Add the support dygraph attribute of op mm, support the out attribute

chery-pick from the pr#23978

paddle/fluid/pybind/op_function_generator.cc

@@ -37,7 +37,8 @@ std::map<std::string, std::set<std::string>> op_passing_out_map = {
     {"momentum", {"ParamOut", "VelocityOut"}},
     {"batch_norm", {"MeanOut", "VarianceOut"}},
     {"accuracy", {"Correct", "Total"}},
-    {"fill_constant", {"Out"}}};
+    {"fill_constant", {"Out"}},
+    {"matmul", {"Out"}}};
 
 // clang-format off
 const char* OUT_INITIALIZER_TEMPLATE =

python/paddle/fluid/dygraph/nn.py

@@ -936,7 +936,8 @@ class Linear(layers.Layer):
 
     def forward(self, input):
         if in_dygraph_mode():
-            pre_bias = core.ops.matmul(input, self.weight, 'transpose_X', False,
+            pre_bias = _varbase_creator(dtype=input.dtype)
+            core.ops.matmul(input, self.weight, pre_bias, 'transpose_X', False,
                             'transpose_Y', False, "alpha", 1)
             pre_act = dygraph_utils._append_bias_in_dygraph(
                 pre_bias, self.bias, axis=len(input.shape) - 1)

python/paddle/fluid/tests/unittests/test_matmul_op.py

@@ -280,6 +280,42 @@ class API_TestMm(unittest.TestCase):
             "two value is\
             {}\n{}, check diff!".format(np_res, expected_result))
 
+    def test_dygraph_with_out(self):
+        device = fluid.CPUPlace()
+        with fluid.dygraph.guard(device):
+            input_array1 = np.random.rand(3, 4).astype("float64")
+            input_array2 = np.random.rand(4, 3).astype("float64")
+            out_array = np.random.rand(3, 3).astype("float64")
+            data1 = fluid.dygraph.to_variable(input_array1)
+            data2 = fluid.dygraph.to_variable(input_array2)
+            paddle_out_holder = fluid.dygraph.to_variable(out_array)
+            out = paddle.mm(data1, data2, out=paddle_out_holder)
+        self.assertTrue(np.allclose(paddle_out_holder.numpy(), out.numpy()))
+
+    def test_dygraph_without_out(self):
+        device = fluid.CPUPlace()
+        with fluid.dygraph.guard(device):
+            input_array1 = np.random.rand(3, 4).astype("float64")
+            input_array2 = np.random.rand(4, 3).astype("float64")
+            data1 = fluid.dygraph.to_variable(input_array1)
+            data2 = fluid.dygraph.to_variable(input_array2)
+            out = paddle.mm(data1, data2)
+            expected_result = np.matmul(input_array1, input_array2)
+        self.assertTrue(np.allclose(expected_result, out.numpy()))
+
+
+class Test_API_Matmul(unittest.TestCase):
+    def test_dygraph_without_out(self):
+        device = fluid.CPUPlace()
+        with fluid.dygraph.guard(device):
+            input_array1 = np.random.rand(3, 4).astype("float64")
+            input_array2 = np.random.rand(4, 3).astype("float64")
+            data1 = fluid.dygraph.to_variable(input_array1)
+            data2 = fluid.dygraph.to_variable(input_array2)
+            out = paddle.matmul(data1, data2)
+            expected_result = np.matmul(input_array1, input_array2)
+        self.assertTrue(np.allclose(expected_result, out.numpy()))
+
 
 class API_TestMmError(unittest.TestCase):
     def test_errors(self):

python/paddle/tensor/linalg.py

@@ -14,7 +14,7 @@
 from paddle.common_ops_import import *
 from ..fluid.layer_helper import LayerHelper
 from ..fluid.data_feeder import check_variable_and_dtype, check_type
-from ..fluid.framework import in_dygraph_mode
+from ..fluid.framework import in_dygraph_mode, _varbase_creator
 
 __all__ = [
     'matmul',
@@ -109,8 +109,10 @@ def matmul(x, y, transpose_x=False, transpose_y=False, alpha=1.0, name=None):
     }
 
     if in_dygraph_mode():
-        return core.ops.matmul(x, y, 'transpose_X', transpose_x, 'transpose_Y',
+        out = _varbase_creator(dtype=x.dtype)
+        core.ops.matmul(x, y, out, 'transpose_X', transpose_x, 'transpose_Y',
                         transpose_y, 'alpha', float(alpha))
+        return out
 
     def __check_input(x, y):
         var_names = {'x': x, 'y': y}

python/paddle/tensor/math.py

@@ -19,7 +19,7 @@ from __future__ import print_function
 
 from paddle.common_ops_import import *
 from ..fluid import layers
-from ..fluid.framework import core
+from ..fluid.framework import core, _varbase_creator
 from ..fluid.layers.layer_function_generator import _generate_doc_string_
 
 # TODO: define math functions
@@ -902,7 +902,10 @@ def mm(input, mat2, out=None, name=None):
             out = paddle.mm(x, mat2) # out shape is [2, 2]
     """
     if in_dygraph_mode():
-        return core.ops.matmul(input, mat2)
+        if out is None:
+            out = _varbase_creator(dtype=input.dtype)
+        core.ops.matmul(input, mat2, out)
+        return out
 
     def __check_input(x, y):
         var_names = {'x': x, 'y': y}