Spaces:
Sleeping
Sleeping
File size: 10,807 Bytes
b5dbcf3 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 |
import os
import logging
from tqdm import tqdm, trange
import numpy as np
import torch
from torch.utils.data import DataLoader, RandomSampler, SequentialSampler
from transformers import BertConfig, AdamW, get_linear_schedule_with_warmup
from utils import MODEL_CLASSES, compute_metrics, get_intent_labels, get_slot_labels
logger = logging.getLogger(__name__)
class Trainer(object):
def __init__(self, args, train_dataset=None, dev_dataset=None, test_dataset=None):
self.args = args
self.train_dataset = train_dataset
self.dev_dataset = dev_dataset
self.test_dataset = test_dataset
self.intent_label_lst = get_intent_labels(args)
self.slot_label_lst = get_slot_labels(args)
# Use cross entropy ignore index as padding label id so that only real label ids contribute to the loss later
self.pad_token_label_id = args.ignore_index
self.config_class, self.model_class, _ = MODEL_CLASSES[args.model_type]
self.config = self.config_class.from_pretrained(args.model_name_or_path, finetuning_task=args.task)
self.model = self.model_class.from_pretrained(args.model_name_or_path,
config=self.config,
args=args,
intent_label_lst=self.intent_label_lst,
slot_label_lst=self.slot_label_lst)
# GPU or CPU
self.device = "cuda" if torch.cuda.is_available() and not args.no_cuda else "cpu"
self.model.to(self.device)
def train(self):
train_sampler = RandomSampler(self.train_dataset)
train_dataloader = DataLoader(self.train_dataset, sampler=train_sampler, batch_size=self.args.train_batch_size)
if self.args.max_steps > 0:
t_total = self.args.max_steps
self.args.num_train_epochs = self.args.max_steps // (len(train_dataloader) // self.args.gradient_accumulation_steps) + 1
else:
t_total = len(train_dataloader) // self.args.gradient_accumulation_steps * self.args.num_train_epochs
# Prepare optimizer and schedule (linear warmup and decay)
no_decay = ['bias', 'LayerNorm.weight']
optimizer_grouped_parameters = [
{'params': [p for n, p in self.model.named_parameters() if not any(nd in n for nd in no_decay)],
'weight_decay': self.args.weight_decay},
{'params': [p for n, p in self.model.named_parameters() if any(nd in n for nd in no_decay)], 'weight_decay': 0.0}
]
optimizer = AdamW(optimizer_grouped_parameters, lr=self.args.learning_rate, eps=self.args.adam_epsilon)
scheduler = get_linear_schedule_with_warmup(optimizer, num_warmup_steps=self.args.warmup_steps, num_training_steps=t_total)
# Train!
logger.info("***** Running training *****")
logger.info(" Num examples = %d", len(self.train_dataset))
logger.info(" Num Epochs = %d", self.args.num_train_epochs)
logger.info(" Total train batch size = %d", self.args.train_batch_size)
logger.info(" Gradient Accumulation steps = %d", self.args.gradient_accumulation_steps)
logger.info(" Total optimization steps = %d", t_total)
logger.info(" Logging steps = %d", self.args.logging_steps)
logger.info(" Save steps = %d", self.args.save_steps)
global_step = 0
tr_loss = 0.0
self.model.zero_grad()
train_iterator = trange(int(self.args.num_train_epochs), desc="Epoch")
for _ in train_iterator:
epoch_iterator = tqdm(train_dataloader, desc="Iteration")
for step, batch in enumerate(epoch_iterator):
self.model.train()
batch = tuple(t.to(self.device) for t in batch) # GPU or CPU
inputs = {'input_ids': batch[0],
'attention_mask': batch[1],
'intent_label_ids': batch[3],
'slot_labels_ids': batch[4]}
if self.args.model_type != 'distilbert':
inputs['token_type_ids'] = batch[2]
outputs = self.model(**inputs)
loss = outputs[0]
if self.args.gradient_accumulation_steps > 1:
loss = loss / self.args.gradient_accumulation_steps
loss.backward()
tr_loss += loss.item()
if (step + 1) % self.args.gradient_accumulation_steps == 0:
torch.nn.utils.clip_grad_norm_(self.model.parameters(), self.args.max_grad_norm)
optimizer.step()
scheduler.step() # Update learning rate schedule
self.model.zero_grad()
global_step += 1
if self.args.logging_steps > 0 and global_step % self.args.logging_steps == 0:
self.evaluate("dev")
if self.args.save_steps > 0 and global_step % self.args.save_steps == 0:
self.save_model()
if 0 < self.args.max_steps < global_step:
epoch_iterator.close()
break
if 0 < self.args.max_steps < global_step:
train_iterator.close()
break
return global_step, tr_loss / global_step
def evaluate(self, mode):
if mode == 'test':
dataset = self.test_dataset
elif mode == 'dev':
dataset = self.dev_dataset
else:
raise Exception("Only dev and test dataset available")
eval_sampler = SequentialSampler(dataset)
eval_dataloader = DataLoader(dataset, sampler=eval_sampler, batch_size=self.args.eval_batch_size)
# Eval!
logger.info("***** Running evaluation on %s dataset *****", mode)
logger.info(" Num examples = %d", len(dataset))
logger.info(" Batch size = %d", self.args.eval_batch_size)
eval_loss = 0.0
nb_eval_steps = 0
intent_preds = None
slot_preds = None
out_intent_label_ids = None
out_slot_labels_ids = None
self.model.eval()
for batch in tqdm(eval_dataloader, desc="Evaluating"):
batch = tuple(t.to(self.device) for t in batch)
with torch.no_grad():
inputs = {'input_ids': batch[0],
'attention_mask': batch[1],
'intent_label_ids': batch[3],
'slot_labels_ids': batch[4]}
if self.args.model_type != 'distilbert':
inputs['token_type_ids'] = batch[2]
outputs = self.model(**inputs)
tmp_eval_loss, (intent_logits, slot_logits) = outputs[:2]
eval_loss += tmp_eval_loss.mean().item()
nb_eval_steps += 1
# Intent prediction
if intent_preds is None:
intent_preds = intent_logits.detach().cpu().numpy()
out_intent_label_ids = inputs['intent_label_ids'].detach().cpu().numpy()
else:
intent_preds = np.append(intent_preds, intent_logits.detach().cpu().numpy(), axis=0)
out_intent_label_ids = np.append(
out_intent_label_ids, inputs['intent_label_ids'].detach().cpu().numpy(), axis=0)
# Slot prediction
if slot_preds is None:
if self.args.use_crf:
# decode() in `torchcrf` returns list with best index directly
slot_preds = np.array(self.model.crf.decode(slot_logits))
else:
slot_preds = slot_logits.detach().cpu().numpy()
out_slot_labels_ids = inputs["slot_labels_ids"].detach().cpu().numpy()
else:
if self.args.use_crf:
slot_preds = np.append(slot_preds, np.array(self.model.crf.decode(slot_logits)), axis=0)
else:
slot_preds = np.append(slot_preds, slot_logits.detach().cpu().numpy(), axis=0)
out_slot_labels_ids = np.append(out_slot_labels_ids, inputs["slot_labels_ids"].detach().cpu().numpy(), axis=0)
eval_loss = eval_loss / nb_eval_steps
results = {
"loss": eval_loss
}
# Intent result
intent_preds = np.argmax(intent_preds, axis=1)
# Slot result
if not self.args.use_crf:
slot_preds = np.argmax(slot_preds, axis=2)
slot_label_map = {i: label for i, label in enumerate(self.slot_label_lst)}
out_slot_label_list = [[] for _ in range(out_slot_labels_ids.shape[0])]
slot_preds_list = [[] for _ in range(out_slot_labels_ids.shape[0])]
for i in range(out_slot_labels_ids.shape[0]):
for j in range(out_slot_labels_ids.shape[1]):
if out_slot_labels_ids[i, j] != self.pad_token_label_id:
out_slot_label_list[i].append(slot_label_map[out_slot_labels_ids[i][j]])
slot_preds_list[i].append(slot_label_map[slot_preds[i][j]])
total_result = compute_metrics(intent_preds, out_intent_label_ids, slot_preds_list, out_slot_label_list)
results.update(total_result)
logger.info("***** Eval results *****")
for key in sorted(results.keys()):
logger.info(" %s = %s", key, str(results[key]))
return results
def save_model(self):
# Save model checkpoint (Overwrite)
if not os.path.exists(self.args.model_dir):
os.makedirs(self.args.model_dir)
model_to_save = self.model.module if hasattr(self.model, 'module') else self.model
model_to_save.save_pretrained(self.args.model_dir)
# Save training arguments together with the trained model
torch.save(self.args, os.path.join(self.args.model_dir, 'training_args.bin'))
logger.info("Saving model checkpoint to %s", self.args.model_dir)
def load_model(self):
# Check whether model exists
if not os.path.exists(self.args.model_dir):
raise Exception("Model doesn't exists! Train first!")
try:
self.model = self.model_class.from_pretrained(self.args.model_dir,
args=self.args,
intent_label_lst=self.intent_label_lst,
slot_label_lst=self.slot_label_lst)
self.model.to(self.device)
logger.info("***** Model Loaded *****")
except:
raise Exception("Some model files might be missing...")
|