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Update tiny model information and pipeline tests (#26285)

Browse Source 
* Update tiny model summary file

* add to pipeline tests

* revert

* fix import

* fix import

* fix

* fix

* update

* update

* update

* fix

* remove BarkModelTest

* fix

---------

Co-authored-by: ydshieh <ydshieh@users.noreply.github.com>
This commit is contained in:
Yih-Dar
2023-09-25 18:08:12 +02:00
committed by GitHub
parent 546e7679e7
commit d9e4bc2895
9 changed files with 174 additions and 30 deletions
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1
src/transformers/models/auto/modeling_tf_auto.py
View File

@@ -362,6 +362,7 @@ TF_MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING_NAMES = OrderedDict([("wav2vec2", "TFW
TF_MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING_NAMES = OrderedDict(
[
("layoutlm", "TFLayoutLMForQuestionAnswering"),
("layoutlmv3", "TFLayoutLMv3ForQuestionAnswering"),
]
)

23
tests/models/bark/test_modeling_bark.py
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@@ -493,13 +493,6 @@ class BarkModelTester:
self.is_training = is_training
def prepare_config_and_inputs(self):
# TODO: @Yoach: Preapre `inputs_dict`
inputs_dict = {}
config = self.get_config()
return config, inputs_dict
def get_config(self):
return BarkConfig.from_sub_model_configs(
self.semantic_model_tester.get_config(),
@@ -522,22 +515,6 @@ class BarkModelTester:
return config
def prepare_config_and_inputs_for_common(self):
# TODO: @Yoach
pass
# return config, inputs_dict
# Need this class in oder to create tiny model for `bark`
# TODO (@Yoach) Implement actual test methods
@unittest.skip("So far all tests will fail.")
class BarkModelTest(ModelTesterMixin, GenerationTesterMixin, unittest.TestCase):
all_model_classes = (BarkModel,) if is_torch_available() else ()
def setUp(self):
self.model_tester = BarkModelTester(self)
self.config_tester = ConfigTester(self, config_class=BarkConfig, n_embd=37)
@require_torch
class BarkSemanticModelTest(ModelTesterMixin, GenerationTesterMixin, unittest.TestCase):

6
tests/models/blip_2/test_modeling_blip_2.py
View File

@@ -666,7 +666,11 @@ class Blip2ModelTester:
class Blip2ModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase):
all_model_classes = (Blip2ForConditionalGeneration, Blip2Model) if is_torch_available() else ()
pipeline_model_mapping = (
{"feature-extraction": Blip2Model, "image-to-text": Blip2ForConditionalGeneration}
{
"feature-extraction": Blip2Model,
"image-to-text": Blip2ForConditionalGeneration,
"visual-question-answering": Blip2ForConditionalGeneration,
}
if is_torch_available()
else {}
)

15
tests/models/bros/test_modeling_bros.py
View File

@@ -22,6 +22,7 @@ from transformers.utils import is_torch_available
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
@@ -272,7 +273,7 @@ class BrosModelTester:
@require_torch
class BrosModelTest(ModelTesterMixin, unittest.TestCase):
class BrosModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase):
test_pruning = False
test_torchscript = False
test_mismatched_shapes = False
@@ -288,6 +289,18 @@ class BrosModelTest(ModelTesterMixin, unittest.TestCase):
else ()
)
all_generative_model_classes = () if is_torch_available() else ()
pipeline_model_mapping = (
{"feature-extraction": BrosModel, "token-classification": BrosForTokenClassification}
if is_torch_available()
else {}
)
# BROS requires `bbox` in the inputs which doesn't fit into the above 2 pipelines' input formats.
# see https://github.com/huggingface/transformers/pull/26294
def is_pipeline_test_to_skip(
self, pipeline_test_casse_name, config_class, model_architecture, tokenizer_name, processor_name
):
return True
def setUp(self):
self.model_tester = BrosModelTester(self)

2
tests/models/idefics/test_modeling_idefics.py
View File

@@ -260,7 +260,7 @@ class IdeficsModelTester:
@require_torch
class IdeficsModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase):
all_model_classes = (IdeficsModel, IdeficsForVisionText2Text) if is_torch_available() else ()
pipeline_model_mapping = {}
pipeline_model_mapping = {"feature-extraction": IdeficsModel} if is_torch_available() else {}
test_pruning = False
test_headmasking = False
test_torchscript = False

6
tests/models/pop2piano/test_modeling_pop2piano.py
View File

@@ -37,6 +37,7 @@ from transformers.utils import is_essentia_available, is_librosa_available, is_s
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
@@ -509,9 +510,12 @@ class Pop2PianoModelTester:
@require_torch
class Pop2PianoModelTest(ModelTesterMixin, GenerationTesterMixin, unittest.TestCase):
class Pop2PianoModelTest(ModelTesterMixin, GenerationTesterMixin, PipelineTesterMixin, unittest.TestCase):
all_model_classes = (Pop2PianoForConditionalGeneration,) if is_torch_available() else ()
all_generative_model_classes = ()
pipeline_model_mapping = (
{"automatic-speech-recognition": Pop2PianoForConditionalGeneration} if is_torch_available() else {}
)
all_parallelizable_model_classes = ()
fx_compatible = False
test_pruning = False

4
tests/models/vits/test_modeling_vits.py
View File

@@ -156,7 +156,9 @@ class VitsModelTester:
@require_torch
class VitsModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase):
all_model_classes = (VitsModel,) if is_torch_available() else ()
pipeline_model_mapping = {"text-to-audio": VitsModel} if is_torch_available() else {}
pipeline_model_mapping = (
{"feature-extraction": VitsModel, "text-to-audio": VitsModel} if is_torch_available() else {}
)
is_encoder_decoder = False
test_pruning = False
test_headmasking = False

4
tests/pipelines/test_pipelines_text_to_audio.py
View File

@@ -181,7 +181,9 @@ class TextToAudioPipelineTests(unittest.TestCase):
outputs = speech_generator("This is a test")
self.assertEqual(ANY(np.ndarray), outputs["audio"])
forward_params = {"num_return_sequences": 2, "do_sample": True}
forward_params = (
{"num_return_sequences": 2, "do_sample": True} if speech_generator.model.can_generate() else {}
)
outputs = speech_generator(["This is great !", "Something else"], forward_params=forward_params)
audio = [output["audio"] for output in outputs]
self.assertEqual([ANY(np.ndarray), ANY(np.ndarray)], audio)

143
tests/utils/tiny_model_summary.json
View File

@@ -128,6 +128,17 @@
],
"sha": "3106af0fd503970717c05f27218e5cacf19ba872"
},
"BarkModel": {
"tokenizer_classes": [
"BertTokenizer",
"BertTokenizerFast"
],
"processor_classes": [],
"model_classes": [
"BarkModel"
],
"sha": "187e590fd87359cea47693e8cb11a604cd7b673c"
},
"BartForCausalLM": {
"tokenizer_classes": [
"BartTokenizer",
@@ -708,6 +719,28 @@
],
"sha": "28b600fcfdc4f4938406fb518abf895620048cb2"
},
"BrosForTokenClassification": {
"tokenizer_classes": [
"BertTokenizer",
"BertTokenizerFast"
],
"processor_classes": [],
"model_classes": [
"BrosForTokenClassification"
],
"sha": "4ec2c91936f96b93667e8946fc7abbdeeb08a6d7"
},
"BrosModel": {
"tokenizer_classes": [
"BertTokenizer",
"BertTokenizerFast"
],
"processor_classes": [],
"model_classes": [
"BrosModel"
],
"sha": "e2464830b1874eeaf9f4b425fbe0ce8e7c7643e9"
},
"CLIPModel": {
"tokenizer_classes": [
"CLIPTokenizer",
@@ -1323,7 +1356,8 @@
],
"processor_classes": [],
"model_classes": [
"DebertaV2ForMultipleChoice"
"DebertaV2ForMultipleChoice",
"TFDebertaV2ForMultipleChoice"
],
"sha": "07e39f520ce239b39ef8cb24cd7874d06c791063"
},
@@ -1519,6 +1553,16 @@
],
"sha": "d6c75bc51196f0a683afb12de6310fdda13efefd"
},
"Dinov2Backbone": {
"tokenizer_classes": [],
"processor_classes": [
"BitImageProcessor"
],
"model_classes": [
"Dinov2Backbone"
],
"sha": "dbf8d2ff3092ac53c11e6525e6cbae7ace84769a"
},
"Dinov2ForImageClassification": {
"tokenizer_classes": [],
"processor_classes": [
@@ -2768,6 +2812,30 @@
],
"sha": "6749164c678d4883d455f98b1dfc98c62da8f08b"
},
"IdeficsForVisionText2Text": {
"tokenizer_classes": [
"LlamaTokenizerFast"
],
"processor_classes": [
"IdeficsImageProcessor"
],
"model_classes": [
"IdeficsForVisionText2Text"
],
"sha": "2c2f2e2cd6b02a77d0cdd8c3767ba9a6267dbd20"
},
"IdeficsModel": {
"tokenizer_classes": [
"LlamaTokenizerFast"
],
"processor_classes": [
"IdeficsImageProcessor"
],
"model_classes": [
"IdeficsModel"
],
"sha": "649df2e35e067efd573ff2d083784a5cf876545e"
},
"ImageGPTForCausalImageModeling": {
"tokenizer_classes": [],
"processor_classes": [
@@ -4077,6 +4145,24 @@
],
"sha": "315f34f30bcc4b0b66b11987726df2a80c50e271"
},
"MusicgenForCausalLM": {
"tokenizer_classes": [
"T5TokenizerFast"
],
"processor_classes": [],
"model_classes": [],
"sha": "37e9ae5dafb601daa8364e9ac17da31cd82b274b"
},
"MusicgenForConditionalGeneration": {
"tokenizer_classes": [
"T5TokenizerFast"
],
"processor_classes": [],
"model_classes": [
"MusicgenForConditionalGeneration"
],
"sha": "b71611b88832e53370e676da53b65042f7fc78ee"
},
"MvpForCausalLM": {
"tokenizer_classes": [
"MvpTokenizer",
@@ -4641,6 +4727,39 @@
],
"sha": "83ec4d2d61ed62525ee033e13d144817beb29d19"
},
"PersimmonForCausalLM": {
"tokenizer_classes": [
"LlamaTokenizer",
"LlamaTokenizerFast"
],
"processor_classes": [],
"model_classes": [
"PersimmonForCausalLM"
],
"sha": "454234d6496c3857f5bf3eafb784616e2cd3ea82"
},
"PersimmonForSequenceClassification": {
"tokenizer_classes": [
"LlamaTokenizer",
"LlamaTokenizerFast"
],
"processor_classes": [],
"model_classes": [
"PersimmonForSequenceClassification"
],
"sha": "1d2674846543a181ca67bafa8b8f3a48bd2eefd1"
},
"PersimmonModel": {
"tokenizer_classes": [
"LlamaTokenizer",
"LlamaTokenizerFast"
],
"processor_classes": [],
"model_classes": [
"PersimmonModel"
],
"sha": "b8c8d479e29e9ee048e2d0b05b001ac835ad8859"
},
"Pix2StructForConditionalGeneration": {
"tokenizer_classes": [
"T5TokenizerFast"
@@ -5432,6 +5551,18 @@
],
"sha": "d46f0a83324e5865420a27a738ef203292de3479"
},
"SpeechT5ForTextToSpeech": {
"tokenizer_classes": [
"SpeechT5Tokenizer"
],
"processor_classes": [
"SpeechT5FeatureExtractor"
],
"model_classes": [
"SpeechT5ForTextToSpeech"
],
"sha": "922e748d9e1ea256a8d9259782021cd3820d5924"
},
"SpeechT5Model": {
"tokenizer_classes": [
"SpeechT5Tokenizer"
@@ -6254,6 +6385,16 @@
],
"sha": "85020189fb7bf1217eb9370b09bca8ec5bcfdafa"
},
"VitsModel": {
"tokenizer_classes": [
"VitsTokenizer"
],
"processor_classes": [],
"model_classes": [
"VitsModel"
],
"sha": "b9a20ca5b6a7874576e485850260578895587dd2"
},
"Wav2Vec2ConformerForAudioFrameClassification": {
"tokenizer_classes": [
"Wav2Vec2CTCTokenizer"