import json import random import time from argparse import ArgumentParser import torch from auto_gptq import AutoGPTQForCausalLM, BaseQuantizeConfig from datasets import Dataset from transformers import AutoTokenizer, TextGenerationPipeline def load_data(data_path, tokenizer, n_samples): with open(data_path, "r", encoding="utf-8") as f: raw_data = json.load(f) raw_data = random.sample(raw_data, k=min(n_samples, len(raw_data))) def dummy_gen(): return raw_data def tokenize(examples): instructions = examples["instruction"] inputs = examples["input"] outputs = examples["output"] prompts = [] texts = [] input_ids = [] attention_mask = [] for istr, inp, opt in zip(instructions, inputs, outputs): if inp: prompt = f"### User:\n{istr}\n\n### Input:\n{inp}\n\nResponse:\n" text = prompt + opt else: prompt = f"### User:\n{istr}\n\nResponse:\n" text = prompt + opt if len(tokenizer(prompt)["input_ids"]) >= tokenizer.model_max_length: continue tokenized_data = tokenizer(text) input_ids.append(tokenized_data["input_ids"][: tokenizer.model_max_length]) attention_mask.append(tokenized_data["attention_mask"][: tokenizer.model_max_length]) prompts.append(prompt) texts.append(text) return { "input_ids": input_ids, "attention_mask": attention_mask, "prompt": prompts } dataset = Dataset.from_generator(dummy_gen) dataset = dataset.map( tokenize, batched=True, batch_size=len(dataset), num_proc=1, keep_in_memory=True, load_from_cache_file=False, remove_columns=["instruction", "input"] ) dataset = dataset.to_list() for sample in dataset: sample["input_ids"] = torch.LongTensor(sample["input_ids"]) sample["attention_mask"] = torch.LongTensor(sample["attention_mask"]) return dataset def main(): parser = ArgumentParser() parser.add_argument("--pretrained_model_dir", type=str) parser.add_argument("--quantized_model_dir", type=str, default=None) parser.add_argument("--bits", type=int, default=4, choices=[2, 3, 4, 8]) parser.add_argument("--group_size", type=int, default=128, help="group size, -1 means no grouping or full rank") parser.add_argument("--desc_act", action="store_true", help="whether to quantize with desc_act") parser.add_argument("--num_samples", type=int, default=128, help="how many samples will be used to quantize model") parser.add_argument("--save_and_reload", action="store_true", help="whether save quantized model to disk and reload back") parser.add_argument("--fast_tokenizer", action="store_true", help="whether use fast tokenizer") parser.add_argument("--use_triton", action="store_true", help="whether use triton to speedup at inference") parser.add_argument("--per_gpu_max_memory", type=int, default=None, help="max memory used to load model per gpu") parser.add_argument("--cpu_max_memory", type=int, default=None, help="max memory used to offload model to cpu") parser.add_argument("--quant_batch_size", type=int, default=1, help="examples batch size for quantization") parser.add_argument("--trust_remote_code", action="store_true", help="whether to trust remote code when loading model") args = parser.parse_args() max_memory = dict() if args.per_gpu_max_memory is not None and args.per_gpu_max_memory > 0: if torch.cuda.is_available(): max_memory.update( {i: f"{args.per_gpu_max_memory}GIB" for i in range(torch.cuda.device_count())} ) if args.cpu_max_memory is not None and args.cpu_max_memory > 0 and max_memory: max_memory["cpu"] = f"{args.cpu_max_memory}GIB" if not max_memory: max_memory = None tokenizer = AutoTokenizer.from_pretrained( args.pretrained_model_dir, use_fast=args.fast_tokenizer, trust_remote_code=args.trust_remote_code ) model = AutoGPTQForCausalLM.from_pretrained( args.pretrained_model_dir, quantize_config=BaseQuantizeConfig(bits=args.bits, group_size=args.group_size, desc_act=args.desc_act), max_memory=max_memory, trust_remote_code=args.trust_remote_code ) examples = load_data("dataset/alpaca_data_cleaned.json", tokenizer, args.num_samples) examples_for_quant = [ {"input_ids": example["input_ids"], "attention_mask": example["attention_mask"]} for example in examples ] start = time.time() model.quantize( examples_for_quant, batch_size=args.quant_batch_size, use_triton=args.use_triton, autotune_warmup_after_quantized=args.use_triton, ) end = time.time() print(f"quantization took: {end - start: .4f}s") if not args.quantized_model_dir: args.quantized_model_dir = args.pretrained_model_dir if args.save_and_reload: model.save_quantized(args.quantized_model_dir, use_safetensors=True) del model if torch.cuda.is_available(): torch.cuda.empty_cache() model = AutoGPTQForCausalLM.from_quantized( args.quantized_model_dir, device="cuda:0", use_triton=args.use_triton, max_memory=max_memory, inject_fused_mlp=True, inject_fused_attention=True, trust_remote_code=args.trust_remote_code ) pipeline_init_kwargs = {"model": model, "tokenizer": tokenizer} if not max_memory: pipeline_init_kwargs["device"] = "cuda:0" pipeline = TextGenerationPipeline(**pipeline_init_kwargs) for example in random.sample(examples, k=min(4, len(examples))): print(f"prompt: {example['prompt']}") print("-" * 42) print(f"golden: {example['output']}") print("-" * 42) start = time.time() generated_text = pipeline( example['prompt'], return_full_text=False, num_beams=1, max_length=len(example["input_ids"]) + 128 # use this instead of max_new_token to disable UserWarning when integrate with logging )[0]['generated_text'] end = time.time() print(f"quant: {generated_text}") num_new_tokens = len(tokenizer(generated_text)["input_ids"]) print(f"generate {num_new_tokens} tokens using {end-start: .4f}s, {num_new_tokens / (end - start)} tokens/s.") print("=" * 42) if __name__ == "__main__": import logging logging.basicConfig( format="%(asctime)s %(levelname)s [%(name)s] %(message)s", level=logging.INFO, datefmt="%Y-%m-%d %H:%M:%S" ) main()