def predict(self, utterance, context=list()):
# utterance
ori_word_seq = self.dataloader.tokenizer.tokenize(utterance)
# tag
ori_tag_seq = ['O'] * len(ori_word_seq)
context_seq = self.dataloader.tokenizer.encode(
'[CLS] ' + ' [SEP] '.join(context[-3:]))
da = {}
word_seq, tag_seq, new2ori = ori_word_seq, ori_tag_seq, None
batch_data = [[
ori_word_seq, ori_tag_seq, da, context_seq, new2ori, word_seq,
self.dataloader.seq_tag2id(tag_seq)
]]
pad_batch = self.dataloader.pad_batch(batch_data)
pad_batch = tuple(t.to('cpu') for t in pad_batch)
word_seq_tensor, tag_seq_tensor, word_mask_tensor, \
tag_mask_tensor, context_seq_tensor, context_mask_tensor = pad_batch
# inference
slot_logits, batch_slot_loss = self.model(
word_seq_tensor, word_mask_tensor, tag_seq_tensor, tag_mask_tensor,
context_seq_tensor, context_mask_tensor)
# postprocess
predicts = recover_intent(self.dataloader, slot_logits[0],
tag_mask_tensor[0], batch_data[0][0],
batch_data[0][1])
return predicts
def main():
global scheduler
data_urls = {
"slot_train_data.json": "http://xbot.bslience.cn/slot_train_data.json",
"slot_val_data.json": "http://xbot.bslience.cn/slot_val_data.json",
"slot_test_data.json": "http://xbot.bslience.cn/slot_test_data.json",
}
# load config
root_path = get_root_path()
config_path = os.path.join(
root_path, "src/xbot/config/nlu/crosswoz_all_context_nlu_slot.json")
config = json.load(open(config_path))
data_path = config["data_dir"]
data_path = os.patgith.join(root_path, data_path)
output_dir = config["output_dir"]
output_dir = os.path.join(root_path, output_dir)
log_dir = config["log_dir"]
output_dir = os.path.join(root_path, output_dir)
device = config["DEVICE"]
# download data
for data_key, url in data_urls.items():
dst = os.path.join(os.path.join(data_path, data_key))
if not os.path.exists(dst):
download_from_url(url, dst)
set_seed(config["seed"])
intent_vocab = json.load(
open(os.path.join(data_path, "intent_vocab.json"), encoding="utf-8"))
tag_vocab = json.load(
open(os.path.join(data_path, "tag_vocab.json"), encoding="utf-8"))
dataloader = Dataloader(
intent_vocab=intent_vocab,
tag_vocab=tag_vocab,
pretrained_weights=config["model"]["pretrained_weights"],
)
for data_key in ["train", "val", "test"]:
dataloader.load_data(
json.load(
open(os.path.join(data_path,
"slot_{}_data.json".format(data_key)),
encoding="utf-8")),
data_key,
cut_sen_len=config["cut_sen_len"],
use_bert_tokenizer=config["use_bert_tokenizer"],
)
print("{} set size: {}".format(data_key,
len(dataloader.data[data_key])))
# output and log dir
if not os.path.exists(output_dir):
os.makedirs(output_dir)
if not os.path.exists(log_dir):
os.makedirs(log_dir)
writer = SummaryWriter(log_dir)
# model
model = SlotWithBert(config["model"], device, dataloader.tag_dim)
model.to(device)
if config["model"]["finetune"]:
no_decay = ["bias", "LayerNorm.weight"]
optimizer_grouped_parameters = [
{
"params": [
p for n, p in model.named_parameters()
if not any(nd in n for nd in no_decay) and p.requires_grad
],
"weight_decay":
config["model"]["weight_decay"],
},
{
"params": [
p for n, p in model.named_parameters()
if any(nd in n for nd in no_decay) and p.requires_grad
],
"weight_decay":
0.0,
},
]
optimizer = AdamW(
optimizer_grouped_parameters,
lr=config["model"]["learning_rate"],
eps=config["model"]["adam_epsilon"],
)
scheduler = get_linear_schedule_with_warmup(
optimizer,
num_warmup_steps=config["model"]["warmup_steps"],
num_training_steps=config["model"]["max_step"],
)
else:
for n, p in model.named_parameters():
if "bert_policy" in n:
p.requires_grad = False
optimizer = torch.optim.Adam(
filter(lambda p: p.requires_grad, model.parameters()),
lr=config["model"]["learning_rate"],
)
max_step = config["model"]["max_step"]
check_step = config["model"]["check_step"]
batch_size = config["model"]["batch_size"]
model.zero_grad()
train_slot_loss = 0
best_val_f1 = 0.0
writer.add_text("config", json.dumps(config))
for step in range(1, max_step + 1):
model.train()
batched_data = dataloader.get_train_batch(batch_size)
batched_data = tuple(t.to(device) for t in batched_data)
(
word_seq_tensor,
tag_seq_tensor,
word_mask_tensor,
tag_mask_tensor,
context_seq_tensor,
context_mask_tensor,
) = batched_data
if not config["model"]["context"]:
context_seq_tensor, context_mask_tensor = None, None
_, slot_loss = model(
word_seq_tensor,
word_mask_tensor,
tag_seq_tensor,
tag_mask_tensor,
context_seq_tensor,
context_mask_tensor,
)
train_slot_loss += slot_loss.item()
loss = slot_loss
loss.backward()
torch.nn.utils.clip_grad_norm_(model.parameters(), 1.0)
optimizer.step()
if config["model"]["finetune"]:
scheduler.step() # Update learning rate schedule
model.zero_grad()
if step % check_step == 0:
train_slot_loss = train_slot_loss / check_step
print("[%d|%d] step" % (step, max_step))
print("\t slot loss:", train_slot_loss)
predict_golden = {"slot": [], "overall": []}
val_slot_loss = 0
model.eval()
for pad_batch, ori_batch, real_batch_size in dataloader.yield_batches(
batch_size, data_key="val"):
pad_batch = tuple(t.to(device) for t in pad_batch)
(
word_seq_tensor,
tag_seq_tensor,
word_mask_tensor,
tag_mask_tensor,
context_seq_tensor,
context_mask_tensor,
) = pad_batch
if not config["model"]["context"]:
context_seq_tensor, context_mask_tensor = None, None
with torch.no_grad():
slot_logits, slot_loss = model(
word_seq_tensor,
word_mask_tensor,
tag_seq_tensor,
tag_mask_tensor,
context_seq_tensor,
context_mask_tensor,
)
val_slot_loss += slot_loss.item() * real_batch_size
for j in range(real_batch_size):
predicts = recover_intent(
dataloader,
slot_logits[j],
tag_mask_tensor[j],
ori_batch[j][0],
ori_batch[j][1],
)
labels = ori_batch[j][2]
predict_golden["slot"].append({
"predict": [x for x in predicts if is_slot_da(x)],
"golden": [x for x in labels if is_slot_da(x)],
})
total = len(dataloader.data["val"])
val_slot_loss /= total
print("%d samples val" % total)
print("\t slot loss:", val_slot_loss)
writer.add_scalar("slot_loss/train",
train_slot_loss,
global_step=step)
writer.add_scalar("slot_loss/val", val_slot_loss, global_step=step)
precision, recall, F1 = calculate_f1(predict_golden["slot"])
print("-" * 20 + "slot" + "-" * 20)
print("\t Precision: %.2f" % (100 * precision))
print("\t Recall: %.2f" % (100 * recall))
print("\t F1: %.2f" % (100 * F1))
writer.add_scalar("val_{}/precision".format("slot"),
precision,
global_step=step)
writer.add_scalar("val_{}/recall".format("slot"),
recall,
global_step=step)
writer.add_scalar("val_{}/F1".format("slot"), F1, global_step=step)
if F1 > best_val_f1:
best_val_f1 = F1
torch.save(
model.state_dict(),
os.path.join(output_dir, "pytorch_model_nlu_slot.pt"),
)
print("best val F1 %.4f" % best_val_f1)
print("save on", output_dir)
train_slot_loss = 0
writer.add_text("val overall F1", "%.2f" % (100 * best_val_f1))
writer.close()
model_path = os.path.join(output_dir, "pytorch_model_nlu_slot.pt")
torch.save(model.state_dict(), model_path)
word_seq_tensor, tag_seq_tensor, word_mask_tensor, tag_mask_tensor, context_seq_tensor, context_mask_tensor = pad_batch
if not config['model']['context']:
context_seq_tensor, context_mask_tensor = None, None
with torch.no_grad():
slot_logits, batch_slot_loss = model.forward(word_seq_tensor,
word_mask_tensor,
tag_seq_tensor,
tag_mask_tensor,
context_seq_tensor,
context_mask_tensor)
slot_loss += batch_slot_loss.item() * real_batch_size
for j in range(real_batch_size):
predicts = recover_intent(dataloader, slot_logits[j], tag_mask_tensor[j],
ori_batch[j][0], ori_batch[j][1])
labels = ori_batch[j][2]
predict_golden['slot'].append({
'predict': [x for x in predicts if is_slot_da(x)],
'golden': [x for x in labels if is_slot_da(x)]
})
total = len(dataloader.data[data_key])
slot_loss /= total
precision, recall, F1 = calculate_f1(predict_golden['slot'])
print('-' * 20 + 'slot' + '-' * 20)
print('\t Precision: %.2f' % (100 * precision))
print('\t Recall: %.2f' % (100 * recall))
print('\t F1: %.2f' % (100 * F1))
