Merge pull request #279 from guru4elephant/refine_bert_example

Refine bert example

python/examples/bert/benchmark.py

@@ -34,8 +34,7 @@ args = benchmark_args()
 def single_func(idx, resource):
     fin = open("data-c.txt")
     if args.request == "rpc":
-        reader = BertReader(vocab_file="vocab.txt", max_seq_len=20)
-        config_file = './serving_client_conf/serving_client_conf.prototxt'
+        reader = BertReader(vocab_file="vocab.txt", max_seq_len=128)
         fetch = ["pooled_output"]
         client = Client()
         client.load_client_config(args.model)
@@ -50,7 +49,6 @@ def single_func(idx, resource):
         start = time.time()
         header = {"Content-Type": "application/json"}
         for line in fin:
-            #dict_data = {"words": "this is for output ", "fetch": ["pooled_output"]}
             dict_data = {"words": line, "fetch": ["pooled_output"]}
             r = requests.post(
                 'http://{}/bert/prediction'.format(resource["endpoint"][0]),
@@ -62,10 +60,11 @@ def single_func(idx, resource):
 
 if __name__ == '__main__':
     multi_thread_runner = MultiThreadRunner()
-    endpoint_list = [
-        "127.0.0.1:9494", "127.0.0.1:9495", "127.0.0.1:9496", "127.0.0.1:9497"
-    ]
-    #endpoint_list = endpoint_list + endpoint_list + endpoint_list
-    #result = multi_thread_runner.run(single_func, args.thread, {"endpoint":endpoint_list})
-    result = single_func(0, {"endpoint": endpoint_list})
+    endpoint_list = []
+    card_num = 4
+    for i in range(args.thread):
+        endpoint_list.append("127.0.0.1:{}".format(9494 + i % card_num))
+    print(endpoint_list)
+    result = multi_thread_runner.run(single_func, args.thread,
+                                     {"endpoint": endpoint_list})
     print(result)

python/examples/bert/benchmark_batch.py

+# -*- coding: utf-8 -*-
+#
 # Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
@@ -11,61 +13,66 @@
 # 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 __future__ import unicode_literals, absolute_import
+import os
 import sys
+import time
 from paddle_serving_client import Client
-from paddle_serving_client.metric import auc
 from paddle_serving_client.utils import MultiThreadRunner
-import time
-from bert_client import BertService
+from paddle_serving_client.utils import benchmark_args
+from batching import pad_batch_data
+import tokenization
+import requests
+import json
+from bert_reader import BertReader
 
+args = benchmark_args()
 
-def predict(thr_id, resource, batch_size):
-    bc = BertService(
-        model_name="bert_chinese_L-12_H-768_A-12",
-        max_seq_len=20,
-        do_lower_case=True)
-    bc.load_client(resource["conf_file"], resource["server_endpoint"])
-    thread_num = resource["thread_num"]
-    file_list = resource["filelist"]
-    line_id = 0
-    result = []
-    label_list = []
-    dataset = []
-    for fn in file_list:
-        fin = open(fn)
-        for line in fin:
-            if line_id % thread_num == thr_id - 1:
-                dataset.append(line.strip())
-            line_id += 1
-        fin.close()
+batch_size = 24
 
-    start = time.time()
+
+def single_func(idx, resource):
+    fin = open("data-c.txt")
+    if args.request == "rpc":
+        reader = BertReader(vocab_file="vocab.txt", max_seq_len=128)
         fetch = ["pooled_output"]
-    batch = []
-    for inst in dataset:
-        if len(batch) < batch_size:
-            batch.append([inst])
-        else:
-            fetch_map_batch = bc.run_batch_general(batch, fetch)
-            batch = []
-            result.append(fetch_map_batch)
+        client = Client()
+        client.load_client_config(args.model)
+        client.connect([resource["endpoint"][idx % 4]])
+
+        start = time.time()
+        idx = 0
+        batch_data = []
+        for line in fin:
+            feed_dict = reader.process(line)
+            batch_data.append(feed_dict)
+            idx += 1
+            if idx % batch_size == 0:
+                result = client.batch_predict(
+                    feed_batch=batch_data, fetch=fetch)
+                batch_data = []
+        end = time.time()
+    elif args.request == "http":
+        header = {"Content-Type": "application/json"}
+        for line in fin:
+            dict_data = {"words": line, "fetch": ["pooled_output"]}
+            r = requests.post(
+                'http://{}/bert/prediction'.format(resource["endpoint"][0]),
+                data=json.dumps(dict_data),
+                headers=header)
         end = time.time()
-    return [result, label_list, [end - start]]
+    return [[end - start]]
 
 
 if __name__ == '__main__':
-    conf_file = sys.argv[1]
-    data_file = sys.argv[2]
-    thread_num = sys.argv[3]
-    batch_size = sys.ragv[4]
-    resource = {}
-    resource["conf_file"] = conf_file
-    resource["server_endpoint"] = ["127.0.0.1:9293"]
-    resource["filelist"] = [data_file]
-    resource["thread_num"] = int(thread_num)
-
-    thread_runner = MultiThreadRunner()
-    result = thread_runner.run(predict, int(sys.argv[3]), resource, batch_size)
-
-    print("total time {} s".format(sum(result[-1]) / len(result[-1])))
+    multi_thread_runner = MultiThreadRunner()
+    endpoint_list = []
+    card_num = 4
+    for i in range(args.thread):
+        endpoint_list.append("127.0.0.1:{}".format(9494 + i % card_num))
+    print(endpoint_list)
+    result = multi_thread_runner.run(single_func, args.thread,
+                                     {"endpoint": endpoint_list})
+    print(result)

python/examples/bert/bert_client.py

 # coding:utf-8
 # pylint: disable=doc-string-missing
+# 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.
+
 import os
 import sys
 import numpy as np
@@ -10,146 +24,17 @@ import time
 from paddlehub.common.logger import logger
 import socket
 from paddle_serving_client import Client
-from paddle_serving_client.utils import MultiThreadRunner
 from paddle_serving_client.utils import benchmark_args
 args = benchmark_args()
 
-_ver = sys.version_info
-is_py2 = (_ver[0] == 2)
-is_py3 = (_ver[0] == 3)
-
-if is_py2:
-    import httplib
-if is_py3:
-    import http.client as httplib
-
-
-class BertService():
-    def __init__(self,
-                 max_seq_len=128,
-                 model_name="bert_uncased_L-12_H-768_A-12",
-                 show_ids=False,
-                 do_lower_case=True,
-                 process_id=0,
-                 retry=3):
-        self.process_id = process_id
-        self.reader_flag = False
-        self.batch_size = 0
-        self.max_seq_len = max_seq_len
-        self.model_name = model_name
-        self.show_ids = show_ids
-        self.do_lower_case = do_lower_case
-        self.retry = retry
-        self.pid = os.getpid()
-        self.profile = True if ("FLAGS_profile_client" in os.environ and
-                                os.environ["FLAGS_profile_client"]) else False
-
-        module = hub.Module(name=self.model_name)
-        inputs, outputs, program = module.context(
-            trainable=True, max_seq_len=self.max_seq_len)
-        input_ids = inputs["input_ids"]
-        position_ids = inputs["position_ids"]
-        segment_ids = inputs["segment_ids"]
-        input_mask = inputs["input_mask"]
-        self.reader = hub.reader.ClassifyReader(
-            vocab_path=module.get_vocab_path(),
-            dataset=None,
-            max_seq_len=self.max_seq_len,
-            do_lower_case=self.do_lower_case)
-        self.reader_flag = True
-
-    def load_client(self, config_file, server_addr):
-        self.client = Client()
-        self.client.load_client_config(config_file)
-        self.client.connect(server_addr)
-
-    def run_general(self, text, fetch):
-        self.batch_size = len(text)
-        data_generator = self.reader.data_generator(
-            batch_size=self.batch_size, phase='predict', data=text)
-        result = []
-        prepro_start = time.time()
-        for run_step, batch in enumerate(data_generator(), start=1):
-            token_list = batch[0][0].reshape(-1).tolist()
-            pos_list = batch[0][1].reshape(-1).tolist()
-            sent_list = batch[0][2].reshape(-1).tolist()
-            mask_list = batch[0][3].reshape(-1).tolist()
-            for si in range(self.batch_size):
-                feed = {
-                    "input_ids": token_list,
-                    "position_ids": pos_list,
-                    "segment_ids": sent_list,
-                    "input_mask": mask_list
-                }
-                prepro_end = time.time()
-                if self.profile:
-                    print("PROFILE\tpid:{}\tbert_pre_0:{} bert_pre_1:{}".format(
-                        self.pid,
-                        int(round(prepro_start * 1000000)),
-                        int(round(prepro_end * 1000000))))
-                fetch_map = self.client.predict(feed=feed, fetch=fetch)
-
-        return fetch_map
-
-    def run_batch_general(self, text, fetch):
-        self.batch_size = len(text)
-        data_generator = self.reader.data_generator(
-            batch_size=self.batch_size, phase='predict', data=text)
-        result = []
-        prepro_start = time.time()
-        for run_step, batch in enumerate(data_generator(), start=1):
-            token_list = batch[0][0].reshape(-1).tolist()
-            pos_list = batch[0][1].reshape(-1).tolist()
-            sent_list = batch[0][2].reshape(-1).tolist()
-            mask_list = batch[0][3].reshape(-1).tolist()
-            feed_batch = []
-            for si in range(self.batch_size):
-                feed = {
-                    "input_ids": token_list[si * self.max_seq_len:(si + 1) *
-                                            self.max_seq_len],
-                    "position_ids":
-                    pos_list[si * self.max_seq_len:(si + 1) * self.max_seq_len],
-                    "segment_ids": sent_list[si * self.max_seq_len:(si + 1) *
-                                             self.max_seq_len],
-                    "input_mask":
-                    mask_list[si * self.max_seq_len:(si + 1) * self.max_seq_len]
-                }
-                feed_batch.append(feed)
-            prepro_end = time.time()
-            if self.profile:
-                print("PROFILE\tpid:{}\tbert_pre_0:{} bert_pre_1:{}".format(
-                    self.pid,
-                    int(round(prepro_start * 1000000)),
-                    int(round(prepro_end * 1000000))))
-            fetch_map_batch = self.client.batch_predict(
-                feed_batch=feed_batch, fetch=fetch)
-        return fetch_map_batch
-
-
-def single_func(idx, resource):
-    bc = BertService(
-        model_name='bert_chinese_L-12_H-768_A-12',
-        max_seq_len=20,
-        show_ids=False,
-        do_lower_case=True)
-    config_file = './serving_client_conf/serving_client_conf.prototxt'
-    fetch = ["pooled_output"]
-    server_addr = [resource["endpoint"][idx]]
-    bc.load_client(config_file, server_addr)
-    batch_size = 1
-    start = time.time()
-    fin = open("data-c.txt")
-    for line in fin:
-        result = bc.run_general([[line.strip()]], fetch)
-    end = time.time()
-    return [[end - start]]
-
-
-if __name__ == '__main__':
-    multi_thread_runner = MultiThreadRunner()
-    result = multi_thread_runner.run(single_func, args.thread, {
-        "endpoint": [
-            "127.0.0.1:9494", "127.0.0.1:9495", "127.0.0.1:9496",
-            "127.0.0.1:9497"
-        ]
-    })
+fin = open("data-c.txt")
+reader = BertReader(vocab_file="vocab.txt", max_seq_len=128)
+fetch = ["pooled_output"]
+endpoint_list = ["127.0.0.1:9494"]
+client = Client()
+client.load_client_config(args.model)
+client.connect(endpoint_list)
+
+for line in fin:
+    feed_dict = reader.process(line)
+    result = client.predict(feed=feed_dict, fetch=fetch)

python/paddle_serving_server_gpu/serve.py

@@ -17,8 +17,8 @@ Usage:
     Example:
         python -m paddle_serving_server.serve --model ./serving_server_model --port 9292
 """
-import os
 import argparse
+import os
 from multiprocessing import Pool, Process
 from paddle_serving_server_gpu import serve_args
 
@@ -64,12 +64,14 @@ def start_gpu_card_model(gpuid, args):  # pylint: disable=doc-string-missing
 def start_multi_card(args):  # pylint: disable=doc-string-missing
     gpus = ""
     if args.gpu_ids == "":
-        import os
+        if "CUDA_VISIBLE_DEVICES" in os.environ:
             gpus = os.environ["CUDA_VISIBLE_DEVICES"]
+        else:
+            gpus = []
     else:
         gpus = args.gpu_ids.split(",")
     if len(gpus) <= 0:
-        start_gpu_card_model(-1)
+        start_gpu_card_model(-1, args)
     else:
         gpu_processes = []
         for i, gpu_id in enumerate(gpus):