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Big TF test cleanup (#24282)

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* Fix one BLIP arg not being optional, remove misspelled arg

* Remove the lxmert test overrides and just use the base test_saved_model_creation

* saved_model_creation fixes and re-enabling tests across the board

* Remove unnecessary skip

* Stop caching sinusoidal embeddings in speech_to_text

* Fix transfo_xl compilation

* Fix transfo_xl compilation

* Fix the conditionals in xglm

* Set the save spec only when building

* Clarify comment

* Move comment correctly

* Correct embeddings generation for speech2text

* Mark RAG generation tests as @slow

* Remove redundant else:

* Add comment to clarify the save_spec line in build()

* Fix size tests for XGLM at last!

* make fixup

* Remove one band_part operation

* Mark test_keras_fit as @slow
This commit is contained in:
Matt
2023-06-16 15:40:49 +01:00
committed by GitHub
parent 896a58de15
commit 3403712958
31 changed files with 68 additions and 217 deletions
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70
tests/models/lxmert/test_modeling_tf_lxmert.py
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@@ -15,14 +15,13 @@
from __future__ import annotations
import os
import tempfile
import unittest
import numpy as np
from transformers import LxmertConfig, is_tf_available
from transformers.testing_utils import require_tf, slow, tooslow
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
@@ -532,73 +531,6 @@ class TFLxmertModelTest(TFModelTesterMixin, PipelineTesterMixin, unittest.TestCa
self.assert_outputs_same(after_outputs, outputs)
@tooslow
def test_saved_model_creation(self):
pass
@slow
def test_saved_model_creation_extended(self):
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
config.output_hidden_states = True
config.output_attentions = True
if hasattr(config, "use_cache"):
config.use_cache = True
encoder_seq_length = getattr(self.model_tester, "encoder_seq_length", self.model_tester.seq_length)
encoder_key_length = getattr(self.model_tester, "key_length", encoder_seq_length)
for model_class in self.all_model_classes:
class_inputs_dict = self._prepare_for_class(inputs_dict, model_class)
model = model_class(config)
num_out = len(model(class_inputs_dict))
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)
outputs = model(class_inputs_dict)
language_hidden_states = outputs["language_hidden_states"]
vision_hidden_states = outputs["vision_hidden_states"]
language_attentions = outputs["language_attentions"]
vision_attentions = outputs["vision_attentions"]
cross_encoder_attentions = outputs["cross_encoder_attentions"]
self.assertEqual(len(outputs), num_out)
self.assertEqual(len(language_hidden_states), self.model_tester.num_hidden_layers["language"] + 1)
self.assertEqual(len(vision_hidden_states), self.model_tester.num_hidden_layers["vision"] + 1)
seq_length = self.model_tester.seq_length
num_visual_features = self.model_tester.num_visual_features
self.assertListEqual(
list(language_hidden_states[0].shape[-2:]),
[seq_length, self.model_tester.hidden_size],
)
self.assertListEqual(
list(vision_hidden_states[0].shape[-2:]),
[num_visual_features, self.model_tester.hidden_size],
)
self.assertEqual(len(language_attentions), self.model_tester.num_hidden_layers["language"])
self.assertEqual(len(vision_attentions), self.model_tester.num_hidden_layers["vision"])
self.assertEqual(len(cross_encoder_attentions), self.model_tester.num_hidden_layers["cross_encoder"])
attentions = [language_attentions, vision_attentions, cross_encoder_attentions]
attention_shapes = [
[self.model_tester.num_attention_heads, encoder_seq_length, encoder_key_length],
[
self.model_tester.num_attention_heads,
self.model_tester.num_visual_features,
self.model_tester.num_visual_features,
],
[self.model_tester.num_attention_heads, encoder_key_length, self.model_tester.num_visual_features],
]
for attention, attention_shape in zip(attentions, attention_shapes):
self.assertListEqual(list(attention[0].shape[-3:]), attention_shape)
@require_tf
class TFLxmertModelIntegrationTest(unittest.TestCase):