Merge branch 'develop' of https://github.com/PaddlePaddle/Paddle into fix_anakin_in_manylinux1

paddle/fluid/framework/CMakeLists.txt

@@ -100,7 +100,11 @@ else()
 endif()
 
 
-cc_library(parallel_executor SRCS parallel_executor.cc DEPS threaded_ssa_graph_executor scope_buffered_ssa_graph_executor graph graph_viz_pass multi_devices_graph_pass multi_devices_graph_print_pass multi_devices_graph_check_pass)
+cc_library(parallel_executor SRCS parallel_executor.cc DEPS
+        threaded_ssa_graph_executor scope_buffered_ssa_graph_executor
+        graph graph_viz_pass multi_devices_graph_pass
+        multi_devices_graph_print_pass multi_devices_graph_check_pass
+        fast_threaded_ssa_graph_executor)
 
 cc_library(prune SRCS prune.cc DEPS framework_proto)
 cc_test(prune_test SRCS prune_test.cc DEPS op_info prune recurrent_op device_context)

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/execution_strategy.h

@@ -19,10 +19,13 @@ namespace framework {
 namespace details {
 
 struct ExecutionStrategy {
+  enum ExecutorType { kDefault = 0, kExperimental = 1 };
+
   size_t num_threads_{0};
   bool use_cuda_{true};
   bool allow_op_delay_{false};
   size_t num_iteration_per_drop_scope_{100};
+  ExecutorType type_{kDefault};
 };
 
 }  //  namespace details

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 = 0;
+      while (true) {
+        remaining = remaining_;
+        if (remaining == 0) {
+          break;
+        }
+        for (int i = 0; i < remaining; ++i) {
+          complete_q.Pop();
+        }
+      }
+      exception_.ReThrow();
+    }
+    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 (...) {
+        exception_.Catch(std::current_exception());
+        --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);
+  });
+}
+
+const ir::Graph &FastThreadedSSAGraphExecutor::Graph() const { return *graph_; }
+}  // 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;
+  const ir::Graph &Graph() const 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_;
+  ExceptionHolder exception_;
+};
+}  // 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;

paddle/fluid/framework/parallel_executor.cc

@@ -25,6 +25,7 @@ limitations under the License. */
 #include "paddle/fluid/platform/nccl_helper.h"
 #endif
 
+#include "paddle/fluid/framework/details/fast_threaded_ssa_graph_executor.h"
 #include "paddle/fluid/framework/details/multi_devices_graph_check_pass.h"
 #include "paddle/fluid/framework/details/multi_devices_graph_print_pass.h"
 #include "paddle/fluid/framework/details/scope_buffered_ssa_graph_executor.h"
@@ -193,8 +194,14 @@ ParallelExecutor::ParallelExecutor(
       member_->local_scopes_, member_->use_cuda_, build_strategy);
 #endif
 
+  if (exec_strategy.type_ == ExecutionStrategy::kDefault) {
     member_->executor_.reset(new details::ThreadedSSAGraphExecutor(
         exec_strategy, member_->local_scopes_, places, std::move(graph)));
+  } else {
+    member_->executor_.reset(new details::FastThreadedSSAGraphExecutor(
+        exec_strategy, member_->local_scopes_, places, std::move(graph)));
+  }
+
   member_->executor_.reset(new details::ScopeBufferedSSAGraphExecutor(
       exec_strategy, member_->local_scopes_, std::move(var_infos),
       member_->places_, std::move(member_->executor_)));

paddle/fluid/pybind/pybind.cc

@@ -596,8 +596,8 @@ All parameter, weight, gradient are variables in Paddle.
 
   // -- python binds for parallel executor.
   py::class_<ParallelExecutor> pe(m, "ParallelExecutor");
-  py::class_<ExecutionStrategy>(pe, "ExecutionStrategy")
-      .def(py::init())
+  py::class_<ExecutionStrategy> exec_strategy(pe, "ExecutionStrategy");
+  exec_strategy.def(py::init())
       .def_property(
           "num_threads",
           [](const ExecutionStrategy &self) { return self.num_threads_; },
@@ -624,6 +624,16 @@ All parameter, weight, gradient are variables in Paddle.
           [](ExecutionStrategy &self, size_t num_iteration_per_drop_scope) {
             self.num_iteration_per_drop_scope_ = num_iteration_per_drop_scope;
           });
+  exec_strategy.def_property(
+      "use_experimental_executor",
+      [](const ExecutionStrategy &self) {
+        return self.type_ == ExecutionStrategy::kExperimental;
+      },
+      [](ExecutionStrategy &self, bool experimental) {
+        self.type_ = experimental ? ExecutionStrategy::kExperimental
+                                  : ExecutionStrategy::kDefault;
+      });
+
   py::class_<BuildStrategy> build_strategy(pe, "BuildStrategy");
 
   py::enum_<BuildStrategy::ReduceStrategy>(build_strategy, "ReduceStrategy")

python/paddle/fluid/tests/unittests/parallel_executor_test_base.py

@@ -38,7 +38,8 @@ class TestParallelExecutorBase(unittest.TestCase):
                                   seed=None,
                                   use_parallel_executor=True,
                                   use_reduce=False,
-                                  optimizer=fluid.optimizer.Adam):
+                                  optimizer=fluid.optimizer.Adam,
+                                  use_fast_executor=False):
         def run_executor(exe, feed, fetch_list, program=None):
             if isinstance(exe, fluid.ParallelExecutor):
                 res = exe.run(fetch_list=fetch_list, feed=feed)
@@ -71,6 +72,8 @@ class TestParallelExecutorBase(unittest.TestCase):
             startup_exe.run(startup)
             exec_strategy = fluid.ExecutionStrategy()
             exec_strategy.allow_op_delay = allow_op_delay
+            if use_fast_executor:
+                exec_strategy.use_experimental_executor = True
 
             build_strategy = fluid.BuildStrategy()
             build_strategy.reduce_strategy = fluid.BuildStrategy.ReduceStrategy.Reduce \

python/paddle/fluid/tests/unittests/test_parallel_executor_mnist.py

@@ -183,7 +183,9 @@ class TestMNIST(TestParallelExecutorBase):
             use_parallel_executor=True)
 
         self.assertAlmostEquals(
-            np.mean(parallel_first_loss), single_first_loss, delta=1e-6)
+            np.mean(parallel_first_loss),
+            single_first_loss,
+            delta=1e-6, )
         self.assertAlmostEquals(
             np.mean(parallel_last_loss), single_last_loss, delta=1e-6)
 
@@ -191,7 +193,7 @@ class TestMNIST(TestParallelExecutorBase):
         self.check_simple_fc_parallel_accuracy(True)
         self.check_simple_fc_parallel_accuracy(False)
 
-    def check_batchnorm_fc_convergence(self, use_cuda):
+    def check_batchnorm_fc_convergence(self, use_cuda, use_fast_executor):
         if use_cuda and not core.is_compiled_with_cuda():
             return
 
@@ -203,11 +205,13 @@ class TestMNIST(TestParallelExecutorBase):
             fc_with_batchnorm,
             feed_dict={"image": img,
                        "label": label},
-            use_cuda=use_cuda)
+            use_cuda=use_cuda,
+            use_fast_executor=use_fast_executor)
 
     def test_batchnorm_fc(self):
-        self.check_batchnorm_fc_convergence(True)
-        self.check_batchnorm_fc_convergence(False)
+        for use_cuda in (False, True):
+            for use_fast_executor in (False, True):
+                self.check_batchnorm_fc_convergence(use_cuda, use_fast_executor)
 
     def test_batchnorm_fc_with_new_strategy(self):
         # FIXME(zcd): close this test temporally.