Merge branch 'PaddlePaddle:develop' into develop

core/configure/proto/server_configure.proto

@@ -22,11 +22,8 @@ message EngineDesc {
   required string reloadable_type = 4;
   required string model_dir = 5;
   repeated int32 gpu_ids = 6;
-  required int32 runtime_thread_num = 7;
-  required int32 batch_infer_size = 8;
-  required int32 enable_batch_align = 9;
-  optional string version_file = 10;
-  optional string version_type = 11;
+  optional string version_file = 7;
+  optional string version_type = 8;
 
   /*
    * Sparse Parameter Service type. Valid types are:
@@ -39,17 +36,34 @@ message EngineDesc {
     LOCAL = 1;
     REMOTE = 2;
   }
-  optional SparseParamServiceType sparse_param_service_type = 12;
-  optional string sparse_param_service_table_name = 13;
-  optional bool enable_memory_optimization = 14;
-  optional bool enable_ir_optimization = 15;
-  optional bool use_trt = 16;
-  optional bool use_lite = 17;
-  optional bool use_xpu = 18;
-  optional bool use_gpu = 19;
-  optional bool combined_model = 20;
-  optional bool encrypted_model = 21;
-  optional bool gpu_multi_stream = 22;
+  optional SparseParamServiceType sparse_param_service_type = 10;
+  optional string sparse_param_service_table_name = 11;
+  optional bool enable_memory_optimization = 12;
+  optional bool enable_ir_optimization = 13;
+  optional bool use_trt = 14;
+  optional bool use_lite = 15;
+  optional bool use_xpu = 16;
+  optional bool use_gpu = 17;
+  optional bool combined_model = 18;
+  optional bool encrypted_model = 19;
+  optional bool gpu_multi_stream = 20;
+
+  /*
+   * "runtime_thread_num": n == 0 means don`t use Asynchronous task scheduling
+   * mode.
+   * n > 0 means how many Predictor for this engine in Asynchronous task
+   * scheduling mode.
+   * "batch_infer_size": the max batch for this engine in Asynchronous task
+   * scheduling mode.
+   * "enable_overrun": always put a whole task into the TaskQueue even if the
+   * total batch is bigger than "batch_infer_size".
+   * "allow_split_request": allow to split task(which is corresponding to
+   * request).
+   */
+  optional int32 runtime_thread_num = 30 [ default = 0 ];
+  optional int32 batch_infer_size = 31 [ default = 32 ];
+  optional bool enable_overrun = 32 [ default = false ];
+  optional bool allow_split_request = 33 [ default = true ];
 };
 
 // model_toolkit conf

core/predictor/framework/bsf-inl.h

@@ -26,9 +26,90 @@
 #include "core/predictor/common/inner_common.h"
 #include "core/predictor/framework/memory.h"
 
+// this file is included by bsf.h
 namespace im {
 namespace bsf {
 
+template <typename InItemT, typename OutItemT>
+bool Task<InItemT, OutItemT>::task_fetch_init(BatchTasks<TaskT>& batchTask) {
+  // 双检锁，减少加锁的粒度
+  if (!fetch_init) {
+    if (taskmeta_num > 1) {
+      // 对于task被拆分为多个taskmeta,需要加锁。
+      AutoMutex lock(task_mut);
+      task_fetch_create(batchTask);
+    } else {
+      // 对于task只有1个taskmeta,不需要加锁。
+      task_fetch_create(batchTask);
+    }
+  }
+  return true;
+}
+
+template <typename InItemT, typename OutItemT>
+bool Task<InItemT, OutItemT>::task_fetch_create(BatchTasks<TaskT>& batchTask) {
+  if (!fetch_init) {
+    vector_fetch_lod_index = batchTask.vector_fetch_lod_index;
+    set_fetch_nobatch_index = batchTask.set_fetch_nobatch_index;
+    OutVectorT taskMetaOutLodTensor;
+    size_t fetchvar_num = batchTask._batch_out.size();
+    for (size_t fetchvar_index = 0; fetchvar_index < fetchvar_num;
+         ++fetchvar_index) {
+      size_t fetchvar_bytesize_index =
+          batchTask.fetchvar_bytesize(fetchvar_index);
+      size_t fetchvar_batch = 0;
+      // 1. nobatch fetchvar情况
+      if (set_fetch_nobatch_index.size() > 0 &&
+          set_fetch_nobatch_index.find(fetchvar_index) !=
+              set_fetch_nobatch_index.end()) {
+        fetchvar_batch = 1;
+      } else if (vector_fetch_lod_index.size() > 0 &&
+                 std::find(vector_fetch_lod_index.begin(),
+                           vector_fetch_lod_index.end(),
+                           fetchvar_index) != vector_fetch_lod_index.end()) {
+        // lod fetchvar情况，此时无法确定总的shape[0]
+        // 根据task中的task_num总数开辟task_num个临时空间
+        // 每个lod型的fetchvar拷贝到对应的临时空间中
+        // 最后再计算临时空间的总量，合并fetchvar和lod
+        fetchvar_batch = 0;
+
+      } else {
+        // 普通fetchvar情况，此时该Task总的fetchvar_batch =
+        // 输入的总的batch_size()
+        fetchvar_batch = batch_size();
+      }
+      paddle::PaddleTensor tensor_out;
+      tensor_out.name = batchTask._batch_out[fetchvar_index].name;
+      tensor_out.dtype =
+          paddle::PaddleDType(batchTask._batch_out[fetchvar_index].dtype);
+      tensor_out.shape = batchTask._batch_out[fetchvar_index].shape;
+      tensor_out.shape[0] = fetchvar_batch;
+      if (fetchvar_batch != 0) {
+        // 此时 lod 为空。
+        tensor_out.lod = batchTask._batch_out[fetchvar_index].lod;
+        // resize all batch memory at one time
+        size_t databuf_size = fetchvar_batch * fetchvar_bytesize_index;
+        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
+
+        // 当task被分为多个taskMeta时，需要临时对象记录
+        // 收齐后再一起合并
+        if (taskmeta_num > 1) {
+          taskMetaOutLodTensor.push_back(tensor_out);
+        }
+      }
+      outVectorT_ptr->push_back(tensor_out);
+    }
+    // outLodTensorVector实际是一个双层vector
+    // shape为taskmeta_num * vector_fetch_lod_index.size();
+    outLodTensorVector.resize(taskmeta_num, taskMetaOutLodTensor);
+    fetch_init = true;
+  }
+  return true;
+}
+
 template <typename TaskT>
 void* TaskExecutor<TaskT>::thread_entry(void* args) {
   ThreadContext<TaskT>* context = static_cast<ThreadContext<TaskT>*>(args);
@@ -136,7 +217,7 @@ TaskHandler<TaskT> TaskExecutor<TaskT>::schedule(
   }
 
   /*
-  if (!BatchTasks<TaskT>::check_valid(in, out, _batch_align)) {
+  if (!BatchTasks<TaskT>::check_valid(in, out, _overrun)) {
     LOG(ERROR) << "Invalid input & output";
     return TaskHandler<TaskT>::valid_handle();
   }
@@ -156,9 +237,11 @@ TaskHandler<TaskT> TaskExecutor<TaskT>::schedule(
 
   task->inVectorT_ptr = (const InVectorT*)inVectorT_ptr;
   task->outVectorT_ptr = (OutVectorT*)outVectorT_ptr;
+  if (!task->task_init()) {
+    LOG(ERROR) << "task->init() failed";
+  }
   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);
@@ -168,11 +251,12 @@ TaskHandler<TaskT> TaskExecutor<TaskT>::schedule(
 
 // this function is accessed by multi thread.
 // so AutoMutex at first.
-// so batch.append_task is thread safe.
+// so batchTask.append_task is thread safe.
 // you dont need to add extra lock in append_task()
+// task is already init.
 template <typename TaskT>
 bool TaskExecutor<TaskT>::move_task_to_batch(
-    BatchTasks<TaskT>& batch) {  // NOLINT
+    BatchTasks<TaskT>& batchTask) {  // NOLINT
   AutoMutex lock(_mut);
   while (_task_queue.empty()) {
     THREAD_COND_WAIT(&_cond, &_mut);
@@ -183,15 +267,65 @@ bool TaskExecutor<TaskT>::move_task_to_batch(
     return false;
   }
 
+  TaskT* previous_task = nullptr;
   while (!_task_queue.empty()) {
     TaskT* task = _task_queue.front();
-    size_t rem = batch.append_task(task);
+
+    // 由于无法确定fetchVar是否为lod（即使输入是非lod，输出也可能是lod）
+    // 简单的处理方法是：task不能被拆分，即用户的请求可以合并一起预测，但不能拆分两个小部分去预测。
+    // 只需要设置engine的属性allow_split_request = false即可。
+
+    // 复杂的处理方法是允许拆分Task，无论是否包含lod.
+    // 难点：预测前，能够知道被拆成了几个taskmeta,但只有预测后，才知道有多少个fetchvar,多少个lod的fetchvar
+    // 所以，task中先要创建taskmeta_num* fetchvar
+    // num（lod类型的）个临时PaddleTensor（存储data及Lod）
+    // 由于多线程调度的单位是taskmeta，故只能在notify_task中，用taskmeta->task去创建
+    // 此时由于多个taskmeta对应一个task，存在多线程竞争，所以需要在task中加锁。
+    // 原子操作不可行，因为多个线程必须等待创建好上述的PaddleTensor后才能继续。
+    // 对于普通的fetch，也需要加锁去创建PaddleTensor，后续才能往里拷贝。
+
+    // _overrun表示，异步BatchTasks是否允许单次临时超过限制。
+    // _overrun为true时，即使BatchTasks剩下1-batch，也会全放入一个完整的Task，允许临时超限。
+    // _overrun为false时，不允许。
+    // 对于模型本身有最大Batch限制的情况，应将该值设为false，默认为false。
+    // 对于模型本身无最大Batch限制，但自己设置了BatchTasks的最大Batch，可以考虑设置为True。
+
+    // _allow_split_request ==
+    // true，则允许拆分task.BatchTasks剩下1-batch，则会从下一个Task中拆出1-Batch
+    // _allow_split_request ==
+    // false，则每个task不会被拆分。BatchTasks剩下1-batch会被浪费
+    // 默认为true，允许拆分task从而使得空间利用率最大。
+    if (!batchTask.get_allow_split_request()) {
+      if (task->batch_size() > batchTask.get_rem_size() &&
+          !batchTask.get_overrun()) {
+        break;
+      }
+    }
+
+    // combine_task_valid负责判断是否能够合并
+    // 除最外层的shape外，内层shape应一致才能合并。
+    // 否则跳出循环,放入下一个batchTask中。
+    // 以此保证batch.append_task(task)中的task的内层shape相同。
+
+    // 对于Shape[0] = 1 而!=batch的情况，因为合并时，取其中一个的值
+    // 所以要求该feedvar必须相等，才能合并。
+    // 否则跳出循环,放入下一个batchTask中。
+    // 目前没有PaddleTensor和PaddleBuff没有重载==，所以只能比较内存.
+    // TODO(HexToString): 可以考虑后期支持AutoPadding.
+    if (previous_task != nullptr) {
+      if (!task->combine_task_valid(previous_task)) {
+        break;
+      }
+    }
+    size_t rem = batchTask.append_task(task);
+    previous_task = task;
     if (task->rem <= 0) {
       _task_queue.pop_front();
     }
     if (rem <= 0) break;
   }
-
+  LOG(INFO) << "Number of tasks remaining in _task_queue is"
+            << _task_queue.size();
   return true;
 }
 
@@ -201,11 +335,12 @@ bool TaskExecutor<TaskT>::move_task_to_batch(
 // TaskT is from the SingleTon TaskExecutor`s _task_queue
 // although TaskMeta is a local variable, but several TaskMeta may points to
 // the same TaskT which is get from the SingleTon TaskExecutor`s _task_queue.
-// put TaskMeta to the local variable BatchTasks<TaskT> batch.
+// put TaskMeta to the local variable BatchTasks<TaskT> batchTask.
 
-// batch.merge_tasks() and batch.notify_tasks() has no lock.
-// BatchTasks<TaskT> batch itself is a local variable, it`s thread safe.
-// If batch.merge_tasks() and batch.notify_tasks() do something to TaskMeta
+// batchTask.merge_tasks() and batchTask.notify_tasks() has no lock.
+// BatchTasks<TaskT> batchTask itself is a local variable, it`s thread safe.
+// If batchTask.merge_tasks() and batchTask.notify_tasks() do something to
+// TaskMeta
 // you need to pay attention to that.
 // Multi-Thread deal with different TaskMeta(cause it`s created as local
 // variable)
@@ -242,11 +377,23 @@ int TaskExecutor<TaskT>::work(ThreadContext<TaskT>* context) {
       return -1;
     }
 
-    BatchTasks<TaskT> batch(_batch_size, _batch_align);
-    if (move_task_to_batch(batch)) {
-      batch.merge_tasks();
-      _fn(&batch.in(), &batch.out());
-      batch.notify_tasks();
+    // move_task_to_batch() take the original task from the `_task_queue`
+    // put the original task into its own Vector<taskmeta>
+    // the capacity of its own Vector<taskmeta> is decided by `_batch_size` or
+    // `_overrun`
+
+    // merge_tasks() move the imput-data into `_batch_in` from its own
+    // Vector<taskmeta>.
+    // because the predictor`s input is the `_batch_in`
+
+    // notify_tasks() move the output-data into every single taskmeta from
+    // `_batch_out`.
+    // because the predictor`s output is the `_batch_out`
+    BatchTasks<TaskT> batchTask(_batch_size, _overrun, _allow_split_request);
+    if (move_task_to_batch(batchTask)) {
+      batchTask.merge_tasks();
+      _fn(&batchTask.in(), &batchTask.out());
+      batchTask.notify_tasks();
     }
   }
 

core/predictor/framework/infer.cpp

@@ -25,7 +25,8 @@ int ReloadableInferEngine::proc_initialize_impl(
   _model_dir = conf.model_dir();
   _infer_thread_num = conf.runtime_thread_num();
   _infer_batch_size = conf.batch_infer_size();
-  _infer_batch_align = conf.enable_batch_align();
+  _infer_overrun = conf.enable_overrun();
+  _allow_split_request = conf.allow_split_request();
 
   _conf = conf;
 
@@ -56,9 +57,6 @@ int ReloadableInferEngine::proc_initialize(const configure::EngineDesc& conf,
   }
 
   // init bsf framework
-  im::bsf::TaskExecutorVector<TaskT>::instance()[_model_index]
-      .set_thread_init_fn(
-          boost::bind(&InferEngine::thrd_initialize_impl, this));
   im::bsf::TaskExecutorVector<TaskT>::instance()[_model_index]
       .set_thread_init_fn(
           boost::bind(&InferEngine::thrd_initialize_impl, this));
@@ -69,8 +67,10 @@ int ReloadableInferEngine::proc_initialize(const configure::EngineDesc& conf,
           boost::bind(&InferEngine::task_infer_impl, this, _1, _2));
   im::bsf::TaskExecutorVector<TaskT>::instance()[_model_index].set_batch_size(
       _infer_batch_size);
-  im::bsf::TaskExecutorVector<TaskT>::instance()[_model_index].set_batch_align(
-      _infer_batch_align);
+  im::bsf::TaskExecutorVector<TaskT>::instance()[_model_index].set_overrun(
+      _infer_overrun);
+  im::bsf::TaskExecutorVector<TaskT>::instance()[_model_index]
+      .set_allow_split_request(_allow_split_request);
   if (im::bsf::TaskExecutorVector<TaskT>::instance()[_model_index].start(
           _infer_thread_num) != 0) {
     LOG(ERROR) << "Failed start bsf executor, threads:" << _infer_thread_num;
@@ -79,7 +79,8 @@ int ReloadableInferEngine::proc_initialize(const configure::EngineDesc& conf,
 
   LOG(WARNING) << "Enable batch schedule framework, thread_num:"
                << _infer_thread_num << ", batch_size:" << _infer_batch_size
-               << ", enable_batch_align:" << _infer_batch_align;
+               << ", enable_overrun:" << _infer_overrun
+               << ", allow_split_request:" << _allow_split_request;
   return 0;
 }
 
@@ -391,6 +392,11 @@ int VersionedInferEngine::task_infer_impl(const void* in,
   return -1;
 }
 
+int InferManager::set_taskexecutor_num(size_t total_engine_num) {
+  im::bsf::TaskExecutorVector<TaskT>::instance().resize(total_engine_num);
+  return 0;
+}
+
 int InferManager::proc_initialize(const char* path,
                                   const char* file,
                                   std::shared_ptr<int> engine_index_ptr) {
@@ -400,8 +406,6 @@ int InferManager::proc_initialize(const char* path,
     return -1;
   }
   uint32_t engine_num = model_toolkit_conf.engines_size();
-  im::bsf::TaskExecutorVector<TaskT>::instance().resize(*engine_index_ptr +
-                                                        engine_num);
   for (uint32_t ei = 0; ei < engine_num; ++ei) {
     LOG(INFO) << "model_toolkit_conf.engines(" << ei
               << ").name: " << model_toolkit_conf.engines(ei).name();

core/predictor/framework/infer.h

@@ -169,8 +169,10 @@ class ReloadableInferEngine : public InferEngine {
   uint32_t _infer_batch_size;
 
   // Need to align batch_size in inferring
-  bool _infer_batch_align;
+  bool _infer_overrun;
 
+  // allow to split request in inferring
+  bool _allow_split_request;
   // model version
   uint64_t _version;
 };
@@ -645,6 +647,8 @@ class InferManager {
                       const char* file,
                       std::shared_ptr<int> engine_index_ptr);
 
+  int set_taskexecutor_num(size_t total_engine_num);
+
   int thrd_initialize();
 
   int thrd_clear();

core/predictor/framework/resource.cpp

@@ -135,6 +135,17 @@ int Resource::initialize(const std::string& path, const std::string& file) {
 
   if (FLAGS_enable_model_toolkit) {
     size_t model_toolkit_num = resource_conf.model_toolkit_path_size();
+    // 此处暂时认为，每个model_toolkit仅包含一个engine
+    // 故认为 model_toolkit_num == engine总数
+    // 若以后出现model_toolkit仅包含多个engine
+    // 则应先for循环统计engine总数,再set_taskexecutor_num
+    // 切不可动态im::bsf::TaskExecutorVector<TaskT>::instance().resize
+    // TaskExecutor是线程池，内含锁，在engine进程初始化时已开始work加锁循环运行了
+    // 之后再resize内存搬运，会导致work使用原锁，而搬运后的TaskExecutor的锁内存已改变
+    if (InferManager::instance().set_taskexecutor_num(model_toolkit_num) != 0) {
+      LOG(ERROR) << "failed set_taskexecutor_num";
+      return -1;
+    }
     std::shared_ptr<int> engine_index_ptr(new int(0));
     for (size_t mi = 0; mi < model_toolkit_num; ++mi) {
       std::string model_toolkit_path = resource_conf.model_toolkit_path(mi);

doc/HTTP_SERVICE_CN.md

@@ -52,7 +52,9 @@ Java的HttpClient使用示例见[`java/examples/src/main/java/PaddleServingClien
 
 如果不能满足您的需求，您也可以在此基础上添加一些功能。
 
-如需支持https或者自定义Response的Status Code等,则需要对C++端brpc-Server进行一定的二次开发，请参考https://github.com/apache/incubator-brpc/blob/master/docs/cn/http_service.md，后续如果需求很大，我们也会将这部分功能加入到Server中，尽情期待。
+如需支持https或者自定义Response的Status Code等,则需要对C++端brpc-Server进行一定的二次开发，请参考https://github.com/apache/incubator-brpc/blob/master/docs/cn/http_service.md
+
+后续如果需求很大，我们也会将这部分功能加入到Server中，尽情期待。
 
 
 ### curl方式发送Http请求(基本原理)

python/examples/bert/bert_httpclient.py

@@ -23,11 +23,9 @@ args = benchmark_args()
 
 reader = ChineseBertReader({"max_seq_len": 128})
 fetch = ["pooled_output"]
-
-client = HttpClient(ip='127.0.0.1', port='9292')
+endpoint_list = ['127.0.0.1:9292']
+client = HttpClient()
 client.load_client_config(args.model)
-#client.set_ip('127.0.0.1')
-#client.set_port('9292')
 ''' 
 if you want use GRPC-client, set_use_grpc_client(True)
 or you can directly use client.grpc_client_predict(...)
@@ -49,6 +47,7 @@ we recommend use Proto data format in HTTP-body, set True(which is default)
 if you want use JSON data format in HTTP-body, set False
 '''
 #client.set_http_proto(True)
+client.connect(endpoint_list)
 
 for line in sys.stdin:
     feed_dict = reader.process(line)

python/examples/fit_a_line/test_httpclient.py

@@ -20,8 +20,6 @@ import time
 
 client = HttpClient()
 client.load_client_config(sys.argv[1])
-#client.set_ip('127.0.0.1')
-#client.set_port('9393')
 ''' 
 if you want use GRPC-client, set_use_grpc_client(True)
 or you can directly use client.grpc_client_predict(...)
@@ -43,13 +41,14 @@ we recommend use Proto data format in HTTP-body, set True(which is default)
 if you want use JSON data format in HTTP-body, set False
 '''
 #client.set_http_proto(True)
+client.connect(["127.0.0.1:9393"])
+fetch_list = client.get_fetch_names()
 
 import paddle
 test_reader = paddle.batch(
     paddle.reader.shuffle(
         paddle.dataset.uci_housing.test(), buf_size=500),
     batch_size=1)
-fetch_list = client.get_fetch_names()
 for data in test_reader():
     new_data = np.zeros((1, 13)).astype("float32")
     new_data[0] = data[0][0]

python/examples/imagenet/resnet50_http_client.py

@@ -18,10 +18,8 @@ from paddle_serving_app.reader import Sequential, URL2Image, Resize
 from paddle_serving_app.reader import CenterCrop, RGB2BGR, Transpose, Div, Normalize
 import time
 
-client = HttpClient(ip='127.0.0.1', port='9696')
+client = HttpClient()
 client.load_client_config(sys.argv[1])
-#client.set_ip('127.0.0.1')
-#client.set_port('9292')
 ''' 
 if you want use GRPC-client, set_use_grpc_client(True)
 or you can directly use client.grpc_client_predict(...)
@@ -43,6 +41,7 @@ we recommend use Proto data format in HTTP-body, set True(which is default)
 if you want use JSON data format in HTTP-body, set False
 '''
 #client.set_http_proto(True)
+client.connect(["127.0.0.1:9696"])
 
 label_dict = {}
 label_idx = 0

python/examples/imdb/test_http_client.py

@@ -17,10 +17,8 @@ from paddle_serving_app.reader.imdb_reader import IMDBDataset
 import sys
 import numpy as np
 
-client = HttpClient(ip='127.0.0.1', port='9292')
+client = HttpClient()
 client.load_client_config(sys.argv[1])
-#client.set_ip('127.0.0.1')
-#client.set_port('9292')
 ''' 
 if you want use GRPC-client, set_use_grpc_client(True)
 or you can directly use client.grpc_client_predict(...)
@@ -42,6 +40,7 @@ we recommend use Proto data format in HTTP-body, set True(which is default)
 if you want use JSON data format in HTTP-body, set False
 '''
 #client.set_http_proto(True)
+client.connect(["127.0.0.1:9292"])
 
 # you can define any english sentence or dataset here
 # This example reuses imdb reader in training, you

python/examples/lac/lac_http_client.py

@@ -21,10 +21,8 @@ import os
 import io
 import numpy as np
 
-client = HttpClient(ip='127.0.0.1', port='9292')
+client = HttpClient()
 client.load_client_config(sys.argv[1])
-#client.set_ip('127.0.0.1')
-#client.set_port('9292')
 ''' 
 if you want use GRPC-client, set_use_grpc_client(True)
 or you can directly use client.grpc_client_predict(...)
@@ -46,6 +44,7 @@ we recommend use Proto data format in HTTP-body, set True(which is default)
 if you want use JSON data format in HTTP-body, set False
 '''
 #client.set_http_proto(True)
+client.connect(["127.0.0.1:9292"])
 
 reader = LACReader()
 for line in sys.stdin:

python/examples/xpu/bert/bert_web_service.py

-# coding=utf-8
-# Copyright (c) 2020 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.
-# pylint: disable=doc-string-missing
-from paddle_serving_server.web_service import WebService
-from paddle_serving_app.reader import ChineseBertReader
-import sys
-import os
-import numpy as np
-
-
-class BertService(WebService):
-    def load(self):
-        self.reader = ChineseBertReader({
-            "vocab_file": "vocab.txt",
-            "max_seq_len": 128
-        })
-
-    def preprocess(self, feed=[], fetch=[]):
-        feed_res = []
-        is_batch = False
-        for ins in feed:
-            feed_dict = self.reader.process(ins["words"].encode("utf-8"))
-            for key in feed_dict.keys():
-                feed_dict[key] = np.array(feed_dict[key]).reshape(
-                    (len(feed_dict[key]), 1))
-            feed_res.append(feed_dict)
-        return feed_res, fetch, is_batch
-
-
-bert_service = BertService(name="bert")
-bert_service.load()
-bert_service.load_model_config(sys.argv[1])
-bert_service.prepare_server(
-    workdir="workdir", port=int(sys.argv[2]), use_lite=True, use_xpu=True, ir_optim=True)
-bert_service.run_rpc_service()
-bert_service.run_web_service()

python/examples/xpu/ernie/ernie_web_service.py

-# coding=utf-8
-# Copyright (c) 2020 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.
-# pylint: disable=doc-string-missing
-from paddle_serving_server.web_service import WebService
-from paddle_serving_app.reader import ChineseBertReader
-import sys
-import os
-import numpy as np
-
-
-class BertService(WebService):
-    def load(self):
-        self.reader = ChineseBertReader({
-            "vocab_file": "vocab.txt",
-            "max_seq_len": 128
-        })
-
-    def preprocess(self, feed=[], fetch=[]):
-        feed_res = []
-        is_batch = False
-        for ins in feed:
-            feed_dict = self.reader.process(ins["words"].encode("utf-8"))
-            for key in feed_dict.keys():
-                feed_dict[key] = np.array(feed_dict[key]).reshape(
-                    (len(feed_dict[key]), 1))
-            feed_res.append(feed_dict)
-        return feed_res, fetch, is_batch
-
-
-bert_service = BertService(name="bert")
-bert_service.load()
-bert_service.load_model_config(sys.argv[1])
-bert_service.prepare_server(
-    workdir="workdir", port=int(sys.argv[2]), use_lite=True, use_xpu=True, ir_optim=True)
-bert_service.run_rpc_service()
-bert_service.run_web_service()

python/examples/xpu/fit_a_line_xpu/README.md

@@ -23,18 +23,3 @@ The `paddlepaddle` package is used in `test_client.py`, and you may need to down
 ``` shell
 python3 test_client.py uci_housing_client/serving_client_conf.prototxt
 ```
-
-## HTTP service
-
-### Start server
-
-Start a web service with default web service hosting modules:
-``` shell
-python3 -m paddle_serving_server.serve --model uci_housing_model --thread 10 --port 9393 --use_lite --use_xpu --ir_optim --name uci
-```
-
-### Client prediction
-
-``` shell
-curl -H "Content-Type:application/json" -X POST -d '{"feed":[{"x": [0.0137, -0.1136, 0.2553, -0.0692, 0.0582, -0.0727, -0.1583, -0.0584, 0.6283, 0.4919, 0.1856, 0.0795, -0.0332]}], "fetch":["price"]}' http://127.0.0.1:9393/uci/prediction
-```

python/examples/xpu/fit_a_line_xpu/README_CN.md

@@ -31,19 +31,3 @@ python3 -m paddle_serving_server.serve --model uci_housing_model --thread 10 --p
 ``` shell
 python3 test_client.py uci_housing_client/serving_client_conf.prototxt
 ```
-
-## HTTP服务
-
-### 开启服务端
-
-通过下面的一行代码开启默认web服务：
-
-``` shell
-python3 -m paddle_serving_server.serve --model uci_housing_model --thread 10 --port 9393 --use_lite --use_xpu --ir_optim --name uci
-```
-
-### 客户端预测
-
-``` shell
-curl -H "Content-Type:application/json" -X POST -d '{"feed":[{"x": [0.0137, -0.1136, 0.2553, -0.0692, 0.0582, -0.0727, -0.1583, -0.0584, 0.6283, 0.4919, 0.1856, 0.0795, -0.0332]}], "fetch":["price"]}' http://127.0.0.1:9393/uci/prediction
-```

python/paddle_serving_client/client.py

@@ -289,6 +289,7 @@ class Client(object):
                 log_id=0):
         self.profile_.record('py_prepro_0')
 
+        # fetch 可以为空，此时会取所有的输出结果
         if feed is None:
             raise ValueError("You should specify feed for prediction")
 
@@ -297,6 +298,7 @@ class Client(object):
             fetch_list = [fetch]
         elif isinstance(fetch, list):
             fetch_list = fetch
+        # fetch 可以为空，此时会取所有的输出结果
         elif fetch == None:
             pass
         else:
@@ -441,6 +443,7 @@ class Client(object):
         model_engine_names = result_batch_handle.get_engine_names()
         for mi, engine_name in enumerate(model_engine_names):
             result_map = {}
+            # fetch 为空，则会取所有的输出结果
             if len(fetch_names) == 0:
                 fetch_names = result_batch_handle.get_tensor_alias_names(mi)
             # result map needs to be a numpy array

python/paddle_serving_client/httpclient.py

@@ -22,6 +22,7 @@ import gzip
 from collections import Iterable
 import base64
 import sys
+import re
 
 import grpc
 from .proto import general_model_service_pb2
@@ -98,7 +99,7 @@ class HttpClient(object):
         self.headers["Content-Type"] = "application/proto"
         self.max_body_size = 512 * 1024 * 1024
         self.use_grpc_client = False
-        self.url = None
+        self.http_s = "http://"
 
         # 使用连接池能够不用反复建立连接
         self.requests_session = requests.session()
@@ -170,7 +171,6 @@ class HttpClient(object):
 
     def set_max_body_size(self, max_body_size):
         self.max_body_size = max_body_size
-        self.init_grpc_stub()
 
     def set_timeout_ms(self, timeout_ms):
         if not isinstance(timeout_ms, int):
@@ -183,25 +183,46 @@ class HttpClient(object):
             raise ValueError("retry_times must be int type.")
         else:
             self.requests_session.mount(
-                'http://', HTTPAdapter(max_retries=retry_times))
-
-    def set_ip(self, ip):
-        self.ip = ip
-        self.init_grpc_stub()
+                self.http_s, HTTPAdapter(max_retries=retry_times))
 
     def set_service_name(self, service_name):
         self.service_name = service_name
 
-    def set_port(self, port):
-        self.port = port
-        self.server_port = port
-        self.init_grpc_stub()
-
-    def set_url(self, url):
+    def connect(self, url=None, encryption=False):
+        if isinstance(url, (list, tuple)):
+            if len(url) > 1:
+                raise ValueError("HttpClient only support 1 endpoint")
+            else:
+                url = url[0]
         if isinstance(url, str):
-            self.url = url
+            if url.startswith("https://"):
+                url = url[8:]
+                self.http_s = "https://"
+            if url.startswith("http://"):
+                url = url[7:]
+                self.http_s = "http://"
+            url_parts = url.split(':')
+            if len(url_parts) != 2 or self.check_ip(url_parts[0]) == False:
+                raise ValueError(
+                    "url not right, it should be like 127.0.0.1:9393 or http://127.0.0.1:9393"
+                )
+            else:
+                self.ip = url_parts[0]
+                self.port = url_parts[1]
+                self.server_port = url_parts[1]
+        if encryption:
+            self.get_serving_port()
+        if self.use_grpc_client:
+            self.init_grpc_stub()
+
+    def check_ip(self, ipAddr):
+        compile_ip = re.compile(
+            '^(1\d{2}|2[0-4]\d|25[0-5]|[1-9]\d|[1-9])\.(1\d{2}|2[0-4]\d|25[0-5]|[1-9]\d|\d)\.(1\d{2}|2[0-4]\d|25[0-5]|[1-9]\d|\d)\.(1\d{2}|2[0-4]\d|25[0-5]|[1-9]\d|\d)$'
+        )
+        if compile_ip.match(ipAddr):
+            return True
         else:
-            print("url must be str")
+            return False
 
     def add_http_headers(self, headers):
         if isinstance(headers, dict):
@@ -229,10 +250,9 @@ class HttpClient(object):
     def use_key(self, key_filename):
         with open(key_filename, "rb") as f:
             self.key = f.read()
-            self.get_serving_port()
 
     def get_serving_port(self):
-        encrypt_url = "http://" + str(self.ip) + ":" + str(self.port)
+        encrypt_url = self.http_s + str(self.ip) + ":" + str(self.port)
         if self.key is not None:
             req = json.dumps({"key": base64.b64encode(self.key).decode()})
         else:
@@ -481,13 +501,7 @@ class HttpClient(object):
             postData = self.process_json_data(feed_dict, fetch_list, batch,
                                               log_id)
 
-        web_url = "http://" + self.ip + ":" + self.server_port + self.service_name
-        if self.url != None:
-            if "http" not in self.url:
-                self.url = "http://" + self.url
-            if "self.service_name" not in self.url:
-                self.url = self.url + self.service_name
-            web_url = self.url
+        web_url = self.http_s + self.ip + ":" + self.server_port + self.service_name
         # 当数据区长度大于512字节时才压缩.
         self.headers.pop("Content-Encoding", "nokey")
         try:

python/paddle_serving_server/server.py

@@ -228,7 +228,8 @@ class Server(object):
             engine.batch_infer_size = self.op_max_batch[index %
                                                         len(self.op_max_batch)]
 
-            engine.enable_batch_align = 1
+            engine.enable_overrun = False
+            engine.allow_split_request = True
             engine.model_dir = model_config_path
             engine.enable_memory_optimization = self.memory_optimization
             engine.enable_ir_optimization = self.ir_optimization

tools/scripts/ipipe_py3.sh

@@ -40,9 +40,9 @@ go env -w GO111MODULE=auto
 
 build_whl_list=(build_cpu_server build_gpu_server build_client build_app)
 rpc_model_list=(grpc_fit_a_line grpc_yolov4 pipeline_imagenet bert_rpc_gpu bert_rpc_cpu ResNet50_rpc \
-lac_rpc cnn_rpc bow_rpc lstm_rpc fit_a_line_rpc deeplabv3_rpc mobilenet_rpc unet_rpc resnetv2_rpc \
+lac_rpc_asyn cnn_rpc_asyn bow_rpc lstm_rpc fit_a_line_rpc deeplabv3_rpc mobilenet_rpc unet_rpc resnetv2_rpc \
 criteo_ctr_rpc_cpu criteo_ctr_rpc_gpu ocr_rpc yolov4_rpc_gpu faster_rcnn_hrnetv2p_w18_1x_encrypt \
-faster_rcnn_model_rpc low_precision_resnet50_int8 ocr_c++_service)
+faster_rcnn_model_rpc low_precision_resnet50_int8 ocr_c++_service ocr_c++_service_asyn)
 http_model_list=(fit_a_line_http lac_http imdb_http_proto imdb_http_json imdb_grpc ResNet50_http bert_http \
 pipeline_ocr_cpu_http)
 
@@ -492,7 +492,7 @@ function ResNet101_rpc() {
     kill_server_process
 }
 
-function cnn_rpc() {
+function cnn_rpc_asyn() {
     dir=${log_dir}rpc_model/cnn_rpc/
     check_dir ${dir}
     unsetproxy
@@ -500,8 +500,9 @@ function cnn_rpc() {
     data_dir=${data}imdb/
     link_data ${data_dir}
     sed -i 's/9292/8865/g' test_client.py
-    ${py_version} -m paddle_serving_server.serve --model imdb_cnn_model/ --port 8865 > ${dir}server_log.txt 2>&1 &
-    check_result server 5
+    ${py_version} -m paddle_serving_server.serve --model imdb_cnn_model/ --port 8865 --op_num 4 --thread 10 --gpu_ids 0 > ${dir}server_log.txt 2>&1 &
+    check_result server 8
+    check_gpu_memory 0
     head test_data/part-0 | ${py_version} test_client.py imdb_cnn_client_conf/serving_client_conf.prototxt imdb.vocab > ${dir}client_log.txt 2>&1
     check_result client "cnn_CPU_RPC server test completed"
     kill_server_process
@@ -537,7 +538,7 @@ function lstm_rpc() {
     kill_server_process
 }
 
-function lac_rpc() {
+function lac_rpc_asyn() {
     dir=${log_dir}rpc_model/lac_rpc/
     check_dir ${dir}
     unsetproxy
@@ -545,8 +546,9 @@ function lac_rpc() {
     data_dir=${data}lac/
     link_data ${data_dir}
     sed -i 's/9292/8868/g' lac_client.py
-    ${py_version} -m paddle_serving_server.serve --model lac_model/ --port 8868 > ${dir}server_log.txt 2>&1 &
-    check_result server 5
+    ${py_version} -m paddle_serving_server.serve --model lac_model/ --port 8868 --gpu_ids 0 --op_num 2 > ${dir}server_log.txt 2>&1 &
+    check_result server 8
+    check_gpu_memory 0
     echo "我爱北京天安门" | ${py_version} lac_client.py lac_client/serving_client_conf.prototxt lac_dict/ > ${dir}client_log.txt 2>&1
     check_result client "lac_CPU_RPC server test completed"
     kill_server_process
@@ -923,6 +925,23 @@ function ocr_c++_service() {
     kill_server_process
 }
 
+function ocr_c++_service_asyn() {
+    dir=${log_dir}rpc_model/ocr_c++_serving/
+    cd ${build_path}/python/examples/ocr
+    check_dir ${dir}
+    echo -e "${GREEN_COLOR}OCR_C++_Service_GPU_RPC asyn_server started${RES}"
+    $py_version -m paddle_serving_server.serve --model ocr_det_model ocr_rec_model --port 9293 --gpu_id 0 --op_num 4 > ${dir}server_log.txt 2>&1 &
+    check_result server 8
+    check_gpu_memory 0
+    echo -e "${GREEN_COLOR}OCR_C++_Service_GPU_RPC client started${RES}"
+    echo "------------------first:"
+    $py_version ocr_cpp_client.py ocr_det_client ocr_rec_client
+    echo "------------------second:"
+    $py_version ocr_cpp_client.py ocr_det_client ocr_rec_client > ${dir}client_log.txt 2>&1
+    check_result client "OCR_C++_Service_GPU_RPC server test completed"
+    kill_server_process
+}
+
 function build_all_whl() {
     for whl in ${build_whl_list[@]}
     do