""" 由于autogptq已经不更新很久了，使用gptq量化前，请先安装我们的autogptq分支,否则代码无法正常运行。 ‘’‘bash git clone https://github.com/LDLINGLINGLING/AutoGPTQ/tree/minicpm_gptq cd Autogptq pip install e . ‘’‘ """ import json import random import time from argparse import ArgumentParser import torch from datasets import Dataset from transformers import AutoTokenizer from auto_gptq import AutoGPTQForCausalLM, BaseQuantizeConfig import os import shutil def copy_missing_files(src_path, dst_path): src_files=os.listdir(src_path) dst_files=os.listdir(dst_path) for src_file in src_files: if src_file not in dst_files and src_file.endswith(('.bin', '.json'))!=True and src_file.startswith('.')!=True: src_file_path = os.path.join(src_path, src_file) dst_file_path = os.path.join(dst_path, src_file) shutil.copy2(src_file_path, dst_file_path) 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":\n{istr+inp}:\n" text = "" + prompt + opt + "" else: prompt = f"\n{istr}\n:\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,default='/root/ld/ld_model_pretrained/MiniCPM-1B-sft-bf16') parser.add_argument("--quantized_model_dir", type=str, default='/root/ld/ld_project/AutoGPTQ/examples/quantization/minicpm_1b_4bit') parser.add_argument("--bits", type=int, default=4, choices=[2, 3, 4])#do not use 8 bit 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", default=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", default=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=8, help="examples batch size for quantization", ) parser.add_argument( "--trust_remote_code", default=True, action="store_true", help="whether to trust remote code when loading model", ) parser.add_argument( "--quant_data", default='quantize_data/alpaca_data_cleaned.json', help="the quant data path", ) args = parser.parse_args() max_memory = {} 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(args.quant_data, 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) tokenizer.save_pretrained(args.quantized_model_dir) copy_missing_files(args.pretrained_model_dir,args.quantized_model_dir) 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" 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() print(tokenizer.decode(model.generate(**tokenizer("{}".format(example['prompt']), return_tensors="pt").to(model.device),max_new_tokens=100)[0])) 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()