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dvclive callback: warn instead of fail when logging non-scalars #27608

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Nov 21, 2023
Merged

dvclive callback: warn instead of fail when logging non-scalars #27608

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64370c5
dvclive callback: warn instead of fail when logging non-scalars
dberenbaum Nov 20, 2023
a95bd2d
tests: log lr as scalar
dberenbaum Nov 20, 2023
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11 changes: 10 additions & 1 deletion src/transformers/integrations/integration_utils.py
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Original file line number Diff line number Diff line change
Expand Up @@ -1680,10 +1680,19 @@ def on_log(self, args, state, control, model=None, logs=None, **kwargs):
if not self._initialized:
self.setup(args, state, model)
if state.is_world_process_zero:
from dvclive.plots import Metric
from dvclive.utils import standardize_metric_name

for key, value in logs.items():
self.live.log_metric(standardize_metric_name(key, "dvclive.huggingface"), value)
if Metric.could_log(value):
self.live.log_metric(standardize_metric_name(key, "dvclive.huggingface"), value)
else:
logger.warning(
"Trainer is attempting to log a value of "
f'"{value}" of type {type(value)} for key "{key}" as a scalar. '
"This invocation of DVCLive's Live.log_metric() "
"is incorrect so we dropped this attribute."
)
self.live.next_step()

def on_save(self, args, state, control, **kwargs):
Expand Down
6 changes: 3 additions & 3 deletions tests/trainer/test_trainer.py
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Original file line number Diff line number Diff line change
Expand Up @@ -672,7 +672,7 @@ class TrainerWithLRLogs(Trainer):
def log(self, logs):
# the LR is computed after metrics and does not exist for the first epoch
if hasattr(self.lr_scheduler, "_last_lr"):
logs["learning_rate"] = self.lr_scheduler._last_lr
logs["learning_rate"] = self.lr_scheduler._last_lr[0]
super().log(logs)

train_dataset = RegressionDataset(length=64)
Expand Down Expand Up @@ -702,14 +702,14 @@ def log(self, logs):
if loss > best_loss:
bad_epochs += 1
if bad_epochs > patience:
self.assertLess(logs[i + 1]["learning_rate"][0], log["learning_rate"][0])
self.assertLess(logs[i + 1]["learning_rate"], log["learning_rate"])
just_decreased = True
bad_epochs = 0
else:
best_loss = loss
bad_epochs = 0
if not just_decreased:
self.assertEqual(logs[i + 1]["learning_rate"][0], log["learning_rate"][0])
self.assertEqual(logs[i + 1]["learning_rate"], log["learning_rate"])

def test_adafactor_lr_none(self):
# test the special case where lr=None, since Trainer can't not have lr_scheduler
Expand Down
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