dzhwinter
7 years ago
commit
89f95ea25e
@ -0,0 +1,48 @@
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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 <cstdint>
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#include "paddle/fluid/platform/hostdevice.h"
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namespace paddle {
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namespace framework {
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template <typename T, size_t N>
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class Array {
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static_assert(N > 0, "The size of array must be larger than 0");
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public:
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HOSTDEVICE Array() {}
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HOSTDEVICE explicit Array(const T &val) {
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for (size_t i = 0; i < N; ++i) data_[i] = val;
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}
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HOSTDEVICE const T *Get() const { return data_; }
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HOSTDEVICE T *GetMutable() { return data_; }
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HOSTDEVICE T &operator[](size_t index) { return data_[index]; }
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HOSTDEVICE const T &operator[](size_t index) const { return data_[index]; }
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HOSTDEVICE constexpr size_t size() const { return N; }
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private:
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T data_[N];
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};
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} // namespace framework
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} // namespace paddle
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@ -0,0 +1,175 @@
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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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@ -0,0 +1,192 @@
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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/ir/fc_fuse_pass.h"
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#include <string>
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#include <vector>
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#include "paddle/fluid/platform/enforce.h"
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namespace paddle {
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namespace framework {
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namespace ir {
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bool VarOutLinksToOp(Node* node, const std::string& op_type) {
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for (auto* out : node->outputs) {
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if (out->IsOp() && out->Op()->Type() == op_type) {
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return true;
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}
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}
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return false;
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}
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void BuildFCPattern(PDPattern* pattern) {
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// make sure the selected MUL op has one input argument is a parameter.
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auto* mul_parameter_var = pattern->NewNode(
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[](Node* node) {
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return node->IsVar() && node->outputs.size() == 1UL &&
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node->outputs.front()->Op()->Type() == "mul" && node->Var() &&
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node->Var()->Persistable(); // check is a parameter
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},
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"mul_weight" /*name*/);
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auto* mul_tmp_input_var = pattern->NewNode(
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[](Node* node) {
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bool result =
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node->IsVar() && node->outputs.size() >= 1UL && node->Var() &&
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!node->Var()->Persistable(); // this input is not an parameter.
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if (!result) return false;
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// check whether one output is MUL op.
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for (auto* op : node->outputs) {
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if (op->IsOp() && op->Op()->Type() == "mul") return true;
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}
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return false;
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},
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"mul_tmp_var" /*name*/);
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// select a MUL op
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auto* mul_op = pattern->NewNode(
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[](Node* node) {
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return node->IsOp() && // start from an Op
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node->Op()->Type() == "mul"; // type is mul
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// the output should be consumed only by one element_add, that check
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// leaves in a Var PDNode.
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},
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"mul" /*name*/);
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// make sure the MUL op's output has only one consumer and links to an
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// ELEMENTWISE_ADD op.
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auto* mul_out_var = pattern->NewNode(
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[](Node* node) {
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return node->IsVar() && // starts from a Var
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node->outputs.size() == 1UL && // only has one consumer
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node->outputs.front()->IsOp() && // check basic logic
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node->Var() && // not a ControlDepVar
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node->outputs.front()->Op()->Type() ==
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"elementwise_add"; // a very strong validation
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},
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"mul_out");
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// this check is not essential, just to make the corresponding variable Node
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// retrival easier.
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auto* elementwise_add_tmp_var = pattern->NewNode(
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[](Node* node) {
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return node->IsVar() && node->outputs.size() >= 1UL && node->Var() &&
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VarOutLinksToOp(node, "elementwise_add");
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},
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"elementwise_add_tmpvar");
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// select an ELEMENTWISE_ADD op
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auto* elementwise_add_op = pattern->NewNode(
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[](Node* node) {
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return node->IsOp() && node->Op()->Type() == "elementwise_add";
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},
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"elementwise_add" /*name*/);
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// get the ELEMENTWISE_ADD op's output
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auto* elementwise_add_out_var = pattern->NewNode(
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[](Node* node) {
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return node->IsVar() && node->inputs.size() == 1UL && node->Var() &&
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node->inputs.front()->Op()->Type() == "elementwise_add";
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},
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"elementwise_add_out");
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pattern->AddEdge(mul_parameter_var, mul_op);
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pattern->AddEdge(mul_tmp_input_var, mul_op);
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pattern->AddEdge(mul_op, mul_out_var);
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pattern->AddEdge(mul_out_var, elementwise_add_op);
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pattern->AddEdge(elementwise_add_tmp_var, elementwise_add_op);
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pattern->AddEdge(elementwise_add_op, elementwise_add_out_var);
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}
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// Replace the node `from` in the links to `to`
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bool LinksReplace(std::vector<Node*>* links, Node* from, Node* to) {
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for (auto*& n : *links) {
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if (n == from) {
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n = to;
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return true;
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}
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}
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return false;
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}
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std::unique_ptr<ir::Graph> FCFusePass::ApplyImpl(
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std::unique_ptr<ir::Graph> graph) const {
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PADDLE_ENFORCE(graph.get());
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std::unordered_set<Node*> nodes2delete;
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GraphPatternDetecter gpd;
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BuildFCPattern(gpd.mutable_pattern());
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#define GET_NODE(id) \
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PADDLE_ENFORCE(subgraph.count(gpd.pattern().RetriveNode(#id)), \
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"pattern has no Node called %s", #id); \
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auto* id = subgraph.at(gpd.pattern().RetriveNode(#id)); \
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PADDLE_ENFORCE_NOT_NULL(id, "subgraph has no node %s", #id);
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auto handler = [&](const GraphPatternDetecter::subgraph_t& subgraph,
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Graph* g) {
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VLOG(4) << "handle FC fuse";
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// Currently, there is no FC op available, so I will just simulate the
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// scenerio.
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// FC's fusion is simple, just op fuse, no need to process the
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// parameters.
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GET_NODE(mul_tmp_var); // x
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GET_NODE(mul_weight); // Y
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GET_NODE(elementwise_add_tmpvar); // bias
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GET_NODE(elementwise_add_out); // Out
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GET_NODE(mul); // MUL op
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GET_NODE(elementwise_add); // ELEMENT_ADD op
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GET_NODE(mul_out); // tmp
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#undef GET_NODE
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// Create an FC Node.
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OpDesc desc;
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std::string fc_x_in = mul_tmp_var->Name();
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std::string fc_Y_in = mul_weight->Name();
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std::string fc_bias_in = elementwise_add_tmpvar->Name();
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std::string fc_out = elementwise_add_out->Name();
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desc.SetInput("Input", std::vector<std::string>({fc_x_in}));
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desc.SetInput("W", std::vector<std::string>({fc_Y_in}));
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desc.SetInput("Bias", std::vector<std::string>({fc_bias_in}));
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desc.SetOutput("Out", std::vector<std::string>({fc_out}));
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desc.SetType("fc");
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auto fc_node = g->CreateOpNode(&desc); // OpDesc will be copied.
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fc_node->inputs =
|
||||
std::vector<Node*>({mul_tmp_var, mul_weight, elementwise_add_tmpvar});
|
||||
fc_node->outputs.push_back(elementwise_add_out);
|
||||
|
||||
// Update link relatons
|
||||
PADDLE_ENFORCE(LinksReplace(&mul_tmp_var->outputs, mul, fc_node));
|
||||
PADDLE_ENFORCE(LinksReplace(&mul_weight->outputs, mul, fc_node));
|
||||
PADDLE_ENFORCE(LinksReplace(&elementwise_add_tmpvar->outputs,
|
||||
elementwise_add, fc_node));
|
||||
PADDLE_ENFORCE(
|
||||
LinksReplace(&elementwise_add_out->inputs, elementwise_add, fc_node));
|
||||
|
||||
// Drop old nodes
|
||||
graph->RemoveNode(mul);
|
||||
graph->RemoveNode(elementwise_add);
|
||||
graph->RemoveNode(mul_out); // tmp variable
|
||||
};
|
||||
|
||||
gpd(graph.get(), handler);
|
||||
|
||||
return graph;
|
||||
}
|
||||
|
||||
} // namespace ir
|
||||
} // namespace framework
|
||||
} // namespace paddle
|
||||
|
||||
REGISTER_PASS(fc_fuse_pass, paddle::framework::ir::FCFusePass);
|
||||
@ -0,0 +1,36 @@
|
||||
// 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.
|
||||
|
||||
#include "paddle/fluid/framework/ir/graph.h"
|
||||
#include "paddle/fluid/framework/ir/graph_pattern_detecter.h"
|
||||
#include "paddle/fluid/framework/ir/pass.h"
|
||||
|
||||
namespace paddle {
|
||||
namespace framework {
|
||||
namespace ir {
|
||||
|
||||
/*
|
||||
* Fuse the MUL and ELEMENTWISE_ADD to a FCOp.
|
||||
*/
|
||||
class FCFusePass : public Pass {
|
||||
public:
|
||||
virtual ~FCFusePass() {}
|
||||
|
||||
protected:
|
||||
std::unique_ptr<ir::Graph> ApplyImpl(std::unique_ptr<ir::Graph> graph) const;
|
||||
};
|
||||
|
||||
} // namespace ir
|
||||
} // namespace framework
|
||||
} // namespace paddle
|
||||
@ -0,0 +1,90 @@
|
||||
// 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.
|
||||
|
||||
#include "paddle/fluid/framework/ir/fc_fuse_pass.h"
|
||||
|
||||
#include <gtest/gtest.h>
|
||||
|
||||
namespace paddle {
|
||||
namespace framework {
|
||||
namespace ir {
|
||||
|
||||
void SetOp(ProgramDesc* prog, const std::string& type,
|
||||
const std::vector<std::string>& inputs,
|
||||
const std::vector<std::string>& outputs) {
|
||||
auto* op = prog->MutableBlock(0)->AppendOp();
|
||||
op->SetType(type);
|
||||
op->SetInput("Xs", inputs);
|
||||
op->SetOutput("Ys", outputs);
|
||||
}
|
||||
|
||||
// a->OP0->b
|
||||
// a->OP1->c
|
||||
// (b, c)->mul->d
|
||||
// (d, e)->elementwise_add->f
|
||||
ProgramDesc BuildProgramDesc() {
|
||||
ProgramDesc prog;
|
||||
for (auto& v : std::vector<std::string>({"a", "b", "c", "d", "e", "f"})) {
|
||||
auto* var = prog.MutableBlock(0)->Var(v);
|
||||
var->SetType(proto::VarType::SELECTED_ROWS);
|
||||
if (v == "c") {
|
||||
var->SetPersistable(true);
|
||||
}
|
||||
}
|
||||
|
||||
SetOp(&prog, "OP0", std::vector<std::string>({"a"}),
|
||||
std::vector<std::string>({"b"}));
|
||||
SetOp(&prog, "OP1", std::vector<std::string>({"a"}),
|
||||
std::vector<std::string>({"c"}));
|
||||
SetOp(&prog, "mul", std::vector<std::string>({"b", "c"}),
|
||||
std::vector<std::string>({"d"}));
|
||||
SetOp(&prog, "elementwise_add", std::vector<std::string>({"d", "e"}),
|
||||
std::vector<std::string>({"f"}));
|
||||
|
||||
return prog;
|
||||
}
|
||||
|
||||
TEST(FCFusePass, basic) {
|
||||
auto prog = BuildProgramDesc();
|
||||
|
||||
std::unique_ptr<ir::Graph> graph(new ir::Graph(prog));
|
||||
|
||||
auto pass = PassRegistry::Instance().Get("fc_fuse_pass");
|
||||
|
||||
int pre_nodes = graph->Nodes().size();
|
||||
|
||||
graph = pass->Apply(std::move(graph));
|
||||
|
||||
int after_nodes = graph->Nodes().size();
|
||||
|
||||
// Remove 3 Nodes: MUL,ELEMENTWISE_ADD, mul_out
|
||||
// Add 1 Node: FC
|
||||
EXPECT_EQ(pre_nodes - 2, after_nodes);
|
||||
|
||||
// Assert fc op in newly generated graph
|
||||
int fc_count = 0;
|
||||
|
||||
for (auto* node : graph->Nodes()) {
|
||||
if (node->IsOp() && node->Op()->Type() == "fc") {
|
||||
++fc_count;
|
||||
}
|
||||
}
|
||||
EXPECT_EQ(fc_count, 1);
|
||||
}
|
||||
|
||||
} // namespace ir
|
||||
} // namespace framework
|
||||
} // namespace paddle
|
||||
|
||||
USE_PASS(fc_fuse_pass);
|
||||
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Reference in new issue