Review comments

Author: AlexanderKalistratov

dpnp/dpnp_iface_manipulation.py

@@ -958,6 +958,9 @@ def atleast_1d(*arys):
     dpnp.check_supported_arrays_type(*arys)
     for ary in arys:
         if ary.ndim == 0:
+            # Scalars cannot be empty
+            # Scalars always have a size of 1, so
+            # reshape(1) is guaranteed to succeed
             result = ary.reshape(1)
         else:
             result = ary

dpnp/dpnp_iface_statistics.py

@@ -392,10 +392,9 @@ def convolve(a, v, mode="full", method="auto"):
     Parameters
     ----------
     a : {dpnp.ndarray, usm_ndarray}
-        First 1-D array.
+        First input array.
     v : {dpnp.ndarray, usm_ndarray}
-        Second 1-D array. The length of `v` must be less than or equal to
-        the length of `a`.
+        Second input array.
     mode : {'full', 'valid', 'same'}, optional
         - 'full': This returns the convolution
           at each point of overlap, with an output shape of (N+M-1,). At
@@ -475,7 +474,7 @@ def convolve(a, v, mode="full", method="auto"):
 
     """
 
-    dpnp.check_supported_arrays_type(a, v)
+    a, v = dpnp.atleast_1d(a, v)
 
     if a.size == 0 or v.size == 0:
         raise ValueError(
@@ -488,11 +487,6 @@ def convolve(a, v, mode="full", method="auto"):
             f"Received shapes: a.shape={a.shape}, v.shape={v.shape}"
         )
 
-    if a.ndim == 0:
-        a = dpnp.reshape(a, (1,))
-    if v.ndim == 0:
-        v = dpnp.reshape(v, (1,))
-
     device = a.sycl_device
     rdtype = result_type_for_device([a.dtype, v.dtype], device)
 

dpnp/tests/test_statistics.py

@@ -140,9 +140,6 @@ def _get_kwargs(mode=None, method=None):
             dpnp_kwargs["method"] = method
         return dpnp_kwargs, numpy_kwargs
 
-    def setup_method(self):
-        numpy.random.seed(0)
-
     @pytest.mark.parametrize(
         "a, v", [([1], [1, 2, 3]), ([1, 2, 3], [1]), ([1, 2, 3], [1, 2])]
     )
@@ -171,10 +168,10 @@ def test_convolve_random(self, a_size, v_size, mode, dtype, method):
         if dtype in [numpy.int8, numpy.uint8, numpy.int16, numpy.uint16]:
             pytest.skip("avoid overflow.")
         an = generate_random_numpy_array(
-            a_size, dtype, low=-3, high=3, probability=0.9
+            a_size, dtype, low=-3, high=3, probability=0.9, seed_value=0
         )
         vn = generate_random_numpy_array(
-            v_size, dtype, low=-3, high=3, probability=0.9
+            v_size, dtype, low=-3, high=3, probability=0.9, seed_value=1
         )
 
         ad = dpnp.array(an)
@@ -282,8 +279,12 @@ def test_convolve_shape_error(self, a, v):
 
     @pytest.mark.parametrize("size", [2, 10**1, 10**2, 10**3, 10**4, 10**5])
     def test_convolve_different_sizes(self, size):
-        a = generate_random_numpy_array(size, dtype=numpy.float32, low=0, high=1)
-        v = generate_random_numpy_array(size // 2, dtype=numpy.float32, low=0, high=1)
+        a = generate_random_numpy_array(
+            size, dtype=numpy.float32, low=0, high=1, seed_value=0
+        )
+        v = generate_random_numpy_array(
+            size // 2, dtype=numpy.float32, low=0, high=1, seed_value=1
+        )
 
         ad = dpnp.array(a)
         vd = dpnp.array(v)
@@ -375,9 +376,6 @@ def _get_kwargs(mode=None, method=None):
             dpnp_kwargs["method"] = method
         return dpnp_kwargs, numpy_kwargs
 
-    def setup_method(self):
-        numpy.random.seed(0)
-
     @pytest.mark.parametrize(
         "a, v", [([1], [1, 2, 3]), ([1, 2, 3], [1]), ([1, 2, 3], [1, 2])]
     )
@@ -406,10 +404,10 @@ def test_correlate_random(self, a_size, v_size, mode, dtype, method):
         if dtype in [numpy.int8, numpy.uint8, numpy.int16, numpy.uint16]:
             pytest.skip("avoid overflow.")
         an = generate_random_numpy_array(
-            a_size, dtype, low=-3, high=3, probability=0.9
+            a_size, dtype, low=-3, high=3, probability=0.9, seed_value=0
         )
         vn = generate_random_numpy_array(
-            v_size, dtype, low=-3, high=3, probability=0.9
+            v_size, dtype, low=-3, high=3, probability=0.9, seed_value=1
         )
 
         ad = dpnp.array(an)
@@ -505,8 +503,12 @@ def test_correlate_shape_error(self, a, v):
 
     @pytest.mark.parametrize("size", [2, 10**1, 10**2, 10**3, 10**4, 10**5])
     def test_correlate_different_sizes(self, size):
-        a = numpy.random.rand(size).astype(numpy.float32)
-        v = numpy.random.rand(size // 2).astype(numpy.float32)
+        a = generate_random_numpy_array(
+            size, dtype=numpy.float32, low=0, high=1, seed_value=0
+        )
+        v = generate_random_numpy_array(
+            size // 2, dtype=numpy.float32, low=0, high=1, seed_value=1
+        )
 
         ad = dpnp.array(a)
         vd = dpnp.array(v)

dpnp/tests/third_party/cupy/math_tests/test_misc.py

@@ -545,14 +545,14 @@ def test_convolve(self, xp, dtype):
 class TestConvolve:
 
     @testing.for_all_dtypes(no_float16=True)
-    @testing.numpy_cupy_allclose(rtol=1e-6, type_check=has_support_aspect64())
+    @testing.numpy_cupy_allclose(rtol=1e-6)
     def test_convolve_non_contiguous(self, xp, dtype, mode):
         a = testing.shaped_arange((300,), xp, dtype)
         b = testing.shaped_arange((100,), xp, dtype)
         return xp.convolve(a[::200], b[10::70], mode=mode)
 
     @testing.for_all_dtypes(no_float16=True)
-    @testing.numpy_cupy_allclose(rtol=5e-4, type_check=has_support_aspect64())
+    @testing.numpy_cupy_allclose(rtol=5e-4)
     def test_convolve_large_non_contiguous(self, xp, dtype, mode):
         a = testing.shaped_arange((10000,), xp, dtype)
         b = testing.shaped_arange((100,), xp, dtype)