【HETERPS】pipeline adaptive for heterps

paddle/fluid/framework/fleet/ps_gpu_wrapper.cc

@@ -40,8 +40,7 @@ namespace framework {
 std::shared_ptr<PSGPUWrapper> PSGPUWrapper::s_instance_ = NULL;
 bool PSGPUWrapper::is_initialized_ = false;
 
-void PSGPUWrapper::BuildTask(std::shared_ptr<HeterContext> gpu_task,
-                             uint64_t table_id, int feature_dim) {
+void PSGPUWrapper::BuildTask(std::shared_ptr<HeterContext> gpu_task) {
   VLOG(3) << "PSGPUWrapper::BuildGPUPSTask begin";
   platform::Timer timeline;
   timeline.Start();
@@ -137,17 +136,16 @@ void PSGPUWrapper::BuildTask(std::shared_ptr<HeterContext> gpu_task,
     local_ptr[i].resize(local_keys[i].size());
   }
   timeline.Start();
-  auto ptl_func = [this, &local_keys, &local_ptr, &table_id,
-                   &fleet_ptr](int i) {
+  auto ptl_func = [this, &local_keys, &local_ptr, &fleet_ptr](int i) {
     size_t key_size = local_keys[i].size();
 #ifdef PADDLE_WITH_PSLIB
     auto tt = fleet_ptr->pslib_ptr_->_worker_ptr->pull_sparse_ptr(
-        reinterpret_cast<char**>(local_ptr[i].data()), table_id,
+        reinterpret_cast<char**>(local_ptr[i].data()), this->table_id_,
         local_keys[i].data(), key_size);
 #endif
 #ifdef PADDLE_WITH_PSCORE
     auto tt = fleet_ptr->_worker_ptr->pull_sparse_ptr(
-        reinterpret_cast<char**>(local_ptr[i].data()), table_id,
+        reinterpret_cast<char**>(local_ptr[i].data()), this->table_id_,
         local_keys[i].data(), key_size);
 #endif
     tt.wait();
@@ -270,11 +268,8 @@ void PSGPUWrapper::BuildTask(std::shared_ptr<HeterContext> gpu_task,
           << " seconds.";
 }
 
-void PSGPUWrapper::BuildGPUPS(uint64_t table_id, int feature_dim) {
+void PSGPUWrapper::BuildGPUTask(std::shared_ptr<HeterContext> gpu_task) {
   int device_num = heter_devices_.size();
-  std::shared_ptr<HeterContext> gpu_task = gpu_task_pool_.Get();
-  gpu_task->Reset();
-  BuildTask(gpu_task, table_id, feature_dim);
   platform::Timer timeline;
   timeline.Start();
 
@@ -289,6 +284,10 @@ void PSGPUWrapper::BuildGPUPS(uint64_t table_id, int feature_dim) {
     delete HeterPs_;
     HeterPs_ = nullptr;
   }
+  if (size_max <= 0) {
+    VLOG(1) << "Skip build gpu ps cause feasign nums = " << size_max;
+    return;
+  }
   std::vector<std::thread> threads(device_num);
   HeterPs_ = HeterPsBase::get_instance(size_max, resource_);
   HeterPs_->set_nccl_comm_and_size(inner_comms_, inter_comms_, node_size_);
@@ -297,7 +296,9 @@ void PSGPUWrapper::BuildGPUPS(uint64_t table_id, int feature_dim) {
     this->HeterPs_->build_ps(i, gpu_task->device_keys_[i].data(),
                              gpu_task->device_values_[i].data(),
                              feature_keys_count[i], 500000, 2);
-    HeterPs_->show_one_table(i);
+    if (feature_keys_count[i] > 0) {
+      HeterPs_->show_one_table(i);
+    }
   };
   for (size_t i = 0; i < threads.size(); i++) {
     threads[i] = std::thread(build_func, i);
@@ -308,7 +309,109 @@ void PSGPUWrapper::BuildGPUPS(uint64_t table_id, int feature_dim) {
   timeline.Pause();
   VLOG(1) << "GpuPs build table total costs: " << timeline.ElapsedSec()
           << " s.";
-  gpu_task_pool_.Push(gpu_task);
+}
+
+void PSGPUWrapper::LoadIntoMemory(bool is_shuffle) {
+  platform::Timer timer;
+  VLOG(3) << "Begin LoadIntoMemory(), dataset[" << dataset_ << "]";
+  timer.Start();
+  dataset_->LoadIntoMemory();
+  timer.Pause();
+  VLOG(0) << "LoadIntoMemory cost: " << timer.ElapsedSec() << "s";
+
+  // local shuffle
+  if (is_shuffle) {
+    dataset_->LocalShuffle();
+  }
+
+  std::shared_ptr<HeterContext> gpu_task = gpu_task_pool_.Get();
+  gpu_task->Reset();
+  data_ready_channel_->Put(gpu_task);
+  VLOG(3) << "End LoadIntoMemory(), dataset[" << dataset_ << "]";
+}
+
+void PSGPUWrapper::start_build_thread() {
+  running_ = true;
+  VLOG(3) << "start build CPU&GPU ps thread.";
+  build_cpu_threads_ = std::thread([this] { build_cpu_thread(); });
+  build_gpu_threads_ = std::thread([this] { build_gpu_thread(); });
+}
+
+void PSGPUWrapper::build_cpu_thread() {
+  while (running_) {
+    std::shared_ptr<HeterContext> gpu_task = nullptr;
+    if (!data_ready_channel_->Get(gpu_task)) {
+      continue;
+    }
+    VLOG(3) << "thread BuildTask start.";
+    platform::Timer timer;
+    timer.Start();
+    // build cpu ps data process
+    BuildTask(gpu_task);
+    timer.Pause();
+    VLOG(1) << "thread BuildTask end, cost time: " << timer.ElapsedSec() << "s";
+    buildcpu_ready_channel_->Put(gpu_task);
+  }
+  VLOG(3) << "build cpu thread end";
+}
+
+void PSGPUWrapper::build_gpu_thread() {
+  while (running_) {
+    std::shared_ptr<HeterContext> gpu_task = nullptr;
+    if (!gpu_free_channel_->Get(gpu_task)) {
+      continue;
+    }
+    if (!buildcpu_ready_channel_->Get(gpu_task)) {
+      continue;
+    }
+    VLOG(3) << "thread BuildGPUTask start.";
+    platform::Timer timer;
+    timer.Start();
+    BuildGPUTask(gpu_task);
+    timer.Pause();
+    VLOG(1) << "thread BuildGPUTask end, cost time: " << timer.ElapsedSec()
+            << "s";
+
+    gpu_task_pool_.Push(gpu_task);
+    train_ready_channel_->Put(gpu_task);
+  }
+  VLOG(3) << "build gpu thread end";
+}
+
+void PSGPUWrapper::BeginPass() {
+  platform::Timer timer;
+  timer.Start();
+  if (current_task_) {
+    PADDLE_THROW(
+        platform::errors::Fatal("[BeginPass] current task is not ended."));
+  }
+  // load+build done
+  if (!train_ready_channel_->Get(current_task_)) {
+    PADDLE_THROW(platform::errors::Fatal("train_ready_channel_ failed."));
+  }
+  timer.Pause();
+  VLOG(1) << "BeginPass end, cost time: " << timer.ElapsedSec() << "s";
+}
+
+void PSGPUWrapper::EndPass() {
+  if (!current_task_) {
+    PADDLE_THROW(
+        platform::errors::Fatal("[EndPass] current task has been ended."));
+  }
+  platform::Timer timer;
+  timer.Start();
+  size_t keysize_max = 0;
+  // in case of feasign_num = 0, skip dump_to_cpu
+  for (size_t i = 0; i < heter_devices_.size(); i++) {
+    keysize_max = std::max(keysize_max, current_task_->device_keys_[i].size());
+  }
+  if (keysize_max != 0) {
+    HeterPs_->end_pass();
+  }
+  current_task_ = nullptr;
+  gpu_free_channel_->Put(current_task_);
+  timer.Pause();
+  VLOG(1) << "EndPass end, cost time: " << timer.ElapsedSec() << "s";
 }
 
 void PSGPUWrapper::PullSparse(const paddle::platform::Place& place,

paddle/fluid/framework/fleet/ps_gpu_wrapper.h

@@ -82,9 +82,33 @@ class PSGPUWrapper {
                    const int hidden_size, const int64_t total_length,
                    const int batch_size);
 
-  void BuildGPUPS(const uint64_t table_id, int feature_dim);
-  void BuildTask(std::shared_ptr<HeterContext> gpu_task, uint64_t table_id,
-                 int feature_dim);
+  void BuildGPUTask(std::shared_ptr<HeterContext> gpu_task);
+  void BuildTask(std::shared_ptr<HeterContext> gpu_task);
+  void LoadIntoMemory(bool is_shuffle);
+  void BeginPass();
+  void EndPass();
+  void start_build_thread();
+  void build_cpu_thread();
+  void build_gpu_thread();
+
+  void Finalize() {
+    VLOG(3) << "PSGPUWrapper Begin Finalize.";
+    if (s_instance_ == nullptr) {
+      return;
+    }
+    data_ready_channel_->Close();
+    buildcpu_ready_channel_->Close();
+    gpu_free_channel_->Close();
+    train_ready_channel_->Close();
+    running_ = false;
+    VLOG(3) << "begin stop build_cpu_threads_";
+    build_cpu_threads_.join();
+    VLOG(3) << "begin stop build_gpu_threads_";
+    build_gpu_threads_.join();
+    s_instance_ = nullptr;
+    VLOG(3) << "PSGPUWrapper Finalize Finished.";
+  }
+
   void InitializeGPU(const std::vector<int>& dev_ids) {
     if (s_instance_ != NULL && is_initialized_ == false) {
       VLOG(3) << "PSGPUWrapper Begin InitializeGPU";
@@ -129,6 +153,24 @@ class PSGPUWrapper {
 #endif
       }
       heter_devices_ = dev_ids;
+      data_ready_channel_->Open();
+      data_ready_channel_->SetCapacity(3);
+      buildcpu_ready_channel_->Open();
+      buildcpu_ready_channel_->SetCapacity(3);
+      gpu_free_channel_->Open();
+      gpu_free_channel_->SetCapacity(1);
+      train_ready_channel_->Open();
+      train_ready_channel_->SetCapacity(1);
+
+      current_task_ = nullptr;
+      gpu_free_channel_->Put(current_task_);
+
+      table_id_ = 1;
+#ifdef PADDLE_WITH_PSLIB
+      table_id_ = 0;
+#endif
+      // start build cpu&gpu ps thread
+      start_build_thread();
     }
   }
 
@@ -206,18 +248,8 @@ class PSGPUWrapper {
     slot_vector_ = slot_vector;
   }
 
-  void EndPass() { HeterPs_->end_pass(); }
   void ShowOneTable(int index) { HeterPs_->show_one_table(index); }
 
-  void Finalize() {
-    VLOG(3) << "PSGPUWrapper Begin Finalize.";
-    if (s_instance_ == nullptr) {
-      return;
-    }
-    s_instance_ = nullptr;
-    VLOG(3) << "PSGPUWrapper Finalize Finished.";
-  }
-
  private:
   static std::shared_ptr<PSGPUWrapper> s_instance_;
   Dataset* dataset_;
@@ -231,6 +263,7 @@ class PSGPUWrapper {
   std::vector<int> slot_vector_;
   int multi_node_{0};
   int node_size_;
+  uint64_t table_id_;
   std::vector<ncclComm_t> inner_comms_;
   std::vector<ncclComm_t> inter_comms_;
   std::vector<ncclUniqueId> inter_ncclids_;
@@ -242,6 +275,27 @@ class PSGPUWrapper {
   int thread_keys_shard_num_ = 37;
   uint64_t max_fea_num_per_pass_ = 5000000000;
 
+  std::shared_ptr<
+      paddle::framework::ChannelObject<std::shared_ptr<HeterContext>>>
+      data_ready_channel_ =
+          paddle::framework::MakeChannel<std::shared_ptr<HeterContext>>();
+  std::shared_ptr<
+      paddle::framework::ChannelObject<std::shared_ptr<HeterContext>>>
+      buildcpu_ready_channel_ =
+          paddle::framework::MakeChannel<std::shared_ptr<HeterContext>>();
+  std::shared_ptr<
+      paddle::framework::ChannelObject<std::shared_ptr<HeterContext>>>
+      gpu_free_channel_ =
+          paddle::framework::MakeChannel<std::shared_ptr<HeterContext>>();
+  std::shared_ptr<
+      paddle::framework::ChannelObject<std::shared_ptr<HeterContext>>>
+      train_ready_channel_ =
+          paddle::framework::MakeChannel<std::shared_ptr<HeterContext>>();
+  std::shared_ptr<HeterContext> current_task_ = nullptr;
+  std::thread build_cpu_threads_;
+  std::thread build_gpu_threads_;
+  bool running_ = false;
+
  protected:
   static bool is_initialized_;
 };

paddle/fluid/framework/heter_util.h

@@ -36,7 +36,7 @@ enum HeterTaskState { PULL_SPARSE, OP_RUN, XPU, OP_RUN_END, PUSH_GRAD, DONE };
 class HeterTask {
  public:
   HeterTask() {}
-  virtual ~HeterTask(){};
+  virtual ~HeterTask() {}
 
   void Update() {
     if (state_ == PULL_SPARSE) {
@@ -111,7 +111,7 @@ template <class T>
 class HeterObjectPool {
  public:
   HeterObjectPool() {}
-  virtual ~HeterObjectPool(){};
+  virtual ~HeterObjectPool() {}
   std::shared_ptr<T> Get() {
     std::lock_guard<std::mutex> lock(mutex_);
     if (pool_.empty()) {
@@ -131,6 +131,10 @@ class HeterObjectPool {
     std::lock_guard<std::mutex> lock(mutex_);
     return pool_.size();
   }
+  bool Empty() {
+    std::lock_guard<std::mutex> lock(mutex_);
+    return pool_.empty();
+  }
   std::shared_ptr<T>& GetElement(int i) { return pool_[i]; }
 
  private:
@@ -160,7 +164,7 @@ class BtObjectPool {
   virtual ~BtObjectPool() {
     bthread_cond_destroy(&cond_);
     bthread_mutex_destroy(&mutex_);
-  };
+  }
 
   std::shared_ptr<T> Get() {
     BthreadMutextGuard guard(&mutex_);

paddle/fluid/pybind/ps_gpu_wrapper_py.cc

@@ -47,7 +47,9 @@ void BindPSGPUWrapper(py::module* m) {
            py::call_guard<py::gil_scoped_release>())
       .def("end_pass", &framework::PSGPUWrapper::EndPass,
            py::call_guard<py::gil_scoped_release>())
-      .def("build_gpu_ps", &framework::PSGPUWrapper::BuildGPUPS,
+      .def("begin_pass", &framework::PSGPUWrapper::BeginPass,
+           py::call_guard<py::gil_scoped_release>())
+      .def("load_into_memory", &framework::PSGPUWrapper::LoadIntoMemory,
            py::call_guard<py::gil_scoped_release>())
       .def("finalize", &framework::PSGPUWrapper::Finalize,
            py::call_guard<py::gil_scoped_release>());

paddle/fluid/pybind/pybind.cc

@@ -185,6 +185,14 @@ bool IsCompiledWithMKLDNN() {
 #endif
 }
 
+bool IsCompiledWithHETERPS() {
+#ifndef PADDLE_WITH_HETERPS
+  return false;
+#else
+  return true;
+#endif
+}
+
 bool SupportsBfloat16() {
 #ifndef PADDLE_WITH_MKLDNN
   return false;
@@ -1910,6 +1918,7 @@ All parameter, weight, gradient are variables in Paddle.
   m.def("is_compiled_with_npu", IsCompiledWithNPU);
   m.def("is_compiled_with_xpu", IsCompiledWithXPU);
   m.def("is_compiled_with_mkldnn", IsCompiledWithMKLDNN);
+  m.def("_is_compiled_with_heterps", IsCompiledWithHETERPS);
   m.def("supports_bfloat16", SupportsBfloat16);
   m.def("supports_bfloat16_fast_performance", SupportsBfloat16FastPerformance);
   m.def("op_supported_infos", OpSupportedInfos);