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from primitiv import optimizers as O
from primitiv import Optimizer, Parameter, Device, Graph, Shape
from primitiv import initializers as I
from primitiv import devices as D
from primitiv import functions as F
from primitiv import tensor_functions as tF
import unittest
import tempfile
import numpy as np
class TestAdam(Optimizer):
def __init__(self, alpha, beta1, beta2, eps):
super().__init__()
self.alpha_ = np.float32(alpha)
self.beta1_ = np.float32(beta1)
self.beta2_ = np.float32(beta2)
self.eps_ = np.float32(eps)
def configure_parameter(self, param):
for name in ("testadam-m1", "testadam-m2"):
if name not in param.stats:
param.add_stats(name, param.shape())
param.stats[name].reset(0)
def update_parameter(self, scale, param):
epoch = self.get_epoch() + 1
g = param.gradient
param.stats["testadam-m1"] = self.beta1_ * param.stats["testadam-m1"] + (1 - self.beta1_) * g
param.stats["testadam-m2"] = self.beta2_ * param.stats["testadam-m2"] + (1 - self.beta2_) * g * g
mm1 = param.stats["testadam-m1"] / (1 - self.beta1_ ** epoch)
mm2 = param.stats["testadam-m2"] / (1 - self.beta2_ ** epoch)
param.value -= (scale * self.alpha_) * mm1 / (tF.sqrt(mm2) + self.eps_)
def get_configs(self):
uint_configs = {}
float_configs = {
"TestAdam.alpha": self.alpha_,
"TestAdam.beta1": self.beta1_,
"TestAdam.beta2": self.beta2_,
"TestAdam.eps": self.eps_,
}
return uint_configs, float_configs
def set_configs(self, uint_configs, float_configs):
self.alpha_ = float_configs["TestAdam.alpha"]
self.beta1_ = float_configs["TestAdam.beta1"]
self.beta2_ = float_configs["TestAdam.beta2"]
self.eps_ = float_configs["TestAdam.eps"]
class TestException(Exception):
pass
class ExceptionOptimizer(Optimizer):
def configure_parameter(self, param):
raise TestException("configure_parameter")
def update_parameter(self, scale, param):
raise TestException("update_parameter")
def get_configs(self):
raise TestException("get_configs")
def set_configs(self, uint_configs, float_configs):
raise TestException("set_configs")
class IncompleteOptimizer(Optimizer):
pass
def train_func(optimizer):
dev = D.Naive(12345)
Device.set_default(dev)
g = Graph()
Graph.set_default(g)
pw1 = Parameter([8, 2], I.XavierUniform())
pb1 = Parameter([8], I.Constant(0))
pw2 = Parameter([1, 8], I.XavierUniform())
pb2 = Parameter([1], I.Constant(0))
optimizer.add(pw1, pb1, pw2, pb2)
input_data = [1, 1, 1, -1, -1, 1, -1, -1]
output_data = [1, -1, -1, 1]
for i in range(10):
g.clear()
x = F.raw_input(Shape([2], 4), input_data)
w1 = F.parameter(pw1)
b1 = F.parameter(pb1)
w2 = F.parameter(pw2)
b2 = F.parameter(pb2)
h = F.tanh(w1 @ x + b1)
y = w2 @ h + b2
t = F.raw_input(Shape([], 4), output_data)
diff = t - y
loss = F.batch.mean(diff * diff)
optimizer.reset_gradients()
loss.backward()
optimizer.update()
return [pw1.value.to_list(),
pb1.value.to_list(),
pw2.value.to_list(),
pb2.value.to_list()]
class PythonOptimizerTest(unittest.TestCase):
@classmethod
def setUpClass(cls):
pass
@classmethod
def tearDownClass(cls):
pass
def setUp(self):
self.t = TestAdam(alpha = 0.001, beta1 = 0.9, beta2 = 0.999, eps = 1e-8)
def tearDown(self):
pass
def test_pyoptimizer_get_set_config(self):
uint_configs, float_configs = Optimizer.get_configs(self.t)
self.assertAlmostEqual(uint_configs['Optimizer.epoch'], 0)
self.assertAlmostEqual(float_configs['TestAdam.alpha'], 0.001)
self.assertAlmostEqual(float_configs['TestAdam.beta1'], 0.9)
self.assertAlmostEqual(float_configs['TestAdam.beta2'], 0.999)
self.assertAlmostEqual(float_configs['TestAdam.eps'], 1e-8, places=10)
float_configs['TestAdam.beta1'] = 200
Optimizer.set_configs(self.t, uint_configs, float_configs)
self.assertEqual(self.t.beta1_, 200)
def test_pyoptimizer_parameter(self):
dev = D.Naive()
Device.set_default(dev)
pw1 = Parameter([8, 2], I.XavierUniform())
self.t.add(pw1)
self.assertIn("testadam-m1", pw1.stats)
self.assertIn("testadam-m2", pw1.stats)
def test_pyoptimizer_compare_with_cpp(self):
c_optimizer = O.Adam(alpha = 0.001, beta1 = 0.9, beta2 = 0.999, eps = 1e-8)
py_params = train_func(self.t)
c_params = train_func(c_optimizer)
py_uint_configs, py_float_configs = Optimizer.get_configs(self.t)
c_uint_configs, c_float_configs = c_optimizer.get_configs()
self.assertEqual(py_uint_configs["Optimizer.epoch"], c_uint_configs["Optimizer.epoch"])
self.assertEqual(py_float_configs["TestAdam.alpha"], c_float_configs["Adam.alpha"])
self.assertEqual(py_float_configs["TestAdam.beta1"], c_float_configs["Adam.beta1"])
self.assertEqual(py_float_configs["TestAdam.beta2"], c_float_configs["Adam.beta2"])
self.assertEqual(py_float_configs["TestAdam.eps"], c_float_configs["Adam.eps"])
self.assertEqual(py_float_configs["Optimizer.clip_threshold"], c_float_configs["Optimizer.clip_threshold"])
self.assertEqual(py_float_configs["Optimizer.l2_strength"], c_float_configs["Optimizer.l2_strength"])
self.assertEqual(py_float_configs["Optimizer.lr_scale"], c_float_configs["Optimizer.lr_scale"])
self.assertTrue(np.isclose(py_params[0], c_params[0]).all())
self.assertTrue(np.isclose(py_params[1], c_params[1]).all())
self.assertTrue(np.isclose(py_params[2], c_params[2]).all())
self.assertTrue(np.isclose(py_params[3], c_params[3]).all())
def test_pyoptimizer_loadsave(self):
t_loaded = TestAdam(alpha = 0, beta1 = 0, beta2 = 0, eps = 0)
self.assertEqual(t_loaded.alpha_, 0)
self.assertEqual(t_loaded.beta1_, 0)
self.assertEqual(t_loaded.beta2_, 0)
self.assertEqual(t_loaded.eps_, 0)
with tempfile.NamedTemporaryFile() as fp:
self.t.save(fp.name)
t_loaded.load(fp.name)
self.assertAlmostEqual(t_loaded.alpha_, 0.001)
self.assertAlmostEqual(t_loaded.beta1_, 0.9)
self.assertAlmostEqual(t_loaded.beta2_, 0.999)
self.assertAlmostEqual(t_loaded.eps_, 1e-8, places=10)
def test_pyoptimizer_propagate_exception(self):
dev = D.Naive()
Device.set_default(dev)
optimizer = ExceptionOptimizer()
p = Parameter()
with self.assertRaises(TestException) as ctx:
optimizer.add(p)
self.assertEqual(str(ctx.exception), "configure_parameter")
with self.assertRaises(TestException) as ctx:
optimizer.update()
self.assertEqual(str(ctx.exception), "update_parameter")
with self.assertRaises(TestException) as ctx:
Optimizer.get_configs(optimizer)
self.assertEqual(str(ctx.exception), "get_configs")
with self.assertRaises(TestException) as ctx:
Optimizer.set_configs(optimizer, {'Optimizer.epoch': 1},
{'Optimizer.clip_threshold': 0.0,
'Optimizer.lr_scale': 1.0,
'Optimizer.l2_strength': 0.0})
self.assertEqual(str(ctx.exception), "set_configs")
def test_pyoptimizer_not_implemented(self):
dev = D.Naive()
Device.set_default(dev)
optimizer = IncompleteOptimizer()
p = Parameter()
with self.assertRaises(NotImplementedError):
optimizer.add(p)
with self.assertRaises(NotImplementedError):
optimizer.update()
with self.assertRaises(NotImplementedError):
Optimizer.get_configs(optimizer)
with self.assertRaises(NotImplementedError):
Optimizer.set_configs(optimizer, {'Optimizer.epoch': 1},
{'Optimizer.clip_threshold': 0.0,
'Optimizer.lr_scale': 1.0,
'Optimizer.l2_strength': 0.0})