def checked(cls, value: float, name: str) -> "Quantile":
if not 0 <= value <= 1:
raise GluonTSUserError(
f"quantile value should be in [0, 1] but found {value}"
)
return Quantile(value, name)
def on_epoch_end(
self,
epoch_no: int,
epoch_loss: float,
training_network: nn.HybridBlock,
trainer: gluon.Trainer,
best_epoch_info: Dict[str, Any],
ctx: mx.Context,
) -> bool:
should_continue = self.lr_scheduler.step(metric_value=epoch_loss)
if not should_continue:
print(
"Early stopping based on learning rate scheduler callback (min_lr was reached)."
)
return False
pre_step_learning_rate = trainer.learning_rate
trainer.optimizer.set_learning_rate(
self.lr_scheduler(trainer.optimizer.num_update))
if not trainer.learning_rate == pre_step_learning_rate:
if best_epoch_info["epoch_no"] == -1:
raise GluonTSUserError(
"Got NaN in first epoch. Try reducing initial learning rate."
)
logger.info(f"Loading parameters from best epoch "
f"({best_epoch_info['epoch_no']})")
training_network.load_parameters(best_epoch_info["params_path"],
ctx)
return True
def from_str(cls, quantile: str) -> "Quantile":
assert isinstance(quantile, str)
try:
return cls.checked(value=float(quantile), name=quantile)
except ValueError:
m = re.match(r"^p(\d{2})$", quantile)
if m is None:
raise GluonTSUserError(
"Quantile string should be of the form "
f'"p10", "p50", ... or "0.1", "0.5", ... but found {quantile}'
)
else:
quantile_float: float = int(m.group(1)) / 100
return cls(value=quantile_float, name=str(quantile_float))
def __call__(
self,
net: nn.HybridBlock,
input_names: List[str],
train_iter: TrainDataLoader,
validation_iter: Optional[ValidationDataLoader] = None,
) -> None: # TODO: we may want to return some training information here
is_validation_available = validation_iter is not None
self.halt = False
with tempfile.TemporaryDirectory(
prefix="gluonts-trainer-temp-") as gluonts_temp:
def base_path() -> str:
return os.path.join(
gluonts_temp,
"{}_{}".format(STATE_ARTIFACT_FILE_NAME, uuid.uuid4()),
)
logger.info("Start model training")
net.initialize(ctx=self.ctx, init=self.init)
with HybridContext(
net=net,
hybridize=self.hybridize,
static_alloc=True,
static_shape=True,
):
batch_size = train_iter.batch_size
best_epoch_info = {
"params_path": "%s-%s.params" % (base_path(), "init"),
"epoch_no": -1,
"score": np.Inf,
}
lr_scheduler = lrs.MetricAttentiveScheduler(
objective="min",
patience=self.patience,
decay_factor=self.learning_rate_decay_factor,
min_lr=self.minimum_learning_rate,
)
optimizer = mx.optimizer.Adam(
learning_rate=self.learning_rate,
lr_scheduler=lr_scheduler,
wd=self.weight_decay,
clip_gradient=self.clip_gradient,
)
trainer = mx.gluon.Trainer(
net.collect_params(),
optimizer=optimizer,
kvstore="device", # FIXME: initialize properly
)
first_forward = True
def loop(epoch_no,
batch_iter,
is_training: bool = True) -> mx.metric.Loss:
nonlocal first_forward
tic = time.time()
epoch_loss = mx.metric.Loss()
# use averaged model for validation
if not is_training and isinstance(
self.avg_strategy, IterationAveragingStrategy):
self.avg_strategy.load_averaged_model(net)
with tqdm(batch_iter) as it:
for batch_no, data_entry in enumerate(it, start=1):
if self.halt:
break
inputs = [data_entry[k] for k in input_names]
if first_forward:
first_forward = False
_ = net(*inputs)
if self.post_initialize_cb:
self.post_initialize_cb(net)
with mx.autograd.record():
output = net(*inputs)
# network can returns several outputs, the first being always the loss
# when having multiple outputs, the forward returns a list in the case of hybrid and a
# tuple otherwise
# we may wrap network outputs in the future to avoid this type check
if isinstance(output, (list, tuple)):
loss = output[0]
else:
loss = output
if is_training:
loss.backward()
trainer.step(batch_size)
# iteration averaging in training
if isinstance(
self.avg_strategy,
IterationAveragingStrategy,
):
self.avg_strategy.apply(net)
epoch_loss.update(None, preds=loss)
lv = loss_value(epoch_loss)
if not np.isfinite(lv):
logger.warning("Epoch[%d] gave nan loss",
epoch_no)
return epoch_loss
it.set_postfix(
ordered_dict={
"epoch":
f"{epoch_no + 1}/{self.epochs}",
("" if is_training else "validation_") + "avg_epoch_loss":
lv,
},
refresh=False,
)
# print out parameters of the network at the first pass
if batch_no == 1 and epoch_no == 0:
net_name = type(net).__name__
num_model_param = self.count_model_params(net)
logger.info(
f"Number of parameters in {net_name}: {num_model_param}"
)
# mark epoch end time and log time cost of current epoch
toc = time.time()
logger.info(
"Epoch[%d] Elapsed time %.3f seconds",
epoch_no,
(toc - tic),
)
logger.info(
"Epoch[%d] Evaluation metric '%s'=%f",
epoch_no,
("" if is_training else "validation_") + "epoch_loss",
lv,
)
if not is_training and isinstance(
self.avg_strategy, IterationAveragingStrategy):
# bring back the cached model
self.avg_strategy.load_cached_model(net)
return epoch_loss
for epoch_no in range(self.epochs):
if self.halt:
logger.info(f"Epoch[{epoch_no}] Interrupting training")
break
curr_lr = trainer.learning_rate
logger.info(
f"Epoch[{epoch_no}] Learning rate is {curr_lr}")
epoch_loss = loop(epoch_no, train_iter)
if is_validation_available:
epoch_loss = loop(epoch_no,
validation_iter,
is_training=False)
# update average trigger
if isinstance(self.avg_strategy,
IterationAveragingStrategy):
self.avg_strategy.update_average_trigger(
metric=loss_value(epoch_loss), epoch=epoch_no + 1)
# once triggered, update the average immediately
self.avg_strategy.apply(net)
should_continue = lr_scheduler.step(loss_value(epoch_loss))
if isinstance(self.avg_strategy,
IterationAveragingStrategy):
logging.info(
"Overriding early stopping for iteration-based averaging strategies."
)
should_continue = True
if not should_continue:
logger.info("Stopping training")
break
# save model and epoch info
bp = base_path()
epoch_info = {
"params_path": f"{bp}-0000.params",
"epoch_no": epoch_no,
"score": loss_value(epoch_loss),
}
net.save_parameters(epoch_info["params_path"]
) # TODO: handle possible exception
save_epoch_info(bp, epoch_info)
# update best epoch info - needed for the learning rate scheduler
if loss_value(epoch_loss) < best_epoch_info["score"]:
best_epoch_info = epoch_info.copy()
if not trainer.learning_rate == curr_lr:
if best_epoch_info["epoch_no"] == -1:
raise GluonTSUserError(
"Got NaN in first epoch. Try reducing initial learning rate."
)
logger.info(f"Loading parameters from best epoch "
f"({best_epoch_info['epoch_no']})")
net.load_parameters(best_epoch_info["params_path"],
self.ctx)
if isinstance(self.avg_strategy, AveragingStrategy):
logging.info("Computing averaged parameters.")
averaged_params_path = self.avg_strategy.apply(
gluonts_temp)
logging.info("Loading averaged parameters.")
net.load_parameters(averaged_params_path, self.ctx)
if isinstance(self.avg_strategy, IterationAveragingStrategy):
logging.info("Loading averaged parameters.")
self.avg_strategy.load_averaged_model(net)
logger.info("End model training")
def __call__(
self,
net: nn.HybridBlock,
train_iter: DataLoader,
validation_iter: Optional[DataLoader] = None,
) -> None: # TODO: we may want to return some training information here
"""
Train a network, given an iterable over training (and optionally validation) batches.
Parameters
----------
net
Network to be trained. This a Gluon HybridBlock, assumed to produce a tensor
of loss values as output.
train_iter
An iterable over batches to be used for training. Batches are assumed to be
dictionaries, whose values are MXNet arrays that correspond to the network
inputs.
validation_iter
Similar to `train_iter` but the batches produced here are used to compute
validation metrics.
"""
is_validation_available = validation_iter is not None
with tempfile.TemporaryDirectory(
prefix="gluonts-trainer-temp-"
) as gluonts_temp:
def base_path() -> str:
return os.path.join(
gluonts_temp,
"{}_{}".format(STATE_ARTIFACT_FILE_NAME, uuid.uuid4()),
)
logger.info("Start model training")
net.initialize(ctx=self.ctx, init=self.init)
with HybridContext(
net=net,
hybridize=self.hybridize,
static_alloc=True,
static_shape=True,
):
best_epoch_info = {
"params_path": "%s-%s.params" % (base_path(), "init"),
"epoch_no": -1,
"score": np.Inf,
}
lr_scheduler = lrs.MetricAttentiveScheduler(
objective="min",
patience=self.patience,
decay_factor=self.learning_rate_decay_factor,
min_lr=self.minimum_learning_rate,
)
optimizer = mx.optimizer.Adam(
learning_rate=self.learning_rate,
lr_scheduler=lr_scheduler,
wd=self.weight_decay,
clip_gradient=self.clip_gradient,
)
trainer = mx.gluon.Trainer(
net.collect_params(),
optimizer=optimizer,
kvstore="device", # FIXME: initialize properly
)
first_forward = True
def loop(
epoch_no,
batch_iter,
num_batches_to_use: Optional[int] = None,
is_training: bool = True,
) -> mx.metric.Loss:
nonlocal first_forward
tic = time.time()
epoch_loss = mx.metric.Loss()
# use averaged model for validation
if not is_training and isinstance(
self.avg_strategy, IterationAveragingStrategy
):
self.avg_strategy.load_averaged_model(net)
batch_iter = itertools.islice(
batch_iter, num_batches_to_use
)
with tqdm(batch_iter, total=num_batches_to_use) as it:
for batch_no, batch in enumerate(it, start=1):
# `batch` here is expected to be a dictionary whose fields
# should correspond 1-to-1 with the network inputs
# see below how `batch.values()` is fed into the network
if first_forward:
first_forward = False
_ = net(*batch.values())
if self.post_initialize_cb:
self.post_initialize_cb(net)
with mx.autograd.record():
# we set the mode explicitly as by default mxnet assumes predict mode and hence
# dropout layers are not used if the mode is not explicitly set to training
mode = (
autograd.train_mode
if is_training
else autograd.predict_mode
)
with mode():
output = net(*batch.values())
# network can returns several outputs, the first being always the loss
# when having multiple outputs, the forward returns a list in the case of hybrid and a
# tuple otherwise
# we may wrap network outputs in the future to avoid this type check
if isinstance(output, (list, tuple)):
loss = output[0]
else:
loss = output
batch_size = loss.shape[0]
if not np.isfinite(ndarray.sum(loss).asscalar()):
logger.warning(
"Batch [%d] of Epoch[%d] gave NaN loss and it will be ignored",
batch_no,
epoch_no,
)
else:
if is_training:
loss.backward()
trainer.step(batch_size)
# iteration averaging in training
if isinstance(
self.avg_strategy,
IterationAveragingStrategy,
):
self.avg_strategy.apply(net)
epoch_loss.update(None, preds=loss)
lv = loss_value(epoch_loss)
it.set_postfix(
ordered_dict={
"epoch": f"{epoch_no + 1}/{self.epochs}",
("" if is_training else "validation_")
+ "avg_epoch_loss": lv,
},
refresh=False,
)
# print out parameters of the network at the first pass
if batch_no == 1 and epoch_no == 0:
net_name = type(net).__name__
num_model_param = self.count_model_params(net)
logger.info(
f"Number of parameters in {net_name}: {num_model_param}"
)
# mark epoch end time and log time cost of current epoch
toc = time.time()
logger.info(
"Epoch[%d] Elapsed time %.3f seconds",
epoch_no,
(toc - tic),
)
logger.info(
"Epoch[%d] Evaluation metric '%s'=%f",
epoch_no,
("" if is_training else "validation_") + "epoch_loss",
lv,
)
if not is_training and isinstance(
self.avg_strategy, IterationAveragingStrategy
):
# bring back the cached model
self.avg_strategy.load_cached_model(net)
return epoch_loss
for epoch_no in range(self.epochs):
curr_lr = trainer.learning_rate
logger.info(
f"Epoch[{epoch_no}] Learning rate is {curr_lr}"
)
epoch_loss = loop(
epoch_no,
train_iter,
num_batches_to_use=self.num_batches_per_epoch,
)
if is_validation_available:
epoch_loss = loop(
epoch_no, validation_iter, is_training=False
)
# update average trigger
if isinstance(
self.avg_strategy, IterationAveragingStrategy
):
self.avg_strategy.update_average_trigger(
metric=loss_value(epoch_loss), epoch=epoch_no + 1
)
# once triggered, update the average immediately
self.avg_strategy.apply(net)
should_continue = lr_scheduler.step(loss_value(epoch_loss))
if isinstance(
self.avg_strategy, IterationAveragingStrategy
):
logging.info(
"Overriding early stopping for iteration-based averaging strategies."
)
should_continue = True
if not should_continue:
logger.info("Stopping training")
break
# save model and epoch info
bp = base_path()
epoch_info = {
"params_path": f"{bp}-0000.params",
"epoch_no": epoch_no,
"score": loss_value(epoch_loss),
}
net.save_parameters(
epoch_info["params_path"]
) # TODO: handle possible exception
save_epoch_info(bp, epoch_info)
# update best epoch info - needed for the learning rate scheduler
if loss_value(epoch_loss) < best_epoch_info["score"]:
best_epoch_info = epoch_info.copy()
if not trainer.learning_rate == curr_lr:
if best_epoch_info["epoch_no"] == -1:
raise GluonTSUserError(
"Got NaN in first epoch. Try reducing initial learning rate."
)
logger.info(
f"Loading parameters from best epoch "
f"({best_epoch_info['epoch_no']})"
)
net.load_parameters(
best_epoch_info["params_path"], self.ctx
)
if isinstance(self.avg_strategy, AveragingStrategy):
logging.info("Computing averaged parameters.")
averaged_params_path = self.avg_strategy.apply(
gluonts_temp
)
logging.info("Loading averaged parameters.")
net.load_parameters(averaged_params_path, self.ctx)
if isinstance(self.avg_strategy, IterationAveragingStrategy):
logging.info("Loading averaged parameters.")
self.avg_strategy.load_averaged_model(net)
logger.info("End model training")