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111 lines
4.0 KiB
111 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 <functional>
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#include <memory>
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#include <string>
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#include <unordered_map>
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#include "ngraph/ngraph.hpp"
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#include "ngraph/op/experimental/generate_mask.hpp"
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#include "paddle/fluid/operators/ngraph/ops/elementwise_scalar_op.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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static void BuildDropoutNode(
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const std::shared_ptr<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 input = platform::GetInputNode(op, "X", ngb_node_map);
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auto op_attrs = framework::AttrReader(op->Attrs());
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auto dropout_prob = op_attrs.Get<float>("dropout_prob");
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auto dropout_implementation =
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op_attrs.Get<std::string>("dropout_implementation");
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auto is_test = op_attrs.Get<bool>("is_test");
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auto seed = op_attrs.Get<int>("seed");
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float value = 1.0f - dropout_prob;
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bool upscale_in_train = (dropout_implementation == "upscale_in_train");
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if (is_test) {
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if (upscale_in_train) {
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platform::SetOutputNode(op, "Out", input, ngb_node_map);
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} else {
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auto mask_val = paddle::platform::CreateConstant(
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input->get_element_type(), input->get_shape(), {value});
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auto out = input * mask_val;
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platform::SetOutputNode(op, "Out", out, ngb_node_map);
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}
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} else {
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auto one = paddle::platform::CreateConstant(input->get_element_type(),
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ngraph::Shape{}, {1});
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auto gen_mask = std::make_shared<ngraph::op::GenerateMask>(
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one, input->get_shape(), input->get_element_type(), seed, value);
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if (upscale_in_train) {
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auto mask_val = paddle::platform::CreateConstant(
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input->get_element_type(), input->get_shape(), {value});
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auto out = value ? input * gen_mask / mask_val : input * gen_mask;
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platform::SetOutputNode(op, "Mask", gen_mask, ngb_node_map);
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platform::SetOutputNode(op, "Out", out, ngb_node_map);
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} else {
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auto out = input * gen_mask;
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platform::SetOutputNode(op, "Mask", gen_mask, ngb_node_map);
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platform::SetOutputNode(op, "Out", out, ngb_node_map);
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}
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}
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}
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static void BuildDropoutGradNode(
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const std::shared_ptr<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 dy = platform::GetInputNode(op, "Out@GRAD", ngb_node_map);
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auto mask = platform::GetInputNode(op, "Mask", ngb_node_map);
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if (dy->get_element_type() != mask->get_element_type()) {
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mask = std::make_shared<ngraph::op::Convert>(mask, dy->get_element_type());
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}
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auto op_attrs = framework::AttrReader(op->Attrs());
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auto dropout_prob = op_attrs.Get<float>("dropout_prob");
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auto dropout_implementation =
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op_attrs.Get<std::string>("dropout_implementation");
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auto dx = dy * mask;
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if (dropout_implementation == "upscale_in_train") {
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if (dropout_prob == 1.0f) {
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dx = ElementwiseScalar<ngraph::op::Multiply>(0., dy);
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} else {
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dx =
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ElementwiseScalar<ngraph::op::Multiply>(1. / (1. - dropout_prob), dx);
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}
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}
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platform::SetOutputNode(op, "X@GRAD", dx, ngb_node_map);
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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(dropout, BuildDropoutNode);
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REGISTER_NG_OP(dropout_grad, BuildDropoutGradNode);
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