compare-shape

Author: Vahid Tavanashad

dpnp/dpnp_iface_linearalgebra.py

@@ -1113,13 +1113,13 @@ def vdot(a, b):
         if b.size != 1:
             raise ValueError("The second array should be of size one.")
         a_conj = numpy.conj(a)
-        return dpnp.multiply(a_conj, b)
+        return dpnp.squeeze(dpnp.multiply(a_conj, b))
 
     if dpnp.isscalar(b):
         if a.size != 1:
             raise ValueError("The first array should be of size one.")
         a_conj = dpnp.conj(a)
-        return dpnp.multiply(a_conj, b)
+        return dpnp.squeeze(dpnp.multiply(a_conj, b))
 
     if a.ndim == 1 and b.ndim == 1:
         return dpnp_dot(a, b, out=None, conjugate=True)

dpnp/dpnp_utils/dpnp_utils_linearalgebra.py

@@ -977,7 +977,7 @@ def dpnp_matmul(
                 result = dpnp.moveaxis(result, (-2, -1), axes_res)
             elif len(axes_res) == 1:
                 result = dpnp.moveaxis(result, (-1,), axes_res)
-            return dpnp.ascontiguousarray(result)
+            return result
 
         return dpnp.asarray(result, order=order)
 

dpnp/tests/helper.py

@@ -36,6 +36,12 @@ def assert_dtype_allclose(
 
     """
 
+    if isinstance(numpy_arr, numpy.ndarray):
+        assert dpnp_arr.shape == numpy_arr.shape
+    else:
+        # numpy output is scalar, then dpnp is 0-D array
+        assert dpnp_arr.shape == ()
+
     list_64bit_types = [numpy.float64, numpy.complex128]
     is_inexact = lambda x: hasattr(x, "dtype") and dpnp.issubdtype(
         x.dtype, dpnp.inexact

dpnp/tests/test_mathematical.py

@@ -3030,10 +3030,13 @@ def test_matmul_strided1(self, stride):
             expected = numpy.matmul(a, a)
             assert_dtype_allclose(result, expected)
 
-            OUT = dpnp.empty(shape, dtype=result.dtype)
+            OUT = numpy.empty(shape, dtype=result.dtype)
             out = OUT[slices]
-            result = dpnp.matmul(a_dp, a_dp, out=out)
-            assert result is out
+            iOUT = dpnp.array(OUT)
+            iout = iOUT[slices]
+            result = dpnp.matmul(a_dp, a_dp, out=iout)
+            assert result is iout
+            expected = numpy.matmul(a, a, out=out)
             assert_dtype_allclose(result, expected)
 
     @pytest.mark.parametrize(

dpnp/tests/test_nanfunctions.py

@@ -122,6 +122,10 @@ def test_allnans(self, dtype, array):
 
         result = getattr(dpnp, self.func)(ia)
         expected = getattr(numpy, self.func)(a)
+        if array.shape == ():
+            # for "0d" case, dpnp returns 0D array, numpy returns 1D array
+            # with one element. dpnp result is correct based on Array API
+            expected = numpy.squeeze(expected)
         assert_dtype_allclose(result, expected)
 
     @pytest.mark.parametrize("axis", [None, 0, 1])