mirror of
https://github.com/NVIDIA/TensorRT-LLM.git
synced 2026-01-14 06:27:45 +08:00
75057cd036
* Update TensorRT-LLM --------- Co-authored-by: Puneesh Khanna <puneesh.khanna@tii.ae> Co-authored-by: Ethan Zhang <26497102+ethnzhng@users.noreply.github.com>
78 lines
2.8 KiB
Python
78 lines
2.8 KiB
Python
import argparse
|
|
import os
|
|
from pathlib import Path
|
|
|
|
from transformers import AutoTokenizer
|
|
|
|
import tensorrt_llm
|
|
from tensorrt_llm import BuildConfig, build
|
|
from tensorrt_llm.executor import GenerationExecutor
|
|
from tensorrt_llm.llmapi import SamplingParams
|
|
from tensorrt_llm.models import LLaMAForCausalLM
|
|
from tensorrt_llm.models.modeling_utils import QuantConfig
|
|
from tensorrt_llm.quantization import QuantAlgo
|
|
|
|
|
|
def parse_args():
|
|
parser = argparse.ArgumentParser(description="Llama single model example")
|
|
parser.add_argument(
|
|
"--cache_dir",
|
|
type=str,
|
|
required=True,
|
|
help=
|
|
"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"
|
|
)
|
|
parser.add_argument(
|
|
"--hf_model_dir",
|
|
type=str,
|
|
required=True,
|
|
help="Read the model data and tokenizer from this directory")
|
|
parser.add_argument(
|
|
"-c",
|
|
"--clean_build",
|
|
default=False,
|
|
action="store_true",
|
|
help=
|
|
"Clean build the engine even if the cache dir exists, be careful, this overwrites the cache dir!!"
|
|
)
|
|
return parser.parse_args()
|
|
|
|
|
|
def main():
|
|
tensorrt_llm.logger.set_level('verbose')
|
|
args = parse_args()
|
|
max_batch_size, max_isl, max_osl = 1, 256, 20
|
|
build_config = BuildConfig(max_input_len=max_isl,
|
|
max_seq_len=max_osl + max_isl,
|
|
max_batch_size=max_batch_size)
|
|
cache_dir = Path(args.cache_dir)
|
|
checkpoint_dir = cache_dir / "trtllm_checkpoint"
|
|
engine_dir = cache_dir / "trtllm_engine"
|
|
|
|
if args.clean_build or not cache_dir.exists():
|
|
os.makedirs(cache_dir, exist_ok=True)
|
|
quant_config = QuantConfig()
|
|
quant_config.quant_algo = QuantAlgo.W4A16_AWQ
|
|
if not checkpoint_dir.exists():
|
|
LLaMAForCausalLM.quantize(args.hf_model_dir,
|
|
checkpoint_dir,
|
|
quant_config=quant_config,
|
|
calib_batches=1)
|
|
llama = LLaMAForCausalLM.from_checkpoint(checkpoint_dir)
|
|
engine = build(llama, build_config)
|
|
engine.save(engine_dir)
|
|
|
|
tokenizer = AutoTokenizer.from_pretrained(args.hf_model_dir)
|
|
with GenerationExecutor.create(engine_dir) as executor:
|
|
sampling_params = SamplingParams(max_tokens=5)
|
|
|
|
input_str = "What should you say when someone gives you a gift? You should say:"
|
|
output = executor.generate(tokenizer.encode(input_str),
|
|
sampling_params=sampling_params)
|
|
output_str = tokenizer.decode(output.outputs[0].token_ids)
|
|
print(f"{input_str} {output_str}")
|
|
|
|
|
|
if __name__ == "__main__":
|
|
main()
|