[cherry-pick] Refine thread pool config of interpretercore

paddle/fluid/framework/new_executor/interpretercore.cc

@@ -19,6 +19,7 @@
 #include "paddle/fluid/framework/details/nan_inf_utils.h"
 #include "paddle/fluid/framework/details/share_tensor_buffer_functor.h"
 #include "paddle/fluid/framework/new_executor/interpretercore_util.h"
+#include "paddle/fluid/framework/new_executor/threadpool_config.h"
 #include "paddle/fluid/framework/operator.h"
 #include "paddle/fluid/platform/device/gpu/gpu_info.h"
 #include "paddle/fluid/platform/os_info.h"
@@ -47,9 +48,6 @@ constexpr const char* kTaskCompletion = "TaskCompletion";
 
 namespace paddle {
 namespace framework {
-// NOTE(Aurelius84): Need a better strategy to determine it.
-static constexpr size_t kHostNumThreads = 4;
-static constexpr size_t kDeviceNumThreads = 1;
 
 InterpreterCore::InterpreterCore(const platform::Place& place,
                                  const BlockDesc& block,
@@ -293,8 +291,14 @@ bool InterpreterCore::BuildInplaceCheckVarIsOnlyInput(size_t var_index) {
 
 std::shared_ptr<interpreter::AsyncWorkQueue> InterpreterCore::GetWorkQueue() {
   if (async_work_queue_ == nullptr) {
-    async_work_queue_ = std::make_shared<interpreter::AsyncWorkQueue>(
-        kHostNumThreads, kDeviceNumThreads, &main_thread_blocker_);
+    int host_num_threads = 1, deivce_num_threads = 1, prepare_num_threads = 1;
+    std::tie(host_num_threads, deivce_num_threads, prepare_num_threads) =
+        interpreter::GetThreadPoolConfig(place_, vec_instruction_.size());
+    async_work_queue_ =
+        std::make_shared<interpreter::AsyncWorkQueue>(host_num_threads,
+                                                      deivce_num_threads,
+                                                      prepare_num_threads,
+                                                      &main_thread_blocker_);
   }
   return async_work_queue_;
 }
@@ -773,14 +777,23 @@ void InterpreterCore::ExecuteInstructionList(
 
   platform::RecordEvent record_prepare(
       "PrepareAtomic", platform::TracerEventType::UserDefined, 1);
-  // NOTE(zhiqiu): get the prepared deps from std::future, and async prepare
-  // those for the next step
-  auto atomic_deps = atomic_deps_.get();
-  auto atomic_var_ref = atomic_var_ref_.get();
-
-  atomic_deps_ = async_work_queue_->PrepareAtomicDeps(dependecy_count_);
-  atomic_var_ref_ =
-      async_work_queue_->PrepareAtomicVarRef(var_scope_.VecMetaInfo());
+
+  std::unique_ptr<std::vector<std::atomic<size_t>>> atomic_deps = nullptr;
+  std::unique_ptr<std::vector<std::atomic<size_t>>> atomic_var_ref = nullptr;
+
+  if (async_work_queue_->QueueNumThreads(kPrepareWorkQueueIdx)) {
+    // NOTE(zhiqiu): get the prepared deps from std::future, and async prepare
+    // those for the next step
+    atomic_deps = atomic_deps_.get();
+    atomic_var_ref = atomic_var_ref_.get();
+
+    atomic_deps_ = async_work_queue_->PrepareAtomicDeps(dependecy_count_);
+    atomic_var_ref_ =
+        async_work_queue_->PrepareAtomicVarRef(var_scope_.VecMetaInfo());
+  } else {
+    atomic_deps = interpreter::PrepareAtomicDeps(dependecy_count_);
+    atomic_var_ref = interpreter::PrepareAtomicVarRef(var_scope_.VecMetaInfo());
+  }
   record_prepare.End();
 
   exception_holder_.Clear();

paddle/fluid/framework/new_executor/interpretercore.h

@@ -129,7 +129,7 @@ class InterpreterCore {
 
   std::vector<Instruction> vec_instruction_;  // deconstruct before OpFuncNode
 
-  // last_live_ops_[i] contains the id of operatos that last access var[i]
+  // last_live_ops_[i] contains the id of operators that last access var[i]
   std::map<size_t, std::set<size_t>> last_live_ops_;
 
   std::vector<size_t> dependecy_count_;

paddle/fluid/framework/new_executor/interpretercore_util.cc

@@ -42,10 +42,12 @@ namespace framework {
 namespace interpreter {
 
 using VariableIdMap = std::map<std::string, std::vector<int>>;
-constexpr size_t kPrepareWorkQueueIdx = 2;
 
 const std::vector<WorkQueueOptions> ConstructWorkQueueOptions(
-    size_t host_num_threads, size_t device_num_threads, EventsWaiter* waiter) {
+    size_t host_num_threads,
+    size_t device_num_threads,
+    size_t prepare_num_threads,
+    EventsWaiter* waiter) {
   std::vector<WorkQueueOptions> group_options;
   // for execute host Kernel
   group_options.emplace_back(/*name*/ "HostTasks",
@@ -65,7 +67,7 @@ const std::vector<WorkQueueOptions> ConstructWorkQueueOptions(
                              /*events_waiter*/ waiter);
   // for prepare deps and others
   group_options.emplace_back(/*name*/ "Prepare",
-                             /*num_threads*/ 1,
+                             /*num_threads*/ prepare_num_threads,
                              /*allow_spinning*/ true,
                              /*always_spinning*/ false,
                              /*track_task*/ false,
@@ -76,10 +78,11 @@ const std::vector<WorkQueueOptions> ConstructWorkQueueOptions(
 
 AsyncWorkQueue::AsyncWorkQueue(size_t host_num_threads,
                                size_t device_num_threads,
+                               size_t prepare_num_threads,
                                EventsWaiter* waiter)
     : host_num_thread_(host_num_threads) {
-  queue_group_ = CreateWorkQueueGroup(
-      ConstructWorkQueueOptions(host_num_threads, device_num_threads, waiter));
+  queue_group_ = CreateWorkQueueGroup(ConstructWorkQueueOptions(
+      host_num_threads, device_num_threads, prepare_num_threads, waiter));
 }
 
 void AsyncWorkQueue::AddTask(const OpFuncType& op_func_type,

paddle/fluid/framework/new_executor/interpretercore_util.h

@@ -39,6 +39,7 @@
 #include "paddle/fluid/platform/init.h"
 
 using AtomicVectorSizeT = std::vector<std::atomic<size_t>>;
+constexpr size_t kPrepareWorkQueueIdx = 2;
 
 namespace paddle {
 namespace framework {
@@ -48,6 +49,7 @@ class AsyncWorkQueue {
  public:
   AsyncWorkQueue(size_t host_num_threads,
                  size_t deivce_num_threads,
+                 size_t prepare_num_threads,
                  EventsWaiter* waiter);
 
   std::future<std::unique_ptr<AtomicVectorSizeT>> PrepareAtomicDeps(
@@ -61,6 +63,10 @@ class AsyncWorkQueue {
 
   void Cancel() { queue_group_->Cancel(); }
 
+  size_t QueueNumThreads(size_t idx) {
+    return queue_group_->QueueNumThreads(idx);
+  }
+
  private:
   size_t host_num_thread_;
   std::unique_ptr<WorkQueueGroup> queue_group_;

paddle/fluid/framework/new_executor/threadpool_config.h

@@ -0,0 +1,136 @@
+// Copyright (c) 2022 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 <thread>
+#include "paddle/fluid/platform/device/ipu/ipu_info.h"
+#include "paddle/fluid/platform/device/npu/npu_info.h"
+#include "paddle/fluid/platform/place.h"
+#include "paddle/phi/backends/device_manager.h"
+#include "paddle/phi/backends/gpu/gpu_info.h"
+#include "paddle/phi/backends/xpu/xpu_info.h"
+
+DECLARE_bool(new_executor_serial_run);
+
+namespace paddle {
+namespace framework {
+namespace interpreter {
+
+static constexpr size_t kHostNumThreads = 4;
+static constexpr size_t kDeviceNumThreads = 1;
+static constexpr size_t kNumGcThreads = 1;
+static constexpr size_t kNumPrepareThreads = 0;
+
+static constexpr size_t kMinOpNumForAsyncPrepare = 1000;
+
+// By default, one interpretercore contains:
+// 1-size thread pool for device kernel launch (or 0 for cpu execution),
+// 1-size thread pool for host kernel launch (or more if the system contains
+// enough processors).
+
+// And it may contain:
+// 1-size thread pool for gc if it is can not use FastGC,
+// 1-size thread pool for preparation if the program contains two many ops
+// (1000+).
+
+// Note that the purpose of the config is to limit the total 'possible'
+// threads introduced by interpretercore to avoid hurting performance.
+
+inline std::tuple<int, int, int> GetThreadPoolConfig(const phi::Place place,
+                                                     size_t op_num) {
+  int num_device_threads = kDeviceNumThreads,
+      num_host_threads = kHostNumThreads,
+      num_prepare_threads = kNumPrepareThreads;
+
+  if (op_num > kMinOpNumForAsyncPrepare) {
+    num_prepare_threads = 1;
+  }
+
+  int device_count = 0, processor_count = 0;
+  if (platform::is_cpu_place(place)) {
+    num_device_threads = 0;
+    num_host_threads = 4;
+  } else {
+    processor_count = std::thread::hardware_concurrency();
+    if (processor_count) {
+      if (platform::is_gpu_place(place)) {
+#if defined(PADDLE_WITH_CUDA) || defined(PADDLE_WITH_HIP)
+        device_count = phi::backends::gpu::GetGPUDeviceCount();
+#endif
+      }
+      if (platform::is_xpu_place(place)) {
+#if defined(PADDLE_WITH_XPU)
+        device_count = phi::backends::xpu::GetXPUDeviceCount();
+#endif
+      }
+      if (platform::is_npu_place(place)) {
+#if defined(PADDLE_WITH_ASCEND_CL)
+        device_count = platform::GetNPUDeviceCount();
+#endif
+      }
+      if (platform::is_ipu_place(place)) {
+#if defined(PADDLE_WITH_IPU)
+        device_count = platform::GetIPUDeviceCount();
+#endif
+      }
+      if (platform::is_custom_place(place)) {
+#if defined(PADDLE_WITH_CUSTOM_DEVICE)
+        device_count =
+            phi::DeviceManager::GetDeviceCount(place.GetDeviceType());
+#endif
+      }
+
+      // Tricky implementation.
+      // In multi-card training, each card may set env like
+      // CUDA_VISIBLE_DEVICE=0 In that case, device_count is set to 8.
+      if (device_count == 1) {
+        device_count = 8;  // in many case, the accelerator has 8 cards.
+      }
+
+      // We expect processor_count = 2 * (the possible total threads when doing
+      // multi-card training), to make sure that the system will not slow down
+      // because of too many threads. Here, 2 is experience value. Since each
+      // device has one interpretercore, the possible total threads when doing
+      // multi-card training = device_count * (the possible total threads in one
+      // interpretercore).
+
+      if (device_count) {
+        auto num = processor_count / device_count / 2 -
+                   (kNumGcThreads + kNumPrepareThreads + num_device_threads);
+        num_host_threads =
+            num > 0 ? (num > kHostNumThreads ? kHostNumThreads : num) : 1;
+      }
+    }
+  }
+
+  // In serial run, only one 1-size thread pool is used
+  if (FLAGS_new_executor_serial_run) {
+    num_host_threads = 0;
+    num_device_threads = 1;
+  }
+
+  VLOG(4) << "place:" << place << ", processor_count:" << processor_count
+          << ", device_count:" << device_count
+          << ", serial_run:" << FLAGS_new_executor_serial_run
+          << ", num_host_threads:" << num_host_threads
+          << ", num_device_threads:" << num_device_threads
+          << ", num_prepare_threads:" << num_prepare_threads;
+  return std::make_tuple(
+      num_host_threads, num_device_threads, num_prepare_threads);
+}
+
+}  // namespace interpreter
+}  // namespace framework
+}  // namespace paddle

paddle/fluid/framework/new_executor/workqueue/workqueue.cc

@@ -121,8 +121,13 @@ WorkQueueGroupImpl::WorkQueueGroupImpl(
   queues_.resize(num_queues);
   void* buffer = malloc(sizeof(NonblockingThreadPool) * num_queues);
   queues_storage_ = reinterpret_cast<NonblockingThreadPool*>(buffer);
+
   for (size_t idx = 0; idx < num_queues; ++idx) {
     const auto& options = queues_options_[idx];
+    if (options.num_threads == 0) {
+      queues_[idx] = nullptr;
+      continue;
+    }
     if (options.track_task && tracker_ == nullptr &&
         options.events_waiter != nullptr) {
       empty_notifier_ = options.events_waiter->RegisterEvent(kQueueEmptyEvent);
@@ -144,7 +149,9 @@ WorkQueueGroupImpl::WorkQueueGroupImpl(
 
 WorkQueueGroupImpl::~WorkQueueGroupImpl() {
   for (auto queue : queues_) {
-    queue->~NonblockingThreadPool();
+    if (queue) {
+      queue->~NonblockingThreadPool();
+    }
   }
   if (tracker_ != nullptr) {
     tracker_->~TaskTracker();
@@ -161,6 +168,10 @@ void WorkQueueGroupImpl::AddTask(size_t queue_idx, std::function<void()> fn) {
                                platform::TracerEventType::UserDefined,
                                10 /*level*/);
   assert(queue_idx < queues_.size());
+  PADDLE_ENFORCE_NOT_NULL(
+      queues_.at(queue_idx),
+      platform::errors::NotFound("Workqueue of index %d is not initialized.",
+                                 queue_idx));
   if (queues_options_.at(queue_idx).track_task) {
     fn = [task = std::move(fn),
           raii = CounterGuard<TaskTracker>(tracker_)]() mutable { task(); };
@@ -170,6 +181,9 @@ void WorkQueueGroupImpl::AddTask(size_t queue_idx, std::function<void()> fn) {
 
 size_t WorkQueueGroupImpl::QueueNumThreads(size_t queue_idx) const {
   assert(queue_idx < queues_.size());
+  if (!queues_.at(queue_idx)) {
+    return 0;
+  }
   return queues_.at(queue_idx)->NumThreads();
 }
 
@@ -183,10 +197,14 @@ size_t WorkQueueGroupImpl::QueueGroupNumThreads() const {
 
 void WorkQueueGroupImpl::Cancel() {
   for (auto queue : queues_) {
-    queue->Cancel();
+    if (queue) {
+      queue->Cancel();
+    }
   }
   for (auto queue : queues_) {
-    queue->WaitThreadsExit();
+    if (queue) {
+      queue->WaitThreadsExit();
+    }
   }
 }