merge code

cmake/external/protobuf.cmake

@@ -143,7 +143,6 @@ function(grpc_protobuf_generate_python SRCS)
     set(${SRCS} ${${SRCS}} PARENT_SCOPE)
 endfunction()
 
-
 # Print and set the protobuf library information,
 # finish this cmake process and exit from this file.
 macro(PROMPT_PROTOBUF_LIB)

core/configure/CMakeLists.txt

@@ -39,27 +39,37 @@ py_grpc_proto_compile(multi_lang_general_model_service_py_proto SRCS proto/multi
 add_custom_target(multi_lang_general_model_service_py_proto_init ALL COMMAND ${CMAKE_COMMAND} -E touch __init__.py)
 add_dependencies(multi_lang_general_model_service_py_proto multi_lang_general_model_service_py_proto_init)
 
+py_grpc_proto_compile(general_python_service_py_proto SRCS proto/general_python_service.proto)
+add_custom_target(general_python_service_py_proto_init ALL COMMAND ${CMAKE_COMMAND} -E touch __init__.py)
+add_dependencies(general_python_service_py_proto general_python_service_py_proto_init)
 if (CLIENT)
 py_proto_compile(sdk_configure_py_proto SRCS proto/sdk_configure.proto)
 add_custom_target(sdk_configure_py_proto_init ALL COMMAND ${CMAKE_COMMAND} -E touch __init__.py)
 add_dependencies(sdk_configure_py_proto sdk_configure_py_proto_init)
 add_custom_command(TARGET sdk_configure_py_proto POST_BUILD
 		COMMAND ${CMAKE_COMMAND} -E make_directory ${PADDLE_SERVING_BINARY_DIR}/python/paddle_serving_client/proto
-		COMMAND cp *.py ${PADDLE_SERVING_BINARY_DIR}/python/paddle_serving_client/proto
+		COMMAND cp __init__.py ${PADDLE_SERVING_BINARY_DIR}/python/paddle_serving_client/proto
+		COMMAND cp sdk_configure*.py ${PADDLE_SERVING_BINARY_DIR}/python/paddle_serving_client/proto
 		COMMENT "Copy generated python proto into directory paddle_serving_client/proto."
 		WORKING_DIRECTORY ${CMAKE_CURRENT_BINARY_DIR})
 
 add_custom_command(TARGET general_model_config_py_proto POST_BUILD
                 COMMAND ${CMAKE_COMMAND} -E make_directory ${PADDLE_SERVING_BINARY_DIR}/python/paddle_serving_client/proto
-                COMMAND cp *.py ${PADDLE_SERVING_BINARY_DIR}/python/paddle_serving_client/proto
+                COMMAND cp general_model_config*.py ${PADDLE_SERVING_BINARY_DIR}/python/paddle_serving_client/proto
                 COMMENT "Copy generated general_model_config proto file into directory paddle_serving_client/proto."
                 WORKING_DIRECTORY ${CMAKE_CURRENT_BINARY_DIR})
 
 add_custom_command(TARGET multi_lang_general_model_service_py_proto POST_BUILD
                 COMMAND ${CMAKE_COMMAND} -E make_directory ${PADDLE_SERVING_BINARY_DIR}/python/paddle_serving_client/proto
-                COMMAND cp *.py ${PADDLE_SERVING_BINARY_DIR}/python/paddle_serving_client/proto
+                COMMAND cp multi_lang_general_model_service*.py ${PADDLE_SERVING_BINARY_DIR}/python/paddle_serving_client/proto
                 COMMENT "Copy generated multi_lang_general_model_service proto file into directory paddle_serving_client/proto."
                 WORKING_DIRECTORY ${CMAKE_CURRENT_BINARY_DIR})
+
+add_custom_command(TARGET general_python_service_py_proto POST_BUILD
+        COMMAND ${CMAKE_COMMAND} -E make_directory ${PADDLE_SERVING_BINARY_DIR}/python/paddle_serving_client/proto
+        COMMAND cp general_python_service*.py ${PADDLE_SERVING_BINARY_DIR}/python/paddle_serving_client/proto
+        COMMENT "Copy generated general_python_service proto file into directory paddle_serving_client/proto."
+        WORKING_DIRECTORY ${CMAKE_CURRENT_BINARY_DIR})
 endif()
 
 if (APP)
@@ -74,28 +84,49 @@ if (SERVER)
 py_proto_compile(server_config_py_proto SRCS proto/server_configure.proto)
 add_custom_target(server_config_py_proto_init ALL COMMAND ${CMAKE_COMMAND} -E touch __init__.py)
 add_dependencies(server_config_py_proto server_config_py_proto_init)
+
+py_proto_compile(pyserving_channel_py_proto SRCS proto/pyserving_channel.proto)
+add_custom_target(pyserving_channel_py_proto_init ALL COMMAND ${CMAKE_COMMAND} -E touch __init__.py)
+add_dependencies(pyserving_channel_py_proto pyserving_channel_py_proto_init)
+
 if (NOT WITH_GPU)
 add_custom_command(TARGET server_config_py_proto POST_BUILD
 		COMMAND ${CMAKE_COMMAND} -E make_directory ${PADDLE_SERVING_BINARY_DIR}/python/paddle_serving_server/proto
-		COMMAND cp *.py ${PADDLE_SERVING_BINARY_DIR}/python/paddle_serving_server/proto
+		COMMAND cp __init__.py ${PADDLE_SERVING_BINARY_DIR}/python/paddle_serving_server/proto
+		COMMAND cp server_config*.py ${PADDLE_SERVING_BINARY_DIR}/python/paddle_serving_server/proto
 		COMMENT "Copy generated python proto into directory paddle_serving_server/proto."
 		WORKING_DIRECTORY ${CMAKE_CURRENT_BINRARY_DIR})
 
 add_custom_command(TARGET general_model_config_py_proto POST_BUILD
 		COMMAND ${CMAKE_COMMAND} -E make_directory ${PADDLE_SERVING_BINARY_DIR}/python/paddle_serving_server/proto
-		COMMAND cp *.py ${PADDLE_SERVING_BINARY_DIR}/python/paddle_serving_server/proto
+		COMMAND cp general_model_config*.py ${PADDLE_SERVING_BINARY_DIR}/python/paddle_serving_server/proto
 		COMMENT "Copy generated general_model_config proto file into directory paddle_serving_server/proto."
 		WORKING_DIRECTORY ${CMAKE_CURRENT_BINARY_DIR})
+
+add_custom_command(TARGET general_python_service_py_proto POST_BUILD
+        COMMAND ${CMAKE_COMMAND} -E make_directory ${PADDLE_SERVING_BINARY_DIR}/python/paddle_serving_server/proto
+        COMMAND cp general_python_service*.py ${PADDLE_SERVING_BINARY_DIR}/python/paddle_serving_server/proto
+        COMMENT "Copy generated general_python_service proto file into directory paddle_serving_server/proto."
+        WORKING_DIRECTORY ${CMAKE_CURRENT_BINARY_DIR})
+            
+add_custom_command(TARGET pyserving_channel_py_proto POST_BUILD
+        COMMAND ${CMAKE_COMMAND} -E make_directory ${PADDLE_SERVING_BINARY_DIR}/python/paddle_serving_server/proto
+        COMMAND cp pyserving_channel*.py ${PADDLE_SERVING_BINARY_DIR}/python/paddle_serving_server/proto
+        COMMENT "Copy generated pyserving_channel proto file into directory paddle_serving_server/proto."
+        WORKING_DIRECTORY ${CMAKE_CURRENT_BINARY_DIR})
+
 add_custom_command(TARGET multi_lang_general_model_service_py_proto POST_BUILD
                 COMMAND ${CMAKE_COMMAND} -E make_directory ${PADDLE_SERVING_BINARY_DIR}/python/paddle_serving_server/proto
-                COMMAND cp *.py ${PADDLE_SERVING_BINARY_DIR}/python/paddle_serving_server/proto
+                COMMAND cp multi_lang_general_model_service*.py ${PADDLE_SERVING_BINARY_DIR}/python/paddle_serving_server/proto
                 COMMENT "Copy generated multi_lang_general_model_service proto file into directory paddle_serving_server/proto."
                 WORKING_DIRECTORY ${CMAKE_CURRENT_BINARY_DIR})
 else()
 add_custom_command(TARGET server_config_py_proto POST_BUILD
 		COMMAND ${CMAKE_COMMAND} -E make_directory
         ${PADDLE_SERVING_BINARY_DIR}/python/paddle_serving_server_gpu/proto
-		COMMAND cp *.py
+		COMMAND cp __init__.py
+        ${PADDLE_SERVING_BINARY_DIR}/python/paddle_serving_server_gpu/proto
+		COMMAND cp server_config*.py
         ${PADDLE_SERVING_BINARY_DIR}/python/paddle_serving_server_gpu/proto
 		COMMENT "Copy generated python proto into directory
         paddle_serving_server_gpu/proto."
@@ -104,15 +135,27 @@ add_custom_command(TARGET server_config_py_proto POST_BUILD
 add_custom_command(TARGET general_model_config_py_proto POST_BUILD
 		COMMAND ${CMAKE_COMMAND} -E make_directory
         ${PADDLE_SERVING_BINARY_DIR}/python/paddle_serving_server_gpu/proto
-		COMMAND cp *.py
+		COMMAND cp general_model_config*.py
         ${PADDLE_SERVING_BINARY_DIR}/python/paddle_serving_server_gpu/proto
 		COMMENT "Copy generated general_model_config proto file into directory
         paddle_serving_server_gpu/proto."
 		WORKING_DIRECTORY ${CMAKE_CURRENT_BINARY_DIR})
 
+add_custom_command(TARGET general_python_service_py_proto POST_BUILD
+        COMMAND ${CMAKE_COMMAND} -E make_directory ${PADDLE_SERVING_BINARY_DIR}/python/paddle_serving_server_gpu/proto
+        COMMAND cp general_python_service*.py ${PADDLE_SERVING_BINARY_DIR}/python/paddle_serving_server_gpu/proto
+        COMMENT "Copy generated general_python_service proto file into directory paddle_serving_server_gpu/proto."
+        WORKING_DIRECTORY ${CMAKE_CURRENT_BINARY_DIR})
+
+add_custom_command(TARGET pyserving_channel_py_proto POST_BUILD
+        COMMAND ${CMAKE_COMMAND} -E make_directory ${PADDLE_SERVING_BINARY_DIR}/python/paddle_serving_server_gpu/proto
+        COMMAND cp pyserving_channel*.py ${PADDLE_SERVING_BINARY_DIR}/python/paddle_serving_server_gpu/proto
+        COMMENT "Copy generated pyserving_channel proto file into directory paddle_serving_server_gpu/proto."
+        WORKING_DIRECTORY ${CMAKE_CURRENT_BINARY_DIR})
+
 add_custom_command(TARGET multi_lang_general_model_service_py_proto POST_BUILD
                 COMMAND ${CMAKE_COMMAND} -E make_directory ${PADDLE_SERVING_BINARY_DIR}/python/paddle_serving_server_gpu/proto
-                COMMAND cp *.py ${PADDLE_SERVING_BINARY_DIR}/python/paddle_serving_server_gpu/proto
+                COMMAND cp multi_lang_general_model_service*.py ${PADDLE_SERVING_BINARY_DIR}/python/paddle_serving_server_gpu/proto
                 COMMENT "Copy generated multi_lang_general_model_service proto file into directory paddle_serving_server_gpu/proto."
                 WORKING_DIRECTORY ${CMAKE_CURRENT_BINARY_DIR})
 endif()

core/configure/proto/general_python_service.proto

+// 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.
+
+syntax = "proto2";
+package baidu.paddle_serving.pyserving;
+
+service GeneralPythonService {
+  rpc inference(Request) returns (Response) {}
+}
+
+message Request {
+  repeated bytes feed_insts = 1;
+  repeated string feed_var_names = 2;
+  repeated bytes shape = 3;
+  repeated string type = 4;
+}
+
+message Response {
+  repeated bytes fetch_insts = 1;
+  repeated string fetch_var_names = 2;
+  required int32 ecode = 3;
+  optional string error_info = 4;
+  repeated bytes shape = 5;
+  repeated string type = 6;
+}

core/configure/proto/pyserving_channel.proto

+// 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.
+
+syntax = "proto2";
+package baidu.paddle_serving.pyserving;
+
+message ChannelData {
+  repeated Inst insts = 1;
+  required int32 id = 2;
+  required int32 ecode = 4;
+  optional string error_info = 5;
+}
+
+message Inst {
+  required bytes data = 1;
+  required string name = 2;
+  required bytes shape = 3;
+  required string type = 4;
+}

python/CMakeLists.txt

@@ -7,8 +7,10 @@ endif()
 
 if (SERVER)
     if (NOT WITH_GPU)
+        file(INSTALL pipeline DESTINATION paddle_serving_server)
         file(GLOB_RECURSE SERVING_SERVER_PY_FILES paddle_serving_server/*.py)
     else()
+        file(INSTALL pipeline DESTINATION paddle_serving_server_gpu)
         file(GLOB_RECURSE SERVING_SERVER_PY_FILES paddle_serving_server_gpu/*.py)
     endif()
         set(PY_FILES ${SERVING_SERVER_PY_FILES})

python/examples/pipeline/imdb_model_ensemble/get_data.sh

+wget --no-check-certificate https://fleet.bj.bcebos.com/text_classification_data.tar.gz
+wget --no-check-certificate https://paddle-serving.bj.bcebos.com/imdb-demo/imdb_model.tar.gz
+tar -zxvf text_classification_data.tar.gz
+tar -zxvf imdb_model.tar.gz

python/examples/pipeline/imdb_model_ensemble/test_pipeline_client.py

+# 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.
+from paddle_serving_client.pipeline import PipelineClient
+import numpy as np
+from paddle_serving_app.reader import IMDBDataset
+from line_profiler import LineProfiler
+
+client = PipelineClient()
+client.connect('localhost:8080')
+
+lp = LineProfiler()
+lp_wrapper = lp(client.predict)
+
+words = 'i am very sad | 0'
+imdb_dataset = IMDBDataset()
+imdb_dataset.load_resource('imdb.vocab')
+
+for i in range(1):
+    word_ids, label = imdb_dataset.get_words_and_label(words)
+    fetch_map = lp_wrapper(feed={"words": word_ids}, fetch=["prediction"])
+    print(fetch_map)
+
+#lp.print_stats()

python/examples/pipeline/imdb_model_ensemble/test_pipeline_server.py

+# 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.pipeline import Op
+from paddle_serving_server.pipeline import PipelineServer
+import numpy as np
+import logging
+
+logging.basicConfig(
+    format='%(asctime)s %(levelname)-8s [%(filename)s:%(lineno)d] %(message)s',
+    datefmt='%Y-%m-%d %H:%M',
+    #level=logging.DEBUG)
+    level=logging.INFO)
+
+
+class CombineOp(Op):
+    def preprocess(self, input_data):
+        combined_prediction = 0
+        for op_name, channeldata in input_data.items():
+            data = channeldata.parse()
+            logging.info("{}: {}".format(op_name, data["prediction"]))
+            combined_prediction += data["prediction"]
+        data = {"prediction": combined_prediction / 2}
+        return data
+
+
+read_op = Op(name="read", inputs=None)
+bow_op = Op(name="bow",
+            inputs=[read_op],
+            server_model="imdb_bow_model",
+            server_port="9393",
+            device="cpu",
+            client_config="imdb_bow_client_conf/serving_client_conf.prototxt",
+            server_name="127.0.0.1:9393",
+            fetch_names=["prediction"],
+            concurrency=1,
+            timeout=0.1,
+            retry=2)
+cnn_op = Op(name="cnn",
+            inputs=[read_op],
+            server_model="imdb_cnn_model",
+            server_port="9292",
+            device="cpu",
+            client_config="imdb_cnn_client_conf/serving_client_conf.prototxt",
+            server_name="127.0.0.1:9292",
+            fetch_names=["prediction"],
+            concurrency=1,
+            timeout=-1,
+            retry=1)
+combine_op = CombineOp(
+    name="combine", inputs=[bow_op, cnn_op], concurrency=1, timeout=-1, retry=1)
+
+pyserver = PipelineServer(
+    use_multithread=True,
+    client_type='grpc',
+    use_future=False,
+    profile=False,
+    retry=1)
+pyserver.add_ops([read_op, bow_op, cnn_op, combine_op])
+pyserver.prepare_server(port=8080, worker_num=2)
+pyserver.run_server()

python/examples/util/timeline_trace.py

@@ -16,10 +16,16 @@ def prase(pid_str, time_str, counter):
         if len(name_list) == 2:
             name = name_list[0]
         else:
-            name = name_list[0] + "_" + name_list[1]
+            name = "_".join(name_list[:-1])
+        name_list = name.split("#")
+        if len(name_list) > 1:
+            tid = name_list[-1]
+            name = "#".join(name_list[:-1])
+        else:
+            tid = 0
         event_dict = {}
         event_dict["name"] = name
-        event_dict["tid"] = 0
+        event_dict["tid"] = tid
         event_dict["pid"] = pid
         event_dict["ts"] = ts
         event_dict["ph"] = ph

python/paddle_serving_client/pipeline.py

+# 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
+import grpc
+from .proto import general_python_service_pb2
+from .proto import general_python_service_pb2_grpc
+import numpy as np
+
+
+class PipelineClient(object):
+    def __init__(self):
+        self._channel = None
+
+    def connect(self, endpoint):
+        self._channel = grpc.insecure_channel(endpoint)
+        self._stub = general_python_service_pb2_grpc.GeneralPythonServiceStub(
+            self._channel)
+
+    def _pack_data_for_infer(self, feed_data):
+        req = general_python_service_pb2.Request()
+        for name, data in feed_data.items():
+            if isinstance(data, list):
+                data = np.array(data)
+            elif not isinstance(data, np.ndarray):
+                raise TypeError("only list and numpy array type is supported.")
+            req.feed_var_names.append(name)
+            req.feed_insts.append(data.tobytes())
+            req.shape.append(np.array(data.shape, dtype="int32").tobytes())
+            req.type.append(str(data.dtype))
+        return req
+
+    def predict(self, feed, fetch):
+        if not isinstance(feed, dict):
+            raise TypeError(
+                "feed must be dict type with format: {name: value}.")
+        if not isinstance(fetch, list):
+            raise TypeError(
+                "fetch_with_type must be list type with format: [name].")
+        req = self._pack_data_for_infer(feed)
+        resp = self._stub.inference(req)
+        if resp.ecode != 0:
+            return {"ecode": resp.ecode, "error_info": resp.error_info}
+        fetch_map = {"ecode": resp.ecode}
+        for idx, name in enumerate(resp.fetch_var_names):
+            if name not in fetch:
+                continue
+            fetch_map[name] = np.frombuffer(
+                resp.fetch_insts[idx], dtype=resp.type[idx])
+            fetch_map[name].shape = np.frombuffer(
+                resp.shape[idx], dtype="int32")
+        return fetch_map

python/paddle_serving_server/serve.py

@@ -53,6 +53,10 @@ def parse_args():  # pylint: disable=doc-string-missing
         type=int,
         default=512 * 1024 * 1024,
         help="Limit sizes of messages")
+    parser.add_argument(
+        "--use_multilang",
+        action='store_true',
+        help="Use Multi-language-service")
     return parser.parse_args()
 
 
@@ -67,6 +71,7 @@ def start_standard_model():  # pylint: disable=doc-string-missing
     ir_optim = args.ir_optim
     max_body_size = args.max_body_size
     use_mkl = args.use_mkl
+    use_multilang = args.use_multilang
 
     if model == "":
         print("You must specify your serving model")
@@ -83,14 +88,19 @@ def start_standard_model():  # pylint: disable=doc-string-missing
     op_seq_maker.add_op(general_infer_op)
     op_seq_maker.add_op(general_response_op)
 
-    server = serving.Server()
-    server.set_op_sequence(op_seq_maker.get_op_sequence())
-    server.set_num_threads(thread_num)
-    server.set_memory_optimize(mem_optim)
-    server.set_ir_optimize(ir_optim)
-    server.use_mkl(use_mkl)
-    server.set_max_body_size(max_body_size)
-    server.set_port(port)
+    server = None
+    if use_multilang:
+        server = serving.MultiLangServer()
+        server.set_op_sequence(op_seq_maker.get_op_sequence())
+    else:
+        server = serving.Server()
+        server.set_op_sequence(op_seq_maker.get_op_sequence())
+        server.set_num_threads(thread_num)
+        server.set_memory_optimize(mem_optim)
+        server.set_ir_optimize(ir_optim)
+        server.use_mkl(use_mkl)
+        server.set_max_body_size(max_body_size)
+        server.set_port(port)
 
     server.load_model_config(model)
     server.prepare_server(workdir=workdir, port=port, device=device)

python/paddle_serving_server_gpu/__init__.py

@@ -68,6 +68,10 @@ def serve_args():
         type=int,
         default=512 * 1024 * 1024,
         help="Limit sizes of messages")
+    parser.add_argument(
+        "--use_multilang",
+        action='store_true',
+        help="Use Multi-language-service")
     return parser.parse_args()
 
 

python/paddle_serving_server_gpu/serve.py

@@ -54,17 +54,26 @@ def start_gpu_card_model(index, gpuid, args):  # pylint: disable=doc-string-miss
     op_seq_maker.add_op(general_infer_op)
     op_seq_maker.add_op(general_response_op)
 
-    server = serving.Server()
-    server.set_op_sequence(op_seq_maker.get_op_sequence())
-    server.set_num_threads(thread_num)
-    server.set_memory_optimize(mem_optim)
-    server.set_ir_optimize(ir_optim)
-    server.set_max_body_size(max_body_size)
-
-    server.load_model_config(model)
-    server.prepare_server(workdir=workdir, port=port, device=device)
-    if gpuid >= 0:
-        server.set_gpuid(gpuid)
+    use_multilang = args.use_multilang
+    if use_multilang:
+        server = serving.MultiLangServer()
+        server.set_op_sequence(op_seq_maker.get_op_sequence())
+        server.load_model_config(model)
+        server.prepare_server(workdir=workdir, port=port, device=device)
+        if gpuid >= 0:
+            raise ValueError("gpuid can not >= 0 in MultiLangServer")
+    else:
+        server = serving.Server()
+        server.set_op_sequence(op_seq_maker.get_op_sequence())
+        server.set_num_threads(thread_num)
+        server.set_memory_optimize(mem_optim)
+        server.set_ir_optimize(ir_optim)
+        server.set_max_body_size(max_body_size)
+
+        server.load_model_config(model)
+        server.prepare_server(workdir=workdir, port=port, device=device)
+        if gpuid >= 0:
+            server.set_gpuid(gpuid)
     server.run_server()
 
 

python/pipeline/__init__.py

+# 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.
+
+from operator import Op
+from pipeline_server import PipelineServer

python/pipeline/channel.py

@@ -22,6 +22,15 @@ elif sys.version_info.major == 3:
     import queue as Queue
 else:
     raise Exception("Error Python version")
+<<<<<<< HEAD
+=======
+from concurrent import futures
+import numpy as np
+from ..proto import pyserving_channel_pb2 as channel_pb2
+import logging
+import enum
+import copy
+>>>>>>> d84910a1180061b57c51824e35e3ca5c857eb3b5
 
 
 class ChannelDataEcode(enum.Enum):
@@ -34,6 +43,7 @@ class ChannelDataEcode(enum.Enum):
 
 
 class ChannelDataType(enum.Enum):
+<<<<<<< HEAD
     DICT = 0
     CHANNEL_NPDATA = 1
     ERROR = 2
@@ -49,3 +59,554 @@ class ThreadChannel(Queue.Queue):
 
 class ProcessChannel(multiprocessing.queues.Queue):
     pass
+=======
+    CHANNEL_PBDATA = 0
+    CHANNEL_FUTURE = 1
+    CHANNEL_NPDATA = 2
+    ERROR = 3
+
+
+class ChannelData(object):
+    def __init__(self,
+                 datatype=None,
+                 future=None,
+                 pbdata=None,
+                 npdata=None,
+                 data_id=None,
+                 callback_func=None,
+                 ecode=None,
+                 error_info=None):
+        '''
+        There are several ways to use it:
+        
+        1. ChannelData(ChannelDataType.CHANNEL_FUTURE.value, future, pbdata[, callback_func])
+        2. ChannelData(ChannelDataType.CHANNEL_FUTURE.value, future, data_id[, callback_func])
+        3. ChannelData(ChannelDataType.CHANNEL_PBDATA.value, pbdata)
+        4. ChannelData(ChannelDataType.CHANNEL_PBDATA.value, npdata, data_id)
+        5. ChannelData(ChannelDataType.CHANNEL_NPDATA.value, npdata, data_id)
+        6. ChannelData(ecode, error_info, data_id)
+
+        Protobufs are not pickle-able:
+        https://stackoverflow.com/questions/55344376/how-to-import-protobuf-module
+        '''
+        if ecode is not None:
+            if data_id is None or error_info is None:
+                raise ValueError("data_id and error_info cannot be None")
+            datatype = ChannelDataType.ERROR.value
+        else:
+            if datatype == ChannelDataType.CHANNEL_FUTURE.value:
+                if data_id is None:
+                    raise ValueError("data_id cannot be None")
+                ecode = ChannelDataEcode.OK.value
+            elif datatype == ChannelDataType.CHANNEL_PBDATA.value:
+                if pbdata is None:
+                    if data_id is None:
+                        raise ValueError("data_id cannot be None")
+                    pbdata = channel_pb2.ChannelData()
+                    ecode, error_info = self._check_npdata(npdata)
+                    if ecode != ChannelDataEcode.OK.value:
+                        logging.error(error_info)
+                    else:
+                        for name, value in npdata.items():
+                            inst = channel_pb2.Inst()
+                            inst.data = value.tobytes()
+                            inst.name = name
+                            inst.shape = np.array(
+                                value.shape, dtype="int32").tobytes()
+                            inst.type = str(value.dtype)
+                            pbdata.insts.append(inst)
+            elif datatype == ChannelDataType.CHANNEL_NPDATA.value:
+                ecode, error_info = self._check_npdata(npdata)
+                if ecode != ChannelDataEcode.OK.value:
+                    logging.error(error_info)
+            else:
+                raise ValueError("datatype not match")
+        self.future = future
+        self.pbdata = pbdata
+        self.npdata = npdata
+        self.datatype = datatype
+        self.id = data_id
+        self.ecode = ecode
+        self.error_info = error_info
+        self.callback_func = callback_func
+
+    def _check_npdata(self, npdata):
+        ecode = ChannelDataEcode.OK.value
+        error_info = None
+        for _, value in npdata.items():
+            if not isinstance(value, np.ndarray):
+                ecode = ChannelDataEcode.TYPE_ERROR.value
+                error_info = log("the value of postped_data must " \
+                        "be np.ndarray, but get {}".format(type(value)))
+                break
+        return ecode, error_info
+
+    def parse(self):
+        # return narray
+        feed = None
+        if self.datatype == ChannelDataType.CHANNEL_PBDATA.value:
+            feed = {}
+            for inst in self.pbdata.insts:
+                feed[inst.name] = np.frombuffer(inst.data, dtype=inst.type)
+                feed[inst.name].shape = np.frombuffer(inst.shape, dtype="int32")
+        elif self.datatype == ChannelDataType.CHANNEL_FUTURE.value:
+            feed = self.future.result()
+            if self.callback_func is not None:
+                feed = self.callback_func(feed)
+        elif self.datatype == ChannelDataType.CHANNEL_NPDATA.value:
+            feed = self.npdata
+        else:
+            raise TypeError("Error type({}) in datatype.".format(self.datatype))
+        return feed
+
+    def __str__(self):
+        return "type[{}], ecode[{}], id[{}]".format(
+            ChannelDataType(self.datatype).name, self.ecode, self.id)
+
+
+class ProcessChannel(multiprocessing.queues.Queue):
+    """ 
+    (Process version) The channel used for communication between Ops.
+
+    1. Support multiple different Op feed data (multiple producer)
+        Different types of data will be packaged through the data ID
+    2. Support multiple different Op fetch data (multiple consumer)
+        Only when all types of Ops get the data of the same ID,
+        the data will be poped; The Op of the same type will not
+        get the data of the same ID.
+    3. (TODO) Timeout and BatchSize are not fully supported.
+
+    Note:
+    1. The ID of the data in the channel must be different.
+    2. The function add_producer() and add_consumer() are not thread safe,
+       and can only be called during initialization.
+    """
+
+    def __init__(self, manager, name=None, maxsize=0, timeout=None):
+        # https://stackoverflow.com/questions/39496554/cannot-subclass-multiprocessing-queue-in-python-3-5/
+        if sys.version_info.major == 2:
+            super(ProcessChannel, self).__init__(maxsize=maxsize)
+        elif sys.version_info.major == 3:
+            super(ProcessChannel, self).__init__(
+                maxsize=maxsize, ctx=multiprocessing.get_context())
+        else:
+            raise Exception("Error Python version")
+        self._maxsize = maxsize
+        self._timeout = timeout
+        self.name = name
+        self._stop = False
+
+        self._cv = multiprocessing.Condition()
+
+        self._producers = []
+        self._producer_res_count = manager.dict()  # {data_id: count}
+        self._push_res = manager.dict()  # {data_id: {op_name: data}}
+
+        self._consumers = manager.dict()  # {op_name: idx}
+        self._idx_consumer_num = manager.dict()  # {idx: num}
+        self._consumer_base_idx = manager.Value('i', 0)
+        self._front_res = manager.list()
+
+    def get_producers(self):
+        return self._producers
+
+    def get_consumers(self):
+        return self._consumers.keys()
+
+    def _log(self, info_str):
+        return "[{}] {}".format(self.name, info_str)
+
+    def debug(self):
+        return self._log("p: {}, c: {}".format(self.get_producers(),
+                                               self.get_consumers()))
+
+    def add_producer(self, op_name):
+        """ not thread safe, and can only be called during initialization. """
+        if op_name in self._producers:
+            raise ValueError(
+                self._log("producer({}) is already in channel".format(op_name)))
+        self._producers.append(op_name)
+
+    def add_consumer(self, op_name):
+        """ not thread safe, and can only be called during initialization. """
+        if op_name in self._consumers:
+            raise ValueError(
+                self._log("consumer({}) is already in channel".format(op_name)))
+        self._consumers[op_name] = 0
+
+        if self._idx_consumer_num.get(0) is None:
+            self._idx_consumer_num[0] = 0
+        self._idx_consumer_num[0] += 1
+
+    def push(self, channeldata, op_name=None):
+        logging.debug(
+            self._log("{} try to push data: {}".format(op_name,
+                                                       channeldata.__str__())))
+        if len(self._producers) == 0:
+            raise Exception(
+                self._log(
+                    "expected number of producers to be greater than 0, but the it is 0."
+                ))
+        elif len(self._producers) == 1:
+            with self._cv:
+                while self._stop is False:
+                    try:
+                        self.put(channeldata, timeout=0)
+                        break
+                    except Queue.Full:
+                        self._cv.wait()
+                logging.debug(
+                    self._log("{} channel size: {}".format(op_name,
+                                                           self.qsize())))
+                self._cv.notify_all()
+                logging.debug(self._log("{} notify all".format(op_name)))
+            logging.debug(self._log("{} push data succ!".format(op_name)))
+            return True
+        elif op_name is None:
+            raise Exception(
+                self._log(
+                    "There are multiple producers, so op_name cannot be None."))
+
+        producer_num = len(self._producers)
+        data_id = channeldata.id
+        put_data = None
+        with self._cv:
+            logging.debug(self._log("{} get lock".format(op_name)))
+            if data_id not in self._push_res:
+                self._push_res[data_id] = {
+                    name: None
+                    for name in self._producers
+                }
+                self._producer_res_count[data_id] = 0
+            # see: https://docs.python.org/3.6/library/multiprocessing.html?highlight=multiprocess#proxy-objects
+            # self._push_res[data_id][op_name] = channeldata
+            tmp_push_res = self._push_res[data_id]
+            tmp_push_res[op_name] = channeldata
+            self._push_res[data_id] = tmp_push_res
+
+            if self._producer_res_count[data_id] + 1 == producer_num:
+                put_data = self._push_res[data_id]
+                self._push_res.pop(data_id)
+                self._producer_res_count.pop(data_id)
+            else:
+                self._producer_res_count[data_id] += 1
+
+            if put_data is None:
+                logging.debug(
+                    self._log("{} push data succ, but not push to queue.".
+                              format(op_name)))
+            else:
+                while self._stop is False:
+                    try:
+                        logging.debug(
+                            self._log("{} push data succ: {}".format(
+                                op_name, put_data.__str__())))
+                        self.put(put_data, timeout=0)
+                        break
+                    except Queue.Empty:
+                        self._cv.wait()
+
+                logging.debug(
+                    self._log("multi | {} push data succ!".format(op_name)))
+            self._cv.notify_all()
+        return True
+
+    def front(self, op_name=None):
+        logging.debug(self._log("{} try to get data...".format(op_name)))
+        if len(self._consumers) == 0:
+            raise Exception(
+                self._log(
+                    "expected number of consumers to be greater than 0, but the it is 0."
+                ))
+        elif len(self._consumers) == 1:
+            resp = None
+            with self._cv:
+                while self._stop is False and resp is None:
+                    try:
+                        logging.debug(
+                            self._log("{} try to get(with channel empty: {})".
+                                      format(op_name, self.empty())))
+                        # For queue multiprocess: after putting an object on 
+                        # an empty queue there may be an infinitessimal delay
+                        # before the queue's :meth:`~Queue.empty`
+                        # see more:
+                        # - https://bugs.python.org/issue18277
+                        # - https://hg.python.org/cpython/rev/860fc6a2bd21
+                        resp = self.get(timeout=1e-3)
+                        break
+                    except Queue.Empty:
+                        logging.debug(
+                            self._log(
+                                "{} wait for empty queue(with channel empty: {})".
+                                format(op_name, self.empty())))
+                        self._cv.wait()
+            logging.debug(
+                self._log("{} get data succ: {}".format(op_name, resp.__str__(
+                ))))
+            return resp
+        elif op_name is None:
+            raise Exception(
+                self._log(
+                    "There are multiple consumers, so op_name cannot be None."))
+
+        with self._cv:
+            # data_idx = consumer_idx - base_idx
+            while self._stop is False and self._consumers[
+                    op_name] - self._consumer_base_idx.value >= len(
+                        self._front_res):
+                logging.debug(
+                    self._log(
+                        "({}) B self._consumers: {}, self._consumer_base_idx: {}, len(self._front_res): {}".
+                        format(op_name, self._consumers, self.
+                               _consumer_base_idx.value, len(self._front_res))))
+                try:
+                    logging.debug(
+                        self._log("{} try to get(with channel size: {})".format(
+                            op_name, self.qsize())))
+                    # For queue multiprocess: after putting an object on 
+                    # an empty queue there may be an infinitessimal delay
+                    # before the queue's :meth:`~Queue.empty`
+                    # see more:
+                    # - https://bugs.python.org/issue18277
+                    # - https://hg.python.org/cpython/rev/860fc6a2bd21
+                    channeldata = self.get(timeout=1e-3)
+                    self._front_res.append(channeldata)
+                    break
+                except Queue.Empty:
+                    logging.debug(
+                        self._log(
+                            "{} wait for empty queue(with channel size: {})".
+                            format(op_name, self.qsize())))
+                    self._cv.wait()
+
+            consumer_idx = self._consumers[op_name]
+            base_idx = self._consumer_base_idx.value
+            data_idx = consumer_idx - base_idx
+            resp = self._front_res[data_idx]
+            logging.debug(self._log("{} get data: {}".format(op_name, resp)))
+
+            self._idx_consumer_num[consumer_idx] -= 1
+            if consumer_idx == base_idx and self._idx_consumer_num[
+                    consumer_idx] == 0:
+                self._idx_consumer_num.pop(consumer_idx)
+                self._front_res.pop(0)
+                self._consumer_base_idx.value += 1
+
+            self._consumers[op_name] += 1
+            new_consumer_idx = self._consumers[op_name]
+            if self._idx_consumer_num.get(new_consumer_idx) is None:
+                self._idx_consumer_num[new_consumer_idx] = 0
+            self._idx_consumer_num[new_consumer_idx] += 1
+            logging.debug(
+                self._log(
+                    "({}) A self._consumers: {}, self._consumer_base_idx: {}, len(self._front_res): {}".
+                    format(op_name, self._consumers, self._consumer_base_idx.
+                           value, len(self._front_res))))
+            logging.debug(self._log("{} notify all".format(op_name)))
+            self._cv.notify_all()
+
+        logging.debug(self._log("multi | {} get data succ!".format(op_name)))
+        return resp  # reference, read only
+
+    def stop(self):
+        #TODO
+        self.close()
+        self._stop = True
+        self._cv.notify_all()
+
+
+class ThreadChannel(Queue.Queue):
+    """ 
+    (Thread version)The channel used for communication between Ops.
+
+    1. Support multiple different Op feed data (multiple producer)
+        Different types of data will be packaged through the data ID
+    2. Support multiple different Op fetch data (multiple consumer)
+        Only when all types of Ops get the data of the same ID,
+        the data will be poped; The Op of the same type will not
+        get the data of the same ID.
+    3. (TODO) Timeout and BatchSize are not fully supported.
+
+    Note:
+    1. The ID of the data in the channel must be different.
+    2. The function add_producer() and add_consumer() are not thread safe,
+       and can only be called during initialization.
+    """
+
+    def __init__(self, name=None, maxsize=-1, timeout=None):
+        Queue.Queue.__init__(self, maxsize=maxsize)
+        self._maxsize = maxsize
+        self._timeout = timeout
+        self.name = name
+        self._stop = False
+
+        self._cv = threading.Condition()
+
+        self._producers = []
+        self._producer_res_count = {}  # {data_id: count}
+        self._push_res = {}  # {data_id: {op_name: data}}
+
+        self._consumers = {}  # {op_name: idx}
+        self._idx_consumer_num = {}  # {idx: num}
+        self._consumer_base_idx = 0
+        self._front_res = []
+
+    def get_producers(self):
+        return self._producers
+
+    def get_consumers(self):
+        return self._consumers.keys()
+
+    def _log(self, info_str):
+        return "[{}] {}".format(self.name, info_str)
+
+    def debug(self):
+        return self._log("p: {}, c: {}".format(self.get_producers(),
+                                               self.get_consumers()))
+
+    def add_producer(self, op_name):
+        """ not thread safe, and can only be called during initialization. """
+        if op_name in self._producers:
+            raise ValueError(
+                self._log("producer({}) is already in channel".format(op_name)))
+        self._producers.append(op_name)
+
+    def add_consumer(self, op_name):
+        """ not thread safe, and can only be called during initialization. """
+        if op_name in self._consumers:
+            raise ValueError(
+                self._log("consumer({}) is already in channel".format(op_name)))
+        self._consumers[op_name] = 0
+
+        if self._idx_consumer_num.get(0) is None:
+            self._idx_consumer_num[0] = 0
+        self._idx_consumer_num[0] += 1
+
+    def push(self, channeldata, op_name=None):
+        logging.debug(
+            self._log("{} try to push data: {}".format(op_name,
+                                                       channeldata.__str__())))
+        if len(self._producers) == 0:
+            raise Exception(
+                self._log(
+                    "expected number of producers to be greater than 0, but the it is 0."
+                ))
+        elif len(self._producers) == 1:
+            with self._cv:
+                while self._stop is False:
+                    try:
+                        self.put(channeldata, timeout=0)
+                        break
+                    except Queue.Full:
+                        self._cv.wait()
+                self._cv.notify_all()
+            logging.debug(self._log("{} push data succ!".format(op_name)))
+            return True
+        elif op_name is None:
+            raise Exception(
+                self._log(
+                    "There are multiple producers, so op_name cannot be None."))
+
+        producer_num = len(self._producers)
+        data_id = channeldata.id
+        put_data = None
+        with self._cv:
+            logging.debug(self._log("{} get lock".format(op_name)))
+            if data_id not in self._push_res:
+                self._push_res[data_id] = {
+                    name: None
+                    for name in self._producers
+                }
+                self._producer_res_count[data_id] = 0
+            self._push_res[data_id][op_name] = channeldata
+            if self._producer_res_count[data_id] + 1 == producer_num:
+                put_data = self._push_res[data_id]
+                self._push_res.pop(data_id)
+                self._producer_res_count.pop(data_id)
+            else:
+                self._producer_res_count[data_id] += 1
+
+            if put_data is None:
+                logging.debug(
+                    self._log("{} push data succ, but not push to queue.".
+                              format(op_name)))
+            else:
+                while self._stop is False:
+                    try:
+                        self.put(put_data, timeout=0)
+                        break
+                    except Queue.Empty:
+                        self._cv.wait()
+
+                logging.debug(
+                    self._log("multi | {} push data succ!".format(op_name)))
+            self._cv.notify_all()
+        return True
+
+    def front(self, op_name=None):
+        logging.debug(self._log("{} try to get data".format(op_name)))
+        if len(self._consumers) == 0:
+            raise Exception(
+                self._log(
+                    "expected number of consumers to be greater than 0, but the it is 0."
+                ))
+        elif len(self._consumers) == 1:
+            resp = None
+            with self._cv:
+                while self._stop is False and resp is None:
+                    try:
+                        resp = self.get(timeout=0)
+                        break
+                    except Queue.Empty:
+                        self._cv.wait()
+            logging.debug(
+                self._log("{} get data succ: {}".format(op_name, resp.__str__(
+                ))))
+            return resp
+        elif op_name is None:
+            raise Exception(
+                self._log(
+                    "There are multiple consumers, so op_name cannot be None."))
+
+        with self._cv:
+            # data_idx = consumer_idx - base_idx
+            while self._stop is False and self._consumers[
+                    op_name] - self._consumer_base_idx >= len(self._front_res):
+                try:
+                    channeldata = self.get(timeout=0)
+                    self._front_res.append(channeldata)
+                    break
+                except Queue.Empty:
+                    self._cv.wait()
+
+            consumer_idx = self._consumers[op_name]
+            base_idx = self._consumer_base_idx
+            data_idx = consumer_idx - base_idx
+            resp = self._front_res[data_idx]
+            logging.debug(self._log("{} get data: {}".format(op_name, resp)))
+
+            self._idx_consumer_num[consumer_idx] -= 1
+            if consumer_idx == base_idx and self._idx_consumer_num[
+                    consumer_idx] == 0:
+                self._idx_consumer_num.pop(consumer_idx)
+                self._front_res.pop(0)
+                self._consumer_base_idx += 1
+
+            self._consumers[op_name] += 1
+            new_consumer_idx = self._consumers[op_name]
+            if self._idx_consumer_num.get(new_consumer_idx) is None:
+                self._idx_consumer_num[new_consumer_idx] = 0
+            self._idx_consumer_num[new_consumer_idx] += 1
+
+            self._cv.notify_all()
+
+        logging.debug(self._log("multi | {} get data succ!".format(op_name)))
+        # return resp  # reference, read only
+        return copy.deepcopy(resp)
+
+    def stop(self):
+        #TODO
+        self.close()
+        self._stop = True
+        self._cv.notify_all()
+>>>>>>> d84910a1180061b57c51824e35e3ca5c857eb3b5

python/pipeline/operator.py

@@ -13,37 +13,398 @@
 # limitations under the License.
 # pylint: disable=doc-string-missing
 
+import threading
+import multiprocessing
+from paddle_serving_client import MultiLangClient, Client
+from concurrent import futures
+import logging
+import func_timeout
+
+from .channel import ThreadChannel, ProcessChannel, ChannelDataEcode, ChannelData, ChannelDataType
+from .util import NameGenerator
+
+_name_gen = NameGenerator("Op")
 
 class Op(object):
     def __init__(self,
-                 name="",
+                 name=None,
                  input_ops=[],
                  server_endpoints=[],
                  concurrency=1,
                  timeout=-1,
                  retry=1):
-        pass
+        if name is None:
+            name = _name_gen.next()
+        self._is_run = False
+        self.name = name  # to identify the type of OP, it must be globally unique
+        self._concurrency = concurrency  # amount of concurrency
+        self.set_input_ops(input_ops)
+        self.with_serving = False
+        '''
+        self._client_config = client_config
+        self._server_name = server_name
+        self._fetch_names = fetch_names
+        self._server_model = server_model
+        self._server_port = server_port
+         self._device = device
+        if self._client_config is not None and \
+                self._server_name is not None and \
+                self._fetch_names is not None:
+            self.with_serving = True
+        '''
+        self._timeout = timeout
+        self._retry = max(1, retry)
+        self._input = None
+        self._outputs = []
+        self._profiler = None
+
+    def init_profiler(self, profiler):
+        self._profiler = profiler
+
+    def _profiler_record(self, string):
+        if self._profiler is None:
+            return
+        self._profiler.record(string)
+
+    def init_client(self, client_type, client_config, server_name, fetch_names):
+        if self.with_serving == False:
+            logging.debug("{} no client".format(self.name))
+            return
+        logging.debug("{} client_config: {}".format(self.name, client_config))
+        logging.debug("{} server_name: {}".format(self.name, server_name))
+        logging.debug("{} fetch_names: {}".format(self.name, fetch_names))
+        if client_type == 'brpc':
+            self._client = Client()
+        elif client_type == 'grpc':
+            self._client = MultiLangClient()
+        else:
+            raise ValueError("unknow client type: {}".format(client_type))
+        self._client.load_client_config(client_config)
+        self._client.connect([server_name])
+        self._fetch_names = fetch_names
+
+    def get_input_channel(self):
+        return self._input
+
+    def get_input_ops(self):
+        return self._input_ops
+
+    def set_input_ops(self, ops):
+        if not isinstance(ops, list):
+            ops = [] if ops is None else [ops]
+        self._input_ops = []
+        for op in ops:
+            if not isinstance(op, Op):
+                raise TypeError(
+                    self._log('input op must be Op type, not {}'.format(
+                        type(op))))
+            self._input_ops.append(op)
 
-    def _get_input_channel(self):
-        pass
+    def add_input_channel(self, channel):
+        if not isinstance(channel, (ThreadChannel, ProcessChannel)):
+            raise TypeError(
+                self._log('input channel must be Channel type, not {}'.format(
+                    type(channel))))
+        channel.add_consumer(self.name)
+        self._input = channel
 
-    def _get_output_channel(self):
-        pass
+    def get_output_channels(self):
+        return self._outputs
 
-    def preprocess(self, input_dict):
-        pass
+    def add_output_channel(self, channel):
+        if not isinstance(channel, (ThreadChannel, ProcessChannel)):
+            raise TypeError(
+                self._log('output channel must be Channel type, not {}'.format(
+                    type(channel))))
+        channel.add_producer(self.name)
+        self._outputs.append(channel)
 
-    def process(self, feed_dict):
-        pass
+    def preprocess(self, channeldata):
+        if isinstance(channeldata, dict):
+            raise NotImplementedError(
+                'this Op has multiple previous inputs. Please override this method'
+            )
+        feed = channeldata.parse()
+        return feed
 
-    def postprocess(self, fetch_dict):
-        pass
+    def midprocess(self, data, use_future=True):
+        if not isinstance(data, dict):
+            raise Exception(
+                self._log(
+                    'data must be dict type(the output of preprocess()), but get {}'.
+                    format(type(data))))
+        logging.debug(self._log('data: {}'.format(data)))
+        logging.debug(self._log('fetch: {}'.format(self._fetch_names)))
+        if isinstance(self._client, MultiLangClient):
+            call_result = self._client.predict(
+                feed=data, fetch=self._fetch_names, asyn=use_future)
+        else:
+            call_result = self._client.predict(
+                feed=data, fetch=self._fetch_names)
+        logging.debug(self._log("get call_result"))
+        return call_result
+
+    def postprocess(self, output_data):
+        return output_data
 
     def stop(self):
-        pass
+        self._input.stop()
+        for channel in self._outputs:
+            channel.stop()
+        self._is_run = False
+
+    def _parse_channeldata(self, channeldata):
+        data_id, error_channeldata = None, None
+        if isinstance(channeldata, dict):
+            parsed_data = {}
+            key = list(channeldata.keys())[0]
+            data_id = channeldata[key].id
+            for _, data in channeldata.items():
+                if data.ecode != ChannelDataEcode.OK.value:
+                    error_channeldata = data
+                    break
+        else:
+            data_id = channeldata.id
+            if channeldata.ecode != ChannelDataEcode.OK.value:
+                error_channeldata = channeldata
+        return data_id, error_channeldata
+
+    def _push_to_output_channels(self, data, channels, name=None):
+        if name is None:
+            name = self.name
+        for channel in channels:
+            channel.push(data, name)
+
+    def start_with_process(self, client_type, use_future):
+        proces = []
+        for concurrency_idx in range(self._concurrency):
+            p = multiprocessing.Process(
+                target=self._run,
+                args=(concurrency_idx, self.get_input_channel(),
+                      self.get_output_channels(), client_type, use_future))
+            p.start()
+            proces.append(p)
+        return proces
+
+    def start_with_thread(self, client_type, use_future):
+        threads = []
+        for concurrency_idx in range(self._concurrency):
+            t = threading.Thread(
+                target=self._run,
+                args=(concurrency_idx, self.get_input_channel(),
+                      self.get_output_channels(), client_type, use_future))
+            t.start()
+            threads.append(t)
+        return threads
+
+    def _run(self, concurrency_idx, input_channel, output_channels, client_type,
+             use_future):
+        # create client based on client_type
+        self.init_client(client_type, self._client_config, self._server_name,
+                         self._fetch_names)
+        op_info_prefix = "[{}|{}]".format(self.name, concurrency_idx)
+        log = self._get_log_func(op_info_prefix)
+        self._is_run = True
+        tid = threading.current_thread().ident
+        while self._is_run:
+            self._profiler_record("{}-get#{}_0".format(op_info_prefix, tid))
+            channeldata = input_channel.front(self.name)
+            self._profiler_record("{}-get#{}_1".format(op_info_prefix, tid))
+            logging.debug(log("input_data: {}".format(channeldata)))
+
+            data_id, error_channeldata = self._parse_channeldata(channeldata)
+
+            # error data in predecessor Op
+            if error_channeldata is not None:
+                self._push_to_output_channels(error_channeldata,
+                                              output_channels)
+                continue
+
+            # preprecess
+            try:
+                self._profiler_record("{}-prep#{}_0".format(op_info_prefix,
+                                                            tid))
+                preped_data = self.preprocess(channeldata)
+                self._profiler_record("{}-prep#{}_1".format(op_info_prefix,
+                                                            tid))
+            except NotImplementedError as e:
+                # preprocess function not implemented
+                error_info = log(e)
+                logging.error(error_info)
+                self._push_to_output_channels(
+                    ChannelData(
+                        ecode=ChannelDataEcode.NOT_IMPLEMENTED.value,
+                        error_info=error_info,
+                        data_id=data_id),
+                    output_channels)
+                continue
+            except TypeError as e:
+                # Error type in channeldata.datatype
+                error_info = log(e)
+                logging.error(error_info)
+                self._push_to_output_channels(
+                    ChannelData(
+                        ecode=ChannelDataEcode.TYPE_ERROR.value,
+                        error_info=error_info,
+                        data_id=data_id),
+                    output_channels)
+                continue
+            except Exception as e:
+                error_info = log(e)
+                logging.error(error_info)
+                self._push_to_output_channels(
+                    ChannelData(
+                        ecode=ChannelDataEcode.UNKNOW.value,
+                        error_info=error_info,
+                        data_id=data_id),
+                    output_channels)
+                continue
+
+            # midprocess
+            midped_data = None
+            if self.with_serving:
+                ecode = ChannelDataEcode.OK.value
+                self._profiler_record("{}-midp#{}_0".format(op_info_prefix,
+                                                            tid))
+                if self._timeout <= 0:
+                    try:
+                        midped_data = self.midprocess(preped_data, use_future)
+                    except Exception as e:
+                        ecode = ChannelDataEcode.UNKNOW.value
+                        error_info = log(e)
+                        logging.error(error_info)
+                else:
+                    for i in range(self._retry):
+                        try:
+                            midped_data = func_timeout.func_timeout(
+                                self._timeout,
+                                self.midprocess,
+                                args=(preped_data, use_future))
+                        except func_timeout.FunctionTimedOut as e:
+                            if i + 1 >= self._retry:
+                                ecode = ChannelDataEcode.TIMEOUT.value
+                                error_info = log(e)
+                                logging.error(error_info)
+                            else:
+                                logging.warn(
+                                    log("timeout, retry({})".format(i + 1)))
+                        except Exception as e:
+                            ecode = ChannelDataEcode.UNKNOW.value
+                            error_info = log(e)
+                            logging.error(error_info)
+                            break
+                        else:
+                            break
+                if ecode != ChannelDataEcode.OK.value:
+                    self._push_to_output_channels(
+                        ChannelData(
+                            ecode=ecode, error_info=error_info,
+                            data_id=data_id),
+                        output_channels)
+                    continue
+                self._profiler_record("{}-midp#{}_1".format(op_info_prefix,
+                                                            tid))
+            else:
+                midped_data = preped_data
+
+            # postprocess
+            output_data = None
+            self._profiler_record("{}-postp#{}_0".format(op_info_prefix, tid))
+            if self.with_serving and client_type == 'grpc' and use_future:
+                # use call_future
+                output_data = ChannelData(
+                    datatype=ChannelDataType.CHANNEL_FUTURE.value,
+                    future=midped_data,
+                    data_id=data_id,
+                    callback_func=self.postprocess)
+            else:
+                try:
+                    postped_data = self.postprocess(midped_data)
+                except Exception as e:
+                    ecode = ChannelDataEcode.UNKNOW.value
+                    error_info = log(e)
+                    logging.error(error_info)
+                    self._push_to_output_channels(
+                        ChannelData(
+                            ecode=ecode, error_info=error_info,
+                            data_id=data_id),
+                        output_channels)
+                    continue
+                if not isinstance(postped_data, dict):
+                    ecode = ChannelDataEcode.TYPE_ERROR.value
+                    error_info = log("output of postprocess funticon must be " \
+                            "dict type, but get {}".format(type(postped_data)))
+                    logging.error(error_info)
+                    self._push_to_output_channels(
+                        ChannelData(
+                            ecode=ecode, error_info=error_info,
+                            data_id=data_id),
+                        output_channels)
+                    continue
+
+                output_data = ChannelData(
+                    ChannelDataType.CHANNEL_NPDATA.value,
+                    npdata=postped_data,
+                    data_id=data_id)
+            self._profiler_record("{}-postp#{}_1".format(op_info_prefix, tid))
+
+            # push data to channel (if run succ)
+            self._profiler_record("{}-push#{}_0".format(op_info_prefix, tid))
+            self._push_to_output_channels(output_data, output_channels)
+            self._profiler_record("{}-push#{}_1".format(op_info_prefix, tid))
+
+    def _log(self, info):
+        return "{} {}".format(self.name, info)
+
+    def _get_log_func(self, op_info_prefix):
+        def log_func(info_str):
+            return "{} {}".format(op_info_prefix, info_str)
+
+        return log_func
+
+    def get_concurrency(self):
+        return self._concurrency
+
+
+class VirtualOp(Op):
+    ''' For connecting two channels. '''
+
+    def __init__(self, name, concurrency=1):
+        super(VirtualOp, self).__init__(
+            name=name, inputs=None, concurrency=concurrency)
+        self._virtual_pred_ops = []
+
+    def add_virtual_pred_op(self, op):
+        self._virtual_pred_ops.append(op)
+
+    def add_output_channel(self, channel):
+        if not isinstance(channel, (ThreadChannel, ProcessChannel)):
+            raise TypeError(
+                self._log('output channel must be Channel type, not {}'.format(
+                    type(channel))))
+        for op in self._virtual_pred_ops:
+            channel.add_producer(op.name)
+        self._outputs.append(channel)
 
-    def start_with_process(self):
-        pass
+    def _run(self, concurrency_idx, input_channel, output_channels, client_type,
+             use_future):
+        op_info_prefix = "[{}|{}]".format(self.name, concurrency_idx)
+        log = self._get_log_func(op_info_prefix)
+        self._is_run = True
+        while self._is_run:
+            self._profiler_record("{}-get#{}_0".format(op_info_prefix, tid))
+            channeldata = input_channel.front(self.name)
+            self._profiler_record("{}-get#{}_1".format(op_info_prefix, tid))
 
-    def start_with_thread(self):
-        pass
+            self._profiler_record("{}-push#{}_0".format(op_info_prefix, tid))
+            if isinstance(channeldata, dict):
+                for name, data in channeldata.items():
+                    self._push_to_output_channels(
+                        data, channels=output_channels, name=name)
+            else:
+                self._push_to_output_channels(
+                    channeldata,
+                    channels=output_channels,
+                    name=self._virtual_pred_ops[0].name)
+            self._profiler_record("{}-push#{}_1".format(op_info_prefix, tid))
+>>>>>>> d84910a1180061b57c51824e35e3ca5c857eb3b5

python/pipeline/pipeline_server.py

@@ -12,6 +12,7 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 # pylint: disable=doc-string-missing
+<<<<<<< HEAD
 
 
 class PipelineService(pipeline_service_pb2_grpc.PipelineServiceServicer):
@@ -32,3 +33,469 @@ class PipelineServer(object):
 
     def run_server(self):
         pass
+=======
+import threading
+import multiprocessing
+import multiprocessing.queues
+import sys
+if sys.version_info.major == 2:
+    import Queue
+elif sys.version_info.major == 3:
+    import queue as Queue
+else:
+    raise Exception("Error Python version")
+import os
+from paddle_serving_client import MultiLangClient, Client
+from concurrent import futures
+import numpy as np
+import grpc
+from ..proto import general_python_service_pb2 as pyservice_pb2
+from ..proto import pyserving_channel_pb2 as channel_pb2
+from ..proto import general_python_service_pb2_grpc
+import logging
+import random
+import time
+import func_timeout
+import enum
+import collections
+import copy
+
+from .operator import Op, VirtualOp
+from .channel import ThreadChannel, ProcessChannel, ChannelData, ChannelDataEcode, ChannelDataType
+from .profiler import TimeProfiler
+
+_profiler = TimeProfiler()
+
+
+class GeneralPythonService(
+        general_python_service_pb2_grpc.GeneralPythonServiceServicer):
+    def __init__(self, in_channel, out_channel, retry=2):
+        super(GeneralPythonService, self).__init__()
+        self.name = "#G"
+        self.set_in_channel(in_channel)
+        self.set_out_channel(out_channel)
+        logging.debug(self._log(in_channel.debug()))
+        logging.debug(self._log(out_channel.debug()))
+        #TODO: 
+        #  multi-lock for different clients
+        #  diffenert lock for server and client
+        self._id_lock = threading.Lock()
+        self._cv = threading.Condition()
+        self._globel_resp_dict = {}
+        self._id_counter = 0
+        self._retry = retry
+        self._recive_func = threading.Thread(
+            target=GeneralPythonService._recive_out_channel_func, args=(self, ))
+        self._recive_func.start()
+
+    def _log(self, info_str):
+        return "[{}] {}".format(self.name, info_str)
+
+    def set_in_channel(self, in_channel):
+        if not isinstance(in_channel, (ThreadChannel, ProcessChannel)):
+            raise TypeError(
+                self._log('in_channel must be Channel type, but get {}'.format(
+                    type(in_channel))))
+        in_channel.add_producer(self.name)
+        self._in_channel = in_channel
+
+    def set_out_channel(self, out_channel):
+        if not isinstance(out_channel, (ThreadChannel, ProcessChannel)):
+            raise TypeError(
+                self._log('out_channel must be Channel type, but get {}'.format(
+                    type(out_channel))))
+        out_channel.add_consumer(self.name)
+        self._out_channel = out_channel
+
+    def _recive_out_channel_func(self):
+        while True:
+            channeldata = self._out_channel.front(self.name)
+            if not isinstance(channeldata, ChannelData):
+                raise TypeError(
+                    self._log('data must be ChannelData type, but get {}'.
+                              format(type(channeldata))))
+            with self._cv:
+                data_id = channeldata.id
+                self._globel_resp_dict[data_id] = channeldata
+                self._cv.notify_all()
+
+    def _get_next_id(self):
+        with self._id_lock:
+            self._id_counter += 1
+            return self._id_counter - 1
+
+    def _get_data_in_globel_resp_dict(self, data_id):
+        resp = None
+        with self._cv:
+            while data_id not in self._globel_resp_dict:
+                self._cv.wait()
+            resp = self._globel_resp_dict.pop(data_id)
+            self._cv.notify_all()
+        return resp
+
+    def _pack_data_for_infer(self, request):
+        logging.debug(self._log('start inferce'))
+        data_id = self._get_next_id()
+        npdata = {}
+        try:
+            for idx, name in enumerate(request.feed_var_names):
+                logging.debug(
+                    self._log('name: {}'.format(request.feed_var_names[idx])))
+                logging.debug(
+                    self._log('data: {}'.format(request.feed_insts[idx])))
+                npdata[name] = np.frombuffer(
+                    request.feed_insts[idx], dtype=request.type[idx])
+                npdata[name].shape = np.frombuffer(
+                    request.shape[idx], dtype="int32")
+        except Exception as e:
+            return ChannelData(
+                ecode=ChannelDataEcode.RPC_PACKAGE_ERROR.value,
+                error_info="rpc package error",
+                data_id=data_id), data_id
+        else:
+            return ChannelData(
+                datatype=ChannelDataType.CHANNEL_NPDATA.value,
+                npdata=npdata,
+                data_id=data_id), data_id
+
+    def _pack_data_for_resp(self, channeldata):
+        logging.debug(self._log('get channeldata'))
+        resp = pyservice_pb2.Response()
+        resp.ecode = channeldata.ecode
+        if resp.ecode == ChannelDataEcode.OK.value:
+            if channeldata.datatype == ChannelDataType.CHANNEL_PBDATA.value:
+                for inst in channeldata.pbdata.insts:
+                    resp.fetch_insts.append(inst.data)
+                    resp.fetch_var_names.append(inst.name)
+                    resp.shape.append(inst.shape)
+                    resp.type.append(inst.type)
+            elif channeldata.datatype in (ChannelDataType.CHANNEL_FUTURE.value,
+                                          ChannelDataType.CHANNEL_NPDATA.value):
+                feed = channeldata.parse()
+                for name, var in feed.items():
+                    resp.fetch_insts.append(var.tobytes())
+                    resp.fetch_var_names.append(name)
+                    resp.shape.append(
+                        np.array(
+                            var.shape, dtype="int32").tobytes())
+                    resp.type.append(str(var.dtype))
+            else:
+                raise TypeError(
+                    self._log("Error type({}) in datatype.".format(
+                        channeldata.datatype)))
+        else:
+            resp.error_info = channeldata.error_info
+        return resp
+
+    def inference(self, request, context):
+        _profiler.record("{}-prepack_0".format(self.name))
+        data, data_id = self._pack_data_for_infer(request)
+        _profiler.record("{}-prepack_1".format(self.name))
+
+        resp_channeldata = None
+        for i in range(self._retry):
+            logging.debug(self._log('push data'))
+            _profiler.record("{}-push_0".format(self.name))
+            self._in_channel.push(data, self.name)
+            _profiler.record("{}-push_1".format(self.name))
+
+            logging.debug(self._log('wait for infer'))
+            _profiler.record("{}-fetch_0".format(self.name))
+            resp_channeldata = self._get_data_in_globel_resp_dict(data_id)
+            _profiler.record("{}-fetch_1".format(self.name))
+
+            if resp_channeldata.ecode == ChannelDataEcode.OK.value:
+                break
+            if i + 1 < self._retry:
+                logging.warn("retry({}): {}".format(
+                    i + 1, resp_channeldata.error_info))
+
+        _profiler.record("{}-postpack_0".format(self.name))
+        resp = self._pack_data_for_resp(resp_channeldata)
+        _profiler.record("{}-postpack_1".format(self.name))
+        _profiler.print_profile()
+        return resp
+
+
+class PipelineServer(object):
+    def __init__(self,
+                 use_multithread=True,
+                 client_type='brpc',
+                 use_future=False,
+                 retry=2,
+                 profile=False):
+        self._channels = []
+        self._user_ops = []
+        self._actual_ops = []
+        self._port = None
+        self._worker_num = None
+        self._in_channel = None
+        self._out_channel = None
+        self._retry = retry
+        self._use_multithread = use_multithread
+        self._client_type = client_type
+        self._use_future = use_future
+        if not self._use_multithread:
+            self._manager = multiprocessing.Manager()
+            if profile:
+                raise Exception(
+                    "profile cannot be used in multiprocess version temporarily")
+            if self._use_future:
+                raise Exception("cannot use future in multiprocess")
+        if self._client_type == 'brpc' and self._use_future:
+            logging.warn("brpc impl cannot use future")
+        _profiler.enable(profile)
+
+    def add_channel(self, channel):
+        self._channels.append(channel)
+
+    def add_op(self, op):
+        self._user_ops.append(op)
+
+    def add_ops(self, ops):
+        self._user_ops.extend(ops)
+
+    def gen_desc(self):
+        logging.info('here will generate desc for PAAS')
+        pass
+
+    def _topo_sort(self):
+        indeg_num = {}
+        que_idx = 0  # scroll queue 
+        ques = [Queue.Queue() for _ in range(2)]
+        for op in self._user_ops:
+            if len(op.get_input_ops()) == 0:
+                op.name = "#G"  # update read_op.name
+                break
+        outdegs = {op.name: [] for op in self._user_ops}
+        zero_indeg_num, zero_outdeg_num = 0, 0
+        for idx, op in enumerate(self._user_ops):
+            # check the name of op is globally unique
+            if op.name in indeg_num:
+                raise Exception("the name of Op must be unique")
+            indeg_num[op.name] = len(op.get_input_ops())
+            if indeg_num[op.name] == 0:
+                ques[que_idx].put(op)
+                zero_indeg_num += 1
+            for pred_op in op.get_input_ops():
+                outdegs[pred_op.name].append(op)
+        if zero_indeg_num != 1:
+            raise Exception("DAG contains multiple input Ops")
+        for _, succ_list in outdegs.items():
+            if len(succ_list) == 0:
+                zero_outdeg_num += 1
+        if zero_outdeg_num != 1:
+            raise Exception("DAG contains multiple output Ops")
+
+        # topo sort to get dag_views
+        dag_views = []
+        sorted_op_num = 0
+        while True:
+            que = ques[que_idx]
+            next_que = ques[(que_idx + 1) % 2]
+            dag_view = []
+            while que.qsize() != 0:
+                op = que.get()
+                dag_view.append(op)
+                sorted_op_num += 1
+                for succ_op in outdegs[op.name]:
+                    indeg_num[succ_op.name] -= 1
+                    if indeg_num[succ_op.name] == 0:
+                        next_que.put(succ_op)
+            dag_views.append(dag_view)
+            if next_que.qsize() == 0:
+                break
+            que_idx = (que_idx + 1) % 2
+        if sorted_op_num < len(self._user_ops):
+            raise Exception("not legal DAG")
+
+        # create channels and virtual ops
+        def name_generator(prefix):
+            def number_generator():
+                idx = 0
+                while True:
+                    yield "{}{}".format(prefix, idx)
+                    idx += 1
+
+            return number_generator()
+
+        def gen_channel(name_gen):
+            channel = None
+            if self._use_multithread:
+                if sys.version_info.major == 2:
+                    channel = ThreadChannel(name=name_gen.next())
+                elif sys.version_info.major == 3:
+                    channel = ThreadChannel(name=name_gen.__next__())
+                else:
+                    raise Exception("Error Python version")
+            else:
+                if sys.version_info.major == 2:
+                    channel = ProcessChannel(
+                        self._manager, name=name_gen.next())
+                elif sys.version_info.major == 3:
+                    channel = ProcessChannel(
+                        self._manager, name=name_gen.__next__())
+                else:
+                    raise Exception("Error Python version")
+            return channel
+
+        def gen_virtual_op(name_gen):
+            virtual_op = None
+            if sys.version_info.major == 2:
+                virtual_op = VirtualOp(name=name_gen.next())
+            elif sys.version_info.major == 3:
+                virtual_op = VirtualOp(name=op_name_gen.__next__())
+            else:
+                raise Exception("Error Python version")
+            return virtual_op
+
+        virtual_op_name_gen = name_generator("vir")
+        channel_name_gen = name_generator("chl")
+        virtual_ops = []
+        channels = []
+        input_channel = None
+        actual_view = None
+        for v_idx, view in enumerate(dag_views):
+            if v_idx + 1 >= len(dag_views):
+                break
+            next_view = dag_views[v_idx + 1]
+            if actual_view is None:
+                actual_view = view
+            actual_next_view = []
+            pred_op_of_next_view_op = {}
+            for op in actual_view:
+                # find actual succ op in next view and create virtual op
+                for succ_op in outdegs[op.name]:
+                    if succ_op in next_view:
+                        if succ_op not in actual_next_view:
+                            actual_next_view.append(succ_op)
+                        if succ_op.name not in pred_op_of_next_view_op:
+                            pred_op_of_next_view_op[succ_op.name] = []
+                        pred_op_of_next_view_op[succ_op.name].append(op)
+                    else:
+                        # create virtual op
+                        virtual_op = gen_virtual_op(virtual_op_name_gen)
+                        virtual_ops.append(virtual_op)
+                        outdegs[virtual_op.name] = [succ_op]
+                        actual_next_view.append(virtual_op)
+                        pred_op_of_next_view_op[virtual_op.name] = [op]
+                        virtual_op.add_virtual_pred_op(op)
+            actual_view = actual_next_view
+            # create channel
+            processed_op = set()
+            for o_idx, op in enumerate(actual_next_view):
+                if op.name in processed_op:
+                    continue
+                channel = gen_channel(channel_name_gen)
+                channels.append(channel)
+                logging.debug("{} => {}".format(channel.name, op.name))
+                op.add_input_channel(channel)
+                pred_ops = pred_op_of_next_view_op[op.name]
+                if v_idx == 0:
+                    input_channel = channel
+                else:
+                    # if pred_op is virtual op, it will use ancestors as producers to channel
+                    for pred_op in pred_ops:
+                        logging.debug("{} => {}".format(pred_op.name,
+                                                        channel.name))
+                        pred_op.add_output_channel(channel)
+                processed_op.add(op.name)
+                # find same input op to combine channel
+                for other_op in actual_next_view[o_idx + 1:]:
+                    if other_op.name in processed_op:
+                        continue
+                    other_pred_ops = pred_op_of_next_view_op[other_op.name]
+                    if len(other_pred_ops) != len(pred_ops):
+                        continue
+                    same_flag = True
+                    for pred_op in pred_ops:
+                        if pred_op not in other_pred_ops:
+                            same_flag = False
+                            break
+                    if same_flag:
+                        logging.debug("{} => {}".format(channel.name,
+                                                        other_op.name))
+                        other_op.add_input_channel(channel)
+                        processed_op.add(other_op.name)
+        output_channel = gen_channel(channel_name_gen)
+        channels.append(output_channel)
+        last_op = dag_views[-1][0]
+        last_op.add_output_channel(output_channel)
+
+        self._actual_ops = virtual_ops
+        for op in self._user_ops:
+            if len(op.get_input_ops()) == 0:
+                # pass read op
+                continue
+            self._actual_ops.append(op)
+        self._channels = channels
+        for c in channels:
+            logging.debug(c.debug())
+        return input_channel, output_channel
+
+    def prepare_server(self, port, worker_num):
+        self._port = port
+        self._worker_num = worker_num
+
+        input_channel, output_channel = self._topo_sort()
+        self._in_channel = input_channel
+        self._out_channel = output_channel
+        for op in self._actual_ops:
+            if op.with_serving:
+                self.prepare_serving(op)
+        self.gen_desc()
+
+    def _run_ops(self):
+        threads_or_proces = []
+        for op in self._actual_ops:
+            op.init_profiler(_profiler)
+            if self._use_multithread:
+                threads_or_proces.extend(
+                    op.start_with_thread(self._client_type, self._use_future))
+            else:
+                threads_or_proces.extend(
+                    op.start_with_process(self._client_type, self._use_future))
+        return threads_or_proces
+
+    def _stop_ops(self):
+        for op in self._actual_ops:
+            op.stop()
+
+    def run_server(self):
+        op_threads_or_proces = self._run_ops()
+        server = grpc.server(
+            futures.ThreadPoolExecutor(max_workers=self._worker_num))
+        general_python_service_pb2_grpc.add_GeneralPythonServiceServicer_to_server(
+            GeneralPythonService(self._in_channel, self._out_channel,
+                                 self._retry), server)
+        server.add_insecure_port('[::]:{}'.format(self._port))
+        server.start()
+        server.wait_for_termination()
+        self._stop_ops()  # TODO
+        for x in op_threads_or_proces:
+            x.join()
+
+    def prepare_serving(self, op):
+        model_path = op._server_model
+        port = op._server_port
+        device = op._device
+
+        if self._client_type == "grpc":
+            if device == "cpu":
+                cmd = "(Use grpc impl) python -m paddle_serving_server.serve" \
+                      " --model {} --thread 4 --port {} --use_multilang &>/dev/null &".format(model_path, port)
+            else:
+                cmd = "(Use grpc impl) python -m paddle_serving_server_gpu.serve" \
+                      " --model {} --thread 4 --port {} --use_multilang &>/dev/null &".format(model_path, port)
+        elif self._client_type == "brpc":
+            if device == "cpu":
+                cmd = "(Use brpc impl) python -m paddle_serving_server.serve" \
+                      " --model {} --thread 4 --port {} &>/dev/null &".format(model_path, port)
+            else:
+                cmd = "(Use brpc impl) python -m paddle_serving_server_gpu.serve" \
+                      " --model {} --thread 4 --port {} &>/dev/null &".format(model_path, port)
+        else:
+            raise Exception("unknow client type: {}".format(self._client_type))
+        # run a server (not in PyServing)
+        logging.info("run a server (not in PyServing): {}".format(cmd))
+>>>>>>> d84910a1180061b57c51824e35e3ca5c857eb3b5

python/pipeline/profiler.py

@@ -13,6 +13,19 @@
 # limitations under the License.
 # pylint: disable=doc-string-missing
 
+<<<<<<< HEAD
+=======
+import os
+import sys
+if sys.version_info.major == 2:
+    import Queue
+elif sys.version_info.major == 3:
+    import queue as Queue
+else:
+    raise Exception("Error Python version")
+import time
+
+>>>>>>> d84910a1180061b57c51824e35e3ca5c857eb3b5
 
 class TimeProfiler(object):
     def __init__(self):
@@ -35,18 +48,30 @@ class TimeProfiler(object):
     def print_profile(self):
         if self._enable is False:
             return
+<<<<<<< HEAD
         print_str = self._print_head
+=======
+        sys.stderr.write(self._print_head)
+>>>>>>> d84910a1180061b57c51824e35e3ca5c857eb3b5
         tmp = {}
         while not self._time_record.empty():
             name, tag, timestamp = self._time_record.get()
             if name in tmp:
                 ptag, ptimestamp = tmp.pop(name)
+<<<<<<< HEAD
                 print_str += "{}_{}:{} ".format(name, ptag, ptimestamp)
                 print_str += "{}_{}:{} ".format(name, tag, timestamp)
             else:
                 tmp[name] = (tag, timestamp)
         print_str += "\n"
         sys.stderr.write(print_str)
+=======
+                sys.stderr.write("{}_{}:{} ".format(name, ptag, ptimestamp))
+                sys.stderr.write("{}_{}:{} ".format(name, tag, timestamp))
+            else:
+                tmp[name] = (tag, timestamp)
+        sys.stderr.write('\n')
+>>>>>>> d84910a1180061b57c51824e35e3ca5c857eb3b5
         for name, item in tmp.items():
             tag, timestamp = item
             self._time_record.put((name, tag, timestamp))

python/requirements.txt

 numpy>=1.12, <=1.16.4 ; python_version<"3.5"
 grpcio-tools>=1.28.1
 grpcio>=1.28.1
+func-timeout>=4.3.5

python/setup.py.server.in

@@ -42,7 +42,8 @@ REQUIRED_PACKAGES = [
 ]
 
 packages=['paddle_serving_server',
-          'paddle_serving_server.proto']
+          'paddle_serving_server.proto',
+          'paddle_serving_server.pipeline']
 
 package_dir={'paddle_serving_server':
              '${PADDLE_SERVING_BINARY_DIR}/python/paddle_serving_server',

python/setup.py.server_gpu.in

@@ -43,7 +43,8 @@ REQUIRED_PACKAGES = [
 
 
 packages=['paddle_serving_server_gpu',
-          'paddle_serving_server_gpu.proto']
+          'paddle_serving_server_gpu.proto',
+          'paddle_serving_server.pipeline']
 
 package_dir={'paddle_serving_server_gpu':
              '${PADDLE_SERVING_BINARY_DIR}/python/paddle_serving_server_gpu',