Revert "fix async while"

core/predictor/framework/bsf-inl.h

@@ -89,12 +89,11 @@ bool Task<InItemT, OutItemT>::task_fetch_create(BatchTasks<TaskT>& batchTask) {
         
         size_t databuf_size = fetchvar_batch * fetchvar_bytesize_index;
         
-        void* databuf_data =
-            MempoolWrapper::instance().malloc(databuf_size, memoryPtr);
+        void* databuf_data = MempoolWrapper::instance().malloc(databuf_size,memoryPtr);
         paddle::PaddleBuf paddleBuf(databuf_data, databuf_size);
         tensor_out.data = paddleBuf;
         
-        // tensor_out.data.Resize(databuf_size);
+        //tensor_out.data.Resize(databuf_size);
       } else {
         // 当taskmeta_num = 1时，由于同时只有一个taskMeta操作task
         // 不涉及线程安全问题，所以此时可以直接由taskMeta->task->resize->copy
@@ -212,38 +211,48 @@ void TaskExecutor<TaskT>::stop() {
 }
 
 template <typename TaskT>
-int TaskExecutor<TaskT>::schedule(
+TaskHandler<TaskT> TaskExecutor<TaskT>::schedule(
     const void* inVectorT_ptr,
-    void* outVectorT_ptr,
-    MempoolRegion* memoryPtr,
-    THREAD_MUTEX_T* thread_mutex_ptr,
-    THREAD_COND_T* thread_cond_ptr,
-    TaskManager<InType, OutType>* task_manager_ptr) {  // NOLINT
+    void* outVectorT_ptr, MempoolRegion* memoryPtr) {  // NOLINT
   TaskT* task = butil::get_object<TaskT>();
   if (!task) {
     LOG(ERROR) << "Failed get TaskT from object pool";
-    return -1;
+    return TaskHandler<TaskT>::valid_handle();
   }
   task->clear();
 
-  task->task_manager_ptr = task_manager_ptr;
-  task->thread_mutex_ptr = thread_mutex_ptr;
-  task->thread_cond_ptr = thread_cond_ptr;
+  /*
+  if (!BatchTasks<TaskT>::check_valid(in, out, _overrun)) {
+    LOG(ERROR) << "Invalid input & output";
+    return TaskHandler<TaskT>::valid_handle();
+  }
+  */
+
+  int fds[2];
+  int rc = pipe(fds);
+  if (rc != 0) {
+    LOG(ERROR) << "call pipe() failed, errno=" << errno << ":"
+               << strerror(errno);
+    return TaskHandler<TaskT>::valid_handle();
+  }
+
+  task->read_fd = fds[0];
+  task->write_fd = fds[1];
   task->owner_tid = ::syscall(SYS_gettid);
   task->memoryPtr = memoryPtr;
-  // task->_bspec_key = _bspec_key;
+  //task->_bspec_key = _bspec_key;
   task->inVectorT_ptr = (const InVectorT*)inVectorT_ptr;
   task->outVectorT_ptr = (OutVectorT*)outVectorT_ptr;
   if (!task->task_init()) {
     LOG(ERROR) << "task->init() failed";
-    return -1;
   }
   task->rem = task->batch_size();
   task->index.store(0, butil::memory_order_relaxed);
   AutoMutex lock(_mut);
   _task_queue.push_back(task);
   THREAD_COND_SIGNAL(&_cond);
-  return 0;
+
+  return TaskHandler<TaskT>(*task);
 }
 
 // this function is accessed by multi thread.
@@ -398,19 +407,13 @@ int TaskExecutor<TaskT>::work(ThreadContext<TaskT>* context) {
 }
 
 template <typename InItemT, typename OutItemT>
-bool TaskManager<InItemT, OutItemT>::schedule(
-    const void* in,
-    void* out,
-    MempoolRegion* memoryPtr,
-    THREAD_MUTEX_T* thread_mutex_ptr,
-    THREAD_COND_T* thread_cond_ptr) {  // NOLINT
-  int error_no = TaskExecutorVector<TaskT>::instance()[_model_index].schedule(
-      in, out, memoryPtr, thread_mutex_ptr, thread_cond_ptr, this);
-
-  if (error_no >= 0) {
-    _task_ready = false;
-    this->thread_mutex_ptr = thread_mutex_ptr;
-    this->thread_cond_ptr = thread_cond_ptr;
+bool TaskManager<InItemT, OutItemT>::schedule(const void* in,
+                                              void* out, MempoolRegion* memoryPtr) {  // NOLINT
+  TaskHandler<TaskT> handler =
+      TaskExecutorVector<TaskT>::instance()[_model_index].schedule(in, out, memoryPtr);
+
+  if (handler.valid()) {
+    _task_owned = handler;
     return true;
   } else {
     LOG(ERROR) << "failed to schedule task";
@@ -420,13 +423,17 @@ bool TaskManager<InItemT, OutItemT>::schedule(
 
 template <typename InItemT, typename OutItemT>
 void TaskManager<InItemT, OutItemT>::wait() {
-  THREAD_MUTEX_LOCK(thread_mutex_ptr);
-  while (!_task_ready) {
-    THREAD_COND_WAIT(thread_cond_ptr, thread_mutex_ptr);
+  char buffer[128];
+  while (read(_task_owned.read_fd, buffer, sizeof(buffer)) < 0 &&
+         errno == EINTR) {
   }
-  THREAD_MUTEX_UNLOCK(thread_mutex_ptr);
+
+  close(_task_owned.read_fd);
+  close(_task_owned.write_fd);
+
+  _task_owned.read_fd = -1;
+  _task_owned.write_fd = -1;
   return;
 }
-
 }  // namespace bsf
 }  // namespace im

core/predictor/framework/bsf.h

@@ -52,9 +52,6 @@ typedef baidu::paddle_serving::predictor::MempoolRegion MempoolRegion;
 // `put`.
 template <typename TaskT>
 class BatchTasks;
-
-template <typename InItemT, typename OutItemT>
-class TaskManager;
 // size_t `index` records how many batch have been processing completed.
 // `index` need to be atomic, cause the operation 'notify' is asynchronous.
 template <typename InItemT, typename OutItemT>
@@ -68,6 +65,8 @@ struct Task {
   typedef std::vector<ShapeVector> VectorOfShapeVector;
   typedef baidu::paddle_serving::predictor::MempoolWrapper MempoolWrapper;
 
+  int read_fd;
+  int write_fd;
   pid_t owner_tid;
   const InVectorT* inVectorT_ptr;
   OutVectorT* outVectorT_ptr;
@@ -85,17 +84,13 @@ struct Task {
   // taskmeta_num * set_feed_lod_index.size()
   std::vector<OutVectorT> outLodTensorVector;
   MempoolRegion* memoryPtr;
-  TaskManager<InItemT, OutItemT>* task_manager_ptr;
-  THREAD_MUTEX_T* thread_mutex_ptr;
-  THREAD_COND_T* thread_cond_ptr;
 
   Task() {
+    read_fd = -1;
+    write_fd = -1;
     owner_tid = -1;
     inVectorT_ptr = NULL;
     outVectorT_ptr = NULL;
-    thread_mutex_ptr = NULL;
-    thread_cond_ptr = NULL;
-    task_manager_ptr = NULL;
     set_feed_lod_index.clear();
     set_feed_nobatch_index.clear();
     vector_fetch_lod_index.clear();
@@ -110,9 +105,8 @@ struct Task {
     outLodTensorVector.clear();
   }
   ~Task() {
-    thread_mutex_ptr = NULL;
-    thread_cond_ptr = NULL;
-    task_manager_ptr = NULL;
+    read_fd = -1;
+    write_fd = -1;
     owner_tid = -1;
     inVectorT_ptr = NULL;
     outVectorT_ptr = NULL;
@@ -130,10 +124,9 @@ struct Task {
     outLodTensorVector.clear();
   }
 
-  void clear() {
-    thread_mutex_ptr = NULL;
-    thread_cond_ptr = NULL;
-    task_manager_ptr = NULL;
+  void clear(){
+    read_fd = -1;
+    write_fd = -1;
     owner_tid = -1;
     inVectorT_ptr = NULL;
     outVectorT_ptr = NULL;
@@ -380,12 +373,11 @@ struct Task {
         // 一次性扩容PaddleTensor中的data和lod
         paddle::PaddleTensor& fetchVarTensor = (*outVectorT_ptr)[feedvar_index];
         fetchVarTensor.shape[0] = total_shape0;
-        void* databuf_data =
-            MempoolWrapper::instance().malloc(data_length, memoryPtr);
+        void* databuf_data = MempoolWrapper::instance().malloc(data_length,memoryPtr);
         paddle::PaddleBuf paddleBuf(databuf_data, data_length);
         fetchVarTensor.data = paddleBuf;
          
-        // fetchVarTensor.data.Resize(data_length);
+        //fetchVarTensor.data.Resize(data_length);
         // task中的lod补0
         if (fetchVarTensor.lod.size() <= 0) {
           fetchVarTensor.lod.push_back({0});
@@ -401,7 +393,7 @@ struct Task {
         size_t once_lod_length = 0;
         for (size_t taskmeta_index = 0; taskmeta_index < total_taskmeta_num;
              ++taskmeta_index) {
-          // process data
+          //process data
           void* dst_ptr = fetchVarTensor.data.data() + data_length_offset;
           void* source_ptr =
               outLodTensorVector[taskmeta_index][index].data.data();
@@ -409,7 +401,7 @@ struct Task {
               outLodTensorVector[taskmeta_index][index].data.length();
           memcpy(dst_ptr, source_ptr, once_data_length);
           data_length_offset += once_data_length;
-          // process lod
+          //process lod
           size_t last_lod_value = fetchVarTensor.lod[0][lod_length_offset];
           once_lod_length =
               outLodTensorVector[taskmeta_index][index].lod[0].size();
@@ -420,6 +412,7 @@ struct Task {
                 outLodTensorVector[taskmeta_index][index].lod[0][once_index];
             lod_length_offset++;
           }
+
         }
       }
     }
@@ -552,9 +545,8 @@ class BatchTasks {
     TaskMetaT tm(task, start_index, add, task->taskmeta_num);
     task->rem -= add;
     _rem_size -= add;
-    if (task->taskmeta_num == 0) {
-      task->total_taskmeta_num =
-          1 + (task->rem + _batch_size - 1) / _batch_size;
+    if(task->taskmeta_num == 0){
+      task->total_taskmeta_num = 1 + (task->rem + _batch_size - 1)/_batch_size;
     }
     task->taskmeta_num += 1;
     _taskmeta_vector.push_back(tm);
@@ -651,8 +643,7 @@ class BatchTasks {
           paddleTensor.lod = _batch_in_lod[feedvar_index];
           paddleTensor.shape = feedVarTensor.shape;
           paddleTensor.shape[0] = _total_shape0_batch_in[feedvar_index];
-          size_t databuf_size =
-              feedvar_bytesize * _total_shape0_batch_in[feedvar_index];
+          size_t databuf_size = feedvar_bytesize * _total_shape0_batch_in[feedvar_index];
           void* databuf_data = MempoolWrapper::instance().malloc(databuf_size);
           paddle::PaddleBuf paddleBuf(databuf_data, databuf_size);
           paddleTensor.data = paddleBuf;
@@ -762,26 +753,25 @@ class BatchTasks {
       // 此时，无法分辨是否是天然nobatch，此时set_fetch_nobatch_index会漏掉
       // 后续希望在其他地方能够区分两者。
       if (fetchvar_batch_size(fetchvar_index) != _total_fetch_batch) {
-        if (fetchvar_batch_size(fetchvar_index) <= 0) {
+        if(fetchvar_batch_size(fetchvar_index) <= 0){
           // which means error.
           return false;
-        } else if (fetchvar_batch_size(fetchvar_index) == 1) {
+        }else if(fetchvar_batch_size(fetchvar_index) == 1){
           // which means fetchvar shape[0] = 1.
           // shape[0] does not change with batch
           set_fetch_nobatch_index.insert(fetchvar_index);
           _total_fetch_batch =
               std::max(fetchvar_batch_size(fetchvar_index), _total_fetch_batch);
-        } else if (_total_fetch_batch == 1) {
-          // 这时意味着，之前的fetchvar shape[0] 全部都= 1
-          // 当前的fetchvar shape[0] > 1
-          // 所以，之前的都是no_batch
-          for (size_t temp_index = fetchvar_index - 1; temp_index >= 0;
-               --temp_index) {
+        }else if(_total_fetch_batch == 1){
+          //这时意味着，之前的fetchvar shape[0] 全部都= 1
+          //当前的fetchvar shape[0] > 1
+          //所以，之前的都是no_batch
+          for(size_t temp_index = fetchvar_index-1; temp_index >= 0; --temp_index){
             set_fetch_nobatch_index.insert(fetchvar_index);
           }
           _total_fetch_batch =
               std::max(fetchvar_batch_size(fetchvar_index), _total_fetch_batch);
-        } else {
+        }else{
           // which means error.
           return false;
         }
@@ -866,11 +856,10 @@ class BatchTasks {
             fetchVarTensor.shape[0] = shape0_length;
             fetch_lod_index++;
 
-            void* databuf_data =
-                MempoolWrapper::instance().malloc(length, task->memoryPtr);
+            void* databuf_data = MempoolWrapper::instance().malloc(length,task->memoryPtr);
             paddle::PaddleBuf paddleBuf(databuf_data, length);
             fetchVarTensor.data = paddleBuf;
-            // fetchVarTensor.data.Resize(length);
+            //fetchVarTensor.data.Resize(length);
             void* dst_ptr = fetchVarTensor.data.data();
             void* source_ptr = _batch_out[fetchvar_index].data.data() +
                                shape0_index_start * fetchvar_bytesize_index;
@@ -897,12 +886,11 @@ class BatchTasks {
             size_t length = fetchvar_bytesize_index * shape0_length;
             fetchVarTensor.shape[0] = shape0_length;
             
-            void* databuf_data =
-                MempoolWrapper::instance().malloc(length, task->memoryPtr);
+            void* databuf_data = MempoolWrapper::instance().malloc(length,task->memoryPtr);
             paddle::PaddleBuf paddleBuf(databuf_data, length);
             fetchVarTensor.data = paddleBuf;
             
-            // fetchVarTensor.data.Resize(length);
+            //fetchVarTensor.data.Resize(length);
             void* dst_ptr = fetchVarTensor.data.data();
             void* source_ptr = _batch_out[fetchvar_index].data.data() +
                                shape0_index_start * fetchvar_bytesize_index;
@@ -952,15 +940,12 @@ class BatchTasks {
       // index是局部变量，fetch_add是原子操作，成功则返回原值。
       // 只有最后一个taskmeta都完成后，该线程的index+add才能>task->batch_size()
       // 故只有一个线程能进入if{}内.不会造成多线程竞争的问题。
-
       size_t index = task->index.fetch_add(add);
       if ((index + add) >= task->batch_size()) {
         task->combine_taskmeta();
         char c = 0;
-        THREAD_MUTEX_LOCK(task->thread_mutex_ptr);
-        task->task_manager_ptr->_task_ready = true;
-        THREAD_COND_SIGNAL(task->thread_cond_ptr);
-        THREAD_MUTEX_UNLOCK(task->thread_mutex_ptr);
+        while (write(task->write_fd, &c, 1) != 1 && errno == EINTR) {
+        }
         butil::return_object(task);
       }
     }
@@ -1000,6 +985,36 @@ class BatchTasks {
   bool _allow_split_request;
 };
 
+// BSF task handle
+// TaskHandler is the handle of Task.
+// `read_fd` is used for receive signal in brpc Thread.
+// 'write_fd' is used for write signal in bsf Thread.
+// when TaskMeta is done, bsf Thread will write to 'write_fd'.
+// brpc Thread is keeping reading 'read_fd' in a while loop.
+// brpc Thread will receive signal when TaskMeta is done.
+// so `read_fd` and 'write_fd' is used for communicate in different Thread.
+template <typename TaskT>
+struct TaskHandler {
+  int read_fd;
+  int write_fd;
+
+  TaskHandler() : read_fd(-1), write_fd(-1) {
+    // do nothing
+  }
+
+  explicit TaskHandler(TaskT const& task)
+      : read_fd(task.read_fd), write_fd(task.write_fd) {
+    // do nothing
+  }
+
+  inline bool valid() const { return read_fd >= 0 && write_fd >= 0; }
+
+  static TaskHandler<TaskT>& valid_handle() {
+    static TaskHandler<TaskT> vhandle;
+    return vhandle;
+  }
+};
+
 // TaskExecutor is a Thread pool.
 template <typename TaskT>
 class TaskExecutor;
@@ -1100,12 +1115,7 @@ class TaskExecutor {
 
   int work(ThreadContext<TaskT>* context);
 
-  int schedule(const void*,
-               void*,
-               MempoolRegion* memoryPtr,
-               THREAD_MUTEX_T* thread_mutex_ptr,
-               THREAD_COND_T* thread_cond_ptr,
-               TaskManager<InType, OutType>* task_manager_ptr);
+  TaskHandler<TaskT> schedule(const void*, void*, MempoolRegion* memoryPtr);
 
   bool move_task_to_batch(BatchTasks<TaskT>& batchTask);  // NOLINT
 
@@ -1184,25 +1194,18 @@ class TaskManager {
   typedef typename TaskT::OutVectorT OutVectorT;
 
   explicit TaskManager(uint32_t model_index)  // NOLINT
-      : _model_index(model_index),
-        _task_ready(false) {}
+      : _model_index(model_index) {}
 
   ~TaskManager() { wait(); }
 
-  bool schedule(const void* in,
-                void* out,
-                MempoolRegion* memoryPtr,
-                THREAD_MUTEX_T* thread_mutex_ptr,
-                THREAD_COND_T* thread_cond_ptr);  // NOLINT
+  bool schedule(const void* in, void* out, MempoolRegion* memoryPtr);  // NOLINT
   void wait();
 
   inline void clear() { wait(); }
-  bool _task_ready = false;
 
  private:
+  TaskHandler<TaskT> _task_owned;
   uint32_t _model_index;
-  THREAD_MUTEX_T* thread_mutex_ptr;
-  THREAD_COND_T* thread_cond_ptr;
 };  // class TaskManager
 
 class AutoMutex {

core/predictor/framework/infer.cpp

@@ -98,11 +98,7 @@ int ReloadableInferEngine::infer(const void* in,
 
   im::bsf::TaskManager<paddle::PaddleTensor, paddle::PaddleTensor> task_manager(
       _model_index);
-  task_manager.schedule(in,
-                        out,
-                        MempoolWrapper::instance().get_thread_memory_ptr(),
-                        ThreadMutex::instance().get_thread_mutex_ptr(),
-                        ThreadMutex::instance().get_thread_cond_ptr());
+  task_manager.schedule(in, out, MempoolWrapper::instance().get_thread_memory_ptr());
   task_manager.wait();
   return 0;
 }

core/predictor/framework/infer.h

@@ -31,9 +31,8 @@
 #include "core/predictor/framework/infer_data.h"
 #include "core/predictor/framework/memory.h"
 #include "core/predictor/framework/predictor_metric.h"
-#include "core/predictor/framework/thread_mutex.h"
-#include "experimental/float16.h"
 #include "paddle_inference_api.h"  // NOLINT
+#include "experimental/float16.h"
 namespace baidu {
 namespace paddle_serving {
 namespace predictor {

core/predictor/framework/resource.cpp

@@ -97,12 +97,6 @@ int Resource::initialize(const std::string& path, const std::string& file) {
   }
   LOG(WARNING) << "Successfully proc initialized mempool wrapper";
 
-  if (ThreadMutex::instance().initialize() != 0) {
-    LOG(ERROR) << "Failed proc initialized mempool wrapper";
-    return -1;
-  }
-  LOG(WARNING) << "Successfully proc initialized ThreadMutex";
-
 #ifdef WITH_AUTH
   std::string product_name_str = resource_conf.auth_product_name();
   std::string container_id_str = resource_conf.auth_container_id();
@@ -307,12 +301,6 @@ int Resource::thread_initialize() {
   }
   LOG(WARNING) << "Successfully thread initialized mempool wrapper";
 
-  if (ThreadMutex::instance().thread_initialize() != 0) {
-    LOG(ERROR) << "Failed thread initialized ThreadMutex";
-    return -1;
-  }
-  LOG(WARNING) << "Successfully thread initialized ThreadMutex";
-
   // infer manager
   if (FLAGS_enable_model_toolkit &&
       InferManager::instance().thrd_initialize() != 0) {
@@ -356,12 +344,6 @@ int Resource::finalize() {
     LOG(ERROR) << "Failed proc finalize infer manager";
     return -1;
   }
-
-  if (ThreadMutex::instance().finalize() != 0) {
-    LOG(ERROR) << "Failed proc finalize ThreadMutex";
-    return -1;
-  }
-
   if (CubeAPI::instance()->destroy() != 0) {
     LOG(ERROR) << "Destory cube api failed ";
     return -1;

core/predictor/framework/resource.h

@@ -24,7 +24,6 @@
 #include "core/predictor/common/inner_common.h"
 #include "core/predictor/framework/infer.h"
 #include "core/predictor/framework/memory.h"
-#include "core/predictor/framework/thread_mutex.h"
 
 namespace baidu {
 namespace paddle_serving {

core/predictor/framework/thread_mutex.cpp

-// Copyright (c) 2019 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 "core/predictor/framework/thread_mutex.h"
-#include "core/predictor/common/inner_common.h"
-namespace baidu {
-namespace paddle_serving {
-namespace predictor {
-
-int ThreadMutex::initialize() {
-  if (THREAD_KEY_CREATE(&_bspec_key_mutex, NULL) != 0) {
-    LOG(ERROR) << "unable to create thread_key of thrd_data";
-    return -1;
-  }
-  if (THREAD_SETSPECIFIC(_bspec_key_mutex, NULL) != 0) {
-    LOG(ERROR) << "failed initialize bsepecific key to null";
-    return -1;
-  }
-
-  if (THREAD_KEY_CREATE(&_bspec_key_cond, NULL) != 0) {
-    LOG(ERROR) << "unable to create thread_key of thrd_data";
-    return -1;
-  }
-  if (THREAD_SETSPECIFIC(_bspec_key_cond, NULL) != 0) {
-    LOG(ERROR) << "failed initialize bsepecific key to null";
-    return -1;
-  }
-
-  return 0;
-}
-
-int ThreadMutex::thread_initialize() {
-  THREAD_MUTEX_T* mutex_ptr = new THREAD_MUTEX_T();
-  THREAD_MUTEX_INIT(mutex_ptr, NULL);
-  if (THREAD_SETSPECIFIC(_bspec_key_mutex, mutex_ptr) != 0) {
-    LOG(ERROR) << "unable to set the thrd_data";
-    delete mutex_ptr;
-    return -1;
-  }
-
-  THREAD_COND_T* cont_ptr = new THREAD_COND_T();
-  THREAD_COND_INIT(cont_ptr, NULL);
-  if (THREAD_SETSPECIFIC(_bspec_key_cond, cont_ptr) != 0) {
-    LOG(ERROR) << "unable to set the thrd_data";
-    delete cont_ptr;
-    return -1;
-  }
-  LOG(WARNING) << "Succ thread initialize ThreadMutex";
-  return 0;
-}
-
-int ThreadMutex::thread_finalize() {
-  THREAD_MUTEX_T* mutex_ptr =
-      (THREAD_MUTEX_T*)THREAD_GETSPECIFIC(_bspec_key_mutex);
-  if (mutex_ptr != NULL) {
-    THREAD_MUTEX_DESTROY(mutex_ptr);
-    delete mutex_ptr;
-  }
-
-  THREAD_COND_T* cont_ptr = (THREAD_COND_T*)THREAD_GETSPECIFIC(_bspec_key_cond);
-  if (cont_ptr != NULL) {
-    THREAD_COND_DESTROY(cont_ptr);
-    delete cont_ptr;
-  }
-  LOG(WARNING) << "Succ thread initialize ThreadMutex";
-  return 0;
-}
-
-int ThreadMutex::finalize() {
-  THREAD_KEY_DELETE(_bspec_key_mutex);
-  THREAD_KEY_DELETE(_bspec_key_cond);
-  return 0;
-}
-
-THREAD_MUTEX_T* ThreadMutex::get_thread_mutex_ptr() {
-  THREAD_MUTEX_T* mutex_ptr =
-      (THREAD_MUTEX_T*)THREAD_GETSPECIFIC(_bspec_key_mutex);
-  return mutex_ptr;
-}
-
-THREAD_COND_T* ThreadMutex::get_thread_cond_ptr() {
-  THREAD_COND_T* cont_ptr = (THREAD_COND_T*)THREAD_GETSPECIFIC(_bspec_key_cond);
-  return cont_ptr;
-}
-
-}  // namespace predictor
-}  // namespace paddle_serving
-}  // namespace baidu

core/predictor/framework/thread_mutex.h

-// Copyright (c) 2019 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 "core/predictor/common/inner_common.h"
-
-namespace baidu {
-namespace paddle_serving {
-namespace predictor {
-
-class ThreadMutex {
- public:
-  ThreadMutex() {}
-
-  static ThreadMutex& instance() {
-    static ThreadMutex thread_mutex;
-    return thread_mutex;
-  }
-
-  int initialize();
-
-  int thread_initialize();
-
-  THREAD_MUTEX_T* get_thread_mutex_ptr();
-
-  THREAD_COND_T* get_thread_cond_ptr();
-
-  int thread_finalize();
-
-  int finalize();
-
- private:
-  THREAD_KEY_T _bspec_key_mutex;
-  THREAD_KEY_T _bspec_key_cond;
-};
-
-}  // namespace predictor
-}  // namespace paddle_serving
-}  // namespace baidu