From 23a52259de98b255a6243b4cfa0f79cf694d1efe Mon Sep 17 00:00:00 2001 From: root <403644786@qq.com> Date: Mon, 15 Jul 2024 14:48:53 +0800 Subject: [PATCH] =?UTF-8?q?=E5=A2=9E=E5=8A=A0=E4=BA=86bnb=E9=87=8F?= =?UTF-8?q?=E5=8C=96=E7=9A=84=E6=B5=8B=E8=AF=95?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- quantize/awq_quantize.py | 8 ++++---- quantize/gptq_quantize.py | 4 ++-- quantize/quantize_eval.py | 29 +++++++++++++++++++++++------ quantize/quantize_eval.sh | 10 +++++----- 4 files changed, 34 insertions(+), 17 deletions(-) diff --git a/quantize/awq_quantize.py b/quantize/awq_quantize.py index b40b14e..1554b6c 100644 --- a/quantize/awq_quantize.py +++ b/quantize/awq_quantize.py @@ -5,12 +5,12 @@ from awq import AutoAWQForCausalLM from transformers import AutoTokenizer import os -model_path = '/root/ld/ld_model_pretrained/MiniCPM-1B-sft-bf16' # model_path or model_id -quant_path = '/root/ld/ld_project/pull_request/MiniCPM/quantize/awq_cpm_1b_4bit' # quant_save_path -quant_data_path='/root/ld/ld_project/pull_request/MiniCPM/quantize/quantize_data/wikitext'# 写入自带数据集地址 +model_path = '/root/ld/ld_model_pretrain/MiniCPM-1B-sft-bf16' # model_path or model_id +quant_path = '/root/ld/pull_request/MiniCPM/quantize/awq_cpm_1b_4bit' # quant_save_path +quant_data_path='/root/ld/pull_request/MiniCPM/quantize/quantize_data/wikitext'# 写入自带数据集地址 quant_config = { "zero_point": True, "q_group_size": 128, "w_bit": 4, "version": "GEMM" } # "w_bit":4 or 8 quant_samples=512 # how many samples to use for calibration -custom_data=[{'question':'你叫什么名字。','answer':'我是openmbmb开源的小钢炮minicpm。'}, # 自定义数据集可用 +custom_data=[{'question':'鼻炎犯了怎么办','answer':'可以使用生理盐水进行清洗。'}, # 自定义数据集可用 {'question':'你有什么特色。','answer':'我很小，但是我很强。'}] # Load model model = AutoAWQForCausalLM.from_pretrained(model_path) diff --git a/quantize/gptq_quantize.py b/quantize/gptq_quantize.py index a98285b..78e10a4 100644 --- a/quantize/gptq_quantize.py +++ b/quantize/gptq_quantize.py @@ -98,8 +98,8 @@ def load_data(data_path, tokenizer, n_samples): 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("--pretrained_model_dir", type=str,default='/root/ld/ld_model_pretrain/MiniCPM-1B-sft-bf16') + parser.add_argument("--quantized_model_dir", type=str, default='/root/ld/pull_request/MiniCPM/quantize/gptq_cpm_1b_4bit') parser.add_argument("--bits", type=int, default=4, choices=[2, 3, 4])#do not use 8 bit parser.add_argument( "--group_size", diff --git a/quantize/quantize_eval.py b/quantize/quantize_eval.py index 82d350c..220d807 100644 --- a/quantize/quantize_eval.py +++ b/quantize/quantize_eval.py @@ -2,7 +2,7 @@ import torch import torch.nn as nn from tqdm import tqdm from datasets import load_dataset -from transformers import AutoModelForCausalLM, AutoTokenizer +from transformers import AutoModelForCausalLM, AutoTokenizer,AutoConfig import GPUtil import argparse @@ -13,6 +13,12 @@ parser.add_argument( default='', help="未量化前的模型路径。" ) +parser.add_argument( + "--bnb_path", + type=str, + default='', + help="bnb量化后的模型路径。" +) parser.add_argument( "--awq_path", type=str, @@ -83,9 +89,9 @@ if __name__ == "__main__": args = parser.parse_args() if args.model_path != "": + print("pretrained model：",args.model_path.split('/')[-1]) model = AutoModelForCausalLM.from_pretrained(args.model_path, torch_dtype=torch.bfloat16, device_map='cuda', trust_remote_code=True) tokenizer = AutoTokenizer.from_pretrained(args.model_path) - print("pretrained model：",args.model_path.split('/')[-1]) gpu_usage = GPUtil.getGPUs()[0].memoryUsed print(f"gpu usage: {round(gpu_usage/1024,2)}GB") evaluate_perplexity(model, tokenizer, args.data_path) @@ -93,10 +99,9 @@ if __name__ == "__main__": if args.awq_path != "": from awq import AutoAWQForCausalLM - + print("awq model：",args.awq_path.split('/')[-1]) model = AutoAWQForCausalLM.from_quantized(args.awq_path, fuse_layers=True,device_map={"":'cuda:0'}) tokenizer = AutoTokenizer.from_pretrained(args.awq_path) - print("awq model：",args.awq_path.split('/')[-1]) gpu_usage = GPUtil.getGPUs()[0].memoryUsed print(f"gpu usage: {round(gpu_usage/1024,2)}GB") evaluate_perplexity(model, tokenizer, args.data_path) @@ -105,11 +110,23 @@ if __name__ == "__main__": #we will support the autogptq later if args.gptq_path != "": from auto_gptq import AutoGPTQForCausalLM - + print("gptq model：",args.gptq_path.split('/')[-1]) tokenizer = AutoTokenizer.from_pretrained(args.gptq_path, use_fast=True) model = AutoGPTQForCausalLM.from_quantized(args.gptq_path, device="cuda:0",trust_remote_code=True) - print("gptq model：",args.gptq_path.split('/')[-1]) gpu_usage = GPUtil.getGPUs()[0].memoryUsed print(f"gpu usage: {round(gpu_usage/1024,2)}GB") evaluate_perplexity(model, tokenizer, args.data_path) + del model + if args.bnb_path != "": + from accelerate.utils import BnbQuantizationConfig + bnb_quantization_config = BnbQuantizationConfig(load_in_4bit=True, bnb_4bit_compute_dtype=torch.bfloat16, bnb_4bit_use_double_quant=True, bnb_4bit_quant_type="nf4") + print("bnb model：",args.gptq_path.split('/')[-1]) + # config=AutoConfig.from_pretrained(args.bnb_path,trust_remote_code=True) + # bnb_config=config.quantization_config + tokenizer = AutoTokenizer.from_pretrained(args.bnb_path, use_fast=True) + model = AutoModelForCausalLM.from_pretrained(args.bnb_path, trust_remote_code=True,)#quantization_config=bnb_config,) + gpu_usage = GPUtil.getGPUs()[0].memoryUsed + print(f"gpu usage: {round(gpu_usage/1024,2)}GB") + evaluate_perplexity(model, tokenizer, args.data_path) + del model diff --git a/quantize/quantize_eval.sh b/quantize/quantize_eval.sh index 4002303..e080659 100644 --- a/quantize/quantize_eval.sh +++ b/quantize/quantize_eval.sh @@ -1,8 +1,8 @@ #!/bin/bash -awq_path="/root/ld/ld_project/AutoAWQ/examples/awq_cpm_1b_4bit" -gptq_path="" -model_path="" - +awq_path="/root/ld/pull_request/MiniCPM/quantize/awq_cpm_1b_4bit" +gptq_path="/root/ld/pull_request/MiniCPM/quantize/gptq_cpm_1b_4bit" +model_path="/root/ld/ld_model_pretrain/MiniCPM-1B-sft-bf16" +bnb_path="/root/ld/ld_model_pretrain/MiniCPM-1B-sft-bf16_int4" python quantize_eval.py --awq_path "${awq_path}" \ - --model_path "${model_path}" --gptq_path "${gptq_path}" \ No newline at end of file + --model_path "${model_path}" --gptq_path "${gptq_path}" --bnb_path "${bnb_path}" \ No newline at end of file