def _compute(self, predictions, references, suffix=False):
report = classification_report(y_true=references,
y_pred=predictions,
suffix=suffix,
output_dict=True)
report.pop("macro avg")
report.pop("weighted avg")
overall_score = report.pop("micro avg")
scores = {}
for type_name, score in report.items():
scores[type_name]["precision"] = score["precision"]
scores[type_name]["recall"] = score["recall"]
scores[type_name]["f1"] = score["f1-score"]
scores[type_name]["number"] = score["support"]
scores["overall_precision"] = overall_score["precision"]
scores["overall_recall"] = overall_score["recall"]
scores["overall_f1"] = overall_score["f1-score"]
scores["overall_accuracy"] = accuracy_score(y_true=references,
y_pred=predictions)
return scores
def model_evaluate_roberta(model, data, label, tag2id, batch_size,
seq_len_list):
id2tag = {value: key for key, value in tag2id.items()}
pred_logits = model.predict(data, batch_size=batch_size)[0]
# pred shape [batch_size, max_len]
preds = np.argmax(pred_logits, axis=2).tolist()
assert len(preds) == len(seq_len_list)
# get predcit label
predict_label = []
target_label = []
for i in range(len(preds)):
pred = preds[i][1:]
temp = []
true_label = label[i][:min(seq_len_list[i], len(pred))]
for j in range(min(seq_len_list[i], len(pred))):
temp.append(id2tag[pred[j]])
assert len(temp) == len(true_label)
target_label.append(true_label)
predict_label.append(temp)
# 计算 precision， recall， f1_score
precision = precision_score(target_label,
predict_label,
average="macro",
zero_division=0)
recall = recall_score(target_label,
predict_label,
average="macro",
zero_division=0)
f1 = f1_score(target_label,
predict_label,
average="macro",
zero_division=0)
logger.info(classification_report(target_label, predict_label))
return precision, recall, f1
def evaluate(self, data, labels):
"""Evaluate the performance of ner model.
Args:
data: list of tokenized texts (, like ``[['我', '是', '中', '国', '人']]``
labels: list of list of str, the corresponding label strings
"""
features, y = self.preprocessor.prepare_input(data, labels)
pred_probs = self.model.predict(features)
lengths = [
min(len(label), pred_prob.shape[0])
for label, pred_prob in zip(labels, pred_probs)
]
y_pred = self.preprocessor.label_decode(pred_probs, lengths)
r = metrics.recall_score(labels, y_pred)
p = metrics.precision_score(labels, y_pred)
f1 = metrics.f1_score(labels, y_pred)
print('Recall: {}, Precision: {}, F1: {}'.format(r, p, f1))
print(metrics.classification_report(labels, y_pred))
return f1
def benchmark_flair_mdl():
tagger = load_flair_ner_model()
start = time.time()
flair_sentences = []
for i, sentence in enumerate(sentences_tokens):
flair_sentence = Sentence()
for token_txt in sentence:
flair_sentence.add_token(Token(token_txt))
flair_sentences.append(flair_sentence)
tagger.predict(flair_sentences, verbose=True)
predictions = [[tok.tags['ner'].value for tok in fs] for fs in flair_sentences]
print("Made predictions on {} sentences and {} tokens in {}s".format(
num_sentences, num_tokens, time.time() - start)
)
assert len(predictions) == num_sentences
print(classification_report(sentences_entities, remove_miscs(predictions),
digits=4))
def train(args, strategy, train_dataset, tokenizer, model, num_train_examples,
labels, train_batch_size, pad_token_label_id):
if args["max_steps"] > 0:
num_train_steps = args["max_steps"] * args[
"gradient_accumulation_steps"]
args["num_train_epochs"] = 1
else:
num_train_steps = (math.ceil(num_train_examples / train_batch_size) //
args["gradient_accumulation_steps"] *
args["num_train_epochs"])
writer = tf.summary.create_file_writer("/tmp/mylogs")
with strategy.scope():
loss_fct = tf.keras.losses.SparseCategoricalCrossentropy(
from_logits=True, reduction=tf.keras.losses.Reduction.NONE)
optimizer = create_optimizer(args["learning_rate"], num_train_steps,
args["warmup_steps"])
if args["fp16"]:
optimizer = tf.keras.mixed_precision.experimental.LossScaleOptimizer(
optimizer, "dynamic")
loss_metric = tf.keras.metrics.Mean(name="loss", dtype=tf.float32)
gradient_accumulator = GradientAccumulator()
logging.info("***** Running training *****")
logging.info(" Num examples = %d", num_train_examples)
logging.info(" Num Epochs = %d", args["num_train_epochs"])
logging.info(" Instantaneous batch size per device = %d",
args["per_device_train_batch_size"])
logging.info(
" Total train batch size (w. parallel, distributed & accumulation) = %d",
train_batch_size * args["gradient_accumulation_steps"],
)
logging.info(" Gradient Accumulation steps = %d",
args["gradient_accumulation_steps"])
logging.info(" Total training steps = %d", num_train_steps)
model.summary()
@tf.function
def apply_gradients():
grads_and_vars = []
for gradient, variable in zip(gradient_accumulator.gradients,
model.trainable_variables):
if gradient is not None:
scaled_gradient = gradient / (
args["n_device"] * args["gradient_accumulation_steps"])
grads_and_vars.append((scaled_gradient, variable))
else:
grads_and_vars.append((gradient, variable))
optimizer.apply_gradients(grads_and_vars, args["max_grad_norm"])
gradient_accumulator.reset()
@tf.function
def train_step(train_features, train_labels):
def step_fn(train_features, train_labels):
inputs = {
"attention_mask": train_features["attention_mask"],
"training": True
}
if "token_type_ids" in train_features:
inputs["token_type_ids"] = train_features["token_type_ids"]
with tf.GradientTape() as tape:
logits = model(train_features["input_ids"], **inputs)[0]
active_loss = tf.reshape(train_labels,
(-1, )) != pad_token_label_id
active_logits = tf.boolean_mask(
tf.reshape(logits, (-1, len(labels))), active_loss)
active_labels = tf.boolean_mask(
tf.reshape(train_labels, (-1, )), active_loss)
cross_entropy = loss_fct(active_labels, active_logits)
loss = tf.reduce_sum(cross_entropy) * (1.0 / train_batch_size)
grads = tape.gradient(loss, model.trainable_variables)
gradient_accumulator(grads)
return cross_entropy
per_example_losses = strategy.experimental_run_v2(step_fn,
args=(train_features,
train_labels))
mean_loss = strategy.reduce(tf.distribute.ReduceOp.MEAN,
per_example_losses,
axis=0)
return mean_loss
current_time = datetime.datetime.now()
train_iterator = master_bar(range(args["num_train_epochs"]))
global_step = 0
logging_loss = 0.0
for epoch in train_iterator:
epoch_iterator = progress_bar(train_dataset,
total=num_train_steps,
parent=train_iterator,
display=args["n_device"] > 1)
step = 1
with strategy.scope():
for train_features, train_labels in epoch_iterator:
loss = train_step(train_features, train_labels)
if step % args["gradient_accumulation_steps"] == 0:
strategy.experimental_run_v2(apply_gradients)
loss_metric(loss)
global_step += 1
if args["logging_steps"] > 0 and global_step % args[
"logging_steps"] == 0:
# Log metrics
if (
args["n_device"] == 1
and args["evaluate_during_training"]
): # Only evaluate when single GPU otherwise metrics may not average well
y_true, y_pred, eval_loss = evaluate(
args,
strategy,
model,
tokenizer,
labels,
pad_token_label_id,
mode="dev")
report = metrics.classification_report(y_true,
y_pred,
digits=4)
logging.info("Eval at step " + str(global_step) +
"\n" + report)
logging.info("eval_loss: " + str(eval_loss))
precision = metrics.precision_score(y_true, y_pred)
recall = metrics.recall_score(y_true, y_pred)
f1 = metrics.f1_score(y_true, y_pred)
with writer.as_default():
tf.summary.scalar("eval_loss", eval_loss,
global_step)
tf.summary.scalar("precision", precision,
global_step)
tf.summary.scalar("recall", recall,
global_step)
tf.summary.scalar("f1", f1, global_step)
lr = optimizer.learning_rate
learning_rate = lr(step)
with writer.as_default():
tf.summary.scalar("lr", learning_rate, global_step)
tf.summary.scalar(
"loss", (loss_metric.result() - logging_loss) /
args["logging_steps"], global_step)
logging_loss = loss_metric.result()
with writer.as_default():
tf.summary.scalar("loss",
loss_metric.result(),
step=step)
if args["save_steps"] > 0 and global_step % args[
"save_steps"] == 0:
# Save model checkpoint
output_dir = os.path.join(
args["output_dir"],
"checkpoint-{}".format(global_step))
if not os.path.exists(output_dir):
os.makedirs(output_dir)
model.save_pretrained(output_dir)
logging.info("Saving model checkpoint to %s",
output_dir)
train_iterator.child.comment = f"loss : {loss_metric.result()}"
step += 1
train_iterator.write(f"loss epoch {epoch + 1}: {loss_metric.result()}")
loss_metric.reset_states()
logging.info(" Training took time = {}".format(datetime.datetime.now() -
current_time))
def main(args):
with open(args.cache_dir / "vocab.pkl", "rb") as f:
vocab: Vocab = pickle.load(f)
tag_idx_path = args.cache_dir / "tag2idx.json"
tag2idx: Dict[str, int] = json.loads(tag_idx_path.read_text())
data_paths = {split: args.data_dir / f"{split}.json" for split in SPLITS}
data = {
split: json.loads(path.read_text())
for split, path in data_paths.items()
}
datasets: Dict[str, SeqSlotDataset] = {
split: SeqSlotDataset(split_data, vocab, tag2idx, args.max_len)
for split, split_data in data.items()
}
# TODO: crecate DataLoader for train / dev datasets
dataloaders = {
split: DataLoader(dataset,
batch_size=args.batch_size,
shuffle=True,
collate_fn=dataset.collate_fn)
for split, dataset in datasets.items()
}
# COMPLETE
embeddings = torch.load(args.cache_dir / "embeddings.pt")
# TODO: init model and move model to target device(cpu / gpu)
model = SeqSlot(embeddings, args.hidden_size, args.num_layers,
args.dropout, args.bidirectional,
datasets[TRAIN].num_classes)
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model.to(device)
# COMPLETE
# TODO: init optimizer
optimizer = torch.optim.Adam(model.parameters(), lr=args.lr)
# COMPLETE
epoch_pbar = trange(args.num_epoch, desc="Epoch")
for epoch in epoch_pbar:
# TODO: Training loop - iterate over train dataloader and update model weights
size = len(dataloaders[TRAIN].dataset)
loss_fn = torch.nn.CrossEntropyLoss()
model.train()
for batch_num, batch in enumerate(dataloaders[TRAIN]):
encoded = batch["encoded"]
tag = batch["tag"]
lens = batch["lens"]
if torch.cuda.is_available():
encoded = encoded.cuda()
tag = tag.cuda()
pred = model(encoded, lens)
pred = pred.view(-1, pred.shape[-1])
tag = tag.reshape(-1)
loss = loss_fn(pred, tag)
optimizer.zero_grad()
loss.backward()
optimizer.step()
if batch_num % 50 == 0:
loss, current = loss, batch_num * len(encoded)
print(f"loss: {loss:>7f} [{current:>5d}/{size:>5d}]")
# COMPLETE
# TODO: Evaluation loop - calculate accuracy and save model weights
loss, size = 0, 0
y_true = []
y_pred = []
model.eval()
with torch.no_grad():
for batch_num, batch in enumerate(dataloaders[DEV]):
encoded = batch["encoded"]
tag = batch["tag"]
lens = batch["lens"]
if torch.cuda.is_available():
encoded = encoded.cuda()
tag = tag.cuda()
pred = model(encoded, lens)
pred = pred.view(-1, pred.shape[-1])
tag = tag.reshape(-1)
loss += loss_fn(pred, tag)
pred_tag = torch.argmax(pred, dim=1)
pred_tag = pred_tag.view(-1, len(encoded))
tag = tag.view(-1, len(encoded))
size += len(encoded)
tran_pred = pred_tag.t()
tran_true = tag.t()
tran_pred = [
list(
map(datasets[DEV].idx2tag,
(tran_pred[i][:lens[i]]).tolist()))
for i in range(len(tran_pred))
]
tran_true = [
list(
map(datasets[DEV].idx2tag,
(tran_true[i][:lens[i]]).tolist()))
for i in range(len(tran_true))
]
for i in range(len(tran_pred)):
y_pred.append(tran_pred[i])
y_true.append(tran_true[i])
report = classification_report(y_pred,
y_true,
mode='strict',
scheme=IOB2)
print(report)
join_correct = correct = 0
join_count = count = 0
for i in range(len(y_pred)):
join = True
for j in range(len(y_pred[i])):
count += 1
if y_pred[i][j] == y_true[i][j]:
correct += 1
else:
join = False
join_count += 1
if join:
join_correct += 1
accuracy = correct / count
join_ac = join_correct / join_count
loss /= size
print(
f"Dev Error: \n Accuracy: {(100*accuracy):>0.1f}%, Avg loss: {loss:>8f} JoinAC: {(100*join_ac):>0.1f}% \n"
)
torch.save(model, args.ckpt_dir / "best.pt")
def evaluate_on_model(ds_X_word, ds_X_char, ds_y):
# load padding length
padding_len = load_dict_after('padding_len.json')
max_len = min(padding_len['max_len'], MAX_LEN)
max_len_char = min(padding_len['max_len_char'], MAX_LEN_CHAR)
# load the model in terms of CRF as output layer or Dense as output layer
model = load_saved_model()
# prepare the tags in terms of multiple output model, or single output
y = []
if USE_CRF and MULTI_OUT:
y = convert_to_multi_output(ds_y, max_len)
else:
for ds in ds_y:
y.extend(ds)
X_word = []
X_char = []
for x_word in ds_X_word:
X_word.extend(x_word)
for x_char in ds_X_char:
X_char.extend(x_char)
X_word = np.array(X_word, dtype="float32")
X_char = np.array(X_char, dtype="float32")
print(model.metrics_names)
if MULTI_OUT:
scores = model.evaluate([X_word, X_char], [
np.array(y[0], dtype="float32").reshape(len(X_word), max_len, 1),
np.array(y[1], dtype="float32").reshape(len(X_word), max_len, 1),
np.array(y[2], dtype="float32").reshape(len(X_word), max_len, 1),
np.array(y[3], dtype="float32").reshape(len(X_word), max_len, 1),
np.array(y[4], dtype="float32").reshape(len(X_word), max_len, 1)
],
verbose=1)
print(scores)
else: # single output
# get scores for test sets from each data-set
for i in range(len(ds_X_word)):
scores = model.evaluate([ds_X_word[i], ds_X_char[i]],
np.array(ds_y[i], dtype="float32").reshape(
len(ds_X_word[i]), max_len, 1),
verbose=1)
print(scores)
if MULTI_OUT:
test_pred = model.predict([X_word, X_char])
for i in range(len(ds_X_word)):
tag2idx = load_dict_after('tag2idx' + str(i) + '.json')
n_tags = len(tag2idx)
idx2tag = flip_dict(tag2idx)
conv_pred = []
conv_gold = []
for sentence_tag in test_pred[i]:
p = np.argmax(sentence_tag, axis=-1)
p = [idx2tag[tag_idx] for tag_idx in p]
conv_pred.append(p)
for sentence_tag in y[i]:
sentence_tag = [idx2tag[tag_idx] for tag_idx in sentence_tag]
conv_gold.append(sentence_tag)
print("F1-score: {:.1%}".format(f1_score(conv_gold, conv_pred)))
print(classification_report(conv_gold, conv_pred))
else:
for i in range(len(ds_X_word)):
x_word = ds_X_word[
i] # all the sentences in word-indexed form for dataset i
x_char = ds_X_char[
i] # all the sentences in character-indexed form for dataset i
y_sen = ds_y[i] # all the corresponding tags of the sentences
y_sen = np.array(y_sen)
#predict
test_pred = model.predict([
np.array(x_word, dtype="float32"),
np.array(x_char, dtype="float32")
])
tag2idx = load_dict_after('tag2idx.json')
n_tags = len(tag2idx)
idx2tag = flip_dict(tag2idx)
conv_pred = [
] # list to store the predicted tags, converted from indices
conv_gold = [
] # list to store the actual/ gold tags, converted from indices
for sentence_tag in test_pred:
p = np.argmax(
sentence_tag, axis=-1
) # for each word, get the tag with maximum probabiliity out of all the possible tags
p = [idx2tag[tag_idx]
for tag_idx in p] # convert each tag from indice to name
conv_pred.append(p)
for sentence_tag in y_sen:
sentence_tag = [idx2tag[tag_idx] for tag_idx in sentence_tag]
conv_gold.append(sentence_tag)
print("F1-score: {:.1%}".format(f1_score(conv_gold, conv_pred)))
print(classification_report(conv_gold, conv_pred))
def run_ner_w_args(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 = {"ner": NerProcessor}
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:
raise ValueError(
"At least one of `do_train` or `do_eval` 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]()
label_list = processor.get_labels()
num_labels = len(label_list) + 1
tokenizer = BertTokenizer.from_pretrained(args.bert_model,
do_lower_case=args.do_lower_case)
train_examples = None
num_train_optimization_steps = None
if args.do_train:
train_examples = processor.get_train_examples(args.data_dir)
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(
)
# 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))
model = BertForNer.from_pretrained(args.bert_model,
cache_dir=cache_dir,
config_dir=args.config_dir,
num_labels=num_labels,
config=args.config)
model_to_save = model.module if hasattr(model, 'module') else model
# print(model_to_save.config, cache_dir)
# print(args.config_dir, args.config)
# exit()
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)
if args.do_train:
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)
# def resolve_opt(pre_model_path, optimizer):
# opt_path = os.path.join(args.bert_model, "opt.pth")
# if os.path.exists(opt_path):
# optimizer.load_state_dict( torch.load( opt_path ) )
# return optimizer
# optimizer = resolve_opt(args.bert_model, optimizer)
global_step = 0
nb_tr_steps = 0
tr_loss = 0
label_map = {i: label for i, label in enumerate(label_list, 1)}
if args.do_train:
train_features = convert_examples_to_features(train_examples,
label_list,
args.max_seq_length,
tokenizer)
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)
all_valid_ids = torch.tensor([f.valid_ids for f in train_features],
dtype=torch.long)
all_lmask_ids = torch.tensor([f.label_mask for f in train_features],
dtype=torch.long)
train_data = TensorDataset(all_input_ids, all_input_mask,
all_segment_ids, all_label_ids,
all_valid_ids, all_lmask_ids)
# 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)
model.train()
def warmup_linear(progress, warmup):
if progress < warmup:
return progress / warmup
return max((progress - 1.) / (warmup - 1.), 0.)
for _ in trange(int(args.num_train_epochs), desc="Epoch"):
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, valid_ids, l_mask = batch
loss = model(input_ids, segment_ids, input_mask, label_ids,
valid_ids, l_mask)
# 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
# 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)
torch.save(model_to_save.state_dict(), output_model_file)
# Save optimizer
output_optimizer_file = os.path.join(args.output_dir, "opt.pth")
torch.save(optimizer.state_dict(), output_optimizer_file)
output_config_file = os.path.join(args.output_dir, CONFIG_NAME)
tokenizer.save_vocabulary(args.output_dir)
with open(output_config_file, 'w') as f:
f.write(model_to_save.config.to_json_string())
label_map = {i: label for i, label in enumerate(label_list, 1)}
model_config = {
"bert_model": args.bert_model,
"do_lower": args.do_lower_case,
"max_seq_length": args.max_seq_length,
"num_labels": len(label_list) + 1,
"label_map": label_map
}
json.dump(
model_config,
open(os.path.join(args.output_dir, "model_config.json"), "w"))
# Load a trained model and config that you have fine-tuned
else:
# output_config_file = os.path.join(args.output_dir, CONFIG_NAME)
# output_model_file = os.path.join(args.output_dir, WEIGHTS_NAME)
# config = BertConfig(output_config_file)
# model = BertForTokenClassification(config, num_labels=num_labels)
# model.load_state_dict(torch.load(output_model_file))
model = BertForNer.from_pretrained(args.bert_model,
num_labels=num_labels)
tokenizer = BertTokenizer.from_pretrained(
args.bert_model, do_lower_case=args.do_lower_case)
model.to(device)
if args.do_eval and (args.local_rank == -1
or torch.distributed.get_rank() == 0):
eval_examples = processor.get_dev_examples(args.data_dir)
eval_features = convert_examples_to_features(eval_examples, label_list,
args.max_seq_length,
tokenizer)
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)
all_valid_ids = torch.tensor([f.valid_ids for f in eval_features],
dtype=torch.long)
all_lmask_ids = torch.tensor([f.label_mask for f in eval_features],
dtype=torch.long)
eval_data = TensorDataset(all_input_ids, all_input_mask,
all_segment_ids, all_label_ids,
all_valid_ids, all_lmask_ids)
# 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
y_true = []
y_pred = []
label_map = {i: label for i, label in enumerate(label_list, 1)}
# for input_ids, input_mask, segment_ids, label_ids in
# tqdm(eval_dataloader, desc="Evaluating"):
for input_ids, input_mask, segment_ids, label_ids, valid_ids, l_mask in tqdm(
eval_dataloader, desc="Evaluating"):
input_ids = input_ids.to(device)
input_mask = input_mask.to(device)
segment_ids = segment_ids.to(device)
valid_ids = valid_ids.to(device)
label_ids = label_ids.to(device)
l_mask = l_mask.to(device)
with torch.no_grad():
logits = model(input_ids,
segment_ids,
input_mask,
valid_ids=valid_ids,
attention_mask_label=l_mask)
logits = torch.argmax(F.log_softmax(logits, dim=2), dim=2)
logits = logits.detach().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] == 11:
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]])
loss = tr_loss / global_step if args.do_train else None
result = dict()
result['loss'] = loss
report = classification_report(y_true, y_pred, digits=4)
logger.info("\n%s", report)
print(report)
result['f1'] = f1_score(y_true, y_pred)
output_eval_file = os.path.join(args.output_dir, "eval_results.txt")
with open(output_eval_file, "w") as writer:
logger.info("***** Eval results *****")
logger.info("\n%s", report)
# writer.write(report)
for key in sorted(result.keys()):
writer.write("%s = %s\n" % (key, str(result[key])))
return result
def test_classification_report(self):
print(classification_report(self.y_true, self.y_pred))
val_batch_labels = label_ids.to("cpu").numpy()
predictions.extend(val_batch_preds)
true_labels.extend(val_batch_labels)
tmp_eval_accuracy = flat_accuracy(val_batch_labels, val_batch_preds)
eval_loss += tmp_eval_loss.mean().item()
eval_accuracy += tmp_eval_accuracy
nb_eval_examples += b_input_ids.size(0)
nb_eval_steps += 1
# Evaluate loss, acc, conf. matrix, and class. report on devset
pred_tags = [[tag2name[i] for i in predictions]]
valid_tags = [[tag2name[i] for i in true_labels]]
cl_report = classification_report(valid_tags, pred_tags)
eval_loss = eval_loss / nb_eval_steps
tmp_accuracy = accuracy_score(valid_tags, pred_tags)
if tmp_accuracy > dev_best_acc:
dev_best_acc = tmp_accuracy
model_to_save = model.module if hasattr(model, 'module') else model # Only save the model it-self
output_model_file = os.path.join(bert_out_address, "pytorch_model.bin")
output_config_file = os.path.join(bert_out_address, "config.json")
torch.save(model_to_save.state_dict(), output_model_file)
model_to_save.config.to_json_file(output_config_file)
# Report metrics
f1 = f1_score(valid_tags, pred_tags)
if f1 > dev_best_f1:
def evaluate(args, model, tokenizer, ngram_dict, processor, label_list):
num_labels = len(label_list) + 1
eval_dataset = load_examples(args,
tokenizer,
ngram_dict,
processor,
label_list,
mode="test")
# Run prediction for full data
eval_sampler = SequentialSampler(eval_dataset)
eval_dataloader = DataLoader(eval_dataset,
sampler=eval_sampler,
batch_size=args.eval_batch_size)
# Eval!
logger.info("***** Running evaluation *****")
logger.info(" Num examples = %d", len(eval_dataset))
logger.info(" Batch size = %d", args.eval_batch_size)
model.eval()
y_true = []
y_pred = []
label_map = {i: label for i, label in enumerate(label_list, 1)}
for batch in tqdm(eval_dataloader, desc="Evaluating"):
batch = tuple(t.to(args.device) for t in batch)
input_ids, input_mask, segment_ids, label_ids, ngram_ids, ngram_positions, \
ngram_lengths, ngram_seg_ids, ngram_masks, valid_ids, l_mask = batch
with torch.no_grad():
logits = model(input_ids,
token_type_ids=None,
attention_mask=None,
labels=None,
valid_ids=valid_ids,
attention_mask_label=None,
ngram_ids=ngram_ids,
ngram_positions=ngram_positions)
logits = torch.argmax(F.log_softmax(logits, dim=2), dim=2)
logits = logits.detach().cpu().numpy()
label_ids = label_ids.detach().cpu().numpy()
for i, label in enumerate(label_ids):
for j, m in enumerate(label):
if j == 0:
continue
if label_ids[i][j] == num_labels - 1:
break
y_true.append(label_map[label_ids[i][j]])
y_pred.append(label_map[logits[i][j]])
if args.task_name == 'cwsmsra' or args.task_name == 'cwspku':
#evaluating CWS
result = cws_evaluate_word_PRF(y_pred, y_true)
logger.info("=======entity level========")
logger.info(
"\n%s",
', '.join("%s: %s" % (key, val) for key, val in result.items()))
logger.info("=======entity level========")
else:
#evaluating NER, POS
report = classification_report(y_true, y_pred, digits=4)
f = f1_score(y_true, y_pred)
result = {"report": report, "f1": f}
logger.info("=======entity level========")
logger.info(report)
logger.info("=======entity level========")
return result
def show_ner_report(labels, preds):
return classification_report(labels, preds, suffix=True)
def show_ner_report(labels, preds):
return seqeval_metrics.classification_report(labels, preds, suffix=True)
def main():
logging.basicConfig(format='%(asctime)s - %(levelname)s - %(message)s',
datefmt='%m/%d/%Y ',
level=logging.INFO)
logger = logging.getLogger(__name__)
parser = argparse.ArgumentParser()
# Required parameters
parser.add_argument("--data",
default=None,
type=str,
required=True,
help="Directory which has the data files for the task")
parser.add_argument(
"--output",
default=None,
type=str,
required=True,
help=
"The output directory where the model predictions and checkpoints will be written."
)
parser.add_argument("--overwrite",
default=False,
type=bool,
help="Set it to True to overwrite output directory")
args = parser.parse_args()
if os.path.exists(args.output) and os.listdir(
args.output) and not args.overwrite:
raise ValueError(
"Output directory ({}) already exists and is not empty. Set the overwrite flag to overwrite"
.format(args.output))
if not os.path.exists(args.output):
os.makedirs(args.output)
train_batch_size = 32
valid_batch_size = 64
test_batch_size = 64
# padding sentences and labels to max_length of 128
max_seq_len = 128
EMBEDDING_DIM = 100
epochs = 10
split_train = split_text_label(os.path.join(args.data, "train.txt"))
split_valid = split_text_label(os.path.join(args.data, "valid.txt"))
split_test = split_text_label(os.path.join(args.data, "test.txt"))
labelSet = set()
wordSet = set()
# words and labels
for data in [split_train, split_valid, split_test]:
for labeled_text in data:
for word, label in labeled_text:
labelSet.add(label)
wordSet.add(word.lower())
# Sort the set to ensure '0' is assigned to 0
sorted_labels = sorted(list(labelSet), key=len)
# Create mapping for labels
label2Idx = {}
for label in sorted_labels:
label2Idx[label] = len(label2Idx)
num_labels = len(label2Idx)
idx2Label = {v: k for k, v in label2Idx.items()}
pickle.dump(idx2Label,
open(os.path.join(args.output, "idx2Label.pkl"), 'wb'))
logger.info("Saved idx2Label pickle file")
# Create mapping for words
word2Idx = {}
if len(word2Idx) == 0:
word2Idx["PADDING_TOKEN"] = len(word2Idx)
word2Idx["UNKNOWN_TOKEN"] = len(word2Idx)
for word in wordSet:
word2Idx[word] = len(word2Idx)
logger.info("Total number of words is : %d ", len(word2Idx))
pickle.dump(word2Idx, open(os.path.join(args.output, "word2Idx.pkl"),
'wb'))
logger.info("Saved word2Idx pickle file")
# Loading glove embeddings
embeddings_index = {}
f = open('embeddings/glove.6B.100d.txt', encoding="utf-8")
for line in f:
values = line.strip().split(' ')
word = values[0] # the first entry is the word
coefs = np.asarray(
values[1:], dtype='float32') #100d vectors representing the word
embeddings_index[word] = coefs
f.close()
logger.info("Glove data loaded")
#print(str(dict(itertools.islice(embeddings_index.items(), 2))))
embedding_matrix = np.zeros((len(word2Idx), EMBEDDING_DIM))
# Word embeddings for the tokens
for word, i in word2Idx.items():
embedding_vector = embeddings_index.get(word)
if embedding_vector is not None:
embedding_matrix[i] = embedding_vector
pickle.dump(embedding_matrix,
open(os.path.join(args.output, "embedding.pkl"), 'wb'))
logger.info("Saved Embedding matrix pickle")
# Interesting - to check how many words were not there in Glove Embedding
# indices = np.where(np.all(np.isclose(embedding_matrix, 0), axis=1))
# print(len(indices[0]))
train_sentences, train_labels = createMatrices(split_train, word2Idx,
label2Idx)
valid_sentences, valid_labels = createMatrices(split_valid, word2Idx,
label2Idx)
test_sentences, test_labels = createMatrices(split_test, word2Idx,
label2Idx)
train_features, train_labels = padding(train_sentences,
train_labels,
max_seq_len,
padding='post')
valid_features, valid_labels = padding(valid_sentences,
valid_labels,
max_seq_len,
padding='post')
test_features, test_labels = padding(test_sentences,
test_labels,
max_seq_len,
padding='post')
logger.info(
f"Train features shape is {train_features.shape} and labels shape is{train_labels.shape}"
)
logger.info(
f"Valid features shape is {valid_features.shape} and labels shape is{valid_labels.shape}"
)
logger.info(
f"Test features shape is {test_features.shape} and labels shape is{test_labels.shape}"
)
train_dataset = tf.data.Dataset.from_tensor_slices(
(train_features, train_labels))
valid_dataset = tf.data.Dataset.from_tensor_slices(
(valid_features, valid_labels))
test_dataset = tf.data.Dataset.from_tensor_slices(
(test_features, test_labels))
shuffled_train_dataset = train_dataset.shuffle(
buffer_size=train_features.shape[0], reshuffle_each_iteration=True)
batched_train_dataset = shuffled_train_dataset.batch(train_batch_size,
drop_remainder=True)
batched_valid_dataset = valid_dataset.batch(valid_batch_size,
drop_remainder=True)
batched_test_dataset = test_dataset.batch(test_batch_size,
drop_remainder=True)
epoch_bar = master_bar(range(epochs))
train_pb_max_len = math.ceil(
float(len(train_features)) / float(train_batch_size))
valid_pb_max_len = math.ceil(
float(len(valid_features)) / float(valid_batch_size))
test_pb_max_len = math.ceil(
float(len(test_features)) / float(test_batch_size))
model = TFNer(max_seq_len=max_seq_len,
embed_input_dim=len(word2Idx),
embed_output_dim=EMBEDDING_DIM,
weights=[embedding_matrix],
num_labels=num_labels)
optimizer = tf.keras.optimizers.Adam(learning_rate=0.01)
scce = tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True)
train_log_dir = f"{args.output}/logs/train"
valid_log_dir = f"{args.output}/logs/valid"
train_summary_writer = tf.summary.create_file_writer(train_log_dir)
valid_summary_writer = tf.summary.create_file_writer(valid_log_dir)
train_loss_metric = tf.keras.metrics.Mean('training_loss',
dtype=tf.float32)
valid_loss_metric = tf.keras.metrics.Mean('valid_loss', dtype=tf.float32)
def train_step_fn(sentences_batch, labels_batch):
with tf.GradientTape() as tape:
logits = model(
sentences_batch) # batchsize, max_seq_len, num_labels
loss = scce(labels_batch, logits) #batchsize,max_seq_len
grads = tape.gradient(loss, model.trainable_variables)
optimizer.apply_gradients(list(zip(grads, model.trainable_variables)))
return loss, logits
def valid_step_fn(sentences_batch, labels_batch):
logits = model(sentences_batch)
loss = scce(labels_batch, logits)
return loss, logits
for epoch in epoch_bar:
with train_summary_writer.as_default():
for sentences_batch, labels_batch in progress_bar(
batched_train_dataset,
total=train_pb_max_len,
parent=epoch_bar):
loss, logits = train_step_fn(sentences_batch, labels_batch)
train_loss_metric(loss)
epoch_bar.child.comment = f'training loss : {train_loss_metric.result()}'
tf.summary.scalar('training loss',
train_loss_metric.result(),
step=epoch)
train_loss_metric.reset_states()
with valid_summary_writer.as_default():
for sentences_batch, labels_batch in progress_bar(
batched_valid_dataset,
total=valid_pb_max_len,
parent=epoch_bar):
loss, logits = valid_step_fn(sentences_batch, labels_batch)
valid_loss_metric.update_state(loss)
epoch_bar.child.comment = f'validation loss : {valid_loss_metric.result()}'
# Logging after each Epoch !
tf.summary.scalar('valid loss',
valid_loss_metric.result(),
step=epoch)
valid_loss_metric.reset_states()
model.save_weights(f"{args.output}/model_weights", save_format='tf')
logger.info(f"Model weights saved")
#Evaluating on test dataset
test_model = TFNer(max_seq_len=max_seq_len,
embed_input_dim=len(word2Idx),
embed_output_dim=EMBEDDING_DIM,
weights=[embedding_matrix],
num_labels=num_labels)
test_model.load_weights(f"{args.output}/model_weights")
logger.info(f"Model weights restored")
true_labels = []
pred_labels = []
for sentences_batch, labels_batch in progress_bar(batched_test_dataset,
total=test_pb_max_len):
logits = test_model(sentences_batch)
temp1 = tf.nn.softmax(logits)
preds = tf.argmax(temp1, axis=2)
true_labels.append(np.asarray(labels_batch))
pred_labels.append(np.asarray(preds))
label_correct, label_pred = idx_to_label(pred_labels, true_labels,
idx2Label)
report = classification_report(label_correct, label_pred, digits=4)
logger.info(f"Results for the test dataset")
logger.info(f"\n{report}")
def evaluate(eval_ATE=True, eval_APC=True):
# evaluate
apc_result = {'max_apc_test_acc': 0, 'max_apc_test_f1': 0}
ate_result = 0
y_true = []
y_pred = []
n_test_correct, n_test_total = 0, 0
test_apc_logits_all, test_polarities_all = None, None
model.eval()
label_map = {i: label for i, label in enumerate(label_list, 1)}
for input_ids_spc, input_mask, segment_ids, label_ids, polarities, valid_ids, l_mask in eval_dataloader:
input_ids_spc = input_ids_spc.to(device)
input_mask = input_mask.to(device)
segment_ids = segment_ids.to(device)
valid_ids = valid_ids.to(device)
label_ids = label_ids.to(device)
polarities = polarities.to(device)
l_mask = l_mask.to(device)
with torch.no_grad():
ate_logits, apc_logits = model(input_ids_spc,
segment_ids,
input_mask,
valid_ids=valid_ids,
polarities=polarities,
attention_mask_label=l_mask)
if eval_APC:
polarities = model.get_batch_polarities(polarities)
n_test_correct += (torch.argmax(
apc_logits, -1) == polarities).sum().item()
n_test_total += len(polarities)
if test_polarities_all is None:
test_polarities_all = polarities
test_apc_logits_all = apc_logits
else:
test_polarities_all = torch.cat(
(test_polarities_all, polarities), dim=0)
test_apc_logits_all = torch.cat(
(test_apc_logits_all, apc_logits), dim=0)
if eval_ATE:
if not args.use_bert_spc:
label_ids = model.get_batch_token_labels_bert_base_indices(
label_ids)
ate_logits = torch.argmax(F.log_softmax(ate_logits, dim=2),
dim=2)
ate_logits = ate_logits.detach().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] == len(label_list):
y_true.append(temp_1)
y_pred.append(temp_2)
break
else:
temp_1.append(label_map.get(label_ids[i][j], 'O'))
temp_2.append(label_map.get(ate_logits[i][j], 'O'))
if eval_APC:
test_acc = n_test_correct / n_test_total
if args.dataset in {'camera', 'car', 'phone', 'notebook'}:
test_f1 = f1_score(torch.argmax(test_apc_logits_all, -1).cpu(),
test_polarities_all.cpu(),
labels=[0, 1],
average='macro')
else:
test_f1 = f1_score(torch.argmax(test_apc_logits_all, -1).cpu(),
test_polarities_all.cpu(),
labels=[0, 1, 2],
average='macro')
test_acc = round(test_acc * 100, 2)
test_f1 = round(test_f1 * 100, 2)
apc_result = {
'max_apc_test_acc': test_acc,
'max_apc_test_f1': test_f1
}
if eval_ATE:
report = classification_report(y_true, y_pred, digits=4)
tmps = report.split()
ate_result = round(float(tmps[7]) * 100, 2)
return apc_result, ate_result
X_train = [sent2features(s) for s in data[996:]]
y_train = [sent2labels(s) for s in data[996:]]
X_test = [sent2features(s) for s in data[:996]]
y_test = [sent2labels(s) for s in data[:996]]
crf = sklearn_crfsuite.CRF(
algorithm='lbfgs',
c1=0.1,
c2=0.1,
max_iterations=20,
all_possible_transitions=False,
)
if __name__ == '__main__':
crf.fit(X_train, y_train)
y_pred = crf.predict(X_test)
y_p, y_t = [], []
for i in range(len(y_pred)):
for j in range(len(y_pred[i])):
y_p.append(y_pred[i][j])
y_t.append(y_test[i][j])
print(
metrics.flat_classification_report(y_test,
y_pred,
labels=corpus.labels))
print(classification_report(y_t, y_p))
def get_classification_report(self,
index2label: [List[str], Dict[int, str]]):
golds, preds = self._map_to_labels(index2label)
cr = classification_report(golds, preds, digits=5)
return report2dict(cr)
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=True,
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.",
)
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("--do_eval",
action="store_true",
help="Whether to run eval or not.")
parser.add_argument(
"--eval_on",
default="dev",
help="Whether to run eval on the dev set or test set.",
)
parser.add_argument(
"--do_lower_case",
action="store_true",
help="Set this flag if you are using an uncased model.",
)
parser.add_argument(
"--train_batch_size",
default=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(
"--weight_decay",
default=0.01,
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(
"--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(
"--fp16_opt_level",
type=str,
default="O1",
help=
"For fp16: Apex AMP optimization level selected in ['O0', 'O1', 'O2', and 'O3']."
"See details at https://nvidia.github.io/apex/amp.html",
)
parser.add_argument(
"--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 = {"ner": NerProcessor}
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 args.do_train and not args.do_eval:
raise ValueError(
"At least one of `do_train` or `do_eval` 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]()
label_list = processor.get_labels()
num_labels = len(label_list) + 1
tokenizer = BertTokenizer.from_pretrained(args.bert_model,
do_lower_case=args.do_lower_case)
train_examples = None
num_train_optimization_steps = 0
if args.do_train:
train_examples = processor.get_train_examples(args.data_dir)
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.local_rank not in [-1, 0]:
torch.distributed.barrier(
) # Make sure only the first process in distributed training will download model & vocab
# Prepare model
config = BertConfig.from_pretrained(args.bert_model,
num_labels=num_labels,
finetuning_task=args.task_name)
model = Ner.from_pretrained(args.bert_model, from_tf=False, config=config)
if args.local_rank == 0:
torch.distributed.barrier(
) # Make sure only the first process in distributed training will download model & vocab
model.to(device)
param_optimizer = list(model.named_parameters())
no_decay = ["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":
args.weight_decay,
},
{
"params":
[p for n, p in param_optimizer if any(nd in n for nd in no_decay)],
"weight_decay":
0.0,
},
]
warmup_steps = int(args.warmup_proportion * num_train_optimization_steps)
optimizer = AdamW(
optimizer_grouped_parameters,
lr=args.learning_rate,
eps=args.adam_epsilon,
)
scheduler = WarmupLinearSchedule(
optimizer,
warmup_steps=warmup_steps,
t_total=num_train_optimization_steps,
)
if args.fp16:
try:
from apex import amp
except ImportError:
raise ImportError(
"Please install apex from https://www.github.com/nvidia/apex to use fp16 training."
)
model, optimizer = amp.initialize(model,
optimizer,
opt_level=args.fp16_opt_level)
# multi-gpu training (should be after apex fp16 initialization)
if n_gpu > 1:
model = torch.nn.DataParallel(model)
if args.local_rank != -1:
model = torch.nn.parallel.DistributedDataParallel(
model,
device_ids=[args.local_rank],
output_device=args.local_rank,
find_unused_parameters=True,
)
global_step = 0
nb_tr_steps = 0
tr_loss = 0
label_map = {i: label for i, label in enumerate(label_list, 1)}
if args.do_train:
train_features = convert_examples_to_features(train_examples,
label_list,
args.max_seq_length,
tokenizer)
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)
all_valid_ids = torch.tensor([f.valid_ids for f in train_features],
dtype=torch.long)
all_lmask_ids = torch.tensor([f.label_mask for f in train_features],
dtype=torch.long)
train_data = TensorDataset(
all_input_ids,
all_input_mask,
all_segment_ids,
all_label_ids,
all_valid_ids,
all_lmask_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)
model.train()
for _ in trange(int(args.num_train_epochs), desc="Epoch"):
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, valid_ids, l_mask = (
batch)
loss = model(
input_ids,
segment_ids,
input_mask,
label_ids,
valid_ids,
l_mask,
)
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:
with amp.scale_loss(loss, optimizer) as scaled_loss:
scaled_loss.backward()
torch.nn.utils.clip_grad_norm_(
amp.master_params(optimizer), args.max_grad_norm)
else:
loss.backward()
torch.nn.utils.clip_grad_norm_(model.parameters(),
args.max_grad_norm)
tr_loss += loss.item()
nb_tr_examples += input_ids.size(0)
nb_tr_steps += 1
if (step + 1) % args.gradient_accumulation_steps == 0:
optimizer.step()
scheduler.step() # Update learning rate schedule
model.zero_grad()
global_step += 1
# 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
model_to_save.save_pretrained(args.output_dir)
tokenizer.save_pretrained(args.output_dir)
label_map = {i: label for i, label in enumerate(label_list, 1)}
model_config = {
"bert_model": args.bert_model,
"do_lower": args.do_lower_case,
"max_seq_length": args.max_seq_length,
"num_labels": len(label_list) + 1,
"label_map": label_map,
}
json.dump(
model_config,
open(os.path.join(args.output_dir, "model_config.json"), "w"),
)
# Load a trained model and config that you have fine-tuned
else:
# Load a trained model and vocabulary that you have fine-tuned
model = Ner.from_pretrained(args.output_dir)
tokenizer = BertTokenizer.from_pretrained(
args.output_dir, do_lower_case=args.do_lower_case)
model.to(device)
if args.do_eval and (args.local_rank == -1
or torch.distributed.get_rank() == 0):
if args.eval_on == "dev":
eval_examples = processor.get_dev_examples(args.data_dir)
elif args.eval_on == "test":
eval_examples = processor.get_test_examples(args.data_dir)
else:
raise ValueError("eval on dev or test set only")
eval_features = convert_examples_to_features(eval_examples, label_list,
args.max_seq_length,
tokenizer)
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)
all_valid_ids = torch.tensor([f.valid_ids for f in eval_features],
dtype=torch.long)
all_lmask_ids = torch.tensor([f.label_mask for f in eval_features],
dtype=torch.long)
eval_data = TensorDataset(
all_input_ids,
all_input_mask,
all_segment_ids,
all_label_ids,
all_valid_ids,
all_lmask_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
y_true = []
y_pred = []
label_map = {i: label for i, label in enumerate(label_list, 1)}
for (
input_ids,
input_mask,
segment_ids,
label_ids,
valid_ids,
l_mask,
) in tqdm(eval_dataloader, desc="Evaluating"):
input_ids = input_ids.to(device)
input_mask = input_mask.to(device)
segment_ids = segment_ids.to(device)
valid_ids = valid_ids.to(device)
label_ids = label_ids.to(device)
l_mask = l_mask.to(device)
with torch.no_grad():
logits = model(
input_ids,
segment_ids,
input_mask,
valid_ids=valid_ids,
attention_mask_label=l_mask,
)
logits = torch.argmax(F.log_softmax(logits, dim=2), dim=2)
logits = logits.detach().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] == len(label_map):
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]])
report = classification_report(y_true, y_pred, digits=4)
logger.info("\n%s", report)
output_eval_file = os.path.join(args.output_dir, "eval_results.txt")
with open(output_eval_file, "w") as writer:
logger.info("***** Eval results *****")
logger.info("\n%s", report)
writer.write(report)
async def accuracy(self, sources: Sources):
if not os.path.isfile(
os.path.join(self.parent.config.output_dir, "tf_model.h5")
):
raise ModelNotTrained("Train model before assessing for accuracy.")
config = self.parent.config._asdict()
config["strategy"] = self.parent.config.strategy
config["n_device"] = self.parent.config.n_device
self.tokenizer = self.tokenizer_class.from_pretrained(
config["output_dir"], do_lower_case=config["do_lower_case"]
)
eval_batch_size = (
config["per_device_eval_batch_size"] * config["n_device"]
)
data_df = await self._preprocess_data(sources)
eval_dataset, num_eval_examples = self.get_dataset(
data_df,
self.tokenizer,
self.pad_token_label_id,
eval_batch_size,
mode="accuracy",
)
eval_dataset = self.parent.config.strategy.experimental_distribute_dataset(
eval_dataset
)
checkpoints = []
results = []
if config["eval_all_checkpoints"]:
checkpoints = list(
os.path.dirname(c)
for c in sorted(
pathlib(
config["output_dir"] + "/**/" + TF2_WEIGHTS_NAME
).glob(recursive=True),
key=lambda f: int("".join(filter(str.isdigit, f)) or -1),
)
)
if len(checkpoints) == 0:
checkpoints.append(config["output_dir"])
self.logger.info("Evaluate the following checkpoints: %s", checkpoints)
for checkpoint in checkpoints:
global_step = (
checkpoint.split("-")[-1]
if re.match(".*checkpoint-[0-9]", checkpoint)
else "final"
)
with self.parent.config.strategy.scope():
self.model = self.model_class.from_pretrained(checkpoint)
y_true, y_pred, eval_loss = self._custom_accuracy(
eval_dataset,
self.tokenizer,
self.model,
num_eval_examples,
eval_batch_size,
)
report = classification_report(y_true, y_pred, digits=4)
if global_step:
results.append(
{
global_step + "_report": report,
global_step + "_loss": eval_loss,
}
)
output_eval_file = os.path.join(
config["output_dir"], "accuracy_results.txt"
)
# create the report and save in output_dir
with self.tf.io.gfile.GFile(output_eval_file, "w") as writer:
for res in results:
for key, val in res.items():
if "loss" in key:
self.logger.debug(key + " = " + str(val))
writer.write(key + " = " + str(val))
writer.write("\n")
else:
self.logger.debug(key)
self.logger.debug("\n" + report)
writer.write(key + "\n")
writer.write(report)
writer.write("\n")
# Return accuracy for the last checkpoint
return Accuracy(f1_score(y_true, y_pred))
def test(padded_X,
X_lengths,
padded_Y,
model,
batch_size,
longest_sent,
optimizer,
label_map,
device,
upos=None,
feats=None,
fixes=None,
results_dir=None,
save_file=True,
results_file="eval_results.txt"):
y_corr_all = []
y_pred_all = []
for example_i in range(0, len(padded_X), batch_size):
# TODO Erase this
# If last batch size != 16 break
if example_i + batch_size > len(padded_X):
break
X_ids = padded_X[example_i:min(example_i + batch_size, len(padded_X))]
upos_ids, feats_ids, fixes_ids = None, None, None
if upos is not None:
upos_ids = upos[example_i:min(example_i + batch_size, len(upos))]
if feats is not None:
feats_ids = [
feat[example_i:min(example_i + batch_size, len(feat))]
for feat in feats
]
if fixes is not None:
fixes_ids = fixes[example_i:min(example_i +
batch_size, len(fixes))]
X_leng = X_lengths[example_i:min(example_i +
batch_size, len(X_lengths))]
Y_ids = padded_Y[example_i:min(example_i + batch_size, len(padded_Y))]
if upos is not None:
if feats is not None:
if fixes is not None:
sorted_data = sorted(zip(X_leng, X_ids, Y_ids, upos_ids,
feats_ids, fixes_ids),
key=lambda pair: pair[0],
reverse=True)
X_leng, X_ids, Y_ids, upos_ids, feats_ids, fixes_ids = zip(
*sorted_data)
X_leng, X_ids, Y_ids, upos_ids, feats_ids, fixes_ids = list(
X_leng), list(X_ids), list(Y_ids), list(
upos_ids), list(feats_ids), list(fixes_ids)
else:
sorted_data = sorted(zip(X_leng, X_ids, Y_ids, upos_ids,
*feats_ids),
key=lambda pair: pair[0],
reverse=True)
X_leng, X_ids, Y_ids, upos_ids, *feats_ids = zip(
*sorted_data)
X_leng, X_ids, Y_ids, upos_ids = list(X_leng), list(
X_ids), list(Y_ids), list(upos_ids)
feats_ids = [list(feat_ids) for feat_ids in feats_ids]
else:
sorted_data = sorted(zip(X_leng, X_ids, Y_ids, upos_ids),
key=lambda pair: pair[0],
reverse=True)
X_leng, X_ids, Y_ids, upos_ids = zip(*sorted_data)
X_leng, X_ids, Y_ids, upos_ids = list(X_leng), list(
X_ids), list(Y_ids), list(upos_ids)
elif feats is not None:
if fixes is not None:
sorted_data = sorted(zip(X_leng, X_ids, Y_ids, feats_ids,
fixes_ids),
key=lambda pair: pair[0],
reverse=True)
X_leng, X_ids, Y_ids, feats_ids, fixes_ids = zip(*sorted_data)
X_leng, X_ids, Y_ids, feats_ids, fixes_ids = list(
X_leng), list(X_ids), list(Y_ids), list(feats_ids), list(
fixes_ids)
else:
sorted_data = sorted(zip(X_leng, X_ids, Y_ids, *feats_ids),
key=lambda pair: pair[0],
reverse=True)
X_leng, X_ids, Y_ids, *feats_ids = zip(*sorted_data)
X_leng, X_ids, Y_ids, = list(X_leng), list(X_ids), list(Y_ids)
feats_ids = [list(feat_ids) for feat_ids in feats_ids]
elif fixes is not None:
sorted_data = sorted(zip(X_leng, X_ids, Y_ids, upos_ids, feats_ids,
fixes_ids),
key=lambda pair: pair[0],
reverse=True)
X_leng, X_ids, Y_ids, fixes_ids = zip(*sorted_data)
X_leng, X_ids, Y_ids, fixes_ids = list(X_leng), list(X_ids), list(
Y_ids), list(fixes_ids)
else:
sorted_data = sorted(zip(X_leng, X_ids, Y_ids),
key=lambda pair: pair[0],
reverse=True)
X_leng, X_ids, Y_ids = zip(*sorted_data)
X_leng, X_ids, Y_ids = list(X_leng), list(X_ids), list(Y_ids)
Y_ids = torch.tensor([index for exam in Y_ids for index in exam],
dtype=torch.long) - 1
Y_ids = Y_ids.to(device)
X_ids = torch.tensor(X_ids, dtype=torch.float32)
X_ids = X_ids.to(device)
if upos is not None:
upos_ids = torch.tensor(upos_ids, dtype=torch.long)
upos_ids = upos_ids.to(device)
if feats is not None:
for feat_i, feat_ids in enumerate(feats_ids):
feat_ids = torch.tensor(feat_ids, dtype=torch.long)
feats_ids[feat_i] = feat_ids.to(device)
if fixes is not None:
fixes_ids = torch.tensor(fixes_ids, dtype=torch.long)
fixes_ids = fixes_ids.to(device)
with torch.no_grad():
if fixes is not None:
y_pred = model(X_ids, X_leng, upos_ids, feats_ids, fixes_ids)
elif feats is not None:
y_pred = model(X_ids, X_leng, upos_ids, feats_ids)
elif upos is not None:
y_pred = model(X_ids, X_leng, upos_ids)
else:
y_pred = model(X_ids, X_leng)
y_pred = y_pred.detach().cpu()
# reshape out_label_ids, create dict for mapping, map all out_label_ids to out_labels, stack them in one array, classification_report(Y_words, out_labels)
y_pred_reshaped = torch.argmax(y_pred, dim=1)
y_pred_reshaped = y_pred_reshaped.view(-1, longest_sent).numpy()
y_corr_reshaped = Y_ids.view(-1, longest_sent).cpu().numpy()
y_corr = []
y_pred_tags = []
for i_y in range(batch_size):
y_corr_row = []
y_pred_tags_row = []
for j_y in range(X_leng[i_y]):
y_corr_row.append(label_map[y_corr_reshaped[i_y][j_y]])
y_pred_tags_row.append(label_map[y_pred_reshaped[i_y][j_y]])
y_corr.append(y_corr_row)
y_pred_tags.append(y_pred_tags_row)
y_corr_all.extend(y_corr)
y_pred_all.extend(y_pred_tags)
optimizer.zero_grad()
report = classification_report(y_corr_all, y_pred_all, digits=4)
if save_file:
if not os.path.exists(results_dir):
os.mkdir(results_dir)
output_eval_file = os.path.join(results_dir, results_file)
with open(output_eval_file, "w") as writer:
writer.write(report)
print(report)
def test_inv_classification_report(self):
print(
classification_report(self.y_true_inv,
self.y_pred_inv,
suffix=True))
def evaluate(self, eval_dataset, output_dir):
"""
Evaluates the model on eval_dataset.
Utility function to be used by the eval_model() method. Not intended to be used directly.
"""
device = self.device
model = self.model
args = self.args
pad_token_label_id = self.pad_token_label_id
eval_output_dir = output_dir
results = {}
eval_sampler = SequentialSampler(eval_dataset)
eval_dataloader = DataLoader(eval_dataset,
sampler=eval_sampler,
batch_size=args["eval_batch_size"])
eval_loss = 0.0
nb_eval_steps = 0
preds = None
out_label_ids = None
model.eval()
for batch in tqdm(eval_dataloader, disable=args["silent"]):
batch = tuple(t.to(device) for t in batch)
with torch.no_grad():
inputs = {
"input_ids": batch[0],
"attention_mask": batch[1],
"labels": batch[3],
}
# XLM and RoBERTa don"t use segment_ids
if args["model_type"] in ["bert", "xlnet"]:
inputs["token_type_ids"] = batch[2]
outputs = model(**inputs)
tmp_eval_loss, logits = outputs[:2]
eval_loss += tmp_eval_loss.mean().item()
nb_eval_steps += 1
if preds is None:
preds = logits.detach().cpu().numpy()
out_label_ids = inputs["labels"].detach().cpu().numpy()
else:
preds = np.append(preds, logits.detach().cpu().numpy(), axis=0)
out_label_ids = np.append(
out_label_ids,
inputs["labels"].detach().cpu().numpy(),
axis=0)
eval_loss = eval_loss / nb_eval_steps
model_outputs = preds
preds = np.argmax(preds, axis=2)
label_map = {i: label for i, label in enumerate(self.labels)}
out_label_list = [[] for _ in range(out_label_ids.shape[0])]
preds_list = [[] for _ in range(out_label_ids.shape[0])]
for i in range(out_label_ids.shape[0]):
for j in range(out_label_ids.shape[1]):
if out_label_ids[i, j] != pad_token_label_id:
out_label_list[i].append(label_map[out_label_ids[i][j]])
preds_list[i].append(label_map[preds[i][j]])
result = {
"eval_loss": eval_loss,
"precision": precision_score(out_label_list, preds_list),
"recall": recall_score(out_label_list, preds_list),
"f1_score": f1_score(out_label_list, preds_list),
}
results.update(result)
output_eval_file = os.path.join(eval_output_dir, "eval_results.txt")
with open(output_eval_file, "w") as writer:
if args["classification_report"]:
cls_report = classification_report(out_label_list, preds_list)
writer.write("{}\n".format(cls_report))
for key in sorted(result.keys()):
writer.write("{} = {}\n".format(key, str(result[key])))
return results, model_outputs, preds_list
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=True,
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.")
parser.add_argument("--task_name",
default=None,
type=str,
required=True,
help="The name of the task to train.")
parser.add_argument("--test_file", default='', type=str, help="Test file")
parser.add_argument(
"--output_dir",
default=None,
type=str,
required=True,
help=
"The output directory where the model predictions and checkpoints will be written."
)
## Other parameters
parser.add_argument(
"--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("--validate_per_epoch",
default=3,
type=int,
help="validations number per epoch")
parser.add_argument("--do_train",
action='store_true',
help="Whether to run training.")
parser.add_argument("--do_validation",
action='store_true',
help="Whether to run validation.")
parser.add_argument("--do_eval",
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("--train_batch_size",
default=32,
type=int,
help="Total batch size for training.")
parser.add_argument("--eval_batch_size",
default=32,
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("--drop",
default=0.1,
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("--weight_decay",
default=0.01,
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("--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(
'--fp16_opt_level',
type=str,
default='O1',
help=
"For fp16: Apex AMP optimization level selected in ['O0', 'O1', 'O2', and 'O3']."
"See details at https://nvidia.github.io/apex/amp.html")
parser.add_argument(
'--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 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)
if args.do_train:
logger.addHandler(
logging.FileHandler(os.path.join(args.output_dir, "train.log"),
'w'))
else:
logger.addHandler(
logging.FileHandler(os.path.join(args.output_dir, "eval.log"),
'w'))
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 = {"ner": NerProcessor}
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 args.do_train and not args.do_eval:
raise ValueError(
"At least one of `do_train` or `do_eval` must be True.")
task_name = args.task_name.lower()
if task_name not in processors:
raise ValueError("Task not found: %s" % (task_name))
processor = processors[task_name]()
label_list = processor.get_tag_labels(args.data_dir)
global EVAL_TAGS
EVAL_TAGS = [
label for label in label_list if label not in ['O', '[CLS]', '[SEP]']
]
# EVAL_TAGS = [f'{x}-{y}' for x in ['B', 'I'] for y in EVAL_TAGS]
logger.info(EVAL_TAGS)
num_labels = len(label_list) + 1
allowed_tags = set(EVAL_TAGS + ['O'])
do_lower_case = 'uncased' in args.bert_model
tokenizer = BertTokenizer.from_pretrained(args.bert_model,
do_lower_case=do_lower_case)
train_examples = None
num_train_optimization_steps = 0
if args.do_train:
train_examples = processor.get_train_examples(args.data_dir)
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.local_rank not in [-1, 0]:
torch.distributed.barrier(
) # Make sure only the first process in distributed training will download model & vocab
# Prepare model
config = BertConfig.from_pretrained(args.bert_model,
num_labels=num_labels,
finetuning_task=args.task_name,
hidden_dropout_prob=args.drop)
print(config)
model = Ner.from_pretrained(args.bert_model, from_tf=False, config=config)
if args.local_rank == 0:
torch.distributed.barrier(
) # Make sure only the first process in distributed training will download model & vocab
model.to(device)
param_optimizer = list(model.named_parameters())
no_decay = ['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':
args.weight_decay
}, {
'params':
[p for n, p in param_optimizer if any(nd in n for nd in no_decay)],
'weight_decay':
0.0
}]
warmup_steps = int(args.warmup_proportion * num_train_optimization_steps)
optimizer = AdamW(optimizer_grouped_parameters,
lr=args.learning_rate,
eps=args.adam_epsilon)
scheduler = WarmupLinearSchedule(optimizer,
warmup_steps=warmup_steps,
t_total=num_train_optimization_steps)
if args.fp16:
try:
from apex import amp
except ImportError:
raise ImportError(
"Please install apex from https://www.github.com/nvidia/apex to use fp16 training."
)
model, optimizer = amp.initialize(model,
optimizer,
opt_level=args.fp16_opt_level)
# multi-gpu training (should be after apex fp16 initialization)
if n_gpu > 1:
model = torch.nn.DataParallel(model)
if args.local_rank != -1:
model = torch.nn.parallel.DistributedDataParallel(
model,
device_ids=[args.local_rank],
output_device=args.local_rank,
find_unused_parameters=True)
global_step = 0
nb_tr_steps = 0
tr_loss = 0
label_map = {i: label for i, label in enumerate(label_list, 1)}
best_dev = 0.0
if args.do_validation:
dev_examples = processor.get_dev_examples(args.data_dir, label_list)
dev_features = convert_examples_to_features(dev_examples, label_list,
args.max_seq_length,
tokenizer)
logger.info("***** Dev set *****")
logger.info(" Num examples = %d", len(dev_examples))
all_input_ids = torch.tensor([f.input_ids for f in dev_features],
dtype=torch.long)
all_input_mask = torch.tensor([f.input_mask for f in dev_features],
dtype=torch.long)
all_segment_ids = torch.tensor([f.segment_ids for f in dev_features],
dtype=torch.long)
all_label_ids = torch.tensor([f.label_id for f in dev_features],
dtype=torch.long)
all_valid_ids = torch.tensor([f.valid_ids for f in dev_features],
dtype=torch.long)
all_lmask_ids = torch.tensor([f.label_mask for f in dev_features],
dtype=torch.long)
dev_data = TensorDataset(all_input_ids, all_input_mask,
all_segment_ids, all_label_ids, all_valid_ids,
all_lmask_ids)
dev_sampler = SequentialSampler(dev_data)
dev_dataloader = DataLoader(dev_data,
sampler=dev_sampler,
batch_size=args.eval_batch_size)
validation_steps = int(
len(train_examples) /
args.train_batch_size) // args.validate_per_epoch
if args.do_train:
train_features = convert_examples_to_features(train_examples,
label_list,
args.max_seq_length,
tokenizer)
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)
all_valid_ids = torch.tensor([f.valid_ids for f in train_features],
dtype=torch.long)
all_lmask_ids = torch.tensor([f.label_mask for f in train_features],
dtype=torch.long)
train_data = TensorDataset(all_input_ids, all_input_mask,
all_segment_ids, all_label_ids,
all_valid_ids, all_lmask_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)
model.train()
start_time = time.time()
for epoch in trange(int(args.num_train_epochs), desc="Epoch"):
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, valid_ids, l_mask = batch
loss = model(input_ids,
segment_ids,
input_mask,
label_ids,
valid_ids,
l_mask,
device=device)
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:
with amp.scale_loss(loss, optimizer) as scaled_loss:
scaled_loss.backward()
torch.nn.utils.clip_grad_norm_(
amp.master_params(optimizer), args.max_grad_norm)
else:
loss.backward()
torch.nn.utils.clip_grad_norm_(model.parameters(),
args.max_grad_norm)
tr_loss += loss.item()
nb_tr_examples += input_ids.size(0)
nb_tr_steps += 1
if (step + 1) % args.gradient_accumulation_steps == 0:
optimizer.step()
scheduler.step() # Update learning rate schedule
model.zero_grad()
global_step += 1
if args.do_validation and (step + 1) % validation_steps == 0:
logger.info(
'Epoch: {}, Step: {} / {}, used_time = {:.2f}s, loss = {:.6f}'
.format(epoch, step + 1, len(train_dataloader),
time.time() - start_time,
tr_loss / nb_tr_steps))
model.eval()
y_true = []
y_pred = []
label_map = {
i: label
for i, label in enumerate(label_list, 1)
}
label_map[0] = '[PAD]'
for batch in tqdm(dev_dataloader, desc='Validation'):
batch = tuple(t.to(device) for t in batch)
input_ids, input_mask, segment_ids, label_ids, valid_ids, l_mask = batch
with torch.no_grad():
logits = model(input_ids,
segment_ids,
input_mask,
None,
valid_ids,
l_mask,
device=device)
logits = torch.argmax(F.log_softmax(logits, dim=2),
dim=2)
logits = logits.detach().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] == len(label_map) - 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_copy = [[
x if x in allowed_tags else 'O' for x in y
] for y in y_true]
y_pred_copy = [[
x if x in allowed_tags else 'O' for x in y
] for y in y_pred]
report = classification_report(y_true_copy,
y_pred_copy,
digits=6)
# report_dict = classification_report(y_true_copy,
# y_pred_copy,
# output_dict=True)
# report_dict = report
logger.info("***** Validation results *****")
logger.info("\n%s", report)
fscore = float([
line.strip().split()[4] for line in report.split('\n')
if line.strip().startswith('micro')
][0])
if fscore > best_dev:
logger.info(f'!!!Best dev: {fscore}')
logger.info(f'at epoch: {epoch}')
best_dev = fscore
model_to_save = model.module if hasattr(
model,
'module') else model # Only save the model it-self
model_to_save.save_pretrained(args.output_dir)
tokenizer.save_pretrained(args.output_dir)
label_map = {
i: label
for i, label in enumerate(label_list, 1)
}
label_map[0] = '[PAD]'
model_config = {
"bert_model": args.bert_model,
"do_lower": args.do_lower_case,
"max_seq_length": args.max_seq_length,
"num_labels": len(label_list) + 1,
"label_map": label_map
}
json.dump(
model_config,
open(
os.path.join(args.output_dir,
"model_config.json"), "w"))
model.train()
model = Ner.from_pretrained(args.output_dir)
do_lower_case = 'uncased' in args.bert_model
tokenizer = BertTokenizer.from_pretrained(args.output_dir,
do_lower_case=do_lower_case)
model.to(device)
if args.do_eval and (args.local_rank == -1
or torch.distributed.get_rank() == 0):
args.test_file = os.path.join(
args.data_dir,
'test.json') if args.test_file == '' else args.test_file
eval_examples = processor.get_test_examples(args.test_file)
eval_features = convert_examples_to_features(eval_examples, label_list,
args.max_seq_length,
tokenizer)
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)
all_valid_ids = torch.tensor([f.valid_ids for f in eval_features],
dtype=torch.long)
all_lmask_ids = torch.tensor([f.label_mask for f in eval_features],
dtype=torch.long)
eval_data = TensorDataset(all_input_ids, all_input_mask,
all_segment_ids, all_label_ids,
all_valid_ids, all_lmask_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
y_true = []
y_pred = []
tag_scores = []
label_map = {i: label for i, label in enumerate(label_list, 1)}
label_map[0] = '[PAD]'
for input_ids, input_mask, segment_ids, label_ids, valid_ids, l_mask in tqdm(
eval_dataloader, desc="Evaluating"):
input_ids = input_ids.to(device)
input_mask = input_mask.to(device)
segment_ids = segment_ids.to(device)
valid_ids = valid_ids.to(device)
label_ids = label_ids.to(device)
l_mask = l_mask.to(device)
with torch.no_grad():
logits = model(input_ids,
segment_ids,
input_mask,
valid_ids=valid_ids,
attention_mask_label=l_mask,
device=device)
scores = np.max(F.softmax(logits, dim=-1).cpu().numpy(), axis=-1)
logits = torch.argmax(F.log_softmax(logits, dim=2), dim=2)
logits = logits.detach().cpu().numpy()
# scores = scores.detach().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 = []
temp_3 = []
for j, m in enumerate(label):
if j == 0:
continue
elif label_ids[i][j] == len(label_map) - 1:
y_true.append(temp_1)
y_pred.append(temp_2)
tag_scores.append(temp_3)
break
else:
temp_1.append(label_map[label_ids[i][j]])
temp_2.append(label_map[logits[i][j]])
temp_3.append(scores[i][j])
y_true_copy = [[x if x in allowed_tags else 'O' for x in y]
for y in y_true]
y_pred_copy = [[x if x in allowed_tags else 'O' for x in y]
for y in y_pred]
# report = classification_report(y_true_copy,
# y_pred_copy, digits=4)
report = 'all scores are 0!\n'
logger.info("\n%s", report)
output_eval_file = os.path.join(
args.output_dir,
f"{args.test_file.split('/')[-1]}_eval_results.txt")
output_preds_file = os.path.join(
args.output_dir,
f"{args.test_file.split('/')[-1]}_predictions.tsv")
with open(output_eval_file, "w") as writer:
logger.info("***** Eval results *****")
logger.info("\n%s", report)
writer.write(report)
prediction_results = {
'id': [ex.guid for ex in eval_examples],
'token': [ex.text_a for ex in eval_examples],
'tag_label': [' '.join(ex.label) for ex in eval_examples],
'tag_pred': [' '.join(pred) for pred in y_pred],
'scores':
[' '.join([str(x) for x in score]) for score in tag_scores]
}
pd.DataFrame(prediction_results).to_csv(output_preds_file,
sep='\t',
index=False)
def main():
args: ModelArguments = get_arguments()
process = NerProcessor(args.data_dir)
label_list = process.get_labels()
num_labels = len(label_list) + 1
if os.path.exists(args.output_dir) and os.listdir(
args.output_dir) and args.do_train:
raise ValueError(
'Output directory ({}) already exist and the dir is not empty')
if not args.output_dir:
os.makedirs(args.output_dir)
if args.do_train:
tokenizer = FullTokenizer(os.path.join(args.bert_model, "vocab.txt"),
args.do_lower_case)
if args.multi_gpu:
if len(args.gpu.split(',')) == 1:
strategy = tf.distribute.MirroredStrategy()
else:
# build the gpu device name arr
gpus = [f'/gpu:{gpu}' for gpu in args.gpu.split(',')]
strategy = tf.distribute.MirroredStrategy(devices=gpus)
else:
strategy = tf.distribute.OneDeviceStrategy(device=args.gpu)
if args.do_train:
train_examples = process.get_train_examples()
# optimization total steps -> learning_rate scheduler, weight decay, warmup learning rate
num_train_optimization_steps = int(
len(train_examples) /
args.train_batch_size) * args.num_train_epochs
warmup_steps = int(args.warmup_proportion *
num_train_optimization_steps)
# keep the final learning should be zero
learning_rate_fn = tf.keras.optimizers.schedules.PolynomialDecay(
initial_learning_rate=args.learning_rate,
decay_steps=num_train_optimization_steps,
end_learning_rate=0.)
if warmup_steps:
# layer norm and bias should not weight decay
learning_rate_fn = AdamWeightDecay(
learning_rate=args.learning_rate,
weight_decay_rate=args.weight_decay,
beta_1=0.9,
beta_2=0.99,
epsilon=args.adam_epsilon,
exclude_from_weight_decay=['layer_norm', 'bias'])
with strategy.scope():
ner = BertNer(args.bert_model, tf.float32, args.num_labels,
args.max_seq_length)
# can define the specific meaning of the reduction
loss_fct = tf.keras.losses.SparseCategoricalCrossentropy(
reduction=tf.keras.losses.Reduction.NONE)
label_map = {label: index for index, label in enumerate(label_list, 1)}
if args.do_train:
train_features = convert_examples_to_features(train_examples,
label_list,
args.max_seq_length,
tokenizer)
logger.info('*** Running training ***')
logger.info(' Num Examples = %d', len(train_examples))
logger.info(' Batch Size = %d', len(args.train_batch_size))
logger.info(' Num Steps = %d', len(num_train_optimization_steps))
all_input_ids = tf.data.Dataset.from_tensor_slices(
np.asarray([f.input_ids for f in train_features]))
all_input_mask = tf.data.Dataset.from_tensor_slices(
np.asarray([f.input_mask for f in train_features]))
all_label_ids = tf.data.Dataset.from_tensor_slices(
np.asarray([f.label_ids for f in train_features]))
all_label_mask = tf.data.Dataset.from_tensor_slices(
np.asarray([f.label_mask for f in train_features]))
all_valid_ids = tf.data.Dataset.from_tensor_slices(
np.asarray([f.valid_ids for f in train_features]))
all_segment_ids = tf.data.Dataset.from_tensor_slices(
np.asarray([f.segment_ids for f in train_features]))
train_data = tf.data.Dataset.zip(
(all_input_ids, all_input_mask, all_segment_ids, all_valid_ids,
all_label_ids, all_label_mask))
# set the shuffle buffer size, reshuffle the train data in each iteration
shuffled_train_data = train_data.shuffle(
buffer_size=int(len(train_features) * 0.1),
seed=args.seed,
reshuffle_each_iteration=True).batch(args.train_batch_size)
distributed_data = strategy.experimental_distribute_dataset(
shuffled_train_data)
loss_metric = tf.keras.metrics.Mean()
epoch_bar = master_bar(range(1))
optimizer: tf.keras.optimizers.Optimizer = None
def train_steps(input_ids, input_mask, segment_id, valid_ids,
label_ids, label_mask):
def step_fn(_input_ids, _input_mask, _segment_id, _valid_ids,
_label_ids, _label_mask):
with tf.GradientTape() as tape:
# _input_ids, one-axis, which will be run on pattern
output = ner(_input_ids,
_input_mask,
_segment_id,
_label_ids,
training=True)
# flatten all of the outputs
_label_mask = tf.reshape(_label_mask, (-1))
output = tf.reshape(output, (-1, num_labels))
output = tf.boolean_mask(output, _input_mask)
_label_ids = tf.reshape(_label_ids, (-1, ))
_label_ids = tf.boolean_mask(_label_ids, _label_mask)
cross_entropy = loss_fct(_label_ids, output)
# this is for single one train data
loss = tf.reduce_sum(
cross_entropy) * 1. / args.train_batch_size
gradients = tape.gradient(loss, ner.trainable_variables)
optimizer.apply_gradients(
grads_and_vars=zip(gradients, ner.trainable_variables))
return cross_entropy
# 在多个gpu上并行跑训练数据
per_example_loss = strategy.experimental_run_v2(
step_fn,
args=(input_ids, input_mask, segment_id, valid_ids, label_ids,
label_mask))
mean_loss = strategy.reduce(tf.distribute)
return mean_loss
pb_max_length = math.ceil(len(train_features) / args.train_batch_size)
for epoch in epoch_bar:
with strategy.scope():
for (input_ids, input_mask, segment_ids, valid_ids, label_ids,
label_mask) in progress_bar(distributed_data,
total=pb_max_length,
parent=epoch_bar):
loss = train_steps(input_ids, input_mask, segment_ids,
valid_ids, label_ids, label_mask)
loss_metric(loss)
epoch_bar.child.comment = f'loss: {loss}'
loss_metric.reset_states()
ner.save_weights(os.path.join(args.output_dir, 'model.h5'))
if args.do_eval:
tokenizer = FullTokenizer(os.path.join(args.bert_model, 'vocab.txt'),
do_lower_case=args.do_lower_case)
ner = BertNer(args.bert_model, tf.float32, args.num_labels,
args.max_seq_length)
# create example eval data to build the model
ids = tf.ones((1, 128), dtype=tf.float32)
ner(ids, ids, ids, ids, ids, training=False)
ner.load_weights(os.path.join(args.output_dir, 'model.h5'))
# load the data
if args.eval_on == 'dev':
eval_examples = process.get_dev_examples()
elif args.eval_on == 'test':
eval_examples = process.get_test_examples()
else:
raise KeyError(f'eval_on arguments is expected in [dev, test]')
eval_features = convert_examples_to_features(eval_examples, label_list,
args.max_seq_length,
tokenizer)
# print the eval info
logger.info('*** Eval Examples ***')
logger.info(' Num Examples = %d', len(eval_features))
logger.info(' Batch Size = %d', args.eval_batch_size)
all_input_ids = tf.data.Dataset.from_tensor_slices(
[f.input_ids for f in eval_features])
all_input_mask = tf.data.Dataset.from_tensor_slices(
[f.input_mask for f in eval_features])
all_segment_ids = tf.data.Dataset.from_tensor_slices(
[f.segment_ids for f in eval_features])
all_valid_ids = tf.data.Dataset.from_tensor_slices(
[f.valid_ids for f in eval_features])
all_label_ids = tf.data.Dataset.from_tensor_slices(
[f.label_ids for f in eval_features])
all_label_mask = tf.data.Dataset.from_tensor_slices(
[f.label_mask for f in eval_features])
eval_data = tf.data.Dataset.zip(
(all_input_ids, all_input_mask, all_segment_ids, all_valid_ids,
all_label_ids, all_label_mask)).batch(args.eval_batch_size)
loss_metric = tf.metrics.Mean()
epoch_bar = master_bar(range(1))
processor_bar_length = math.ceil(
len(eval_features) / args.eval_batch_size)
y_true, y_predict = [], []
for epoch in epoch_bar:
for (input_ids, input_mask, segment_ids, valid_ids, label_ids,
label_mask) in progress_bar(eval_data,
total=processor_bar_length,
parent=epoch_bar):
logits = ner(input_ids,
input_mask,
segment_ids,
valid_ids,
training=False)
logits = tf.argmax(logits, axis=-1)
label_predict = tf.boolean_mask(logits, label_mask)
y_true.append(label_ids)
y_predict.append(label_predict)
report = classification_report(y_true, y_predict, digits=4)
output_eval_file = os.path.join(args.output_dir, 'eval_result.txt')
with open(output_eval_file, 'w', encoding='utf-8') as f:
logger.info('*** Eval Result ***')
logger.info(report)
f.write(report)
def evaluate(opt):
# set config
config = load_config(opt)
if opt.num_threads > 0: torch.set_num_threads(opt.num_threads)
config['opt'] = opt
logger.info("%s", config)
# set path
set_path(config)
# prepare test dataset
test_loader = prepare_datasets(config)
# load pytorch model checkpoint
checkpoint = load_checkpoint(config)
# prepare model and load parameters
model = load_model(config, checkpoint)
model.eval()
# convert to onnx format
if opt.convert_onnx:
(x, y) = next(iter(test_loader))
x = to_device(x, opt.device)
y = to_device(y, opt.device)
convert_onnx(config, model, x)
check_onnx(config)
logger.info("[ONNX model saved at {}".format(opt.onnx_path))
# quantize onnx
if opt.quantize_onnx:
quantize_onnx(opt.onnx_path, opt.quantized_onnx_path)
logger.info("[Quantized ONNX model saved at {}".format(
opt.quantized_onnx_path))
return
# load onnx model for using onnxruntime
if opt.enable_ort:
import onnxruntime as ort
sess_options = ort.SessionOptions()
sess_options.inter_op_num_threads = opt.num_threads
sess_options.intra_op_num_threads = opt.num_threads
ort_session = ort.InferenceSession(opt.onnx_path,
sess_options=sess_options)
# enable to use dynamic quantized model (pytorch>=1.3.0)
if opt.enable_dqm and opt.device == 'cpu':
model = torch.quantization.quantize_dynamic(model, {torch.nn.Linear},
dtype=torch.qint8)
print(model)
# evaluation
preds = None
ys = None
n_batches = len(test_loader)
total_examples = 0
whole_st_time = time.time()
first_time = time.time()
first_examples = 0
total_duration_time = 0.0
with torch.no_grad():
for i, (x, y) in enumerate(tqdm(test_loader, total=n_batches)):
start_time = time.time()
x = to_device(x, opt.device)
y = to_device(y, opt.device)
if opt.enable_ort:
x = to_numpy(x)
if config['emb_class'] == 'glove':
ort_inputs = {
ort_session.get_inputs()[0].name: x[0],
ort_session.get_inputs()[1].name: x[1]
}
if opt.use_char_cnn:
ort_inputs[ort_session.get_inputs()[2].name] = x[2]
if config['emb_class'] in [
'bert', 'distilbert', 'albert', 'roberta', 'bart',
'electra'
]:
if config['emb_class'] in ['distilbert', 'bart']:
ort_inputs = {
ort_session.get_inputs()[0].name: x[0],
ort_session.get_inputs()[1].name: x[1]
}
else:
ort_inputs = {
ort_session.get_inputs()[0].name: x[0],
ort_session.get_inputs()[1].name: x[1],
ort_session.get_inputs()[2].name: x[2]
}
if opt.bert_use_pos:
ort_inputs[ort_session.get_inputs()[3].name] = x[3]
if opt.use_crf:
logits, prediction = ort_session.run(None, ort_inputs)
prediction = to_device(torch.tensor(prediction),
opt.device)
logits = to_device(torch.tensor(logits), opt.device)
else:
logits = ort_session.run(None, ort_inputs)[0]
logits = to_device(torch.tensor(logits), opt.device)
else:
if opt.use_crf: logits, prediction = model(x)
else: logits = model(x)
if preds is None:
if opt.use_crf: preds = to_numpy(prediction)
else: preds = to_numpy(logits)
ys = to_numpy(y)
else:
if opt.use_crf:
preds = np.append(preds, to_numpy(prediction), axis=0)
else:
preds = np.append(preds, to_numpy(logits), axis=0)
ys = np.append(ys, to_numpy(y), axis=0)
cur_examples = y.size(0)
total_examples += cur_examples
if i == 0: # first one may take longer time, so ignore in computing duration.
first_time = float((time.time() - first_time) * 1000)
first_examples = cur_examples
if opt.num_examples != 0 and total_examples >= opt.num_examples:
logger.info("[Stop Evaluation] : up to the {} examples".format(
total_examples))
break
duration_time = float((time.time() - start_time) * 1000)
if i != 0: total_duration_time += duration_time
'''
logger.info("[Elapsed Time] : {}ms".format(duration_time))
'''
whole_time = float((time.time() - whole_st_time) * 1000)
avg_time = (whole_time - first_time) / (total_examples - first_examples)
if not opt.use_crf: preds = np.argmax(preds, axis=2)
# compute measure using seqeval
labels = model.labels
ys_lbs = [[] for _ in range(ys.shape[0])]
preds_lbs = [[] for _ in range(ys.shape[0])]
pad_label_id = config['pad_label_id']
for i in range(ys.shape[0]): # foreach sentence
for j in range(ys.shape[1]): # foreach token
if ys[i][j] != pad_label_id:
ys_lbs[i].append(labels[ys[i][j]])
preds_lbs[i].append(labels[preds[i][j]])
ret = {
"precision": precision_score(ys_lbs, preds_lbs),
"recall": recall_score(ys_lbs, preds_lbs),
"f1": f1_score(ys_lbs, preds_lbs),
"report": classification_report(ys_lbs, preds_lbs, digits=4),
}
print(ret['report'])
f1 = ret['f1']
# write predicted labels to file
default_label = config['default_label']
write_prediction(opt, ys, preds, labels, pad_label_id, default_label)
logger.info("[F1] : {}, {}".format(f1, total_examples))
logger.info("[Elapsed Time] : {} examples, {}ms, {}ms on average".format(
total_examples, whole_time, avg_time))
logger.info(
"[Elapsed Time(total_duration_time, average)] : {}ms, {}ms".format(
total_duration_time, total_duration_time / (total_examples - 1)))
def report(labels, preds):
return classification_report(labels, preds)
attention_mask=b_input_mask,
labels=b_labels)
logits = model(b_input_ids,
token_type_ids=None,
attention_mask=b_input_mask)
logits = logits.detach().cpu().numpy()
label_ids = b_labels.to('cpu').numpy()
# print(np.argmax(logits, axis=2).shape)
predictions.extend([list(p) for p in np.argmax(logits, axis=2)])
true_labels.extend(label_ids)
tmp_eval_accuracy = flat_accuracy(logits, label_ids)
eval_loss += tmp_eval_loss.mean().item()
eval_accuracy += tmp_eval_accuracy
nb_eval_examples += b_input_ids.size(0)
nb_eval_steps += 1
eval_loss = eval_loss / nb_eval_steps
print("Validation loss: {}".format(eval_loss))
print("Validation Accuracy: {}".format(eval_accuracy / nb_eval_steps))
pred_tags = [[id2label[p_i] for p_i in p] for p in predictions]
valid_tags = [[id2label[l_i] for l_i in l] for l in true_labels]
with open("logs/logs_epoch_{}.txt".format(epoch), "w") as f:
for tokens, pred, valid in zip(tokenized_test_text, pred_tags,
valid_tags):
f.write(" ".join(tokens) + "\n")
f.write(" ".join(pred) + "\n")
f.write(" ".join(valid) + "\n\n")
print("F1-Score: {}".format(f1_score(pred_tags, valid_tags)))
print(classification_report(pred_tags, valid_tags))
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=True,
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.")
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(
"--percent",
default=100,
type=int,
help="The percentage of examples used in the training data.\n")
parser.add_argument("--do_train",
action='store_true',
help="Whether to run training.")
parser.add_argument("--do_eval",
action='store_true',
help="Whether to run eval on the dev set.")
parser.add_argument(
"--do_lower_case",
action='store_true',
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(
'--pretrain',
action='store_true',
help="Whether to load a pre-trained model for continuing training")
parser.add_argument('--pretrained_model_file',
type=str,
help="The path of the pretrained_model_file")
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 = {"ner": NerProcessor}
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 args.do_train and not args.do_eval:
raise ValueError(
"At least one of `do_train` or `do_eval` 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]()
label_list = processor.get_labels()
num_labels = len(label_list) + 1
tokenizer = BertTokenizer.from_pretrained(args.bert_model,
do_lower_case=args.do_lower_case)
train_examples = None
num_train_optimization_steps = None
if args.do_train:
train_examples = processor.get_train_examples(args.data_dir)
train_examples = train_examples[:int(
len(train_examples) * args.percent / 100)]
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(
)
# 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))
model = BertForTokenClassification.from_pretrained(args.bert_model,
cache_dir=cache_dir,
num_labels=num_labels)
if args.pretrain:
# Load a pre-trained model
print('load a pre-trained model from ' + args.pretrained_model_file)
pretrained_state_dict = torch.load(args.pretrained_model_file)
model_state_dict = model.state_dict()
print('pretrained_state_dict', pretrained_state_dict.keys())
print('model_state_dict', model_state_dict.keys())
pretrained_state = {
k: v
for k, v in pretrained_state_dict.items() if k in model_state_dict
and v.size() == model_state_dict[k].size()
}
model_state_dict.update(pretrained_state)
print('updated_state_dict', model_state_dict.keys())
model.load_state_dict(model_state_dict)
model.to(device)
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)
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
if args.do_train:
train_features = convert_examples_to_features(train_examples,
label_list,
args.max_seq_length,
tokenizer)
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)
model.train()
for _ in trange(int(args.num_train_epochs), desc="Epoch"):
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
print('train loss', tr_loss)
# 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)
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())
label_map = {i: label for i, label in enumerate(label_list, 0)}
model_config = {
"bert_model": args.bert_model,
"do_lower": args.do_lower_case,
"max_seq_length": args.max_seq_length,
"num_labels": len(label_list) + 1,
"label_map": label_map
}
json.dump(
model_config,
open(os.path.join(args.output_dir, "model_config.json"), "w"))
# Load a trained model and config that you have fine-tuned
else:
output_model_file = os.path.join(args.output_dir, WEIGHTS_NAME)
model_state_dict = torch.load(output_model_file)
model = BertForTokenClassification.from_pretrained(
args.bert_model,
state_dict=model_state_dict,
num_labels=num_labels)
model.load_state_dict(torch.load(output_model_file))
model.to(device)
if args.do_eval and (args.local_rank == -1
or torch.distributed.get_rank() == 0):
eval_examples = processor.get_dev_examples(args.data_dir)
eval_features = convert_examples_to_features(eval_examples, label_list,
args.max_seq_length,
tokenizer)
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()
y_true = []
y_pred = []
label_map = {i: label for i, label in enumerate(label_list, 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():
logits = model(input_ids, segment_ids, input_mask)
logits = torch.argmax(logits, dim=2)
logits = logits.detach().cpu().numpy()
label_ids = label_ids.to('cpu').numpy()
input_mask = input_mask.to('cpu').numpy()
for i, mask in enumerate(input_mask):
temp_1 = []
temp_2 = []
for j, m in enumerate(mask):
if j == 0:
continue
if m:
if label_map[label_ids[i][j]] != "X":
temp_1.append(label_map[label_ids[i][j]])
temp_2.append(label_map[logits[i][j]])
else:
temp_1.pop()
temp_2.pop()
break
if temp_1[-1] == '[SEP]':
temp_1.pop()
temp_2.pop()
y_true.append(temp_1)
y_pred.append(temp_2)
report = classification_report(y_true, y_pred, digits=4)
prediction_file = os.path.join(args.output_dir, 'predictions.txt')
write_predictions(eval_examples, y_true, y_pred, prediction_file)
output_eval_file = os.path.join(args.output_dir, "eval_results.txt")
with open(output_eval_file, "w") as writer:
logger.info("***** Eval results *****")
logger.info("\n%s", report)
writer.write(report)
def main(_):
logging.set_verbosity(logging.INFO)
args = flags.FLAGS.flag_values_dict()
if (os.path.exists(args["output_dir"]) and os.listdir(args["output_dir"])
and args["do_train"] and not args["overwrite_output_dir"]):
raise ValueError(
"Output directory ({}) already exists and is not empty. Use --overwrite_output_dir to overcome."
.format(args["output_dir"]))
if args["fp16"]:
tf.config.optimizer.set_experimental_options(
{"auto_mixed_precision": True})
if args["tpu"]:
resolver = tf.distribute.cluster_resolver.TPUClusterResolver(
tpu=args["tpu"])
tf.config.experimental_connect_to_cluster(resolver)
tf.tpu.experimental.initialize_tpu_system(resolver)
strategy = tf.distribute.experimental.TPUStrategy(resolver)
args["n_device"] = args["num_tpu_cores"]
elif len(args["gpus"].split(",")) > 1:
args["n_device"] = len(
[f"/gpu:{gpu}" for gpu in args["gpus"].split(",")])
strategy = tf.distribute.MirroredStrategy(
devices=[f"/gpu:{gpu}" for gpu in args["gpus"].split(",")])
elif args["no_cuda"]:
args["n_device"] = 1
strategy = tf.distribute.OneDeviceStrategy(device="/cpu:0")
else:
args["n_device"] = len(args["gpus"].split(","))
strategy = tf.distribute.OneDeviceStrategy(device="/gpu:" +
args["gpus"].split(",")[0])
logging.warning(
"n_device: %s, distributed training: %s, 16-bits training: %s",
args["n_device"],
bool(args["n_device"] > 1),
args["fp16"],
)
labels = get_labels(args["labels"])
num_labels = len(labels)
pad_token_label_id = -1
# IBO
print(args["config_name"]
if args["config_name"] else args["model_name_or_path"])
config = AutoConfig.from_pretrained(
args["config_name"]
if args["config_name"] else args["model_name_or_path"],
num_labels=num_labels,
cache_dir=args["cache_dir"],
)
logging.info("Training/evaluation parameters %s", args)
args["model_type"] = config.model_type
# Training
if args["do_train"]:
tokenizer = AutoTokenizer.from_pretrained(
args["tokenizer_name"]
if args["tokenizer_name"] else args["model_name_or_path"],
do_lower_case=args["do_lower_case"],
cache_dir=args["cache_dir"],
)
with strategy.scope():
model = TFAutoModelForTokenClassification.from_pretrained(
args["model_name_or_path"],
from_pt=bool(".bin" in args["model_name_or_path"]),
config=config,
cache_dir=args["cache_dir"],
)
train_batch_size = args["per_device_train_batch_size"] * args[
"n_device"]
train_dataset, num_train_examples = load_and_cache_examples(
args,
tokenizer,
labels,
pad_token_label_id,
train_batch_size,
mode="train")
train_dataset = strategy.experimental_distribute_dataset(train_dataset)
train(
args,
strategy,
train_dataset,
tokenizer,
model,
num_train_examples,
labels,
train_batch_size,
pad_token_label_id,
)
os.makedirs(args["output_dir"], exist_ok=True)
logging.info("Saving model to %s", args["output_dir"])
model.save_pretrained(args["output_dir"])
tokenizer.save_pretrained(args["output_dir"])
# Evaluation
if args["do_eval"]:
tokenizer = AutoTokenizer.from_pretrained(
args["output_dir"], do_lower_case=args["do_lower_case"])
checkpoints = []
results = []
if args["eval_all_checkpoints"]:
checkpoints = list(
os.path.dirname(c) for c in sorted(
glob.glob(args["output_dir"] + "/**/" + TF2_WEIGHTS_NAME,
recursive=True),
key=lambda f: int("".join(filter(str.isdigit, f)) or -1),
))
logging.info("Evaluate the following checkpoints: %s", checkpoints)
if len(checkpoints) == 0:
checkpoints.append(args["output_dir"])
for checkpoint in checkpoints:
global_step = checkpoint.split("-")[-1] if re.match(
".*checkpoint-[0-9]", checkpoint) else "final"
with strategy.scope():
model = TFAutoModelForTokenClassification.from_pretrained(
checkpoint)
y_true, y_pred, eval_loss = evaluate(args,
strategy,
model,
tokenizer,
labels,
pad_token_label_id,
mode="dev")
report = metrics.classification_report(y_true, y_pred, digits=4)
if global_step:
results.append({
global_step + "_report": report,
global_step + "_loss": eval_loss
})
output_eval_file = os.path.join(args["output_dir"], "eval_results.txt")
with tf.io.gfile.GFile(output_eval_file, "w") as writer:
for res in results:
for key, val in res.items():
if "loss" in key:
logging.info(key + " = " + str(val))
writer.write(key + " = " + str(val))
writer.write("\n")
else:
logging.info(key)
logging.info("\n" + report)
writer.write(key + "\n")
writer.write(report)
writer.write("\n")
if args["do_predict"]:
tokenizer = AutoTokenizer.from_pretrained(
args["output_dir"], do_lower_case=args["do_lower_case"])
model = TFAutoModelForTokenClassification.from_pretrained(
args["output_dir"])
eval_batch_size = args["per_device_eval_batch_size"] * args["n_device"]
predict_dataset, _ = load_and_cache_examples(args,
tokenizer,
labels,
pad_token_label_id,
eval_batch_size,
mode="test")
y_true, y_pred, pred_loss = evaluate(args,
strategy,
model,
tokenizer,
labels,
pad_token_label_id,
mode="test")
output_test_results_file = os.path.join(args["output_dir"],
"test_results.txt")
output_test_predictions_file = os.path.join(args["output_dir"],
"test_predictions.txt")
report = metrics.classification_report(y_true, y_pred, digits=4)
with tf.io.gfile.GFile(output_test_results_file, "w") as writer:
report = metrics.classification_report(y_true, y_pred, digits=4)
logging.info("\n" + report)
writer.write(report)
writer.write("\n\nloss = " + str(pred_loss))
with tf.io.gfile.GFile(output_test_predictions_file, "w") as writer:
with tf.io.gfile.GFile(os.path.join(args["data_dir"], "test.txt"),
"r") as f:
example_id = 0
for line in f:
if line.startswith(
"-DOCSTART-") or line == "" or line == "\n":
writer.write(line)
if not y_pred[example_id]:
example_id += 1
elif y_pred[example_id]:
output_line = line.split(
)[0] + " " + y_pred[example_id].pop(0) + "\n"
writer.write(output_line)
else:
logging.warning(
"Maximum sequence length exceeded: No prediction for '%s'.",
line.split()[0])
def evaluate(args, model, tokenizer, labels, pad_token_label_id, mode, prefix=""):
eval_dataset = FunsdDataset(args, tokenizer, labels, pad_token_label_id, mode=mode)
args.eval_batch_size = args.per_gpu_eval_batch_size * max(1, args.n_gpu)
eval_sampler = SequentialSampler(eval_dataset)
eval_dataloader = DataLoader(
eval_dataset,
sampler=eval_sampler,
batch_size=args.eval_batch_size,
collate_fn=None,
)
# Eval!
logger.info("***** Running evaluation %s *****", prefix)
logger.info(" Num examples = %d", len(eval_dataset))
logger.info(" Batch size = %d", args.eval_batch_size)
eval_loss = 0.0
nb_eval_steps = 0
preds = None
out_label_ids = None
model.eval()
for batch in tqdm(eval_dataloader, desc="Evaluating"):
with torch.no_grad():
inputs = {
"input_ids": batch[0].to(args.device),
"attention_mask": batch[1].to(args.device),
"labels": batch[3].to(args.device),
}
if args.model_type in ["layoutlm"]:
inputs["bbox"] = batch[4].to(args.device)
inputs["token_type_ids"] = (
batch[2].to(args.device)
if args.model_type in ["bert", "layoutlm"]
else None
) # RoBERTa don"t use segment_ids
outputs = model(**inputs)
tmp_eval_loss, logits = outputs[:2]
if args.n_gpu > 1:
tmp_eval_loss = (
tmp_eval_loss.mean()
) # mean() to average on multi-gpu parallel evaluating
eval_loss += tmp_eval_loss.item()
nb_eval_steps += 1
if preds is None:
preds = logits.detach().cpu().numpy()
out_label_ids = inputs["labels"].detach().cpu().numpy()
else:
preds = np.append(preds, logits.detach().cpu().numpy(), axis=0)
out_label_ids = np.append(
out_label_ids, inputs["labels"].detach().cpu().numpy(), axis=0
)
eval_loss = eval_loss / nb_eval_steps
preds = np.argmax(preds, axis=2)
label_map = {i: label for i, label in enumerate(labels)}
out_label_list = [[] for _ in range(out_label_ids.shape[0])]
preds_list = [[] for _ in range(out_label_ids.shape[0])]
for i in range(out_label_ids.shape[0]):
for j in range(out_label_ids.shape[1]):
if out_label_ids[i, j] != pad_token_label_id:
out_label_list[i].append(label_map[out_label_ids[i][j]])
preds_list[i].append(label_map[preds[i][j]])
results = {
"loss": eval_loss,
"precision": precision_score(out_label_list, preds_list),
"recall": recall_score(out_label_list, preds_list),
"f1": f1_score(out_label_list, preds_list),
}
report = classification_report(out_label_list, preds_list)
logger.info("\n" + report)
logger.info("***** Eval results %s *****", prefix)
for key in sorted(results.keys()):
logger.info(" %s = %s", key, str(results[key]))
return results, preds_list