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import argparse, gc, shutil
from transformers import AutoTokenizer
from auto_gptq import AutoGPTQForCausalLM, BaseQuantizeConfig
from datasets import load_dataset
parser = argparse.ArgumentParser()
parser.add_argument("--model-id", type=str)
parser.add_argument("--save-dir", type=str)
parser.add_argument("--channelwise", action="store_true")
parser.add_argument("--num-samples", type=int, default=512)
parser.add_argument("--max-seq-len", type=int, default=2048)
def preprocess(example):
return {"text": tokenizer.apply_chat_template(example["messages"], tokenize=False)}
if __name__ == "__main__":
args = parser.parse_args()
dataset = load_dataset("HuggingFaceH4/ultrachat_200k", split="train_sft[:5%]")
tokenizer = AutoTokenizer.from_pretrained(args.model_id)
ds = dataset.shuffle().select(range(args.num_samples))
ds = ds.map(preprocess)
examples = [
tokenizer(
example["text"], padding=False, max_length=args.max_seq_len, truncation=True,
) for example in ds
]
if args.channelwise:
group_size = -1
else:
group_size = 128
quantize_config = BaseQuantizeConfig(
bits=4, # Only support 4 bit
group_size=group_size, # Set to g=128 or -1 (for channelwise)
desc_act=False, # Marlin does not suport act_order=True
model_file_base_name="model" # Name of the model.safetensors when we call save_pretrained
)
model = AutoGPTQForCausalLM.from_pretrained(
args.model_id,
quantize_config,
device_map="auto")
model.quantize(examples)
gptq_save_dir = "./tmp-gptq"
print(f"Saving gptq model to {gptq_save_dir}")
model.save_pretrained(gptq_save_dir)
tokenizer.save_pretrained(gptq_save_dir)
del model
gc.collect()
print("Reloading in marlin format")
marlin_model = AutoGPTQForCausalLM.from_quantized(
gptq_save_dir,
use_marlin=True,
device_map="auto")
print("Saving in marlin format")
marlin_model.save_pretrained(args.save_dir)
tokenizer.save_pretrained(args.save_dir)
shutil.rmtree(gptq_save_dir)
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