in rat-sql-gap/seq2struct/commands/train.py [0:0]
def train(self, config, modeldir):
# slight difference here vs. unrefactored train: The init_random starts over here. Could be fixed if it was important by saving random state at end of init
with self.init_random:
# We may be able to move optimizer and lr_scheduler to __init__ instead. Empirically it works fine. I think that's because saver.restore
# resets the state by calling optimizer.load_state_dict.
# But, if there is no saved file yet, I think this is not true, so might need to reset the optimizer manually?
# For now, just creating it from scratch each time is safer and appears to be the same speed, but also means you have to pass in the config to train which is kind of ugly.
# TODO: not nice
if config["optimizer"].get("name", None) == 'bertAdamw':
bert_params = list(self.model.encoder.bert_model.parameters())
assert len(bert_params) > 0
non_bert_params = []
for name, _param in self.model.named_parameters():
if "bert" not in name:
non_bert_params.append(_param)
assert len(non_bert_params) + len(bert_params) == len(list(self.model.parameters()))
optimizer = registry.construct('optimizer', config['optimizer'], non_bert_params=non_bert_params, \
bert_params=bert_params)
lr_scheduler = registry.construct( 'lr_scheduler',
config.get('lr_scheduler', {'name': 'noop'}),
param_groups=[optimizer.non_bert_param_group, \
optimizer.bert_param_group])
else:
optimizer = registry.construct('optimizer', config['optimizer'], params=self.model.parameters())
lr_scheduler = registry.construct( 'lr_scheduler',
config.get('lr_scheduler', {'name': 'noop'}),
param_groups=optimizer.param_groups)
# 2. Restore model parameters
saver = saver_mod.Saver(
{"model": self.model, "optimizer": optimizer}, keep_every_n=self.train_config.keep_every_n)
last_step = saver.restore(modeldir, map_location=self.device)
if "pretrain" in config and last_step == 0:
pretrain_config = config["pretrain"]
_path = pretrain_config["pretrained_path"]
_step = pretrain_config["checkpoint_step"]
pretrain_step = saver.restore(_path, step=_step, map_location=self.device, item_keys=["model"])
saver.save(modeldir, pretrain_step) # for evaluating pretrained models
last_step = pretrain_step
# 3. Get training data somewhere
with self.data_random:
train_data = self.model_preproc.dataset('train')
train_data_loader = self._yield_batches_from_epochs(
torch.utils.data.DataLoader(
train_data,
batch_size=self.train_config.batch_size,
shuffle=True,
drop_last=True,
collate_fn=lambda x: x))
train_eval_data_loader = torch.utils.data.DataLoader(
train_data,
batch_size=self.train_config.eval_batch_size,
collate_fn=lambda x: x)
val_data = self.model_preproc.dataset('val')
val_data_loader = torch.utils.data.DataLoader(
val_data,
batch_size=self.train_config.eval_batch_size,
collate_fn=lambda x: x)
# 4. Start training loop
with self.data_random:
for batch in train_data_loader:
# Quit if too long
if last_step >= self.train_config.max_steps:
break
# Evaluate model
if last_step % self.train_config.eval_every_n == 0:
if self.train_config.eval_on_train:
self._eval_model(self.logger, self.model, last_step, train_eval_data_loader, 'train', num_eval_items=self.train_config.num_eval_items)
if self.train_config.eval_on_val:
self._eval_model(self.logger, self.model, last_step, val_data_loader, 'val', num_eval_items=self.train_config.num_eval_items)
# Compute and apply gradient
with self.model_random:
for _i in range(self.train_config.num_batch_accumulated):
if _i > 0: batch = next(train_data_loader)
loss = self.model.compute_loss(batch)
norm_loss = loss / self.train_config.num_batch_accumulated
norm_loss.backward()
if self.train_config.clip_grad:
torch.nn.utils.clip_grad_norm_(optimizer.bert_param_group["params"], \
self.train_config.clip_grad)
optimizer.step()
lr_scheduler.update_lr(last_step)
optimizer.zero_grad()
# Report metrics
if last_step % self.train_config.report_every_n == 0:
self.logger.log('Step {}: loss={:.4f}'.format(last_step, loss.item()))
last_step += 1
# Run saver
if last_step % self.train_config.save_every_n == 0:
saver.save(modeldir, last_step)
# Save final model
saver.save(modeldir, last_step)