Merge pull request #1267 from bjjwwang/uni_benchmark

add benchmark script

python/examples/pipeline/PaddleClas/DarkNet53/benchmark.sh

@@ -15,7 +15,7 @@ for thread_num in 1 2 4 8 12 16
 do
   for batch_size in 1
   do
-    echo "----${modelname} thread num: ${thread_num} batch size: ${batch_size} mode:http ----" >>profile_log_$modelname
+    echo "#----${modelname} thread num: ${thread_num} batch size: ${batch_size} mode:http ----" >>profile_log_$modelname
     # Start one web service, If you start the service yourself, you can ignore it here.
     #python3 web_service.py >web.log 2>&1 &
     #sleep 3
@@ -23,15 +23,15 @@ do
     # --id is the serial number of the GPU card, Must be the same as the gpu id used by the server.
     nvidia-smi --id=3 --query-gpu=memory.used --format=csv -lms 1000 > gpu_use.log 2>&1 &
     nvidia-smi --id=3 --query-gpu=utilization.gpu --format=csv -lms 1000 > gpu_utilization.log 2>&1 &
-    echo "import psutil\ncpu_utilization=psutil.cpu_percent(1,False)\nprint('CPU_UTILIZATION:', cpu_utilization)\n" > cpu_utilization.py
+    echo "import psutil\ncpu_utilization=psutil.cpu_percent(1,False)\nprint('CPU_UTIL:', cpu_utilization)\n" > cpu_utilization.py
     # Start http client
     python3 benchmark.py run http $thread_num $batch_size > profile 2>&1
 
     # Collect CPU metrics, Filter data that is zero momentarily, Record the maximum value of GPU memory and the average value of GPU utilization
     python3 cpu_utilization.py >> profile_log_$modelname
     grep -av '^0 %' gpu_utilization.log > gpu_utilization.log.tmp
-    awk 'BEGIN {max = 0} {if(NR>1){if ($modelname > max) max=$modelname}} END {print "MAX_GPU_MEMORY:", max}' gpu_use.log >> profile_log_$modelname
-    awk -F' ' '{sum+=$1} END {print "GPU_UTILIZATION:", sum/NR, sum, NR }' gpu_utilization.log.tmp >> profile_log_$modelname
+    awk 'BEGIN {max = 0} {if(NR>1){if ($modelname > max) max=$modelname}} END {print "GPU_MEM:", max}' gpu_use.log >> profile_log_$modelname
+    awk -F' ' '{sum+=$1} END {print "GPU_UTIL:", sum/NR, sum, NR }' gpu_utilization.log.tmp >> profile_log_$modelname
 
     # Show profiles
     python3 ../../../util/show_profile.py profile $thread_num >> profile_log_$modelname

python/paddle_serving_client/utils/__init__.py

@@ -41,9 +41,9 @@ def show_latency(latency_list):
     info = "latency:\n"
     info += "mean: {}ms\n".format(np.mean(latency_array))
     info += "median: {}ms\n".format(np.median(latency_array))
-    info += "80 percent: {}ms\n".format(np.percentile(latency_array, 80))
-    info += "90 percent: {}ms\n".format(np.percentile(latency_array, 90))
-    info += "99 percent: {}ms\n".format(np.percentile(latency_array, 99))
+    info += "80_percent: {}ms\n".format(np.percentile(latency_array, 80))
+    info += "90_percent: {}ms\n".format(np.percentile(latency_array, 90))
+    info += "99_percent: {}ms\n".format(np.percentile(latency_array, 99))
     sys.stderr.write(info)
 
 

python/paddle_serving_server/benchmark_utils.py

+# Copyright (c) 2021 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 argparse
+import os
+import time
+import logging
+
+import paddle
+import paddle.inference as paddle_infer
+
+from pathlib import Path
+
+CUR_DIR = os.path.dirname(os.path.abspath(__file__))
+#LOG_PATH_ROOT = f"{CUR_DIR}/../../tools/output"
+LOG_PATH_ROOT = f"."
+
+class PaddleInferBenchmark(object):
+    def __init__(self,
+                 config,
+                 model_info: dict={},
+                 data_info: dict={},
+                 perf_info: dict={},
+                 resource_info: dict={},
+                 **kwargs):
+        """
+        Construct PaddleInferBenchmark Class to format logs.
+        args:
+            config(paddle.inference.Config): paddle inference config
+            model_info(dict): basic model info
+                {'model_name': 'resnet50'
+                 'precision': 'fp32'}
+            data_info(dict): input data info
+                {'batch_size': 1
+                 'shape': '3,224,224'
+                 'data_num': 1000}
+            perf_info(dict): performance result
+                {'preprocess_time_s': 1.0
+                'inference_time_s': 2.0
+                'postprocess_time_s': 1.0
+                'total_time_s': 4.0}
+            resource_info(dict): 
+                cpu and gpu resources
+                {'cpu_rss': 100
+                 'gpu_rss': 100
+                 'gpu_util': 60}
+        """
+        # PaddleInferBenchmark Log Version
+        self.log_version = "1.0.3"
+
+        # Paddle Version
+        self.paddle_version = paddle.__version__
+        self.paddle_commit = paddle.__git_commit__
+        paddle_infer_info = paddle_infer.get_version()
+        self.paddle_branch = paddle_infer_info.strip().split(': ')[-1]
+
+        # model info
+        self.model_info = model_info
+
+        # data info
+        self.data_info = data_info
+
+        # perf info
+        self.perf_info = perf_info
+        try:
+            # required value
+            self.model_name = model_info['model_name']
+            self.precision = model_info['precision']
+
+            self.batch_size = data_info['batch_size']
+            self.shape = data_info['shape']
+            self.data_num = data_info['data_num']
+
+            self.inference_time_s = round(perf_info['inference_time_s'], 4)
+        except:
+            self.print_help()
+            raise ValueError(
+                "Set argument wrong, please check input argument and its type")
+
+        self.preprocess_time_s = perf_info.get('preprocess_time_s', 0)
+        self.postprocess_time_s = perf_info.get('postprocess_time_s', 0)
+        self.total_time_s = perf_info.get('total_time_s', 0)
+
+        self.inference_time_s_90 = perf_info.get("inference_time_s_90", 0)
+        self.inference_time_s_99 = perf_info.get("inference_time_s_99", 0)
+        self.succ_rate = perf_info.get("succ_rate", "")
+        self.qps = perf_info.get("qps", "")
+
+        # conf info
+        self.config_status = self.parse_config(config)
+
+        # mem info
+        if isinstance(resource_info, dict):
+            self.cpu_rss_mb = int("-1" if 'cpu_rss_mb' not in resource_info or resource_info.get('cpu_rss_mb').strip()=="" else resource_info.get('cpu_rss_mb', 0))
+            self.cpu_vms_mb = int("-1" if 'cpu_vms_mb' not in resource_info or resource_info.get('cpu_vms_mb').strip()=="" else resource_info.get('cpu_vms_mb', 0))
+            self.cpu_shared_mb = int("-1" if 'cpu_shared_mb' not in resource_info or resource_info.get('cpu_shared_mb').strip()=="" else resource_info.get('cpu_shared_mb', 0))
+            self.cpu_dirty_mb = int("-1" if 'cpu_dirty_mb' not in resource_info or resource_info.get('cpu_dirty_mb').strip()=="" else resource_info.get('cpu_dirty_mb', 0))
+            self.cpu_util = round(resource_info.get('cpu_util', 0), 2)
+
+            self.gpu_rss_mb = int("-1" if 'gpu_rss_mb' not in resource_info or resource_info.get('gpu_rss_mb').strip()=="" else resource_info.get('gpu_rss_mb', 0))
+            #self.gpu_util = round(resource_info.get('gpu_util', 0), 2)
+            self.gpu_util = resource_info.get('gpu_util', 0)
+            #self.gpu_mem_util = round(resource_info.get('gpu_mem_util', 0), 2)
+            self.gpu_mem_util =  resource_info.get('gpu_mem_util', 0)
+        else:
+            self.cpu_rss_mb = 0
+            self.cpu_vms_mb = 0
+            self.cpu_shared_mb = 0
+            self.cpu_dirty_mb = 0
+            self.cpu_util = 0
+
+            self.gpu_rss_mb = 0
+            self.gpu_util = 0
+            self.gpu_mem_util = 0
+
+        # init benchmark logger
+        self.benchmark_logger()
+
+    def benchmark_logger(self):
+        """
+        benchmark logger
+        """
+        # remove other logging handler
+        for handler in logging.root.handlers[:]:
+            logging.root.removeHandler(handler)
+
+        # Init logger
+        FORMAT = '%(asctime)s - %(name)s - %(levelname)s - %(message)s'
+        log_output = f"{LOG_PATH_ROOT}/{self.model_name}.log"
+        Path(f"{LOG_PATH_ROOT}").mkdir(parents=True, exist_ok=True)
+        logging.basicConfig(
+            level=logging.INFO,
+            format=FORMAT,
+            handlers=[
+                logging.FileHandler(
+                    filename=log_output, mode='w'),
+                logging.StreamHandler(),
+            ])
+        self.logger = logging.getLogger(__name__)
+        self.logger.info(
+            f"Paddle Inference benchmark log will be saved to {log_output}")
+
+    def parse_config(self, config) -> dict:
+        """
+        parse paddle predictor config
+        args:
+            config(paddle.inference.Config): paddle inference config
+        return:
+            config_status(dict): dict style config info
+        """
+        if isinstance(config, paddle_infer.Config):
+            config_status = {}
+            config_status['runtime_device'] = "gpu" if config.use_gpu(
+            ) else "cpu"
+            config_status['ir_optim'] = config.ir_optim()
+            config_status['enable_tensorrt'] = config.tensorrt_engine_enabled()
+            config_status['precision'] = self.precision
+            config_status['enable_mkldnn'] = config.mkldnn_enabled()
+            config_status[
+                'cpu_math_library_num_threads'] = config.cpu_math_library_num_threads(
+                )
+        elif isinstance(config, dict):
+            config_status = {}
+            config_status['runtime_device'] = config.get('runtime_device', "")
+            config_status['ir_optim'] = config.get('ir_optim', "")
+            config_status['enable_tensorrt'] = config.get('enable_tensorrt', "")
+            config_status['precision'] = config.get('precision', "")
+            config_status['enable_mkldnn'] = config.get('enable_mkldnn', "")
+            config_status['cpu_math_library_num_threads'] = config.get(
+                'cpu_math_library_num_threads', "")
+        else:
+            self.print_help()
+            raise ValueError(
+                "Set argument config wrong, please check input argument and its type"
+            )
+        return config_status
+
+    def report(self, identifier=None):
+        """
+        print log report
+        args:
+            identifier(string): identify log
+        """
+        if identifier:
+            identifier = f"[{identifier}]"
+        else:
+            identifier = ""
+
+        self.logger.info("\n")
+        self.logger.info(
+            "---------------------- Paddle info ----------------------")
+        self.logger.info(f"{identifier} paddle_version: {self.paddle_version}")
+        self.logger.info(f"{identifier} paddle_commit: {self.paddle_commit}")
+        self.logger.info(f"{identifier} paddle_branch: {self.paddle_branch}")
+        self.logger.info(f"{identifier} log_api_version: {self.log_version}")
+        self.logger.info(
+            "----------------------- Conf info -----------------------")
+        self.logger.info(
+            f"{identifier} runtime_device: {self.config_status['runtime_device']}"
+        )
+        self.logger.info(
+            f"{identifier} ir_optim: {self.config_status['ir_optim']}")
+        self.logger.info(f"{identifier} enable_memory_optim: {True}")
+        self.logger.info(
+            f"{identifier} enable_tensorrt: {self.config_status['enable_tensorrt']}"
+        )
+        self.logger.info(
+            f"{identifier} enable_mkldnn: {self.config_status['enable_mkldnn']}")
+        self.logger.info(
+            f"{identifier} cpu_math_library_num_threads: {self.config_status['cpu_math_library_num_threads']}"
+        )
+        self.logger.info(
+            "----------------------- Model info ----------------------")
+        self.logger.info(f"{identifier} model_name: {self.model_name}")
+        self.logger.info(f"{identifier} precision: {self.precision}")
+        self.logger.info(
+            "----------------------- Data info -----------------------")
+        self.logger.info(f"{identifier} batch_size: {self.batch_size}")
+        self.logger.info(f"{identifier} input_shape: {self.shape}")
+        self.logger.info(f"{identifier} data_num: {self.data_num}")
+        self.logger.info(
+            "----------------------- Perf info -----------------------")
+        self.logger.info(
+            f"{identifier} cpu_rss(MB): {self.cpu_rss_mb}, cpu_vms: {self.cpu_vms_mb}, cpu_shared_mb: {self.cpu_shared_mb}, cpu_dirty_mb: {self.cpu_dirty_mb}, cpu_util: {self.cpu_util}%"
+        )
+        self.logger.info(
+            f"{identifier} gpu_rss(MB): {self.gpu_rss_mb}, gpu_util: {self.gpu_util}%, gpu_mem_util: {self.gpu_mem_util}%"
+        )
+        self.logger.info(
+            f"{identifier} total time spent(s): {self.total_time_s}")
+        self.logger.info(
+            f"{identifier} preprocess_time(ms): {self.preprocess_time_s}, inference_time(ms): {self.inference_time_s}, postprocess_time(ms): {self.postprocess_time_s}"
+        )
+        if self.inference_time_s_90:
+            self.logger.info(
+                f"{identifier} 90%_cost: {self.inference_time_s_90}, 99%_cost: {self.inference_time_s_99}, succ_rate: {self.succ_rate}"
+            )
+        if self.qps:
+            self.logger.info(f"{identifier} QPS: {self.qps}")
+
+    def print_help(self):
+        """
+        print function help
+        """
+        print("""Usage: 
+            ==== Print inference benchmark logs. ====
+            config = paddle.inference.Config()
+            model_info = {'model_name': 'resnet50'
+                          'precision': 'fp32'}
+            data_info = {'batch_size': 1
+                         'shape': '3,224,224'
+                         'data_num': 1000}
+            perf_info = {'preprocess_time_s': 1.0
+                         'inference_time_s': 2.0
+                         'postprocess_time_s': 1.0
+                         'total_time_s': 4.0}
+            resource_info = {'cpu_rss_mb': 100
+                             'gpu_rss_mb': 100
+                             'gpu_util': 60}
+            log = PaddleInferBenchmark(config, model_info, data_info, perf_info, resource_info)
+            log('Test')
+            """)
+
+    def __call__(self, identifier=None):
+        """
+        __call__
+        args:
+            identifier(string): identify log
+        """
+        self.report(identifier)

python/paddle_serving_server/parse_profile.py

+# Copyright (c) 2021 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 sys
+import os
+import yaml
+import argparse
+
+from .benchmark_utils import PaddleInferBenchmark
+"""
+{'CPU_UTILIZATION': 0.8, 'MAX_GPU_MEMORY': 0, 'GPU_UTILIZATION': '0 %', 'DAG': {'50': 670.256, '60': 670.256, '70': 670.765, '80': 671.23, '90': 687.546, '95': 687.546, '99': 687.546, 'avg': 670.755625, 'qps': 0.8, 'query_count': 8, 'succ': 1.0}, 'demo': {'midp': 669.484375, 'postp': 0.184875, 'prep': 1.001875}}
+"""
+
+
+class LogHandler(object):
+    def __init__(self):
+        self.fstr = ""
+
+    def print(self):
+        print(self.fstr)
+
+    def dump(self, filename):
+        with open(filename, 'w') as fout:
+            fout.write(self.fstr)
+
+    def append(self, new_str):
+        self.fstr += new_str + "\n"
+
+def handle_benchmark(benchmark_config, benchmark_raw, indentifier):
+    model_info = {
+        'model_name': benchmark_config["model_name"],
+        'precision': benchmark_config["precision"]
+    }
+    data_info = {
+        'batch_size': benchmark_config["batch_size"],
+        'shape': benchmark_config["input_shape"],
+        'data_num': benchmark_config["num_of_samples"]
+    }
+    perf_info = {
+        'preprocess_time_s': "",
+        'inference_time_s': float(benchmark_raw["median"][0:-2])/1000, # *** ms
+        'postprocess_time_s': "",
+        'total_time_s': "",
+        'inference_time_s_90': float(benchmark_raw["90_percent"][0:-2])/1000,  # *** ms
+        'inference_time_s_99': float(benchmark_raw["99_percent"][0:-2])/1000,  # *** ms
+        'qps': benchmark_raw["AVG_QPS"]
+    }
+    resource_info = {
+        'cpu_rss_mb': "",
+        'cpu_vms_mb': "",
+        'cpu_shared_mb': "",
+        'cpu_dirty_mb': "",
+        'cpu_util': benchmark_raw["CPU_UTIL"],
+        'gpu_rss_mb': "",
+        'gpu_util': benchmark_raw["GPU_UTIL"],
+        'gpu_mem_util': benchmark_raw["GPU_MEM"]
+    }
+
+    server_log = PaddleInferBenchmark(
+        benchmark_config, model_info, data_info, perf_info, resource_info)
+    server_log(indentifier)
+
+def parse_args():  # pylint: disable=doc-string-missing
+    parser = argparse.ArgumentParser("serve")
+    parser.add_argument(
+        "--benchmark_cfg",
+        type=str,
+        required=True,
+        help="benchmark config yaml. including general info, model info, data info, conf info"
+    )
+    parser.add_argument(
+        "--benchmark_log",
+        type=str,
+        required=True,
+        help="benchmark log, generated by a web service or pipeline.")
+    parser.add_argument(
+        "--output",
+        type=str,
+        default="std_benchmark.log",
+        help="the output filename, default std_benchmark.log")
+    return parser.parse_args()
+
+
+if __name__ == "__main__":
+    args = parse_args()
+    benchmark_cfg_filename = args.benchmark_cfg
+    f = open(benchmark_cfg_filename, 'r')
+    benchmark_config = yaml.load(f)
+    f.close()
+    benchmark_log_filename = args.benchmark_log
+    f = open(benchmark_log_filename, 'r')
+    lines = f.readlines()
+    line_no = 0
+    while line_no < len(lines):
+        if len(lines[line_no]) > 5 and lines[line_no].startswith("#---"):
+            iden = lines[line_no][5: -5]
+            line_no += 1
+            sub_log = lines[line_no: line_no + 13]
+            sub_dict = yaml.safe_load("".join(sub_log))
+            handle_benchmark(benchmark_config, sub_dict, iden)
+            line_no += 13
+        else:
+            line_no += 1
+