mirror of
https://github.com/NVIDIA/TensorRT-LLM.git
synced 2026-01-14 06:27:45 +08:00
32ed92e449
Co-authored-by: Rong Zhou <130957722+ReginaZh@users.noreply.github.com> Co-authored-by: Onur Galoglu <33498883+ogaloglu@users.noreply.github.com> Co-authored-by: Fabian Joswig <fjosw@users.noreply.github.com>
113 lines
3.8 KiB
Python
Executable File
113 lines
3.8 KiB
Python
Executable File
#!/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 init_model_spec_module, run_command, wincopy
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init_model_spec_module()
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import model_spec
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import tensorrt_llm.bindings as _tb
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def build_engine(weight_dir: _pl.Path, medusa_dir: _pl.Path,
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engine_dir: _pl.Path, *args):
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covert_cmd = [_sys.executable, "examples/medusa/convert_checkpoint.py"] + (
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['--model_dir', str(weight_dir)] if weight_dir else []) + [
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'--medusa_model_dir', str(medusa_dir), \
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'--output_dir', str(engine_dir), '--dtype=float16', '--num_medusa_heads=4'
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] + list(args)
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run_command(covert_cmd)
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build_args = ["trtllm-build"] + (
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['--checkpoint_dir', str(engine_dir)] if engine_dir else []) + [
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'--output_dir',
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str(engine_dir),
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'--gemm_plugin=float16',
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'--max_batch_size=8',
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'--max_input_len=12',
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'--max_seq_len=140',
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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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'--speculative_decoding_mode=medusa',
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]
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run_command(build_args)
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def build_engines(model_cache: str):
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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 = 'vicuna-7b-v1.3'
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medusa_name = 'medusa-vicuna-7b-v1.3'
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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) / model_name
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medusa_cache_dir = _pl.Path(model_cache) / medusa_name
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assert model_cache_dir.is_dir(), model_cache_dir
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assert medusa_cache_dir.is_dir(), model_cache_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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wincopy(source=str(medusa_cache_dir),
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dest=medusa_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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run_command(
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["rsync", "-av", str(medusa_cache_dir), "."], cwd=models_dir)
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model_dir = models_dir / model_name
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medusa_dir = models_dir / medusa_name
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assert model_dir.is_dir()
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assert medusa_dir.is_dir()
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engine_dir = models_dir / 'rt_engine' / model_name
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model_spec_obj = model_spec.ModelSpec('input_tokens.npy', _tb.DataType.HALF)
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model_spec_obj.use_gpt_plugin()
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model_spec_obj.set_kv_cache_type(_tb.KVCacheType.PAGED)
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model_spec_obj.use_packed_input()
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model_spec_obj.use_medusa()
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print(f"\nBuilding fp16 engine")
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build_engine(model_dir, medusa_dir,
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engine_dir / model_spec_obj.get_model_path() / 'tp1-pp1-gpu')
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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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build_engines(**vars(parser.parse_args()))
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