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7 years ago
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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/framework/details/fast_threaded_ssa_graph_executor.h"
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#include <string>
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#include <vector>
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#include "paddle/fluid/framework/details/fetch_op_handle.h"
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#include "paddle/fluid/framework/details/multi_devices_helper.h"
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namespace paddle {
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namespace framework {
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namespace details {
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FastThreadedSSAGraphExecutor::FastThreadedSSAGraphExecutor(
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const ExecutionStrategy &strategy, const std::vector<Scope *> &local_scopes,
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const std::vector<platform::Place> &places,
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std::unique_ptr<ir::Graph> &&graph)
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: strategy_(strategy),
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local_scopes_(local_scopes),
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places_(places),
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graph_(std::move(graph)),
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pool_(strategy.num_threads_ +
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1), // add one more thread for generate op_deps
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fetch_ctxs_(places) {
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auto &ops = graph_->Get<details::GraphOps>("ops");
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for (auto &op : ops) {
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int dep = static_cast<int>(op->NotReadyInputSize());
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op_deps_.emplace(op.get(), dep);
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if (dep == 0) {
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bootstrap_ops_.emplace_back(op.get());
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}
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}
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PrepareAtomicOpDeps();
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}
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FeedFetchList FastThreadedSSAGraphExecutor::Run(
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const std::vector<std::string> &fetch_tensors) {
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std::unique_ptr<std::unordered_map<OpHandleBase *, std::atomic<int>>>
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op_deps = atomic_op_deps_.get();
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PrepareAtomicOpDeps();
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paddle::framework::FeedFetchList fetches;
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fetches.resize(fetch_tensors.size());
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std::unordered_map<std::string, std::vector<VarHandleBase *>> fetched_vars;
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std::vector<std::unique_ptr<ir::Node>> fetch_nodes;
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std::vector<std::unique_ptr<FetchOpHandle>> fetch_ops;
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for (auto &fetch_var_name : fetch_tensors) {
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for (auto &var_map : graph_->Get<details::GraphVars>("vars")) {
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auto it = var_map.find(fetch_var_name);
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if (it != var_map.end()) {
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fetched_vars[fetch_var_name].push_back(it->second.rbegin()->get());
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}
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}
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}
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for (size_t i = 0; i < fetch_tensors.size(); ++i) {
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auto &var_name = fetch_tensors[i];
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auto fetched_var_it = fetched_vars.find(var_name);
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PADDLE_ENFORCE(fetched_var_it != fetched_vars.end(),
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"Cannot find fetched variable.(Perhaps the main_program "
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"is not set to ParallelExecutor)");
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auto &vars = fetched_var_it->second;
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fetch_nodes.emplace_back(new ir::Node("fetch", ir::Node::Type::kOperation));
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auto *op = new FetchOpHandle(fetch_nodes.back().get(), &fetches, i,
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&local_scopes_);
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fetch_ops.emplace_back(op);
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for (auto &p : places_) {
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op->SetDeviceContext(p, fetch_ctxs_.Get(p));
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}
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for (auto *var : vars) {
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op->AddInput(var);
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}
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(*op_deps)[op] = static_cast<int>(op->NotReadyInputSize());
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}
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size_t num_complete = 0;
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remaining_ = 0;
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BlockingQueue<size_t> complete_q;
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for (auto op : bootstrap_ops_) {
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RunOpAsync(op_deps.get(), op, &complete_q);
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}
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while (num_complete != op_deps->size()) {
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size_t num_comp = complete_q.Pop();
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if (num_comp == -1UL) {
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int remaining = 0;
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while (true) {
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remaining = remaining_;
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if (remaining == 0) {
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break;
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}
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for (int i = 0; i < remaining; ++i) {
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complete_q.Pop();
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}
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}
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exception_.ReThrow();
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}
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num_complete += num_comp;
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}
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// Wait FetchOps.
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if (!fetch_ops.empty()) {
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fetch_ops.clear();
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}
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return fetches;
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}
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void FastThreadedSSAGraphExecutor::RunOpAsync(
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std::unordered_map<OpHandleBase *, std::atomic<int>> *op_deps,
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OpHandleBase *op, BlockingQueue<size_t> *complete_q) {
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++remaining_;
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this->pool_.enqueue([=] {
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OpHandleBase *op_to_run = op;
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size_t complete = 0;
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while (op_to_run != nullptr) {
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try {
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op_to_run->Run(strategy_.use_cuda_);
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++complete;
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} catch (...) {
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exception_.Catch(std::current_exception());
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--remaining_;
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complete_q->Push(-1UL);
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return;
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}
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auto &outputs = op_to_run->Outputs();
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op_to_run = nullptr;
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for (auto &output : outputs) {
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for (auto &pending_op : output->PendingOps()) {
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std::atomic<int> &deps = op_deps->at(pending_op);
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if (deps.fetch_sub(1) == 1) { // pending_op ready
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if (op_to_run == nullptr) {
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op_to_run = pending_op;
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} else {
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this->RunOpAsync(op_deps, pending_op, complete_q);
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}
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}
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}
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}
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}
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--remaining_;
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complete_q->Push(complete);
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});
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}
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void FastThreadedSSAGraphExecutor::PrepareAtomicOpDeps() {
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atomic_op_deps_ = pool_.enqueue([&] {
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std::unordered_map<OpHandleBase *, std::atomic<int>> *op_deps =
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new std::unordered_map<OpHandleBase *, std::atomic<int>>;
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for (auto &pair : op_deps_) {
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(*op_deps)[pair.first] = pair.second;
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}
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return std::unique_ptr<
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std::unordered_map<OpHandleBase *, std::atomic<int>>>(op_deps);
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});
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}
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const ir::Graph &FastThreadedSSAGraphExecutor::Graph() const { return *graph_; }
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} // namespace details
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} // namespace framework
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} // namespace paddle
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@ -0,0 +1,64 @@
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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 <vector>
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#include "ThreadPool.h"
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#include "paddle/fluid/framework/blocking_queue.h"
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#include "paddle/fluid/framework/details/exception_holder.h"
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#include "paddle/fluid/framework/details/execution_strategy.h"
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#include "paddle/fluid/framework/details/ssa_graph_executor.h"
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namespace paddle {
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namespace framework {
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class Scope;
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namespace details {
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class OpHandleBase;
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class FastThreadedSSAGraphExecutor : public SSAGraphExecutor {
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public:
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FastThreadedSSAGraphExecutor(const ExecutionStrategy &strategy,
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const std::vector<Scope *> &local_scopes,
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const std::vector<platform::Place> &places,
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std::unique_ptr<ir::Graph> &&graph);
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FeedFetchList Run(const std::vector<std::string> &fetch_tensors) override;
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const ir::Graph &Graph() const override;
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private:
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ExecutionStrategy strategy_;
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std::vector<Scope *> local_scopes_;
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std::vector<platform::Place> places_;
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std::unique_ptr<ir::Graph> graph_;
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std::unordered_map<OpHandleBase *, int> op_deps_;
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std::vector<OpHandleBase *> bootstrap_ops_;
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::ThreadPool pool_;
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platform::DeviceContextPool fetch_ctxs_;
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std::atomic<int> remaining_;
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void RunOpAsync(std::unordered_map<OpHandleBase *, std::atomic<int>> *op_deps,
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OpHandleBase *op, BlockingQueue<size_t> *complete_q);
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void PrepareAtomicOpDeps();
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std::future<
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std::unique_ptr<std::unordered_map<OpHandleBase *, std::atomic<int>>>>
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atomic_op_deps_;
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ExceptionHolder exception_;
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};
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} // namespace details
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} // namespace framework
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} // namespace paddle
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@ -1,4 +1,4 @@
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nv_library(tensorrt_engine SRCS engine.cc DEPS framework_proto)
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nv_library(tensorrt_engine SRCS engine.cc DEPS framework_proto device_context)
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nv_test(test_tensorrt SRCS test_tensorrt.cc DEPS dynload_cuda device_context dynamic_loader)
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nv_test(test_tensorrt SRCS test_tensorrt.cc DEPS dynload_cuda device_context dynamic_loader)
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nv_test(test_tensorrt_engine SRCS test_engine.cc DEPS dynload_cuda tensorrt_engine)
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nv_test(test_tensorrt_engine SRCS test_engine.cc DEPS dynload_cuda tensorrt_engine)
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add_subdirectory(convert)
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add_subdirectory(convert)
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