loss = model(input_ids, segment_ids, input_mask,
start_positions, end_positions)
if n_gpu > 1:
loss = loss.mean() # mean() to average on multi-gpu.
total_loss += loss.item()
pbar.set_postfix({
'loss':
'{0:1.5f}'.format(total_loss / (iteration + 1e-5))
})
pbar.update(1)
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.lr * warmup_linear(
global_steps / total_steps, args.warmup_rate)
for param_group in optimizer.param_groups:
param_group['lr'] = lr_this_step
else:
loss.backward()
optimizer.step()
model.zero_grad()
global_steps += 1
iteration += 1
if global_steps % eval_steps == 0:
best_f1, best_em, best_f1_em = evaluate(
model, args, dev_examples, dev_features, device,
global_steps, best_f1, best_em, best_f1_em)
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=True,
action='store_true',
help="Whether to run training.")
parser.add_argument("--do_eval",
default=True,
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',
type=bool,
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(" ")
def main():
parser = argparse.ArgumentParser()
parser.add_argument("--gpu_ids", default='', required=True, type=str)
parser.add_argument(
"--bert_config_file",
required=True,
default=
'check_points/pretrain_models/roberta_wwm_ext_large/bert_config.json')
parser.add_argument(
"--vocab_file",
required=True,
default='check_points/pretrain_models/roberta_wwm_ext_large/vocab.txt')
parser.add_argument(
"--init_restore_dir",
required=True,
default=
'check_points/pretrain_models/roberta_wwm_ext_large/pytorch_model.pth')
parser.add_argument("--input_dir", required=True, default='dataset/CHID')
parser.add_argument("--output_dir",
required=True,
default='check_points/CHID')
## Other parameters
parser.add_argument("--train_file",
default='./origin_data/CHID/train.json',
type=str,
help="SQuAD json for training. E.g., train-v1.1.json")
parser.add_argument(
"--train_ans_file",
default='./origin_data/CHID/train_answer.json',
type=str,
help="SQuAD answer for training. E.g., train-v1.1.json")
parser.add_argument(
"--predict_file",
default='./origin_data/CHID/dev.json',
type=str,
help="SQuAD json for predictions. E.g., dev-v1.1.json or test-v1.1.json"
)
parser.add_argument(
"--predict_ans_file",
default='origin_data/CHID/dev_answer.json',
type=str,
help="SQuAD json for predictions. E.g., dev-v1.1.json or test-v1.1.json"
)
parser.add_argument(
"--max_seq_length",
default=64,
type=int,
help=
"The maximum total input sequence length after WordPiece tokenization. Sequences "
"longer than this will be truncated, and sequences shorter than this will be padded."
)
parser.add_argument(
"--max_num_choices",
default=10,
type=int,
help=
"The maximum number of cadicate answer, shorter than this will be padded."
)
parser.add_argument("--train_batch_size",
default=20,
type=int,
help="Total batch size for training.")
parser.add_argument("--predict_batch_size",
default=16,
type=int,
help="Total batch size for predictions.")
parser.add_argument("--learning_rate",
default=2e-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=0.06,
type=float,
help=
"Proportion of training to perform linear learning rate warmup for. E.g., 0.1 = 10% "
"of training.")
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(
"--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(
'--fp16',
default=False,
action='store_true',
help="Whether to use 16-bit float precision instead of 32-bit")
args = parser.parse_args()
print(args)
os.environ["CUDA_VISIBLE_DEVICES"] = args.gpu_ids
device = torch.device(
"cuda" if torch.cuda.is_available() and not args.no_cuda else "cpu")
n_gpu = torch.cuda.device_count()
print("device: {} n_gpu: {}, 16-bits training: {}".format(
device, n_gpu, 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 = 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 os.path.exists(args.input_dir) == False:
os.makedirs(args.input_dir, exist_ok=True)
if os.path.exists(args.output_dir) == False:
os.makedirs(args.output_dir, exist_ok=True)
tokenizer = BertTokenizer(vocab_file=args.vocab_file,
do_lower_case=args.do_lower_case)
print('ready for train dataset')
train_example_file = os.path.join(
args.input_dir,
'train_examples_{}.pkl'.format(str(args.max_seq_length)))
train_feature_file = os.path.join(
args.input_dir,
'train_features_{}.pkl'.format(str(args.max_seq_length)))
train_features = generate_input(args.train_file,
args.train_ans_file,
train_example_file,
train_feature_file,
tokenizer,
max_seq_length=args.max_seq_length,
max_num_choices=args.max_num_choices,
is_training=True)
dev_example_file = os.path.join(
args.input_dir, 'dev_examples_{}.pkl'.format(str(args.max_seq_length)))
dev_feature_file = os.path.join(
args.input_dir, 'dev_features_{}.pkl'.format(str(args.max_seq_length)))
eval_features = generate_input(args.predict_file,
None,
dev_example_file,
dev_feature_file,
tokenizer,
max_seq_length=args.max_seq_length,
max_num_choices=args.max_num_choices,
is_training=False)
print("train features {}".format(len(train_features)))
num_train_steps = int(
len(train_features) / args.train_batch_size /
args.gradient_accumulation_steps * args.num_train_epochs)
print("loaded train dataset")
print("Num generate examples = {}".format(len(train_features)))
print("Batch size = {}".format(args.train_batch_size))
print("Num steps for a epoch = {}".format(num_train_steps))
all_input_ids = torch.tensor([f.input_ids for f in train_features],
dtype=torch.long)
all_input_masks = torch.tensor([f.input_masks 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_choice_masks = torch.tensor([f.choice_masks for f in train_features],
dtype=torch.long)
all_labels = torch.tensor([f.label for f in train_features],
dtype=torch.long)
train_data = TensorDataset(all_input_ids, all_input_masks, all_segment_ids,
all_choice_masks, all_labels)
train_sampler = RandomSampler(train_data)
train_dataloader = DataLoader(train_data,
sampler=train_sampler,
batch_size=args.train_batch_size,
drop_last=True)
all_example_ids = [f.example_id for f in eval_features]
all_tags = [f.tag for f in eval_features]
all_input_ids = torch.tensor([f.input_ids for f in eval_features],
dtype=torch.long)
all_input_masks = torch.tensor([f.input_masks 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_choice_masks = torch.tensor([f.choice_masks for f in eval_features],
dtype=torch.long)
all_example_index = torch.arange(all_input_ids.size(0), dtype=torch.long)
eval_data = TensorDataset(all_input_ids, all_input_masks, all_segment_ids,
all_choice_masks, all_example_index)
# Run prediction for full data
eval_sampler = SequentialSampler(eval_data)
eval_dataloader = DataLoader(eval_data,
sampler=eval_sampler,
batch_size=args.predict_batch_size)
# Prepare model
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 = reset_model(args, bert_config, AlbertForMultipleChoice)
else:
bert_config = ALBertConfig.from_json_file(args.bert_config_file)
model = reset_model(args, bert_config, ALBertForMultipleChoice)
else:
bert_config = BertConfig.from_json_file(args.bert_config_file)
model = reset_model(args, bert_config, BertForMultipleChoice)
model = model.to(device)
if n_gpu > 1:
model = torch.nn.DataParallel(model)
optimizer = get_optimization(model,
float16=args.fp16,
learning_rate=args.learning_rate,
total_steps=num_train_steps,
schedule='warmup_linear',
warmup_rate=args.warmup_proportion,
weight_decay_rate=0.01,
max_grad_norm=1.0,
opt_pooler=True)
global_step = 0
best_acc = 0
acc = 0
for i in range(int(args.num_train_epochs)):
num_step = 0
average_loss = 0
model.train()
model.zero_grad() # 等价于optimizer.zero_grad()
steps_per_epoch = num_train_steps // args.num_train_epochs
with tqdm(total=int(steps_per_epoch),
desc='Epoch %d' % (i + 1)) as pbar:
for step, batch in enumerate(train_dataloader):
if n_gpu == 1:
batch = tuple(
t.to(device)
for t in batch) # multi-gpu does scattering it-self
input_ids, input_masks, segment_ids, choice_masks, labels = batch
if step == 0 and i == 0:
print('shape of input_ids: {}'.format(input_ids.shape))
print('shape of labels: {}'.format(labels.shape))
loss = model(input_ids=input_ids,
token_type_ids=segment_ids,
attention_mask=input_masks,
labels=labels)
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)
# 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()
if (step + 1) % args.gradient_accumulation_steps == 0:
optimizer.step()
optimizer.zero_grad()
global_step += 1
average_loss += loss.item()
num_step += 1
pbar.set_postfix({
'loss':
'{0:1.5f}'.format(average_loss / (num_step + 1e-5))
})
pbar.update(1)
print("***** Running predictions *****")
print("Num split examples = {}".format(len(eval_features)))
print("Batch size = {}".format(args.predict_batch_size))
model.eval()
all_results = []
print("Start evaluating")
for input_ids, input_masks, segment_ids, choice_masks, example_indices in tqdm(
eval_dataloader, desc="Evaluating", disable=None):
if len(all_results) == 0:
print('shape of input_ids: {}'.format(input_ids.shape))
input_ids = input_ids.to(device)
input_masks = input_masks.to(device)
segment_ids = segment_ids.to(device)
with torch.no_grad():
batch_logits = model(input_ids=input_ids,
token_type_ids=segment_ids,
attention_mask=input_masks,
labels=None)
for i, example_index in enumerate(example_indices):
logits = batch_logits[i].detach().cpu().tolist()
eval_feature = eval_features[example_index.item()]
unique_id = int(eval_feature.unique_id)
all_results.append(
RawResult(unique_id=unique_id,
example_id=all_example_ids[unique_id],
tag=all_tags[unique_id],
logit=logits))
predict_file = 'dev_predictions.json'
print('decoder raw results')
tmp_predict_file = os.path.join(args.output_dir, "raw_predictions.pkl")
output_prediction_file = os.path.join(args.output_dir, predict_file)
results = get_final_predictions(all_results, tmp_predict_file, g=True)
write_predictions(results, output_prediction_file)
print('predictions saved to {}'.format(output_prediction_file))
if args.predict_ans_file:
acc = evaluate(args.predict_ans_file, output_prediction_file)
print(f'{args.predict_file} 预测精度：{acc}')
# Save a epoch trained model
if acc > best_acc:
best_acc = acc
output_model_file = os.path.join(args.output_dir,
"best_checkpoint.bin")
print('save trained model from {}'.format(output_model_file))
model_to_save = model.module if hasattr(
model, 'module') else model # Only save the model it-self
torch.save(model_to_save.state_dict(), output_model_file)