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token_by_token_icl.py
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import torch,os
from args import DeepArgs
from utils import set_gpu,get_datasets,generate_figure
from transformers import HfArgumentParser,AutoTokenizer,GPT2LMHeadModel
from circuit_model import trunk_model,assert_model
import logging
import json
from tqdm import tqdm
import copy
from demo_representation_vocb import assert_circuits_equal_output
hf_parser = HfArgumentParser((DeepArgs,))
args: DeepArgs = hf_parser.parse_args_into_dataclasses()[0]
torch.cuda.empty_cache()
set_gpu(args.gpu)
os.environ["CUDA_VISIBLE_DEVICES"] = '0,1,2,3,4'
# os.environ['CUDA_LAUNCH_BLOCKING'] = '1'
def get_logger(filename, verbosity=1, name=None):
level_dict = {0: logging.DEBUG, 1: logging.INFO, 2: logging.WARNING}
formatter = logging.Formatter(
"[%(asctime)s][%(filename)s][line:%(lineno)d][%(levelname)s] %(message)s"
)
logger = logging.getLogger(name)
logger.setLevel(level_dict[verbosity])
fh = logging.FileHandler(filename, "w")
fh.setFormatter(formatter)
logger.addHandler(fh)
# sh = logging.StreamHandler()
# sh.setFormatter(formatter)
# logger.addHandler(sh)
return logger
if args.task_name=='token_by_token':
if args.model_name=='gpt2xl':
tokenizer = AutoTokenizer.from_pretrained("openai-community/gpt2")
model=trunk_model(args)
orig_model = GPT2LMHeadModel.from_pretrained("openai-community/gpt2")
#check_model=assert_model(args)
#equal_model=assert_circuits_equal_output(args)
layer=12
circuit_layer=29
circuit_num=12*29
if args.case_type=='icl_sst2':
with open('dataset/icl_sst2.json','r') as f:
input_text=json.load(f)
if args.case_type=='icl_oc':
with open('dataset/icl_object_counting.json','r') as f:
input_text=json.load(f)
if args.case_type=='icl_raco':
with open('dataset/icl_reasoning_about_colored_objects.json','r') as f:
input_text=json.load(f)
if args.case_type=='icl_qawiki':
with open('dataset/icl_qa_wikidata.json','r') as f:
input_text=json.load(f)
input_self_text=':'
inputs_self = tokenizer(input_self_text, return_tensors="pt")
outputs_self = orig_model(**inputs_self, labels=inputs_self["input_ids"])
_,self_label_ids=torch.topk(outputs_self.logits[0][-1],1)
branch_cut=torch.zeros((circuit_num,circuit_num))
#init the input matrix
token_num=inputs_self['input_ids'].size()[-1]
input_matrix=torch.zeros((12,29,token_num,768)).to(args.device)
cut_circuit_tensor_all=None
#equal_model(inputs)
with torch.no_grad():
top_token,input_matrix,cut_circuit_tensor_all=model(inputs_self,self_label_ids,0,0,input_matrix,cut_circuit_tensor_all)
assert top_token[0].item()==self_label_ids.item()
for m in tqdm(range(circuit_num)):
for n in range(circuit_num):
if m//circuit_layer > n//circuit_layer and (m+1)%29!=27 and (m+1)%29!=28 and (m+1)%29!=0 and (m+1)%29!=1 and (n+1)%29!=27 and (n+1)%29!=28 and (n+1)%29!=0 and (n+1)%29!=1 and branch_cut[m][n]!=1:
branch_cut[m][n]=1
top_token,input_matrix_new,cut_circuit_tensor_all=model(inputs_self,self_label_ids,m,n,input_matrix,cut_circuit_tensor_all)
if top_token[0].item()!=self_label_ids.item():
branch_cut[m][n]=0
else:
# if t>0:
# check_model(inputs,label_ids,branch_cut)
input_matrix=input_matrix_new
torch.cuda.empty_cache()
all_branch_cut=[]
for id in range(29,348):
all_branch_cut_dict={}
all_branch_cut_dict['layer {} and circuit {}'.format(id//circuit_layer,id%circuit_layer)]=branch_cut[id].tolist()
all_branch_cut.append(all_branch_cut_dict)
old_data=[]
old_data.append(all_branch_cut)
with open('json_logs/token_by_token/gpt2xl/'+args.case_type+'/'+'self.json','w',encoding='utf-8') as data:
json.dump(old_data,data,ensure_ascii=False,sort_keys=True)
for i in range (0,len(input_text)):
#sst2 571
case_idx=input_text[i]['id']
print('To record {}-th case'.format(i))
for t in range(0,2):
if t==0:
input_case=input_text[i]['question']
inputs = tokenizer(input_case, return_tensors="pt")
token_length=inputs["input_ids"].size()[-1]
if t==1:
input_case=input_text[i]['text']
inputs = tokenizer(input_case, return_tensors="pt")
token_length=inputs["input_ids"].size()[-1]
with torch.no_grad():
outputs = orig_model(**inputs, labels=inputs["input_ids"])
_,label_ids=torch.topk(outputs.logits[0][-1],1)
branch_cut=torch.zeros((circuit_num,circuit_num))
#init the input matrix
token_num=inputs['input_ids'].size()[-1]
input_matrix=torch.zeros((12,29,token_num,768)).to(args.device)
cut_circuit_tensor_all=None
#equal_model(inputs)
top_token,input_matrix,cut_circuit_tensor_all=model(inputs,label_ids,0,0,input_matrix,cut_circuit_tensor_all)
assert top_token[0].item()==label_ids.item()
for m in tqdm(range(circuit_num)):
for n in range(circuit_num):
if m//circuit_layer > n//circuit_layer and (m+1)%29!=27 and (m+1)%29!=28 and (m+1)%29!=0 and (m+1)%29!=1 and (n+1)%29!=27 and (n+1)%29!=28 and (n+1)%29!=0 and (n+1)%29!=1 and branch_cut[m][n]!=1:
branch_cut[m][n]=1
top_token,input_matrix_new,cut_circuit_tensor_all=model(inputs,label_ids,m,n,input_matrix,cut_circuit_tensor_all)
if top_token[0].item()!=label_ids.item():
branch_cut[m][n]=0
else:
# if t>0:
# check_model(inputs,label_ids,branch_cut)
input_matrix=input_matrix_new
torch.cuda.empty_cache()
all_branch_cut=[]
for id in range(29,348):
all_branch_cut_dict={}
all_branch_cut_dict['layer {} and circuit {}'.format(id//circuit_layer,id%circuit_layer)]=branch_cut[id].tolist()
all_branch_cut.append(all_branch_cut_dict)
if t>0:
with open('json_logs/token_by_token/gpt2xl/'+args.case_type+'/'+'self.json') as self_data:
self_matrix=json.load(self_data)
self_cut=self_matrix[0]
with open('json_logs/token_by_token/gpt2xl/'+args.case_type+'/'+str(case_idx)+'.json') as read_data:
old_data=json.load(read_data)
old_data.append(all_branch_cut)
old_data.append(self_cut)
else:
old_data=[]
old_data.append(all_branch_cut)
with open('json_logs/token_by_token/gpt2xl/'+args.case_type+'/'+str(case_idx)+'.json','w',encoding='utf-8') as data:
json.dump(old_data,data,ensure_ascii=False,sort_keys=True)