mirror of
https://github.com/NVIDIA/TensorRT-LLM.git
synced 2026-01-14 06:27:45 +08:00
132 lines
4.8 KiB
Python
132 lines
4.8 KiB
Python
#!/usr/bin/env python3
|
|
# 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 as _arg
|
|
import os as _os
|
|
import pathlib as _pl
|
|
import platform as _pf
|
|
import sys as _sys
|
|
import typing as _tp
|
|
|
|
from build_engines_utils import run_command, wincopy
|
|
|
|
|
|
def build_engine(weight_dir: _pl.Path, ckpt_dir: _pl.Path, engine_dir: _pl.Path,
|
|
*args):
|
|
convert_args = [
|
|
_sys.executable, "examples/recurrentgemma/convert_checkpoint.py"
|
|
] + (['--model_dir', str(weight_dir)] if weight_dir else []) + [
|
|
'--output_dir',
|
|
str(ckpt_dir),
|
|
'--ckpt_type=hf',
|
|
'--dtype=float16',
|
|
]
|
|
run_command(convert_args)
|
|
build_args = ["trtllm-build"] + ['--checkpoint_dir',
|
|
str(ckpt_dir)] + [
|
|
'--output_dir',
|
|
str(engine_dir),
|
|
'--gpt_attention_plugin=float16',
|
|
'--paged_kv_cache=enable',
|
|
'--gemm_plugin=float16',
|
|
'--max_batch_size=8',
|
|
'--max_input_len=924',
|
|
'--max_output_len=100',
|
|
'--max_beam_width=1',
|
|
] + list(args)
|
|
run_command(build_args)
|
|
|
|
|
|
def build_engines(model_cache: _tp.Optional[str] = None):
|
|
resources_dir = _pl.Path(__file__).parent.resolve().parent
|
|
models_dir = resources_dir / 'models'
|
|
model_name = 'recurrentgemma-2b'
|
|
hf_dir = models_dir / model_name
|
|
|
|
# Clone or update the model directory without lfs
|
|
if model_cache:
|
|
print("Copy model from model_cache")
|
|
model_cache_dir = _pl.Path(model_cache) / 'recurrentgemma' / model_name
|
|
print(model_cache_dir)
|
|
assert (model_cache_dir.is_dir())
|
|
if _pf.system() == "Windows":
|
|
wincopy(source=str(model_cache_dir),
|
|
dest=model_name,
|
|
isdir=True,
|
|
cwd=models_dir)
|
|
else:
|
|
run_command(
|
|
["rsync", "-av", str(model_cache_dir), "."], cwd=models_dir)
|
|
else:
|
|
if not hf_dir.is_dir():
|
|
if _pf.system() == "Windows":
|
|
url_prefix = ""
|
|
else:
|
|
url_prefix = "file://"
|
|
model_url = "https://huggingface.co/google/recurrentgemma-2b"
|
|
run_command([
|
|
"git", "clone", model_url, "--single-branch", "--no-local",
|
|
model_name
|
|
],
|
|
cwd=models_dir,
|
|
env={
|
|
**_os.environ, "GIT_LFS_SKIP_SMUDGE": "1"
|
|
})
|
|
|
|
assert (hf_dir.is_dir())
|
|
|
|
# Download the model file
|
|
model_file_name = "*"
|
|
if not model_cache:
|
|
run_command(["git", "lfs", "pull", "--include", model_file_name],
|
|
cwd=hf_dir)
|
|
|
|
tp_size = 1
|
|
pp_size = 1
|
|
tp_pp_dir = f"tp{tp_size}-pp{pp_size}-gpu"
|
|
|
|
ckpt_dir = models_dir / 'rt_ckpt' / model_name
|
|
engine_dir = models_dir / 'rt_engine' / model_name
|
|
|
|
python_exe = _sys.executable
|
|
run_command([python_exe, "-m", "pip", "install", "transformers>=4.40.0"],
|
|
env=_os.environ,
|
|
timeout=300)
|
|
|
|
print("\nBuilding fp16-plugin-packed-paged engine")
|
|
build_engine(hf_dir, ckpt_dir / 'fp16-plugin-packed-paged' / tp_pp_dir,
|
|
engine_dir / 'fp16-plugin-packed-paged' / tp_pp_dir,
|
|
'--remove_input_padding=enable', '--paged_state=enable')
|
|
|
|
# Restore transformers version
|
|
run_command([python_exe, "-m", "pip", "uninstall", "transformers", "-y"],
|
|
env=_os.environ,
|
|
timeout=300)
|
|
run_command([python_exe, "-m", "pip", "install", "transformers==4.38.2"],
|
|
env=_os.environ,
|
|
timeout=300)
|
|
|
|
print("Done.")
|
|
|
|
|
|
if __name__ == "__main__":
|
|
parser = _arg.ArgumentParser()
|
|
parser.add_argument("--model_cache",
|
|
type=str,
|
|
help="Directory where models are stored")
|
|
|
|
build_engines(**vars(parser.parse_args()))
|