def get_data_loaders(self):
data_dir = self.args.data_dir
self.train_dataset = PickleDataset(
os.path.join(data_dir, f'{self.args.train_set}.pkl'),
os.path.join(data_dir, self.args.train_index_file),
segment_size=self.config.segment_size)
self.val_dataset = PickleDataset(
os.path.join(data_dir, f'{self.args.val_set}.pkl'),
os.path.join(data_dir, self.args.val_index_file),
segment_size=self.config.segment_size)
self.train_loader = get_data_loader(self.train_dataset,
batch_size=self.config.batch_size,
shuffle=self.config.shuffle,
num_workers=4,
drop_last=False)
self.val_loader = get_data_loader(self.val_dataset,
batch_size=self.config.batch_size,
shuffle=self.config.shuffle,
num_workers=4,
drop_last=False)
self.train_iter = infinite_iter(self.train_loader)
return
def get_data_loaders(self):
data_dir = self.args.data_dir
self.gpu_num = torch.cuda.device_count() if torch.cuda.is_available(
) else 1
self.train_dataset = PickleDataset(
os.path.join(data_dir, f'{self.args.train_set}.pkl'),
os.path.join(data_dir, self.args.train_index_file),
segment_size=self.config['data_loader']['segment_size'])
self.train_loader = get_data_loader(
self.train_dataset,
frame_size=self.config['data_loader']['frame_size'],
batch_size=self.config['data_loader']['batch_size'] * self.gpu_num,
num_workers=0,
shuffle=self.config['data_loader']['shuffle'],
drop_last=False)
self.train_iter = infinite_iter(self.train_loader)
if self.args.use_eval_set:
self.eval_dataset = PickleDataset(
os.path.join(data_dir, f'{self.args.eval_set}.pkl'),
os.path.join(data_dir, self.args.eval_index_file),
segment_size=self.config['data_loader']['segment_size'])
self.eval_loader = get_data_loader(
self.eval_dataset,
frame_size=self.config['data_loader']['frame_size'],
batch_size=self.config['data_loader']['batch_size'] *
self.gpu_num,
shuffle=self.config['data_loader']['shuffle'],
num_workers=0,
drop_last=False)
self.eval_iter = infinite_iter(self.eval_loader)
if self.args.use_test_set:
self.test_dataset = PickleDataset(
os.path.join(data_dir, f'{self.args.test_set}.pkl'),
os.path.join(data_dir, self.args.test_index_file),
segment_size=self.config['data_loader']['segment_size'])
self.test_loader = get_data_loader(
self.test_dataset,
frame_size=self.config['data_loader']['frame_size'],
batch_size=self.config['data_loader']['batch_size'],
shuffle=False,
num_workers=0,
drop_last=False)
self.test_iter = infinite_iter(self.test_loader)
return
def _get_loader(network_loc, molecule_loc, exclude_ids_loc, split_by,
batch_size, batch_size_test, num_iterations, num_workers, full,
training_only, k, p, ms: MoleculeSpec):
"""Helper function for getting data loaders
Args:
network_loc (str): Location of the bipartite network
molecule_loc (str): Location of molecule SMILES strings
exclude_ids_loc (str): The location storing the ids to be excluded from the training set
split_by (str): Whether to split by scaffold or molecule
batch_size (int): The batch size for training
batch_size_test (int): The batch size for testing
num_iterations (int): The number of total iterations for model training
num_workers (int): The number of workers for loading dataset
full (bool): Whether to use the full dataset for training
training_only (bool): Only record training loss
k (int): The number of importance samples
p (float): The degree of stochasticity of importance sampling 0.0 for fully stochastic decoding, 1.0 for fully
deterministic decoding
ms (MoleculeSpec)
Returns:
t.Tuple[t.Iterable, t.Iterable]:
"""
if full:
loader_train = get_data_loader_full(scaffold_network_loc=network_loc,
molecule_smiles_loc=molecule_loc,
batch_size=batch_size,
num_iterations=num_iterations,
num_workers=num_workers,
k=k,
p=p,
ms=ms)
loader_test = None
else:
loader_train, loader_test = get_data_loader(
scaffold_network_loc=network_loc,
molecule_smiles_loc=molecule_loc,
exclude_ids_loc=exclude_ids_loc,
split_type=split_by,
batch_size=batch_size,
batch_size_test=batch_size_test,
num_iterations=num_iterations,
num_workers=num_workers,
k=k,
p=p,
ms=ms)
if training_only:
loader_test = None
return loader_train, loader_test
def get_data_loaders(self):
data_dir = self.args.data_dir
self.test_dataset = PickleDataset(
os.path.join(data_dir, f'{self.args.test_set}.pkl'),
os.path.join(data_dir, self.args.test_index_file),
segment_size=self.config['data_loader']['segment_size'])
self.test_loader = get_data_loader(
self.test_dataset,
frame_size=self.config['data_loader']['frame_size'],
batch_size=self.config['data_loader']['batch_size'],
shuffle=False,
drop_last=False)
def get_data_loaders(self):
data_dir = self.args.data_dir
self.train_dataset = PickleDataset(
os.path.join(data_dir, f'{self.args.train_set}.pkl'),
os.path.join(data_dir, self.args.train_index_file),
segment_size=self.config['data_loader']['segment_size'])
self.train_loader = get_data_loader(
self.train_dataset,
frame_size=self.config['data_loader']['frame_size'],
batch_size=self.config['data_loader']['batch_size'],
shuffle=self.config['data_loader']['shuffle'],
num_workers=4,
drop_last=False)
self.train_iter = infinite_iter(self.train_loader)
return
def get_data_loaders(self):
data_dir = self.args.data_dir
self.train_dataset = PickleDataset(
os.path.join(data_dir, f"{self.args.train_set}.pkl"),
os.path.join(data_dir, self.args.train_index_file),
segment_size=self.config["data_loader"]["segment_size"],
)
self.train_loader = get_data_loader(
self.train_dataset,
frame_size=self.config["data_loader"]["frame_size"],
batch_size=self.config["data_loader"]["batch_size"],
shuffle=self.config["data_loader"]["shuffle"],
num_workers=0,
drop_last=False,
)
self.train_iter = infinite_iter(self.train_loader)
return
def train(args):
# Define Tokenizer
tokenizer_module = getattr(import_module("transformers"),
f"{args.model_name}Tokenizer")
tokenizer = tokenizer_module.from_pretrained(args.pretrained_name_or_path)
slot_meta, train_examples, dev_examples, dev_labels = train_data_loading(
args, isUserFirst=False, isDialogueLevel=False)
# Define Preprocessor
processor = TRADEPreprocessor(slot_meta,
tokenizer,
max_seq_length=args.max_seq_length,
use_n_gate=args.use_n_gate)
train_features = processor.convert_examples_to_features(train_examples)
dev_features = processor.convert_examples_to_features(dev_examples)
train_loader = get_data_loader(processor, train_features,
args.train_batch_size)
dev_loader = get_data_loader(processor, dev_features, args.eval_batch_size)
args.vocab_size = len(tokenizer)
args.n_gate = len(
processor.gating2id
) # gating 갯수 : (none, dontcare, ptr) or (none, yes, no, dontcare, ptr)
# Slot Meta tokenizing for the decoder initial inputs
tokenized_slot_meta = []
for slot in slot_meta:
tokenized_slot_meta.append(
tokenizer.encode(slot.replace("-", " "), add_special_tokens=False))
# Model 선언
model = TRADE(args, tokenized_slot_meta)
# model.set_subword_embedding(args) # Subword Embedding 초기화
print(f"Subword Embeddings is loaded from {args.pretrained_name_or_path}")
model.to(device)
print("Model is initialized")
# Optimizer 및 Scheduler 선언
n_epochs = args.epochs
t_total = len(train_loader) * n_epochs
# get_optimizer 부분에서 자동으로 warmup_steps를 계산할 수 있도록 바꿨음 (아래가 원래의 code)
# warmup_steps = int(t_total * args.warmup_ratio)
optimizer = get_optimizer(model,
args) # get optimizer (Adam, sgd, AdamP, ..)
scheduler = get_scheduler(
optimizer, t_total, args) # get scheduler (custom, linear, cosine, ..)
loss_fnc_1 = masked_cross_entropy_for_value # generation - # classes: vocab_size
loss_fnc_2 = nn.CrossEntropyLoss()
# loss_fnc_2 = LabelSmoothingLoss(classes=model.decoder.n_gate,smoothing=args.smoothing_factor)
json.dump(
vars(args),
open(f"{args.model_dir}/{args.model_fold}/exp_config.json", "w"),
indent=2,
ensure_ascii=False,
)
json.dump(
slot_meta,
open(f"{args.model_dir}/{args.model_fold}/slot_meta.json", "w"),
indent=2,
ensure_ascii=False,
)
best_score, best_checkpoint = 0, 0
for epoch in range(n_epochs):
model.train()
for step, batch in enumerate(train_loader):
input_ids, segment_ids, input_masks, gating_ids, target_ids, guids = [
b.to(device) if not isinstance(b, list) else b for b in batch
]
# teacher forcing
if (args.teacher_forcing_ratio > 0.0
and random.random() < args.teacher_forcing_ratio):
tf = target_ids
else:
tf = None
all_point_outputs, all_gate_outputs = model(
input_ids, segment_ids, input_masks, target_ids.size(-1), tf)
# generation loss
loss_1 = loss_fnc_1(
all_point_outputs.contiguous(),
target_ids.contiguous().view(-1),
tokenizer.pad_token_id,
)
# gating loss
loss_2 = loss_fnc_2(
all_gate_outputs.contiguous().view(-1, args.n_gate),
gating_ids.contiguous().view(-1),
)
loss = loss_1 + loss_2
loss.backward()
nn.utils.clip_grad_norm_(model.parameters(), args.max_grad_norm)
optimizer.step()
scheduler.step()
for learning_rate in scheduler.get_lr():
wandb.log({"learning_rate": learning_rate})
optimizer.zero_grad()
if step % 100 == 0:
print(
f"[{epoch}/{n_epochs}] [{step}/{len(train_loader)}] loss: {loss.item()} gen: {loss_1.item()} gate: {loss_2.item()}"
)
wandb.log({
"epoch": epoch,
"Train epoch loss": loss.item(),
"Train epoch gen loss": loss_1.item(),
"Train epoch gate loss": loss_2.item(),
})
predictions = inference_TRADE(model, dev_loader, processor, device)
eval_result = _evaluation(predictions, dev_labels, slot_meta)
for k, v in eval_result.items():
if k in ("joint_goal_accuracy", 'turn_slot_accuracy',
'turn_slot_f1'):
print(f"{k}: {v}")
if best_score < eval_result["joint_goal_accuracy"]:
print("Update Best checkpoint!")
best_score = eval_result["joint_goal_accuracy"]
best_checkpoint = epoch
wandb.log({
"epoch":
epoch,
"Best joint goal accuracy":
best_score,
"Best turn slot accuracy":
eval_result['turn_slot_accuracy'],
"Best turn slot f1":
eval_result['turn_slot_f1']
})
if args.logging_accuracy_per_domain_slot:
wandb.log({
k: v
for k, v in eval_result.items()
if k not in ("joint_goal_accuracy", 'turn_slot_accuracy',
'turn_slot_f1')
})
torch.save(model.state_dict(),
f"{args.model_dir}/{args.model_fold}/model-{epoch}.bin")
print(f"Best checkpoint: {args.model_dir}/model-{best_checkpoint}.bin")
wandb.log(
{"Best checkpoint": f"{args.model_dir}/model-{best_checkpoint}.bin"})
def main_inference(args, config):
slot_meta = json.load(
open(f"{args.model_dir}/{args.model_fold}/slot_meta.json", "r"))
ontology = json.load(open(f"{CFG.TrainOntology}", "r"))
if config.replace_word_data:
slot_meta = [meta.replace('택시', '버스') for meta in slot_meta]
ontology = {
domain_slot_key.replace('택시', '버스'): domain_slot_value
for domain_slot_key, domain_slot_value in ontology.items()
}
# Define Tokenizer
tokenizer_module = getattr(import_module("transformers"),
f"{config.model_name}Tokenizer")
tokenizer = tokenizer_module.from_pretrained(
config.pretrained_name_or_path)
# Extracting Featrues
if config.dst == 'TRADE':
eval_examples = test_data_loading(args,
isUserFirst=False,
isDialogueLevel=False)
processor = TRADEPreprocessor(slot_meta, tokenizer)
tokenized_slot_meta = []
for slot in slot_meta:
tokenized_slot_meta.append(
tokenizer.encode(slot.replace("-", " "),
add_special_tokens=False))
# Model 선언
model = TRADE(config, tokenized_slot_meta)
model.set_subword_embedding(config) # Subword Embedding 초기화
elif config.dst == 'SUMBT':
eval_examples = test_data_loading(args,
isUserFirst=True,
isDialogueLevel=True)
max_turn = max([len(e) * 2 for e in eval_examples])
processor = SUMBTPreprocessor(
slot_meta,
tokenizer,
ontology=ontology, # predefined ontology
max_seq_length=config.max_seq_length, # 각 turn마다 최대 길이
max_turn_length=max_turn) # 각 dialogue의 최대 turn 길이
slot_type_ids, slot_values_ids = tokenize_ontology(
ontology, tokenizer, config.max_label_length)
# Model 선언
num_labels = [len(s)
for s in slot_values_ids] # 각 Slot 별 후보 Values의 갯수
model = SUMBT(config, num_labels, device)
model.initialize_slot_value_lookup(
slot_values_ids,
slot_type_ids) # Tokenized Ontology의 Pre-encoding using BERT_SV
eval_features = processor.convert_examples_to_features(eval_examples)
eval_loader = get_data_loader(processor, eval_features,
config.eval_batch_size)
print("# eval:", len(eval_loader))
ckpt = torch.load(
f'{args.model_dir}/{args.model_fold}/model-{args.chkpt_idx}.bin',
map_location="cpu")
model.load_state_dict(ckpt)
model.to(device)
print("Model is loaded")
inference_module = getattr(import_module("inference"),
f"inference_{config.dst}")
predictions = inference_module(model, eval_loader, processor, device)
os.makedirs(args.output_dir, exist_ok=True)
json.dump(
predictions,
open(f"{args.output_dir}/{args.model_fold}-predictions.csv", "w"),
indent=2,
ensure_ascii=False,
)
def train(args):
# Define Tokenizer
tokenizer_module = getattr(import_module("transformers"),
f"{args.model_name}Tokenizer")
tokenizer = tokenizer_module.from_pretrained(args.pretrained_name_or_path)
slot_meta, train_examples, dev_examples, dev_labels = train_data_loading(
args, isUserFirst=True, isDialogueLevel=True)
ontology = json.load(open("../input/data/train_dataset/ontology.json"))
# Define Preprocessor
max_turn = max([len(e) * 2 for e in train_examples])
processor = SUMBTPreprocessor(
slot_meta,
tokenizer,
ontology=ontology, # predefined ontology
max_seq_length=args.max_seq_length, # 각 turn마다 최대 길이
max_turn_length=max_turn) # 각 dialogue의 최대 turn 길이
train_features = processor.convert_examples_to_features(train_examples)
dev_features = processor.convert_examples_to_features(dev_examples)
train_loader = get_data_loader(processor, train_features,
args.train_batch_size)
dev_loader = get_data_loader(processor, dev_features, args.eval_batch_size)
if args.replace_word_data:
ontology = {
domain_slot_key.replace('택시', '버스'): domain_slot_value
for domain_slot_key, domain_slot_value in ontology.items()
}
slot_type_ids, slot_values_ids = tokenize_ontology(ontology, tokenizer,
args.max_label_length)
# Model 선언
num_labels = [len(s) for s in slot_values_ids] # 각 Slot 별 후보 Values의 갯수
n_gpu = 1 if torch.cuda.device_count() < 2 else torch.cuda.device_count()
model = SUMBT(args, num_labels, device)
model.initialize_slot_value_lookup(
slot_values_ids,
slot_type_ids) # Tokenized Ontology의 Pre-encoding using BERT_SV
model.to(device)
print("Model is initialized")
"""## Optimizer & Scheduler 선언 """
n_epochs = args.epochs
t_total = len(train_loader) * n_epochs
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)
],
"weight_decay":
args.weight_decay,
},
{
"params": [
p for n, p in model.named_parameters()
if any(nd in n for nd in no_decay)
],
"weight_decay":
0.0,
},
]
optimizer = get_optimizer(optimizer_grouped_parameters,
args) # get optimizer (Adam, sgd, AdamP, ..)
scheduler = get_scheduler(
optimizer, t_total, args) # get scheduler (custom, linear, cosine, ..)
json.dump(
vars(args),
open(f"{args.model_dir}/{args.model_fold}/exp_config.json", "w"),
indent=2,
ensure_ascii=False,
)
json.dump(
slot_meta,
open(f"{args.model_dir}/{args.model_fold}/slot_meta.json", "w"),
indent=2,
ensure_ascii=False,
)
best_score, best_checkpoint = 0, 0
for epoch in range(n_epochs):
batch_loss = []
model.train()
for step, batch in enumerate(train_loader):
input_ids, segment_ids, input_masks, target_ids, num_turns, guids = \
[b.to(device) if not isinstance(b, list) else b for b in batch]
# Forward
if n_gpu == 1:
loss, loss_slot, acc, acc_slot, _ = model(
input_ids, segment_ids, input_masks, target_ids, n_gpu)
else:
loss, _, acc, acc_slot, _ = model(input_ids, segment_ids,
input_masks, target_ids,
n_gpu)
batch_loss.append(loss.item())
loss.backward()
nn.utils.clip_grad_norm_(model.parameters(), 1.0)
optimizer.step()
scheduler.step()
for learning_rate in scheduler.get_lr():
wandb.log({"learning_rate": learning_rate})
optimizer.zero_grad()
if step % 100 == 0:
print('[%d/%d] [%d/%d] %f' %
(epoch, n_epochs, step, len(train_loader), loss.item()))
wandb.log({"epoch": epoch, "Train epoch loss": loss.item()})
predictions = inference_SUMBT(model, dev_loader, processor, device)
eval_result = _evaluation(predictions, dev_labels, slot_meta)
for k, v in eval_result.items():
if k in ("joint_goal_accuracy", 'turn_slot_accuracy',
'turn_slot_f1'):
print(f"{k}: {v}")
if best_score < eval_result["joint_goal_accuracy"]:
print("Update Best checkpoint!")
best_score = eval_result["joint_goal_accuracy"]
best_checkpoint = epoch
wandb.log({
"epoch":
epoch,
"Best joint goal accuracy":
best_score,
"Best turn slot accuracy":
eval_result['turn_slot_accuracy'],
"Best turn slot f1":
eval_result['turn_slot_f1']
})
if args.logging_accuracy_per_domain_slot:
wandb.log({
k: v
for k, v in eval_result.items()
if k not in ("joint_goal_accuracy", 'turn_slot_accuracy',
'turn_slot_f1')
})
torch.save(model.state_dict(),
f"{args.model_dir}/{args.model_fold}/model-{epoch}.bin")
print(f"Best checkpoint: {args.model_dir}/model-{best_checkpoint}.bin")
wandb.log(
{"Best checkpoint": f"{args.model_dir}/model-{best_checkpoint}.bin"})
def _get_loader(
network_loc: str, molecule_loc: str, exclude_ids_loc: str,
split_by: str, batch_size: int, batch_size_test: int,
num_iterations: int, num_workers: int, full: bool, training_only: bool,
k: int, p: float,
ms: MoleculeSpec) -> t.Tuple[t.Iterable, t.Optional[t.Iterable]]:
"""Helper function for getting data loaders
Args:
network_loc (str):
Location of the bipartite network
molecule_loc (str):
Location of molecule SMILES strings
exclude_ids_loc (str):
The location storing the ids to be excluded from the training set
split_by (str):
Whether to split by scaffold or molecule
batch_size (int):
The batch size for training
batch_size_test (int):
The batch size for testing
num_iterations (int):
The number of total iterations for model training
num_workers (int):
The number of workers for loading dataset
full (bool):
Whether to use the full dataset for training
training_only (bool):
Only record training loss
k (int):
The number of importance samples
p (float):
The degree of stochasticity of importance sampling 0.0 for fully
stochastic decoding, 1.0 for fully deterministic decoding
ms (MoleculeSpec)
Returns:
t.Tuple[t.Iterable, t.Iterable]:
DataLoaders for training and test data
"""
if full:
training_only = True
loader_train = \
get_data_loader_full(network_loc,
molecule_loc,
batch_size,
num_iterations,
num_workers,
k, p, ms)
loader_test = None
else:
loader_train, loader_test = \
get_data_loader(network_loc,
molecule_loc,
exclude_ids_loc,
split_by,
batch_size,
batch_size_test,
num_iterations,
num_workers,
k, p, ms)
if training_only:
loader_test = None
return loader_train, loader_test
