Simplify lu_factor by moving logic to dpnp_lu_factor

IntelPython · vlad-perevezentsev · Aug 29, 2025 · Aug 5, 2025 · Aug 5, 2025 · Aug 13, 2025
commit 02b645e8500fb7635756a2f01929077a08e10e4a
@@ -1009,7 +1009,7 @@ def _is_empty_2d(arr):
     return arr.size == 0 and numpy.prod(arr.shape[-2:]) == 0
 
 
-def _lu_factor(a, res_type, scipy=False, overwrite_a=False):
+def _lu_factor(a, res_type):
     """
     Compute pivoted LU decomposition.
 
@@ -1050,41 +1050,18 @@ def _lu_factor(a, res_type, scipy=False, overwrite_a=False):
 
     a_usm_arr = dpnp.get_usm_ndarray(a)
 
-    if not scipy:
-        # Internal use case (e.g., det(), slogdet()). Always copy.
-        # `a` must be copied because getrf destroys the input matrix
-        a_h = dpnp.empty_like(a, order="C", dtype=res_type)
+    # `a` must be copied because getrf destroys the input matrix
+    a_h = dpnp.empty_like(a, order="C", dtype=res_type)
 
-        # use DPCTL tensor function to fill the сopy of the input array
-        # from the input array
-        ht_ev, copy_ev = ti._copy_usm_ndarray_into_usm_ndarray(
-            src=a_usm_arr,
-            dst=a_h.get_array(),
-            sycl_queue=a_sycl_queue,
-            depends=_manager.submitted_events,
-        )
-        _manager.add_event_pair(ht_ev, copy_ev)
-
-    else:
-        # SciPy-compatible behavior
-        # Copy is required if:
-        # - overwrite_a is False (always copy),
-        # - dtype mismatch,
-        # - not F-contiguous,
-        # - not writeable
-        if not overwrite_a or _is_copy_required(a, res_type):
-            a_h = dpnp.empty_like(a, order="F", dtype=res_type)
-            ht_ev, copy_ev = ti._copy_usm_ndarray_into_usm_ndarray(
-                src=a_usm_arr,
-                dst=a_h.get_array(),
-                sycl_queue=a_sycl_queue,
-                depends=_manager.submitted_events,
-            )
-            _manager.add_event_pair(ht_ev, copy_ev)
-        else:
-            # input is suitable for in-place modification
-            a_h = a
-            copy_ev = None
+    # use DPCTL tensor function to fill the сopy of the input array
+    # from the input array
+    ht_ev, copy_ev = ti._copy_usm_ndarray_into_usm_ndarray(
+        src=a_usm_arr,
+        dst=a_h.get_array(),
+        sycl_queue=a_sycl_queue,
+        depends=_manager.submitted_events,
+    )
+    _manager.add_event_pair(ht_ev, copy_ev)
 
     ipiv_h = dpnp.empty(
         n,
@@ -1102,55 +1079,20 @@ def _lu_factor(a, res_type, scipy=False, overwrite_a=False):
         a_h.get_array(),
         ipiv_h.get_array(),
         dev_info_h,
-        depends=[copy_ev] if copy_ev is not None else [],
+        depends=[copy_ev],
     )
     _manager.add_event_pair(ht_ev, getrf_ev)
 
-    # Return list if called in SciPy-compatible mode
-    # else dpnp.ndarray
-    if scipy:
-        dev_info_array = dev_info_h
-    else:
-        dev_info_array = dpnp.array(
-            dev_info_h, usm_type=a_usm_type, sycl_queue=a_sycl_queue
-        )
+    dev_info_array = dpnp.array(
+        dev_info_h, usm_type=a_usm_type, sycl_queue=a_sycl_queue
+    )
 
     # Return a tuple containing the factorized matrix 'a_h',
     # pivot indices 'ipiv_h'
     # and the status 'dev_info_h' from the LAPACK getrf call
     return (a_h, ipiv_h, dev_info_array)
 
 
-def dpnp_lu_factor(a, overwrite_a=False, check_finite=True):
-    """Compute pivoted LU decomposition."""
-
-    res_type = _common_type(a)
-
-    # accommodate empty arrays
-    if a.size == 0:
-        lu = dpnp.empty_like(a)
-        piv = dpnp.arange(0, dtype=dpnp.int32)
-        return lu, piv
-
-    if check_finite:
-        if not dpnp.isfinite(a).all():
-            raise ValueError("array must not contain infs or NaNs")
-
-    lu, piv, dev_info = _lu_factor(
-        a, res_type, scipy=True, overwrite_a=overwrite_a
-    )
-
-    if any(dev_info):
-        diag_nums = ", ".join(str(v) for v in dev_info if v > 0)
-        warn(
-            f"Diagonal number {diag_nums} are exactly zero. Singular matrix.",
-            RuntimeWarning,
-            stacklevel=2,
-        )
-
-    return lu, piv
-
-
 def _multi_dot(arrays, order, i, j, out=None):
     """Actually do the multiplication with the given order."""