def main(args):
ontology = json.load(open(os.path.join(args.data_root, args.ontology_data)))
slot_meta, _ = make_slot_meta(ontology)
tokenizer = BertTokenizer.from_pretrained("dsksd/bert-ko-small-minimal")
out_file = '/opt/ml/code/p3-dst-chatting-day/SomDST/pickles/test_data_raw.pkl'
if os.path.exists(out_file):
print("Pickles are exist!")
with open(out_file, 'rb') as f:
test_data_raw = pickle.load(f)
# with open(out_path+'/test_data.pkl', 'rb') as f:
# test_data = pickle.load(f)
print("Pickles brought!")
else:
print("Pickles are not exist!")
test_data_raw = prepare_dataset_eval(data_path=args.test_data_path,
tokenizer=tokenizer,
slot_meta=slot_meta,
n_history=args.n_history,
max_seq_length=args.max_seq_length,
op_code=args.op_code)
# test_data = WosDataset(train_data_raw,
# tokenizer,
# slot_meta,
# args.max_seq_length,
# rng,
# ontology,
# args.word_dropout,
# args.shuffle_state,
# args.shuffle_p)
with open(out_file, 'wb') as f:
pickle.dump(test_data_raw, f)
# with open(out_path+'/test_data.pkl', 'wb') as f:
# pickle.dump(test_data, f)
print("Pickles saved!")
print("# test examples %d" % len(test_data_raw))
model_config = BertConfig.from_json_file(args.bert_config_path)
model_config.dropout = 0.1
op2id = OP_SET[args.op_code]
model = SomDST(model_config, len(op2id), len(domain2id), op2id['update'])
ckpt = torch.load(args.model_ckpt_path, map_location='cpu')
model.load_state_dict(ckpt)
model.eval()
model.to(device)
print("Model is loaded")
inference_model(model, test_data_raw, tokenizer, slot_meta, args.op_code)
def main(args):
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
ontology = json.load(open(os.path.join(args.data_root,
args.ontology_data)))
slot_meta, _ = make_slot_meta(ontology)
tokenizer = BertTokenizer.from_pretrained(args.bert_config)
special_tokens = ['[SLOT]', '[NULL]']
special_tokens_dict = {'additional_special_tokens': special_tokens}
tokenizer.add_special_tokens(special_tokens_dict)
data = prepare_dataset(data_path=os.path.join(args.data_root,
args.test_data),
data_list=None,
tokenizer=tokenizer,
slot_meta=slot_meta,
n_history=args.n_history,
max_seq_length=args.max_seq_length,
op_code=args.op_code)
model_config = BertConfig.from_json_file(args.bert_config_path)
model_config.dropout = 0.1
op2id = OP_SET[args.op_code]
model = TransformerDST(model_config, len(op2id), len(domain2id),
op2id['update'])
ckpt = torch.load(args.model_ckpt_path, map_location='cpu')
model.load_state_dict(ckpt)
model.eval()
model.to(device)
if args.eval_all:
model_evaluation(model, data, tokenizer, slot_meta, 0, args.op_code,
False, False, False)
model_evaluation(model, data, tokenizer, slot_meta, 0, args.op_code,
False, False, True)
model_evaluation(model, data, tokenizer, slot_meta, 0, args.op_code,
False, True, False)
model_evaluation(model, data, tokenizer, slot_meta, 0, args.op_code,
False, True, True)
model_evaluation(model, data, tokenizer, slot_meta, 0, args.op_code,
True, False, False)
model_evaluation(model, data, tokenizer, slot_meta, 0, args.op_code,
True, True, False)
model_evaluation(model, data, tokenizer, slot_meta, 0, args.op_code,
True, False, True)
model_evaluation(model, data, tokenizer, slot_meta, 0, args.op_code,
True, True, True)
else:
model_evaluation(model, data, tokenizer, slot_meta, 0, args.op_code,
args.gt_op, args.gt_p_state, args.gt_gen)
def main(args):
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
ontology = json.load(open(os.path.join(args.data_root,
args.ontology_data)))
slot_meta, _ = make_slot_meta(ontology)
tokenizer = BertTokenizer(args.vocab_path, do_lower_case=True)
data = prepare_dataset(os.path.join(args.data_root,
args.test_data), tokenizer, slot_meta,
args.n_history, args.max_seq_length, args.op_code)
model_config = BertConfig.from_json_file(args.bert_config_path)
model_config.dropout = 0.1
op2id = OP_SET[args.op_code]
model = TransformerDST(model_config, len(op2id), len(domain2id),
op2id['update'])
ckpt = torch.load(args.model_ckpt_path, map_location='cpu')
model.load_state_dict(ckpt)
model.eval()
model.to(device)
if args.eval_all:
model_evaluation(model, data, tokenizer, slot_meta, 0, args.op_code,
False, False, False)
model_evaluation(model, data, tokenizer, slot_meta, 0, args.op_code,
False, False, True)
model_evaluation(model, data, tokenizer, slot_meta, 0, args.op_code,
False, True, False)
model_evaluation(model, data, tokenizer, slot_meta, 0, args.op_code,
False, True, True)
model_evaluation(model, data, tokenizer, slot_meta, 0, args.op_code,
True, False, False)
model_evaluation(model, data, tokenizer, slot_meta, 0, args.op_code,
True, True, False)
model_evaluation(model, data, tokenizer, slot_meta, 0, args.op_code,
True, False, True)
model_evaluation(model, data, tokenizer, slot_meta, 0, args.op_code,
True, True, True)
else:
model_evaluation(model, data, tokenizer, slot_meta, 0, args.op_code,
args.gt_op, args.gt_p_state, args.gt_gen)
def main(args):
ontology = json.load(open(os.path.join(args.data_root,
args.ontology_data)))
slot_meta, _ = make_slot_meta(ontology)
tokenizer = BertTokenizer.from_pretrained("dsksd/bert-ko-small-minimal")
data = prepare_dataset(os.path.join(args.data_root,
args.test_data), tokenizer, slot_meta,
args.n_history, args.max_seq_length, args.op_code)
model_config = BertConfig.from_json_file(args.bert_config_path)
model_config.dropout = 0.1
op2id = OP_SET[args.op_code]
model = SomDST(model_config, len(op2id), len(domain2id), op2id['update'])
ckpt = torch.load(args.model_ckpt_path, map_location='cpu')
model.load_state_dict(ckpt)
model.eval()
model.to(device)
if args.eval_all:
model_evaluation(model, data, tokenizer, slot_meta, 0, args.op_code,
False, False, False)
model_evaluation(model, data, tokenizer, slot_meta, 0, args.op_code,
False, False, True)
model_evaluation(model, data, tokenizer, slot_meta, 0, args.op_code,
False, True, False)
model_evaluation(model, data, tokenizer, slot_meta, 0, args.op_code,
False, True, True)
model_evaluation(model, data, tokenizer, slot_meta, 0, args.op_code,
True, False, False)
model_evaluation(model, data, tokenizer, slot_meta, 0, args.op_code,
True, True, False)
model_evaluation(model, data, tokenizer, slot_meta, 0, args.op_code,
True, False, True)
model_evaluation(model, data, tokenizer, slot_meta, 0, args.op_code,
True, True, True)
else:
model_evaluation(model, data, tokenizer, slot_meta, 0, args.op_code,
args.gt_op, args.gt_p_state, args.gt_gen)
def main(args):
assert args.use_one_optim is True
if args.use_cls_only:
args.no_dial = True
print("### use_cls_only: {:}".format(args.use_cls_only))
print("### no_dial: {:}".format(args.no_dial))
if args.recover_e > 0:
raise NotImplementedError("This option is from my oldest code version. "
"I have not checked it for this code version.")
if not os.path.exists(args.save_dir):
os.mkdir(args.save_dir)
print("### mkdir {:}".format(args.save_dir))
def worker_init_fn(worker_id):
np.random.seed(args.random_seed + worker_id)
n_gpu = 0
if torch.cuda.is_available() and (not args.use_cpu):
n_gpu = torch.cuda.device_count()
device = torch.device('cuda')
print("### Device: {:}".format(device))
else:
print("### Use CPU (Debugging)")
device = torch.device("cpu")
if args.random_seed < 0:
print("### Pick a random seed")
args.random_seed = random.sample(list(range(0, 100000)), 1)[0]
print("### Random Seed: {:}".format(args.random_seed))
np.random.seed(args.random_seed)
random.seed(args.random_seed)
rng = random.Random(args.random_seed)
torch.manual_seed(args.random_seed)
if n_gpu > 0:
if args.random_seed >= 0:
torch.cuda.manual_seed(args.random_seed)
torch.cuda.manual_seed_all(args.random_seed)
torch.backends.cudnn.benchmark = False
torch.backends.cudnn.deterministic = True
if not os.path.exists(args.save_dir):
os.mkdir(args.save_dir)
ontology = json.load(open(args.ontology_data))
slot_meta, ontology = make_slot_meta(ontology)
op2id = OP_SET[args.op_code]
print(op2id)
tokenizer = BertTokenizer(args.vocab_path, do_lower_case=True)
train_path = os.path.join(args.data_root, "train.pt")
dev_path = os.path.join(args.data_root, "dev.pt")
test_path = os.path.join(args.data_root, "test.pt")
if not os.path.exists(test_path):
test_data_raw = prepare_dataset(data_path=args.test_data_path,
tokenizer=tokenizer,
slot_meta=slot_meta,
n_history=args.n_history,
max_seq_length=args.max_seq_length,
op_code=args.op_code)
torch.save(test_data_raw, test_path)
else:
test_data_raw = torch.load(test_path)
print("# test examples %d" % len(test_data_raw))
if not os.path.exists(train_path):
train_data_raw = prepare_dataset(data_path=args.train_data_path,
tokenizer=tokenizer,
slot_meta=slot_meta,
n_history=args.n_history,
max_seq_length=args.max_seq_length,
op_code=args.op_code)
torch.save(train_data_raw, train_path)
else:
train_data_raw = torch.load(train_path)
train_data = MultiWozDataset(train_data_raw,
tokenizer,
slot_meta,
args.max_seq_length,
rng,
ontology,
args.word_dropout,
args.shuffle_state,
args.shuffle_p, pad_id=tokenizer.convert_tokens_to_ids(['[PAD]'])[0],
slot_id=tokenizer.convert_tokens_to_ids(['[SLOT]'])[0],
decoder_teacher_forcing=args.decoder_teacher_forcing,
use_full_slot=args.use_full_slot,
use_dt_only=args.use_dt_only, no_dial=args.no_dial,
use_cls_only=args.use_cls_only)
print("# train examples %d" % len(train_data_raw))
if not os.path.exists(dev_path):
dev_data_raw = prepare_dataset(data_path=args.dev_data_path,
tokenizer=tokenizer,
slot_meta=slot_meta,
n_history=args.n_history,
max_seq_length=args.max_seq_length,
op_code=args.op_code)
torch.save(dev_data_raw, dev_path)
else:
dev_data_raw = torch.load(dev_path)
print("# dev examples %d" % len(dev_data_raw))
model_config = BertConfig.from_json_file(args.bert_config_path)
model_config.dropout = args.dropout
model_config.attention_probs_dropout_prob = args.attention_probs_dropout_prob
model_config.hidden_dropout_prob = args.hidden_dropout_prob
type_vocab_size = 4
dec_config = args
model = TransformerDST(model_config, dec_config, len(op2id), len(domain2id),
op2id['update'],
tokenizer.convert_tokens_to_ids(['[MASK]'])[0],
tokenizer.convert_tokens_to_ids(['[SEP]'])[0],
tokenizer.convert_tokens_to_ids(['[PAD]'])[0],
tokenizer.convert_tokens_to_ids(['-'])[0],
type_vocab_size, args.exclude_domain)
if not os.path.exists(args.bert_ckpt_path):
args.bert_ckpt_path = download_ckpt(args.bert_ckpt_path, args.bert_config_path, 'assets')
state_dict = torch.load(args.bert_ckpt_path, map_location='cpu')
_k = 'embeddings.token_type_embeddings.weight'
print("config.type_vocab_size != state_dict[bert.embeddings.token_type_embeddings.weight] ({0} != {1})".format(
type_vocab_size, state_dict[_k].shape[0]))
state_dict[_k].resize_(
type_vocab_size, state_dict[_k].shape[1])
state_dict[_k].data[2, :].copy_(state_dict[_k].data[0, :])
state_dict[_k].data[3, :].copy_(state_dict[_k].data[0, :])
model.bert.load_state_dict(state_dict)
print("\n### Done Load BERT")
sys.stdout.flush()
# re-initialize added special tokens ([SLOT], [NULL], [EOS])
model.bert.embeddings.word_embeddings.weight.data[1].normal_(mean=0.0, std=0.02)
model.bert.embeddings.word_embeddings.weight.data[2].normal_(mean=0.0, std=0.02)
model.bert.embeddings.word_embeddings.weight.data[3].normal_(mean=0.0, std=0.02)
# re-initialize seg-2, seg-3
model.bert.embeddings.token_type_embeddings.weight.data[2].normal_(mean=0.0, std=0.02)
model.bert.embeddings.token_type_embeddings.weight.data[3].normal_(mean=0.0, std=0.02)
model.to(device)
num_train_steps = int(len(train_data_raw) / args.batch_size * args.n_epochs)
if args.use_one_optim:
print("### Use One Optim")
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}
]
optimizer = AdamW(optimizer_grouped_parameters, lr=args.enc_lr)
scheduler = WarmupLinearSchedule(optimizer, int(num_train_steps * args.enc_warmup),
t_total=num_train_steps)
else:
no_decay = ['bias', 'LayerNorm.bias', 'LayerNorm.weight']
enc_param_optimizer = list(model.bert.named_parameters()) # TODO: For BERT only
print('### Optim BERT: {:}'.format(len(enc_param_optimizer)))
enc_optimizer_grouped_parameters = [
{'params': [p for n, p in enc_param_optimizer if not any(nd in n for nd in no_decay)], 'weight_decay': 0.01},
{'params': [p for n, p in enc_param_optimizer if any(nd in n for nd in no_decay)], 'weight_decay': 0.0}
]
enc_optimizer = AdamW(enc_optimizer_grouped_parameters, lr=args.enc_lr)
enc_scheduler = WarmupLinearSchedule(enc_optimizer, int(num_train_steps * args.enc_warmup),
t_total=num_train_steps)
dec_param_optimizer = list(model.named_parameters()) # TODO: For other parameters
print('### Optim All: {:}'.format(len(dec_param_optimizer)))
dec_param_optimizer = [p for (n, p) in dec_param_optimizer if 'bert' not in n]
print('### Optim OTH: {:}'.format(len(dec_param_optimizer)))
dec_optimizer = AdamW(dec_param_optimizer, lr=args.dec_lr)
dec_scheduler = WarmupLinearSchedule(dec_optimizer, int(num_train_steps * args.dec_warmup),
t_total=num_train_steps)
if args.recover_e > 0:
model_recover, enc_recover, dec_recover = load(args, str(args.recover_e))
print("### Recover Model E{:}".format(args.recover_e))
sys.stdout.flush()
model.load_state_dict(model_recover)
print("### Recover Optim E{:}".format(args.recover_e))
sys.stdout.flush()
enc_optimizer.load_state_dict(enc_recover)
dec_optimizer.load_state_dict(dec_optimizer)
if n_gpu > 1:
model = torch.nn.DataParallel(model)
train_sampler = RandomSampler(train_data)
train_dataloader = DataLoader(train_data,
sampler=train_sampler,
batch_size=args.batch_size,
collate_fn=train_data.collate_fn,
num_workers=args.num_workers,
worker_init_fn=worker_init_fn)
loss_fnc = nn.CrossEntropyLoss()
best_score = {'epoch': 0, 'joint_acc': 0, 'op_acc': 0, 'final_slot_f1': 0}
start_time = time.time()
for epoch in range(args.n_epochs):
batch_loss = []
model.train()
for step, batch in enumerate(train_dataloader):
batch = [b.to(device) if (not isinstance(b, int)) and (not isinstance(b, dict) and (not isinstance(b, list)) and (not isinstance(b, np.ndarray))) else b for b in batch]
input_ids_p, segment_ids_p, input_mask_p, \
state_position_ids, op_ids, domain_ids, input_ids_g, segment_ids_g, position_ids_g, input_mask_g, \
masked_pos, masked_weights, lm_label_ids, id_n_map, gen_max_len, n_total_pred = batch
domain_scores, state_scores, loss_g = model(input_ids_p, segment_ids_p, input_mask_p, state_position_ids,
input_ids_g, segment_ids_g, position_ids_g, input_mask_g,
masked_pos, masked_weights, lm_label_ids, id_n_map, gen_max_len, only_pred_op=args.only_pred_op, n_gpu=n_gpu)
if n_total_pred > 0:
loss_g = loss_g.sum() / n_total_pred
else:
loss_g = 0
loss_s = loss_fnc(state_scores.view(-1, len(op2id)), op_ids.view(-1))
if args.only_pred_op:
loss = loss_s
else:
loss = loss_s + loss_g
if args.exclude_domain is not True:
loss_d = loss_fnc(domain_scores.view(-1, len(domain2id)), domain_ids.view(-1))
loss = loss + loss_d
batch_loss.append(loss.item())
loss.backward()
if args.use_one_optim:
optimizer.step()
scheduler.step()
else:
enc_optimizer.step()
enc_scheduler.step()
dec_optimizer.step()
dec_scheduler.step()
model.zero_grad()
if step % 100 == 0:
try:
loss_g = loss_g.item()
except AttributeError:
loss_g = loss_g
if args.exclude_domain is not True:
print("time %.1f min, [%d/%d] [%d/%d] mean_loss : %.3f, state_loss : %.3f, gen_loss : %.3f, dom_loss : %.3f" \
% ((time.time()-start_time)/60, epoch+1, args.n_epochs, step,
len(train_dataloader), np.mean(batch_loss),
loss_s.item(), loss_g, loss_d.item()))
else:
print("time %.1f min, [%d/%d] [%d/%d] mean_loss : %.3f, state_loss : %.3f, gen_loss : %.3f" \
% ((time.time()-start_time)/60, epoch+1, args.n_epochs, step,
len(train_dataloader), np.mean(batch_loss),
loss_s.item(), loss_g))
sys.stdout.flush()
batch_loss = []
if args.use_one_optim:
save(args, epoch + 1, model, optimizer)
else:
save(args, epoch + 1, model, enc_optimizer, dec_optimizer)
if ((epoch+1) % args.eval_epoch == 0) and (epoch+1 >= 8):
eval_res = model_evaluation(model, dev_data_raw, tokenizer, slot_meta, epoch+1, args.op_code,
use_full_slot=args.use_full_slot, use_dt_only=args.use_dt_only, no_dial=args.no_dial, use_cls_only=args.use_cls_only, n_gpu=n_gpu)
print("### Epoch {:} Score : ".format(epoch+1), eval_res)
if eval_res['joint_acc'] > best_score['joint_acc']:
best_score = eval_res
print("### Best Joint Acc: {:} ###".format(best_score['joint_acc']))
print('\n')
if epoch+1 >= 8: # To speed up
eval_res_test = model_evaluation(model, test_data_raw, tokenizer, slot_meta, epoch + 1, args.op_code,
use_full_slot=args.use_full_slot, use_dt_only=args.use_dt_only, no_dial=args.no_dial, use_cls_only=args.use_cls_only, n_gpu=n_gpu)
print("### Epoch {:} Test Score : ".format(epoch + 1), eval_res_test)
def main(args):
def worker_init_fn(worker_id):
np.random.seed(args.random_seed + worker_id)
n_gpu = 0
if torch.cuda.is_available():
n_gpu = torch.cuda.device_count()
np.random.seed(args.random_seed)
random.seed(args.random_seed)
rng = random.Random(args.random_seed)
torch.manual_seed(args.random_seed)
if n_gpu > 0:
torch.cuda.manual_seed(args.random_seed)
torch.cuda.manual_seed_all(args.random_seed)
torch.backends.cudnn.benchmark = False
torch.backends.cudnn.deterministic = True
if not os.path.exists(args.save_dir):
os.mkdir(args.save_dir)
ontology = json.load(open(args.ontology_data))
slot_meta, ontology = make_slot_meta(ontology)
op2id = OP_SET[args.op_code]
print(op2id)
tokenizer = BertTokenizer(args.vocab_path, do_lower_case=True)
train_data_raw = prepare_dataset(data_path=args.train_data_path,
tokenizer=tokenizer,
slot_meta=slot_meta,
n_history=args.n_history,
max_seq_length=args.max_seq_length,
op_code=args.op_code)
train_data = MultiWozDataset(train_data_raw,
tokenizer,
slot_meta,
args.max_seq_length,
rng,
ontology,
args.word_dropout,
args.shuffle_state,
args.shuffle_p)
print("# train examples %d" % len(train_data_raw))
dev_data_raw = prepare_dataset(data_path=args.dev_data_path,
tokenizer=tokenizer,
slot_meta=slot_meta,
n_history=args.n_history,
max_seq_length=args.max_seq_length,
op_code=args.op_code)
print("# dev examples %d" % len(dev_data_raw))
test_data_raw = prepare_dataset(data_path=args.test_data_path,
tokenizer=tokenizer,
slot_meta=slot_meta,
n_history=args.n_history,
max_seq_length=args.max_seq_length,
op_code=args.op_code)
print("# test examples %d" % len(test_data_raw))
model_config = BertConfig.from_json_file(args.bert_config_path)
model_config.dropout = args.dropout
model_config.attention_probs_dropout_prob = args.attention_probs_dropout_prob
model_config.hidden_dropout_prob = args.hidden_dropout_prob
model = SomDST(model_config, len(op2id), len(domain2id), op2id['update'], args.exclude_domain)
if not os.path.exists(args.bert_ckpt_path):
args.bert_ckpt_path = download_ckpt(args.bert_ckpt_path, args.bert_config_path, 'assets')
ckpt = torch.load(args.bert_ckpt_path, map_location='cpu')
model.encoder.bert.load_state_dict(ckpt)
# re-initialize added special tokens ([SLOT], [NULL], [EOS])
model.encoder.bert.embeddings.word_embeddings.weight.data[1].normal_(mean=0.0, std=0.02)
model.encoder.bert.embeddings.word_embeddings.weight.data[2].normal_(mean=0.0, std=0.02)
model.encoder.bert.embeddings.word_embeddings.weight.data[3].normal_(mean=0.0, std=0.02)
model.to(device)
num_train_steps = int(len(train_data_raw) / args.batch_size * args.n_epochs)
no_decay = ['bias', 'LayerNorm.bias', 'LayerNorm.weight']
enc_param_optimizer = list(model.encoder.named_parameters())
enc_optimizer_grouped_parameters = [
{'params': [p for n, p in enc_param_optimizer if not any(nd in n for nd in no_decay)], 'weight_decay': 0.01},
{'params': [p for n, p in enc_param_optimizer if any(nd in n for nd in no_decay)], 'weight_decay': 0.0}
]
enc_optimizer = AdamW(enc_optimizer_grouped_parameters, lr=args.enc_lr)
enc_scheduler = WarmupLinearSchedule(enc_optimizer, int(num_train_steps * args.enc_warmup),
t_total=num_train_steps)
dec_param_optimizer = list(model.decoder.parameters())
dec_optimizer = AdamW(dec_param_optimizer, lr=args.dec_lr)
dec_scheduler = WarmupLinearSchedule(dec_optimizer, int(num_train_steps * args.dec_warmup),
t_total=num_train_steps)
if n_gpu > 1:
model = torch.nn.DataParallel(model)
train_sampler = RandomSampler(train_data)
train_dataloader = DataLoader(train_data,
sampler=train_sampler,
batch_size=args.batch_size,
collate_fn=train_data.collate_fn,
num_workers=args.num_workers,
worker_init_fn=worker_init_fn)
loss_fnc = nn.CrossEntropyLoss()
best_score = {'epoch': 0, 'joint_acc': 0, 'op_acc': 0, 'final_slot_f1': 0}
for epoch in range(args.n_epochs):
batch_loss = []
model.train()
for step, batch in enumerate(train_dataloader):
batch = [b.to(device) if not isinstance(b, int) else b for b in batch]
input_ids, input_mask, segment_ids, state_position_ids, op_ids,\
domain_ids, gen_ids, max_value, max_update = batch
if rng.random() < args.decoder_teacher_forcing: # teacher forcing
teacher = gen_ids
else:
teacher = None
domain_scores, state_scores, gen_scores = model(input_ids=input_ids,
token_type_ids=segment_ids,
state_positions=state_position_ids,
attention_mask=input_mask,
max_value=max_value,
op_ids=op_ids,
max_update=max_update,
teacher=teacher)
loss_s = loss_fnc(state_scores.view(-1, len(op2id)), op_ids.view(-1))
loss_g = masked_cross_entropy_for_value(gen_scores.contiguous(),
gen_ids.contiguous(),
tokenizer.vocab['[PAD]'])
loss = loss_s + loss_g
if args.exclude_domain is not True:
loss_d = loss_fnc(domain_scores.view(-1, len(domain2id)), domain_ids.view(-1))
loss = loss + loss_d
batch_loss.append(loss.item())
loss.backward()
enc_optimizer.step()
enc_scheduler.step()
dec_optimizer.step()
dec_scheduler.step()
model.zero_grad()
if step % 100 == 0:
if args.exclude_domain is not True:
print("[%d/%d] [%d/%d] mean_loss : %.3f, state_loss : %.3f, gen_loss : %.3f, dom_loss : %.3f" \
% (epoch+1, args.n_epochs, step,
len(train_dataloader), np.mean(batch_loss),
loss_s.item(), loss_g.item(), loss_d.item()))
else:
print("[%d/%d] [%d/%d] mean_loss : %.3f, state_loss : %.3f, gen_loss : %.3f" \
% (epoch+1, args.n_epochs, step,
len(train_dataloader), np.mean(batch_loss),
loss_s.item(), loss_g.item()))
batch_loss = []
if (epoch+1) % args.eval_epoch == 0:
eval_res = model_evaluation(model, dev_data_raw, tokenizer, slot_meta, epoch+1, args.op_code)
if eval_res['joint_acc'] > best_score['joint_acc']:
best_score = eval_res
model_to_save = model.module if hasattr(model, 'module') else model
save_path = os.path.join(args.save_dir, 'model_best.bin')
torch.save(model_to_save.state_dict(), save_path)
print("Best Score : ", best_score)
print("\n")
print("Test using best model...")
best_epoch = best_score['epoch']
ckpt_path = os.path.join(args.save_dir, 'model_best.bin')
model = SomDST(model_config, len(op2id), len(domain2id), op2id['update'], args.exclude_domain)
ckpt = torch.load(ckpt_path, map_location='cpu')
model.load_state_dict(ckpt)
model.to(device)
model_evaluation(model, test_data_raw, tokenizer, slot_meta, best_epoch, args.op_code,
is_gt_op=False, is_gt_p_state=False, is_gt_gen=False)
model_evaluation(model, test_data_raw, tokenizer, slot_meta, best_epoch, args.op_code,
is_gt_op=False, is_gt_p_state=False, is_gt_gen=True)
model_evaluation(model, test_data_raw, tokenizer, slot_meta, best_epoch, args.op_code,
is_gt_op=False, is_gt_p_state=True, is_gt_gen=False)
model_evaluation(model, test_data_raw, tokenizer, slot_meta, best_epoch, args.op_code,
is_gt_op=False, is_gt_p_state=True, is_gt_gen=True)
model_evaluation(model, test_data_raw, tokenizer, slot_meta, best_epoch, args.op_code,
is_gt_op=True, is_gt_p_state=False, is_gt_gen=False)
model_evaluation(model, test_data_raw, tokenizer, slot_meta, best_epoch, args.op_code,
is_gt_op=True, is_gt_p_state=True, is_gt_gen=False)
model_evaluation(model, test_data_raw, tokenizer, slot_meta, best_epoch, args.op_code,
is_gt_op=True, is_gt_p_state=False, is_gt_gen=True)
model_evaluation(model, test_data_raw, tokenizer, slot_meta, best_epoch, args.op_code,
is_gt_op=True, is_gt_p_state=True, is_gt_gen=True)
def main(args):
assert args.use_one_optim is True
if args.recover_e > 0:
raise NotImplementedError("This option is from my oldest code version. "
"I have not checked it for this code version.")
if not os.path.exists(args.save_dir):
os.mkdir(args.save_dir)
print("### mkdir {:}".format(args.save_dir))
def worker_init_fn(worker_id):
np.random.seed(args.random_seed + worker_id)
n_gpu = 0
if torch.cuda.is_available() and (not args.use_cpu):
n_gpu = torch.cuda.device_count()
device = torch.device('cuda')
print("### Device: {:}".format(device))
else:
print("### Use CPU (Debugging)")
device = torch.device("cpu")
if args.random_seed < 0:
print("### Pick a random seed")
args.random_seed = random.sample(list(range(1, 100000)), 1)[0]
print("### Random Seed: {:}".format(args.random_seed))
np.random.seed(args.random_seed)
random.seed(args.random_seed)
rng = random.Random(args.random_seed)
torch.manual_seed(args.random_seed)
if n_gpu > 0:
if args.random_seed >= 0:
torch.cuda.manual_seed(args.random_seed)
torch.cuda.manual_seed_all(args.random_seed)
torch.backends.cudnn.benchmark = False
torch.backends.cudnn.deterministic = True
if not os.path.exists(args.save_dir):
os.mkdir(args.save_dir)
ontology = json.load(open(args.ontology_data))
slot_meta, ontology = make_slot_meta(ontology)
op2id = OP_SET[args.op_code]
print(op2id)
tokenizer = BertTokenizer(args.vocab_path, do_lower_case=True)
train_path = os.path.join(args.data_root, "train.pt")
train_data_raw = torch.load(train_path)[:5000]
print("# train examples %d" % len(train_data_raw))
test_path = os.path.join(args.data_root, "test.pt")
test_data_raw = torch.load(test_path)
print("# test examples %d" % len(test_data_raw))
model_config = BertConfig.from_json_file(args.bert_config_path)
model_config.dropout = args.dropout
model_config.attention_probs_dropout_prob = args.attention_probs_dropout_prob
model_config.hidden_dropout_prob = args.hidden_dropout_prob
type_vocab_size = 4
dec_config = args
model = TransformerDST(model_config, dec_config, len(op2id), len(domain2id),
op2id['update'],
tokenizer.convert_tokens_to_ids(['[MASK]'])[0],
tokenizer.convert_tokens_to_ids(['[SEP]'])[0],
tokenizer.convert_tokens_to_ids(['[PAD]'])[0],
tokenizer.convert_tokens_to_ids(['-'])[0],
type_vocab_size, args.exclude_domain)
test_epochs = [int(e) for e in args.load_epoch.strip().lower().split('-')]
for best_epoch in test_epochs:
print("### Epoch {:}...".format(best_epoch))
sys.stdout.flush()
ckpt_path = os.path.join(args.save_dir, 'model.e{:}.bin'.format(best_epoch))
ckpt = torch.load(ckpt_path, map_location='cpu')
model.load_state_dict(ckpt)
model.to(device)
# eval_res = model_evaluation(model, train_data_raw, tokenizer, slot_meta, best_epoch, args.op_code,
# use_full_slot=args.use_full_slot, use_dt_only=args.use_dt_only, no_dial=args.no_dial, n_gpu=n_gpu,
# is_gt_op=False, is_gt_p_state=False, is_gt_gen=False)
#
# print("### Epoch {:} Train Score : ".format(best_epoch), eval_res)
# print('\n'*2)
# sys.stdout.flush()
eval_res = model_evaluation(model, test_data_raw, tokenizer, slot_meta, best_epoch, args.op_code,
use_full_slot=args.use_full_slot, use_dt_only=args.use_dt_only, no_dial=args.no_dial, n_gpu=n_gpu,
is_gt_op=False, is_gt_p_state=False, is_gt_gen=False)
print("### Epoch {:} Test Score : ".format(best_epoch), eval_res)
print('\n'*2)
sys.stdout.flush()
def main(args):
def worker_init_fn(worker_id):
np.random.seed(args.random_seed + worker_id)
n_gpu = 0
if torch.cuda.is_available():
n_gpu = torch.cuda.device_count()
np.random.seed(args.random_seed)
random.seed(args.random_seed)
rng = random.Random(args.random_seed)
torch.manual_seed(args.random_seed)
if n_gpu > 0:
torch.cuda.manual_seed(args.random_seed)
torch.cuda.manual_seed_all(args.random_seed)
torch.backends.cudnn.benchmark = False
torch.backends.cudnn.deterministic = True
if not os.path.exists(args.out_dir):
os.mkdir(args.out_dir)
ontology = json.load(open(args.ontology_data))
slot_meta, ontology = make_slot_meta(ontology)
op2id = OP_SET[args.op_code]
tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')
if os.path.exists(args.train_data_path + ".pk"):
train_data_raw = load_data(args.train_data_path + ".pk")
else:
train_data_raw = prepare_dataset(data_path=args.train_data_path,
tokenizer=tokenizer,
slot_meta=slot_meta,
n_history=args.n_history,
max_seq_length=args.max_seq_length,
op_code=args.op_code)
print("# train examples %d" % len(train_data_raw))
# maxlen = 0
# for i in range(len(train_data_raw)):
# maxlen = max(maxlen, len(train_data_raw[i].turn_utter.split()))
# print(maxlen)
if os.path.exists(args.dev_data_path + ".pk"):
dev_data_raw = load_data(args.dev_data_path + ".pk")
else:
dev_data_raw = prepare_dataset(data_path=args.dev_data_path,
tokenizer=tokenizer,
slot_meta=slot_meta,
n_history=args.n_history,
max_seq_length=args.max_seq_length,
op_code=args.op_code)
print("# dev examples %d" % len(dev_data_raw))
if os.path.exists(args.test_data_path + ".pk"):
test_data_raw = load_data(args.test_data_path + ".pk")
else:
test_data_raw = prepare_dataset(data_path=args.test_data_path,
tokenizer=tokenizer,
slot_meta=slot_meta,
n_history=args.n_history,
max_seq_length=args.max_seq_length,
op_code=args.op_code)
print("# test examples %d" % len(test_data_raw))
dst = DST(args, ontology, slot_meta)
best_score = {'epoch': float("-inf"), 'joint_acc_score': float("-inf"), 'op_acc': float("-inf"),
'final_slot_f1': float("-inf")}
dst.model.train_model(train_data_raw, test_data_raw, ontology, slot_meta, args.out_dir, args.filename, show_running_loss=True)
def main(args):
if not os.path.exists(args.save_dir):
os.mkdir(args.save_dir)
print("### mkdir {:}".format(args.save_dir))
def worker_init_fn(worker_id):
np.random.seed(args.random_seed + worker_id)
n_gpu = 0
if torch.cuda.is_available() and args.use_cpu:
n_gpu = torch.cuda.device_count()
device = torch.device('cuda')
print("### Device: {:}".format(device))
else:
print("### Use CPU (Debugging)")
device = torch.device("cpu")
if args.random_seed < 0:
print("### Pick a random seed")
args.random_seed = random.sample(list(range(1, 100000)), 1)[0]
print("### Random Seed: {:}".format(args.random_seed))
np.random.seed(args.random_seed)
random.seed(args.random_seed)
rng = random.Random(args.random_seed)
torch.manual_seed(args.random_seed)
if n_gpu > 0:
if args.random_seed >= 0:
torch.cuda.manual_seed(args.random_seed)
torch.cuda.manual_seed_all(args.random_seed)
torch.backends.cudnn.benchmark = False
torch.backends.cudnn.deterministic = True
if not os.path.exists(args.save_dir):
os.mkdir(args.save_dir)
ontology = json.load(open(args.ontology_data))
slot_meta, ontology = make_slot_meta(ontology)
op2id = OP_SET[args.op_code]
print(op2id)
tokenizer = BertTokenizer.from_pretrained(args.bert_config)
special_tokens = ['[SLOT]', '[NULL]']
special_tokens_dict = {'additional_special_tokens': special_tokens}
tokenizer.add_special_tokens(special_tokens_dict)
test_path = os.path.join(args.data_root_test, "test.pt")
if not os.path.exists(test_path):
test_data_raw = prepare_dataset_for_inference(data_path=args.test_data_path,
data_list=None,
tokenizer=tokenizer,
slot_meta=slot_meta,
n_history=args.n_history,
max_seq_length=args.max_seq_length,
op_code=args.op_code)
torch.save(test_data_raw, test_path)
else:
test_data_raw = torch.load(test_path)
print("# test examples %d" % len(test_data_raw))
model_config = BertConfig.from_json_file(args.bert_config_path)
model_config.dropout = 0.
model_config.attention_probs_dropout_prob = 0.
model_config.hidden_dropout_prob = 0.
type_vocab_size = 4
dec_config = args
model = TransformerDST(model_config, dec_config, len(op2id), len(domain2id),
op2id['update'],
tokenizer.convert_tokens_to_ids(['[MASK]'])[0],
tokenizer.convert_tokens_to_ids(['[SEP]'])[0],
tokenizer.convert_tokens_to_ids(['[PAD]'])[0],
tokenizer.convert_tokens_to_ids(['-'])[0],
type_vocab_size, args.exclude_domain)
state_dict = torch.load(args.bert_ckpt_path, map_location='cpu')
_k = 'bert.embeddings.token_type_embeddings.weight'
print("config.type_vocab_size != state_dict[bert.embeddings.token_type_embeddings.weight] ({0} != {1})".format(
type_vocab_size, state_dict[_k].shape[0]))
state_dict[_k] = state_dict[_k].repeat(int(type_vocab_size / state_dict[_k].shape[0]), 1)
state_dict[_k].data[2, :].copy_(state_dict[_k].data[0, :])
state_dict[_k].data[3, :].copy_(state_dict[_k].data[0, :])
model.bert.load_state_dict(state_dict, strict=False)
# re-initialize added special tokens ([SLOT], [NULL], [EOS])
model.bert.embeddings.word_embeddings.weight.data[1].normal_(mean=0.0, std=0.02)
model.bert.embeddings.word_embeddings.weight.data[2].normal_(mean=0.0, std=0.02)
model.bert.embeddings.word_embeddings.weight.data[3].normal_(mean=0.0, std=0.02)
# re-initialize seg-2, seg-3
model.bert.embeddings.token_type_embeddings.weight.data[2].normal_(mean=0.0, std=0.02)
model.bert.embeddings.token_type_embeddings.weight.data[3].normal_(mean=0.0, std=0.02)
model.bert.resize_token_embeddings(len(tokenizer))
test_epochs = [int(e) for e in args.load_epoch.strip().lower().split('-')]
for best_epoch in test_epochs:
print("### Epoch {:}...".format(best_epoch))
sys.stdout.flush()
ckpt_path = os.path.join('/opt/ml/code/transformer_dst', args.save_dir, 'model.e{:}.bin'.format(best_epoch))
ckpt = torch.load(ckpt_path, map_location='cpu')
model.load_state_dict(ckpt)
model.to(device)
eval_res = model_evaluation(model, test_data_raw, tokenizer, slot_meta, best_epoch, args.op_code,
use_full_slot=args.use_full_slot, use_dt_only=args.use_dt_only,
no_dial=args.no_dial, n_gpu=n_gpu,
is_gt_op=False, is_gt_p_state=False, is_gt_gen=False, submission=True)
print("### Epoch {:} Test Score : ".format(best_epoch), eval_res)
print('\n' * 2)
sys.stdout.flush()
def main(args):
def worker_init_fn(worker_id):
np.random.seed(args.random_seed + worker_id)
n_gpu = 0
if torch.cuda.is_available():
n_gpu = torch.cuda.device_count()
np.random.seed(args.random_seed)
random.seed(args.random_seed)
rng = random.Random(args.random_seed)
torch.manual_seed(args.random_seed)
if n_gpu > 0:
torch.cuda.manual_seed(args.random_seed)
torch.cuda.manual_seed_all(args.random_seed)
torch.backends.cudnn.benchmark = False
torch.backends.cudnn.deterministic = True
if not os.path.exists(args.out_dir):
os.mkdir(args.out_dir)
ontology = json.load(open(args.ontology_data))
slot_meta, ontology = make_slot_meta(ontology)
op2id = OP_SET[args.op_code]
tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')
if os.path.exists(args.train_data_path + ".pk"):
train_data_raw = load_data(args.train_data_path + ".pk")
else:
train_data_raw = prepare_dataset(data_path=args.train_data_path,
tokenizer=tokenizer,
slot_meta=slot_meta,
n_history=args.n_history,
max_seq_length=args.max_seq_length,
op_code=args.op_code)
print("# train examples %d" % len(train_data_raw))
if os.path.exists(args.dev_data_path + ".pk"):
dev_data_raw = load_data(args.dev_data_path + ".pk")
else:
dev_data_raw = prepare_dataset(data_path=args.dev_data_path,
tokenizer=tokenizer,
slot_meta=slot_meta,
n_history=args.n_history,
max_seq_length=args.max_seq_length,
op_code=args.op_code)
print("# dev examples %d" % len(dev_data_raw))
if os.path.exists(args.test_data_path + ".pk"):
test_data_raw = load_data(args.test_data_path + ".pk")
else:
test_data_raw = prepare_dataset(data_path=args.test_data_path,
tokenizer=tokenizer,
slot_meta=slot_meta,
n_history=args.n_history,
max_seq_length=args.max_seq_length,
op_code=args.op_code)
print("# test examples %d" % len(test_data_raw))
model = DST_PICK.from_pretrained('bert-base-uncased')
if not os.path.exists(args.bert_ckpt_path):
args.bert_ckpt_path = download_ckpt(args.bert_ckpt_path,
args.bert_config_path, 'assets')
ckpt = torch.load(args.bert_ckpt_path, map_location='cpu')
model.bert.load_state_dict(ckpt)
no_decay = ["bias", "LayerNorm.weight", "LayerNorm.bias"]
optimizer_grouped_parameters = [
{
"params": [
p for n, p in model.named_parameters()
if not any(nd in n for nd in no_decay)
],
"weight_decay":
0.0,
},
{
"params": [
p for n, p in model.named_parameters()
if any(nd in n for nd in no_decay)
],
"weight_decay":
0.0,
},
]
optimizer = AdamW(optimizer_grouped_parameters)
model.to(device)
# if n_gpu > 1:
# model = torch.nn.DataParallel(model)
best_score = {
'epoch': float("-inf"),
'joint_acc_score': float("-inf"),
'op_acc': float("-inf"),
'final_slot_f1': float("-inf")
}
best_epoch = 0
for epoch in range(args.n_epochs):
batch_loss = []
model.train()
for step in tqdm(range(len(train_data_raw)), desc="training"):
inp = model.generate_train_instances(train_data_raw[step],
ontology, tokenizer, device)
# ignore dialog with no training slots
if not inp:
continue
loss = model(**inp)
batch_loss.append(loss.item())
loss.backward()
optimizer.step()
model.zero_grad()
if step % 100 == 0:
print("[%d/%d] [%d/%d] mean_loss : %.3f" %
(epoch + 1, args.n_epochs, step, len(train_data_raw),
np.mean(batch_loss)))
batch_loss = []
if (epoch + 1) % args.eval_epoch == 0:
eval_res, res_per_domain, pred = model.evaluate(
dev_data_raw, tokenizer, ontology, slot_meta, epoch + 1,
device)
#
if eval_res['joint_acc_score'] > best_score['joint_acc_score']:
best_score['joint_acc_score'] = eval_res['joint_acc_score']
model_to_save = model.module if hasattr(model,
'module') else model
save_path = os.path.join(args.out_dir, args.filename + '.bin')
torch.save(model_to_save.state_dict(), save_path)
best_epoch = epoch + 1
print("Best Score : ", best_score['joint_acc_score'])
print("\n")
print("Test using best model...")
ckpt_path = os.path.join(args.out_dir, args.filename + '.bin')
model = DST_PICK.from_pretrained('bert-base-uncased')
ckpt = torch.load(ckpt_path, map_location='cpu')
model.load_state_dict(ckpt)
model.to(device)
eval_res, res_per_domain, pred = model.evaluate(dev_data_raw, tokenizer,
ontology, slot_meta,
best_epoch, device)
# save to file
save_result_to_file(args.out_dir + "/" + args.filename + ".res", eval_res,
res_per_domain)
json.dump(pred, open('%s.pred' % (args.out_dir + "/" + args.filename),
'w'))
def main(args):
def worker_init_fn(worker_id):
np.random.seed(args.random_seed + worker_id)
n_gpu = 0
if torch.cuda.is_available():
n_gpu = torch.cuda.device_count()
np.random.seed(args.random_seed)
random.seed(args.random_seed)
rng = random.Random(args.random_seed)
torch.manual_seed(args.random_seed)
if n_gpu > 0:
torch.cuda.manual_seed(args.random_seed)
torch.cuda.manual_seed_all(args.random_seed)
torch.backends.cudnn.benchmark = False
torch.backends.cudnn.deterministic = True
if not os.path.exists(args.save_dir):
os.mkdir(args.save_dir)
ontology = json.load(open(args.ontology_data))
slot_meta, ontology = make_slot_meta(ontology)
op2id = OP_SET[args.op_code]
# print(op2id)
tokenizer = BertTokenizer.from_pretrained("dsksd/bert-ko-small-minimal")
out_path = '/opt/ml/code/new-som-dst/pickles'
if os.path.exists(out_path):
print("Pickles are exist!")
with open(out_path + '/train_data_raw.pkl', 'rb') as f:
train_data_raw = pickle.load(f)
with open(out_path + '/train_data.pkl', 'rb') as f:
train_data = pickle.load(f)
with open(out_path + '/dev_data_raw.pkl', 'rb') as f:
dev_data_raw = pickle.load(f)
print("Pickles brought!")
else:
print("Pickles are not exist!")
train_dials, dev_dials = load_dataset(args.train_data_path)
print(f"t_d_len : {len(train_dials)}, d_d_len : {len(dev_dials)}")
train_data_raw = prepare_dataset(dials=train_dials,
tokenizer=tokenizer,
slot_meta=slot_meta,
n_history=args.n_history,
max_seq_length=args.max_seq_length,
op_code=args.op_code)
# print("train_data_raw is ready")
train_data = WosDataset(train_data_raw, tokenizer, slot_meta,
args.max_seq_length, rng, ontology,
args.word_dropout, args.shuffle_state,
args.shuffle_p)
dev_data_raw = prepare_dataset(dials=dev_dials,
tokenizer=tokenizer,
slot_meta=slot_meta,
n_history=args.n_history,
max_seq_length=args.max_seq_length,
op_code=args.op_code)
# print(len(dev_data_raw))
os.makedirs(out_path, exist_ok=True)
with open(out_path + '/train_data_raw.pkl', 'wb') as f:
pickle.dump(train_data_raw, f)
with open(out_path + '/train_data.pkl', 'wb') as f:
pickle.dump(train_data, f)
with open(out_path + '/dev_data_raw.pkl', 'wb') as f:
pickle.dump(dev_data_raw, f)
print("Pickles saved!")
print("# train examples %d" % len(train_data_raw))
print("# dev examples %d" % len(dev_data_raw))
# test_data_raw = prepare_dataset(data_path=args.test_data_path,
# tokenizer=tokenizer,
# slot_meta=slot_meta,
# n_history=args.n_history,
# max_seq_length=args.max_seq_length,
# op_code=args.op_code)
# print("# test examples %d" % len(test_data_raw))
model_config = BertConfig.from_json_file(args.bert_config_path)
model_config.dropout = args.dropout
model_config.attention_probs_dropout_prob = args.attention_probs_dropout_prob
model_config.hidden_dropout_prob = args.hidden_dropout_prob
model = SomDST(model_config, len(op2id), len(domain2id), op2id['update'],
args.exclude_domain)
ckpt = torch.load('/opt/ml/outputs/model_20.bin', map_location='cpu')
model.load_state_dict(ckpt)
print(f"model is loaded!")
# if not os.path.exists(args.bert_ckpt_path):
# args.bert_ckpt_path = download_ckpt(args.bert_ckpt_path, args.bert_config_path, '/opt/ml/code/new-som-dst/assets')
# ckpt = torch.load(args.bert_ckpt_path, map_location='cpu')
# model.encoder.bert.load_state_dict(ckpt, strict=False)
# # re-initialize added special tokens ([SLOT], [NULL], [EOS])
# model.encoder.bert.embeddings.word_embeddings.weight.data[1].normal_(mean=0.0, std=0.02)
# model.encoder.bert.embeddings.word_embeddings.weight.data[2].normal_(mean=0.0, std=0.02)
# model.encoder.bert.embeddings.word_embeddings.weight.data[3].normal_(mean=0.0, std=0.02)
model.to(device)
print()
wandb.watch(model)
num_train_steps = int(
len(train_data_raw) / args.batch_size * args.n_epochs)
no_decay = ['bias', 'LayerNorm.bias', 'LayerNorm.weight']
enc_param_optimizer = list(model.encoder.named_parameters())
enc_optimizer_grouped_parameters = [{
'params': [
p for n, p in enc_param_optimizer
if not any(nd in n for nd in no_decay)
],
'weight_decay':
0.01
}, {
'params':
[p for n, p in enc_param_optimizer if any(nd in n for nd in no_decay)],
'weight_decay':
0.0
}]
enc_optimizer = AdamW(enc_optimizer_grouped_parameters, lr=args.enc_lr)
enc_scheduler = get_linear_schedule_with_warmup(
enc_optimizer,
num_warmup_steps=int(num_train_steps * args.enc_warmup),
num_training_steps=num_train_steps)
dec_param_optimizer = list(model.decoder.parameters())
dec_optimizer = AdamW(dec_param_optimizer, lr=args.dec_lr)
dec_scheduler = get_linear_schedule_with_warmup(
dec_optimizer,
num_warmup_steps=int(num_train_steps * args.dec_warmup),
num_training_steps=num_train_steps)
if n_gpu > 1:
model = torch.nn.DataParallel(model)
train_sampler = RandomSampler(train_data)
train_dataloader = DataLoader(train_data,
sampler=train_sampler,
batch_size=args.batch_size,
collate_fn=train_data.collate_fn,
num_workers=args.num_workers,
worker_init_fn=worker_init_fn)
loss_fnc = nn.CrossEntropyLoss()
best_score = {'epoch': 0, 'joint_acc': 0, 'op_acc': 0, 'final_slot_f1': 0}
for epoch in range(args.n_epochs):
batch_loss = []
model.train()
for step, batch in enumerate(train_dataloader):
batch = [
b.to(device) if not isinstance(b, int) else b for b in batch
]
input_ids, input_mask, segment_ids, state_position_ids, op_ids,\
domain_ids, gen_ids, max_value, max_update = batch
if rng.random() < args.decoder_teacher_forcing: # teacher forcing
teacher = gen_ids
else:
teacher = None
domain_scores, state_scores, gen_scores = model(
input_ids=input_ids,
token_type_ids=segment_ids,
state_positions=state_position_ids,
attention_mask=input_mask,
max_value=max_value,
op_ids=op_ids,
max_update=max_update,
teacher=teacher)
# print(f"input_id : {input_ids[0].shape} {input_ids[0]}")
# print(f"segment_id : {segment_ids[0].shape} {segment_ids[0]}")
# print(f"slot_position : {state_position_ids[0].shape} {state_position_ids[0]}")
# print(f"input_mask : {input_mask[0].shape} {input_mask[0]}")
# print(f"state_scores : {state_scores[0].shape} {state_scores[0]}")
# print(f"gen_scores : {gen_scores[0].shape} {gen_scores[0]}")
# print(f"op_ids : {op_ids.shape, op_ids}")
loss_s = loss_fnc(state_scores.view(-1, len(op2id)),
op_ids.view(-1))
# print("loss_s", loss_s.shape, loss_s)
loss_g = masked_cross_entropy_for_value(
gen_scores.contiguous(), # B, J', K, V
gen_ids.contiguous(), # B, J', K
tokenizer.vocab['[PAD]'])
# print("loss_g", loss_g)
# print(f"gen_scores : {gen_scores.shape, torch.argmax(gen_scores[0][0], -1)}")
# print(f"gen_ids : {gen_ids.shape, gen_ids[0][0], tokenizer.decode(gen_ids[0][0])}")
loss = loss_s + loss_g
if args.exclude_domain is not True:
loss_d = loss_fnc(domain_scores.view(-1, len(domain2id)),
domain_ids.view(-1))
loss = loss + loss_d
batch_loss.append(loss.item())
loss.backward()
enc_optimizer.step()
enc_scheduler.step()
dec_optimizer.step()
dec_scheduler.step()
model.zero_grad()
if (step + 1) % 100 == 0:
if args.exclude_domain is not True:
print("[%d/%d] [%d/%d] mean_loss : %.3f, state_loss : %.3f, gen_loss : %.3f, dom_loss : %.3f" \
% (epoch+1, args.n_epochs, step+1,
len(train_dataloader), np.mean(batch_loss),
loss_s.item(), loss_g.item(), loss_d.item()))
else:
print("[%d/%d] [%d/%d] mean_loss : %.3f, state_loss : %.3f, gen_loss : %.3f" \
% (epoch+1, args.n_epochs, step+1,
len(train_dataloader), np.mean(batch_loss),
loss_s.item(), loss_g.item()))
batch_loss = []
if (epoch + 1) % args.eval_epoch == 0:
eval_res = model_evaluation(model, dev_data_raw, tokenizer,
slot_meta, epoch + 1, args.op_code)
if eval_res['joint_acc'] > best_score['joint_acc']:
best_score = eval_res
model_to_save = model.module if hasattr(model,
'module') else model
save_path = os.path.join(args.save_dir, 'model_best.bin')
torch.save(model_to_save.state_dict(), save_path)
print("Best Score : ", best_score)
print("\n")
wandb.log({
'joint_acc': eval_res['joint_acc'],
'slot_acc': eval_res['slot_acc'],
'slot_f1': eval_res['slot_f1'],
'op_acc': eval_res['op_acc'],
'op_f1': eval_res['op_f1'],
'final_slot_f1': eval_res['final_slot_f1']
})
# save model at 10 epochs
if (epoch + 1) % 10 == 0:
model_to_save = model.module if hasattr(model, 'module') else model
save_path = os.path.join(args.save_dir, f'model_{epoch+1}.bin')
torch.save(model_to_save.state_dict(), save_path)
print(f"model_{epoch}.bin is saved!")