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104 lines
4.0 KiB
104 lines
4.0 KiB
/*Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved.
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Licensed under the Apache License, Version 2.0 (the "License");
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you may not use this file except in compliance with the License.
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You may obtain a copy of the License at
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http://www.apache.org/licenses/LICENSE-2.0
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Unless required by applicable law or agreed to in writing, software
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distributed under the License is distributed on an "AS IS" BASIS,
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WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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See the License for the specific language governing permissions and
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limitations under the License. */
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#pragma once
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#include <memory>
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#include <string>
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#include <unordered_map>
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#include <vector>
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#include "ngraph/ngraph.hpp"
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#include "paddle/fluid/operators/ngraph/ops/elementwise_node.h"
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#include "paddle/fluid/operators/ngraph/ops/op_bridge.h"
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#include "paddle/fluid/platform/ngraph_helper.h"
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namespace paddle {
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namespace operators {
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namespace ngraphs {
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void BuildElementwiseDivGradNode(
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const std::shared_ptr<paddle::framework::OperatorBase>& op,
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std::shared_ptr<
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std::unordered_map<std::string, std::shared_ptr<ngraph::Node>>>
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ngb_node_map) {
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auto op_attrs = paddle::framework::AttrReader(op->Attrs());
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int axis = op_attrs.Get<int>("axis");
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auto dout = paddle::platform::GetInputNode(op, "Out@GRAD", ngb_node_map);
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auto y = paddle::platform::GetInputNode(op, "Y", ngb_node_map);
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auto out = paddle::platform::GetInputNode(op, "Out", ngb_node_map);
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auto dout_shape = dout->get_shape();
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auto y_shape = y->get_shape();
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if (dout->get_element_type() != y->get_element_type()) {
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y = std::make_shared<ngraph::op::Convert>(y, dout->get_element_type());
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}
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auto dy_hd = std::make_shared<ngraph::op::Multiply>(out, dout);
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if (dout_shape == y_shape) {
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auto dx = std::make_shared<ngraph::op::Divide>(dout, y);
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auto dy = std::make_shared<ngraph::op::Divide>(dy_hd, -y);
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paddle::platform::SetOutputNode(op, "X@GRAD", dx, ngb_node_map);
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paddle::platform::SetOutputNode(op, "Y@GRAD", dy, ngb_node_map);
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} else {
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auto dy_hd_shape = dy_hd->get_shape();
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axis = (axis == -1 ? dy_hd_shape.size() - y_shape.size() : axis);
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paddle::platform::TrimTrailingSingularDims(&y_shape);
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axis = (y_shape.size() == 0 ? dy_hd_shape.size() : axis);
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int pre, n, post;
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paddle::platform::GetMidDims(dy_hd_shape, y_shape, axis, &pre, &n, &post);
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ngraph::Shape lhs_shape{};
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lhs_shape.push_back(pre);
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lhs_shape.push_back(n);
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if (post != 1) {
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lhs_shape.push_back(post);
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}
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std::vector<size_t> dy_order(dout_shape.size());
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std::iota(std::begin(dy_order), std::end(dy_order), 0);
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auto dy_hd_reshape = std::make_shared<ngraph::op::Reshape>(
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dy_hd, ngraph::AxisVector(dy_order), lhs_shape);
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ngraph::AxisSet axis_set{0};
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if (post != 1) {
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axis_set.insert(2);
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}
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auto dy_sum = std::make_shared<ngraph::op::Sum>(dy_hd_reshape, axis_set);
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auto dy_sum_yshape = std::make_shared<ngraph::op::Reshape>(
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dy_sum, ngraph::AxisVector{0}, y->get_shape());
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auto dy_ = std::make_shared<ngraph::op::Divide>(dy_sum_yshape, -y);
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paddle::platform::SetOutputNode(op, "Y@GRAD", dy_, ngb_node_map);
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y_shape = y->get_shape();
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std::vector<size_t> y_order(y_shape.size() == 0 ? 1 : y_shape.size());
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std::iota(std::begin(y_order), std::end(y_order), 0);
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auto y_reshape = std::make_shared<ngraph::op::Reshape>(
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y, ngraph::AxisVector(y_order), ngraph::Shape{(size_t)n});
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auto y_broadcast =
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std::make_shared<ngraph::op::Broadcast>(y_reshape, lhs_shape, axis_set);
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std::vector<size_t> lhs_order(lhs_shape.size());
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std::iota(std::begin(lhs_order), std::end(lhs_order), 0);
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auto y_broadcast_reshape = std::make_shared<ngraph::op::Reshape>(
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y_broadcast, ngraph::AxisVector(lhs_order), dout_shape);
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auto dx = std::make_shared<ngraph::op::Divide>(dout, y_broadcast_reshape);
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paddle::platform::SetOutputNode(op, "X@GRAD", dx, ngb_node_map);
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}
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}
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} // namespace ngraphs
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} // namespace operators
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} // namespace paddle
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REGISTER_NG_OP(elementwise_div_grad, BuildElementwiseDivGradNode);
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