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Replace build() with build_in_name_scope() for some TF tests (#28046)

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Replace build() with build_in_name_scope() for some tests
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Matt 2023-12-14 17:42:25 +00:00 committed by GitHub
parent 050e0b44f6
commit 3060899be5
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4 changed files with 10 additions and 10 deletions
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2
tests/models/bart/test_modeling_tf_bart.py
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@ -304,7 +304,7 @@ class TFBartModelTest(TFModelTesterMixin, TFCoreModelTesterMixin, PipelineTester
old_total_size = config.vocab_size old_total_size = config.vocab_size
new_total_size = old_total_size + new_tokens_size new_total_size = old_total_size + new_tokens_size
model = model_class(config=copy.deepcopy(config)) # `resize_token_embeddings` mutates `config` model = model_class(config=copy.deepcopy(config)) # `resize_token_embeddings` mutates `config`
model.build() model.build_in_name_scope()
model.resize_token_embeddings(new_total_size) model.resize_token_embeddings(new_total_size)
# fetch the output for an input exclusively made of new members of the vocabulary # fetch the output for an input exclusively made of new members of the vocabulary

2
tests/models/ctrl/test_modeling_tf_ctrl.py
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@ -225,7 +225,7 @@ class TFCTRLModelTest(TFModelTesterMixin, PipelineTesterMixin, unittest.TestCase
for model_class in self.all_model_classes: for model_class in self.all_model_classes:
model = model_class(config) model = model_class(config)
model.build() # may be needed for the get_bias() call below model.build_in_name_scope() # may be needed for the get_bias() call below
assert isinstance(model.get_input_embeddings(), tf.keras.layers.Layer) assert isinstance(model.get_input_embeddings(), tf.keras.layers.Layer)
if model_class in list_lm_models: if model_class in list_lm_models:

8
tests/test_modeling_tf_common.py
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@ -316,7 +316,7 @@ class TFModelTesterMixin:
with tf.Graph().as_default() as g: with tf.Graph().as_default() as g:
model = model_class(config) model = model_class(config)
model.build() model.build_in_name_scope()
for op in g.get_operations(): for op in g.get_operations():
model_op_names.add(op.node_def.op) model_op_names.add(op.node_def.op)
@ -346,7 +346,7 @@ class TFModelTesterMixin:
for model_class in self.all_model_classes[:2]: for model_class in self.all_model_classes[:2]:
model = model_class(config) model = model_class(config)
model.build() model.build_in_name_scope()
onnx_model_proto, _ = tf2onnx.convert.from_keras(model, opset=self.onnx_min_opset) onnx_model_proto, _ = tf2onnx.convert.from_keras(model, opset=self.onnx_min_opset)
@ -1088,7 +1088,7 @@ class TFModelTesterMixin:
def _get_word_embedding_weight(model, embedding_layer): def _get_word_embedding_weight(model, embedding_layer):
if isinstance(embedding_layer, tf.keras.layers.Embedding): if isinstance(embedding_layer, tf.keras.layers.Embedding):
# builds the embeddings layer # builds the embeddings layer
model.build() model.build_in_name_scope()
return embedding_layer.embeddings return embedding_layer.embeddings
else: else:
return model._get_word_embedding_weight(embedding_layer) return model._get_word_embedding_weight(embedding_layer)
@ -1151,7 +1151,7 @@ class TFModelTesterMixin:
old_total_size = config.vocab_size old_total_size = config.vocab_size
new_total_size = old_total_size + new_tokens_size new_total_size = old_total_size + new_tokens_size
model = model_class(config=copy.deepcopy(config)) # `resize_token_embeddings` mutates `config` model = model_class(config=copy.deepcopy(config)) # `resize_token_embeddings` mutates `config`
model.build() model.build_in_name_scope()
model.resize_token_embeddings(new_total_size) model.resize_token_embeddings(new_total_size)
# fetch the output for an input exclusively made of new members of the vocabulary # fetch the output for an input exclusively made of new members of the vocabulary

8
tests/test_modeling_tf_utils.py
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@ -402,8 +402,8 @@ class TFModelUtilsTest(unittest.TestCase):
# Finally, check the model can be reloaded # Finally, check the model can be reloaded
new_model = TFBertModel.from_pretrained(tmp_dir) new_model = TFBertModel.from_pretrained(tmp_dir)
model.build() model.build_in_name_scope()
new_model.build() new_model.build_in_name_scope()
for p1, p2 in zip(model.weights, new_model.weights): for p1, p2 in zip(model.weights, new_model.weights):
self.assertTrue(np.allclose(p1.numpy(), p2.numpy())) self.assertTrue(np.allclose(p1.numpy(), p2.numpy()))
@ -632,7 +632,7 @@ class TFModelPushToHubTester(unittest.TestCase):
) )
model = TFBertModel(config) model = TFBertModel(config)
# Make sure model is properly initialized # Make sure model is properly initialized
model.build() model.build_in_name_scope()
logging.set_verbosity_info() logging.set_verbosity_info()
logger = logging.get_logger("transformers.utils.hub") logger = logging.get_logger("transformers.utils.hub")
@ -701,7 +701,7 @@ class TFModelPushToHubTester(unittest.TestCase):
) )
model = TFBertModel(config) model = TFBertModel(config)
# Make sure model is properly initialized # Make sure model is properly initialized
model.build() model.build_in_name_scope()
model.push_to_hub("valid_org/test-model-tf-org", token=self._token) model.push_to_hub("valid_org/test-model-tf-org", token=self._token)