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D2-mixtral.py
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import argparse
import os
import torch
import torch.backends.cudnn as cudnn
from config import cfg, process_args
from dataset import make_dataset, make_data_loader, process_dataset, make_batchnorm_stats, make_calibration_dataloader
from metric import make_metric, make_logger
from model import make_prune_model
from module import to_device, process_control, makedir_exist_ok, check_dense_model, save_calib_info
from deepspeed.profiling.flops_profiler import FlopsProfiler
from utils import run_lm_eval, ppl_eval_sharing
from transformers import AutoTokenizer, AutoModelForCausalLM
from tqdm import tqdm
from model.merge_mixtral import Merge_MixtralSparseMoeBlock
cudnn.benchmark = False
parser = argparse.ArgumentParser(description='cfg')
parser.add_argument('--base_model_path', type=str, default="mistralai/Mixtral-8x7B", help='Path to base model')
parser.add_argument('--expert_freq_path', type=str, default="cache/Mixtral_wikitext_20000_expert_frequencies.json", help='Path to expert frequencies')
parser.add_argument('--fisher_path', type=str, default="Model/fisher_Mixtral-8x7B.pt", help='Path to fisher info')
parser.add_argument('--svd_scale_path', type=str, default="Model/SVD_scale_mixtral_0-31_512.pt", help='Path to svd scale')
parser.add_argument('--result_path', type=str, default="result", help='Path to result')
parser.add_argument("--pp_ratio", type=float, default=0.2)
parser.add_argument("--delta_ratio", type=float, default=1)
parser.add_argument("--share_ratio", type=float, default=1)
parser.add_argument("--share_V", action='store_true', default=False)
parser.add_argument("--share_U", action='store_true', default=False)
parser.add_argument("--merge_method", type=str, default="fisher")
for k in cfg:
exec('parser.add_argument(\'--{0}\', default=cfg[\'{0}\'], type=type(cfg[\'{0}\']))'.format(k))
parser.add_argument('--control_name', default=None, type=str)
parser.add_argument('--output_dir', default=None, type=str)
args = vars(parser.parse_args())
process_args(args)
def main():
process_control()
seeds = list(range(cfg['init_seed'], cfg['init_seed'] + cfg['num_experiments']))
for i in range(cfg['num_experiments']):
model_tag_list = [str(seeds[i]), cfg['control_name']]
cfg['model_tag'] = '_'.join([x for x in model_tag_list if x])
runExperiment()
return
def runExperiment():
cfg['seed'] = int(cfg['model_tag'].split('_')[0])
cfg['prune_ratio'] = args['pp_ratio']
torch.manual_seed(cfg['seed'])
torch.cuda.manual_seed(cfg['seed'])
result_path = os.path.join('output', 'result')
makedir_exist_ok(result_path)
if check_dense_model() is None:
pass
cfg['epoch'] = 0
cfg['data_name'] = 'wikitext'
dataset = make_dataset(cfg['data_name'], cfg['subset_name'])
cfg['model_name'] = 'mixtral'
cfg['skip_layers'] = []
cfg['test_stage'] = False
cfg['no_probe_process'] = False
cfg['merge_model'] = True
cfg['shared_infer'] = False
def merge_model(base_model_path, expert_freq_path, svd_scale_path, fisher_path, delta_ratio, share_ratio, share_V, share_U, merge_method):
import json
with open(expert_freq_path, 'r') as f:
expert_freq = json.load(f)
svd_scale = torch.load(svd_scale_path, map_location='cpu')
fisher_info = torch.load(fisher_path, map_location="cpu")
model = AutoModelForCausalLM.from_pretrained(base_model_path,
device_map="auto",
trust_remote_code=True,
torch_dtype=torch.bfloat16)
for i in tqdm(range(len(model.model.layers)), desc="Merging layers"):
Merge_MoE_Block = Merge_MixtralSparseMoeBlock(model.config, share_ratio=share_ratio,
delta_ratio=delta_ratio, expert_freq=expert_freq[str(i)],
delta_share_V=share_V, delta_share_U=share_U,
merge_method=merge_method, shared_infer=cfg['shared_infer']).to(model.model.layers[i].block_sparse_moe.gate.weight.device)
Merge_MoE_Block.merge_experts(model.model.layers[i].block_sparse_moe, svd_scale=svd_scale[i], hessian = fisher_info[i], scale_type='svdllm')
model.model.layers[i].block_sparse_moe = Merge_MoE_Block
return model
model = merge_model(args['base_model_path'], args['expert_freq_path'], args['svd_scale_path'], args['fisher_path'],
args['delta_ratio'], args['share_ratio'], args['share_V'], args['share_U'], args['merge_method'])
tokenizer = AutoTokenizer.from_pretrained(args['base_model_path'], use_fast=False)
if tokenizer.pad_token is None:
tokenizer.pad_token = tokenizer.eos_token
# -----------------------------------------------------------------------------------------
cfg['tokenizer'] = tokenizer
cfg['model_name'] = 'llama-2-7b'
cfg['model_type'] = 'mixtral'
test_logger = make_logger(os.path.join('output', 'runs', 'test_{}'.format(cfg['model_tag'])))
dataset = process_dataset(dataset, tokenizer)
if cfg['model_name'] in ['cnn', 'resnet18', 'wresnet28x2']:
model = make_batchnorm_stats(dataset['train'], model, cfg['model_name'])
model = make_prune_model(model)
if cfg['merge_model']:
for i in range(len(model.model.model.layers)):
model.model.model.layers[i].block_sparse_moe.update_Wmean()
if 'calib' in cfg['prune_method']:
print('Running Calibration ...', flush=True)
cfg['calibration_stage'] = True
cfg['calibration_dataset'] = 'wikitest'
calibration_data_loader = make_calibration_dataloader(tokenizer)
run_calibration(model, calibration_data_loader['train'])
save_calib_info(model)
if 'flapratio' in cfg['prune_method']:
from model import HiddenRepresentationPruning
pruning_module = HiddenRepresentationPruning(cfg, 'flapratio')
pruning_module.flap_ratio(model, test_logger)
cfg['calibration_stage'] = False
print('Calibration Done...', flush=True)
save_dir = f"{args['result_path']}/mixtral_delta-{args['delta_ratio']}-pp_ratio-{args['pp_ratio']}-shareV-{args['share_V']}"
os.makedirs(save_dir, exist_ok=True)
result_str = ppl_eval_sharing(model, tokenizer, experiment_name=f"D2-Mixtral", datasets=['wikitext2', 'ptb', 'c4'], params_only=False, batch_size=16)
with open(f"{save_dir}/ppl_eval_sharing.txt", "w") as f:
f.write(result_str)
run_lm_eval(model, tokenizer, batch_size=16, task_names=["openbookqa", "arc_easy", "winogrande", "hellaswag",
"arc_challenge", "piqa", "mathqa"], output_dir=save_dir)
return
def run_calibration(model, data_loader):
with torch.no_grad():
model.eval()
for i, input in enumerate(data_loader):
# now, the wikitext and c4 datsets used for calibration are clm tasks
# input_size = input['labels'].size(0)
input = {'input_ids': input['input_ids'], 'attention_mask': input['attention_mask'],
'labels': input['labels']}
input = to_device(input, "cuda")
output = model(**input)
# input_ = {'target': input['labels']}
# output_ = {'target': output['logits'], 'loss': output['loss']}
return
if __name__ == "__main__":
main()