mirror of
https://github.com/NVIDIA/TensorRT-LLM.git
synced 2026-01-14 06:27:45 +08:00
148 lines
4.7 KiB
Python
148 lines
4.7 KiB
Python
import argparse
|
|
import os
|
|
import time
|
|
import traceback
|
|
from concurrent.futures import ThreadPoolExecutor, as_completed
|
|
|
|
import tensorrt_llm
|
|
from tensorrt_llm._utils import release_gc
|
|
from tensorrt_llm.logger import logger
|
|
from tensorrt_llm.mapping import Mapping
|
|
from tensorrt_llm.models import LlavaNextVisionWrapper
|
|
from tensorrt_llm.models.modeling_utils import QuantConfig
|
|
|
|
|
|
def parse_arguments():
|
|
parser = argparse.ArgumentParser()
|
|
parser.add_argument('--model_dir', type=str, required=True, default=None)
|
|
|
|
parser.add_argument(
|
|
'--vision_tp_size',
|
|
type=int,
|
|
default=1,
|
|
help='N-way tensor parallelism size for the vision tower')
|
|
|
|
parser.add_argument(
|
|
'--vision_cp_size',
|
|
type=int,
|
|
default=1,
|
|
help='N-way context parallelism size for the vision tower')
|
|
|
|
parser.add_argument('--dtype',
|
|
type=str,
|
|
default='float16',
|
|
choices=['float32', 'bfloat16', 'float16'])
|
|
|
|
parser.add_argument('--load_by_shard',
|
|
action='store_true',
|
|
help='Load a pretrained model shard-by-shard.')
|
|
|
|
parser.add_argument("--load_model_on_cpu", action="store_true")
|
|
|
|
parser.add_argument('--output_dir',
|
|
type=str,
|
|
default='tllm_checkpoint',
|
|
help='The path to save the TensorRT-LLM checkpoint')
|
|
parser.add_argument(
|
|
'--workers',
|
|
type=int,
|
|
default=1,
|
|
help='The number of workers for converting checkpoint in parallel')
|
|
|
|
parser.add_argument(
|
|
'--save_config_only',
|
|
action="store_true",
|
|
default=False,
|
|
help=
|
|
'Only save the model config w/o read and converting weights, be careful, this is for debug only'
|
|
)
|
|
parser.add_argument('--log_level', type=str, default='info')
|
|
|
|
args = parser.parse_args()
|
|
return args
|
|
|
|
|
|
def args_to_build_options(args):
|
|
return {
|
|
'load_model_on_cpu': args.load_model_on_cpu,
|
|
}
|
|
|
|
|
|
def convert_and_save_hf(args):
|
|
model_dir = args.model_dir
|
|
load_by_shard = args.load_by_shard
|
|
world_size = args.vision_tp_size * args.vision_cp_size
|
|
# Need to convert the cli args to the kay-value pairs and override them in the generate config dict.
|
|
# Ideally these fields will be moved out of the config and pass them into build API, keep them here for compatibility purpose for now,
|
|
# before the refactor is done.
|
|
override_fields = {}
|
|
override_fields.update(args_to_build_options(args))
|
|
|
|
quant_config = QuantConfig()
|
|
|
|
# When not loading by shard, preload one complete model and then slice per rank weights from this
|
|
# this saves the disk reloading time
|
|
def convert_and_save_rank(args, rank):
|
|
mapping = Mapping(world_size=world_size,
|
|
rank=rank,
|
|
tp_size=args.vision_tp_size,
|
|
cp_size=args.vision_cp_size)
|
|
tik = time.time()
|
|
llava_next_vision_wrapper = LlavaNextVisionWrapper.from_hugging_face(
|
|
model_dir,
|
|
args.dtype,
|
|
mapping=mapping,
|
|
quant_config=quant_config,
|
|
load_by_shard=load_by_shard,
|
|
**override_fields,
|
|
)
|
|
print(f'Total time of reading and converting {time.time()-tik} s')
|
|
tik = time.time()
|
|
llava_next_vision_wrapper.save_checkpoint(args.output_dir,
|
|
save_config=(rank == 0))
|
|
del llava_next_vision_wrapper
|
|
print(f'Total time of saving checkpoint {time.time()-tik} s')
|
|
|
|
execute(args.workers, [convert_and_save_rank] * world_size, args)
|
|
release_gc()
|
|
|
|
|
|
def execute(workers, func, args):
|
|
if workers == 1:
|
|
for rank, f in enumerate(func):
|
|
f(args, rank)
|
|
else:
|
|
with ThreadPoolExecutor(max_workers=workers) as p:
|
|
futures = [p.submit(f, args, rank) for rank, f in enumerate(func)]
|
|
exceptions = []
|
|
for future in as_completed(futures):
|
|
try:
|
|
future.result()
|
|
except Exception as e:
|
|
traceback.print_exc()
|
|
exceptions.append(e)
|
|
assert len(
|
|
exceptions
|
|
) == 0, "Checkpoint conversion failed, please check error log."
|
|
|
|
|
|
def main():
|
|
print(tensorrt_llm.__version__)
|
|
args = parse_arguments()
|
|
logger.set_level(args.log_level)
|
|
|
|
tik = time.time()
|
|
|
|
if not os.path.exists(args.output_dir):
|
|
os.makedirs(args.output_dir)
|
|
|
|
convert_and_save_hf(args)
|
|
|
|
tok = time.time()
|
|
t = time.strftime('%H:%M:%S', time.gmtime(tok - tik))
|
|
print(f'Total time of converting checkpoints: {t}')
|
|
|
|
|
|
if __name__ == '__main__':
|
|
main()
|