remove temporary workaround for dpnp.prod

dpnp/dpnp_iface_mathematical.py

@@ -39,7 +39,6 @@
 
 
 import dpctl.tensor as dpt
-import dpctl.utils as du
 import numpy
 from numpy.core.numeric import (
     normalize_axis_index,
@@ -2266,32 +2265,7 @@ def prod(
 
     """
 
-    # Product reduction for complex output are known to fail for Gen9 with 2024.0 compiler
-    # TODO: get rid of this temporary work around when OneAPI 2024.1 is released
-    dpnp.check_supported_arrays_type(a)
-    _dtypes = (a.dtype, dtype)
-    _any_complex = any(
-        dpnp.issubdtype(dt, dpnp.complexfloating) for dt in _dtypes
-    )
-    device_mask = (
-        du.intel_device_info(a.sycl_device).get("device_id", 0) & 0xFF00
-    )
-    if _any_complex and device_mask in [0x3E00, 0x9B00]:
-        res = call_origin(
-            numpy.prod,
-            a,
-            axis=axis,
-            dtype=dtype,
-            out=out,
-            keepdims=keepdims,
-            initial=initial,
-            where=where,
-        )
-        if dpnp.isscalar(res):
-            # numpy may return a scalar, convert it back to dpnp array
-            return dpnp.array(res, sycl_queue=a.sycl_queue, usm_type=a.usm_type)
-        return res
-    elif initial is not None:
+    if initial is not None:
         raise NotImplementedError(
             "initial keyword argument is only supported with its default value."
         )

tests/test_linalg.py

@@ -229,9 +229,6 @@ def test_cond(arr, p):
 
 
 class TestDet:
-    # TODO: Remove the use of fixture for test_det
-    # when dpnp.prod() will support complex dtypes on Gen9
-    @pytest.mark.usefixtures("allow_fall_back_on_numpy")
     @pytest.mark.parametrize(
         "array",
         [
@@ -1054,9 +1051,6 @@ def test_solve_errors(self):
 
 
 class TestSlogdet:
-    # TODO: Remove the use of fixture for test_slogdet_2d and test_slogdet_3d
-    # when dpnp.prod() will support complex dtypes on Gen9
-    @pytest.mark.usefixtures("allow_fall_back_on_numpy")
     @pytest.mark.parametrize("dtype", get_all_dtypes(no_bool=True))
     def test_slogdet_2d(self, dtype):
         a_np = numpy.array([[1, 2], [3, 4]], dtype=dtype)
@@ -1068,7 +1062,6 @@ def test_slogdet_2d(self, dtype):
         assert_allclose(sign_expected, sign_result)
         assert_allclose(logdet_expected, logdet_result, rtol=1e-3, atol=1e-4)
 
-    @pytest.mark.usefixtures("allow_fall_back_on_numpy")
     @pytest.mark.parametrize("dtype", get_all_dtypes(no_bool=True))
     def test_slogdet_3d(self, dtype):
         a_np = numpy.array(

tests/test_mathematical.py

@@ -752,7 +752,6 @@ def test_positive_boolean():
 
 
 class TestProd:
-    @pytest.mark.usefixtures("allow_fall_back_on_numpy")
     @pytest.mark.parametrize("func", ["prod", "nanprod"])
     @pytest.mark.parametrize("axis", [None, 0, 1, -1, 2, -2, (1, 2), (0, -2)])
     @pytest.mark.parametrize("keepdims", [False, True])
@@ -790,7 +789,6 @@ def test_prod_nanprod_bool(self, func, axis, keepdims):
         dpnp_res = getattr(dpnp, func)(ia, axis=axis, keepdims=keepdims)
         assert_dtype_allclose(dpnp_res, np_res)
 
-    @pytest.mark.usefixtures("allow_fall_back_on_numpy")
     @pytest.mark.usefixtures("suppress_complex_warning")
     @pytest.mark.usefixtures("suppress_invalid_numpy_warnings")
     @pytest.mark.parametrize("func", ["prod", "nanprod"])