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run_gen.py
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run_gen.py
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# 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.
"""
import os
import ast
import json
import math
import time
import scipy
import wandb
import random
import pickle
import logging
import argparse
import numpy as np
from tqdm import tqdm
import multiprocessing
from functools import partial
import torch
from torch.nn import CrossEntropyLoss
from torch.utils.tensorboard import SummaryWriter
from torch.utils.data import DataLoader, SequentialSampler, RandomSampler
from torch.utils.data.distributed import DistributedSampler
from transformers import AdamW, get_linear_schedule_with_warmup
from models import build_or_load_gen_model
from evaluator import smooth_bleu
from evaluator.CodeBLEU import calc_code_bleu
from evaluator.bleu import _bleu
from utils import get_filenames, get_elapse_time, load_and_cache_gen_data
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 add_args(parser):
parser.add_argument("--task", type=str, required=True,
choices=['summarize', 'cmt_msg_gen', 'concode', 'translate', 'refine', 'defect', 'clone', 'multi_task'])
parser.add_argument("--sub_task", type=str, default='')
parser.add_argument("--lang", type=str, default='')
parser.add_argument("--eval_task", type=str, default='')
parser.add_argument("--model_type", default="codet5", type=str, choices=['roberta', 'bart', 'codet5'])
parser.add_argument("--add_lang_ids", action='store_true')
parser.add_argument("--data_num", default=-1, type=int)
parser.add_argument("--start_epoch", default=0, type=int)
parser.add_argument("--num_train_epochs", default=100, type=int)
parser.add_argument("--patience", default=5, type=int)
parser.add_argument("--cache_path", type=str, required=True)
parser.add_argument("--summary_dir", type=str, required=True)
parser.add_argument("--data_dir", type=str, required=True)
parser.add_argument("--res_dir", type=str, required=True)
parser.add_argument("--res_fn", type=str, default=None)
parser.add_argument("--add_task_prefix", action='store_true', help="Whether to add task prefix for t5 and codet5")
parser.add_argument("--save_last_checkpoints", action='store_true')
parser.add_argument("--always_save_model", action='store_true')
parser.add_argument("--do_eval_bleu", action='store_true', help="Whether to evaluate bleu on dev set.")
## Required parameters
parser.add_argument("--model_name_or_path", default="roberta-base", type=str,
help="Path to pre-trained model: e.g. roberta-base")
parser.add_argument("--output_dir", default=None, type=str, required=True,
help="The output directory where the model predictions and checkpoints will be written.")
parser.add_argument("--load_model_path", default=None, type=str,
help="Path to trained model: Should contain the .bin files")
## Other parameters
parser.add_argument("--train_filename", default=None, type=str,
help="The train filename. Should contain the .jsonl files for this task.")
parser.add_argument("--dev_filename", default=None, type=str,
help="The dev filename. Should contain the .jsonl files for this task.")
parser.add_argument("--test_filename", default=None, type=str,
help="The test filename. Should contain the .jsonl files for this task.")
parser.add_argument("--config_name", default="", type=str,
help="Pretrained config name or path if not the same as model_name")
parser.add_argument("--tokenizer_name", default="roberta-base", type=str,
help="Pretrained tokenizer name or path if not the same as model_name")
parser.add_argument("--max_source_length", default=200, type=int,
help="The maximum total source sequence length after tokenization. Sequences longer "
"than this will be truncated, sequences shorter will be padded.")
parser.add_argument("--max_target_length", default=50, type=int,
help="The maximum total target sequence length after tokenization. Sequences longer "
"than this will be truncated, sequences shorter will be padded.")
parser.add_argument("--do_train", action='store_true',
help="Whether to run eval on the train set.")
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("--do_lower_case", action='store_true',
help="Set this flag if you are using an uncased model.")
parser.add_argument("--no_cuda", action='store_true',
help="Avoid using CUDA when available")
parser.add_argument("--train_batch_size", default=8, type=int,
help="Batch size per GPU/CPU for training.")
parser.add_argument("--eval_batch_size", default=8, 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("--beam_size", default=10, type=int,
help="beam size for beam search")
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("--save_steps", default=-1, type=int, )
parser.add_argument("--log_steps", default=-1, type=int, )
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("--eval_steps", default=-1, type=int,
help="")
parser.add_argument("--train_steps", default=-1, type=int,
help="")
parser.add_argument("--warmup_steps", default=100, type=int,
help="Linear warmup over warmup_steps.")
parser.add_argument("--local_rank", type=int, default=-1,
help="For distributed training: local_rank")
parser.add_argument('--seed', type=int, default=1234,
help="random seed for initialization")
# new design and its switch
parser.add_argument("--setting", default={'known_part':['type','scope'], 'unknown_part': ['subject']}, # TODO default value should be None;
help="choose which types of commit message are used in the training", type=ast.literal_eval)
parser.add_argument("--max_unknown_part_length", default=None, type=int, # TODO default:None
help="The maximum length of unknown part(such as <type> and <scope>) in the target sequence after tokenization. Sequences longer "
"than this will be truncated, sequences shorter will be padded.")
parser.add_argument("--max_part_length", default={'type_scope': 10, 'type':10, 'scope':10, 'subject': 40, 'type_scope_subject': 50},
help="The maximum length of (<type>, <scope>) or (<subject>) in the target sequence after tokenization. Sequences longer "
"than this will be truncated, sequences shorter will be padded.")
parser.add_argument("--type_as_special_token", action='store_true',
help="set the value of list `unknwon_part_start_limited_choice_list` as special token in tokenizer")
parser.add_argument("--filter_none_scope", action='store_true',
help="filter_none_scope")
parser.add_argument("--target_is_knwon_part", action='store_true',
help="target_is_knwon_part")
parser.add_argument("--no_pos_no_segment", action='store_true',
help="no_pos_no_segment")
parser.add_argument("--co_teaching", action='store_true',
help="co_teaching")
parser.add_argument("--co_training", action='store_true',
help="co_training")
parser.add_argument("--co_training_method", default=None, type=int,
help="co_training_method")
parser.add_argument("--multi_task", action='store_true',
help="multi_task")
parser.add_argument("--valid_data_not_sample", action='store_true',
help="valid_data_not_sample")
parser.add_argument("--add_prompt", default=None, type=ast.literal_eval,
help="Example:{\"type_scope\":<type_scope>, \"subject\":<subject>}; {\"type\":<type>, \"scope\":<scope>, \"subject\":<subject>};")
parser.add_argument("--select_train_idx_list_file", nargs='+',
help="select_train_idx_list_file")
parser.add_argument("--select_valid_idx_list_file", nargs='+',
help="select_valid_idx_list_file")
parser.add_argument("--select_test_idx_list_file", nargs='+',
help="select_test_idx_list_file")
parser.add_argument("--setting_only_used_to_train", action='store_true',
help="setting_only_used_to_train")
parser.add_argument("--get_training_loss_list", action='store_true',
help="get_training_loss_list")
parser.add_argument("--first_token", default=None,
help="first_token")
parser.add_argument("--eval_bleu_part", default=None, type=ast.literal_eval)
parser.add_argument("--get_idx_at_last", action='store_true',
help="get_idx_at_last")
parser.add_argument("--base_number", default=np.e, type=float,
help="base_number")
parser.add_argument("--wait_confidence", default=3, type=int,
help="wait_confidence")
parser.add_argument("--max_cpu_count", default=128, type=int,
help="wait_confidence")
parser.add_argument("--calculate_after_each_epoch", action='store_true',
help="calculate_after_each_epoch")
# REF TODO
parser.add_argument('--noise_rate', type = float,
help = 'corruption rate, should be less than 1', default = 0.91)
parser.add_argument('--forget_rate', type = float,
help = 'forget rate', default = None)
parser.add_argument('--num_gradual', type = int, default = 10, #???
help='how many epochs for linear drop rate, can be 5, 10, 15. This parameter is equal to Tk for R(T) in Co-teaching paper.')
parser.add_argument('--exponent', type = float, default = 1,
help='exponent of the forget rate, can be 0.5, 1, 2. This parameter is equal to c in Tc for R(T) in Co-teaching paper.')
## fixed
parser.add_argument("--loss_focus_unknown", action='store_true',
help="loss_focus_unknown")
parser.add_argument("--unknwon_part_start_limited_choice_list", default=None, #["chore", "docs", "feat", "fix", "perf", "refactor", "style", "test"],
help="`limited_type_list`")
parser.add_argument("--skip_ref_token", default=None, # TODO default:None
help="ignore_special_token")
parser.add_argument("--wandb_username", type=str, required=False, default=None,
help="username in wandb.ai")
parser.add_argument("--wandb_project_name", default=None, type=str, required=False,
help="project name showing in wandb")
parser.add_argument("--wandb_run_name", default="only_type", type=str, required=False,
help="experiment running name showing in wandb")
args = parser.parse_args()
if not args.wandb_project_name:
args.wandb_project_name = "CodeT5_cmtgen_{}".format(args.sub_task.replace("/","_")) # "/,\,#,?,%,:"
if args.forget_rate is None:
args.forget_rate=args.noise_rate
if args.task in ['summarize']:
args.lang = args.sub_task
elif args.task in ['cmt_msg_gen']:
args.lang = args.sub_task
elif args.task in ['refine', 'concode', 'clone']:
args.lang = 'java'
elif args.task == 'defect':
args.lang = 'c'
elif args.task == 'translate':
args.lang = 'c_sharp' if args.sub_task == 'java-cs' else 'java'
if args.multi_task:
args.setting = None
if args.add_prompt is not None:
args.max_target_length += len(args.add_prompt.keys())
if isinstance(args.setting, dict) and args.max_unknown_part_length is None:
args.max_unknown_part_length = args.max_part_length['_'.join(args.setting['unknown_part'])]
if args.co_training_method == 0:
args.co_training = True
args.get_idx_at_last = True
args.get_training_loss_list = True
if args.get_training_loss_list:
args.get_idx_at_last = True
return args
def set_dist(args):
# 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:
# Setup for distributed data parallel
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
cpu_cont = min(multiprocessing.cpu_count(), args.max_cpu_count)
logger.warning("Process rank: %s, device: %s, n_gpu: %s, distributed training: %s, cpu count: %d",
args.local_rank, device, args.n_gpu, bool(args.local_rank != -1), cpu_cont)
args.device = device
args.cpu_cont = cpu_cont
if isinstance(args.setting, dict) and "type" == args.setting["unknown_part"][0]:
args.unknwon_part_start_limited_choice = ["feat", "fix", "docs", "style", "refactor", "perf", "test", "chore"]
if args.filter_none_scope:
args.skip_ref_token = None
def set_seed(args):
"""set random seed."""
random.seed(args.seed)
np.random.seed(args.seed)
torch.manual_seed(args.seed)
if args.n_gpu > 0:
torch.cuda.manual_seed_all(args.seed)
def get_limited_type_dict(args, tokenizer):
limited_type_dict=dict()
if args.unknwon_part_start_limited_choice_list is not None:
for limited_type in args.unknwon_part_start_limited_choice_list:
limited_type_dict[limited_type] = tokenizer.encode("{}".format(limited_type))[1:-1]
else:
limited_type_dict=None
return limited_type_dict
def get_skip_ref_token_id(args, tokenizer):
skip_ref_token_id = None
if args.skip_ref_token is not None:
skip_ref_token_id = tokenizer.encode("{}".format(args.skip_ref_token))[1:-1][0]
return skip_ref_token_id
def eval_ppl_epoch(args, eval_data, eval_examples, model, tokenizer, tag=None, output_result=False, teacher_forcing=True, get_loss_list=False):
eval_sampler = SequentialSampler(eval_data)
eval_dataloader = DataLoader(eval_data, sampler=eval_sampler, batch_size=args.eval_batch_size,
num_workers=args.cpu_cont, pin_memory=True)
# Start evaluating model
logger.info(" " + "***** Running ppl evaluation *****")
logger.info(" Num examples = %d", len(eval_examples))
logger.info(" Batch size = %d", args.eval_batch_size)
model.eval()
eval_loss, batch_num = 0, 0
output_part_gen = list()
output_part_ref = list()
output_unknown_part_gen = list()
output_unknown_part_ref = list()
if args.task == 'cmt_msg_gen':
limited_type_dict = get_limited_type_dict(args, tokenizer)
skip_ref_token_id = get_skip_ref_token_id(args, tokenizer)
if get_loss_list:
loss_list = list()
commit_index_list = list()
for batch in tqdm(eval_dataloader, total=len(eval_dataloader), desc="Eval ppl"):
batch = tuple(t.to(args.device) for t in batch)
if args.task == 'cmt_msg_gen':
if args.co_teaching:
if args.get_idx_at_last:
source_ids, target_ids, position_ids, segment_ids, noise_or_not, commit_index = batch
else:
source_ids, target_ids, position_ids, segment_ids, noise_or_not = batch
else:
if args.get_idx_at_last:
source_ids, target_ids, position_ids, segment_ids, commit_index = batch
else:
source_ids, target_ids, position_ids, segment_ids = batch
if args.no_pos_no_segment:
position_ids = None
segment_ids = None
else:
source_ids, target_ids = batch
source_mask = source_ids.ne(tokenizer.pad_token_id)
target_mask = target_ids.ne(tokenizer.pad_token_id)
with torch.no_grad():
if args.model_type == 'roberta':
loss, _, _ = model(source_ids=source_ids, source_mask=source_mask,
target_ids=target_ids, target_mask=target_mask)
else:
if args.task == 'cmt_msg_gen':
if args.setting_only_used_to_train:
outputs = model(input_ids=source_ids, attention_mask=source_mask,
labels=target_ids, decoder_attention_mask=target_mask, \
position_ids=position_ids, segment_ids=segment_ids, \
skip_ref_token_id = skip_ref_token_id, \
teacher_forcing=teacher_forcing)
else:
outputs = model(input_ids=source_ids, attention_mask=source_mask,
labels=target_ids, decoder_attention_mask=target_mask, \
position_ids=position_ids, segment_ids=segment_ids, \
unknown_part_length=args.max_unknown_part_length, unknwon_part_start_flag=tokenizer.sep_token_id, \
unknwon_part_start_limited_choice_dict=limited_type_dict, \
skip_ref_token_id = skip_ref_token_id, \
teacher_forcing=teacher_forcing, loss_focus_unknown=args.loss_focus_unknown) ## TODO revise `decoder_attention_mask`?
else:
outputs = model(input_ids=source_ids, attention_mask=source_mask,
labels=target_ids, decoder_attention_mask=target_mask)
loss = outputs.loss
if args.task == 'cmt_msg_gen' and output_result:
for idx in range(len(outputs.logits)):
ref_token_list = tokenizer.convert_ids_to_tokens(target_ids[idx])
unknwon_part_start_idx = ref_token_list.index(tokenizer.eos_token)+1
unknown_part_end_idx = ref_token_list[unknwon_part_start_idx:].index(tokenizer.eos_token) + unknwon_part_start_idx
if unknown_part_end_idx <= unknwon_part_start_idx:
unknwon_part_start_idx = 0
unknown_part_end_idx = -1
pred_ids = [torch.argmax(each) for each in outputs.logits[idx]]
clean_pred_ids = pred_ids[unknwon_part_start_idx:unknown_part_end_idx]
ref_ids = target_ids[idx]
clean_ref_ids = ref_ids[unknwon_part_start_idx:unknown_part_end_idx]
gen_sentence = tokenizer.decode(pred_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)
clean_gen_sentence = tokenizer.decode(clean_pred_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)
ref_sentence = tokenizer.decode(ref_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)
clean_ref_sentence = tokenizer.decode(clean_ref_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)
output_part_gen.append(gen_sentence)
output_part_ref.append(ref_sentence)
output_unknown_part_gen.append(clean_gen_sentence)
output_unknown_part_ref.append(clean_ref_sentence)
if args.task == 'cmt_msg_gen' and get_loss_list:
lm_logits = outputs.logits
loss_fct = CrossEntropyLoss(ignore_index=0, reduction='none')
loss_batch = loss_fct(lm_logits.view(-1, lm_logits.size(-1)), target_ids.view(-1)).view(-1, lm_logits.size(1))
loss_batch = torch.nanmean(loss_batch.masked_fill(target_ids==tokenizer.pad_token_id, torch.nan), dim=1)
loss_list += loss_batch.tolist()
if args.get_idx_at_last:
commit_index_list += commit_index.tolist()
eval_loss += loss.item()
batch_num += 1
eval_loss = eval_loss / batch_num
eval_ppl = round(np.exp(eval_loss), 5)
if args.task == 'cmt_msg_gen' and output_result:
os.makedirs(args.res_dir, exist_ok=True)
output_fn = os.path.join(args.res_dir, "eval_ppl_result_{}.output".format(tag))
clean_output_fn = os.path.join(args.res_dir, "eval_ppl_result_clean_{}.output".format(tag))
gold_fn = os.path.join(args.res_dir, "eval_ppl_result_{}.gold".format(tag))
clean_gold_fn = os.path.join(args.res_dir, "eval_ppl_result_clean_{}.gold".format(tag))
with open(output_fn, "w") as f:
f.write("\n".join(output_part_gen))
with open(gold_fn, "w") as f:
f.write("\n".join(output_part_ref))
with open(clean_output_fn, "w") as f:
f.write("\n".join(output_unknown_part_gen))
with open(clean_gold_fn, "w") as f:
f.write("\n".join(output_unknown_part_ref))
if get_loss_list:
os.makedirs(args.res_dir, exist_ok=True)
loss_fn = os.path.join(args.res_dir, "eval_ppl_result_{}.loss".format(tag))
with open(loss_fn, "w") as f:
f.write("\n".join(map(lambda x: str(x), loss_list)))
with open(os.path.join(args.res_dir, "commit_index_list_eval_ppl_{}.pickle".format(tag)), "wb") as f:
pickle.dump(commit_index_list, f)
return eval_ppl
def eval_bleu_epoch(args, eval_data, eval_examples, model, tokenizer, split_tag, criteria, target_is_knwon_part=False, get_score_list=False, get_token_list=False):
logger.info(" ***** Running bleu evaluation on {} data*****".format(split_tag))
logger.info(" Num examples = %d", len(eval_examples))
logger.info(" Batch size = %d", args.eval_batch_size)
eval_sampler = SequentialSampler(eval_data)
if args.data_num == -1:
eval_dataloader = DataLoader(eval_data, sampler=eval_sampler, batch_size=args.eval_batch_size,
num_workers=args.cpu_cont, pin_memory=True)
else:
eval_dataloader = DataLoader(eval_data, sampler=eval_sampler, batch_size=args.eval_batch_size)
model.eval()
pred_ids = []
bleu, codebleu = 0.0, 0.0
if args.task == 'cmt_msg_gen':
limited_type_dict = get_limited_type_dict(args, tokenizer)
skip_ref_token_id = get_skip_ref_token_id(args, tokenizer)
gold_ids = []
first_token_id=None
if args.first_token is not None:
first_token_id = tokenizer.encode("{}".format(args.first_token))
logger.info("First token is set to `{}`, its token_id is {}".format(args.first_token, first_token_id))
gen_part_start_idx_batch_idx_list = list()
score_list = list()
for batch in tqdm(eval_dataloader, total=len(eval_dataloader), desc="Eval bleu for {} set".format(split_tag)):
batch = tuple(t.to(args.device) for t in batch)
if args.task == 'cmt_msg_gen':
if args.co_teaching:
source_ids, target_token_ids, target_position_ids, target_segment_ids, noise_or_not = batch
else:
source_ids, target_token_ids, target_position_ids, target_segment_ids = batch
if args.no_pos_no_segment:
target_position_ids = None
target_segment_ids = None
else:
source_ids, _ = batch
source_mask = source_ids.ne(tokenizer.pad_token_id)
gen_part_start_idx_before_list = list()
if args.task == 'cmt_msg_gen':
known_part_token_ids, known_part_position_ids, known_part_segment_ids = None, None, None
if first_token_id is not None:
known_part_token_ids = torch.Tensor(first_token_id*batch[1].shape[0]).view(batch[1].shape[0], len(first_token_id))
known_part_token_ids = known_part_token_ids.to(args.device)
if not args.setting_only_used_to_train:
## Get known_part (<subject>)
## target_token_ids: known_part(e.g.: <subject>) + <eos> +unknown_part(e.g.: <type> <scope>)
known_part_token_ids = target_token_ids.clone()
known_part_position_ids = target_position_ids.clone()
known_part_segment_ids = target_segment_ids.clone()
for idx in range(batch[1].shape[0]):
gen_part_start_idx_before = -1
for i, token_id in enumerate(known_part_token_ids[idx]):
if i > 1 and token_id == tokenizer.pad_token_id: # 0: # tokenizer.pad_token_id:
gen_part_start_idx_before = i
break
gen_part_start_idx_before_list.append(gen_part_start_idx_before)
if not target_is_knwon_part:
for idx in range(batch[1].shape[0]):
gen_part_start_idx_before = gen_part_start_idx_before_list[idx]
max_known_part_length = args.max_target_length - args.max_unknown_part_length
if gen_part_start_idx_before <= max_known_part_length:
### <subject> token_id
known_part_token_ids[idx] = torch.cat((target_token_ids[idx][:gen_part_start_idx_before], \
torch.zeros(args.max_target_length-gen_part_start_idx_before, dtype=target_token_ids.dtype, device=args.device)), -1)
### <subject> position_id
known_part_position_ids[idx] = torch.cat((target_position_ids[idx][:gen_part_start_idx_before], \
torch.zeros(args.max_target_length-gen_part_start_idx_before, dtype=target_position_ids.dtype, device=args.device)), -1)
### <subject> segment_id
known_part_segment_ids[idx] = torch.cat((target_segment_ids[idx][:gen_part_start_idx_before], \
torch.zeros(args.max_target_length-gen_part_start_idx_before, dtype=target_segment_ids.dtype, device=args.device)), -1)
known_part_token_ids = known_part_token_ids[:,:max_known_part_length]
known_part_position_ids = known_part_position_ids[:,:max_known_part_length]
known_part_segment_ids = known_part_segment_ids[:,:max_known_part_length]
gold = list(target_token_ids.cpu().numpy())
gold_ids.extend(gold)
del target_token_ids, target_position_ids, target_segment_ids
with torch.no_grad():
if args.model_type == 'roberta':
preds = model(source_ids=source_ids, source_mask=source_mask)
top_preds = [pred[0].cpu().numpy() for pred in preds]
else:
if args.task == 'cmt_msg_gen':
return_dict_in_generate = None
output_scores = None
if get_score_list:
return_dict_in_generate = True
output_scores = True
gen_part_length = None
if not args.setting_only_used_to_train:
gen_part_length = args.max_unknown_part_length
preds = model.generate(source_ids,
attention_mask=source_mask,
use_cache=True,
num_beams=args.beam_size,
early_stopping=True,
max_length=args.max_target_length,
known_output_ids=known_part_token_ids,
known_output_position_ids = known_part_position_ids,
known_output_segment_ids = known_part_segment_ids,
unknown_part_length=gen_part_length,
unknwon_part_start_flag=tokenizer.sep_token_id,
unknwon_part_start_limited_choice_dict=limited_type_dict,
skip_ref_token_id = skip_ref_token_id,
eos_token_id_num = 2,
return_dict_in_generate = return_dict_in_generate,
output_scores = output_scores)
if get_score_list:
preds, score = preds.sequences, preds.sequences_scores
if args.setting_only_used_to_train:
for idx, pred in enumerate(preds):
gen_part_start_idx_before = pred.tolist().index(tokenizer.sep_token_id)
gen_part_start_idx_before_list.append(gen_part_start_idx_before)
# gen_part_start_idx_before_list = (preds==tokenizer.sep_token_id).nonzero()[:, 1]
# gen_part_start_idx_before_list = list(gen_part_start_idx_before_list.cpu().numpy())
else:
preds = model.generate(source_ids,
attention_mask=source_mask,
use_cache=True,
num_beams=args.beam_size,
early_stopping=args.task == 'summarize',
max_length=args.max_target_length)
top_preds = preds
pred_ids.extend(top_preds)
if get_score_list:
score_list += list(score.cpu().numpy())
gen_part_start_idx_batch_idx_list += gen_part_start_idx_before_list
pred_nls = [tokenizer.convert_ids_to_tokens(preds) for preds in pred_ids]
if args.task == 'cmt_msg_gen':
pred_nls_clean, gold_nls_clean = list(), list()
for idx in range(len(pred_ids)):
pred_nls_clean.append(tokenizer.decode(pred_ids[idx][gen_part_start_idx_batch_idx_list[idx]:], \
skip_special_tokens=True, clean_up_tokenization_spaces=False))
gold_nls_clean.append(tokenizer.decode(gold_ids[idx][gen_part_start_idx_batch_idx_list[idx]:], \
skip_special_tokens=True, clean_up_tokenization_spaces=False))
output_fn = os.path.join(args.res_dir, "test_{}.output".format(criteria))
gold_fn = os.path.join(args.res_dir, "test_{}.gold".format(criteria))
src_fn = os.path.join(args.res_dir, "test_{}.src".format(criteria))
if get_score_list:
score_fn = os.path.join(args.res_dir, "test_{}.score".format(criteria))
if args.task in ['defect']:
target_dict = {0: 'false', 1: 'true'}
golds = [target_dict[ex.target] for ex in eval_examples]
eval_acc = np.mean([int(p == g) for p, g in zip(pred_nls, golds)])
result = {'em': eval_acc * 100, 'bleu': 0, 'codebleu': 0}
with open(output_fn, 'w') as f, open(gold_fn, 'w') as f1, open(src_fn, 'w') as f2:
for pred_nl, gold in zip(pred_nls, eval_examples):
f.write(pred_nl.strip() + '\n')
f1.write(target_dict[gold.target] + '\n')
f2.write(gold.source.strip() + '\n')
logger.info("Save the predictions into %s", output_fn)
else:
dev_accs, predictions = [], []
if args.task == 'cmt_msg_gen':
output_fn_clean = os.path.join(args.res_dir, "test_{}_clean.output".format(criteria))
gold_fn_clean = os.path.join(args.res_dir, "test_{}_clean.gold".format(criteria))
output_fn_idx_clean = os.path.join(args.res_dir, "test_{}_idx_clean.output".format(criteria))
gold_fn_idx_clean = os.path.join(args.res_dir, "test_{}_idx_clean.gold".format(criteria))
with open(output_fn, 'w') as f, open(gold_fn, 'w') as f1, open(src_fn, 'w') as f2, \
open(output_fn_clean, "w") as out_clean, open(gold_fn_clean, 'w') as gold_clean, \
open(output_fn_idx_clean, "w") as out_idx_clean, open(gold_fn_idx_clean, "w") as gold_idx_clean:
for gold_raw, preds, pred_nl_clean, gold_nl_clean in zip(eval_examples, \
pred_ids, pred_nls_clean, gold_nls_clean):
dev_accs.append(pred_nl_clean.strip() == gold_nl_clean.strip())
predictions.append(f"{gold_raw.idx}\t{pred_nl_clean}")
f.write(f"{gold_raw.idx}\t{tokenizer.decode(preds).strip()}\n")
f1.write(f"{gold_raw.idx}\t{gold_raw.target.strip()}\n")
f2.write(f"{gold_raw.idx}\t{gold_raw.source.strip()}\n")
out_idx_clean.write(f"{gold_raw.idx}\t{pred_nl_clean.strip()}\n")
gold_idx_clean.write(f"{gold_raw.idx}\t{gold_nl_clean.strip()}\n")
out_clean.write(pred_nl_clean.strip() + '\n')
gold_clean.write(gold_nl_clean.strip() + '\n')
if get_score_list:
with open(score_fn, 'w') as f:
f.write("\n".join(map(lambda x: str(x), score_list)))
if get_token_list:
pred_ids_pkl_file_path = os.path.join(args.res_dir, "test_{}_pred_ids.pickle".format(criteria))
with open(pred_ids_pkl_file_path, "wb") as f:
pickle.dump(pred_ids, f)
else:
with open(output_fn, 'w') as f, open(gold_fn, 'w') as f1, open(src_fn, 'w') as f2:
for pred_nl, gold in zip(pred_nls, eval_examples):
dev_accs.append(pred_nl.strip() == gold.target.strip())
if args.task in ['summarize']:
# for smooth-bleu4 evaluation
predictions.append(str(gold.idx) + '\t' + pred_nl)
f.write(str(gold.idx) + '\t' + pred_nl.strip() + '\n')
f1.write(str(gold.idx) + '\t' + gold.target.strip() + '\n')
f2.write(str(gold.idx) + '\t' + gold.source.strip() + '\n')
else:
f.write(pred_nl.strip() + '\n')
f1.write(gold.target.strip() + '\n')
f2.write(gold.source.strip() + '\n')
if args.task == 'summarize':
(goldMap, predictionMap) = smooth_bleu.computeMaps(predictions, gold_fn)
bleu = round(smooth_bleu.bleuFromMaps(goldMap, predictionMap)[0], 2)
elif args.task == 'cmt_msg_gen':
(goldMap, predictionMap) = smooth_bleu.computeMaps(predictions, gold_fn_idx_clean)
bleu = round(smooth_bleu.bleuFromMaps(goldMap, predictionMap)[0], 2)
else:
bleu = round(_bleu(gold_fn, output_fn), 2)
if args.task in ['concode', 'translate', 'refine']:
codebleu = calc_code_bleu.get_codebleu(gold_fn, output_fn, args.lang)
result = {'em': np.mean(dev_accs) * 100, 'bleu': bleu}
if args.task == 'concode':
result['codebleu'] = codebleu * 100
logger.info("***** Eval results *****")
for key in sorted(result.keys()):
logger.info(" %s = %s", key, str(round(result[key], 4)))
return result
def cal_Prob(one_commit_data):
idx, score, phais, mean, std = one_commit_data
return idx, scipy.stats.norm(mean, std).pdf(score)*phais
def EM(data, k = 2, max_step = 200, threhold = 0.00001):
phais = torch.tensor([[1.0/k] for i in range(k)], device=data.device)
mean = torch.tensor([[i] for i in range(k)], device=data.device)
std = torch.tensor([[1] for i in range(k)], device=data.device)
pi_times_2 = torch.tensor(2 * math.pi)
for i in range(max_step):
# Qs = e_step(data,phais,mean,std)
data_k = data.repeat(k).reshape(k, data.shape[0])
exponent = torch.pow((data_k - mean),2)*(-1/(2*std))
Qs = (torch.exp(exponent)/torch.sqrt(pi_times_2*std)*phais)
Qs = Qs / torch.sum(Qs, dim=0, keepdim=True)
# phais, mean, std= m_step(data,phais,mean,std,Qs)
gama_j = torch.sum(Qs, dim=1)
new_phais = (gama_j/data.shape[0]).reshape(k, 1)
new_mean = (torch.sum(data*Qs, dim=1)/gama_j).reshape(k, 1)
X_i_mu_j = torch.pow((data_k - mean),2)
# new_std = (torch.sum((X_i_mu_j*Qs).transpose(0,1), axis=1) / gama_j).reshape(k, 1)
new_std = (torch.sum(X_i_mu_j*Qs, axis=1) / gama_j).reshape(k, 1)
if i > 0 and False not in (torch.abs(new_mean - mean) < threhold):
break
phais, mean, std = new_phais, new_mean, new_std
return phais[:,0].tolist(), mean[:,0].tolist(), std[:,0].tolist()
def main():
parser = argparse.ArgumentParser()
args = add_args(parser)
logger.info(args)
if args.wandb_username:
if not args.wandb_project_name:
args.wandb_project_name = "default"
wandb.init(project=f"{args.wandb_project_name}", name=args.wandb_run_name, entity="bigcode")
wandb.config.update(args)
t0 = time.time()
set_dist(args)
set_seed(args)
config, model, tokenizer = build_or_load_gen_model(args)
if args.co_teaching:
_, model2, _ = build_or_load_gen_model(args)
if args.task == 'cmt_msg_gen':
if args.type_as_special_token:
tokenizer.add_tokens(args.unknwon_part_start_limited_choice_list, special_tokens=True)
tokenizer.add_tokens(["NNNone"], special_tokens=True) ### TODO special_token "NNNone"
if args.add_prompt is not None and isinstance(args.add_prompt, dict):
for value in args.add_prompt.values():
tokenizer.add_tokens(value, special_tokens=True)
model.resize_token_embeddings(len(tokenizer))
if args.co_teaching:
model2.resize_token_embeddings(len(tokenizer))
limited_type_dict = get_limited_type_dict(args, tokenizer)
skip_ref_token_id = get_skip_ref_token_id(args, tokenizer)
model.to(args.device)
if args.co_teaching:
model2.to(args.device)
if args.n_gpu > 1:
# for DataParallel
model = torch.nn.DataParallel(model)
if args.co_teaching:
model2 = torch.nn.DataParallel(model2)
# pool = multiprocessing.Pool(args.cpu_cont)
args.train_filename, args.dev_filename, args.test_filename = get_filenames(args.data_dir, args.task, args.sub_task)
fa = open(os.path.join(args.output_dir, 'summary.log'), 'a+')
if args.co_teaching:
# define drop rate schedule
rate_schedule = np.ones(args.num_train_epochs) * args.forget_rate
rate_schedule[:args.num_gradual] = np.linspace(0, args.forget_rate**args.exponent, args.num_gradual)
if args.do_train:
if args.co_training:
with open(os.path.join(os.path.dirname(args.train_filename), 'train.msg.txt')) as train_msg_f:
train_total_num = len(train_msg_f.read().split("\n"))
logger.info("There are at last {} code diff and commit message can be used to train".format(train_total_num))
select_train_idx_list = None
if args.select_train_idx_list_file is not None:
select_train_idx_list = []
for each_file_path in args.select_train_idx_list_file:
with open(each_file_path) as f:
select_train_idx_list+=[int(line) for line in f.read().split("\n")]
select_train_idx_list = sorted(list(set(select_train_idx_list)))
if args.local_rank in [-1, 0] and args.data_num == -1:
summary_fn = '{}/{}'.format(args.summary_dir, '/'.join(args.output_dir.split('/')[1:]))
tb_writer = SummaryWriter(summary_fn)
# Prepare training data loader
with multiprocessing.Pool(args.cpu_cont) as pool:
train_examples, train_data = load_and_cache_gen_data(args, args.train_filename, pool, tokenizer, 'train',
setting=args.setting, filter_none_scope=args.filter_none_scope, is_sample=False,
no_pos_no_segment=args.no_pos_no_segment, select_idx_list=select_train_idx_list,
get_idx_at_last = args.get_idx_at_last)
train_sampler = RandomSampler(train_data) if args.local_rank == -1 else DistributedSampler(train_data)
train_dataloader = DataLoader(train_data, sampler=train_sampler, batch_size=args.train_batch_size,
num_workers=args.cpu_cont, pin_memory=True)
if args.get_training_loss_list:
os.makedirs(args.res_dir, exist_ok=True)
with open(os.path.join(args.res_dir, "train_sampler.pickle"), "wb") as f:
pickle.dump(train_sampler, f)
# 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}
]
optimizer = AdamW(optimizer_grouped_parameters, lr=args.learning_rate, eps=args.adam_epsilon)
num_train_optimization_steps = args.num_train_epochs * len(train_dataloader)
scheduler = get_linear_schedule_with_warmup(optimizer,
num_warmup_steps=args.warmup_steps,
num_training_steps=num_train_optimization_steps)
if args.co_teaching:
optimizer_grouped_parameters2 = [
{'params': [p for n, p in model2.named_parameters() if not any(nd in n for nd in no_decay)],
'weight_decay': args.weight_decay},
{'params': [p for n, p in model2.named_parameters() if any(nd in n for nd in no_decay)], 'weight_decay': 0.0}
]
optimizer2 = AdamW(optimizer_grouped_parameters2, lr=args.learning_rate, eps=args.adam_epsilon)
scheduler2 = get_linear_schedule_with_warmup(optimizer,
num_warmup_steps=args.warmup_steps,
num_training_steps=num_train_optimization_steps)
# Start training
train_example_num = len(train_data)
logger.info("***** Running training *****")
logger.info(" Num examples = %d", train_example_num)
logger.info(" Batch size = %d", args.train_batch_size)
logger.info(" Batch num = %d", math.ceil(train_example_num / args.train_batch_size))
logger.info(" Num epoch = %d", args.num_train_epochs)
dev_dataset = {}
global_step, best_bleu_em, best_ppl = 0, -1, 1e6
not_loss_dec_cnt, not_bleu_em_inc_cnt = 0, 0 if args.do_eval_bleu else 1e6
if args.co_teaching:
global_step2, best_bleu_em2, best_ppl2 = 0, -1, 1e6
for cur_epoch in range(args.start_epoch, int(args.num_train_epochs)):
if args.co_training and cur_epoch > 0 and not args.co_training_method == 0:
with multiprocessing.Pool(args.cpu_cont) as pool:
train_examples, train_data = load_and_cache_gen_data(args, args.train_filename, pool, tokenizer, 'train',
setting=args.setting, filter_none_scope=args.filter_none_scope, is_sample=False,
no_pos_no_segment=args.no_pos_no_segment, select_idx_list=select_train_idx_list,
get_idx_at_last = args.get_idx_at_last)
train_sampler = RandomSampler(train_data) if args.local_rank == -1 else DistributedSampler(train_data)
train_dataloader = DataLoader(train_data, sampler=train_sampler, batch_size=args.train_batch_size,
num_workers=args.cpu_cont, pin_memory=True)
bar = tqdm(train_dataloader, total=len(train_dataloader), desc="Training")
nb_tr_examples, nb_tr_steps, tr_loss = 0, 0, 0
if args.co_teaching:
tr_loss2 = 0
model.train()
if args.co_teaching:
model2.train()
pure_ratio_1_list=[]
pure_ratio_2_list=[]
if args.get_training_loss_list:
loss_list = list()
commit_index_list = list()
if args.co_training_method == 0:
confidence_mul_loss_list = list()
for step, batch in enumerate(bar):
batch = tuple(t.to(args.device) for t in batch)
if args.task == 'cmt_msg_gen' and args.co_teaching:
if args.multi_task:
if args.get_idx_at_last:
source_ids, target_ids_gen_TS, position_ids_gen_TS, segment_ids_gen_TS, \
target_ids_gen_subject, position_ids_gen_subject, segment_ids_gen_subject, noise_or_not, commit_index = batch
else:
source_ids, target_ids_gen_TS, position_ids_gen_TS, segment_ids_gen_TS, \
target_ids_gen_subject, position_ids_gen_subject, segment_ids_gen_subject, noise_or_not = batch
else:
if args.get_idx_at_last:
source_ids, target_ids, position_ids, segment_ids, noise_or_not, commit_index = batch
else:
source_ids, target_ids, position_ids, segment_ids, noise_or_not = batch
if args.no_pos_no_segment:
position_ids, segment_ids = None, None
else:
if args.task == 'cmt_msg_gen' and args.multi_task:
if args.get_idx_at_last:
source_ids, target_ids_gen_TS, position_ids_gen_TS, segment_ids_gen_TS, \
target_ids_gen_subject, position_ids_gen_subject, segment_ids_gen_subject, commit_index = batch
else:
source_ids, target_ids_gen_TS, position_ids_gen_TS, segment_ids_gen_TS, \
target_ids_gen_subject, position_ids_gen_subject, segment_ids_gen_subject = batch
else:
if args.get_idx_at_last:
source_ids, target_ids, position_ids, segment_ids, commit_index = batch
else:
source_ids, target_ids, position_ids, segment_ids = batch
if args.no_pos_no_segment:
position_ids, segment_ids = None, None
source_mask = source_ids.ne(tokenizer.pad_token_id)
if args.task == 'cmt_msg_gen' and args.multi_task:
target_mask_gen_TS = target_ids_gen_TS.ne(tokenizer.pad_token_id)
target_mask_gen_subject = target_ids_gen_subject.ne(tokenizer.pad_token_id)
else:
target_mask = target_ids.ne(tokenizer.pad_token_id)
if args.model_type == 'roberta':
loss, _, _ = model(source_ids=source_ids, source_mask=source_mask,
target_ids=target_ids, target_mask=target_mask)
else:
if args.task == 'cmt_msg_gen':
if args.multi_task:
source_ids = torch.concat([source_ids, source_ids], dim=0)
source_mask = torch.concat([source_mask, source_mask], dim=0)
target_ids = torch.concat([target_ids_gen_TS, target_ids_gen_subject], dim=0)
target_mask = torch.concat([target_mask_gen_TS, target_mask_gen_subject], dim=0)
position_ids = torch.concat([position_ids_gen_TS, position_ids_gen_subject], dim=0)
segment_ids = torch.concat([segment_ids_gen_TS, segment_ids_gen_subject], dim=0)
if args.no_pos_no_segment:
position_ids, segment_ids = None, None
outputs = model(input_ids=source_ids, attention_mask=source_mask,
labels=target_ids, decoder_attention_mask=target_mask, \
position_ids=position_ids, segment_ids=segment_ids, \
max_part_length=args.max_part_length,
unknwon_part_start_flag=tokenizer.sep_token_id, \
unknwon_part_start_limited_choice_dict=limited_type_dict, \
skip_ref_token_id = skip_ref_token_id, loss_focus_unknown=args.loss_focus_unknown)
if args.co_teaching:
if args.no_pos_no_segment:
position_ids, segment_ids = None, None
outputs2 = model2(input_ids=source_ids, attention_mask=source_mask,
labels=target_ids, decoder_attention_mask=target_mask, \
position_ids=position_ids, segment_ids=segment_ids, \
unknown_part_length=args.max_unknown_part_length, unknwon_part_start_flag=tokenizer.sep_token_id, \
unknwon_part_start_limited_choice_dict=limited_type_dict, \
skip_ref_token_id = skip_ref_token_id, loss_focus_unknown=args.loss_focus_unknown)
else:
outputs = model(input_ids=source_ids, attention_mask=source_mask,
labels=target_ids, decoder_attention_mask=target_mask)
loss = outputs.loss
if args.get_training_loss_list:
lm_logits = outputs.logits
loss_fct = CrossEntropyLoss(ignore_index=0, reduction='none')
# logger.info("lm_logits.size:{}".format(lm_logits.size))
loss_batch = loss_fct(lm_logits.view(-1, lm_logits.size(-1)), target_ids.view(-1)).view(-1, lm_logits.size(1))
loss_batch = torch.nanmean(loss_batch.masked_fill(target_ids==tokenizer.pad_token_id, torch.nan), dim=1)
loss_list += loss_batch.tolist()
if args.get_idx_at_last:
commit_index_list += commit_index.tolist()
if cur_epoch > args.wait_confidence and args.co_training and args.co_training_method == 0:
loss_batch = [loss * confidence_list[commit_index[idx]] for idx, loss in enumerate(loss_batch)]
confidence_mul_loss_list += loss_batch
loss_batch = torch.stack(loss_batch, dim=0)
loss = torch.mean(loss_batch)
if args.co_teaching:
lm_logits_1 = outputs.logits
lm_logits_2 = outputs2.logits
loss_fct = CrossEntropyLoss(ignore_index=0, reduction='none')
loss_1 = loss_fct(lm_logits_1.view(-1, lm_logits_1.size(-1)), target_ids.view(-1)).view(-1, 50)
loss_2 = loss_fct(lm_logits_2.view(-1, lm_logits_2.size(-1)), target_ids.view(-1)).view(-1, 50)
loss_1 = torch.nanmean(loss_1.masked_fill(target_ids==tokenizer.pad_token_id, torch.nan), dim=1)
loss_2 = torch.nanmean(loss_2.masked_fill(target_ids==tokenizer.pad_token_id, torch.nan), dim=1)
wandb.log({"train_loss (before)": outputs.loss}, step=global_step)
wandb.log({"train_loss2 (before)": outputs2.loss}, step=global_step)
# Ref: https://github.com/bhanML/Co-teaching/blob/7c7fbe23e15e517db76a0882b6d108e4508e09d6/loss.py#L8-L29
ind_1_sorted = torch.argsort(loss_1)
loss_1_sorted = loss_1[ind_1_sorted]
ind_2_sorted = torch.argsort(loss_2)
loss_2_sorted = loss_2[ind_2_sorted]
remember_rate = 1 - rate_schedule[cur_epoch]
num_remember = int(remember_rate * len(loss_1_sorted))
pure_ratio_1 = torch.sum(noise_or_not[ind_1_sorted[:num_remember]])/float(num_remember)
pure_ratio_2 = torch.sum(noise_or_not[ind_2_sorted[:num_remember]])/float(num_remember)
ind_1_update = ind_1_sorted[:num_remember]
ind_2_update = ind_2_sorted[:num_remember]
# exchange
loss_1_update = loss_fct(lm_logits_1[ind_1_update].view(-1, lm_logits_1[ind_1_update].size(-1)),
target_ids[ind_1_update].view(-1))
loss_2_update = loss_fct(lm_logits_2[ind_2_update].view(-1, lm_logits_2[ind_2_update].size(-1)),
target_ids[ind_2_update].view(-1))
loss, loss2 = torch.sum(loss_1_update)/num_remember, torch.sum(loss_2_update)/num_remember
pure_ratio_1_list.append(100*pure_ratio_1)
pure_ratio_2_list.append(100*pure_ratio_2)
if args.wandb_username:
wandb.log({"train_loss": loss}, step=global_step)
if args.co_teaching:
wandb.log({"train_loss2": outputs2.loss}, step=global_step)
if args.n_gpu > 1:
loss = loss.mean() # mean() to average on multi-gpu.
if args.co_teaching:
loss2 = loss2.mean()
if args.gradient_accumulation_steps > 1:
loss = loss / args.gradient_accumulation_steps
if args.co_teaching:
loss2 = loss2 / args.gradient_accumulation_steps
tr_loss += loss.item()
if args.co_teaching:
tr_loss2 += loss2.item()
nb_tr_examples += source_ids.size(0)
nb_tr_steps += 1
loss.backward()
if args.co_teaching:
loss2.backward()
if nb_tr_steps % args.gradient_accumulation_steps == 0:
# Update parameters
optimizer.step()
optimizer.zero_grad()
scheduler.step()
if args.co_teaching:
optimizer2.step()
optimizer2.zero_grad()
scheduler2.step()
global_step += 1
train_loss = round(tr_loss * args.gradient_accumulation_steps / (nb_tr_steps + 1), 4)
bar.set_description("[{}] Train loss {}".format(cur_epoch, round(train_loss, 3)))
if args.co_teaching:
train_loss2 = round(tr_loss2 * args.gradient_accumulation_steps / (nb_tr_steps + 1), 4)
mean_pure_ratio1 = torch.sum(torch.stack(pure_ratio_1_list))/len(pure_ratio_1_list)
mean_pure_ratio2 = torch.sum(torch.stack(pure_ratio_2_list))/len(pure_ratio_2_list)
bar.set_description("[{}] Train loss1 {} loss2 {} Pure Ratio 1 {} Ratio 2 {}".format(cur_epoch,\
round(train_loss, 3), round(train_loss2, 3), round(mean_pure_ratio1.item(),3), round(mean_pure_ratio2.item(),3)))
if args.get_training_loss_list: