def main(args):
print(20 * "=", " Preparing for training ", 20 * "=")
if not os.path.exists(args.result):
os.makedirs(args.result)
tokenizer = tokenization.BertTokenizer(args.vocab, do_lower_case=True)
# -------------------- Data loading ------------------- #
print("\t* Loading testing data...")
# train_data = LCQMC_dataset(args.train_file, args.vocab_file, args.max_length, test_flag=False)
test_data = DataProcessForSentence(tokenizer,
args.test_file,
args.max_length,
test_flag=True)
test_loader = DataLoader(test_data, batch_size=args.batch_size)
# -------------------- Model definition ------------------- #
print("\t* Building model...")
model = Roberta_pooling(args).to(args.device)
all_predict = predict(model, test_loader, args)
index = np.array([], dtype=int)
for i in range(len(all_predict)):
index = np.append(index, i)
# ---------------------生成文件--------------------------
df_test = pd.DataFrame(columns=['index', 'prediction'])
df_test['index'] = index
df_test['prediction'] = all_predict
df_test.to_csv(args.submit_example_path,
index=False,
columns=['index', 'prediction'],
sep='\t')
n_gpu = torch.cuda.device_count()
print("device %s n_gpu %d" % (device, n_gpu))
print("device: {} n_gpu: {} 16-bits training: {}".format(device, n_gpu, args.float16))
# load the bert setting
if 'albert' not in args.bert_config_file:
bert_config = BertConfig.from_json_file(args.bert_config_file)
else:
if 'google' in args.bert_config_file:
bert_config = AlbertConfig.from_json_file(args.bert_config_file)
else:
bert_config = ALBertConfig.from_json_file(args.bert_config_file)
# load data
print('loading data...')
tokenizer = tokenization.BertTokenizer(vocab_file=args.vocab_file, do_lower_case=True)
assert args.vocab_size == len(tokenizer.vocab)
if not os.path.exists(args.test_dir1) or not os.path.exists(args.test_dir2):
json2features(args.test_file, [args.test_dir1, args.test_dir2], tokenizer, is_training=False,
max_seq_length=args.max_seq_length)
if not os.path.exists(args.test_dir1):
json2features(input_file=args.test_file, output_files=[args.test_dir1, args.test_dir2],
tokenizer=tokenizer, is_training=False, repeat_limit=3, max_query_length=96,
max_seq_length=args.max_seq_length, doc_stride=128)
test_examples = json.load(open(args.test_dir1, 'r'))
test_features = json.load(open(args.test_dir2, 'r'))
dev_steps_per_epoch = len(test_features) // args.n_batch
if len(test_features) % args.n_batch != 0:
def main(args):
print(20 * "=", " Preparing for training ", 20 * "=")
# 保存模型的路径
if not os.path.exists(args.target_dir):
os.makedirs(args.target_dir)
tokenizer = tokenization.BertTokenizer(args.vocab, do_lower_case=True)
# tokenizer = BertTokenizer.from_pretrained(args.vocab)
# -------------------- Data loading ------------------- #
print("\t* Loading training data...")
# train_data = LCQMC_dataset(args.train_file, args.vocab_file, args.max_length, test_flag=False)
train_data = DataProcessForSentence(tokenizer,
args.train_file,
args.max_length,
test_flag=False)
train_loader = DataLoader(train_data,
batch_size=args.batch_size,
shuffle=True)
print("\t* Loading valid data...")
dev_data = DataProcessForSentence(tokenizer,
args.dev_file,
args.max_length,
test_flag=False)
dev_loader = DataLoader(dev_data, batch_size=args.batch_size, shuffle=True)
# -------------------- Model definition ------------------- #
print("\t* Building model...")
# model = Bert_model(args).to(args.device)
model = Roberta_pooling(args).to(args.device)
# -------------------- Preparation for training ------------------- #
criterion = nn.CrossEntropyLoss() # 交叉熵损失函数
# 列出所有需要更新权重的参数
param_optimizer = list(model.named_parameters())
# 不需要权重衰减的
no_decay = ['bias', 'LearyNorm.bias', 'LayerNorm.weight']
# 不是这几种类型的就需要进行权重衰减，这里中是不需要进行权重衰减的，保持正常的梯度更新即可
optimizer_grouped_parameters = [{
'params':
[p for n, p in param_optimizer if not any(nd in n for nd in no_decay)],
'weight_decay':
0.01
}, {
'params':
[p for n, p in param_optimizer if any(nd in n for nd in no_decay)],
'weight_decay':
0.00
}]
optimizer_params = {'lr': args.lr, 'eps': 1e-6, 'correct_bias': False}
optimizer = transformers.AdamW(optimizer_grouped_parameters,
**optimizer_params)
scheduler = transformers.get_linear_schedule_with_warmup(
optimizer,
num_warmup_steps=0.5,
num_training_steps=len(train_loader) * args.epochs)
best_score = 0.0
start_epoch = 1
epochs_count = []
train_losses = []
valid_losses = []
# Continuing training from a checkpoint if one was given as argument
if args.checkpoint:
# 从文件中加载checkpoint数据, 从而继续训练模型
checkpoints = torch.load(args.checkpoint)
start_epoch = checkpoints["epoch"] + 1
best_score = checkpoints["best_score"]
print("\t* Training will continue on existing model from epoch {}...".
format(start_epoch))
model.load_state_dict(checkpoints["model"]) # 模型部分
optimizer.load_state_dict(checkpoints["optimizer"])
epochs_count = checkpoints["epochs_count"]
train_losses = checkpoints["train_losses"]
valid_losses = checkpoints["valid_losses"]
# 这里改为只有从以前加载的checkpoint中才进行计算 valid, Compute loss and accuracy before starting (or resuming) training.
_, valid_loss, valid_accuracy, auc = validate(model, dev_loader,
criterion, args)
print(
"\t* Validation loss before training: {:.4f}, accuracy: {:.4f}%, auc: {:.4f}"
.format(valid_loss, (valid_accuracy * 100), auc))
# -------------------- Training epochs ------------------- #
print("\n", 20 * "=",
"Training Bert model on device: {}".format(args.device), 20 * "=")
patience_counter = 0
for epoch in range(start_epoch, args.epochs + 1):
epochs_count.append(epoch)
# -------------------- train --------------------------
print("* Training epoch {}:".format(epoch))
epoch_time, epoch_loss, epoch_accuracy = train(model, train_loader,
optimizer, scheduler,
criterion, args)
train_losses.append(epoch_loss)
print("-> Training time: {:.4f}s, loss = {:.4f}, accuracy: {:.4f}%".
format(epoch_time, epoch_loss, (epoch_accuracy * 100)))
# -------------------- valid --------------------------
print("* Validation for epoch {}:".format(epoch))
epoch_time, epoch_loss, epoch_accuracy, epoch_auc = validate(
model, train_loader, criterion, args)
valid_losses.append(epoch_loss)
print(
"-> Valid. time: {:.4f}s, loss: {:.4f}, accuracy: {:.4f}%, auc: {:.4f}\n"
.format(epoch_time, epoch_loss, (epoch_accuracy * 100), epoch_auc))
# Update the optimizer's learning rate with the scheduler.
scheduler.step(epoch_accuracy)
# Early stopping on validation accuracy.
if epoch_accuracy < best_score:
patience_counter += 1
else:
best_score = epoch_accuracy
patience_counter = 0
# 保存最好的结果，需要保存的参数，这些参数在checkpoint中都能找到
torch.save(
{
"epoch": epoch,
"model": model.state_dict(),
"best_score": best_score,
"epochs_count": epochs_count,
"train_losses": train_losses,
"valid_losses": valid_losses
}, os.path.join(args.target_dir, "pooling_bert_best.bin"))
if patience_counter >= args.patience:
print("-> Early stopping: patience limit reached, stopping...")
break
del model
def main():
parser = argparse.ArgumentParser()
## Required parameters
parser.add_argument(
"--data_dir",
default=None,
type=str,
required=True,
help=
"The input data dir. Should contain the .tsv files (or other data files) for the task."
)
parser.add_argument(
"--bert_model",
default=None,
type=str,
required=False,
help="Bert pre-trained model selected in the list: bert-base-uncased, "
"bert-large-uncased, bert-base-cased, bert-large-cased, bert-base-multilingual-uncased, "
"bert-base-multilingual-cased, bert-base-chinese.")
# trained_model_file
parser.add_argument("--trained_model_dir",
default=None,
type=str,
help="trained model for eval or predict")
parser.add_argument("--task_name",
default=None,
type=str,
required=True,
help="The name of the task to train.")
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(
"--cache_dir",
default="",
type=str,
help=
"Where do you want to store the pre-trained models downloaded from s3")
parser.add_argument(
"--max_seq_length",
default=128,
type=int,
help=
"The maximum total input sequence length after WordPiece tokenization. \n"
"Sequences longer than this will be truncated, and sequences shorter \n"
"than this will be padded.")
parser.add_argument("--do_train",
action='store_true',
help="Whether to run training.")
parser.add_argument("--my_tokenization",
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 test set.")
parser.add_argument(
"--do_lower_case",
default=False,
help="Set this flag if you are using an uncased model.")
parser.add_argument("--train_batch_size",
default=32,
type=int,
help="Total batch size for training.")
parser.add_argument("--eval_batch_size",
default=8,
type=int,
help="Total batch size for eval.")
parser.add_argument("--learning_rate",
default=5e-5,
type=float,
help="The initial learning rate for Adam.")
parser.add_argument("--num_train_epochs",
default=3.0,
type=float,
help="Total number of training epochs to perform.")
parser.add_argument(
"--warmup_proportion",
default=0.1,
type=float,
help=
"Proportion of training to perform linear learning rate warmup for. "
"E.g., 0.1 = 10%% of training.")
parser.add_argument("--no_cuda",
action='store_true',
help="Whether not to use CUDA when available")
parser.add_argument("--local_rank",
type=int,
default=-1,
help="local_rank for distributed training on gpus")
parser.add_argument('--seed',
type=int,
default=42,
help="random seed for initialization")
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(
'--fp16',
action='store_true',
help="Whether to use 16-bit float precision instead of 32-bit")
parser.add_argument(
'--loss_scale',
type=float,
default=0,
help=
"Loss scaling to improve fp16 numeric stability. Only used when fp16 set to True.\n"
"0 (default value): dynamic loss scaling.\n"
"Positive power of 2: static loss scaling value.\n")
parser.add_argument('--server_ip',
type=str,
default='',
help="Can be used for distant debugging.")
parser.add_argument('--server_port',
type=str,
default='',
help="Can be used for distant debugging.")
args = parser.parse_args()
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()
processors = {
"cola": ColaProcessor,
"mnli": MnliProcessor,
"mrpc": MrpcProcessor,
"lcqmc": LcqmcProcessor,
"sst-2": Sst2Processor,
"text-clf": TextClfProcessor,
"xnli": XnliProcessor
}
num_labels_task = {
"cola": 2,
"sst-2": 2,
"mnli": 3,
"mrpc": 2,
"lcqmc": 2,
"xnli": 3,
"text-clf": 0
}
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")
n_gpu = torch.cuda.device_count()
else:
torch.cuda.set_device(args.local_rank)
device = torch.device("cuda", args.local_rank)
n_gpu = 1
# Initializes the distributed backend which will take care of sychronizing nodes/GPUs
torch.distributed.init_process_group(backend='nccl')
logger.info(
"device: {} n_gpu: {}, distributed training: {}, 16-bits training: {}".
format(device, n_gpu, bool(args.local_rank != -1), args.fp16))
if args.gradient_accumulation_steps < 1:
raise ValueError(
"Invalid gradient_accumulation_steps parameter: {}, should be >= 1"
.format(args.gradient_accumulation_steps))
args.train_batch_size = args.train_batch_size // args.gradient_accumulation_steps
random.seed(args.seed)
np.random.seed(args.seed)
torch.manual_seed(args.seed)
if n_gpu > 0:
torch.cuda.manual_seed_all(args.seed)
if not args.do_train and not args.do_eval and not args.do_test:
raise ValueError(
"At least one of `do_train` or `do_eval(test)` must be True.")
if os.path.exists(args.output_dir) and os.listdir(
args.output_dir) and args.do_train:
raise ValueError(
"Output directory ({}) already exists and is not empty.".format(
args.output_dir))
if not os.path.exists(args.output_dir):
os.makedirs(args.output_dir)
task_name = args.task_name.lower()
if task_name not in processors:
raise ValueError("Task not found: %s" % (task_name))
processor = processors[task_name]()
num_labels = num_labels_task[task_name]
label_list = processor.get_labels()
if args.bert_model:
tokenizer = tokenization.BertTokenizer(vocab_file=os.path.join(
args.bert_model, 'vocab.txt'),
do_lower_case=True)
elif args.trained_model_dir:
tokenization.BertTokenizer(vocab_file=os.path.join(
args.trained_model_dir, 'vocab.txt'),
do_lower_case=True)
logger.info('vocab size is %d' % (len(tokenizer.vocab)))
label_map_reverse = {}
train_examples = None
num_train_optimization_steps = None
if args.do_train:
train_examples = processor.get_train_examples(args.data_dir)
if task_name == 'text-clf':
num_labels = len(label_list)
label_map = {label: i for i, label in enumerate(label_list)}
label_file = os.path.join(args.output_dir, "label_map_training.txt")
with open(label_file, "w") as writer:
for (k, v) in label_map.items():
writer.write(str(k))
writer.write('\t')
writer.write(str(v))
writer.write('\n')
label_map_reverse = {v: k for k, v in label_map.items()}
num_train_optimization_steps = int(
len(train_examples) / args.train_batch_size /
args.gradient_accumulation_steps) * args.num_train_epochs
if args.local_rank != -1:
num_train_optimization_steps = num_train_optimization_steps // torch.distributed.get_world_size(
)
else:
train_examples = processor.get_train_examples(args.data_dir)
label_map = {label: i for i, label in enumerate(label_list)}
label_map_reverse = {v: k for k, v in label_map.items()}
num_labels = len(label_list)
# Prepare model
cache_dir = args.cache_dir if args.cache_dir else os.path.join(
str(PYTORCH_PRETRAINED_BERT_CACHE), 'distributed_{}'.format(
args.local_rank))
if args.trained_model_dir:
config = BertConfig(
os.path.join(args.trained_model_dir, 'bert_config.json'))
model = BertForSequenceClassification(config, num_labels=num_labels)
model.load_state_dict(
torch.load(
os.path.join(args.trained_model_dir, 'pytorch_model.bin')))
logger.info('finish trained model loading!')
elif args.bert_model: #
# model = BertForSequenceClassification.from_pretrained(args.bert_model,
# cache_dir=cache_dir,
# num_labels=num_labels)
print('init model...')
bert_config = BertConfig.from_json_file(
os.path.join(args.bert_model, 'bert_config.json'))
model = BertForSequenceClassification(bert_config,
num_labels=num_labels)
utils.torch_show_all_params(model)
utils.torch_init_model(
model, os.path.join(args.bert_model, 'pytorch_model.bin'))
if args.fp16:
model.half()
model.to(device)
if args.local_rank != -1:
try:
from apex.parallel import DistributedDataParallel as DDP
except ImportError:
raise ImportError(
"Please install apex from https://www.github.com/nvidia/apex to use distributed and fp16 training."
)
model = DDP(model)
elif n_gpu > 1:
model = torch.nn.DataParallel(model)
# Prepare optimizer
param_optimizer = list(model.named_parameters())
no_decay = ['bias', 'LayerNorm.bias', 'LayerNorm.weight']
optimizer_grouped_parameters = [{
'params':
[p for n, p in param_optimizer if not any(nd in n for nd in no_decay)],
'weight_decay':
0.01
}, {
'params':
[p for n, p in param_optimizer if any(nd in n for nd in no_decay)],
'weight_decay':
0.0
}]
if args.fp16:
try:
from apex.optimizers import FP16_Optimizer
from apex.optimizers import FusedAdam
except ImportError:
raise ImportError(
"Please install apex from https://www.github.com/nvidia/apex to use distributed and fp16 training."
)
optimizer = FusedAdam(optimizer_grouped_parameters,
lr=args.learning_rate,
bias_correction=False,
max_grad_norm=1.0)
if args.loss_scale == 0:
optimizer = FP16_Optimizer(optimizer, dynamic_loss_scale=True)
else:
optimizer = FP16_Optimizer(optimizer,
static_loss_scale=args.loss_scale)
else:
optimizer = BertAdam(optimizer_grouped_parameters,
lr=args.learning_rate,
warmup=args.warmup_proportion,
t_total=num_train_optimization_steps)
global_step = 0
nb_tr_steps = 0
tr_loss = 0
if args.do_train:
train_features = convert_examples_to_features(
train_examples,
label_list,
args.max_seq_length,
tokenizer,
my_tokenization=args.my_tokenization)
logger.info("***** Running training *****")
logger.info(" Num examples = %d", len(train_examples))
logger.info(" Batch size = %d", args.train_batch_size)
logger.info(" Num steps = %d", num_train_optimization_steps)
all_input_ids = torch.tensor([f.input_ids for f in train_features],
dtype=torch.long)
all_input_mask = torch.tensor([f.input_mask for f in train_features],
dtype=torch.long)
all_segment_ids = torch.tensor([f.segment_ids for f in train_features],
dtype=torch.long)
all_label_ids = torch.tensor([f.label_id for f in train_features],
dtype=torch.long)
train_data = TensorDataset(all_input_ids, all_input_mask,
all_segment_ids, all_label_ids)
if args.local_rank == -1:
train_sampler = RandomSampler(train_data)
else:
train_sampler = DistributedSampler(train_data)
train_dataloader = DataLoader(train_data,
sampler=train_sampler,
batch_size=args.train_batch_size)
num_epoch = 0
for _ in trange(int(args.num_train_epochs), desc="Epoch"):
model.train()
tr_loss = 0
nb_tr_examples, nb_tr_steps = 0, 0
for step, batch in enumerate(
tqdm(train_dataloader, desc="Iteration")):
batch = tuple(t.to(device) for t in batch)
input_ids, input_mask, segment_ids, label_ids = batch
loss = model(input_ids, segment_ids, input_mask, label_ids)
if n_gpu > 1:
loss = loss.mean() # mean() to average on multi-gpu.
if args.gradient_accumulation_steps > 1:
loss = loss / args.gradient_accumulation_steps
if args.fp16:
optimizer.backward(loss)
else:
loss.backward()
tr_loss += loss.item()
nb_tr_examples += input_ids.size(0)
nb_tr_steps += 1
if (step + 1) % args.gradient_accumulation_steps == 0:
if args.fp16:
# modify learning rate with special warm up BERT uses
# if args.fp16 is False, BertAdam is used that handles this automatically
lr_this_step = args.learning_rate * warmup_linear(
global_step / num_train_optimization_steps,
args.warmup_proportion)
for param_group in optimizer.param_groups:
param_group['lr'] = lr_this_step
optimizer.step()
optimizer.zero_grad()
global_step += 1
num_epoch += 1
## begin to evaluate
eval_all_result = []
eval_examples = processor.get_dev_examples(args.data_dir)
eval_features = convert_examples_to_features(
eval_examples,
label_list,
args.max_seq_length,
tokenizer,
my_tokenization=args.my_tokenization)
logger.info("***** Running %d -th evaluation *****" % num_epoch)
logger.info(" Num examples = %d", len(eval_examples))
logger.info(" Batch size = %d", args.eval_batch_size)
all_input_ids = torch.tensor([f.input_ids for f in eval_features],
dtype=torch.long)
all_input_mask = torch.tensor(
[f.input_mask for f in eval_features], dtype=torch.long)
all_segment_ids = torch.tensor(
[f.segment_ids for f in eval_features], dtype=torch.long)
all_label_ids = torch.tensor([f.label_id for f in eval_features],
dtype=torch.long)
eval_data = TensorDataset(all_input_ids, all_input_mask,
all_segment_ids, all_label_ids)
# Run prediction for full data
eval_sampler = SequentialSampler(eval_data)
eval_dataloader = DataLoader(eval_data,
sampler=eval_sampler,
batch_size=args.eval_batch_size)
model.eval()
eval_loss, eval_accuracy = 0, 0
nb_eval_steps, nb_eval_examples = 0, 0
for input_ids, input_mask, segment_ids, label_ids in tqdm(
eval_dataloader, desc="Evaluating"):
input_ids = input_ids.to(device)
input_mask = input_mask.to(device)
segment_ids = segment_ids.to(device)
label_ids = label_ids.to(device)
with torch.no_grad():
tmp_eval_loss = model(input_ids, segment_ids, input_mask,
label_ids)
logits = model(input_ids, segment_ids, input_mask)
logits = logits.detach().cpu().numpy()
label_ids = label_ids.to('cpu').numpy()
tmp_eval_accuracy, batch_result = accuracy(logits, label_ids)
for i in range(input_ids.size()[0]):
eval_all_result.append(batch_result[i])
eval_loss += tmp_eval_loss.mean().item()
eval_accuracy += tmp_eval_accuracy
nb_eval_examples += input_ids.size(0)
nb_eval_steps += 1
eval_loss = eval_loss / nb_eval_steps
eval_accuracy = eval_accuracy / nb_eval_examples
loss = tr_loss / nb_tr_steps if args.do_train else None
result = {
'eval_loss': eval_loss,
'eval_accuracy': eval_accuracy,
'global_step': global_step,
'loss': loss
}
output_eval_file = os.path.join(args.output_dir,
"eval_results.txt")
epoch_eval_result_file = os.path.join(
args.output_dir,
str(num_epoch) + "th_epoch_eval_results.txt")
with open(output_eval_file, "a") as writer:
logger.info("***** %d th epoch eval results *****" % num_epoch)
writer.write("***%d th epoch result***" % num_epoch)
for key in sorted(result.keys()):
logger.info(" %s = %s", key, str(result[key]))
writer.write("%s = %s\n" % (key, str(result[key])))
if num_epoch < 0:
continue
with open(epoch_eval_result_file, "w") as writer:
for element in eval_all_result:
tokens_sample = 'Text'
result_sample = element
writer.write(str(tokens_sample))
writer.write('\t')
for ele in result_sample:
writer.write(label_map_reverse[ele])
writer.write('\t')
writer.write('\n')
# Save a trained model and the associated configuration
model_to_save = model.module if hasattr(
model, 'module') else model # Only save the model it-self
output_model_file = os.path.join(
args.output_dir, WEIGHTS_NAME + '.ep' + str(num_epoch))
torch.save(model_to_save.state_dict(), output_model_file)
output_config_file = os.path.join(args.output_dir, CONFIG_NAME)
with open(output_config_file, 'w') as f:
f.write(model_to_save.config.to_json_string())
model.to(device)
logger.info('%s' % str(args.do_eval))
if args.do_eval and (args.local_rank == -1
or torch.distributed.get_rank() == 0):
eval_all_result = []
stime = datetime.datetime.now()
eval_examples = processor.get_dev_examples(args.data_dir)
eval_features = convert_examples_to_features(
eval_examples,
label_list,
args.max_seq_length,
tokenizer,
my_tokenization=args.my_tokenization)
logger.info("***** Running evaluation *****")
logger.info(" Num examples = %d", len(eval_examples))
logger.info(" Batch size = %d", args.eval_batch_size)
all_input_ids = torch.tensor([f.input_ids for f in eval_features],
dtype=torch.long)
all_input_mask = torch.tensor([f.input_mask for f in eval_features],
dtype=torch.long)
all_segment_ids = torch.tensor([f.segment_ids for f in eval_features],
dtype=torch.long)
all_label_ids = torch.tensor([f.label_id for f in eval_features],
dtype=torch.long)
eval_data = TensorDataset(all_input_ids, all_input_mask,
all_segment_ids, all_label_ids)
# Run prediction for full data
eval_sampler = SequentialSampler(eval_data)
eval_dataloader = DataLoader(eval_data,
sampler=eval_sampler,
batch_size=args.eval_batch_size)
model.eval()
eval_loss, eval_accuracy = 0, 0
nb_eval_steps, nb_eval_examples = 0, 0
for input_ids, input_mask, segment_ids, label_ids in tqdm(
eval_dataloader, desc="Evaluating"):
input_ids = input_ids.to(device)
input_mask = input_mask.to(device)
segment_ids = segment_ids.to(device)
label_ids = label_ids.to(device)
with torch.no_grad():
tmp_eval_loss = model(input_ids, segment_ids, input_mask,
label_ids)
logits = model(input_ids, segment_ids, input_mask)
logits = logits.detach().cpu().numpy()
label_ids = label_ids.to('cpu').numpy()
tmp_eval_accuracy, batch_result = accuracy(logits, label_ids)
for i in range(input_ids.size()[0]):
eval_all_result.append(batch_result[i])
eval_loss += tmp_eval_loss.mean().item()
eval_accuracy += tmp_eval_accuracy
nb_eval_examples += input_ids.size(0)
nb_eval_steps += 1
eval_loss = eval_loss / nb_eval_steps
eval_accuracy = eval_accuracy / nb_eval_examples
loss = tr_loss / nb_tr_steps if args.do_train else None
result = {
'eval_loss': eval_loss,
'eval_accuracy': eval_accuracy,
'global_step': global_step,
'loss': loss
}
output_eval_file = os.path.join(args.output_dir, "eval_metrics.txt")
eval_result_file = os.path.join(args.output_dir,
"eval_all_results.txt")
with open(output_eval_file, "a") as writer:
logger.info("***** Final Eval results *****")
for key in sorted(result.keys()):
logger.info(" %s = %s", key, str(result[key]))
writer.write("%s = %s\n" % (key, str(result[key])))
etime = datetime.datetime.now()
logger.info('eval precoess cost time: %s' % str(etime - stime))
with open(eval_result_file, "w") as writer:
logger.info("***** eval_all_results *****")
for element in eval_all_result:
tokens_sample = 'Text'
result_sample = element
writer.write(str(tokens_sample))
writer.write('\t')
for ele in result_sample:
writer.write(label_map_reverse[ele])
writer.write('\t')
writer.write('\n')
if args.do_test:
eval_all_result = []
eval_examples = processor.get_test_examples(args.data_dir)
eval_features = convert_examples_to_features(eval_examples, label_list,
args.max_seq_length,
tokenizer)
logger.info("***** Running testing *****")
logger.info(" Num examples = %d", len(eval_examples))
logger.info(" Batch size = %d", args.eval_batch_size)
all_input_ids = torch.tensor([f.input_ids for f in eval_features],
dtype=torch.long)
all_input_mask = torch.tensor([f.input_mask for f in eval_features],
dtype=torch.long)
all_segment_ids = torch.tensor([f.segment_ids for f in eval_features],
dtype=torch.long)
all_label_ids = torch.tensor([f.label_id for f in eval_features],
dtype=torch.long)
eval_data = TensorDataset(all_input_ids, all_input_mask,
all_segment_ids, all_label_ids)
# Run prediction for full data
eval_sampler = SequentialSampler(eval_data)
eval_dataloader = DataLoader(eval_data,
sampler=eval_sampler,
batch_size=args.eval_batch_size)
model.eval()
eval_loss, eval_accuracy = 0, 0
nb_eval_steps, nb_eval_examples = 0, 0
for input_ids, input_mask, segment_ids, label_ids in tqdm(
eval_dataloader, desc="Evaluating"):
input_ids = input_ids.to(device)
input_mask = input_mask.to(device)
segment_ids = segment_ids.to(device)
label_ids = label_ids.to(device)
with torch.no_grad():
tmp_eval_loss = model(input_ids, segment_ids, input_mask,
label_ids)
logits = model(input_ids, segment_ids, input_mask)
logits = logits.detach().cpu().numpy()
label_ids = label_ids.to('cpu').numpy()
tmp_eval_accuracy, batch_result = accuracy(logits, label_ids)
for i in range(input_ids.size()[0]):
eval_all_result.append(batch_result[i])
eval_loss += tmp_eval_loss.mean().item()
eval_accuracy += tmp_eval_accuracy
nb_eval_examples += input_ids.size(0)
nb_eval_steps += 1
eval_loss = eval_loss / nb_eval_steps
eval_accuracy = eval_accuracy / nb_eval_examples
loss = tr_loss / nb_tr_steps if args.do_train else None
result = {
'eval_loss': eval_loss,
'eval_accuracy': eval_accuracy,
'global_step': global_step,
'loss': loss
}
output_test_file = os.path.join(args.output_dir, "test_results.txt")
test_metric_file = os.path.join(args.output_dir, "test_metric.txt")
with open(test_metric_file, "w") as writer:
for key in sorted(result.keys()):
logger.info(" %s = %s", key, str(result[key]))
writer.write("%s = %s\n" % (key, str(result[key])))
with open(output_test_file, "w") as writer:
for element in eval_all_result:
result_sample = element
for ele in result_sample:
writer.write(str(ele))
writer.write('\t')
writer.write('\n')
def main():
parser = argparse.ArgumentParser()
## Required parameters
parser.add_argument("--gpu_ids", default='0', type=str, required=True)
parser.add_argument(
"--data_dir",
default=None,
type=str,
required=True,
help=
"The input data dir. Should contain the .tsv files (or other data files) for the task."
)
parser.add_argument("--task_name", default='c3', type=str, required=True)
parser.add_argument(
"--bert_config_file",
default=None,
type=str,
required=True,
help=
"The config json file corresponding to the pre-trained BERT model. \n"
"This specifies the model architecture.")
parser.add_argument(
"--vocab_file",
default=None,
type=str,
required=True,
help="The vocabulary file that the BERT model was trained on.")
parser.add_argument(
"--output_dir",
default=None,
type=str,
required=True,
help="The output directory where the model checkpoints will be written."
)
## Other parameters
parser.add_argument(
"--init_checkpoint",
default=
'check_points/pretrain_models/albert_xxlarge_google_zh_v1121/pytorch_model.pth',
type=str,
help="Initial checkpoint (usually from a pre-trained BERT model).")
parser.add_argument(
"--do_lower_case",
default=True,
action='store_true',
help=
"Whether to lower case the input text. True for uncased models, False for cased models."
)
parser.add_argument(
"--max_seq_length",
default=512,
type=int,
help=
"The maximum total input sequence length after WordPiece tokenization. \n"
"Sequences longer than this will be truncated, and sequences shorter \n"
"than this will be padded.")
parser.add_argument("--do_train",
default=False,
action='store_true',
help="Whether to run training.")
parser.add_argument("--do_eval",
default=False,
action='store_true',
help="Whether to run eval on the dev set.")
parser.add_argument("--train_batch_size",
default=16,
type=int,
help="Total batch size for training.")
parser.add_argument("--eval_batch_size",
default=16,
type=int,
help="Total batch size for eval.")
parser.add_argument("--learning_rate",
default=2e-5,
type=float,
help="The initial learning rate for Adam.")
parser.add_argument("--schedule",
default='warmup_linear',
type=str,
help='schedule')
parser.add_argument("--weight_decay_rate",
default=0.01,
type=float,
help='weight_decay_rate')
parser.add_argument('--clip_norm', type=float, default=1.0)
parser.add_argument("--num_train_epochs",
default=8.0,
type=float,
help="Total number of training epochs to perform.")
parser.add_argument(
"--warmup_proportion",
default=0.1,
type=float,
help=
"Proportion of training to perform linear learning rate warmup for. "
"E.g., 0.1 = 10%% of training.")
parser.add_argument("--no_cuda",
default=False,
action='store_true',
help="Whether not to use CUDA when available")
parser.add_argument('--float16', action='store_true', default=False)
parser.add_argument("--local_rank",
type=int,
default=-1,
help="local_rank for distributed training on gpus")
parser.add_argument('--seed',
type=int,
default=422,
help="random seed for initialization")
parser.add_argument(
'--gradient_accumulation_steps',
type=int,
default=1,
help=
"Number of updates steps to accumualte before performing a backward/update pass."
)
parser.add_argument('--setting_file', type=str, default='setting.txt')
parser.add_argument('--log_file', type=str, default='log.txt')
args = parser.parse_args()
args.setting_file = os.path.join(args.output_dir, args.setting_file)
args.log_file = os.path.join(args.output_dir, args.log_file)
os.makedirs(args.output_dir, exist_ok=True)
with open(args.setting_file, 'wt') as opt_file:
opt_file.write('------------ Options -------------\n')
print('------------ Options -------------')
for k in args.__dict__:
v = args.__dict__[k]
opt_file.write('%s: %s\n' % (str(k), str(v)))
print('%s: %s' % (str(k), str(v)))
opt_file.write('-------------- End ----------------\n')
print('------------ End -------------')
os.environ["CUDA_VISIBLE_DEVICES"] = args.gpu_ids
if os.path.exists(args.log_file):
os.remove(args.log_file)
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")
n_gpu = torch.cuda.device_count()
else:
device = torch.device("cuda", args.local_rank)
n_gpu = 1
# Initializes the distributed backend which will take care of sychronizing nodes/GPUs
torch.distributed.init_process_group(backend='nccl')
logger.info("device %s n_gpu %d distributed training %r", device, n_gpu,
bool(args.local_rank != -1))
if args.gradient_accumulation_steps < 1:
raise ValueError(
"Invalid gradient_accumulation_steps parameter: {}, should be >= 1"
.format(args.gradient_accumulation_steps))
args.train_batch_size = int(args.train_batch_size /
args.gradient_accumulation_steps)
random.seed(args.seed)
np.random.seed(args.seed)
torch.manual_seed(args.seed)
if n_gpu > 0:
torch.cuda.manual_seed_all(args.seed)
if not args.do_train and not args.do_eval:
raise ValueError(
"At least one of `do_train` or `do_eval` must be True.")
processor = c3Processor(args.data_dir)
label_list = processor.get_labels()
tokenizer = tokenization.BertTokenizer(vocab_file=args.vocab_file,
do_lower_case=args.do_lower_case)
train_examples = None
num_train_steps = None
if args.do_train:
train_examples = processor.get_train_examples()
num_train_steps = int(
len(train_examples) / n_class / args.train_batch_size /
args.gradient_accumulation_steps * args.num_train_epochs)
if 'albert' in args.bert_config_file:
if 'google' in args.bert_config_file:
bert_config = AlbertConfig.from_json_file(args.bert_config_file)
model = AlbertForMultipleChoice(bert_config, num_choices=n_class)
else:
bert_config = ALBertConfig.from_json_file(args.bert_config_file)
model = ALBertForMultipleChoice(bert_config, num_choices=n_class)
else:
bert_config = BertConfig.from_json_file(args.bert_config_file)
model = BertForMultipleChoice(bert_config, num_choices=n_class)
if args.max_seq_length > bert_config.max_position_embeddings:
raise ValueError(
"Cannot use sequence length {} because the BERT model was only trained up to sequence length {}"
.format(args.max_seq_length, bert_config.max_position_embeddings))
if args.init_checkpoint is not None:
utils.torch_show_all_params(model)
utils.torch_init_model(model, args.init_checkpoint)
if args.float16:
model.half()
model.to(device)
if args.local_rank != -1:
model = torch.nn.parallel.DistributedDataParallel(
model, device_ids=[args.local_rank], output_device=args.local_rank)
elif n_gpu > 1:
model = torch.nn.DataParallel(model)
optimizer = get_optimization(
model=model,
float16=args.float16,
learning_rate=args.learning_rate,
total_steps=num_train_steps,
schedule=args.schedule,
warmup_rate=args.warmup_proportion,
max_grad_norm=args.clip_norm,
weight_decay_rate=args.weight_decay_rate,
opt_pooler=True) # multi_choice must update pooler
global_step = 0
eval_dataloader = None
if args.do_eval:
eval_examples = processor.get_dev_examples()
feature_dir = os.path.join(
args.data_dir, 'dev_features{}.pkl'.format(args.max_seq_length))
if os.path.exists(feature_dir):
eval_features = pickle.load(open(feature_dir, 'rb'))
else:
eval_features = convert_examples_to_features(
eval_examples, label_list, args.max_seq_length, tokenizer)
with open(feature_dir, 'wb') as w:
pickle.dump(eval_features, w)
input_ids = []
input_mask = []
segment_ids = []
label_id = []
for f in eval_features:
input_ids.append([])
input_mask.append([])
segment_ids.append([])
for i in range(n_class):
input_ids[-1].append(f[i].input_ids)
input_mask[-1].append(f[i].input_mask)
segment_ids[-1].append(f[i].segment_ids)
label_id.append(f[0].label_id)
all_input_ids = torch.tensor(input_ids, dtype=torch.long)
all_input_mask = torch.tensor(input_mask, dtype=torch.long)
all_segment_ids = torch.tensor(segment_ids, dtype=torch.long)
all_label_ids = torch.tensor(label_id, dtype=torch.long)
eval_data = TensorDataset(all_input_ids, all_input_mask,
all_segment_ids, all_label_ids)
if args.local_rank == -1:
eval_sampler = SequentialSampler(eval_data)
else:
eval_sampler = DistributedSampler(eval_data)
eval_dataloader = DataLoader(eval_data,
sampler=eval_sampler,
batch_size=args.eval_batch_size)
if args.do_train:
best_accuracy = 0
feature_dir = os.path.join(
args.data_dir, 'train_features{}.pkl'.format(args.max_seq_length))
if os.path.exists(feature_dir):
train_features = pickle.load(open(feature_dir, 'rb'))
else:
train_features = convert_examples_to_features(
train_examples, label_list, args.max_seq_length, tokenizer)
with open(feature_dir, 'wb') as w:
pickle.dump(train_features, w)
logger.info("***** Running training *****")
logger.info(" Num examples = %d", len(train_examples))
logger.info(" Batch size = %d", args.train_batch_size)
logger.info(" Num steps = %d", num_train_steps)
input_ids = []
input_mask = []
segment_ids = []
label_id = []
for f in train_features:
input_ids.append([])
input_mask.append([])
segment_ids.append([])
for i in range(n_class):
input_ids[-1].append(f[i].input_ids)
input_mask[-1].append(f[i].input_mask)
segment_ids[-1].append(f[i].segment_ids)
label_id.append(f[0].label_id)
all_input_ids = torch.tensor(input_ids, dtype=torch.long)
all_input_mask = torch.tensor(input_mask, dtype=torch.long)
all_segment_ids = torch.tensor(segment_ids, dtype=torch.long)
all_label_ids = torch.tensor(label_id, dtype=torch.long)
train_data = TensorDataset(all_input_ids, all_input_mask,
all_segment_ids, all_label_ids)
if args.local_rank == -1:
train_sampler = RandomSampler(train_data)
else:
train_sampler = DistributedSampler(train_data)
train_dataloader = DataLoader(train_data,
sampler=train_sampler,
batch_size=args.train_batch_size,
drop_last=True)
steps_per_epoch = int(num_train_steps / args.num_train_epochs)
for ie in range(int(args.num_train_epochs)):
model.train()
tr_loss = 0
nb_tr_examples, nb_tr_steps = 0, 0
with tqdm(total=int(steps_per_epoch),
desc='Epoch %d' % (ie + 1)) as pbar:
for step, batch in enumerate(train_dataloader):
batch = tuple(t.to(device) for t in batch)
input_ids, input_mask, segment_ids, label_ids = batch
loss = model(input_ids, segment_ids, input_mask, label_ids)
if n_gpu > 1:
loss = loss.mean() # mean() to average on multi-gpu.
if args.gradient_accumulation_steps > 1:
loss = loss / args.gradient_accumulation_steps
tr_loss += loss.item()
if args.float16:
optimizer.backward(loss)
# modify learning rate with special warm up BERT uses
# if args.fp16 is False, BertAdam is used and handles this automatically
lr_this_step = args.learning_rate * warmup_linear(
global_step / num_train_steps,
args.warmup_proportion)
for param_group in optimizer.param_groups:
param_group['lr'] = lr_this_step
else:
loss.backward()
nb_tr_examples += input_ids.size(0)
if (step + 1) % args.gradient_accumulation_steps == 0:
optimizer.step(
) # We have accumulated enought gradients
model.zero_grad()
global_step += 1
nb_tr_steps += 1
pbar.set_postfix({
'loss':
'{0:1.5f}'.format(tr_loss / (nb_tr_steps + 1e-5))
})
pbar.update(1)
if args.do_eval:
model.eval()
eval_loss, eval_accuracy = 0, 0
nb_eval_steps, nb_eval_examples = 0, 0
logits_all = []
for input_ids, input_mask, segment_ids, label_ids in tqdm(
eval_dataloader):
input_ids = input_ids.to(device)
input_mask = input_mask.to(device)
segment_ids = segment_ids.to(device)
label_ids = label_ids.to(device)
with torch.no_grad():
tmp_eval_loss, logits = model(input_ids,
segment_ids,
input_mask,
label_ids,
return_logits=True)
logits = logits.detach().cpu().numpy()
label_ids = label_ids.cpu().numpy()
for i in range(len(logits)):
logits_all += [logits[i]]
tmp_eval_accuracy = accuracy(logits, label_ids.reshape(-1))
eval_loss += tmp_eval_loss.mean().item()
eval_accuracy += tmp_eval_accuracy
nb_eval_examples += input_ids.size(0)
nb_eval_steps += 1
eval_loss = eval_loss / nb_eval_steps
eval_accuracy = eval_accuracy / nb_eval_examples
if args.do_train:
result = {
'eval_loss': eval_loss,
'eval_accuracy': eval_accuracy,
'global_step': global_step,
'loss': tr_loss / nb_tr_steps
}
else:
result = {
'eval_loss': eval_loss,
'eval_accuracy': eval_accuracy
}
logger.info("***** Eval results *****")
for key in sorted(result.keys()):
logger.info(" %s = %s", key, str(result[key]))
with open(args.log_file, 'a') as aw:
aw.write(
"-------------------global steps:{}-------------------\n"
.format(global_step))
aw.write(str(json.dumps(result, indent=2)) + '\n')
if eval_accuracy >= best_accuracy:
torch.save(model.state_dict(),
os.path.join(args.output_dir, "model_best.pt"))
best_accuracy = eval_accuracy
model.load_state_dict(
torch.load(os.path.join(args.output_dir, "model_best.pt")))
torch.save(model.state_dict(), os.path.join(args.output_dir,
"model.pt"))
model.load_state_dict(torch.load(os.path.join(args.output_dir,
"model.pt")))
if args.do_eval:
logger.info("***** Running evaluation *****")
logger.info(" Num examples = %d", len(eval_examples))
logger.info(" Batch size = %d", args.eval_batch_size)
model.eval()
eval_loss, eval_accuracy = 0, 0
nb_eval_steps, nb_eval_examples = 0, 0
logits_all = []
for input_ids, input_mask, segment_ids, label_ids in tqdm(
eval_dataloader):
input_ids = input_ids.to(device)
input_mask = input_mask.to(device)
segment_ids = segment_ids.to(device)
label_ids = label_ids.to(device)
with torch.no_grad():
tmp_eval_loss, logits = model(input_ids,
segment_ids,
input_mask,
label_ids,
return_logits=True)
logits = logits.detach().cpu().numpy()
label_ids = label_ids.cpu().numpy()
for i in range(len(logits)):
logits_all += [logits[i]]
tmp_eval_accuracy = accuracy(logits, label_ids.reshape(-1))
eval_loss += tmp_eval_loss.mean().item()
eval_accuracy += tmp_eval_accuracy
nb_eval_examples += input_ids.size(0)
nb_eval_steps += 1
eval_loss = eval_loss / nb_eval_steps
eval_accuracy = eval_accuracy / nb_eval_examples
result = {'eval_loss': eval_loss, 'eval_accuracy': eval_accuracy}
output_eval_file = os.path.join(args.output_dir, "results_dev.txt")
with open(output_eval_file, "w") as writer:
logger.info("***** Eval results *****")
for key in sorted(result.keys()):
logger.info(" %s = %s", key, str(result[key]))
writer.write("%s = %s\n" % (key, str(result[key])))
output_eval_file = os.path.join(args.output_dir, "logits_dev.txt")
with open(output_eval_file, "w") as f:
for i in range(len(logits_all)):
for j in range(len(logits_all[i])):
f.write(str(logits_all[i][j]))
if j == len(logits_all[i]) - 1:
f.write("\n")
else:
f.write(" ")
test_examples = processor.get_test_examples()
feature_dir = os.path.join(
args.data_dir, 'test_features{}.pkl'.format(args.max_seq_length))
if os.path.exists(feature_dir):
test_features = pickle.load(open(feature_dir, 'rb'))
else:
test_features = convert_examples_to_features(
test_examples, label_list, args.max_seq_length, tokenizer)
with open(feature_dir, 'wb') as w:
pickle.dump(test_features, w)
logger.info("***** Running testing *****")
logger.info(" Num examples = %d", len(test_examples))
logger.info(" Batch size = %d", args.eval_batch_size)
input_ids = []
input_mask = []
segment_ids = []
label_id = []
for f in test_features:
input_ids.append([])
input_mask.append([])
segment_ids.append([])
for i in range(n_class):
input_ids[-1].append(f[i].input_ids)
input_mask[-1].append(f[i].input_mask)
segment_ids[-1].append(f[i].segment_ids)
label_id.append(f[0].label_id)
all_input_ids = torch.tensor(input_ids, dtype=torch.long)
all_input_mask = torch.tensor(input_mask, dtype=torch.long)
all_segment_ids = torch.tensor(segment_ids, dtype=torch.long)
all_label_ids = torch.tensor(label_id, dtype=torch.long)
test_data = TensorDataset(all_input_ids, all_input_mask,
all_segment_ids, all_label_ids)
if args.local_rank == -1:
test_sampler = SequentialSampler(test_data)
else:
test_sampler = DistributedSampler(test_data)
test_dataloader = DataLoader(test_data,
sampler=test_sampler,
batch_size=args.eval_batch_size)
model.eval()
test_loss, test_accuracy = 0, 0
nb_test_steps, nb_test_examples = 0, 0
logits_all = []
for input_ids, input_mask, segment_ids, label_ids in tqdm(
test_dataloader):
input_ids = input_ids.to(device)
input_mask = input_mask.to(device)
segment_ids = segment_ids.to(device)
label_ids = label_ids.to(device)
with torch.no_grad():
tmp_test_loss, logits = model(input_ids,
segment_ids,
input_mask,
label_ids,
return_logits=True)
logits = logits.detach().cpu().numpy()
label_ids = label_ids.to('cpu').numpy()
for i in range(len(logits)):
logits_all += [logits[i]]
tmp_test_accuracy = accuracy(logits, label_ids.reshape(-1))
test_loss += tmp_test_loss.mean().item()
test_accuracy += tmp_test_accuracy
nb_test_examples += input_ids.size(0)
nb_test_steps += 1
test_loss = test_loss / nb_test_steps
test_accuracy = test_accuracy / nb_test_examples
result = {'test_loss': test_loss, 'test_accuracy': test_accuracy}
output_test_file = os.path.join(args.output_dir, "results_test.txt")
with open(output_test_file, "w") as writer:
logger.info("***** Test results *****")
for key in sorted(result.keys()):
logger.info(" %s = %s", key, str(result[key]))
writer.write("%s = %s\n" % (key, str(result[key])))
output_test_file = os.path.join(args.output_dir, "logits_test.txt")
with open(output_test_file, "w") as f:
for i in range(len(logits_all)):
for j in range(len(logits_all[i])):
f.write(str(logits_all[i][j]))
if j == len(logits_all[i]) - 1:
f.write("\n")
else:
f.write(" ")
# the test submission order can't be changed
submission_test = os.path.join(args.output_dir, "submission_test.json")
test_preds = [int(np.argmax(logits_)) for logits_ in logits_all]
with open(submission_test, "w") as f:
json.dump(test_preds, f)