mirror of
https://github.com/NVIDIA/TensorRT-LLM.git
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202bed4574
Signed-off-by: nv-guomingz <137257613+nv-guomingz@users.noreply.github.com> Signed-off-by: Wangshanshan <30051912+dominicshanshan@users.noreply.github.com>
122 lines
4.1 KiB
Python
122 lines
4.1 KiB
Python
import argparse
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import json
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import os
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import time
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import traceback
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from concurrent.futures import ThreadPoolExecutor, as_completed
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import tensorrt_llm
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from tensorrt_llm._utils import release_gc
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from tensorrt_llm.logger import logger
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from tensorrt_llm.mapping import Mapping
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from tensorrt_llm.models import SD3Transformer2DModel
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def parse_arguments():
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parser = argparse.ArgumentParser()
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parser.add_argument('--model_path',
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type=str,
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default="stabilityai/stable-diffusion-3.5-medium")
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parser.add_argument('--tp_size',
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type=int,
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default=1,
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help='N-way tensor parallelism size')
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parser.add_argument('--cp_size',
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type=int,
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default=1,
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help='N-way context parallelism size')
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parser.add_argument(
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'--dtype',
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type=str,
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default='float16',
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choices=['auto', 'float16', 'bfloat16', 'float32'],
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help=
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"The data type for the model weights and activations if not quantized. "
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"If 'auto', the data type is automatically inferred from the source model; "
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"however, if the source dtype is float32, it is converted to float16.")
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parser.add_argument('--output_dir',
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type=str,
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default='tllm_checkpoint',
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help='The path to save the TensorRT LLM checkpoint')
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parser.add_argument(
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'--workers',
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type=int,
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default=1,
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help='The number of workers for converting checkpoint in parallel')
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parser.add_argument('--log_level', type=str, default='info')
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args = parser.parse_args()
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return args
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def convert_and_save_hf(args):
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world_size = args.tp_size * args.cp_size
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def convert_and_save_rank(args, rank):
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mapping = Mapping(world_size=world_size,
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rank=rank,
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tp_size=args.tp_size,
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cp_size=args.cp_size)
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tik = time.time()
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mmdit = SD3Transformer2DModel.from_pretrained(args.model_path,
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args.dtype,
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mapping=mapping)
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print(f'Total time of reading and converting: {time.time()-tik:.3f} s')
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tik = time.time()
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mmdit.save_checkpoint(args.output_dir, save_config=(rank == 0))
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# post-process checkpoint config
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with open(f"{args.output_dir}/config.json", 'r') as f:
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config = json.load(f)
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config["model_path"] = args.model_path
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config["use_pretrained_pos_emb"] = True if "pos_embed.pos_embed" in [
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name for name, _ in mmdit.named_parameters()
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] else False
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with open(f"{args.output_dir}/config.json", 'w') as f:
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json.dump(config, f, indent=4)
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del mmdit
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print(f'Total time of saving checkpoint: {time.time()-tik:.3f} s')
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execute(args.workers, [convert_and_save_rank] * world_size, args)
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release_gc()
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def execute(workers, func, args):
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if workers == 1:
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for rank, f in enumerate(func):
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f(args, rank)
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else:
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with ThreadPoolExecutor(max_workers=workers) as p:
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futures = [p.submit(f, args, rank) for rank, f in enumerate(func)]
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exceptions = []
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for future in as_completed(futures):
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try:
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future.result()
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except Exception as e:
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traceback.print_exc()
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exceptions.append(e)
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assert len(
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exceptions
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) == 0, "Checkpoint conversion failed, please check error log."
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def main():
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print(tensorrt_llm.__version__)
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args = parse_arguments()
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logger.set_level(args.log_level)
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tik = time.time()
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if not os.path.exists(args.output_dir):
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os.makedirs(args.output_dir)
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assert args.model_path is not None
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convert_and_save_hf(args)
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tok = time.time()
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t = time.strftime('%H:%M:%S', time.gmtime(tok - tik))
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print(f'Total time of converting checkpoints: {t}')
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if __name__ == '__main__':
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main()
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