[NGraph] Enable elementwise mul operator (#17552)

paddle/fluid/operators/ngraph/ops/elementwise_mul_op.h

@@ -0,0 +1,111 @@
+/*Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License. */
+
+#pragma once
+
+#include <memory>
+#include <string>
+#include <unordered_map>
+#include <vector>
+
+#include "ngraph/ngraph.hpp"
+#include "paddle/fluid/operators/ngraph/ops/elementwise_node.h"
+#include "paddle/fluid/operators/ngraph/ops/op_bridge.h"
+#include "paddle/fluid/platform/ngraph_helper.h"
+
+namespace paddle {
+namespace operators {
+namespace ngraphs {
+
+void BuildElementwiseMulNode(
+    const std::shared_ptr<paddle::framework::OperatorBase>& op,
+    std::shared_ptr<
+        std::unordered_map<std::string, std::shared_ptr<ngraph::Node>>>
+        ngb_node_map) {
+  BuildElementwiseBinaryNode<ngraph::op::Multiply>(op, ngb_node_map);
+}
+
+void BuildElementwiseMulGradNode(
+    const std::shared_ptr<paddle::framework::OperatorBase>& op,
+    std::shared_ptr<
+        std::unordered_map<std::string, std::shared_ptr<ngraph::Node>>>
+        ngb_node_map) {
+  auto op_attrs = paddle::framework::AttrReader(op->Attrs());
+  int axis = op_attrs.Get<int>("axis");
+
+  auto dout = paddle::platform::GetInputNode(op, "Out@GRAD", ngb_node_map);
+  auto y = paddle::platform::GetInputNode(op, "Y", ngb_node_map);
+  auto x = paddle::platform::GetInputNode(op, "X", ngb_node_map);
+  auto dout_shape = dout->get_shape();
+  auto y_shape = y->get_shape();
+  auto x_shape = x->get_shape();
+  if (dout->get_element_type() != y->get_element_type()) {
+    y = std::make_shared<ngraph::op::Convert>(y, dout->get_element_type());
+  }
+  if (dout_shape == y_shape) {
+    auto dx = std::make_shared<ngraph::op::Multiply>(dout, y);
+    auto dy = std::make_shared<ngraph::op::Multiply>(dout, x);
+    paddle::platform::SetOutputNode(op, "X@GRAD", dx, ngb_node_map);
+    paddle::platform::SetOutputNode(op, "Y@GRAD", dy, ngb_node_map);
+  } else {
+    auto dy_hd = std::make_shared<ngraph::op::Multiply>(dout, x);
+    auto dy_hd_shape = dy_hd->get_shape();
+    axis = (axis == -1 ? dy_hd_shape.size() - y_shape.size() : axis);
+    paddle::platform::TrimTrailingSingularDims(&y_shape);
+    axis = (y_shape.size() == 0 ? dy_hd_shape.size() : axis);
+    int pre, n, post;
+    paddle::platform::GetMidDims(dy_hd_shape, y_shape, axis, &pre, &n, &post);
+    ngraph::Shape lhs_shape{};
+    lhs_shape.push_back(pre);
+    lhs_shape.push_back(n);
+    if (post != 1) {
+      lhs_shape.push_back(post);
+    }
+
+    std::vector<size_t> dy_order(dout_shape.size());
+    std::iota(std::begin(dy_order), std::end(dy_order), 0);
+    auto dy_hd_reshape = std::make_shared<ngraph::op::Reshape>(
+        dy_hd, ngraph::AxisVector(dy_order), lhs_shape);
+
+    ngraph::AxisSet axis_set{0};
+    if (post != 1) {
+      axis_set.insert(2);
+    }
+
+    auto dy_sum = std::make_shared<ngraph::op::Sum>(dy_hd_reshape, axis_set);
+    auto dy_sum_yshape = std::make_shared<ngraph::op::Reshape>(
+        dy_sum, ngraph::AxisVector{0}, y->get_shape());
+    paddle::platform::SetOutputNode(op, "Y@GRAD", dy_sum_yshape, ngb_node_map);
+
+    y_shape = y->get_shape();
+    std::vector<size_t> y_order(y_shape.size() == 0 ? 1 : y_shape.size());
+    std::iota(std::begin(y_order), std::end(y_order), 0);
+    auto y_reshape = std::make_shared<ngraph::op::Reshape>(
+        y, ngraph::AxisVector(y_order), ngraph::Shape{(size_t)n});
+    auto y_broadcast =
+        std::make_shared<ngraph::op::Broadcast>(y_reshape, lhs_shape, axis_set);
+    std::vector<size_t> lhs_order(lhs_shape.size());
+    std::iota(std::begin(lhs_order), std::end(lhs_order), 0);
+    auto y_broadcast_reshape = std::make_shared<ngraph::op::Reshape>(
+        y_broadcast, ngraph::AxisVector(lhs_order), dout_shape);
+    auto dx = std::make_shared<ngraph::op::Multiply>(y_broadcast_reshape, dout);
+    paddle::platform::SetOutputNode(op, "X@GRAD", dx, ngb_node_map);
+  }
+}
+}  // namespace ngraphs
+}  // namespace operators
+}  // namespace paddle
+
+REGISTER_NG_OP(elementwise_mul, BuildElementwiseMulNode);
+REGISTER_NG_OP(elementwise_mul_grad, BuildElementwiseMulGradNode);

python/paddle/fluid/tests/unittests/ngraph/test_elementwise_mul_ngraph_op.py

@@ -0,0 +1,22 @@
+#	Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#    http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from __future__ import print_function
+
+import unittest, sys
+sys.path.append("../")
+from test_elementwise_mul_op import TestElementwiseMulOp_scalar, TestElementwiseMulOp_Vector, TestElementwiseMulOp_broadcast_0, TestElementwiseMulOp_broadcast_1, TestElementwiseMulOp_broadcast_2, TestElementwiseMulOp_broadcast_3
+
+if __name__ == '__main__':
+    unittest.main()