def test(args):
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')
print("device: {} n_gpu: {}, distributed training: {}, 16-bits training: {}".format(
device, n_gpu, bool(args.local_rank != -1), args.fp16))
seg_model_checkpoint = torch.load(args.eval_model)
seg_model = WMSeg.from_spec(seg_model_checkpoint['spec'], seg_model_checkpoint['state_dict'], args)
eval_examples = seg_model.load_data(args.eval_data_path)
convert_examples_to_features = seg_model.convert_examples_to_features
feature2input = seg_model.feature2input
num_labels = seg_model.num_labels
word2id = seg_model.word2id
label_map = {v: k for k, v in seg_model.labelmap.items()}
if args.fp16:
seg_model.half()
seg_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.")
seg_model = DDP(seg_model)
elif n_gpu > 1:
seg_model = torch.nn.DataParallel(seg_model)
seg_model.to(device)
seg_model.eval()
eval_loss, eval_accuracy = 0, 0
nb_eval_steps, nb_eval_examples = 0, 0
y_true = []
y_pred = []
for start_index in tqdm(range(0, len(eval_examples), args.eval_batch_size)):
eval_batch_examples = eval_examples[start_index: min(start_index + args.eval_batch_size,
len(eval_examples))]
eval_features = convert_examples_to_features(eval_batch_examples)
input_ids, input_mask, l_mask, label_ids, matching_matrix, ngram_ids, ngram_positions, \
segment_ids, valid_ids, word_ids, word_mask = feature2input(device, eval_features)
with torch.no_grad():
_, tag_seq = seg_model(input_ids, segment_ids, input_mask, labels=label_ids,
valid_ids=valid_ids, attention_mask_label=l_mask,
word_seq=word_ids, label_value_matrix=matching_matrix,
word_mask=word_mask,
input_ngram_ids=ngram_ids, ngram_position_matrix=ngram_positions)
# logits = torch.argmax(F.log_softmax(logits, dim=2),dim=2)
# logits = logits.detach().cpu().numpy()
logits = tag_seq.to('cpu').numpy()
label_ids = label_ids.to('cpu').numpy()
input_mask = input_mask.to('cpu').numpy()
for i, label in enumerate(label_ids):
temp_1 = []
temp_2 = []
for j, m in enumerate(label):
if j == 0:
continue
elif label_ids[i][j] == num_labels - 1:
y_true.append(temp_1)
y_pred.append(temp_2)
break
else:
temp_1.append(label_map[label_ids[i][j]])
temp_2.append(label_map[logits[i][j]])
y_true_all = []
y_pred_all = []
sentence_all = []
for y_true_item in y_true:
y_true_all += y_true_item
for y_pred_item in y_pred:
y_pred_all += y_pred_item
for example, y_true_item in zip(eval_examples, y_true):
sen = example.text_a
sen = sen.strip()
sen = sen.split(' ')
if len(y_true_item) != len(sen):
sen = sen[:len(y_true_item)]
sentence_all.append(sen)
p, r, f = cws_evaluate_word_PRF(y_pred_all, y_true_all)
oov = cws_evaluate_OOV(y_pred, y_true, sentence_all, word2id)
print(args.eval_data_path)
print("\nP: %f, R: %f, F: %f, OOV: %f" % (p, r, f, oov))
def predict(args):
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')
print("device: {} n_gpu: {}, distributed training: {}, 16-bits training: {}".format(
device, n_gpu, bool(args.local_rank != -1), args.fp16))
seg_model_checkpoint = torch.load(args.eval_model)
seg_model = WMSeg.from_spec(seg_model_checkpoint['spec'], seg_model_checkpoint['state_dict'], args)
eval_examples = seg_model.load_data(args.input_file, do_predict=True)
convert_examples_to_features = seg_model.convert_examples_to_features
feature2input = seg_model.feature2input
num_labels = seg_model.num_labels
word2id = seg_model.word2id
label_map = {v: k for k, v in seg_model.labelmap.items()}
if args.fp16:
seg_model.half()
seg_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.")
seg_model = DDP(seg_model)
elif n_gpu > 1:
seg_model = torch.nn.DataParallel(seg_model)
seg_model.to(device)
seg_model.eval()
y_pred = []
for start_index in tqdm(range(0, len(eval_examples), args.eval_batch_size)):
eval_batch_examples = eval_examples[start_index: min(start_index + args.eval_batch_size,
len(eval_examples))]
eval_features = convert_examples_to_features(eval_batch_examples)
input_ids, input_mask, l_mask, label_ids, matching_matrix, ngram_ids, ngram_positions, \
segment_ids, valid_ids, word_ids, word_mask = feature2input(device, eval_features)
with torch.no_grad():
_, tag_seq = seg_model(input_ids, segment_ids, input_mask, labels=label_ids,
valid_ids=valid_ids, attention_mask_label=l_mask,
word_seq=word_ids, label_value_matrix=matching_matrix,
word_mask=word_mask,
input_ngram_ids=ngram_ids, ngram_position_matrix=ngram_positions)
logits = tag_seq.to('cpu').numpy()
label_ids = label_ids.to('cpu').numpy()
for i, label in enumerate(label_ids):
temp = []
for j, m in enumerate(label):
if j == 0:
continue
elif label_ids[i][j] == num_labels - 1:
y_pred.append(temp)
break
else:
temp.append(label_map[logits[i][j]])
print('write results to %s' % str(args.output_file))
with open(args.output_file, 'w', encoding='utf8') as writer:
for i in range(len(y_pred)):
sentence = eval_examples[i].text_a
_, seg_pred_str = eval_sentence(y_pred[i], None, sentence, word2id)
writer.write('%s\n' % seg_pred_str)
def train(args):
if args.use_bert and args.use_zen:
raise ValueError('We cannot use both BERT and ZEN')
if not os.path.exists('./logs'):
os.mkdir('./logs')
now_time = datetime.datetime.now().strftime('%Y-%m-%d-%H-%M-%S')
log_file_name = './logs/log-' + now_time
logging.basicConfig(format='%(asctime)s - %(levelname)s - %(name)s - %(message)s',
datefmt='%m/%d/%Y %H:%M:%S',
filename=log_file_name,
filemode='w',
level=logging.INFO)
logger = logging.getLogger(__name__)
console_handler = logging.StreamHandler()
logger.addHandler(console_handler)
logger = logging.getLogger(__name__)
logger.info(vars(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()
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 not os.path.exists('./models'):
os.mkdir('./models')
if args.model_name is None:
raise Warning('model name is not specified, the model will NOT be saved!')
output_model_dir = os.path.join('./models', args.model_name + '_' + now_time)
word2id = get_word2id(args.train_data_path)
logger.info('# of word in train: %d: ' % len(word2id))
if args.use_memory:
gram2id = get_gram2id(args.train_data_path, args.eval_data_path,
args.ngram_num_threshold, args.ngram_flag, args.av_threshold)
logger.info('# of n-gram in memory: %d' % len(gram2id))
else:
gram2id = None
label_list = ["O", "B", "I", "E", "S", "[CLS]", "[SEP]"]
label_map = {label: i for i, label in enumerate(label_list, 1)}
hpara = WMSeg.init_hyper_parameters(args)
seg_model = WMSeg(word2id, gram2id, label_map, hpara, args)
train_examples = seg_model.load_data(args.train_data_path)
eval_examples = seg_model.load_data(args.eval_data_path)
num_labels = seg_model.num_labels
convert_examples_to_features = seg_model.convert_examples_to_features
feature2input = seg_model.feature2input
total_params = sum(p.numel() for p in seg_model.parameters() if p.requires_grad)
logger.info('# of trainable parameters: %d' % total_params)
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()
if args.fp16:
seg_model.half()
seg_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.")
seg_model = DDP(seg_model)
elif n_gpu > 1:
seg_model = torch.nn.DataParallel(seg_model)
param_optimizer = list(seg_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)
warmup_linear = WarmupLinearSchedule(warmup=args.warmup_proportion,
t_total=num_train_optimization_steps)
else:
# num_train_optimization_steps=-1
optimizer = BertAdam(optimizer_grouped_parameters,
lr=args.learning_rate,
warmup=args.warmup_proportion,
t_total=num_train_optimization_steps)
best_epoch = -1
best_p = -1
best_r = -1
best_f = -1
best_oov = -1
history = {'epoch': [], 'p': [], 'r': [], 'f': [], 'oov': []}
num_of_no_improvement = 0
patient = args.patient
global_step = 0
nb_tr_steps = 0
tr_loss = 0
if args.do_train:
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)
for epoch in trange(int(args.num_train_epochs), desc="Epoch"):
np.random.shuffle(train_examples)
seg_model.train()
tr_loss = 0
nb_tr_examples, nb_tr_steps = 0, 0
for step, start_index in enumerate(tqdm(range(0, len(train_examples), args.train_batch_size))):
seg_model.train()
batch_examples = train_examples[start_index: min(start_index +
args.train_batch_size, len(train_examples))]
if len(batch_examples) == 0:
continue
train_features = convert_examples_to_features(batch_examples)
input_ids, input_mask, l_mask, label_ids, matching_matrix, ngram_ids, ngram_positions, \
segment_ids, valid_ids, word_ids, word_mask = feature2input(device, train_features)
loss, _ = seg_model(input_ids, segment_ids, input_mask, label_ids, valid_ids, l_mask, word_ids,
matching_matrix, word_mask, ngram_ids, ngram_positions)
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
seg_model.to(device)
if args.local_rank == -1 or torch.distributed.get_rank() == 0:
seg_model.eval()
eval_loss, eval_accuracy = 0, 0
nb_eval_steps, nb_eval_examples = 0, 0
y_true = []
y_pred = []
label_map = {i: label for i, label in enumerate(label_list, 1)}
for start_index in range(0, len(eval_examples), args.eval_batch_size):
eval_batch_examples = eval_examples[start_index: min(start_index + args.eval_batch_size,
len(eval_examples))]
eval_features = convert_examples_to_features(eval_batch_examples)
input_ids, input_mask, l_mask, label_ids, matching_matrix, ngram_ids, ngram_positions, \
segment_ids, valid_ids, word_ids, word_mask = feature2input(device, eval_features)
with torch.no_grad():
_, tag_seq = seg_model(input_ids, segment_ids, input_mask, labels=label_ids,
valid_ids=valid_ids, attention_mask_label=l_mask,
word_seq=word_ids, label_value_matrix=matching_matrix,
word_mask=word_mask,
input_ngram_ids=ngram_ids, ngram_position_matrix=ngram_positions)
# logits = torch.argmax(F.log_softmax(logits, dim=2),dim=2)
# logits = logits.detach().cpu().numpy()
logits = tag_seq.to('cpu').numpy()
label_ids = label_ids.to('cpu').numpy()
input_mask = input_mask.to('cpu').numpy()
for i, label in enumerate(label_ids):
temp_1 = []
temp_2 = []
for j, m in enumerate(label):
if j == 0:
continue
elif label_ids[i][j] == num_labels - 1:
y_true.append(temp_1)
y_pred.append(temp_2)
break
else:
temp_1.append(label_map[label_ids[i][j]])
temp_2.append(label_map[logits[i][j]])
y_true_all = []
y_pred_all = []
sentence_all = []
for y_true_item in y_true:
y_true_all += y_true_item
for y_pred_item in y_pred:
y_pred_all += y_pred_item
for example, y_true_item in zip(eval_examples, y_true):
sen = example.text_a
sen = sen.strip()
sen = sen.split(' ')
if len(y_true_item) != len(sen):
sen = sen[:len(y_true_item)]
sentence_all.append(sen)
p, r, f = cws_evaluate_word_PRF(y_pred_all, y_true_all)
oov = cws_evaluate_OOV(y_pred, y_true, sentence_all, word2id)
logger.info('OOV: %f' % oov)
history['epoch'].append(epoch)
history['p'].append(p)
history['r'].append(r)
history['f'].append(f)
history['oov'].append(oov)
logger.info("=======entity level========")
logger.info("\nEpoch: %d, P: %f, R: %f, F: %f, OOV: %f", epoch + 1, p, r, f, oov)
logger.info("=======entity level========")
# the evaluation method of NER
report = classification_report(y_true, y_pred, digits=4)
if args.model_name is not None:
if not os.path.exists(output_model_dir):
os.mkdir(output_model_dir)
output_eval_file = os.path.join(args.model_name, "eval_results.txt")
if os.path.exists(output_eval_file):
with open(output_eval_file, "a") as writer:
logger.info("***** Eval results *****")
logger.info("=======token level========")
logger.info("\n%s", report)
logger.info("=======token level========")
writer.write(report)
if f > best_f:
best_epoch = epoch + 1
best_p = p
best_r = r
best_f = f
best_oov = oov
num_of_no_improvement = 0
if args.model_name:
with open(os.path.join(output_model_dir, 'CWS_result.txt'), "w") as writer:
for i in range(len(y_pred)):
sentence = eval_examples[i].text_a
seg_true_str, seg_pred_str = eval_sentence(y_pred[i], y_true[i], sentence, word2id)
# logger.info("true: %s", seg_true_str)
# logger.info("pred: %s", seg_pred_str)
writer.write('True: %s\n' % seg_true_str)
writer.write('Pred: %s\n\n' % seg_pred_str)
best_eval_model_path = os.path.join(output_model_dir, 'model.pt')
if n_gpu > 1:
torch.save({
'spec': seg_model.module.spec,
'state_dict': seg_model.module.state_dict(),
# 'trainer': optimizer.state_dict(),
}, best_eval_model_path)
else:
torch.save({
'spec': seg_model.spec,
'state_dict': seg_model.state_dict(),
# 'trainer': optimizer.state_dict(),
}, best_eval_model_path)
else:
num_of_no_improvement += 1
if num_of_no_improvement >= patient:
logger.info('\nEarly stop triggered at epoch %d\n' % epoch)
break
logger.info("\n=======best f entity level========")
logger.info("\nEpoch: %d, P: %f, R: %f, F: %f, OOV: %f\n", best_epoch, best_p, best_r, best_f, best_oov)
logger.info("\n=======best f entity level========")
if os.path.exists(output_model_dir):
with open(os.path.join(output_model_dir, 'history.json'), 'w', encoding='utf8') as f:
json.dump(history, f)
f.write('\n')