import argparse
import os
import time
import traceback
from concurrent.futures import ThreadPoolExecutor, as_completed
from transformers import AutoConfig
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 QWenForCausalLM
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, required=True)
parser.add_argument('--tp_size',
type=int,
default=1,
help='N-way tensor parallelism size')
parser.add_argument('--pp_size',
type=int,
default=1,
help='N-way pipeline parallelism size')
parser.add_argument('--cp_size',
type=int,
default=1,
help='N-way context parallelism size')
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(
'--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(
'--disable_weight_only_quant_plugin',
default=False,
action="store_true",
help='By default, using plugin implementation for weight quantization. Enabling disable_weight_only_quant_plugin flag will use ootb implementation instead of plugin.'
'You must also use --use_weight_only for that argument to have an impact.'
)
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(
'--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(
"--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(
'--per_channel',
action="store_true",
default=False,
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',
action="store_true",
default=False,
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(
'--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'
)
parser.add_argument(
'--per_group',
default=False,
action="store_true",
help='By default, we use a single static scaling factor to scale weights in the int4 range. '
'per_group chooses at run time, and for each group, a custom scaling factor. '
'The flag is built for GPTQ/AWQ quantization.')
parser.add_argument('--group_size',
type=int,
default=128,
help='Group size used in GPTQ quantization.')
parser.add_argument("--load_model_on_cpu", action="store_true")
parser.add_argument(
'--use_parallel_embedding',
action="store_true",
default=False,
help='By default embedding parallelism is disabled. By setting this flag, embedding parallelism is enabled'
)
parser.add_argument(
'--embedding_sharding_dim',
type=int,
default=0,
choices=[0, 1],
help='By default the embedding lookup table is sharded along vocab dimension (embedding_sharding_dim=0). '
'To shard it along hidden dimension, set embedding_sharding_dim=1'
'Note: embedding sharing is only enabled when embedding_sharding_dim = 0'
)
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(
'--moe_tp_size',
type=int,
default=-1,
help='N-way tensor parallelism size for MOE, default is tp_size, which will do tp-only for MoE'
)
parser.add_argument(
'--moe_ep_size',
type=int,
default=-1,
help='N-way expert parallelism size for MOE, default is 1, which will do tp-only for MoE'
)
args = parser.parse_args()
return args
def args_to_quant_config(args: argparse.Namespace) -> QuantConfig:
'''return config dict with quantization info based on the command line args
'''
quant_config = QuantConfig()
if args.use_weight_only:
if args.weight_only_precision == 'int8':
quant_config.quant_algo = QuantAlgo.W8A16
elif args.weight_only_precision == 'int4':
quant_config.quant_algo = QuantAlgo.W4A16
elif args.smoothquant:
quant_config.smoothquant_val = args.smoothquant
if args.per_channel:
if args.per_token:
quant_config.quant_algo = QuantAlgo.W8A8_SQ_PER_CHANNEL_PER_TOKEN_PLUGIN
else:
quant_config.quant_algo = QuantAlgo.W8A8_SQ_PER_CHANNEL_PER_TENSOR_PLUGIN
else:
if args.per_token:
quant_config.quant_algo = QuantAlgo.W8A8_SQ_PER_TENSOR_PER_TOKEN_PLUGIN
else:
quant_config.quant_algo = QuantAlgo.W8A8_SQ_PER_TENSOR_PLUGIN
if args.int8_kv_cache:
quant_config.kv_cache_quant_algo = QuantAlgo.INT8
if args.weight_only_precision == 'int4_gptq':
quant_config.group_size = args.group_size
quant_config.has_zero_point = True
quant_config.pre_quant_scale = False
quant_config.quant_algo = QuantAlgo.W4A16_GPTQ
return quant_config
def update_quant_config_from_hf(quant_config, hf_config,
override_fields) -> tuple[QuantConfig, dict]:
hf_config_dict = hf_config.to_dict()
if hf_config_dict.get('quantization_config'):
if hf_config_dict['quantization_config'].get('quant_method') == 'awq':
logger.info(
"Load quantization configs from huggingface model_config.")
quant_config.quant_algo = QuantAlgo.W4A16_GPTQ
quant_config.group_size = hf_config_dict['quantization_config'].get(
'group_size', 128)
quant_config.has_zero_point = hf_config_dict[
'quantization_config'].get('zero_point', False)
override_fields.update({"use_autoawq": True})
elif hf_config_dict['quantization_config'].get(
'quant_method') == 'gptq':
logger.info(
"Load quantization configs from huggingface model_config.")
desc_act = hf_config_dict['quantization_config'].get(
'desc_act', False)
if desc_act:
raise ValueError("GPTQ with desc_act=True is not implemented!")
quant_config.quant_algo = QuantAlgo.W4A16_GPTQ
quant_config.group_size = hf_config_dict['quantization_config'].get(
'group_size', 128)
quant_config.has_zero_point = hf_config_dict[
'quantization_config'].get('sym', False)
return quant_config, override_fields
def args_to_build_options(args):
return {
'use_parallel_embedding': args.use_parallel_embedding,
'embedding_sharding_dim': args.embedding_sharding_dim,
'disable_weight_only_quant_plugin':
args.disable_weight_only_quant_plugin
}
def convert_and_save_hf(args):
model_dir = args.model_dir
world_size = args.tp_size * args.pp_size
override_fields = {}
override_fields.update(args_to_build_options(args))
quant_config = args_to_quant_config(args)
try:
hf_config = AutoConfig.from_pretrained(model_dir,
trust_remote_code=True)
quant_config, override_fields = update_quant_config_from_hf(
quant_config, hf_config, override_fields)
except BaseException:
logger.warning("AutoConfig cannot load the huggingface config.")
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,
moe_tp_size=args.moe_tp_size,
moe_ep_size=args.moe_ep_size,
cp_size=args.cp_size)
QWenForCausalLM.quantize(args.model_dir,
args.output_dir,
dtype=args.dtype,
mapping=mapping,
quant_config=quant_config,
calib_dataset=args.calib_dataset,
**override_fields)
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,
moe_tp_size=args.moe_tp_size,
moe_ep_size=args.moe_ep_size)
qwen = QWenForCausalLM.from_hugging_face(model_dir,
args.dtype,
mapping=mapping,
quant_config=quant_config,
**override_fields)
qwen.config.mapping.cp_size = args.cp_size
qwen.config.mapping.attn_tp_size = -1
qwen.config.mapping.attn_cp_size = -1
qwen.config.mapping.world_size *= args.cp_size
qwen.save_checkpoint(args.output_dir, save_config=(rank == 0))
del qwen
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()
if (args.moe_tp_size == -1 and args.moe_ep_size == -1):
args.moe_tp_size = args.tp_size
args.moe_ep_size = 1
elif (args.moe_tp_size == -1):
args.moe_tp_size = args.tp_size // args.moe_ep_size
elif (args.moe_ep_size == -1):
args.moe_ep_size = args.tp_size // args.moe_tp_size
assert (args.moe_tp_size * args.moe_ep_size == args.tp_size
), "moe_tp_size * moe_ep_size must equal to tp_size"
tik = time.time()
if not os.path.exists(args.output_dir):
os.makedirs(args.output_dir)
assert args.model_dir is not None
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()