mirror of
https://github.com/NVIDIA/TensorRT-LLM.git
synced 2026-01-25 05:02:59 +08:00
202bed4574
Signed-off-by: nv-guomingz <137257613+nv-guomingz@users.noreply.github.com> Signed-off-by: Wangshanshan <30051912+dominicshanshan@users.noreply.github.com>
262 lines
9.6 KiB
Python
262 lines
9.6 KiB
Python
# SPDX-FileCopyrightText: Copyright (c) 2022-2024 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
|
||
# SPDX-License-Identifier: Apache-2.0
|
||
#
|
||
# 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.
|
||
import argparse
|
||
import os
|
||
import time
|
||
import traceback
|
||
from concurrent.futures import ThreadPoolExecutor, as_completed
|
||
|
||
import tensorrt_llm
|
||
from tensorrt_llm.mapping import Mapping
|
||
from tensorrt_llm.models.baichuan.config import BaichuanConfig
|
||
from tensorrt_llm.models.baichuan.convert import load_weights_from_gptq
|
||
from tensorrt_llm.models.baichuan.model import BaichuanForCausalLM
|
||
from tensorrt_llm.models.modeling_utils import QuantConfig
|
||
from tensorrt_llm.quantization import QuantAlgo
|
||
|
||
|
||
def parse_arguments():
|
||
parser = argparse.ArgumentParser()
|
||
parser.add_argument('--model_dir', type=str, default=None)
|
||
parser.add_argument('--quant_ckpt_path', type=str, default=None)
|
||
parser.add_argument('--tp_size', type=int, default=1)
|
||
parser.add_argument('--pp_size', type=int, default=1)
|
||
parser.add_argument('--model_version',
|
||
type=str,
|
||
default='v1_13b',
|
||
choices=['v1_7b', 'v1_13b', 'v2_7b', 'v2_13b'])
|
||
parser.add_argument(
|
||
'--dtype',
|
||
type=str,
|
||
default='auto',
|
||
choices=['auto', 'float16', 'bfloat16', 'float32'],
|
||
help=
|
||
"The data type for the model weights and activations if not quantized. "
|
||
"If 'auto', the data type is automatically inferred from the source model; "
|
||
"however, if the source dtype is float32, it is converted to float16.")
|
||
parser.add_argument('--logits_dtype',
|
||
type=str,
|
||
default='float32',
|
||
choices=['float16', 'float32'])
|
||
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(
|
||
'--calib_dataset',
|
||
type=str,
|
||
default='ccdv/cnn_dailymail',
|
||
help=
|
||
"The huggingface dataset name or the local directory of the dataset for calibration."
|
||
)
|
||
parser.add_argument(
|
||
'--per_channel',
|
||
default=False,
|
||
action="store_true",
|
||
help=
|
||
'By default, we use a single static scaling factor for the GEMM\'s result. '
|
||
'per_channel instead uses a different static scaling factor for each channel. '
|
||
'The latter is usually more accurate, but a little slower.')
|
||
parser.add_argument(
|
||
'--per_token',
|
||
default=False,
|
||
action="store_true",
|
||
help=
|
||
'By default, we use a single static scaling factor to scale activations in the int8 range. '
|
||
'per_token chooses at run time, and for each token, a custom scaling factor. '
|
||
'The latter is usually more accurate, but a little slower.')
|
||
parser.add_argument(
|
||
"--smoothquant",
|
||
"-sq",
|
||
type=float,
|
||
default=None,
|
||
help="Set the α parameter (see https://arxiv.org/pdf/2211.10438.pdf)"
|
||
" to Smoothquant the model, and output int8 weights."
|
||
" A good first try is 0.5. Must be in [0, 1]")
|
||
parser.add_argument(
|
||
'--use_weight_only',
|
||
default=False,
|
||
action="store_true",
|
||
help='Quantize weights for the various GEMMs to INT4/INT8.'
|
||
'See --weight_only_precision to set the precision')
|
||
parser.add_argument(
|
||
'--weight_only_precision',
|
||
const='int8',
|
||
type=str,
|
||
nargs='?',
|
||
default='int8',
|
||
choices=['int8', 'int4', 'int4_gptq'],
|
||
help=
|
||
'Define the precision for the weights when using weight-only quantization.'
|
||
'You must also use --use_weight_only for that argument to have an impact.'
|
||
)
|
||
parser.add_argument('--group_size',
|
||
type=int,
|
||
default=128,
|
||
help='Group size used in GPTQ/AWQ quantization.')
|
||
parser.add_argument(
|
||
'--int8_kv_cache',
|
||
default=False,
|
||
action="store_true",
|
||
help=
|
||
'By default, we use dtype for KV cache. int8_kv_cache chooses int8 quantization for KV'
|
||
)
|
||
args = parser.parse_args()
|
||
return args
|
||
|
||
|
||
def args_to_quant_config(args: argparse.Namespace) -> QuantConfig:
|
||
config = QuantConfig(group_size=args.group_size)
|
||
|
||
if args.smoothquant:
|
||
config.smoothquant_val = args.smoothquant
|
||
if args.per_token and args.per_channel:
|
||
config.quant_algo = QuantAlgo.W8A8_SQ_PER_CHANNEL_PER_TOKEN_PLUGIN
|
||
elif not args.per_token and not args.per_channel:
|
||
config.quant_algo = QuantAlgo.W8A8_SQ_PER_TENSOR_PLUGIN
|
||
elif not args.per_token and args.per_channel:
|
||
config.quant_algo = QuantAlgo.W8A8_SQ_PER_CHANNEL_PER_TENSOR_PLUGIN
|
||
elif args.per_token and not args.per_channel:
|
||
config.quant_algo = QuantAlgo.W8A8_SQ_PER_TENSOR_PER_TOKEN_PLUGIN
|
||
else:
|
||
if args.use_weight_only and args.weight_only_precision == 'int8':
|
||
config.quant_algo = QuantAlgo.W8A16
|
||
elif args.use_weight_only and args.weight_only_precision == 'int4':
|
||
config.quant_algo = QuantAlgo.W4A16
|
||
elif args.use_weight_only and args.weight_only_precision == 'int4_gptq':
|
||
config.quant_algo = QuantAlgo.W4A16_GPTQ
|
||
|
||
if args.int8_kv_cache:
|
||
config.kv_cache_quant_algo = QuantAlgo.INT8
|
||
|
||
if args.use_weight_only and args.weight_only_precision == 'int4_gptq':
|
||
config.has_zero_point = True
|
||
|
||
return config
|
||
|
||
|
||
def convert_and_save_hf(args):
|
||
world_size = args.tp_size * args.pp_size
|
||
quantization_config = args_to_quant_config(args)
|
||
|
||
if args.smoothquant is not None or args.int8_kv_cache:
|
||
mapping = Mapping(
|
||
world_size=world_size,
|
||
tp_size=args.tp_size,
|
||
pp_size=args.pp_size,
|
||
)
|
||
BaichuanForCausalLM.quantize(
|
||
args.model_dir,
|
||
args.output_dir,
|
||
dtype=args.dtype,
|
||
mapping=mapping,
|
||
quant_config=quantization_config,
|
||
calib_dataset=args.calib_dataset,
|
||
model_version=args.model_version,
|
||
)
|
||
else:
|
||
|
||
def convert_and_save_rank(args, rank):
|
||
mapping = Mapping(
|
||
world_size=world_size,
|
||
rank=rank,
|
||
tp_size=args.tp_size,
|
||
pp_size=args.pp_size,
|
||
)
|
||
|
||
model = BaichuanForCausalLM.from_hugging_face(
|
||
args.model_dir,
|
||
dtype=args.dtype,
|
||
mapping=mapping,
|
||
quant_config=quantization_config,
|
||
model_version=args.model_version,
|
||
logits_dtype=args.logits_dtype,
|
||
)
|
||
model.save_checkpoint(args.output_dir, save_config=(rank == 0))
|
||
del model
|
||
|
||
execute(args.workers, [convert_and_save_rank] * world_size, args)
|
||
|
||
|
||
def convert_and_save_gptq(args, rank):
|
||
mapping = Mapping(world_size=args.tp_size * args.pp_size,
|
||
rank=rank,
|
||
tp_size=args.tp_size,
|
||
pp_size=args.pp_size)
|
||
config = BaichuanConfig.from_hugging_face(
|
||
args.model_dir,
|
||
dtype=args.dtype,
|
||
mapping=mapping,
|
||
quant_config=args_to_quant_config(args),
|
||
model_version=args.model_version,
|
||
logits_dtype=args.logits_dtype)
|
||
|
||
config.vocab_size = int((config.vocab_size + 63) / 64) * 64
|
||
model = BaichuanForCausalLM(config)
|
||
weights = load_weights_from_gptq(config, args.quant_ckpt_path)
|
||
model.load(weights)
|
||
model.save_checkpoint(args.output_dir, save_config=(rank == 0))
|
||
|
||
|
||
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()
|
||
world_size = args.tp_size * args.pp_size
|
||
tik = time.time()
|
||
|
||
if not os.path.exists(args.output_dir):
|
||
os.makedirs(args.output_dir)
|
||
|
||
if args.use_weight_only and args.weight_only_precision == 'int4_gptq':
|
||
assert args.model_dir is not None
|
||
assert args.quant_ckpt_path is not None
|
||
execute(args.workers, [convert_and_save_gptq] * world_size, args)
|
||
else:
|
||
assert args.model_dir is not None
|
||
assert args.quant_ckpt_path is None, "only gptq weights only needs this option"
|
||
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()
|