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Add tf_keras imports to prepare for Keras 3 (#28588)

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* Port core files + ESM (because ESM code is odd)

* Search-replace in modelling code

* Fix up transfo_xl as well

* Fix other core files + tests (still need to add correct import to tests)

* Fix cookiecutter

* make fixup, fix imports in some more core files

* Auto-add imports to tests

* Cleanup, add imports to sagemaker tests

* Use correct exception for importing tf_keras

* Fixes in modeling_tf_utils

* make fixup

* Correct version parsing code

* Ensure the pipeline tests correctly revert to float32 after each test

* Ensure the pipeline tests correctly revert to float32 after each test

* More tf.keras -> keras

* Add dtype cast

* Better imports of tf_keras

* Add a cast for tf.assign, just in case

* Fix callback imports
This commit is contained in:
Matt
2024-01-30 17:26:36 +00:00
committed by GitHub
parent 1d489b3e61
commit 415e9a0980
109 changed files with 2801 additions and 2658 deletions
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35
tests/test_modeling_tf_common.py
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@@ -80,6 +80,7 @@ if is_tf_available():
TFSampleDecoderOnlyOutput,
TFSampleEncoderDecoderOutput,
)
from transformers.modeling_tf_utils import keras
tf.config.experimental.enable_tensor_float_32_execution(False)
@@ -365,7 +366,7 @@ class TFModelTesterMixin:
and module_member_name[: -len("MainLayer")] == model_class.__name__[: -len("Model")]
for module_member in (getattr(module, module_member_name),)
if isinstance(module_member, type)
and tf.keras.layers.Layer in module_member.__bases__
and keras.layers.Layer in module_member.__bases__
and getattr(module_member, "_keras_serializable", False)
}
for main_layer_class in tf_main_layer_classes:
@@ -379,17 +380,17 @@ class TFModelTesterMixin:
main_layer = main_layer_class(config)
symbolic_inputs = {
name: tf.keras.Input(tensor.shape[1:], dtype=tensor.dtype) for name, tensor in inputs_dict.items()
name: keras.Input(tensor.shape[1:], dtype=tensor.dtype) for name, tensor in inputs_dict.items()
}
model = tf.keras.Model(symbolic_inputs, outputs=main_layer(symbolic_inputs))
model = keras.Model(symbolic_inputs, outputs=main_layer(symbolic_inputs))
outputs = model(inputs_dict)
with tempfile.TemporaryDirectory() as tmpdirname:
filepath = os.path.join(tmpdirname, "keras_model.h5")
model.save(filepath)
if "T5" in main_layer_class.__name__:
model = tf.keras.models.load_model(
model = keras.models.load_model(
filepath,
custom_objects={
main_layer_class.__name__: main_layer_class,
@@ -397,10 +398,10 @@ class TFModelTesterMixin:
},
)
else:
model = tf.keras.models.load_model(
model = keras.models.load_model(
filepath, custom_objects={main_layer_class.__name__: main_layer_class}
)
assert isinstance(model, tf.keras.Model)
assert isinstance(model, keras.Model)
after_outputs = model(inputs_dict)
self.assert_outputs_same(after_outputs, outputs)
@@ -610,7 +611,7 @@ class TFModelTesterMixin:
tf_outputs = tf_model(tf_inputs_dict)
# tf models returned loss is usually a tensor rather than a scalar.
# (see `hf_compute_loss`: it uses `tf.keras.losses.Reduction.NONE`)
# (see `hf_compute_loss`: it uses `keras.losses.Reduction.NONE`)
# Change it here to a scalar to match PyTorch models' loss
tf_loss = getattr(tf_outputs, "loss", None)
if tf_loss is not None:
@@ -697,7 +698,7 @@ class TFModelTesterMixin:
# These are maximally general inputs for the model, with multiple None dimensions
# Hopefully this will catch any conditionals that fail for flexible shapes
functional_inputs = {
key: tf.keras.Input(shape=val.shape[1:], dtype=val.dtype, name=key)
key: keras.Input(shape=val.shape[1:], dtype=val.dtype, name=key)
for key, val in model.input_signature.items()
if key in model.dummy_inputs
}
@@ -706,7 +707,7 @@ class TFModelTesterMixin:
hidden_states = outputs_dict[0]
# Compile extended model
functional_model = tf.keras.Model(inputs=functional_inputs, outputs=hidden_states)
functional_model = keras.Model(inputs=functional_inputs, outputs=hidden_states)
model_out = functional_model.predict(model.dummy_inputs) # Check we can pass inputs with the Keras API
self.assertTrue(model_out is not None)
with tempfile.TemporaryDirectory() as tmpdirname:
@@ -918,12 +919,12 @@ class TFModelTesterMixin:
for model_class in self.all_model_classes:
model = model_class(config)
self.assertIsInstance(model.get_input_embeddings(), tf.keras.layers.Layer)
self.assertIsInstance(model.get_input_embeddings(), keras.layers.Layer)
legacy_text_in_text_out = model.get_lm_head() is not None
if model_class in text_in_text_out_models or legacy_text_in_text_out:
out_embeddings = model.get_output_embeddings()
self.assertIsInstance(out_embeddings, tf.keras.layers.Layer)
self.assertIsInstance(out_embeddings, keras.layers.Layer)
bias = model.get_bias()
if bias is not None:
self.assertIsInstance(bias, dict)
@@ -931,7 +932,7 @@ class TFModelTesterMixin:
self.assertIsInstance(v, tf.Variable)
elif model_class in speech_in_text_out_models:
out_embeddings = model.get_output_embeddings()
self.assertIsInstance(out_embeddings, tf.keras.layers.Layer)
self.assertIsInstance(out_embeddings, keras.layers.Layer)
bias = model.get_bias()
self.assertIsNone(bias)
else:
@@ -1079,14 +1080,14 @@ class TFModelTesterMixin:
def test_resize_token_embeddings(self):
# TODO (joao): after the embeddings refactor is complete, rework this test so as to rely exclusively on
# tf.keras.layers.Embedding
# keras.layers.Embedding
if not self.test_resize_embeddings:
return
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
def _get_word_embedding_weight(model, embedding_layer):
if isinstance(embedding_layer, tf.keras.layers.Embedding):
if isinstance(embedding_layer, keras.layers.Embedding):
# builds the embeddings layer
model.build_in_name_scope()
return embedding_layer.embeddings
@@ -1456,7 +1457,7 @@ class TFModelTesterMixin:
]
for accuracy_class in accuracy_classes:
if model.__class__.__name__.endswith(accuracy_class):
metrics = [tf.keras.metrics.SparseCategoricalAccuracy()]
metrics = [keras.metrics.SparseCategoricalAccuracy()]
break
else:
metrics = []
@@ -1472,7 +1473,7 @@ class TFModelTesterMixin:
model_weights = model.get_weights()
# Run eagerly to save some expensive compilation times
model.compile(optimizer=tf.keras.optimizers.SGD(0.0), run_eagerly=True, metrics=metrics)
model.compile(optimizer=keras.optimizers.SGD(0.0), run_eagerly=True, metrics=metrics)
# Make sure the model fits without crashing regardless of where we pass the labels
history1 = model.fit(
prepared_for_class,
@@ -1557,7 +1558,7 @@ class TFModelTesterMixin:
# After testing that the model accepts all int inputs, confirm that its dummies are int32
for key, tensor in model.dummy_inputs.items():
self.assertTrue(
isinstance(tensor, tf.Tensor) or tf.keras.backend.is_keras_tensor(tensor),
isinstance(tensor, tf.Tensor) or keras.backend.is_keras_tensor(tensor),
"Dummy inputs should be tf.Tensor!",
)
if tensor.dtype.is_integer: