def run_epoch(self, state: TrainingState, data, metric_reporter: MetricReporter):
"""Our run_epoch is a bit different, because we're wrapping the model forward
call with model.train_batch, which arranges tensors and gets loss, etc."""
report_metric = state.stage != Stage.TRAIN or self.config.report_train_metrics
model = state.model
for batch_id, batch in enumerate(data):
self.zero_grads(state)
with timing.time("model.train_batch"):
loss, metric_data = model.train_batch(model, batch)
self.backprop(state, loss)
if report_metric:
with timing.time("add metrics"):
metric_reporter.add_batch_stats(
batch_id, *metric_data, **metric_reporter.batch_context(batch)
)
metrics = None
if report_metric:
with timing.time("report metrics"):
metrics = metric_reporter.report_metric(
model, state.stage, state.epoch, print_to_channels=(state.rank == 0)
)
else:
metric_reporter._reset()
return metrics
def run_step(
self,
samples: List[Any],
state: TrainingState,
metric_reporter: MetricReporter,
report_metric: bool,
):
"""Our run_step is a bit different, because we're wrapping the model forward
call with model.train_batch, which arranges tensors and gets loss, etc.
Whenever "samples" contains more than one mini-batch (sample_size > 1),
we want to accumulate gradients locally and only call all-reduce in the
last backwards pass.
"""
sample_size = len(samples)
assert sample_size <= self.config.num_accumulated_batches
model = state.model
self.zero_grads(state)
for idx, (batch_id, batch) in enumerate(samples):
with contextlib_ExitStack() as exit_stack:
# enter ddp no_sync context and fp16 delay_scale context if needed
maybe_accumulate_gradients(exit_stack, model, idx, sample_size)
logits = model(batch)
targets = batch["label_ids"]
loss = self.loss(logits, targets)
if sample_size > 1:
# gradients averaged per batch and accumulated across samples.
# divide sample_size to let gradients averaged per example
loss = loss / sample_size
self.backprop(state, loss)
if report_metric:
with timing.time("add metrics"):
predictions = torch.max(logits, -1)[1]
scores = F.log_softmax(logits)
# [len(targets)] means the batch_size, it's required by add_batch_stats
# Will rewrite metric_reporter rather than fixing it
metric_data = (predictions, targets, scores, loss,
[targets])
metric_reporter.add_batch_stats(
batch_id,
*metric_data,
# TODO merge this step into add_batch_stats once all data
# migration is done
# in new data API, we don't have raw_batch
**metric_reporter.batch_context(raw_batch=[],
batch=batch),
)
if batch_id % self.config.num_samples_to_log_progress == 0:
metric_reporter.report_realtime_metric(state.stage)
# update gradients after #len(samples) forward & backward
self.optimizer_step(state)
self.sparsification_step(state)
def run_step(
self,
samples: List[Any],
state: TrainingState,
metric_reporter: MetricReporter,
report_metric: bool,
):
"""Our run_step is a bit different, because we're wrapping the model forward
call with model.train_batch, which arranges tensors and gets loss, etc.
Whenever "samples" contains more than one mini-batch (sample_size > 1),
we want to accumulate gradients locally and only call all-reduce in the
last backwards pass.
"""
sample_size = len(samples)
assert sample_size <= self.config.num_accumulated_batches
if self('begin_batch'): return
model = state.model
self.zero_grads(state)
for idx, (batch_id, (raw_batch, batch)) in enumerate(samples):
with contextlib_ExitStack() as exit_stack:
# enter ddp no_sync context and fp16 delay_scale context if needed
maybe_accumulate_gradients(exit_stack, model, idx, sample_size)
with timing.time("model.train_batch"):
loss, metric_data = model.train_batch(model, batch, state)
if sample_size > 1:
# gradients averaged per batch and accumulated across samples.
# divide sample_size to let gradients averaged per example
loss = loss / sample_size
self.backprop(state, loss)
self.samples, self.state, self.loss = samples, state, loss
self('after_loss')
if report_metric:
with timing.time("add metrics"):
metric_reporter.add_batch_stats(
batch_id,
*metric_data,
# TODO merge this step into add_batch_stats once all data
# migration is done
**metric_reporter.batch_context(raw_batch, batch),
)
if batch_id % self.config.num_samples_to_log_progress == 0:
metric_reporter.report_realtime_metric(state.stage)
# update gradients after #len(samples) forward & backward
self.optimizer_step(state)
self.sparsification_step(state)
self('after_batch')
def run_step(
self,
samples: List[Any],
state: TrainingState,
metric_reporter: MetricReporter,
report_metric: bool,
):
"""Our run_step is a bit different, because we're wrapping the model forward
call with model.train_batch, which arranges tensors and gets loss, etc."""
sample_size = len(samples)
assert sample_size <= self.config.num_accumulated_batches
model = state.model
self.zero_grads(state)
for i, (batch_id, (raw_batch, batch)) in enumerate(samples):
if cuda.DISTRIBUTED_WORLD_SIZE > 1:
# Whenever *samples* contains more than one mini-batch, we
# want to accumulate gradients locally and only call
# all-reduce in the last backwards pass.
if i < sample_size - 1:
# sync gradients in the last sample backward
model.accumulate_gradients(True)
else:
model.accumulate_gradients(False)
with timing.time("model.train_batch"):
loss, metric_data = model.train_batch(model, batch)
if sample_size > 1:
# gradients averaged per each batch and accumulated across samples.
# divide sample_size to let gradients averaged per example
loss = loss / sample_size
self.backprop(state, loss)
if report_metric:
with timing.time("add metrics"):
metric_reporter.add_batch_stats(
batch_id,
*metric_data,
**metric_reporter.batch_context(raw_batch, batch),
)
# update gradients after #len(samples) forward & backward
self.optimizer_step(state)
def _run_epoch(
self,
stage: Stage,
epoch: int,
batches,
model: Model,
metric_reporter: MetricReporter,
pre_batch=lambda: None,
backprop=lambda loss: None,
rank=0,
num_samples_to_log_progress: int = None,
):
"""Our run_epoch is a bit different, because we're wrapping the model forward
call with model.train_batch, which arranges tensors and gets loss, etc."""
print(f"Rank {rank} worker: Running epoch #{epoch} for {stage}")
report_metric = stage != Stage.TRAIN or self.config.report_train_metrics
for batch_id, batch in enumerate(batches):
pre_batch()
with timing.time("model.train_batch"):
loss, metric_data = model.train_batch(batch)
with timing.time("backprop"):
backprop(loss)
if report_metric:
with timing.time("add metrics"):
metric_reporter.add_batch_stats(
batch_id, *metric_data, **metric_reporter.batch_context(batch)
)
metrics = None
if report_metric:
with timing.time("report metrics"):
metrics = metric_reporter.report_metric(
model, stage, epoch, print_to_channels=(rank == 0)
)
else:
metric_reporter._reset()
return metrics
def run_step(
self,
samples: List[Any],
state: TrainingState,
metric_reporter: MetricReporter,
report_metric: bool,
):
"""Our run_step is a bit different, because we're wrapping the model forward
call with model.train_batch, which arranges tensors and gets loss, etc.
Whenever "samples" contains more than one mini-batch (sample_size > 1),
we want to accumulate gradients locally and only call all-reduce in the
last backwards pass.
"""
sample_size = len(samples)
assert sample_size <= self.config.num_accumulated_batches
model = state.model
self.zero_grads(state)
for idx, (batch_id, (raw_batch, batch)) in enumerate(samples):
with maybe_no_sync(model, idx, sample_size):
with timing.time("model.train_batch"):
loss, metric_data = model.train_batch(model, batch, state)
if sample_size > 1:
# gradients averaged per batch and accumulated across samples.
# divide sample_size to let gradients averaged per example
loss = loss / sample_size
self.backprop(state, loss)
if report_metric:
with timing.time("add metrics"):
metric_reporter.add_batch_stats(
batch_id,
*metric_data,
**metric_reporter.batch_context(raw_batch, batch),
)
# update gradients after #len(samples) forward & backward
self.optimizer_step(state)
def _run_epoch(
self,
stage: Stage,
epoch: int,
batches,
model: Model,
metric_reporter: MetricReporter,
pre_batch=lambda: None,
backprop=lambda loss, timer=None: None,
rank=0,
):
"""Our run_epoch is a bit different, because we're wrapping the model forward
call with model.train_batch, which arranges tensors and gets loss, etc."""
print(f"Rank {rank} worker: Running epoch #{epoch} for {stage}")
report_metric = stage != Stage.TRAIN or self.config.report_train_metrics
for batch_id, (batch, tensors) in enumerate(batches):
print(f"Batch {batch_id} has {len(batch)} examples")
pre_batch()
context = metric_reporter.batch_context(batch)
# pass context to model to use in forward call if needed
model.contextualize(context)
loss, metric_data = model.train_batch(tensors)
if BatchContext.IGNORE_LOSS in context:
loss *= 0
backprop(loss)
if report_metric:
metric_reporter.add_batch_stats(batch_id, *metric_data,
**context)
metrics = None
if report_metric:
metrics = metric_reporter.report_metric(
stage, epoch, print_to_channels=(rank == 0))
else:
metric_reporter._reset()
return metrics