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https://github.com/NVIDIA/TensorRT-LLM.git
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655524dd82
* Update TensorRT-LLM --------- Co-authored-by: Bhuvanesh Sridharan <bhuvan.sridharan@gmail.com> Co-authored-by: Shixiaowei02 <39303645+Shixiaowei02@users.noreply.github.com>
104 lines
3.4 KiB
Python
104 lines
3.4 KiB
Python
#!/usr/bin/env python3
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# 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 as _arg
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import pathlib as _pl
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import platform as _pf
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import sys as _sys
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from build_engines_utils import run_command, wincopy
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def build_engine(weight_dir: _pl.Path, engine_dir: _pl.Path, *args):
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ckpt_dir = engine_dir / 'ckpt'
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covert_cmd = [_sys.executable, "examples/llama/convert_checkpoint.py"
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] + ([f'--model_dir={weight_dir}'] if weight_dir else []) + [
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f'--output_dir={ckpt_dir}',
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'--dtype=float16',
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] + list(args)
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run_command(covert_cmd)
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build_args = [
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'trtllm-build',
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f'--checkpoint_dir={ckpt_dir}',
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f'--output_dir={engine_dir}',
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'--gpt_attention_plugin=float16',
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'--use_custom_all_reduce=enable',
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'--gemm_plugin=float16',
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'--max_batch_size=32',
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'--max_input_len=40',
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'--max_output_len=20',
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'--max_beam_width=2',
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'--log_level=error',
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'--paged_kv_cache=enable',
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'--remove_input_padding=enable',
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]
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run_command(build_args)
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def build_engines(model_cache: str, only_multi_gpu: bool):
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resources_dir = _pl.Path(__file__).parent.resolve().parent
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models_dir = resources_dir / 'models'
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model_name = 'llama-7b-hf'
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if model_cache:
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print("Copy model from model_cache")
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model_cache_dir = _pl.Path(model_cache) / 'llama-models' / model_name
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assert (model_cache_dir.is_dir())
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if _pf.system() == "Windows":
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wincopy(source=str(model_cache_dir),
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dest=model_name,
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isdir=True,
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cwd=models_dir)
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else:
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run_command(
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["rsync", "-av", str(model_cache_dir), "."], cwd=models_dir)
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hf_dir = models_dir / model_name
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assert hf_dir.is_dir()
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engine_dir = models_dir / 'rt_engine' / model_name
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tp_pp_sizes = [(1, 1)]
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if only_multi_gpu:
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tp_pp_sizes = [(1, 4), (4, 1), (2, 2)]
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for tp_size, pp_size in tp_pp_sizes:
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tp_pp_dir = f"tp{tp_size}-pp{pp_size}-gpu"
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print(f"\nBuilding fp16 tp{tp_size} pp{pp_size} engine")
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build_engine(hf_dir,
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engine_dir / f'fp16-plugin-packed-paged/{tp_pp_dir}',
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f'--tp_size={tp_size}', f'--pp_size={pp_size}')
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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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help="Directory where models are stored")
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parser.add_argument(
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"--only_multi_gpu",
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action="store_true",
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help="Flag to build only for Tensor and Pipeline parallelism")
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build_engines(**vars(parser.parse_args()))
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