address comments

dpnp/dpnp_iface_linearalgebra.py

@@ -147,11 +147,11 @@ def dot(a, b, out=None):
         # TODO: use specific scalar-vector kernel
         return dpnp.multiply(a, b, out=out)
 
+    # numpy.dot does not allow casting even if it is safe
+    # casting="no" is used in the following
     if a_ndim == 1 and b_ndim == 1:
-        return dpnp_dot(a, b, out=out)
+        return dpnp_dot(a, b, out=out, casting="no")
 
-    # NumPy does not allow casting even if it is safe
-    # casting="no" is used in the following
     if a_ndim == 2 and b_ndim == 2:
         return dpnp.matmul(a, b, out=out, casting="no")
 
@@ -753,6 +753,7 @@ def matmul(
         Type to use in computing the matrix product. By default, the returned
         array will have data type that is determined by considering
         Promotion Type Rule and device capabilities.
+        Default: ``None``.
     casting : {"no", "equiv", "safe", "same_kind", "unsafe"}, optional
         Controls what kind of data casting may occur.
         Default: ``"same_kind"``.
@@ -1203,7 +1204,7 @@ def vecdot(
     .. math::
        \mathbf{a} \cdot \mathbf{b} = \sum_{i=0}^{n-1} \overline{a_i}b_i
 
-    where the sum is over the last dimension (unless axis is specified) and
+    where the sum is over the last dimension (unless `axis` is specified) and
     where :math:`\overline{a_i}` denotes the complex conjugate if :math:`a_i`
     is complex and the identity otherwise.
 
@@ -1221,16 +1222,17 @@ def vecdot(
         removed. If not provided or ``None``, a freshly-allocated array is
         used.
         Default: ``None``.
-    dtype : {None, dtype}, optional
-        Type to use in computing the vector dot product. By default, the
-        returned array will have data type that is determined by considering
-        Promotion Type Rule and device capabilities.
     casting : {"no", "equiv", "safe", "same_kind", "unsafe"}, optional
         Controls what kind of data casting may occur.
         Default: ``"same_kind"``.
     order : {"C", "F", "A", "K", None}, optional
         Memory layout of the newly output array, if parameter `out` is ``None``.
         Default: ``"K"``.
+    dtype : {None, dtype}, optional
+        Type to use in computing the vector dot product. By default, the
+        returned array will have data type that is determined by considering
+        Promotion Type Rule and device capabilities.
+        Default: ``None``.
     axes : {None, list of tuples}, optional
         A list of tuples with indices of axes the matrix product should operate
         on. For instance, for the signature of ``(i),(i)->()``, the base

dpnp/dpnp_utils/dpnp_utils_linearalgebra.py

@@ -189,7 +189,7 @@ def _define_contig_flag(x):
 
 def _define_dim_flags(x, axis):
     """
-    Define useful flags for the main calculation in dpnp_matmul.
+    Define useful flags for the calculations in dpnp_matmul and dpnp_vecdot.
     x_is_1D: `x` is 1D array or inherently 1D (all dimensions are equal to one
     except for one of them), for instance, if x.shape = (1, 1, 1, 2),
     then x_is_1D = True
@@ -220,7 +220,7 @@ def _define_dim_flags(x, axis):
     return x_is_2D, x_is_1D, x_base_is_1D
 
 
-def _get_result_shape(x1, x2, out, func, np_flag):
+def _get_result_shape(x1, x2, out, _get_result_shape_fn, np_flag):
     """
     Three task are completed in this function:
         - Get the shape of the result array.
@@ -239,15 +239,7 @@ def _get_result_shape(x1, x2, out, func, np_flag):
             "The second input array does not have enough dimensions (has 0, but requires at least 1)"
         )
 
-    if func == "matmul":
-        x1, x2, result_shape = _get_result_shape_matmul(
-            x1, x2, x1_ndim, x2_ndim
-        )
-    else:  # func == "vecdot"
-        assert func == "vecdot"
-        x1, x2, result_shape = _get_result_shape_vecdot(
-            x1, x2, x1_ndim, x2_ndim
-        )
+    x1, x2, result_shape = _get_result_shape_fn(x1, x2, x1_ndim, x2_ndim)
 
     if out is not None:
         out_shape = out.shape
@@ -474,7 +466,7 @@ def _shape_error(shape1, shape2, func, err_msg):
     elif func == "vecdot":
         signature = "(n?,),(n?,)->()"
     else:
-        # applicable when err_msg == 3
+        # applicable when err_msg == 2
         assert func is None
 
     if err_msg == 0:
@@ -655,7 +647,7 @@ def dpnp_cross(a, b, cp):
     return cp
 
 
-def dpnp_dot(a, b, /, out=None, *, conjugate=False):
+def dpnp_dot(a, b, /, out=None, *, casting="same_kind", conjugate=False):
     """
     Return the dot product of two arrays.
 
@@ -717,8 +709,7 @@ def dpnp_dot(a, b, /, out=None, *, conjugate=False):
     if dot_dtype != res_dtype:
         result = result.astype(res_dtype, copy=False)
 
-    # numpy.dot does not allow casting even if it is safe
-    return dpnp.get_result_array(result, out, casting="no")
+    return dpnp.get_result_array(result, out, casting=casting)
 
 
 def dpnp_kron(a, b, a_ndim, b_ndim):
@@ -773,8 +764,10 @@ def dpnp_matmul(
             order = "F"
         else:
             order = "C"
-
-    if order in "kK":
+    elif order in "kK":
+        # For order="K", we return order="C" to align with NumPy behavior
+        # It is different than logic used in dpnp_vecdot because NumPy
+        # behaves differently for matmul and vecdot
         order = "C"
 
     x1_ndim = x1.ndim
@@ -806,7 +799,7 @@ def dpnp_matmul(
     )
 
     x1, x2, result_shape = _get_result_shape(
-        x1, x2, out, "matmul", NumPy_special_behavior
+        x1, x2, out, _get_result_shape_matmul, NumPy_special_behavior
     )
 
     # Determine the appropriate data types
@@ -1000,6 +993,9 @@ def dpnp_vecdot(
     _validate_out_array(out, exec_q)
 
     if order in "aAkK":
+        # This logic is also used for order="K" to align with NumPy behavior.
+        # It is different than logic used in dpnp_matmul because NumPy
+        # behaves differently for matmul and vecdot
         if x1.flags.fnc and x2.flags.fnc:
             order = "F"
         else:
@@ -1035,7 +1031,7 @@ def dpnp_vecdot(
     )
 
     x1, x2, result_shape = _get_result_shape(
-        x1, x2, out, "vecdot", NumPy_special_behavior
+        x1, x2, out, _get_result_shape_vecdot, NumPy_special_behavior
     )
 
     # Determine the appropriate data types
@@ -1047,21 +1043,7 @@ def dpnp_vecdot(
     _, x2_is_1D, _ = _define_dim_flags(x2, axis=-1)
 
     if x1.size == 0 or x2.size == 0:
-        order = "C" if order in "kK" else order
-        result = _create_result_array(
-            x1,
-            x2,
-            out,
-            shape=result_shape,
-            dtype=res_dtype,
-            usm_type=res_usm_type,
-            sycl_queue=exec_q,
-            order=order,
-        )
-        if numpy.prod(result_shape) == 0:
-            return result
-        result.fill(0)
-        return result
+        call_flag = "trivial"
     elif x1_is_1D and x2_is_1D:
         call_flag = "dot"
         # arrays are inehrently 1D, make them 1D
@@ -1072,7 +1054,20 @@ def dpnp_vecdot(
         call_flag = "vecdot"
 
     # dispatch to proper function call
-    if call_flag == "dot":
+    if call_flag == "trivial":
+        result = _create_result_array(
+            x1,
+            x2,
+            out,
+            shape=result_shape,
+            dtype=res_dtype,
+            usm_type=res_usm_type,
+            sycl_queue=exec_q,
+            order=order,
+        )
+        if numpy.prod(result_shape) != 0:
+            result.fill(0)
+    elif call_flag == "dot":
         if out is not None and out.shape != ():
             result = dpnp_dot(x1, x2, out=None, conjugate=True)
         else:

tests/test_product.py

@@ -1525,6 +1525,27 @@ def test_order(self, order1, order2, order, shape):
         assert result.flags.f_contiguous == expected.flags.f_contiguous
         assert_dtype_allclose(result, expected)
 
+    @pytest.mark.parametrize("order", ["C", "F", "K", "A"])
+    @pytest.mark.parametrize(
+        "shape", [(2, 4, 0), (4, 0, 5)], ids=["(2, 4, 0)", "(4, 0, 5)"]
+    )
+    def test_order_trivial(self, order, shape):
+        # input is both c-contiguous and f-contiguous
+        a = numpy.ones(shape)
+        a_dp = dpnp.asarray(a)
+
+        result = dpnp.vecdot(a_dp, a_dp, order=order)
+        expected = numpy.vecdot(a, a, order=order)
+        if shape == (2, 4, 0) and order == "A":
+            # NumPy does not behave correctly for this case, for order="A",
+            # if input is both c- and f-contiguous, output is c-contiguous
+            assert result.flags.c_contiguous
+            assert not result.flags.f_contiguous
+        else:
+            assert result.flags.c_contiguous == expected.flags.c_contiguous
+            assert result.flags.f_contiguous == expected.flags.f_contiguous
+        assert_dtype_allclose(result, expected)
+
     @pytest.mark.parametrize(
         "order1, order2, out_order",
         [
@@ -1538,7 +1559,7 @@ def test_order(self, order1, order2, order, shape):
             ("F", "F", "C"),
         ],
     )
-    def test_out(self, order1, order2, out_order):
+    def test_out_order(self, order1, order2, out_order):
         a1 = numpy.arange(20).reshape(5, 4, order=order1)
         a2 = numpy.arange(20).reshape(5, 4, order=order2)
 
@@ -1555,6 +1576,34 @@ def test_out(self, order1, order2, out_order):
         assert result.flags.f_contiguous == expected.flags.f_contiguous
         assert_dtype_allclose(result, expected)
 
+    @pytest.mark.parametrize("dtype1", get_all_dtypes(no_none=True))
+    @pytest.mark.parametrize("dtype2", get_all_dtypes(no_none=True))
+    @pytest.mark.parametrize(
+        "shape_pair",
+        [
+            ((4,), ()),
+            ((1, 1, 4), (1, 1)),
+            ((6, 7, 4, 3), (6, 7, 4)),
+            ((2, 0), (2,)),  # zero-size inputs, 1D output
+            ((3, 0, 4), (3, 0)),  # zero-size output
+        ],
+    )
+    def test_out_dtype(self, dtype1, dtype2, shape_pair):
+        shape1, shape2 = shape_pair
+        a = numpy.ones(shape1, dtype=dtype1)
+        b = dpnp.asarray(a)
+
+        out_np = numpy.empty(shape2, dtype=dtype2)
+        out_dp = dpnp.asarray(out_np)
+
+        if dpnp.can_cast(dtype1, dtype2, casting="same_kind"):
+            result = dpnp.vecdot(b, b, out=out_dp)
+            expected = numpy.vecdot(a, a, out=out_np)
+            assert_dtype_allclose(result, expected)
+        else:
+            with pytest.raises(TypeError):
+                dpnp.vecdot(b, b, out=out_dp)
+
     @pytest.mark.parametrize(
         "out_shape",
         [