CodedOutputStream output)
       if (streamMergeThreshold_ != 0) {
         output.writeInt32(31, streamMergeThreshold_);
       }
+      if (tfrtUseIfrt_ != false) {
+        output.writeBool(32, tfrtUseIfrt_);
+      }
       getUnknownFields().writeTo(output);
     }
 
@@ -1484,6 +1514,10 @@ public int getSerializedSize() {
         size += com.google.protobuf.CodedOutputStream
           .computeInt32Size(31, streamMergeThreshold_);
       }
+      if (tfrtUseIfrt_ != false) {
+        size += com.google.protobuf.CodedOutputStream
+          .computeBoolSize(32, tfrtUseIfrt_);
+      }
       size += getUnknownFields().getSerializedSize();
       memoizedSize = size;
       return size;
@@ -1537,6 +1571,8 @@ public boolean equals(final java.lang.Object obj) {
           != other.getUseTfrt()) return false;
       if (getEnableMultiHost()
           != other.getEnableMultiHost()) return false;
+      if (getTfrtUseIfrt()
+          != other.getTfrtUseIfrt()) return false;
       if (getBackendServerPort()
           != other.getBackendServerPort()) return false;
       if (getTargetTpu()
@@ -1620,6 +1656,9 @@ public int hashCode() {
       hash = (37 * hash) + ENABLE_MULTI_HOST_FIELD_NUMBER;
       hash = (53 * hash) + com.google.protobuf.Internal.hashBoolean(
           getEnableMultiHost());
+      hash = (37 * hash) + TFRT_USE_IFRT_FIELD_NUMBER;
+      hash = (53 * hash) + com.google.protobuf.Internal.hashBoolean(
+          getTfrtUseIfrt());
       hash = (37 * hash) + BACKEND_SERVER_PORT_FIELD_NUMBER;
       hash = (53 * hash) + getBackendServerPort();
       hash = (37 * hash) + TARGET_TPU_FIELD_NUMBER;
@@ -1820,6 +1859,8 @@ public Builder clear() {
 
         enableMultiHost_ = false;
 
+        tfrtUseIfrt_ = false;
+
         backendServerPort_ = 0;
 
         targetTpu_ = false;
@@ -1890,6 +1931,7 @@ public org.tensorflow.proto.ConfigProto.Experimental buildPartial() {
         result.xlaFusionAutotunerThresh_ = xlaFusionAutotunerThresh_;
         result.useTfrt_ = useTfrt_;
         result.enableMultiHost_ = enableMultiHost_;
+        result.tfrtUseIfrt_ = tfrtUseIfrt_;
         result.backendServerPort_ = backendServerPort_;
         result.targetTpu_ = targetTpu_;
         result.targetGpu_ = targetGpu_;
@@ -2007,6 +2049,9 @@ public Builder mergeFrom(org.tensorflow.proto.ConfigProto.Experimental other) {
         if (other.getEnableMultiHost() != false) {
           setEnableMultiHost(other.getEnableMultiHost());
         }
+        if (other.getTfrtUseIfrt() != false) {
+          setTfrtUseIfrt(other.getTfrtUseIfrt());
+        }
         if (other.getBackendServerPort() != 0) {
           setBackendServerPort(other.getBackendServerPort());
         }
@@ -2199,6 +2244,11 @@ public Builder mergeFrom(
 
                 break;
               } // case 248
+              case 256: {
+                tfrtUseIfrt_ = input.readBool();
+
+                break;
+              } // case 256
               default: {
                 if (!super.parseUnknownField(input, extensionRegistry, tag)) {
                   done = true; // was an endgroup tag
@@ -3423,6 +3473,52 @@ public Builder clearEnableMultiHost() {
         return this;
       }
 
+      private boolean tfrtUseIfrt_ ;
+      /**
+       * 
    +       * If true, use ifrt as the backend for TFRT. This is only used when
+       * `use_tfrt` is true.
+       * 
    
+       *
+       * bool tfrt_use_ifrt = 32;
+       * @return The tfrtUseIfrt.
+       */
+      @java.lang.Override
+      public boolean getTfrtUseIfrt() {
+        return tfrtUseIfrt_;
+      }
+      /**
+       * 
    +       * If true, use ifrt as the backend for TFRT. This is only used when
+       * `use_tfrt` is true.
+       * 
    
+       *
+       * bool tfrt_use_ifrt = 32;
+       * @param value The tfrtUseIfrt to set.
+       * @return This builder for chaining.
+       */
+      public Builder setTfrtUseIfrt(boolean value) {
+        
+        tfrtUseIfrt_ = value;
+        onChanged();
+        return this;
+      }
+      /**
+       * 
    +       * If true, use ifrt as the backend for TFRT. This is only used when
+       * `use_tfrt` is true.
+       * 
    
+       *
+       * bool tfrt_use_ifrt = 32;
+       * @return This builder for chaining.
+       */
+      public Builder clearTfrtUseIfrt() {
+        
+        tfrtUseIfrt_ = false;
+        onChanged();
+        return this;
+      }
+
       private int backendServerPort_ ;
       /**
        * 
    @@ -4461,6 +4557,44 @@ public org.tensorflow.proto.GPUOptionsOrBuilder getGpuOptionsOrBuilder() {
     return getGpuOptions();
   }
 
+  public static final int PLUGGABLE_DEVICE_OPTIONS_FIELD_NUMBER = 18;
+  private org.tensorflow.proto.GPUOptions pluggableDeviceOptions_;
+  /**
+   * 
    +   * Options that apply to pluggable devices.
+   * 
    
+   *
+   * .tensorflow.GPUOptions pluggable_device_options = 18;
+   * @return Whether the pluggableDeviceOptions field is set.
+   */
+  @java.lang.Override
+  public boolean hasPluggableDeviceOptions() {
+    return pluggableDeviceOptions_ != null;
+  }
+  /**
+   * 
    +   * Options that apply to pluggable devices.
+   * 
    
+   *
+   * .tensorflow.GPUOptions pluggable_device_options = 18;
+   * @return The pluggableDeviceOptions.
+   */
+  @java.lang.Override
+  public org.tensorflow.proto.GPUOptions getPluggableDeviceOptions() {
+    return pluggableDeviceOptions_ == null ? org.tensorflow.proto.GPUOptions.getDefaultInstance() : pluggableDeviceOptions_;
+  }
+  /**
+   * 
    +   * Options that apply to pluggable devices.
+   * 
    
+   *
+   * .tensorflow.GPUOptions pluggable_device_options = 18;
+   */
+  @java.lang.Override
+  public org.tensorflow.proto.GPUOptionsOrBuilder getPluggableDeviceOptionsOrBuilder() {
+    return getPluggableDeviceOptions();
+  }
+
   public static final int ALLOW_SOFT_PLACEMENT_FIELD_NUMBER = 7;
   private boolean allowSoftPlacement_;
   /**
@@ -4757,6 +4891,9 @@ public void writeTo(com.google.protobuf.CodedOutputStream output)
     if (shareClusterDevicesInSession_ != false) {
       output.writeBool(17, shareClusterDevicesInSession_);
     }
+    if (pluggableDeviceOptions_ != null) {
+      output.writeMessage(18, getPluggableDeviceOptions());
+    }
     getUnknownFields().writeTo(output);
   }
 
@@ -4844,6 +4981,10 @@ public int getSerializedSize() {
       size += com.google.protobuf.CodedOutputStream
         .computeBoolSize(17, shareClusterDevicesInSession_);
     }
+    if (pluggableDeviceOptions_ != null) {
+      size += com.google.protobuf.CodedOutputStream
+        .computeMessageSize(18, getPluggableDeviceOptions());
+    }
     size += getUnknownFields().getSerializedSize();
     memoizedSize = size;
     return size;
@@ -4878,6 +5019,11 @@ public boolean equals(final java.lang.Object obj) {
       if (!getGpuOptions()
           .equals(other.getGpuOptions())) return false;
     }
+    if (hasPluggableDeviceOptions() != other.hasPluggableDeviceOptions()) return false;
+    if (hasPluggableDeviceOptions()) {
+      if (!getPluggableDeviceOptions()
+          .equals(other.getPluggableDeviceOptions())) return false;
+    }
     if (getAllowSoftPlacement()
         != other.getAllowSoftPlacement()) return false;
     if (getLogDevicePlacement()
@@ -4944,6 +5090,10 @@ public int hashCode() {
       hash = (37 * hash) + GPU_OPTIONS_FIELD_NUMBER;
       hash = (53 * hash) + getGpuOptions().hashCode();
     }
+    if (hasPluggableDeviceOptions()) {
+      hash = (37 * hash) + PLUGGABLE_DEVICE_OPTIONS_FIELD_NUMBER;
+      hash = (53 * hash) + getPluggableDeviceOptions().hashCode();
+    }
     hash = (37 * hash) + ALLOW_SOFT_PLACEMENT_FIELD_NUMBER;
     hash = (53 * hash) + com.google.protobuf.Internal.hashBoolean(
         getAllowSoftPlacement());
@@ -5154,6 +5304,12 @@ public Builder clear() {
         gpuOptions_ = null;
         gpuOptionsBuilder_ = null;
       }
+      if (pluggableDeviceOptionsBuilder_ == null) {
+        pluggableDeviceOptions_ = null;
+      } else {
+        pluggableDeviceOptions_ = null;
+        pluggableDeviceOptionsBuilder_ = null;
+      }
       allowSoftPlacement_ = false;
 
       logDevicePlacement_ = false;
@@ -5240,6 +5396,11 @@ public org.tensorflow.proto.ConfigProto buildPartial() {
       } else {
         result.gpuOptions_ = gpuOptionsBuilder_.build();
       }
+      if (pluggableDeviceOptionsBuilder_ == null) {
+        result.pluggableDeviceOptions_ = pluggableDeviceOptions_;
+      } else {
+        result.pluggableDeviceOptions_ = pluggableDeviceOptionsBuilder_.build();
+      }
       result.allowSoftPlacement_ = allowSoftPlacement_;
       result.logDevicePlacement_ = logDevicePlacement_;
       if (graphOptionsBuilder_ == null) {
@@ -5366,6 +5527,9 @@ public Builder mergeFrom(org.tensorflow.proto.ConfigProto other) {
       if (other.hasGpuOptions()) {
         mergeGpuOptions(other.getGpuOptions());
       }
+      if (other.hasPluggableDeviceOptions()) {
+        mergePluggableDeviceOptions(other.getPluggableDeviceOptions());
+      }
       if (other.getAllowSoftPlacement() != false) {
         setAllowSoftPlacement(other.getAllowSoftPlacement());
       }
@@ -5526,6 +5690,13 @@ public Builder mergeFrom(
 
               break;
             } // case 136
+            case 146: {
+              input.readMessage(
+                  getPluggableDeviceOptionsFieldBuilder().getBuilder(),
+                  extensionRegistry);
+
+              break;
+            } // case 146
             default: {
               if (!super.parseUnknownField(input, extensionRegistry, tag)) {
                 done = true; // was an endgroup tag
@@ -6886,6 +7057,161 @@ public org.tensorflow.proto.GPUOptionsOrBuilder getGpuOptionsOrBuilder() {
       return gpuOptionsBuilder_;
     }
 
+    private org.tensorflow.proto.GPUOptions pluggableDeviceOptions_;
+    private com.google.protobuf.SingleFieldBuilderV3<
+        org.tensorflow.proto.GPUOptions, org.tensorflow.proto.GPUOptions.Builder, org.tensorflow.proto.GPUOptionsOrBuilder> pluggableDeviceOptionsBuilder_;
+    /**
+     * 
    +     * Options that apply to pluggable devices.
+     * 
    
+     *
+     * .tensorflow.GPUOptions pluggable_device_options = 18;
+     * @return Whether the pluggableDeviceOptions field is set.
+     */
+    public boolean hasPluggableDeviceOptions() {
+      return pluggableDeviceOptionsBuilder_ != null || pluggableDeviceOptions_ != null;
+    }
+    /**
+     * 
    +     * Options that apply to pluggable devices.
+     * 
    
+     *
+     * .tensorflow.GPUOptions pluggable_device_options = 18;
+     * @return The pluggableDeviceOptions.
+     */
+    public org.tensorflow.proto.GPUOptions getPluggableDeviceOptions() {
+      if (pluggableDeviceOptionsBuilder_ == null) {
+        return pluggableDeviceOptions_ == null ? org.tensorflow.proto.GPUOptions.getDefaultInstance() : pluggableDeviceOptions_;
+      } else {
+        return pluggableDeviceOptionsBuilder_.getMessage();
+      }
+    }
+    /**
+     * 
    +     * Options that apply to pluggable devices.
+     * 
    
+     *
+     * .tensorflow.GPUOptions pluggable_device_options = 18;
+     */
+    public Builder setPluggableDeviceOptions(org.tensorflow.proto.GPUOptions value) {
+      if (pluggableDeviceOptionsBuilder_ == null) {
+        if (value == null) {
+          throw new NullPointerException();
+        }
+        pluggableDeviceOptions_ = value;
+        onChanged();
+      } else {
+        pluggableDeviceOptionsBuilder_.setMessage(value);
+      }
+
+      return this;
+    }
+    /**
+     * 
    +     * Options that apply to pluggable devices.
+     * 
    
+     *
+     * .tensorflow.GPUOptions pluggable_device_options = 18;
+     */
+    public Builder setPluggableDeviceOptions(
+        org.tensorflow.proto.GPUOptions.Builder builderForValue) {
+      if (pluggableDeviceOptionsBuilder_ == null) {
+        pluggableDeviceOptions_ = builderForValue.build();
+        onChanged();
+      } else {
+        pluggableDeviceOptionsBuilder_.setMessage(builderForValue.build());
+      }
+
+      return this;
+    }
+    /**
+     * 
    +     * Options that apply to pluggable devices.
+     * 
    
+     *
+     * .tensorflow.GPUOptions pluggable_device_options = 18;
+     */
+    public Builder mergePluggableDeviceOptions(org.tensorflow.proto.GPUOptions value) {
+      if (pluggableDeviceOptionsBuilder_ == null) {
+        if (pluggableDeviceOptions_ != null) {
+          pluggableDeviceOptions_ =
+            org.tensorflow.proto.GPUOptions.newBuilder(pluggableDeviceOptions_).mergeFrom(value).buildPartial();
+        } else {
+          pluggableDeviceOptions_ = value;
+        }
+        onChanged();
+      } else {
+        pluggableDeviceOptionsBuilder_.mergeFrom(value);
+      }
+
+      return this;
+    }
+    /**
+     * 
    +     * Options that apply to pluggable devices.
+     * 
    
+     *
+     * .tensorflow.GPUOptions pluggable_device_options = 18;
+     */
+    public Builder clearPluggableDeviceOptions() {
+      if (pluggableDeviceOptionsBuilder_ == null) {
+        pluggableDeviceOptions_ = null;
+        onChanged();
+      } else {
+        pluggableDeviceOptions_ = null;
+        pluggableDeviceOptionsBuilder_ = null;
+      }
+
+      return this;
+    }
+    /**
+     * 
    +     * Options that apply to pluggable devices.
+     * 
    
+     *
+     * .tensorflow.GPUOptions pluggable_device_options = 18;
+     */
+    public org.tensorflow.proto.GPUOptions.Builder getPluggableDeviceOptionsBuilder() {
+      
+      onChanged();
+      return getPluggableDeviceOptionsFieldBuilder().getBuilder();
+    }
+    /**
+     * 
    +     * Options that apply to pluggable devices.
+     * 
    
+     *
+     * .tensorflow.GPUOptions pluggable_device_options = 18;
+     */
+    public org.tensorflow.proto.GPUOptionsOrBuilder getPluggableDeviceOptionsOrBuilder() {
+      if (pluggableDeviceOptionsBuilder_ != null) {
+        return pluggableDeviceOptionsBuilder_.getMessageOrBuilder();
+      } else {
+        return pluggableDeviceOptions_ == null ?
+            org.tensorflow.proto.GPUOptions.getDefaultInstance() : pluggableDeviceOptions_;
+      }
+    }
+    /**
+     * 
    +     * Options that apply to pluggable devices.
+     * 
    
+     *
+     * .tensorflow.GPUOptions pluggable_device_options = 18;
+     */
+    private com.google.protobuf.SingleFieldBuilderV3<
+        org.tensorflow.proto.GPUOptions, org.tensorflow.proto.GPUOptions.Builder, org.tensorflow.proto.GPUOptionsOrBuilder> 
+        getPluggableDeviceOptionsFieldBuilder() {
+      if (pluggableDeviceOptionsBuilder_ == null) {
+        pluggableDeviceOptionsBuilder_ = new com.google.protobuf.SingleFieldBuilderV3<
+            org.tensorflow.proto.GPUOptions, org.tensorflow.proto.GPUOptions.Builder, org.tensorflow.proto.GPUOptionsOrBuilder>(
+                getPluggableDeviceOptions(),
+                getParentForChildren(),
+                isClean());
+        pluggableDeviceOptions_ = null;
+      }
+      return pluggableDeviceOptionsBuilder_;
+    }
+
     private boolean allowSoftPlacement_ ;
     /**
      * 
    diff --git a/tensorflow-core/tensorflow-core-native/src/gen/java/org/tensorflow/proto/ConfigProtoOrBuilder.java b/tensorflow-core/tensorflow-core-native/src/gen/java/org/tensorflow/proto/ConfigProtoOrBuilder.java
index d158b44e08f..29a052555c6 100644
--- a/tensorflow-core/tensorflow-core-native/src/gen/java/org/tensorflow/proto/ConfigProtoOrBuilder.java
+++ b/tensorflow-core/tensorflow-core-native/src/gen/java/org/tensorflow/proto/ConfigProtoOrBuilder.java
@@ -341,6 +341,33 @@ org.tensorflow.proto.ThreadPoolOptionProtoOrBuilder getSessionInterOpThreadPoolO
    */
   org.tensorflow.proto.GPUOptionsOrBuilder getGpuOptionsOrBuilder();
 
+  /**
+   * 
    +   * Options that apply to pluggable devices.
+   * 
    
+   *
+   * .tensorflow.GPUOptions pluggable_device_options = 18;
+   * @return Whether the pluggableDeviceOptions field is set.
+   */
+  boolean hasPluggableDeviceOptions();
+  /**
+   * 
    +   * Options that apply to pluggable devices.
+   * 
    
+   *
+   * .tensorflow.GPUOptions pluggable_device_options = 18;
+   * @return The pluggableDeviceOptions.
+   */
+  org.tensorflow.proto.GPUOptions getPluggableDeviceOptions();
+  /**
+   * 
    +   * Options that apply to pluggable devices.
+   * 
    
+   *
+   * .tensorflow.GPUOptions pluggable_device_options = 18;
+   */
+  org.tensorflow.proto.GPUOptionsOrBuilder getPluggableDeviceOptionsOrBuilder();
+
   /**
    * 
        * Whether soft placement is allowed. If allow_soft_placement is true,
diff --git a/tensorflow-core/tensorflow-core-native/src/gen/java/org/tensorflow/proto/ConfigProtos.java b/tensorflow-core/tensorflow-core-native/src/gen/java/org/tensorflow/proto/ConfigProtos.java
index bca6f96f8b0..ee8eb70f710 100644
--- a/tensorflow-core/tensorflow-core-native/src/gen/java/org/tensorflow/proto/ConfigProtos.java
+++ b/tensorflow-core/tensorflow-core-native/src/gen/java/org/tensorflow/proto/ConfigProtos.java
@@ -29,6 +29,11 @@ public static void registerAllExtensions(
   static final 
     com.google.protobuf.GeneratedMessageV3.FieldAccessorTable
       internal_static_tensorflow_GPUOptions_Experimental_VirtualDevices_fieldAccessorTable;
+  static final com.google.protobuf.Descriptors.Descriptor
+    internal_static_tensorflow_GPUOptions_Experimental_StreamMergeOptions_descriptor;
+  static final 
+    com.google.protobuf.GeneratedMessageV3.FieldAccessorTable
+      internal_static_tensorflow_GPUOptions_Experimental_StreamMergeOptions_fieldAccessorTable;
   static final com.google.protobuf.Descriptors.Descriptor
     internal_static_tensorflow_OptimizerOptions_descriptor;
   static final 
@@ -127,7 +132,7 @@ public static void registerAllExtensions(
       "obuf/debug.proto\032.tensorflow/core/protob" +
       "uf/rewriter_config.proto\032*tensorflow/cor" +
       "e/protobuf/rpc_options.proto\032&tsl/protob" +
-      "uf/coordination_config.proto\"\352\007\n\nGPUOpti" +
+      "uf/coordination_config.proto\"\211\n\n\nGPUOpti" +
       "ons\022\'\n\037per_process_gpu_memory_fraction\030\001" +
       " \001(\001\022\024\n\014allow_growth\030\004 \001(\010\022\026\n\016allocator_" +
       "type\030\002 \001(\t\022\037\n\027deferred_deletion_bytes\030\003 " +
@@ -135,7 +140,7 @@ public static void registerAllExtensions(
       "ing_active_delay_usecs\030\006 \001(\005\022$\n\034polling_" +
       "inactive_delay_msecs\030\007 \001(\005\022\034\n\024force_gpu_" +
       "compatible\030\010 \001(\010\0229\n\014experimental\030\t \001(\0132#" +
-      ".tensorflow.GPUOptions.Experimental\032\243\005\n\014" +
+      ".tensorflow.GPUOptions.Experimental\032\302\007\n\014" +
       "Experimental\022K\n\017virtual_devices\030\001 \003(\01322." +
       "tensorflow.GPUOptions.Experimental.Virtu" +
       "alDevices\022#\n\033num_virtual_devices_per_gpu" +
@@ -150,126 +155,135 @@ public static void registerAllExtensions(
       "llow_retry_on_allocation_failure\030\014 \001(\010\022 " +
       "\n\030gpu_host_mem_limit_in_mb\030\r \001(\002\022$\n\034gpu_" +
       "host_mem_disallow_growth\030\016 \001(\010\022$\n\034gpu_sy" +
-      "stem_memory_size_in_mb\030\020 \001(\005\032S\n\016VirtualD" +
-      "evices\022\027\n\017memory_limit_mb\030\001 \003(\002\022\020\n\010prior" +
-      "ity\030\002 \003(\005\022\026\n\016device_ordinal\030\003 \003(\005\"\235\003\n\020Op" +
-      "timizerOptions\022+\n#do_common_subexpressio" +
-      "n_elimination\030\001 \001(\010\022\033\n\023do_constant_foldi" +
-      "ng\030\002 \001(\010\022$\n\034max_folded_constant_in_bytes" +
-      "\030\006 \001(\003\022\034\n\024do_function_inlining\030\004 \001(\010\0225\n\t" +
-      "opt_level\030\003 \001(\0162\".tensorflow.OptimizerOp" +
-      "tions.Level\022E\n\020global_jit_level\030\005 \001(\0162+." +
-      "tensorflow.OptimizerOptions.GlobalJitLev" +
-      "el\022\026\n\016cpu_global_jit\030\007 \001(\010\" \n\005Level\022\006\n\002L" +
-      "1\020\000\022\017\n\002L0\020\377\377\377\377\377\377\377\377\377\001\"C\n\016GlobalJitLevel\022\013" +
-      "\n\007DEFAULT\020\000\022\020\n\003OFF\020\377\377\377\377\377\377\377\377\377\001\022\010\n\004ON_1\020\001\022" +
-      "\010\n\004ON_2\020\002\"\356\002\n\014GraphOptions\022\036\n\026enable_rec" +
-      "v_scheduling\030\002 \001(\010\0227\n\021optimizer_options\030" +
-      "\003 \001(\0132\034.tensorflow.OptimizerOptions\022\030\n\020b" +
-      "uild_cost_model\030\004 \001(\003\022\036\n\026build_cost_mode" +
-      "l_after\030\t \001(\003\022\024\n\014infer_shapes\030\005 \001(\010\022\032\n\022p" +
-      "lace_pruned_graph\030\006 \001(\010\022 \n\030enable_bfloat" +
-      "16_sendrecv\030\007 \001(\010\022\025\n\rtimeline_step\030\010 \001(\005" +
-      "\0223\n\017rewrite_options\030\n \001(\0132\032.tensorflow.R" +
-      "ewriterConfigJ\004\010\001\020\002R%skip_common_subexpr" +
-      "ession_elimination\"A\n\025ThreadPoolOptionPr" +
-      "oto\022\023\n\013num_threads\030\001 \001(\005\022\023\n\013global_name\030" +
-      "\002 \001(\t\"0\n\017SessionMetadata\022\014\n\004name\030\001 \001(\t\022\017" +
-      "\n\007version\030\002 \001(\003\"\225\020\n\013ConfigProto\022>\n\014devic" +
-      "e_count\030\001 \003(\0132(.tensorflow.ConfigProto.D" +
-      "eviceCountEntry\022$\n\034intra_op_parallelism_" +
-      "threads\030\002 \001(\005\022$\n\034inter_op_parallelism_th" +
-      "reads\030\005 \001(\005\022\037\n\027use_per_session_threads\030\t" +
-      " \001(\010\022G\n\034session_inter_op_thread_pool\030\014 \003" +
-      "(\0132!.tensorflow.ThreadPoolOptionProto\022\030\n" +
-      "\020placement_period\030\003 \001(\005\022\026\n\016device_filter" +
-      "s\030\004 \003(\t\022+\n\013gpu_options\030\006 \001(\0132\026.tensorflo" +
-      "w.GPUOptions\022\034\n\024allow_soft_placement\030\007 \001" +
-      "(\010\022\034\n\024log_device_placement\030\010 \001(\010\022/\n\rgrap" +
-      "h_options\030\n \001(\0132\030.tensorflow.GraphOption" +
-      "s\022\037\n\027operation_timeout_in_ms\030\013 \001(\003\022+\n\013rp" +
-      "c_options\030\r \001(\0132\026.tensorflow.RPCOptions\022" +
-      "+\n\013cluster_def\030\016 \001(\0132\026.tensorflow.Cluste" +
-      "rDef\022\035\n\025isolate_session_state\030\017 \001(\010\022(\n s" +
-      "hare_cluster_devices_in_session\030\021 \001(\010\022:\n" +
-      "\014experimental\030\020 \001(\0132$.tensorflow.ConfigP" +
-      "roto.Experimental\0322\n\020DeviceCountEntry\022\013\n" +
-      "\003key\030\001 \001(\t\022\r\n\005value\030\002 \001(\005:\0028\001\032\217\n\n\014Experi" +
-      "mental\022\037\n\027collective_group_leader\030\001 \001(\t\022" +
-      "\025\n\rexecutor_type\030\003 \001(\t\022\032\n\022recv_buf_max_c" +
-      "hunk\030\004 \001(\005\022\031\n\021use_numa_affinity\030\005 \001(\010\0225\n" +
-      "-collective_deterministic_sequential_exe" +
-      "cution\030\006 \001(\010\022\027\n\017collective_nccl\030\007 \001(\010\0226\n" +
-      ".share_session_state_in_clusterspec_prop" +
-      "agation\030\010 \001(\010\022\037\n\027disable_thread_spinning" +
-      "\030\t \001(\010\022(\n share_cluster_devices_in_sessi" +
-      "on\030\n \001(\010\0225\n\020session_metadata\030\013 \001(\0132\033.ten" +
-      "sorflow.SessionMetadata\022!\n\031optimize_for_" +
-      "static_graph\030\014 \001(\010\022\032\n\022enable_mlir_bridge" +
-      "\030\r \001(\010\022S\n\023mlir_bridge_rollout\030\021 \001(\01626.te" +
-      "nsorflow.ConfigProto.Experimental.MlirBr" +
-      "idgeRollout\022&\n\036enable_mlir_graph_optimiz" +
-      "ation\030\020 \001(\010\022\'\n\037disable_output_partition_" +
-      "graphs\030\016 \001(\010\022#\n\033xla_fusion_autotuner_thr" +
-      "esh\030\017 \001(\003\022\020\n\010use_tfrt\030\022 \001(\010\022\031\n\021enable_mu" +
-      "lti_host\030\033 \001(\010\022\033\n\023backend_server_port\030\034 " +
-      "\001(\005\022\022\n\ntarget_tpu\030\035 \001(\010\022\022\n\ntarget_gpu\030\036 " +
-      "\001(\010\022\036\n\026stream_merge_threshold\030\037 \001(\005\022\'\n\037d" +
-      "isable_functional_ops_lowering\030\025 \001(\010\022\'\n\037" +
-      "xla_prefer_single_graph_cluster\030\026 \001(\010\022B\n" +
-      "\023coordination_config\030\027 \001(\0132%.tensorflow." +
-      "CoordinationServiceConfig\022)\n!disable_opt" +
-      "imize_for_static_graph\030\030 \001(\010\0220\n(disable_" +
-      "eager_executor_streaming_enqueue\030\032 \001(\010\"\336" +
-      "\001\n\021MlirBridgeRollout\022#\n\037MLIR_BRIDGE_ROLL" +
-      "OUT_UNSPECIFIED\020\000\022\037\n\033MLIR_BRIDGE_ROLLOUT" +
-      "_ENABLED\020\001\022 \n\034MLIR_BRIDGE_ROLLOUT_DISABL" +
-      "ED\020\002\"\004\010\003\020\003\"\004\010\004\020\004*%MLIR_BRIDGE_ROLLOUT_SA" +
-      "FE_MODE_ENABLED*.MLIR_BRIDGE_ROLLOUT_SAF" +
-      "E_MODE_FALLBACK_ENABLEDJ\004\010\002\020\003J\004\010\023\020\024J\004\010\024\020" +
-      "\025J\004\010\031\020\032\"\341\004\n\nRunOptions\0226\n\013trace_level\030\001 " +
-      "\001(\0162!.tensorflow.RunOptions.TraceLevel\022\025" +
-      "\n\rtimeout_in_ms\030\002 \001(\003\022\034\n\024inter_op_thread" +
-      "_pool\030\003 \001(\005\022\037\n\027output_partition_graphs\030\005" +
-      " \001(\010\022/\n\rdebug_options\030\006 \001(\0132\030.tensorflow" +
-      ".DebugOptions\022*\n\"report_tensor_allocatio" +
-      "ns_upon_oom\030\007 \001(\010\0229\n\014experimental\030\010 \001(\0132" +
-      "#.tensorflow.RunOptions.Experimental\032\322\001\n" +
-      "\014Experimental\022\034\n\024collective_graph_key\030\001 " +
-      "\001(\003\022\034\n\024use_run_handler_pool\030\002 \001(\010\022[\n\030run" +
-      "_handler_pool_options\030\003 \001(\01329.tensorflow" +
-      ".RunOptions.Experimental.RunHandlerPoolO" +
-      "ptions\032)\n\025RunHandlerPoolOptions\022\020\n\010prior" +
-      "ity\030\001 \001(\003\"R\n\nTraceLevel\022\014\n\010NO_TRACE\020\000\022\022\n" +
-      "\016SOFTWARE_TRACE\020\001\022\022\n\016HARDWARE_TRACE\020\002\022\016\n" +
-      "\nFULL_TRACE\020\003J\004\010\004\020\005\"\276\003\n\013RunMetadata\022)\n\ns" +
-      "tep_stats\030\001 \001(\0132\025.tensorflow.StepStats\022," +
-      "\n\ncost_graph\030\002 \001(\0132\030.tensorflow.CostGrap" +
-      "hDef\022.\n\020partition_graphs\030\003 \003(\0132\024.tensorf" +
-      "low.GraphDef\022?\n\017function_graphs\030\004 \003(\0132&." +
-      "tensorflow.RunMetadata.FunctionGraphs\0225\n" +
-      "\020session_metadata\030\005 \001(\0132\033.tensorflow.Ses" +
-      "sionMetadata\032\255\001\n\016FunctionGraphs\022.\n\020parti" +
-      "tion_graphs\030\001 \003(\0132\024.tensorflow.GraphDef\022" +
-      "4\n\026pre_optimization_graph\030\002 \001(\0132\024.tensor" +
-      "flow.GraphDef\0225\n\027post_optimization_graph" +
-      "\030\003 \001(\0132\024.tensorflow.GraphDef\":\n\020TensorCo" +
-      "nnection\022\023\n\013from_tensor\030\001 \001(\t\022\021\n\tto_tens" +
-      "or\030\002 \001(\t\"\260\003\n\017CallableOptions\022\014\n\004feed\030\001 \003" +
-      "(\t\022\r\n\005fetch\030\002 \003(\t\022\016\n\006target\030\003 \003(\t\022+\n\013run" +
-      "_options\030\004 \001(\0132\026.tensorflow.RunOptions\0227" +
-      "\n\021tensor_connection\030\005 \003(\0132\034.tensorflow.T" +
-      "ensorConnection\022B\n\014feed_devices\030\006 \003(\0132,." +
-      "tensorflow.CallableOptions.FeedDevicesEn" +
-      "try\022D\n\rfetch_devices\030\007 \003(\0132-.tensorflow." +
-      "CallableOptions.FetchDevicesEntry\022\027\n\017fet" +
-      "ch_skip_sync\030\010 \001(\010\0322\n\020FeedDevicesEntry\022\013" +
-      "\n\003key\030\001 \001(\t\022\r\n\005value\030\002 \001(\t:\0028\001\0323\n\021FetchD" +
-      "evicesEntry\022\013\n\003key\030\001 \001(\t\022\r\n\005value\030\002 \001(\t:" +
-      "\0028\001B\200\001\n\024org.tensorflow.protoB\014ConfigProt" +
-      "osP\001ZUgithub.com/tensorflow/tensorflow/t" +
-      "ensorflow/go/core/protobuf/for_core_prot" +
-      "os_go_proto\370\001\001b\006proto3"
+      "stem_memory_size_in_mb\030\020 \001(\005\022.\n&populate" +
+      "_pjrt_gpu_client_creation_info\030\021 \001(\010\022\017\n\007" +
+      "node_id\030\022 \001(\005\022T\n\024stream_merge_options\030\023 " +
+      "\001(\01326.tensorflow.GPUOptions.Experimental" +
+      ".StreamMergeOptions\032S\n\016VirtualDevices\022\027\n" +
+      "\017memory_limit_mb\030\001 \003(\002\022\020\n\010priority\030\002 \003(\005" +
+      "\022\026\n\016device_ordinal\030\003 \003(\005\032\205\001\n\022StreamMerge" +
+      "Options\022#\n\033merge_host_to_device_stream\030\001" +
+      " \001(\010\022#\n\033merge_device_to_host_stream\030\002 \001(" +
+      "\010\022%\n\035merge_device_to_device_stream\030\003 \001(\010" +
+      "\"\235\003\n\020OptimizerOptions\022+\n#do_common_subex" +
+      "pression_elimination\030\001 \001(\010\022\033\n\023do_constan" +
+      "t_folding\030\002 \001(\010\022$\n\034max_folded_constant_i" +
+      "n_bytes\030\006 \001(\003\022\034\n\024do_function_inlining\030\004 " +
+      "\001(\010\0225\n\topt_level\030\003 \001(\0162\".tensorflow.Opti" +
+      "mizerOptions.Level\022E\n\020global_jit_level\030\005" +
+      " \001(\0162+.tensorflow.OptimizerOptions.Globa" +
+      "lJitLevel\022\026\n\016cpu_global_jit\030\007 \001(\010\" \n\005Lev" +
+      "el\022\006\n\002L1\020\000\022\017\n\002L0\020\377\377\377\377\377\377\377\377\377\001\"C\n\016GlobalJit" +
+      "Level\022\013\n\007DEFAULT\020\000\022\020\n\003OFF\020\377\377\377\377\377\377\377\377\377\001\022\010\n\004" +
+      "ON_1\020\001\022\010\n\004ON_2\020\002\"\356\002\n\014GraphOptions\022\036\n\026ena" +
+      "ble_recv_scheduling\030\002 \001(\010\0227\n\021optimizer_o" +
+      "ptions\030\003 \001(\0132\034.tensorflow.OptimizerOptio" +
+      "ns\022\030\n\020build_cost_model\030\004 \001(\003\022\036\n\026build_co" +
+      "st_model_after\030\t \001(\003\022\024\n\014infer_shapes\030\005 \001" +
+      "(\010\022\032\n\022place_pruned_graph\030\006 \001(\010\022 \n\030enable" +
+      "_bfloat16_sendrecv\030\007 \001(\010\022\025\n\rtimeline_ste" +
+      "p\030\010 \001(\005\0223\n\017rewrite_options\030\n \001(\0132\032.tenso" +
+      "rflow.RewriterConfigJ\004\010\001\020\002R%skip_common_" +
+      "subexpression_elimination\"A\n\025ThreadPoolO" +
+      "ptionProto\022\023\n\013num_threads\030\001 \001(\005\022\023\n\013globa" +
+      "l_name\030\002 \001(\t\"0\n\017SessionMetadata\022\014\n\004name\030" +
+      "\001 \001(\t\022\017\n\007version\030\002 \001(\003\"\346\020\n\013ConfigProto\022>" +
+      "\n\014device_count\030\001 \003(\0132(.tensorflow.Config" +
+      "Proto.DeviceCountEntry\022$\n\034intra_op_paral" +
+      "lelism_threads\030\002 \001(\005\022$\n\034inter_op_paralle" +
+      "lism_threads\030\005 \001(\005\022\037\n\027use_per_session_th" +
+      "reads\030\t \001(\010\022G\n\034session_inter_op_thread_p" +
+      "ool\030\014 \003(\0132!.tensorflow.ThreadPoolOptionP" +
+      "roto\022\030\n\020placement_period\030\003 \001(\005\022\026\n\016device" +
+      "_filters\030\004 \003(\t\022+\n\013gpu_options\030\006 \001(\0132\026.te" +
+      "nsorflow.GPUOptions\0228\n\030pluggable_device_" +
+      "options\030\022 \001(\0132\026.tensorflow.GPUOptions\022\034\n" +
+      "\024allow_soft_placement\030\007 \001(\010\022\034\n\024log_devic" +
+      "e_placement\030\010 \001(\010\022/\n\rgraph_options\030\n \001(\013" +
+      "2\030.tensorflow.GraphOptions\022\037\n\027operation_" +
+      "timeout_in_ms\030\013 \001(\003\022+\n\013rpc_options\030\r \001(\013" +
+      "2\026.tensorflow.RPCOptions\022+\n\013cluster_def\030" +
+      "\016 \001(\0132\026.tensorflow.ClusterDef\022\035\n\025isolate" +
+      "_session_state\030\017 \001(\010\022(\n share_cluster_de" +
+      "vices_in_session\030\021 \001(\010\022:\n\014experimental\030\020" +
+      " \001(\0132$.tensorflow.ConfigProto.Experiment" +
+      "al\0322\n\020DeviceCountEntry\022\013\n\003key\030\001 \001(\t\022\r\n\005v" +
+      "alue\030\002 \001(\005:\0028\001\032\246\n\n\014Experimental\022\037\n\027colle" +
+      "ctive_group_leader\030\001 \001(\t\022\025\n\rexecutor_typ" +
+      "e\030\003 \001(\t\022\032\n\022recv_buf_max_chunk\030\004 \001(\005\022\031\n\021u" +
+      "se_numa_affinity\030\005 \001(\010\0225\n-collective_det" +
+      "erministic_sequential_execution\030\006 \001(\010\022\027\n" +
+      "\017collective_nccl\030\007 \001(\010\0226\n.share_session_" +
+      "state_in_clusterspec_propagation\030\010 \001(\010\022\037" +
+      "\n\027disable_thread_spinning\030\t \001(\010\022(\n share" +
+      "_cluster_devices_in_session\030\n \001(\010\0225\n\020ses" +
+      "sion_metadata\030\013 \001(\0132\033.tensorflow.Session" +
+      "Metadata\022!\n\031optimize_for_static_graph\030\014 " +
+      "\001(\010\022\032\n\022enable_mlir_bridge\030\r \001(\010\022S\n\023mlir_" +
+      "bridge_rollout\030\021 \001(\01626.tensorflow.Config" +
+      "Proto.Experimental.MlirBridgeRollout\022&\n\036" +
+      "enable_mlir_graph_optimization\030\020 \001(\010\022\'\n\037" +
+      "disable_output_partition_graphs\030\016 \001(\010\022#\n" +
+      "\033xla_fusion_autotuner_thresh\030\017 \001(\003\022\020\n\010us" +
+      "e_tfrt\030\022 \001(\010\022\031\n\021enable_multi_host\030\033 \001(\010\022" +
+      "\025\n\rtfrt_use_ifrt\030  \001(\010\022\033\n\023backend_server" +
+      "_port\030\034 \001(\005\022\022\n\ntarget_tpu\030\035 \001(\010\022\022\n\ntarge" +
+      "t_gpu\030\036 \001(\010\022\036\n\026stream_merge_threshold\030\037 " +
+      "\001(\005\022\'\n\037disable_functional_ops_lowering\030\025" +
+      " \001(\010\022\'\n\037xla_prefer_single_graph_cluster\030" +
+      "\026 \001(\010\022B\n\023coordination_config\030\027 \001(\0132%.ten" +
+      "sorflow.CoordinationServiceConfig\022)\n!dis" +
+      "able_optimize_for_static_graph\030\030 \001(\010\0220\n(" +
+      "disable_eager_executor_streaming_enqueue" +
+      "\030\032 \001(\010\"\336\001\n\021MlirBridgeRollout\022#\n\037MLIR_BRI" +
+      "DGE_ROLLOUT_UNSPECIFIED\020\000\022\037\n\033MLIR_BRIDGE" +
+      "_ROLLOUT_ENABLED\020\001\022 \n\034MLIR_BRIDGE_ROLLOU" +
+      "T_DISABLED\020\002\"\004\010\003\020\003\"\004\010\004\020\004*%MLIR_BRIDGE_RO" +
+      "LLOUT_SAFE_MODE_ENABLED*.MLIR_BRIDGE_ROL" +
+      "LOUT_SAFE_MODE_FALLBACK_ENABLEDJ\004\010\002\020\003J\004\010" +
+      "\023\020\024J\004\010\024\020\025J\004\010\031\020\032\"\341\004\n\nRunOptions\0226\n\013trace_" +
+      "level\030\001 \001(\0162!.tensorflow.RunOptions.Trac" +
+      "eLevel\022\025\n\rtimeout_in_ms\030\002 \001(\003\022\034\n\024inter_o" +
+      "p_thread_pool\030\003 \001(\005\022\037\n\027output_partition_" +
+      "graphs\030\005 \001(\010\022/\n\rdebug_options\030\006 \001(\0132\030.te" +
+      "nsorflow.DebugOptions\022*\n\"report_tensor_a" +
+      "llocations_upon_oom\030\007 \001(\010\0229\n\014experimenta" +
+      "l\030\010 \001(\0132#.tensorflow.RunOptions.Experime" +
+      "ntal\032\322\001\n\014Experimental\022\034\n\024collective_grap" +
+      "h_key\030\001 \001(\003\022\034\n\024use_run_handler_pool\030\002 \001(" +
+      "\010\022[\n\030run_handler_pool_options\030\003 \001(\01329.te" +
+      "nsorflow.RunOptions.Experimental.RunHand" +
+      "lerPoolOptions\032)\n\025RunHandlerPoolOptions\022" +
+      "\020\n\010priority\030\001 \001(\003\"R\n\nTraceLevel\022\014\n\010NO_TR" +
+      "ACE\020\000\022\022\n\016SOFTWARE_TRACE\020\001\022\022\n\016HARDWARE_TR" +
+      "ACE\020\002\022\016\n\nFULL_TRACE\020\003J\004\010\004\020\005\"\276\003\n\013RunMetad" +
+      "ata\022)\n\nstep_stats\030\001 \001(\0132\025.tensorflow.Ste" +
+      "pStats\022,\n\ncost_graph\030\002 \001(\0132\030.tensorflow." +
+      "CostGraphDef\022.\n\020partition_graphs\030\003 \003(\0132\024" +
+      ".tensorflow.GraphDef\022?\n\017function_graphs\030" +
+      "\004 \003(\0132&.tensorflow.RunMetadata.FunctionG" +
+      "raphs\0225\n\020session_metadata\030\005 \001(\0132\033.tensor" +
+      "flow.SessionMetadata\032\255\001\n\016FunctionGraphs\022" +
+      ".\n\020partition_graphs\030\001 \003(\0132\024.tensorflow.G" +
+      "raphDef\0224\n\026pre_optimization_graph\030\002 \001(\0132" +
+      "\024.tensorflow.GraphDef\0225\n\027post_optimizati" +
+      "on_graph\030\003 \001(\0132\024.tensorflow.GraphDef\":\n\020" +
+      "TensorConnection\022\023\n\013from_tensor\030\001 \001(\t\022\021\n" +
+      "\tto_tensor\030\002 \001(\t\"\260\003\n\017CallableOptions\022\014\n\004" +
+      "feed\030\001 \003(\t\022\r\n\005fetch\030\002 \003(\t\022\016\n\006target\030\003 \003(" +
+      "\t\022+\n\013run_options\030\004 \001(\0132\026.tensorflow.RunO" +
+      "ptions\0227\n\021tensor_connection\030\005 \003(\0132\034.tens" +
+      "orflow.TensorConnection\022B\n\014feed_devices\030" +
+      "\006 \003(\0132,.tensorflow.CallableOptions.FeedD" +
+      "evicesEntry\022D\n\rfetch_devices\030\007 \003(\0132-.ten" +
+      "sorflow.CallableOptions.FetchDevicesEntr" +
+      "y\022\027\n\017fetch_skip_sync\030\010 \001(\010\0322\n\020FeedDevice" +
+      "sEntry\022\013\n\003key\030\001 \001(\t\022\r\n\005value\030\002 \001(\t:\0028\001\0323" +
+      "\n\021FetchDevicesEntry\022\013\n\003key\030\001 \001(\t\022\r\n\005valu" +
+      "e\030\002 \001(\t:\0028\001B\200\001\n\024org.tensorflow.protoB\014Co" +
+      "nfigProtosP\001ZUgithub.com/tensorflow/tens" +
+      "orflow/tensorflow/go/core/protobuf/for_c" +
+      "ore_protos_go_proto\370\001\001b\006proto3"
     };
     descriptor = com.google.protobuf.Descriptors.FileDescriptor
       .internalBuildGeneratedFileFrom(descriptorData,
@@ -294,13 +308,19 @@ public static void registerAllExtensions(
     internal_static_tensorflow_GPUOptions_Experimental_fieldAccessorTable = new
       com.google.protobuf.GeneratedMessageV3.FieldAccessorTable(
         internal_static_tensorflow_GPUOptions_Experimental_descriptor,
-        new java.lang.String[] { "VirtualDevices", "NumVirtualDevicesPerGpu", "UseUnifiedMemory", "NumDevToDevCopyStreams", "CollectiveRingOrder", "TimestampedAllocator", "KernelTrackerMaxInterval", "KernelTrackerMaxBytes", "KernelTrackerMaxPending", "InternalFragmentationFraction", "UseCudaMallocAsync", "DisallowRetryOnAllocationFailure", "GpuHostMemLimitInMb", "GpuHostMemDisallowGrowth", "GpuSystemMemorySizeInMb", });
+        new java.lang.String[] { "VirtualDevices", "NumVirtualDevicesPerGpu", "UseUnifiedMemory", "NumDevToDevCopyStreams", "CollectiveRingOrder", "TimestampedAllocator", "KernelTrackerMaxInterval", "KernelTrackerMaxBytes", "KernelTrackerMaxPending", "InternalFragmentationFraction", "UseCudaMallocAsync", "DisallowRetryOnAllocationFailure", "GpuHostMemLimitInMb", "GpuHostMemDisallowGrowth", "GpuSystemMemorySizeInMb", "PopulatePjrtGpuClientCreationInfo", "NodeId", "StreamMergeOptions", });
     internal_static_tensorflow_GPUOptions_Experimental_VirtualDevices_descriptor =
       internal_static_tensorflow_GPUOptions_Experimental_descriptor.getNestedTypes().get(0);
     internal_static_tensorflow_GPUOptions_Experimental_VirtualDevices_fieldAccessorTable = new
       com.google.protobuf.GeneratedMessageV3.FieldAccessorTable(
         internal_static_tensorflow_GPUOptions_Experimental_VirtualDevices_descriptor,
         new java.lang.String[] { "MemoryLimitMb", "Priority", "DeviceOrdinal", });
+    internal_static_tensorflow_GPUOptions_Experimental_StreamMergeOptions_descriptor =
+      internal_static_tensorflow_GPUOptions_Experimental_descriptor.getNestedTypes().get(1);
+    internal_static_tensorflow_GPUOptions_Experimental_StreamMergeOptions_fieldAccessorTable = new
+      com.google.protobuf.GeneratedMessageV3.FieldAccessorTable(
+        internal_static_tensorflow_GPUOptions_Experimental_StreamMergeOptions_descriptor,
+        new java.lang.String[] { "MergeHostToDeviceStream", "MergeDeviceToHostStream", "MergeDeviceToDeviceStream", });
     internal_static_tensorflow_OptimizerOptions_descriptor =
       getDescriptor().getMessageTypes().get(1);
     internal_static_tensorflow_OptimizerOptions_fieldAccessorTable = new
@@ -330,7 +350,7 @@ public static void registerAllExtensions(
     internal_static_tensorflow_ConfigProto_fieldAccessorTable = new
       com.google.protobuf.GeneratedMessageV3.FieldAccessorTable(
         internal_static_tensorflow_ConfigProto_descriptor,
-        new java.lang.String[] { "DeviceCount", "IntraOpParallelismThreads", "InterOpParallelismThreads", "UsePerSessionThreads", "SessionInterOpThreadPool", "PlacementPeriod", "DeviceFilters", "GpuOptions", "AllowSoftPlacement", "LogDevicePlacement", "GraphOptions", "OperationTimeoutInMs", "RpcOptions", "ClusterDef", "IsolateSessionState", "ShareClusterDevicesInSession", "Experimental", });
+        new java.lang.String[] { "DeviceCount", "IntraOpParallelismThreads", "InterOpParallelismThreads", "UsePerSessionThreads", "SessionInterOpThreadPool", "PlacementPeriod", "DeviceFilters", "GpuOptions", "PluggableDeviceOptions", "AllowSoftPlacement", "LogDevicePlacement", "GraphOptions", "OperationTimeoutInMs", "RpcOptions", "ClusterDef", "IsolateSessionState", "ShareClusterDevicesInSession", "Experimental", });
     internal_static_tensorflow_ConfigProto_DeviceCountEntry_descriptor =
       internal_static_tensorflow_ConfigProto_descriptor.getNestedTypes().get(0);
     internal_static_tensorflow_ConfigProto_DeviceCountEntry_fieldAccessorTable = new
@@ -342,7 +362,7 @@ public static void registerAllExtensions(
     internal_static_tensorflow_ConfigProto_Experimental_fieldAccessorTable = new
       com.google.protobuf.GeneratedMessageV3.FieldAccessorTable(
         internal_static_tensorflow_ConfigProto_Experimental_descriptor,
-        new java.lang.String[] { "CollectiveGroupLeader", "ExecutorType", "RecvBufMaxChunk", "UseNumaAffinity", "CollectiveDeterministicSequentialExecution", "CollectiveNccl", "ShareSessionStateInClusterspecPropagation", "DisableThreadSpinning", "ShareClusterDevicesInSession", "SessionMetadata", "OptimizeForStaticGraph", "EnableMlirBridge", "MlirBridgeRollout", "EnableMlirGraphOptimization", "DisableOutputPartitionGraphs", "XlaFusionAutotunerThresh", "UseTfrt", "EnableMultiHost", "BackendServerPort", "TargetTpu", "TargetGpu", "StreamMergeThreshold", "DisableFunctionalOpsLowering", "XlaPreferSingleGraphCluster", "CoordinationConfig", "DisableOptimizeForStaticGraph", "DisableEagerExecutorStreamingEnqueue", });
+        new java.lang.String[] { "CollectiveGroupLeader", "ExecutorType", "RecvBufMaxChunk", "UseNumaAffinity", "CollectiveDeterministicSequentialExecution", "CollectiveNccl", "ShareSessionStateInClusterspecPropagation", "DisableThreadSpinning", "ShareClusterDevicesInSession", "SessionMetadata", "OptimizeForStaticGraph", "EnableMlirBridge", "MlirBridgeRollout", "EnableMlirGraphOptimization", "DisableOutputPartitionGraphs", "XlaFusionAutotunerThresh", "UseTfrt", "EnableMultiHost", "TfrtUseIfrt", "BackendServerPort", "TargetTpu", "TargetGpu", "StreamMergeThreshold", "DisableFunctionalOpsLowering", "XlaPreferSingleGraphCluster", "CoordinationConfig", "DisableOptimizeForStaticGraph", "DisableEagerExecutorStreamingEnqueue", });
     internal_static_tensorflow_RunOptions_descriptor =
       getDescriptor().getMessageTypes().get(6);
     internal_static_tensorflow_RunOptions_fieldAccessorTable = new
diff --git a/tensorflow-core/tensorflow-core-native/src/gen/java/org/tensorflow/proto/CoordinationConfig.java b/tensorflow-core/tensorflow-core-native/src/gen/java/org/tensorflow/proto/CoordinationConfig.java
index 6c1f875d2f6..5dfed710211 100644
--- a/tensorflow-core/tensorflow-core-native/src/gen/java/org/tensorflow/proto/CoordinationConfig.java
+++ b/tensorflow-core/tensorflow-core-native/src/gen/java/org/tensorflow/proto/CoordinationConfig.java
@@ -853,6 +853,17 @@ org.tensorflow.proto.CoordinationConfig.CoordinatedJobOrBuilder getCoordinatedJo
      * @return The forceDisable.
      */
     boolean getForceDisable();
+
+    /**
+     * 
    +     * Use long polling to get error from coordination service as the error
+     * propagation mechanism.
+     * 
    
+     *
+     * bool poll_for_error_from_service_at_startup = 13;
+     * @return The pollForErrorFromServiceAtStartup.
+     */
+    boolean getPollForErrorFromServiceAtStartup();
   }
   /**
    * 
    @@ -1223,6 +1234,22 @@ public boolean getForceDisable() {
       return forceDisable_;
     }
 
+    public static final int POLL_FOR_ERROR_FROM_SERVICE_AT_STARTUP_FIELD_NUMBER = 13;
+    private boolean pollForErrorFromServiceAtStartup_;
+    /**
+     * 
    +     * Use long polling to get error from coordination service as the error
+     * propagation mechanism.
+     * 
    
+     *
+     * bool poll_for_error_from_service_at_startup = 13;
+     * @return The pollForErrorFromServiceAtStartup.
+     */
+    @java.lang.Override
+    public boolean getPollForErrorFromServiceAtStartup() {
+      return pollForErrorFromServiceAtStartup_;
+    }
+
     private byte memoizedIsInitialized = -1;
     @java.lang.Override
     public final boolean isInitialized() {
@@ -1270,6 +1297,9 @@ public void writeTo(com.google.protobuf.CodedOutputStream output)
       if (forceDisable_ != false) {
         output.writeBool(12, forceDisable_);
       }
+      if (pollForErrorFromServiceAtStartup_ != false) {
+        output.writeBool(13, pollForErrorFromServiceAtStartup_);
+      }
       getUnknownFields().writeTo(output);
     }
 
@@ -1325,6 +1355,10 @@ public int getSerializedSize() {
         size += com.google.protobuf.CodedOutputStream
           .computeBoolSize(12, forceDisable_);
       }
+      if (pollForErrorFromServiceAtStartup_ != false) {
+        size += com.google.protobuf.CodedOutputStream
+          .computeBoolSize(13, pollForErrorFromServiceAtStartup_);
+      }
       size += getUnknownFields().getSerializedSize();
       memoizedSize = size;
       return size;
@@ -1362,6 +1396,8 @@ public boolean equals(final java.lang.Object obj) {
           != other.getAllowNewIncarnationToReconnect()) return false;
       if (getForceDisable()
           != other.getForceDisable()) return false;
+      if (getPollForErrorFromServiceAtStartup()
+          != other.getPollForErrorFromServiceAtStartup()) return false;
       if (!getUnknownFields().equals(other.getUnknownFields())) return false;
       return true;
     }
@@ -1406,6 +1442,9 @@ public int hashCode() {
       hash = (37 * hash) + FORCE_DISABLE_FIELD_NUMBER;
       hash = (53 * hash) + com.google.protobuf.Internal.hashBoolean(
           getForceDisable());
+      hash = (37 * hash) + POLL_FOR_ERROR_FROM_SERVICE_AT_STARTUP_FIELD_NUMBER;
+      hash = (53 * hash) + com.google.protobuf.Internal.hashBoolean(
+          getPollForErrorFromServiceAtStartup());
       hash = (29 * hash) + getUnknownFields().hashCode();
       memoizedHashCode = hash;
       return hash;
@@ -1566,6 +1605,8 @@ public Builder clear() {
 
         forceDisable_ = false;
 
+        pollForErrorFromServiceAtStartup_ = false;
+
         return this;
       }
 
@@ -1616,6 +1657,7 @@ public org.tensorflow.proto.CoordinationConfig.CoordinationServiceConfig buildPa
         result.recoverableJobs_ = recoverableJobs_;
         result.allowNewIncarnationToReconnect_ = allowNewIncarnationToReconnect_;
         result.forceDisable_ = forceDisable_;
+        result.pollForErrorFromServiceAtStartup_ = pollForErrorFromServiceAtStartup_;
         onBuilt();
         return result;
       }
@@ -1729,6 +1771,9 @@ public Builder mergeFrom(org.tensorflow.proto.CoordinationConfig.CoordinationSer
         if (other.getForceDisable() != false) {
           setForceDisable(other.getForceDisable());
         }
+        if (other.getPollForErrorFromServiceAtStartup() != false) {
+          setPollForErrorFromServiceAtStartup(other.getPollForErrorFromServiceAtStartup());
+        }
         this.mergeUnknownFields(other.getUnknownFields());
         onChanged();
         return this;
@@ -1819,6 +1864,11 @@ public Builder mergeFrom(
 
                 break;
               } // case 96
+              case 104: {
+                pollForErrorFromServiceAtStartup_ = input.readBool();
+
+                break;
+              } // case 104
               default: {
                 if (!super.parseUnknownField(input, extensionRegistry, tag)) {
                   done = true; // was an endgroup tag
@@ -2798,6 +2848,52 @@ public Builder clearForceDisable() {
         onChanged();
         return this;
       }
+
+      private boolean pollForErrorFromServiceAtStartup_ ;
+      /**
+       * 
    +       * Use long polling to get error from coordination service as the error
+       * propagation mechanism.
+       * 
    
+       *
+       * bool poll_for_error_from_service_at_startup = 13;
+       * @return The pollForErrorFromServiceAtStartup.
+       */
+      @java.lang.Override
+      public boolean getPollForErrorFromServiceAtStartup() {
+        return pollForErrorFromServiceAtStartup_;
+      }
+      /**
+       * 
    +       * Use long polling to get error from coordination service as the error
+       * propagation mechanism.
+       * 
    
+       *
+       * bool poll_for_error_from_service_at_startup = 13;
+       * @param value The pollForErrorFromServiceAtStartup to set.
+       * @return This builder for chaining.
+       */
+      public Builder setPollForErrorFromServiceAtStartup(boolean value) {
+        
+        pollForErrorFromServiceAtStartup_ = value;
+        onChanged();
+        return this;
+      }
+      /**
+       * 
    +       * Use long polling to get error from coordination service as the error
+       * propagation mechanism.
+       * 
    
+       *
+       * bool poll_for_error_from_service_at_startup = 13;
+       * @return This builder for chaining.
+       */
+      public Builder clearPollForErrorFromServiceAtStartup() {
+        
+        pollForErrorFromServiceAtStartup_ = false;
+        onChanged();
+        return this;
+      }
       @java.lang.Override
       public final Builder setUnknownFields(
           final com.google.protobuf.UnknownFieldSet unknownFields) {
@@ -2883,7 +2979,7 @@ public org.tensorflow.proto.CoordinationConfig.CoordinationServiceConfig getDefa
     java.lang.String[] descriptorData = {
       "\n&tsl/protobuf/coordination_config.proto" +
       "\022\ntensorflow\"1\n\016CoordinatedJob\022\014\n\004name\030\001" +
-      " \001(\t\022\021\n\tnum_tasks\030\002 \001(\005\"\240\003\n\031Coordination" +
+      " \001(\t\022\021\n\tnum_tasks\030\002 \001(\005\"\320\003\n\031Coordination" +
       "ServiceConfig\022\024\n\014service_type\030\001 \001(\t\022\026\n\016s" +
       "ervice_leader\030\002 \001(\t\022\033\n\023enable_health_che" +
       "ck\030\003 \001(\010\022&\n\036cluster_register_timeout_in_" +
@@ -2893,11 +2989,12 @@ public org.tensorflow.proto.CoordinationConfig.CoordinationServiceConfig getDefa
       "timeout_in_ms\030\007 \001(\003\022*\n\"agent_destruction" +
       "_without_shutdown\030\010 \001(\010\022\030\n\020recoverable_j" +
       "obs\030\t \003(\t\022*\n\"allow_new_incarnation_to_re" +
-      "connect\030\013 \001(\010\022\025\n\rforce_disable\030\014 \001(\010J\004\010\006" +
-      "\020\007Bm\n\024org.tensorflow.protoZUgithub.com/t" +
-      "ensorflow/tensorflow/tensorflow/go/core/" +
-      "protobuf/for_core_protos_go_protob\006proto" +
-      "3"
+      "connect\030\013 \001(\010\022\025\n\rforce_disable\030\014 \001(\010\022.\n&" +
+      "poll_for_error_from_service_at_startup\030\r" +
+      " \001(\010J\004\010\006\020\007Bm\n\024org.tensorflow.protoZUgith" +
+      "ub.com/tensorflow/tensorflow/tensorflow/" +
+      "go/core/protobuf/for_core_protos_go_prot" +
+      "ob\006proto3"
     };
     descriptor = com.google.protobuf.Descriptors.FileDescriptor
       .internalBuildGeneratedFileFrom(descriptorData,
@@ -2914,7 +3011,7 @@ public org.tensorflow.proto.CoordinationConfig.CoordinationServiceConfig getDefa
     internal_static_tensorflow_CoordinationServiceConfig_fieldAccessorTable = new
       com.google.protobuf.GeneratedMessageV3.FieldAccessorTable(
         internal_static_tensorflow_CoordinationServiceConfig_descriptor,
-        new java.lang.String[] { "ServiceType", "ServiceLeader", "EnableHealthCheck", "ClusterRegisterTimeoutInMs", "HeartbeatTimeoutInMs", "CoordinatedJobList", "ShutdownBarrierTimeoutInMs", "AgentDestructionWithoutShutdown", "RecoverableJobs", "AllowNewIncarnationToReconnect", "ForceDisable", });
+        new java.lang.String[] { "ServiceType", "ServiceLeader", "EnableHealthCheck", "ClusterRegisterTimeoutInMs", "HeartbeatTimeoutInMs", "CoordinatedJobList", "ShutdownBarrierTimeoutInMs", "AgentDestructionWithoutShutdown", "RecoverableJobs", "AllowNewIncarnationToReconnect", "ForceDisable", "PollForErrorFromServiceAtStartup", });
   }
 
   // @@protoc_insertion_point(outer_class_scope)
diff --git a/tensorflow-core/tensorflow-core-native/src/gen/java/org/tensorflow/proto/EntryValue.java b/tensorflow-core/tensorflow-core-native/src/gen/java/org/tensorflow/proto/EntryValue.java
index 44deff4cb4d..0b6ce2fef52 100644
--- a/tensorflow-core/tensorflow-core-native/src/gen/java/org/tensorflow/proto/EntryValue.java
+++ b/tensorflow-core/tensorflow-core-native/src/gen/java/org/tensorflow/proto/EntryValue.java
@@ -1,5 +1,5 @@
 // Generated by the protocol buffer compiler.  DO NOT EDIT!
-// source: tsl/protobuf/test_log.proto
+// source: xla/tsl/protobuf/test_log.proto
 
 package org.tensorflow.proto;
 
diff --git a/tensorflow-core/tensorflow-core-native/src/gen/java/org/tensorflow/proto/EntryValueOrBuilder.java b/tensorflow-core/tensorflow-core-native/src/gen/java/org/tensorflow/proto/EntryValueOrBuilder.java
index 525dfd70275..6338554d477 100644
--- a/tensorflow-core/tensorflow-core-native/src/gen/java/org/tensorflow/proto/EntryValueOrBuilder.java
+++ b/tensorflow-core/tensorflow-core-native/src/gen/java/org/tensorflow/proto/EntryValueOrBuilder.java
@@ -1,5 +1,5 @@
 // Generated by the protocol buffer compiler.  DO NOT EDIT!
-// source: tsl/protobuf/test_log.proto
+// source: xla/tsl/protobuf/test_log.proto
 
 package org.tensorflow.proto;
 
diff --git a/tensorflow-core/tensorflow-core-native/src/gen/java/org/tensorflow/proto/GPUInfo.java b/tensorflow-core/tensorflow-core-native/src/gen/java/org/tensorflow/proto/GPUInfo.java
index f07305dc1aa..858f216fb45 100644
--- a/tensorflow-core/tensorflow-core-native/src/gen/java/org/tensorflow/proto/GPUInfo.java
+++ b/tensorflow-core/tensorflow-core-native/src/gen/java/org/tensorflow/proto/GPUInfo.java
@@ -1,5 +1,5 @@
 // Generated by the protocol buffer compiler.  DO NOT EDIT!
-// source: tsl/protobuf/test_log.proto
+// source: xla/tsl/protobuf/test_log.proto
 
 package org.tensorflow.proto;
 
diff --git a/tensorflow-core/tensorflow-core-native/src/gen/java/org/tensorflow/proto/GPUInfoOrBuilder.java b/tensorflow-core/tensorflow-core-native/src/gen/java/org/tensorflow/proto/GPUInfoOrBuilder.java
index 6aefc92ee8c..02d2cc61740 100644
--- a/tensorflow-core/tensorflow-core-native/src/gen/java/org/tensorflow/proto/GPUInfoOrBuilder.java
+++ b/tensorflow-core/tensorflow-core-native/src/gen/java/org/tensorflow/proto/GPUInfoOrBuilder.java
@@ -1,5 +1,5 @@
 // Generated by the protocol buffer compiler.  DO NOT EDIT!
-// source: tsl/protobuf/test_log.proto
+// source: xla/tsl/protobuf/test_log.proto
 
 package org.tensorflow.proto;
 
diff --git a/tensorflow-core/tensorflow-core-native/src/gen/java/org/tensorflow/proto/GPUOptions.java b/tensorflow-core/tensorflow-core-native/src/gen/java/org/tensorflow/proto/GPUOptions.java
index 8eac8bc4ef1..d9db2330adb 100644
--- a/tensorflow-core/tensorflow-core-native/src/gen/java/org/tensorflow/proto/GPUOptions.java
+++ b/tensorflow-core/tensorflow-core-native/src/gen/java/org/tensorflow/proto/GPUOptions.java
@@ -467,6 +467,43 @@ org.tensorflow.proto.GPUOptions.Experimental.VirtualDevicesOrBuilder getVirtualD
      * @return The gpuSystemMemorySizeInMb.
      */
     int getGpuSystemMemorySizeInMb();
+
+    /**
+     * 
    +     * If true, save information needed for created a PjRt GPU client for
+     * creating a client with remote devices.
+     * 
    
+     *
+     * bool populate_pjrt_gpu_client_creation_info = 17;
+     * @return The populatePjrtGpuClientCreationInfo.
+     */
+    boolean getPopulatePjrtGpuClientCreationInfo();
+
+    /**
+     * 
    +     * node_id for use when creating a PjRt GPU client with remote devices,
+     * which enumerates jobs*tasks from a ServerDef.
+     * 
    
+     *
+     * int32 node_id = 18;
+     * @return The nodeId.
+     */
+    int getNodeId();
+
+    /**
+     * .tensorflow.GPUOptions.Experimental.StreamMergeOptions stream_merge_options = 19;
+     * @return Whether the streamMergeOptions field is set.
+     */
+    boolean hasStreamMergeOptions();
+    /**
+     * .tensorflow.GPUOptions.Experimental.StreamMergeOptions stream_merge_options = 19;
+     * @return The streamMergeOptions.
+     */
+    org.tensorflow.proto.GPUOptions.Experimental.StreamMergeOptions getStreamMergeOptions();
+    /**
+     * .tensorflow.GPUOptions.Experimental.StreamMergeOptions stream_merge_options = 19;
+     */
+    org.tensorflow.proto.GPUOptions.Experimental.StreamMergeOptionsOrBuilder getStreamMergeOptionsOrBuilder();
   }
   /**
    * Protobuf type {@code tensorflow.GPUOptions.Experimental}
@@ -1673,105 +1710,846 @@ private void ensureDeviceOrdinalIsMutable() {
         }
         /**
          * 
    -         * Virtual Device ordinal number determines the device ID of the device.
-         * A Virtual device with a lower ordinal number always receives the a
-         * smaller device id. The phyiscal device id and location in the
-         * virtual device list is used to break ties.
+         * Virtual Device ordinal number determines the device ID of the device.
+         * A Virtual device with a lower ordinal number always receives the a
+         * smaller device id. The phyiscal device id and location in the
+         * virtual device list is used to break ties.
+         * 
    
+         *
+         * repeated int32 device_ordinal = 3;
+         * @return The count of deviceOrdinal.
+         */
+        public int getDeviceOrdinalCount() {
+          return deviceOrdinal_.size();
+        }
+        /**
+         * 
    +         * Virtual Device ordinal number determines the device ID of the device.
+         * A Virtual device with a lower ordinal number always receives the a
+         * smaller device id. The phyiscal device id and location in the
+         * virtual device list is used to break ties.
+         * 
    
+         *
+         * repeated int32 device_ordinal = 3;
+         * @param index The index of the element to return.
+         * @return The deviceOrdinal at the given index.
+         */
+        public int getDeviceOrdinal(int index) {
+          return deviceOrdinal_.getInt(index);
+        }
+        /**
+         * 
    +         * Virtual Device ordinal number determines the device ID of the device.
+         * A Virtual device with a lower ordinal number always receives the a
+         * smaller device id. The phyiscal device id and location in the
+         * virtual device list is used to break ties.
+         * 
    
+         *
+         * repeated int32 device_ordinal = 3;
+         * @param index The index to set the value at.
+         * @param value The deviceOrdinal to set.
+         * @return This builder for chaining.
+         */
+        public Builder setDeviceOrdinal(
+            int index, int value) {
+          ensureDeviceOrdinalIsMutable();
+          deviceOrdinal_.setInt(index, value);
+          onChanged();
+          return this;
+        }
+        /**
+         * 
    +         * Virtual Device ordinal number determines the device ID of the device.
+         * A Virtual device with a lower ordinal number always receives the a
+         * smaller device id. The phyiscal device id and location in the
+         * virtual device list is used to break ties.
+         * 
    
+         *
+         * repeated int32 device_ordinal = 3;
+         * @param value The deviceOrdinal to add.
+         * @return This builder for chaining.
+         */
+        public Builder addDeviceOrdinal(int value) {
+          ensureDeviceOrdinalIsMutable();
+          deviceOrdinal_.addInt(value);
+          onChanged();
+          return this;
+        }
+        /**
+         * 
    +         * Virtual Device ordinal number determines the device ID of the device.
+         * A Virtual device with a lower ordinal number always receives the a
+         * smaller device id. The phyiscal device id and location in the
+         * virtual device list is used to break ties.
+         * 
    
+         *
+         * repeated int32 device_ordinal = 3;
+         * @param values The deviceOrdinal to add.
+         * @return This builder for chaining.
+         */
+        public Builder addAllDeviceOrdinal(
+            java.lang.Iterable values) {
+          ensureDeviceOrdinalIsMutable();
+          com.google.protobuf.AbstractMessageLite.Builder.addAll(
+              values, deviceOrdinal_);
+          onChanged();
+          return this;
+        }
+        /**
+         * 
    +         * Virtual Device ordinal number determines the device ID of the device.
+         * A Virtual device with a lower ordinal number always receives the a
+         * smaller device id. The phyiscal device id and location in the
+         * virtual device list is used to break ties.
+         * 
    
+         *
+         * repeated int32 device_ordinal = 3;
+         * @return This builder for chaining.
+         */
+        public Builder clearDeviceOrdinal() {
+          deviceOrdinal_ = emptyIntList();
+          bitField0_ = (bitField0_ & ~0x00000004);
+          onChanged();
+          return this;
+        }
+        @java.lang.Override
+        public final Builder setUnknownFields(
+            final com.google.protobuf.UnknownFieldSet unknownFields) {
+          return super.setUnknownFields(unknownFields);
+        }
+
+        @java.lang.Override
+        public final Builder mergeUnknownFields(
+            final com.google.protobuf.UnknownFieldSet unknownFields) {
+          return super.mergeUnknownFields(unknownFields);
+        }
+
+
+        // @@protoc_insertion_point(builder_scope:tensorflow.GPUOptions.Experimental.VirtualDevices)
+      }
+
+      // @@protoc_insertion_point(class_scope:tensorflow.GPUOptions.Experimental.VirtualDevices)
+      private static final org.tensorflow.proto.GPUOptions.Experimental.VirtualDevices DEFAULT_INSTANCE;
+      static {
+        DEFAULT_INSTANCE = new org.tensorflow.proto.GPUOptions.Experimental.VirtualDevices();
+      }
+
+      public static org.tensorflow.proto.GPUOptions.Experimental.VirtualDevices getDefaultInstance() {
+        return DEFAULT_INSTANCE;
+      }
+
+      private static final com.google.protobuf.Parser
+          PARSER = new com.google.protobuf.AbstractParser() {
+        @java.lang.Override
+        public VirtualDevices parsePartialFrom(
+            com.google.protobuf.CodedInputStream input,
+            com.google.protobuf.ExtensionRegistryLite extensionRegistry)
+            throws com.google.protobuf.InvalidProtocolBufferException {
+          Builder builder = newBuilder();
+          try {
+            builder.mergeFrom(input, extensionRegistry);
+          } catch (com.google.protobuf.InvalidProtocolBufferException e) {
+            throw e.setUnfinishedMessage(builder.buildPartial());
+          } catch (com.google.protobuf.UninitializedMessageException e) {
+            throw e.asInvalidProtocolBufferException().setUnfinishedMessage(builder.buildPartial());
+          } catch (java.io.IOException e) {
+            throw new com.google.protobuf.InvalidProtocolBufferException(e)
+                .setUnfinishedMessage(builder.buildPartial());
+          }
+          return builder.buildPartial();
+        }
+      };
+
+      public static com.google.protobuf.Parser parser() {
+        return PARSER;
+      }
+
+      @java.lang.Override
+      public com.google.protobuf.Parser getParserForType() {
+        return PARSER;
+      }
+
+      @java.lang.Override
+      public org.tensorflow.proto.GPUOptions.Experimental.VirtualDevices getDefaultInstanceForType() {
+        return DEFAULT_INSTANCE;
+      }
+
+    }
+
+    public interface StreamMergeOptionsOrBuilder extends
+        // @@protoc_insertion_point(interface_extends:tensorflow.GPUOptions.Experimental.StreamMergeOptions)
+        com.google.protobuf.MessageOrBuilder {
+
+      /**
+       * 
    +       * If true, the compute stream will be used for host_to_device copy as
+       * well. It's no longer necessary to record an event before the copy to
+       * let the copy stream wait for the compute stream to finish. There is
+       * also no need to wait for the copy to complete before executing the
+       * callback function.
+       * 
    
+       *
+       * bool merge_host_to_device_stream = 1;
+       * @return The mergeHostToDeviceStream.
+       */
+      boolean getMergeHostToDeviceStream();
+
+      /**
+       * 
    +       * If true, the compute stream will be used for device_to_host copy as
+       * well. It's no longer necessary to record an event before the copy to
+       * let the copy stream wait for the compute stream to finish.
+       * 
    
+       *
+       * bool merge_device_to_host_stream = 2;
+       * @return The mergeDeviceToHostStream.
+       */
+      boolean getMergeDeviceToHostStream();
+
+      /**
+       * 
    +       * If true, the compute stream will be used for device_to_device copy as
+       * well. It's no longer necessary to record an event before the copy to
+       * let the copy stream wait for the compute stream of the sending device
+       * to finish. There is also no need to wait for the compute stream of the
+       * receiving device to finish if the copy is within the same device.
+       * 
    
+       *
+       * bool merge_device_to_device_stream = 3;
+       * @return The mergeDeviceToDeviceStream.
+       */
+      boolean getMergeDeviceToDeviceStream();
+    }
+    /**
+     * 
    +     * Whether to merge data transfer streams into the compute stream in the
+     * same stream group. Stream merging helps reduce the overhead caused by
+     * stream synchronization, especially when data transfers are frequent. For
+     * example, setting "merge_host_to_device_stream = true" will make the
+     * compute stream responsible for both computation and host to device memory
+     * copy.
+     * 
    
+     *
+     * Protobuf type {@code tensorflow.GPUOptions.Experimental.StreamMergeOptions}
+     */
+    public static final class StreamMergeOptions extends
+        com.google.protobuf.GeneratedMessageV3 implements
+        // @@protoc_insertion_point(message_implements:tensorflow.GPUOptions.Experimental.StreamMergeOptions)
+        StreamMergeOptionsOrBuilder {
+    private static final long serialVersionUID = 0L;
+      // Use StreamMergeOptions.newBuilder() to construct.
+      private StreamMergeOptions(com.google.protobuf.GeneratedMessageV3.Builder builder) {
+        super(builder);
+      }
+      private StreamMergeOptions() {
+      }
+
+      @java.lang.Override
+      @SuppressWarnings({"unused"})
+      protected java.lang.Object newInstance(
+          UnusedPrivateParameter unused) {
+        return new StreamMergeOptions();
+      }
+
+      @java.lang.Override
+      public final com.google.protobuf.UnknownFieldSet
+      getUnknownFields() {
+        return this.unknownFields;
+      }
+      public static final com.google.protobuf.Descriptors.Descriptor
+          getDescriptor() {
+        return org.tensorflow.proto.ConfigProtos.internal_static_tensorflow_GPUOptions_Experimental_StreamMergeOptions_descriptor;
+      }
+
+      @java.lang.Override
+      protected com.google.protobuf.GeneratedMessageV3.FieldAccessorTable
+          internalGetFieldAccessorTable() {
+        return org.tensorflow.proto.ConfigProtos.internal_static_tensorflow_GPUOptions_Experimental_StreamMergeOptions_fieldAccessorTable
+            .ensureFieldAccessorsInitialized(
+                org.tensorflow.proto.GPUOptions.Experimental.StreamMergeOptions.class, org.tensorflow.proto.GPUOptions.Experimental.StreamMergeOptions.Builder.class);
+      }
+
+      public static final int MERGE_HOST_TO_DEVICE_STREAM_FIELD_NUMBER = 1;
+      private boolean mergeHostToDeviceStream_;
+      /**
+       * 
    +       * If true, the compute stream will be used for host_to_device copy as
+       * well. It's no longer necessary to record an event before the copy to
+       * let the copy stream wait for the compute stream to finish. There is
+       * also no need to wait for the copy to complete before executing the
+       * callback function.
+       * 
    
+       *
+       * bool merge_host_to_device_stream = 1;
+       * @return The mergeHostToDeviceStream.
+       */
+      @java.lang.Override
+      public boolean getMergeHostToDeviceStream() {
+        return mergeHostToDeviceStream_;
+      }
+
+      public static final int MERGE_DEVICE_TO_HOST_STREAM_FIELD_NUMBER = 2;
+      private boolean mergeDeviceToHostStream_;
+      /**
+       * 
    +       * If true, the compute stream will be used for device_to_host copy as
+       * well. It's no longer necessary to record an event before the copy to
+       * let the copy stream wait for the compute stream to finish.
+       * 
    
+       *
+       * bool merge_device_to_host_stream = 2;
+       * @return The mergeDeviceToHostStream.
+       */
+      @java.lang.Override
+      public boolean getMergeDeviceToHostStream() {
+        return mergeDeviceToHostStream_;
+      }
+
+      public static final int MERGE_DEVICE_TO_DEVICE_STREAM_FIELD_NUMBER = 3;
+      private boolean mergeDeviceToDeviceStream_;
+      /**
+       * 
    +       * If true, the compute stream will be used for device_to_device copy as
+       * well. It's no longer necessary to record an event before the copy to
+       * let the copy stream wait for the compute stream of the sending device
+       * to finish. There is also no need to wait for the compute stream of the
+       * receiving device to finish if the copy is within the same device.
+       * 
    
+       *
+       * bool merge_device_to_device_stream = 3;
+       * @return The mergeDeviceToDeviceStream.
+       */
+      @java.lang.Override
+      public boolean getMergeDeviceToDeviceStream() {
+        return mergeDeviceToDeviceStream_;
+      }
+
+      private byte memoizedIsInitialized = -1;
+      @java.lang.Override
+      public final boolean isInitialized() {
+        byte isInitialized = memoizedIsInitialized;
+        if (isInitialized == 1) return true;
+        if (isInitialized == 0) return false;
+
+        memoizedIsInitialized = 1;
+        return true;
+      }
+
+      @java.lang.Override
+      public void writeTo(com.google.protobuf.CodedOutputStream output)
+                          throws java.io.IOException {
+        if (mergeHostToDeviceStream_ != false) {
+          output.writeBool(1, mergeHostToDeviceStream_);
+        }
+        if (mergeDeviceToHostStream_ != false) {
+          output.writeBool(2, mergeDeviceToHostStream_);
+        }
+        if (mergeDeviceToDeviceStream_ != false) {
+          output.writeBool(3, mergeDeviceToDeviceStream_);
+        }
+        getUnknownFields().writeTo(output);
+      }
+
+      @java.lang.Override
+      public int getSerializedSize() {
+        int size = memoizedSize;
+        if (size != -1) return size;
+
+        size = 0;
+        if (mergeHostToDeviceStream_ != false) {
+          size += com.google.protobuf.CodedOutputStream
+            .computeBoolSize(1, mergeHostToDeviceStream_);
+        }
+        if (mergeDeviceToHostStream_ != false) {
+          size += com.google.protobuf.CodedOutputStream
+            .computeBoolSize(2, mergeDeviceToHostStream_);
+        }
+        if (mergeDeviceToDeviceStream_ != false) {
+          size += com.google.protobuf.CodedOutputStream
+            .computeBoolSize(3, mergeDeviceToDeviceStream_);
+        }
+        size += getUnknownFields().getSerializedSize();
+        memoizedSize = size;
+        return size;
+      }
+
+      @java.lang.Override
+      public boolean equals(final java.lang.Object obj) {
+        if (obj == this) {
+         return true;
+        }
+        if (!(obj instanceof org.tensorflow.proto.GPUOptions.Experimental.StreamMergeOptions)) {
+          return super.equals(obj);
+        }
+        org.tensorflow.proto.GPUOptions.Experimental.StreamMergeOptions other = (org.tensorflow.proto.GPUOptions.Experimental.StreamMergeOptions) obj;
+
+        if (getMergeHostToDeviceStream()
+            != other.getMergeHostToDeviceStream()) return false;
+        if (getMergeDeviceToHostStream()
+            != other.getMergeDeviceToHostStream()) return false;
+        if (getMergeDeviceToDeviceStream()
+            != other.getMergeDeviceToDeviceStream()) return false;
+        if (!getUnknownFields().equals(other.getUnknownFields())) return false;
+        return true;
+      }
+
+      @java.lang.Override
+      public int hashCode() {
+        if (memoizedHashCode != 0) {
+          return memoizedHashCode;
+        }
+        int hash = 41;
+        hash = (19 * hash) + getDescriptor().hashCode();
+        hash = (37 * hash) + MERGE_HOST_TO_DEVICE_STREAM_FIELD_NUMBER;
+        hash = (53 * hash) + com.google.protobuf.Internal.hashBoolean(
+            getMergeHostToDeviceStream());
+        hash = (37 * hash) + MERGE_DEVICE_TO_HOST_STREAM_FIELD_NUMBER;
+        hash = (53 * hash) + com.google.protobuf.Internal.hashBoolean(
+            getMergeDeviceToHostStream());
+        hash = (37 * hash) + MERGE_DEVICE_TO_DEVICE_STREAM_FIELD_NUMBER;
+        hash = (53 * hash) + com.google.protobuf.Internal.hashBoolean(
+            getMergeDeviceToDeviceStream());
+        hash = (29 * hash) + getUnknownFields().hashCode();
+        memoizedHashCode = hash;
+        return hash;
+      }
+
+      public static org.tensorflow.proto.GPUOptions.Experimental.StreamMergeOptions parseFrom(
+          java.nio.ByteBuffer data)
+          throws com.google.protobuf.InvalidProtocolBufferException {
+        return PARSER.parseFrom(data);
+      }
+      public static org.tensorflow.proto.GPUOptions.Experimental.StreamMergeOptions parseFrom(
+          java.nio.ByteBuffer data,
+          com.google.protobuf.ExtensionRegistryLite extensionRegistry)
+          throws com.google.protobuf.InvalidProtocolBufferException {
+        return PARSER.parseFrom(data, extensionRegistry);
+      }
+      public static org.tensorflow.proto.GPUOptions.Experimental.StreamMergeOptions parseFrom(
+          com.google.protobuf.ByteString data)
+          throws com.google.protobuf.InvalidProtocolBufferException {
+        return PARSER.parseFrom(data);
+      }
+      public static org.tensorflow.proto.GPUOptions.Experimental.StreamMergeOptions parseFrom(
+          com.google.protobuf.ByteString data,
+          com.google.protobuf.ExtensionRegistryLite extensionRegistry)
+          throws com.google.protobuf.InvalidProtocolBufferException {
+        return PARSER.parseFrom(data, extensionRegistry);
+      }
+      public static org.tensorflow.proto.GPUOptions.Experimental.StreamMergeOptions parseFrom(byte[] data)
+          throws com.google.protobuf.InvalidProtocolBufferException {
+        return PARSER.parseFrom(data);
+      }
+      public static org.tensorflow.proto.GPUOptions.Experimental.StreamMergeOptions parseFrom(
+          byte[] data,
+          com.google.protobuf.ExtensionRegistryLite extensionRegistry)
+          throws com.google.protobuf.InvalidProtocolBufferException {
+        return PARSER.parseFrom(data, extensionRegistry);
+      }
+      public static org.tensorflow.proto.GPUOptions.Experimental.StreamMergeOptions parseFrom(java.io.InputStream input)
+          throws java.io.IOException {
+        return com.google.protobuf.GeneratedMessageV3
+            .parseWithIOException(PARSER, input);
+      }
+      public static org.tensorflow.proto.GPUOptions.Experimental.StreamMergeOptions parseFrom(
+          java.io.InputStream input,
+          com.google.protobuf.ExtensionRegistryLite extensionRegistry)
+          throws java.io.IOException {
+        return com.google.protobuf.GeneratedMessageV3
+            .parseWithIOException(PARSER, input, extensionRegistry);
+      }
+      public static org.tensorflow.proto.GPUOptions.Experimental.StreamMergeOptions parseDelimitedFrom(java.io.InputStream input)
+          throws java.io.IOException {
+        return com.google.protobuf.GeneratedMessageV3
+            .parseDelimitedWithIOException(PARSER, input);
+      }
+      public static org.tensorflow.proto.GPUOptions.Experimental.StreamMergeOptions parseDelimitedFrom(
+          java.io.InputStream input,
+          com.google.protobuf.ExtensionRegistryLite extensionRegistry)
+          throws java.io.IOException {
+        return com.google.protobuf.GeneratedMessageV3
+            .parseDelimitedWithIOException(PARSER, input, extensionRegistry);
+      }
+      public static org.tensorflow.proto.GPUOptions.Experimental.StreamMergeOptions parseFrom(
+          com.google.protobuf.CodedInputStream input)
+          throws java.io.IOException {
+        return com.google.protobuf.GeneratedMessageV3
+            .parseWithIOException(PARSER, input);
+      }
+      public static org.tensorflow.proto.GPUOptions.Experimental.StreamMergeOptions parseFrom(
+          com.google.protobuf.CodedInputStream input,
+          com.google.protobuf.ExtensionRegistryLite extensionRegistry)
+          throws java.io.IOException {
+        return com.google.protobuf.GeneratedMessageV3
+            .parseWithIOException(PARSER, input, extensionRegistry);
+      }
+
+      @java.lang.Override
+      public Builder newBuilderForType() { return newBuilder(); }
+      public static Builder newBuilder() {
+        return DEFAULT_INSTANCE.toBuilder();
+      }
+      public static Builder newBuilder(org.tensorflow.proto.GPUOptions.Experimental.StreamMergeOptions prototype) {
+        return DEFAULT_INSTANCE.toBuilder().mergeFrom(prototype);
+      }
+      @java.lang.Override
+      public Builder toBuilder() {
+        return this == DEFAULT_INSTANCE
+            ? new Builder() : new Builder().mergeFrom(this);
+      }
+
+      @java.lang.Override
+      protected Builder newBuilderForType(
+          com.google.protobuf.GeneratedMessageV3.BuilderParent parent) {
+        Builder builder = new Builder(parent);
+        return builder;
+      }
+      /**
+       * 
    +       * Whether to merge data transfer streams into the compute stream in the
+       * same stream group. Stream merging helps reduce the overhead caused by
+       * stream synchronization, especially when data transfers are frequent. For
+       * example, setting "merge_host_to_device_stream = true" will make the
+       * compute stream responsible for both computation and host to device memory
+       * copy.
+       * 
    
+       *
+       * Protobuf type {@code tensorflow.GPUOptions.Experimental.StreamMergeOptions}
+       */
+      public static final class Builder extends
+          com.google.protobuf.GeneratedMessageV3.Builder implements
+          // @@protoc_insertion_point(builder_implements:tensorflow.GPUOptions.Experimental.StreamMergeOptions)
+          org.tensorflow.proto.GPUOptions.Experimental.StreamMergeOptionsOrBuilder {
+        public static final com.google.protobuf.Descriptors.Descriptor
+            getDescriptor() {
+          return org.tensorflow.proto.ConfigProtos.internal_static_tensorflow_GPUOptions_Experimental_StreamMergeOptions_descriptor;
+        }
+
+        @java.lang.Override
+        protected com.google.protobuf.GeneratedMessageV3.FieldAccessorTable
+            internalGetFieldAccessorTable() {
+          return org.tensorflow.proto.ConfigProtos.internal_static_tensorflow_GPUOptions_Experimental_StreamMergeOptions_fieldAccessorTable
+              .ensureFieldAccessorsInitialized(
+                  org.tensorflow.proto.GPUOptions.Experimental.StreamMergeOptions.class, org.tensorflow.proto.GPUOptions.Experimental.StreamMergeOptions.Builder.class);
+        }
+
+        // Construct using org.tensorflow.proto.GPUOptions.Experimental.StreamMergeOptions.newBuilder()
+        private Builder() {
+
+        }
+
+        private Builder(
+            com.google.protobuf.GeneratedMessageV3.BuilderParent parent) {
+          super(parent);
+
+        }
+        @java.lang.Override
+        public Builder clear() {
+          super.clear();
+          mergeHostToDeviceStream_ = false;
+
+          mergeDeviceToHostStream_ = false;
+
+          mergeDeviceToDeviceStream_ = false;
+
+          return this;
+        }
+
+        @java.lang.Override
+        public com.google.protobuf.Descriptors.Descriptor
+            getDescriptorForType() {
+          return org.tensorflow.proto.ConfigProtos.internal_static_tensorflow_GPUOptions_Experimental_StreamMergeOptions_descriptor;
+        }
+
+        @java.lang.Override
+        public org.tensorflow.proto.GPUOptions.Experimental.StreamMergeOptions getDefaultInstanceForType() {
+          return org.tensorflow.proto.GPUOptions.Experimental.StreamMergeOptions.getDefaultInstance();
+        }
+
+        @java.lang.Override
+        public org.tensorflow.proto.GPUOptions.Experimental.StreamMergeOptions build() {
+          org.tensorflow.proto.GPUOptions.Experimental.StreamMergeOptions result = buildPartial();
+          if (!result.isInitialized()) {
+            throw newUninitializedMessageException(result);
+          }
+          return result;
+        }
+
+        @java.lang.Override
+        public org.tensorflow.proto.GPUOptions.Experimental.StreamMergeOptions buildPartial() {
+          org.tensorflow.proto.GPUOptions.Experimental.StreamMergeOptions result = new org.tensorflow.proto.GPUOptions.Experimental.StreamMergeOptions(this);
+          result.mergeHostToDeviceStream_ = mergeHostToDeviceStream_;
+          result.mergeDeviceToHostStream_ = mergeDeviceToHostStream_;
+          result.mergeDeviceToDeviceStream_ = mergeDeviceToDeviceStream_;
+          onBuilt();
+          return result;
+        }
+
+        @java.lang.Override
+        public Builder clone() {
+          return super.clone();
+        }
+        @java.lang.Override
+        public Builder setField(
+            com.google.protobuf.Descriptors.FieldDescriptor field,
+            java.lang.Object value) {
+          return super.setField(field, value);
+        }
+        @java.lang.Override
+        public Builder clearField(
+            com.google.protobuf.Descriptors.FieldDescriptor field) {
+          return super.clearField(field);
+        }
+        @java.lang.Override
+        public Builder clearOneof(
+            com.google.protobuf.Descriptors.OneofDescriptor oneof) {
+          return super.clearOneof(oneof);
+        }
+        @java.lang.Override
+        public Builder setRepeatedField(
+            com.google.protobuf.Descriptors.FieldDescriptor field,
+            int index, java.lang.Object value) {
+          return super.setRepeatedField(field, index, value);
+        }
+        @java.lang.Override
+        public Builder addRepeatedField(
+            com.google.protobuf.Descriptors.FieldDescriptor field,
+            java.lang.Object value) {
+          return super.addRepeatedField(field, value);
+        }
+        @java.lang.Override
+        public Builder mergeFrom(com.google.protobuf.Message other) {
+          if (other instanceof org.tensorflow.proto.GPUOptions.Experimental.StreamMergeOptions) {
+            return mergeFrom((org.tensorflow.proto.GPUOptions.Experimental.StreamMergeOptions)other);
+          } else {
+            super.mergeFrom(other);
+            return this;
+          }
+        }
+
+        public Builder mergeFrom(org.tensorflow.proto.GPUOptions.Experimental.StreamMergeOptions other) {
+          if (other == org.tensorflow.proto.GPUOptions.Experimental.StreamMergeOptions.getDefaultInstance()) return this;
+          if (other.getMergeHostToDeviceStream() != false) {
+            setMergeHostToDeviceStream(other.getMergeHostToDeviceStream());
+          }
+          if (other.getMergeDeviceToHostStream() != false) {
+            setMergeDeviceToHostStream(other.getMergeDeviceToHostStream());
+          }
+          if (other.getMergeDeviceToDeviceStream() != false) {
+            setMergeDeviceToDeviceStream(other.getMergeDeviceToDeviceStream());
+          }
+          this.mergeUnknownFields(other.getUnknownFields());
+          onChanged();
+          return this;
+        }
+
+        @java.lang.Override
+        public final boolean isInitialized() {
+          return true;
+        }
+
+        @java.lang.Override
+        public Builder mergeFrom(
+            com.google.protobuf.CodedInputStream input,
+            com.google.protobuf.ExtensionRegistryLite extensionRegistry)
+            throws java.io.IOException {
+          if (extensionRegistry == null) {
+            throw new java.lang.NullPointerException();
+          }
+          try {
+            boolean done = false;
+            while (!done) {
+              int tag = input.readTag();
+              switch (tag) {
+                case 0:
+                  done = true;
+                  break;
+                case 8: {
+                  mergeHostToDeviceStream_ = input.readBool();
+
+                  break;
+                } // case 8
+                case 16: {
+                  mergeDeviceToHostStream_ = input.readBool();
+
+                  break;
+                } // case 16
+                case 24: {
+                  mergeDeviceToDeviceStream_ = input.readBool();
+
+                  break;
+                } // case 24
+                default: {
+                  if (!super.parseUnknownField(input, extensionRegistry, tag)) {
+                    done = true; // was an endgroup tag
+                  }
+                  break;
+                } // default:
+              } // switch (tag)
+            } // while (!done)
+          } catch (com.google.protobuf.InvalidProtocolBufferException e) {
+            throw e.unwrapIOException();
+          } finally {
+            onChanged();
+          } // finally
+          return this;
+        }
+
+        private boolean mergeHostToDeviceStream_ ;
+        /**
+         * 
    +         * If true, the compute stream will be used for host_to_device copy as
+         * well. It's no longer necessary to record an event before the copy to
+         * let the copy stream wait for the compute stream to finish. There is
+         * also no need to wait for the copy to complete before executing the
+         * callback function.
+         * 
    
+         *
+         * bool merge_host_to_device_stream = 1;
+         * @return The mergeHostToDeviceStream.
+         */
+        @java.lang.Override
+        public boolean getMergeHostToDeviceStream() {
+          return mergeHostToDeviceStream_;
+        }
+        /**
+         * 
    +         * If true, the compute stream will be used for host_to_device copy as
+         * well. It's no longer necessary to record an event before the copy to
+         * let the copy stream wait for the compute stream to finish. There is
+         * also no need to wait for the copy to complete before executing the
+         * callback function.
+         * 
    
+         *
+         * bool merge_host_to_device_stream = 1;
+         * @param value The mergeHostToDeviceStream to set.
+         * @return This builder for chaining.
+         */
+        public Builder setMergeHostToDeviceStream(boolean value) {
+          
+          mergeHostToDeviceStream_ = value;
+          onChanged();
+          return this;
+        }
+        /**
+         * 
    +         * If true, the compute stream will be used for host_to_device copy as
+         * well. It's no longer necessary to record an event before the copy to
+         * let the copy stream wait for the compute stream to finish. There is
+         * also no need to wait for the copy to complete before executing the
+         * callback function.
          * 
    
          *
-         * repeated int32 device_ordinal = 3;
-         * @return The count of deviceOrdinal.
+         * bool merge_host_to_device_stream = 1;
+         * @return This builder for chaining.
          */
-        public int getDeviceOrdinalCount() {
-          return deviceOrdinal_.size();
+        public Builder clearMergeHostToDeviceStream() {
+          
+          mergeHostToDeviceStream_ = false;
+          onChanged();
+          return this;
         }
+
+        private boolean mergeDeviceToHostStream_ ;
         /**
          * 
    -         * Virtual Device ordinal number determines the device ID of the device.
-         * A Virtual device with a lower ordinal number always receives the a
-         * smaller device id. The phyiscal device id and location in the
-         * virtual device list is used to break ties.
+         * If true, the compute stream will be used for device_to_host copy as
+         * well. It's no longer necessary to record an event before the copy to
+         * let the copy stream wait for the compute stream to finish.
          * 
    
          *
-         * repeated int32 device_ordinal = 3;
-         * @param index The index of the element to return.
-         * @return The deviceOrdinal at the given index.
+         * bool merge_device_to_host_stream = 2;
+         * @return The mergeDeviceToHostStream.
          */
-        public int getDeviceOrdinal(int index) {
-          return deviceOrdinal_.getInt(index);
+        @java.lang.Override
+        public boolean getMergeDeviceToHostStream() {
+          return mergeDeviceToHostStream_;
         }
         /**
          * 
    -         * Virtual Device ordinal number determines the device ID of the device.
-         * A Virtual device with a lower ordinal number always receives the a
-         * smaller device id. The phyiscal device id and location in the
-         * virtual device list is used to break ties.
+         * If true, the compute stream will be used for device_to_host copy as
+         * well. It's no longer necessary to record an event before the copy to
+         * let the copy stream wait for the compute stream to finish.
          * 
    
          *
-         * repeated int32 device_ordinal = 3;
-         * @param index The index to set the value at.
-         * @param value The deviceOrdinal to set.
+         * bool merge_device_to_host_stream = 2;
+         * @param value The mergeDeviceToHostStream to set.
          * @return This builder for chaining.
          */
-        public Builder setDeviceOrdinal(
-            int index, int value) {
-          ensureDeviceOrdinalIsMutable();
-          deviceOrdinal_.setInt(index, value);
+        public Builder setMergeDeviceToHostStream(boolean value) {
+          
+          mergeDeviceToHostStream_ = value;
           onChanged();
           return this;
         }
         /**
          * 
    -         * Virtual Device ordinal number determines the device ID of the device.
-         * A Virtual device with a lower ordinal number always receives the a
-         * smaller device id. The phyiscal device id and location in the
-         * virtual device list is used to break ties.
+         * If true, the compute stream will be used for device_to_host copy as
+         * well. It's no longer necessary to record an event before the copy to
+         * let the copy stream wait for the compute stream to finish.
          * 
    
          *
-         * repeated int32 device_ordinal = 3;
-         * @param value The deviceOrdinal to add.
+         * bool merge_device_to_host_stream = 2;
          * @return This builder for chaining.
          */
-        public Builder addDeviceOrdinal(int value) {
-          ensureDeviceOrdinalIsMutable();
-          deviceOrdinal_.addInt(value);
+        public Builder clearMergeDeviceToHostStream() {
+          
+          mergeDeviceToHostStream_ = false;
           onChanged();
           return this;
         }
+
+        private boolean mergeDeviceToDeviceStream_ ;
         /**
          * 
    -         * Virtual Device ordinal number determines the device ID of the device.
-         * A Virtual device with a lower ordinal number always receives the a
-         * smaller device id. The phyiscal device id and location in the
-         * virtual device list is used to break ties.
+         * If true, the compute stream will be used for device_to_device copy as
+         * well. It's no longer necessary to record an event before the copy to
+         * let the copy stream wait for the compute stream of the sending device
+         * to finish. There is also no need to wait for the compute stream of the
+         * receiving device to finish if the copy is within the same device.
          * 
    
          *
-         * repeated int32 device_ordinal = 3;
-         * @param values The deviceOrdinal to add.
+         * bool merge_device_to_device_stream = 3;
+         * @return The mergeDeviceToDeviceStream.
+         */
+        @java.lang.Override
+        public boolean getMergeDeviceToDeviceStream() {
+          return mergeDeviceToDeviceStream_;
+        }
+        /**
+         * 
    +         * If true, the compute stream will be used for device_to_device copy as
+         * well. It's no longer necessary to record an event before the copy to
+         * let the copy stream wait for the compute stream of the sending device
+         * to finish. There is also no need to wait for the compute stream of the
+         * receiving device to finish if the copy is within the same device.
+         * 
    
+         *
+         * bool merge_device_to_device_stream = 3;
+         * @param value The mergeDeviceToDeviceStream to set.
          * @return This builder for chaining.
          */
-        public Builder addAllDeviceOrdinal(
-            java.lang.Iterable values) {
-          ensureDeviceOrdinalIsMutable();
-          com.google.protobuf.AbstractMessageLite.Builder.addAll(
-              values, deviceOrdinal_);
+        public Builder setMergeDeviceToDeviceStream(boolean value) {
+          
+          mergeDeviceToDeviceStream_ = value;
           onChanged();
           return this;
         }
         /**
          * 
    -         * Virtual Device ordinal number determines the device ID of the device.
-         * A Virtual device with a lower ordinal number always receives the a
-         * smaller device id. The phyiscal device id and location in the
-         * virtual device list is used to break ties.
+         * If true, the compute stream will be used for device_to_device copy as
+         * well. It's no longer necessary to record an event before the copy to
+         * let the copy stream wait for the compute stream of the sending device
+         * to finish. There is also no need to wait for the compute stream of the
+         * receiving device to finish if the copy is within the same device.
          * 
    
          *
-         * repeated int32 device_ordinal = 3;
+         * bool merge_device_to_device_stream = 3;
          * @return This builder for chaining.
          */
-        public Builder clearDeviceOrdinal() {
-          deviceOrdinal_ = emptyIntList();
-          bitField0_ = (bitField0_ & ~0x00000004);
+        public Builder clearMergeDeviceToDeviceStream() {
+          
+          mergeDeviceToDeviceStream_ = false;
           onChanged();
           return this;
         }
@@ -1788,23 +2566,23 @@ public final Builder mergeUnknownFields(
         }
 
 
-        // @@protoc_insertion_point(builder_scope:tensorflow.GPUOptions.Experimental.VirtualDevices)
+        // @@protoc_insertion_point(builder_scope:tensorflow.GPUOptions.Experimental.StreamMergeOptions)
       }
 
-      // @@protoc_insertion_point(class_scope:tensorflow.GPUOptions.Experimental.VirtualDevices)
-      private static final org.tensorflow.proto.GPUOptions.Experimental.VirtualDevices DEFAULT_INSTANCE;
+      // @@protoc_insertion_point(class_scope:tensorflow.GPUOptions.Experimental.StreamMergeOptions)
+      private static final org.tensorflow.proto.GPUOptions.Experimental.StreamMergeOptions DEFAULT_INSTANCE;
       static {
-        DEFAULT_INSTANCE = new org.tensorflow.proto.GPUOptions.Experimental.VirtualDevices();
+        DEFAULT_INSTANCE = new org.tensorflow.proto.GPUOptions.Experimental.StreamMergeOptions();
       }
 
-      public static org.tensorflow.proto.GPUOptions.Experimental.VirtualDevices getDefaultInstance() {
+      public static org.tensorflow.proto.GPUOptions.Experimental.StreamMergeOptions getDefaultInstance() {
         return DEFAULT_INSTANCE;
       }
 
-      private static final com.google.protobuf.Parser
-          PARSER = new com.google.protobuf.AbstractParser() {
+      private static final com.google.protobuf.Parser
+          PARSER = new com.google.protobuf.AbstractParser() {
         @java.lang.Override
-        public VirtualDevices parsePartialFrom(
+        public StreamMergeOptions parsePartialFrom(
             com.google.protobuf.CodedInputStream input,
             com.google.protobuf.ExtensionRegistryLite extensionRegistry)
             throws com.google.protobuf.InvalidProtocolBufferException {
@@ -1823,17 +2601,17 @@ public VirtualDevices parsePartialFrom(
         }
       };
 
-      public static com.google.protobuf.Parser parser() {
+      public static com.google.protobuf.Parser parser() {
         return PARSER;
       }
 
       @java.lang.Override
-      public com.google.protobuf.Parser getParserForType() {
+      public com.google.protobuf.Parser getParserForType() {
         return PARSER;
       }
 
       @java.lang.Override
-      public org.tensorflow.proto.GPUOptions.Experimental.VirtualDevices getDefaultInstanceForType() {
+      public org.tensorflow.proto.GPUOptions.Experimental.StreamMergeOptions getDefaultInstanceForType() {
         return DEFAULT_INSTANCE;
       }
 
@@ -2365,6 +3143,64 @@ public int getGpuSystemMemorySizeInMb() {
       return gpuSystemMemorySizeInMb_;
     }
 
+    public static final int POPULATE_PJRT_GPU_CLIENT_CREATION_INFO_FIELD_NUMBER = 17;
+    private boolean populatePjrtGpuClientCreationInfo_;
+    /**
+     * 
    +     * If true, save information needed for created a PjRt GPU client for
+     * creating a client with remote devices.
+     * 
    
+     *
+     * bool populate_pjrt_gpu_client_creation_info = 17;
+     * @return The populatePjrtGpuClientCreationInfo.
+     */
+    @java.lang.Override
+    public boolean getPopulatePjrtGpuClientCreationInfo() {
+      return populatePjrtGpuClientCreationInfo_;
+    }
+
+    public static final int NODE_ID_FIELD_NUMBER = 18;
+    private int nodeId_;
+    /**
+     * 
    +     * node_id for use when creating a PjRt GPU client with remote devices,
+     * which enumerates jobs*tasks from a ServerDef.
+     * 
    
+     *
+     * int32 node_id = 18;
+     * @return The nodeId.
+     */
+    @java.lang.Override
+    public int getNodeId() {
+      return nodeId_;
+    }
+
+    public static final int STREAM_MERGE_OPTIONS_FIELD_NUMBER = 19;
+    private org.tensorflow.proto.GPUOptions.Experimental.StreamMergeOptions streamMergeOptions_;
+    /**
+     * .tensorflow.GPUOptions.Experimental.StreamMergeOptions stream_merge_options = 19;
+     * @return Whether the streamMergeOptions field is set.
+     */
+    @java.lang.Override
+    public boolean hasStreamMergeOptions() {
+      return streamMergeOptions_ != null;
+    }
+    /**
+     * .tensorflow.GPUOptions.Experimental.StreamMergeOptions stream_merge_options = 19;
+     * @return The streamMergeOptions.
+     */
+    @java.lang.Override
+    public org.tensorflow.proto.GPUOptions.Experimental.StreamMergeOptions getStreamMergeOptions() {
+      return streamMergeOptions_ == null ? org.tensorflow.proto.GPUOptions.Experimental.StreamMergeOptions.getDefaultInstance() : streamMergeOptions_;
+    }
+    /**
+     * .tensorflow.GPUOptions.Experimental.StreamMergeOptions stream_merge_options = 19;
+     */
+    @java.lang.Override
+    public org.tensorflow.proto.GPUOptions.Experimental.StreamMergeOptionsOrBuilder getStreamMergeOptionsOrBuilder() {
+      return getStreamMergeOptions();
+    }
+
     private byte memoizedIsInitialized = -1;
     @java.lang.Override
     public final boolean isInitialized() {
@@ -2424,6 +3260,15 @@ public void writeTo(com.google.protobuf.CodedOutputStream output)
       if (gpuSystemMemorySizeInMb_ != 0) {
         output.writeInt32(16, gpuSystemMemorySizeInMb_);
       }
+      if (populatePjrtGpuClientCreationInfo_ != false) {
+        output.writeBool(17, populatePjrtGpuClientCreationInfo_);
+      }
+      if (nodeId_ != 0) {
+        output.writeInt32(18, nodeId_);
+      }
+      if (streamMergeOptions_ != null) {
+        output.writeMessage(19, getStreamMergeOptions());
+      }
       getUnknownFields().writeTo(output);
     }
 
@@ -2492,6 +3337,18 @@ public int getSerializedSize() {
         size += com.google.protobuf.CodedOutputStream
           .computeInt32Size(16, gpuSystemMemorySizeInMb_);
       }
+      if (populatePjrtGpuClientCreationInfo_ != false) {
+        size += com.google.protobuf.CodedOutputStream
+          .computeBoolSize(17, populatePjrtGpuClientCreationInfo_);
+      }
+      if (nodeId_ != 0) {
+        size += com.google.protobuf.CodedOutputStream
+          .computeInt32Size(18, nodeId_);
+      }
+      if (streamMergeOptions_ != null) {
+        size += com.google.protobuf.CodedOutputStream
+          .computeMessageSize(19, getStreamMergeOptions());
+      }
       size += getUnknownFields().getSerializedSize();
       memoizedSize = size;
       return size;
@@ -2539,6 +3396,15 @@ public boolean equals(final java.lang.Object obj) {
           != other.getGpuHostMemDisallowGrowth()) return false;
       if (getGpuSystemMemorySizeInMb()
           != other.getGpuSystemMemorySizeInMb()) return false;
+      if (getPopulatePjrtGpuClientCreationInfo()
+          != other.getPopulatePjrtGpuClientCreationInfo()) return false;
+      if (getNodeId()
+          != other.getNodeId()) return false;
+      if (hasStreamMergeOptions() != other.hasStreamMergeOptions()) return false;
+      if (hasStreamMergeOptions()) {
+        if (!getStreamMergeOptions()
+            .equals(other.getStreamMergeOptions())) return false;
+      }
       if (!getUnknownFields().equals(other.getUnknownFields())) return false;
       return true;
     }
@@ -2589,6 +3455,15 @@ public int hashCode() {
           getGpuHostMemDisallowGrowth());
       hash = (37 * hash) + GPU_SYSTEM_MEMORY_SIZE_IN_MB_FIELD_NUMBER;
       hash = (53 * hash) + getGpuSystemMemorySizeInMb();
+      hash = (37 * hash) + POPULATE_PJRT_GPU_CLIENT_CREATION_INFO_FIELD_NUMBER;
+      hash = (53 * hash) + com.google.protobuf.Internal.hashBoolean(
+          getPopulatePjrtGpuClientCreationInfo());
+      hash = (37 * hash) + NODE_ID_FIELD_NUMBER;
+      hash = (53 * hash) + getNodeId();
+      if (hasStreamMergeOptions()) {
+        hash = (37 * hash) + STREAM_MERGE_OPTIONS_FIELD_NUMBER;
+        hash = (53 * hash) + getStreamMergeOptions().hashCode();
+      }
       hash = (29 * hash) + getUnknownFields().hashCode();
       memoizedHashCode = hash;
       return hash;
@@ -2752,6 +3627,16 @@ public Builder clear() {
 
         gpuSystemMemorySizeInMb_ = 0;
 
+        populatePjrtGpuClientCreationInfo_ = false;
+
+        nodeId_ = 0;
+
+        if (streamMergeOptionsBuilder_ == null) {
+          streamMergeOptions_ = null;
+        } else {
+          streamMergeOptions_ = null;
+          streamMergeOptionsBuilder_ = null;
+        }
         return this;
       }
 
@@ -2802,6 +3687,13 @@ public org.tensorflow.proto.GPUOptions.Experimental buildPartial() {
         result.gpuHostMemLimitInMb_ = gpuHostMemLimitInMb_;
         result.gpuHostMemDisallowGrowth_ = gpuHostMemDisallowGrowth_;
         result.gpuSystemMemorySizeInMb_ = gpuSystemMemorySizeInMb_;
+        result.populatePjrtGpuClientCreationInfo_ = populatePjrtGpuClientCreationInfo_;
+        result.nodeId_ = nodeId_;
+        if (streamMergeOptionsBuilder_ == null) {
+          result.streamMergeOptions_ = streamMergeOptions_;
+        } else {
+          result.streamMergeOptions_ = streamMergeOptionsBuilder_.build();
+        }
         onBuilt();
         return result;
       }
@@ -2919,6 +3811,15 @@ public Builder mergeFrom(org.tensorflow.proto.GPUOptions.Experimental other) {
         if (other.getGpuSystemMemorySizeInMb() != 0) {
           setGpuSystemMemorySizeInMb(other.getGpuSystemMemorySizeInMb());
         }
+        if (other.getPopulatePjrtGpuClientCreationInfo() != false) {
+          setPopulatePjrtGpuClientCreationInfo(other.getPopulatePjrtGpuClientCreationInfo());
+        }
+        if (other.getNodeId() != 0) {
+          setNodeId(other.getNodeId());
+        }
+        if (other.hasStreamMergeOptions()) {
+          mergeStreamMergeOptions(other.getStreamMergeOptions());
+        }
         this.mergeUnknownFields(other.getUnknownFields());
         onChanged();
         return this;
@@ -3028,6 +3929,23 @@ public Builder mergeFrom(
 
                 break;
               } // case 128
+              case 136: {
+                populatePjrtGpuClientCreationInfo_ = input.readBool();
+
+                break;
+              } // case 136
+              case 144: {
+                nodeId_ = input.readInt32();
+
+                break;
+              } // case 144
+              case 154: {
+                input.readMessage(
+                    getStreamMergeOptionsFieldBuilder().getBuilder(),
+                    extensionRegistry);
+
+                break;
+              } // case 154
               default: {
                 if (!super.parseUnknownField(input, extensionRegistry, tag)) {
                   done = true; // was an endgroup tag
@@ -4783,6 +5701,217 @@ public Builder clearGpuSystemMemorySizeInMb() {
         onChanged();
         return this;
       }
+
+      private boolean populatePjrtGpuClientCreationInfo_ ;
+      /**
+       * 
    +       * If true, save information needed for created a PjRt GPU client for
+       * creating a client with remote devices.
+       * 
    
+       *
+       * bool populate_pjrt_gpu_client_creation_info = 17;
+       * @return The populatePjrtGpuClientCreationInfo.
+       */
+      @java.lang.Override
+      public boolean getPopulatePjrtGpuClientCreationInfo() {
+        return populatePjrtGpuClientCreationInfo_;
+      }
+      /**
+       * 
    +       * If true, save information needed for created a PjRt GPU client for
+       * creating a client with remote devices.
+       * 
    
+       *
+       * bool populate_pjrt_gpu_client_creation_info = 17;
+       * @param value The populatePjrtGpuClientCreationInfo to set.
+       * @return This builder for chaining.
+       */
+      public Builder setPopulatePjrtGpuClientCreationInfo(boolean value) {
+        
+        populatePjrtGpuClientCreationInfo_ = value;
+        onChanged();
+        return this;
+      }
+      /**
+       * 
    +       * If true, save information needed for created a PjRt GPU client for
+       * creating a client with remote devices.
+       * 
    
+       *
+       * bool populate_pjrt_gpu_client_creation_info = 17;
+       * @return This builder for chaining.
+       */
+      public Builder clearPopulatePjrtGpuClientCreationInfo() {
+        
+        populatePjrtGpuClientCreationInfo_ = false;
+        onChanged();
+        return this;
+      }
+
+      private int nodeId_ ;
+      /**
+       * 
    +       * node_id for use when creating a PjRt GPU client with remote devices,
+       * which enumerates jobs*tasks from a ServerDef.
+       * 
    
+       *
+       * int32 node_id = 18;
+       * @return The nodeId.
+       */
+      @java.lang.Override
+      public int getNodeId() {
+        return nodeId_;
+      }
+      /**
+       * 
    +       * node_id for use when creating a PjRt GPU client with remote devices,
+       * which enumerates jobs*tasks from a ServerDef.
+       * 
    
+       *
+       * int32 node_id = 18;
+       * @param value The nodeId to set.
+       * @return This builder for chaining.
+       */
+      public Builder setNodeId(int value) {
+        
+        nodeId_ = value;
+        onChanged();
+        return this;
+      }
+      /**
+       * 
    +       * node_id for use when creating a PjRt GPU client with remote devices,
+       * which enumerates jobs*tasks from a ServerDef.
+       * 
    
+       *
+       * int32 node_id = 18;
+       * @return This builder for chaining.
+       */
+      public Builder clearNodeId() {
+        
+        nodeId_ = 0;
+        onChanged();
+        return this;
+      }
+
+      private org.tensorflow.proto.GPUOptions.Experimental.StreamMergeOptions streamMergeOptions_;
+      private com.google.protobuf.SingleFieldBuilderV3<
+          org.tensorflow.proto.GPUOptions.Experimental.StreamMergeOptions, org.tensorflow.proto.GPUOptions.Experimental.StreamMergeOptions.Builder, org.tensorflow.proto.GPUOptions.Experimental.StreamMergeOptionsOrBuilder> streamMergeOptionsBuilder_;
+      /**
+       * .tensorflow.GPUOptions.Experimental.StreamMergeOptions stream_merge_options = 19;
+       * @return Whether the streamMergeOptions field is set.
+       */
+      public boolean hasStreamMergeOptions() {
+        return streamMergeOptionsBuilder_ != null || streamMergeOptions_ != null;
+      }
+      /**
+       * .tensorflow.GPUOptions.Experimental.StreamMergeOptions stream_merge_options = 19;
+       * @return The streamMergeOptions.
+       */
+      public org.tensorflow.proto.GPUOptions.Experimental.StreamMergeOptions getStreamMergeOptions() {
+        if (streamMergeOptionsBuilder_ == null) {
+          return streamMergeOptions_ == null ? org.tensorflow.proto.GPUOptions.Experimental.StreamMergeOptions.getDefaultInstance() : streamMergeOptions_;
+        } else {
+          return streamMergeOptionsBuilder_.getMessage();
+        }
+      }
+      /**
+       * .tensorflow.GPUOptions.Experimental.StreamMergeOptions stream_merge_options = 19;
+       */
+      public Builder setStreamMergeOptions(org.tensorflow.proto.GPUOptions.Experimental.StreamMergeOptions value) {
+        if (streamMergeOptionsBuilder_ == null) {
+          if (value == null) {
+            throw new NullPointerException();
+          }
+          streamMergeOptions_ = value;
+          onChanged();
+        } else {
+          streamMergeOptionsBuilder_.setMessage(value);
+        }
+
+        return this;
+      }
+      /**
+       * .tensorflow.GPUOptions.Experimental.StreamMergeOptions stream_merge_options = 19;
+       */
+      public Builder setStreamMergeOptions(
+          org.tensorflow.proto.GPUOptions.Experimental.StreamMergeOptions.Builder builderForValue) {
+        if (streamMergeOptionsBuilder_ == null) {
+          streamMergeOptions_ = builderForValue.build();
+          onChanged();
+        } else {
+          streamMergeOptionsBuilder_.setMessage(builderForValue.build());
+        }
+
+        return this;
+      }
+      /**
+       * .tensorflow.GPUOptions.Experimental.StreamMergeOptions stream_merge_options = 19;
+       */
+      public Builder mergeStreamMergeOptions(org.tensorflow.proto.GPUOptions.Experimental.StreamMergeOptions value) {
+        if (streamMergeOptionsBuilder_ == null) {
+          if (streamMergeOptions_ != null) {
+            streamMergeOptions_ =
+              org.tensorflow.proto.GPUOptions.Experimental.StreamMergeOptions.newBuilder(streamMergeOptions_).mergeFrom(value).buildPartial();
+          } else {
+            streamMergeOptions_ = value;
+          }
+          onChanged();
+        } else {
+          streamMergeOptionsBuilder_.mergeFrom(value);
+        }
+
+        return this;
+      }
+      /**
+       * .tensorflow.GPUOptions.Experimental.StreamMergeOptions stream_merge_options = 19;
+       */
+      public Builder clearStreamMergeOptions() {
+        if (streamMergeOptionsBuilder_ == null) {
+          streamMergeOptions_ = null;
+          onChanged();
+        } else {
+          streamMergeOptions_ = null;
+          streamMergeOptionsBuilder_ = null;
+        }
+
+        return this;
+      }
+      /**
+       * .tensorflow.GPUOptions.Experimental.StreamMergeOptions stream_merge_options = 19;
+       */
+      public org.tensorflow.proto.GPUOptions.Experimental.StreamMergeOptions.Builder getStreamMergeOptionsBuilder() {
+        
+        onChanged();
+        return getStreamMergeOptionsFieldBuilder().getBuilder();
+      }
+      /**
+       * .tensorflow.GPUOptions.Experimental.StreamMergeOptions stream_merge_options = 19;
+       */
+      public org.tensorflow.proto.GPUOptions.Experimental.StreamMergeOptionsOrBuilder getStreamMergeOptionsOrBuilder() {
+        if (streamMergeOptionsBuilder_ != null) {
+          return streamMergeOptionsBuilder_.getMessageOrBuilder();
+        } else {
+          return streamMergeOptions_ == null ?
+              org.tensorflow.proto.GPUOptions.Experimental.StreamMergeOptions.getDefaultInstance() : streamMergeOptions_;
+        }
+      }
+      /**
+       * .tensorflow.GPUOptions.Experimental.StreamMergeOptions stream_merge_options = 19;
+       */
+      private com.google.protobuf.SingleFieldBuilderV3<
+          org.tensorflow.proto.GPUOptions.Experimental.StreamMergeOptions, org.tensorflow.proto.GPUOptions.Experimental.StreamMergeOptions.Builder, org.tensorflow.proto.GPUOptions.Experimental.StreamMergeOptionsOrBuilder> 
+          getStreamMergeOptionsFieldBuilder() {
+        if (streamMergeOptionsBuilder_ == null) {
+          streamMergeOptionsBuilder_ = new com.google.protobuf.SingleFieldBuilderV3<
+              org.tensorflow.proto.GPUOptions.Experimental.StreamMergeOptions, org.tensorflow.proto.GPUOptions.Experimental.StreamMergeOptions.Builder, org.tensorflow.proto.GPUOptions.Experimental.StreamMergeOptionsOrBuilder>(
+                  getStreamMergeOptions(),
+                  getParentForChildren(),
+                  isClean());
+          streamMergeOptions_ = null;
+        }
+        return streamMergeOptionsBuilder_;
+      }
       @java.lang.Override
       public final Builder setUnknownFields(
           final com.google.protobuf.UnknownFieldSet unknownFields) {
diff --git a/tensorflow-core/tensorflow-core-native/src/gen/java/org/tensorflow/proto/MachineConfiguration.java b/tensorflow-core/tensorflow-core-native/src/gen/java/org/tensorflow/proto/MachineConfiguration.java
index 56ab6b425d1..6dbc6ce6f3b 100644
--- a/tensorflow-core/tensorflow-core-native/src/gen/java/org/tensorflow/proto/MachineConfiguration.java
+++ b/tensorflow-core/tensorflow-core-native/src/gen/java/org/tensorflow/proto/MachineConfiguration.java
@@ -1,5 +1,5 @@
 // Generated by the protocol buffer compiler.  DO NOT EDIT!
-// source: tsl/protobuf/test_log.proto
+// source: xla/tsl/protobuf/test_log.proto
 
 package org.tensorflow.proto;
 
diff --git a/tensorflow-core/tensorflow-core-native/src/gen/java/org/tensorflow/proto/MachineConfigurationOrBuilder.java b/tensorflow-core/tensorflow-core-native/src/gen/java/org/tensorflow/proto/MachineConfigurationOrBuilder.java
index 5821218bf8f..e3c944d06be 100644
--- a/tensorflow-core/tensorflow-core-native/src/gen/java/org/tensorflow/proto/MachineConfigurationOrBuilder.java
+++ b/tensorflow-core/tensorflow-core-native/src/gen/java/org/tensorflow/proto/MachineConfigurationOrBuilder.java
@@ -1,5 +1,5 @@
 // Generated by the protocol buffer compiler.  DO NOT EDIT!
-// source: tsl/protobuf/test_log.proto
+// source: xla/tsl/protobuf/test_log.proto
 
 package org.tensorflow.proto;
 
diff --git a/tensorflow-core/tensorflow-core-native/src/gen/java/org/tensorflow/proto/MemoryInfo.java b/tensorflow-core/tensorflow-core-native/src/gen/java/org/tensorflow/proto/MemoryInfo.java
index 8c4b5b692a6..d351a728e2a 100644
--- a/tensorflow-core/tensorflow-core-native/src/gen/java/org/tensorflow/proto/MemoryInfo.java
+++ b/tensorflow-core/tensorflow-core-native/src/gen/java/org/tensorflow/proto/MemoryInfo.java
@@ -1,5 +1,5 @@
 // Generated by the protocol buffer compiler.  DO NOT EDIT!
-// source: tsl/protobuf/test_log.proto
+// source: xla/tsl/protobuf/test_log.proto
 
 package org.tensorflow.proto;
 
diff --git a/tensorflow-core/tensorflow-core-native/src/gen/java/org/tensorflow/proto/MemoryInfoOrBuilder.java b/tensorflow-core/tensorflow-core-native/src/gen/java/org/tensorflow/proto/MemoryInfoOrBuilder.java
index 265206a7c19..6a2f7e6c9e8 100644
--- a/tensorflow-core/tensorflow-core-native/src/gen/java/org/tensorflow/proto/MemoryInfoOrBuilder.java
+++ b/tensorflow-core/tensorflow-core-native/src/gen/java/org/tensorflow/proto/MemoryInfoOrBuilder.java
@@ -1,5 +1,5 @@
 // Generated by the protocol buffer compiler.  DO NOT EDIT!
-// source: tsl/protobuf/test_log.proto
+// source: xla/tsl/protobuf/test_log.proto
 
 package org.tensorflow.proto;
 
diff --git a/tensorflow-core/tensorflow-core-native/src/gen/java/org/tensorflow/proto/MetricEntry.java b/tensorflow-core/tensorflow-core-native/src/gen/java/org/tensorflow/proto/MetricEntry.java
index 70a5e1ba8bc..d9454e9bc70 100644
--- a/tensorflow-core/tensorflow-core-native/src/gen/java/org/tensorflow/proto/MetricEntry.java
+++ b/tensorflow-core/tensorflow-core-native/src/gen/java/org/tensorflow/proto/MetricEntry.java
@@ -1,5 +1,5 @@
 // Generated by the protocol buffer compiler.  DO NOT EDIT!
-// source: tsl/protobuf/test_log.proto
+// source: xla/tsl/protobuf/test_log.proto
 
 package org.tensorflow.proto;
 
diff --git a/tensorflow-core/tensorflow-core-native/src/gen/java/org/tensorflow/proto/MetricEntryOrBuilder.java b/tensorflow-core/tensorflow-core-native/src/gen/java/org/tensorflow/proto/MetricEntryOrBuilder.java
index 9898de2810f..e8f2867a14a 100644
--- a/tensorflow-core/tensorflow-core-native/src/gen/java/org/tensorflow/proto/MetricEntryOrBuilder.java
+++ b/tensorflow-core/tensorflow-core-native/src/gen/java/org/tensorflow/proto/MetricEntryOrBuilder.java
@@ -1,5 +1,5 @@
 // Generated by the protocol buffer compiler.  DO NOT EDIT!
-// source: tsl/protobuf/test_log.proto
+// source: xla/tsl/protobuf/test_log.proto
 
 package org.tensorflow.proto;
 
diff --git a/tensorflow-core/tensorflow-core-native/src/gen/java/org/tensorflow/proto/PlatformInfo.java b/tensorflow-core/tensorflow-core-native/src/gen/java/org/tensorflow/proto/PlatformInfo.java
index 782524cf4d4..d2875cf5041 100644
--- a/tensorflow-core/tensorflow-core-native/src/gen/java/org/tensorflow/proto/PlatformInfo.java
+++ b/tensorflow-core/tensorflow-core-native/src/gen/java/org/tensorflow/proto/PlatformInfo.java
@@ -1,5 +1,5 @@
 // Generated by the protocol buffer compiler.  DO NOT EDIT!
-// source: tsl/protobuf/test_log.proto
+// source: xla/tsl/protobuf/test_log.proto
 
 package org.tensorflow.proto;
 
diff --git a/tensorflow-core/tensorflow-core-native/src/gen/java/org/tensorflow/proto/PlatformInfoOrBuilder.java b/tensorflow-core/tensorflow-core-native/src/gen/java/org/tensorflow/proto/PlatformInfoOrBuilder.java
index fd9455571a2..caade7d2f32 100644
--- a/tensorflow-core/tensorflow-core-native/src/gen/java/org/tensorflow/proto/PlatformInfoOrBuilder.java
+++ b/tensorflow-core/tensorflow-core-native/src/gen/java/org/tensorflow/proto/PlatformInfoOrBuilder.java
@@ -1,5 +1,5 @@
 // Generated by the protocol buffer compiler.  DO NOT EDIT!
-// source: tsl/protobuf/test_log.proto
+// source: xla/tsl/protobuf/test_log.proto
 
 package org.tensorflow.proto;
 
diff --git a/tensorflow-core/tensorflow-core-native/src/gen/java/org/tensorflow/proto/ResourceHandleProto.java b/tensorflow-core/tensorflow-core-native/src/gen/java/org/tensorflow/proto/ResourceHandleProto.java
index 159c9574b47..df26d1e77cd 100644
--- a/tensorflow-core/tensorflow-core-native/src/gen/java/org/tensorflow/proto/ResourceHandleProto.java
+++ b/tensorflow-core/tensorflow-core-native/src/gen/java/org/tensorflow/proto/ResourceHandleProto.java
@@ -59,27 +59,47 @@ public interface DtypeAndShapeOrBuilder extends
       com.google.protobuf.MessageOrBuilder {
 
     /**
+     * 
    +     * Data type of the tensor.
+     * 
    
+     *
      * .tensorflow.DataType dtype = 1;
      * @return The enum numeric value on the wire for dtype.
      */
     int getDtypeValue();
     /**
+     * 
    +     * Data type of the tensor.
+     * 
    
+     *
      * .tensorflow.DataType dtype = 1;
      * @return The dtype.
      */
     org.tensorflow.proto.DataType getDtype();
 
     /**
+     * 
    +     * Shape of the tensor.
+     * 
    
+     *
      * .tensorflow.TensorShapeProto shape = 2;
      * @return Whether the shape field is set.
      */
     boolean hasShape();
     /**
+     * 
    +     * Shape of the tensor.
+     * 
    
+     *
      * .tensorflow.TensorShapeProto shape = 2;
      * @return The shape.
      */
     org.tensorflow.proto.TensorShapeProto getShape();
     /**
+     * 
    +     * Shape of the tensor.
+     * 
    
+     *
      * .tensorflow.TensorShapeProto shape = 2;
      */
     org.tensorflow.proto.TensorShapeProtoOrBuilder getShapeOrBuilder();
@@ -132,6 +152,10 @@ protected java.lang.Object newInstance(
     public static final int DTYPE_FIELD_NUMBER = 1;
     private int dtype_;
     /**
+     * 
    +     * Data type of the tensor.
+     * 
    
+     *
      * .tensorflow.DataType dtype = 1;
      * @return The enum numeric value on the wire for dtype.
      */
@@ -139,6 +163,10 @@ protected java.lang.Object newInstance(
       return dtype_;
     }
     /**
+     * 
    +     * Data type of the tensor.
+     * 
    
+     *
      * .tensorflow.DataType dtype = 1;
      * @return The dtype.
      */
@@ -151,6 +179,10 @@ protected java.lang.Object newInstance(
     public static final int SHAPE_FIELD_NUMBER = 2;
     private org.tensorflow.proto.TensorShapeProto shape_;
     /**
+     * 
    +     * Shape of the tensor.
+     * 
    
+     *
      * .tensorflow.TensorShapeProto shape = 2;
      * @return Whether the shape field is set.
      */
@@ -159,6 +191,10 @@ public boolean hasShape() {
       return shape_ != null;
     }
     /**
+     * 
    +     * Shape of the tensor.
+     * 
    
+     *
      * .tensorflow.TensorShapeProto shape = 2;
      * @return The shape.
      */
@@ -167,6 +203,10 @@ public org.tensorflow.proto.TensorShapeProto getShape() {
       return shape_ == null ? org.tensorflow.proto.TensorShapeProto.getDefaultInstance() : shape_;
     }
     /**
+     * 
    +     * Shape of the tensor.
+     * 
    
+     *
      * .tensorflow.TensorShapeProto shape = 2;
      */
     @java.lang.Override
@@ -531,6 +571,10 @@ public Builder mergeFrom(
 
       private int dtype_ = 0;
       /**
+       * 
    +       * Data type of the tensor.
+       * 
    
+       *
        * .tensorflow.DataType dtype = 1;
        * @return The enum numeric value on the wire for dtype.
        */
@@ -538,6 +582,10 @@ public Builder mergeFrom(
         return dtype_;
       }
       /**
+       * 
    +       * Data type of the tensor.
+       * 
    
+       *
        * .tensorflow.DataType dtype = 1;
        * @param value The enum numeric value on the wire for dtype to set.
        * @return This builder for chaining.
@@ -549,6 +597,10 @@ public Builder setDtypeValue(int value) {
         return this;
       }
       /**
+       * 
    +       * Data type of the tensor.
+       * 
    
+       *
        * .tensorflow.DataType dtype = 1;
        * @return The dtype.
        */
@@ -559,6 +611,10 @@ public org.tensorflow.proto.DataType getDtype() {
         return result == null ? org.tensorflow.proto.DataType.UNRECOGNIZED : result;
       }
       /**
+       * 
    +       * Data type of the tensor.
+       * 
    
+       *
        * .tensorflow.DataType dtype = 1;
        * @param value The dtype to set.
        * @return This builder for chaining.
@@ -573,6 +629,10 @@ public Builder setDtype(org.tensorflow.proto.DataType value) {
         return this;
       }
       /**
+       * 
    +       * Data type of the tensor.
+       * 
    
+       *
        * .tensorflow.DataType dtype = 1;
        * @return This builder for chaining.
        */
@@ -587,6 +647,10 @@ public Builder clearDtype() {
       private com.google.protobuf.SingleFieldBuilderV3<
           org.tensorflow.proto.TensorShapeProto, org.tensorflow.proto.TensorShapeProto.Builder, org.tensorflow.proto.TensorShapeProtoOrBuilder> shapeBuilder_;
       /**
+       * 
    +       * Shape of the tensor.
+       * 
    
+       *
        * .tensorflow.TensorShapeProto shape = 2;
        * @return Whether the shape field is set.
        */
@@ -594,6 +658,10 @@ public boolean hasShape() {
         return shapeBuilder_ != null || shape_ != null;
       }
       /**
+       * 
    +       * Shape of the tensor.
+       * 
    
+       *
        * .tensorflow.TensorShapeProto shape = 2;
        * @return The shape.
        */
@@ -605,6 +673,10 @@ public org.tensorflow.proto.TensorShapeProto getShape() {
         }
       }
       /**
+       * 
    +       * Shape of the tensor.
+       * 
    
+       *
        * .tensorflow.TensorShapeProto shape = 2;
        */
       public Builder setShape(org.tensorflow.proto.TensorShapeProto value) {
@@ -621,6 +693,10 @@ public Builder setShape(org.tensorflow.proto.TensorShapeProto value) {
         return this;
       }
       /**
+       * 
    +       * Shape of the tensor.
+       * 
    
+       *
        * .tensorflow.TensorShapeProto shape = 2;
        */
       public Builder setShape(
@@ -635,6 +711,10 @@ public Builder setShape(
         return this;
       }
       /**
+       * 
    +       * Shape of the tensor.
+       * 
    
+       *
        * .tensorflow.TensorShapeProto shape = 2;
        */
       public Builder mergeShape(org.tensorflow.proto.TensorShapeProto value) {
@@ -653,6 +733,10 @@ public Builder mergeShape(org.tensorflow.proto.TensorShapeProto value) {
         return this;
       }
       /**
+       * 
    +       * Shape of the tensor.
+       * 
    
+       *
        * .tensorflow.TensorShapeProto shape = 2;
        */
       public Builder clearShape() {
@@ -667,6 +751,10 @@ public Builder clearShape() {
         return this;
       }
       /**
+       * 
    +       * Shape of the tensor.
+       * 
    
+       *
        * .tensorflow.TensorShapeProto shape = 2;
        */
       public org.tensorflow.proto.TensorShapeProto.Builder getShapeBuilder() {
@@ -675,6 +763,10 @@ public org.tensorflow.proto.TensorShapeProto.Builder getShapeBuilder() {
         return getShapeFieldBuilder().getBuilder();
       }
       /**
+       * 
    +       * Shape of the tensor.
+       * 
    
+       *
        * .tensorflow.TensorShapeProto shape = 2;
        */
       public org.tensorflow.proto.TensorShapeProtoOrBuilder getShapeOrBuilder() {
@@ -686,6 +778,10 @@ public org.tensorflow.proto.TensorShapeProtoOrBuilder getShapeOrBuilder() {
         }
       }
       /**
+       * 
    +       * Shape of the tensor.
+       * 
    
+       *
        * .tensorflow.TensorShapeProto shape = 2;
        */
       private com.google.protobuf.SingleFieldBuilderV3<
diff --git a/tensorflow-core/tensorflow-core-native/src/gen/java/org/tensorflow/proto/RewriterConfig.java b/tensorflow-core/tensorflow-core-native/src/gen/java/org/tensorflow/proto/RewriterConfig.java
index ae97c9cc75f..c235fb30634 100644
--- a/tensorflow-core/tensorflow-core-native/src/gen/java/org/tensorflow/proto/RewriterConfig.java
+++ b/tensorflow-core/tensorflow-core-native/src/gen/java/org/tensorflow/proto/RewriterConfig.java
@@ -2002,8 +2002,8 @@ public boolean getDisableModelPruning() {
   private int autoMixedPrecision_;
   /**
    * 
    -   * Optimize data types for CUDA (default is OFF).
-   * This will try to use float16 on GPU which is faster.
+   * Optimize data types for CUDA/oneDNN (default is OFF).
+   * This will try to use float16 on GPU/CPU which is faster.
    * Note that this can change the numerical stability of the graph and may
    * require the use of loss scaling to maintain model convergence.
    * 
    
@@ -2016,8 +2016,8 @@ public boolean getDisableModelPruning() {
   }
   /**
    * 
    -   * Optimize data types for CUDA (default is OFF).
-   * This will try to use float16 on GPU which is faster.
+   * Optimize data types for CUDA/oneDNN (default is OFF).
+   * This will try to use float16 on GPU/CPU which is faster.
    * Note that this can change the numerical stability of the graph and may
    * require the use of loss scaling to maintain model convergence.
    * 
    
@@ -5074,8 +5074,8 @@ public Builder clearImplementationSelector() {
     private int autoMixedPrecision_ = 0;
     /**
      * 
    -     * Optimize data types for CUDA (default is OFF).
-     * This will try to use float16 on GPU which is faster.
+     * Optimize data types for CUDA/oneDNN (default is OFF).
+     * This will try to use float16 on GPU/CPU which is faster.
      * Note that this can change the numerical stability of the graph and may
      * require the use of loss scaling to maintain model convergence.
      * 
    
@@ -5088,8 +5088,8 @@ public Builder clearImplementationSelector() {
     }
     /**
      * 
    -     * Optimize data types for CUDA (default is OFF).
-     * This will try to use float16 on GPU which is faster.
+     * Optimize data types for CUDA/oneDNN (default is OFF).
+     * This will try to use float16 on GPU/CPU which is faster.
      * Note that this can change the numerical stability of the graph and may
      * require the use of loss scaling to maintain model convergence.
      * 
    
@@ -5106,8 +5106,8 @@ public Builder setAutoMixedPrecisionValue(int value) {
     }
     /**
      * 
    -     * Optimize data types for CUDA (default is OFF).
-     * This will try to use float16 on GPU which is faster.
+     * Optimize data types for CUDA/oneDNN (default is OFF).
+     * This will try to use float16 on GPU/CPU which is faster.
      * Note that this can change the numerical stability of the graph and may
      * require the use of loss scaling to maintain model convergence.
      * 
    
@@ -5123,8 +5123,8 @@ public org.tensorflow.proto.RewriterConfig.Toggle getAutoMixedPrecision() {
     }
     /**
      * 
    -     * Optimize data types for CUDA (default is OFF).
-     * This will try to use float16 on GPU which is faster.
+     * Optimize data types for CUDA/oneDNN (default is OFF).
+     * This will try to use float16 on GPU/CPU which is faster.
      * Note that this can change the numerical stability of the graph and may
      * require the use of loss scaling to maintain model convergence.
      * 
    
@@ -5144,8 +5144,8 @@ public Builder setAutoMixedPrecision(org.tensorflow.proto.RewriterConfig.Toggle
     }
     /**
      * 
    -     * Optimize data types for CUDA (default is OFF).
-     * This will try to use float16 on GPU which is faster.
+     * Optimize data types for CUDA/oneDNN (default is OFF).
+     * This will try to use float16 on GPU/CPU which is faster.
      * Note that this can change the numerical stability of the graph and may
      * require the use of loss scaling to maintain model convergence.
      * 
    
diff --git a/tensorflow-core/tensorflow-core-native/src/gen/java/org/tensorflow/proto/RewriterConfigOrBuilder.java b/tensorflow-core/tensorflow-core-native/src/gen/java/org/tensorflow/proto/RewriterConfigOrBuilder.java
index 9ad4b3cf401..2676ca54911 100644
--- a/tensorflow-core/tensorflow-core-native/src/gen/java/org/tensorflow/proto/RewriterConfigOrBuilder.java
+++ b/tensorflow-core/tensorflow-core-native/src/gen/java/org/tensorflow/proto/RewriterConfigOrBuilder.java
@@ -305,8 +305,8 @@ public interface RewriterConfigOrBuilder extends
 
   /**
    * 
    -   * Optimize data types for CUDA (default is OFF).
-   * This will try to use float16 on GPU which is faster.
+   * Optimize data types for CUDA/oneDNN (default is OFF).
+   * This will try to use float16 on GPU/CPU which is faster.
    * Note that this can change the numerical stability of the graph and may
    * require the use of loss scaling to maintain model convergence.
    * 
    
@@ -317,8 +317,8 @@ public interface RewriterConfigOrBuilder extends
   int getAutoMixedPrecisionValue();
   /**
    * 
    -   * Optimize data types for CUDA (default is OFF).
-   * This will try to use float16 on GPU which is faster.
+   * Optimize data types for CUDA/oneDNN (default is OFF).
+   * This will try to use float16 on GPU/CPU which is faster.
    * Note that this can change the numerical stability of the graph and may
    * require the use of loss scaling to maintain model convergence.
    * 
    
diff --git a/tensorflow-core/tensorflow-core-native/src/gen/java/org/tensorflow/proto/RunConfiguration.java b/tensorflow-core/tensorflow-core-native/src/gen/java/org/tensorflow/proto/RunConfiguration.java
index 2a17bdafaf1..f8f244b522c 100644
--- a/tensorflow-core/tensorflow-core-native/src/gen/java/org/tensorflow/proto/RunConfiguration.java
+++ b/tensorflow-core/tensorflow-core-native/src/gen/java/org/tensorflow/proto/RunConfiguration.java
@@ -1,5 +1,5 @@
 // Generated by the protocol buffer compiler.  DO NOT EDIT!
-// source: tsl/protobuf/test_log.proto
+// source: xla/tsl/protobuf/test_log.proto
 
 package org.tensorflow.proto;
 
diff --git a/tensorflow-core/tensorflow-core-native/src/gen/java/org/tensorflow/proto/RunConfigurationOrBuilder.java b/tensorflow-core/tensorflow-core-native/src/gen/java/org/tensorflow/proto/RunConfigurationOrBuilder.java
index a3b3ca982e8..4f2ef9a6b2c 100644
--- a/tensorflow-core/tensorflow-core-native/src/gen/java/org/tensorflow/proto/RunConfigurationOrBuilder.java
+++ b/tensorflow-core/tensorflow-core-native/src/gen/java/org/tensorflow/proto/RunConfigurationOrBuilder.java
@@ -1,5 +1,5 @@
 // Generated by the protocol buffer compiler.  DO NOT EDIT!
-// source: tsl/protobuf/test_log.proto
+// source: xla/tsl/protobuf/test_log.proto
 
 package org.tensorflow.proto;
 
diff --git a/tensorflow-core/tensorflow-core-native/src/gen/java/org/tensorflow/proto/SignatureDef.java b/tensorflow-core/tensorflow-core-native/src/gen/java/org/tensorflow/proto/SignatureDef.java
index ecb73cc96e7..b701daabd03 100644
--- a/tensorflow-core/tensorflow-core-native/src/gen/java/org/tensorflow/proto/SignatureDef.java
+++ b/tensorflow-core/tensorflow-core-native/src/gen/java/org/tensorflow/proto/SignatureDef.java
@@ -7,58 +7,6 @@
  * 
      * SignatureDef defines the signature of a computation supported by a TensorFlow
  * graph.
- * For example, a model with two loss computations, sharing a single input,
- * might have the following signature_def map, in a MetaGraphDef message.
- * Note that across the two SignatureDefs "loss_A" and "loss_B", the input key,
- * output key, and method_name are identical, and will be used by system(s) that
- * implement or rely upon this particular loss method. The output tensor names
- * differ, demonstrating how different outputs can exist for the same method.
- * signature_def {
- *   key: "loss_A"
- *   value {
- *     inputs {
- *       key: "input"
- *       value {
- *         name: "input:0"
- *         dtype: DT_STRING
- *         tensor_shape: ...
- *       }
- *     }
- *     outputs {
- *       key: "loss_output"
- *       value {
- *         name: "loss_output_A:0"
- *         dtype: DT_FLOAT
- *         tensor_shape: ...
- *       }
- *     }
- *     method_name: "some/package/compute_loss"
- *   }
- *   ...
- * }
- * signature_def {
- *   key: "loss_B"
- *   value {
- *     inputs {
- *       key: "input"
- *       value {
- *         name: "input:0"
- *         dtype: DT_STRING
- *         tensor_shape: ...
- *       }
- *     }
- *     outputs {
- *       key: "loss_output"
- *       value {
- *         name: "loss_output_B:0"
- *         dtype: DT_FLOAT
- *         tensor_shape: ...
- *       }
- *     }
- *     method_name: "some/package/compute_loss"
- *   }
- *   ...
- * }
  * 
    
  *
  * Protobuf type {@code tensorflow.SignatureDef}
@@ -315,13 +263,12 @@ public org.tensorflow.proto.TensorInfo getOutputsOrThrow(
   private volatile java.lang.Object methodName_;
   /**
    * 
    -   * Extensible method_name information enabling third-party users to mark a
-   * SignatureDef as supporting a particular method. This enables producers and
-   * consumers of SignatureDefs, e.g. a model definition library and a serving
-   * library to have a clear hand-off regarding the semantics of a computation.
-   * Note that multiple SignatureDefs in a single MetaGraphDef may have the same
-   * method_name. This is commonly used to support multi-headed computation,
-   * where a single graph computation may return multiple results.
+   * Deprecated: TensorFlow 2 always sets this to a fixed value;
+   * open-source TF Serving stopped checking by default since release 2.4.
+   * In TensorFlow 1, the method_name enabled users to mark a SignatureDef as
+   * supporting a particular method. Multiple SignatureDefs in a single
+   * MetaGraphDef could have the same method_name (e.g., to support multi-headed
+   * computation).
    * 
    
    *
    * string method_name = 3;
@@ -342,13 +289,12 @@ public java.lang.String getMethodName() {
   }
   /**
    * 
    -   * Extensible method_name information enabling third-party users to mark a
-   * SignatureDef as supporting a particular method. This enables producers and
-   * consumers of SignatureDefs, e.g. a model definition library and a serving
-   * library to have a clear hand-off regarding the semantics of a computation.
-   * Note that multiple SignatureDefs in a single MetaGraphDef may have the same
-   * method_name. This is commonly used to support multi-headed computation,
-   * where a single graph computation may return multiple results.
+   * Deprecated: TensorFlow 2 always sets this to a fixed value;
+   * open-source TF Serving stopped checking by default since release 2.4.
+   * In TensorFlow 1, the method_name enabled users to mark a SignatureDef as
+   * supporting a particular method. Multiple SignatureDefs in a single
+   * MetaGraphDef could have the same method_name (e.g., to support multi-headed
+   * computation).
    * 
    
    *
    * string method_name = 3;
@@ -690,58 +636,6 @@ protected Builder newBuilderForType(
    * 
        * SignatureDef defines the signature of a computation supported by a TensorFlow
    * graph.
-   * For example, a model with two loss computations, sharing a single input,
-   * might have the following signature_def map, in a MetaGraphDef message.
-   * Note that across the two SignatureDefs "loss_A" and "loss_B", the input key,
-   * output key, and method_name are identical, and will be used by system(s) that
-   * implement or rely upon this particular loss method. The output tensor names
-   * differ, demonstrating how different outputs can exist for the same method.
-   * signature_def {
-   *   key: "loss_A"
-   *   value {
-   *     inputs {
-   *       key: "input"
-   *       value {
-   *         name: "input:0"
-   *         dtype: DT_STRING
-   *         tensor_shape: ...
-   *       }
-   *     }
-   *     outputs {
-   *       key: "loss_output"
-   *       value {
-   *         name: "loss_output_A:0"
-   *         dtype: DT_FLOAT
-   *         tensor_shape: ...
-   *       }
-   *     }
-   *     method_name: "some/package/compute_loss"
-   *   }
-   *   ...
-   * }
-   * signature_def {
-   *   key: "loss_B"
-   *   value {
-   *     inputs {
-   *       key: "input"
-   *       value {
-   *         name: "input:0"
-   *         dtype: DT_STRING
-   *         tensor_shape: ...
-   *       }
-   *     }
-   *     outputs {
-   *       key: "loss_output"
-   *       value {
-   *         name: "loss_output_B:0"
-   *         dtype: DT_FLOAT
-   *         tensor_shape: ...
-   *       }
-   *     }
-   *     method_name: "some/package/compute_loss"
-   *   }
-   *   ...
-   * }
    * 
    
    *
    * Protobuf type {@code tensorflow.SignatureDef}
@@ -1296,13 +1190,12 @@ public Builder putAllOutputs(
     private java.lang.Object methodName_ = "";
     /**
      * 
    -     * Extensible method_name information enabling third-party users to mark a
-     * SignatureDef as supporting a particular method. This enables producers and
-     * consumers of SignatureDefs, e.g. a model definition library and a serving
-     * library to have a clear hand-off regarding the semantics of a computation.
-     * Note that multiple SignatureDefs in a single MetaGraphDef may have the same
-     * method_name. This is commonly used to support multi-headed computation,
-     * where a single graph computation may return multiple results.
+     * Deprecated: TensorFlow 2 always sets this to a fixed value;
+     * open-source TF Serving stopped checking by default since release 2.4.
+     * In TensorFlow 1, the method_name enabled users to mark a SignatureDef as
+     * supporting a particular method. Multiple SignatureDefs in a single
+     * MetaGraphDef could have the same method_name (e.g., to support multi-headed
+     * computation).
      * 
    
      *
      * string method_name = 3;
@@ -1322,13 +1215,12 @@ public java.lang.String getMethodName() {
     }
     /**
      * 
    -     * Extensible method_name information enabling third-party users to mark a
-     * SignatureDef as supporting a particular method. This enables producers and
-     * consumers of SignatureDefs, e.g. a model definition library and a serving
-     * library to have a clear hand-off regarding the semantics of a computation.
-     * Note that multiple SignatureDefs in a single MetaGraphDef may have the same
-     * method_name. This is commonly used to support multi-headed computation,
-     * where a single graph computation may return multiple results.
+     * Deprecated: TensorFlow 2 always sets this to a fixed value;
+     * open-source TF Serving stopped checking by default since release 2.4.
+     * In TensorFlow 1, the method_name enabled users to mark a SignatureDef as
+     * supporting a particular method. Multiple SignatureDefs in a single
+     * MetaGraphDef could have the same method_name (e.g., to support multi-headed
+     * computation).
      * 
    
      *
      * string method_name = 3;
@@ -1349,13 +1241,12 @@ public java.lang.String getMethodName() {
     }
     /**
      * 
    -     * Extensible method_name information enabling third-party users to mark a
-     * SignatureDef as supporting a particular method. This enables producers and
-     * consumers of SignatureDefs, e.g. a model definition library and a serving
-     * library to have a clear hand-off regarding the semantics of a computation.
-     * Note that multiple SignatureDefs in a single MetaGraphDef may have the same
-     * method_name. This is commonly used to support multi-headed computation,
-     * where a single graph computation may return multiple results.
+     * Deprecated: TensorFlow 2 always sets this to a fixed value;
+     * open-source TF Serving stopped checking by default since release 2.4.
+     * In TensorFlow 1, the method_name enabled users to mark a SignatureDef as
+     * supporting a particular method. Multiple SignatureDefs in a single
+     * MetaGraphDef could have the same method_name (e.g., to support multi-headed
+     * computation).
      * 
    
      *
      * string method_name = 3;
@@ -1374,13 +1265,12 @@ public Builder setMethodName(
     }
     /**
      * 
    -     * Extensible method_name information enabling third-party users to mark a
-     * SignatureDef as supporting a particular method. This enables producers and
-     * consumers of SignatureDefs, e.g. a model definition library and a serving
-     * library to have a clear hand-off regarding the semantics of a computation.
-     * Note that multiple SignatureDefs in a single MetaGraphDef may have the same
-     * method_name. This is commonly used to support multi-headed computation,
-     * where a single graph computation may return multiple results.
+     * Deprecated: TensorFlow 2 always sets this to a fixed value;
+     * open-source TF Serving stopped checking by default since release 2.4.
+     * In TensorFlow 1, the method_name enabled users to mark a SignatureDef as
+     * supporting a particular method. Multiple SignatureDefs in a single
+     * MetaGraphDef could have the same method_name (e.g., to support multi-headed
+     * computation).
      * 
    
      *
      * string method_name = 3;
@@ -1394,13 +1284,12 @@ public Builder clearMethodName() {
     }
     /**
      * 
    -     * Extensible method_name information enabling third-party users to mark a
-     * SignatureDef as supporting a particular method. This enables producers and
-     * consumers of SignatureDefs, e.g. a model definition library and a serving
-     * library to have a clear hand-off regarding the semantics of a computation.
-     * Note that multiple SignatureDefs in a single MetaGraphDef may have the same
-     * method_name. This is commonly used to support multi-headed computation,
-     * where a single graph computation may return multiple results.
+     * Deprecated: TensorFlow 2 always sets this to a fixed value;
+     * open-source TF Serving stopped checking by default since release 2.4.
+     * In TensorFlow 1, the method_name enabled users to mark a SignatureDef as
+     * supporting a particular method. Multiple SignatureDefs in a single
+     * MetaGraphDef could have the same method_name (e.g., to support multi-headed
+     * computation).
      * 
    
      *
      * string method_name = 3;
diff --git a/tensorflow-core/tensorflow-core-native/src/gen/java/org/tensorflow/proto/SignatureDefOrBuilder.java b/tensorflow-core/tensorflow-core-native/src/gen/java/org/tensorflow/proto/SignatureDefOrBuilder.java
index 86ae1bcf3d1..28bd86c8f8a 100644
--- a/tensorflow-core/tensorflow-core-native/src/gen/java/org/tensorflow/proto/SignatureDefOrBuilder.java
+++ b/tensorflow-core/tensorflow-core-native/src/gen/java/org/tensorflow/proto/SignatureDefOrBuilder.java
@@ -121,13 +121,12 @@ org.tensorflow.proto.TensorInfo getOutputsOrThrow(
 
   /**
    * 
    -   * Extensible method_name information enabling third-party users to mark a
-   * SignatureDef as supporting a particular method. This enables producers and
-   * consumers of SignatureDefs, e.g. a model definition library and a serving
-   * library to have a clear hand-off regarding the semantics of a computation.
-   * Note that multiple SignatureDefs in a single MetaGraphDef may have the same
-   * method_name. This is commonly used to support multi-headed computation,
-   * where a single graph computation may return multiple results.
+   * Deprecated: TensorFlow 2 always sets this to a fixed value;
+   * open-source TF Serving stopped checking by default since release 2.4.
+   * In TensorFlow 1, the method_name enabled users to mark a SignatureDef as
+   * supporting a particular method. Multiple SignatureDefs in a single
+   * MetaGraphDef could have the same method_name (e.g., to support multi-headed
+   * computation).
    * 
    
    *
    * string method_name = 3;
@@ -136,13 +135,12 @@ org.tensorflow.proto.TensorInfo getOutputsOrThrow(
   java.lang.String getMethodName();
   /**
    * 
    -   * Extensible method_name information enabling third-party users to mark a
-   * SignatureDef as supporting a particular method. This enables producers and
-   * consumers of SignatureDefs, e.g. a model definition library and a serving
-   * library to have a clear hand-off regarding the semantics of a computation.
-   * Note that multiple SignatureDefs in a single MetaGraphDef may have the same
-   * method_name. This is commonly used to support multi-headed computation,
-   * where a single graph computation may return multiple results.
+   * Deprecated: TensorFlow 2 always sets this to a fixed value;
+   * open-source TF Serving stopped checking by default since release 2.4.
+   * In TensorFlow 1, the method_name enabled users to mark a SignatureDef as
+   * supporting a particular method. Multiple SignatureDefs in a single
+   * MetaGraphDef could have the same method_name (e.g., to support multi-headed
+   * computation).
    * 
    
    *
    * string method_name = 3;
diff --git a/tensorflow-core/tensorflow-core-native/src/gen/java/org/tensorflow/proto/TensorProto.java b/tensorflow-core/tensorflow-core-native/src/gen/java/org/tensorflow/proto/TensorProto.java
index 0440777955e..ef4157a3352 100644
--- a/tensorflow-core/tensorflow-core-native/src/gen/java/org/tensorflow/proto/TensorProto.java
+++ b/tensorflow-core/tensorflow-core-native/src/gen/java/org/tensorflow/proto/TensorProto.java
@@ -66,6 +66,10 @@ protected java.lang.Object newInstance(
   public static final int DTYPE_FIELD_NUMBER = 1;
   private int dtype_;
   /**
+   * 
    +   * Data type of the tensor.
+   * 
    
+   *
    * .tensorflow.DataType dtype = 1;
    * @return The enum numeric value on the wire for dtype.
    */
@@ -73,6 +77,10 @@ protected java.lang.Object newInstance(
     return dtype_;
   }
   /**
+   * 
    +   * Data type of the tensor.
+   * 
    
+   *
    * .tensorflow.DataType dtype = 1;
    * @return The dtype.
    */
@@ -1929,6 +1937,10 @@ public Builder mergeFrom(
 
     private int dtype_ = 0;
     /**
+     * 
    +     * Data type of the tensor.
+     * 
    
+     *
      * .tensorflow.DataType dtype = 1;
      * @return The enum numeric value on the wire for dtype.
      */
@@ -1936,6 +1948,10 @@ public Builder mergeFrom(
       return dtype_;
     }
     /**
+     * 
    +     * Data type of the tensor.
+     * 
    
+     *
      * .tensorflow.DataType dtype = 1;
      * @param value The enum numeric value on the wire for dtype to set.
      * @return This builder for chaining.
@@ -1947,6 +1963,10 @@ public Builder setDtypeValue(int value) {
       return this;
     }
     /**
+     * 
    +     * Data type of the tensor.
+     * 
    
+     *
      * .tensorflow.DataType dtype = 1;
      * @return The dtype.
      */
@@ -1957,6 +1977,10 @@ public org.tensorflow.proto.DataType getDtype() {
       return result == null ? org.tensorflow.proto.DataType.UNRECOGNIZED : result;
     }
     /**
+     * 
    +     * Data type of the tensor.
+     * 
    
+     *
      * .tensorflow.DataType dtype = 1;
      * @param value The dtype to set.
      * @return This builder for chaining.
@@ -1971,6 +1995,10 @@ public Builder setDtype(org.tensorflow.proto.DataType value) {
       return this;
     }
     /**
+     * 
    +     * Data type of the tensor.
+     * 
    
+     *
      * .tensorflow.DataType dtype = 1;
      * @return This builder for chaining.
      */
diff --git a/tensorflow-core/tensorflow-core-native/src/gen/java/org/tensorflow/proto/TensorProtoOrBuilder.java b/tensorflow-core/tensorflow-core-native/src/gen/java/org/tensorflow/proto/TensorProtoOrBuilder.java
index fe901586e8c..9eafe8177e2 100644
--- a/tensorflow-core/tensorflow-core-native/src/gen/java/org/tensorflow/proto/TensorProtoOrBuilder.java
+++ b/tensorflow-core/tensorflow-core-native/src/gen/java/org/tensorflow/proto/TensorProtoOrBuilder.java
@@ -8,11 +8,19 @@ public interface TensorProtoOrBuilder extends
     com.google.protobuf.MessageOrBuilder {
 
   /**
+   * 
    +   * Data type of the tensor.
+   * 
    
+   *
    * .tensorflow.DataType dtype = 1;
    * @return The enum numeric value on the wire for dtype.
    */
   int getDtypeValue();
   /**
+   * 
    +   * Data type of the tensor.
+   * 
    
+   *
    * .tensorflow.DataType dtype = 1;
    * @return The dtype.
    */
diff --git a/tensorflow-core/tensorflow-core-native/src/gen/java/org/tensorflow/proto/TestLogProtos.java b/tensorflow-core/tensorflow-core-native/src/gen/java/org/tensorflow/proto/TestLogProtos.java
index fb587acc9e8..f56cbf6b82b 100644
--- a/tensorflow-core/tensorflow-core-native/src/gen/java/org/tensorflow/proto/TestLogProtos.java
+++ b/tensorflow-core/tensorflow-core-native/src/gen/java/org/tensorflow/proto/TestLogProtos.java
@@ -1,5 +1,5 @@
 // Generated by the protocol buffer compiler.  DO NOT EDIT!
-// source: tsl/protobuf/test_log.proto
+// source: xla/tsl/protobuf/test_log.proto
 
 package org.tensorflow.proto;
 
@@ -108,68 +108,68 @@ public static void registerAllExtensions(
       descriptor;
   static {
     java.lang.String[] descriptorData = {
-      "\n\033tsl/protobuf/test_log.proto\022\ntensorflo" +
-      "w\032\031google/protobuf/any.proto\032\036google/pro" +
-      "tobuf/wrappers.proto\"D\n\nEntryValue\022\026\n\014do" +
-      "uble_value\030\001 \001(\001H\000\022\026\n\014string_value\030\002 \001(\t" +
-      "H\000B\006\n\004kind\"\214\001\n\013MetricEntry\022\014\n\004name\030\001 \001(\t" +
-      "\022\r\n\005value\030\002 \001(\001\022/\n\tmin_value\030\003 \001(\0132\034.goo" +
-      "gle.protobuf.DoubleValue\022/\n\tmax_value\030\004 " +
-      "\001(\0132\034.google.protobuf.DoubleValue\"\217\002\n\016Be" +
-      "nchmarkEntry\022\014\n\004name\030\001 \001(\t\022\r\n\005iters\030\002 \001(" +
-      "\003\022\020\n\010cpu_time\030\003 \001(\001\022\021\n\twall_time\030\004 \001(\001\022\022" +
-      "\n\nthroughput\030\005 \001(\001\0226\n\006extras\030\006 \003(\0132&.ten" +
-      "sorflow.BenchmarkEntry.ExtrasEntry\022(\n\007me" +
-      "trics\030\007 \003(\0132\027.tensorflow.MetricEntry\032E\n\013" +
-      "ExtrasEntry\022\013\n\003key\030\001 \001(\t\022%\n\005value\030\002 \001(\0132" +
-      "\026.tensorflow.EntryValue:\0028\001\"=\n\020Benchmark" +
-      "Entries\022)\n\005entry\030\001 \003(\0132\032.tensorflow.Benc" +
-      "hmarkEntry\"B\n\022BuildConfiguration\022\014\n\004mode" +
-      "\030\001 \001(\t\022\020\n\010cc_flags\030\002 \003(\t\022\014\n\004opts\030\003 \003(\t\"f" +
-      "\n\010CommitId\022\024\n\nchangelist\030\001 \001(\003H\000\022\016\n\004hash" +
-      "\030\002 \001(\tH\000\022\020\n\010snapshot\030\003 \001(\t\022\032\n\022pending_ch" +
-      "angelist\030\004 \001(\003B\006\n\004kind\"\336\001\n\007CPUInfo\022\021\n\tnu" +
-      "m_cores\030\001 \001(\003\022\031\n\021num_cores_allowed\030\002 \001(\003" +
-      "\022\023\n\013mhz_per_cpu\030\003 \001(\001\022\020\n\010cpu_info\030\004 \001(\t\022" +
-      "\024\n\014cpu_governor\030\005 \001(\t\0226\n\ncache_size\030\006 \003(" +
-      "\0132\".tensorflow.CPUInfo.CacheSizeEntry\0320\n" +
-      "\016CacheSizeEntry\022\013\n\003key\030\001 \001(\t\022\r\n\005value\030\002 " +
-      "\001(\003:\0028\001\".\n\nMemoryInfo\022\r\n\005total\030\001 \001(\003\022\021\n\t" +
-      "available\030\002 \001(\003\"6\n\007GPUInfo\022\r\n\005model\030\001 \001(" +
-      "\t\022\014\n\004uuid\030\002 \001(\t\022\016\n\006bus_id\030\003 \001(\t\"p\n\014Platf" +
-      "ormInfo\022\014\n\004bits\030\001 \001(\t\022\017\n\007linkage\030\002 \001(\t\022\017" +
-      "\n\007machine\030\003 \001(\t\022\017\n\007release\030\004 \001(\t\022\016\n\006syst" +
-      "em\030\005 \001(\t\022\017\n\007version\030\006 \001(\t\"e\n\023AvailableDe" +
-      "viceInfo\022\014\n\004name\030\001 \001(\t\022\014\n\004type\030\002 \001(\t\022\024\n\014" +
-      "memory_limit\030\003 \001(\003\022\034\n\024physical_descripti" +
-      "on\030\004 \001(\t\"\263\002\n\024MachineConfiguration\022\020\n\010hos" +
-      "tname\030\001 \001(\t\022\031\n\021serial_identifier\030\007 \001(\t\022/" +
-      "\n\rplatform_info\030\002 \001(\0132\030.tensorflow.Platf" +
-      "ormInfo\022%\n\010cpu_info\030\003 \001(\0132\023.tensorflow.C" +
-      "PUInfo\022)\n\013device_info\030\004 \003(\0132\024.google.pro" +
-      "tobuf.Any\022>\n\025available_device_info\030\005 \003(\013" +
-      "2\037.tensorflow.AvailableDeviceInfo\022+\n\013mem" +
-      "ory_info\030\006 \001(\0132\026.tensorflow.MemoryInfo\"\221" +
-      "\001\n\020RunConfiguration\022\020\n\010argument\030\001 \003(\t\022;\n" +
-      "\010env_vars\030\002 \003(\0132).tensorflow.RunConfigur" +
-      "ation.EnvVarsEntry\032.\n\014EnvVarsEntry\022\013\n\003ke" +
-      "y\030\001 \001(\t\022\r\n\005value\030\002 \001(\t:\0028\001\"\320\004\n\013TestResul" +
-      "ts\022\016\n\006target\030\001 \001(\t\022-\n\007entries\030\002 \001(\0132\034.te" +
-      "nsorflow.BenchmarkEntries\022;\n\023build_confi" +
-      "guration\030\003 \001(\0132\036.tensorflow.BuildConfigu" +
-      "ration\022\'\n\tcommit_id\030\004 \001(\0132\024.tensorflow.C" +
-      "ommitId\022\022\n\nstart_time\030\005 \001(\003\022\020\n\010run_time\030" +
-      "\006 \001(\001\022?\n\025machine_configuration\030\007 \001(\0132 .t" +
-      "ensorflow.MachineConfiguration\0227\n\021run_co" +
-      "nfiguration\030\010 \001(\0132\034.tensorflow.RunConfig" +
-      "uration\022\014\n\004name\030\t \001(\t\022=\n\016benchmark_type\030" +
-      "\n \001(\0162%.tensorflow.TestResults.Benchmark" +
-      "Type\022\020\n\010run_mode\030\013 \001(\t\022\022\n\ntf_version\030\014 \001" +
-      "(\t\"\210\001\n\rBenchmarkType\022\013\n\007UNKNOWN\020\000\022\026\n\022CPP" +
-      "_MICROBENCHMARK\020\001\022\024\n\020PYTHON_BENCHMARK\020\002\022" +
-      "\025\n\021ANDROID_BENCHMARK\020\003\022\022\n\016EDGE_BENCHMARK" +
-      "\020\004\022\021\n\rIOS_BENCHMARK\020\005B*\n\024org.tensorflow." +
-      "protoB\rTestLogProtosP\001\370\001\001b\006proto3"
+      "\n\037xla/tsl/protobuf/test_log.proto\022\ntenso" +
+      "rflow\032\031google/protobuf/any.proto\032\036google" +
+      "/protobuf/wrappers.proto\"D\n\nEntryValue\022\026" +
+      "\n\014double_value\030\001 \001(\001H\000\022\026\n\014string_value\030\002" +
+      " \001(\tH\000B\006\n\004kind\"\214\001\n\013MetricEntry\022\014\n\004name\030\001" +
+      " \001(\t\022\r\n\005value\030\002 \001(\001\022/\n\tmin_value\030\003 \001(\0132\034" +
+      ".google.protobuf.DoubleValue\022/\n\tmax_valu" +
+      "e\030\004 \001(\0132\034.google.protobuf.DoubleValue\"\217\002" +
+      "\n\016BenchmarkEntry\022\014\n\004name\030\001 \001(\t\022\r\n\005iters\030" +
+      "\002 \001(\003\022\020\n\010cpu_time\030\003 \001(\001\022\021\n\twall_time\030\004 \001" +
+      "(\001\022\022\n\nthroughput\030\005 \001(\001\0226\n\006extras\030\006 \003(\0132&" +
+      ".tensorflow.BenchmarkEntry.ExtrasEntry\022(" +
+      "\n\007metrics\030\007 \003(\0132\027.tensorflow.MetricEntry" +
+      "\032E\n\013ExtrasEntry\022\013\n\003key\030\001 \001(\t\022%\n\005value\030\002 " +
+      "\001(\0132\026.tensorflow.EntryValue:\0028\001\"=\n\020Bench" +
+      "markEntries\022)\n\005entry\030\001 \003(\0132\032.tensorflow." +
+      "BenchmarkEntry\"B\n\022BuildConfiguration\022\014\n\004" +
+      "mode\030\001 \001(\t\022\020\n\010cc_flags\030\002 \003(\t\022\014\n\004opts\030\003 \003" +
+      "(\t\"f\n\010CommitId\022\024\n\nchangelist\030\001 \001(\003H\000\022\016\n\004" +
+      "hash\030\002 \001(\tH\000\022\020\n\010snapshot\030\003 \001(\t\022\032\n\022pendin" +
+      "g_changelist\030\004 \001(\003B\006\n\004kind\"\336\001\n\007CPUInfo\022\021" +
+      "\n\tnum_cores\030\001 \001(\003\022\031\n\021num_cores_allowed\030\002" +
+      " \001(\003\022\023\n\013mhz_per_cpu\030\003 \001(\001\022\020\n\010cpu_info\030\004 " +
+      "\001(\t\022\024\n\014cpu_governor\030\005 \001(\t\0226\n\ncache_size\030" +
+      "\006 \003(\0132\".tensorflow.CPUInfo.CacheSizeEntr" +
+      "y\0320\n\016CacheSizeEntry\022\013\n\003key\030\001 \001(\t\022\r\n\005valu" +
+      "e\030\002 \001(\003:\0028\001\".\n\nMemoryInfo\022\r\n\005total\030\001 \001(\003" +
+      "\022\021\n\tavailable\030\002 \001(\003\"6\n\007GPUInfo\022\r\n\005model\030" +
+      "\001 \001(\t\022\014\n\004uuid\030\002 \001(\t\022\016\n\006bus_id\030\003 \001(\t\"p\n\014P" +
+      "latformInfo\022\014\n\004bits\030\001 \001(\t\022\017\n\007linkage\030\002 \001" +
+      "(\t\022\017\n\007machine\030\003 \001(\t\022\017\n\007release\030\004 \001(\t\022\016\n\006" +
+      "system\030\005 \001(\t\022\017\n\007version\030\006 \001(\t\"e\n\023Availab" +
+      "leDeviceInfo\022\014\n\004name\030\001 \001(\t\022\014\n\004type\030\002 \001(\t" +
+      "\022\024\n\014memory_limit\030\003 \001(\003\022\034\n\024physical_descr" +
+      "iption\030\004 \001(\t\"\263\002\n\024MachineConfiguration\022\020\n" +
+      "\010hostname\030\001 \001(\t\022\031\n\021serial_identifier\030\007 \001" +
+      "(\t\022/\n\rplatform_info\030\002 \001(\0132\030.tensorflow.P" +
+      "latformInfo\022%\n\010cpu_info\030\003 \001(\0132\023.tensorfl" +
+      "ow.CPUInfo\022)\n\013device_info\030\004 \003(\0132\024.google" +
+      ".protobuf.Any\022>\n\025available_device_info\030\005" +
+      " \003(\0132\037.tensorflow.AvailableDeviceInfo\022+\n" +
+      "\013memory_info\030\006 \001(\0132\026.tensorflow.MemoryIn" +
+      "fo\"\221\001\n\020RunConfiguration\022\020\n\010argument\030\001 \003(" +
+      "\t\022;\n\010env_vars\030\002 \003(\0132).tensorflow.RunConf" +
+      "iguration.EnvVarsEntry\032.\n\014EnvVarsEntry\022\013" +
+      "\n\003key\030\001 \001(\t\022\r\n\005value\030\002 \001(\t:\0028\001\"\320\004\n\013TestR" +
+      "esults\022\016\n\006target\030\001 \001(\t\022-\n\007entries\030\002 \001(\0132" +
+      "\034.tensorflow.BenchmarkEntries\022;\n\023build_c" +
+      "onfiguration\030\003 \001(\0132\036.tensorflow.BuildCon" +
+      "figuration\022\'\n\tcommit_id\030\004 \001(\0132\024.tensorfl" +
+      "ow.CommitId\022\022\n\nstart_time\030\005 \001(\003\022\020\n\010run_t" +
+      "ime\030\006 \001(\001\022?\n\025machine_configuration\030\007 \001(\013" +
+      "2 .tensorflow.MachineConfiguration\0227\n\021ru" +
+      "n_configuration\030\010 \001(\0132\034.tensorflow.RunCo" +
+      "nfiguration\022\014\n\004name\030\t \001(\t\022=\n\016benchmark_t" +
+      "ype\030\n \001(\0162%.tensorflow.TestResults.Bench" +
+      "markType\022\020\n\010run_mode\030\013 \001(\t\022\022\n\ntf_version" +
+      "\030\014 \001(\t\"\210\001\n\rBenchmarkType\022\013\n\007UNKNOWN\020\000\022\026\n" +
+      "\022CPP_MICROBENCHMARK\020\001\022\024\n\020PYTHON_BENCHMAR" +
+      "K\020\002\022\025\n\021ANDROID_BENCHMARK\020\003\022\022\n\016EDGE_BENCH" +
+      "MARK\020\004\022\021\n\rIOS_BENCHMARK\020\005B*\n\024org.tensorf" +
+      "low.protoB\rTestLogProtosP\001\370\001\001b\006proto3"
     };
     descriptor = com.google.protobuf.Descriptors.FileDescriptor
       .internalBuildGeneratedFileFrom(descriptorData,
diff --git a/tensorflow-core/tensorflow-core-native/src/gen/java/org/tensorflow/proto/TestResults.java b/tensorflow-core/tensorflow-core-native/src/gen/java/org/tensorflow/proto/TestResults.java
index 09ed588ef20..4bc27cbdde7 100644
--- a/tensorflow-core/tensorflow-core-native/src/gen/java/org/tensorflow/proto/TestResults.java
+++ b/tensorflow-core/tensorflow-core-native/src/gen/java/org/tensorflow/proto/TestResults.java
@@ -1,5 +1,5 @@
 // Generated by the protocol buffer compiler.  DO NOT EDIT!
-// source: tsl/protobuf/test_log.proto
+// source: xla/tsl/protobuf/test_log.proto
 
 package org.tensorflow.proto;
 
diff --git a/tensorflow-core/tensorflow-core-native/src/gen/java/org/tensorflow/proto/TestResultsOrBuilder.java b/tensorflow-core/tensorflow-core-native/src/gen/java/org/tensorflow/proto/TestResultsOrBuilder.java
index 3afd736b478..1d6f1545988 100644
--- a/tensorflow-core/tensorflow-core-native/src/gen/java/org/tensorflow/proto/TestResultsOrBuilder.java
+++ b/tensorflow-core/tensorflow-core-native/src/gen/java/org/tensorflow/proto/TestResultsOrBuilder.java
@@ -1,5 +1,5 @@
 // Generated by the protocol buffer compiler.  DO NOT EDIT!
-// source: tsl/protobuf/test_log.proto
+// source: xla/tsl/protobuf/test_log.proto
 
 package org.tensorflow.proto;
 
diff --git a/tensorflow-core/tensorflow-core-native/src/gen/java/org/tensorflow/proto/data/DatasetOptions.java b/tensorflow-core/tensorflow-core-native/src/gen/java/org/tensorflow/proto/data/DatasetOptions.java
index c7d43294b14..424adecedbd 100644
--- a/tensorflow-core/tensorflow-core-native/src/gen/java/org/tensorflow/proto/data/DatasetOptions.java
+++ b/tensorflow-core/tensorflow-core-native/src/gen/java/org/tensorflow/proto/data/DatasetOptions.java
@@ -381,6 +381,17 @@ public interface AutotuneOptionsOrBuilder extends
      */
     org.tensorflow.proto.data.model.Model.AutotuneAlgorithm getAutotuneAlgorithm();
 
+    /**
+     * int64 initial_parallelism = 5;
+     * @return Whether the initialParallelism field is set.
+     */
+    boolean hasInitialParallelism();
+    /**
+     * int64 initial_parallelism = 5;
+     * @return The initialParallelism.
+     */
+    long getInitialParallelism();
+
     public org.tensorflow.proto.data.DatasetOptions.AutotuneOptions.OptionalEnabledCase getOptionalEnabledCase();
 
     public org.tensorflow.proto.data.DatasetOptions.AutotuneOptions.OptionalCpuBudgetCase getOptionalCpuBudgetCase();
@@ -388,10 +399,12 @@ public interface AutotuneOptionsOrBuilder extends
     public org.tensorflow.proto.data.DatasetOptions.AutotuneOptions.OptionalRamBudgetCase getOptionalRamBudgetCase();
 
     public org.tensorflow.proto.data.DatasetOptions.AutotuneOptions.OptionalAutotuneAlgorithmCase getOptionalAutotuneAlgorithmCase();
+
+    public org.tensorflow.proto.data.DatasetOptions.AutotuneOptions.OptionalInitialParallelismCase getOptionalInitialParallelismCase();
   }
   /**
    * 
    -   * next: 5
+   * next: 6
    * 
    
    *
    * Protobuf type {@code tensorflow.data.AutotuneOptions}
@@ -589,6 +602,45 @@ public int getNumber() {
           optionalAutotuneAlgorithmCase_);
     }
 
+    private int optionalInitialParallelismCase_ = 0;
+    private java.lang.Object optionalInitialParallelism_;
+    public enum OptionalInitialParallelismCase
+        implements com.google.protobuf.Internal.EnumLite,
+            com.google.protobuf.AbstractMessage.InternalOneOfEnum {
+      INITIAL_PARALLELISM(5),
+      OPTIONALINITIALPARALLELISM_NOT_SET(0);
+      private final int value;
+      private OptionalInitialParallelismCase(int value) {
+        this.value = value;
+      }
+      /**
+       * @param value The number of the enum to look for.
+       * @return The enum associated with the given number.
+       * @deprecated Use {@link #forNumber(int)} instead.
+       */
+      @java.lang.Deprecated
+      public static OptionalInitialParallelismCase valueOf(int value) {
+        return forNumber(value);
+      }
+
+      public static OptionalInitialParallelismCase forNumber(int value) {
+        switch (value) {
+          case 5: return INITIAL_PARALLELISM;
+          case 0: return OPTIONALINITIALPARALLELISM_NOT_SET;
+          default: return null;
+        }
+      }
+      public int getNumber() {
+        return this.value;
+      }
+    };
+
+    public OptionalInitialParallelismCase
+    getOptionalInitialParallelismCase() {
+      return OptionalInitialParallelismCase.forNumber(
+          optionalInitialParallelismCase_);
+    }
+
     public static final int ENABLED_FIELD_NUMBER = 1;
     /**
      * bool enabled = 1;
@@ -684,6 +736,27 @@ public org.tensorflow.proto.data.model.Model.AutotuneAlgorithm getAutotuneAlgori
       return org.tensorflow.proto.data.model.Model.AutotuneAlgorithm.DEFAULT;
     }
 
+    public static final int INITIAL_PARALLELISM_FIELD_NUMBER = 5;
+    /**
+     * int64 initial_parallelism = 5;
+     * @return Whether the initialParallelism field is set.
+     */
+    @java.lang.Override
+    public boolean hasInitialParallelism() {
+      return optionalInitialParallelismCase_ == 5;
+    }
+    /**
+     * int64 initial_parallelism = 5;
+     * @return The initialParallelism.
+     */
+    @java.lang.Override
+    public long getInitialParallelism() {
+      if (optionalInitialParallelismCase_ == 5) {
+        return (java.lang.Long) optionalInitialParallelism_;
+      }
+      return 0L;
+    }
+
     private byte memoizedIsInitialized = -1;
     @java.lang.Override
     public final boolean isInitialized() {
@@ -713,6 +786,10 @@ public void writeTo(com.google.protobuf.CodedOutputStream output)
       if (optionalAutotuneAlgorithmCase_ == 4) {
         output.writeEnum(4, ((java.lang.Integer) optionalAutotuneAlgorithm_));
       }
+      if (optionalInitialParallelismCase_ == 5) {
+        output.writeInt64(
+            5, (long)((java.lang.Long) optionalInitialParallelism_));
+      }
       getUnknownFields().writeTo(output);
     }
 
@@ -741,6 +818,11 @@ public int getSerializedSize() {
         size += com.google.protobuf.CodedOutputStream
           .computeEnumSize(4, ((java.lang.Integer) optionalAutotuneAlgorithm_));
       }
+      if (optionalInitialParallelismCase_ == 5) {
+        size += com.google.protobuf.CodedOutputStream
+          .computeInt64Size(
+              5, (long)((java.lang.Long) optionalInitialParallelism_));
+      }
       size += getUnknownFields().getSerializedSize();
       memoizedSize = size;
       return size;
@@ -792,6 +874,15 @@ public boolean equals(final java.lang.Object obj) {
         case 0:
         default:
       }
+      if (!getOptionalInitialParallelismCase().equals(other.getOptionalInitialParallelismCase())) return false;
+      switch (optionalInitialParallelismCase_) {
+        case 5:
+          if (getInitialParallelism()
+              != other.getInitialParallelism()) return false;
+          break;
+        case 0:
+        default:
+      }
       if (!getUnknownFields().equals(other.getUnknownFields())) return false;
       return true;
     }
@@ -837,6 +928,15 @@ public int hashCode() {
         case 0:
         default:
       }
+      switch (optionalInitialParallelismCase_) {
+        case 5:
+          hash = (37 * hash) + INITIAL_PARALLELISM_FIELD_NUMBER;
+          hash = (53 * hash) + com.google.protobuf.Internal.hashLong(
+              getInitialParallelism());
+          break;
+        case 0:
+        default:
+      }
       hash = (29 * hash) + getUnknownFields().hashCode();
       memoizedHashCode = hash;
       return hash;
@@ -934,7 +1034,7 @@ protected Builder newBuilderForType(
     }
     /**
      * 
    -     * next: 5
+     * next: 6
      * 
    
      *
      * Protobuf type {@code tensorflow.data.AutotuneOptions}
@@ -977,6 +1077,8 @@ public Builder clear() {
         optionalRamBudget_ = null;
         optionalAutotuneAlgorithmCase_ = 0;
         optionalAutotuneAlgorithm_ = null;
+        optionalInitialParallelismCase_ = 0;
+        optionalInitialParallelism_ = null;
         return this;
       }
 
@@ -1015,10 +1117,14 @@ public org.tensorflow.proto.data.DatasetOptions.AutotuneOptions buildPartial() {
         if (optionalAutotuneAlgorithmCase_ == 4) {
           result.optionalAutotuneAlgorithm_ = optionalAutotuneAlgorithm_;
         }
+        if (optionalInitialParallelismCase_ == 5) {
+          result.optionalInitialParallelism_ = optionalInitialParallelism_;
+        }
         result.optionalEnabledCase_ = optionalEnabledCase_;
         result.optionalCpuBudgetCase_ = optionalCpuBudgetCase_;
         result.optionalRamBudgetCase_ = optionalRamBudgetCase_;
         result.optionalAutotuneAlgorithmCase_ = optionalAutotuneAlgorithmCase_;
+        result.optionalInitialParallelismCase_ = optionalInitialParallelismCase_;
         onBuilt();
         return result;
       }
@@ -1103,6 +1209,15 @@ public Builder mergeFrom(org.tensorflow.proto.data.DatasetOptions.AutotuneOption
             break;
           }
         }
+        switch (other.getOptionalInitialParallelismCase()) {
+          case INITIAL_PARALLELISM: {
+            setInitialParallelism(other.getInitialParallelism());
+            break;
+          }
+          case OPTIONALINITIALPARALLELISM_NOT_SET: {
+            break;
+          }
+        }
         this.mergeUnknownFields(other.getUnknownFields());
         onChanged();
         return this;
@@ -1150,6 +1265,11 @@ public Builder mergeFrom(
                 optionalAutotuneAlgorithm_ = rawValue;
                 break;
               } // case 32
+              case 40: {
+                optionalInitialParallelism_ = input.readInt64();
+                optionalInitialParallelismCase_ = 5;
+                break;
+              } // case 40
               default: {
                 if (!super.parseUnknownField(input, extensionRegistry, tag)) {
                   done = true; // was an endgroup tag
@@ -1225,6 +1345,21 @@ public Builder clearOptionalAutotuneAlgorithm() {
         return this;
       }
 
+      private int optionalInitialParallelismCase_ = 0;
+      private java.lang.Object optionalInitialParallelism_;
+      public OptionalInitialParallelismCase
+          getOptionalInitialParallelismCase() {
+        return OptionalInitialParallelismCase.forNumber(
+            optionalInitialParallelismCase_);
+      }
+
+      public Builder clearOptionalInitialParallelism() {
+        optionalInitialParallelismCase_ = 0;
+        optionalInitialParallelism_ = null;
+        onChanged();
+        return this;
+      }
+
 
       /**
        * bool enabled = 1;
@@ -1419,6 +1554,47 @@ public Builder clearAutotuneAlgorithm() {
         }
         return this;
       }
+
+      /**
+       * int64 initial_parallelism = 5;
+       * @return Whether the initialParallelism field is set.
+       */
+      public boolean hasInitialParallelism() {
+        return optionalInitialParallelismCase_ == 5;
+      }
+      /**
+       * int64 initial_parallelism = 5;
+       * @return The initialParallelism.
+       */
+      public long getInitialParallelism() {
+        if (optionalInitialParallelismCase_ == 5) {
+          return (java.lang.Long) optionalInitialParallelism_;
+        }
+        return 0L;
+      }
+      /**
+       * int64 initial_parallelism = 5;
+       * @param value The initialParallelism to set.
+       * @return This builder for chaining.
+       */
+      public Builder setInitialParallelism(long value) {
+        optionalInitialParallelismCase_ = 5;
+        optionalInitialParallelism_ = value;
+        onChanged();
+        return this;
+      }
+      /**
+       * int64 initial_parallelism = 5;
+       * @return This builder for chaining.
+       */
+      public Builder clearInitialParallelism() {
+        if (optionalInitialParallelismCase_ == 5) {
+          optionalInitialParallelismCase_ = 0;
+          optionalInitialParallelism_ = null;
+          onChanged();
+        }
+        return this;
+      }
       @java.lang.Override
       public final Builder setUnknownFields(
           final com.google.protobuf.UnknownFieldSet unknownFields) {
@@ -5349,60 +5525,47 @@ public org.tensorflow.proto.data.DatasetOptions.OptimizationOptions getDefaultIn
 
   }
 
-  public interface ThreadingOptionsOrBuilder extends
-      // @@protoc_insertion_point(interface_extends:tensorflow.data.ThreadingOptions)
+  public interface ServiceOptionsOrBuilder extends
+      // @@protoc_insertion_point(interface_extends:tensorflow.data.ServiceOptions)
       com.google.protobuf.MessageOrBuilder {
 
     /**
-     * int32 max_intra_op_parallelism = 1;
-     * @return Whether the maxIntraOpParallelism field is set.
-     */
-    boolean hasMaxIntraOpParallelism();
-    /**
-     * int32 max_intra_op_parallelism = 1;
-     * @return The maxIntraOpParallelism.
-     */
-    int getMaxIntraOpParallelism();
-
-    /**
-     * int32 private_threadpool_size = 2;
-     * @return Whether the privateThreadpoolSize field is set.
+     * bool pinned = 1;
+     * @return Whether the pinned field is set.
      */
-    boolean hasPrivateThreadpoolSize();
+    boolean hasPinned();
     /**
-     * int32 private_threadpool_size = 2;
-     * @return The privateThreadpoolSize.
+     * bool pinned = 1;
+     * @return The pinned.
      */
-    int getPrivateThreadpoolSize();
-
-    public org.tensorflow.proto.data.DatasetOptions.ThreadingOptions.OptionalMaxIntraOpParallelismCase getOptionalMaxIntraOpParallelismCase();
+    boolean getPinned();
 
-    public org.tensorflow.proto.data.DatasetOptions.ThreadingOptions.OptionalPrivateThreadpoolSizeCase getOptionalPrivateThreadpoolSizeCase();
+    public org.tensorflow.proto.data.DatasetOptions.ServiceOptions.OptionalPinnedCase getOptionalPinnedCase();
   }
   /**
    * 
    -   * next: 3
+   * next: 2
    * 
    
    *
-   * Protobuf type {@code tensorflow.data.ThreadingOptions}
+   * Protobuf type {@code tensorflow.data.ServiceOptions}
    */
-  public static final class ThreadingOptions extends
+  public static final class ServiceOptions extends
       com.google.protobuf.GeneratedMessageV3 implements
-      // @@protoc_insertion_point(message_implements:tensorflow.data.ThreadingOptions)
-      ThreadingOptionsOrBuilder {
+      // @@protoc_insertion_point(message_implements:tensorflow.data.ServiceOptions)
+      ServiceOptionsOrBuilder {
   private static final long serialVersionUID = 0L;
-    // Use ThreadingOptions.newBuilder() to construct.
-    private ThreadingOptions(com.google.protobuf.GeneratedMessageV3.Builder builder) {
+    // Use ServiceOptions.newBuilder() to construct.
+    private ServiceOptions(com.google.protobuf.GeneratedMessageV3.Builder builder) {
       super(builder);
     }
-    private ThreadingOptions() {
+    private ServiceOptions() {
     }
 
     @java.lang.Override
     @SuppressWarnings({"unused"})
     protected java.lang.Object newInstance(
         UnusedPrivateParameter unused) {
-      return new ThreadingOptions();
+      return new ServiceOptions();
     }
 
     @java.lang.Override
@@ -5412,65 +5575,26 @@ protected java.lang.Object newInstance(
     }
     public static final com.google.protobuf.Descriptors.Descriptor
         getDescriptor() {
-      return org.tensorflow.proto.data.DatasetOptions.internal_static_tensorflow_data_ThreadingOptions_descriptor;
+      return org.tensorflow.proto.data.DatasetOptions.internal_static_tensorflow_data_ServiceOptions_descriptor;
     }
 
     @java.lang.Override
     protected com.google.protobuf.GeneratedMessageV3.FieldAccessorTable
         internalGetFieldAccessorTable() {
-      return org.tensorflow.proto.data.DatasetOptions.internal_static_tensorflow_data_ThreadingOptions_fieldAccessorTable
+      return org.tensorflow.proto.data.DatasetOptions.internal_static_tensorflow_data_ServiceOptions_fieldAccessorTable
           .ensureFieldAccessorsInitialized(
-              org.tensorflow.proto.data.DatasetOptions.ThreadingOptions.class, org.tensorflow.proto.data.DatasetOptions.ThreadingOptions.Builder.class);
-    }
-
-    private int optionalMaxIntraOpParallelismCase_ = 0;
-    private java.lang.Object optionalMaxIntraOpParallelism_;
-    public enum OptionalMaxIntraOpParallelismCase
-        implements com.google.protobuf.Internal.EnumLite,
-            com.google.protobuf.AbstractMessage.InternalOneOfEnum {
-      MAX_INTRA_OP_PARALLELISM(1),
-      OPTIONALMAXINTRAOPPARALLELISM_NOT_SET(0);
-      private final int value;
-      private OptionalMaxIntraOpParallelismCase(int value) {
-        this.value = value;
-      }
-      /**
-       * @param value The number of the enum to look for.
-       * @return The enum associated with the given number.
-       * @deprecated Use {@link #forNumber(int)} instead.
-       */
-      @java.lang.Deprecated
-      public static OptionalMaxIntraOpParallelismCase valueOf(int value) {
-        return forNumber(value);
-      }
-
-      public static OptionalMaxIntraOpParallelismCase forNumber(int value) {
-        switch (value) {
-          case 1: return MAX_INTRA_OP_PARALLELISM;
-          case 0: return OPTIONALMAXINTRAOPPARALLELISM_NOT_SET;
-          default: return null;
-        }
-      }
-      public int getNumber() {
-        return this.value;
-      }
-    };
-
-    public OptionalMaxIntraOpParallelismCase
-    getOptionalMaxIntraOpParallelismCase() {
-      return OptionalMaxIntraOpParallelismCase.forNumber(
-          optionalMaxIntraOpParallelismCase_);
+              org.tensorflow.proto.data.DatasetOptions.ServiceOptions.class, org.tensorflow.proto.data.DatasetOptions.ServiceOptions.Builder.class);
     }
 
-    private int optionalPrivateThreadpoolSizeCase_ = 0;
-    private java.lang.Object optionalPrivateThreadpoolSize_;
-    public enum OptionalPrivateThreadpoolSizeCase
+    private int optionalPinnedCase_ = 0;
+    private java.lang.Object optionalPinned_;
+    public enum OptionalPinnedCase
         implements com.google.protobuf.Internal.EnumLite,
             com.google.protobuf.AbstractMessage.InternalOneOfEnum {
-      PRIVATE_THREADPOOL_SIZE(2),
-      OPTIONALPRIVATETHREADPOOLSIZE_NOT_SET(0);
+      PINNED(1),
+      OPTIONALPINNED_NOT_SET(0);
       private final int value;
-      private OptionalPrivateThreadpoolSizeCase(int value) {
+      private OptionalPinnedCase(int value) {
         this.value = value;
       }
       /**
@@ -5479,14 +5603,14 @@ private OptionalPrivateThreadpoolSizeCase(int value) {
        * @deprecated Use {@link #forNumber(int)} instead.
        */
       @java.lang.Deprecated
-      public static OptionalPrivateThreadpoolSizeCase valueOf(int value) {
+      public static OptionalPinnedCase valueOf(int value) {
         return forNumber(value);
       }
 
-      public static OptionalPrivateThreadpoolSizeCase forNumber(int value) {
+      public static OptionalPinnedCase forNumber(int value) {
         switch (value) {
-          case 2: return PRIVATE_THREADPOOL_SIZE;
-          case 0: return OPTIONALPRIVATETHREADPOOLSIZE_NOT_SET;
+          case 1: return PINNED;
+          case 0: return OPTIONALPINNED_NOT_SET;
           default: return null;
         }
       }
@@ -5495,52 +5619,31 @@ public int getNumber() {
       }
     };
 
-    public OptionalPrivateThreadpoolSizeCase
-    getOptionalPrivateThreadpoolSizeCase() {
-      return OptionalPrivateThreadpoolSizeCase.forNumber(
-          optionalPrivateThreadpoolSizeCase_);
-    }
-
-    public static final int MAX_INTRA_OP_PARALLELISM_FIELD_NUMBER = 1;
-    /**
-     * int32 max_intra_op_parallelism = 1;
-     * @return Whether the maxIntraOpParallelism field is set.
-     */
-    @java.lang.Override
-    public boolean hasMaxIntraOpParallelism() {
-      return optionalMaxIntraOpParallelismCase_ == 1;
-    }
-    /**
-     * int32 max_intra_op_parallelism = 1;
-     * @return The maxIntraOpParallelism.
-     */
-    @java.lang.Override
-    public int getMaxIntraOpParallelism() {
-      if (optionalMaxIntraOpParallelismCase_ == 1) {
-        return (java.lang.Integer) optionalMaxIntraOpParallelism_;
-      }
-      return 0;
+    public OptionalPinnedCase
+    getOptionalPinnedCase() {
+      return OptionalPinnedCase.forNumber(
+          optionalPinnedCase_);
     }
 
-    public static final int PRIVATE_THREADPOOL_SIZE_FIELD_NUMBER = 2;
+    public static final int PINNED_FIELD_NUMBER = 1;
     /**
-     * int32 private_threadpool_size = 2;
-     * @return Whether the privateThreadpoolSize field is set.
+     * bool pinned = 1;
+     * @return Whether the pinned field is set.
      */
     @java.lang.Override
-    public boolean hasPrivateThreadpoolSize() {
-      return optionalPrivateThreadpoolSizeCase_ == 2;
+    public boolean hasPinned() {
+      return optionalPinnedCase_ == 1;
     }
     /**
-     * int32 private_threadpool_size = 2;
-     * @return The privateThreadpoolSize.
+     * bool pinned = 1;
+     * @return The pinned.
      */
     @java.lang.Override
-    public int getPrivateThreadpoolSize() {
-      if (optionalPrivateThreadpoolSizeCase_ == 2) {
-        return (java.lang.Integer) optionalPrivateThreadpoolSize_;
+    public boolean getPinned() {
+      if (optionalPinnedCase_ == 1) {
+        return (java.lang.Boolean) optionalPinned_;
       }
-      return 0;
+      return false;
     }
 
     private byte memoizedIsInitialized = -1;
@@ -5557,13 +5660,9 @@ public final boolean isInitialized() {
     @java.lang.Override
     public void writeTo(com.google.protobuf.CodedOutputStream output)
                         throws java.io.IOException {
-      if (optionalMaxIntraOpParallelismCase_ == 1) {
-        output.writeInt32(
-            1, (int)((java.lang.Integer) optionalMaxIntraOpParallelism_));
-      }
-      if (optionalPrivateThreadpoolSizeCase_ == 2) {
-        output.writeInt32(
-            2, (int)((java.lang.Integer) optionalPrivateThreadpoolSize_));
+      if (optionalPinnedCase_ == 1) {
+        output.writeBool(
+            1, (boolean)((java.lang.Boolean) optionalPinned_));
       }
       getUnknownFields().writeTo(output);
     }
@@ -5574,15 +5673,10 @@ public int getSerializedSize() {
       if (size != -1) return size;
 
       size = 0;
-      if (optionalMaxIntraOpParallelismCase_ == 1) {
-        size += com.google.protobuf.CodedOutputStream
-          .computeInt32Size(
-              1, (int)((java.lang.Integer) optionalMaxIntraOpParallelism_));
-      }
-      if (optionalPrivateThreadpoolSizeCase_ == 2) {
+      if (optionalPinnedCase_ == 1) {
         size += com.google.protobuf.CodedOutputStream
-          .computeInt32Size(
-              2, (int)((java.lang.Integer) optionalPrivateThreadpoolSize_));
+          .computeBoolSize(
+              1, (boolean)((java.lang.Boolean) optionalPinned_));
       }
       size += getUnknownFields().getSerializedSize();
       memoizedSize = size;
@@ -5594,25 +5688,16 @@ public boolean equals(final java.lang.Object obj) {
       if (obj == this) {
        return true;
       }
-      if (!(obj instanceof org.tensorflow.proto.data.DatasetOptions.ThreadingOptions)) {
+      if (!(obj instanceof org.tensorflow.proto.data.DatasetOptions.ServiceOptions)) {
         return super.equals(obj);
       }
-      org.tensorflow.proto.data.DatasetOptions.ThreadingOptions other = (org.tensorflow.proto.data.DatasetOptions.ThreadingOptions) obj;
+      org.tensorflow.proto.data.DatasetOptions.ServiceOptions other = (org.tensorflow.proto.data.DatasetOptions.ServiceOptions) obj;
 
-      if (!getOptionalMaxIntraOpParallelismCase().equals(other.getOptionalMaxIntraOpParallelismCase())) return false;
-      switch (optionalMaxIntraOpParallelismCase_) {
+      if (!getOptionalPinnedCase().equals(other.getOptionalPinnedCase())) return false;
+      switch (optionalPinnedCase_) {
         case 1:
-          if (getMaxIntraOpParallelism()
-              != other.getMaxIntraOpParallelism()) return false;
-          break;
-        case 0:
-        default:
-      }
-      if (!getOptionalPrivateThreadpoolSizeCase().equals(other.getOptionalPrivateThreadpoolSizeCase())) return false;
-      switch (optionalPrivateThreadpoolSizeCase_) {
-        case 2:
-          if (getPrivateThreadpoolSize()
-              != other.getPrivateThreadpoolSize()) return false;
+          if (getPinned()
+              != other.getPinned()) return false;
           break;
         case 0:
         default:
@@ -5628,18 +5713,693 @@ public int hashCode() {
       }
       int hash = 41;
       hash = (19 * hash) + getDescriptor().hashCode();
-      switch (optionalMaxIntraOpParallelismCase_) {
+      switch (optionalPinnedCase_) {
         case 1:
-          hash = (37 * hash) + MAX_INTRA_OP_PARALLELISM_FIELD_NUMBER;
-          hash = (53 * hash) + getMaxIntraOpParallelism();
-          break;
-        case 0:
-        default:
-      }
-      switch (optionalPrivateThreadpoolSizeCase_) {
-        case 2:
-          hash = (37 * hash) + PRIVATE_THREADPOOL_SIZE_FIELD_NUMBER;
-          hash = (53 * hash) + getPrivateThreadpoolSize();
+          hash = (37 * hash) + PINNED_FIELD_NUMBER;
+          hash = (53 * hash) + com.google.protobuf.Internal.hashBoolean(
+              getPinned());
+          break;
+        case 0:
+        default:
+      }
+      hash = (29 * hash) + getUnknownFields().hashCode();
+      memoizedHashCode = hash;
+      return hash;
+    }
+
+    public static org.tensorflow.proto.data.DatasetOptions.ServiceOptions parseFrom(
+        java.nio.ByteBuffer data)
+        throws com.google.protobuf.InvalidProtocolBufferException {
+      return PARSER.parseFrom(data);
+    }
+    public static org.tensorflow.proto.data.DatasetOptions.ServiceOptions parseFrom(
+        java.nio.ByteBuffer data,
+        com.google.protobuf.ExtensionRegistryLite extensionRegistry)
+        throws com.google.protobuf.InvalidProtocolBufferException {
+      return PARSER.parseFrom(data, extensionRegistry);
+    }
+    public static org.tensorflow.proto.data.DatasetOptions.ServiceOptions parseFrom(
+        com.google.protobuf.ByteString data)
+        throws com.google.protobuf.InvalidProtocolBufferException {
+      return PARSER.parseFrom(data);
+    }
+    public static org.tensorflow.proto.data.DatasetOptions.ServiceOptions parseFrom(
+        com.google.protobuf.ByteString data,
+        com.google.protobuf.ExtensionRegistryLite extensionRegistry)
+        throws com.google.protobuf.InvalidProtocolBufferException {
+      return PARSER.parseFrom(data, extensionRegistry);
+    }
+    public static org.tensorflow.proto.data.DatasetOptions.ServiceOptions parseFrom(byte[] data)
+        throws com.google.protobuf.InvalidProtocolBufferException {
+      return PARSER.parseFrom(data);
+    }
+    public static org.tensorflow.proto.data.DatasetOptions.ServiceOptions parseFrom(
+        byte[] data,
+        com.google.protobuf.ExtensionRegistryLite extensionRegistry)
+        throws com.google.protobuf.InvalidProtocolBufferException {
+      return PARSER.parseFrom(data, extensionRegistry);
+    }
+    public static org.tensorflow.proto.data.DatasetOptions.ServiceOptions parseFrom(java.io.InputStream input)
+        throws java.io.IOException {
+      return com.google.protobuf.GeneratedMessageV3
+          .parseWithIOException(PARSER, input);
+    }
+    public static org.tensorflow.proto.data.DatasetOptions.ServiceOptions parseFrom(
+        java.io.InputStream input,
+        com.google.protobuf.ExtensionRegistryLite extensionRegistry)
+        throws java.io.IOException {
+      return com.google.protobuf.GeneratedMessageV3
+          .parseWithIOException(PARSER, input, extensionRegistry);
+    }
+    public static org.tensorflow.proto.data.DatasetOptions.ServiceOptions parseDelimitedFrom(java.io.InputStream input)
+        throws java.io.IOException {
+      return com.google.protobuf.GeneratedMessageV3
+          .parseDelimitedWithIOException(PARSER, input);
+    }
+    public static org.tensorflow.proto.data.DatasetOptions.ServiceOptions parseDelimitedFrom(
+        java.io.InputStream input,
+        com.google.protobuf.ExtensionRegistryLite extensionRegistry)
+        throws java.io.IOException {
+      return com.google.protobuf.GeneratedMessageV3
+          .parseDelimitedWithIOException(PARSER, input, extensionRegistry);
+    }
+    public static org.tensorflow.proto.data.DatasetOptions.ServiceOptions parseFrom(
+        com.google.protobuf.CodedInputStream input)
+        throws java.io.IOException {
+      return com.google.protobuf.GeneratedMessageV3
+          .parseWithIOException(PARSER, input);
+    }
+    public static org.tensorflow.proto.data.DatasetOptions.ServiceOptions parseFrom(
+        com.google.protobuf.CodedInputStream input,
+        com.google.protobuf.ExtensionRegistryLite extensionRegistry)
+        throws java.io.IOException {
+      return com.google.protobuf.GeneratedMessageV3
+          .parseWithIOException(PARSER, input, extensionRegistry);
+    }
+
+    @java.lang.Override
+    public Builder newBuilderForType() { return newBuilder(); }
+    public static Builder newBuilder() {
+      return DEFAULT_INSTANCE.toBuilder();
+    }
+    public static Builder newBuilder(org.tensorflow.proto.data.DatasetOptions.ServiceOptions prototype) {
+      return DEFAULT_INSTANCE.toBuilder().mergeFrom(prototype);
+    }
+    @java.lang.Override
+    public Builder toBuilder() {
+      return this == DEFAULT_INSTANCE
+          ? new Builder() : new Builder().mergeFrom(this);
+    }
+
+    @java.lang.Override
+    protected Builder newBuilderForType(
+        com.google.protobuf.GeneratedMessageV3.BuilderParent parent) {
+      Builder builder = new Builder(parent);
+      return builder;
+    }
+    /**
+     * 
    +     * next: 2
+     * 
    
+     *
+     * Protobuf type {@code tensorflow.data.ServiceOptions}
+     */
+    public static final class Builder extends
+        com.google.protobuf.GeneratedMessageV3.Builder implements
+        // @@protoc_insertion_point(builder_implements:tensorflow.data.ServiceOptions)
+        org.tensorflow.proto.data.DatasetOptions.ServiceOptionsOrBuilder {
+      public static final com.google.protobuf.Descriptors.Descriptor
+          getDescriptor() {
+        return org.tensorflow.proto.data.DatasetOptions.internal_static_tensorflow_data_ServiceOptions_descriptor;
+      }
+
+      @java.lang.Override
+      protected com.google.protobuf.GeneratedMessageV3.FieldAccessorTable
+          internalGetFieldAccessorTable() {
+        return org.tensorflow.proto.data.DatasetOptions.internal_static_tensorflow_data_ServiceOptions_fieldAccessorTable
+            .ensureFieldAccessorsInitialized(
+                org.tensorflow.proto.data.DatasetOptions.ServiceOptions.class, org.tensorflow.proto.data.DatasetOptions.ServiceOptions.Builder.class);
+      }
+
+      // Construct using org.tensorflow.proto.data.DatasetOptions.ServiceOptions.newBuilder()
+      private Builder() {
+
+      }
+
+      private Builder(
+          com.google.protobuf.GeneratedMessageV3.BuilderParent parent) {
+        super(parent);
+
+      }
+      @java.lang.Override
+      public Builder clear() {
+        super.clear();
+        optionalPinnedCase_ = 0;
+        optionalPinned_ = null;
+        return this;
+      }
+
+      @java.lang.Override
+      public com.google.protobuf.Descriptors.Descriptor
+          getDescriptorForType() {
+        return org.tensorflow.proto.data.DatasetOptions.internal_static_tensorflow_data_ServiceOptions_descriptor;
+      }
+
+      @java.lang.Override
+      public org.tensorflow.proto.data.DatasetOptions.ServiceOptions getDefaultInstanceForType() {
+        return org.tensorflow.proto.data.DatasetOptions.ServiceOptions.getDefaultInstance();
+      }
+
+      @java.lang.Override
+      public org.tensorflow.proto.data.DatasetOptions.ServiceOptions build() {
+        org.tensorflow.proto.data.DatasetOptions.ServiceOptions result = buildPartial();
+        if (!result.isInitialized()) {
+          throw newUninitializedMessageException(result);
+        }
+        return result;
+      }
+
+      @java.lang.Override
+      public org.tensorflow.proto.data.DatasetOptions.ServiceOptions buildPartial() {
+        org.tensorflow.proto.data.DatasetOptions.ServiceOptions result = new org.tensorflow.proto.data.DatasetOptions.ServiceOptions(this);
+        if (optionalPinnedCase_ == 1) {
+          result.optionalPinned_ = optionalPinned_;
+        }
+        result.optionalPinnedCase_ = optionalPinnedCase_;
+        onBuilt();
+        return result;
+      }
+
+      @java.lang.Override
+      public Builder clone() {
+        return super.clone();
+      }
+      @java.lang.Override
+      public Builder setField(
+          com.google.protobuf.Descriptors.FieldDescriptor field,
+          java.lang.Object value) {
+        return super.setField(field, value);
+      }
+      @java.lang.Override
+      public Builder clearField(
+          com.google.protobuf.Descriptors.FieldDescriptor field) {
+        return super.clearField(field);
+      }
+      @java.lang.Override
+      public Builder clearOneof(
+          com.google.protobuf.Descriptors.OneofDescriptor oneof) {
+        return super.clearOneof(oneof);
+      }
+      @java.lang.Override
+      public Builder setRepeatedField(
+          com.google.protobuf.Descriptors.FieldDescriptor field,
+          int index, java.lang.Object value) {
+        return super.setRepeatedField(field, index, value);
+      }
+      @java.lang.Override
+      public Builder addRepeatedField(
+          com.google.protobuf.Descriptors.FieldDescriptor field,
+          java.lang.Object value) {
+        return super.addRepeatedField(field, value);
+      }
+      @java.lang.Override
+      public Builder mergeFrom(com.google.protobuf.Message other) {
+        if (other instanceof org.tensorflow.proto.data.DatasetOptions.ServiceOptions) {
+          return mergeFrom((org.tensorflow.proto.data.DatasetOptions.ServiceOptions)other);
+        } else {
+          super.mergeFrom(other);
+          return this;
+        }
+      }
+
+      public Builder mergeFrom(org.tensorflow.proto.data.DatasetOptions.ServiceOptions other) {
+        if (other == org.tensorflow.proto.data.DatasetOptions.ServiceOptions.getDefaultInstance()) return this;
+        switch (other.getOptionalPinnedCase()) {
+          case PINNED: {
+            setPinned(other.getPinned());
+            break;
+          }
+          case OPTIONALPINNED_NOT_SET: {
+            break;
+          }
+        }
+        this.mergeUnknownFields(other.getUnknownFields());
+        onChanged();
+        return this;
+      }
+
+      @java.lang.Override
+      public final boolean isInitialized() {
+        return true;
+      }
+
+      @java.lang.Override
+      public Builder mergeFrom(
+          com.google.protobuf.CodedInputStream input,
+          com.google.protobuf.ExtensionRegistryLite extensionRegistry)
+          throws java.io.IOException {
+        if (extensionRegistry == null) {
+          throw new java.lang.NullPointerException();
+        }
+        try {
+          boolean done = false;
+          while (!done) {
+            int tag = input.readTag();
+            switch (tag) {
+              case 0:
+                done = true;
+                break;
+              case 8: {
+                optionalPinned_ = input.readBool();
+                optionalPinnedCase_ = 1;
+                break;
+              } // case 8
+              default: {
+                if (!super.parseUnknownField(input, extensionRegistry, tag)) {
+                  done = true; // was an endgroup tag
+                }
+                break;
+              } // default:
+            } // switch (tag)
+          } // while (!done)
+        } catch (com.google.protobuf.InvalidProtocolBufferException e) {
+          throw e.unwrapIOException();
+        } finally {
+          onChanged();
+        } // finally
+        return this;
+      }
+      private int optionalPinnedCase_ = 0;
+      private java.lang.Object optionalPinned_;
+      public OptionalPinnedCase
+          getOptionalPinnedCase() {
+        return OptionalPinnedCase.forNumber(
+            optionalPinnedCase_);
+      }
+
+      public Builder clearOptionalPinned() {
+        optionalPinnedCase_ = 0;
+        optionalPinned_ = null;
+        onChanged();
+        return this;
+      }
+
+
+      /**
+       * bool pinned = 1;
+       * @return Whether the pinned field is set.
+       */
+      public boolean hasPinned() {
+        return optionalPinnedCase_ == 1;
+      }
+      /**
+       * bool pinned = 1;
+       * @return The pinned.
+       */
+      public boolean getPinned() {
+        if (optionalPinnedCase_ == 1) {
+          return (java.lang.Boolean) optionalPinned_;
+        }
+        return false;
+      }
+      /**
+       * bool pinned = 1;
+       * @param value The pinned to set.
+       * @return This builder for chaining.
+       */
+      public Builder setPinned(boolean value) {
+        optionalPinnedCase_ = 1;
+        optionalPinned_ = value;
+        onChanged();
+        return this;
+      }
+      /**
+       * bool pinned = 1;
+       * @return This builder for chaining.
+       */
+      public Builder clearPinned() {
+        if (optionalPinnedCase_ == 1) {
+          optionalPinnedCase_ = 0;
+          optionalPinned_ = null;
+          onChanged();
+        }
+        return this;
+      }
+      @java.lang.Override
+      public final Builder setUnknownFields(
+          final com.google.protobuf.UnknownFieldSet unknownFields) {
+        return super.setUnknownFields(unknownFields);
+      }
+
+      @java.lang.Override
+      public final Builder mergeUnknownFields(
+          final com.google.protobuf.UnknownFieldSet unknownFields) {
+        return super.mergeUnknownFields(unknownFields);
+      }
+
+
+      // @@protoc_insertion_point(builder_scope:tensorflow.data.ServiceOptions)
+    }
+
+    // @@protoc_insertion_point(class_scope:tensorflow.data.ServiceOptions)
+    private static final org.tensorflow.proto.data.DatasetOptions.ServiceOptions DEFAULT_INSTANCE;
+    static {
+      DEFAULT_INSTANCE = new org.tensorflow.proto.data.DatasetOptions.ServiceOptions();
+    }
+
+    public static org.tensorflow.proto.data.DatasetOptions.ServiceOptions getDefaultInstance() {
+      return DEFAULT_INSTANCE;
+    }
+
+    private static final com.google.protobuf.Parser
+        PARSER = new com.google.protobuf.AbstractParser() {
+      @java.lang.Override
+      public ServiceOptions parsePartialFrom(
+          com.google.protobuf.CodedInputStream input,
+          com.google.protobuf.ExtensionRegistryLite extensionRegistry)
+          throws com.google.protobuf.InvalidProtocolBufferException {
+        Builder builder = newBuilder();
+        try {
+          builder.mergeFrom(input, extensionRegistry);
+        } catch (com.google.protobuf.InvalidProtocolBufferException e) {
+          throw e.setUnfinishedMessage(builder.buildPartial());
+        } catch (com.google.protobuf.UninitializedMessageException e) {
+          throw e.asInvalidProtocolBufferException().setUnfinishedMessage(builder.buildPartial());
+        } catch (java.io.IOException e) {
+          throw new com.google.protobuf.InvalidProtocolBufferException(e)
+              .setUnfinishedMessage(builder.buildPartial());
+        }
+        return builder.buildPartial();
+      }
+    };
+
+    public static com.google.protobuf.Parser parser() {
+      return PARSER;
+    }
+
+    @java.lang.Override
+    public com.google.protobuf.Parser getParserForType() {
+      return PARSER;
+    }
+
+    @java.lang.Override
+    public org.tensorflow.proto.data.DatasetOptions.ServiceOptions getDefaultInstanceForType() {
+      return DEFAULT_INSTANCE;
+    }
+
+  }
+
+  public interface ThreadingOptionsOrBuilder extends
+      // @@protoc_insertion_point(interface_extends:tensorflow.data.ThreadingOptions)
+      com.google.protobuf.MessageOrBuilder {
+
+    /**
+     * int32 max_intra_op_parallelism = 1;
+     * @return Whether the maxIntraOpParallelism field is set.
+     */
+    boolean hasMaxIntraOpParallelism();
+    /**
+     * int32 max_intra_op_parallelism = 1;
+     * @return The maxIntraOpParallelism.
+     */
+    int getMaxIntraOpParallelism();
+
+    /**
+     * int32 private_threadpool_size = 2;
+     * @return Whether the privateThreadpoolSize field is set.
+     */
+    boolean hasPrivateThreadpoolSize();
+    /**
+     * int32 private_threadpool_size = 2;
+     * @return The privateThreadpoolSize.
+     */
+    int getPrivateThreadpoolSize();
+
+    public org.tensorflow.proto.data.DatasetOptions.ThreadingOptions.OptionalMaxIntraOpParallelismCase getOptionalMaxIntraOpParallelismCase();
+
+    public org.tensorflow.proto.data.DatasetOptions.ThreadingOptions.OptionalPrivateThreadpoolSizeCase getOptionalPrivateThreadpoolSizeCase();
+  }
+  /**
+   * 
    +   * next: 3
+   * 
    
+   *
+   * Protobuf type {@code tensorflow.data.ThreadingOptions}
+   */
+  public static final class ThreadingOptions extends
+      com.google.protobuf.GeneratedMessageV3 implements
+      // @@protoc_insertion_point(message_implements:tensorflow.data.ThreadingOptions)
+      ThreadingOptionsOrBuilder {
+  private static final long serialVersionUID = 0L;
+    // Use ThreadingOptions.newBuilder() to construct.
+    private ThreadingOptions(com.google.protobuf.GeneratedMessageV3.Builder builder) {
+      super(builder);
+    }
+    private ThreadingOptions() {
+    }
+
+    @java.lang.Override
+    @SuppressWarnings({"unused"})
+    protected java.lang.Object newInstance(
+        UnusedPrivateParameter unused) {
+      return new ThreadingOptions();
+    }
+
+    @java.lang.Override
+    public final com.google.protobuf.UnknownFieldSet
+    getUnknownFields() {
+      return this.unknownFields;
+    }
+    public static final com.google.protobuf.Descriptors.Descriptor
+        getDescriptor() {
+      return org.tensorflow.proto.data.DatasetOptions.internal_static_tensorflow_data_ThreadingOptions_descriptor;
+    }
+
+    @java.lang.Override
+    protected com.google.protobuf.GeneratedMessageV3.FieldAccessorTable
+        internalGetFieldAccessorTable() {
+      return org.tensorflow.proto.data.DatasetOptions.internal_static_tensorflow_data_ThreadingOptions_fieldAccessorTable
+          .ensureFieldAccessorsInitialized(
+              org.tensorflow.proto.data.DatasetOptions.ThreadingOptions.class, org.tensorflow.proto.data.DatasetOptions.ThreadingOptions.Builder.class);
+    }
+
+    private int optionalMaxIntraOpParallelismCase_ = 0;
+    private java.lang.Object optionalMaxIntraOpParallelism_;
+    public enum OptionalMaxIntraOpParallelismCase
+        implements com.google.protobuf.Internal.EnumLite,
+            com.google.protobuf.AbstractMessage.InternalOneOfEnum {
+      MAX_INTRA_OP_PARALLELISM(1),
+      OPTIONALMAXINTRAOPPARALLELISM_NOT_SET(0);
+      private final int value;
+      private OptionalMaxIntraOpParallelismCase(int value) {
+        this.value = value;
+      }
+      /**
+       * @param value The number of the enum to look for.
+       * @return The enum associated with the given number.
+       * @deprecated Use {@link #forNumber(int)} instead.
+       */
+      @java.lang.Deprecated
+      public static OptionalMaxIntraOpParallelismCase valueOf(int value) {
+        return forNumber(value);
+      }
+
+      public static OptionalMaxIntraOpParallelismCase forNumber(int value) {
+        switch (value) {
+          case 1: return MAX_INTRA_OP_PARALLELISM;
+          case 0: return OPTIONALMAXINTRAOPPARALLELISM_NOT_SET;
+          default: return null;
+        }
+      }
+      public int getNumber() {
+        return this.value;
+      }
+    };
+
+    public OptionalMaxIntraOpParallelismCase
+    getOptionalMaxIntraOpParallelismCase() {
+      return OptionalMaxIntraOpParallelismCase.forNumber(
+          optionalMaxIntraOpParallelismCase_);
+    }
+
+    private int optionalPrivateThreadpoolSizeCase_ = 0;
+    private java.lang.Object optionalPrivateThreadpoolSize_;
+    public enum OptionalPrivateThreadpoolSizeCase
+        implements com.google.protobuf.Internal.EnumLite,
+            com.google.protobuf.AbstractMessage.InternalOneOfEnum {
+      PRIVATE_THREADPOOL_SIZE(2),
+      OPTIONALPRIVATETHREADPOOLSIZE_NOT_SET(0);
+      private final int value;
+      private OptionalPrivateThreadpoolSizeCase(int value) {
+        this.value = value;
+      }
+      /**
+       * @param value The number of the enum to look for.
+       * @return The enum associated with the given number.
+       * @deprecated Use {@link #forNumber(int)} instead.
+       */
+      @java.lang.Deprecated
+      public static OptionalPrivateThreadpoolSizeCase valueOf(int value) {
+        return forNumber(value);
+      }
+
+      public static OptionalPrivateThreadpoolSizeCase forNumber(int value) {
+        switch (value) {
+          case 2: return PRIVATE_THREADPOOL_SIZE;
+          case 0: return OPTIONALPRIVATETHREADPOOLSIZE_NOT_SET;
+          default: return null;
+        }
+      }
+      public int getNumber() {
+        return this.value;
+      }
+    };
+
+    public OptionalPrivateThreadpoolSizeCase
+    getOptionalPrivateThreadpoolSizeCase() {
+      return OptionalPrivateThreadpoolSizeCase.forNumber(
+          optionalPrivateThreadpoolSizeCase_);
+    }
+
+    public static final int MAX_INTRA_OP_PARALLELISM_FIELD_NUMBER = 1;
+    /**
+     * int32 max_intra_op_parallelism = 1;
+     * @return Whether the maxIntraOpParallelism field is set.
+     */
+    @java.lang.Override
+    public boolean hasMaxIntraOpParallelism() {
+      return optionalMaxIntraOpParallelismCase_ == 1;
+    }
+    /**
+     * int32 max_intra_op_parallelism = 1;
+     * @return The maxIntraOpParallelism.
+     */
+    @java.lang.Override
+    public int getMaxIntraOpParallelism() {
+      if (optionalMaxIntraOpParallelismCase_ == 1) {
+        return (java.lang.Integer) optionalMaxIntraOpParallelism_;
+      }
+      return 0;
+    }
+
+    public static final int PRIVATE_THREADPOOL_SIZE_FIELD_NUMBER = 2;
+    /**
+     * int32 private_threadpool_size = 2;
+     * @return Whether the privateThreadpoolSize field is set.
+     */
+    @java.lang.Override
+    public boolean hasPrivateThreadpoolSize() {
+      return optionalPrivateThreadpoolSizeCase_ == 2;
+    }
+    /**
+     * int32 private_threadpool_size = 2;
+     * @return The privateThreadpoolSize.
+     */
+    @java.lang.Override
+    public int getPrivateThreadpoolSize() {
+      if (optionalPrivateThreadpoolSizeCase_ == 2) {
+        return (java.lang.Integer) optionalPrivateThreadpoolSize_;
+      }
+      return 0;
+    }
+
+    private byte memoizedIsInitialized = -1;
+    @java.lang.Override
+    public final boolean isInitialized() {
+      byte isInitialized = memoizedIsInitialized;
+      if (isInitialized == 1) return true;
+      if (isInitialized == 0) return false;
+
+      memoizedIsInitialized = 1;
+      return true;
+    }
+
+    @java.lang.Override
+    public void writeTo(com.google.protobuf.CodedOutputStream output)
+                        throws java.io.IOException {
+      if (optionalMaxIntraOpParallelismCase_ == 1) {
+        output.writeInt32(
+            1, (int)((java.lang.Integer) optionalMaxIntraOpParallelism_));
+      }
+      if (optionalPrivateThreadpoolSizeCase_ == 2) {
+        output.writeInt32(
+            2, (int)((java.lang.Integer) optionalPrivateThreadpoolSize_));
+      }
+      getUnknownFields().writeTo(output);
+    }
+
+    @java.lang.Override
+    public int getSerializedSize() {
+      int size = memoizedSize;
+      if (size != -1) return size;
+
+      size = 0;
+      if (optionalMaxIntraOpParallelismCase_ == 1) {
+        size += com.google.protobuf.CodedOutputStream
+          .computeInt32Size(
+              1, (int)((java.lang.Integer) optionalMaxIntraOpParallelism_));
+      }
+      if (optionalPrivateThreadpoolSizeCase_ == 2) {
+        size += com.google.protobuf.CodedOutputStream
+          .computeInt32Size(
+              2, (int)((java.lang.Integer) optionalPrivateThreadpoolSize_));
+      }
+      size += getUnknownFields().getSerializedSize();
+      memoizedSize = size;
+      return size;
+    }
+
+    @java.lang.Override
+    public boolean equals(final java.lang.Object obj) {
+      if (obj == this) {
+       return true;
+      }
+      if (!(obj instanceof org.tensorflow.proto.data.DatasetOptions.ThreadingOptions)) {
+        return super.equals(obj);
+      }
+      org.tensorflow.proto.data.DatasetOptions.ThreadingOptions other = (org.tensorflow.proto.data.DatasetOptions.ThreadingOptions) obj;
+
+      if (!getOptionalMaxIntraOpParallelismCase().equals(other.getOptionalMaxIntraOpParallelismCase())) return false;
+      switch (optionalMaxIntraOpParallelismCase_) {
+        case 1:
+          if (getMaxIntraOpParallelism()
+              != other.getMaxIntraOpParallelism()) return false;
+          break;
+        case 0:
+        default:
+      }
+      if (!getOptionalPrivateThreadpoolSizeCase().equals(other.getOptionalPrivateThreadpoolSizeCase())) return false;
+      switch (optionalPrivateThreadpoolSizeCase_) {
+        case 2:
+          if (getPrivateThreadpoolSize()
+              != other.getPrivateThreadpoolSize()) return false;
+          break;
+        case 0:
+        default:
+      }
+      if (!getUnknownFields().equals(other.getUnknownFields())) return false;
+      return true;
+    }
+
+    @java.lang.Override
+    public int hashCode() {
+      if (memoizedHashCode != 0) {
+        return memoizedHashCode;
+      }
+      int hash = 41;
+      hash = (19 * hash) + getDescriptor().hashCode();
+      switch (optionalMaxIntraOpParallelismCase_) {
+        case 1:
+          hash = (37 * hash) + MAX_INTRA_OP_PARALLELISM_FIELD_NUMBER;
+          hash = (53 * hash) + getMaxIntraOpParallelism();
+          break;
+        case 0:
+        default:
+      }
+      switch (optionalPrivateThreadpoolSizeCase_) {
+        case 2:
+          hash = (37 * hash) + PRIVATE_THREADPOOL_SIZE_FIELD_NUMBER;
+          hash = (53 * hash) + getPrivateThreadpoolSize();
           break;
         case 0:
         default:
@@ -6261,6 +7021,33 @@ public interface OptionsOrBuilder extends
      */
     org.tensorflow.proto.data.DatasetOptions.OptimizationOptionsOrBuilder getOptimizationOptionsOrBuilder();
 
+    /**
+     * 
    +     * The tf.data service options associated with the dataset.
+     * 
    
+     *
+     * .tensorflow.data.ServiceOptions service_options = 12;
+     * @return Whether the serviceOptions field is set.
+     */
+    boolean hasServiceOptions();
+    /**
+     * 
    +     * The tf.data service options associated with the dataset.
+     * 
    
+     *
+     * .tensorflow.data.ServiceOptions service_options = 12;
+     * @return The serviceOptions.
+     */
+    org.tensorflow.proto.data.DatasetOptions.ServiceOptions getServiceOptions();
+    /**
+     * 
    +     * The tf.data service options associated with the dataset.
+     * 
    
+     *
+     * .tensorflow.data.ServiceOptions service_options = 12;
+     */
+    org.tensorflow.proto.data.DatasetOptions.ServiceOptionsOrBuilder getServiceOptionsOrBuilder();
+
     /**
      * bool slack = 4;
      * @return Whether the slack field is set.
@@ -6353,7 +7140,7 @@ public interface OptionsOrBuilder extends
    * 
        * Message stored with Dataset objects to control how datasets are processed and
    * optimized.
-   * next: 12
+   * next: 13
    * 
    
    *
    * Protobuf type {@code tensorflow.data.Options}
@@ -6868,6 +7655,44 @@ public org.tensorflow.proto.data.DatasetOptions.OptimizationOptionsOrBuilder get
       return getOptimizationOptions();
     }
 
+    public static final int SERVICE_OPTIONS_FIELD_NUMBER = 12;
+    private org.tensorflow.proto.data.DatasetOptions.ServiceOptions serviceOptions_;
+    /**
+     * 
    +     * The tf.data service options associated with the dataset.
+     * 
    
+     *
+     * .tensorflow.data.ServiceOptions service_options = 12;
+     * @return Whether the serviceOptions field is set.
+     */
+    @java.lang.Override
+    public boolean hasServiceOptions() {
+      return serviceOptions_ != null;
+    }
+    /**
+     * 
    +     * The tf.data service options associated with the dataset.
+     * 
    
+     *
+     * .tensorflow.data.ServiceOptions service_options = 12;
+     * @return The serviceOptions.
+     */
+    @java.lang.Override
+    public org.tensorflow.proto.data.DatasetOptions.ServiceOptions getServiceOptions() {
+      return serviceOptions_ == null ? org.tensorflow.proto.data.DatasetOptions.ServiceOptions.getDefaultInstance() : serviceOptions_;
+    }
+    /**
+     * 
    +     * The tf.data service options associated with the dataset.
+     * 
    
+     *
+     * .tensorflow.data.ServiceOptions service_options = 12;
+     */
+    @java.lang.Override
+    public org.tensorflow.proto.data.DatasetOptions.ServiceOptionsOrBuilder getServiceOptionsOrBuilder() {
+      return getServiceOptions();
+    }
+
     public static final int SLACK_FIELD_NUMBER = 4;
     /**
      * bool slack = 4;
@@ -7052,6 +7877,9 @@ public void writeTo(com.google.protobuf.CodedOutputStream output)
       for (int i = 0; i < frameworkType_.size(); i++) {
         com.google.protobuf.GeneratedMessageV3.writeString(output, 11, frameworkType_.getRaw(i));
       }
+      if (serviceOptions_ != null) {
+        output.writeMessage(12, getServiceOptions());
+      }
       getUnknownFields().writeTo(output);
     }
 
@@ -7112,6 +7940,10 @@ public int getSerializedSize() {
         size += dataSize;
         size += 1 * getFrameworkTypeList().size();
       }
+      if (serviceOptions_ != null) {
+        size += com.google.protobuf.CodedOutputStream
+          .computeMessageSize(12, getServiceOptions());
+      }
       size += getUnknownFields().getSerializedSize();
       memoizedSize = size;
       return size;
@@ -7144,6 +7976,11 @@ public boolean equals(final java.lang.Object obj) {
         if (!getOptimizationOptions()
             .equals(other.getOptimizationOptions())) return false;
       }
+      if (hasServiceOptions() != other.hasServiceOptions()) return false;
+      if (hasServiceOptions()) {
+        if (!getServiceOptions()
+            .equals(other.getServiceOptions())) return false;
+      }
       if (hasThreadingOptions() != other.hasThreadingOptions()) return false;
       if (hasThreadingOptions()) {
         if (!getThreadingOptions()
@@ -7230,6 +8067,10 @@ public int hashCode() {
         hash = (37 * hash) + OPTIMIZATION_OPTIONS_FIELD_NUMBER;
         hash = (53 * hash) + getOptimizationOptions().hashCode();
       }
+      if (hasServiceOptions()) {
+        hash = (37 * hash) + SERVICE_OPTIONS_FIELD_NUMBER;
+        hash = (53 * hash) + getServiceOptions().hashCode();
+      }
       if (hasThreadingOptions()) {
         hash = (37 * hash) + THREADING_OPTIONS_FIELD_NUMBER;
         hash = (53 * hash) + getThreadingOptions().hashCode();
@@ -7385,7 +8226,7 @@ protected Builder newBuilderForType(
      * 
          * Message stored with Dataset objects to control how datasets are processed and
      * optimized.
-     * next: 12
+     * next: 13
      * 
    
      *
      * Protobuf type {@code tensorflow.data.Options}
@@ -7440,6 +8281,12 @@ public Builder clear() {
           optimizationOptions_ = null;
           optimizationOptionsBuilder_ = null;
         }
+        if (serviceOptionsBuilder_ == null) {
+          serviceOptions_ = null;
+        } else {
+          serviceOptions_ = null;
+          serviceOptionsBuilder_ = null;
+        }
         if (threadingOptionsBuilder_ == null) {
           threadingOptions_ = null;
         } else {
@@ -7511,6 +8358,11 @@ public org.tensorflow.proto.data.DatasetOptions.Options buildPartial() {
         } else {
           result.optimizationOptions_ = optimizationOptionsBuilder_.build();
         }
+        if (serviceOptionsBuilder_ == null) {
+          result.serviceOptions_ = serviceOptions_;
+        } else {
+          result.serviceOptions_ = serviceOptionsBuilder_.build();
+        }
         if (optionalSlackCase_ == 4) {
           result.optionalSlack_ = optionalSlack_;
         }
@@ -7601,6 +8453,9 @@ public Builder mergeFrom(org.tensorflow.proto.data.DatasetOptions.Options other)
         if (other.hasOptimizationOptions()) {
           mergeOptimizationOptions(other.getOptimizationOptions());
         }
+        if (other.hasServiceOptions()) {
+          mergeServiceOptions(other.getServiceOptions());
+        }
         if (other.hasThreadingOptions()) {
           mergeThreadingOptions(other.getThreadingOptions());
         }
@@ -7752,6 +8607,13 @@ public Builder mergeFrom(
                 frameworkType_.add(s);
                 break;
               } // case 90
+              case 98: {
+                input.readMessage(
+                    getServiceOptionsFieldBuilder().getBuilder(),
+                    extensionRegistry);
+
+                break;
+              } // case 98
               default: {
                 if (!super.parseUnknownField(input, extensionRegistry, tag)) {
                   done = true; // was an endgroup tag
@@ -8608,6 +9470,161 @@ public org.tensorflow.proto.data.DatasetOptions.OptimizationOptionsOrBuilder get
         return optimizationOptionsBuilder_;
       }
 
+      private org.tensorflow.proto.data.DatasetOptions.ServiceOptions serviceOptions_;
+      private com.google.protobuf.SingleFieldBuilderV3<
+          org.tensorflow.proto.data.DatasetOptions.ServiceOptions, org.tensorflow.proto.data.DatasetOptions.ServiceOptions.Builder, org.tensorflow.proto.data.DatasetOptions.ServiceOptionsOrBuilder> serviceOptionsBuilder_;
+      /**
+       * 
    +       * The tf.data service options associated with the dataset.
+       * 
    
+       *
+       * .tensorflow.data.ServiceOptions service_options = 12;
+       * @return Whether the serviceOptions field is set.
+       */
+      public boolean hasServiceOptions() {
+        return serviceOptionsBuilder_ != null || serviceOptions_ != null;
+      }
+      /**
+       * 
    +       * The tf.data service options associated with the dataset.
+       * 
    
+       *
+       * .tensorflow.data.ServiceOptions service_options = 12;
+       * @return The serviceOptions.
+       */
+      public org.tensorflow.proto.data.DatasetOptions.ServiceOptions getServiceOptions() {
+        if (serviceOptionsBuilder_ == null) {
+          return serviceOptions_ == null ? org.tensorflow.proto.data.DatasetOptions.ServiceOptions.getDefaultInstance() : serviceOptions_;
+        } else {
+          return serviceOptionsBuilder_.getMessage();
+        }
+      }
+      /**
+       * 
    +       * The tf.data service options associated with the dataset.
+       * 
    
+       *
+       * .tensorflow.data.ServiceOptions service_options = 12;
+       */
+      public Builder setServiceOptions(org.tensorflow.proto.data.DatasetOptions.ServiceOptions value) {
+        if (serviceOptionsBuilder_ == null) {
+          if (value == null) {
+            throw new NullPointerException();
+          }
+          serviceOptions_ = value;
+          onChanged();
+        } else {
+          serviceOptionsBuilder_.setMessage(value);
+        }
+
+        return this;
+      }
+      /**
+       * 
    +       * The tf.data service options associated with the dataset.
+       * 
    
+       *
+       * .tensorflow.data.ServiceOptions service_options = 12;
+       */
+      public Builder setServiceOptions(
+          org.tensorflow.proto.data.DatasetOptions.ServiceOptions.Builder builderForValue) {
+        if (serviceOptionsBuilder_ == null) {
+          serviceOptions_ = builderForValue.build();
+          onChanged();
+        } else {
+          serviceOptionsBuilder_.setMessage(builderForValue.build());
+        }
+
+        return this;
+      }
+      /**
+       * 
    +       * The tf.data service options associated with the dataset.
+       * 
    
+       *
+       * .tensorflow.data.ServiceOptions service_options = 12;
+       */
+      public Builder mergeServiceOptions(org.tensorflow.proto.data.DatasetOptions.ServiceOptions value) {
+        if (serviceOptionsBuilder_ == null) {
+          if (serviceOptions_ != null) {
+            serviceOptions_ =
+              org.tensorflow.proto.data.DatasetOptions.ServiceOptions.newBuilder(serviceOptions_).mergeFrom(value).buildPartial();
+          } else {
+            serviceOptions_ = value;
+          }
+          onChanged();
+        } else {
+          serviceOptionsBuilder_.mergeFrom(value);
+        }
+
+        return this;
+      }
+      /**
+       * 
    +       * The tf.data service options associated with the dataset.
+       * 
    
+       *
+       * .tensorflow.data.ServiceOptions service_options = 12;
+       */
+      public Builder clearServiceOptions() {
+        if (serviceOptionsBuilder_ == null) {
+          serviceOptions_ = null;
+          onChanged();
+        } else {
+          serviceOptions_ = null;
+          serviceOptionsBuilder_ = null;
+        }
+
+        return this;
+      }
+      /**
+       * 
    +       * The tf.data service options associated with the dataset.
+       * 
    
+       *
+       * .tensorflow.data.ServiceOptions service_options = 12;
+       */
+      public org.tensorflow.proto.data.DatasetOptions.ServiceOptions.Builder getServiceOptionsBuilder() {
+        
+        onChanged();
+        return getServiceOptionsFieldBuilder().getBuilder();
+      }
+      /**
+       * 
    +       * The tf.data service options associated with the dataset.
+       * 
    
+       *
+       * .tensorflow.data.ServiceOptions service_options = 12;
+       */
+      public org.tensorflow.proto.data.DatasetOptions.ServiceOptionsOrBuilder getServiceOptionsOrBuilder() {
+        if (serviceOptionsBuilder_ != null) {
+          return serviceOptionsBuilder_.getMessageOrBuilder();
+        } else {
+          return serviceOptions_ == null ?
+              org.tensorflow.proto.data.DatasetOptions.ServiceOptions.getDefaultInstance() : serviceOptions_;
+        }
+      }
+      /**
+       * 
    +       * The tf.data service options associated with the dataset.
+       * 
    
+       *
+       * .tensorflow.data.ServiceOptions service_options = 12;
+       */
+      private com.google.protobuf.SingleFieldBuilderV3<
+          org.tensorflow.proto.data.DatasetOptions.ServiceOptions, org.tensorflow.proto.data.DatasetOptions.ServiceOptions.Builder, org.tensorflow.proto.data.DatasetOptions.ServiceOptionsOrBuilder> 
+          getServiceOptionsFieldBuilder() {
+        if (serviceOptionsBuilder_ == null) {
+          serviceOptionsBuilder_ = new com.google.protobuf.SingleFieldBuilderV3<
+              org.tensorflow.proto.data.DatasetOptions.ServiceOptions, org.tensorflow.proto.data.DatasetOptions.ServiceOptions.Builder, org.tensorflow.proto.data.DatasetOptions.ServiceOptionsOrBuilder>(
+                  getServiceOptions(),
+                  getParentForChildren(),
+                  isClean());
+          serviceOptions_ = null;
+        }
+        return serviceOptionsBuilder_;
+      }
+
       /**
        * bool slack = 4;
        * @return Whether the slack field is set.
@@ -9040,6 +10057,11 @@ public org.tensorflow.proto.data.DatasetOptions.Options getDefaultInstanceForTyp
   private static final 
     com.google.protobuf.GeneratedMessageV3.FieldAccessorTable
       internal_static_tensorflow_data_OptimizationOptions_fieldAccessorTable;
+  private static final com.google.protobuf.Descriptors.Descriptor
+    internal_static_tensorflow_data_ServiceOptions_descriptor;
+  private static final 
+    com.google.protobuf.GeneratedMessageV3.FieldAccessorTable
+      internal_static_tensorflow_data_ServiceOptions_fieldAccessorTable;
   private static final com.google.protobuf.Descriptors.Descriptor
     internal_static_tensorflow_data_ThreadingOptions_descriptor;
   private static final 
@@ -9061,70 +10083,74 @@ public org.tensorflow.proto.data.DatasetOptions.Options getDefaultInstanceForTyp
     java.lang.String[] descriptorData = {
       "\n/tensorflow/core/framework/dataset_opti" +
       "ons.proto\022\017tensorflow.data\032%tensorflow/c" +
-      "ore/framework/model.proto\"\371\001\n\017AutotuneOp" +
+      "ore/framework/model.proto\"\270\002\n\017AutotuneOp" +
       "tions\022\021\n\007enabled\030\001 \001(\010H\000\022\024\n\ncpu_budget\030\002" +
       " \001(\005H\001\022\024\n\nram_budget\030\003 \001(\003H\002\022F\n\022autotune" +
       "_algorithm\030\004 \001(\0162(.tensorflow.data.model" +
-      ".AutotuneAlgorithmH\003B\022\n\020optional_enabled" +
-      "B\025\n\023optional_cpu_budgetB\025\n\023optional_ram_" +
-      "budgetB\035\n\033optional_autotune_algorithm\"\321\001" +
-      "\n\022CardinalityOptions\022G\n\rcompute_level\030\001 " +
-      "\001(\01620.tensorflow.data.CardinalityOptions" +
-      ".ComputeLevel\"r\n\014ComputeLevel\022#\n\037CARDINA" +
-      "LITY_COMPUTE_UNSPECIFIED\020\000\022\033\n\027CARDINALIT" +
-      "Y_COMPUTE_LOW\020\001\022 \n\034CARDINALITY_COMPUTE_M" +
-      "ODERATE\020\002\"\177\n\021DistributeOptions\022;\n\021auto_s" +
-      "hard_policy\030\001 \001(\0162 .tensorflow.data.Auto" +
-      "ShardPolicy\022\025\n\013num_devices\030\002 \001(\005H\000B\026\n\024op" +
-      "tional_num_devices\"\271\006\n\023OptimizationOptio" +
-      "ns\022%\n\033apply_default_optimizations\030\001 \001(\010H" +
-      "\000\022\027\n\rfilter_fusion\030\006 \001(\010H\001\022\036\n\024map_and_ba" +
-      "tch_fusion\030\t \001(\010H\002\022\037\n\025map_and_filter_fus" +
-      "ion\030\n \001(\010H\003\022\024\n\nmap_fusion\030\013 \001(\010H\004\022\035\n\023map" +
-      "_parallelization\030\014 \001(\010H\005\022\032\n\020noop_elimina" +
-      "tion\030\016 \001(\010H\006\022\030\n\016parallel_batch\030\017 \001(\010H\007\022#" +
-      "\n\031shuffle_and_repeat_fusion\030\021 \001(\010H\010\022 \n\026f" +
-      "ilter_parallelization\030\022 \001(\010H\t\022\031\n\017inject_" +
-      "prefetch\030\023 \001(\010H\n\022!\n\027seq_interleave_prefe" +
-      "tch\030\025 \001(\010H\013B&\n$optional_apply_default_op" +
-      "timizationsB\030\n\026optional_filter_fusionB\037\n" +
-      "\035optional_map_and_batch_fusionB \n\036option" +
-      "al_map_and_filter_fusionB\025\n\023optional_map" +
-      "_fusionB\036\n\034optional_map_parallelizationB" +
-      "\033\n\031optional_noop_eliminationB\031\n\027optional" +
-      "_parallel_batchB$\n\"optional_shuffle_and_" +
-      "repeat_fusionB!\n\037optional_filter_paralle" +
-      "lizationB\032\n\030optional_inject_prefetchB\"\n " +
-      "optional_seq_interleave_prefetchJ\004\010\002\020\003J\004" +
-      "\010\003\020\004J\004\010\004\020\005J\004\010\005\020\006J\004\010\007\020\010J\004\010\010\020\tJ\004\010\r\020\016J\004\010\020\020\021" +
-      "J\004\010\024\020\025\"\242\001\n\020ThreadingOptions\022\"\n\030max_intra" +
-      "_op_parallelism\030\001 \001(\005H\000\022!\n\027private_threa" +
-      "dpool_size\030\002 \001(\005H\001B#\n!optional_max_intra" +
-      "_op_parallelismB\"\n optional_private_thre" +
-      "adpool_size\"\373\004\n\007Options\022\026\n\014dataset_name\030" +
-      "\n \001(\tH\000\022\026\n\016framework_type\030\013 \003(\t\022\027\n\rdeter" +
-      "ministic\030\001 \001(\010H\001\022:\n\020autotune_options\030\007 \001" +
-      "(\0132 .tensorflow.data.AutotuneOptions\022>\n\022" +
-      "distribute_options\030\002 \001(\0132\".tensorflow.da" +
-      "ta.DistributeOptions\022B\n\024optimization_opt" +
-      "ions\030\003 \001(\0132$.tensorflow.data.Optimizatio" +
-      "nOptions\022\017\n\005slack\030\004 \001(\010H\002\022<\n\021threading_o" +
-      "ptions\030\005 \001(\0132!.tensorflow.data.Threading" +
-      "Options\022E\n\025external_state_policy\030\006 \001(\0162$" +
-      ".tensorflow.data.ExternalStatePolicyH\003\022\035" +
-      "\n\023symbolic_checkpoint\030\010 \001(\010H\004\022\024\n\nwarm_st" +
-      "art\030\t \001(\010H\005B\027\n\025optional_dataset_nameB\030\n\026" +
-      "optional_deterministicB\020\n\016optional_slack" +
-      "B \n\036optional_external_state_policyB\036\n\034op" +
-      "tional_symbolic_checkpointB\025\n\023optional_w" +
-      "arm_start*K\n\017AutoShardPolicy\022\010\n\004AUTO\020\000\022\010" +
-      "\n\004FILE\020\001\022\010\n\004DATA\020\002\022\010\n\004HINT\020\003\022\020\n\003OFF\020\377\377\377\377" +
-      "\377\377\377\377\377\001*J\n\023ExternalStatePolicy\022\017\n\013POLICY_" +
-      "WARN\020\000\022\021\n\rPOLICY_IGNORE\020\001\022\017\n\013POLICY_FAIL" +
-      "\020\002Bs\n\031org.tensorflow.proto.dataZVgithub." +
-      "com/tensorflow/tensorflow/tensorflow/go/" +
-      "core/framework/dataset_options_go_protob" +
-      "\006proto3"
+      ".AutotuneAlgorithmH\003\022\035\n\023initial_parallel" +
+      "ism\030\005 \001(\003H\004B\022\n\020optional_enabledB\025\n\023optio" +
+      "nal_cpu_budgetB\025\n\023optional_ram_budgetB\035\n" +
+      "\033optional_autotune_algorithmB\036\n\034optional" +
+      "_initial_parallelism\"\321\001\n\022CardinalityOpti" +
+      "ons\022G\n\rcompute_level\030\001 \001(\01620.tensorflow." +
+      "data.CardinalityOptions.ComputeLevel\"r\n\014" +
+      "ComputeLevel\022#\n\037CARDINALITY_COMPUTE_UNSP" +
+      "ECIFIED\020\000\022\033\n\027CARDINALITY_COMPUTE_LOW\020\001\022 " +
+      "\n\034CARDINALITY_COMPUTE_MODERATE\020\002\"\177\n\021Dist" +
+      "ributeOptions\022;\n\021auto_shard_policy\030\001 \001(\016" +
+      "2 .tensorflow.data.AutoShardPolicy\022\025\n\013nu" +
+      "m_devices\030\002 \001(\005H\000B\026\n\024optional_num_device" +
+      "s\"\271\006\n\023OptimizationOptions\022%\n\033apply_defau" +
+      "lt_optimizations\030\001 \001(\010H\000\022\027\n\rfilter_fusio" +
+      "n\030\006 \001(\010H\001\022\036\n\024map_and_batch_fusion\030\t \001(\010H" +
+      "\002\022\037\n\025map_and_filter_fusion\030\n \001(\010H\003\022\024\n\nma" +
+      "p_fusion\030\013 \001(\010H\004\022\035\n\023map_parallelization\030" +
+      "\014 \001(\010H\005\022\032\n\020noop_elimination\030\016 \001(\010H\006\022\030\n\016p" +
+      "arallel_batch\030\017 \001(\010H\007\022#\n\031shuffle_and_rep" +
+      "eat_fusion\030\021 \001(\010H\010\022 \n\026filter_paralleliza" +
+      "tion\030\022 \001(\010H\t\022\031\n\017inject_prefetch\030\023 \001(\010H\n\022" +
+      "!\n\027seq_interleave_prefetch\030\025 \001(\010H\013B&\n$op" +
+      "tional_apply_default_optimizationsB\030\n\026op" +
+      "tional_filter_fusionB\037\n\035optional_map_and" +
+      "_batch_fusionB \n\036optional_map_and_filter" +
+      "_fusionB\025\n\023optional_map_fusionB\036\n\034option" +
+      "al_map_parallelizationB\033\n\031optional_noop_" +
+      "eliminationB\031\n\027optional_parallel_batchB$" +
+      "\n\"optional_shuffle_and_repeat_fusionB!\n\037" +
+      "optional_filter_parallelizationB\032\n\030optio" +
+      "nal_inject_prefetchB\"\n optional_seq_inte" +
+      "rleave_prefetchJ\004\010\002\020\003J\004\010\003\020\004J\004\010\004\020\005J\004\010\005\020\006J" +
+      "\004\010\007\020\010J\004\010\010\020\tJ\004\010\r\020\016J\004\010\020\020\021J\004\010\024\020\025\"5\n\016Service" +
+      "Options\022\020\n\006pinned\030\001 \001(\010H\000B\021\n\017optional_pi" +
+      "nned\"\242\001\n\020ThreadingOptions\022\"\n\030max_intra_o" +
+      "p_parallelism\030\001 \001(\005H\000\022!\n\027private_threadp" +
+      "ool_size\030\002 \001(\005H\001B#\n!optional_max_intra_o" +
+      "p_parallelismB\"\n optional_private_thread" +
+      "pool_size\"\265\005\n\007Options\022\026\n\014dataset_name\030\n " +
+      "\001(\tH\000\022\026\n\016framework_type\030\013 \003(\t\022\027\n\rdetermi" +
+      "nistic\030\001 \001(\010H\001\022:\n\020autotune_options\030\007 \001(\013" +
+      "2 .tensorflow.data.AutotuneOptions\022>\n\022di" +
+      "stribute_options\030\002 \001(\0132\".tensorflow.data" +
+      ".DistributeOptions\022B\n\024optimization_optio" +
+      "ns\030\003 \001(\0132$.tensorflow.data.OptimizationO" +
+      "ptions\0228\n\017service_options\030\014 \001(\0132\037.tensor" +
+      "flow.data.ServiceOptions\022\017\n\005slack\030\004 \001(\010H" +
+      "\002\022<\n\021threading_options\030\005 \001(\0132!.tensorflo" +
+      "w.data.ThreadingOptions\022E\n\025external_stat" +
+      "e_policy\030\006 \001(\0162$.tensorflow.data.Externa" +
+      "lStatePolicyH\003\022\035\n\023symbolic_checkpoint\030\010 " +
+      "\001(\010H\004\022\024\n\nwarm_start\030\t \001(\010H\005B\027\n\025optional_" +
+      "dataset_nameB\030\n\026optional_deterministicB\020" +
+      "\n\016optional_slackB \n\036optional_external_st" +
+      "ate_policyB\036\n\034optional_symbolic_checkpoi" +
+      "ntB\025\n\023optional_warm_start*K\n\017AutoShardPo" +
+      "licy\022\010\n\004AUTO\020\000\022\010\n\004FILE\020\001\022\010\n\004DATA\020\002\022\010\n\004HI" +
+      "NT\020\003\022\020\n\003OFF\020\377\377\377\377\377\377\377\377\377\001*J\n\023ExternalStateP" +
+      "olicy\022\017\n\013POLICY_WARN\020\000\022\021\n\rPOLICY_IGNORE\020" +
+      "\001\022\017\n\013POLICY_FAIL\020\002Bs\n\031org.tensorflow.pro" +
+      "to.dataZVgithub.com/tensorflow/tensorflo" +
+      "w/tensorflow/go/core/framework/dataset_o" +
+      "ptions_go_protob\006proto3"
     };
     descriptor = com.google.protobuf.Descriptors.FileDescriptor
       .internalBuildGeneratedFileFrom(descriptorData,
@@ -9136,7 +10162,7 @@ public org.tensorflow.proto.data.DatasetOptions.Options getDefaultInstanceForTyp
     internal_static_tensorflow_data_AutotuneOptions_fieldAccessorTable = new
       com.google.protobuf.GeneratedMessageV3.FieldAccessorTable(
         internal_static_tensorflow_data_AutotuneOptions_descriptor,
-        new java.lang.String[] { "Enabled", "CpuBudget", "RamBudget", "AutotuneAlgorithm", "OptionalEnabled", "OptionalCpuBudget", "OptionalRamBudget", "OptionalAutotuneAlgorithm", });
+        new java.lang.String[] { "Enabled", "CpuBudget", "RamBudget", "AutotuneAlgorithm", "InitialParallelism", "OptionalEnabled", "OptionalCpuBudget", "OptionalRamBudget", "OptionalAutotuneAlgorithm", "OptionalInitialParallelism", });
     internal_static_tensorflow_data_CardinalityOptions_descriptor =
       getDescriptor().getMessageTypes().get(1);
     internal_static_tensorflow_data_CardinalityOptions_fieldAccessorTable = new
@@ -9155,18 +10181,24 @@ public org.tensorflow.proto.data.DatasetOptions.Options getDefaultInstanceForTyp
       com.google.protobuf.GeneratedMessageV3.FieldAccessorTable(
         internal_static_tensorflow_data_OptimizationOptions_descriptor,
         new java.lang.String[] { "ApplyDefaultOptimizations", "FilterFusion", "MapAndBatchFusion", "MapAndFilterFusion", "MapFusion", "MapParallelization", "NoopElimination", "ParallelBatch", "ShuffleAndRepeatFusion", "FilterParallelization", "InjectPrefetch", "SeqInterleavePrefetch", "OptionalApplyDefaultOptimizations", "OptionalFilterFusion", "OptionalMapAndBatchFusion", "OptionalMapAndFilterFusion", "OptionalMapFusion", "OptionalMapParallelization", "OptionalNoopElimination", "OptionalParallelBatch", "OptionalShuffleAndRepeatFusion", "OptionalFilterParallelization", "OptionalInjectPrefetch", "OptionalSeqInterleavePrefetch", });
-    internal_static_tensorflow_data_ThreadingOptions_descriptor =
+    internal_static_tensorflow_data_ServiceOptions_descriptor =
       getDescriptor().getMessageTypes().get(4);
+    internal_static_tensorflow_data_ServiceOptions_fieldAccessorTable = new
+      com.google.protobuf.GeneratedMessageV3.FieldAccessorTable(
+        internal_static_tensorflow_data_ServiceOptions_descriptor,
+        new java.lang.String[] { "Pinned", "OptionalPinned", });
+    internal_static_tensorflow_data_ThreadingOptions_descriptor =
+      getDescriptor().getMessageTypes().get(5);
     internal_static_tensorflow_data_ThreadingOptions_fieldAccessorTable = new
       com.google.protobuf.GeneratedMessageV3.FieldAccessorTable(
         internal_static_tensorflow_data_ThreadingOptions_descriptor,
         new java.lang.String[] { "MaxIntraOpParallelism", "PrivateThreadpoolSize", "OptionalMaxIntraOpParallelism", "OptionalPrivateThreadpoolSize", });
     internal_static_tensorflow_data_Options_descriptor =
-      getDescriptor().getMessageTypes().get(5);
+      getDescriptor().getMessageTypes().get(6);
     internal_static_tensorflow_data_Options_fieldAccessorTable = new
       com.google.protobuf.GeneratedMessageV3.FieldAccessorTable(
         internal_static_tensorflow_data_Options_descriptor,
-        new java.lang.String[] { "DatasetName", "FrameworkType", "Deterministic", "AutotuneOptions", "DistributeOptions", "OptimizationOptions", "Slack", "ThreadingOptions", "ExternalStatePolicy", "SymbolicCheckpoint", "WarmStart", "OptionalDatasetName", "OptionalDeterministic", "OptionalSlack", "OptionalExternalStatePolicy", "OptionalSymbolicCheckpoint", "OptionalWarmStart", });
+        new java.lang.String[] { "DatasetName", "FrameworkType", "Deterministic", "AutotuneOptions", "DistributeOptions", "OptimizationOptions", "ServiceOptions", "Slack", "ThreadingOptions", "ExternalStatePolicy", "SymbolicCheckpoint", "WarmStart", "OptionalDatasetName", "OptionalDeterministic", "OptionalSlack", "OptionalExternalStatePolicy", "OptionalSymbolicCheckpoint", "OptionalWarmStart", });
     org.tensorflow.proto.data.model.Model.getDescriptor();
   }
 
diff --git a/tensorflow-core/tensorflow-core-native/src/gen/java/org/tensorflow/proto/data/experimental/ServiceConfig.java b/tensorflow-core/tensorflow-core-native/src/gen/java/org/tensorflow/proto/data/experimental/ServiceConfig.java
index 5d143f7c9f8..de029b2baa5 100644
--- a/tensorflow-core/tensorflow-core-native/src/gen/java/org/tensorflow/proto/data/experimental/ServiceConfig.java
+++ b/tensorflow-core/tensorflow-core-native/src/gen/java/org/tensorflow/proto/data/experimental/ServiceConfig.java
@@ -2261,7 +2261,11 @@ public interface WorkerConfigOrBuilder extends
 
     /**
      * 
    -     * The protocol for the worker to use when transferring data to clients.
+     * If set, the name of an alternative data transfer protocol for which the
+     * worker starts an additional server ("data transfer server"); the trainer
+     * can then get data from this server. If not set, no such server is started,
+     * and the trainer can only get data from the regular worker server over
+     * `protocol`.
      * 
    
      *
      * string data_transfer_protocol = 7;
@@ -2270,7 +2274,11 @@ public interface WorkerConfigOrBuilder extends
     java.lang.String getDataTransferProtocol();
     /**
      * 
    -     * The protocol for the worker to use when transferring data to clients.
+     * If set, the name of an alternative data transfer protocol for which the
+     * worker starts an additional server ("data transfer server"); the trainer
+     * can then get data from this server. If not set, no such server is started,
+     * and the trainer can only get data from the regular worker server over
+     * `protocol`.
      * 
    
      *
      * string data_transfer_protocol = 7;
@@ -2281,9 +2289,21 @@ public interface WorkerConfigOrBuilder extends
 
     /**
      * 
    -     * The data transfer address of the worker server. The substring "%port%", if
-     * specified, will be replaced with the worker's bound port. This is useful
-     * when the port is set to `0`.
+     * If `data_transfer_protocol` is set, the port to which the data transfer
+     * server binds. If set to `0`, the server binds to any available port.
+     * 
    
+     *
+     * int64 data_transfer_port = 13;
+     * @return The dataTransferPort.
+     */
+    long getDataTransferPort();
+
+    /**
+     * 
    +     * If `data_transfer_protocol` is set, the address of the data transfer
+     * server. The substring "%dts_port%" can be used to represent -- and is
+     * replaced with -- the bound port of the data transfer server; this is useful
+     * when `data_transfer_port` is set to `0`.
      * 
    
      *
      * string data_transfer_address = 8;
@@ -2292,9 +2312,10 @@ public interface WorkerConfigOrBuilder extends
     java.lang.String getDataTransferAddress();
     /**
      * 
    -     * The data transfer address of the worker server. The substring "%port%", if
-     * specified, will be replaced with the worker's bound port. This is useful
-     * when the port is set to `0`.
+     * If `data_transfer_protocol` is set, the address of the data transfer
+     * server. The substring "%dts_port%" can be used to represent -- and is
+     * replaced with -- the bound port of the data transfer server; this is useful
+     * when `data_transfer_port` is set to `0`.
      * 
    
      *
      * string data_transfer_address = 8;
@@ -2340,7 +2361,7 @@ public interface WorkerConfigOrBuilder extends
   /**
    * 
        * Configuration for a tf.data service WorkerServer.
-   * Next id: 13
+   * Next id: 14
    * 
    
    *
    * Protobuf type {@code tensorflow.data.experimental.WorkerConfig}
@@ -2646,7 +2667,11 @@ public long getDispatcherTimeoutMs() {
     private volatile java.lang.Object dataTransferProtocol_;
     /**
      * 
    -     * The protocol for the worker to use when transferring data to clients.
+     * If set, the name of an alternative data transfer protocol for which the
+     * worker starts an additional server ("data transfer server"); the trainer
+     * can then get data from this server. If not set, no such server is started,
+     * and the trainer can only get data from the regular worker server over
+     * `protocol`.
      * 
    
      *
      * string data_transfer_protocol = 7;
@@ -2667,7 +2692,11 @@ public java.lang.String getDataTransferProtocol() {
     }
     /**
      * 
    -     * The protocol for the worker to use when transferring data to clients.
+     * If set, the name of an alternative data transfer protocol for which the
+     * worker starts an additional server ("data transfer server"); the trainer
+     * can then get data from this server. If not set, no such server is started,
+     * and the trainer can only get data from the regular worker server over
+     * `protocol`.
      * 
    
      *
      * string data_transfer_protocol = 7;
@@ -2688,13 +2717,30 @@ public java.lang.String getDataTransferProtocol() {
       }
     }
 
+    public static final int DATA_TRANSFER_PORT_FIELD_NUMBER = 13;
+    private long dataTransferPort_;
+    /**
+     * 
    +     * If `data_transfer_protocol` is set, the port to which the data transfer
+     * server binds. If set to `0`, the server binds to any available port.
+     * 
    
+     *
+     * int64 data_transfer_port = 13;
+     * @return The dataTransferPort.
+     */
+    @java.lang.Override
+    public long getDataTransferPort() {
+      return dataTransferPort_;
+    }
+
     public static final int DATA_TRANSFER_ADDRESS_FIELD_NUMBER = 8;
     private volatile java.lang.Object dataTransferAddress_;
     /**
      * 
    -     * The data transfer address of the worker server. The substring "%port%", if
-     * specified, will be replaced with the worker's bound port. This is useful
-     * when the port is set to `0`.
+     * If `data_transfer_protocol` is set, the address of the data transfer
+     * server. The substring "%dts_port%" can be used to represent -- and is
+     * replaced with -- the bound port of the data transfer server; this is useful
+     * when `data_transfer_port` is set to `0`.
      * 
    
      *
      * string data_transfer_address = 8;
@@ -2715,9 +2761,10 @@ public java.lang.String getDataTransferAddress() {
     }
     /**
      * 
    -     * The data transfer address of the worker server. The substring "%port%", if
-     * specified, will be replaced with the worker's bound port. This is useful
-     * when the port is set to `0`.
+     * If `data_transfer_protocol` is set, the address of the data transfer
+     * server. The substring "%dts_port%" can be used to represent -- and is
+     * replaced with -- the bound port of the data transfer server; this is useful
+     * when `data_transfer_port` is set to `0`.
      * 
    
      *
      * string data_transfer_address = 8;
@@ -2837,6 +2884,9 @@ public void writeTo(com.google.protobuf.CodedOutputStream output)
       if (snapshotMaxChunkSizeBytes_ != 0L) {
         output.writeInt64(12, snapshotMaxChunkSizeBytes_);
       }
+      if (dataTransferPort_ != 0L) {
+        output.writeInt64(13, dataTransferPort_);
+      }
       getUnknownFields().writeTo(output);
     }
 
@@ -2893,6 +2943,10 @@ public int getSerializedSize() {
         size += com.google.protobuf.CodedOutputStream
           .computeInt64Size(12, snapshotMaxChunkSizeBytes_);
       }
+      if (dataTransferPort_ != 0L) {
+        size += com.google.protobuf.CodedOutputStream
+          .computeInt64Size(13, dataTransferPort_);
+      }
       size += getUnknownFields().getSerializedSize();
       memoizedSize = size;
       return size;
@@ -2924,6 +2978,8 @@ public boolean equals(final java.lang.Object obj) {
           != other.getDispatcherTimeoutMs()) return false;
       if (!getDataTransferProtocol()
           .equals(other.getDataTransferProtocol())) return false;
+      if (getDataTransferPort()
+          != other.getDataTransferPort()) return false;
       if (!getDataTransferAddress()
           .equals(other.getDataTransferAddress())) return false;
       if (getCrossTrainerCacheSizeBytes()
@@ -2964,6 +3020,9 @@ public int hashCode() {
           getDispatcherTimeoutMs());
       hash = (37 * hash) + DATA_TRANSFER_PROTOCOL_FIELD_NUMBER;
       hash = (53 * hash) + getDataTransferProtocol().hashCode();
+      hash = (37 * hash) + DATA_TRANSFER_PORT_FIELD_NUMBER;
+      hash = (53 * hash) + com.google.protobuf.Internal.hashLong(
+          getDataTransferPort());
       hash = (37 * hash) + DATA_TRANSFER_ADDRESS_FIELD_NUMBER;
       hash = (53 * hash) + getDataTransferAddress().hashCode();
       hash = (37 * hash) + CROSS_TRAINER_CACHE_SIZE_BYTES_FIELD_NUMBER;
@@ -3073,7 +3132,7 @@ protected Builder newBuilderForType(
     /**
      * 
          * Configuration for a tf.data service WorkerServer.
-     * Next id: 13
+     * Next id: 14
      * 
    
      *
      * Protobuf type {@code tensorflow.data.experimental.WorkerConfig}
@@ -3124,6 +3183,8 @@ public Builder clear() {
 
         dataTransferProtocol_ = "";
 
+        dataTransferPort_ = 0L;
+
         dataTransferAddress_ = "";
 
         crossTrainerCacheSizeBytes_ = 0L;
@@ -3171,6 +3232,7 @@ public org.tensorflow.proto.data.experimental.ServiceConfig.WorkerConfig buildPa
         result.heartbeatIntervalMs_ = heartbeatIntervalMs_;
         result.dispatcherTimeoutMs_ = dispatcherTimeoutMs_;
         result.dataTransferProtocol_ = dataTransferProtocol_;
+        result.dataTransferPort_ = dataTransferPort_;
         result.dataTransferAddress_ = dataTransferAddress_;
         result.crossTrainerCacheSizeBytes_ = crossTrainerCacheSizeBytes_;
         result.snapshotMaxChunkSizeBytes_ = snapshotMaxChunkSizeBytes_;
@@ -3258,6 +3320,9 @@ public Builder mergeFrom(org.tensorflow.proto.data.experimental.ServiceConfig.Wo
           dataTransferProtocol_ = other.dataTransferProtocol_;
           onChanged();
         }
+        if (other.getDataTransferPort() != 0L) {
+          setDataTransferPort(other.getDataTransferPort());
+        }
         if (!other.getDataTransferAddress().isEmpty()) {
           dataTransferAddress_ = other.dataTransferAddress_;
           onChanged();
@@ -3358,6 +3423,11 @@ public Builder mergeFrom(
 
                 break;
               } // case 96
+              case 104: {
+                dataTransferPort_ = input.readInt64();
+
+                break;
+              } // case 104
               default: {
                 if (!super.parseUnknownField(input, extensionRegistry, tag)) {
                   done = true; // was an endgroup tag
@@ -3990,7 +4060,11 @@ public Builder clearDispatcherTimeoutMs() {
       private java.lang.Object dataTransferProtocol_ = "";
       /**
        * 
    -       * The protocol for the worker to use when transferring data to clients.
+       * If set, the name of an alternative data transfer protocol for which the
+       * worker starts an additional server ("data transfer server"); the trainer
+       * can then get data from this server. If not set, no such server is started,
+       * and the trainer can only get data from the regular worker server over
+       * `protocol`.
        * 
    
        *
        * string data_transfer_protocol = 7;
@@ -4010,7 +4084,11 @@ public java.lang.String getDataTransferProtocol() {
       }
       /**
        * 
    -       * The protocol for the worker to use when transferring data to clients.
+       * If set, the name of an alternative data transfer protocol for which the
+       * worker starts an additional server ("data transfer server"); the trainer
+       * can then get data from this server. If not set, no such server is started,
+       * and the trainer can only get data from the regular worker server over
+       * `protocol`.
        * 
    
        *
        * string data_transfer_protocol = 7;
@@ -4031,7 +4109,11 @@ public java.lang.String getDataTransferProtocol() {
       }
       /**
        * 
    -       * The protocol for the worker to use when transferring data to clients.
+       * If set, the name of an alternative data transfer protocol for which the
+       * worker starts an additional server ("data transfer server"); the trainer
+       * can then get data from this server. If not set, no such server is started,
+       * and the trainer can only get data from the regular worker server over
+       * `protocol`.
        * 
    
        *
        * string data_transfer_protocol = 7;
@@ -4050,7 +4132,11 @@ public Builder setDataTransferProtocol(
       }
       /**
        * 
    -       * The protocol for the worker to use when transferring data to clients.
+       * If set, the name of an alternative data transfer protocol for which the
+       * worker starts an additional server ("data transfer server"); the trainer
+       * can then get data from this server. If not set, no such server is started,
+       * and the trainer can only get data from the regular worker server over
+       * `protocol`.
        * 
    
        *
        * string data_transfer_protocol = 7;
@@ -4064,7 +4150,11 @@ public Builder clearDataTransferProtocol() {
       }
       /**
        * 
    -       * The protocol for the worker to use when transferring data to clients.
+       * If set, the name of an alternative data transfer protocol for which the
+       * worker starts an additional server ("data transfer server"); the trainer
+       * can then get data from this server. If not set, no such server is started,
+       * and the trainer can only get data from the regular worker server over
+       * `protocol`.
        * 
    
        *
        * string data_transfer_protocol = 7;
@@ -4083,12 +4173,59 @@ public Builder setDataTransferProtocolBytes(
         return this;
       }
 
+      private long dataTransferPort_ ;
+      /**
+       * 
    +       * If `data_transfer_protocol` is set, the port to which the data transfer
+       * server binds. If set to `0`, the server binds to any available port.
+       * 
    
+       *
+       * int64 data_transfer_port = 13;
+       * @return The dataTransferPort.
+       */
+      @java.lang.Override
+      public long getDataTransferPort() {
+        return dataTransferPort_;
+      }
+      /**
+       * 
    +       * If `data_transfer_protocol` is set, the port to which the data transfer
+       * server binds. If set to `0`, the server binds to any available port.
+       * 
    
+       *
+       * int64 data_transfer_port = 13;
+       * @param value The dataTransferPort to set.
+       * @return This builder for chaining.
+       */
+      public Builder setDataTransferPort(long value) {
+        
+        dataTransferPort_ = value;
+        onChanged();
+        return this;
+      }
+      /**
+       * 
    +       * If `data_transfer_protocol` is set, the port to which the data transfer
+       * server binds. If set to `0`, the server binds to any available port.
+       * 
    
+       *
+       * int64 data_transfer_port = 13;
+       * @return This builder for chaining.
+       */
+      public Builder clearDataTransferPort() {
+        
+        dataTransferPort_ = 0L;
+        onChanged();
+        return this;
+      }
+
       private java.lang.Object dataTransferAddress_ = "";
       /**
        * 
    -       * The data transfer address of the worker server. The substring "%port%", if
-       * specified, will be replaced with the worker's bound port. This is useful
-       * when the port is set to `0`.
+       * If `data_transfer_protocol` is set, the address of the data transfer
+       * server. The substring "%dts_port%" can be used to represent -- and is
+       * replaced with -- the bound port of the data transfer server; this is useful
+       * when `data_transfer_port` is set to `0`.
        * 
    
        *
        * string data_transfer_address = 8;
@@ -4108,9 +4245,10 @@ public java.lang.String getDataTransferAddress() {
       }
       /**
        * 
    -       * The data transfer address of the worker server. The substring "%port%", if
-       * specified, will be replaced with the worker's bound port. This is useful
-       * when the port is set to `0`.
+       * If `data_transfer_protocol` is set, the address of the data transfer
+       * server. The substring "%dts_port%" can be used to represent -- and is
+       * replaced with -- the bound port of the data transfer server; this is useful
+       * when `data_transfer_port` is set to `0`.
        * 
    
        *
        * string data_transfer_address = 8;
@@ -4131,9 +4269,10 @@ public java.lang.String getDataTransferAddress() {
       }
       /**
        * 
    -       * The data transfer address of the worker server. The substring "%port%", if
-       * specified, will be replaced with the worker's bound port. This is useful
-       * when the port is set to `0`.
+       * If `data_transfer_protocol` is set, the address of the data transfer
+       * server. The substring "%dts_port%" can be used to represent -- and is
+       * replaced with -- the bound port of the data transfer server; this is useful
+       * when `data_transfer_port` is set to `0`.
        * 
    
        *
        * string data_transfer_address = 8;
@@ -4152,9 +4291,10 @@ public Builder setDataTransferAddress(
       }
       /**
        * 
    -       * The data transfer address of the worker server. The substring "%port%", if
-       * specified, will be replaced with the worker's bound port. This is useful
-       * when the port is set to `0`.
+       * If `data_transfer_protocol` is set, the address of the data transfer
+       * server. The substring "%dts_port%" can be used to represent -- and is
+       * replaced with -- the bound port of the data transfer server; this is useful
+       * when `data_transfer_port` is set to `0`.
        * 
    
        *
        * string data_transfer_address = 8;
@@ -4168,9 +4308,10 @@ public Builder clearDataTransferAddress() {
       }
       /**
        * 
    -       * The data transfer address of the worker server. The substring "%port%", if
-       * specified, will be replaced with the worker's bound port. This is useful
-       * when the port is set to `0`.
+       * If `data_transfer_protocol` is set, the address of the data transfer
+       * server. The substring "%dts_port%" can be used to represent -- and is
+       * replaced with -- the bound port of the data transfer server; this is useful
+       * when `data_transfer_port` is set to `0`.
        * 
    
        *
        * string data_transfer_address = 8;
@@ -4424,19 +4565,20 @@ public org.tensorflow.proto.data.experimental.ServiceConfig.WorkerConfig getDefa
       "s\030\006 \001(\003\022 \n\030gc_dynamic_sharding_jobs\030\013 \001(" +
       "\010\022\031\n\021client_timeout_ms\030\010 \001(\003\022\031\n\021worker_t" +
       "imeout_ms\030\n \001(\003\022\'\n\037worker_max_concurrent" +
-      "_snapshots\030\014 \001(\003\"\345\002\n\014WorkerConfig\022\014\n\004por" +
+      "_snapshots\030\014 \001(\003\"\201\003\n\014WorkerConfig\022\014\n\004por" +
       "t\030\001 \001(\003\022\020\n\010protocol\030\002 \001(\t\022\032\n\022dispatcher_" +
       "address\030\003 \001(\t\022\026\n\016worker_address\030\004 \001(\t\022\023\n" +
       "\013worker_tags\030\n \003(\t\022\035\n\025heartbeat_interval" +
       "_ms\030\005 \001(\003\022\035\n\025dispatcher_timeout_ms\030\006 \001(\003" +
-      "\022\036\n\026data_transfer_protocol\030\007 \001(\t\022\035\n\025data" +
-      "_transfer_address\030\010 \001(\t\022&\n\036cross_trainer" +
-      "_cache_size_bytes\030\013 \001(\003\022%\n\035snapshot_max_" +
-      "chunk_size_bytes\030\014 \001(\003\022 \n\030shutdown_quiet" +
-      "_period_ms\030\t \001(\003B\177\n&org.tensorflow.proto" +
-      ".data.experimentalZUgithub.com/tensorflo" +
-      "w/tensorflow/tensorflow/go/core/protobuf" +
-      "/for_core_protos_go_protob\006proto3"
+      "\022\036\n\026data_transfer_protocol\030\007 \001(\t\022\032\n\022data" +
+      "_transfer_port\030\r \001(\003\022\035\n\025data_transfer_ad" +
+      "dress\030\010 \001(\t\022&\n\036cross_trainer_cache_size_" +
+      "bytes\030\013 \001(\003\022%\n\035snapshot_max_chunk_size_b" +
+      "ytes\030\014 \001(\003\022 \n\030shutdown_quiet_period_ms\030\t" +
+      " \001(\003B\177\n&org.tensorflow.proto.data.experi" +
+      "mentalZUgithub.com/tensorflow/tensorflow" +
+      "/tensorflow/go/core/protobuf/for_core_pr" +
+      "otos_go_protob\006proto3"
     };
     descriptor = com.google.protobuf.Descriptors.FileDescriptor
       .internalBuildGeneratedFileFrom(descriptorData,
@@ -4454,7 +4596,7 @@ public org.tensorflow.proto.data.experimental.ServiceConfig.WorkerConfig getDefa
     internal_static_tensorflow_data_experimental_WorkerConfig_fieldAccessorTable = new
       com.google.protobuf.GeneratedMessageV3.FieldAccessorTable(
         internal_static_tensorflow_data_experimental_WorkerConfig_descriptor,
-        new java.lang.String[] { "Port", "Protocol", "DispatcherAddress", "WorkerAddress", "WorkerTags", "HeartbeatIntervalMs", "DispatcherTimeoutMs", "DataTransferProtocol", "DataTransferAddress", "CrossTrainerCacheSizeBytes", "SnapshotMaxChunkSizeBytes", "ShutdownQuietPeriodMs", });
+        new java.lang.String[] { "Port", "Protocol", "DispatcherAddress", "WorkerAddress", "WorkerTags", "HeartbeatIntervalMs", "DispatcherTimeoutMs", "DataTransferProtocol", "DataTransferPort", "DataTransferAddress", "CrossTrainerCacheSizeBytes", "SnapshotMaxChunkSizeBytes", "ShutdownQuietPeriodMs", });
     org.tensorflow.proto.data.DataService.getDescriptor();
   }
 
diff --git a/tensorflow-core/tensorflow-core-native/src/gen/java/org/tensorflow/proto/dummy/BfcMemoryMap.java b/tensorflow-core/tensorflow-core-native/src/gen/java/org/tensorflow/proto/dummy/BfcMemoryMap.java
index 9ddd1a3d74f..38f0ce96ef4 100644
--- a/tensorflow-core/tensorflow-core-native/src/gen/java/org/tensorflow/proto/dummy/BfcMemoryMap.java
+++ b/tensorflow-core/tensorflow-core-native/src/gen/java/org/tensorflow/proto/dummy/BfcMemoryMap.java
@@ -24,11 +24,11 @@ public static void registerAllExtensions(
   static {
     java.lang.String[] descriptorData = {
       "\n-tensorflow/core/protobuf/bfc_memory_ma" +
-      "p.proto\022\020tensorflow.dummy\032!tsl/protobuf/" +
-      "bfc_memory_map.protoBs\n\032org.tensorflow.p" +
-      "roto.dummyZUgithub.com/tensorflow/tensor" +
-      "flow/tensorflow/go/core/protobuf/for_cor" +
-      "e_protos_go_protoP\000b\006proto3"
+      "p.proto\022\020tensorflow.dummy\032%xla/tsl/proto" +
+      "buf/bfc_memory_map.protoBs\n\032org.tensorfl" +
+      "ow.proto.dummyZUgithub.com/tensorflow/te" +
+      "nsorflow/tensorflow/go/core/protobuf/for" +
+      "_core_protos_go_protoP\000b\006proto3"
     };
     descriptor = com.google.protobuf.Descriptors.FileDescriptor
       .internalBuildGeneratedFileFrom(descriptorData,
diff --git a/tensorflow-core/tensorflow-core-native/src/gen/java/org/tensorflow/proto/dummy/TestLog.java b/tensorflow-core/tensorflow-core-native/src/gen/java/org/tensorflow/proto/dummy/TestLog.java
index 7f4925fa6b5..95f0ab4c9c2 100644
--- a/tensorflow-core/tensorflow-core-native/src/gen/java/org/tensorflow/proto/dummy/TestLog.java
+++ b/tensorflow-core/tensorflow-core-native/src/gen/java/org/tensorflow/proto/dummy/TestLog.java
@@ -24,9 +24,9 @@ public static void registerAllExtensions(
   static {
     java.lang.String[] descriptorData = {
       "\n#tensorflow/core/util/test_log.proto\022\020t" +
-      "ensorflow.dummy\032\033tsl/protobuf/test_log.p" +
-      "rotoB\034\n\032org.tensorflow.proto.dummyP\000b\006pr" +
-      "oto3"
+      "ensorflow.dummy\032\037xla/tsl/protobuf/test_l" +
+      "og.protoB\034\n\032org.tensorflow.proto.dummyP\000" +
+      "b\006proto3"
     };
     descriptor = com.google.protobuf.Descriptors.FileDescriptor
       .internalBuildGeneratedFileFrom(descriptorData,
diff --git a/tensorflow-core/tensorflow-core-native/src/gen/resources/org/tensorflow/base_api/api_def_AssignVariableXlaConcatND.pbtxt b/tensorflow-core/tensorflow-core-native/src/gen/resources/org/tensorflow/base_api/api_def_AssignVariableXlaConcatND.pbtxt
index 646f602af22..6bd6bcd8d05 100644
--- a/tensorflow-core/tensorflow-core-native/src/gen/resources/org/tensorflow/base_api/api_def_AssignVariableXlaConcatND.pbtxt
+++ b/tensorflow-core/tensorflow-core-native/src/gen/resources/org/tensorflow/base_api/api_def_AssignVariableXlaConcatND.pbtxt
@@ -5,17 +5,13 @@ op {
     name: "resource"
     description: <

    Values in {@code arr} outside of the range [0, size) are ignored. * - * @param data type for {@code output} output * @param input 1D or 2D int {@code Tensor}. * @param sizeOutput non-negative int scalar {@code Tensor}. * @param weights is an int32, int64, float32, or float64 {@code Tensor} with the same @@ -900,7 +867,6 @@ public DenseBincount denseBincount(Ope * Computes Psi, the derivative of Lgamma (the log of the absolute value of * {@code Gamma(x)}), element-wise. * - * @param data type for {@code y} output * @param x The x value * @param data type for {@code Digamma} output and operands * @return a new instance of Digamma @@ -914,7 +880,6 @@ public Digamma digamma(Operand x) { * NOTE: {@code math.Div} supports broadcasting. More about broadcasting * here * - * @param data type for {@code z} output * @param x The x value * @param y The y value * @param data type for {@code Div} output and operands @@ -929,7 +894,6 @@ public Div div(Operand x, Operand y) { * NOTE: {@code math.DivNoNan} supports broadcasting. More about broadcasting * here * - * @param data type for {@code z} output * @param x The x value * @param y The y value * @param data type for {@code DivNoNan} output and operands @@ -966,7 +930,6 @@ public Equal equal(Operand x, Operand y, Equal.Options.. /** * Computes the Gauss error function of {@code x} element-wise. In statistics, for non-negative values of $x$, the error function has the following interpretation: for a random variable $Y$ that is normally distributed with mean 0 and variance $1/\sqrt{2}$, $erf(x)$ is the probability that $Y$ falls in the range $[−x, x]$. * - * @param data type for {@code y} output * @param x The x value * @param data type for {@code Erf} output and operands * @return a new instance of Erf @@ -978,7 +941,6 @@ public Erf erf(Operand x) { /** * Computes the complementary error function of {@code x} element-wise. * - * @param data type for {@code y} output * @param x The x value * @param data type for {@code Erfc} output and operands * @return a new instance of Erfc @@ -990,7 +952,6 @@ public Erfc erfc(Operand x) { /** * The Erfinv operation * - * @param data type for {@code y} output * @param x The x value * @param data type for {@code Erfinv} output and operands * @return a new instance of erfinv @@ -1023,7 +984,6 @@ public erfinv erfinv(Operand x) { * tf.math.exp(x) ==> 1.4686939399158851+2.2873552871788423j * * - * @param data type for {@code y} output * @param x The x value * @param data type for {@code Exp} output and operands * @return a new instance of Exp @@ -1047,7 +1007,6 @@ public Exp exp(Operand x) { * tf.math.expm1(x) ==> (0.46869393991588515+2.2873552871788423j) * * - * @param data type for {@code y} output * @param x The x value * @param data type for {@code Expm1} output and operands * @return a new instance of Expm1 @@ -1068,7 +1027,6 @@ public Fact fact() { /** * Returns element-wise largest integer not greater than x. * - * @param data type for {@code y} output * @param x The x value * @param data type for {@code Floor} output and operands * @return a new instance of Floor @@ -1082,7 +1040,6 @@ public Floor floor(Operand x) { * NOTE: {@code math.FloorDiv} supports broadcasting. More about broadcasting * here * - * @param data type for {@code z} output * @param x The x value * @param y The y value * @param data type for {@code FloorDiv} output and operands @@ -1100,7 +1057,6 @@ public FloorDiv floorDiv(Operand x, Operand y) { *