135 lines
4.5 KiB
Python
135 lines
4.5 KiB
Python
import json
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import random
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import time
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from argparse import ArgumentParser
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import torch
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from auto_gptq import AutoGPTQForCausalLM, BaseQuantizeConfig
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from datasets import Dataset
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from transformers import AutoTokenizer, TextGenerationPipeline
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def load_data(data_path, tokenizer, n_samples):
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with open(data_path, "r", encoding="utf-8") as f:
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raw_data = json.load(f)
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raw_data = random.sample(raw_data, k=min(n_samples, len(raw_data)))
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def dummy_gen():
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return raw_data
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def tokenize(examples):
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instructions = examples["instruction"]
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inputs = examples["input"]
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outputs = examples["output"]
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prompts = []
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texts = []
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input_ids = []
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attention_mask = []
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for istr, inp, opt in zip(instructions, inputs, outputs):
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if inp:
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prompt = f"Instruction:\n{istr}\nInput:\n{inp}\nOutput:\n"
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text = prompt + opt
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else:
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prompt = f"Instruction:\n{istr}\nOutput:\n"
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text = prompt + opt
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if len(tokenizer(prompt)["input_ids"]) >= tokenizer.model_max_length:
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continue
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tokenized_data = tokenizer(text)
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input_ids.append(tokenized_data["input_ids"][: tokenizer.model_max_length])
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attention_mask.append(tokenized_data["attention_mask"][: tokenizer.model_max_length])
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prompts.append(prompt)
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texts.append(text)
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return {
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"input_ids": input_ids,
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"attention_mask": attention_mask,
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"prompt": prompts
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}
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dataset = Dataset.from_generator(dummy_gen)
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dataset = dataset.map(
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tokenize,
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batched=True,
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batch_size=len(dataset),
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num_proc=1,
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keep_in_memory=True,
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load_from_cache_file=False,
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remove_columns=["instruction", "input"]
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)
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dataset = dataset.to_list()
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for sample in dataset:
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sample["input_ids"] = torch.LongTensor(sample["input_ids"])
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sample["attention_mask"] = torch.LongTensor(sample["attention_mask"])
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return dataset
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def main():
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parser = ArgumentParser()
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parser.add_argument("--pretrained_model_dir", type=str)
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parser.add_argument("--quantized_model_dir", type=str, default=None)
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parser.add_argument("--bits", type=int, default=4, choices=[2, 3, 4, 8])
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parser.add_argument("--group_size", type=int, default=128)
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parser.add_argument("--num_samples", type=int, default=128)
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parser.add_argument("--save_and_reload", action="store_true")
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parser.add_argument("--fast_tokenizer", action="store_true")
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args = parser.parse_args()
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tokenizer = AutoTokenizer.from_pretrained(
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args.pretrained_model_dir,
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use_fast=args.fast_tokenizer,
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trust_remote_code=True
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)
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model = AutoGPTQForCausalLM.from_pretrained(
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args.pretrained_model_dir,
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quantize_config=BaseQuantizeConfig(bits=args.bits, group_size=args.group_size)
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)
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examples = load_data("dataset/alpaca_data_cleaned.json", tokenizer, args.num_samples)
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examples_for_quant = [
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{"input_ids": example["input_ids"], "attention_mask": example["attention_mask"]}
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for example in examples
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]
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model.quantize(examples_for_quant)
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if not args.quantized_model_dir:
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args.quantized_model_dir = args.pretrained_model_dir
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if args.save_and_reload:
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model.save_quantized(args.quantized_model_dir)
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model = AutoGPTQForCausalLM.from_quantized(args.quantized_model_dir, device="cuda:0")
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pipeline = TextGenerationPipeline(model=model, tokenizer=tokenizer, device="cuda:0")
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for example in random.sample(examples, k=min(4, len(examples))):
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print(f"prompt: {example['prompt']}")
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print(f"origin: {example['output']}")
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start = time.time()
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generated_text = pipeline(
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example['prompt'],
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return_full_text=False,
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num_beams=1,
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max_length=len(example["input_ids"]) + 128 # use this instead of max_new_token to disable UserWarning when integrate with logging
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)[0]['generated_text']
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end = time.time()
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print(f"quant: {generated_text}")
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num_new_tokens = len(tokenizer(generated_text)["input_ids"])
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print(f"generate {num_new_tokens} tokens using {end-start: .4f}s")
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print("=" * 42)
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if __name__ == "__main__":
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import logging
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logging.basicConfig(
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format="%(asctime)s %(levelname)s [%(name)s] %(message)s", level=logging.INFO, datefmt="%Y-%m-%d %H:%M:%S"
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)
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main()
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