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# coding=utf-8
# Quantizes fine-tuned models with AutoGPTQ (https://github.com/PanQiWei/AutoGPTQ).
# Usage: python quantize.py --input_dir path_to_llama_model --output_dir path_to_quant_model --data_file alpaca.json
# --max_length 1024 --max_samples 1024
# dataset format: instruction (string), input (string), output (string), history (List[string])
import fire
from datasets import load_dataset
from transformers import AutoTokenizer
from auto_gptq import AutoGPTQForCausalLM, BaseQuantizeConfig
def quantize(input_dir: str, output_dir: str, data_file: str, max_length: int, max_samples: int):
tokenizer = AutoTokenizer.from_pretrained(input_dir, use_fast=False, padding_side="left")
def format_example(examples):
prefix=("A chat between a curious user and an artificial intelligence assistant. "
"The assistant gives helpful, detailed, and polite answers to the user's questions.")
texts = []
for i in range(len(examples["instruction"])):
prompt = prefix + "\n"
if "history" in examples:
for user_query, bot_resp in examples["history"][i]:
prompt += "Human: {}\nAssistant: {}\n".format(user_query, bot_resp)
prompt += "Human: {}\nAssistant: {}".format(
examples["instruction"][i] + "\n" + examples["input"][i], examples["output"][i]
)
texts.append(prompt)
return tokenizer(texts, truncation=True, max_length=max_length)
dataset = load_dataset("json", data_files=data_file)["train"]
column_names = list(dataset.column_names)
dataset = dataset.select(range(min(len(dataset), max_samples)))
dataset = dataset.map(format_example, batched=True, remove_columns=column_names)
dataset = dataset.shuffle()
quantize_config = BaseQuantizeConfig(
bits=4,
group_size=128,
desc_act=False
)
model = AutoGPTQForCausalLM.from_pretrained(input_dir, quantize_config, trust_remote_code=True)
model.quantize(dataset)
model.save_quantized(output_dir)
if __name__ == "__main__":
fire.Fire(quantize)
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