Use thread pool

paddle/fluid/framework/parallel_executor.cc

@@ -15,6 +15,7 @@ limitations under the License. */
 #include "paddle/fluid/framework/parallel_executor.h"
 #include "lod_tensor.h"
 #include "op_registry.h"
+#include "threadpool.h"
 
 namespace paddle {
 namespace framework {
@@ -34,7 +35,6 @@ struct VarHandle {
 struct OpHandle {
   std::vector<VarHandle *> inputs_;
   std::vector<VarHandle *> outputs_;
-  platform::DeviceContext *dev_ctx_;
 
   std::string DebugString() {
     std::stringstream ss;
@@ -66,6 +66,9 @@ struct NCCLAllReduceOpHandle : public OpHandle {};
 
 class ParallelExecutorPrivate {
  public:
+  explicit ParallelExecutorPrivate(size_t num_threads = 12)
+      : pool_(num_threads) {}
+
   std::unordered_map<platform::Place, Scope *, platform::PlaceHash>
       local_scopes_;
   std::unordered_map<platform::Place, platform::CUDADeviceContext,
@@ -78,6 +81,8 @@ class ParallelExecutorPrivate {
                      platform::PlaceHash>
       vars_;
   std::vector<std::unique_ptr<OpHandle>> ops_;
+
+  ThreadPool pool_;
 };
 
 // TODO(yy): Move this function somewhere
@@ -285,13 +290,15 @@ void ParallelExecutor::BCastParamsToGPUs(
 std::vector<LoDTensor> ParallelExecutor::Run(
     const std::vector<std::string> &fetch_tensors) {
   // Version --> VarHandle
-  std::unordered_set<VarHandle *> pending_vars;
+
+  std::unordered_map<VarHandle *, bool> pending_vars;
   std::unordered_map<OpHandle *, size_t> pending_ops;
 
   for (auto &place_pair : member_->vars_) {
     for (auto &name_pair : place_pair.second) {
       for (auto &version_pair : name_pair.second) {
-        pending_vars.insert(&version_pair.second);
+        pending_vars[&version_pair.second] =
+            version_pair.second.generated_op_ == nullptr;
       }
     }
   }
@@ -300,56 +307,50 @@ std::vector<LoDTensor> ParallelExecutor::Run(
     pending_ops.insert({op.get(), op->inputs_.size()});
   }
 
-  std::unordered_set<OpHandle *> complete_op;
-
-  size_t num_op = pending_ops.size();
-
-  while (complete_op.size() != num_op) {
-    std::vector<VarHandle *> to_remove;
-    for (auto &var : pending_vars) {
-      if (var->generated_op_ == nullptr ||
-          complete_op.count(var->generated_op_) != 0) {
-        to_remove.push_back(var);
+  while (!pending_ops.empty()) {
+    VarHandle *ready_var = nullptr;
+    for (auto &pair : pending_vars) {
+      if (pair.second) {
+        ready_var = pair.first;
       }
     }
-    for (auto *var : to_remove) {
-      pending_vars.erase(var);
+
+    if (ready_var == nullptr) {
+      member_->pool_.Wait();  // Wait thread pool;
+      continue;
     }
 
+    pending_vars.erase(ready_var);
+
     std::vector<OpHandle *> to_run;
-    for (auto *var : to_remove) {
-      for (auto *op : var->pending_ops_) {
-        if (var->name_ == "mean_0.tmp_0@GRAD") {
-          LOG(INFO) << op->DebugString();
-        }
-        auto &num = pending_ops[op];
-        --num;
-        if (num == 0) {
-          to_run.emplace_back(op);
-        }
+
+    for (auto *op : ready_var->pending_ops_) {
+      auto &deps = pending_ops[op];
+      --deps;
+      if (deps == 0) {
+        to_run.emplace_back(op);
       }
     }
 
     for (auto *op : to_run) {
       pending_ops.erase(op);
-      complete_op.insert(op);
-    }
 
-    if (to_run.empty()) break;
+      std::vector<bool *> ready_buffer;
+      for (auto *var : op->outputs_) {
+        ready_buffer.emplace_back(&pending_vars[var]);
+      }
 
-    // TODO(yy): Use thead pool to run OpHandle. Operators in ToRun can be
-    // paralleled. We can also use another schedule method. Just a demo here.
+      auto op_run = [ready_buffer, op] {
+        // TODO(yy) Check Previous Op has same dev ctx.
+        LOG(INFO) << "Run " << op->DebugString();
+        for (auto *ready : ready_buffer) {
+          *ready = true;
+        }
+      };
 
-    std::stringstream ss;
-    ss << "\n";
-    for (auto *op : to_run) {
-      ss << op->DebugString() << "\n";
+      member_->pool_.Run(op_run);
     }
-    ss << std::endl;
-    LOG(INFO) << ss.str();
   }
-
-  PADDLE_ENFORCE_EQ(complete_op.size(), num_op);
   return std::vector<LoDTensor>();
 }
 }  // namespace framework

paddle/fluid/framework/threadpool.h

@@ -32,6 +32,8 @@ namespace framework {
 // number of threads.
 class ThreadPool {
  public:
+  explicit ThreadPool(int num_threads);
+
   using Task = std::packaged_task<std::unique_ptr<platform::EnforceNotMet>()>;
 
   // Returns the singleton of ThreadPool.
@@ -103,8 +105,6 @@ class ThreadPool {
 
   DISABLE_COPY_AND_ASSIGN(ThreadPool);
 
-  explicit ThreadPool(int num_threads);
-
   // If the task queue is empty and avaialbe is equal to the number of
   // threads, means that all tasks are completed.  Note: this function
   // is not thread-safe.  Returns true if all tasks are completed.