文件结构如下所示：
mode.py
# Copyright (c) Microsoft Corporation.
# Licensed under the MIT License.
import torch
import torch.nn as nn
import torch
from torch.autograd import Variable
import copy
from torch.nn import CrossEntropyLoss, MSELoss
class Model(nn.Module):
def __init__(self, encoder,config,tokenizer,args):
super(Model, self).__init__()
self.encoder = encoder
self.config=config
self.tokenizer=tokenizer
self.args=args
# Define dropout layer, dropout_probability is taken from args.
self.dropout = nn.Dropout(args.dropout_probability)
def forward(self, input_ids=None,labels=None):
outputs=self.encoder(input_ids,attention_mask=input_ids.ne(1))[0]
# Apply dropout
outputs = self.dropout(outputs)
logits=outputs
prob=torch.sigmoid(logits)
if labels is not None:
labels=labels.float()
loss=torch.log(prob[:,0]+1e-10)*labels+torch.log((1-prob)[:,0]+1e-10)*(1-labels)
loss=-loss.mean()
return loss,prob
else:
return prob
run.py
# coding=utf-8
# Copyright 2018 The Google AI Language Team Authors and The HuggingFace Inc. team.
# Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""
Fine-tuning the library models for language modeling on a text file (GPT, GPT-2, BERT, RoBERTa).
GPT and GPT-2 are fine-tuned using a causal language modeling (CLM) loss while BERT and RoBERTa are fine-tuned
using a masked language modeling (MLM) loss.
"""
from __future__ import absolute_import, division, print_function
import argparse
import glob
import logging
import os
import pickle
import random
import re
import shutil
import time
import numpy as np
import torch
from torch.utils.data import DataLoader, Dataset, SequentialSampler, RandomSampler,TensorDataset
from torch.utils.data.distributed import DistributedSampler
import json
try:
from torch.utils.tensorboard import SummaryWriter
except:
from tensorboardX import SummaryWriter
from tqdm import tqdm, trange
import multiprocessing
from model import Model
from sklearn.metrics import precision_score, recall_score, f1_score,accuracy_score
cpu_cont = multiprocessing.cpu_count()
from transformers import (WEIGHTS_NAME, AdamW, get_linear_schedule_with_warmup,
BertConfig, BertForMaskedLM, BertTokenizer, BertForSequenceClassification,
GPT2Config, GPT2LMHeadModel, GPT2Tokenizer,
OpenAIGPTConfig, OpenAIGPTLMHeadModel, OpenAIGPTTokenizer,
RobertaConfig, RobertaForSequenceClassification, RobertaTokenizer,
DistilBertConfig, DistilBertForMaskedLM, DistilBertForSequenceClassification, DistilBertTokenizer)
logger = logging.getLogger(__name__)
MODEL_CLASSES = {
'gpt2': (GPT2Config, GPT2LMHeadModel, GPT2Tokenizer),
'openai-gpt': (OpenAIGPTConfig, OpenAIGPTLMHeadModel, OpenAIGPTTokenizer),
'bert': (BertConfig, BertForSequenceClassification, BertTokenizer),
'roberta': (RobertaConfig, RobertaForSequenceClassification, RobertaTokenizer),
'distilbert': (DistilBertConfig, DistilBertForSequenceClassification, DistilBertTokenizer)
}
class InputFeatures(object):
"""A single training/test features for a example."""
def __init__(self,
input_tokens,
input_ids,
idx,
label,
):
self.input_tokens = input_tokens
self.input_ids = input_ids
self.idx=str(idx)
self.label=label
def convert_examples_to_features(js,tokenizer,args):
#source
code=' '.join(js['func'].split())
code_tokens=tokenizer.tokenize(code)[:args.block_size-2]
source_tokens =[tokenizer.cls_token]+code_tokens+[tokenizer.sep_token]
source_ids = tokenizer.convert_tokens_to_ids(source_tokens)
padding_length = args.block_size - len(source_ids)
source_ids+=[tokenizer.pad_token_id]*padding_length
return InputFeatures(source_tokens,source_ids,js['idx'],js['target'])
class TextDataset(Dataset):
def __init__(self, tokenizer, args, file_path=None):
self.examples = []
with open(file_path) as f:
for line in f:
js=json.loads(line.strip())
self.examples.append(convert_examples_to_features(js,tokenizer,args))
if 'train' in file_path:
for idx, example in enumerate(self.examples[:3]):
logger.info("*** Example ***")
logger.info("idx: {}".format(idx))
logger.info("label: {}".format(example.label))
logger.info("input_tokens: {}".format([x.replace('\u0120','_') for x in example.input_tokens]))
logger.info("input_ids: {}".format(' '.join(map(str, example.input_ids))))
def __len__(self):
return len(self.examples)
def __getitem__(self, i):
return torch.tensor(self.examples[i].input_ids),torch.tensor(self.examples[i].label)
def set_seed(seed=42):
random.seed(seed)
os.environ['PYHTONHASHSEED'] = str(seed)
np.random.seed(seed)
torch.manual_seed(seed)
torch.cuda.manual_seed(seed)
torch.backends.cudnn.deterministic = True
def train(args, train_dataset, model, tokenizer):
""" Train the model """
args.train_batch_size = args.per_gpu_train_batch_size * max(1, args.n_gpu)
train_sampler = RandomSampler(train_dataset) if args.local_rank == -1 else DistributedSampler(train_dataset)
train_dataloader = DataLoader(train_dataset, sampler=train_sampler,
batch_size=args.train_batch_size,num_workers=4,pin_memory=True)
args.max_steps=args.epoch*len( train_dataloader)
args.save_steps=len( train_dataloader)
args.warmup_steps=len( train_dataloader)
args.logging_steps=len( train_dataloader)
args.num_train_epochs=args.epoch
model.to(args.device)
# Prepare optimizer and schedule (linear warmup and decay)
no_decay = ['bias', 'LayerNorm.weight']
optimizer_grouped_parameters = [
{'params': [p for n, p in model.named_parameters() if not any(nd in n for nd in no_decay)],
'weight_decay': args.weight_decay},
{'params': [p for n, p in model.named_parameters() if any(nd in n for nd in no_decay)], 'weight_decay': 0.0}
]
num_params = sum(p.numel() for p in model.parameters())
trainable_param = sum(p.numel() for p in model.parameters() if p.requires_grad )
logger.info(f"Number of model parameters: {num_params}")
logger.info(f"Number of model trainable_param: {trainable_param}")
optimizer = AdamW(optimizer_grouped_parameters, lr=args.learning_rate, eps=args.adam_epsilon)
scheduler = get_linear_schedule_with_warmup(optimizer, num_warmup_steps=args.max_steps*0.1,
num_training_steps=args.max_steps)
if args.fp16:
try:
from apex import amp
except ImportError:
raise ImportError("Please install apex from https://www.github.com/nvidia/apex to use fp16 training.")
model, optimizer = amp.initialize(model, optimizer, opt_level=args.fp16_opt_level)
# multi-gpu training (should be after apex fp16 initialization)
if args.n_gpu > 1:
model = torch.nn.DataParallel(model)
# Distributed training (should be after apex fp16 initialization)
if args.local_rank != -1:
model = torch.nn.parallel.DistributedDataParallel(model, device_ids=[args.local_rank],
output_device=args.local_rank,
find_unused_parameters=True)
checkpoint_last = os.path.join(args.output_dir, 'checkpoint-last')
scheduler_last = os.path.join(checkpoint_last, 'scheduler.pt')
optimizer_last = os.path.join(checkpoint_last, 'optimizer.pt')
if os.path.exists(scheduler_last):
scheduler.load_state_dict(torch.load(scheduler_last))
if os.path.exists(optimizer_last):
optimizer.load_state_dict(torch.load(optimizer_last))
# Train!
logger.info("***** Running training *****")
logger.info(" Num examples = %d", len(train_dataset))
logger.info(" Num Epochs = %d", args.num_train_epochs)
logger.info(" Instantaneous batch size per GPU = %d", args.per_gpu_train_batch_size)
logger.info(" Total train batch size (w. parallel, distributed & accumulation) = %d",
args.train_batch_size * args.gradient_accumulation_steps * (
torch.distributed.get_world_size() if args.local_rank != -1 else 1))
logger.info(" Gradient Accumulation steps = %d", args.gradient_accumulation_steps)
logger.info(" Total optimization steps = %d", args.max_steps)
global_step = args.start_step
tr_loss, logging_loss,avg_loss,tr_nb,tr_num,train_loss = 0.0, 0.0,0.0,0,0,0
best_mrr=0.0
best_acc=0.0
# model.resize_token_embeddings(len(tokenizer))
model.zero_grad()
# Initialize early stopping parameters at the start of training
early_stopping_counter = 0
best_loss = None
for idx in range(args.start_epoch, int(args.num_train_epochs)):
bar = tqdm(train_dataloader,total=len(train_dataloader))
tr_num=0
train_loss=0
for step, batch in enumerate(bar):
inputs = batch[0].to(args.device)
labels=batch[1].to(args.device)
model.train()
loss,logits = model(inputs,labels)
preds = logits[:, 0] > 0.5
if args.n_gpu > 1:
loss = loss.mean() # mean() to average on multi-gpu parallel training
if args.gradient_accumulation_steps > 1:
loss = loss / args.gradient_accumulation_steps
if args.fp16:
with amp.scale_loss(loss, optimizer) as scaled_loss:
scaled_loss.backward()
torch.nn.utils.clip_grad_norm_(amp.master_params(optimizer), args.max_grad_norm)
else:
loss.backward()
torch.nn.utils.clip_grad_norm_(model.parameters(), args.max_grad_norm)
tr_loss += loss.item()
tr_num+=1
train_loss+=loss.item()
if avg_loss==0:
avg_loss=tr_loss
avg_loss=round(train_loss/tr_num,5)
bar.set_description("epoch {} loss {}".format(idx,avg_loss))
if (step + 1) % args.gradient_accumulation_steps == 0:
optimizer.step()
optimizer.zero_grad()
scheduler.step()
global_step += 1
output_flag=True
avg_loss=round(np.exp((tr_loss - logging_loss) /(global_step- tr_nb)),4)
if args.local_rank in [-1, 0] and args.logging_steps > 0 and global_step % args.logging_steps == 0:
logging_loss = tr_loss
tr_nb=global_step
if args.local_rank in [-1, 0] and args.save_steps > 0 and global_step % args.save_steps == 0:
if args.local_rank == -1 and args.evaluate_during_training: # Only evaluate when single GPU otherwise metrics may not average well
results = evaluate(args, model, tokenizer,eval_when_training=True)
for key, value in results.items():
logger.info(" %s = %s", key, round(value,4))
# Save model checkpoint
if results['eval_acc']>best_acc:
best_acc=results['eval_acc']
logger.info(" "+"*"*20)
logger.info(" Best acc:%s",round(best_acc,4))
logger.info(" "+"*"*20)
checkpoint_prefix = 'checkpoint-best-acc'
output_dir = os.path.join(args.output_dir, '{}'.format(checkpoint_prefix))
if not os.path.exists(output_dir):
os.makedirs(output_dir)
model_to_save = model.module if hasattr(model,'module') else model
output_dir = os.path.join(output_dir, '{}'.format('model.bin'))
torch.save(model_to_save.state_dict(), output_dir)
logger.info("Saving model checkpoint to %s", output_dir)
# Calculate average loss for the epoch
avg_loss = train_loss / tr_num
# Check for early stopping condition
if args.early_stopping_patience is not None:
if best_loss is None or avg_loss < best_loss - args.min_loss_delta:
best_loss = avg_loss
early_stopping_counter = 0
else:
early_stopping_counter += 1
if early_stopping_counter >= args.early_stopping_patience:
logger.info("Early stopping")
break # Exit the loop early
def evaluate(args, model, tokenizer,eval_when_training=False):
# Loop to handle MNLI double evaluation (matched, mis-matched)
eval_output_dir = args.output_dir
eval_dataset = TextDataset(tokenizer, args,args.eval_data_file)
if not os.path.exists(eval_output_dir) and args.local_rank in [-1, 0]:
os.makedirs(eval_output_dir)
args.eval_batch_size = args.per_gpu_eval_batch_size * max(1, args.n_gpu)
# Note that DistributedSampler samples randomly
eval_sampler = SequentialSampler(eval_dataset) if args.local_rank == -1 else DistributedSampler(eval_dataset)
eval_dataloader = DataLoader(eval_dataset, sampler=eval_sampler, batch_size=args.eval_batch_size,num_workers=4,pin_memory=True)
# multi-gpu evaluate
if args.n_gpu > 1 and eval_when_training is False:
model = torch.nn.DataParallel(model)
# Eval!
logger.info("***** Running evaluation *****")
logger.info(" Num examples = %d", len(eval_dataset))
logger.info(" Batch size = %d", args.eval_batch_size)
eval_loss = 0.0
nb_eval_steps = 0
model.eval()
logits=[]
labels=[]
for batch in eval_dataloader:
inputs = batch[0].to(args.device)
label=batch[1].to(args.device)
with torch.no_grad():
lm_loss,logit = model(inputs,label)
eval_loss += lm_loss.mean().item()
logits.append(logit.cpu().numpy())
labels.append(label.cpu().numpy())
nb_eval_steps += 1
logits=np.concatenate(logits,0)
labels=np.concatenate(labels,0)
preds=logits[:,0]>0.5
eval_acc=np.mean(labels==preds)
precision = precision_score(labels, preds)
recall = recall_score(labels, preds)
f1 = f1_score(labels, preds)
eval_loss = eval_loss / nb_eval_steps
perplexity = torch.tensor(eval_loss)
logger.info(f"test_Evaluation Accuracy: {eval_acc}\n")
logger.info(f"test_Precision: {precision}")
logger.info(f"test_Recall: {recall}")
logger.info(f"test_F1 Score: {f1}")
result = {
"eval_loss": float(perplexity),
"eval_acc":round(eval_acc,4),
}
end_time = time.time()
elapsed_time = end_time - start_time
# Log timing information
logger.info(f"{ elapsed_time:.2f}")
return result
def test(args, model, tokenizer):
# Loop to handle MNLI double evaluation (matched, mis-matched)
eval_dataset = TextDataset(tokenizer, args,args.test_data_file)
args.eval_batch_size = args.per_gpu_eval_batch_size * max(1, args.n_gpu)
# Note that DistributedSampler samples randomly
eval_sampler = SequentialSampler(eval_dataset) if args.local_rank == -1 else DistributedSampler(eval_dataset)
eval_dataloader = DataLoader(eval_dataset, sampler=eval_sampler, batch_size=args.eval_batch_size)
# multi-gpu evaluate
if args.n_gpu > 1:
model = torch.nn.DataParallel(model)
# Eval!
logger.info("***** Running Test *****")
logger.info(" Num examples = %d", len(eval_dataset))
logger.info(" Batch size = %d", args.eval_batch_size)
eval_loss = 0.0
nb_eval_steps = 0
model.eval()
logits=[]
labels=[]
for batch in tqdm(eval_dataloader,total=len(eval_dataloader)):
inputs = batch[0].to(args.device)
label=batch[1].to(args.device)
with torch.no_grad():
logit = model(inputs)
logits.append(logit.cpu().numpy())
labels.append(label.cpu().numpy())
logits=np.concatenate(logits,0)
labels=np.concatenate(labels,0)
preds=logits[:,0]>0.5
with open(os.path.join(args.output_dir,"predictions.txt"),'w') as f:
for example,pred in zip(eval_dataset.examples,preds):
if pred:
f.write(example.idx+'\t1\n')
else:
f.write(example.idx+'\t0\n')
def main():
parser = argparse.ArgumentParser()
## Required parameters
parser.add_argument("--train_data_file", default=None, type=str, required=True,
help="The input training data file (a text file).")
parser.add_argument("--output_dir", default=None, type=str, required=True,
help="The output directory where the model predictions and checkpoints will be written.")
## Other parameters
parser.add_argument("--eval_data_file", default=None, type=str,
help="An optional input evaluation data file to evaluate the perplexity on (a text file).")
parser.add_argument("--test_data_file", default=None, type=str,
help="An optional input evaluation data file to evaluate the perplexity on (a text file).")
parser.add_argument("--model_type", default="bert", type=str,
help="The model architecture to be fine-tuned.")
parser.add_argument("--model_name_or_path", default=None, type=str,
help="The model checkpoint for weights initialization.")
parser.add_argument("--mlm", action='store_true',
help="Train with masked-language modeling loss instead of language modeling.")
parser.add_argument("--mlm_probability", type=float, default=0.15,
help="Ratio of tokens to mask for masked language modeling loss")
parser.add_argument("--config_name", default="", type=str,
help="Optional pretrained config name or path if not the same as model_name_or_path")
parser.add_argument("--tokenizer_name", default="", type=str,
help="Optional pretrained tokenizer name or path if not the same as model_name_or_path")
parser.add_argument("--cache_dir", default="", type=str,
help="Optional directory to store the pre-trained models downloaded from s3 (instread of the default one)")
parser.add_argument("--block_size", default=-1, type=int,
help="Optional input sequence length after tokenization."
"The training dataset will be truncated in block of this size for training."
"Default to the model max input length for single sentence inputs (take into account special tokens).")
parser.add_argument("--do_train", action='store_true',
help="Whether to run training.")
parser.add_argument("--do_eval", action='store_true',
help="Whether to run eval on the dev set.")
parser.add_argument("--do_test", action='store_true',
help="Whether to run eval on the dev set.")
parser.add_argument("--evaluate_during_training", action='store_true',
help="Run evaluation during training at each logging step.")
parser.add_argument("--do_lower_case", action='store_true',
help="Set this flag if you are using an uncased model.")
parser.add_argument("--train_batch_size", default=4, type=int,
help="Batch size per GPU/CPU for training.")
parser.add_argument("--eval_batch_size", default=4, type=int,
help="Batch size per GPU/CPU for evaluation.")
parser.add_argument('--gradient_accumulation_steps', type=int, default=1,
help="Number of updates steps to accumulate before performing a backward/update pass.")
parser.add_argument("--learning_rate", default=5e-5, type=float,
help="The initial learning rate for Adam.")
parser.add_argument("--weight_decay", default=0.0, type=float,
help="Weight deay if we apply some.")
parser.add_argument("--adam_epsilon", default=1e-8, type=float,
help="Epsilon for Adam optimizer.")
parser.add_argument("--max_grad_norm", default=1.0, type=float,
help="Max gradient norm.")
parser.add_argument("--num_train_epochs", default=1.0, type=float,
help="Total number of training epochs to perform.")
parser.add_argument("--max_steps", default=-1, type=int,
help="If > 0: set total number of training steps to perform. Override num_train_epochs.")
parser.add_argument("--warmup_steps", default=0, type=int,
help="Linear warmup over warmup_steps.")
parser.add_argument('--logging_steps', type=int, default=50,
help="Log every X updates steps.")
parser.add_argument('--save_steps', type=int, default=50,
help="Save checkpoint every X updates steps.")
parser.add_argument('--save_total_limit', type=int, default=None,
help='Limit the total amount of checkpoints, delete the older checkpoints in the output_dir, does not delete by default')
parser.add_argument("--eval_all_checkpoints", action='store_true',
help="Evaluate all checkpoints starting with the same prefix as model_name_or_path ending and ending with step number")
parser.add_argument("--no_cuda", action='store_true',
help="Avoid using CUDA when available")
parser.add_argument('--overwrite_output_dir', action='store_true',
help="Overwrite the content of the output directory")
parser.add_argument('--overwrite_cache', action='store_true',
help="Overwrite the cached training and evaluation sets")
parser.add_argument('--seed', type=int, default=42,
help="random seed for initialization")
parser.add_argument('--epoch', type=int, default=42,
help="random seed for initialization")
parser.add_argument('--fp16', action='store_true',
help="Whether to use 16-bit (mixed) precision (through NVIDIA apex) instead of 32-bit")
parser.add_argument('--fp16_opt_level', type=str, default='O1',
help="For fp16: Apex AMP optimization level selected in ['O0', 'O1', 'O2', and 'O3']."
"See details at https://nvidia.github.io/apex/amp.html")
parser.add_argument("--local_rank", type=int, default=-1,
help="For distributed training: local_rank")
parser.add_argument('--server_ip', type=str, default='', help="For distant debugging.")
parser.add_argument('--server_port', type=str, default='', help="For distant debugging.")
# Add early stopping parameters and dropout probability parameters
parser.add_argument("--early_stopping_patience", type=int, default=None,
help="Number of epochs with no improvement after which training will be stopped.")
parser.add_argument("--min_loss_delta", type=float, default=0.001,
help="Minimum change in the loss required to qualify as an improvement.")
parser.add_argument('--dropout_probability', type=float, default=0, help='dropout probability')
args = parser.parse_args()
# Setup distant debugging if needed
if args.server_ip and args.server_port:
# Distant debugging - see https://code.visualstudio.com/docs/python/debugging#_attach-to-a-local-script
import ptvsd
print("Waiting for debugger attach")
ptvsd.enable_attach(address=(args.server_ip, args.server_port), redirect_output=True)
ptvsd.wait_for_attach()
# Setup CUDA, GPU & distributed training
if args.local_rank == -1 or args.no_cuda:
device = torch.device("cuda" if torch.cuda.is_available() and not args.no_cuda else "cpu")
args.n_gpu = torch.cuda.device_count()
else: # Initializes the distributed backend which will take care of sychronizing nodes/GPUs
torch.cuda.set_device(args.local_rank)
device = torch.device("cuda", args.local_rank)
torch.distributed.init_process_group(backend='nccl')
args.n_gpu = 1
args.device = device
args.per_gpu_train_batch_size=args.train_batch_size//args.n_gpu
args.per_gpu_eval_batch_size=args.eval_batch_size//args.n_gpu
# Setup logging
logging.basicConfig(format='%(asctime)s - %(levelname)s - %(name)s - %(message)s',
datefmt='%m/%d/%Y %H:%M:%S',
level=logging.INFO if args.local_rank in [-1, 0] else logging.WARN)
logger.warning("Process rank: %s, device: %s, n_gpu: %s, distributed training: %s, 16-bits training: %s",
args.local_rank, device, args.n_gpu, bool(args.local_rank != -1), args.fp16)
# Set seed
set_seed(args.seed)
# Load pretrained model and tokenizer
if args.local_rank not in [-1, 0]:
torch.distributed.barrier() # Barrier to make sure only the first process in distributed training download model & vocab
args.start_epoch = 0
args.start_step = 0
checkpoint_last = os.path.join(args.output_dir, 'checkpoint-last')
if os.path.exists(checkpoint_last) and os.listdir(checkpoint_last):
args.model_name_or_path = os.path.join(checkpoint_last, 'pytorch_model.bin')
args.config_name = os.path.join(checkpoint_last, 'config.json')
idx_file = os.path.join(checkpoint_last, 'idx_file.txt')
with open(idx_file, encoding='utf-8') as idxf:
args.start_epoch = int(idxf.readlines()[0].strip()) + 1
step_file = os.path.join(checkpoint_last, 'step_file.txt')
if os.path.exists(step_file):
with open(step_file, encoding='utf-8') as stepf:
args.start_step = int(stepf.readlines()[0].strip())
logger.info("reload model from {}, resume from {} epoch".format(checkpoint_last, args.start_epoch))
config_class, model_class, tokenizer_class = MODEL_CLASSES[args.model_type]
config = config_class.from_pretrained(args.config_name if args.config_name else args.model_name_or_path,
cache_dir=args.cache_dir if args.cache_dir else None)
config.num_labels=1
tokenizer = tokenizer_class.from_pretrained(args.tokenizer_name,
do_lower_case=args.do_lower_case,
cache_dir=args.cache_dir if args.cache_dir else None)
if args.block_size <= 0:
args.block_size = tokenizer.max_len_single_sentence # Our input block size will be the max possible for the model
args.block_size = min(args.block_size, tokenizer.max_len_single_sentence)
if args.model_name_or_path:
model = model_class.from_pretrained(args.model_name_or_path,
from_tf=bool('.ckpt' in args.model_name_or_path),
config=config,
cache_dir=args.cache_dir if args.cache_dir else None)
else:
model = model_class(config)
model=Model(model,config,tokenizer,args)
if args.local_rank == 0:
torch.distributed.barrier() # End of barrier to make sure only the first process in distributed training download model & vocab
logger.info("Training/evaluation parameters %s", args)
# Training
if args.do_train:
if args.local_rank not in [-1, 0]:
torch.distributed.barrier() # Barrier to make sure only the first process in distributed training process the dataset, and the others will use the cache
train_dataset = TextDataset(tokenizer, args,args.train_data_file)
if args.local_rank == 0:
torch.distributed.barrier()
train(args, train_dataset, model, tokenizer)
# Evaluation
results = {}
if args.do_eval and args.local_rank in [-1, 0]:
checkpoint_prefix = 'checkpoint-best-acc/model.bin'
output_dir = os.path.join(args.output_dir, '{}'.format(checkpoint_prefix))
model.load_state_dict(torch.load(output_dir))
model.to(args.device)
result=evaluate(args, model, tokenizer)
logger.info("***** Eval results *****")
for key in sorted(result.keys()):
logger.info(" %s = %s", key, str(round(result[key],4)))
if args.do_test and args.local_rank in [-1, 0]:
checkpoint_prefix = 'checkpoint-best-acc/model.bin'
output_dir = os.path.join(args.output_dir, '{}'.format(checkpoint_prefix))
model.load_state_dict(torch.load(output_dir))
model.to(args.device)
test(args, model, tokenizer)
return results
if __name__ == "__main__":
start_time = time.time()
main()
run.sh
python run.pq \
--output_dir=./saved_models \
--model_type=roberta \
--tokenizer_name=microsoft/unixcoder-base \
--model_name_or_path=microsoft/unixcoder-base \
--do_train \
--train_data_file=/data/code/codebert/dataset/dataset/d2a/d2a_train.json \
--eval_data_file=/data/code/codebert/dataset/dataset/d2a/d2a_test.json \
--epoch 20 \
--block_size 400 \
--train_batch_size 4 \
--eval_batch_size 8 \
--learning_rate 2e-6 \
--max_grad_norm 1.0 \
--evaluate_during_training \
--seed 123213 2>&1 | tee ada.log
![[ZJCTF 2019]NiZhuanSiWei - 伪协议+文件包含+反序列化](https://img-blog.csdnimg.cn/cfcabeaa8ff14d6ca7e5e821a911c6b9.png#pic_center)
