mirror of
https://github.com/NVIDIA/TensorRT-LLM.git
synced 2026-01-14 06:27:45 +08:00
146 lines
4.6 KiB
Python
Executable File
146 lines
4.6 KiB
Python
Executable File
#!/usr/bin/env python3
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# SPDX-FileCopyrightText: Copyright (c) 2022-2023 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 as _arg
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import os as _os
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import pathlib as _pl
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import subprocess as _sp
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import sys
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import typing as _tp
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from glob import glob as _glob
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import torch.multiprocessing as _mp
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resources_dir = _pl.Path(
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__file__).parent.parent.parent.parent.parent / "examples/chatglm6b"
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sys.path.insert(0, str(resources_dir))
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engine_target_path = _pl.Path(
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__file__).parent.parent / "models/rt_engine/chatglm6b"
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import build as _ecb
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def build_engine(weigth_dir: _pl.Path, engine_dir: _pl.Path, *args):
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print("Additional parameters: " + " ".join(args[0]))
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arg = _ecb.parse_arguments()
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arg.model_dir = str(weigth_dir)
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arg.output_dir = str(engine_dir)
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arg.max_batch_size = 2
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arg.max_beam_width = 2
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for item in args[0]:
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key, value = item.split(" ")
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if key[2:] in dir(arg):
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arg.__setattr__(key, value)
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else:
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print("Error parameter name:", key)
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return
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_ecb.build(0, arg)
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def run_command(command: _tp.Sequence[str], *, cwd=None, **kwargs) -> None:
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command = [str(i) for i in command]
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print(f"Running: cd %s && %s" %
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(str(cwd or _pl.Path.cwd()), " ".join(command)))
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_sp.check_call(command, cwd=cwd, **kwargs)
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def build_engines(model_cache: _tp.Optional[str] = None, world_size: int = 1):
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# Clone the model directory
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hf_dir = resources_dir / "pyTorchModel"
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ft_dir = resources_dir / "ftModel"
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trt_dir = resources_dir / "trtModel"
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run_command(
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["pip", "install", "-r",
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str(resources_dir) + "/requirements.txt"],
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cwd=resources_dir)
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if not _os.path.exists(hf_dir):
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_os.mkdir(hf_dir)
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if len(_glob(str(hf_dir) + "/*")) == 0:
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run_command(
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["git", "clone", "https://huggingface.co/THUDM/chatglm-6b", hf_dir],
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cwd=resources_dir)
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if not _os.path.exists(resources_dir / "lm.npy"):
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print("Exporting weight of LM")
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run_command([
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"cp",
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str(hf_dir) + "/modeling_chatglm.py",
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str(hf_dir) + "/modeling_chatglm.py-backup"
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],
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cwd=resources_dir)
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run_command([
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"cp",
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str(resources_dir) + "/modeling_chatglm.py",
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str(hf_dir) + "/modeling_chatglm.py"
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],
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cwd=resources_dir)
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run_command(["python3", str(resources_dir) + "/exportLM.py"],
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cwd=resources_dir)
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assert (_os.path.exists(resources_dir / "lm.npy"))
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run_command([
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"mv",
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str(hf_dir) + "/modeling_chatglm.py-backup",
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str(hf_dir) + "/modeling_chatglm.py"
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],
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cwd=resources_dir)
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if len(_glob(str(ft_dir) + "/*")) == 0:
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print("\nConverting weight")
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run_command([
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"python3",
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str(resources_dir) + "/hf_chatglm6b_convert.py", "-i", hf_dir, "-o",
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ft_dir, "--storage-type", "fp16"
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],
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cwd=resources_dir)
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if len(_glob(str(trt_dir) + "/*")) == 0:
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print("\nBuilding engine")
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arg_list = [
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"--dtype float16",
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"--use_gpt_attention_plugin float16",
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"--use_gemm_plugin float16",
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]
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build_engine(ft_dir / "1-gpu", trt_dir, arg_list)
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if not _os.path.exists(str(engine_target_path)):
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_os.system(f"mkdir -p {str(engine_target_path)}")
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_os.system(f"cp -r {str(trt_dir) + '/*'} {engine_target_path}")
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print("Done.")
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if __name__ == "__main__":
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parser = _arg.ArgumentParser()
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parser.add_argument("--model_cache",
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type=str,
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default="examples/chatglm6b/pyTorchModel",
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help="Directory where models are stored")
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parser.add_argument('--world_size',
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type=int,
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default=1,
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help='world size, only support tensor parallelism now')
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_mp.set_start_method("spawn")
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build_engines(**vars(parser.parse_args()))
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