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https://github.com/NVIDIA/TensorRT-LLM.git
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62 lines
1.8 KiB
Python
62 lines
1.8 KiB
Python
import argparse
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import os
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from pathlib import Path
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from tensorrt_llm.executor import GenerationExecutor
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from tensorrt_llm.models import LLaMAForCausalLM
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def read_input():
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while (True):
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input_text = input("<")
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if input_text in ("q", "quit"):
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break
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yield input_text
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def parse_args():
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parser = argparse.ArgumentParser(description="Llama single model example")
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parser.add_argument(
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"--engine_dir",
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type=Path,
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required=True,
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help=
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"Directory to save and load the engine. When -c is specified, always rebuild and save to this dir. When -c is not specified, load engine when the engine_dir exists, rebuild otherwise"
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)
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parser.add_argument(
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"--hf_model_dir",
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type=str,
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required=True,
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help="Read the model data and tokenizer from this directory")
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parser.add_argument(
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"-c",
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"--clean_build",
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default=False,
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action="store_true",
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help=
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"Clean build the engine even if the engine_dir exists, be careful, this overwrites the engine_dir!!"
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)
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return parser.parse_args()
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def main():
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args = parse_args()
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tokenizer_dir = args.hf_model_dir
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max_batch_size, max_isl, max_osl = 1, 256, 20
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if args.clean_build or not args.engine_dir.exists():
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args.engine_dir.mkdir(exist_ok=True, parents=True)
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os.makedirs(args.engine_dir, exist_ok=True)
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llama = LLaMAForCausalLM.from_hugging_face(args.hf_model_dir)
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llama.to_trt(max_batch_size, max_isl, max_osl)
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llama.save(str(args.engine_dir))
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executor = GenerationExecutor.create(args.engine_dir, tokenizer_dir)
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for inp in read_input():
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output = executor.generate(inp, max_new_tokens=20)
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print(f">{output.text}")
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main()
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