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https://github.com/PaddlePaddle/FastDeploy.git
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6343b0db47
* Add notes for tensors * Optimize some apis * move some warnings * Support build with Paddle2ONNX * Add protobuf support * Fix compile on mac * add clearn package script * Add paddle2onnx code * remove submodule * Add onnx ocde * remove softlink * add onnx code * fix error * Add cmake file * fix patchelf * update paddle2onnx * Delete .gitmodules --------- Co-authored-by: PaddleCI <paddle_ci@example.com> Co-authored-by: pangyoki <pangyoki@126.com> Co-authored-by: jiangjiajun <jiangjiajun@baidu.lcom>
54 lines
1.7 KiB
C++
54 lines
1.7 KiB
C++
/*
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* SPDX-License-Identifier: Apache-2.0
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*/
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// ATTENTION: The code in this file is highly EXPERIMENTAL.
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// Adventurous users should note that the APIs will probably change.
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#pragma once
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#include "onnxoptimizer/pass.h"
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namespace ONNX_NAMESPACE {
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namespace optimization {
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struct FuseConsecutiveLogSoftmax final : public PredicateBasedPass {
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explicit FuseConsecutiveLogSoftmax()
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: PredicateBasedPass(PassType::Fuse, PassEfficiency::Complete,
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PassOptimizationType::Compute) {}
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std::string getPassName() const override {
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return "fuse_consecutive_log_softmax";
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}
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bool patternMatchPredicate(Node* node) override {
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return node->kind() == kLog && node->input()->node()->kind() == kSoftmax &&
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node->input()->uses().size() == 1;
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}
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bool runTransform(Node* log_node, Graph& graph,
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NodeDestroyType& destroy_current) override {
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Value* log_node_output = log_node->output();
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Node* softmax_node = log_node->inputs()[0]->node();
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Node* log_softmax_node = graph.create(kLogSoftmax, 1);
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// log_softmax_node construction
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log_softmax_node->i_(kaxis, softmax_node->i(kaxis));
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log_softmax_node->addInput(softmax_node->input());
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log_softmax_node->insertBefore(softmax_node);
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log_softmax_node->output()->setSizes(log_node_output->sizes());
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log_softmax_node->output()->setElemType(log_node_output->elemType());
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const bool replacing_success =
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tryReplacingAllUsesWith(log_node, log_softmax_node);
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if (!replacing_success) {
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return false;
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}
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log_node->removeAllInputs();
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destroy_current = NodeDestroyType::DestroyTwo;
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return true;
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}
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};
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} // namespace optimization
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} // namespace ONNX_NAMESPACE
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