unmute tests

Author: vtavana

tests/test_fft.py

@@ -13,14 +13,8 @@
     get_all_dtypes,
     get_complex_dtypes,
     get_float_dtypes,
-    is_cpu_device,
 )
 
-# aspects of default device:
-_def_device = dpctl.SyclQueue().sycl_device
-_def_dev_has_fp64 = _def_device.has_aspect_fp64
-is_gpu_with_fp64 = not is_cpu_device() and _def_dev_has_fp64
-
 
 # TODO: `assert_dtype_allclose` calls in this file have `check_only_type_kind=True`
 # since stock NumPy is currently used in public CI for code coverege which
@@ -498,13 +492,13 @@ def test_fft_1D(self, dtype, n, norm):
         # but dpnp return float32 if input is float32
         assert_dtype_allclose(result, expected, check_only_type_kind=True)
 
-    @pytest.mark.skipif(is_gpu_with_fp64, reason="MKLD17702")
     @pytest.mark.parametrize("dtype", get_complex_dtypes())
     @pytest.mark.parametrize("n", [None, 5, 20])
     @pytest.mark.parametrize("norm", ["forward", "backward", "ortho"])
     def test_fft_1D_complex(self, dtype, n, norm):
         x = dpnp.linspace(-1, 1, 11)
         a = dpnp.sin(x) + 1j * dpnp.cos(x)
+        a[0].imag = 0  # input should be complex Hermitian
         a = dpnp.asarray(a, dtype=dtype)
         a_np = dpnp.asnumpy(a)
 
@@ -525,29 +519,37 @@ def test_fft_1D_on_2D_array(self, dtype, n, axis, norm, order):
         expected = numpy.fft.irfft(a_np, n=n, axis=axis, norm=norm)
         assert_dtype_allclose(result, expected, check_only_type_kind=True)
 
-    @pytest.mark.skipif(is_gpu_with_fp64, reason="MKLD17702")
     @pytest.mark.parametrize("dtype", get_complex_dtypes())
     @pytest.mark.parametrize("n", [None, 5, 8])
     @pytest.mark.parametrize("axis", [0, 1, 2])
     @pytest.mark.parametrize("norm", ["forward", "backward", "ortho"])
     @pytest.mark.parametrize("order", ["C", "F"])
     def test_fft_1D_on_3D_array(self, dtype, n, axis, norm, order):
-        x1 = numpy.random.uniform(-10, 10, 24)
-        x2 = numpy.random.uniform(-10, 10, 24)
+        x1 = numpy.random.uniform(-10, 10, 36)
+        x2 = numpy.random.uniform(-10, 10, 36)
         a_np = numpy.array(x1 + 1j * x2, dtype=dtype).reshape(
-            2, 3, 4, order=order
+            3, 3, 4, order=order
         )
+        # each 1-D array of input should be complex Hermitian
+        a_np[0].imag = 0
+        a_np[:, 0, :].imag = 0
+        a_np[..., 0].imag = 0
+        a_np[..., -1].imag = 0  # Nyquist mode for 3rd dimension
+
+        a_np = numpy.array(x1, dtype=dtype).reshape(
+            2, 3, 4, order=order
+        )  # input should be complex Hermitian
         a = dpnp.asarray(a_np)
 
         result = dpnp.fft.irfft(a, n=n, axis=axis, norm=norm)
         expected = numpy.fft.irfft(a_np, n=n, axis=axis, norm=norm)
         assert_dtype_allclose(result, expected, check_only_type_kind=True)
 
-    @pytest.mark.skipif(is_gpu_with_fp64, reason="MKLD17702")
     @pytest.mark.parametrize("n", [None, 5, 20])
     def test_fft_usm_ndarray(self, n):
         x = dpt.linspace(-1, 1, 11)
         a = dpt.sin(x) + 1j * dpt.cos(x)
+        a[0] = dpt.sin(x[0])  # input should be complex Hermitian
         a_usm = dpt.asarray(a, dtype=dpt.complex64)
         a_np = dpt.asnumpy(a_usm)
         out_shape = n if n is not None else 2 * (a_usm.shape[0] - 1)
@@ -558,13 +560,13 @@ def test_fft_usm_ndarray(self, n):
         expected = numpy.fft.irfft(a_np, n=n)
         assert_dtype_allclose(result, expected, check_only_type_kind=True)
 
-    @pytest.mark.skipif(is_gpu_with_fp64, reason="MKLD17702")
     @pytest.mark.parametrize("dtype", get_complex_dtypes())
     @pytest.mark.parametrize("n", [None, 5, 20])
     @pytest.mark.parametrize("norm", ["forward", "backward", "ortho"])
     def test_fft_1D_out(self, dtype, n, norm):
         x = dpnp.linspace(-1, 1, 11)
         a = dpnp.sin(x) + 1j * dpnp.cos(x)
+        a[0].imag = 0  # input should be complex Hermitian
         a = dpnp.asarray(a, dtype=dtype)
         a_np = dpnp.asnumpy(a)
 

tests/test_umath.py

@@ -313,7 +313,7 @@ def test_large_values(self, dtype):
         assert_dtype_allclose(result, expected)
 
 
-class TestLogaddexp:
+class TestLogAddExp:
     @pytest.mark.parametrize("dtype", get_all_dtypes(no_complex=True))
     def test_logaddexp(self, dtype):
         np_array1, np_array2, expected = _get_numpy_arrays_2in_1out(