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activate_neuron_T5Large.py
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activate_neuron_T5Large.py
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# -*- coding: utf-8 -*-
"""pipeline.ipynb
Automatically generated by Colaboratory.
Original file is located at
https://colab.research.google.com/drive/1-m2ywJVcfgCHOcEN-4agAbLz7tRGqMvM
"""
'''准备模型和数据'''
'''这里模型就用model这个变量'''
'''数据之后用example作为演示'''
'''使用的时候替换成自己的model就可以了'''
#import numpy as np
import torch
import config
#from activate_neuron.mymodel import *
#import activate_neuron.mymodel as mymodel
#from activate_neuron.utils import *
#import activate_neuron.utils as utils
#from transformers import AutoConfig, AutoModelForMaskedLM
#from model.modelling_roberta import RobertaForMaskedLM
#from reader.reader import init_dataset, init_formatter, init_test_dataset
import argparse
import os
import torch
import logging
import random
import numpy as np
from tools.init_tool import init_all
from config_parser import create_config
from tools.valid_tool import valid
from torch.autograd import Variable
logging.basicConfig(format='%(asctime)s - %(levelname)s - %(name)s - %(message)s',
datefmt='%m/%d/%Y %H:%M:%S',
level=logging.INFO)
logger = logging.getLogger(__name__)
def set_random_seed(seed):
"""Set random seed for reproducability."""
if seed is not None and seed > 0:
random.seed(seed)
np.random.seed(seed)
torch.manual_seed(seed)
torch.cuda.manual_seed_all(seed)
def relu(tmp):
return 1*(tmp > 0)*tmp
def topk(obj, k):
M=-10000
obj = list(obj)[:]
idlist = []
for i in range(k):
idlist.append(obj.index(max(obj)))
obj[obj.index(max(obj))]=M
return idlist
def relu(tmp):
return 1*(tmp > 0)*tmp
def topk(obj, k):
M=-10000
obj = list(obj)[:]
idlist = []
for i in range(k):
idlist.append(obj.index(max(obj)))
obj[obj.index(max(obj))]=M
return idlist
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument('--config', '-c', help="specific config file", required=True)
parser.add_argument('--gpu', '-g', help="gpu id list")
parser.add_argument('--local_rank', type=int, help='local rank', default=-1)
parser.add_argument('--do_test', help="do test while training or not", action="store_true")
parser.add_argument('--checkpoint', help="checkpoint file path", type=str, default=None)
parser.add_argument('--comment', help="checkpoint file path", default=None)
parser.add_argument("--seed", type=int, default=None)
parser.add_argument("--prompt_emb_output", type=bool, default=False)
parser.add_argument("--save_name", type=str, default=None)
parser.add_argument("--replacing_prompt", type=str, default=None)
parser.add_argument("--pre_train_mlm", default=False, action='store_true')
parser.add_argument("--task_transfer_projector", default=False, action='store_true')
parser.add_argument("--model_transfer_projector", default=False, action='store_true')
parser.add_argument("--activate_neuron", default=True, action='store_true')
parser.add_argument("--mode", type=str, default="valid")
parser.add_argument("--projector", type=str, default=None)
args = parser.parse_args()
configFilePath = args.config
config = create_config(configFilePath)
use_gpu = True
gpu_list = []
if args.gpu is None:
use_gpu = False
else:
use_gpu = True
os.environ["CUDA_VISIBLE_DEVICES"] = args.gpu
device_list = args.gpu.split(",")
for a in range(0, len(device_list)):
gpu_list.append(int(a))
os.system("clear")
config.set('distributed', 'local_rank', args.local_rank)
config.set("distributed", "use", False)
if config.getboolean("distributed", "use") and len(gpu_list)>1:
torch.cuda.set_device(gpu_list[args.local_rank])
torch.distributed.init_process_group(backend=config.get("distributed", "backend"))
config.set('distributed', 'gpu_num', len(gpu_list))
cuda = torch.cuda.is_available()
logger.info("CUDA available: %s" % str(cuda))
if not cuda and len(gpu_list) > 0:
logger.error("CUDA is not available but specific gpu id")
raise NotImplementedError
set_random_seed(args.seed)
########
'''
formatter = "mlmPrompt"
config.set("data","train_formatter_type",formatter)
config.set("data","valid_formatter_type",formatter)
config.set("data","test_formatter_type",formatter)
config.set("model","model_name","mlmPrompt")
'''
########
parameters = init_all(config, gpu_list, args.checkpoint, args.mode, local_rank = args.local_rank, args=args)
do_test = False
model = parameters["model"]
valid_dataset = parameters["valid_dataset"]
##########################
##########################
'''准备hook'''
'''这是提取特征的代码'''
outputs=[[] for _ in range(24)]
def save_ppt_outputs1_hook(n):
def fn(_,__,output):
#print("=====")
#print(output)
#print("----")
#print(output.shape) #torch.Size([1, 1, 3072])
#print("=====")
#exit()
outputs[n].append(output.detach().to("cpu"))
#outputs[n].append(output.detach())
return fn
for n in range(24):
#这里面提取feature的模组可以改变,这里因为我自定义模型的原因要两层roberta
#for l in model.state_dict().keys():
# print(l)
#print("====")
#exit()
#decoder
model.encoder.decoder.block[n].layer[2].DenseReluDense.wi.register_forward_hook(save_ppt_outputs1_hook(n))
#encoder
#model.encoder.encoder.block[n].layer[1].DenseReluDense.wi.register_forward_hook(save_ppt_outputs1_hook(n))
'''将数据通过模型'''
'''hook会自动将中间层的激活储存在outputs中'''
model.eval()
valid(model, parameters["valid_dataset"], 1, None, config, gpu_list, parameters["output_function"], mode=args.mode, args=args)
#################################################
#################################################
#################################################
'''
print(len(outputs)) #12
print(len(outputs[0])) #17 epoch
print(len(outputs[0][0])) #64
print(len(outputs[0][0][0])) #231
print(len(outputs[0][0][0][0])) #3072
#outputs[][][][][] , layer:12, epoch:17, batch_size:64, input_length:231, neuron:3072
'''
#merge 17 epoch
for k in range(24):
#outputs[k] = relu(np.concatenate(outputs[k]))
#outputs[k] = torch.relu(torch.cat(outputs[k]))
outputs[k] = torch.cat(outputs[k])
#print(outputs[k])
#print(outputs[k].shape)
#exit()
'''
print(len(outputs)) #12
print(len(outputs[0])) #17 epoch
print(len(outputs[0][0])) #64
print(len(outputs[0][0][0])) #231
print(len(outputs[0][0][0][0])) #3072
#outputs[][][][][] , layer:12, epoch:17, batch_size:64, input_length:231, neuron:3072
'''
'''这部分是根据论文里的代码找到某个neuron的最大激活'''
'''
#划定层数
#layer = np.random.randint(12)
layer = torch.randint(1,12,(1,))
#决定neuron
#neuron = np.random.randint(3072)
neuron = torch.randint(1,3072,(1,))
#这里面是得到了某层的某个neuron的所有激活
neuron_activation = outputs[layer][:,:,neuron]
max_activation = [neuron_activation[i,:length[i]].max() for i in range(size)]
print(neuron_activation)
print(max_activation)
exit()
'''
outputs = torch.stack(outputs)
#decoder
#print(outputs.shape)
outputs = outputs[:,:1,:1,:] #12 layers, [mask]
#print(outputs.shape)
#exit()
#encoder
#print(outputs.shape)
#outputs = outputs[:,:,100:101,:] #12 layers, [mask]
#print(outputs.shape)
#exit()
#print(outputs.shape)
# [12, 1, 1, 3072] --> 12, 1(batch_size), (target_length), 3072
# [12, 2, 1, 3072] --> 12, 1(batch_size), (target_length), 3072
#print(outputs)
#print(save_dir)
#exit()
save_name = args.replacing_prompt.strip().split("/")[-1].split(".")[0]
#print(save_name)
#exit()
dir = "task_activated_neuron"
if os.path.isdir(dir):
save_dir = dir+"/"+save_name
if os.path.isdir(save_dir):
torch.save(outputs,save_dir+"/task_activated_neuron")
else:
os.mkdir(save_dir)
torch.save(outputs,save_dir+"/task_activated_neuron")
else:
os.mkdir(dir)
save_dir = dir+"/"+save_name
os.mkdir(save_dir)
torch.save(outputs,save_dir+"/task_activated_neuron")
print("==Prompt emb==")
print(outputs.shape)
print("Save Done")
print("==============")
'''
size = 8 # number of the sentences
length = 231 #sentence length
#Activated neuron for a task-specific prompt
for layer in range(1,12):
for neuron in range(1,3072):
neuron_activation = outputs[layer][:,:,neuron]
print(outputs[layer].shape)
print(neuron_activation.shape)
exit()
max_activation = [neuron_activation[i,:length[i]].max() for i in range(size)]
print(neuron_activation)
print("------------")
print(max_activation)
print("============")
exit()
'''
'''选择头几个句子展示'''
'''
N = 4
indexes = topk(max_activation,N)
for ids in indexes:
print(tokenizer.decode(example['input_ids'][ids,:length[ids]]))
'''