mirror of
https://github.com/NVIDIA/TensorRT-LLM.git
synced 2026-01-14 06:27:45 +08:00
190 lines
6.6 KiB
Python
190 lines
6.6 KiB
Python
# SPDX-FileCopyrightText: Copyright (c) 2022-2024 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
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# SPDX-License-Identifier: Apache-2.0
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import argparse
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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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from transformers import AutoConfig
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import tensorrt_llm
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from tensorrt_llm.mapping import Mapping
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from tensorrt_llm.models import Phi3ForCausalLM, PhiForCausalLM
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from tensorrt_llm.models.modeling_utils import QuantConfig
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from tensorrt_llm.quantization import QuantAlgo
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def parse_arguments():
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parser = argparse.ArgumentParser()
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parser.add_argument('--model_dir', type=str, default=None)
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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('--pp_size',
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type=int,
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default=1,
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help='N-way pipeline 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='auto',
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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(
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'--use_weight_only',
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default=False,
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action="store_true",
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help='Quantize weights for the various GEMMs to INT4/INT8.'
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'See --weight_only_precision to set the precision')
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parser.add_argument(
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'--weight_only_precision',
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const='int8',
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type=str,
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nargs='?',
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default='int8',
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choices=['int8', 'int4'],
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help=
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'Define the precision for the weights when using weight-only quantization.'
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'You must also use --use_weight_only for that argument to have an impact.'
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)
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parser.add_argument(
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'--moe_tp_size',
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type=int,
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default=-1,
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help=
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'N-way tensor parallelism size for MOE, default is tp_size, which will do tp-only for MoE'
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)
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parser.add_argument(
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'--moe_ep_size',
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type=int,
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default=-1,
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help=
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'N-way expert parallelism size for MOE, default is 1, which will do tp-only for MoE'
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)
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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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args = parser.parse_args()
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return args
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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 args_to_quant_config(args: argparse.Namespace) -> QuantConfig:
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'''return config dict with quantization info based on the command line args
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'''
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quant_config = QuantConfig()
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if args.use_weight_only:
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if args.weight_only_precision == 'int8':
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quant_config.quant_algo = QuantAlgo.W8A16
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elif args.weight_only_precision == 'int4':
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quant_config.quant_algo = QuantAlgo.W4A16
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return quant_config
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if __name__ == '__main__':
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print(tensorrt_llm.__version__)
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args = parse_arguments()
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assert args.pp_size == 1, "Pipeline parallelism is not supported."
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world_size = args.tp_size * args.pp_size
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if (args.moe_tp_size == -1 and args.moe_ep_size == -1):
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# moe default to tp-only
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args.moe_tp_size = args.tp_size
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args.moe_ep_size = 1
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elif (args.moe_tp_size == -1):
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args.moe_tp_size = args.tp_size // args.moe_ep_size
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elif (args.moe_ep_size == -1):
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args.moe_ep_size = args.tp_size // args.moe_tp_size
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assert (args.moe_tp_size * args.moe_ep_size == args.tp_size
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), "moe_tp_size * moe_ep_size must equal to tp_size"
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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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model_config = AutoConfig.from_pretrained(args.model_dir,
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trust_remote_code=True)
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if hasattr(model_config, "llm_config"):
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model_config = model_config.llm_config
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model_type = model_config.architectures[0]
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supported_models = [
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'PhiForCausalLM', 'Phi3ForCausalLM', 'Phi3VForCausalLM',
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'Phi3SmallForCausalLM', 'PhiMoEForCausalLM'
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]
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if model_type not in supported_models:
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assert False, "Invalid model type"
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is_phi3 = 'Phi3' in model_type or 'MoE' in model_type
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phi_model_cls = Phi3ForCausalLM if is_phi3 else PhiForCausalLM
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quant_config = args_to_quant_config(args)
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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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pp_size=args.pp_size,
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moe_tp_size=args.moe_tp_size,
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moe_ep_size=args.moe_ep_size)
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phi = phi_model_cls.from_hugging_face(
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args.model_dir,
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args.dtype,
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mapping=mapping,
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quant_config=quant_config,
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)
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phi.save_checkpoint(args.output_dir, save_config=(rank == 0))
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del phi
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execute(args.workers, [convert_and_save_rank] * world_size, 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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