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More TF int dtype fixes (#20384)

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* Add a test to ensure int dummy inputs are int64

* Move the test into the existing int64 test and update a lot of existing dummies

* Fix remaining dummies

* Fix remaining dummies

* Test for int64 serving sigs as well

* Update core tests to use tf.int64

* Add better messages to the assertions

* Update all serving sigs to int64

* More sneaky hiding tf.int32s

* Add an optional int32 signature in save_pretrained

* make fixup

* Add Amy's suggestions

* Switch all serving sigs back to tf.int32

* Switch all dummies to tf.int32

* Adjust tests to check for tf.int32 instead of tf.int64

* Fix base dummy_inputs dtype

* Start casting to tf.int32 in input_processing

* Change dtype for unpack_inputs test

* Add proper tf.int32 test

* Make the alternate serving signature int64
This commit is contained in:
Matt
2022-11-28 13:24:44 +00:00
committed by GitHub
parent 72b19ca680
commit de4159a318
47 changed files with 215 additions and 167 deletions
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28
tests/test_modeling_tf_common.py
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@@ -1643,7 +1643,7 @@ class TFModelTesterMixin:
if metrics:
self.assertTrue(len(accuracy1) == len(accuracy3) > 0, "Missing metrics!")
def test_int64_inputs(self):
def test_int_support(self):
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
prepared_for_class = self._prepare_for_class(
@@ -1662,6 +1662,26 @@ class TFModelTesterMixin:
}
model = model_class(config)
model(**prepared_for_class) # No assertion, we're just checking this doesn't throw an error
int32_prepared_for_class = {
key: tf.cast(tensor, tf.int32) if isinstance(tensor, tf.Tensor) and tensor.dtype.is_integer else tensor
for key, tensor in prepared_for_class.items()
}
model(**int32_prepared_for_class) # No assertion, we're just checking this doesn't throw an error
# 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), "Dummy inputs should be tf.Tensor!")
if tensor.dtype.is_integer:
self.assertTrue(tensor.dtype == tf.int32, "Integer dummy inputs should be tf.int32!")
# Also confirm that the serving sig uses int32
if hasattr(model, "serving"):
serving_sig = model.serving.input_signature
for key, tensor_spec in serving_sig[0].items():
if tensor_spec.dtype.is_integer:
self.assertTrue(
tensor_spec.dtype == tf.int32, "Serving signatures should use tf.int32 for ints!"
)
def test_generate_with_headmasking(self):
attention_names = ["encoder_attentions", "decoder_attentions", "cross_attentions"]
@@ -2005,9 +2025,9 @@ class UtilsFunctionsTest(unittest.TestCase):
return pixel_values, output_attentions, output_hidden_states, return_dict
dummy_model = DummyModel()
input_ids = tf.constant([0, 1, 2, 3], dtype=tf.int64)
past_key_values = tf.constant([4, 5, 6, 7], dtype=tf.int64)
pixel_values = tf.constant([8, 9, 10, 11], dtype=tf.int64)
input_ids = tf.constant([0, 1, 2, 3], dtype=tf.int32)
past_key_values = tf.constant([4, 5, 6, 7], dtype=tf.int32)
pixel_values = tf.constant([8, 9, 10, 11], dtype=tf.int32)
# test case 1: Pass inputs as keyword arguments; Booleans are inherited from the config.
output = dummy_model.call(input_ids=input_ids, past_key_values=past_key_values)