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Adding new train_step logic to make things less confusing for users (#15994)

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* Adding new train_step logic to make things less confusing for users

* DO NOT ASK WHY WE NEED THAT SUBCLASS

* Metrics now working, at least for single-output models with type annotations!

* Updates and TODOs for the new train_step

* Make fixup

* Temporary test workaround until T5 has types

* Temporary test workaround until T5 has types

* I think this actually works! Needs a lot of tests though

* MAke style/quality

* Revert changes to T5 tests

* Deleting the aforementioned unmentionable subclass

* Deleting the aforementioned unmentionable subclass

* Adding a Keras API test

* Style fixes

* Removing unneeded TODO and comments

* Update test_step too

* Stop trying to compute metrics with the dummy_loss, patch up test

* Make style

* make fixup

* Docstring cleanup

* make fixup

* make fixup

* Stop expanding 1D input tensors when using dummy loss

* Adjust T5 test given the new compile()

* make fixup

* Skipping test for convnext

* Removing old T5-specific Keras test now that we have a common one

* make fixup

* make fixup

* Only skip convnext test on CPU

* Update src/transformers/modeling_tf_utils.py

Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>

* Update src/transformers/modeling_tf_utils.py

Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>

* Avoiding TF import issues

* make fixup

* Update compile() to support TF 2.3

* Skipping model.fit() on template classes for now

* Skipping model.fit() on template class tests for now

* Replace ad-hoc solution with find_labels

* make fixup

Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
This commit is contained in:
Matt
2022-04-05 14:23:27 +01:00
committed by GitHub
parent 7ccacdf10f
commit 4354005291
5 changed files with 184 additions and 83 deletions
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7
tests/convnext/test_modeling_tf_convnext.py
View File

@@ -143,6 +143,13 @@ class TFConvNextModelTest(TFModelTesterMixin, unittest.TestCase):
def test_inputs_embeds(self):
pass
@unittest.skipIf(
not is_tf_available() or len(tf.config.list_physical_devices("GPU")) == 0,
reason="TF (<=2.8) does not support backprop for grouped convolutions on CPU.",
)
def test_keras_fit(self):
pass
@unittest.skip(reason="ConvNext does not support input and output embeddings")
def test_model_common_attributes(self):
pass

30
tests/t5/test_modeling_tf_t5.py
View File

@@ -804,33 +804,3 @@ class TFT5ModelIntegrationTests(unittest.TestCase):
translation = tok.decode(output[0], skip_special_tokens=True, clean_up_tokenization_spaces=False)
self.assertEqual(translation, expected_translation)
def test_finetune_keras_trainer(self):
"""Ensure that the model can be fine-tuned via the keras API and
that metrics work as expected.
"""
# This metric expects to be called with the logits output
def _accuracy(y_true, y_pred):
return tf.keras.metrics.sparse_categorical_crossentropy(y_true[:, 0], y_pred[:, 0])
# measure the accuracy of the first token
class FirstTokenAccuracy(tf.keras.metrics.MeanMetricWrapper):
def __init__(self, name="accuracy", **kwargs):
super().__init__(_accuracy, name=name, **kwargs)
model = self.model
model.compile("adam", metrics=FirstTokenAccuracy())
tokenizer = T5Tokenizer.from_pretrained("t5-small")
examples = [
("sentiment: Everything is awesome!", "positive"),
("sentiment: Tensorflow datasets are hard to use", "negative"),
]
inputs = dict(tokenizer([x[0] for x in examples], padding=True, return_tensors="tf"))
inputs["labels"] = tokenizer([x[1] for x in examples], return_tensors="tf").input_ids
model.fit(inputs)
m = model.evaluate(inputs)
self.assertEqual(len(m), 2)

50
tests/test_modeling_tf_common.py
View File

@@ -1302,6 +1302,56 @@ class TFModelTesterMixin:
self.assertEqual(loss.shape, [loss_size])
def test_keras_fit(self):
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
model = model_class(config)
if getattr(model, "hf_compute_loss", None):
# Test that model correctly compute the loss with kwargs
prepared_for_class = self._prepare_for_class(inputs_dict.copy(), model_class, return_labels=True)
# Is there a better way to remove these decoder inputs?
prepared_for_class = {
key: val
for key, val in prepared_for_class.items()
if key not in ("head_mask", "decoder_head_mask", "cross_attn_head_mask", "decoder_input_ids")
}
possible_label_cols = {
"labels",
"label",
"label_ids",
"start_positions",
"start_position",
"end_positions",
"end_position",
"next_sentence_label",
}
label_names = possible_label_cols.intersection(set(prepared_for_class))
self.assertGreater(len(label_names), 0, msg="No matching label names found!")
labels = {key: val for key, val in prepared_for_class.items() if key in label_names}
inputs_minus_labels = {key: val for key, val in prepared_for_class.items() if key not in label_names}
self.assertGreater(len(inputs_minus_labels), 0)
model.compile(optimizer=tf.keras.optimizers.SGD(0.0), run_eagerly=True)
# Make sure the model fits without crashing regardless of where we pass the labels
history1 = model.fit(
prepared_for_class,
validation_data=prepared_for_class,
steps_per_epoch=1,
validation_steps=1,
shuffle=False,
)
val_loss1 = history1.history["val_loss"][0]
history2 = model.fit(
inputs_minus_labels,
labels,
validation_data=(inputs_minus_labels, labels),
steps_per_epoch=1,
validation_steps=1,
shuffle=False,
)
val_loss2 = history2.history["val_loss"][0]
self.assertTrue(np.allclose(val_loss1, val_loss2, atol=1e-2, rtol=1e-3))
def test_generate_with_headmasking(self):
attention_names = ["encoder_attentions", "decoder_attentions", "cross_attentions"]
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()