Support parameter out in dpnp.multiply()

Author: antonwolfy

dpnp/dpnp_array.py

@@ -211,7 +211,10 @@ def __ilshift__(self, other):
 
  # '__imatmul__',
  # '__imod__',
- # '__imul__',
+
+    def __imul__(self, other):
+        dpnp.multiply(self, other, out=self)
+        return self
 
     def __index__(self):
         return self._array_obj.__index__()

dpnp/dpnp_iface_mathematical.py

@@ -1155,7 +1155,7 @@ def multiply(x1,
     -----------
     Parameters `x1` and `x2` are supported as either scalar, :class:`dpnp.ndarray`
     or :class:`dpctl.tensor.usm_ndarray`, but both `x1` and `x2` can not be scalars at the same time.
-    Parameters `out`, `where`, `dtype` and `subok` are supported with their default values.
+    Parameters `where`, `dtype` and `subok` are supported with their default values.
     Keyword arguments ``kwargs`` are currently unsupported.
     Otherwise the functions will be executed sequentially on CPU.
     Input array data types are limited by supported DPNP :ref:`Data types`.
@@ -1170,29 +1170,7 @@ def multiply(x1,
 
     """
 
-    if out is not None:
-        pass
-    elif where is not True:
-        pass
-    elif dtype is not None:
-        pass
-    elif subok is not True:
-        pass
-    elif dpnp.isscalar(x1) and dpnp.isscalar(x2):
-        # at least either x1 or x2 has to be an array
-        pass
-    else:
-        # get USM type and queue to copy scalar from the host memory into a USM allocation
-        usm_type, queue = get_usm_allocations([x1, x2]) if dpnp.isscalar(x1) or dpnp.isscalar(x2) else (None, None)
-
-        x1_desc = dpnp.get_dpnp_descriptor(x1, copy_when_strides=False, copy_when_nondefault_queue=False,
-                                           alloc_usm_type=usm_type, alloc_queue=queue)
-        x2_desc = dpnp.get_dpnp_descriptor(x2, copy_when_strides=False, copy_when_nondefault_queue=False,
-                                           alloc_usm_type=usm_type, alloc_queue=queue)
-        if x1_desc and x2_desc:
-            return dpnp_multiply(x1_desc, x2_desc, dtype=dtype, out=out, where=where).get_pyobj()
-
-    return call_origin(numpy.multiply, x1, x2, out=out, where=where, dtype=dtype, subok=subok, **kwargs)
+    return _check_nd_call(numpy.multiply, dpnp_multiply, x1, x2, out=out, where=where, dtype=dtype, subok=subok, **kwargs)
 
 
 def nancumprod(x1, **kwargs):

tests/test_mathematical.py

@@ -718,6 +718,89 @@ def test_invalid_out(self, out):
         assert_raises(TypeError, numpy.add, a.asnumpy(), 2, out)
 
 
+class TestMultiply:
+    @pytest.mark.parametrize("dtype", get_all_dtypes(no_none=True))
+    def test_multiply(self, dtype):
+        array1_data = numpy.arange(10)
+        array2_data = numpy.arange(5, 15)
+        out = numpy.empty(10, dtype=dtype)
+
+        # DPNP
+        dp_array1 = dpnp.array(array1_data, dtype=dtype)
+        dp_array2 = dpnp.array(array2_data, dtype=dtype)
+        dp_out = dpnp.array(out, dtype=dtype)
+        result = dpnp.multiply(dp_array1, dp_array2, out=dp_out)
+
+        # original
+        np_array1 = numpy.array(array1_data, dtype=dtype)
+        np_array2 = numpy.array(array2_data, dtype=dtype)
+        expected = numpy.multiply(np_array1, np_array2, out=out)
+
+        assert_allclose(expected, result)
+        assert_allclose(out, dp_out)
+
+    @pytest.mark.parametrize("dtype", get_all_dtypes(no_none=True))
+    def test_out_dtypes(self, dtype):
+        size = 2 if dtype == dpnp.bool else 10
+
+        np_array1 = numpy.arange(size, 2 * size, dtype=dtype)
+        np_array2 = numpy.arange(size, dtype=dtype)
+        np_out = numpy.empty(size, dtype=numpy.complex64)
+        expected = numpy.multiply(np_array1, np_array2, out=np_out)
+
+        dp_array1 = dpnp.arange(size, 2 * size, dtype=dtype)
+        dp_array2 = dpnp.arange(size, dtype=dtype)
+        dp_out = dpnp.empty(size, dtype=dpnp.complex64)
+        result = dpnp.multiply(dp_array1, dp_array2, out=dp_out)
+
+        assert_array_equal(expected, result)
+
+    @pytest.mark.parametrize("dtype", get_all_dtypes(no_none=True))
+    def test_out_overlap(self, dtype):
+        size = 1 if dtype == dpnp.bool else 15
+
+        np_a = numpy.arange(2 * size, dtype=dtype)
+        expected = numpy.multiply(np_a[size::], np_a[::2], out=np_a[:size:])
+
+        dp_a = dpnp.arange(2 * size, dtype=dtype)
+        result = dpnp.multiply(dp_a[size::], dp_a[::2], out=dp_a[:size:])
+
+        assert_allclose(expected, result)
+        assert_allclose(dp_a, np_a)
+
+    @pytest.mark.parametrize("dtype", get_all_dtypes(no_bool=True, no_none=True))
+    def test_inplace_strided_out(self, dtype):
+        size = 21
+
+        np_a = numpy.arange(size, dtype=dtype)
+        np_a[::3] *= 4
+
+        dp_a = dpnp.arange(size, dtype=dtype)
+        dp_a[::3] *= 4
+
+        assert_allclose(dp_a, np_a)
+
+    @pytest.mark.parametrize("shape",
+                             [(0,), (15, ), (2, 2)],
+                             ids=['(0,)', '(15, )', '(2,2)'])
+    def test_invalid_shape(self, shape):
+        dp_array1 = dpnp.arange(10, dtype=dpnp.float64)
+        dp_array2 = dpnp.arange(5, 15, dtype=dpnp.float64)
+        dp_out = dpnp.empty(shape, dtype=dpnp.float64)
+
+        with pytest.raises(ValueError):
+            dpnp.multiply(dp_array1, dp_array2, out=dp_out)
+
+    @pytest.mark.parametrize("out",
+                             [4, (), [], (3, 7), [2, 4]],
+                             ids=['4', '()', '[]', '(3, 7)', '[2, 4]'])
+    def test_invalid_out(self, out):
+        a = dpnp.arange(10)
+
+        assert_raises(TypeError, dpnp.multiply, a, 2, out)
+        assert_raises(TypeError, numpy.multiply, a.asnumpy(), 2, out)
+
+
 class TestPower:
     @pytest.mark.parametrize("dtype", get_float_complex_dtypes())
     def test_power(self, dtype):

tests/test_strides.py

@@ -217,7 +217,7 @@ def test_strides_true_devide(dtype, shape):
 
 
 @pytest.mark.parametrize("func_name",
-                         ["add", "power"])
+                         ["add", "multiply", "power"])
 @pytest.mark.parametrize("dtype", get_all_dtypes(no_bool=True, no_complex=True))
 def test_strided_out_2args(func_name, dtype):
     np_out = numpy.ones((5, 3, 2))[::3]
@@ -236,7 +236,7 @@ def test_strided_out_2args(func_name, dtype):
 
 
 @pytest.mark.parametrize("func_name",
-                         ["add", "power"])
+                         ["add", "multiply", "power"])
 @pytest.mark.parametrize("dtype", get_all_dtypes(no_bool=True, no_complex=True))
 def test_strided_in_out_2args(func_name, dtype):
     sh = (3, 4, 2)
@@ -258,7 +258,7 @@ def test_strided_in_out_2args(func_name, dtype):
 
 
 @pytest.mark.parametrize("func_name",
-                         ["add", "power"])
+                         ["add", "multiply", "power"])
 @pytest.mark.parametrize("dtype", get_all_dtypes(no_bool=True, no_complex=True))
 def test_strided_in_out_2args_diff_out_dtype(func_name, dtype):
     sh = (3, 3, 2)
@@ -280,7 +280,7 @@ def test_strided_in_out_2args_diff_out_dtype(func_name, dtype):
 
 
 @pytest.mark.parametrize("func_name",
-                         ["add", "power"])
+                         ["add", "multiply", "power"])
 @pytest.mark.parametrize("dtype", get_all_dtypes(no_bool=True, no_complex=True, no_none=True))
 def test_strided_in_2args_overlap(func_name, dtype):
     size = 5
@@ -296,7 +296,7 @@ def test_strided_in_2args_overlap(func_name, dtype):
 
 
 @pytest.mark.parametrize("func_name",
-                         ["add", "power"])
+                         ["add", "multiply", "power"])
 @pytest.mark.parametrize("dtype", get_all_dtypes(no_bool=True, no_complex=True, no_none=True))
 def test_strided_in_out_2args_overlap(func_name, dtype):
     sh = (4, 3, 2)

tests/test_usm_type.py

@@ -33,6 +33,8 @@ def test_coerced_usm_types_mul(usm_type_x, usm_type_y):
     y = dp.arange(10, usm_type = usm_type_y)
 
     z = 3 * x * y * 1.5
+    z *= x
+    z *= 4.8
 
     assert x.usm_type == usm_type_x
     assert y.usm_type == usm_type_y