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Add test for proper TF input signatures (#24320)

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* Add test for proper input signatures

* No more signature pruning

* Test the dummy inputs are valid too

* fine-tine -> fine-tune

* Fix indent in test_dataset_conversion
This commit is contained in:
Matt
2023-06-16 17:03:13 +01:00
committed by GitHub
parent bdfd57d1d1
commit 9138995025
6 changed files with 21 additions and 19 deletions
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12
tests/test_modeling_tf_common.py
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@@ -1065,6 +1065,16 @@ class TFModelTesterMixin:
output_for_kw_input = model(**inputs_np)
self.assert_outputs_same(output_for_dict_input, output_for_kw_input)
def test_valid_input_signature_and_dummies(self):
config, _ = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
model = model_class(config)
call_args = inspect.signature(model.call).parameters
for key in model.input_signature:
self.assertIn(key, call_args)
for key in model.dummy_inputs:
self.assertIn(key, call_args)
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
@@ -1700,7 +1710,7 @@ class TFModelTesterMixin:
for tensor in test_batch.values():
self.assertTrue(isinstance(tensor, tf.Tensor))
self.assertEqual(len(tensor), len(input_dataset)) # Assert we didn't lose any data
model(test_batch, training=False)
model(test_batch, training=False)
if "labels" in inspect.signature(model_class.call).parameters.keys():
tf_inputs_dict = self._prepare_for_class(inputs_dict, model_class, return_labels=True)

5
tests/utils/test_modeling_tf_core.py
View File

@@ -217,18 +217,17 @@ class TFCoreModelTesterMixin:
for model_class in self.all_model_classes:
class_inputs_dict = self._prepare_for_class(inputs_dict, model_class)
model = model_class(config)
class_sig = model._prune_signature(model.input_signature)
num_out = len(model(class_inputs_dict))
for key in list(class_inputs_dict.keys()):
# Remove keys not in the serving signature, as the SavedModel will not be compiled to deal with them
if key not in class_sig:
if key not in model.input_signature:
del class_inputs_dict[key]
# Check it's a tensor, in case the inputs dict has some bools in it too
elif isinstance(class_inputs_dict[key], tf.Tensor) and class_inputs_dict[key].dtype.is_integer:
class_inputs_dict[key] = tf.cast(class_inputs_dict[key], tf.int32)
if set(class_inputs_dict.keys()) != set(class_sig.keys()):
if set(class_inputs_dict.keys()) != set(model.input_signature.keys()):
continue # Some models have inputs that the preparation functions don't create, we skip those
with tempfile.TemporaryDirectory() as tmpdirname: