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https://github.com/NVIDIA/TensorRT-LLM.git
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9dbc5b38ba
* Update TensorRT-LLM --------- Co-authored-by: Marks101 <markus.schnoes@gmx.de> Co-authored-by: lkm2835 <lkm2835@gmail.com>
84 lines
2.8 KiB
Python
84 lines
2.8 KiB
Python
import argparse
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import os
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from pathlib import Path
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from transformers import AutoTokenizer
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import tensorrt_llm
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from tensorrt_llm import BuildConfig, build
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from tensorrt_llm.executor import GenerationExecutor
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from tensorrt_llm.hlapi import SamplingParams
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from tensorrt_llm.models import LLaMAForCausalLM
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from tensorrt_llm.models.modeling_utils import QuantConfig
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from tensorrt_llm.quantization import QuantAlgo
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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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"--cache_dir",
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type=str,
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required=True,
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help=
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"Directory to save and load the engine and checkpoint. 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 cache dir exists, be careful, this overwrites the cache dir!!"
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)
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return parser.parse_args()
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def main():
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tensorrt_llm.logger.set_level('verbose')
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args = parse_args()
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max_batch_size, max_isl, max_osl = 1, 256, 20
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build_config = BuildConfig(max_input_len=max_isl,
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max_seq_len=max_osl + max_isl,
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max_batch_size=max_batch_size)
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cache_dir = Path(args.cache_dir)
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checkpoint_dir = cache_dir / "trtllm_checkpoint"
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engine_dir = cache_dir / "trtllm_engine"
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if args.clean_build or not cache_dir.exists():
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os.makedirs(cache_dir, exist_ok=True)
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quant_config = QuantConfig()
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quant_config.quant_algo = QuantAlgo.W4A16_AWQ
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if not checkpoint_dir.exists():
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LLaMAForCausalLM.quantize(args.hf_model_dir,
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checkpoint_dir,
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quant_config=quant_config,
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calib_batches=1)
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llama = LLaMAForCausalLM.from_checkpoint(checkpoint_dir)
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engine = build(llama, build_config)
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engine.save(engine_dir)
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tokenizer = AutoTokenizer.from_pretrained(args.hf_model_dir)
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executor = GenerationExecutor.create(engine_dir)
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sampling_params = SamplingParams(max_new_tokens=20)
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for inp in read_input():
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output = executor.generate(tokenizer.encode(inp),
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sampling_params=sampling_params)
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print(f"> {tokenizer.decode(output.outputs[0].token_ids)}")
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if __name__ == "__main__":
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main()
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