def load_model(self, logdir, step):
'''Load a model (identified by the config used for construction) and return it'''
# 1. Construct model 创建model
model = registry.construct('model', self.config['model'], preproc=self.model_preproc, device=self.device)
model.to(self.device)
model.eval()
# 2. Restore its parameters
saver = saver_mod.Saver({"model": model})
last_step = saver.restore(logdir, step=step, map_location=self.device, item_keys=["model"])
if not last_step:
raise Exception(f"Attempting to infer on untrained model in {logdir}, step={step}")
return model
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(
f'Step {last_step}: loss={loss.item():.4f}')
last_step += 1
# Run saver
if last_step == 1 or last_step % self.train_config.save_every_n == 0:
saver.save(modeldir, last_step)
# Save final model
saver.save(modeldir, last_step)
def train(self, config, modeldir, trainset, valset):
# 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)
else:
optimizer = registry.construct('optimizer',
config['optimizer'],
params=self.model.parameters())
# 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"])
print("pretrain restored! pretrain step: %d" % pretrain_step)
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(trainset)
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(valset)
print("train: ")
for _item in train_data.components[1]:
if _item.tree is None:
print("?")
dev_badidxs = []
print("val: ")
for _idx, _item in enumerate(val_data.components[1]):
if _item.tree is None:
dev_badidxs.append(_idx)
print("!")
assert len(val_data.components[0]) == len(val_data.components[1])
new_first = []
new_second = []
for _idx in range(len(val_data.components[1])):
if _idx not in dev_badidxs:
new_first.append(val_data.components[0][_idx])
new_second.append(val_data.components[1][_idx])
val_data.components = copy.deepcopy((new_first, new_second))
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:
last_val_loss = None
lr_decay_countdown = MAX_LRDECAY_COUNTDOWN
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:
train_loss = 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:
eval_loss = self._eval_model(
self.logger,
self.model,
last_step,
val_data_loader,
'val',
num_eval_items=self.train_config.num_eval_items)
if last_val_loss is None or eval_loss < last_val_loss:
last_val_loss = eval_loss
lr_decay_countdown = MAX_LRDECAY_COUNTDOWN
elif lr_decay_countdown > 0:
lr_decay_countdown -= 1
else:
current_lr = None
for p in optimizer.param_groups:
p['lr'] *= LR_DECAY_RATE
current_lr = p['lr']
self.logger.log(f'LR decay: down to {current_lr}')
if DEBUG:
current_lr = None
for p in optimizer.param_groups:
p['lr'] *= LR_DECAY_RATE
current_lr = p['lr']
self.logger.log(f'LR decay: down to {current_lr}')
# 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()
optimizer.zero_grad()
# Report metrics
if last_step % self.train_config.report_every_n == 0:
self.logger.log(
f'Step {last_step}: loss={loss.item():.4f}')
last_step += 1
# Run saver
if last_step == 1 or last_step % self.train_config.save_every_n == 0:
saver.save(modeldir, last_step)
print("model saved at %d step" % last_step)
# Save final model
saver.save(modeldir, last_step)
def finetune_on_database(self,
infer_output_path,
database,
config,
model_load_dir,
beam_size,
output_history,
use_heuristic,
metrics_list,
scores,
take_grad_steps=True,
batch_size="1"):
if database:
current_infer_output_path = infer_output_path + "/" + database
else:
current_infer_output_path = infer_output_path + "/" + "entire_val"
os.makedirs(os.path.dirname(current_infer_output_path), exist_ok=True)
infer_output = open(current_infer_output_path, 'w')
spider_data = registry.construct('dataset',
self.config['data']['val'],
database=database)
val_data = self.model_preproc.dataset('val', database=database)
# val_data_loader = self._yield_batches_from_epochs(
# torch.utils.data.DataLoader(val_data, batch_size=1, collate_fn=lambda x: x,
# shuffle=False))
assert len(val_data) == len(spider_data)
if len(val_data) == 0:
return
if batch_size == "32":
if len(val_data) < 32:
return
print("database:", database)
if batch_size == "n^2":
indices = np.random.permutation(
self.get_no_repeat_data_indices(spider_data))
print("length of data:", len(val_data))
print("length of data after removing repeat entries:",
len(indices))
else:
indices = np.random.permutation(len(val_data))
# TODO: RANDOMIZE DATA
optimizer, lr_scheduler = self.construct_optimizer_and_lr_scheduler(
config)
saver = saver_mod.Saver({
"model": self.model,
"optimizer": optimizer
},
keep_every_n=self.finetune_config.keep_every_n)
last_step = saver.restore(model_load_dir, map_location=self.device)
self.logger.log(f"Loaded trained model; last_step:{last_step}")
current_batch = []
clear_batch = False
current_number = 0
for i in tqdm.tqdm(indices):
current_number += 1
orig_item, preproc_item = spider_data[i], val_data[i]
with torch.no_grad():
decoded = self._infer_one(self.model, orig_item, preproc_item,
beam_size, output_history,
use_heuristic)
infer_output.write(
json.dumps({
'index': int(i),
'beams': decoded,
}) + '\n')
infer_output.flush()
if take_grad_steps:
if batch_size == "1":
current_batch = [preproc_item]
elif batch_size == "32":
if current_number % 32 != 0:
current_batch.append(preproc_item)
clear_batch = False
continue
else:
clear_batch = True
else:
current_batch.append(preproc_item)
try:
with self.model_random:
loss = self.model.compute_loss(current_batch)
norm_loss = loss / self.finetune_config.num_batch_accumulated
norm_loss.backward()
if self.finetune_config.clip_grad:
torch.nn.utils.clip_grad_norm_(optimizer.bert_param_group["params"], \
self.finetune_config.clip_grad)
optimizer.step()
lr_scheduler.update_lr(last_step)
optimizer.zero_grad()
if clear_batch:
current_batch = []
# stats = self._eval_model(self.logger, self.model, last_step, batch, 'val',
# self.finetune_config.report_every_n)
# val_losses.append(stats['loss'])
except KeyError:
self.logger.log("keyError")
current_batch = []
continue
# except AssertionError:
# self.logger.log("AssertionError")
# continue
inferred = open(current_infer_output_path)
metrics = spider_data.Metrics(spider_data)
inferred_lines = list(inferred)
# if len(inferred_lines) < len(spider_data):
# raise Exception(f'Not enough inferred: {len(inferred_lines)} vs {len(spider_data)}')
for line in inferred_lines:
infer_results = json.loads(line)
if infer_results['beams']:
inferred_code = infer_results['beams'][0]['inferred_code']
else:
inferred_code = None
if 'index' in infer_results:
metrics.add(spider_data[infer_results['index']], inferred_code)
else:
metrics.add(None,
inferred_code,
obsolete_gold_code=infer_results['gold_code'])
final_metrics = metrics.finalize()
metrics_list.append(final_metrics)
#print(final_metrics['total_scores']['all']['exact'])
scores.append((database, final_metrics['total_scores']['all']['exact'],
len(indices)))
# if last_step % self.finetune_config.save_every_n == 0:
# saver.save(model_save_dir+'/seed_'+seed, last_step)
#print('scores', scores)
#print("average score:", self.aggregate_score(scores))
return scores
