def default_callbacks(
self,
validation_metric: str = "-loss",
patience: int = None,
max_checkpoints: int = 20,
checkpoint_every: int = None,
model_save_interval: float = None,
serialization_dir: str = "__DEFAULT__",
validation_data: Iterable[Instance] = None,
validation_iterator: DataIterator = None,
batch_size: int = 2,
):
if serialization_dir == "__DEFAULT__":
serialization_dir = self.TEST_DIR
checkpointer = Checkpointer(serialization_dir, checkpoint_every,
max_checkpoints)
tensorboard = TensorboardWriter(get_batch_num_total=lambda: None)
if validation_iterator is None:
validation_iterator = BasicIterator(batch_size=batch_size)
validation_iterator.index_with(self.vocab)
return [
LogToTensorboard(log_batch_size_period=10,
tensorboard=tensorboard),
Checkpoint(checkpointer, model_save_interval),
Validate(
validation_data=self.instances
if validation_data is None else validation_data,
validation_iterator=validation_iterator,
),
TrackMetrics(patience, validation_metric),
GradientNormAndClip(),
]
def default_callbacks(self,
validation_metric: str = "-loss",
patience: int = None,
max_checkpoints: int = 20,
checkpoint_every: int = None,
serialization_dir: str = "__DEFAULT__",
iterator: DataIterator = None,
validation_data: Iterable[Instance] = None,
validation_iterator: DataIterator = None,
batch_size: int = 2):
if serialization_dir == "__DEFAULT__":
serialization_dir = self.TEST_DIR
checkpointer = Checkpointer(serialization_dir,
checkpoint_every,
max_checkpoints)
tensorboard = TensorboardWriter(get_batch_num_total=lambda: None)
if iterator is None:
iterator = BasicIterator(batch_size=batch_size)
iterator.index_with(self.vocab)
return [
LogToTensorboard(log_batch_size_period=10, tensorboard=tensorboard),
Checkpoint(checkpointer),
Validate(validation_data=self.instances if validation_data is None else validation_data,
validation_iterator=iterator if validation_iterator is None else validation_iterator),
TrackMetrics(patience, validation_metric),
TrainSupervised(),
GenerateTrainingBatches(self.instances, iterator, True)
]
def from_params( # type: ignore
cls, serialization_dir: str, params: Params) -> "LogToTensorboard":
log_batch_size_period = params.pop_int("log_batch_size_period", None)
tensorboard = TensorboardWriter.from_params(
params=params,
serialization_dir=serialization_dir,
get_batch_num_total=lambda: None)
return LogToTensorboard(tensorboard, log_batch_size_period)
def from_params( # type: ignore
cls, serialization_dir: str, params: Params,
**extras) -> "LogToTensorboard":
log_batch_size_period = params.pop_int("log_batch_size_period", None)
tensorboard = TensorboardWriter.from_params(
params=params,
serialization_dir=serialization_dir,
get_batch_num_total=lambda: None)
# TODO(mattg): remove get_batch_num_total from TensorboardWriter, and instead just add a
# method / arguments to tell the writer what batch num we're at.
return LogToTensorboard(tensorboard, log_batch_size_period)
def test_trainer_can_log_learning_rates_tensorboard(self):
callbacks = [cb for cb in self.default_callbacks() if not isinstance(cb, LogToTensorboard)]
# The lambda: None is unfortunate, but it will get replaced by the callback.
tensorboard = TensorboardWriter(lambda: None, should_log_learning_rate=True, summary_interval=2)
callbacks.append(LogToTensorboard(tensorboard))
trainer = CallbackTrainer(self.model, self.optimizer,
num_epochs=2,
serialization_dir=self.TEST_DIR,
callbacks=callbacks)
trainer.train()
def test_trainer_can_log_histograms(self):
# enable activation logging
for module in self.model.modules():
module.should_log_activations = True
callbacks = [cb for cb in self.default_callbacks() if not isinstance(cb, LogToTensorboard)]
# The lambda: None is unfortunate, but it will get replaced by the callback.
tensorboard = TensorboardWriter(lambda: None, histogram_interval=2)
callbacks.append(LogToTensorboard(tensorboard))
trainer = CallbackTrainer(self.model, self.optimizer,
num_epochs=3,
serialization_dir=self.TEST_DIR,
callbacks=callbacks)
trainer.train()
def test_model_training(self):
training_dataset = self.sample_instances if self.sample_only else self.train_instances
#training_dataset = training_dataset[:500]
validation_dataset = self.sample_instances if self.sample_only else self.test_instances
serialization_dir = self.TEST_DATA_ROOT / "serialized_sample" if self.sample_only else "serialized"
tensorboard_dir = self.TEST_DATA_ROOT / "tensorboard"
batch_size = 64
train_iterator = BucketIterator(sorting_keys=[("question",
"num_tokens")],
padding_noise=0.0,
batch_size=batch_size)
val_iterator = BucketIterator(sorting_keys=[("question", "num_tokens")
],
padding_noise=0.0,
batch_size=batch_size)
train_iterator.index_with(vocab=self.vocab)
val_iterator.index_with(vocab=self.vocab)
tensorboard = TensorboardWriter(get_batch_num_total=lambda: np.ceil(
len(training_dataset) / batch_size),
serialization_dir=tensorboard_dir,
summary_interval=5,
histogram_interval=5,
should_log_parameter_statistics=True)
trainer = CallbackTrainer(
model=self.model,
serialization_dir=serialization_dir,
iterator=train_iterator,
training_data=training_dataset,
num_epochs=20,
cuda_device=0,
optimizer=torch.optim.Adagrad(self.model.parameters()),
callbacks=[
LogToTensorboard(tensorboard),
Validate(validation_data=validation_dataset,
validation_iterator=val_iterator),
TrackMetrics(),
ResetMetricsCallback()
])
trainer.train()
self.val_outputs_fp.close()
def test_model_training(self):
serialization_dir = self.TEST_DATA_ROOT / "serialized_sample"
tensorboard_dir = self.TEST_DATA_ROOT / "tensorboard.seq2seq"
batch_size = 64
train_iterator = BucketIterator(sorting_keys=[("source_tokens", "num_tokens")], padding_noise=0.0, batch_size=batch_size)
train_iterator.index_with(vocab=self.vocab)
tensorboard = TensorboardWriter(
get_batch_num_total=lambda: np.ceil(len(self.train_instances) / batch_size),
serialization_dir=tensorboard_dir,
summary_interval=5,
histogram_interval=5,
should_log_parameter_statistics=True)
trainer = CallbackTrainer(model=self.model,
serialization_dir=serialization_dir,
iterator=train_iterator,
training_data=self.train_instances,
num_epochs=1,
cuda_device=0,
optimizer=torch.optim.Adam(self.model.parameters(), lr=1e-3),
callbacks=[LogToTensorboard(tensorboard),
Validate(validation_data=self.dev_instances, validation_iterator=train_iterator),
TrackMetrics(), ResetMetricsCallback()]
)
for i in range(50):
print('Epoch: {}'.format(i))
trainer.train()
import itertools
predictor = Seq2SeqPredictor(self.model, self.reader)
for instance in itertools.islice(self.dev_instances, 10):
print('SOURCE:', instance.fields['source_tokens'].tokens)
print('GOLD:', instance.fields['target_tokens'].tokens)
print('PRED:', predictor.predict_instance(instance)['predicted_tokens'])
def __init__(
self,
model: Model,
optimizer: torch.optim.Optimizer,
iterator: DataIterator,
train_datasets: List[Iterable[Instance]],
validation_datasets: List[Iterable[Instance]] = None,
patience: Optional[int] = None,
validation_metric: str = "-loss",
validation_iterator: DataIterator = None,
shuffle: bool = True,
num_epochs: int = 20,
serialization_dir: Optional[str] = None,
num_serialized_models_to_keep: int = 20,
keep_serialized_model_every_num_seconds: int = None,
checkpointer: Checkpointer = None,
model_save_interval: float = None,
cuda_device: Union[int, List] = [0, 1], #int = -1,
grad_norm: Optional[float] = None,
grad_clipping: Optional[float] = None,
learning_rate_scheduler: Optional[LearningRateScheduler] = None,
momentum_scheduler: Optional[MomentumScheduler] = None,
summary_interval: int = 100,
histogram_interval: int = None,
should_log_parameter_statistics: bool = True,
should_log_learning_rate: bool = False,
log_batch_size_period: Optional[int] = None,
moving_average: Optional[MovingAverage] = None,
# meta learner parameters
meta_batches: int = 200,
inner_steps: int = 1,
meta_batch_size: int = 3,
batch_norm=True,
) -> None:
"""
A metatrainer for doing meta-learning. It just takes a list of labeled datasets
and a ``DataIterator``, and uses the supplied meta-learner to learn the weights
for your model over some fixed number of epochs. You can also pass in a validation
datasets and enable early stopping. There are many other bells and whistles as well.
Parameters
----------
model : ``Model``, required.
"""
print('[info]============================ metatrainer.init is running')
print(
'[info] cuda_device in metatrainer.init is:{}'.format(cuda_device))
# I am not calling move_to_gpu here, because if the model is
# not already on the GPU then the optimizer is going to be wrong.
super().__init__(serialization_dir, cuda_device)
self.train_data = train_datasets
self._validation_data = validation_datasets
self.model = model
self.iterator = iterator[0]
self._validation_iterator = validation_iterator
self.shuffle = shuffle
self.optimizer = optimizer
# Meta Trainer specific params
self.meta_batches = meta_batches
self.inner_steps = inner_steps
self.innerstepsize = .001
self.meta_batch_size = meta_batch_size
self.meta_step_size = .1
self.batch_norm = batch_norm
if patience is None: # no early stopping
if validation_dataset:
logger.warning(
'You provided a validation dataset but patience was set to None, '
'meaning that early stopping is disabled')
elif (not isinstance(patience, int)) or patience <= 0:
raise ConfigurationError(
'{} is an invalid value for "patience": it must be a positive integer '
'or None (if you want to disable early stopping)'.format(
patience))
# For tracking is_best_so_far and should_stop_early
self._metric_tracker = MetricTracker(patience, validation_metric)
# Get rid of + or -
self._validation_metric = validation_metric[1:]
self._num_epochs = num_epochs
if checkpointer is not None:
# We can't easily check if these parameters were passed in, so check against their default values.
# We don't check against serialization_dir since it is also used by the parent class.
if num_serialized_models_to_keep != 20 or \
keep_serialized_model_every_num_seconds is not None:
raise ConfigurationError(
"When passing a custom Checkpointer, you may not also pass in separate checkpointer "
"args 'num_serialized_models_to_keep' or 'keep_serialized_model_every_num_seconds'."
)
self._checkpointer = checkpointer
else:
self._checkpointer = Checkpointer(
serialization_dir, keep_serialized_model_every_num_seconds,
num_serialized_models_to_keep)
self._model_save_interval = model_save_interval
self._grad_norm = grad_norm
self._grad_clipping = grad_clipping
self._learning_rate_scheduler = learning_rate_scheduler
self._momentum_scheduler = momentum_scheduler
self._moving_average = moving_average
# We keep the total batch number as an instance variable because it
# is used inside a closure for the hook which logs activations in
# ``_enable_activation_logging``.
self._batch_num_total = 0
self._tensorboard = TensorboardWriter(
get_batch_num_total=lambda: self._batch_num_total,
serialization_dir=serialization_dir,
summary_interval=summary_interval,
histogram_interval=histogram_interval,
should_log_parameter_statistics=should_log_parameter_statistics,
should_log_learning_rate=should_log_learning_rate)
self._log_batch_size_period = log_batch_size_period
self._last_log = 0.0 # time of last logging
# Enable activation logging.
if histogram_interval is not None:
self._tensorboard.enable_activation_logging(self.model)
def __init__(self,
model: Model,
task_list: List[Task],
optimizer_params: Params,
lr_scheduler_params: Params,
patience: Optional[int] = None,
num_epochs: int = 20,
serialization_dir: Optional[str] = None,
cuda_device: int = -1,
gradient_accumulation_steps: int = 1,
grad_norm: Optional[float] = None,
grad_clipping: Optional[float] = None,
min_lr: float = 0.00001,
no_tqdm: bool = False,
momentum_scheduler: Optional[MomentumScheduler] = None,
summary_interval: int = 50,
histogram_interval: int = 50,
should_log_parameter_statistics: bool = True,
should_log_learning_rate: bool = True,
sampling_method: str = "proportional",
moving_average: Optional[MovingAverage] = None) -> None:
self._model = model
self._task_list = task_list
self._n_tasks = len(self._task_list)
self._optimizer_params = optimizer_params
self._optimizers = {}
self._lr_scheduler_params = lr_scheduler_params
self._schedulers = {}
self._all_params = [(n, p) for n, p in self._model.named_parameters() if p.requires_grad]
self._params_exclude_share_encoder = [(n, p) for n, p in self._model.named_parameters() if
p.requires_grad and "_share_encoder" not in n]
self._params_exclude_share_discriminator = [(n, p) for n, p in self._model.named_parameters() if
p.requires_grad and "_s_domain_discriminator" not in n]
for task in self._task_list:
task_name = task._name
self._optimizers[task_name] = {}
self._optimizers[task_name]["all_params"] = Optimizer.from_params(
model_parameters=self._all_params, params=deepcopy(optimizer_params)
)
self._optimizers[task_name]["exclude_share_encoder"] = Optimizer.from_params(
model_parameters=self._params_exclude_share_encoder, params=deepcopy(optimizer_params)
)
self._optimizers[task_name]["exclude_share_discriminator"] = Optimizer.from_params(
model_parameters=self._params_exclude_share_discriminator, params=deepcopy(optimizer_params)
)
self._schedulers[task_name] = {}
self._schedulers[task_name] = LearningRateScheduler.from_params(
optimizer=self._optimizers[task_name]["all_params"], params=deepcopy(lr_scheduler_params)
)
# self._schedulers[task_name]["all_params"] = LearningRateScheduler.from_params(
# optimizer=self._optimizers[task_name]["all_params"], params=deepcopy(lr_scheduler_params)
# )
# self._schedulers[task_name]["exclude_share_encoder"] = LearningRateScheduler.from_params(
# optimizer=self._optimizers[task_name]["exclude_share_encoder"], params=deepcopy(lr_scheduler_params)
# )
# self._schedulers[task_name]["exclude_share_discriminator"] = LearningRateScheduler.from_params(
# optimizer=self._optimizers[task_name]["exclude_share_discriminator"],
# params=deepcopy(lr_scheduler_params)
# )
self._serialization_dir = serialization_dir
self._cuda_device = cuda_device
if self._cuda_device >= 0:
check_for_gpu(self._cuda_device)
self._model = self._model.cuda(self._cuda_device)
self._patience = patience
self._num_epochs = num_epochs
self._epoch_trained = 0
self._gradient_accumulation_steps = gradient_accumulation_steps
self._grad_norm = grad_norm
self._grad_clipping = grad_clipping
self._min_lr = min_lr
self._no_tqdm = no_tqdm
self._sampling_method = sampling_method
self._task_infos = None
self._metric_infos = None
self._tr_generators = None
self._global_step = 0
self._batch_num_total = 0
self._tensorboard = TensorboardWriter(
get_batch_num_total=lambda: self._batch_num_total,
serialization_dir=serialization_dir,
summary_interval=summary_interval,
histogram_interval=histogram_interval,
should_log_parameter_statistics=should_log_parameter_statistics,
should_log_learning_rate=should_log_learning_rate)
self._last_log = 0.0 # time of last logging
# Enable activation logging.
if histogram_interval is not None:
self._tensorboard.enable_activation_logging(self._model)
class GanMtlTrainer:
def __init__(self,
model: Model,
task_list: List[Task],
optimizer_params: Params,
lr_scheduler_params: Params,
patience: Optional[int] = None,
num_epochs: int = 20,
serialization_dir: Optional[str] = None,
cuda_device: int = -1,
gradient_accumulation_steps: int = 1,
grad_norm: Optional[float] = None,
grad_clipping: Optional[float] = None,
min_lr: float = 0.00001,
no_tqdm: bool = False,
momentum_scheduler: Optional[MomentumScheduler] = None,
summary_interval: int = 50,
histogram_interval: int = 50,
should_log_parameter_statistics: bool = True,
should_log_learning_rate: bool = True,
sampling_method: str = "proportional",
moving_average: Optional[MovingAverage] = None) -> None:
self._model = model
self._task_list = task_list
self._n_tasks = len(self._task_list)
self._optimizer_params = optimizer_params
self._optimizers = {}
self._lr_scheduler_params = lr_scheduler_params
self._schedulers = {}
self._all_params = [(n, p) for n, p in self._model.named_parameters() if p.requires_grad]
self._params_exclude_share_encoder = [(n, p) for n, p in self._model.named_parameters() if
p.requires_grad and "_share_encoder" not in n]
self._params_exclude_share_discriminator = [(n, p) for n, p in self._model.named_parameters() if
p.requires_grad and "_s_domain_discriminator" not in n]
for task in self._task_list:
task_name = task._name
self._optimizers[task_name] = {}
self._optimizers[task_name]["all_params"] = Optimizer.from_params(
model_parameters=self._all_params, params=deepcopy(optimizer_params)
)
self._optimizers[task_name]["exclude_share_encoder"] = Optimizer.from_params(
model_parameters=self._params_exclude_share_encoder, params=deepcopy(optimizer_params)
)
self._optimizers[task_name]["exclude_share_discriminator"] = Optimizer.from_params(
model_parameters=self._params_exclude_share_discriminator, params=deepcopy(optimizer_params)
)
self._schedulers[task_name] = {}
self._schedulers[task_name] = LearningRateScheduler.from_params(
optimizer=self._optimizers[task_name]["all_params"], params=deepcopy(lr_scheduler_params)
)
# self._schedulers[task_name]["all_params"] = LearningRateScheduler.from_params(
# optimizer=self._optimizers[task_name]["all_params"], params=deepcopy(lr_scheduler_params)
# )
# self._schedulers[task_name]["exclude_share_encoder"] = LearningRateScheduler.from_params(
# optimizer=self._optimizers[task_name]["exclude_share_encoder"], params=deepcopy(lr_scheduler_params)
# )
# self._schedulers[task_name]["exclude_share_discriminator"] = LearningRateScheduler.from_params(
# optimizer=self._optimizers[task_name]["exclude_share_discriminator"],
# params=deepcopy(lr_scheduler_params)
# )
self._serialization_dir = serialization_dir
self._cuda_device = cuda_device
if self._cuda_device >= 0:
check_for_gpu(self._cuda_device)
self._model = self._model.cuda(self._cuda_device)
self._patience = patience
self._num_epochs = num_epochs
self._epoch_trained = 0
self._gradient_accumulation_steps = gradient_accumulation_steps
self._grad_norm = grad_norm
self._grad_clipping = grad_clipping
self._min_lr = min_lr
self._no_tqdm = no_tqdm
self._sampling_method = sampling_method
self._task_infos = None
self._metric_infos = None
self._tr_generators = None
self._global_step = 0
self._batch_num_total = 0
self._tensorboard = TensorboardWriter(
get_batch_num_total=lambda: self._batch_num_total,
serialization_dir=serialization_dir,
summary_interval=summary_interval,
histogram_interval=histogram_interval,
should_log_parameter_statistics=should_log_parameter_statistics,
should_log_learning_rate=should_log_learning_rate)
self._last_log = 0.0 # time of last logging
# Enable activation logging.
if histogram_interval is not None:
self._tensorboard.enable_activation_logging(self._model)
def _rescale_gradients(self) -> Optional[float]:
return training_util.rescale_gradients(self._model, self._grad_norm)
def _enable_gradient_clipping(self) -> None:
training_util.enable_gradient_clipping(self._model, self._grad_clipping)
def _train_epoch(self, total_n_tr_batches: int, sampling_prob: List, reverse=False, train_D=False) -> Dict[
str, float]:
self._model.train() # Set the model to "train" mode.
if reverse:
logger.info("Training Generator- Begin")
elif not train_D:
logger.info("Training Init Generator- Begin")
if train_D:
logger.info("Training Discriminator- Begin")
logger.info("reverse is {}, train_D is {}", reverse, train_D)
### Reset training and trained batches counter before new training epoch ###
for _, task_info in self._task_infos.items():
task_info["tr_loss_cum"] = 0.0
task_info['stm_loss'] = 0.0
task_info['p_d_loss'] = 0.0
task_info['s_d_loss'] = 0.0
task_info['valid_loss'] = 0.0
task_info["n_batches_trained_this_epoch"] = 0
all_tr_metrics = {} # BUG TO COMPLETE COMMENT TO MAKE IT MORE CLEAR
### Start training epoch ###
epoch_tqdm = tqdm.tqdm(range(total_n_tr_batches), total=total_n_tr_batches)
histogram_parameters = set(self._model.get_parameters_for_histogram_tensorboard_logging())
for step, _ in enumerate(epoch_tqdm):
task_idx = np.argmax(np.random.multinomial(1, sampling_prob))
task = self._task_list[task_idx]
task_info = self._task_infos[task._name]
### One forward + backward pass ###
# Call next batch to train
batch = next(self._tr_generators[task._name])
self._batch_num_total += 1
task_info["n_batches_trained_this_epoch"] += 1
# Load optimizer
if not train_D:
optimizer = self._optimizers[task._name]["all_params"]
else:
optimizer = self._optimizers[task._name]["exclude_share_encoder"]
# Get the loss for this batch
output_dict = self._forward(tensor_batch=batch, task=task, for_training=True, reverse=reverse)
# if reverse or train_D:
# output_dict_fake = self._forward(tensor_batch=batch, task=task, for_training=True, reverse=True)
# loss = output_dict["stm_loss"]
# if train_D:
# loss = (output_dict["stm_loss"] + output_dict["s_d_loss"] + output_dict_fake["stm_loss"] +
# output_dict_fake["s_d_loss"]) / 2.0
# if reverse:
# # loss = (output_dict["stm_loss"] + output_dict["p_d_loss"] + 0.005 * output_dict["s_d_loss"] +
# # output_dict_fake["stm_loss"] + output_dict_fake["p_d_loss"] + 0.005 * output_dict_fake[
# # "s_d_loss"]) / 2.0
# loss = (output_dict['loss'] + output_dict_fake['loss']) / 2.0
loss = output_dict['loss']
if self._gradient_accumulation_steps > 1:
loss /= self._gradient_accumulation_steps
loss.backward()
task_info["tr_loss_cum"] += loss.item()
task_info['stm_loss'] += output_dict['stm_loss'].item()
task_info['p_d_loss'] += output_dict['p_d_loss'].item()
task_info['s_d_loss'] += output_dict['s_d_loss'].item()
task_info['valid_loss'] += output_dict['valid_loss'].item()
# if reverse or train_D:
# task_info['stm_loss'] += output_dict_fake['stm_loss'].item()
# task_info['stm_loss'] /= 2.0
# task_info['p_d_loss'] += output_dict_fake['p_d_loss'].item()
# task_info['p_d_loss'] /= 2.0
# task_info['s_d_loss'] += output_dict_fake['s_d_loss'].item()
# task_info['s_d_loss'] /= 2.0
# task_info['valid_loss'] += output_dict_fake['valid_loss'].item()
# task_info['valid_loss'] /= 2.0
del loss
if (step + 1) % self._gradient_accumulation_steps == 0:
batch_grad_norm = self._rescale_gradients()
if self._tensorboard.should_log_histograms_this_batch():
param_updates = {name: param.detach().cpu().clone()
for name, param in self._model.named_parameters()}
optimizer.step()
for name, param in self._model.named_parameters():
param_updates[name].sub_(param.detach().cpu())
update_norm = torch.norm(param_updates[name].view(-1, ))
param_norm = torch.norm(param.view(-1, )).cpu()
self._tensorboard.add_train_scalar("gradient_update/" + name,
update_norm / (param_norm + 1e-7))
else:
optimizer.step()
optimizer.zero_grad()
### Get metrics for all progress so far, update tqdm, display description ###
task_metrics = self._get_metrics(task=task)
task_metrics["loss"] = float(
task_info["tr_loss_cum"] / (task_info["n_batches_trained_this_epoch"] + 0.000_001)
)
task_metrics["stm_loss"] = float(
task_info["stm_loss"] / (task_info["n_batches_trained_this_epoch"] + 0.000_001)
)
task_metrics["p_d_loss"] = float(
task_info["p_d_loss"] / (task_info["n_batches_trained_this_epoch"] + 0.000_001)
)
task_metrics["s_d_loss"] = float(
task_info["s_d_loss"] / (task_info["n_batches_trained_this_epoch"] + 0.000_001)
)
task_metrics["valid_loss"] = float(
task_info["valid_loss"] / (task_info["n_batches_trained_this_epoch"] + 0.000_001)
)
description = training_util.description_from_metrics(task_metrics)
epoch_tqdm.set_description(task._name + ", " + description)
# Log parameter values to Tensorboard
if self._tensorboard.should_log_this_batch():
self._tensorboard.log_parameter_and_gradient_statistics(self._model, batch_grad_norm)
self._tensorboard.log_learning_rates(self._model, optimizer)
self._tensorboard.log_metrics(
{"epoch_metrics/" + task._name + "/" + k: v for k, v in task_metrics.items()})
if self._tensorboard.should_log_histograms_this_batch():
self._tensorboard.log_histograms(self._model, histogram_parameters)
self._global_step += 1
### Bookkeeping all the training metrics for all the tasks on the training epoch that just finished ###
for task in self._task_list:
task_info = self._task_infos[task._name]
task_info["total_n_batches_trained"] += task_info["n_batches_trained_this_epoch"]
task_info["last_log"] = time.time()
task_metrics = self._get_metrics(task=task, reset=True)
if task._name not in all_tr_metrics:
all_tr_metrics[task._name] = {}
for name, value in task_metrics.items():
all_tr_metrics[task._name][name] = value
all_tr_metrics[task._name]["loss"] = float(
task_info["tr_loss_cum"] / (task_info["n_batches_trained_this_epoch"] + 0.000_000_01)
)
all_tr_metrics[task._name]["stm_loss"] = float(
task_info["stm_loss"] / (task_info["n_batches_trained_this_epoch"] + 0.000_000_01)
)
all_tr_metrics[task._name]["p_d_loss"] = float(
task_info["p_d_loss"] / (task_info["n_batches_trained_this_epoch"] + 0.000_000_01)
)
all_tr_metrics[task._name]["s_d_loss"] = float(
task_info["s_d_loss"] / (task_info["n_batches_trained_this_epoch"] + 0.000_000_01)
)
all_tr_metrics[task._name]["valid_loss"] = float(
task_info["valid_loss"] / (task_info["n_batches_trained_this_epoch"] + 0.000_000_01)
)
# Tensorboard - Training metrics for this epoch
for metric_name, value in all_tr_metrics[task._name].items():
self._tensorboard.add_train_scalar(
name="task_" + task._name + "/" + metric_name, value=value
)
logger.info("Train - End")
return all_tr_metrics
def _validation(self, n_epoch: int) -> Tuple[float, int]:
### Begin validation of the model ###
logger.info("Validation - Begin")
all_val_metrics = {}
self._model.eval() # Set the model into evaluation mode
avg_accuracy = 0.0
for task_idx, task in enumerate(self._task_list):
logger.info("Validation - Task {}/{}: {}", task_idx + 1, self._n_tasks, task._name)
val_loss = 0.0
n_batches_val_this_epoch_this_task = 0
n_val_batches = self._task_infos[task._name]["n_val_batches"]
scheduler = self._schedulers[task._name]
# Create tqdm generator for current tasks's validation
data_iterator = task._data_iterator
val_generator = data_iterator(task._validation_data, num_epochs=1, shuffle=False)
val_generator_tqdm = tqdm.tqdm(val_generator, total=n_val_batches)
# Iterate over each validation batch for this tasks
for batch in val_generator_tqdm:
n_batches_val_this_epoch_this_task += 1
# Get the loss
val_output_dict = self._forward(batch, task=task, for_training=False)
loss = val_output_dict["stm_loss"]
val_loss += loss.item()
del loss
# Get metrics for all progress so far, update tqdm, display description
task_metrics = self._get_metrics(task=task)
task_metrics["loss"] = float(val_loss / n_batches_val_this_epoch_this_task)
description = training_util.description_from_metrics(task_metrics)
val_generator_tqdm.set_description(description)
# Get tasks validation metrics and store them in all_val_metrics
task_metrics = self._get_metrics(task=task, reset=True)
if task._name not in all_val_metrics:
all_val_metrics[task._name] = {}
for name, value in task_metrics.items():
all_val_metrics[task._name][name] = value
all_val_metrics[task._name]["loss"] = float(val_loss / n_batches_val_this_epoch_this_task)
avg_accuracy += task_metrics["sentiment_acc"]
# Tensorboard - Validation metrics for this epoch
for metric_name, value in all_val_metrics[task._name].items():
self._tensorboard.add_validation_scalar(
name="task_" + task._name + "/" + metric_name, value=value
)
### Perform a patience check and update the history of validation metric for this tasks ###
this_epoch_val_metric = all_val_metrics[task._name][task._val_metric]
metric_history = self._metric_infos[task._name]["hist"]
metric_history.append(this_epoch_val_metric)
is_best_so_far, out_of_patience = self._check_history(
metric_history=metric_history,
cur_score=this_epoch_val_metric,
should_decrease=task._val_metric_decreases,
)
if is_best_so_far:
logger.info("Best model found for {}.", task._name)
self._metric_infos[task._name]["best"] = (n_epoch, all_val_metrics)
if out_of_patience and not self._metric_infos[task._name]["is_out_of_patience"]:
self._metric_infos[task._name]["is_out_of_patience"] = True
logger.info("Task {} is out of patience and vote to stop the training.", task._name)
# The LRScheduler API is agnostic to whether your schedule requires a validation metric -
# if it doesn't, the validation metric passed here is ignored.
scheduler.step(this_epoch_val_metric, n_epoch)
logger.info("Validation - End")
return all_val_metrics, avg_accuracy
def train(self, recover: bool = False) -> Dict[str, Any]:
# 1 train sentiment classifier & private classifier & domain embeddings => init G 50 epoch
# 2 fix share encoder(+domain embeddings?), train share classifier(cls&real/fake) & others => train D
# 3 fix share classifier, train share encoder, reverse share classifier input gradient min loss => train G
training_start_time = time.time()
if recover:
try:
n_epoch, should_stop = self._restore_checkpoint()
logger.info("Loaded model from checkpoint. Starting at epoch {}", n_epoch)
except RuntimeError:
raise ConfigurationError(
"Could not recover training from the checkpoint. Did you mean to output to "
"a different serialization directory or delete the existing serialization "
"directory?"
)
else:
n_epoch, should_stop = 0, False
### Store all the necessary informations and attributes about the tasks ###
task_infos = {task._name: {} for task in self._task_list}
for task_idx, task in enumerate(self._task_list):
task_info = task_infos[task._name]
# Store statistiscs on training and validation batches
data_iterator = task._data_iterator
n_tr_batches = data_iterator.get_num_batches(task._train_data)
n_val_batches = data_iterator.get_num_batches(task._validation_data)
task_info["n_tr_batches"] = n_tr_batches
task_info["n_val_batches"] = n_val_batches
# Create counter for number of batches trained during the whole
# training for this specific tasks
task_info["total_n_batches_trained"] = 0
task_info["last_log"] = time.time() # Time of last logging
self._task_infos = task_infos
### Bookkeeping the validation metrics ###
metric_infos = {
task._name: {
"val_metric": task._val_metric,
"hist": [],
"is_out_of_patience": False,
"min_lr_hit": False,
"best": (-1, {}),
}
for task in self._task_list
}
self._metric_infos = metric_infos
### Write log ###
total_n_tr_batches = 0 # The total number of training batches across all the datasets.
for task_name, info in self._task_infos.items():
total_n_tr_batches += info["n_tr_batches"]
logger.info("Task {}:", task_name)
logger.info("\t{} training batches", info["n_tr_batches"])
logger.info("\t{} validation batches", info["n_val_batches"])
### Create the training generators/iterators tqdm ###
self._tr_generators = {}
for task in self._task_list:
data_iterator = task._data_iterator
tr_generator = data_iterator(task._train_data, num_epochs=None)
self._tr_generators[task._name] = tr_generator
### Create sampling probability distribution ###
if self._sampling_method == "uniform":
sampling_prob = [float(1 / self._n_tasks)] * self._n_tasks
elif self._sampling_method == "proportional":
sampling_prob = [float(info["n_tr_batches"] / total_n_tr_batches) for info in self._task_infos.values()]
### Enable gradient clipping ###
# Only if self._grad_clipping is specified
self._enable_gradient_clipping()
### Setup is ready. Training of the model can begin ###
logger.info("Set up ready. Beginning training/validation.")
avg_accuracies = []
best_accuracy = 0.0
### Begin Training of the model ###
while not should_stop:
### Log Infos: current epoch count and CPU/GPU usage ###
logger.info("")
logger.info("Epoch {}/{} - Begin", n_epoch, self._num_epochs - 1)
logger.info(f"Peak CPU memory usage MB: {peak_memory_mb()}")
for gpu, memory in gpu_memory_mb().items():
logger.info(f"GPU {gpu} memory usage MB: {memory}")
# if n_epoch <= 10:
# # init generator
# all_tr_metrics = self._train_epoch(total_n_tr_batches, sampling_prob)
# # train discriminator 3 epochs
# # elif 10 < n_epoch < 20 or n_epoch % 2 == 0:
# # all_tr_metrics = self._train_epoch(total_n_tr_batches, sampling_prob, train_D=True)
# else:
# train adversarial generator every 3 epoch
all_tr_metrics = self._train_epoch(total_n_tr_batches, sampling_prob, reverse=True)
all_val_metrics, avg_accuracy = self._validation(n_epoch)
is_best = False
if best_accuracy < avg_accuracy:
best_accuracy = avg_accuracy
logger.info("Best accuracy found --- {}", best_accuracy / self._n_tasks)
is_best = True
### Print all training and validation metrics for this epoch ###
logger.info("***** Epoch {}/{} Statistics *****", n_epoch, self._num_epochs - 1)
for task in self._task_list:
logger.info("Statistic: {}", task._name)
logger.info(
"\tTraining - {}: {:3d}",
"Nb batches trained",
self._task_infos[task._name]["n_batches_trained_this_epoch"],
)
for metric_name, value in all_tr_metrics[task._name].items():
logger.info("\tTraining - {}: {:.3f}", metric_name, value)
for metric_name, value in all_val_metrics[task._name].items():
logger.info("\tValidation - {}: {:.3f}", metric_name, value)
logger.info("***** Average accuracy is {:.6f} *****", avg_accuracy / self._n_tasks)
avg_accuracies.append(avg_accuracy / self._n_tasks)
logger.info("**********")
### Check to see if should stop ###
stop_tr, stop_val = True, True
for task in self._task_list:
# task_info = self._task_infos[tasks._name]
if self._optimizers[task._name]['exclude_share_encoder'].param_groups[0]["lr"] < self._min_lr and \
self._optimizers[task._name]['exclude_share_discriminator'].param_groups[0][
"lr"] < self._min_lr:
logger.info("Minimum lr hit on {}.", task._name)
logger.info("Task {} vote to stop training.", task._name)
metric_infos[task._name]["min_lr_hit"] = True
stop_tr = stop_tr and self._metric_infos[task._name]["min_lr_hit"]
stop_val = stop_val and self._metric_infos[task._name]["is_out_of_patience"]
if stop_tr:
should_stop = True
logger.info("All tasks hit minimum lr. Stopping training.")
if stop_val:
should_stop = True
logger.info("All metrics ran out of patience. Stopping training.")
if n_epoch >= self._num_epochs - 1:
should_stop = True
logger.info("Maximum number of epoch hit. Stopping training.")
self._save_checkpoint(n_epoch, should_stop, is_best)
### Update n_epoch ###
# One epoch = doing N (forward + backward) pass where N is the total number of training batches.
n_epoch += 1
self._epoch_trained = n_epoch
logger.info("Max accuracy is {:.6f}", max(avg_accuracies))
### Summarize training at the end ###
logger.info("***** Training is finished *****")
logger.info("Stopped training after {} epochs", n_epoch)
return_metrics = {}
for task_name, task_info in self._task_infos.items():
nb_epoch_trained = int(task_info["total_n_batches_trained"] / task_info["n_tr_batches"])
logger.info(
"Trained {} for {} batches ~= {} epochs",
task_name,
task_info["total_n_batches_trained"],
nb_epoch_trained,
)
return_metrics[task_name] = {
"best_epoch": self._metric_infos[task_name]["best"][0],
"nb_epoch_trained": nb_epoch_trained,
"best_epoch_val_metrics": self._metric_infos[task_name]["best"][1],
}
training_elapsed_time = time.time() - training_start_time
return_metrics["training_duration"] = time.strftime("%d:%H:%M:%S", time.gmtime(training_elapsed_time))
return_metrics["nb_epoch_trained"] = n_epoch
return return_metrics
def _check_history(self, metric_history: List[float], cur_score: float, should_decrease: bool = False):
patience = self._patience + 1
best_fn = min if should_decrease else max
best_score = best_fn(metric_history)
if best_score == cur_score:
best_so_far = metric_history.index(best_score) == len(metric_history) - 1
else:
best_so_far = False
out_of_patience = False
if len(metric_history) > patience:
if should_decrease:
out_of_patience = max(metric_history[-patience:]) <= cur_score
else:
out_of_patience = min(metric_history[-patience:]) >= cur_score
if best_so_far and out_of_patience: # then something is up
print("Something is up")
return best_so_far, out_of_patience
def _forward(self, tensor_batch: torch.Tensor, task: Task = None, for_training: bool = False, reverse=False):
if task is not None:
# tensor_batch = move_to_device(tensor_batch, self._cuda_device)
output_dict = self._model.forward(
task_name=task._name, tensor_batch=tensor_batch, reverse=reverse, for_training=for_training,
epoch_trained=self._epoch_trained
)
if for_training:
try:
# loss = output_dict["stm_loss"]
output_dict["p_d_loss"] += self._model.get_regularization_penalty()
except KeyError:
raise RuntimeError(
"The model you are trying to optimize does not contain a"
" `loss` key in the output of model.forward(inputs)."
)
return output_dict
else:
raise ConfigurationError("Cannot call forward through tasks `None`")
def _get_metrics(self, task: Task, reset: bool = False):
task_tagger = getattr(self._model, "_tagger_" + task._name)
return task_tagger.get_metrics(reset)
def _save_checkpoint(self, epoch: int, should_stop: bool, is_best: bool = False) -> None:
### Saving training state ###
training_state = {
"epoch": epoch,
"should_stop": should_stop,
"metric_infos": self._metric_infos,
"task_infos": self._task_infos,
"schedulers": {},
"optimizers": {},
}
if self._optimizers is not None:
for task_name, optimizers in self._optimizers.items():
training_state["optimizers"][task_name] = {}
for params_name, optimizer in optimizers.items():
training_state["optimizers"][task_name][params_name] = optimizer.state_dict()
if self._schedulers is not None:
for task_name, scheduler in self._schedulers.items():
training_state["schedulers"][task_name] = scheduler.lr_scheduler.state_dict()
training_path = os.path.join(self._serialization_dir, "training_state.th")
torch.save(training_state, training_path)
logger.info("Checkpoint - Saved training state to {}", training_path)
### Saving model state ###
model_path = os.path.join(self._serialization_dir, "model_state.th")
model_state = self._model.state_dict()
torch.save(model_state, model_path)
logger.info("Checkpoint - Saved model state to {}", model_path)
if is_best:
logger.info("Checkpoint - Best validation performance so far for all tasks")
logger.info("Checkpoint - Copying weights to '{}/best_all.th'.", self._serialization_dir)
shutil.copyfile(model_path, os.path.join(self._serialization_dir, "best_all.th"))
### Saving best models for each tasks ###
for task_name, infos in self._metric_infos.items():
best_epoch, _ = infos["best"]
if best_epoch == epoch:
logger.info("Checkpoint - Best validation performance so far for {} tasks", task_name)
logger.info("Checkpoint - Copying weights to '{}/best_{}.th'.", self._serialization_dir, task_name)
shutil.copyfile(model_path, os.path.join(self._serialization_dir, "best_{}.th".format(task_name)))
@classmethod
def from_params(cls, model: Model, task_list: List[Task], serialization_dir: str,
params: Params) -> "GanMtlTrainer":
optimizer_params = params.pop("optimizer")
lr_scheduler_params = params.pop("scheduler")
patience = params.pop_int("patience", 2)
num_epochs = params.pop_int("num_epochs", 20)
cuda_device = params.pop_int("cuda_device", -1)
gradient_accumulation_steps = params.pop_int("gradient_accumulation_steps", 1)
grad_norm = params.pop_float("grad_norm", None)
grad_clipping = params.pop_float("grad_clipping", None)
min_lr = params.pop_float("min_lr", 0.00001)
no_tqdm = params.pop_bool("no_tqdm", False)
summary_interval = params.pop("summary_interval", 30)
histogram_interval = params.pop("histogram_interval", 30)
sampling_method = params.pop("sampling_method", "proportional")
params.assert_empty(cls.__name__)
return GanMtlTrainer(
model=model,
task_list=task_list,
optimizer_params=optimizer_params,
lr_scheduler_params=lr_scheduler_params,
patience=patience,
num_epochs=num_epochs,
serialization_dir=serialization_dir,
cuda_device=cuda_device,
gradient_accumulation_steps=gradient_accumulation_steps,
grad_norm=grad_norm,
grad_clipping=grad_clipping,
min_lr=min_lr,
no_tqdm=no_tqdm,
summary_interval=summary_interval,
histogram_interval=histogram_interval,
sampling_method=sampling_method
)
def __init__(
self,
model: Model,
optimizer: torch.optim.Optimizer,
data_loader: torch.utils.data.DataLoader,
adv_policy: adv_utils.AdvTrainingPolicy,
patience: Optional[int] = None,
validation_metric: str = "-loss",
validation_data_loader: torch.utils.data.DataLoader = None,
num_epochs: int = 20,
serialization_dir: Optional[str] = None,
num_serialized_models_to_keep: int = 20,
keep_serialized_model_every_num_seconds: int = None,
checkpointer: Checkpointer = None,
model_save_interval: float = None,
cuda_device: int = -1,
grad_norm: Optional[float] = None,
grad_clipping: Optional[float] = None,
learning_rate_scheduler: Optional[LearningRateScheduler] = None,
momentum_scheduler: Optional[MomentumScheduler] = None,
summary_interval: int = 100,
histogram_interval: int = None,
should_log_parameter_statistics: bool = True,
should_log_learning_rate: bool = False,
log_batch_size_period: Optional[int] = None,
moving_average: Optional[MovingAverage] = None,
batch_callbacks: List[BatchCallback] = None,
epoch_callbacks: List[EpochCallback] = None,
distributed: bool = False,
local_rank: int = 0,
world_size: int = 1,
opt_level: Optional[str] = None,
) -> None:
"""
A trainer for doing supervised learning. It just takes a labeled dataset
and a `DataLoader`, and uses the supplied `Optimizer` to learn the weights
for your model over some fixed number of epochs. You can also pass in a validation
dataloader and enable early stopping. There are many other bells and whistles as well.
# Parameters
model : `Model`, required.
An AllenNLP model to be optimized. Pytorch Modules can also be optimized if
their `forward` method returns a dictionary with a "loss" key, containing a
scalar tensor representing the loss function to be optimized.
If you are training your model using GPUs, your model should already be
on the correct device. (If you use `Trainer.from_params` this will be
handled for you.)
optimizer : `torch.nn.Optimizer`, required.
An instance of a Pytorch Optimizer, instantiated with the parameters of the
model to be optimized.
data_loader : `DataLoader`, required.
A pytorch `DataLoader` containing your `Dataset`, yielding padded indexed batches.
patience : Optional[int] > 0, optional (default=None)
Number of epochs to be patient before early stopping: the training is stopped
after `patience` epochs with no improvement. If given, it must be `> 0`.
If None, early stopping is disabled.
validation_metric : str, optional (default="loss")
Validation metric to measure for whether to stop training using patience
and whether to serialize an `is_best` model each epoch. The metric name
must be prepended with either "+" or "-", which specifies whether the metric
is an increasing or decreasing function.
validation_dataloader : `DataLoader`, optional (default=None)
A `DataLoader` to use for the validation set. If `None`, then
use the training `DataLoader` with the validation data.
num_epochs : int, optional (default = 20)
Number of training epochs.
serialization_dir : str, optional (default=None)
Path to directory for saving and loading model files. Models will not be saved if
this parameter is not passed.
num_serialized_models_to_keep : `int`, optional (default=20)
Number of previous model checkpoints to retain. Default is to keep 20 checkpoints.
A value of None or -1 means all checkpoints will be kept.
keep_serialized_model_every_num_seconds : `int`, optional (default=None)
If num_serialized_models_to_keep is not None, then occasionally it's useful to
save models at a given interval in addition to the last num_serialized_models_to_keep.
To do so, specify keep_serialized_model_every_num_seconds as the number of seconds
between permanently saved checkpoints. Note that this option is only used if
num_serialized_models_to_keep is not None, otherwise all checkpoints are kept.
checkpointer : `Checkpointer`, optional (default=None)
An instance of class Checkpointer to use instead of the default. If a checkpointer is specified,
the arguments num_serialized_models_to_keep and keep_serialized_model_every_num_seconds should
not be specified. The caller is responsible for initializing the checkpointer so that it is
consistent with serialization_dir.
model_save_interval : `float`, optional (default=None)
If provided, then serialize models every `model_save_interval`
seconds within single epochs. In all cases, models are also saved
at the end of every epoch if `serialization_dir` is provided.
cuda_device : `int`, optional (default = -1)
An integer specifying the CUDA device(s) to use for this process. If -1, the CPU is used.
Data parallelism is controlled at the allennlp train level, so each trainer will have a single
GPU.
grad_norm : `float`, optional, (default = None).
If provided, gradient norms will be rescaled to have a maximum of this value.
grad_clipping : `float`, optional (default = `None`).
If provided, gradients will be clipped `during the backward pass` to have an (absolute)
maximum of this value. If you are getting `NaNs` in your gradients during training
that are not solved by using `grad_norm`, you may need this.
learning_rate_scheduler : `LearningRateScheduler`, optional (default = None)
If specified, the learning rate will be decayed with respect to
this schedule at the end of each epoch (or batch, if the scheduler implements
the `step_batch` method). If you use `torch.optim.lr_scheduler.ReduceLROnPlateau`,
this will use the `validation_metric` provided to determine if learning has plateaued.
To support updating the learning rate on every batch, this can optionally implement
`step_batch(batch_num_total)` which updates the learning rate given the batch number.
momentum_scheduler : `MomentumScheduler`, optional (default = None)
If specified, the momentum will be updated at the end of each batch or epoch
according to the schedule.
summary_interval : `int`, optional, (default = 100)
Number of batches between logging scalars to tensorboard
histogram_interval : `int`, optional, (default = `None`)
If not None, then log histograms to tensorboard every `histogram_interval` batches.
When this parameter is specified, the following additional logging is enabled:
* Histograms of model parameters
* The ratio of parameter update norm to parameter norm
* Histogram of layer activations
We log histograms of the parameters returned by
`model.get_parameters_for_histogram_tensorboard_logging`.
The layer activations are logged for any modules in the `Model` that have
the attribute `should_log_activations` set to `True`. Logging
histograms requires a number of GPU-CPU copies during training and is typically
slow, so we recommend logging histograms relatively infrequently.
Note: only Modules that return tensors, tuples of tensors or dicts
with tensors as values currently support activation logging.
should_log_parameter_statistics : `bool`, optional, (default = True)
Whether to send parameter statistics (mean and standard deviation
of parameters and gradients) to tensorboard.
should_log_learning_rate : `bool`, optional, (default = False)
Whether to send parameter specific learning rate to tensorboard.
log_batch_size_period : `int`, optional, (default = `None`)
If defined, how often to log the average batch size.
moving_average : `MovingAverage`, optional, (default = None)
If provided, we will maintain moving averages for all parameters. During training, we
employ a shadow variable for each parameter, which maintains the moving average. During
evaluation, we backup the original parameters and assign the moving averages to corresponding
parameters. Be careful that when saving the checkpoint, we will save the moving averages of
parameters. This is necessary because we want the saved model to perform as well as the validated
model if we load it later. But this may cause problems if you restart the training from checkpoint.
distributed : `bool`, optional, (default = False)
If set, PyTorch's `DistributedDataParallel` is used to train the model in multiple GPUs. This also
requires `world_size` to be greater than 1.
local_rank : `int`, optional, (default = 0)
This is the unique identifier of the `Trainer` in a distributed process group. The GPU device id is
used as the rank.
world_size : `int`, (default = 1)
The number of `Trainer` workers participating in the distributed training.
opt_level : `str`, optional, (default = `None`)
Each opt_level establishes a set of properties that govern Amp’s implementation of pure or mixed
precision training. Must be a choice of `"O0"`, `"O1"`, `"O2"`, or `"O3"`.
See the Apex [documentation](https://nvidia.github.io/apex/amp.html#opt-levels-and-properties) for
more details. If `None`, Amp is not used. Defaults to `None`.
"""
super().__init__(serialization_dir, cuda_device, distributed,
local_rank, world_size)
# I am not calling move_to_gpu here, because if the model is
# not already on the GPU then the optimizer is going to be wrong.
self.model = model
self.data_loader = data_loader
self._validation_data_loader = validation_data_loader
self.optimizer = optimizer
self.adv_policy = adv_policy
if patience is None: # no early stopping
if validation_data_loader:
logger.warning(
"You provided a validation dataset but patience was set to None, "
"meaning that early stopping is disabled")
elif (not isinstance(patience, int)) or patience <= 0:
raise ConfigurationError(
'{} is an invalid value for "patience": it must be a positive integer '
"or None (if you want to disable early stopping)".format(
patience))
# For tracking is_best_so_far and should_stop_early
self._metric_tracker = MetricTracker(patience, validation_metric)
# Get rid of + or -
self._validation_metric = validation_metric[1:]
self._num_epochs = num_epochs
if checkpointer is not None:
# We can't easily check if these parameters were passed in, so check against their default values.
# We don't check against serialization_dir since it is also used by the parent class.
if (num_serialized_models_to_keep != 20
or keep_serialized_model_every_num_seconds is not None):
raise ConfigurationError(
"When passing a custom Checkpointer, you may not also pass in separate checkpointer "
"args 'num_serialized_models_to_keep' or 'keep_serialized_model_every_num_seconds'."
)
self._checkpointer = checkpointer
else:
self._checkpointer = Checkpointer(
serialization_dir,
keep_serialized_model_every_num_seconds,
num_serialized_models_to_keep,
)
self._model_save_interval = model_save_interval
self._grad_norm = grad_norm
self._grad_clipping = grad_clipping
self._learning_rate_scheduler = learning_rate_scheduler
self._momentum_scheduler = momentum_scheduler
self._moving_average = moving_average
self._batch_callbacks = batch_callbacks or []
self._epoch_callbacks = epoch_callbacks or []
# We keep the total batch number as an instance variable because it
# is used inside a closure for the hook which logs activations in
# `_enable_activation_logging`.
self._batch_num_total = 0
self._tensorboard = TensorboardWriter(
get_batch_num_total=lambda: self._batch_num_total,
serialization_dir=serialization_dir,
summary_interval=summary_interval,
histogram_interval=histogram_interval,
should_log_parameter_statistics=should_log_parameter_statistics,
should_log_learning_rate=should_log_learning_rate,
)
self._log_batch_size_period = log_batch_size_period
self._last_log = 0.0 # time of last logging
# Enable activation logging.
if histogram_interval is not None:
self._tensorboard.enable_activation_logging(self.model)
# Enable automatic mixed precision training with NVIDIA Apex.
self._opt_level = opt_level
if self._opt_level is not None:
if amp is None:
raise ConfigurationError((
"Apex not installed but opt_level was provided. Please install NVIDIA's Apex to enable"
" automatic mixed precision (AMP) training. See: https://github.com/NVIDIA/apex."
))
self.model, self.optimizer = amp.initialize(
self.model, self.optimizer, opt_level=self._opt_level)
# Using `DistributedDataParallel`(ddp) brings in a quirk wrt AllenNLP's `Model` interface and its
# usage. A `Model` object is wrapped by `ddp`, but assigning the wrapped model to `self.model`
# will break the usages such as `Model.get_regularization_penalty`, `Model.get_metrics`, etc.
#
# Hence a reference to Pytorch's object is maintained in the case of distributed training and in the
# normal case, reference to `Model` is retained. This reference is only used in
# these places: `model.__call__`, `model.train` and `model.eval`.
if self._distributed:
self._pytorch_model = DistributedDataParallel(
self.model,
device_ids=[self.cuda_device],
find_unused_parameters=True)
else:
self._pytorch_model = self.model
def test_model_training(self):
training_dataset = self.sample_instances if self.sample_only else self.train_instances
#training_dataset = training_dataset[:500]
validation_dataset = self.sample_instances if self.sample_only else self.test_instances
serialization_dir = self.TEST_DATA_ROOT / "serialized_sample" if self.sample_only else "serialized"
tensorboard_dir = self.TEST_DATA_ROOT / "tensorboard.seq2seq"
batch_size = 64
train_iterator = BucketIterator(sorting_keys=[("source_tokens",
"num_tokens")],
padding_noise=0.1,
batch_size=batch_size)
train_iterator.index_with(vocab=self.vocab)
multiproc_iterator = MultiprocessIterator(train_iterator,
num_workers=4,
output_queue_size=6000)
tensorboard = TensorboardWriter(get_batch_num_total=lambda: np.ceil(
len(training_dataset) / batch_size),
serialization_dir=tensorboard_dir,
summary_interval=5,
histogram_interval=5,
should_log_parameter_statistics=True,
should_log_learning_rate=True)
optimizer = torch.optim.Adam(self.model.parameters(), lr=1e-3)
scheduler = CosineWithRestarts(optimizer=optimizer, t_initial=5)
trainer = CallbackTrainer(
model=self.model,
serialization_dir=serialization_dir,
iterator=multiproc_iterator,
training_data=self.train_instances,
num_epochs=100,
cuda_device=0,
optimizer=optimizer,
callbacks=[
LogToTensorboard(tensorboard),
Validate(validation_data=self.test_instances,
validation_iterator=multiproc_iterator),
TrackMetrics(),
ResetMetricsCallback(),
UpdateLearningRate(scheduler),
ValidationLogCallback(self.train_reader, self.test_instances)
])
# trainer = Trainer(model=self.model,
# serialization_dir=serialization_dir,
# iterator=train_iterator,
# train_dataset=training_dataset,
# num_epochs=1,
# cuda_device=0,
# optimizer=torch.optim.Adam(self.model.parameters(), lr=1e-3),
# validation_dataset=training_dataset,
# validation_iterator=train_iterator,
# should_log_learning_rate=True,
# learning_rate_scheduler=scheduler
# )
# for i in range(50):
# print('Epoch: {}'.format(i))
# trainer.train()
#
# import itertools
#
# predictor = Seq2SeqPredictor(self.model, self.train_reader)
#
# for instance in itertools.islice(training_dataset, 10):
# print('SOURCE:', instance.fields['source_tokens'].tokens)
# print('GOLD:', instance.fields['target_tokens'].tokens)
# print('PRED:', predictor.predict_instance(instance)['predicted_tokens'])
#
# self.val_outputs_fp.close()
trainer.train()
def __init__(
self,
model: Model,
task_list: List[Task],
optimizer_params: Params,
lr_scheduler_params: Params,
patience: Optional[int] = None,
num_epochs: int = 20,
serialization_dir: str = None,
cuda_device: int = -1,
gradient_accumulation_steps: int = 1,
grad_norm: Optional[float] = None,
grad_clipping: Optional[float] = None,
min_lr: float = 0.00001,
no_tqdm: bool = False,
summary_interval: int = 10,
histogram_interval: int = 10,
log_parameter_statistics: bool = False,
log_gradient_statistics: bool = False,
):
"""
Parameters
----------
model: ``Model``, required.
An AllenNLP model to be optimized. Pytorch Modules can also be optimized if
their ``forward`` method returns a dictionary with a "loss" key, containing a
scalar tensor representing the loss function to be optimized.
iterator: ``DataIterator``, required.
A method for iterating over a ``Dataset``, yielding padded indexed batches.
patience: Optional[int] > 0, optional (default=None)
Number of epochs to be patient before early stopping: the training is stopped
after ``patience`` epochs with no improvement. If given, it must be ``> 0``.
If None, early stopping is disabled.
num_epochs: int, optional (default = 20)
Number of training epochs.
serialization_dir: str, optional (default=None)
Path to directory for saving and loading model files. Models will not be saved if
this parameter is not passed.
cuda_device: int, optional (default = -1)
An integer specifying the CUDA device to use. If -1, the CPU is used.
Multi-gpu training is not currently supported, but will be once the
Pytorch DataParallel API stabilises.
grad_norm: float, optional, (default = None).
If provided, gradient norms will be rescaled to have a maximum of this value.
grad_clipping : float, optional (default = None).
If provided, gradients will be clipped `during the backward pass` to have an (absolute)
maximum of this value. If you are getting ``NaNs`` in your gradients during training
that are not solved by using ``grad_norm``, you may need this.
no_tqdm : bool, optional (default=False)
We use ``tqdm`` for logging, which will print a nice progress bar that updates in place
after every batch. This is nice if you're running training on a local shell, but can
cause problems with log files from, e.g., a docker image running on kubernetes. If
``no_tqdm`` is ``True``, we will not use tqdm, and instead log batch statistics using
``logger.info``.
"""
self._model = model
parameters_to_train = [(n, p)
for n, p in self._model.named_parameters()
if p.requires_grad]
self._task_list = task_list
self._n_tasks = len(self._task_list)
self._optimizer_params = optimizer_params
self._optimizers = {}
self._lr_scheduler_params = lr_scheduler_params
self._schedulers = {}
for task in self._task_list:
task_name = task._name
self._optimizers[task_name] = Optimizer.from_params(
model_parameters=parameters_to_train,
params=deepcopy(optimizer_params))
self._schedulers[task_name] = LearningRateScheduler.from_params(
optimizer=self._optimizers[task_name],
params=deepcopy(lr_scheduler_params))
self._serialization_dir = serialization_dir
self._patience = patience
self._num_epochs = num_epochs
self._cuda_device = cuda_device
if self._cuda_device >= 0:
check_for_gpu(self._cuda_device)
self._model = self._model.cuda(self._cuda_device)
self._gradient_accumulation_steps = gradient_accumulation_steps
self._grad_norm = grad_norm
self._grad_clipping = grad_clipping
self._min_lr = min_lr
self._task_infos = None
self._metric_infos = None
self._tr_generators = None
self._no_tqdm = no_tqdm
self._summary_interval = summary_interval # num batches between logging to tensorboard
# self._log_parameter_statistics = log_parameter_statistics
# self._log_gradient_statistics = log_gradient_statistics
self._global_step = 0
# train_log = SummaryWriter(os.path.join(self._serialization_dir, "log", "train"))
# validation_log = SummaryWriter(os.path.join(self._serialization_dir, "log", "validation"))
# self._tensorboard = TensorboardWriter(train_log=train_log, validation_log=validation_log)
self._batch_num_total = 0
self._tensorboard = TensorboardWriter(
get_batch_num_total=lambda: self._batch_num_total,
serialization_dir=serialization_dir,
summary_interval=summary_interval,
histogram_interval=histogram_interval,
should_log_learning_rate=True)
# Enable activation logging.
if histogram_interval is not None:
self._tensorboard.enable_activation_logging(self._model)
def record_loss(outputs_dict: Dict, tensorboard: TensorboardWriter):
for key, value in outputs_dict.items():
if key.endswith("loss"):
tensorboard.add_train_scalar("loss/%s" % key, value)
class Trainer(TrainerBase):
"""
1. epoch todo
2. loss todo
"""
def __init__(self,
model: Model,
optimizer: torch.optim.Optimizer,
iterator: DataIterator,
train_dataset: Iterable[Instance],
validation_dataset: Optional[Iterable[Instance]] = None,
patience: Optional[int] = None,
validation_metric: str = "-loss",
validation_iterator: DataIterator = None,
shuffle: bool = True,
num_epochs: int = 20,
serialization_dir: Optional[str] = None,
num_serialized_models_to_keep: int = 0,
keep_serialized_model_every_num_seconds: int = None,
checkpointer: Checkpointer = None,
model_save_interval: float = None,
cuda_device: Union[int, List] = -1,
grad_norm: Optional[float] = None,
grad_clipping: Optional[float] = None,
learning_rate_scheduler: Optional[LearningRateScheduler] = None,
momentum_scheduler: Optional[MomentumScheduler] = None,
summary_interval: int = 100,
histogram_interval: int = None,
should_log_parameter_statistics: bool = True,
should_log_learning_rate: bool = False,
log_batch_size_period: Optional[int] = None,
moving_average: Optional[MovingAverage] = None,
callbacks: List[allennlp_callback.Callback]=None,
early_stopping_by_batch: bool=True,
estimator: Estimator=None,
) -> None:
"""
A trainer for doing supervised learning. It just takes a labeled dataset
and a ``DataIterator``, and uses the supplied ``Optimizer`` to learn the weights
for your model over some fixed number of epochs. You can also pass in a validation
dataset and enable early stopping. There are many other bells and whistles as well.
Parameters
----------
model : ``Model``, required.
An AllenNLP model to be optimized. Pytorch Modules can also be optimized if
their ``forward`` method returns a dictionary with a "loss" key, containing a
scalar tensor representing the loss function to be optimized.
If you are training your model using GPUs, your model should already be
on the correct device. (If you use `Trainer.from_params` this will be
handled for you.)
optimizer : ``torch.nn.Optimizer``, required.
An instance of a Pytorch Optimizer, instantiated with the parameters of the
model to be optimized.
iterator : ``DataIterator``, required.
A method for iterating over a ``Dataset``, yielding padded indexed batches.
train_dataset : ``Dataset``, required.
A ``Dataset`` to train on. The dataset should have already been indexed.
validation_dataset : ``Dataset``, optional, (default = None).
A ``Dataset`` to evaluate on. The dataset should have already been indexed.
patience : Optional[int] > 0, optional (default=None)
Number of epochs to be patient before early stopping: the training is stopped
after ``patience`` epochs with no improvement. If given, it must be ``> 0``.
If None, early stopping is disabled.
validation_metric : str, optional (default="loss")
Validation metric to measure for whether to stop training using patience
and whether to serialize an ``is_best`` model each epoch. The metric name
must be prepended with either "+" or "-", which specifies whether the metric
is an increasing or decreasing function.
validation_iterator : ``DataIterator``, optional (default=None)
An iterator to use for the validation set. If ``None``, then
use the training `iterator`.
shuffle: ``bool``, optional (default=True)
Whether to shuffle the instances in the iterator or not.
num_epochs : int, optional (default = 20)
Number of training epochs.
serialization_dir : str, optional (default=None)
Path to directory for saving and loading model files. Models will not be saved if
this parameter is not passed.
num_serialized_models_to_keep : ``int``, optional (default=20)
Number of previous model checkpoints to retain. Default is to keep 20 checkpoints.
A value of None or -1 means all checkpoints will be kept.
keep_serialized_model_every_num_seconds : ``int``, optional (default=None)
If num_serialized_models_to_keep is not None, then occasionally it's useful to
save models at a given interval in addition to the last num_serialized_models_to_keep.
To do so, specify keep_serialized_model_every_num_seconds as the number of seconds
between permanently saved checkpoints. Note that this option is only used if
num_serialized_models_to_keep is not None, otherwise all checkpoints are kept.
checkpointer : ``Checkpointer``, optional (default=None)
An instance of class Checkpointer to use instead of the default. If a checkpointer is specified,
the arguments num_serialized_models_to_keep and keep_serialized_model_every_num_seconds should
not be specified. The caller is responsible for initializing the checkpointer so that it is
consistent with serialization_dir.
model_save_interval : ``float``, optional (default=None)
If provided, then serialize models every ``model_save_interval``
seconds within single epochs. In all cases, models are also saved
at the end of every epoch if ``serialization_dir`` is provided.
cuda_device : ``Union[int, List[int]]``, optional (default = -1)
An integer or list of integers specifying the CUDA device(s) to use. If -1, the CPU is used.
grad_norm : ``float``, optional, (default = None).
If provided, gradient norms will be rescaled to have a maximum of this value.
grad_clipping : ``float``, optional (default = ``None``).
If provided, gradients will be clipped `during the backward pass` to have an (absolute)
maximum of this value. If you are getting ``NaNs`` in your gradients during training
that are not solved by using ``grad_norm``, you may need this.
learning_rate_scheduler : ``LearningRateScheduler``, optional (default = None)
If specified, the learning rate will be decayed with respect to
this schedule at the end of each epoch (or batch, if the scheduler implements
the ``step_batch`` method). If you use :class:`torch.optim.lr_scheduler.ReduceLROnPlateau`,
this will use the ``validation_metric`` provided to determine if learning has plateaued.
To support updating the learning rate on every batch, this can optionally implement
``step_batch(batch_num_total)`` which updates the learning rate given the batch number.
momentum_scheduler : ``MomentumScheduler``, optional (default = None)
If specified, the momentum will be updated at the end of each batch or epoch
according to the schedule.
summary_interval: ``int``, optional, (default = 100)
Number of batches between logging scalars to tensorboard
histogram_interval : ``int``, optional, (default = ``None``)
If not None, then log histograms to tensorboard every ``histogram_interval`` batches.
When this parameter is specified, the following additional logging is enabled:
* Histograms of model parameters
* The ratio of parameter update norm to parameter norm
* Histogram of layer activations
We log histograms of the parameters returned by
``model.get_parameters_for_histogram_tensorboard_logging``.
The layer activations are logged for any modules in the ``Model`` that have
the attribute ``should_log_activations`` set to ``True``. Logging
histograms requires a number of GPU-CPU copies during training and is typically
slow, so we recommend logging histograms relatively infrequently.
Note: only Modules that return tensors, tuples of tensors or dicts
with tensors as values currently support activation logging.
should_log_parameter_statistics : ``bool``, optional, (default = True)
Whether to send parameter statistics (mean and standard deviation
of parameters and gradients) to tensorboard.
should_log_learning_rate : ``bool``, optional, (default = False)
Whether to send parameter specific learning rate to tensorboard.
log_batch_size_period : ``int``, optional, (default = ``None``)
If defined, how often to log the average batch size.
moving_average: ``MovingAverage``, optional, (default = None)
If provided, we will maintain moving averages for all parameters. During training, we
employ a shadow variable for each parameter, which maintains the moving average. During
evaluation, we backup the original parameters and assign the moving averages to corresponding
parameters. Be careful that when saving the checkpoint, we will save the moving averages of
parameters. This is necessary because we want the saved model to perform as well as the validated
model if we load it later. But this may cause problems if you restart the training from checkpoint.
"""
super().__init__(serialization_dir, cuda_device)
# I am not calling move_to_gpu here, because if the model is
# not already on the GPU then the optimizer is going to be wrong.
self.model = model
self.iterator = iterator
self._validation_iterator = validation_iterator
self.shuffle = shuffle
self.optimizer = optimizer
self.train_data = train_dataset
self._validation_data = validation_dataset
if patience is None: # no early stopping
if validation_dataset:
logger.warning('You provided a validation dataset but patience was set to None, '
'meaning that early stopping is disabled')
elif (not isinstance(patience, int)) or patience <= 0:
raise ConfigurationError('{} is an invalid value for "patience": it must be a positive integer '
'or None (if you want to disable early stopping)'.format(patience))
# For tracking is_best_so_far and should_stop_early
self._metric_tracker = MetricTracker(patience, validation_metric)
# Get rid of + or -
self._validation_metric = validation_metric[1:]
self._num_epochs = num_epochs
if checkpointer is not None:
# We can't easily check if these parameters were passed in, so check against their default values.
# We don't check against serialization_dir since it is also used by the parent class.
if num_serialized_models_to_keep != 20 or \
keep_serialized_model_every_num_seconds is not None:
raise ConfigurationError(
"When passing a custom Checkpointer, you may not also pass in separate checkpointer "
"args 'num_serialized_models_to_keep' or 'keep_serialized_model_every_num_seconds'.")
self._checkpointer = checkpointer
else:
self._checkpointer = Checkpointer(serialization_dir,
keep_serialized_model_every_num_seconds,
num_serialized_models_to_keep)
self._model_save_interval = model_save_interval
self._grad_norm = grad_norm
self._grad_clipping = grad_clipping
self._learning_rate_scheduler = learning_rate_scheduler
self._momentum_scheduler = momentum_scheduler
self._moving_average = moving_average
# We keep the total batch number as an instance variable because it
# is used inside a closure for the hook which logs activations in
# ``_enable_activation_logging``.
self._batch_num_total = 0
self._tensorboard = TensorboardWriter(
get_batch_num_total=lambda: self._batch_num_total,
serialization_dir=serialization_dir,
summary_interval=summary_interval,
histogram_interval=histogram_interval,
should_log_parameter_statistics=should_log_parameter_statistics,
should_log_learning_rate=should_log_learning_rate)
self._log_batch_size_period = log_batch_size_period
self._last_log = 0.0 # time of last logging
# Enable activation logging.
if histogram_interval is not None:
self._tensorboard.enable_activation_logging(self.model)
self.callbacks = callbacks
self._early_stopping_by_batch = early_stopping_by_batch
self._estimator = estimator
def rescale_gradients(self) -> Optional[float]:
return training_util.rescale_gradients(self.model, self._grad_norm)
def batch_loss(self, batch_group: List[TensorDict], for_training: bool) -> torch.Tensor:
"""
Does a forward pass on the given batches and returns the ``loss`` value in the result.
If ``for_training`` is `True` also applies regularization penalty.
"""
if self._multiple_gpu:
output_dict = training_util.data_parallel(batch_group, self.model, self._cuda_devices)
else:
assert len(batch_group) == 1
batch = batch_group[0]
batch = nn_util.move_to_device(batch, self._cuda_devices[0])
output_dict = self.model(**batch)
try:
loss = output_dict["loss"]
if for_training:
loss += self.model.get_regularization_penalty()
except KeyError:
if for_training:
raise RuntimeError("The model you are trying to optimize does not contain a"
" 'loss' key in the output of model.forward(inputs).")
loss = None
return loss
def _train_epoch(self, epoch: int) -> Dict[str, float]:
"""
Trains one epoch and returns metrics.
"""
logger.info("Epoch %d/%d", epoch, self._num_epochs - 1)
peak_cpu_usage = peak_memory_mb()
logger.info(f"Peak CPU memory usage MB: {peak_cpu_usage}")
gpu_usage = []
for gpu, memory in gpu_memory_mb().items():
gpu_usage.append((gpu, memory))
logger.info(f"GPU {gpu} memory usage MB: {memory}")
train_loss = 0.0
# Set the model to "train" mode.
self.model.train()
num_gpus = len(self._cuda_devices)
# Get tqdm for the training batches
raw_train_generator = self.iterator(self.train_data,
num_epochs=1,
shuffle=self.shuffle)
train_generator = lazy_groups_of(raw_train_generator, num_gpus)
num_training_batches = math.ceil(self.iterator.get_num_batches(self.train_data)/num_gpus)
self._last_log = time.time()
last_save_time = time.time()
batches_this_epoch = 0
if self._batch_num_total is None:
self._batch_num_total = 0
histogram_parameters = set(self.model.get_parameters_for_histogram_tensorboard_logging())
logger.info("Training")
train_generator_tqdm = Tqdm.tqdm(train_generator,
total=num_training_batches)
cumulative_batch_size = 0
for batch_group in train_generator_tqdm:
self.model.train()
batches_this_epoch += 1
self._batch_num_total += 1
batch_num_total = self._batch_num_total
self.optimizer.zero_grad()
loss = self.batch_loss(batch_group, for_training=True)
if torch.isnan(loss):
raise ValueError("nan loss encountered")
loss.backward()
train_loss += loss.item()
batch_grad_norm = self.rescale_gradients()
# This does nothing if batch_num_total is None or you are using a
# scheduler which doesn't update per batch.
if self._learning_rate_scheduler:
self._learning_rate_scheduler.step_batch(batch_num_total)
if self._momentum_scheduler:
self._momentum_scheduler.step_batch(batch_num_total)
if self._tensorboard.should_log_histograms_this_batch():
# get the magnitude of parameter updates for logging
# We need a copy of current parameters to compute magnitude of updates,
# and copy them to CPU so large models won't go OOM on the GPU.
param_updates = {name: param.detach().cpu().clone()
for name, param in self.model.named_parameters()}
self.optimizer.step()
for name, param in self.model.named_parameters():
param_updates[name].sub_(param.detach().cpu())
update_norm = torch.norm(param_updates[name].view(-1, ))
param_norm = torch.norm(param.view(-1, )).cpu()
self._tensorboard.add_train_scalar("gradient_update/" + name,
update_norm / (param_norm + 1e-7))
else:
self.optimizer.step()
# Update moving averages
if self._moving_average is not None:
self._moving_average.apply(batch_num_total)
# Update the description with the latest metrics
metrics = training_util.get_metrics(self.model, train_loss, batches_this_epoch)
description = training_util.description_from_metrics(metrics)
train_generator_tqdm.set_description(description, refresh=False)
# Log parameter values to Tensorboard
if self._tensorboard.should_log_this_batch():
self._tensorboard.log_parameter_and_gradient_statistics(self.model, batch_grad_norm)
self._tensorboard.log_learning_rates(self.model, self.optimizer)
self._tensorboard.add_train_scalar("loss/loss_train", metrics["loss"])
self._tensorboard.log_metrics({"epoch_metrics/" + k: v for k, v in metrics.items()})
if self._tensorboard.should_log_histograms_this_batch():
self._tensorboard.log_histograms(self.model, histogram_parameters)
if self._log_batch_size_period:
cur_batch = sum([training_util.get_batch_size(batch) for batch in batch_group])
cumulative_batch_size += cur_batch
if (batches_this_epoch - 1) % self._log_batch_size_period == 0:
average = cumulative_batch_size/batches_this_epoch
logger.info(f"current batch size: {cur_batch} mean batch size: {average}")
self._tensorboard.add_train_scalar("current_batch_size", cur_batch)
self._tensorboard.add_train_scalar("mean_batch_size", average)
# Save model if needed.
if self._model_save_interval is not None and (
time.time() - last_save_time > self._model_save_interval
):
last_save_time = time.time()
self._save_checkpoint(
'{0}.{1}'.format(epoch, training_util.time_to_str(int(last_save_time)))
)
if self._early_stopping_by_batch and self._batch_num_total % 10 == 0:
if self._validation_data is not None:
with torch.no_grad():
# We have a validation set, so compute all the metrics on it.
val_loss, num_batches = self._validation_loss()
val_metrics = training_util.get_metrics(self.model, val_loss, num_batches, reset=True)
# Check validation metric for early stopping
this_epoch_val_metric = val_metrics[self._validation_metric]
self._metric_tracker.add_metric(this_epoch_val_metric)
if self._metric_tracker.is_best_so_far():
metrics['best_batch'] = self._batch_num_total
for key, value in val_metrics.items():
metrics["best_validation_" + key] = value
self._metric_tracker.best_epoch_metrics = val_metrics
self._save_checkpoint(self._batch_num_total)
if self.callbacks is not None:
for callback in self.callbacks:
callback.on_batch_end(self._batch_num_total)
metrics = training_util.get_metrics(self.model, train_loss, batches_this_epoch, reset=True)
metrics['cpu_memory_MB'] = peak_cpu_usage
for (gpu_num, memory) in gpu_usage:
metrics['gpu_'+str(gpu_num)+'_memory_MB'] = memory
return metrics
def _validation_loss(self) -> Tuple[float, int]:
"""
Computes the validation loss. Returns it and the number of batches.
"""
logger.info("Validating")
self.model.eval()
# Replace parameter values with the shadow values from the moving averages.
if self._moving_average is not None:
self._moving_average.assign_average_value()
if self._validation_iterator is not None:
val_iterator = self._validation_iterator
else:
val_iterator = self.iterator
num_gpus = len(self._cuda_devices)
raw_val_generator = val_iterator(self._validation_data,
num_epochs=1,
shuffle=False)
val_generator = lazy_groups_of(raw_val_generator, num_gpus)
num_validation_batches = math.ceil(val_iterator.get_num_batches(self._validation_data)/num_gpus)
val_generator_tqdm = Tqdm.tqdm(val_generator,
total=num_validation_batches)
batches_this_epoch = 0
val_loss = 0
for batch_group in val_generator_tqdm:
loss = self.batch_loss(batch_group, for_training=False)
if loss is not None:
# You shouldn't necessarily have to compute a loss for validation, so we allow for
# `loss` to be None. We need to be careful, though - `batches_this_epoch` is
# currently only used as the divisor for the loss function, so we can safely only
# count those batches for which we actually have a loss. If this variable ever
# gets used for something else, we might need to change things around a bit.
batches_this_epoch += 1
val_loss += loss.detach().cpu().numpy()
# Update the description with the latest metrics
val_metrics = training_util.get_metrics(self.model, val_loss, batches_this_epoch)
description = training_util.description_from_metrics(val_metrics)
val_generator_tqdm.set_description(description, refresh=False)
# Now restore the original parameter values.
if self._moving_average is not None:
self._moving_average.restore()
return val_loss, batches_this_epoch
def train(self) -> Dict[str, Any]:
"""
Trains the supplied model with the supplied parameters.
"""
try:
epoch_counter = self._restore_checkpoint()
except RuntimeError:
traceback.print_exc()
raise ConfigurationError("Could not recover training from the checkpoint. Did you mean to output to "
"a different serialization directory or delete the existing serialization "
"directory?")
training_util.enable_gradient_clipping(self.model, self._grad_clipping)
logger.info("Beginning training.")
train_metrics: Dict[str, float] = {}
val_metrics: Dict[str, float] = {}
this_epoch_val_metric: float = None
metrics: Dict[str, Any] = {}
epochs_trained = 0
training_start_time = time.time()
metrics['best_epoch'] = self._metric_tracker.best_epoch
for key, value in self._metric_tracker.best_epoch_metrics.items():
metrics["best_validation_" + key] = value
if self.callbacks is not None:
with torch.no_grad():
for callback in self.callbacks:
callback.on_train_begin()
for epoch in range(epoch_counter, self._num_epochs):
epoch_start_time = time.time()
if self.callbacks is not None:
with torch.no_grad():
for callback in self.callbacks:
callback.on_epoch_begin(epoch)
train_metrics = self._train_epoch(epoch)
if not self._early_stopping_by_batch:
# get peak of memory usage
if 'cpu_memory_MB' in train_metrics:
metrics['peak_cpu_memory_MB'] = max(metrics.get('peak_cpu_memory_MB', 0),
train_metrics['cpu_memory_MB'])
for key, value in train_metrics.items():
if key.startswith('gpu_'):
metrics["peak_"+key] = max(metrics.get("peak_"+key, 0), value)
if self._validation_data is not None:
with torch.no_grad():
val_metrics_temp = self._estimator.estimate(self._validation_data)
# We have a validation set, so compute all the metrics on it.
# val_loss, num_batches = self._validation_loss()
# val_metrics = training_util.get_metrics(self.model, val_loss, num_batches, reset=True)
val_metrics = {'loss': 0}
if 'sentiment_acc' in val_metrics_temp:
val_metrics['accuracy'] = val_metrics_temp['sentiment_acc']
if 'category_f1' in val_metrics_temp:
val_metrics['category_f1'] = val_metrics_temp['category_f1']['fscore']
if 'other_metrics' in val_metrics_temp and 'merge_micro_f1' in val_metrics_temp['other_metrics']:
val_metrics['merge_micro_f1'] = val_metrics_temp['other_metrics']['merge_micro_f1']
# Check validation metric for early stopping
val_metrics.update(val_metrics_temp)
this_epoch_val_metric = val_metrics[self._validation_metric]
self._metric_tracker.add_metric(this_epoch_val_metric)
if self._metric_tracker.should_stop_early():
logger.info("Ran out of patience. Stopping training.")
break
self._tensorboard.log_metrics(train_metrics,
val_metrics=val_metrics,
log_to_console=True,
epoch=epoch + 1) # +1 because tensorboard doesn't like 0
# Create overall metrics dict
training_elapsed_time = time.time() - training_start_time
metrics["training_duration"] = str(datetime.timedelta(seconds=training_elapsed_time))
metrics["training_start_epoch"] = epoch_counter
metrics["training_epochs"] = epochs_trained
metrics["epoch"] = epoch
for key, value in train_metrics.items():
metrics["training_" + key] = value
for key, value in val_metrics.items():
metrics["validation_" + key] = value
if self._metric_tracker.is_best_so_far():
# Update all the best_ metrics.
# (Otherwise they just stay the same as they were.)
metrics['best_epoch'] = epoch
for key, value in val_metrics.items():
metrics["best_validation_" + key] = value
self._metric_tracker.best_epoch_metrics = val_metrics
if self._serialization_dir:
dump_metrics(os.path.join(self._serialization_dir, f'metrics_epoch_{epoch}.json'), metrics)
# The Scheduler API is agnostic to whether your schedule requires a validation metric -
# if it doesn't, the validation metric passed here is ignored.
if self._learning_rate_scheduler:
self._learning_rate_scheduler.step(this_epoch_val_metric, epoch)
if self._momentum_scheduler:
self._momentum_scheduler.step(this_epoch_val_metric, epoch)
self._save_checkpoint(epoch)
else:
if self._metric_tracker.should_stop_early():
logger.info("Ran out of patience. Stopping training.")
break
epoch_elapsed_time = time.time() - epoch_start_time
logger.info("Epoch duration: %s", datetime.timedelta(seconds=epoch_elapsed_time))
if epoch < self._num_epochs - 1:
training_elapsed_time = time.time() - training_start_time
estimated_time_remaining = training_elapsed_time * \
((self._num_epochs - epoch_counter) / float(epoch - epoch_counter + 1) - 1)
formatted_time = str(datetime.timedelta(seconds=int(estimated_time_remaining)))
logger.info("Estimated training time remaining: %s", formatted_time)
if self.callbacks is not None:
with torch.no_grad():
for callback in self.callbacks:
callback.on_epoch_end(epoch)
epochs_trained += 1
# make sure pending events are flushed to disk and files are closed properly
# self._tensorboard.close()
# Load the best model state before returning
best_model_state = self._checkpointer.best_model_state()
if best_model_state:
self.model.load_state_dict(best_model_state)
return metrics
def _save_checkpoint(self, epoch: Union[int, str]) -> None:
"""
Saves a checkpoint of the model to self._serialization_dir.
Is a no-op if self._serialization_dir is None.
Parameters
----------
epoch : Union[int, str], required.
The epoch of training. If the checkpoint is saved in the middle
of an epoch, the parameter is a string with the epoch and timestamp.
"""
# If moving averages are used for parameters, we save
# the moving average values into checkpoint, instead of the current values.
if self._moving_average is not None:
self._moving_average.assign_average_value()
# These are the training states we need to persist.
training_states = {
"metric_tracker": self._metric_tracker.state_dict(),
"optimizer": self.optimizer.state_dict(),
"batch_num_total": self._batch_num_total
}
# If we have a learning rate or momentum scheduler, we should persist them too.
if self._learning_rate_scheduler is not None:
training_states["learning_rate_scheduler"] = self._learning_rate_scheduler.state_dict()
if self._momentum_scheduler is not None:
training_states["momentum_scheduler"] = self._momentum_scheduler.state_dict()
self._checkpointer.save_checkpoint(
model_state=self.model.state_dict(),
epoch=epoch,
training_states=training_states,
is_best_so_far=self._metric_tracker.is_best_so_far())
# Restore the original values for parameters so that training will not be affected.
if self._moving_average is not None:
self._moving_average.restore()
def _restore_checkpoint(self) -> int:
"""
Restores the model and training state from the last saved checkpoint.
This includes an epoch count and optimizer state, which is serialized separately
from model parameters. This function should only be used to continue training -
if you wish to load a model for inference/load parts of a model into a new
computation graph, you should use the native Pytorch functions:
`` model.load_state_dict(torch.load("/path/to/model/weights.th"))``
If ``self._serialization_dir`` does not exist or does not contain any checkpointed weights,
this function will do nothing and return 0.
Returns
-------
epoch: int
The epoch at which to resume training, which should be one after the epoch
in the saved training state.
"""
model_state, training_state = self._checkpointer.restore_checkpoint()
if not training_state:
# No checkpoint to restore, start at 0
return 0
self.model.load_state_dict(model_state)
self.optimizer.load_state_dict(training_state["optimizer"])
if self._learning_rate_scheduler is not None and "learning_rate_scheduler" in training_state:
self._learning_rate_scheduler.load_state_dict(training_state["learning_rate_scheduler"])
if self._momentum_scheduler is not None and "momentum_scheduler" in training_state:
self._momentum_scheduler.load_state_dict(training_state["momentum_scheduler"])
training_util.move_optimizer_to_cuda(self.optimizer)
# Currently the ``training_state`` contains a serialized ``MetricTracker``.
if "metric_tracker" in training_state:
self._metric_tracker.load_state_dict(training_state["metric_tracker"])
# It used to be the case that we tracked ``val_metric_per_epoch``.
elif "val_metric_per_epoch" in training_state:
self._metric_tracker.clear()
self._metric_tracker.add_metrics(training_state["val_metric_per_epoch"])
# And before that we didn't track anything.
else:
self._metric_tracker.clear()
if isinstance(training_state["epoch"], int):
epoch_to_return = training_state["epoch"] + 1
else:
epoch_to_return = int(training_state["epoch"].split('.')[0]) + 1
# For older checkpoints with batch_num_total missing, default to old behavior where
# it is unchanged.
batch_num_total = training_state.get('batch_num_total')
if batch_num_total is not None:
self._batch_num_total = batch_num_total
return epoch_to_return
# Requires custom from_params.
@classmethod
def from_params(cls, # type: ignore
model: Model,
serialization_dir: str,
iterator: DataIterator,
train_data: Iterable[Instance],
validation_data: Optional[Iterable[Instance]],
params: Params,
validation_iterator: DataIterator = None) -> 'Trainer':
# pylint: disable=arguments-differ
patience = params.pop_int("patience", None)
validation_metric = params.pop("validation_metric", "-loss")
shuffle = params.pop_bool("shuffle", True)
num_epochs = params.pop_int("num_epochs", 20)
cuda_device = parse_cuda_device(params.pop("cuda_device", -1))
grad_norm = params.pop_float("grad_norm", None)
grad_clipping = params.pop_float("grad_clipping", None)
lr_scheduler_params = params.pop("learning_rate_scheduler", None)
momentum_scheduler_params = params.pop("momentum_scheduler", None)
if isinstance(cuda_device, list):
model_device = cuda_device[0]
else:
model_device = cuda_device
if model_device >= 0:
# Moving model to GPU here so that the optimizer state gets constructed on
# the right device.
model = model.cuda(model_device)
parameters = [[n, p] for n, p in model.named_parameters() if p.requires_grad]
optimizer = Optimizer.from_params(parameters, params.pop("optimizer"))
if "moving_average" in params:
moving_average = MovingAverage.from_params(params.pop("moving_average"), parameters=parameters)
else:
moving_average = None
if lr_scheduler_params:
lr_scheduler = LearningRateScheduler.from_params(optimizer, lr_scheduler_params)
else:
lr_scheduler = None
if momentum_scheduler_params:
momentum_scheduler = MomentumScheduler.from_params(optimizer, momentum_scheduler_params)
else:
momentum_scheduler = None
if 'checkpointer' in params:
if 'keep_serialized_model_every_num_seconds' in params or \
'num_serialized_models_to_keep' in params:
raise ConfigurationError(
"Checkpointer may be initialized either from the 'checkpointer' key or from the "
"keys 'num_serialized_models_to_keep' and 'keep_serialized_model_every_num_seconds'"
" but the passed config uses both methods.")
checkpointer = Checkpointer.from_params(params.pop("checkpointer"))
else:
num_serialized_models_to_keep = params.pop_int("num_serialized_models_to_keep", 20)
keep_serialized_model_every_num_seconds = params.pop_int(
"keep_serialized_model_every_num_seconds", None)
checkpointer = Checkpointer(
serialization_dir=serialization_dir,
num_serialized_models_to_keep=num_serialized_models_to_keep,
keep_serialized_model_every_num_seconds=keep_serialized_model_every_num_seconds)
model_save_interval = params.pop_float("model_save_interval", None)
summary_interval = params.pop_int("summary_interval", 100)
histogram_interval = params.pop_int("histogram_interval", None)
should_log_parameter_statistics = params.pop_bool("should_log_parameter_statistics", True)
should_log_learning_rate = params.pop_bool("should_log_learning_rate", False)
log_batch_size_period = params.pop_int("log_batch_size_period", None)
params.assert_empty(cls.__name__)
return cls(model, optimizer, iterator,
train_data, validation_data,
patience=patience,
validation_metric=validation_metric,
validation_iterator=validation_iterator,
shuffle=shuffle,
num_epochs=num_epochs,
serialization_dir=serialization_dir,
cuda_device=cuda_device,
grad_norm=grad_norm,
grad_clipping=grad_clipping,
learning_rate_scheduler=lr_scheduler,
momentum_scheduler=momentum_scheduler,
checkpointer=checkpointer,
model_save_interval=model_save_interval,
summary_interval=summary_interval,
histogram_interval=histogram_interval,
should_log_parameter_statistics=should_log_parameter_statistics,
should_log_learning_rate=should_log_learning_rate,
log_batch_size_period=log_batch_size_period,
moving_average=moving_average)
def __init__(
self,
model: Model,
optimizer: torch.optim.Optimizer,
data_loader: torch.utils.data.DataLoader,
patience: Optional[int] = None,
validation_metric: str = "-loss",
validation_data_loader: torch.utils.data.DataLoader = None,
num_epochs: int = 20,
serialization_dir: Optional[str] = None,
checkpointer: Checkpointer = None,
cuda_device: int = -1,
grad_norm: Optional[float] = None,
grad_clipping: Optional[float] = None,
learning_rate_scheduler: Optional[LearningRateScheduler] = None,
momentum_scheduler: Optional[MomentumScheduler] = None,
tensorboard_writer: TensorboardWriter = None,
moving_average: Optional[MovingAverage] = None,
batch_callbacks: List[BatchCallback] = None,
epoch_callbacks: List[EpochCallback] = None,
distributed: bool = False,
local_rank: int = 0,
world_size: int = 1,
num_gradient_accumulation_steps: int = 1,
opt_level: Optional[str] = None,
) -> None:
super().__init__(serialization_dir, cuda_device, distributed,
local_rank, world_size)
# I am not calling move_to_gpu here, because if the model is
# not already on the GPU then the optimizer is going to be wrong.
self.model = model
self.data_loader = data_loader
self._validation_data_loader = validation_data_loader
self.optimizer = optimizer
if patience is None: # no early stopping
if validation_data_loader:
logger.warning(
"You provided a validation dataset but patience was set to None, "
"meaning that early stopping is disabled")
elif (not isinstance(patience, int)) or patience <= 0:
raise ConfigurationError(
'{} is an invalid value for "patience": it must be a positive integer '
"or None (if you want to disable early stopping)".format(
patience))
# For tracking is_best_so_far and should_stop_early
self._metric_tracker = MetricTracker(patience, validation_metric)
# Get rid of + or -
self._validation_metric = validation_metric[1:]
self._num_epochs = num_epochs
if checkpointer is not None:
self._checkpointer = checkpointer
else:
self._checkpointer = Checkpointer(serialization_dir)
self._grad_norm = grad_norm
self._grad_clipping = grad_clipping
self._learning_rate_scheduler = learning_rate_scheduler
self._momentum_scheduler = momentum_scheduler
self._moving_average = moving_average
self._batch_callbacks = batch_callbacks or []
self._epoch_callbacks = epoch_callbacks or []
# We keep the total batch number as an instance variable because it
# is used inside a closure for the hook which logs activations in
# `_enable_activation_logging`.
self._batch_num_total = 0
self._tensorboard = tensorboard_writer or TensorboardWriter(
serialization_dir)
self._tensorboard.get_batch_num_total = lambda: self._batch_num_total
self._tensorboard.enable_activation_logging(self.model)
self._last_log = 0.0 # time of last logging
self._num_gradient_accumulation_steps = num_gradient_accumulation_steps
# Enable automatic mixed precision training with NVIDIA Apex.
self._opt_level = opt_level
if self._opt_level is not None:
if amp is None:
raise ConfigurationError((
"Apex not installed but opt_level was provided. Please install NVIDIA's Apex to enable"
" automatic mixed precision (AMP) training. See: https://github.com/NVIDIA/apex."
))
self.model, self.optimizer = amp.initialize(
self.model, self.optimizer, opt_level=self._opt_level)
# Using `DistributedDataParallel`(ddp) brings in a quirk wrt AllenNLP's `Model` interface and its
# usage. A `Model` object is wrapped by `ddp`, but assigning the wrapped model to `self.model`
# will break the usages such as `Model.get_regularization_penalty`, `Model.get_metrics`, etc.
#
# Hence a reference to Pytorch's object is maintained in the case of distributed training and in the
# normal case, reference to `Model` is retained. This reference is only used in
# these places: `model.__call__`, `model.train` and `model.eval`.
if self._distributed:
self._pytorch_model = DistributedDataParallel(
self.model,
device_ids=[self.cuda_device],
find_unused_parameters=True)
else:
self._pytorch_model = self.model
class Trainer(TrainerBase):
def __init__(
self,
model: Model,
optimizer: torch.optim.Optimizer,
iterator: DataIterator,
train_dataset: Iterable[Instance],
validation_dataset: Optional[Iterable[Instance]] = None,
train_low_dataset: Optional[Iterable[Instance]] = None,
patience: Optional[int] = None,
validation_metric: str = "-loss",
validation_iterator: DataIterator = None,
shuffle: bool = True,
num_epochs: int = 20,
serialization_dir: Optional[str] = None,
num_serialized_models_to_keep: int = 20,
keep_serialized_model_every_num_seconds: int = None,
checkpointer: Checkpointer = None,
model_save_interval: float = None,
cuda_device: Union[int, List] = -1,
grad_norm: Optional[float] = None,
grad_clipping: Optional[float] = None,
learning_rate_scheduler: Optional[LearningRateScheduler] = None,
momentum_scheduler: Optional[MomentumScheduler] = None,
summary_interval: int = 100,
histogram_interval: int = None,
should_log_parameter_statistics: bool = True,
should_log_learning_rate: bool = False,
log_batch_size_period: Optional[int] = None,
moving_average: Optional[MovingAverage] = None,
epoch_low_start: Optional[int] = None,
epoch_without_improvement_low_start: Optional[int] = None,
) -> None:
super().__init__(serialization_dir, cuda_device)
# I am not calling move_to_gpu here, because if the model is
# not already on the GPU then the optimizer is going to be wrong.
self.model = model
self.iterator = iterator
self._validation_iterator = validation_iterator
self.shuffle = shuffle
self.optimizer = optimizer
self.train_data = train_dataset
self._validation_data = validation_dataset
self._train_low_data = train_low_dataset
# set when to train with low-data only / with defaults
self._epoch_low_start = epoch_low_start or 10
self._epoch_without_improvement_low_start = epoch_without_improvement_low_start or 5
if patience is None: # no early stopping
if validation_dataset:
logger.warning(
'You provided a validation dataset but patience was set to None, '
'meaning that early stopping is disabled')
elif (not isinstance(patience, int)) or patience <= 0:
raise ConfigurationError(
'{} is an invalid value for "patience": it must be a positive integer '
'or None (if you want to disable early stopping)'.format(
patience))
# For tracking is_best_so_far and should_stop_early
self._metric_tracker = MetricTracker(patience, validation_metric)
# AX: custom parameter for reinforce trainer
self._metric_tracker.reinforce_start_with_low = None
# Get rid of + or -
self._validation_metric = validation_metric[1:]
self._num_epochs = num_epochs
if checkpointer is not None:
# We can't easily check if these parameters were passed in, so check against their default values.
# We don't check against serialization_dir since it is also used by the parent class.
if num_serialized_models_to_keep != 20 or \
keep_serialized_model_every_num_seconds is not None:
raise ConfigurationError(
"When passing a custom Checkpointer, you may not also pass in separate checkpointer "
"args 'num_serialized_models_to_keep' or 'keep_serialized_model_every_num_seconds'."
)
self._checkpointer = checkpointer
else:
self._checkpointer = Checkpointer(
serialization_dir, keep_serialized_model_every_num_seconds,
num_serialized_models_to_keep)
self._model_save_interval = model_save_interval
self._grad_norm = grad_norm
self._grad_clipping = grad_clipping
self._learning_rate_scheduler = learning_rate_scheduler
self._momentum_scheduler = momentum_scheduler
self._moving_average = moving_average
# We keep the total batch number as an instance variable because it
# is used inside a closure for the hook which logs activations in
# ``_enable_activation_logging``.
self._batch_num_total = 0
self._tensorboard = TensorboardWriter(
get_batch_num_total=lambda: self._batch_num_total,
serialization_dir=serialization_dir,
summary_interval=summary_interval,
histogram_interval=histogram_interval,
should_log_parameter_statistics=should_log_parameter_statistics,
should_log_learning_rate=should_log_learning_rate)
self._log_batch_size_period = log_batch_size_period
self._last_log = 0.0 # time of last logging
# Enable activation logging.
if histogram_interval is not None:
self._tensorboard.enable_activation_logging(self.model)
def rescale_gradients(self) -> Optional[float]:
return training_util.rescale_gradients(self.model, self._grad_norm)
def batch_loss(self, batch_group: List[TensorDict],
for_training: bool) -> torch.Tensor:
"""
Does a forward pass on the given batches and returns the ``loss`` value in the result.
If ``for_training`` is `True` also applies regularization penalty.
"""
if self._multiple_gpu:
output_dict = training_util.data_parallel(batch_group, self.model,
self._cuda_devices)
else:
assert len(batch_group) == 1
batch = batch_group[0]
batch = nn_util.move_to_device(batch, self._cuda_devices[0])
output_dict = self.model(**batch)
try:
loss = output_dict["loss"]
if for_training:
loss += self.model.get_regularization_penalty()
except KeyError:
if for_training:
raise RuntimeError(
"The model you are trying to optimize does not contain a"
" 'loss' key in the output of model.forward(inputs).")
loss = None
return loss
def _train_epoch(self, epoch: int) -> Dict[str, float]:
"""
Trains one epoch and returns metrics.
"""
logger.info("Epoch %d/%d", epoch, self._num_epochs - 1)
peak_cpu_usage = peak_memory_mb()
logger.info(f"Peak CPU memory usage MB: {peak_cpu_usage}")
gpu_usage = []
for gpu, memory in gpu_memory_mb().items():
gpu_usage.append((gpu, memory))
logger.info(f"GPU {gpu} memory usage MB: {memory}")
train_loss = 0.0
# Set the model to "train" mode.
self.model.train()
num_gpus = len(self._cuda_devices)
if not self._metric_tracker.reinforce_start_with_low and (
epoch < self._epoch_low_start
or self._metric_tracker._epochs_with_no_improvement <
self._epoch_without_improvement_low_start):
train_data = self.train_data
else:
if not self._metric_tracker.reinforce_start_with_low:
self._metric_tracker.reinforce_start_with_low = epoch
train_data = self._train_low_data
# Get tqdm for the training batches
raw_train_generator = self.iterator(train_data,
num_epochs=1,
shuffle=self.shuffle)
train_generator = lazy_groups_of(raw_train_generator, num_gpus)
num_training_batches = math.ceil(
self.iterator.get_num_batches(train_data) / num_gpus)
self._last_log = time.time()
last_save_time = time.time()
batches_this_epoch = 0
if self._batch_num_total is None:
self._batch_num_total = 0
histogram_parameters = set(
self.model.get_parameters_for_histogram_tensorboard_logging())
logger.info("Training")
train_generator_tqdm = Tqdm.tqdm(train_generator,
total=num_training_batches)
cumulative_batch_size = 0
for batch_group in train_generator_tqdm:
batches_this_epoch += 1
self._batch_num_total += 1
batch_num_total = self._batch_num_total
self.optimizer.zero_grad()
loss = self.batch_loss(batch_group, for_training=True)
if torch.isnan(loss):
raise ValueError("nan loss encountered")
loss.backward()
train_loss += loss.item()
batch_grad_norm = self.rescale_gradients()
# This does nothing if batch_num_total is None or you are using a
# scheduler which doesn't update per batch.
if self._learning_rate_scheduler:
self._learning_rate_scheduler.step_batch(batch_num_total)
if self._momentum_scheduler:
self._momentum_scheduler.step_batch(batch_num_total)
if self._tensorboard.should_log_histograms_this_batch():
# get the magnitude of parameter updates for logging
# We need a copy of current parameters to compute magnitude of updates,
# and copy them to CPU so large models won't go OOM on the GPU.
param_updates = {
name: param.detach().cpu().clone()
for name, param in self.model.named_parameters()
}
self.optimizer.step()
for name, param in self.model.named_parameters():
param_updates[name].sub_(param.detach().cpu())
update_norm = torch.norm(param_updates[name].view(-1, ))
param_norm = torch.norm(param.view(-1, )).cpu()
self._tensorboard.add_train_scalar(
"gradient_update/" + name,
update_norm / (param_norm + 1e-7))
else:
self.optimizer.step()
# Update moving averages
if self._moving_average is not None:
self._moving_average.apply(batch_num_total)
# Update the description with the latest metrics
metrics = training_util.get_metrics(self.model, train_loss,
batches_this_epoch)
description = training_util.description_from_metrics(metrics)
train_generator_tqdm.set_description(description, refresh=False)
# Log parameter values to Tensorboard
if self._tensorboard.should_log_this_batch():
self._tensorboard.log_parameter_and_gradient_statistics(
self.model, batch_grad_norm)
self._tensorboard.log_learning_rates(self.model,
self.optimizer)
self._tensorboard.add_train_scalar("loss/loss_train",
metrics["loss"])
self._tensorboard.log_metrics(
{"epoch_metrics/" + k: v
for k, v in metrics.items()})
if self._tensorboard.should_log_histograms_this_batch():
self._tensorboard.log_histograms(self.model,
histogram_parameters)
if self._log_batch_size_period:
cur_batch = sum([
training_util.get_batch_size(batch)
for batch in batch_group
])
cumulative_batch_size += cur_batch
if (batches_this_epoch - 1) % self._log_batch_size_period == 0:
average = cumulative_batch_size / batches_this_epoch
logger.info(
f"current batch size: {cur_batch} mean batch size: {average}"
)
self._tensorboard.add_train_scalar("current_batch_size",
cur_batch)
self._tensorboard.add_train_scalar("mean_batch_size",
average)
# Save model if needed.
if self._model_save_interval is not None and (
time.time() - last_save_time > self._model_save_interval):
last_save_time = time.time()
self._save_checkpoint('{0}.{1}'.format(
epoch, training_util.time_to_str(int(last_save_time))))
metrics = training_util.get_metrics(self.model,
train_loss,
batches_this_epoch,
reset=True)
metrics['cpu_memory_MB'] = peak_cpu_usage
for (gpu_num, memory) in gpu_usage:
metrics['gpu_' + str(gpu_num) + '_memory_MB'] = memory
return metrics
def _validation_loss(self) -> Tuple[float, int]:
"""
Computes the validation loss. Returns it and the number of batches.
"""
logger.info("Validating")
self.model.eval()
# Replace parameter values with the shadow values from the moving averages.
if self._moving_average is not None:
self._moving_average.assign_average_value()
if self._validation_iterator is not None:
val_iterator = self._validation_iterator
else:
val_iterator = self.iterator
num_gpus = len(self._cuda_devices)
raw_val_generator = val_iterator(self._validation_data,
num_epochs=1,
shuffle=False)
val_generator = lazy_groups_of(raw_val_generator, num_gpus)
num_validation_batches = math.ceil(
val_iterator.get_num_batches(self._validation_data) / num_gpus)
val_generator_tqdm = Tqdm.tqdm(val_generator,
total=num_validation_batches)
batches_this_epoch = 0
val_loss = 0
for batch_group in val_generator_tqdm:
loss = self.batch_loss(batch_group, for_training=False)
if loss is not None:
# You shouldn't necessarily have to compute a loss for validation, so we allow for
# `loss` to be None. We need to be careful, though - `batches_this_epoch` is
# currently only used as the divisor for the loss function, so we can safely only
# count those batches for which we actually have a loss. If this variable ever
# gets used for something else, we might need to change things around a bit.
batches_this_epoch += 1
val_loss += loss.detach().cpu().numpy()
# Update the description with the latest metrics
val_metrics = training_util.get_metrics(self.model, val_loss,
batches_this_epoch)
description = training_util.description_from_metrics(val_metrics)
val_generator_tqdm.set_description(description, refresh=False)
# Now restore the original parameter values.
if self._moving_average is not None:
self._moving_average.restore()
return val_loss, batches_this_epoch
def train(self) -> Dict[str, Any]:
"""
Trains the supplied model with the supplied parameters.
"""
try:
epoch_counter = self._restore_checkpoint()
except RuntimeError:
traceback.print_exc()
raise ConfigurationError(
"Could not recover training from the checkpoint. Did you mean to output to "
"a different serialization directory or delete the existing serialization "
"directory?")
training_util.enable_gradient_clipping(self.model, self._grad_clipping)
logger.info("Beginning training.")
train_metrics: Dict[str, float] = {}
val_metrics: Dict[str, float] = {}
this_epoch_val_metric: float = None
metrics: Dict[str, Any] = {}
epochs_trained = 0
training_start_time = time.time()
metrics['best_epoch'] = self._metric_tracker.best_epoch
for key, value in self._metric_tracker.best_epoch_metrics.items():
metrics["best_validation_" + key] = value
for epoch in range(epoch_counter, self._num_epochs):
epoch_start_time = time.time()
train_metrics = self._train_epoch(epoch)
# AX: add custom value for epoch that low-training was started
metrics[
"reinforce_start_with_low"] = self._metric_tracker.reinforce_start_with_low
# get peak of memory usage
if 'cpu_memory_MB' in train_metrics:
metrics['peak_cpu_memory_MB'] = max(
metrics.get('peak_cpu_memory_MB', 0),
train_metrics['cpu_memory_MB'])
for key, value in train_metrics.items():
if key.startswith('gpu_'):
metrics["peak_" + key] = max(metrics.get("peak_" + key, 0),
value)
if self._validation_data is not None:
with torch.no_grad():
# We have a validation set, so compute all the metrics on it.
val_loss, num_batches = self._validation_loss()
val_metrics = training_util.get_metrics(self.model,
val_loss,
num_batches,
reset=True)
# Check validation metric for early stopping
this_epoch_val_metric = val_metrics[
self._validation_metric]
self._metric_tracker.add_metric(this_epoch_val_metric)
if self._metric_tracker.should_stop_early():
logger.info("Ran out of patience. Stopping training.")
break
self._tensorboard.log_metrics(
train_metrics,
val_metrics=val_metrics,
log_to_console=True,
epoch=epoch + 1) # +1 because tensorboard doesn't like 0
# Create overall metrics dict
training_elapsed_time = time.time() - training_start_time
metrics["training_duration"] = time.strftime(
"%H:%M:%S", time.gmtime(training_elapsed_time))
metrics["training_start_epoch"] = epoch_counter
metrics["training_epochs"] = epochs_trained
metrics["epoch"] = epoch
for key, value in train_metrics.items():
metrics["training_" + key] = value
for key, value in val_metrics.items():
metrics["validation_" + key] = value
if self._metric_tracker.is_best_so_far():
# Update all the best_ metrics.
# (Otherwise they just stay the same as they were.)
metrics['best_epoch'] = epoch
for key, value in val_metrics.items():
metrics["best_validation_" + key] = value
self._metric_tracker.best_epoch_metrics = val_metrics
if self._serialization_dir:
dump_metrics(
os.path.join(self._serialization_dir,
f'metrics_epoch_{epoch}.json'), metrics)
# The Scheduler API is agnostic to whether your schedule requires a validation metric -
# if it doesn't, the validation metric passed here is ignored.
if self._learning_rate_scheduler:
self._learning_rate_scheduler.step(this_epoch_val_metric,
epoch)
if self._momentum_scheduler:
self._momentum_scheduler.step(this_epoch_val_metric, epoch)
self._save_checkpoint(epoch)
epoch_elapsed_time = time.time() - epoch_start_time
logger.info(
"Epoch duration: %s",
time.strftime("%H:%M:%S", time.gmtime(epoch_elapsed_time)))
if epoch < self._num_epochs - 1:
training_elapsed_time = time.time() - training_start_time
estimated_time_remaining = training_elapsed_time * \
((self._num_epochs - epoch_counter) / float(epoch - epoch_counter + 1) - 1)
formatted_time = str(
datetime.timedelta(seconds=int(estimated_time_remaining)))
logger.info("Estimated training time remaining: %s",
formatted_time)
epochs_trained += 1
# Load the best model state before returning
best_model_state = self._checkpointer.best_model_state()
if best_model_state:
self.model.load_state_dict(best_model_state)
return metrics
def _save_checkpoint(self, epoch: Union[int, str]) -> None:
"""
Saves a checkpoint of the model to self._serialization_dir.
Is a no-op if self._serialization_dir is None.
Parameters
----------
epoch : Union[int, str], required.
The epoch of training. If the checkpoint is saved in the middle
of an epoch, the parameter is a string with the epoch and timestamp.
"""
# If moving averages are used for parameters, we save
# the moving average values into checkpoint, instead of the current values.
if self._moving_average is not None:
self._moving_average.assign_average_value()
# These are the training states we need to persist.
training_states = {
"metric_tracker": self._metric_tracker.state_dict(),
"optimizer": self.optimizer.state_dict(),
"batch_num_total": self._batch_num_total
}
# If we have a learning rate or momentum scheduler, we should persist them too.
if self._learning_rate_scheduler is not None:
training_states[
"learning_rate_scheduler"] = self._learning_rate_scheduler.state_dict(
)
if self._momentum_scheduler is not None:
training_states[
"momentum_scheduler"] = self._momentum_scheduler.state_dict()
self._checkpointer.save_checkpoint(
model_state=self.model.state_dict(),
epoch=epoch,
training_states=training_states,
is_best_so_far=self._metric_tracker.is_best_so_far())
# Restore the original values for parameters so that training will not be affected.
if self._moving_average is not None:
self._moving_average.restore()
def _restore_checkpoint(self) -> int:
"""
Restores the model and training state from the last saved checkpoint.
This includes an epoch count and optimizer state, which is serialized separately
from model parameters. This function should only be used to continue training -
if you wish to load a model for inference/load parts of a model into a new
computation graph, you should use the native Pytorch functions:
`` model.load_state_dict(torch.load("/path/to/model/weights.th"))``
If ``self._serialization_dir`` does not exist or does not contain any checkpointed weights,
this function will do nothing and return 0.
Returns
-------
epoch: int
The epoch at which to resume training, which should be one after the epoch
in the saved training state.
"""
model_state, training_state = self._checkpointer.restore_checkpoint()
if not training_state:
# No checkpoint to restore, start at 0
return 0
self.model.load_state_dict(model_state)
self.optimizer.load_state_dict(training_state["optimizer"])
if self._learning_rate_scheduler is not None and "learning_rate_scheduler" in training_state:
self._learning_rate_scheduler.load_state_dict(
training_state["learning_rate_scheduler"])
if self._momentum_scheduler is not None and "momentum_scheduler" in training_state:
self._momentum_scheduler.load_state_dict(
training_state["momentum_scheduler"])
training_util.move_optimizer_to_cuda(self.optimizer)
# Currently the ``training_state`` contains a serialized ``MetricTracker``.
if "metric_tracker" in training_state:
self._metric_tracker.load_state_dict(
training_state["metric_tracker"])
# It used to be the case that we tracked ``val_metric_per_epoch``.
elif "val_metric_per_epoch" in training_state:
self._metric_tracker.clear()
self._metric_tracker.add_metrics(
training_state["val_metric_per_epoch"])
# And before that we didn't track anything.
else:
self._metric_tracker.clear()
if isinstance(training_state["epoch"], int):
epoch_to_return = training_state["epoch"] + 1
else:
epoch_to_return = int(training_state["epoch"].split('.')[0]) + 1
# For older checkpoints with batch_num_total missing, default to old behavior where
# it is unchanged.
batch_num_total = training_state.get('batch_num_total')
if batch_num_total is not None:
self._batch_num_total = batch_num_total
return epoch_to_return
# Requires custom from_params.
@classmethod
def from_params(
cls, # type: ignore
params: Params,
serialization_dir: str,
recover: bool = False) -> 'Trainer':
# modified for second training_data
all_datasets = datasets_from_params(params)
# copied from allennlp.training.trainer.TrainingPieces
# modified for second training_data
datasets_for_vocab_creation = set(
params.pop("datasets_for_vocab_creation", all_datasets))
if recover and os.path.exists(
os.path.join(serialization_dir, "vocabulary")):
vocab = Vocabulary.from_files(
os.path.join(serialization_dir, "vocabulary"))
params.pop("vocabulary", {})
else:
vocab = Vocabulary.from_params(params.pop(
"vocabulary", {}), (instance
for key, dataset in all_datasets.items()
for instance in dataset
if key in datasets_for_vocab_creation))
model = Model.from_params(vocab=vocab, params=params.pop('model'))
model.extend_embedder_vocab()
vocab.save_to_files(os.path.join(serialization_dir, "vocabulary"))
iterator = DataIterator.from_params(params.pop("iterator"))
iterator.index_with(model.vocab)
validation_iterator_params = params.pop("validation_iterator", None)
if validation_iterator_params:
validation_iterator = DataIterator.from_params(
validation_iterator_params)
validation_iterator.index_with(model.vocab)
else:
validation_iterator = None
train_data = all_datasets['train']
validation_data = all_datasets.get('validation')
test_data = all_datasets.get('test')
train_low_data = all_datasets.get('train_low')
trainer_params = params.pop("trainer")
no_grad_regexes = trainer_params.pop("no_grad", ())
for name, parameter in model.named_parameters():
if any(re.search(regex, name) for regex in no_grad_regexes):
parameter.requires_grad_(False)
frozen_parameter_names, tunable_parameter_names = \
get_frozen_and_tunable_parameter_names(model)
logger.info("Following parameters are Frozen (without gradient):")
for name in frozen_parameter_names:
logger.info(name)
logger.info("Following parameters are Tunable (with gradient):")
for name in tunable_parameter_names:
logger.info(name)
# END OF TrainerPieces code
params = trainer_params
# pylint: disable=arguments-differ
patience = params.pop_int("patience", None)
validation_metric = params.pop("validation_metric", "-loss")
shuffle = params.pop_bool("shuffle", True)
num_epochs = params.pop_int("num_epochs", 20)
cuda_device = parse_cuda_device(params.pop("cuda_device", -1))
grad_norm = params.pop_float("grad_norm", None)
grad_clipping = params.pop_float("grad_clipping", None)
lr_scheduler_params = params.pop("learning_rate_scheduler", None)
momentum_scheduler_params = params.pop("momentum_scheduler", None)
if isinstance(cuda_device, list):
model_device = cuda_device[0]
else:
model_device = cuda_device
if model_device >= 0:
# Moving model to GPU here so that the optimizer state gets constructed on
# the right device.
model = model.cuda(model_device)
parameters = [[n, p] for n, p in model.named_parameters()
if p.requires_grad]
optimizer = Optimizer.from_params(parameters, params.pop("optimizer"))
if "moving_average" in params:
moving_average = MovingAverage.from_params(
params.pop("moving_average"), parameters=parameters)
else:
moving_average = None
if lr_scheduler_params:
lr_scheduler = LearningRateScheduler.from_params(
optimizer, lr_scheduler_params)
else:
lr_scheduler = None
if momentum_scheduler_params:
momentum_scheduler = MomentumScheduler.from_params(
optimizer, momentum_scheduler_params)
else:
momentum_scheduler = None
if 'checkpointer' in params:
if 'keep_serialized_model_every_num_seconds' in params or \
'num_serialized_models_to_keep' in params:
raise ConfigurationError(
"Checkpointer may be initialized either from the 'checkpointer' key or from the "
"keys 'num_serialized_models_to_keep' and 'keep_serialized_model_every_num_seconds'"
" but the passed config uses both methods.")
checkpointer = Checkpointer.from_params(params.pop("checkpointer"))
else:
num_serialized_models_to_keep = params.pop_int(
"num_serialized_models_to_keep", 20)
keep_serialized_model_every_num_seconds = params.pop_int(
"keep_serialized_model_every_num_seconds", None)
checkpointer = Checkpointer(
serialization_dir=serialization_dir,
num_serialized_models_to_keep=num_serialized_models_to_keep,
keep_serialized_model_every_num_seconds=
keep_serialized_model_every_num_seconds)
model_save_interval = params.pop_float("model_save_interval", None)
summary_interval = params.pop_int("summary_interval", 100)
histogram_interval = params.pop_int("histogram_interval", None)
should_log_parameter_statistics = params.pop_bool(
"should_log_parameter_statistics", True)
should_log_learning_rate = params.pop_bool("should_log_learning_rate",
False)
log_batch_size_period = params.pop_int("log_batch_size_period", None)
epoch_low_start = params.pop_int("epoch_low_start", None)
epoch_without_improvement_low_start = params.pop_int(
"epoch_without_improvement_low_start", None)
params.assert_empty(cls.__name__)
return cls(
model,
optimizer,
iterator,
train_data,
validation_data,
train_low_dataset=train_low_data,
patience=patience,
validation_metric=validation_metric,
validation_iterator=validation_iterator,
shuffle=shuffle,
num_epochs=num_epochs,
serialization_dir=serialization_dir,
cuda_device=cuda_device,
grad_norm=grad_norm,
grad_clipping=grad_clipping,
learning_rate_scheduler=lr_scheduler,
momentum_scheduler=momentum_scheduler,
checkpointer=checkpointer,
model_save_interval=model_save_interval,
summary_interval=summary_interval,
histogram_interval=histogram_interval,
should_log_parameter_statistics=should_log_parameter_statistics,
should_log_learning_rate=should_log_learning_rate,
log_batch_size_period=log_batch_size_period,
moving_average=moving_average,
epoch_low_start=epoch_low_start,
epoch_without_improvement_low_start=
epoch_without_improvement_low_start,
)
def __init__(
self,
model: Model,
optimizer: torch.optim.Optimizer,
data_loader: torch.utils.data.DataLoader,
patience: Optional[int] = None,
validation_metric: str = "-loss",
validation_data_loader: torch.utils.data.DataLoader = None,
num_epochs: int = 20,
serialization_dir: Optional[str] = None,
checkpointer: Checkpointer = None,
cuda_device: int = -1,
grad_norm: Optional[float] = None,
grad_clipping: Optional[float] = None,
learning_rate_scheduler: Optional[LearningRateScheduler] = None,
momentum_scheduler: Optional[MomentumScheduler] = None,
tensorboard_writer: TensorboardWriter = None,
moving_average: Optional[MovingAverage] = None,
distributed: bool = False,
local_rank: int = 0,
world_size: int = 1,
num_gradient_accumulation_steps: int = 1,
opt_level: Optional[str] = None,
) -> None:
"""
A trainer for doing supervised learning. It just takes a labeled dataset
and a `DataLoader`, and uses the supplied `Optimizer` to learn the weights
for your model over some fixed number of epochs. You can also pass in a validation
dataloader and enable early stopping. There are many other bells and whistles as well.
# Parameters
model : `Model`, required.
An AllenNLP model to be optimized. Pytorch Modules can also be optimized if
their `forward` method returns a dictionary with a "loss" key, containing a
scalar tensor representing the loss function to be optimized.
If you are training your model using GPUs, your model should already be
on the correct device. (If you are using our `train` command this will be
handled for you.)
optimizer : `torch.nn.Optimizer`, required.
An instance of a Pytorch Optimizer, instantiated with the parameters of the
model to be optimized.
data_loader : `DataLoader`, required.
A pytorch `DataLoader` containing your `Dataset`, yielding padded indexed batches.
patience : Optional[int] > 0, optional (default=None)
Number of epochs to be patient before early stopping: the training is stopped
after `patience` epochs with no improvement. If given, it must be `> 0`.
If None, early stopping is disabled.
validation_metric : str, optional (default="loss")
Validation metric to measure for whether to stop training using patience
and whether to serialize an `is_best` model each epoch. The metric name
must be prepended with either "+" or "-", which specifies whether the metric
is an increasing or decreasing function.
validation_dataloader : `DataLoader`, optional (default=None)
A `DataLoader` to use for the validation set. If `None`, then
use the training `DataLoader` with the validation data.
num_epochs : int, optional (default = 20)
Number of training epochs.
serialization_dir : str, optional (default=None)
Path to directory for saving and loading model files. Models will not be saved if
this parameter is not passed.
checkpointer : `Checkpointer`, optional (default=None)
A `Checkpointer` is responsible for periodically saving model weights. If none is given
here, we will construct one with default parameters.
cuda_device : `int`, optional (default = -1)
An integer specifying the CUDA device(s) to use for this process. If -1, the CPU is used.
Data parallelism is controlled at the allennlp train level, so each trainer will have a single
GPU.
grad_norm : `float`, optional, (default = None).
If provided, gradient norms will be rescaled to have a maximum of this value.
grad_clipping : `float`, optional (default = `None`).
If provided, gradients will be clipped `during the backward pass` to have an (absolute)
maximum of this value. If you are getting `NaNs` in your gradients during training
that are not solved by using `grad_norm`, you may need this.
learning_rate_scheduler : `LearningRateScheduler`, optional (default = None)
If specified, the learning rate will be decayed with respect to
this schedule at the end of each epoch (or batch, if the scheduler implements
the `step_batch` method). If you use `torch.optim.lr_scheduler.ReduceLROnPlateau`,
this will use the `validation_metric` provided to determine if learning has plateaued.
To support updating the learning rate on every batch, this can optionally implement
`step_batch(batch_num_total)` which updates the learning rate given the batch number.
momentum_scheduler : `MomentumScheduler`, optional (default = None)
If specified, the momentum will be updated at the end of each batch or epoch
according to the schedule.
tensorboard_writer : `TensorboardWriter`, optional
If this is not provided, we will construct a `TensorboardWriter` with default
parameters and use that.
moving_average : `MovingAverage`, optional, (default = None)
If provided, we will maintain moving averages for all parameters. During training, we
employ a shadow variable for each parameter, which maintains the moving average. During
evaluation, we backup the original parameters and assign the moving averages to corresponding
parameters. Be careful that when saving the checkpoint, we will save the moving averages of
parameters. This is necessary because we want the saved model to perform as well as the validated
model if we load it later. But this may cause problems if you restart the training from checkpoint.
distributed : `bool`, optional, (default = False)
If set, PyTorch's `DistributedDataParallel` is used to train the model in multiple GPUs. This also
requires `world_size` to be greater than 1.
local_rank : `int`, optional, (default = 0)
This is the unique identifier of the `Trainer` in a distributed process group. The GPU device id is
used as the rank.
world_size : `int`, (default = 1)
The number of `Trainer` workers participating in the distributed training.
num_gradient_accumulation_steps : `int`, optional, (default = 1)
Gradients are accumulated for the given number of steps before doing an optimizer step. This can
be useful to accommodate batches that are larger than the RAM size. Refer Thomas Wolf's
[post](https://tinyurl.com/y5mv44fw) for details on Gradient Accumulation.
opt_level : `str`, optional, (default = `None`)
Each opt_level establishes a set of properties that govern Amp’s implementation of pure or mixed
precision training. Must be a choice of `"O0"`, `"O1"`, `"O2"`, or `"O3"`.
See the Apex [documentation](https://nvidia.github.io/apex/amp.html#opt-levels-and-properties) for
more details. If `None`, Amp is not used. Defaults to `None`.
"""
super().__init__(serialization_dir, cuda_device, distributed,
local_rank, world_size)
# I am not calling move_to_gpu here, because if the model is
# not already on the GPU then the optimizer is going to be wrong.
self.model = model
self.data_loader = data_loader
self._validation_data_loader = validation_data_loader
self.optimizer = optimizer
if patience is None: # no early stopping
if validation_data_loader:
logger.warning(
"You provided a validation dataset but patience was set to None, "
"meaning that early stopping is disabled")
elif (not isinstance(patience, int)) or patience <= 0:
raise ConfigurationError(
'{} is an invalid value for "patience": it must be a positive integer '
"or None (if you want to disable early stopping)".format(
patience))
# For tracking is_best_so_far and should_stop_early
self._metric_tracker = MetricTracker(patience, validation_metric)
# Get rid of + or -
self._validation_metric = validation_metric[1:]
self._num_epochs = num_epochs
if checkpointer is not None:
self._checkpointer = checkpointer
else:
self._checkpointer = Checkpointer(serialization_dir)
self._grad_norm = grad_norm
self._grad_clipping = grad_clipping
self._learning_rate_scheduler = learning_rate_scheduler
self._momentum_scheduler = momentum_scheduler
self._moving_average = moving_average
# We keep the total batch number as an instance variable because it
# is used inside a closure for the hook which logs activations in
# `_enable_activation_logging`.
self._batch_num_total = 0
self._tensorboard = tensorboard_writer or TensorboardWriter(
serialization_dir)
self._tensorboard.get_batch_num_total = lambda: self._batch_num_total
self._tensorboard.enable_activation_logging(self.model)
self._last_log = 0.0 # time of last logging
self._num_gradient_accumulation_steps = num_gradient_accumulation_steps
# Enable automatic mixed precision training with NVIDIA Apex.
self._opt_level = opt_level
if self._opt_level is not None:
if amp is None:
raise ConfigurationError((
"Apex not installed but opt_level was provided. Please install NVIDIA's Apex to enable"
" automatic mixed precision (AMP) training. See: https://github.com/NVIDIA/apex."
))
self.model, self.optimizer = amp.initialize(
self.model, self.optimizer, opt_level=self._opt_level)
# Using `DistributedDataParallel`(ddp) brings in a quirk wrt AllenNLP's `Model` interface and its
# usage. A `Model` object is wrapped by `ddp`, but assigning the wrapped model to `self.model`
# will break the usages such as `Model.get_regularization_penalty`, `Model.get_metrics`, etc.
#
# Hence a reference to Pytorch's object is maintained in the case of distributed training and in the
# normal case, reference to `Model` is retained. This reference is only used in
# these places: `model.__call__`, `model.train` and `model.eval`.
if self._distributed:
self._pytorch_model = DistributedDataParallel(
self.model,
device_ids=[self.cuda_device],
find_unused_parameters=True)
else:
self._pytorch_model = self.model
def __init__(
self,
model: Model,
optimizer: torch.optim.Optimizer,
data_loader: DataLoader,
deepspeed_config: DeepspeedConfig,
patience: Optional[int] = None,
validation_metric: str = "-loss",
validation_data_loader: DataLoader = None,
num_epochs: int = 20,
serialization_dir: Optional[str] = None,
checkpointer: Checkpointer = None,
cuda_device: Optional[Union[int, torch.device]] = None,
grad_norm: Optional[float] = None,
grad_clipping: Optional[float] = None,
tensorboard_writer: TensorboardWriter = None,
moving_average: Optional[MovingAverage] = None,
batch_callbacks: List[BatchCallback] = None,
epoch_callbacks: List[EpochCallback] = None,
distributed: bool = False,
local_rank: int = 0,
world_size: int = 1,
num_gradient_accumulation_steps: int = 1,
use_amp: bool = False,
) -> None:
super().__init__(serialization_dir, cuda_device, distributed,
local_rank, world_size)
# I am not calling move_to_gpu here, because if the model is
# not already on the GPU then the optimizer is going to be wrong.
self.model = model
self.data_loader = data_loader
self._validation_data_loader = validation_data_loader
self.optimizer = optimizer
if patience is None: # no early stopping
if validation_data_loader is not None:
logger.warning(
"You provided a validation dataset but patience was set to None, "
"meaning that early stopping is disabled")
elif (not isinstance(patience, int)) or patience <= 0:
raise ConfigurationError(
'{} is an invalid value for "patience": it must be a positive integer '
"or None (if you want to disable early stopping)".format(
patience))
# For tracking is_best_so_far and should_stop_early
self._metric_tracker = MetricTracker(patience, validation_metric)
# Get rid of + or -
self._validation_metric = validation_metric[1:]
self._num_epochs = num_epochs
if checkpointer is not None:
self._checkpointer = checkpointer
else:
self._checkpointer = Checkpointer(serialization_dir)
self._grad_norm = grad_norm
self._grad_clipping = grad_clipping
self._moving_average = moving_average
self._batch_callbacks = batch_callbacks or []
self._epoch_callbacks = epoch_callbacks or []
# We keep the total batch number as an instance variable because it
# is used inside a closure for the hook which logs activations in
# `_enable_activation_logging`.
self._batch_num_total = 0
self._tensorboard = tensorboard_writer or TensorboardWriter(
serialization_dir)
self._tensorboard.get_batch_num_total = lambda: self._batch_num_total
self._tensorboard.enable_activation_logging(self.model)
self._last_log = 0.0 # time of last logging
self._num_gradient_accumulation_steps = num_gradient_accumulation_steps
# Enable automatic mixed precision training.
self._scaler: Optional[amp.GradScaler] = None
self._use_amp = use_amp
if self._use_amp:
if self.cuda_device == torch.device("cpu"):
raise ValueError("Using AMP requires a cuda device")
self._scaler = amp.GradScaler()
self._pytorch_model = self.model
self._ds_config = deepspeed_config
self.model_engine, self.ds_optimizer, _, _ = self._ds_config.launch(
self.model,
None, # self.optimizer,
local_rank,
serialization_dir,
self.data_loader.batch_size,
num_gradient_accumulation_steps)
class MetaTrainer(TrainerBase):
def __init__(
self,
model: Model,
optimizer: torch.optim.Optimizer,
iterator: DataIterator,
train_datasets: List[Iterable[Instance]],
validation_datasets: List[Iterable[Instance]] = None,
patience: Optional[int] = None,
validation_metric: str = "-loss",
validation_iterator: DataIterator = None,
shuffle: bool = True,
num_epochs: int = 20,
serialization_dir: Optional[str] = None,
num_serialized_models_to_keep: int = 20,
keep_serialized_model_every_num_seconds: int = None,
checkpointer: Checkpointer = None,
model_save_interval: float = None,
cuda_device: Union[int, List] = [0, 1], #int = -1,
grad_norm: Optional[float] = None,
grad_clipping: Optional[float] = None,
learning_rate_scheduler: Optional[LearningRateScheduler] = None,
momentum_scheduler: Optional[MomentumScheduler] = None,
summary_interval: int = 100,
histogram_interval: int = None,
should_log_parameter_statistics: bool = True,
should_log_learning_rate: bool = False,
log_batch_size_period: Optional[int] = None,
moving_average: Optional[MovingAverage] = None,
# meta learner parameters
meta_batches: int = 200,
inner_steps: int = 1,
meta_batch_size: int = 3,
batch_norm=True,
) -> None:
"""
A metatrainer for doing meta-learning. It just takes a list of labeled datasets
and a ``DataIterator``, and uses the supplied meta-learner to learn the weights
for your model over some fixed number of epochs. You can also pass in a validation
datasets and enable early stopping. There are many other bells and whistles as well.
Parameters
----------
model : ``Model``, required.
"""
print('[info]============================ metatrainer.init is running')
print(
'[info] cuda_device in metatrainer.init is:{}'.format(cuda_device))
# I am not calling move_to_gpu here, because if the model is
# not already on the GPU then the optimizer is going to be wrong.
super().__init__(serialization_dir, cuda_device)
self.train_data = train_datasets
self._validation_data = validation_datasets
self.model = model
self.iterator = iterator[0]
self._validation_iterator = validation_iterator
self.shuffle = shuffle
self.optimizer = optimizer
# Meta Trainer specific params
self.meta_batches = meta_batches
self.inner_steps = inner_steps
self.innerstepsize = .001
self.meta_batch_size = meta_batch_size
self.meta_step_size = .1
self.batch_norm = batch_norm
if patience is None: # no early stopping
if validation_dataset:
logger.warning(
'You provided a validation dataset but patience was set to None, '
'meaning that early stopping is disabled')
elif (not isinstance(patience, int)) or patience <= 0:
raise ConfigurationError(
'{} is an invalid value for "patience": it must be a positive integer '
'or None (if you want to disable early stopping)'.format(
patience))
# For tracking is_best_so_far and should_stop_early
self._metric_tracker = MetricTracker(patience, validation_metric)
# Get rid of + or -
self._validation_metric = validation_metric[1:]
self._num_epochs = num_epochs
if checkpointer is not None:
# We can't easily check if these parameters were passed in, so check against their default values.
# We don't check against serialization_dir since it is also used by the parent class.
if num_serialized_models_to_keep != 20 or \
keep_serialized_model_every_num_seconds is not None:
raise ConfigurationError(
"When passing a custom Checkpointer, you may not also pass in separate checkpointer "
"args 'num_serialized_models_to_keep' or 'keep_serialized_model_every_num_seconds'."
)
self._checkpointer = checkpointer
else:
self._checkpointer = Checkpointer(
serialization_dir, keep_serialized_model_every_num_seconds,
num_serialized_models_to_keep)
self._model_save_interval = model_save_interval
self._grad_norm = grad_norm
self._grad_clipping = grad_clipping
self._learning_rate_scheduler = learning_rate_scheduler
self._momentum_scheduler = momentum_scheduler
self._moving_average = moving_average
# We keep the total batch number as an instance variable because it
# is used inside a closure for the hook which logs activations in
# ``_enable_activation_logging``.
self._batch_num_total = 0
self._tensorboard = TensorboardWriter(
get_batch_num_total=lambda: self._batch_num_total,
serialization_dir=serialization_dir,
summary_interval=summary_interval,
histogram_interval=histogram_interval,
should_log_parameter_statistics=should_log_parameter_statistics,
should_log_learning_rate=should_log_learning_rate)
self._log_batch_size_period = log_batch_size_period
self._last_log = 0.0 # time of last logging
# Enable activation logging.
if histogram_interval is not None:
self._tensorboard.enable_activation_logging(self.model)
def rescale_gradients(self) -> Optional[float]:
return training_util.rescale_gradients(self.model, self._grad_norm)
# TODO check out overriding
def batch_loss(self, batch: TensorDict,
for_training: bool) -> torch.Tensor:
"""
Does a forward pass on the given batches and returns the ``loss`` value in the result.
If ``for_training`` is `True` also applies regularization penalty.
"""
if self._multiple_gpu: #len(self.cuda_device) > 1:
# print('[info] self.cuda_device is:{}'.format(self.cuda_device))
# print('[info] batch len:{}, is:{}'.format(len(batch), batch))
output_dict = training_util.data_parallel(batch, self.model,
self._cuda_devices)
else:
batch = nn_util.move_to_device(batch, self._cuda_devices[0])
output_dict = self.model(**batch)
try:
loss = output_dict["loss"]
if for_training:
loss += self.model.get_regularization_penalty()
except KeyError:
if for_training:
raise RuntimeError(
"The model you are trying to optimize does not contain a"
" 'loss' key in the output of model.forward(inputs).")
loss = None
return loss
def reptile_inner_update(self, batch_data: TensorDict) -> float:
loss = self.batch_loss(batch_data, True)
if torch.isnan(loss):
raise ValueError("nan loss encountered")
loss.backward()
temp_loss = loss.item()
self.optimizer.step()
# This only place where vary from implementation
# for param in self.model.parameters():
# TODO add innerstepsize
# param.data -= self.innerstepsize * param.grad.data
return temp_loss
def reptile_outer_update(self, train_generators: List[Iterable],
iteration: int, num_gpus: int):
# https://github.com/farbodtm/reptile-pytorch/blob/master/reptile.py
weights_before = deepcopy(self.model.state_dict())
self.optimizer.zero_grad()
random.shuffle(train_generators)
new_weights = []
total_loss = 0.0
# for batch in train_generators[0]:
# print('[info]batch is:{}'.format(batch))
task_wrap = Tqdm.tqdm(zip(train_generators[0], train_generators[1],
train_generators[2]),
total=1)
# , train_generators[3], train_generators[4]), \
for i, batch_group in enumerate(task_wrap):
if not i:
for k in range(self.meta_batch_size): # tasks per batch
total_loss += self.reptile_inner_update(batch_group[k][0])
new_weights.append(deepcopy(self.model.state_dict()))
self.model.load_state_dict({
name: weights_before[name]
for name in weights_before
})
else:
break
weights_after = {
name: new_weights[0][name] / float(self.meta_batch_size)
for name in new_weights[0]
}
for i in range(1, self.meta_batch_size):
for name in new_weights[i]:
weights_after[name] += new_weights[i][name] / float(
self.meta_batch_size)
#They used self.step_size of 1.0 in some of their outer.
outerstepsize = self.meta_step_size * (
1 - iteration / self.meta_batches) # linear schedule
self.model.load_state_dict({
name: weights_before[name] +
(weights_after[name] - weights_before[name]) * outerstepsize
for name in weights_before
})
return total_loss / self.meta_batch_size
def _train_epoch(self, epoch: int) -> Dict[str, float]:
"""
Trains on one epoch. Differs from base trainer in that
it utilizes
"""
logger.info("Epoch %d/%d", epoch, self._num_epochs - 1)
peak_cpu_usage = peak_memory_mb()
logger.info(f"Peak CPU memory usage MB: {peak_cpu_usage}")
gpu_usage = []
for gpu, memory in gpu_memory_mb().items():
gpu_usage.append((gpu, memory))
logger.info(f"GPU {gpu} memory usage MB: {memory}")
train_loss = 0.0
# Set the model to "train" mode.
self.model.train()
num_gpus = len(self._cuda_devices)
raw_generators = []
# fix max number of batches
self._last_log = time.time()
last_save_time = time.time()
batches_this_epoch = 0
if self._batch_num_total is None:
self._batch_num_total = 0
histogram_parameters = set(
self.model.get_parameters_for_histogram_tensorboard_logging())
logger.info("Training")
cumulative_batch_size = 0
for i in range(0, self.meta_batches):
train_generators = []
for i, train_info in enumerate(self.train_data):
raw_train_generator = self.iterator(train_info,
num_epochs=1,
shuffle=self.shuffle)
train_generators.append(
lazy_groups_of(raw_train_generator, num_gpus))
loss_batch = self.reptile_outer_update(train_generators, i,
num_gpus)
# TODO figure out if is important
train_loss = loss_batch
print('[info] train_loss is:{}'.format(train_loss))
# TODO figure out BATCH NORM MAML https://openreview.net/pdf?id=HygBZnRctX
if self.batch_norm:
batch_grad_norm = self.rescale_gradients()
# This does nothing if batch_num_total is None or you are using a
# scheduler which doesn't update per batch.
# TODO investigate learning rate scheduling for meta learning
#if self._learning_rate_scheduler:
#self._learning_rate_scheduler.step_batch(batch_num_total)
#if self._momentum_scheduler:
#self._momentum_scheduler.step_batch(batch_num_total)
if self._tensorboard.should_log_histograms_this_batch():
# get the magnitude of parameter updates for logging
# We need a copy of current parameters to compute magnitude of updates,
# and copy them to CPU so large models won't go OOM on the GPU.
param_updates = {
name: param.detach().cpu().clone()
for name, param in self.model.named_parameters()
}
self.optimizer.step()
for name, param in self.model.named_parameters():
param_updates[name].sub_(param.detach().cpu())
update_norm = torch.norm(param_updates[name].view(-1, ))
param_norm = torch.norm(param.view(-1, )).cpu()
self._tensorboard.add_train_scalar(
"gradient_update/" + name,
update_norm / (param_norm + 1e-7))
else:
self.optimizer.step()
# Update moving averages
if self._moving_average is not None:
self._moving_average.apply(batch_num_total)
# Update the description with the latest metrics
metrics = training_util.get_metrics(self.model, train_loss,
batches_this_epoch)
description = training_util.description_from_metrics(metrics)
# Log parameter values to Tensorboard
if self._tensorboard.should_log_this_batch():
self._tensorboard.log_parameter_and_gradient_statistics(
self.model, batch_grad_norm)
self._tensorboard.log_learning_rates(self.model,
self.optimizer)
self._tensorboard.add_train_scalar("loss/loss_train",
metrics["loss"])
self._tensorboard.log_metrics(
{"epoch_metrics/" + k: v
for k, v in metrics.items()})
if self._tensorboard.should_log_histograms_this_batch():
self._tensorboard.log_histograms(self.model,
histogram_parameters)
if self._log_batch_size_period:
cur_batch = sum([
training_util.get_batch_size(batch)
for batch in batch_group
])
cumulative_batch_size += cur_batch
if (batches_this_epoch - 1) % self._log_batch_size_period == 0:
average = cumulative_batch_size / batches_this_epoch
logger.info(
f"current batch size: {cur_batch} mean batch size: {average}"
)
self._tensorboard.add_train_scalar("current_batch_size",
cur_batch)
self._tensorboard.add_train_scalar("mean_batch_size",
average)
# Save model if needed.
if self._model_save_interval is not None and (
time.time() - last_save_time > self._model_save_interval):
last_save_time = time.time()
self._save_checkpoint('{0}.{1}'.format(
epoch, training_util.time_to_str(int(last_save_time))))
metrics = training_util.get_metrics(self.model,
train_loss,
batches_this_epoch,
reset=True)
metrics['cpu_memory_MB'] = peak_cpu_usage
for (gpu_num, memory) in gpu_usage:
metrics['gpu_' + str(gpu_num) + '_memory_MB'] = memory
return metrics
def _validation_loss(self) -> Tuple[float, int]:
"""
Computes the validation loss. Returns it and the number of batches.
"""
logger.info("Validating")
self.model.eval()
# Replace parameter values with the shadow values from the moving averages.
if self._moving_average is not None:
self._moving_average.assign_average_value()
if self._validation_iterator is not None:
val_iterator = self._validation_iterator[0]
else:
val_iterator = self.iterator
num_gpus = len(self._cuda_devices)
valid_generators = []
for i, valid_info in enumerate(self._validation_data):
raw_val_generator = self.iterator(valid_info,
num_epochs=1,
shuffle=self.shuffle)
valid_generators.append(lazy_groups_of(raw_val_generator,
num_gpus))
num_validation_batches = min(
map(
lambda i: math.ceil(
val_iterator.get_num_batches(self._validation_data[i]) /
num_gpus), range(self.meta_batch_size)))
val_generator_tqdm = Tqdm.tqdm(zip(valid_generators[0],
valid_generators[1],
valid_generators[2]),
total=num_validation_batches)
print("val gene called")
batches_this_epoch = 0
val_loss = 0
for i, batch_group in enumerate(val_generator_tqdm):
for k in range(self.meta_batch_size): # tasks per batch
loss = self.batch_loss(batch_group[k][0], for_training=False)
if loss is not None:
# You shouldn't necessarily have to compute a loss for validation, so we allow for
# `loss` to be None. We need to be careful, though - `batches_this_epoch` is
# currently only used as the divisor for the loss function, so we can safely only
# count those batches for which we actually have a loss. If this variable ever
# gets used for something else, we might need to change things around a bit.
batches_this_epoch += 1
val_loss += loss.detach().cpu().numpy()
# Update the description with the latest metrics
val_metrics = training_util.get_metrics(self.model, val_loss,
batches_this_epoch)
description = training_util.description_from_metrics(val_metrics)
val_generator_tqdm.set_description(description, refresh=False)
# Now restore the original parameter values.
if self._moving_average is not None:
self._moving_average.restore()
return val_loss, batches_this_epoch
def train(self) -> Dict[str, Any]:
"""
Trains the supplied model with the supplied parameters.
"""
try:
epoch_counter = self._restore_checkpoint()
except RuntimeError:
traceback.print_exc()
raise ConfigurationError(
"Could not recover training from the checkpoint. Did you mean to output to "
"a different serialization directory or delete the existing serialization "
"directory?")
training_util.enable_gradient_clipping(self.model, self._grad_clipping)
logger.info("Beginning training.")
train_metrics: Dict[str, float] = {}
val_metrics: Dict[str, float] = {}
this_epoch_val_metric: float = None
metrics: Dict[str, Any] = {}
epochs_trained = 0
training_start_time = time.time()
metrics['best_epoch'] = self._metric_tracker.best_epoch
for key, value in self._metric_tracker.best_epoch_metrics.items():
metrics["best_validation_" + key] = value
for epoch in range(epoch_counter, self._num_epochs):
epoch_start_time = time.time()
train_metrics = self._train_epoch(epoch)
# get peak of memory usage
if 'cpu_memory_MB' in train_metrics:
metrics['peak_cpu_memory_MB'] = max(
metrics.get('peak_cpu_memory_MB', 0),
train_metrics['cpu_memory_MB'])
for key, value in train_metrics.items():
if key.startswith('gpu_'):
metrics["peak_" + key] = max(metrics.get("peak_" + key, 0),
value)
if self._validation_data is not None:
# We have a validation set, so compute all the metrics on it.
val_loss, num_batches = self._validation_loss()
val_metrics = training_util.get_metrics(self.model,
val_loss,
num_batches,
reset=True)
# Check validation metric for early stopping
this_epoch_val_metric = val_metrics[self._validation_metric]
self._metric_tracker.add_metric(this_epoch_val_metric)
if self._metric_tracker.should_stop_early():
logger.info("Ran out of patience. Stopping training.")
break
self._tensorboard.log_metrics(
train_metrics,
val_metrics=val_metrics,
log_to_console=True,
epoch=epoch + 1) # +1 because tensorboard doesn't like 0
# Create overall metrics dict
training_elapsed_time = time.time() - training_start_time
metrics["training_duration"] = str(
datetime.timedelta(seconds=training_elapsed_time))
metrics["training_start_epoch"] = epoch_counter
metrics["training_epochs"] = epochs_trained
metrics["epoch"] = epoch
for key, value in train_metrics.items():
metrics["training_" + key] = value
for key, value in val_metrics.items():
metrics["validation_" + key] = value
if self._metric_tracker.is_best_so_far():
# Update all the best_ metrics.
# (Otherwise they just stay the same as they were.)
metrics['best_epoch'] = epoch
for key, value in val_metrics.items():
metrics["best_validation_" + key] = value
self._metric_tracker.best_epoch_metrics = val_metrics
if self._serialization_dir:
dump_metrics(
os.path.join(self._serialization_dir,
f'metrics_epoch_{epoch}.json'), metrics)
# The Scheduler API is agnostic to whether your schedule requires a validation metric -
# if it doesn't, the validation metric passed here is ignored.
if self._learning_rate_scheduler:
self._learning_rate_scheduler.step(this_epoch_val_metric,
epoch)
if self._momentum_scheduler:
self._momentum_scheduler.step(this_epoch_val_metric, epoch)
self._save_checkpoint(epoch)
epoch_elapsed_time = time.time() - epoch_start_time
logger.info("Epoch duration: %s",
datetime.timedelta(seconds=epoch_elapsed_time))
if epoch < self._num_epochs - 1:
training_elapsed_time = time.time() - training_start_time
estimated_time_remaining = training_elapsed_time * \
((self._num_epochs - epoch_counter) / float(epoch - epoch_counter + 1) - 1)
formatted_time = str(
datetime.timedelta(seconds=int(estimated_time_remaining)))
logger.info("Estimated training time remaining: %s",
formatted_time)
epochs_trained += 1
# Load the best model state before returning
best_model_state = self._checkpointer.best_model_state()
if best_model_state:
self.model.load_state_dict(best_model_state)
return metrics
def _save_checkpoint(self, epoch: Union[int, str]) -> None:
"""
Saves a checkpoint of the model to self._serialization_dir.
Is a no-op if self._serialization_dir is None.
Parameters
----------
epoch : Union[int, str], required.
The epoch of training. If the checkpoint is saved in the middle
of an epoch, the parameter is a string with the epoch and timestamp.
"""
# If moving averages are used for parameters, we save
# the moving average values into checkpoint, instead of the current values.
if self._moving_average is not None:
self._moving_average.assign_average_value()
# These are the training states we need to persist.
training_states = {
"metric_tracker": self._metric_tracker.state_dict(),
"optimizer": self.optimizer.state_dict(),
"batch_num_total": self._batch_num_total
}
# If we have a learning rate or momentum scheduler, we should persist them too.
if self._learning_rate_scheduler is not None:
training_states[
"learning_rate_scheduler"] = self._learning_rate_scheduler.state_dict(
)
if self._momentum_scheduler is not None:
training_states[
"momentum_scheduler"] = self._momentum_scheduler.state_dict()
self._checkpointer.save_checkpoint(
model_state=self.model.state_dict(),
epoch=epoch,
training_states=training_states,
is_best_so_far=self._metric_tracker.is_best_so_far())
# Restore the original values for parameters so that training will not be affected.
if self._moving_average is not None:
self._moving_average.restore()
def _restore_checkpoint(self) -> int:
"""
Restores the model and training state from the last saved checkpoint.
This includes an epoch count and optimizer state, which is serialized separately
from model parameters. This function should only be used to continue training -
if you wish to load a model for inference/load parts of a model into a new
computation graph, you should use the native Pytorch functions:
`` model.load_state_dict(torch.load("/path/to/model/weights.th"))``
If ``self._serialization_dir`` does not exist or does not contain any checkpointed weights,
this function will do nothing and return 0.
Returns
-------
epoch: int
The epoch at which to resume training, which should be one after the epoch
in the saved training state.
"""
model_state, training_state = self._checkpointer.restore_checkpoint()
if not training_state:
# No checkpoint to restore, start at 0
return 0
self.model.load_state_dict(model_state)
self.optimizer.load_state_dict(training_state["optimizer"])
if self._learning_rate_scheduler is not None and "learning_rate_scheduler" in training_state:
self._learning_rate_scheduler.load_state_dict(
training_state["learning_rate_scheduler"])
if self._momentum_scheduler is not None and "momentum_scheduler" in training_state:
self._momentum_scheduler.load_state_dict(
training_state["momentum_scheduler"])
training_util.move_optimizer_to_cuda(self.optimizer)
# Currently the ``training_state`` contains a serialized ``MetricTracker``.
if "metric_tracker" in training_state:
self._metric_tracker.load_state_dict(
training_state["metric_tracker"])
# It used to be the case that we tracked ``val_metric_per_epoch``.
elif "val_metric_per_epoch" in training_state:
self._metric_tracker.clear()
self._metric_tracker.add_metrics(
training_state["val_metric_per_epoch"])
# And before that we didn't track anything.
else:
self._metric_tracker.clear()
if isinstance(training_state["epoch"], int):
epoch_to_return = training_state["epoch"] + 1
else:
epoch_to_return = int(training_state["epoch"].split('.')[0]) + 1
# For older checkpoints with batch_num_total missing, default to old behavior where
# it is unchanged.
batch_num_total = training_state.get('batch_num_total')
if batch_num_total is not None:
self._batch_num_total = batch_num_total
return epoch_to_return
# Requires custom from_params.
@classmethod
def from_params(
cls, # type: ignore
params: Params,
serialization_dir: str,
recover: bool = False,
cache_directory: str = None,
cache_prefix: str = None) -> 'Trainer':
# datasets = meta_dataset_from_params(params, cache_directory=cache_directory, cache_prefix=cache_prefix)
# model = Model.from_params(vocab=vocab, params=params.pop("model"))
# iterator = DataIterator.from_params(params.pop("iterator"))
# iterator.index_with(model.vocab)
pieces = MetaTrainerPieces.from_params(params, serialization_dir,
recover, cache_directory,
cache_prefix)
model = pieces.model
iterator = pieces.iterator,
# params=pieces.params,
train_data = pieces.train_dataset
validation_data = pieces.validation_dataset
validation_iterator = pieces.validation_iterator
params = pieces.params
# pylint: disable=arguments-differ
patience = params.pop_int("patience", None)
validation_metric = params.pop("validation_metric", "-loss")
shuffle = params.pop_bool("shuffle", True)
num_epochs = params.pop_int("num_epochs", 20)
cuda_device = parse_cuda_device(params.pop("cuda_device", [0, 1]))
grad_norm = params.pop_float("grad_norm", None)
grad_clipping = params.pop_float("grad_clipping", None)
lr_scheduler_params = params.pop("learning_rate_scheduler", None)
momentum_scheduler_params = params.pop("momentum_scheduler", None)
if isinstance(cuda_device, list):
model_device = cuda_device[0]
else:
model_device = cuda_device
if model_device >= 0:
# Moving model to GPU here so that the optimizer state gets constructed on
# the right device.
model = model.cuda(model_device)
parameters = [[n, p] for n, p in model.named_parameters()
if p.requires_grad]
optimizer = Optimizer.from_params(parameters, params.pop("optimizer"))
if "moving_average" in params:
moving_average = MovingAverage.from_params(
params.pop("moving_average"), parameters=parameters)
else:
moving_average = None
if lr_scheduler_params:
lr_scheduler = LearningRateScheduler.from_params(
optimizer, lr_scheduler_params)
else:
lr_scheduler = None
if momentum_scheduler_params:
momentum_scheduler = MomentumScheduler.from_params(
optimizer, momentum_scheduler_params)
else:
momentum_scheduler = None
if 'checkpointer' in params:
if 'keep_serialized_model_every_num_seconds' in params or \
'num_serialized_models_to_keep' in params:
raise ConfigurationError(
"Checkpointer may be initialized either from the 'checkpointer' key or from the "
"keys 'num_serialized_models_to_keep' and 'keep_serialized_model_every_num_seconds'"
" but the passed config uses both methods.")
checkpointer = Checkpointer.from_params(params.pop("checkpointer"))
else:
num_serialized_models_to_keep = params.pop_int(
"num_serialized_models_to_keep", 20)
keep_serialized_model_every_num_seconds = params.pop_int(
"keep_serialized_model_every_num_seconds", None)
checkpointer = Checkpointer(
serialization_dir=serialization_dir,
num_serialized_models_to_keep=num_serialized_models_to_keep,
keep_serialized_model_every_num_seconds=
keep_serialized_model_every_num_seconds)
model_save_interval = params.pop_float("model_save_interval", None)
summary_interval = params.pop_int("summary_interval", 100)
histogram_interval = params.pop_int("histogram_interval", None)
should_log_parameter_statistics = params.pop_bool(
"should_log_parameter_statistics", True)
should_log_learning_rate = params.pop_bool("should_log_learning_rate",
False)
log_batch_size_period = params.pop_int("log_batch_size_period", None)
print('[info] cuda_device in metatrainer.from_param is:{}'.format(
cuda_device))
params.assert_empty(cls.__name__)
return cls(
model,
optimizer,
iterator,
train_data,
validation_data,
patience=patience,
validation_metric=validation_metric,
validation_iterator=validation_iterator,
shuffle=shuffle,
num_epochs=num_epochs,
serialization_dir=serialization_dir,
cuda_device=cuda_device,
grad_norm=grad_norm,
grad_clipping=grad_clipping,
learning_rate_scheduler=lr_scheduler,
momentum_scheduler=momentum_scheduler,
checkpointer=checkpointer,
model_save_interval=model_save_interval,
summary_interval=summary_interval,
histogram_interval=histogram_interval,
should_log_parameter_statistics=should_log_parameter_statistics,
should_log_learning_rate=should_log_learning_rate,
log_batch_size_period=log_batch_size_period,
moving_average=moving_average,
# distributed=distributed,
# rank=local_rank,
# world_size=world_size,
# num_gradient_accumulation_steps=num_gradient_accumulation_steps,
)
class Trainer(TrainerBase):
def __init__(
self,
model: Model,
optimizer: torch.optim.Optimizer,
# scheduler 根据epoch调整学习率
scheduler: torch.optim.lr_scheduler,
iterator: DataIterator,
train_dataset: Iterable[Instance],
validation_dataset: Optional[Iterable[Instance]] = None,
patience: Optional[int] = None,
validation_metric: str = "-loss",
validation_iterator: DataIterator = None,
shuffle: bool = True,
num_epochs: int = 20,
accumulated_batch_count: int = 1,
serialization_dir: Optional[str] = None,
num_serialized_models_to_keep: int = 20,
keep_serialized_model_every_num_seconds: int = None,
checkpointer: Checkpointer = None,
model_save_interval: float = None,
cuda_device: Union[int, List] = -1,
grad_norm: Optional[float] = None,
grad_clipping: Optional[float] = None,
learning_rate_scheduler: Optional[LearningRateScheduler] = None,
momentum_scheduler: Optional[MomentumScheduler] = None,
summary_interval: int = 100,
histogram_interval: int = None,
should_log_parameter_statistics: bool = True,
should_log_learning_rate: bool = False,
log_batch_size_period: Optional[int] = None,
moving_average: Optional[MovingAverage] = None,
cold_step_count: int = 0,
cold_lr: float = 1e-3,
cuda_verbose_step=None,
) -> None:
"""
A trainer for doing supervised learning. It just takes a labeled dataset
and a ``DataIterator``, and uses the supplied ``Optimizer`` to learn the weights
for your model over some fixed number of epochs. You can also pass in a validation
dataset and enable early stopping. There are many other bells and whistles as well.
Parameters
----------
model : ``Model``, required.
An AllenNLP model to be optimized. Pytorch Modules can also be optimized if
their ``forward`` method returns a dictionary with a "loss" key, containing a
scalar tensor representing the loss function to be optimized.
If you are training your model using GPUs, your model should already be
on the correct device. (If you use `Trainer.from_params` this will be
handled for you.)
optimizer : ``torch.nn.Optimizer``, required.
An instance of a Pytorch Optimizer, instantiated with the parameters of the
model to be optimized.
iterator : ``DataIterator``, required.
A method for iterating over a ``Dataset``, yielding padded indexed batches.
train_dataset : ``Dataset``, required.
A ``Dataset`` to train on. The dataset should have already been indexed.
validation_dataset : ``Dataset``, optional, (default = None).
A ``Dataset`` to evaluate on. The dataset should have already been indexed.
patience : Optional[int] > 0, optional (default=None)
Number of epochs to be patient before early stopping: the training is stopped
after ``patience`` epochs with no improvement. If given, it must be ``> 0``.
If None, early stopping is disabled.
validation_metric : str, optional (default="loss")
Validation metric to measure for whether to stop training using patience
and whether to serialize an ``is_best`` model each epoch. The metric name
must be prepended with either "+" or "-", which specifies whether the metric
is an increasing or decreasing function.
validation_iterator : ``DataIterator``, optional (default=None)
An iterator to use for the validation set. If ``None``, then
use the training `iterator`.
shuffle: ``bool``, optional (default=True)
Whether to shuffle the instances in the iterator or not.
num_epochs : int, optional (default = 20)
Number of training epochs.
serialization_dir : str, optional (default=None)
Path to directory for saving and loading model files. Models will not be saved if
this parameter is not passed.
num_serialized_models_to_keep : ``int``, optional (default=20)
Number of previous model checkpoints to retain. Default is to keep 20 checkpoints.
A value of None or -1 means all checkpoints will be kept.
keep_serialized_model_every_num_seconds : ``int``, optional (default=None)
If num_serialized_models_to_keep is not None, then occasionally it's useful to
save models at a given interval in addition to the last num_serialized_models_to_keep.
To do so, specify keep_serialized_model_every_num_seconds as the number of seconds
between permanently saved checkpoints. Note that this option is only used if
num_serialized_models_to_keep is not None, otherwise all checkpoints are kept.
checkpointer : ``Checkpointer``, optional (default=None)
An instance of class Checkpointer to use instead of the default. If a checkpointer is specified,
the arguments num_serialized_models_to_keep and keep_serialized_model_every_num_seconds should
not be specified. The caller is responsible for initializing the checkpointer so that it is
consistent with serialization_dir.
model_save_interval : ``float``, optional (default=None)
If provided, then serialize models every ``model_save_interval``
seconds within single epochs. In all cases, models are also saved
at the end of every epoch if ``serialization_dir`` is provided.
cuda_device : ``Union[int, List[int]]``, optional (default = -1)
An integer or list of integers specifying the CUDA device(s) to use. If -1, the CPU is used.
grad_norm : ``float``, optional, (default = None).
If provided, gradient norms will be rescaled to have a maximum of this value.
grad_clipping : ``float``, optional (default = ``None``).
If provided, gradients will be clipped `during the backward pass` to have an (absolute)
maximum of this value. If you are getting ``NaNs`` in your gradients during training
that are not solved by using ``grad_norm``, you may need this.
learning_rate_scheduler : ``LearningRateScheduler``, optional (default = None)
If specified, the learning rate will be decayed with respect to
this schedule at the end of each epoch (or batch, if the scheduler implements
the ``step_batch`` method). If you use :class:`torch.optim.lr_scheduler.ReduceLROnPlateau`,
this will use the ``validation_metric`` provided to determine if learning has plateaued.
To support updating the learning rate on every batch, this can optionally implement
``step_batch(batch_num_total)`` which updates the learning rate given the batch number.
momentum_scheduler : ``MomentumScheduler``, optional (default = None)
If specified, the momentum will be updated at the end of each batch or epoch
according to the schedule.
summary_interval: ``int``, optional, (default = 100)
Number of batches between logging scalars to tensorboard
histogram_interval : ``int``, optional, (default = ``None``)
If not None, then log histograms to tensorboard every ``histogram_interval`` batches.
When this parameter is specified, the following additional logging is enabled:
* Histograms of model parameters
* The ratio of parameter update norm to parameter norm
* Histogram of layer activations
We log histograms of the parameters returned by
``model.get_parameters_for_histogram_tensorboard_logging``.
The layer activations are logged for any modules in the ``Model`` that have
the attribute ``should_log_activations`` set to ``True``. Logging
histograms requires a number of GPU-CPU copies during training and is typically
slow, so we recommend logging histograms relatively infrequently.
Note: only Modules that return tensors, tuples of tensors or dicts
with tensors as values currently support activation logging.
should_log_parameter_statistics : ``bool``, optional, (default = True)
Whether to send parameter statistics (mean and standard deviation
of parameters and gradients) to tensorboard.
should_log_learning_rate : ``bool``, optional, (default = False)
Whether to send parameter specific learning rate to tensorboard.
log_batch_size_period : ``int``, optional, (default = ``None``)
If defined, how often to log the average batch size.
moving_average: ``MovingAverage``, optional, (default = None)
If provided, we will maintain moving averages for all parameters. During training, we
employ a shadow variable for each parameter, which maintains the moving average. During
evaluation, we backup the original parameters and assign the moving averages to corresponding
parameters. Be careful that when saving the checkpoint, we will save the moving averages of
parameters. This is necessary because we want the saved model to perform as well as the validated
model if we load it later. But this may cause problems if you restart the training from checkpoint.
"""
super().__init__(serialization_dir, cuda_device)
# I am not calling move_to_gpu here, because if the model is
# not already on the GPU then the optimizer is going to be wrong.
self.model = model
self.iterator = iterator
self._validation_iterator = validation_iterator
self.shuffle = shuffle
self.optimizer = optimizer
self.scheduler = scheduler
self.train_data = train_dataset
self._validation_data = validation_dataset
self.accumulated_batch_count = accumulated_batch_count
self.cold_step_count = cold_step_count
self.cold_lr = cold_lr
self.cuda_verbose_step = cuda_verbose_step
if patience is None: # no early stopping
if validation_dataset:
logger.warning(
"You provided a validation dataset but patience was set to None, "
"meaning that early stopping is disabled")
elif (not isinstance(patience, int)) or patience <= 0:
raise ConfigurationError(
'{} is an invalid value for "patience": it must be a positive integer '
"or None (if you want to disable early stopping)".format(
patience))
# For tracking is_best_so_far and should_stop_early It mimics the PyTorch state_dict / load_state_dict
self._metric_tracker = MetricTracker(patience, validation_metric)
# Get rid of + or - 取绝对值
self._validation_metric = validation_metric[1:]
# 默认20个 epoch
self._num_epochs = num_epochs
if checkpointer is not None:
# We can't easily check if these parameters were passed in, so check against their default values.
# We don't check against serialization_dir since it is also used by the parent class.
# 默认应该有20个序列化模型 且不应该设置只保存模型一段间隔
if num_serialized_models_to_keep != 20 \
or keep_serialized_model_every_num_seconds is not None:
raise ConfigurationError(
"When passing a custom Checkpointer, you may not also pass in separate checkpointer "
"args 'num_serialized_models_to_keep' or 'keep_serialized_model_every_num_seconds'."
)
self._checkpointer = checkpointer
else:
self._checkpointer = Checkpointer(
serialization_dir,
keep_serialized_model_every_num_seconds,
num_serialized_models_to_keep,
)
self._model_save_interval = model_save_interval
self._grad_norm = grad_norm
self._grad_clipping = grad_clipping
self._learning_rate_scheduler = learning_rate_scheduler
self._momentum_scheduler = momentum_scheduler
self._moving_average = moving_average
# We keep the total batch number as an instance variable because it
# is used inside a closure for the hook which logs activations in
# ``_enable_activation_logging``.
self._batch_num_total = 0
self._tensorboard = TensorboardWriter(
get_batch_num_total=lambda: self._batch_num_total,
serialization_dir=serialization_dir,
summary_interval=summary_interval,
histogram_interval=histogram_interval,
should_log_parameter_statistics=should_log_parameter_statistics,
should_log_learning_rate=should_log_learning_rate,
)
self._log_batch_size_period = log_batch_size_period
self._last_log = 0.0 # time of last logging
# Enable activation logging.
if histogram_interval is not None:
self._tensorboard.enable_activation_logging(self.model)
# 目的是如果多个特征取值范围不一样 梯度下降收敛会慢 这里grad norm 默认是none 即no-op 无操作返回
def rescale_gradients(self) -> Optional[float]:
return training_util.rescale_gradients(self.model, self._grad_norm)
# 计算batch的loss
def batch_loss(self, batch_group: List[TensorDict],
for_training: bool) -> torch.Tensor:
"""
Does a forward pass on the given batches and returns the ``loss`` value in the result.
If ``for_training`` is `True` also applies regularization penalty.
正则化惩罚是要降低不重要特征的影响力，避免过拟合
被train epoch 和evaluate epoch复用
"""
# 处理并行， 但在gector中默认是单gpu
if self._multiple_gpu:
output_dict = training_util.data_parallel(batch_group, self.model,
self._cuda_devices)
else:
assert len(batch_group) == 1
batch = batch_group[0]
batch = nn_util.move_to_device(batch, self._cuda_devices[0])
# 前向传播
output_dict = self.model(**batch)
# 通过正则化惩罚项来计算loss
try:
loss = output_dict["loss"]
if for_training:
loss += self.model.get_regularization_penalty()
except KeyError:
if for_training:
raise RuntimeError(
"The model you are trying to optimize does not contain a"
" 'loss' key in the output of model.forward(inputs).")
loss = None
return loss
def _train_epoch(self, epoch: int) -> Dict[str, float]:
"""
Trains one epoch and returns metrics.
"""
logger.info("Epoch %d/%d", epoch, self._num_epochs - 1)
peak_cpu_usage = peak_memory_mb()
logger.info(f"Peak CPU memory usage MB: {peak_cpu_usage}")
gpu_usage = []
for gpu, memory in gpu_memory_mb().items():
gpu_usage.append((gpu, memory))
logger.info(f"GPU {gpu} memory usage MB: {memory}")
train_loss = 0.0
# Set the model to "train" mode.
self.model.train()
num_gpus = len(self._cuda_devices)
# Get tqdm for the training batches
# 使训练数据可迭代
raw_train_generator = self.iterator(self.train_data,
num_epochs=1,
shuffle=self.shuffle)
# 将可迭代的单实例批处理到list中
train_generator = lazy_groups_of(raw_train_generator, num_gpus)
# 向上取整 获取batch数 （总batch/gpu数）
num_training_batches = math.ceil(
self.iterator.get_num_batches(self.train_data) / num_gpus)
# 默认的accumulated batch count 为4，此处是求accumulate的尾巴
residue = num_training_batches % self.accumulated_batch_count
self._last_log = time.time()
last_save_time = time.time()
batches_this_epoch = 0
if self._batch_num_total is None:
self._batch_num_total = 0
histogram_parameters = set(
self.model.get_parameters_for_histogram_tensorboard_logging())
logger.info("Training")
# 训练进度条
train_generator_tqdm = Tqdm.tqdm(train_generator,
total=num_training_batches)
cumulative_batch_size = 0
# 梯度清零 常规操作
self.optimizer.zero_grad()
# 开始训练
for batch_group in train_generator_tqdm:
batches_this_epoch += 1
self._batch_num_total += 1
batch_num_total = self._batch_num_total
# 一个batch为accumulated_batch_count个iteration，梯度累积
iter_len = self.accumulated_batch_count \
if batches_this_epoch <= (num_training_batches - residue) else residue
if self.cuda_verbose_step is not None and batch_num_total % self.cuda_verbose_step == 0:
print(
f'Before forward pass - Cuda memory allocated: {torch.cuda.memory_allocated() / 1e9}'
)
print(
f'Before forward pass - Cuda memory cached: {torch.cuda.memory_cached() / 1e9}'
)
try: # 平均loss
loss = self.batch_loss(batch_group,
for_training=True) / iter_len
except RuntimeError as e:
print(e)
for x in batch_group:
all_words = [len(y['words']) for y in x['metadata']]
print(f"Total sents: {len(all_words)}. "
f"Min {min(all_words)}. Max {max(all_words)}")
for elem in ['labels', 'd_tags']:
tt = x[elem]
print(
f"{elem} shape {list(tt.shape)} and min {tt.min().item()} and {tt.max().item()}"
)
for elem in ["bert", "mask", "bert-offsets"]:
tt = x['tokens'][elem]
print(
f"{elem} shape {list(tt.shape)} and min {tt.min().item()} and {tt.max().item()}"
)
raise e
if self.cuda_verbose_step is not None and batch_num_total % self.cuda_verbose_step == 0:
print(
f'After forward pass - Cuda memory allocated: {torch.cuda.memory_allocated() / 1e9}'
)
print(
f'After forward pass - Cuda memory cached: {torch.cuda.memory_cached() / 1e9}'
)
if torch.isnan(loss):
raise ValueError("nan loss encountered")
# 反向传播
loss.backward()
if self.cuda_verbose_step is not None and batch_num_total % self.cuda_verbose_step == 0:
print(
f'After backprop - Cuda memory allocated: {torch.cuda.memory_allocated() / 1e9}'
)
print(
f'After backprop - Cuda memory cached: {torch.cuda.memory_cached() / 1e9}'
)
# 计算loss
train_loss += loss.item() * iter_len
# 删除两个变量
del batch_group, loss
# pytorch 训练时无用的临时变量可能会越来越多，导致 out of memory ，可以使用下面语句来清理这些不需要的变量。
torch.cuda.empty_cache()
if self.cuda_verbose_step is not None and batch_num_total % self.cuda_verbose_step == 0:
print(
f'After collecting garbage - Cuda memory allocated: {torch.cuda.memory_allocated() / 1e9}'
)
print(
f'After collecting garbage - Cuda memory cached: {torch.cuda.memory_cached() / 1e9}'
)
# 正则化梯度
batch_grad_norm = self.rescale_gradients()
# This does nothing if batch_num_total is None or you are using a
# scheduler which doesn't update per batch.
# lr会在epoch变大的同时予以调整，一般是逐渐变小
# momentum 动量 防止损失函数陷入局部极小值，跳出鞍点
if self._learning_rate_scheduler:
self._learning_rate_scheduler.step_batch(batch_num_total)
if self._momentum_scheduler:
self._momentum_scheduler.step_batch(batch_num_total)
if self._tensorboard.should_log_histograms_this_batch():
# copy参数 防止爆内存
# get the magnitude of parameter updates for logging
# We need a copy of current parameters to compute magnitude of updates,
# and copy them to CPU so large models won't go OOM on the GPU.
param_updates = {
name: param.detach().cpu().clone()
for name, param in self.model.named_parameters()
}
if batches_this_epoch % self.accumulated_batch_count == 0 or \
batches_this_epoch == num_training_batches:
# 自动计算梯度 optimizer.step()
self.optimizer.step()
self.optimizer.zero_grad()
for name, param in self.model.named_parameters():
param_updates[name].sub_(param.detach().cpu())
# 求l1范数
update_norm = torch.norm(param_updates[name].view(-1))
param_norm = torch.norm(param.view(-1)).cpu()
self._tensorboard.add_train_scalar(
"gradient_update/" + name,
update_norm / (param_norm + 1e-7))
else:
if batches_this_epoch % self.accumulated_batch_count == 0 or \
batches_this_epoch == num_training_batches:
self.optimizer.step()
self.optimizer.zero_grad()
# Update moving averages 在adam或SGD优化中为了平衡模型更新速度一般设置滑动平均来提高模型在测试数据上的健壮性
if self._moving_average is not None:
self._moving_average.apply(batch_num_total)
# Update the description with the latest metrics
metrics = training_util.get_metrics(self.model, train_loss,
batches_this_epoch)
description = training_util.description_from_metrics(metrics)
train_generator_tqdm.set_description(description, refresh=False)
# Log parameter values to Tensorboard
if self._tensorboard.should_log_this_batch():
self._tensorboard.log_parameter_and_gradient_statistics(
self.model, batch_grad_norm)
self._tensorboard.log_learning_rates(self.model,
self.optimizer)
self._tensorboard.add_train_scalar("loss/loss_train",
metrics["loss"])
self._tensorboard.log_metrics(
{"epoch_metrics/" + k: v
for k, v in metrics.items()})
if self._tensorboard.should_log_histograms_this_batch():
self._tensorboard.log_histograms(self.model,
histogram_parameters)
if self._log_batch_size_period:
cur_batch = sum([
training_util.get_batch_size(batch)
for batch in batch_group
])
cumulative_batch_size += cur_batch
if (batches_this_epoch - 1) % self._log_batch_size_period == 0:
average = cumulative_batch_size / batches_this_epoch
logger.info(
f"current batch size: {cur_batch} mean batch size: {average}"
)
self._tensorboard.add_train_scalar("current_batch_size",
cur_batch)
self._tensorboard.add_train_scalar("mean_batch_size",
average)
# Save model if needed.
if self._model_save_interval is not None and (
time.time() - last_save_time > self._model_save_interval):
last_save_time = time.time()
self._save_checkpoint("{0}.{1}".format(
epoch, training_util.time_to_str(int(last_save_time))))
metrics = training_util.get_metrics(self.model,
train_loss,
batches_this_epoch,
reset=True)
metrics["cpu_memory_MB"] = peak_cpu_usage
for (gpu_num, memory) in gpu_usage:
metrics["gpu_" + str(gpu_num) + "_memory_MB"] = memory
return metrics
def _validation_loss(self) -> Tuple[float, int]:
"""
Computes the validation loss. Returns it and the number of batches.
"""
logger.info("Validating")
# 与model.train()类似
self.model.eval()
# Replace parameter values with the shadow values from the moving averages.
if self._moving_average is not None:
self._moving_average.assign_average_value()
if self._validation_iterator is not None:
val_iterator = self._validation_iterator
else:
val_iterator = self.iterator
num_gpus = len(self._cuda_devices)
# 与上面train代码的流程相似
raw_val_generator = val_iterator(self._validation_data,
num_epochs=1,
shuffle=False)
val_generator = lazy_groups_of(raw_val_generator, num_gpus)
num_validation_batches = math.ceil(
val_iterator.get_num_batches(self._validation_data) / num_gpus)
val_generator_tqdm = Tqdm.tqdm(val_generator,
total=num_validation_batches)
batches_this_epoch = 0
val_loss = 0
for batch_group in val_generator_tqdm:
loss = self.batch_loss(batch_group, for_training=False)
if loss is not None:
# You shouldn't necessarily have to compute a loss for validation, so we allow for
# `loss` to be None. We need to be careful, though - `batches_this_epoch` is
# currently only used as the divisor for the loss function, so we can safely only
# count those batches for which we actually have a loss. If this variable ever
# gets used for something else, we might need to change things around a bit.
batches_this_epoch += 1
val_loss += loss.detach().cpu().numpy(
) # loss.detach().cpu().numpy()为了取出loss值
# Update the description with the latest metrics
val_metrics = training_util.get_metrics(self.model, val_loss,
batches_this_epoch)
description = training_util.description_from_metrics(val_metrics)
val_generator_tqdm.set_description(description, refresh=False)
# Now restore the original parameter values.
if self._moving_average is not None:
self._moving_average.restore()
return val_loss, batches_this_epoch
@property
def train(self) -> Dict[str, Any]:
"""
Trains the supplied model with the supplied parameters.
相关的metric字典记录的信息都在训练时产生的json文件中
"""
try:
epoch_counter = self._restore_checkpoint()
except RuntimeError:
traceback.print_exc()
raise ConfigurationError(
"Could not recover training from the checkpoint. Did you mean to output to "
"a different serialization directory or delete the existing serialization "
"directory?")
# 梯度剪裁 防止梯度爆炸跳过最优解
training_util.enable_gradient_clipping(self.model, self._grad_clipping)
logger.info("Beginning training.")
train_metrics: Dict[str, float] = {}
val_metrics: Dict[str, float] = {}
this_epoch_val_metric: float = None
metrics: Dict[str, Any] = {}
epochs_trained = 0
# ------训练开始-------
training_start_time = time.time()
# cold_step_count为只训练最后一层线性层的epoch数
# 训练阶段一，二
# 在前 cold_step_count个epoch
# 不需要训练原来的预训练模型，之后需要训练
# 阶段三直接训练预训练模型参数， 因为预训练模型的参数过多
# 同时需要注意，在cold step阶段也要使用cold lr，
# 此阶段结束后，使用base lr
if self.cold_step_count > 0:
# 1e-5
base_lr = self.optimizer.param_groups[0]['lr']
for param_group in self.optimizer.param_groups:
# 1e-3
param_group['lr'] = self.cold_lr
self.model.text_field_embedder._token_embedders[
'bert'].set_weights(freeze=True)
metrics["best_epoch"] = self._metric_tracker.best_epoch
for key, value in self._metric_tracker.best_epoch_metrics.items():
metrics["best_validation_" + key] = value
# epoch_counter = 0 if restore_checkpoint is none else continue training
for epoch in range(epoch_counter, self._num_epochs):
# 恢复正常
if epoch == self.cold_step_count and epoch != 0:
for param_group in self.optimizer.param_groups:
param_group['lr'] = base_lr
self.model.text_field_embedder._token_embedders[
'bert'].set_weights(freeze=False)
# --开始当前epoch--
epoch_start_time = time.time()
# **训练**
train_metrics = self._train_epoch(epoch)
# get peak of memory usage
if "cpu_memory_MB" in train_metrics:
metrics["peak_cpu_memory_MB"] = max(
metrics.get("peak_cpu_memory_MB", 0),
train_metrics["cpu_memory_MB"])
for key, value in train_metrics.items():
if key.startswith("gpu_"):
metrics["peak_" + key] = max(metrics.get("peak_" + key, 0),
value)
# clear cache before validation
torch.cuda.empty_cache()
# evaluate的函数说了， 不是一定需要进行验证，所以这里要做判断
if self._validation_data is not None:
# 常规操作，验证时不计算梯度，不更新参数
with torch.no_grad():
# We have a validation set, so compute all the metrics on it.
val_loss, num_batches = self._validation_loss()
val_metrics = training_util.get_metrics(self.model,
val_loss,
num_batches,
reset=True)
# Check validation metric for early stopping
# 获取性能指标--loss
this_epoch_val_metric = val_metrics[
self._validation_metric]
self._metric_tracker.add_metric(this_epoch_val_metric)
if self._metric_tracker.should_stop_early():
# 这就是为什么有的时候ckpt不足epoch个数，是因为patience耗光
# patience是配合早停机制的阈值，patience次在验证集的性能下降时，停止训练
logger.info("Ran out of patience. Stopping training.")
break
self._tensorboard.log_metrics(
train_metrics,
val_metrics=val_metrics,
log_to_console=True,
epoch=epoch + 1) # +1 because tensorboard doesn't like 0
# Create overall metrics dict
# **epoch结束**
training_elapsed_time = time.time() - training_start_time
metrics["training_duration"] = str(
datetime.timedelta(seconds=training_elapsed_time))
metrics["training_start_epoch"] = epoch_counter
metrics["training_epochs"] = epochs_trained
metrics["epoch"] = epoch
# 将train， evaluate阶段的metric记录都汇总
for key, value in train_metrics.items():
metrics["training_" + key] = value
for key, value in val_metrics.items():
metrics["validation_" + key] = value
# if self.cold_step_count <= epoch:
# step操作
self.scheduler.step(metrics['validation_loss'])
# 这些更新都在119服务器的pretraingectors目录下
if self._metric_tracker.is_best_so_far():
# Update all the best_ metrics.
# (Otherwise they just stay the same as they were.)
metrics["best_epoch"] = epoch
for key, value in val_metrics.items():
metrics["best_validation_" + key] = value
self._metric_tracker.best_epoch_metrics = val_metrics
# 以json形式存储metrics
if self._serialization_dir:
dump_metrics(
os.path.join(self._serialization_dir,
f"metrics_epoch_{epoch}.json"), metrics)
# The Scheduler API is agnostic to whether your schedule requires a validation metric -
# if it doesn't, the validation metric passed here is ignored.
if self._learning_rate_scheduler:
# step操作
self._learning_rate_scheduler.step(this_epoch_val_metric,
epoch)
if self._momentum_scheduler:
# step操作
self._momentum_scheduler.step(this_epoch_val_metric, epoch)
# 保存ckpt
self._save_checkpoint(epoch)
epoch_elapsed_time = time.time() - epoch_start_time
logger.info("Epoch duration: %s",
datetime.timedelta(seconds=epoch_elapsed_time))
if epoch < self._num_epochs - 1:
training_elapsed_time = time.time() - training_start_time
estimated_time_remaining = training_elapsed_time * (
(self._num_epochs - epoch_counter) /
float(epoch - epoch_counter + 1) - 1)
formatted_time = str(
datetime.timedelta(seconds=int(estimated_time_remaining)))
logger.info("Estimated training time remaining: %s",
formatted_time)
# 一个epoch结束
epochs_trained += 1
# make sure pending events are flushed to disk and files are closed properly
# self._tensorboard.close()
# Load the best model state before returning
best_model_state = self._checkpointer.best_model_state()
if best_model_state:
self.model.load_state_dict(best_model_state)
return metrics
def _save_checkpoint(self, epoch: Union[int, str]) -> None:
"""
主要是存储model状态和train的状态
Saves a checkpoint of the model to self._serialization_dir.
Is a no-op if self._serialization_dir is None.
Parameters
----------
epoch : Union[int, str], required.
The epoch of training. If the checkpoint is saved in the middle
of an epoch, the parameter is a string with the epoch and timestamp.
"""
# If moving averages are used for parameters, we save
# the moving average values into checkpoint, instead of the current values.
if self._moving_average is not None:
self._moving_average.assign_average_value()
# These are the training states we need to persist.
training_states = {
"metric_tracker": self._metric_tracker.state_dict(),
"optimizer": self.optimizer.state_dict(),
"batch_num_total": self._batch_num_total,
}
# If we have a learning rate or momentum scheduler, we should persist them too.
if self._learning_rate_scheduler is not None:
training_states[
"learning_rate_scheduler"] = self._learning_rate_scheduler.state_dict(
)
if self._momentum_scheduler is not None:
training_states[
"momentum_scheduler"] = self._momentum_scheduler.state_dict()
# save checkpoint
self._checkpointer.save_checkpoint(
model_state=self.model.state_dict(),
epoch=epoch,
training_states=training_states,
is_best_so_far=self._metric_tracker.is_best_so_far(),
)
# Restore the original values for parameters so that training will not be affected.
if self._moving_average is not None:
self._moving_average.restore()
def _restore_checkpoint(self) -> int:
"""
恢复上一个检查点的模型和训练状态
Restores the model and training state from the last saved checkpoint.
This includes an epoch count and optimizer state, which is serialized separately
from model parameters. This function should only be used to continue training -
if you wish to load a model for inference/load parts of a model into a new
computation graph, you should use the native Pytorch functions:
`` model.load_state_dict(torch.load("/path/to/model/weights.th"))``
If ``self._serialization_dir`` does not exist or does not contain any checkpointed weights,
this function will do nothing and return 0.
Returns
-------
epoch: int
The epoch at which to resume training, which should be one after the epoch
in the saved training state.
"""
model_state, training_state = self._checkpointer.restore_checkpoint()
if not training_state:
# No checkpoint to restore, start at 0
return 0
self.model.load_state_dict(model_state)
self.optimizer.load_state_dict(training_state["optimizer"])
if self._learning_rate_scheduler is not None \
and "learning_rate_scheduler" in training_state:
self._learning_rate_scheduler.load_state_dict(
training_state["learning_rate_scheduler"])
if self._momentum_scheduler is not None and "momentum_scheduler" in training_state:
self._momentum_scheduler.load_state_dict(
training_state["momentum_scheduler"])
training_util.move_optimizer_to_cuda(self.optimizer)
# Currently the ``training_state`` contains a serialized ``MetricTracker``.
if "metric_tracker" in training_state:
self._metric_tracker.load_state_dict(
training_state["metric_tracker"])
# It used to be the case that we tracked ``val_metric_per_epoch``.
elif "val_metric_per_epoch" in training_state:
self._metric_tracker.clear()
self._metric_tracker.add_metrics(
training_state["val_metric_per_epoch"])
# And before that we didn't track anything.
else:
self._metric_tracker.clear()
if isinstance(training_state["epoch"], int):
epoch_to_return = training_state["epoch"] + 1
else:
epoch_to_return = int(training_state["epoch"].split(".")[0]) + 1
# For older checkpoints with batch_num_total missing, default to old behavior where
# it is unchanged.
batch_num_total = training_state.get("batch_num_total")
if batch_num_total is not None:
self._batch_num_total = batch_num_total
return epoch_to_return
# Requires custom from_params.
# 通过返回和构造函数一样的参数名字，来完成实例化
@classmethod
def from_params( # type: ignore
cls,
model: Model,
serialization_dir: str,
iterator: DataIterator,
train_data: Iterable[Instance],
validation_data: Optional[Iterable[Instance]],
params: Params,
validation_iterator: DataIterator = None,
) -> "Trainer":
# 与python 字典的pop一样 返回值为对应key的value
patience = params.pop_int("patience", None)
validation_metric = params.pop("validation_metric", "-loss")
shuffle = params.pop_bool("shuffle", True)
num_epochs = params.pop_int("num_epochs", 20)
cuda_device = parse_cuda_device(params.pop("cuda_device", -1))
grad_norm = params.pop_float("grad_norm", None)
grad_clipping = params.pop_float("grad_clipping", None)
lr_scheduler_params = params.pop("learning_rate_scheduler", None)
momentum_scheduler_params = params.pop("momentum_scheduler", None)
# 单gpu
if isinstance(cuda_device, list):
model_device = cuda_device[0]
else:
model_device = cuda_device
if model_device >= 0:
# Moving model to GPU here so that the optimizer state gets constructed on
# the right device.
model = model.cuda(model_device)
parameters = [[n, p] for n, p in model.named_parameters()
if p.requires_grad]
optimizer = Optimizer.from_params(parameters, params.pop("optimizer"))
if "moving_average" in params:
moving_average = MovingAverage.from_params(
params.pop("moving_average"), parameters=parameters)
else:
moving_average = None
if lr_scheduler_params:
lr_scheduler = LearningRateScheduler.from_params(
optimizer, lr_scheduler_params)
else:
lr_scheduler = None
if momentum_scheduler_params:
momentum_scheduler = MomentumScheduler.from_params(
optimizer, momentum_scheduler_params)
else:
momentum_scheduler = None
if "checkpointer" in params:
if "keep_serialized_model_every_num_seconds" in params \
or "num_serialized_models_to_keep" in params:
raise ConfigurationError(
"Checkpointer may be initialized either from the 'checkpointer' key or from the "
"keys 'num_serialized_models_to_keep' and 'keep_serialized_model_every_num_seconds'"
" but the passed config uses both methods.")
checkpointer = Checkpointer.from_params(params.pop("checkpointer"))
else:
num_serialized_models_to_keep = params.pop_int(
"num_serialized_models_to_keep", 20)
keep_serialized_model_every_num_seconds = params.pop_int(
"keep_serialized_model_every_num_seconds", None)
checkpointer = Checkpointer(
serialization_dir=serialization_dir,
num_serialized_models_to_keep=num_serialized_models_to_keep,
keep_serialized_model_every_num_seconds=
keep_serialized_model_every_num_seconds,
)
model_save_interval = params.pop_float("model_save_interval", None)
summary_interval = params.pop_int("summary_interval", 100)
histogram_interval = params.pop_int("histogram_interval", None)
should_log_parameter_statistics = params.pop_bool(
"should_log_parameter_statistics", True)
should_log_learning_rate = params.pop_bool("should_log_learning_rate",
False)
log_batch_size_period = params.pop_int("log_batch_size_period", None)
params.assert_empty(cls.__name__)
return cls(
model,
optimizer,
iterator,
train_data,
validation_data,
patience=patience,
validation_metric=validation_metric,
validation_iterator=validation_iterator,
shuffle=shuffle,
num_epochs=num_epochs,
serialization_dir=serialization_dir,
cuda_device=cuda_device,
grad_norm=grad_norm,
grad_clipping=grad_clipping,
learning_rate_scheduler=lr_scheduler,
momentum_scheduler=momentum_scheduler,
checkpointer=checkpointer,
model_save_interval=model_save_interval,
summary_interval=summary_interval,
histogram_interval=histogram_interval,
should_log_parameter_statistics=should_log_parameter_statistics,
should_log_learning_rate=should_log_learning_rate,
log_batch_size_period=log_batch_size_period,
moving_average=moving_average,
)
class MyTrainer(TrainerBase):
def __init__(
self,
model: Model,
optimizer: torch.optim.Optimizer,
iterator: DataIterator,
train_dataset: Iterable[Instance],
validation_dataset: Optional[Iterable[Instance]] = None,
patience: Optional[int] = None,
validation_metric: str = "-loss",
validation_iterator: DataIterator = None,
shuffle: bool = True,
num_epochs: int = 20,
serialization_dir: Optional[str] = None,
num_serialized_models_to_keep: int = 20,
keep_serialized_model_every_num_seconds: int = None,
checkpointer: Checkpointer = None,
model_save_interval: float = None,
cuda_device: Union[int, List] = -1,
grad_norm: Optional[float] = None,
grad_clipping: Optional[float] = None,
learning_rate_scheduler: Optional[LearningRateScheduler] = None,
momentum_scheduler: Optional[MomentumScheduler] = None,
summary_interval: int = 100,
histogram_interval: int = None,
should_log_parameter_statistics: bool = True,
should_log_learning_rate: bool = False,
log_batch_size_period: Optional[int] = None,
moving_average: Optional[MovingAverage] = None,
) -> None:
super().__init__(serialization_dir, cuda_device)
# I am not calling move_to_gpu here, because if the model is
# not already on the GPU then the optimizer is going to be wrong.
self.model = model
self.iterator = iterator
self._validation_iterator = validation_iterator
self.shuffle = shuffle
self.optimizer = optimizer
self.train_data = train_dataset
self._validation_data = validation_dataset
if patience is None: # no early stopping
if validation_dataset:
logger.warning(
"You provided a validation dataset but patience was set to None, "
"meaning that early stopping is disabled")
elif (not isinstance(patience, int)) or patience <= 0:
raise ConfigurationError(
'{} is an invalid value for "patience": it must be a positive integer '
"or None (if you want to disable early stopping)".format(
patience))
# Frequency of metric checking (in batch)
self._summary_interval = summary_interval
# For tracking is_best_so_far and should_stop_early
self._metric_tracker = MetricTracker(patience, validation_metric)
# Get rid of + or -
self._validation_metric = validation_metric[1:]
self._num_epochs = num_epochs
if checkpointer is not None:
# We can't easily check if these parameters were passed in, so check against their default values.
# We don't check against serialization_dir since it is also used by the parent class.
if (num_serialized_models_to_keep != 20
or keep_serialized_model_every_num_seconds is not None):
raise ConfigurationError(
"When passing a custom Checkpointer, you may not also pass in separate checkpointer "
"args 'num_serialized_models_to_keep' or 'keep_serialized_model_every_num_seconds'."
)
self._checkpointer = checkpointer
else:
self._checkpointer = Checkpointer(
serialization_dir,
keep_serialized_model_every_num_seconds,
num_serialized_models_to_keep,
)
self._model_save_interval = model_save_interval
self._grad_norm = grad_norm
self._grad_clipping = grad_clipping
self._learning_rate_scheduler = learning_rate_scheduler
self._momentum_scheduler = momentum_scheduler
self._moving_average = moving_average
# We keep the total batch number as an instance variable because it
# is used inside a closure for the hook which logs activations in
# ``_enable_activation_logging``.
self._batch_num_total = 0
self._tensorboard = TensorboardWriter(
get_batch_num_total=lambda: self._batch_num_total,
serialization_dir=serialization_dir,
summary_interval=summary_interval,
histogram_interval=histogram_interval,
should_log_parameter_statistics=should_log_parameter_statistics,
should_log_learning_rate=should_log_learning_rate,
)
self._log_batch_size_period = log_batch_size_period
self._last_log = 0.0 # time of last logging
# Enable activation logging.
if histogram_interval is not None:
self._tensorboard.enable_activation_logging(self.model)
def rescale_gradients(self) -> Optional[float]:
return training_util.rescale_gradients(self.model, self._grad_norm)
def batch_loss(self, batch_group: List[TensorDict],
for_training: bool) -> torch.Tensor:
"""
Does a forward pass on the given batches and returns the ``loss`` value in the result.
If ``for_training`` is `True` also applies regularization penalty.
"""
if self._multiple_gpu:
output_dict = training_util.data_parallel(batch_group, self.model,
self._cuda_devices)
else:
assert len(batch_group) == 1
batch = batch_group[0]
batch = nn_util.move_to_device(batch, self._cuda_devices[0])
output_dict = self.model(**batch)
try:
loss = output_dict["loss"]
if for_training:
loss += self.model.get_regularization_penalty()
except KeyError:
if for_training:
raise RuntimeError(
"The model you are trying to optimize does not contain a"
" 'loss' key in the output of model.forward(inputs).")
loss = None
return loss
def _train_epoch(self, epoch: int) -> Dict[str, float]:
"""
Trains one epoch and returns metrics.
"""
logger.info("Epoch %d/%d", epoch, self._num_epochs - 1)
peak_cpu_usage = peak_memory_mb()
logger.info(f"Peak CPU memory usage MB: {peak_cpu_usage}")
gpu_usage = []
for gpu, memory in gpu_memory_mb().items():
gpu_usage.append((gpu, memory))
logger.info(f"GPU {gpu} memory usage MB: {memory}")
train_loss = 0.0
# Set the model to "train" mode.
self.model.train()
num_gpus = len(self._cuda_devices)
# Get tqdm for the training batches
raw_train_generator = self.iterator(self.train_data,
num_epochs=1,
shuffle=self.shuffle)
train_generator = lazy_groups_of(raw_train_generator, num_gpus)
num_training_batches = math.ceil(
self.iterator.get_num_batches(self.train_data) / num_gpus)
self._last_log = time.time()
last_save_time = time.time()
batches_this_epoch = 0
if self._batch_num_total is None:
self._batch_num_total = 0
histogram_parameters = set(
self.model.get_parameters_for_histogram_tensorboard_logging())
logger.info("Training")
train_generator_tqdm = Tqdm.tqdm(train_generator,
total=num_training_batches)
cumulative_batch_size = 0
for batch_group in train_generator_tqdm:
batches_this_epoch += 1
self._batch_num_total += 1
batch_num_total = self._batch_num_total
self.optimizer.zero_grad()
loss = self.batch_loss(batch_group, for_training=True)
if torch.isnan(loss):
raise ValueError("nan loss encountered")
loss.backward()
train_loss += loss.item()
batch_grad_norm = self.rescale_gradients()
# This does nothing if batch_num_total is None or you are using a
# scheduler which doesn't update per batch.
if self._learning_rate_scheduler:
self._learning_rate_scheduler.step_batch(batch_num_total)
if self._momentum_scheduler:
self._momentum_scheduler.step_batch(batch_num_total)
if self._tensorboard.should_log_histograms_this_batch():
# get the magnitude of parameter updates for logging
# We need a copy of current parameters to compute magnitude of updates,
# and copy them to CPU so large models won't go OOM on the GPU.
param_updates = {
name: param.detach().cpu().clone()
for name, param in self.model.named_parameters()
}
self.optimizer.step()
for name, param in self.model.named_parameters():
param_updates[name].sub_(param.detach().cpu())
update_norm = torch.norm(param_updates[name].view(-1))
param_norm = torch.norm(param.view(-1)).cpu()
self._tensorboard.add_train_scalar(
"gradient_update/" + name,
update_norm / (param_norm + 1e-7))
else:
self.optimizer.step()
# Update moving averages
if self._moving_average is not None:
self._moving_average.apply(batch_num_total)
# evaluate model performance if reaches a checkpoint.
if batches_this_epoch % self._summary_interval == 0:
# Update the description with the latest metrics
metrics = training_util.get_metrics(self.model, train_loss,
batches_this_epoch)
description = training_util.description_from_metrics(metrics)
train_generator_tqdm.set_description(description,
refresh=False)
# Log parameter values to Tensorboard
if self._tensorboard.should_log_this_batch():
self._tensorboard.log_parameter_and_gradient_statistics(
self.model, batch_grad_norm)
self._tensorboard.log_learning_rates(self.model,
self.optimizer)
self._tensorboard.add_train_scalar("loss/loss_train",
metrics["loss"])
self._tensorboard.log_metrics(
{"epoch_metrics/" + k: v
for k, v in metrics.items()})
if self._tensorboard.should_log_histograms_this_batch():
self._tensorboard.log_histograms(self.model,
histogram_parameters)
if self._log_batch_size_period:
cur_batch = sum([
training_util.get_batch_size(batch)
for batch in batch_group
])
cumulative_batch_size += cur_batch
if (batches_this_epoch - 1) % self._log_batch_size_period == 0:
average = cumulative_batch_size / batches_this_epoch
logger.info(
f"current batch size: {cur_batch} mean batch size: {average}"
)
self._tensorboard.add_train_scalar("current_batch_size",
cur_batch)
self._tensorboard.add_train_scalar("mean_batch_size",
average)
# Save model if needed.
if self._model_save_interval is not None and (
time.time() - last_save_time > self._model_save_interval):
last_save_time = time.time()
self._save_checkpoint("{0}.{1}".format(
epoch, training_util.time_to_str(int(last_save_time))))
metrics = training_util.get_metrics(self.model,
train_loss,
batches_this_epoch,
reset=True)
metrics["cpu_memory_MB"] = peak_cpu_usage
for (gpu_num, memory) in gpu_usage:
metrics["gpu_" + str(gpu_num) + "_memory_MB"] = memory
return metrics
def _save_checkpoint(self, epoch: Union[int, str]) -> None:
"""
Saves a checkpoint of the model to self._serialization_dir.
Is a no-op if self._serialization_dir is None.
Parameters
----------
epoch : Union[int, str], required.
The epoch of training. If the checkpoint is saved in the middle
of an epoch, the parameter is a string with the epoch and timestamp.
"""
# If moving averages are used for parameters, we save
# the moving average values into checkpoint, instead of the current values.
if self._moving_average is not None:
self._moving_average.assign_average_value()
# These are the training states we need to persist.
training_states = {
"metric_tracker": self._metric_tracker.state_dict(),
"optimizer": self.optimizer.state_dict(),
"batch_num_total": self._batch_num_total,
}
# If we have a learning rate or momentum scheduler, we should persist them too.
if self._learning_rate_scheduler is not None:
training_states[
"learning_rate_scheduler"] = self._learning_rate_scheduler.state_dict(
)
if self._momentum_scheduler is not None:
training_states[
"momentum_scheduler"] = self._momentum_scheduler.state_dict()
self._checkpointer.save_checkpoint(
model_state=self.model.state_dict(),
epoch=epoch,
training_states=training_states,
is_best_so_far=self._metric_tracker.is_best_so_far(),
)
# Restore the original values for parameters so that training will not be affected.
if self._moving_average is not None:
self._moving_average.restore()
def _restore_checkpoint(self) -> int:
"""
Restores the model and training state from the last saved checkpoint.
This includes an epoch count and optimizer state, which is serialized separately
from model parameters. This function should only be used to continue training -
if you wish to load a model for inference/load parts of a model into a new
computation graph, you should use the native Pytorch functions:
`` model.load_state_dict(torch.load("/path/to/model/weights.th"))``
If ``self._serialization_dir`` does not exist or does not contain any checkpointed weights,
this function will do nothing and return 0.
Returns
-------
epoch: int
The epoch at which to resume training, which should be one after the epoch
in the saved training state.
"""
model_state, training_state = self._checkpointer.restore_checkpoint()
if not training_state:
# No checkpoint to restore, start at 0
return 0
self.model.load_state_dict(model_state)
self.optimizer.load_state_dict(training_state["optimizer"])
if (self._learning_rate_scheduler is not None
and "learning_rate_scheduler" in training_state):
self._learning_rate_scheduler.load_state_dict(
training_state["learning_rate_scheduler"])
if self._momentum_scheduler is not None and "momentum_scheduler" in training_state:
self._momentum_scheduler.load_state_dict(
training_state["momentum_scheduler"])
training_util.move_optimizer_to_cuda(self.optimizer)
# Currently the ``training_state`` contains a serialized ``MetricTracker``.
if "metric_tracker" in training_state:
self._metric_tracker.load_state_dict(
training_state["metric_tracker"])
# It used to be the case that we tracked ``val_metric_per_epoch``.
elif "val_metric_per_epoch" in training_state:
self._metric_tracker.clear()
self._metric_tracker.add_metrics(
training_state["val_metric_per_epoch"])
# And before that we didn't track anything.
else:
self._metric_tracker.clear()
if isinstance(training_state["epoch"], int):
epoch_to_return = training_state["epoch"] + 1
else:
epoch_to_return = int(training_state["epoch"].split(".")[0]) + 1
# For older checkpoints with batch_num_total missing, default to old behavior where
# it is unchanged.
batch_num_total = training_state.get("batch_num_total")
if batch_num_total is not None:
self._batch_num_total = batch_num_total
return epoch_to_return
# Requires custom from_params.
@classmethod
def from_params( # type: ignore
cls,
model: Model,
serialization_dir: str,
iterator: DataIterator,
train_data: Iterable[Instance],
validation_data: Optional[Iterable[Instance]],
params: Params,
validation_iterator: DataIterator = None,
) -> "Trainer":
patience = params.pop_int("patience", None)
validation_metric = params.pop("validation_metric", "-loss")
shuffle = params.pop_bool("shuffle", True)
num_epochs = params.pop_int("num_epochs", 20)
cuda_device = parse_cuda_device(params.pop("cuda_device", -1))
grad_norm = params.pop_float("grad_norm", None)
grad_clipping = params.pop_float("grad_clipping", None)
lr_scheduler_params = params.pop("learning_rate_scheduler", None)
momentum_scheduler_params = params.pop("momentum_scheduler", None)
if isinstance(cuda_device, list):
model_device = cuda_device[0]
else:
model_device = cuda_device
if model_device >= 0:
# Moving model to GPU here so that the optimizer state gets constructed on
# the right device.
model = model.cuda(model_device)
parameters = [[n, p] for n, p in model.named_parameters()
if p.requires_grad]
optimizer = Optimizer.from_params(parameters, params.pop("optimizer"))
if "moving_average" in params:
moving_average = MovingAverage.from_params(
params.pop("moving_average"), parameters=parameters)
else:
moving_average = None
if lr_scheduler_params:
lr_scheduler = LearningRateScheduler.from_params(
optimizer, lr_scheduler_params)
else:
lr_scheduler = None
if momentum_scheduler_params:
momentum_scheduler = MomentumScheduler.from_params(
optimizer, momentum_scheduler_params)
else:
momentum_scheduler = None
if "checkpointer" in params:
if ("keep_serialized_model_every_num_seconds" in params
or "num_serialized_models_to_keep" in params):
raise ConfigurationError(
"Checkpointer may be initialized either from the 'checkpointer' key or from the "
"keys 'num_serialized_models_to_keep' and 'keep_serialized_model_every_num_seconds'"
" but the passed config uses both methods.")
checkpointer = Checkpointer.from_params(params.pop("checkpointer"))
else:
num_serialized_models_to_keep = params.pop_int(
"num_serialized_models_to_keep", 20)
keep_serialized_model_every_num_seconds = params.pop_int(
"keep_serialized_model_every_num_seconds", None)
checkpointer = Checkpointer(
serialization_dir=serialization_dir,
num_serialized_models_to_keep=num_serialized_models_to_keep,
keep_serialized_model_every_num_seconds=
keep_serialized_model_every_num_seconds,
)
model_save_interval = params.pop_float("model_save_interval", None)
summary_interval = params.pop_int("summary_interval", 100)
histogram_interval = params.pop_int("histogram_interval", None)
should_log_parameter_statistics = params.pop_bool(
"should_log_parameter_statistics", True)
should_log_learning_rate = params.pop_bool("should_log_learning_rate",
False)
log_batch_size_period = params.pop_int("log_batch_size_period", None)
params.assert_empty(cls.__name__)
return cls(
model,
optimizer,
iterator,
train_data,
validation_data,
patience=patience,
validation_metric=validation_metric,
validation_iterator=validation_iterator,
shuffle=shuffle,
num_epochs=num_epochs,
serialization_dir=serialization_dir,
cuda_device=cuda_device,
grad_norm=grad_norm,
grad_clipping=grad_clipping,
learning_rate_scheduler=lr_scheduler,
momentum_scheduler=momentum_scheduler,
checkpointer=checkpointer,
model_save_interval=model_save_interval,
summary_interval=summary_interval,
histogram_interval=histogram_interval,
should_log_parameter_statistics=should_log_parameter_statistics,
should_log_learning_rate=should_log_learning_rate,
log_batch_size_period=log_batch_size_period,
moving_average=moving_average,
)
def from_partial_objects(
cls,
model: MetaWrapper,
serialization_dir: str,
data_loader: DataLoader,
validation_data_loader: DataLoader = None,
patience: int = None,
validation_metric: str = "-loss",
num_epochs: int = 20,
cuda_device: Optional[Union[int, torch.device]] = None,
num_gradient_accumulation_steps: int = 1,
use_amp: bool = False,
no_grad: List[str] = None,
component_optimizers: Dict[str, Lazy[ComponentOptimizer]] = None,
tensorboard_writer: Lazy[TensorboardWriter] = None,
moving_average: Lazy[MovingAverage] = None,
checkpointer: Lazy[Checkpointer] = None,
batch_callbacks: List[BatchCallback] = None,
epoch_callbacks: List[EpochCallback] = None,
) -> "Trainer":
if cuda_device is None:
from torch import cuda
if cuda.device_count() > 0:
cuda_device = 0
else:
cuda_device = -1
check_for_gpu(cuda_device)
if cuda_device >= 0:
# Moving model to GPU here so that the optimizer state gets constructed on
# the right device.
model = model.cuda(cuda_device)
model.meta_model = model.meta_model.cuda(cuda_device)
for name in model.component_models:
model.component_models[name] = model.component_models[
name].cuda(cuda_device)
if no_grad:
for name, parameter in model.named_parameters():
if any(re.search(regex, name) for regex in no_grad):
parameter.requires_grad_(False)
batches_per_epoch: Optional[int]
try:
batches_per_epoch = len(data_loader)
batches_per_epoch = math.ceil(batches_per_epoch /
num_gradient_accumulation_steps)
except TypeError:
batches_per_epoch = None
sub_models = model.get_all_models()
for name, sub_model in sub_models.items():
component_optimizers[name] = component_optimizers[name].construct(
name=name,
model=sub_model,
num_epochs=num_epochs,
batches_per_epoch=batches_per_epoch,
cuda_device=cuda_device)
all_parameters = [[n, p] for n, p in model.named_parameters()
if p.requires_grad]
moving_average_ = moving_average.construct(parameters=all_parameters)
checkpointer_ = checkpointer.construct() or Checkpointer(
serialization_dir)
tensorboard_writer_ = tensorboard_writer.construct(
) or TensorboardWriter(serialization_dir)
return cls(model=model,
component_optimizers=component_optimizers,
data_loader=data_loader,
patience=patience,
validation_metric=validation_metric,
validation_data_loader=validation_data_loader,
num_epochs=num_epochs,
serialization_dir=serialization_dir,
checkpointer=checkpointer_,
moving_average=moving_average_,
cuda_device=cuda_device,
tensorboard_writer=tensorboard_writer_,
batch_callbacks=batch_callbacks,
epoch_callbacks=epoch_callbacks,
use_amp=use_amp)
class Trainer(TrainerBase):
def __init__(
self,
model: Model,
optimizer: torch.optim.Optimizer,
iterator: DataIterator,
train_dataset: Iterable[Instance],
validation_dataset: Optional[Iterable[Instance]] = None,
patience: Optional[int] = None,
validation_metric: str = "-loss",
validation_iterator: DataIterator = None,
shuffle: bool = True,
num_epochs: int = 20,
serialization_dir: Optional[str] = None,
num_serialized_models_to_keep: int = 20,
keep_serialized_model_every_num_seconds: int = None,
checkpointer: Checkpointer = None,
model_save_interval: float = None,
cuda_device: int = -1,
grad_norm: Optional[float] = None,
grad_clipping: Optional[float] = None,
learning_rate_scheduler: Optional[LearningRateScheduler] = None,
momentum_scheduler: Optional[MomentumScheduler] = None,
summary_interval: int = 100,
histogram_interval: int = None,
should_log_parameter_statistics: bool = True,
should_log_learning_rate: bool = False,
log_batch_size_period: Optional[int] = None,
moving_average: Optional[MovingAverage] = None,
distributed: bool = False,
local_rank: int = 0,
world_size: int = 1,
num_gradient_accumulation_steps: int = 1,
) -> None:
"""
A trainer for doing supervised learning. It just takes a labeled dataset
and a `DataIterator`, and uses the supplied `Optimizer` to learn the weights
for your model over some fixed number of epochs. You can also pass in a validation
dataset and enable early stopping. There are many other bells and whistles as well.
# Parameters
model : `Model`, required.
An AllenNLP model to be optimized. Pytorch Modules can also be optimized if
their `forward` method returns a dictionary with a "loss" key, containing a
scalar tensor representing the loss function to be optimized.
If you are training your model using GPUs, your model should already be
on the correct device. (If you use `Trainer.from_params` this will be
handled for you.)
optimizer : `torch.nn.Optimizer`, required.
An instance of a Pytorch Optimizer, instantiated with the parameters of the
model to be optimized.
iterator : `DataIterator`, required.
A method for iterating over a `Dataset`, yielding padded indexed batches.
train_dataset : `Dataset`, required.
A `Dataset` to train on. The dataset should have already been indexed.
validation_dataset : `Dataset`, optional, (default = None).
A `Dataset` to evaluate on. The dataset should have already been indexed.
patience : Optional[int] > 0, optional (default=None)
Number of epochs to be patient before early stopping: the training is stopped
after `patience` epochs with no improvement. If given, it must be `> 0`.
If None, early stopping is disabled.
validation_metric : str, optional (default="loss")
Validation metric to measure for whether to stop training using patience
and whether to serialize an `is_best` model each epoch. The metric name
must be prepended with either "+" or "-", which specifies whether the metric
is an increasing or decreasing function.
validation_iterator : `DataIterator`, optional (default=None)
An iterator to use for the validation set. If `None`, then
use the training `iterator`.
shuffle : `bool`, optional (default=True)
Whether to shuffle the instances in the iterator or not.
num_epochs : int, optional (default = 20)
Number of training epochs.
serialization_dir : str, optional (default=None)
Path to directory for saving and loading model files. Models will not be saved if
this parameter is not passed.
num_serialized_models_to_keep : `int`, optional (default=20)
Number of previous model checkpoints to retain. Default is to keep 20 checkpoints.
A value of None or -1 means all checkpoints will be kept.
keep_serialized_model_every_num_seconds : `int`, optional (default=None)
If num_serialized_models_to_keep is not None, then occasionally it's useful to
save models at a given interval in addition to the last num_serialized_models_to_keep.
To do so, specify keep_serialized_model_every_num_seconds as the number of seconds
between permanently saved checkpoints. Note that this option is only used if
num_serialized_models_to_keep is not None, otherwise all checkpoints are kept.
checkpointer : `Checkpointer`, optional (default=None)
An instance of class Checkpointer to use instead of the default. If a checkpointer is specified,
the arguments num_serialized_models_to_keep and keep_serialized_model_every_num_seconds should
not be specified. The caller is responsible for initializing the checkpointer so that it is
consistent with serialization_dir.
model_save_interval : `float`, optional (default=None)
If provided, then serialize models every `model_save_interval`
seconds within single epochs. In all cases, models are also saved
at the end of every epoch if `serialization_dir` is provided.
cuda_device : `int`, optional (default = -1)
An integer specifying the CUDA device(s) to use for this process. If -1, the CPU is used.
Data parallelism is controlled at the allennlp train level, so each trainer will have a single
GPU.
grad_norm : `float`, optional, (default = None).
If provided, gradient norms will be rescaled to have a maximum of this value.
grad_clipping : `float`, optional (default = `None`).
If provided, gradients will be clipped `during the backward pass` to have an (absolute)
maximum of this value. If you are getting `NaNs` in your gradients during training
that are not solved by using `grad_norm`, you may need this.
learning_rate_scheduler : `LearningRateScheduler`, optional (default = None)
If specified, the learning rate will be decayed with respect to
this schedule at the end of each epoch (or batch, if the scheduler implements
the `step_batch` method). If you use `torch.optim.lr_scheduler.ReduceLROnPlateau`,
this will use the `validation_metric` provided to determine if learning has plateaued.
To support updating the learning rate on every batch, this can optionally implement
`step_batch(batch_num_total)` which updates the learning rate given the batch number.
momentum_scheduler : `MomentumScheduler`, optional (default = None)
If specified, the momentum will be updated at the end of each batch or epoch
according to the schedule.
summary_interval : `int`, optional, (default = 100)
Number of batches between logging scalars to tensorboard
histogram_interval : `int`, optional, (default = `None`)
If not None, then log histograms to tensorboard every `histogram_interval` batches.
When this parameter is specified, the following additional logging is enabled:
* Histograms of model parameters
* The ratio of parameter update norm to parameter norm
* Histogram of layer activations
We log histograms of the parameters returned by
`model.get_parameters_for_histogram_tensorboard_logging`.
The layer activations are logged for any modules in the `Model` that have
the attribute `should_log_activations` set to `True`. Logging
histograms requires a number of GPU-CPU copies during training and is typically
slow, so we recommend logging histograms relatively infrequently.
Note: only Modules that return tensors, tuples of tensors or dicts
with tensors as values currently support activation logging.
should_log_parameter_statistics : `bool`, optional, (default = True)
Whether to send parameter statistics (mean and standard deviation
of parameters and gradients) to tensorboard.
should_log_learning_rate : `bool`, optional, (default = False)
Whether to send parameter specific learning rate to tensorboard.
log_batch_size_period : `int`, optional, (default = `None`)
If defined, how often to log the average batch size.
moving_average : `MovingAverage`, optional, (default = None)
If provided, we will maintain moving averages for all parameters. During training, we
employ a shadow variable for each parameter, which maintains the moving average. During
evaluation, we backup the original parameters and assign the moving averages to corresponding
parameters. Be careful that when saving the checkpoint, we will save the moving averages of
parameters. This is necessary because we want the saved model to perform as well as the validated
model if we load it later. But this may cause problems if you restart the training from checkpoint.
distributed : `bool`, optional, (default = False)
If set, PyTorch's `DistributedDataParallel` is used to train the model in multiple GPUs. This also
requires `world_size` to be greater than 1.
local_rank : `int`, optional, (default = 0)
This is the unique identifier of the `Trainer` in a distributed process group. The GPU device id is
used as the rank.
world_size : `int`, (default = 1)
The number of `Trainer` workers participating in the distributed training.
num_gradient_accumulation_steps : `int`, optional, (default = 1)
Gradients are accumulated for the given number of steps before doing an optimizer step. This can
be useful to accommodate batches that are larger than the RAM size. Refer Thomas Wolf's
[post](https://tinyurl.com/y5mv44fw) for details on Gradient Accumulation.
"""
super().__init__(serialization_dir, cuda_device, distributed,
local_rank, world_size)
# I am not calling move_to_gpu here, because if the model is
# not already on the GPU then the optimizer is going to be wrong.
self.model = model
self.iterator = iterator
self._validation_iterator = validation_iterator
self.shuffle = shuffle
self.optimizer = optimizer
self.train_data = train_dataset
self._validation_data = validation_dataset
if patience is None: # no early stopping
if validation_dataset:
logger.warning(
"You provided a validation dataset but patience was set to None, "
"meaning that early stopping is disabled")
elif (not isinstance(patience, int)) or patience <= 0:
raise ConfigurationError(
'{} is an invalid value for "patience": it must be a positive integer '
"or None (if you want to disable early stopping)".format(
patience))
# For tracking is_best_so_far and should_stop_early
self._metric_tracker = MetricTracker(patience, validation_metric)
# Get rid of + or -
self._validation_metric = validation_metric[1:]
self._num_epochs = num_epochs
if checkpointer is not None:
# We can't easily check if these parameters were passed in, so check against their default values.
# We don't check against serialization_dir since it is also used by the parent class.
if (num_serialized_models_to_keep != 20
or keep_serialized_model_every_num_seconds is not None):
raise ConfigurationError(
"When passing a custom Checkpointer, you may not also pass in separate checkpointer "
"args 'num_serialized_models_to_keep' or 'keep_serialized_model_every_num_seconds'."
)
self._checkpointer = checkpointer
else:
self._checkpointer = Checkpointer(
serialization_dir,
keep_serialized_model_every_num_seconds,
num_serialized_models_to_keep,
)
self._model_save_interval = model_save_interval
self._grad_norm = grad_norm
self._grad_clipping = grad_clipping
self._learning_rate_scheduler = learning_rate_scheduler
self._momentum_scheduler = momentum_scheduler
self._moving_average = moving_average
# We keep the total batch number as an instance variable because it
# is used inside a closure for the hook which logs activations in
# `_enable_activation_logging`.
self._batch_num_total = 0
self._tensorboard = TensorboardWriter(
get_batch_num_total=lambda: self._batch_num_total,
serialization_dir=serialization_dir,
summary_interval=summary_interval,
histogram_interval=histogram_interval,
should_log_parameter_statistics=should_log_parameter_statistics,
should_log_learning_rate=should_log_learning_rate,
)
self._log_batch_size_period = log_batch_size_period
self._last_log = 0.0 # time of last logging
self._num_gradient_accumulation_steps = num_gradient_accumulation_steps
# Enable activation logging.
if histogram_interval is not None:
self._tensorboard.enable_activation_logging(self.model)
# Using `DistributedDataParallel`(ddp) brings in a quirk wrt AllenNLP's `Model` interface and its
# usage. A `Model` object is wrapped by `ddp`, but assigning the wrapped model to `self.model`
# will break the usages such as `Model.get_regularization_penalty`, `Model.get_metrics`, etc.
#
# Hence a reference to Pytorch's object is maintained in the case of distributed training and in the
# normal case, reference to `Model` is retained. This reference is only used in
# these places: `model.__call__`, `model.train` and `model.eval`.
if self._distributed:
self._pytorch_model = DistributedDataParallel(
self.model,
device_ids=[self.cuda_device],
find_unused_parameters=True)
else:
self._pytorch_model = self.model
def rescale_gradients(self) -> Optional[float]:
return training_util.rescale_gradients(self.model, self._grad_norm)
def batch_loss(self, batch: TensorDict,
for_training: bool) -> torch.Tensor:
"""
Does a forward pass on the given batches and returns the `loss` value in the result.
If `for_training` is `True` also applies regularization penalty.
"""
batch = nn_util.move_to_device(batch, self.cuda_device)
output_dict = self._pytorch_model(**batch)
try:
loss = output_dict["loss"]
if for_training:
loss += self.model.get_regularization_penalty()
except KeyError:
if for_training:
raise RuntimeError(
"The model you are trying to optimize does not contain a"
" 'loss' key in the output of model.forward(inputs).")
loss = None
return loss
def _train_epoch(self, epoch: int) -> Dict[str, float]:
"""
Trains one epoch and returns metrics.
"""
logger.info("Epoch %d/%d", epoch, self._num_epochs - 1)
peak_cpu_usage = common_util.peak_memory_mb()
logger.info(f"Peak CPU memory usage MB: {peak_cpu_usage}")
gpu_usage = []
for gpu, memory in common_util.gpu_memory_mb().items():
gpu_usage.append((gpu, memory))
logger.info(f"GPU {gpu} memory usage MB: {memory}")
train_loss = 0.0
# Set the model to "train" mode.
self._pytorch_model.train()
# Get tqdm for the training batches
batch_generator = self.iterator(self.train_data,
num_epochs=1,
shuffle=self.shuffle)
batch_group_generator = common_util.lazy_groups_of(
batch_generator, self._num_gradient_accumulation_steps)
num_training_batches = math.ceil(
self.iterator.get_num_batches(self.train_data) /
self._num_gradient_accumulation_steps)
# Having multiple tqdm bars in case of distributed training will be a mess. Hence only the master's
# progress is shown
if self._master:
batch_group_generator_tqdm = Tqdm.tqdm(batch_group_generator,
total=num_training_batches)
else:
batch_group_generator_tqdm = batch_group_generator
self._last_log = time.time()
last_save_time = time.time()
batches_this_epoch = 0
if self._batch_num_total is None:
self._batch_num_total = 0
histogram_parameters = set(
self.model.get_parameters_for_histogram_tensorboard_logging())
logger.info("Training")
cumulative_batch_group_size = 0
done_early = False
for batch_group in batch_group_generator_tqdm:
if self._distributed:
# Check whether the other workers have stopped already (due to differing amounts of
# data in each). If so, we can't proceed because we would hang when we hit the
# barrier implicit in Model.forward. We use a IntTensor instead a BoolTensor
# here because NCCL process groups apparently don't support BoolTensor.
done = torch.tensor(0, device=self.cuda_device)
torch.distributed.all_reduce(done,
torch.distributed.ReduceOp.SUM)
if done.item() > 0:
done_early = True
logger.warning(
f"Worker {torch.distributed.get_rank()} finishing training early! "
"This implies that there is an imbalance in your training "
"data across the workers and that some amount of it will be "
"ignored. A small amount of this is fine, but a major imbalance "
"should be avoided. Note: This warning will appear unless your "
"data is perfectly balanced.")
break
batches_this_epoch += 1
self._batch_num_total += 1
batch_num_total = self._batch_num_total
self.optimizer.zero_grad()
for batch in batch_group:
loss = self.batch_loss(batch, for_training=True)
if torch.isnan(loss):
raise ValueError("nan loss encountered")
loss = loss / len(batch_group)
loss.backward()
train_loss += loss.item()
batch_grad_norm = self.rescale_gradients()
# This does nothing if batch_num_total is None or you are using a
# scheduler which doesn't update per batch.
if self._learning_rate_scheduler:
self._learning_rate_scheduler.step_batch(batch_num_total)
if self._momentum_scheduler:
self._momentum_scheduler.step_batch(batch_num_total)
if self._tensorboard.should_log_histograms_this_batch(
) and self._master:
# get the magnitude of parameter updates for logging
# We need a copy of current parameters to compute magnitude of updates,
# and copy them to CPU so large models won't go OOM on the GPU.
param_updates = {
name: param.detach().cpu().clone()
for name, param in self.model.named_parameters()
}
self.optimizer.step()
for name, param in self.model.named_parameters():
param_updates[name].sub_(param.detach().cpu())
update_norm = torch.norm(param_updates[name].view(-1))
param_norm = torch.norm(param.view(-1)).cpu()
self._tensorboard.add_train_scalar(
"gradient_update/" + name,
update_norm / (param_norm + 1e-7))
else:
self.optimizer.step()
# Update moving averages
if self._moving_average is not None:
self._moving_average.apply(batch_num_total)
# Update the description with the latest metrics
metrics = training_util.get_metrics(
self.model,
train_loss,
batches_this_epoch,
world_size=self._world_size,
cuda_device=[self.cuda_device],
)
# Updating tqdm only for the master as the trainers wouldn't have one
if self._master:
description = training_util.description_from_metrics(metrics)
batch_group_generator_tqdm.set_description(description,
refresh=False)
# Log parameter values to Tensorboard (only from the master)
if self._tensorboard.should_log_this_batch() and self._master:
self._tensorboard.log_parameter_and_gradient_statistics(
self.model, batch_grad_norm)
self._tensorboard.log_learning_rates(self.model,
self.optimizer)
self._tensorboard.add_train_scalar("loss/loss_train",
metrics["loss"])
self._tensorboard.log_metrics(
{"epoch_metrics/" + k: v
for k, v in metrics.items()})
if self._tensorboard.should_log_histograms_this_batch(
) and self._master:
self._tensorboard.log_histograms(self.model,
histogram_parameters)
if self._log_batch_size_period:
batch_group_size = sum(
training_util.get_batch_size(batch)
for batch in batch_group)
cumulative_batch_group_size += batch_group_size
if (batches_this_epoch - 1) % self._log_batch_size_period == 0:
average = cumulative_batch_group_size / batches_this_epoch
logger.info(
f"current batch size: {batch_group_size} mean batch size: {average}"
)
self._tensorboard.add_train_scalar("current_batch_size",
batch_group_size)
self._tensorboard.add_train_scalar("mean_batch_size",
average)
# Save model if needed.
if (self._model_save_interval is not None and
(time.time() - last_save_time > self._model_save_interval)
and self._master):
last_save_time = time.time()
self._save_checkpoint("{0}.{1}".format(
epoch, training_util.time_to_str(int(last_save_time))))
if self._distributed and not done_early:
logger.warning(
f"Worker {torch.distributed.get_rank()} completed its entire epoch (training)."
)
# Indicate that we're done so that any workers that have remaining data stop the epoch early.
done = torch.tensor(1, device=self.cuda_device)
torch.distributed.all_reduce(done, torch.distributed.ReduceOp.SUM)
assert done.item()
# Let all workers finish their epoch before computing
# the final statistics for the epoch.
if self._distributed:
dist.barrier()
metrics = training_util.get_metrics(
self.model,
train_loss,
batches_this_epoch,
reset=True,
world_size=self._world_size,
cuda_device=[self.cuda_device],
)
metrics["cpu_memory_MB"] = peak_cpu_usage
for (gpu_num, memory) in gpu_usage:
metrics["gpu_" + str(gpu_num) + "_memory_MB"] = memory
return metrics
def _validation_loss(self) -> Tuple[float, int]:
"""
Computes the validation loss. Returns it and the number of batches.
"""
logger.info("Validating")
self._pytorch_model.eval()
# Replace parameter values with the shadow values from the moving averages.
if self._moving_average is not None:
self._moving_average.assign_average_value()
if self._validation_iterator is not None:
val_iterator = self._validation_iterator
else:
val_iterator = self.iterator
val_generator = val_iterator(self._validation_data,
num_epochs=1,
shuffle=False)
num_validation_batches = val_iterator.get_num_batches(
self._validation_data)
val_generator_tqdm = Tqdm.tqdm(val_generator,
total=num_validation_batches)
batches_this_epoch = 0
val_loss = 0
done_early = False
for batch in val_generator_tqdm:
if self._distributed:
# Check whether the other workers have stopped already (due to differing amounts of
# data in each). If so, we can't proceed because we would hang when we hit the
# barrier implicit in Model.forward. We use a IntTensor instead a BoolTensor
# here because NCCL process groups apparently don't support BoolTensor.
done = torch.tensor(0, device=self.cuda_device)
torch.distributed.all_reduce(done,
torch.distributed.ReduceOp.SUM)
if done.item() > 0:
done_early = True
logger.warning(
f"Worker {torch.distributed.get_rank()} finishing validation early! "
"This implies that there is an imbalance in your validation "
"data across the workers and that some amount of it will be "
"ignored. A small amount of this is fine, but a major imbalance "
"should be avoided. Note: This warning will appear unless your "
"data is perfectly balanced.")
break
loss = self.batch_loss(batch, for_training=False)
if loss is not None:
# You shouldn't necessarily have to compute a loss for validation, so we allow for
# `loss` to be None. We need to be careful, though - `batches_this_epoch` is
# currently only used as the divisor for the loss function, so we can safely only
# count those batches for which we actually have a loss. If this variable ever
# gets used for something else, we might need to change things around a bit.
batches_this_epoch += 1
val_loss += loss.detach().cpu().numpy()
# Update the description with the latest metrics
val_metrics = training_util.get_metrics(
self.model,
val_loss,
batches_this_epoch,
world_size=self._world_size,
cuda_device=[self.cuda_device],
)
description = training_util.description_from_metrics(val_metrics)
val_generator_tqdm.set_description(description, refresh=False)
if self._distributed and not done_early:
logger.warning(
f"Worker {torch.distributed.get_rank()} completed its entire epoch (validation)."
)
# Indicate that we're done so that any workers that have remaining data stop validation early.
done = torch.tensor(1, device=self.cuda_device)
torch.distributed.all_reduce(done, torch.distributed.ReduceOp.SUM)
assert done.item()
# Now restore the original parameter values.
if self._moving_average is not None:
self._moving_average.restore()
return val_loss, batches_this_epoch
def train(self) -> Dict[str, Any]:
"""
Trains the supplied model with the supplied parameters.
"""
try:
epoch_counter = self._restore_checkpoint()
except RuntimeError:
traceback.print_exc()
raise ConfigurationError(
"Could not recover training from the checkpoint. Did you mean to output to "
"a different serialization directory or delete the existing serialization "
"directory?")
training_util.enable_gradient_clipping(self.model, self._grad_clipping)
logger.info("Beginning training.")
val_metrics: Dict[str, float] = {}
this_epoch_val_metric: float = None
metrics: Dict[str, Any] = {}
epochs_trained = 0
training_start_time = time.time()
metrics["best_epoch"] = self._metric_tracker.best_epoch
for key, value in self._metric_tracker.best_epoch_metrics.items():
metrics["best_validation_" + key] = value
for epoch in range(epoch_counter, self._num_epochs):
epoch_start_time = time.time()
train_metrics = self._train_epoch(epoch)
# get peak of memory usage
if "cpu_memory_MB" in train_metrics:
metrics["peak_cpu_memory_MB"] = max(
metrics.get("peak_cpu_memory_MB", 0),
train_metrics["cpu_memory_MB"])
for key, value in train_metrics.items():
if key.startswith("gpu_"):
metrics["peak_" + key] = max(metrics.get("peak_" + key, 0),
value)
if self._validation_data is not None:
with torch.no_grad():
# We have a validation set, so compute all the metrics on it.
val_loss, num_batches = self._validation_loss()
# It is safe again to wait till the validation is done. This is
# important to get the metrics right.
if self._distributed:
dist.barrier()
val_metrics = training_util.get_metrics(
self.model,
val_loss,
num_batches,
reset=True,
world_size=self._world_size,
cuda_device=[self.cuda_device],
)
# Check validation metric for early stopping
this_epoch_val_metric = val_metrics[
self._validation_metric]
self._metric_tracker.add_metric(this_epoch_val_metric)
if self._metric_tracker.should_stop_early():
logger.info("Ran out of patience. Stopping training.")
break
if self._master:
self._tensorboard.log_metrics(
train_metrics,
val_metrics=val_metrics,
log_to_console=True,
epoch=epoch + 1) # +1 because tensorboard doesn't like 0
# Create overall metrics dict
training_elapsed_time = time.time() - training_start_time
metrics["training_duration"] = str(
datetime.timedelta(seconds=training_elapsed_time))
metrics["training_start_epoch"] = epoch_counter
metrics["training_epochs"] = epochs_trained
metrics["epoch"] = epoch
for key, value in train_metrics.items():
metrics["training_" + key] = value
for key, value in val_metrics.items():
metrics["validation_" + key] = value
if self._metric_tracker.is_best_so_far():
# Update all the best_ metrics.
# (Otherwise they just stay the same as they were.)
metrics["best_epoch"] = epoch
for key, value in val_metrics.items():
metrics["best_validation_" + key] = value
self._metric_tracker.best_epoch_metrics = val_metrics
if self._serialization_dir and self._master:
common_util.dump_metrics(
os.path.join(self._serialization_dir,
f"metrics_epoch_{epoch}.json"), metrics)
# The Scheduler API is agnostic to whether your schedule requires a validation metric -
# if it doesn't, the validation metric passed here is ignored.
if self._learning_rate_scheduler:
self._learning_rate_scheduler.step(this_epoch_val_metric,
epoch)
if self._momentum_scheduler:
self._momentum_scheduler.step(this_epoch_val_metric, epoch)
if self._master:
self._save_checkpoint(epoch)
# Wait for the master to finish saving the checkpoint
if self._distributed:
dist.barrier()
epoch_elapsed_time = time.time() - epoch_start_time
logger.info("Epoch duration: %s",
datetime.timedelta(seconds=epoch_elapsed_time))
if epoch < self._num_epochs - 1:
training_elapsed_time = time.time() - training_start_time
estimated_time_remaining = training_elapsed_time * (
(self._num_epochs - epoch_counter) /
float(epoch - epoch_counter + 1) - 1)
formatted_time = str(
datetime.timedelta(seconds=int(estimated_time_remaining)))
logger.info("Estimated training time remaining: %s",
formatted_time)
epochs_trained += 1
# make sure pending events are flushed to disk and files are closed properly
self._tensorboard.close()
# Load the best model state before returning
best_model_state = self._checkpointer.best_model_state()
if best_model_state:
self.model.load_state_dict(best_model_state)
return metrics
def _save_checkpoint(self, epoch: Union[int, str]) -> None:
"""
Saves a checkpoint of the model to self._serialization_dir.
Is a no-op if self._serialization_dir is None.
# Parameters
epoch : Union[int, str], required.
The epoch of training. If the checkpoint is saved in the middle
of an epoch, the parameter is a string with the epoch and timestamp.
"""
# If moving averages are used for parameters, we save
# the moving average values into checkpoint, instead of the current values.
if self._moving_average is not None:
self._moving_average.assign_average_value()
# These are the training states we need to persist.
training_states = {
"metric_tracker": self._metric_tracker.state_dict(),
"optimizer": self.optimizer.state_dict(),
"batch_num_total": self._batch_num_total,
}
# If we have a learning rate or momentum scheduler, we should persist them too.
if self._learning_rate_scheduler is not None:
training_states[
"learning_rate_scheduler"] = self._learning_rate_scheduler.state_dict(
)
if self._momentum_scheduler is not None:
training_states[
"momentum_scheduler"] = self._momentum_scheduler.state_dict()
self._checkpointer.save_checkpoint(
model_state=self.model.state_dict(),
epoch=epoch,
training_states=training_states,
is_best_so_far=self._metric_tracker.is_best_so_far(),
)
# Restore the original values for parameters so that training will not be affected.
if self._moving_average is not None:
self._moving_average.restore()
def _restore_checkpoint(self) -> int:
"""
Restores the model and training state from the last saved checkpoint.
This includes an epoch count and optimizer state, which is serialized separately
from model parameters. This function should only be used to continue training -
if you wish to load a model for inference/load parts of a model into a new
computation graph, you should use the native Pytorch functions:
` model.load_state_dict(torch.load("/path/to/model/weights.th"))`
If `self._serialization_dir` does not exist or does not contain any checkpointed weights,
this function will do nothing and return 0.
# Returns
epoch: int
The epoch at which to resume training, which should be one after the epoch
in the saved training state.
"""
model_state, training_state = self._checkpointer.restore_checkpoint()
if not training_state:
# No checkpoint to restore, start at 0
return 0
self.model.load_state_dict(model_state)
self.optimizer.load_state_dict(training_state["optimizer"])
if (self._learning_rate_scheduler is not None
and "learning_rate_scheduler" in training_state):
self._learning_rate_scheduler.load_state_dict(
training_state["learning_rate_scheduler"])
if self._momentum_scheduler is not None and "momentum_scheduler" in training_state:
self._momentum_scheduler.load_state_dict(
training_state["momentum_scheduler"])
training_util.move_optimizer_to_cuda(self.optimizer)
# Currently the `training_state` contains a serialized `MetricTracker`.
if "metric_tracker" in training_state:
self._metric_tracker.load_state_dict(
training_state["metric_tracker"])
# It used to be the case that we tracked `val_metric_per_epoch`.
elif "val_metric_per_epoch" in training_state:
self._metric_tracker.clear()
self._metric_tracker.add_metrics(
training_state["val_metric_per_epoch"])
# And before that we didn't track anything.
else:
self._metric_tracker.clear()
if isinstance(training_state["epoch"], int):
epoch_to_return = training_state["epoch"] + 1
else:
epoch_to_return = int(training_state["epoch"].split(".")[0]) + 1
# For older checkpoints with batch_num_total missing, default to old behavior where
# it is unchanged.
batch_num_total = training_state.get("batch_num_total")
if batch_num_total is not None:
self._batch_num_total = batch_num_total
return epoch_to_return
@classmethod
def from_partial_objects(
cls,
model: Model,
serialization_dir: str,
iterator: DataIterator,
train_data: Iterable[Instance],
validation_iterator: DataIterator = None,
validation_data: Iterable[Instance] = None,
local_rank: int = 0,
patience: int = None,
validation_metric: str = "-loss",
shuffle: bool = True,
num_epochs: int = 20,
cuda_device: int = -1,
grad_norm: float = None,
grad_clipping: float = None,
model_save_interval: float = None,
summary_interval: int = 100,
histogram_interval: int = None,
should_log_parameter_statistics: bool = True,
should_log_learning_rate: bool = False,
log_batch_size_period: int = None,
distributed: bool = None,
world_size: int = 1,
num_gradient_accumulation_steps: int = 1,
no_grad: List[str] = None,
optimizer: Lazy[Optimizer] = None,
learning_rate_scheduler: Lazy[LearningRateScheduler] = None,
momentum_scheduler: Lazy[MomentumScheduler] = None,
moving_average: Lazy[MovingAverage] = None,
checkpointer: Lazy[Checkpointer] = None,
) -> "Trainer":
"""
This method exists so that we can have a documented method to construct this class using
`FromParams`. If you are not using `FromParams` or config files, you can safely ignore this
method.
The reason we can't just use `__init__` with `FromParams` here is because there are
sequential dependencies to this class's arguments. Anything that has a `Lazy[]` type
annotation needs something from one of the non-`Lazy` arguments. The `Optimizer` needs to
have the parameters from the `Model` before it's constructed, and the `Schedulers` need to
have the `Optimizer`. Because of this, the typical way we construct things `FromParams`
doesn't work, so we use `Lazy` to allow for constructing the objects sequentially.
If you're not using `FromParams`, you can just construct these arguments in the right order
yourself in your code and call the constructor directly.
"""
check_for_gpu(cuda_device)
if cuda_device >= 0:
# Moving model to GPU here so that the optimizer state gets constructed on
# the right device.
model = model.cuda(cuda_device)
if no_grad:
for name, parameter in model.named_parameters():
if any(re.search(regex, name) for regex in no_grad):
parameter.requires_grad_(False)
common_util.log_frozen_and_tunable_parameter_names(model)
parameters = [[n, p] for n, p in model.named_parameters()
if p.requires_grad]
optimizer_ = optimizer.construct(model_parameters=parameters)
if not optimizer_:
optimizer_ = Optimizer.default(parameters)
batches_per_epoch = iterator.get_num_batches(train_data)
if batches_per_epoch == 1: # get_num_batches returns 1 when it can't determine the answer
batches_per_epoch = None
moving_average_ = moving_average.construct(parameters=parameters)
learning_rate_scheduler_ = learning_rate_scheduler.construct(
optimizer=optimizer_,
num_epochs=num_epochs,
num_steps_per_epoch=batches_per_epoch)
momentum_scheduler_ = momentum_scheduler.construct(
optimizer=optimizer_)
checkpointer_ = checkpointer.construct() or Checkpointer(
serialization_dir)
return cls(
model,
optimizer_,
iterator,
train_data,
validation_data,
patience=patience,
validation_metric=validation_metric,
validation_iterator=validation_iterator,
shuffle=shuffle,
num_epochs=num_epochs,
serialization_dir=serialization_dir,
cuda_device=cuda_device,
grad_norm=grad_norm,
grad_clipping=grad_clipping,
learning_rate_scheduler=learning_rate_scheduler_,
momentum_scheduler=momentum_scheduler_,
checkpointer=checkpointer_,
model_save_interval=model_save_interval,
summary_interval=summary_interval,
histogram_interval=histogram_interval,
should_log_parameter_statistics=should_log_parameter_statistics,
should_log_learning_rate=should_log_learning_rate,
log_batch_size_period=log_batch_size_period,
moving_average=moving_average_,
distributed=distributed,
local_rank=local_rank,
world_size=world_size,
num_gradient_accumulation_steps=num_gradient_accumulation_steps,
)
def __init__(
self,
model: Model,
optimizer: torch.optim.Optimizer,
iterator: DataIterator,
train_dataset: Iterable[Instance],
validation_dataset: Optional[Iterable[Instance]] = None,
patience: Optional[int] = None,
validation_metric: str = "-loss",
validation_iterator: DataIterator = None,
shuffle: bool = True,
num_epochs: int = 20,
serialization_dir: Optional[str] = None,
num_serialized_models_to_keep: int = 20,
keep_serialized_model_every_num_seconds: int = None,
checkpointer: Checkpointer = None,
model_save_interval: float = None,
cuda_device: Union[int, List] = -1,
grad_norm: Optional[float] = None,
grad_clipping: Optional[float] = None,
learning_rate_scheduler: Optional[LearningRateScheduler] = None,
momentum_scheduler: Optional[MomentumScheduler] = None,
summary_interval: int = 100,
histogram_interval: int = None,
should_log_parameter_statistics: bool = True,
should_log_learning_rate: bool = False,
log_batch_size_period: Optional[int] = None,
moving_average: Optional[MovingAverage] = None,
) -> None:
super().__init__(serialization_dir, cuda_device)
# I am not calling move_to_gpu here, because if the model is
# not already on the GPU then the optimizer is going to be wrong.
self.model = model
self.iterator = iterator
self._validation_iterator = validation_iterator
self.shuffle = shuffle
self.optimizer = optimizer
self.train_data = train_dataset
self._validation_data = validation_dataset
if patience is None: # no early stopping
if validation_dataset:
logger.warning(
"You provided a validation dataset but patience was set to None, "
"meaning that early stopping is disabled")
elif (not isinstance(patience, int)) or patience <= 0:
raise ConfigurationError(
'{} is an invalid value for "patience": it must be a positive integer '
"or None (if you want to disable early stopping)".format(
patience))
# Frequency of metric checking (in batch)
self._summary_interval = summary_interval
# For tracking is_best_so_far and should_stop_early
self._metric_tracker = MetricTracker(patience, validation_metric)
# Get rid of + or -
self._validation_metric = validation_metric[1:]
self._num_epochs = num_epochs
if checkpointer is not None:
# We can't easily check if these parameters were passed in, so check against their default values.
# We don't check against serialization_dir since it is also used by the parent class.
if (num_serialized_models_to_keep != 20
or keep_serialized_model_every_num_seconds is not None):
raise ConfigurationError(
"When passing a custom Checkpointer, you may not also pass in separate checkpointer "
"args 'num_serialized_models_to_keep' or 'keep_serialized_model_every_num_seconds'."
)
self._checkpointer = checkpointer
else:
self._checkpointer = Checkpointer(
serialization_dir,
keep_serialized_model_every_num_seconds,
num_serialized_models_to_keep,
)
self._model_save_interval = model_save_interval
self._grad_norm = grad_norm
self._grad_clipping = grad_clipping
self._learning_rate_scheduler = learning_rate_scheduler
self._momentum_scheduler = momentum_scheduler
self._moving_average = moving_average
# We keep the total batch number as an instance variable because it
# is used inside a closure for the hook which logs activations in
# ``_enable_activation_logging``.
self._batch_num_total = 0
self._tensorboard = TensorboardWriter(
get_batch_num_total=lambda: self._batch_num_total,
serialization_dir=serialization_dir,
summary_interval=summary_interval,
histogram_interval=histogram_interval,
should_log_parameter_statistics=should_log_parameter_statistics,
should_log_learning_rate=should_log_learning_rate,
)
self._log_batch_size_period = log_batch_size_period
self._last_log = 0.0 # time of last logging
# Enable activation logging.
if histogram_interval is not None:
self._tensorboard.enable_activation_logging(self.model)
def from_partial_objects(
cls,
model: Model,
serialization_dir: str,
data_loader: DataLoader,
validation_data_loader: DataLoader = None,
local_rank: int = 0,
patience: int = None,
validation_metric: str = "-loss",
num_epochs: int = 20,
cuda_device: int = -1,
grad_norm: float = None,
grad_clipping: float = None,
distributed: bool = None,
world_size: int = 1,
num_gradient_accumulation_steps: int = 1,
opt_level: Optional[str] = None,
no_grad: List[str] = None,
optimizer: Lazy[Optimizer] = None,
learning_rate_scheduler: Lazy[LearningRateScheduler] = None,
momentum_scheduler: Lazy[MomentumScheduler] = None,
tensorboard_writer: Lazy[TensorboardWriter] = None,
moving_average: Lazy[MovingAverage] = None,
checkpointer: Lazy[Checkpointer] = None,
batch_callbacks: List[BatchCallback] = None,
epoch_callbacks: List[EpochCallback] = None,
) -> "Trainer":
"""
This method exists so that we can have a documented method to construct this class using
`FromParams`. If you are not using `FromParams` or config files, you can safely ignore this
method.
The reason we can't just use `__init__` with `FromParams` here is because there are
sequential dependencies to this class's arguments. Anything that has a `Lazy[]` type
annotation needs something from one of the non-`Lazy` arguments. The `Optimizer` needs to
have the parameters from the `Model` before it's constructed, and the `Schedulers` need to
have the `Optimizer`. Because of this, the typical way we construct things `FromParams`
doesn't work, so we use `Lazy` to allow for constructing the objects sequentially.
If you're not using `FromParams`, you can just construct these arguments in the right order
yourself in your code and call the constructor directly.
"""
check_for_gpu(cuda_device)
if cuda_device >= 0:
# Moving model to GPU here so that the optimizer state gets constructed on
# the right device.
model = model.cuda(cuda_device)
if no_grad:
for name, parameter in model.named_parameters():
if any(re.search(regex, name) for regex in no_grad):
parameter.requires_grad_(False)
common_util.log_frozen_and_tunable_parameter_names(model)
parameters = [[n, p] for n, p in model.named_parameters()
if p.requires_grad]
optimizer_ = optimizer.construct(model_parameters=parameters)
if not optimizer_:
optimizer_ = Optimizer.default(parameters)
try:
batches_per_epoch = len(data_loader)
except TypeError:
# If the dataset is lazy, it won't have a length.
batches_per_epoch = None
moving_average_ = moving_average.construct(parameters=parameters)
learning_rate_scheduler_ = learning_rate_scheduler.construct(
optimizer=optimizer_,
num_epochs=num_epochs,
num_steps_per_epoch=batches_per_epoch)
momentum_scheduler_ = momentum_scheduler.construct(
optimizer=optimizer_)
checkpointer_ = checkpointer.construct() or Checkpointer(
serialization_dir)
tensorboard_writer_ = tensorboard_writer.construct(
) or TensorboardWriter(serialization_dir)
return cls(
model,
optimizer_,
data_loader,
patience=patience,
validation_metric=validation_metric,
validation_data_loader=validation_data_loader,
num_epochs=num_epochs,
serialization_dir=serialization_dir,
cuda_device=cuda_device,
grad_norm=grad_norm,
grad_clipping=grad_clipping,
learning_rate_scheduler=learning_rate_scheduler_,
momentum_scheduler=momentum_scheduler_,
tensorboard_writer=tensorboard_writer_,
checkpointer=checkpointer_,
moving_average=moving_average_,
batch_callbacks=batch_callbacks,
epoch_callbacks=epoch_callbacks,
distributed=distributed,
local_rank=local_rank,
world_size=world_size,
num_gradient_accumulation_steps=num_gradient_accumulation_steps,
opt_level=opt_level,
)
class MultiTaskTrainer(Registrable):
def __init__(
self,
model: Model,
task_list: List[Task],
optimizer_params: Params,
lr_scheduler_params: Params,
patience: Optional[int] = None,
num_epochs: int = 20,
serialization_dir: str = None,
cuda_device: int = -1,
gradient_accumulation_steps: int = 1,
grad_norm: Optional[float] = None,
grad_clipping: Optional[float] = None,
min_lr: float = 0.00001,
no_tqdm: bool = False,
summary_interval: int = 10,
histogram_interval: int = 10,
log_parameter_statistics: bool = False,
log_gradient_statistics: bool = False,
):
"""
Parameters
----------
model: ``Model``, required.
An AllenNLP model to be optimized. Pytorch Modules can also be optimized if
their ``forward`` method returns a dictionary with a "loss" key, containing a
scalar tensor representing the loss function to be optimized.
iterator: ``DataIterator``, required.
A method for iterating over a ``Dataset``, yielding padded indexed batches.
patience: Optional[int] > 0, optional (default=None)
Number of epochs to be patient before early stopping: the training is stopped
after ``patience`` epochs with no improvement. If given, it must be ``> 0``.
If None, early stopping is disabled.
num_epochs: int, optional (default = 20)
Number of training epochs.
serialization_dir: str, optional (default=None)
Path to directory for saving and loading model files. Models will not be saved if
this parameter is not passed.
cuda_device: int, optional (default = -1)
An integer specifying the CUDA device to use. If -1, the CPU is used.
Multi-gpu training is not currently supported, but will be once the
Pytorch DataParallel API stabilises.
grad_norm: float, optional, (default = None).
If provided, gradient norms will be rescaled to have a maximum of this value.
grad_clipping : float, optional (default = None).
If provided, gradients will be clipped `during the backward pass` to have an (absolute)
maximum of this value. If you are getting ``NaNs`` in your gradients during training
that are not solved by using ``grad_norm``, you may need this.
no_tqdm : bool, optional (default=False)
We use ``tqdm`` for logging, which will print a nice progress bar that updates in place
after every batch. This is nice if you're running training on a local shell, but can
cause problems with log files from, e.g., a docker image running on kubernetes. If
``no_tqdm`` is ``True``, we will not use tqdm, and instead log batch statistics using
``logger.info``.
"""
self._model = model
parameters_to_train = [(n, p)
for n, p in self._model.named_parameters()
if p.requires_grad]
self._task_list = task_list
self._n_tasks = len(self._task_list)
self._optimizer_params = optimizer_params
self._optimizers = {}
self._lr_scheduler_params = lr_scheduler_params
self._schedulers = {}
for task in self._task_list:
task_name = task._name
self._optimizers[task_name] = Optimizer.from_params(
model_parameters=parameters_to_train,
params=deepcopy(optimizer_params))
self._schedulers[task_name] = LearningRateScheduler.from_params(
optimizer=self._optimizers[task_name],
params=deepcopy(lr_scheduler_params))
self._serialization_dir = serialization_dir
self._patience = patience
self._num_epochs = num_epochs
self._cuda_device = cuda_device
if self._cuda_device >= 0:
check_for_gpu(self._cuda_device)
self._model = self._model.cuda(self._cuda_device)
self._gradient_accumulation_steps = gradient_accumulation_steps
self._grad_norm = grad_norm
self._grad_clipping = grad_clipping
self._min_lr = min_lr
self._task_infos = None
self._metric_infos = None
self._tr_generators = None
self._no_tqdm = no_tqdm
self._summary_interval = summary_interval # num batches between logging to tensorboard
# self._log_parameter_statistics = log_parameter_statistics
# self._log_gradient_statistics = log_gradient_statistics
self._global_step = 0
# train_log = SummaryWriter(os.path.join(self._serialization_dir, "log", "train"))
# validation_log = SummaryWriter(os.path.join(self._serialization_dir, "log", "validation"))
# self._tensorboard = TensorboardWriter(train_log=train_log, validation_log=validation_log)
self._batch_num_total = 0
self._tensorboard = TensorboardWriter(
get_batch_num_total=lambda: self._batch_num_total,
serialization_dir=serialization_dir,
summary_interval=summary_interval,
histogram_interval=histogram_interval,
should_log_learning_rate=True)
# Enable activation logging.
if histogram_interval is not None:
self._tensorboard.enable_activation_logging(self._model)
def train(
self,
# tasks: List[Task],
# params: Params,
recover: bool = False,
):
raise NotImplementedError
def _check_history(self,
metric_history: List[float],
cur_score: float,
should_decrease: bool = False):
"""
Given a tasks, the history of the performance on that tasks,
and the current score, check if current score is
best so far and if out of patience.
Parameters
----------
metric_history: List[float], required
cur_score: float, required
should_decrease: bool, default = False
Wheter or not the validation metric should increase while training.
For instance, the bigger the f1 score is, the better it is -> should_decrease = False
Returns
-------
best_so_far: bool
Whether or not the current epoch is the best so far in terms of the speicified validation metric.
out_of_patience: bool
Whether or not the training for this specific tasks should stop (patience parameter).
"""
patience = self._patience + 1
best_fn = min if should_decrease else max
best_score = best_fn(metric_history)
if best_score == cur_score:
best_so_far = metric_history.index(
best_score) == len(metric_history) - 1
else:
best_so_far = False
out_of_patience = False
if len(metric_history) > patience:
if should_decrease:
out_of_patience = max(metric_history[-patience:]) <= cur_score
else:
out_of_patience = min(metric_history[-patience:]) >= cur_score
if best_so_far and out_of_patience: # then something is up
print("Something is up")
return best_so_far, out_of_patience
def _forward(self,
tensor_batch: torch.Tensor,
for_training: bool = False,
task: Task = None):
if task is not None:
# tensor_batch = tensor2HalfTensor(tensor_batch)
# for k, val in tensor_batch.items():
# if isinstance(val, Dict):
# for key, value in val.items():
# print(key, value.dtype)
# if isinstance(val, Tensor):
# print(k, val.dtype)
tensor_batch = move_to_device(tensor_batch, self._cuda_device)
output_dict = self._model.forward(task_name=task._name,
tensor_batch=tensor_batch,
for_training=for_training)
if for_training:
try:
loss = output_dict["loss"]
loss += self._model.get_regularization_penalty()
except KeyError:
raise RuntimeError(
"The model you are trying to optimize does not contain a"
" `loss` key in the output of model.forward(inputs).")
return output_dict
else:
raise ConfigurationError(
"Cannot call forward through tasks `None`")
def _get_metrics(self, task: Task, reset: bool = False):
task_tagger = getattr(self._model, "_tagger_" + task._name)
return task_tagger.get_metrics(reset)
def _description_from_metrics(self, metrics: Dict[str, float]):
# pylint: disable=no-self-use
return ", ".join(
["%s: %.4f" % (name, value)
for name, value in metrics.items()]) + " ||"
def _rescale_gradients(self) -> Optional[float]:
"""
Performs gradient rescaling. Is a no-op if gradient rescaling is not enabled.
"""
return training_util.rescale_gradients(self._model, self._grad_norm)
def _enable_gradient_clipping(self) -> None:
if self._grad_clipping is not None:
# Pylint is unable to tell that we're in the case that _grad_clipping is not None...
# pylint: disable=invalid-unary-operand-type
clip_function = lambda grad: grad.clamp(-self._grad_clipping, self.
_grad_clipping)
for parameter in self._model.parameters():
if parameter.requires_grad:
parameter.register_hook(clip_function)
def _save_checkpoint(self, epoch: int, should_stop: bool) -> None:
"""
Save the current states (model, training, optimizers, metrics and tasks).
Parameters
----------
epoch: int, required.
The epoch of training.
should_stop: bool, required
Whether or not the training is finished.
should_save_model: bool, optional (default = True)
Whether or not the model state should be saved.
"""
### Saving training state ###
training_state = {
"epoch": epoch,
"should_stop": should_stop,
"metric_infos": self._metric_infos,
"task_infos": self._task_infos,
"schedulers": {},
"optimizers": {},
}
if self._optimizers is not None:
for task_name, optimizer in self._optimizers.items():
training_state["optimizers"][task_name] = optimizer.state_dict(
)
if self._schedulers is not None:
for task_name, scheduler in self._schedulers.items():
training_state["schedulers"][
task_name] = scheduler.lr_scheduler.state_dict()
training_path = os.path.join(self._serialization_dir,
"training_state.th")
torch.save(training_state, training_path)
logger.info("Checkpoint - Saved training state to {}", training_path)
### Saving model state ###
model_path = os.path.join(self._serialization_dir, "model_state.th")
model_state = self._model.state_dict()
torch.save(model_state, model_path)
logger.info("Checkpoint - Saved model state to {}", model_path)
### Saving best models for each tasks ###
for task_name, infos in self._metric_infos.items():
best_epoch, _ = infos["best"]
if best_epoch == epoch:
logger.info(
"Checkpoint - Best validation performance so far for {} tasks",
task_name)
logger.info("Checkpoint - Copying weights to '{}/best_{}.th'.",
self._serialization_dir, task_name)
shutil.copyfile(
model_path,
os.path.join(self._serialization_dir,
"best_{}.th".format(task_name)))
def find_latest_checkpoint(self) -> Tuple[str, str]:
"""
Return the location of the latest model and training state files.
If there isn't a valid checkpoint then return None.
"""
have_checkpoint = (
self._serialization_dir is not None
and any("model_state" in x
for x in os.listdir(self._serialization_dir))
and any("training_state" in x
for x in os.listdir(self._serialization_dir)))
if not have_checkpoint:
return None
model_path = os.path.join(self._serialization_dir, "model_state.th")
training_state_path = os.path.join(self._serialization_dir,
"training_state.th")
return (model_path, training_state_path)
def _restore_checkpoint(self):
"""
Restores a model from a serialization_dir to the last saved checkpoint.
This includes an epoch count, optimizer state, a model state, a tasks state and
a metric state. All are of which are serialized separately.
This function should only be used to continue training -
if you wish to load a model for inference/load parts of a model into a new
computation graph, you should use the native Pytorch functions:
`` model.load_state_dict(torch.load("/path/to/model/weights.th"))``
Returns
-------
epoch: int,
The epoch at which to resume training.
should_stop: bool
Whether or not the training should already by stopped.
"""
latest_checkpoint = self.find_latest_checkpoint()
if not self._serialization_dir:
raise ConfigurationError(
"`serialization_dir` not specified - cannot "
"restore a model without a directory path.")
if latest_checkpoint is None:
raise ConfigurationError(
"Cannot restore model because one of"
"`model_state.th` or `training_state.th` is not in directory path."
)
model_path, training_state_path = latest_checkpoint
# Load the parameters onto CPU, then transfer to GPU.
# This avoids potential OOM on GPU for large models that
# load parameters onto GPU then make a new GPU copy into the parameter
# buffer. The GPU transfer happens implicitly in load_state_dict.
model_state = torch.load(model_path, map_location=device_mapping(-1))
training_state = torch.load(training_state_path,
map_location=device_mapping(-1))
# Load model
self._model.load_state_dict(model_state)
logger.info("Checkpoint - Model loaded from {}", model_path)
# Load optimizers
for task_name, optimizers_state in training_state["optimizers"].items(
):
self._optimizers[task_name].load_state_dict(optimizers_state)
logger.info("Checkpoint - Optimizers loaded from {}",
training_state_path)
# Load schedulers
for task_name, scheduler_state in training_state["schedulers"].items():
self._schedulers[task_name].lr_scheduler.load_state_dict(
scheduler_state)
logger.info("Checkpoint - Learning rate schedulers loaded from {}",
training_state_path)
self._metric_infos = training_state["metric_infos"]
self._task_infos = training_state["task_infos"]
logger.info("Checkpoint - Task infos loaded from {}",
training_state_path)
logger.info("Checkpoint - Metric infos loaded from {}",
training_state_path)
n_epoch, should_stop = training_state["epoch"], training_state[
"should_stop"]
return n_epoch + 1, should_stop
@classmethod
def from_params(cls, model: Model, task_list: List[Task],
serialization_dir: str,
params: Params) -> "MultiTaskTrainer":
"""
Static method that constructs the multi tasks trainer described by ``params``.
"""
choice = params.pop_choice("type", cls.list_available())
return cls.by_name(choice).from_params(
model=model,
task_list=task_list,
serialization_dir=serialization_dir,
params=params)
def __init__(self,
model: Model,
optimizer: torch.optim.Optimizer,
iterator: DataIterator,
train_dataset: Iterable[Instance],
validation_dataset: Optional[Iterable[Instance]] = None,
patience: Optional[int] = None,
validation_metric: str = "-loss",
validation_iterator: DataIterator = None,
shuffle: bool = True,
num_epochs: int = 20,
serialization_dir: Optional[str] = None,
num_serialized_models_to_keep: int = 0,
keep_serialized_model_every_num_seconds: int = None,
checkpointer: Checkpointer = None,
model_save_interval: float = None,
cuda_device: Union[int, List] = -1,
grad_norm: Optional[float] = None,
grad_clipping: Optional[float] = None,
learning_rate_scheduler: Optional[LearningRateScheduler] = None,
momentum_scheduler: Optional[MomentumScheduler] = None,
summary_interval: int = 100,
histogram_interval: int = None,
should_log_parameter_statistics: bool = True,
should_log_learning_rate: bool = False,
log_batch_size_period: Optional[int] = None,
moving_average: Optional[MovingAverage] = None,
callbacks: List[allennlp_callback.Callback]=None,
early_stopping_by_batch: bool=True,
estimator: Estimator=None,
) -> None:
"""
A trainer for doing supervised learning. It just takes a labeled dataset
and a ``DataIterator``, and uses the supplied ``Optimizer`` to learn the weights
for your model over some fixed number of epochs. You can also pass in a validation
dataset and enable early stopping. There are many other bells and whistles as well.
Parameters
----------
model : ``Model``, required.
An AllenNLP model to be optimized. Pytorch Modules can also be optimized if
their ``forward`` method returns a dictionary with a "loss" key, containing a
scalar tensor representing the loss function to be optimized.
If you are training your model using GPUs, your model should already be
on the correct device. (If you use `Trainer.from_params` this will be
handled for you.)
optimizer : ``torch.nn.Optimizer``, required.
An instance of a Pytorch Optimizer, instantiated with the parameters of the
model to be optimized.
iterator : ``DataIterator``, required.
A method for iterating over a ``Dataset``, yielding padded indexed batches.
train_dataset : ``Dataset``, required.
A ``Dataset`` to train on. The dataset should have already been indexed.
validation_dataset : ``Dataset``, optional, (default = None).
A ``Dataset`` to evaluate on. The dataset should have already been indexed.
patience : Optional[int] > 0, optional (default=None)
Number of epochs to be patient before early stopping: the training is stopped
after ``patience`` epochs with no improvement. If given, it must be ``> 0``.
If None, early stopping is disabled.
validation_metric : str, optional (default="loss")
Validation metric to measure for whether to stop training using patience
and whether to serialize an ``is_best`` model each epoch. The metric name
must be prepended with either "+" or "-", which specifies whether the metric
is an increasing or decreasing function.
validation_iterator : ``DataIterator``, optional (default=None)
An iterator to use for the validation set. If ``None``, then
use the training `iterator`.
shuffle: ``bool``, optional (default=True)
Whether to shuffle the instances in the iterator or not.
num_epochs : int, optional (default = 20)
Number of training epochs.
serialization_dir : str, optional (default=None)
Path to directory for saving and loading model files. Models will not be saved if
this parameter is not passed.
num_serialized_models_to_keep : ``int``, optional (default=20)
Number of previous model checkpoints to retain. Default is to keep 20 checkpoints.
A value of None or -1 means all checkpoints will be kept.
keep_serialized_model_every_num_seconds : ``int``, optional (default=None)
If num_serialized_models_to_keep is not None, then occasionally it's useful to
save models at a given interval in addition to the last num_serialized_models_to_keep.
To do so, specify keep_serialized_model_every_num_seconds as the number of seconds
between permanently saved checkpoints. Note that this option is only used if
num_serialized_models_to_keep is not None, otherwise all checkpoints are kept.
checkpointer : ``Checkpointer``, optional (default=None)
An instance of class Checkpointer to use instead of the default. If a checkpointer is specified,
the arguments num_serialized_models_to_keep and keep_serialized_model_every_num_seconds should
not be specified. The caller is responsible for initializing the checkpointer so that it is
consistent with serialization_dir.
model_save_interval : ``float``, optional (default=None)
If provided, then serialize models every ``model_save_interval``
seconds within single epochs. In all cases, models are also saved
at the end of every epoch if ``serialization_dir`` is provided.
cuda_device : ``Union[int, List[int]]``, optional (default = -1)
An integer or list of integers specifying the CUDA device(s) to use. If -1, the CPU is used.
grad_norm : ``float``, optional, (default = None).
If provided, gradient norms will be rescaled to have a maximum of this value.
grad_clipping : ``float``, optional (default = ``None``).
If provided, gradients will be clipped `during the backward pass` to have an (absolute)
maximum of this value. If you are getting ``NaNs`` in your gradients during training
that are not solved by using ``grad_norm``, you may need this.
learning_rate_scheduler : ``LearningRateScheduler``, optional (default = None)
If specified, the learning rate will be decayed with respect to
this schedule at the end of each epoch (or batch, if the scheduler implements
the ``step_batch`` method). If you use :class:`torch.optim.lr_scheduler.ReduceLROnPlateau`,
this will use the ``validation_metric`` provided to determine if learning has plateaued.
To support updating the learning rate on every batch, this can optionally implement
``step_batch(batch_num_total)`` which updates the learning rate given the batch number.
momentum_scheduler : ``MomentumScheduler``, optional (default = None)
If specified, the momentum will be updated at the end of each batch or epoch
according to the schedule.
summary_interval: ``int``, optional, (default = 100)
Number of batches between logging scalars to tensorboard
histogram_interval : ``int``, optional, (default = ``None``)
If not None, then log histograms to tensorboard every ``histogram_interval`` batches.
When this parameter is specified, the following additional logging is enabled:
* Histograms of model parameters
* The ratio of parameter update norm to parameter norm
* Histogram of layer activations
We log histograms of the parameters returned by
``model.get_parameters_for_histogram_tensorboard_logging``.
The layer activations are logged for any modules in the ``Model`` that have
the attribute ``should_log_activations`` set to ``True``. Logging
histograms requires a number of GPU-CPU copies during training and is typically
slow, so we recommend logging histograms relatively infrequently.
Note: only Modules that return tensors, tuples of tensors or dicts
with tensors as values currently support activation logging.
should_log_parameter_statistics : ``bool``, optional, (default = True)
Whether to send parameter statistics (mean and standard deviation
of parameters and gradients) to tensorboard.
should_log_learning_rate : ``bool``, optional, (default = False)
Whether to send parameter specific learning rate to tensorboard.
log_batch_size_period : ``int``, optional, (default = ``None``)
If defined, how often to log the average batch size.
moving_average: ``MovingAverage``, optional, (default = None)
If provided, we will maintain moving averages for all parameters. During training, we
employ a shadow variable for each parameter, which maintains the moving average. During
evaluation, we backup the original parameters and assign the moving averages to corresponding
parameters. Be careful that when saving the checkpoint, we will save the moving averages of
parameters. This is necessary because we want the saved model to perform as well as the validated
model if we load it later. But this may cause problems if you restart the training from checkpoint.
"""
super().__init__(serialization_dir, cuda_device)
# I am not calling move_to_gpu here, because if the model is
# not already on the GPU then the optimizer is going to be wrong.
self.model = model
self.iterator = iterator
self._validation_iterator = validation_iterator
self.shuffle = shuffle
self.optimizer = optimizer
self.train_data = train_dataset
self._validation_data = validation_dataset
if patience is None: # no early stopping
if validation_dataset:
logger.warning('You provided a validation dataset but patience was set to None, '
'meaning that early stopping is disabled')
elif (not isinstance(patience, int)) or patience <= 0:
raise ConfigurationError('{} is an invalid value for "patience": it must be a positive integer '
'or None (if you want to disable early stopping)'.format(patience))
# For tracking is_best_so_far and should_stop_early
self._metric_tracker = MetricTracker(patience, validation_metric)
# Get rid of + or -
self._validation_metric = validation_metric[1:]
self._num_epochs = num_epochs
if checkpointer is not None:
# We can't easily check if these parameters were passed in, so check against their default values.
# We don't check against serialization_dir since it is also used by the parent class.
if num_serialized_models_to_keep != 20 or \
keep_serialized_model_every_num_seconds is not None:
raise ConfigurationError(
"When passing a custom Checkpointer, you may not also pass in separate checkpointer "
"args 'num_serialized_models_to_keep' or 'keep_serialized_model_every_num_seconds'.")
self._checkpointer = checkpointer
else:
self._checkpointer = Checkpointer(serialization_dir,
keep_serialized_model_every_num_seconds,
num_serialized_models_to_keep)
self._model_save_interval = model_save_interval
self._grad_norm = grad_norm
self._grad_clipping = grad_clipping
self._learning_rate_scheduler = learning_rate_scheduler
self._momentum_scheduler = momentum_scheduler
self._moving_average = moving_average
# We keep the total batch number as an instance variable because it
# is used inside a closure for the hook which logs activations in
# ``_enable_activation_logging``.
self._batch_num_total = 0
self._tensorboard = TensorboardWriter(
get_batch_num_total=lambda: self._batch_num_total,
serialization_dir=serialization_dir,
summary_interval=summary_interval,
histogram_interval=histogram_interval,
should_log_parameter_statistics=should_log_parameter_statistics,
should_log_learning_rate=should_log_learning_rate)
self._log_batch_size_period = log_batch_size_period
self._last_log = 0.0 # time of last logging
# Enable activation logging.
if histogram_interval is not None:
self._tensorboard.enable_activation_logging(self.model)
self.callbacks = callbacks
self._early_stopping_by_batch = early_stopping_by_batch
self._estimator = estimator
class PtTrainer(TrainerBase):
def __init__(
self,
model: Model,
optimizer: torch.optim.Optimizer,
iterator: DataIterator,
train_dataset: Iterable[Instance],
validation_dataset: Optional[Iterable[Instance]] = None,
max_src_len: int = None,
patience: Optional[int] = None,
validation_metric: str = "-loss",
validation_iterator: DataIterator = None,
batch_size: int = 1,
shuffle: bool = True,
num_epochs: int = 20,
serialization_dir: Optional[str] = None,
num_serialized_models_to_keep: int = 20,
keep_serialized_model_every_num_seconds: int = None,
checkpointer: Checkpointer = None,
model_save_interval: float = None,
cuda_device: Union[int, List] = -1,
grad_norm: Optional[float] = None,
grad_clipping: Optional[float] = None,
learning_rate_scheduler: Optional[LearningRateScheduler] = None,
momentum_scheduler: Optional[MomentumScheduler] = None,
summary_interval: int = 100,
histogram_interval: int = None,
should_log_parameter_statistics: bool = True,
should_log_learning_rate: bool = False,
log_batch_size_period: Optional[int] = None,
moving_average: Optional[MovingAverage] = None) -> None:
super().__init__(serialization_dir, cuda_device)
# I am not calling move_to_gpu here, because if the model is
# not already on the GPU then the optimizer is going to be wrong.
self.model = model
self.iterator = iterator
self._validation_iterator = validation_iterator
self.shuffle = shuffle
self.optimizer = optimizer
self.train_data = train_dataset
self._validation_data = validation_dataset
self.max_src_len = max_src_len
self.batch_size = batch_size
# For tracking is_best_so_far and should_stop_early
self._metric_tracker = MetricTracker(patience, validation_metric)
# Get rid of + or -
self._validation_metric = validation_metric[1:]
self._num_epochs = num_epochs
if checkpointer is not None:
# We can't easily check if these parameters were passed in, so check against their default values.
# We don't check against serialization_dir since it is also used by the parent class.
if num_serialized_models_to_keep != 20 or \
keep_serialized_model_every_num_seconds is not None:
raise ConfigurationError(
"When passing a custom Checkpointer, you may not also pass in separate checkpointer "
"args 'num_serialized_models_to_keep' or 'keep_serialized_model_every_num_seconds'."
)
self._checkpointer = checkpointer
else:
self._checkpointer = Checkpointer(
serialization_dir, keep_serialized_model_every_num_seconds,
num_serialized_models_to_keep)
self._model_save_interval = model_save_interval
self._grad_norm = grad_norm
self._grad_clipping = grad_clipping
self._learning_rate_scheduler = learning_rate_scheduler
self._momentum_scheduler = momentum_scheduler
self._moving_average = moving_average
# We keep the total batch number as an instance variable because it
# is used inside a closure for the hook which logs activations in
# ``_enable_activation_logging``.
self._batch_num_total = 0
self._tensorboard = TensorboardWriter(
get_batch_num_total=lambda: self._batch_num_total,
serialization_dir=serialization_dir,
summary_interval=summary_interval,
histogram_interval=histogram_interval,
should_log_parameter_statistics=should_log_parameter_statistics,
should_log_learning_rate=should_log_learning_rate)
self._log_batch_size_period = log_batch_size_period
self._last_log = 0.0 # time of last logging
# Enable activation logging.
if histogram_interval is not None:
self._tensorboard.enable_activation_logging(self.model)
def rescale_gradients(self) -> Optional[float]:
return training_util.rescale_gradients(self.model, self._grad_norm)
def batch_loss(self, batch_group: List[TensorDict],
for_training: bool) -> torch.Tensor:
"""
Does a forward pass on the given batches and returns the ``loss`` value in the result.
If ``for_training`` is `True` also applies regularization penalty.
"""
if self._multiple_gpu:
output_dict = training_util.data_parallel(batch_group, self.model,
self._cuda_devices)
else:
assert len(batch_group) == 1
batch = batch_group[0]
batch = nn_util.move_to_device(batch, self._cuda_devices[0])
output_dict = self.model(**batch)
try:
loss = output_dict["loss"]
if for_training:
loss += self.model.get_regularization_penalty()
except KeyError:
if for_training:
raise RuntimeError(
"The model you are trying to optimize does not contain a"
" 'loss' key in the output of model.forward(inputs).")
loss = None
return loss
def _train_epoch(self, epoch: int) -> Dict[str, float]:
train_loss = 0.0
self.model.train()
num_gpus = len(self._cuda_devices)
if getattr(self, "train_dataset", None) is None:
self.train_dataset = DMDataSet(data=self.train_data[0],
batch_size=self.batch_size,
num_gpus=num_gpus,
shuffle=True)
self.train_dataset.set_epoch(epoch)
num_training_batches = math.ceil(
len(self.train_dataset) / self.batch_size / num_gpus)
self._last_log = time.time()
batches_this_epoch = 0
if self._batch_num_total is None:
self._batch_num_total = 0
logger.info("Training")
train_generator_tqdm = Tqdm.tqdm(self.train_dataset,
total=num_training_batches)
for batch_group in train_generator_tqdm:
# print('gpu num: ', len(batch_group))
# print('batch_size: ', len(batch_group[0]["source_tokens"]["tokens"]))
# gpu_data = batch_group[0]
# src_data = gpu_data["source_tokens"]["tokens"]
# tgt_data = gpu_data["target_tokens"]["tokens"]
# for sdata, tdata in zip(src_data, tgt_data):
# s = ''.join([self.model.vocab.get_token_from_index(x, "source_tokens") if x != 0 else '' for x in sdata.numpy()])
# t = ''.join([self.model.vocab.get_token_from_index(x, "target_tokens") if x != 0 else '' for x in tdata.numpy()])
# print(s)
# print(t)
batches_this_epoch += 1
self._batch_num_total += 1
batch_num_total = self._batch_num_total
self.optimizer.zero_grad()
loss = self.batch_loss(batch_group, for_training=True)
if torch.isnan(loss):
raise ValueError("nan loss encountered")
loss.backward()
train_loss += loss.item()
batch_grad_norm = self.rescale_gradients()
if self._learning_rate_scheduler:
self._learning_rate_scheduler.step_batch(batch_num_total)
if self._momentum_scheduler:
self._momentum_scheduler.step_batch(batch_num_total)
self.optimizer.step()
# Update moving averages
if self._moving_average is not None:
self._moving_average.apply(batch_num_total)
# Update the description with the latest metrics
metrics = training_util.get_metrics(self.model, train_loss,
batches_this_epoch)
description = training_util.description_from_metrics(metrics)
train_generator_tqdm.set_description(description, refresh=False)
# Log parameter values to Tensorboard
if self._tensorboard.should_log_this_batch():
self._tensorboard.log_learning_rates(self.model,
self.optimizer)
self._tensorboard.add_train_scalar("loss/loss_train",
metrics["loss"])
self._tensorboard.log_metrics(
{"epoch_metrics/" + k: v
for k, v in metrics.items()})
metrics = training_util.get_metrics(self.model,
train_loss,
batches_this_epoch,
reset=True)
return metrics
def _validation_loss(self) -> Tuple[float, int]:
logger.info("Validating")
self.model.eval()
# Replace parameter values with the shadow values from the moving averages.
if self._moving_average is not None:
self._moving_average.assign_average_value()
if self._validation_iterator is not None:
val_iterator = self._validation_iterator
else:
val_iterator = self.iterator
num_gpus = len(self._cuda_devices)
if getattr(self, "val_dataset", None) is None:
self.val_dataset = DMDataSet(data=self._validation_data[0],
batch_size=self.batch_size,
num_gpus=num_gpus,
shuffle=False)
num_validation_batches = math.ceil(
len(self.val_dataset) / self.batch_size / num_gpus)
val_generator_tqdm = Tqdm.tqdm(self.val_dataset,
total=num_validation_batches)
batches_this_epoch = 0
val_loss = 0
for batch_group in val_generator_tqdm:
loss = self.batch_loss(batch_group, for_training=False)
if loss is not None:
batches_this_epoch += 1
val_loss += loss.detach().cpu().numpy()
# Update the description with the latest metrics
val_metrics = training_util.get_metrics(self.model, val_loss,
batches_this_epoch)
description = training_util.description_from_metrics(val_metrics)
val_generator_tqdm.set_description(description, refresh=False)
# Now restore the original parameter values.
if self._moving_average is not None:
self._moving_average.restore()
return val_loss, batches_this_epoch
def train(self) -> Dict[str, Any]:
"""
Trains the supplied model with the supplied parameters.
"""
try:
epoch_counter = self._restore_checkpoint()
except RuntimeError:
traceback.print_exc()
raise ConfigurationError(
"Could not recover training from the checkpoint. Did you mean to output to "
"a different serialization directory or delete the existing serialization "
"directory?")
training_util.enable_gradient_clipping(self.model, self._grad_clipping)
logger.info("Beginning training.")
train_metrics: Dict[str, float] = {}
val_metrics: Dict[str, float] = {}
this_epoch_val_metric: float = None
metrics: Dict[str, Any] = {}
epochs_trained = 0
training_start_time = time.time()
metrics['best_epoch'] = self._metric_tracker.best_epoch
for key, value in self._metric_tracker.best_epoch_metrics.items():
metrics["best_validation_" + key] = value
for epoch in range(epoch_counter, self._num_epochs):
epoch_start_time = time.time()
train_metrics = self._train_epoch(epoch)
if self._validation_data is not None:
with torch.no_grad():
# We have a validation set, so compute all the metrics on it.
val_loss, num_batches = self._validation_loss()
val_metrics = training_util.get_metrics(self.model,
val_loss,
num_batches,
reset=True)
# Check validation metric for early stopping
this_epoch_val_metric = val_metrics[
self._validation_metric]
self._metric_tracker.add_metric(this_epoch_val_metric)
if self._metric_tracker.should_stop_early():
logger.info("Ran out of patience. Stopping training.")
break
self._tensorboard.log_metrics(
train_metrics,
val_metrics=val_metrics,
log_to_console=True,
epoch=epoch + 1) # +1 because tensorboard doesn't like 0
# Create overall metrics dict
training_elapsed_time = time.time() - training_start_time
metrics["training_duration"] = str(
datetime.timedelta(seconds=training_elapsed_time))
metrics["training_start_epoch"] = epoch_counter
metrics["training_epochs"] = epochs_trained
metrics["epoch"] = epoch
for key, value in train_metrics.items():
metrics["training_" + key] = value
for key, value in val_metrics.items():
metrics["validation_" + key] = value
if self._metric_tracker.is_best_so_far():
# Update all the best_ metrics.
# (Otherwise they just stay the same as they were.)
metrics['best_epoch'] = epoch
for key, value in val_metrics.items():
metrics["best_validation_" + key] = value
self._metric_tracker.best_epoch_metrics = val_metrics
if self._serialization_dir:
dump_metrics(
os.path.join(self._serialization_dir,
f'metrics_epoch_{epoch}.json'), metrics)
# The Scheduler API is agnostic to whether your schedule requires a validation metric -
# if it doesn't, the validation metric passed here is ignored.
if self._learning_rate_scheduler:
self._learning_rate_scheduler.step(this_epoch_val_metric,
epoch)
if self._momentum_scheduler:
self._momentum_scheduler.step(this_epoch_val_metric, epoch)
self._save_checkpoint(epoch)
epoch_elapsed_time = time.time() - epoch_start_time
logger.info("Epoch duration: %s",
datetime.timedelta(seconds=epoch_elapsed_time))
if epoch < self._num_epochs - 1:
training_elapsed_time = time.time() - training_start_time
estimated_time_remaining = training_elapsed_time * \
((self._num_epochs - epoch_counter) / float(epoch - epoch_counter + 1) - 1)
formatted_time = str(
datetime.timedelta(seconds=int(estimated_time_remaining)))
logger.info("Estimated training time remaining: %s",
formatted_time)
epochs_trained += 1
# make sure pending events are flushed to disk and files are closed properly
self._tensorboard.close()
# Load the best model state before returning
best_model_state = self._checkpointer.best_model_state()
if best_model_state:
self.model.load_state_dict(best_model_state)
return metrics
def _save_checkpoint(self, epoch: Union[int, str]) -> None:
"""
Saves a checkpoint of the model to self._serialization_dir.
Is a no-op if self._serialization_dir is None.
Parameters
----------
epoch : Union[int, str], required.
The epoch of training. If the checkpoint is saved in the middle
of an epoch, the parameter is a string with the epoch and timestamp.
"""
# If moving averages are used for parameters, we save
# the moving average values into checkpoint, instead of the current values.
if self._moving_average is not None:
self._moving_average.assign_average_value()
# These are the training states we need to persist.
training_states = {
"metric_tracker": self._metric_tracker.state_dict(),
"optimizer": self.optimizer.state_dict(),
"batch_num_total": self._batch_num_total
}
# If we have a learning rate or momentum scheduler, we should persist them too.
if self._learning_rate_scheduler is not None:
training_states[
"learning_rate_scheduler"] = self._learning_rate_scheduler.state_dict(
)
if self._momentum_scheduler is not None:
training_states[
"momentum_scheduler"] = self._momentum_scheduler.state_dict()
self._checkpointer.save_checkpoint(
model_state=self.model.state_dict(),
epoch=epoch,
training_states=training_states,
is_best_so_far=self._metric_tracker.is_best_so_far())
# Restore the original values for parameters so that training will not be affected.
if self._moving_average is not None:
self._moving_average.restore()
def _restore_checkpoint(self) -> int:
"""
Restores the model and training state from the last saved checkpoint.
This includes an epoch count and optimizer state, which is serialized separately
from model parameters. This function should only be used to continue training -
if you wish to load a model for inference/load parts of a model into a new
computation graph, you should use the native Pytorch functions:
`` model.load_state_dict(torch.load("/path/to/model/weights.th"))``
If ``self._serialization_dir`` does not exist or does not contain any checkpointed weights,
this function will do nothing and return 0.
Returns
-------
epoch: int
The epoch at which to resume training, which should be one after the epoch
in the saved training state.
"""
model_state, training_state = self._checkpointer.restore_checkpoint()
if not training_state:
# No checkpoint to restore, start at 0
return 0
self.model.load_state_dict(model_state)
self.optimizer.load_state_dict(training_state["optimizer"])
if self._learning_rate_scheduler is not None and "learning_rate_scheduler" in training_state:
self._learning_rate_scheduler.load_state_dict(
training_state["learning_rate_scheduler"])
if self._momentum_scheduler is not None and "momentum_scheduler" in training_state:
self._momentum_scheduler.load_state_dict(
training_state["momentum_scheduler"])
training_util.move_optimizer_to_cuda(self.optimizer)
# Currently the ``training_state`` contains a serialized ``MetricTracker``.
if "metric_tracker" in training_state:
self._metric_tracker.load_state_dict(
training_state["metric_tracker"])
# It used to be the case that we tracked ``val_metric_per_epoch``.
elif "val_metric_per_epoch" in training_state:
self._metric_tracker.clear()
self._metric_tracker.add_metrics(
training_state["val_metric_per_epoch"])
# And before that we didn't track anything.
else:
self._metric_tracker.clear()
if isinstance(training_state["epoch"], int):
epoch_to_return = training_state["epoch"] + 1
else:
epoch_to_return = int(training_state["epoch"].split('.')[0]) + 1
# For older checkpoints with batch_num_total missing, default to old behavior where
# it is unchanged.
batch_num_total = training_state.get('batch_num_total')
if batch_num_total is not None:
self._batch_num_total = batch_num_total
return epoch_to_return
# Requires custom from_params.
@classmethod
def from_params(cls,
params: Params,
serialization_dir: str,
recover: bool = False,
cache_directory: str = None,
cache_prefix: str = None) -> 'PtTrainer':
max_src_len = params.dataset_reader.get('max_src_len', None)
all_datasets = training_util.datasets_from_params(
params, cache_directory, cache_prefix)
datasets_for_vocab_creation = set(
params.pop("datasets_for_vocab_creation", all_datasets))
for dataset in datasets_for_vocab_creation:
if dataset not in all_datasets:
raise ConfigurationError(
f"invalid 'dataset_for_vocab_creation' {dataset}")
logger.info(
"From dataset instances, %s will be considered for vocabulary creation.",
", ".join(datasets_for_vocab_creation))
if recover and os.path.exists(
os.path.join(serialization_dir, "vocabulary")):
vocab = Vocabulary.from_files(
os.path.join(serialization_dir, "vocabulary"))
params.pop("vocabulary", {})
else:
vocab = Vocabulary.from_params(params.pop(
"vocabulary", {}), (instance
for key, dataset in all_datasets.items()
if key in datasets_for_vocab_creation
for instance in dataset))
model = Model.from_params(vocab=vocab, params=params.pop('model'))
# If vocab extension is ON for training, embedding extension should also be
# done. If vocab and embeddings are already in sync, it would be a no-op.
model.extend_embedder_vocab()
# Initializing the model can have side effect of expanding the vocabulary
vocab.save_to_files(os.path.join(serialization_dir, "vocabulary"))
iterator = DataIterator.from_params(params.pop("iterator"))
iterator.index_with(model.vocab)
validation_iterator_params = params.pop("validation_iterator", None)
if validation_iterator_params:
validation_iterator = DataIterator.from_params(
validation_iterator_params)
validation_iterator.index_with(model.vocab)
else:
validation_iterator = None
train_data = all_datasets['train']
validation_data = all_datasets.get('validation')
test_data = all_datasets.get('test')
trainer_params = params.pop("trainer")
no_grad_regexes = trainer_params.pop("no_grad", ())
for name, parameter in model.named_parameters():
if any(re.search(regex, name) for regex in no_grad_regexes):
parameter.requires_grad_(False)
frozen_parameter_names, tunable_parameter_names = \
get_frozen_and_tunable_parameter_names(model)
logger.info("Following parameters are Frozen (without gradient):")
for name in frozen_parameter_names:
logger.info(name)
logger.info("Following parameters are Tunable (with gradient):")
for name in tunable_parameter_names:
logger.info(name)
params = trainer_params
patience = params.pop_int("patience", None)
validation_metric = params.pop("validation_metric", "-loss")
shuffle = params.pop_bool("shuffle", True)
num_epochs = params.pop_int("num_epochs", 20)
cuda_device = parse_cuda_device(params.pop("cuda_device", -1))
grad_norm = params.pop_float("grad_norm", None)
grad_clipping = params.pop_float("grad_clipping", None)
lr_scheduler_params = params.pop("learning_rate_scheduler", None)
momentum_scheduler_params = params.pop("momentum_scheduler", None)
if isinstance(cuda_device, list):
model_device = cuda_device[0]
else:
model_device = cuda_device
if model_device >= 0:
# Moving model to GPU here so that the optimizer state gets constructed on
# the right device.
model = model.cuda(model_device)
parameters = [[n, p] for n, p in model.named_parameters()
if p.requires_grad]
optimizer = Optimizer.from_params(parameters, params.pop("optimizer"))
if "moving_average" in params:
moving_average = MovingAverage.from_params(
params.pop("moving_average"), parameters=parameters)
else:
moving_average = None
if lr_scheduler_params:
lr_scheduler = LearningRateScheduler.from_params(
optimizer, lr_scheduler_params)
else:
lr_scheduler = None
if momentum_scheduler_params:
momentum_scheduler = MomentumScheduler.from_params(
optimizer, momentum_scheduler_params)
else:
momentum_scheduler = None
if 'checkpointer' in params:
if 'keep_serialized_model_every_num_seconds' in params or \
'num_serialized_models_to_keep' in params:
raise ConfigurationError(
"Checkpointer may be initialized either from the 'checkpointer' key or from the "
"keys 'num_serialized_models_to_keep' and 'keep_serialized_model_every_num_seconds'"
" but the passed config uses both methods.")
checkpointer = Checkpointer.from_params(params.pop("checkpointer"))
else:
num_serialized_models_to_keep = params.pop_int(
"num_serialized_models_to_keep", 20)
keep_serialized_model_every_num_seconds = params.pop_int(
"keep_serialized_model_every_num_seconds", None)
checkpointer = Checkpointer(
serialization_dir=serialization_dir,
num_serialized_models_to_keep=num_serialized_models_to_keep,
keep_serialized_model_every_num_seconds=
keep_serialized_model_every_num_seconds)
model_save_interval = params.pop_float("model_save_interval", None)
summary_interval = params.pop_int("summary_interval", 100)
histogram_interval = params.pop_int("histogram_interval", None)
should_log_parameter_statistics = params.pop_bool(
"should_log_parameter_statistics", True)
should_log_learning_rate = params.pop_bool("should_log_learning_rate",
False)
log_batch_size_period = params.pop_int("log_batch_size_period", None)
return cls(
model,
optimizer,
iterator,
train_data,
validation_data,
patience=patience,
validation_metric=validation_metric,
validation_iterator=validation_iterator,
max_src_len=max_src_len,
shuffle=shuffle,
num_epochs=num_epochs,
serialization_dir=serialization_dir,
cuda_device=cuda_device,
grad_norm=grad_norm,
grad_clipping=grad_clipping,
learning_rate_scheduler=lr_scheduler,
momentum_scheduler=momentum_scheduler,
checkpointer=checkpointer,
model_save_interval=model_save_interval,
summary_interval=summary_interval,
histogram_interval=histogram_interval,
should_log_parameter_statistics=should_log_parameter_statistics,
should_log_learning_rate=should_log_learning_rate,
log_batch_size_period=log_batch_size_period,
moving_average=moving_average,
batch_size=iterator._batch_size)
