Merge branch 'develop' of https://github.com/PaddlePaddle/Paddle into imperative_shared_ptr
test=developrevert-15207-remove_op_handle_lock_and_fix_var
minqiyang
6 years ago
commit
ddfb9f1123
@ -0,0 +1,148 @@
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// Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
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//
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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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//
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// http://www.apache.org/licenses/LICENSE-2.0
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//
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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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#include <string>
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#include <vector>
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#include "paddle/fluid/framework/ir/graph_viz_pass.h"
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#include "paddle/fluid/framework/ir/node.h"
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#include "paddle/fluid/framework/ir/transpose_flatten_concat_fuse_pass.h"
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namespace paddle {
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namespace framework {
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namespace ir {
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template <int times>
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std::unique_ptr<ir::Graph> TransposeFlattenConcatFusePass<times>::ApplyImpl(
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std::unique_ptr<ir::Graph> graph) const {
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const std::string pattern_name =
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"transpose_flatten" + std::to_string(times) + "_concat_fuse";
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FusePassBase::Init(pattern_name, graph.get());
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GraphPatternDetector gpd;
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std::vector<PDNode *> input_nodes;
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for (int i = 0; i < times; i++) {
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input_nodes.push_back(gpd.mutable_pattern()
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->NewNode("x" + std::to_string(i))
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->assert_is_op_input("transpose2", "X")
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->AsInput());
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}
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patterns::TransposeFlattenConcat pattern(gpd.mutable_pattern(), pattern_name);
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pattern(input_nodes, times);
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auto handler = [&](const GraphPatternDetector::subgraph_t &subgraph,
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Graph *g) {
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const int kNumFields = 5;
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const int kTransOffset = 1;
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const int kTransOutOffset = 2;
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const int kFlattenOffset = 3;
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const int kFlattenOutOffset = 4;
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std::vector<Node *> nodes;
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for (int i = 0; i < times; i++) {
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PADDLE_ENFORCE(
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subgraph.at(pattern.GetPDNode("transpose" + std::to_string(i))));
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PADDLE_ENFORCE(
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subgraph.at(pattern.GetPDNode("transpose_out" + std::to_string(i))));
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PADDLE_ENFORCE(
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subgraph.at(pattern.GetPDNode("flatten" + std::to_string(i))));
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PADDLE_ENFORCE(
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subgraph.at(pattern.GetPDNode("flatten_out" + std::to_string(i))));
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PADDLE_ENFORCE(subgraph.at(input_nodes[i]));
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nodes.push_back(subgraph.at(input_nodes[i]));
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nodes.push_back(
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subgraph.at(pattern.GetPDNode("transpose" + std::to_string(i))));
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nodes.push_back(
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subgraph.at(pattern.GetPDNode("transpose_out" + std::to_string(i))));
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nodes.push_back(
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subgraph.at(pattern.GetPDNode("flatten" + std::to_string(i))));
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nodes.push_back(
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subgraph.at(pattern.GetPDNode("flatten_out" + std::to_string(i))));
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}
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Node *concat_op = subgraph.at(pattern.GetPDNode("concat"));
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Node *concat_out = subgraph.at(pattern.GetPDNode("concat_out"));
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std::vector<std::string> input_names;
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std::vector<int> trans_axis = boost::get<std::vector<int>>(
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nodes[kTransOffset]->Op()->GetAttr("axis"));
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int flatten_axis =
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boost::get<int>(nodes[kFlattenOffset]->Op()->GetAttr("axis"));
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int concat_axis = boost::get<int>(concat_op->Op()->GetAttr("axis"));
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std::string output_name = concat_out->Name();
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for (int i = 0; i < times; i++) {
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input_names.push_back(nodes[i * kNumFields]->Name());
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}
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framework::OpDesc new_op_desc;
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new_op_desc.SetType("fusion_transpose_flatten_concat");
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new_op_desc.SetInput("X", input_names);
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new_op_desc.SetAttr("trans_axis", trans_axis);
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new_op_desc.SetAttr("flatten_axis", flatten_axis);
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new_op_desc.SetAttr("concat_axis", concat_axis);
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new_op_desc.SetOutput("Out", {output_name});
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new_op_desc.Flush();
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// Create a new node for the fused op.
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auto *new_conv_op = graph->CreateOpNode(&new_op_desc);
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std::unordered_set<const Node *> delete_nodes;
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for (int i = 0; i < times; i++) {
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nodes[i * kNumFields]->outputs.push_back(new_conv_op);
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new_conv_op->inputs.push_back(nodes[i * kNumFields]);
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delete_nodes.insert(nodes[i * kNumFields + kTransOffset]);
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delete_nodes.insert(nodes[i * kNumFields + kTransOutOffset]);
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delete_nodes.insert(nodes[i * kNumFields + kFlattenOffset]);
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delete_nodes.insert(nodes[i * kNumFields + kFlattenOutOffset]);
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}
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delete_nodes.insert(concat_op);
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new_conv_op->outputs.push_back(concat_out);
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concat_out->inputs.push_back(new_conv_op);
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// Delete the unneeded nodes.
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GraphSafeRemoveNodes(graph.get(), delete_nodes);
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};
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gpd(graph.get(), handler);
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return graph;
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}
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template class TransposeFlattenConcatFusePass<1>;
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template class TransposeFlattenConcatFusePass<3>;
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template class TransposeFlattenConcatFusePass<4>;
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template class TransposeFlattenConcatFusePass<5>;
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template class TransposeFlattenConcatFusePass<6>;
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} // namespace ir
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} // namespace framework
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} // namespace paddle
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REGISTER_PASS(transpose_flatten_concat_fuse_pass,
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paddle::framework::ir::TransposeFlattenConcatFusePass<1>);
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REGISTER_PASS(transpose_flatten3_concat_fuse_pass,
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paddle::framework::ir::TransposeFlattenConcatFusePass<3>);
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REGISTER_PASS(transpose_flatten4_concat_fuse_pass,
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paddle::framework::ir::TransposeFlattenConcatFusePass<4>);
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REGISTER_PASS(transpose_flatten5_concat_fuse_pass,
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paddle::framework::ir::TransposeFlattenConcatFusePass<5>);
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REGISTER_PASS(transpose_flatten6_concat_fuse_pass,
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paddle::framework::ir::TransposeFlattenConcatFusePass<6>);
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@ -0,0 +1,38 @@
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// Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
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//
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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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//
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// http://www.apache.org/licenses/LICENSE-2.0
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//
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// Unless required by applicable law or agreed to in writing, software
|
||||
// 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 "paddle/fluid/framework/ir/fuse_pass_base.h"
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#include "paddle/fluid/framework/ir/graph_pattern_detector.h"
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namespace paddle {
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namespace framework {
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namespace ir {
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// There may be many transpose-flatten structures in a model, and the output of
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// these structures will be used as inputs to the concat Op. This pattern will
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// be detected by our pass. The times here represents the repeat times of this
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// structure.
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template <int times>
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class TransposeFlattenConcatFusePass : public FusePassBase {
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public:
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virtual ~TransposeFlattenConcatFusePass() {}
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protected:
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std::unique_ptr<ir::Graph> ApplyImpl(std::unique_ptr<ir::Graph> graph) const;
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};
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} // namespace ir
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} // namespace framework
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} // namespace paddle
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File diff suppressed because it is too large
Load Diff
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/* Copyright (c) 2016 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
|
||||
|
||||
http://www.apache.org/licenses/LICENSE-2.0
|
||||
|
||||
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
|
||||
limitations under the License. */
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#pragma once
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#include "paddle/fluid/framework/eigen.h"
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#include "paddle/fluid/framework/op_registry.h"
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namespace paddle {
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namespace operators {
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template <typename DeviceContext, typename T>
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class DataNormKernel : public framework::OpKernel<T> {
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public:
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void Compute(const framework::ExecutionContext& ctx) const override;
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};
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template <typename DeviceContext, typename T>
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class DataNormGradKernel : public framework::OpKernel<T> {
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public:
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void Compute(const framework::ExecutionContext& ctx) const override;
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};
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} // namespace operators
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} // namespace paddle
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@ -0,0 +1,162 @@
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/* Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserve.
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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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|
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http://www.apache.org/licenses/LICENSE-2.0
|
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|
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Unless required by applicable law or agreed to in writing, software
|
||||
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
|
||||
limitations under the License. */
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#include "paddle/fluid/operators/teacher_student_sigmoid_loss_op.h"
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#include "paddle/fluid/operators/math/math_function.h"
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namespace paddle {
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namespace operators {
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using Tensor = framework::Tensor;
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class TeacherStudentSigmoidLossOp : public framework::OperatorWithKernel {
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public:
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using framework::OperatorWithKernel::OperatorWithKernel;
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void InferShape(framework::InferShapeContext* ctx) const override {
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PADDLE_ENFORCE(ctx->HasInput("X"), "Input(X) should be not null.");
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PADDLE_ENFORCE(ctx->HasInput("Label"), "Input(Label) should be not null.");
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PADDLE_ENFORCE(ctx->HasOutput("Y"), "Output(Y) should be not null.");
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auto x_dims = ctx->GetInputDim("X");
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auto label_dims = ctx->GetInputDim("Label");
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PADDLE_ENFORCE_EQ(x_dims.size(), 2UL, "Input(X)'s rank should be 2.");
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PADDLE_ENFORCE_EQ(label_dims.size(), 2UL,
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"Input(Label)'s rank should be 2.");
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PADDLE_ENFORCE_EQ(x_dims[0], label_dims[0],
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"The 1st dimension of Input(X) and Input(Label) should "
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"be equal.");
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PADDLE_ENFORCE_EQ(label_dims[1], 1UL,
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"The 2nd dimension of "
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"Input(Label) should be 1.");
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ctx->SetOutputDim("Y", {x_dims[0], 1});
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ctx->ShareLoD("X", /*->*/ "Y");
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}
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protected:
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// Explicitly set that the data type of computation kernel of
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// teacher_student_sigmoid_loss
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// is determined by its input "X".
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framework::OpKernelType GetExpectedKernelType(
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const framework::ExecutionContext& ctx) const override {
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return framework::OpKernelType(ctx.Input<Tensor>("X")->type(),
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ctx.device_context());
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}
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};
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class TeacherStudentSigmoidLossGradientOp
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: public framework::OperatorWithKernel {
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public:
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using framework::OperatorWithKernel::OperatorWithKernel;
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void InferShape(framework::InferShapeContext* ctx) const override {
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PADDLE_ENFORCE(ctx->HasInput("X"), "Input(X) should be not null.");
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PADDLE_ENFORCE(ctx->HasInput("Label"), "Input(Label) should be not null.");
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PADDLE_ENFORCE(ctx->HasInput(framework::GradVarName("Y")),
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"Input(Y@GRAD) should be not null.");
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PADDLE_ENFORCE(ctx->HasOutput(framework::GradVarName("X")),
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"Output(X@GRAD) should be not null.");
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auto x_dims = ctx->GetInputDim("X");
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auto label_dims = ctx->GetInputDim("Label");
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auto dy_dims = ctx->GetInputDim(framework::GradVarName("Y"));
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PADDLE_ENFORCE_EQ(x_dims.size(), 2, "Input(X)'s rank should be 2.");
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PADDLE_ENFORCE_EQ(dy_dims.size(), 2, "Input(Y@Grad)'s rank should be 2.");
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PADDLE_ENFORCE_EQ(label_dims.size(), 2, "Input(Label)'s rank should be 2.");
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PADDLE_ENFORCE_EQ(x_dims[0], label_dims[0],
|
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"The 1st dimension of Input(X) and Input(Label) should "
|
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"be equal.");
|
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PADDLE_ENFORCE_EQ(x_dims[0], dy_dims[0],
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"The 1st dimension of Input(X) and Input(Y@Grad) should "
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"be equal.");
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PADDLE_ENFORCE_EQ(dy_dims[1], 1,
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"The 2nd dimension of Input(Y@Grad) should be 1.");
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PADDLE_ENFORCE_EQ(label_dims[1], 1,
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"When Attr(soft_label) == false, the 2nd dimension of "
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"Input(Label) should be 1.");
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ctx->SetOutputDim(framework::GradVarName("X"), x_dims);
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ctx->ShareLoD("X", framework::GradVarName("X"));
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}
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protected:
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// Explicitly set that the data type of computation kernel of
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// teacher_student_sigmoid_loss
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// is determined by its input "X".
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framework::OpKernelType GetExpectedKernelType(
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const framework::ExecutionContext& ctx) const override {
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return framework::OpKernelType(ctx.Input<Tensor>("X")->type(),
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ctx.device_context());
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}
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};
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class TeacherStudentSigmoidLossOpMaker
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: public framework::OpProtoAndCheckerMaker {
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public:
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void Make() override {
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AddInput("X",
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"(Tensor, default Tensor<float>), a 2-D tensor with shape [N x 1],"
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" where N is the batch size and D is the output. "
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"This input is a probability computed by the previous operator, "
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"which is almost always the result of a softmax operator.");
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AddInput("Label",
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"(Tensor), the ground truth which is a 2-D tensor. "
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"Label is a Tensor<float> with shape [N x 1]. ");
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AddOutput("Y",
|
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"(Tensor, default Tensor<float>), a 2-D tensor with shape "
|
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"[N x 1]. The teacher student sigmoid loss.");
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AddAttr<float>(
|
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"soft_max_up_bound",
|
||||
"fp32, if input > soft_max_up_bound, will be bound, default 15.0")
|
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.SetDefault(15.0);
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AddAttr<float>(
|
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"soft_max_lower_bound",
|
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"fp32, if input < soft_max_lower_bound, will be bound, default -15.0")
|
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.SetDefault(-15.0);
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AddComment(R"DOC(
|
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TeacherStudentSigmoidLoss Operator.
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|
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It's similarity to SigmoidCrossEntropyWithLogits Operator. The difference is that
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we add another label(z') to original.
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loss = max(x, 0) - x * z + log(1 + exp(-abs(x))) + max(x, 0) - x * z' + log(1 + exp(-abs(x)))
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z is click or not
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z' is teacher value
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label = {-2, -1, [0, 2]}
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when z' is not exist, clk = 0 : label = -2;
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when z' is not exist, clk = 1 : label = -1;
|
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when z' is exist , clk = 0 : label = 0 + z';
|
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when z' is exist , clk = 1 : label = 1 + z';
|
||||
|
||||
)DOC");
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||||
}
|
||||
};
|
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|
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} // namespace operators
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} // namespace paddle
|
||||
|
||||
namespace ops = paddle::operators;
|
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REGISTER_OPERATOR(teacher_student_sigmoid_loss,
|
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ops::TeacherStudentSigmoidLossOp,
|
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ops::TeacherStudentSigmoidLossOpMaker,
|
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paddle::framework::DefaultGradOpDescMaker<true>);
|
||||
|
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REGISTER_OPERATOR(teacher_student_sigmoid_loss_grad,
|
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ops::TeacherStudentSigmoidLossGradientOp);
|
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|
||||
REGISTER_OP_CPU_KERNEL(teacher_student_sigmoid_loss,
|
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ops::TeacherStudentSigmoidLossOpKernel<float>,
|
||||
ops::TeacherStudentSigmoidLossOpKernel<double>);
|
||||
|
||||
REGISTER_OP_CPU_KERNEL(teacher_student_sigmoid_loss_grad,
|
||||
ops::TeacherStudentSigmoidLossGradOpKernel<float>,
|
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ops::TeacherStudentSigmoidLossGradOpKernel<double>);
|
||||
@ -0,0 +1,118 @@
|
||||
/* 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 "paddle/fluid/framework/eigen.h"
|
||||
#include "paddle/fluid/framework/op_registry.h"
|
||||
|
||||
namespace paddle {
|
||||
namespace operators {
|
||||
|
||||
using Tensor = framework::Tensor;
|
||||
template <typename T>
|
||||
class TeacherStudentSigmoidLossOpKernel : public framework::OpKernel<T> {
|
||||
public:
|
||||
void Compute(const framework::ExecutionContext& context) const override {
|
||||
Tensor* y = context.Output<Tensor>("Y");
|
||||
const Tensor* x = context.Input<Tensor>("X");
|
||||
const Tensor* labels = context.Input<Tensor>("Label");
|
||||
T* y_data = y->mutable_data<T>(context.GetPlace());
|
||||
const T* x_data = x->data<T>();
|
||||
const T* label_data = labels->data<T>();
|
||||
int64_t batch_size = x->dims()[0];
|
||||
// loss = max(x, 0) - x * z + log(1 + exp(-abs(x))) + max(x, 0) - x * z' +
|
||||
// log(1 + exp(-abs(x)))
|
||||
// z is click or not
|
||||
// z' is value q of feed_fine
|
||||
// label = {-2, -1, [0, 2]}
|
||||
// when z' is not exist, clk = 0 : label = -2;
|
||||
// when z' is not exist, clk = 1 : label = -1;
|
||||
// when z' is exist , clk = 0 : label = 0 + z';
|
||||
// when z' is exist , clk = 1 : label = 1 + z';
|
||||
for (int i = 0; i < batch_size; ++i) {
|
||||
if (label_data[i] < -1.0) {
|
||||
y_data[i] = (x_data[i] > 0 ? x_data[i] : 0.0) +
|
||||
log(1.0 + exp(-fabs(x_data[i])));
|
||||
} else if (label_data[i] < 0.0) {
|
||||
y_data[i] = (x_data[i] > 0 ? x_data[i] : 0.0) - x_data[i] +
|
||||
log(1.0 + exp(-fabs(x_data[i])));
|
||||
} else if (label_data[i] < 1.0) {
|
||||
y_data[i] = (x_data[i] > 0 ? x_data[i] : 0.0) +
|
||||
log(1.0 + exp(-fabs(x_data[i]))) +
|
||||
(x_data[i] > 0 ? x_data[i] : 0.0) -
|
||||
x_data[i] * label_data[i] +
|
||||
log(1.0 + exp(-fabs(x_data[i])));
|
||||
} else {
|
||||
y_data[i] = (x_data[i] > 0 ? x_data[i] : 0.0) - x_data[i] +
|
||||
log(1.0 + exp(-fabs(x_data[i]))) +
|
||||
(x_data[i] > 0 ? x_data[i] : 0.0) -
|
||||
x_data[i] * (label_data[i] - 1.0) +
|
||||
log(1.0 + exp(-fabs(x_data[i])));
|
||||
}
|
||||
}
|
||||
}
|
||||
};
|
||||
|
||||
template <typename T>
|
||||
class TeacherStudentSigmoidLossGradOpKernel : public framework::OpKernel<T> {
|
||||
public:
|
||||
void Compute(const framework::ExecutionContext& context) const override {
|
||||
const Tensor* x = context.Input<Tensor>("X");
|
||||
const T* x_data = x->data<T>();
|
||||
|
||||
Tensor* dx = context.Output<Tensor>(framework::GradVarName("X"));
|
||||
T* dx_data = dx->mutable_data<T>(context.GetPlace());
|
||||
|
||||
const Tensor* labels = context.Input<Tensor>("Label");
|
||||
const T* label_data = labels->data<T>();
|
||||
|
||||
T soft_max_up_bound =
|
||||
static_cast<T>(context.Attr<float>("soft_max_up_bound"));
|
||||
T soft_max_lower_bound =
|
||||
static_cast<T>(context.Attr<float>("soft_max_lower_bound"));
|
||||
|
||||
int64_t batch_size = x->dims()[0];
|
||||
|
||||
const framework::Tensor* dOut =
|
||||
context.Input<framework::Tensor>(framework::GradVarName("Y"));
|
||||
|
||||
const T* dout_data = dOut->data<T>();
|
||||
|
||||
for (int i = 0; i < batch_size; ++i) {
|
||||
T sum_val = x_data[i];
|
||||
if (sum_val > soft_max_up_bound) {
|
||||
sum_val = soft_max_up_bound;
|
||||
} else {
|
||||
if (sum_val < soft_max_lower_bound) {
|
||||
sum_val = soft_max_lower_bound;
|
||||
}
|
||||
}
|
||||
|
||||
T pred = 1.0 / (1.0 + exp(-sum_val));
|
||||
if (label_data[i] < -1.0) {
|
||||
dx_data[i] = 0.0 - pred;
|
||||
} else if (label_data[i] < 0.0) {
|
||||
dx_data[i] = 1.0 - pred;
|
||||
} else {
|
||||
dx_data[i] = label_data[i] - 2.0 * pred;
|
||||
}
|
||||
if (sum_val >= soft_max_up_bound || sum_val <= soft_max_lower_bound) {
|
||||
dx_data[i] = 0;
|
||||
}
|
||||
dx_data[i] *= dout_data[i] * -1;
|
||||
}
|
||||
}
|
||||
};
|
||||
} // namespace operators
|
||||
} // namespace paddle
|
||||
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Reference in new issue