[Benchmark] Add uie benchmark

benchmark/benchmark_uie.py

@@ -0,0 +1,321 @@
+import numpy as np
+import os
+import time
+import distutils.util
+import sys
+import json
+
+import fastdeploy as fd
+from fastdeploy.text import UIEModel, SchemaLanguage
+
+
+def parse_arguments():
+    import argparse
+    import ast
+    parser = argparse.ArgumentParser()
+    parser.add_argument(
+        "--model_dir",
+        required=True,
+        help="The directory of model and tokenizer.")
+    parser.add_argument(
+        "--data_path", required=True, help="The path of uie data.")
+    parser.add_argument(
+        "--device",
+        type=str,
+        default='cpu',
+        choices=['gpu', 'cpu'],
+        help="Type of inference device, support 'cpu' or 'gpu'.")
+    parser.add_argument(
+        "--backend",
+        type=str,
+        default='paddle',
+        choices=['ort', 'paddle', 'trt', 'paddle_trt', 'ov'],
+        help="The inference runtime backend.")
+    parser.add_argument(
+        "--device_id", type=int, default=0, help="device(gpu) id")
+    parser.add_argument(
+        "--batch_size", type=int, default=1, help="The batch size of data.")
+    parser.add_argument(
+        "--max_length",
+        type=int,
+        default=128,
+        help="The max length of sequence.")
+    parser.add_argument(
+        "--cpu_num_threads",
+        type=int,
+        default=8,
+        help="The number of threads when inferring on cpu.")
+    parser.add_argument(
+        "--enable_trt_fp16",
+        type=distutils.util.strtobool,
+        default=False,
+        help="whether enable fp16 in trt backend")
+    parser.add_argument(
+        "--epoch", type=int, default=1, help="The epoch of test")
+    parser.add_argument(
+        "--enable_collect_memory_info",
+        type=ast.literal_eval,
+        default=False,
+        help="whether enable collect memory info")
+    return parser.parse_args()
+
+
+def build_option(args):
+    option = fd.RuntimeOption()
+    if args.device == 'cpu':
+        option.use_cpu()
+        option.set_cpu_thread_num(args.cpu_num_threads)
+    else:
+        option.use_gpu(args.device_id)
+    if args.backend == 'paddle':
+        option.use_paddle_backend()
+    elif args.backend == 'ort':
+        option.use_ort_backend()
+    elif args.backend == 'ov':
+        option.use_openvino_backend()
+    else:
+        option.use_trt_backend()
+        if args.backend == 'paddle_trt':
+            option.enable_paddle_to_trt()
+            option.enable_paddle_trt_collect_shape()
+        trt_file = os.path.join(args.model_dir, "infer.trt")
+        option.set_trt_input_shape(
+            'input_ids',
+            min_shape=[1, 1],
+            opt_shape=[args.batch_size, args.max_length // 2],
+            max_shape=[args.batch_size, args.max_length])
+        option.set_trt_input_shape(
+            'token_type_ids',
+            min_shape=[1, 1],
+            opt_shape=[args.batch_size, args.max_length // 2],
+            max_shape=[args.batch_size, args.max_length])
+        option.set_trt_input_shape(
+            'pos_ids',
+            min_shape=[1, 1],
+            opt_shape=[args.batch_size, args.max_length // 2],
+            max_shape=[args.batch_size, args.max_length])
+        option.set_trt_input_shape(
+            'att_mask',
+            min_shape=[1, 1],
+            opt_shape=[args.batch_size, args.max_length // 2],
+            max_shape=[args.batch_size, args.max_length])
+        if args.enable_trt_fp16:
+            option.enable_trt_fp16()
+            trt_file = trt_file + ".fp16"
+        option.set_trt_cache_file(trt_file)
+    return option
+
+
+class StatBase(object):
+    """StatBase"""
+    nvidia_smi_path = "nvidia-smi"
+    gpu_keys = ('index', 'uuid', 'name', 'timestamp', 'memory.total',
+                'memory.free', 'memory.used', 'utilization.gpu',
+                'utilization.memory')
+    nu_opt = ',nounits'
+    cpu_keys = ('cpu.util', 'memory.util', 'memory.used')
+
+
+class Monitor(StatBase):
+    """Monitor"""
+
+    def __init__(self, use_gpu=False, gpu_id=0, interval=0.1):
+        self.result = {}
+        self.gpu_id = gpu_id
+        self.use_gpu = use_gpu
+        self.interval = interval
+        self.cpu_stat_q = multiprocessing.Queue()
+
+    def start(self):
+        cmd = '%s --id=%s --query-gpu=%s --format=csv,noheader%s -lms 50' % (
+            StatBase.nvidia_smi_path, self.gpu_id, ','.join(StatBase.gpu_keys),
+            StatBase.nu_opt)
+        if self.use_gpu:
+            self.gpu_stat_worker = subprocess.Popen(
+                cmd,
+                stderr=subprocess.STDOUT,
+                stdout=subprocess.PIPE,
+                shell=True,
+                close_fds=True,
+                preexec_fn=os.setsid)
+        # cpu stat
+        pid = os.getpid()
+        self.cpu_stat_worker = multiprocessing.Process(
+            target=self.cpu_stat_func,
+            args=(self.cpu_stat_q, pid, self.interval))
+        self.cpu_stat_worker.start()
+
+    def stop(self):
+        try:
+            if self.use_gpu:
+                os.killpg(self.gpu_stat_worker.pid, signal.SIGUSR1)
+            # os.killpg(p.pid, signal.SIGTERM)
+            self.cpu_stat_worker.terminate()
+            self.cpu_stat_worker.join(timeout=0.01)
+        except Exception as e:
+            print(e)
+            return
+
+        # gpu
+        if self.use_gpu:
+            lines = self.gpu_stat_worker.stdout.readlines()
+            lines = [
+                line.strip().decode("utf-8") for line in lines
+                if line.strip() != ''
+            ]
+            gpu_info_list = [{
+                k: v
+                for k, v in zip(StatBase.gpu_keys, line.split(', '))
+            } for line in lines]
+            if len(gpu_info_list) == 0:
+                return
+            result = gpu_info_list[0]
+            for item in gpu_info_list:
+                for k in item.keys():
+                    if k not in ["name", "uuid", "timestamp"]:
+                        result[k] = max(int(result[k]), int(item[k]))
+                    else:
+                        result[k] = max(result[k], item[k])
+            self.result['gpu'] = result
+
+        # cpu
+        cpu_result = {}
+        if self.cpu_stat_q.qsize() > 0:
+            cpu_result = {
+                k: v
+                for k, v in zip(StatBase.cpu_keys, self.cpu_stat_q.get())
+            }
+        while not self.cpu_stat_q.empty():
+            item = {
+                k: v
+                for k, v in zip(StatBase.cpu_keys, self.cpu_stat_q.get())
+            }
+            for k in StatBase.cpu_keys:
+                cpu_result[k] = max(cpu_result[k], item[k])
+        cpu_result['name'] = cpuinfo.get_cpu_info()['brand_raw']
+        self.result['cpu'] = cpu_result
+
+    def output(self):
+        return self.result
+
+    def cpu_stat_func(self, q, pid, interval=0.0):
+        """cpu stat function"""
+        stat_info = psutil.Process(pid)
+        while True:
+            # pid = os.getpid()
+            cpu_util, mem_util, mem_use = stat_info.cpu_percent(
+            ), stat_info.memory_percent(), round(stat_info.memory_info().rss /
+                                                 1024.0 / 1024.0, 4)
+            q.put([cpu_util, mem_util, mem_use])
+            time.sleep(interval)
+        return
+
+
+def get_dataset(data_path, max_seq_len=512):
+    json_lines = []
+    with open(data_path, 'r', encoding='utf-8') as f:
+        for line in f:
+            json_line = json.loads(line)
+            content = json_line['content'].strip()
+            prompt = json_line['prompt']
+            # Model Input is aslike: [CLS] Prompt [SEP] Content [SEP]
+            # It include three summary tokens.
+            if max_seq_len <= len(prompt) + 3:
+                raise ValueError(
+                    "The value of max_seq_len is too small, please set a larger value"
+                )
+            json_lines.append(json_line)
+
+    return json_lines
+
+
+if __name__ == '__main__':
+    args = parse_arguments()
+    runtime_option = build_option(args)
+    model_path = os.path.join(args.model_dir, "inference.pdmodel")
+    param_path = os.path.join(args.model_dir, "inference.pdiparams")
+    vocab_path = os.path.join(args.model_dir, "vocab.txt")
+
+    gpu_id = args.device_id
+    enable_collect_memory_info = args.enable_collect_memory_info
+    dump_result = dict()
+    end2end_statis = list()
+    cpu_mem = list()
+    gpu_mem = list()
+    gpu_util = list()
+    if args.device == "cpu":
+        file_path = args.model_dir + "_model_" + args.backend + "_" + \
+            args.device + "_" + str(args.cpu_num_threads) + ".txt"
+    else:
+        if args.enable_trt_fp16:
+            file_path = args.model_dir + "_model_" + \
+                args.backend + "_fp16_" + args.device + ".txt"
+        else:
+            file_path = args.model_dir + "_model_" + args.backend + "_" + args.device + ".txt"
+    f = open(file_path, "w")
+    f.writelines("===={}====: \n".format(os.path.split(file_path)[-1][:-4]))
+
+    ds = get_dataset(args.data_path)
+    schema = ["时间"]
+    uie = UIEModel(
+        model_path,
+        param_path,
+        vocab_path,
+        position_prob=0.5,
+        max_length=args.max_length,
+        batch_size=args.batch_size,
+        schema=schema,
+        runtime_option=runtime_option,
+        schema_language=SchemaLanguage.ZH)
+
+    try:
+        if enable_collect_memory_info:
+            import multiprocessing
+            import subprocess
+            import psutil
+            import signal
+            import cpuinfo
+            enable_gpu = args.device == "gpu"
+            monitor = Monitor(enable_gpu, gpu_id)
+            monitor.start()
+        uie.enable_record_time_of_runtime()
+
+        for ep in range(args.epoch):
+            for i, sample in enumerate(ds):
+                curr_start = time.time()
+                uie.set_schema([sample['prompt']])
+                result = uie.predict([sample['content']])
+                end2end_statis.append(time.time() - curr_start)
+        runtime_statis = uie.print_statis_info_of_runtime()
+
+        warmup_iter = args.epoch * len(ds) // 5
+
+        end2end_statis_repeat = end2end_statis[warmup_iter:]
+        if enable_collect_memory_info:
+            monitor.stop()
+            mem_info = monitor.output()
+            dump_result["cpu_rss_mb"] = mem_info['cpu'][
+                'memory.used'] if 'cpu' in mem_info else 0
+            dump_result["gpu_rss_mb"] = mem_info['gpu'][
+                'memory.used'] if 'gpu' in mem_info else 0
+            dump_result["gpu_util"] = mem_info['gpu'][
+                'utilization.gpu'] if 'gpu' in mem_info else 0
+
+        dump_result["runtime"] = runtime_statis["avg_time"] * 1000
+        dump_result["end2end"] = np.mean(end2end_statis_repeat) * 1000
+
+        time_cost_str = f"Runtime(ms): {dump_result['runtime']}\n" \
+                        f"End2End(ms): {dump_result['end2end']}\n"
+        f.writelines(time_cost_str)
+        print(time_cost_str)
+
+        if enable_collect_memory_info:
+            mem_info_str = f"cpu_rss_mb: {dump_result['cpu_rss_mb']}\n" \
+                           f"gpu_rss_mb: {dump_result['gpu_rss_mb']}\n" \
+                           f"gpu_util: {dump_result['gpu_util']}\n"
+            f.writelines(mem_info_str)
+            print(mem_info_str)
+    except:
+        f.writelines("!!!!!Infer Failed\n")
+
+    f.close()

benchmark/run_benchmark_uie.sh

@@ -0,0 +1,27 @@
+# wget https://bj.bcebos.com/fastdeploy/benchmark/uie/reimbursement_form_data.txt
+# wget https://bj.bcebos.com/fastdeploy/models/uie/uie-base.tgz
+# tar xvfz uie-base.tgz
+
+DEVICE_ID=0
+
+echo "[FastDeploy]    Running UIE benchmark..."
+
+# GPU
+echo "-------------------------------GPU Benchmark---------------------------------------"
+python benchmark_uie.py --epoch 5 --model_dir uie-base --data_path reimbursement_form_data.txt --backend paddle --device_id $DEVICE_ID --device gpu --enable_collect_memory_info True
+python benchmark_uie.py --epoch 5 --model_dir uie-base --data_path reimbursement_form_data.txt --backend ort --device_id $DEVICE_ID --device gpu --enable_collect_memory_info True
+python benchmark_uie.py --epoch 5 --model_dir uie-base --data_path reimbursement_form_data.txt --backend paddle_trt --device_id $DEVICE_ID --device gpu --enable_trt_fp16 False --enable_collect_memory_info True
+python benchmark_uie.py --epoch 5 --model_dir uie-base --data_path reimbursement_form_data.txt --backend trt --device_id $DEVICE_ID --device gpu --enable_trt_fp16 False --enable_collect_memory_info True
+python benchmark_uie.py --epoch 5 --model_dir uie-base --data_path reimbursement_form_data.txt --backend paddle_trt --device_id $DEVICE_ID --device gpu --enable_trt_fp16 True --enable_collect_memory_info True
+python benchmark_uie.py --epoch 5 --model_dir uie-base --data_path reimbursement_form_data.txt --backend trt --device_id $DEVICE_ID --device gpu --enable_trt_fp16 True --enable_collect_memory_info True
+echo "-----------------------------------------------------------------------------------"
+
+# CPU
+echo "-------------------------------CPU Benchmark---------------------------------------"
+for cpu_num_threads in 1 8;
+do
+  python benchmark_uie.py --epoch 5 --model_dir uie-base --data_path reimbursement_form_data.txt --backend paddle --device cpu --cpu_num_threads ${cpu_num_threads} --enable_collect_memory_info True
+  python benchmark_uie.py --epoch 5 --model_dir uie-base --data_path reimbursement_form_data.txt --backend ort --device cpu --cpu_num_threads ${cpu_num_threads} --enable_collect_memory_info True
+  python benchmark_uie.py --epoch 5 --model_dir uie-base --data_path reimbursement_form_data.txt --backend ov --device cpu --cpu_num_threads ${cpu_num_threads} --enable_collect_memory_info True
+done
+echo "-----------------------------------------------------------------------------------"