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benchmark.py
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benchmark.py
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# -*- coding: utf-8 -*-
# Copyright (C) 2023-2024 Intel Corporation
# SPDX-License-Identifier: Apache-2.0
import os
import sys
import argparse
import time
from pathlib import Path
import logging as log
import utils.ov_utils
import utils.pt_utils
import utils.model_utils
import torch
import numpy as np
from openvino.runtime import get_version
import PIL
import hashlib
import utils.metrics_print
import utils.output_csv
import traceback
from transformers import set_seed
from PIL import Image
from utils.memory_profile import MemConsumption
from utils.hook_forward import StableDiffusionHook
import utils.output_json
import utils.output_file
FW_UTILS = {'pt': utils.pt_utils, 'ov': utils.ov_utils}
DEFAULT_INFERENCE_STEPS = 20
LCM_DEFAULT_INFERENCE_STEPS = 4
DEFAULT_IMAGE_WIDTH = 512
DEFAULT_IMAGE_HEIGHT = 512
DEFAULT_SUPER_RESOLUTION_STEPS = 50
DEFAULT_SUPER_RESOLUTION_WIDTH = 128
DEFAULT_SUPER_RESOLUTION_HEIGHT = 128
DEFAULT_OUTPUT_TOKEN_SIZE = 512
MAX_OUTPUT_TOKEN_SIZE = 64 * 1024
mem_consumption = MemConsumption()
stable_diffusion_hook = StableDiffusionHook()
def gen_iterate_data(
iter_idx='',
in_size='',
infer_count='',
out_size='',
gen_time='',
latency='',
res_md5='',
max_rss_mem='',
max_shared_mem='',
prompt_idx='',
tokenization_time=[],
):
iter_data = {}
iter_data['iteration'] = iter_idx
iter_data['input_size'] = in_size
iter_data['infer_count'] = infer_count
iter_data['output_size'] = out_size
iter_data['generation_time'] = gen_time
iter_data['latency'] = latency
iter_data['result_md5'] = res_md5
iter_data['first_token_latency'] = ''
iter_data['other_tokens_avg_latency'] = ''
iter_data['first_token_infer_latency'] = ''
iter_data['other_tokens_infer_avg_latency'] = ''
iter_data['max_rss_mem_consumption'] = max_rss_mem
iter_data['max_shared_mem_consumption'] = max_shared_mem
iter_data['prompt_idx'] = prompt_idx
iter_data['tokenization_time'] = tokenization_time[0] if len(tokenization_time) > 0 else ''
iter_data['detokenization_time'] = tokenization_time[1] if len(tokenization_time) > 1 else ''
return iter_data
def run_text_generation(input_text, num, model, tokenizer, args, iter_data_list, warmup_md5, prompt_index, bench_hook, model_precision, proc_id):
set_seed(args['seed'])
input_text_list = [input_text] * args['batch_size']
if args["output_dir"] is not None and num == 0:
for bs_index, in_text in enumerate(input_text_list):
utils.output_file.output_input_text(in_text, args, model_precision, prompt_index, bs_index, proc_id)
tok_encode_start = time.perf_counter()
input_data = tokenizer(input_text_list, return_tensors='pt')
tok_encode_end = time.perf_counter()
tok_encode_time = (tok_encode_end - tok_encode_start) * 1000
input_data.pop('token_type_ids', None)
# Remove `token_type_ids` from inputs
input_tokens = input_data['input_ids'] if 'input_ids' in input_data else input_data
input_token_size = input_tokens[0].numel()
max_output_token_size = DEFAULT_OUTPUT_TOKEN_SIZE if args['infer_count'] is None else args['infer_count']
max_output_token_size = MAX_OUTPUT_TOKEN_SIZE if max_output_token_size > MAX_OUTPUT_TOKEN_SIZE else max_output_token_size
if args['batch_size'] > 1:
out_str = '[warm-up]' if num == 0 else '[{}]'.format(num)
out_str += " Batch_size={}, ".format(args['batch_size'])
out_str += 'all input token size after padding: {} * {}, '.format(input_token_size, args['batch_size'])
out_str += 'all max_output_token_size: {} * {}'.format(max_output_token_size, args['batch_size'])
log.info(out_str)
max_rss_mem_consumption = ''
max_shared_mem_consumption = ''
if (args['mem_consumption'] == 1 and num == 0) or args['mem_consumption'] == 2:
mem_consumption.start_collect_memory_consumption()
start = time.perf_counter()
result = model.generate(**input_data, max_new_tokens=int(max_output_token_size), num_beams=args['num_beams'], use_cache=True)
end = time.perf_counter()
if (args['mem_consumption'] == 1 and num == 0) or args['mem_consumption'] == 2:
mem_consumption.end_collect_momory_consumption()
max_rss_mem_consumption, max_shared_mem_consumption = mem_consumption.get_max_memory_consumption()
mem_consumption.clear_max_memory_consumption()
generation_time = end - start
tok_decode_start = time.perf_counter()
generated_text = tokenizer.batch_decode(result)
tok_decode_end = time.perf_counter()
tok_decode_time = (tok_decode_end - tok_decode_start) * 1000
# Only text_gen need to minus length of input_data, because generated_text may include input_text
num_tokens = 0
result_md5_list = []
for bs_idx in range(args['batch_size']):
if 'sum' not in args['model_name'] and result[bs_idx][:input_token_size].equal(input_tokens[bs_idx]):
generated_text_len = len(result[bs_idx]) - input_tokens[bs_idx].numel()
else:
generated_text_len = len(result[bs_idx])
num_tokens += generated_text_len
if generated_text_len > max_output_token_size:
log.error('Output token size is over max output token size!')
result_text = generated_text[bs_idx]
if args["output_dir"] is not None:
utils.output_file.output_gen_text(result_text, args, model_precision, prompt_index, num, bs_idx, proc_id)
result_md5_list.append(hashlib.md5(result_text.encode()).hexdigest())
if num == 0:
warmup_md5[prompt_index] = result_md5_list
per_token_time = generation_time * 1000 / num_tokens
iter_data = gen_iterate_data(
num,
input_token_size * args['batch_size'],
max_output_token_size * args['batch_size'],
num_tokens,
generation_time,
per_token_time,
result_md5_list,
max_rss_mem=max_rss_mem_consumption,
max_shared_mem=max_shared_mem_consumption,
prompt_idx=prompt_index,
tokenization_time=(tok_encode_time, tok_decode_time)
)
iter_data_list.append(iter_data)
tm_list = bench_hook.get_time_list()
tm_infer_list = bench_hook.get_time_infer_list()
utils.metrics_print.print_metrics(
num,
iter_data,
tm_list,
tm_infer_list,
warm_up=(num == 0),
max_rss_mem=max_rss_mem_consumption,
max_shared_mem=max_shared_mem_consumption,
tokenization_time=(tok_encode_time, tok_decode_time)
)
if num > 0:
warmup_md5_list = warmup_md5[prompt_index]
if result_md5_list != warmup_md5_list:
log.warning(f"[{num}] Prompt[{prompt_index}]'s md5 {result_md5_list} is different from warm-up's md5 {warmup_md5_list}")
utils.metrics_print.print_generated(num, warm_up=(num == 0), generated=generated_text[0])
else:
utils.metrics_print.print_generated(num, warm_up=(num == 0), generated=generated_text[0])
bench_hook.clear_time_list()
bench_hook.clear_time_infer_list()
def run_text_generation_benchmark(model_path, framework, device, args, num_iters):
model, tokenizer, pretrain_time, bench_hook = FW_UTILS[framework].create_text_gen_model(model_path, device, **args)
model_precision = utils.model_utils.get_model_precision(model_path.parents._parts)
iter_data_list = []
warmup_md5 = {}
input_text_list = utils.model_utils.get_prompts(args)
if len(input_text_list) == 0:
raise RuntimeError('==Failure prompts is empty ==')
log.info(f"Numbeams: {args['num_beams']}, benchmarking iter nums(exclude warm-up): {num_iters}, "
f'prompt nums: {len(input_text_list)}')
# if num_iters == 0, just output warm-up data
proc_id = os.getpid()
if args['subsequent'] is False:
for num in range(num_iters + 1):
for prompt_idx, input_text in enumerate(input_text_list):
if num == 0:
log.info(f'[warm-up] Input text: {input_text}')
run_text_generation(input_text, num, model, tokenizer, args, iter_data_list, warmup_md5, prompt_idx, bench_hook, model_precision, proc_id)
else:
for prompt_idx, input_text in enumerate(input_text_list):
for num in range(num_iters + 1):
if num == 0:
log.info(f'[warm-up] Input text: {input_text}')
run_text_generation(input_text, num, model, tokenizer, args, iter_data_list, warmup_md5, prompt_idx, bench_hook, model_precision, proc_id)
utils.metrics_print.print_average(iter_data_list)
return iter_data_list, pretrain_time
def run_image_generation(image_param, num, image_id, pipe, args, iter_data_list, proc_id):
set_seed(args['seed'])
input_text = image_param['prompt']
image_width = image_param.get('width', DEFAULT_IMAGE_WIDTH)
image_height = image_param.get('height', DEFAULT_IMAGE_HEIGHT)
nsteps = image_param.get('steps', DEFAULT_INFERENCE_STEPS if 'lcm' not in args["model_name"] else LCM_DEFAULT_INFERENCE_STEPS)
guidance_scale = image_param.get('guidance_scale', None)
log.info(
f"[{'warm-up' if num == 0 else num}] Input params: Batch_size={args['batch_size']}, "
f'steps={nsteps}, width={image_width}, height={image_height}, guidance_scale={guidance_scale}'
)
result_md5_list = []
max_rss_mem_consumption = ''
max_shared_mem_consumption = ''
if (args['mem_consumption'] == 1 and num == 0) or args['mem_consumption'] == 2:
mem_consumption.start_collect_memory_consumption()
additional_args = {}
if guidance_scale is not None:
additional_args["guidance_scale"] = guidance_scale
else:
if 'lcm-sdxl' in args['model_type']:
additional_args["guidance_scale"] = 1.0
if 'turbo' in args['model_name']:
additional_args["guidance_scale"] = 0.0
input_text_list = [input_text] * args['batch_size']
if num == 0 and args["output_dir"] is not None:
for bs_idx, in_text in enumerate(input_text_list):
utils.output_file.output_image_input_text(in_text, args, image_id, bs_idx, proc_id)
start = time.perf_counter()
res = pipe(input_text_list, num_inference_steps=nsteps, height=image_height, width=image_width, **additional_args).images
end = time.perf_counter()
if (args['mem_consumption'] == 1 and num == 0) or args['mem_consumption'] == 2:
mem_consumption.end_collect_momory_consumption()
max_rss_mem_consumption, max_shared_mem_consumption = mem_consumption.get_max_memory_consumption()
mem_consumption.clear_max_memory_consumption()
for bs_idx in range(args['batch_size']):
rslt_img_fn = utils.output_file.output_gen_image(res[bs_idx], args, image_id, num, bs_idx, proc_id, '.png')
result_md5_list.append(hashlib.md5(Image.open(rslt_img_fn).tobytes()).hexdigest())
generation_time = end - start
iter_data = gen_iterate_data(
iter_idx=num,
infer_count=nsteps,
gen_time=generation_time,
res_md5=result_md5_list,
max_rss_mem=max_rss_mem_consumption,
max_shared_mem=max_shared_mem_consumption,
prompt_idx=image_id,
)
iter_data_list.append(iter_data)
utils.metrics_print.print_metrics(
num,
iter_data,
warm_up=(num == 0),
max_rss_mem=max_rss_mem_consumption,
max_shared_mem=max_shared_mem_consumption,
stable_diffusion=stable_diffusion_hook
)
utils.metrics_print.print_generated(num, warm_up=(num == 0), generated=rslt_img_fn)
stable_diffusion_hook.clear_statistics()
def run_image_generation_benchmark(model_path, framework, device, args, num_iters):
pipe, pretrain_time = FW_UTILS[framework].create_image_gen_model(model_path, device, **args)
iter_data_list = []
input_image_list = utils.model_utils.get_image_param_from_prompt_file(args)
if len(input_image_list) == 0:
raise RuntimeError('==Failure prompts is empty ==')
if framework == "ov":
stable_diffusion_hook.new_text_encoder(pipe)
stable_diffusion_hook.new_unet(pipe)
stable_diffusion_hook.new_vae_decoder(pipe)
log.info(f'Benchmarking iter nums(exclude warm-up): {num_iters}, prompt nums: {len(input_image_list)}')
# if num_iters == 0, just output warm-up data
proc_id = os.getpid()
if args['subsequent'] is False:
for num in range(num_iters + 1):
for image_id, image_param in enumerate(input_image_list):
run_image_generation(image_param, num, image_id, pipe, args, iter_data_list, proc_id)
else:
for image_id, image_param in enumerate(input_image_list):
for num in range(num_iters + 1):
run_image_generation(image_param, num, image_id, pipe, args, iter_data_list, proc_id)
utils.metrics_print.print_average(iter_data_list)
return iter_data_list, pretrain_time
def run_image_classification(model_path, framework, device, args, num_iters=10):
model, input_size = FW_UTILS[framework].create_image_classification_model(model_path, device, **args)
data = torch.rand(input_size)
test_time = []
iter_data_list = []
for num in range(num_iters or 10):
start = time.perf_counter()
model(data)
end = time.perf_counter()
generation_time = end - start
test_time.append(generation_time)
iter_data = gen_iterate_data(iter_idx=num, in_size=input_size, infer_count=num_iters, gen_time=generation_time)
iter_data_list.append(iter_data)
log.info(f'Processed {num_iters} images in {np.sum(test_time)}s')
log.info(f'Average processing time {np.mean(test_time)} s')
return iter_data_list
def run_ldm_super_resolution(img, num, pipe, args, framework, iter_data_list, image_id, tm_list, proc_id):
set_seed(args['seed'])
nsteps = img.get('steps', DEFAULT_SUPER_RESOLUTION_STEPS)
resize_image_width = img.get('width', DEFAULT_SUPER_RESOLUTION_WIDTH)
resize_image_height = img.get('height', DEFAULT_SUPER_RESOLUTION_HEIGHT)
log.info(
f"[{'warm-up' if num == 0 else num}] Input params: steps={nsteps}, "
f'resize_width={resize_image_width}, resize_height={resize_image_height}'
)
low_res_img = PIL.Image.open(img['prompt']).convert('RGB')
low_res_img = low_res_img.resize((resize_image_width, resize_image_height))
max_rss_mem_consumption = ''
max_shared_mem_consumption = ''
if (args['mem_consumption'] == 1 and num == 0) or args['mem_consumption'] == 2:
mem_consumption.start_collect_memory_consumption()
start = time.perf_counter()
res = pipe(low_res_img, num_inference_steps=nsteps, tm_list=tm_list)
end = time.perf_counter()
if (args['mem_consumption'] == 1 and num == 0) or args['mem_consumption'] == 2:
mem_consumption.end_collect_momory_consumption()
max_rss_mem_consumption, max_shared_mem_consumption = mem_consumption.get_max_memory_consumption()
mem_consumption.clear_max_memory_consumption()
result_md5_list = []
if framework == 'ov':
rslt_img_fn = utils.output_file.output_gen_image(res[0], args, image_id, num, None, proc_id, '.png')
result_md5_list.append(hashlib.md5(Image.open(rslt_img_fn).tobytes()).hexdigest())
generation_time = end - start
iter_data = gen_iterate_data(
iter_idx=num,
infer_count=nsteps,
gen_time=generation_time,
res_md5=result_md5_list,
max_rss_mem=max_rss_mem_consumption,
max_shared_mem=max_shared_mem_consumption,
prompt_idx=image_id,
)
iter_data_list.append(iter_data)
utils.metrics_print.print_metrics(
num,
iter_data,
warm_up=(num == 0),
max_rss_mem=max_rss_mem_consumption,
max_shared_mem=max_shared_mem_consumption,
)
utils.metrics_print.print_generated(num, warm_up=(num == 0), generated=rslt_img_fn)
utils.metrics_print.print_ldm_unet_vqvae_infer_latency(num, iter_data, tm_list, warm_up=(num == 0))
def run_ldm_super_resolution_benchmark(model_path, framework, device, args, num_iters):
pipe, pretrain_time = FW_UTILS[framework].create_ldm_super_resolution_model(model_path, device, **args)
iter_data_list = []
tm_list = []
input_image_list = utils.model_utils.get_image_param_from_prompt_file(args)
if len(input_image_list) > 0:
images = []
for image in input_image_list:
if args['prompt'] is None and args['prompt_file'] is None:
raise RuntimeError('==Failure image is empty ==')
elif args['prompt_file'] is not None:
image['prompt'] = os.path.join(os.path.dirname(args['prompt_file']), image['prompt'].replace('./', ''))
image['prompt'] = Path(image['prompt'])
images.append(image)
else:
if args['images'] is not None:
images = Path(args['images'])
if images.is_dir():
images = list(images.glob('*'))
else:
images = [images]
else:
raise RuntimeError('==Failure image is empty ==')
log.info(f'Benchmarking iter nums(exclude warm-up): {num_iters}, prompt nums: {len(images)}')
# if num_iters == 0, just output warm-up data
proc_id = os.getpid()
for num in range(num_iters + 1):
image_id = 0
for img in images:
if num == 0:
if args["output_dir"] is not None:
utils.output_file.output_image_input_text(str(img['prompt']), args, image_id, None, proc_id)
log.info(f"[{'warm-up' if num == 0 else num}] Input image={img['prompt']}")
run_ldm_super_resolution(img, num, pipe, args, framework, iter_data_list, image_id, tm_list, proc_id)
tm_list.clear()
image_id = image_id + 1
utils.metrics_print.print_average(iter_data_list)
return iter_data_list, pretrain_time
def num_iters_type(x):
x = int(x)
if x < 0:
raise argparse.ArgumentTypeError('Minimum input value is 0')
return x
def get_argprser():
parser = argparse.ArgumentParser('LLM benchmarking tool', add_help=True, formatter_class=argparse.RawTextHelpFormatter)
parser.add_argument('-m', '--model', help='model folder including IR files or Pytorch files', required=TabError)
parser.add_argument('-d', '--device', default='cpu', help='inference device')
parser.add_argument('-r', '--report', help='report csv')
parser.add_argument('-rj', '--report_json', help='report json')
parser.add_argument('-f', '--framework', default='ov', help='framework')
parser.add_argument('-p', '--prompt', default=None, help='one prompt')
parser.add_argument('-pf', '--prompt_file', default=None, help='prompt file in jsonl format')
parser.add_argument(
'-ic',
'--infer_count',
default=None,
type=int,
help='limit the output token size '
f'(default {DEFAULT_OUTPUT_TOKEN_SIZE}) of text_gen and code_gen models.',
)
parser.add_argument(
'-n',
'--num_iters',
default=0,
type=num_iters_type,
help='number of benchmarking iterations, '
'if the value is greater than 0, the average numbers exclude the first(0th) iteration,\n'
'if the value equals 0 (default), execute the warm-up iteration(0th iteration).',
)
parser.add_argument('-i', '--images', default=None, help='test images for vision tasks. Can be directory or path to single image')
parser.add_argument('-s', '--seed', type=int, default=42, required=False, help='specific random seed to generate fix result. Default 42.')
parser.add_argument(
'-lc',
'--load_config',
default=None,
required=False,
help='path to JSON file to load customized configurations.\n'
'Example for OpenVINO: {\"INFERENCE_NUM_THREADS\":32,\"PERFORMANCE_HINT\":\"LATENCY\"}.\n'
'Example for Pytorch: {\"PREC_BF16\":true}. Pytorch currently only supports bf16 settings.\n',
)
parser.add_argument(
'-mc',
'--memory_consumption',
default=0,
required=False,
type=int,
help='if the value is 1, output the maximum memory consumption in warm-up iterations. If the value is 2,'
' output the maximum memory consumption in all iterations.',
)
parser.add_argument('-bs', '--batch_size', type=int, default=1, required=False, help='Batch size value')
parser.add_argument(
'--fuse_decoding_strategy',
action='store_true',
help='Add decoding postprocessing for next token selection to the model as an extra ops. Original hf_model.generate function will be patched.',
)
parser.add_argument(
'--save_prepared_model',
default=None,
help='Path to .xml file to save IR used for inference with all pre-/post processing included',
)
parser.add_argument('--num_beams', type=int, default=1, help='Number of beams in the decoding strategy, activates beam_search if greater than 1')
parser.add_argument(
'--torch_compile_backend',
default='openvino',
required=False,
help='Enables running the torch.compile() with specified backend: pytorch or openvino (default)',
)
parser.add_argument(
'--convert_tokenizer', action='store_true', help='Convert tokenizer to OpenVINO format'
)
parser.add_argument(
'--interleave',
action='store_true',
help='if the value is True, input prompts are processed in interleave manner'
'if the value is False (default), input prompts are processed in subsequent manner'
)
parser.add_argument(
'--subsequent',
action='store_true',
help='if the value is True, input prompts are processed in subsequent manner'
'if the value is False (default), input prompts are processed in interleave manner'
)
parser.add_argument('-od', '--output_dir', help='Save the input text and generated text, images to files')
utils.model_utils.add_stateful_model_arguments(parser)
return parser.parse_args()
CASE_TO_BENCH = {
'text_gen': run_text_generation_benchmark,
'image_gen': run_image_generation_benchmark,
'image_cls': run_image_classification,
'code_gen': run_text_generation_benchmark,
'ldm_super_resolution': run_ldm_super_resolution_benchmark,
}
def main():
log.basicConfig(format='[ %(levelname)s ] %(message)s', level=log.INFO, stream=sys.stdout)
args = get_argprser()
model_path, framework, model_args, model_name = utils.model_utils.analyze_args(args)
# Set the device for running OpenVINO backend for torch.compile()
if model_args['torch_compile_backend']:
ov_torch_backend_device = str(args.device)
os.putenv('OPENVINO_TORCH_BACKEND_DEVICE', ov_torch_backend_device.upper())
os.system('echo [ INFO ] OPENVINO_TORCH_BACKEND_DEVICE=$OPENVINO_TORCH_BACKEND_DEVICE')
out_str = 'Model path={}'.format(model_path)
if framework == 'ov':
out_str += ', openvino runtime version: {}'.format(get_version())
if model_args['config'].get('PREC_BF16') and model_args['config']['PREC_BF16'] is True:
log.warning('[Warning] Param bf16/prec_bf16 only work for framework pt. It will be disabled.')
log.info(out_str)
if args.memory_consumption:
mem_consumption.start_collect_mem_consumption_thread()
try:
iter_data_list, pretrain_time = CASE_TO_BENCH[model_args['use_case']](model_path, framework, args.device, model_args, args.num_iters)
if args.report is not None or args.report_json is not None:
model_precision = ''
if framework == 'ov':
ir_conversion_frontend = utils.model_utils.get_ir_conversion_frontend(model_name, model_path.parents._parts)
if ir_conversion_frontend != '':
framework = framework + '(' + ir_conversion_frontend + ')'
model_precision = utils.model_utils.get_model_precision(model_path.parents._parts)
if args.report is not None:
utils.output_csv.write_result(
args.report,
model_name,
framework,
args.device,
model_args,
iter_data_list,
pretrain_time,
model_precision,
)
if args.report_json is not None:
utils.output_json.write_result(
args.report_json,
model_name,
framework,
args.device,
model_args,
iter_data_list,
pretrain_time,
model_precision,
)
except Exception:
log.error('An exception occurred')
log.info(traceback.format_exc())
exit(1)
finally:
if args.memory_consumption:
mem_consumption.end_collect_mem_consumption_thread()
if __name__ == '__main__':
main()