Fix BUG: Mask RCNN inference diff When using AnalysisPredictor. (#19213)
* fix mask rcnn bug: 1. affine channel fuse (diff) 2. condition block op (memory leak) 3. merge lod tensor op (diff) 4. memroy optim (diff) test=develop * fix ci aboud PADDLE_ENFOCE fix merge lod infer op ut test=developpadding_in_crf
Zhaolong Xing
6 years ago
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GitHub
parent
5fc8de449a
commit
76c95af000
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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 <algorithm>
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#include "paddle/fluid/framework/ir/fuse_pass_base.h"
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#include "paddle/fluid/framework/ir/graph.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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class InferCleanGraphPass : public FusePassBase {
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public:
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virtual ~InferCleanGraphPass() {}
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protected:
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void ApplyImpl(ir::Graph* graph) const {
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FusePassBase::Init("original_graph", graph);
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PADDLE_ENFORCE(graph);
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auto is_valid_node = [](Node* x) {
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return x && IsControlDepVar(*x) && x->IsVar() && !x->Var();
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};
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std::unordered_set<const Node*> invalid_nodes;
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int valid_op = 0;
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for (auto* node : graph->Nodes()) {
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PADDLE_ENFORCE_NOT_NULL(node);
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if (is_valid_node(node)) {
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invalid_nodes.insert(node);
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} else if (node->IsOp()) {
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// Collect all the operators to help tracking number of operators.
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++valid_op;
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}
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}
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GraphSafeRemoveNodes(graph, invalid_nodes);
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AddStatis(valid_op);
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}
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void CleanEdges(std::vector<Node*>* nodes,
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const std::unordered_set<Node*>& to_remove) const {
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auto it = std::remove_if(nodes->begin(), nodes->end(),
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[&](Node* x) { return to_remove.count(x); });
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nodes->erase(it, nodes->end());
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}
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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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REGISTER_PASS(infer_clean_graph_pass,
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paddle::framework::ir::InferCleanGraphPass);
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@ -0,0 +1,47 @@
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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 "paddle/fluid/inference/analysis/passes/ir_graph_clean_pass.h"
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#include <algorithm>
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#include "paddle/fluid/framework/ir/graph.h"
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#include "paddle/fluid/framework/ir/graph_pattern_detector.h"
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#include "paddle/fluid/framework/ir/node.h"
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namespace paddle {
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namespace inference {
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namespace analysis {
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void IrInferCleanGraphPass::RunImpl(Argument* argument) {
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auto& graph = argument->main_graph();
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auto is_valid_node = [](framework::ir::Node* x) {
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return x && IsControlDepVar(*x) && x->IsVar() && !x->Var();
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};
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std::unordered_set<const framework::ir::Node*> invalid_nodes;
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int valid_op = 0;
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for (auto* node : graph.Nodes()) {
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PADDLE_ENFORCE_NOT_NULL(node);
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if (is_valid_node(node)) {
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invalid_nodes.insert(node);
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} else if (node->IsOp()) {
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++valid_op;
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}
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}
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GraphSafeRemoveNodes(&graph, invalid_nodes);
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}
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} // namespace analysis
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} // namespace inference
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} // namespace paddle
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@ -0,0 +1,34 @@
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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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#pragma once
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#include <string>
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#include <unordered_set>
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#include "paddle/fluid/inference/analysis/analysis_pass.h"
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namespace paddle {
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namespace inference {
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namespace analysis {
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class IrInferCleanGraphPass : public AnalysisPass {
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public:
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void RunImpl(Argument *argument) override;
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std::string repr() const override { return "ir_graph_clean_pass"; }
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};
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} // namespace analysis
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} // namespace inference
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} // namespace paddle
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