benchmark.py

# Copyright (c) Microsoft Corporation.  All rights reserved.
# Copyright 2018 The HuggingFace Inc. team.
# Copyright (c) 2018, NVIDIA CORPORATION.  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.
""" Benchmarking the inference of pretrained transformer models.
    PyTorch/TorchScript benchmark is based on https://github.com/huggingface/transformers/blob/master/examples/benchmarks.py.
    One difference is that random input_ids is generated in this benchmark.

    For onnxruntime, this script will convert a pretrained model to ONNX, and optimize it when -o parameter is used.

    Example commands:
        Export all models to ONNX, optimize and validate them:
            python benchmark.py -b 0 -o -v -i 1 2 3
        Run OnnxRuntime on GPU for all models:
            python benchmark.py -g
        Run OnnxRuntime on GPU for all models with fp32 optimization:
            python benchmark.py -g -o
        Run OnnxRuntime on GPU with fp16 optimization:
            python benchmark.py -g -o -p "fp16"
        Run TorchScript on GPU for all models:
            python benchmark.py -e torchscript -g
        Run TorchScript on GPU for all models with fp16:
            python benchmark.py -e torchscript -g -p "fp16"
        Run ONNXRuntime and TorchScript on CPU for all models with quantization:
            python benchmark.py -e torchscript onnxruntime -p "int8" -o

    It is recommended to use run_benchmark.sh to launch benchmark.
"""

import argparse
import logging
import timeit
from datetime import datetime
import numpy

import os
import psutil
import onnx
from enum import Enum
from benchmark_helper import (create_onnxruntime_session, Precision, setup_logger, get_latency_result, output_details,
                              output_summary, output_fusion_statistics, inference_ort, inference_ort_with_io_binding,
                              allocateOutputBuffers)
from quantize_helper import QuantizeHelper
from onnx_exporter import create_onnxruntime_input, load_pretrained_model, export_onnx_model_from_pt, export_onnx_model_from_tf

logger = logging.getLogger('')

from huggingface_models import MODELS, MODEL_CLASSES

cpu_count = psutil.cpu_count(logical=False)

# Set OMP environment variable before importing onnxruntime or torch.
if "OMP_NUM_THREADS" not in os.environ:
    os.environ["OMP_NUM_THREADS"] = str(cpu_count)

import torch
from transformers import (AutoConfig, AutoTokenizer, AutoModel, GPT2Model, LxmertConfig)


def run_onnxruntime(use_gpu, model_names, model_class, precision, num_threads, batch_sizes, sequence_lengths,
                    repeat_times, input_counts, optimize_onnx, validate_onnx, cache_dir, onnx_dir, verbose, overwrite,
                    disable_ort_io_binding, use_raw_attention_mask, model_fusion_statistics, model_source):
    import onnxruntime

    results = []
    if use_gpu and ('CUDAExecutionProvider' not in onnxruntime.get_available_providers()):
        logger.error(
            "Please install onnxruntime-gpu package instead of onnxruntime, and use a machine with GPU for testing gpu performance."
        )
        return results

    for model_name in model_names:
        all_input_names = MODELS[model_name][0]
        for num_inputs in input_counts:
            if num_inputs > len(all_input_names):
                break

            input_names = all_input_names[:num_inputs]

            if 'pt' in model_source:
                with torch.no_grad():
                    onnx_model_file, is_valid_onnx_model, vocab_size, max_sequence_length = export_onnx_model_from_pt(
                        model_name, MODELS[model_name][1], MODELS[model_name][2], MODELS[model_name][3], model_class,
                        cache_dir, onnx_dir, input_names, use_gpu, precision, optimize_onnx, validate_onnx,
                        use_raw_attention_mask, overwrite, model_fusion_statistics)
            if 'tf' in model_source:
                onnx_model_file, is_valid_onnx_model, vocab_size, max_sequence_length = export_onnx_model_from_tf(
                    model_name, MODELS[model_name][1], MODELS[model_name][2], MODELS[model_name][3], model_class,
                    cache_dir, onnx_dir, input_names, use_gpu, precision, optimize_onnx, validate_onnx,
                    use_raw_attention_mask, overwrite, model_fusion_statistics)

            if not is_valid_onnx_model:
                continue

            ort_session = create_onnxruntime_session(onnx_model_file,
                                                     use_gpu,
                                                     enable_all_optimization=True,
                                                     num_threads=num_threads,
                                                     verbose=verbose)
            if ort_session is None:
                continue

            ort_output_names = [node_arg.name for node_arg in ort_session.get_outputs()]
            output_buffers = []
            device = "cuda" if use_gpu else "cpu"
            config = AutoConfig.from_pretrained(model_name, cache_dir=cache_dir)
            max_last_state_size = numpy.prod(
                [max(batch_sizes), max(sequence_lengths),
                 max(vocab_size, config.hidden_size)])
            max_pooler_size = numpy.prod([max(batch_sizes), config.hidden_size])
            for batch_size in batch_sizes:
                if batch_size <= 0:
                    continue
                for sequence_length in sequence_lengths:
                    if max_sequence_length is not None and sequence_length > max_sequence_length:
                        continue

                    input_value_type = numpy.int64 if 'pt' in model_source else numpy.int32
                    ort_inputs = create_onnxruntime_input(vocab_size, batch_size, sequence_length, input_names, config,
                                                          input_value_type)
                    result_template = {
                        "engine": "onnxruntime",
                        "version": onnxruntime.__version__,
                        "device": device,
                        "optimizer": optimize_onnx,
                        "precision": precision,
                        "io_binding": not disable_ort_io_binding,
                        "model_name": model_name,
                        "inputs": num_inputs,
                        "threads": num_threads,
                        "batch_size": batch_size,
                        "sequence_length": sequence_length,
                        "datetime": str(datetime.now()),
                    }

                    logger.info("Run onnxruntime on {} with input shape {}".format(model_name,
                                                                                   [batch_size, sequence_length]))

                    if disable_ort_io_binding:
                        result = inference_ort(ort_session, ort_inputs, result_template, repeat_times, batch_size)
                    else:
                        # Get output sizes from a dummy ort run
                        ort_outputs = ort_session.run(ort_output_names, ort_inputs)
                        output_buffer_max_sizes = [max_last_state_size]
                        for i in range(len(ort_outputs)):
                            if i == 2 and MODELS[model_name][3] == "gpt":
                                # past state output max size
                                output_buffer_max_sizes.append(max_pooler_size)
                            else:
                                output_buffer_max_sizes.append(max_last_state_size)

                        data_type = numpy.longlong if 'pt' in model_source else numpy.intc
                        result = inference_ort_with_io_binding(ort_session, ort_inputs, result_template, repeat_times,
                                                               ort_output_names, ort_outputs, output_buffers,
                                                               output_buffer_max_sizes, batch_size, device, data_type)
                    logger.info(result)
                    results.append(result)

    return results


def run_pytorch(use_gpu, model_names, model_class, precision, num_threads, batch_sizes, sequence_lengths, repeat_times,
                torchscript, cache_dir, verbose):
    results = []
    if use_gpu and not torch.cuda.is_available():
        logger.error("Please install PyTorch with Cuda, and use a machine with GPU for testing gpu performance.")
        return results

    torch.set_grad_enabled(False)

    for model_name in model_names:
        config = AutoConfig.from_pretrained(model_name, torchscript=torchscript, cache_dir=cache_dir)
        model = load_pretrained_model(model_name, config=config, cache_dir=cache_dir, custom_model_class=model_class)
        tokenizer = AutoTokenizer.from_pretrained(model_name, cache_dir=cache_dir)

        max_input_size = tokenizer.max_model_input_sizes[
            model_name] if model_name in tokenizer.max_model_input_sizes else 1024

        logger.debug(f"Model {model}")
        logger.debug(f"Number of parameters {model.num_parameters()}")

        if precision == Precision.FLOAT16:
            model.half()

        device = torch.device("cuda:0" if use_gpu else "cpu")
        model.to(device)

        if precision == Precision.INT8:
            model = QuantizeHelper.quantize_torch_model(model)

        for batch_size in batch_sizes:
            if batch_size <= 0:
                continue

            for sequence_length in sequence_lengths:
                if max_input_size is not None and sequence_length > max_input_size:
                    continue

                logger.info("Run PyTorch on {} with input shape {}".format(model_name, [batch_size, sequence_length]))
                input_ids = torch.randint(low=0,
                                          high=config.vocab_size - 1,
                                          size=(batch_size, sequence_length),
                                          dtype=torch.long,
                                          device=device)
                try:
                    inference = torch.jit.trace(model, input_ids) if torchscript else model
                    inference(input_ids)

                    runtimes = timeit.repeat(lambda: inference(input_ids), repeat=repeat_times, number=1)

                    result = {
                        "engine": "torchscript" if torchscript else "torch",
                        "version": torch.__version__,
                        "device": "cuda" if use_gpu else "cpu",
                        "optimizer": "",
                        "precision": precision,
                        "io_binding": "",
                        "model_name": model_name,
                        "inputs": 1,
                        "threads": num_threads,
                        "batch_size": batch_size,
                        "sequence_length": sequence_length,
                        "datetime": str(datetime.now()),
                    }
                    result.update(get_latency_result(runtimes, batch_size))
                    logger.info(result)
                    results.append(result)
                except RuntimeError as e:
                    logger.exception(e)
                    torch.cuda.empty_cache()

    return results


def run_mlir(use_gpu, model_names, model_class, precision, num_threads, batch_sizes, sequence_lengths,
                   repeat_times, cache_dir, verbose, use_search):
    results = []

    from iree import runtime as ireert
    from iree.compiler import tf as tfc
    from iree.compiler import compile_str
    import sys
    from absl import app

    import numpy as np
    import os
    import tempfile
    import tensorflow as tf

    import time
    from transformers import BertModel, BertTokenizer, TFBertModel

    # TODO: Adjust run_mlir S.T it can run on multiple batch_szs and sequence_lens
    MAX_SEQUENCE_LENGTH = sequence_lengths[0]
    BATCH_SIZE = 1

    # Create a set of 2-dimensional inputs
    bert_input = [tf.TensorSpec(shape=[BATCH_SIZE,MAX_SEQUENCE_LENGTH],dtype=tf.int32),
            tf.TensorSpec(shape=[BATCH_SIZE,MAX_SEQUENCE_LENGTH], dtype=tf.int32),
            tf.TensorSpec(shape=[BATCH_SIZE,MAX_SEQUENCE_LENGTH], dtype=tf.int32)]

    class BertModule(tf.Module):
        def __init__(self):
            super(BertModule, self).__init__()
            # Create a BERT trainer with the created network.
            self.m = TFBertModel.from_pretrained("microsoft/MiniLM-L12-H384-uncased", from_pt=True)

            # Invoke the trainer model on the inputs. This causes the layer to be built.
            self.m.predict = lambda x,y,z: self.m.call(input_ids=x, attention_mask=y, token_type_ids=z, training=False)

        @tf.function(input_signature=bert_input)
        def predict(self, input_ids, attention_mask, token_type_ids):
            return self.m.predict(input_ids, attention_mask, token_type_ids)

    # Prepping Data
    tokenizer = BertTokenizer.from_pretrained("microsoft/MiniLM-L12-H384-uncased")
    text = "Replace me by any text you'd like."
    encoded_input = tokenizer(text, padding='max_length', truncation=True, max_length=MAX_SEQUENCE_LENGTH)
    for key in encoded_input:
        encoded_input[key] = tf.expand_dims(tf.convert_to_tensor(encoded_input[key]),0)

    # Compile the model using IREE
    backend = "dylib-llvm-aot"
    args = ["--iree-llvm-target-cpu-features=host"]
    backend_config = "dylib"
    if use_gpu:
        backend = "cuda"
        backend_config = "cuda"
        args = ["--iree-cuda-llvm-target-arch=sm_80", "--iree-hal-cuda-disable-loop-nounroll-wa", "--iree-enable-fusion-with-reduction-ops"]

    if use_search:
        ARITFACTS_DIR = os.getcwd()
        mlir_path = os.path.join(ARITFACTS_DIR, "model-tiled.mlir")
        with open(mlir_path, "rb") as input_file:
            compiler_module = input_file.read()
        print("Using model with searched parameters.")
    else:
        compiler_module = tfc.compile_module(BertModule(), exported_names = ["predict"], import_only=True)
    flatbuffer_blob = compile_str(compiler_module, target_backends=[backend], extra_args=args)
    #flatbuffer_blob = compile_str(compiler_module, target_backends=[backend])

    # Save module as MLIR file in a directory
    vm_module = ireert.VmModule.from_flatbuffer(flatbuffer_blob)
    #tracer = ireert.Tracer(os.getcwd())
    # TODO: Remove printing of "Tracing module.predict"
    config = ireert.Config(backend_config)
    ctx = ireert.SystemContext(config=config)
    ctx.add_vm_module(vm_module)
    BertCompiled = ctx.modules.module
    
    if not use_search:
        #Dump module
        ARITFACTS_DIR = os.getcwd()
        mlir_path = os.path.join(ARITFACTS_DIR, "model.mlir")
        with open(mlir_path, "wt") as output_file:
            output_file.write(compiler_module.decode('utf-8'))
        print(f"Wrote MLIR to path '{mlir_path}'")

    #result = BertCompiled.predict(encoded_input["input_ids"], encoded_input["attention_mask"], encoded_input["token_type_ids"])
    #print(result)
    #end iree

    try:
        runtimes = timeit.repeat(lambda: BertCompiled.predict(encoded_input["input_ids"], encoded_input["attention_mask"], encoded_input["token_type_ids"]), repeat=repeat_times, number=1)
        result = {
            "engine": "MLIR",
            "version": tf.__version__,
            "device": "cuda" if use_gpu else "cpu",
            "optimizer": "",
            "precision": precision,
            "io_binding": "",
            "model_name": "microsoft/MiniLM-L12-H384-uncased",
            "inputs": 1,
            "threads": 1,
            "batch_size": batch_sizes[0],
            "sequence_length": sequence_lengths[0],
            "datetime": str(datetime.now()),
        }
        result.update(get_latency_result(runtimes, batch_sizes[0]))
        logger.info(result)
        results.append(result)
    except RuntimeError as e:
        logger.exception(e)
        from numba import cuda
        device = cuda.get_current_device()
        device.reset()

    return results


def run_with_tf_optimizations(do_eager_mode: bool, use_xla: bool):
    import tensorflow as tf
    from functools import wraps

    def run_func(func):
        @wraps(func)
        def run_in_eager_mode(*args, **kwargs):
            return func(*args, **kwargs)

        @wraps(func)
        @tf.function(experimental_compile=use_xla)
        def run_in_graph_mode(*args, **kwargs):
            return func(*args, **kwargs)

        if do_eager_mode is True:
            assert (
                use_xla is False
            ), "Cannot run model in XLA, if `args.eager_mode` is set to `True`. Please set `args.eager_mode=False`."
            return run_in_eager_mode
        else:
            return run_in_graph_mode

    return run_func


def run_tensorflow(use_gpu, model_names, model_class, precision, num_threads, batch_sizes, sequence_lengths,
                   repeat_times, cache_dir, verbose):
    results = []

    import tensorflow as tf
    tf.config.threading.set_intra_op_parallelism_threads(num_threads)

    if not use_gpu:
        tf.config.set_visible_devices([], 'GPU')

    if use_gpu and not tf.test.is_built_with_cuda():
        logger.error("Please install Tensorflow-gpu, and use a machine with GPU for testing gpu performance.")
        return results

    if use_gpu:  # Restrict TensorFlow to only use the first GPU
        physical_devices = tf.config.list_physical_devices('GPU')
        try:
            tf.config.set_visible_devices(physical_devices[0], 'GPU')
            tf.config.experimental.set_memory_growth(physical_devices[0], True)
            tf.distribute.OneDeviceStrategy(device='/gpu:0')
        except RuntimeError as e:
            logger.exception(e)

    if precision == Precision.FLOAT16 or precision == Precision.INT8:
        raise NotImplementedError("Mixed precision is currently not supported.")

    for model_name in model_names:
        config = AutoConfig.from_pretrained(model_name, cache_dir=cache_dir)

        model = load_pretrained_model(model_name,
                                      config=config,
                                      cache_dir=cache_dir,
                                      custom_model_class=model_class,
                                      is_tf_model=True)

        tokenizer = AutoTokenizer.from_pretrained(model_name, cache_dir=cache_dir)

        max_input_size = tokenizer.max_model_input_sizes[
            model_name] if model_name in tokenizer.max_model_input_sizes else 1024

        for batch_size in batch_sizes:
            if batch_size <= 0:
                continue

            for sequence_length in sequence_lengths:
                if max_input_size is not None and sequence_length > max_input_size:
                    continue

                logger.info("Run Tensorflow on {} with input shape {}".format(model_name,
                                                                              [batch_size, sequence_length]))

                import random
                rng = random.Random()
                values = [rng.randint(0, config.vocab_size - 1) for i in range(batch_size * sequence_length)]
                input_ids = tf.constant(values, shape=(batch_size, sequence_length), dtype=tf.int32)

                try:
                    # Disable both for better inference perf
                    @run_with_tf_optimizations(do_eager_mode=False, use_xla=True)
                    def encoder_forward():
                        return model(input_ids, training=False)

                    @run_with_tf_optimizations(do_eager_mode=False, use_xla=True)
                    def encoder_decoder_forward():
                        return model(input_ids, decoder_input_ids=input_ids, training=False)

                    @run_with_tf_optimizations(do_eager_mode=False, use_xla=True)
                    def lxmert_forward():
                        feats = tf.random.normal([1, 1, config.visual_feat_dim])
                        pos = tf.random.normal([1, 1, config.visual_pos_dim])
                        return model(input_ids, visual_feats=feats, visual_pos=pos, training=False)

                    inference = encoder_forward
                    if config.is_encoder_decoder:
                        inference = encoder_decoder_forward
                    elif isinstance(config, LxmertConfig):
                        inference = lxmert_forward

                    inference()

                    runtimes = timeit.repeat(lambda: inference(), repeat=repeat_times, number=1)

                    result = {
                        "engine": "tensorflow",
                        "version": tf.__version__,
                        "device": "cuda" if use_gpu else "cpu",
                        "optimizer": "",
                        "precision": precision,
                        "io_binding": "",
                        "model_name": model_name,
                        "inputs": 1,
                        "threads": num_threads,
                        "batch_size": batch_size,
                        "sequence_length": sequence_length,
                        "datetime": str(datetime.now()),
                    }
                    result.update(get_latency_result(runtimes, batch_size))
                    logger.info(result)
                    results.append(result)
                except RuntimeError as e:
                    logger.exception(e)
                    from numba import cuda
                    device = cuda.get_current_device()
                    device.reset()

    return results


def parse_arguments():
    parser = argparse.ArgumentParser()

    parser.add_argument("-m",
                        "--models",
                        required=False,
                        nargs="+",
                        type=str,
                        default=["bert-base-cased", "roberta-base", "gpt2"],
                        choices=list(MODELS.keys()),
                        help="Pre-trained models in the list: " + ", ".join(MODELS.keys()))

    parser.add_argument("--model_source",
                        required=False,
                        nargs=1,
                        type=str,
                        default='pt',
                        choices=['pt', 'tf'],
                        help="Export onnx from pt or tf")

    parser.add_argument('--model_class',
                        required=False,
                        type=str,
                        default=None,
                        choices=list(MODEL_CLASSES),
                        help='Model type selected in the list: ' + ', '.join(MODEL_CLASSES))

    parser.add_argument("-e",
                        "--engines",
                        required=False,
                        nargs="+",
                        type=str,
                        default=['onnxruntime'],
                        choices=['onnxruntime', 'torch', 'torchscript', 'tensorflow', 'mlir'],
                        help="Engines to benchmark")

    parser.add_argument("-c",
                        "--cache_dir",
                        required=False,
                        type=str,
                        default=os.path.join('.', 'cache_models'),
                        help="Directory to cache pre-trained models")

    parser.add_argument("--onnx_dir",
                        required=False,
                        type=str,
                        default=os.path.join('.', 'onnx_models'),
                        help="Directory to store onnx models")

    parser.add_argument("-g", "--use_gpu", required=False, action="store_true", help="Run on cuda device")

    parser.add_argument(
        "-p",
        "--precision",
        type=Precision,
        default=Precision.FLOAT32,
        choices=list(Precision),
        help="Precision of model to run. fp32 for full precision, fp16 for half precision, and int8 for quantization")

    parser.add_argument("--verbose", required=False, action="store_true", help="Print more information")

    parser.add_argument("--overwrite", required=False, action="store_true", help="Overwrite existing models")

    parser.add_argument("-o",
                        "--optimize_onnx",
                        required=False,
                        action="store_true",
                        help="Use optimizer.py to optimize onnx model")

    parser.add_argument("-v", "--validate_onnx", required=False, action="store_true", help="Validate ONNX model")

    parser.add_argument("-f",
                        "--fusion_csv",
                        required=False,
                        default=None,
                        help="CSV file for saving summary results of graph optimization.")

    parser.add_argument("-d", "--detail_csv", required=False, default=None, help="CSV file for saving detail results.")

    parser.add_argument("-r", "--result_csv", required=False, default=None, help="CSV file for saving summary results.")

    parser.add_argument("-i",
                        "--input_counts",
                        required=False,
                        nargs="+",
                        default=[1],
                        type=int,
                        choices=[1, 2, 3],
                        help="Number of ONNX model inputs. Please use 1 for fair comparison with Torch or TorchScript.")

    parser.add_argument("-t",
                        "--test_times",
                        required=False,
                        default=100,
                        type=int,
                        help="Number of repeat times to get average inference latency.")

    parser.add_argument("-b", "--batch_sizes", nargs="+", type=int, default=[1])

    parser.add_argument("-s", "--sequence_lengths", nargs="+", type=int, default=[4, 8, 16, 32, 64, 128, 256])

    parser.add_argument('--disable_ort_io_binding',
                        required=False,
                        action='store_true',
                        help='Disable running ONNX Runtime with binded inputs and outputs. ')
    parser.set_defaults(disable_ort_io_binding=False)

    parser.add_argument("-n", "--num_threads", required=False, nargs="+", type=int, default=[0], help="Threads to use")
    
    parser.add_argument("--use_search", required=False, action="store_true", help="Use searched model")

    args = parser.parse_args()
    return args


def main():
    args = parse_arguments()

    setup_logger(args.verbose)

    if args.precision == Precision.FLOAT16 and not args.use_gpu:
        logger.error("fp16 is for GPU only")
        return

    if args.precision == Precision.INT8 and args.use_gpu:
        logger.error("int8 is for CPU only")
        return

    args.num_threads = sorted(set(cpu_count if x <= 0 else x for x in args.num_threads))

    logger.info(f"Arguments: {args}")

    if not os.path.exists(args.cache_dir):
        try:
            os.mkdir(args.cache_dir)
        except OSError:
            logger.error("Creation of the directory %s failed" % args.cache_dir)

    enable_torch = "torch" in args.engines
    enable_torchscript = "torchscript" in args.engines
    enable_onnxruntime = "onnxruntime" in args.engines
    enable_tensorflow = "tensorflow" in args.engines
    enable_mlir = "mlir" in args.engines

    results = []

    for num_threads in args.num_threads:
        torch.set_num_threads(num_threads)
        logger.debug(torch.__config__.parallel_info())
        if enable_torch or enable_torchscript:
            if args.input_counts != [1]:
                logger.warning("--input_counts is not implemented for torch or torchscript engine.")

            if enable_torchscript:
                results += run_pytorch(args.use_gpu, args.models, args.model_class, args.precision, num_threads,
                                       args.batch_sizes, args.sequence_lengths, args.test_times, True, args.cache_dir,
                                       args.verbose)

            if enable_torch:
                results += run_pytorch(args.use_gpu, args.models, args.model_class, args.precision, num_threads,
                                       args.batch_sizes, args.sequence_lengths, args.test_times, False, args.cache_dir,
                                       args.verbose)

        if enable_tensorflow:
            results += run_tensorflow(args.use_gpu, args.models, args.model_class, args.precision, num_threads,
                                      args.batch_sizes, args.sequence_lengths, args.test_times, args.cache_dir,
                                      args.verbose)
        if enable_mlir:
            results += run_mlir(args.use_gpu, args.models, args.model_class, args.precision, num_threads,
                            args.batch_sizes, args.sequence_lengths, args.test_times, args.cache_dir,
                            args.verbose, args.use_search)


        model_fusion_statistics = {}
        if enable_onnxruntime:
            try:
                use_raw_attention_mask = True
                results += run_onnxruntime(args.use_gpu, args.models, args.model_class, args.precision, num_threads,
                                           args.batch_sizes, args.sequence_lengths, args.test_times, args.input_counts,
                                           args.optimize_onnx, args.validate_onnx, args.cache_dir, args.onnx_dir,
                                           args.verbose, args.overwrite, args.disable_ort_io_binding,
                                           use_raw_attention_mask, model_fusion_statistics, args.model_source)
            except:
                logger.error(f"Exception", exc_info=True)

    time_stamp = datetime.now().strftime("%Y%m%d-%H%M%S")
    if model_fusion_statistics:
        csv_filename = args.fusion_csv or f"benchmark_fusion_{time_stamp}.csv"
        output_fusion_statistics(model_fusion_statistics, csv_filename)

    if len(results) == 0:
        if args.batch_sizes != [0]:
            logger.warning("No any result avaiable.")
        return

    csv_filename = args.detail_csv or f"benchmark_detail_{time_stamp}.csv"
    output_details(results, csv_filename)

    csv_filename = args.result_csv or f"benchmark_summary_{time_stamp}.csv"
    output_summary(results, csv_filename, args)


if __name__ == "__main__":
    main()