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generate_sent.py
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generate_sent.py
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import transformers
import torch
import os
import json
import random
import numpy as np
import argparse
# from torch.utils.tensorboard import SummaryWriter
from datetime import datetime
from tqdm import tqdm
from torch.nn import DataParallel
# from tokenizations.bpe_tokenizer import get_encoder
# import pre_process_data as ppd
import pickle
from transformers import *
import torch
import os
from torch import randint
import torch.nn as nn
from reformer_pytorch import ReformerLM
from reformer_pytorch.generative_tools import TrainingWrapper
from transformers import BertTokenizer, AdamW
# from processors import *
import pickle
import tkitFile
from reformer_chinese import *
pretrained_weights = 'cache/vocab_small_terry_ai.txt'
tokenizer=tokenizer_plus(pretrained_weights)
tokenizer.max_len = 128
def get_data(path,tokenizer):
# temp=tkitFile.Json('data/cache/train.json')
for it in tkitFile.Json(path).auto_load():
item={}
# print(it)
kw = tokenizer.encode_plus(it['keywords'], max_length=tokenizer.max_len, add_special_tokens=True)
pad_num=tokenizer.max_len-len(kw['input_ids'])
item['keywords']=kw['input_ids']+ [tokenizer.convert_tokens_to_ids('[PAD]')]*pad_num
tx= tokenizer.encode_plus(it['text'], max_length=tokenizer.max_len, add_special_tokens=True)
pad_num=tokenizer.max_len-len(tx['input_ids'])
item['text']=tx['input_ids']+ [tokenizer.convert_tokens_to_ids('[PAD]')]*pad_num
# inputs_1 = tokenizer.encode_plus(sentence_0, sentence_1, add_special_tokens=True, return_tensors='pt')
# print(item)
yield item
def auto_encode(sentence_0):
# sentence_1 = "我是谁啊"
sentence_1=None
inputs_1 = tokenizer.encode_plus(sentence_0, sentence_1, add_special_tokens=False, return_tensors='pt')
# inputs_1=tokenizer.convert_tokens_to_ids(sentence_0)
# inputs_1 = torch.tensor(inputs_1).long()
return inputs_1['input_ids']
def get(start_text,length=50):
"""
获取预测文本
"""
# start_text=x_train_text[0][:5]
initial =auto_encode(start_text)
# print(initial)
sample = model.generate(initial, length, temperature=1., filter_thres = 0.9, eos_token = 1) # assume end token is 1, or omit and it will sample up to 100
# print(sample)
# print(sample.shape) # (1, <=100) token ids
text = tokenizer.convert_ids_to_tokens(sample.tolist()[0])
# if multi_gpu:
# loss = loss.mean()
# total_loss += loss
# total_steps += 1
# if (overall_step + 1) % log_step == 0:
# print('now time: {}:{}. Step {} of piece {}, ppl {}'.format(
# datetime.now().hour,
# datetime.now().minute,
# (step + 1),
# piece_num,
# torch.exp(loss)))
return text
def gen(text):
model = ReformerLM(
num_tokens= 13137,
dim = 128,
depth = 12,
max_seq_len = 4096,
lsh_dropout = 0.1,
causal = True,
full_attn_thres = 128
)
model = TrainingWrapper(model, ignore_index = 0, pad_value = 0).cpu()
output_dir="model"
model_cpu_path=os.path.join(output_dir, 'model_cpu.pt')
model.load_state_dict(torch.load(model_cpu_path))
initial =auto_encode(text)
# print(initial)
sample = model.generate(initial, 10, temperature=1., filter_thres = 0.9, eos_token = 1) # assume end token is 1, or omit and it will sample up to 100
# print(sample)
# print(sample.shape) # (1, <=100) token ids
text = tokenizer.convert_ids_to_tokens(sample.tolist()[0])
# print(text)
return text
def train(device='cpu',output_dir='model',epochs=5,save_step=5,batch_size=4):
model = ReformerLM(
num_tokens= 13137,
dim = 128,
depth = 12,
max_seq_len = 4096,
lsh_dropout = 0.1,
causal = True,
full_attn_thres = 128
)
model = TrainingWrapper(model, ignore_index = 0, pad_value = 0).to(device)
# output_dir="model"
model_cpu_path=os.path.join(output_dir, 'model_cpu.pt')
try:
model.load_state_dict(torch.load(model_cpu_path))
except:
pass
model.train()
optimizer = AdamW(params=model.parameters())
optimizer_path=os.path.join(output_dir, 'optimizer.pt')
try:
optimizer.load_state_dict(torch.load(optimizer_path))
except:
pass
print(optimizer)
total_loss = 0.0
# batch_size=4
loss = []
data=[]
for it in get_data("data/train.json",tokenizer):
data.append(it)
# data=data[:1000]
loss_fn = nn.CrossEntropyLoss() # -100 index = padding token
for n in tqdm(range(epochs)):
# print(n)
random.shuffle(data)
inputs=[]
labels=[]
for i,it in enumerate( data):
# print("it",it)
inputs.append(it['keywords'])
labels.append(it['text'])
if i %batch_size==0 and i!=0:
# print(it)
inputs_batch = torch.tensor(inputs).long().to(device)
labels_batch = torch.tensor(labels).long().to(device)
# print(inputs_batch)
inputs=[]
labels=[]
# inputs = torch.tensor(it['keywords']).long()
# labels = torch.tensor(it['text']).long()
# print("inputs",inputs)
pred = model(inputs_batch)
mlm_loss = loss_fn(pred.view(-1, tokenizer.vocab_size), labels_batch.view(-1))
total_loss += mlm_loss.item()
loss.append(mlm_loss.item())
print('loss',mlm_loss.item())
mlm_loss.backward()
optimizer.step()
model.zero_grad()
# output_dir="model"
if i% save_step==0 and i!=0:
model_cpu_path=os.path.join(output_dir, 'model_cpu.pt')
optimizer_path=os.path.join(output_dir, 'optimizer.pt')
torch.save(model.state_dict(), model_cpu_path)
torch.save(optimizer.state_dict(), optimizer_path)
model_cpu_path=os.path.join(output_dir, 'model_cpu.pt')
optimizer_path=os.path.join(output_dir, 'optimizer.pt')
torch.save(model.state_dict(), model_cpu_path)
torch.save(optimizer.state_dict(), optimizer_path)
# train()
# print("1:训练 2:生成")
# x=input("选择:")
# if x =='1':
# train()
# else:
x=input("关键词")
words=gen(x)
print("".join(words))