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paddle/fluid/framework/details/CMakeLists.txt

@@ -42,3 +42,5 @@ cc_test(gather_op_test SRCS gather_op_handle_test.cc DEPS var_handle op_handle_b
 cc_library(scope_buffered_ssa_graph_executor SRCS scope_buffered_ssa_graph_executor.cc DEPS ssa_graph_executor)
 #cc_test(reduce_op_handle_test SRCS reduce_op_handle_test.cc DEPS var_handle op_handle_base scope ddim memory
 #        device_context reduce_op_handle )
+cc_library(fast_threaded_ssa_graph_executor SRCS fast_threaded_ssa_graph_executor.cc
+        DEPS fetch_op_handle ssa_graph_executor scope simple_threadpool device_context)

paddle/fluid/framework/details/fast_threaded_ssa_graph_executor.cc

+// 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/details/fast_threaded_ssa_graph_executor.h"
+#include <string>
+#include <vector>
+#include "paddle/fluid/framework/details/fetch_op_handle.h"
+#include "paddle/fluid/framework/details/multi_devices_helper.h"
+
+namespace paddle {
+namespace framework {
+namespace details {
+
+FastThreadedSSAGraphExecutor::FastThreadedSSAGraphExecutor(
+    const ExecutionStrategy &strategy, const std::vector<Scope *> &local_scopes,
+    const std::vector<platform::Place> &places,
+    std::unique_ptr<ir::Graph> &&graph)
+    : strategy_(strategy),
+      local_scopes_(local_scopes),
+      places_(places),
+      graph_(std::move(graph)),
+      pool_(strategy.num_threads_ +
+            1),  // add one more thread for generate op_deps
+      fetch_ctxs_(places) {
+  auto &ops = graph_->Get<details::GraphOps>("ops");
+
+  for (auto &op : ops) {
+    int dep = static_cast<int>(op->NotReadyInputSize());
+    op_deps_.emplace(op.get(), dep);
+    if (dep == 0) {
+      bootstrap_ops_.emplace_back(op.get());
+    }
+  }
+
+  PrepareAtomicOpDeps();
+}
+
+FeedFetchList FastThreadedSSAGraphExecutor::Run(
+    const std::vector<std::string> &fetch_tensors) {
+  std::unique_ptr<std::unordered_map<OpHandleBase *, std::atomic<int>>>
+      op_deps = atomic_op_deps_.get();
+  PrepareAtomicOpDeps();
+
+  paddle::framework::FeedFetchList fetches;
+  fetches.resize(fetch_tensors.size());
+  std::unordered_map<std::string, std::vector<VarHandleBase *>> fetched_vars;
+  std::vector<std::unique_ptr<ir::Node>> fetch_nodes;
+  std::vector<std::unique_ptr<FetchOpHandle>> fetch_ops;
+
+  for (auto &fetch_var_name : fetch_tensors) {
+    for (auto &var_map : graph_->Get<details::GraphVars>("vars")) {
+      auto it = var_map.find(fetch_var_name);
+      if (it != var_map.end()) {
+        fetched_vars[fetch_var_name].push_back(it->second.rbegin()->get());
+      }
+    }
+  }
+
+  for (size_t i = 0; i < fetch_tensors.size(); ++i) {
+    auto &var_name = fetch_tensors[i];
+    auto fetched_var_it = fetched_vars.find(var_name);
+    PADDLE_ENFORCE(fetched_var_it != fetched_vars.end(),
+                   "Cannot find fetched variable.(Perhaps the main_program "
+                   "is not set to ParallelExecutor)");
+
+    auto &vars = fetched_var_it->second;
+
+    fetch_nodes.emplace_back(new ir::Node("fetch", ir::Node::Type::kOperation));
+    auto *op = new FetchOpHandle(fetch_nodes.back().get(), &fetches, i,
+                                 &local_scopes_);
+    fetch_ops.emplace_back(op);
+
+    for (auto &p : places_) {
+      op->SetDeviceContext(p, fetch_ctxs_.Get(p));
+    }
+
+    for (auto *var : vars) {
+      op->AddInput(var);
+    }
+
+    (*op_deps)[op] = static_cast<int>(op->NotReadyInputSize());
+  }
+
+  size_t num_complete = 0;
+  remaining_ = 0;
+  BlockingQueue<size_t> complete_q;
+  for (auto op : bootstrap_ops_) {
+    RunOpAsync(op_deps.get(), op, &complete_q);
+  }
+
+  while (num_complete != op_deps->size()) {
+    size_t num_comp = complete_q.Pop();
+    if (num_comp == -1UL) {
+      int remaining = remaining_;
+      for (int i = 0; i < remaining; ++i) {
+        complete_q.Pop();
+      }
+      LOG(FATAL) << "On exception thrown, not implemented";
+    }
+    num_complete += num_comp;
+  }
+  // Wait FetchOps.
+  if (!fetch_ops.empty()) {
+    fetch_ops.clear();
+  }
+  return fetches;
+}
+void FastThreadedSSAGraphExecutor::RunOpAsync(
+    std::unordered_map<OpHandleBase *, std::atomic<int>> *op_deps,
+    OpHandleBase *op, BlockingQueue<size_t> *complete_q) {
+  ++remaining_;
+  this->pool_.enqueue([=] {
+    OpHandleBase *op_to_run = op;
+    size_t complete = 0;
+    while (op_to_run != nullptr) {
+      try {
+        op_to_run->Run(strategy_.use_cuda_);
+        ++complete;
+      } catch (...) {
+        --remaining_;
+        complete_q->Push(-1UL);
+        return;
+      }
+      auto &outputs = op_to_run->Outputs();
+      op_to_run = nullptr;
+      for (auto &output : outputs) {
+        for (auto &pending_op : output->PendingOps()) {
+          std::atomic<int> &deps = op_deps->at(pending_op);
+          if (deps.fetch_sub(1) == 1) {  // pending_op ready
+            if (op_to_run == nullptr) {
+              op_to_run = pending_op;
+            } else {
+              this->RunOpAsync(op_deps, pending_op, complete_q);
+            }
+          }
+        }
+      }
+    }
+    --remaining_;
+    complete_q->Push(complete);
+  });
+}
+void FastThreadedSSAGraphExecutor::PrepareAtomicOpDeps() {
+  atomic_op_deps_ = pool_.enqueue([&] {
+    std::unordered_map<OpHandleBase *, std::atomic<int>> *op_deps =
+        new std::unordered_map<OpHandleBase *, std::atomic<int>>;
+    for (auto &pair : op_deps_) {
+      (*op_deps)[pair.first] = pair.second;
+    }
+    return std::unique_ptr<
+        std::unordered_map<OpHandleBase *, std::atomic<int>>>(op_deps);
+  });
+}
+}  // namespace details
+}  // namespace framework
+}  // namespace paddle

paddle/fluid/framework/details/fast_threaded_ssa_graph_executor.h

+// 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 <string>
+#include <vector>
+#include "ThreadPool.h"
+#include "paddle/fluid/framework/blocking_queue.h"
+#include "paddle/fluid/framework/details/exception_holder.h"
+#include "paddle/fluid/framework/details/execution_strategy.h"
+#include "paddle/fluid/framework/details/ssa_graph_executor.h"
+
+namespace paddle {
+namespace framework {
+class Scope;
+namespace details {
+
+class OpHandleBase;
+class FastThreadedSSAGraphExecutor : public SSAGraphExecutor {
+ public:
+  FastThreadedSSAGraphExecutor(const ExecutionStrategy &strategy,
+                               const std::vector<Scope *> &local_scopes,
+                               const std::vector<platform::Place> &places,
+                               std::unique_ptr<ir::Graph> &&graph);
+  FeedFetchList Run(const std::vector<std::string> &fetch_tensors) override;
+
+ private:
+  ExecutionStrategy strategy_;
+  std::vector<Scope *> local_scopes_;
+  std::vector<platform::Place> places_;
+  std::unique_ptr<ir::Graph> graph_;
+
+  std::unordered_map<OpHandleBase *, int> op_deps_;
+  std::vector<OpHandleBase *> bootstrap_ops_;
+
+  ::ThreadPool pool_;
+  platform::DeviceContextPool fetch_ctxs_;
+  std::atomic<int> remaining_;
+
+  void RunOpAsync(std::unordered_map<OpHandleBase *, std::atomic<int>> *op_deps,
+                  OpHandleBase *op, BlockingQueue<size_t> *complete_q);
+
+  void PrepareAtomicOpDeps();
+
+  std::future<
+      std::unique_ptr<std::unordered_map<OpHandleBase *, std::atomic<int>>>>
+      atomic_op_deps_;
+};
+}  // namespace details
+}  // namespace framework
+}  // namespace paddle

paddle/fluid/framework/details/op_handle_base.cc

@@ -158,6 +158,16 @@ void OpHandleBase::RunAndRecordEvent(platform::Place p,
 #endif
 }
 
+size_t OpHandleBase::NotReadyInputSize() const {
+  std::unordered_set<VarHandleBase *> res;
+  for (auto *var : inputs_) {
+    if (var->GeneratedOp() != nullptr) {
+      res.emplace(var);
+    }
+  }
+  return res.size();
+}
+
 }  // namespace details
 }  // namespace framework
 }  // namespace paddle

paddle/fluid/framework/details/op_handle_base.h

@@ -81,6 +81,8 @@ class OpHandleBase {
     return res.size();
   }
 
+  size_t NotReadyInputSize() const;
+
   const std::vector<VarHandleBase *> &Outputs() const { return outputs_; }
 
   size_t NoDummyInputSize() const;