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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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21
tests/models/groupvit/test_modeling_tf_groupvit.py
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@@ -46,6 +46,7 @@ if is_tf_available():
import tensorflow as tf
from transformers import TFGroupViTModel, TFGroupViTTextModel, TFGroupViTVisionModel, TFSharedEmbeddings
from transformers.modeling_tf_utils import keras
from transformers.models.groupvit.modeling_tf_groupvit import TF_GROUPVIT_PRETRAINED_MODEL_ARCHIVE_LIST
@@ -186,9 +187,9 @@ class TFGroupViTVisionModelTest(TFModelTesterMixin, unittest.TestCase):
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))
x = model.get_output_embeddings()
self.assertTrue(x is None or isinstance(x, tf.keras.layers.Layer))
self.assertTrue(x is None or isinstance(x, keras.layers.Layer))
def test_forward_signature(self):
config, _ = self.model_tester.prepare_config_and_inputs_for_common()
@@ -340,7 +341,7 @@ class TFGroupViTVisionModelTest(TFModelTesterMixin, unittest.TestCase):
with tempfile.TemporaryDirectory() as tmpdirname:
model.save_pretrained(tmpdirname, saved_model=True)
saved_model_dir = os.path.join(tmpdirname, "saved_model", "1")
model = tf.keras.models.load_model(saved_model_dir)
model = keras.models.load_model(saved_model_dir)
outputs = model(class_inputs_dict)
output_hidden_states = outputs["hidden_states"]
output_attentions = outputs["attentions"]
@@ -505,7 +506,7 @@ class TFGroupViTTextModelTest(TFModelTesterMixin, unittest.TestCase):
with tempfile.TemporaryDirectory() as tmpdirname:
model.save_pretrained(tmpdirname, saved_model=True)
saved_model_dir = os.path.join(tmpdirname, "saved_model", "1")
model = tf.keras.models.load_model(saved_model_dir)
model = keras.models.load_model(saved_model_dir)
outputs = model(class_inputs_dict)
output_hidden_states = outputs["hidden_states"]
output_attentions = outputs["attentions"]
@@ -655,7 +656,7 @@ class TFGroupViTModelTest(TFModelTesterMixin, PipelineTesterMixin, unittest.Test
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:
@@ -669,17 +670,17 @@ class TFGroupViTModelTest(TFModelTesterMixin, PipelineTesterMixin, unittest.Test
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,
@@ -687,10 +688,10 @@ class TFGroupViTModelTest(TFModelTesterMixin, PipelineTesterMixin, unittest.Test
},
)
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)