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Rewrite TensorFlow train_step and test_step #17057

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merged 8 commits into from
May 17, 2022
Merged

Rewrite TensorFlow train_step and test_step #17057

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a414e65
Initial commit
Rocketknight1 May 3, 2022
03e5208
Better label renaming
Rocketknight1 May 3, 2022
a3dcca3
Remove breakpoint before pushing (this is your job)
Rocketknight1 May 3, 2022
aa0aee1
Test a lot more in the Keras fit() test
Rocketknight1 May 3, 2022
b7db255
make fixup
Rocketknight1 May 3, 2022
95701a2
Clarify the case where we flatten y dicts into tensors
Rocketknight1 May 16, 2022
af1ae3c
Clarify the case where we flatten y dicts into tensors
Rocketknight1 May 16, 2022
a9fdf07
Extract label name remapping to a method
Rocketknight1 May 17, 2022
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Remove breakpoint before pushing (this is your job)
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@Rocketknight1
Rocketknight1 committed May 9, 2022
commit a3dcca3975f07a2f5b7817717e8e3b729622d5c4
11 changes: 6 additions & 5 deletions src/transformers/modeling_tf_utils.py
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Original file line number Diff line number Diff line change
Expand Up @@ -958,8 +958,7 @@ def train_step(self, data):
that they are available to the model during the forward pass.
"""

# For now, we hardcode the most common renamings - this will hopefully be expanded to model-specific
# attributes in future.
# We hardcode the most common renamings; models with weirder names can set `self._label_to_output_map`
arg_names = list(dict(inspect.signature(self.call).parameters).keys())
label_kwargs = find_labels(self.__class__)
if self._label_to_output_map is not None:
Expand All @@ -970,6 +969,8 @@ def train_step(self, data):
label_to_output = {"labels": "prediction_logits", "sentence_order_label": "sop_logits"}
elif "next_sentence_label" in arg_names:
label_to_output = {"labels": "prediction_logits", "next_sentence_label": "seq_relationship_logits"}
elif "mc_labels" in arg_names:
label_to_output = {"labels": "logits", "mc_labels": "mc_logits"}
else:
label_to_output = dict()
output_to_label = {val: key for key, val in label_to_output.items()}
Expand Down Expand Up @@ -1039,7 +1040,6 @@ def train_step(self, data):
if loss is None:
loss = self.compiled_loss(y, y_pred, sample_weight, regularization_losses=self.losses)

breakpoint()
# Run backwards pass.
self.optimizer.minimize(loss, self.trainable_variables, tape=tape)

Expand All @@ -1061,8 +1061,7 @@ def test_step(self, data):
labels where appropriate. It will also copy label keys into the input dict when using the dummy loss, to ensure
that they are available to the model during the forward pass.
"""
# For now, we hardcode the most common renamings - this will hopefully be expanded to model-specific
# attributes in future.
# We hardcode the most common renamings; models with weirder names can set `self._label_to_output_map`
arg_names = list(dict(inspect.signature(self.call).parameters).keys())
label_kwargs = find_labels(self.__class__)
if self._label_to_output_map is not None:
Expand All @@ -1073,6 +1072,8 @@ def test_step(self, data):
label_to_output = {"labels": "prediction_logits", "sentence_order_label": "sop_logits"}
elif "next_sentence_label" in arg_names:
label_to_output = {"labels": "prediction_logits", "next_sentence_label": "seq_relationship_logits"}
elif "mc_labels" in arg_names:
label_to_output = {"labels": "logits", "mc_labels": "mc_logits"}
else:
label_to_output = dict()
output_to_label = {val: key for key, val in label_to_output.items()}
Expand Down