mirror of
https://github.com/NVIDIA/TensorRT-LLM.git
synced 2026-01-14 06:27:45 +08:00
55 lines
1.8 KiB
Python
55 lines
1.8 KiB
Python
#!/usr/bin/env python3
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import os
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from typing import Optional
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import click
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from tensorrt_llm import LLM as TorchLLM
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from tensorrt_llm._tensorrt_engine import LLM
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from tensorrt_llm.llmapi import KvCacheConfig, SamplingParams
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@click.command()
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@click.option("--model_dir", type=str, required=True)
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@click.option("--tp_size", type=int, required=True)
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@click.option("--engine_dir", type=str, default=None)
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@click.option("--n", type=int, default=1)
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@click.option("--best_of", type=int, default=None)
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@click.option("--top_k", type=int, default=1)
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@click.option("--use_beam_search", is_flag=True)
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@click.option("--use_pytorch", is_flag=True)
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def main(model_dir: str, tp_size: int, engine_dir: Optional[str], n: int,
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best_of: Optional[int], top_k: int, use_beam_search: bool,
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use_pytorch: bool):
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if use_pytorch:
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llm = TorchLLM(
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model_dir,
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tensor_parallel_size=tp_size,
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kv_cache_config=KvCacheConfig(free_gpu_memory_fraction=0.4))
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else:
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llm = LLM(model_dir,
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tensor_parallel_size=tp_size,
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kv_cache_config=KvCacheConfig(free_gpu_memory_fraction=0.4))
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if engine_dir is not None and os.path.abspath(
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engine_dir) != os.path.abspath(model_dir):
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llm.save(engine_dir)
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sampling_params = SamplingParams(max_tokens=10,
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end_id=-1,
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n=n,
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best_of=best_of,
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use_beam_search=use_beam_search,
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top_k=top_k)
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print(sampling_params)
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prompt_token_ids = [45, 12, 13]
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for output in llm.generate([prompt_token_ids],
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sampling_params=sampling_params):
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print(output)
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print("run_llm.py Done")
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if __name__ == '__main__':
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main()
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