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Update tiny models for pipeline testing. (#24364)

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* fix

---------

Co-authored-by: ydshieh <ydshieh@users.noreply.github.com>
This commit is contained in:
Yih-Dar 2023-06-20 14:43:10 +02:00 committed by GitHub
parent 56efbf4301
commit c23d131eab
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GPG Key ID: 4AEE18F83AFDEB23
15 changed files with 110 additions and 29 deletions
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4
src/transformers/__init__.py
View File

@ -2965,11 +2965,13 @@ else:
)
_import_structure["models.auto"].extend(
[
"TF_MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING",
"TF_MODEL_FOR_CAUSAL_LM_MAPPING",
"TF_MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING",
"TF_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING",
"TF_MODEL_FOR_MASKED_IMAGE_MODELING_MAPPING",
"TF_MODEL_FOR_MASKED_LM_MAPPING",
"TF_MODEL_FOR_MASK_GENERATION_MAPPING",
"TF_MODEL_FOR_MULTIPLE_CHOICE_MAPPING",
"TF_MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING",
"TF_MODEL_FOR_PRETRAINING_MAPPING",
@ -6350,9 +6352,11 @@ if TYPE_CHECKING:
TFAlbertPreTrainedModel,
)
from .models.auto import (
TF_MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING,
TF_MODEL_FOR_CAUSAL_LM_MAPPING,
TF_MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING,
TF_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING,
TF_MODEL_FOR_MASK_GENERATION_MAPPING,
TF_MODEL_FOR_MASKED_IMAGE_MODELING_MAPPING,
TF_MODEL_FOR_MASKED_LM_MAPPING,
TF_MODEL_FOR_MULTIPLE_CHOICE_MAPPING,

4
src/transformers/models/auto/__init__.py
View File

@ -114,8 +114,10 @@ except OptionalDependencyNotAvailable:
pass
else:
_import_structure["modeling_tf_auto"] = [
"TF_MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING",
"TF_MODEL_FOR_CAUSAL_LM_MAPPING",
"TF_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING",
"TF_MODEL_FOR_MASK_GENERATION_MAPPING",
"TF_MODEL_FOR_MASKED_IMAGE_MODELING_MAPPING",
"TF_MODEL_FOR_MASKED_LM_MAPPING",
"TF_MODEL_FOR_MULTIPLE_CHOICE_MAPPING",
@ -279,9 +281,11 @@ if TYPE_CHECKING:
pass
else:
from .modeling_tf_auto import (
TF_MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING,
TF_MODEL_FOR_CAUSAL_LM_MAPPING,
TF_MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING,
TF_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING,
TF_MODEL_FOR_MASK_GENERATION_MAPPING,
TF_MODEL_FOR_MASKED_IMAGE_MODELING_MAPPING,
TF_MODEL_FOR_MASKED_LM_MAPPING,
TF_MODEL_FOR_MULTIPLE_CHOICE_MAPPING,

6
src/transformers/utils/dummy_tf_objects.py
View File

@ -216,6 +216,9 @@ class TFAlbertPreTrainedModel(metaclass=DummyObject):
requires_backends(self, ["tf"])
TF_MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING = None
TF_MODEL_FOR_CAUSAL_LM_MAPPING = None
@ -225,6 +228,9 @@ TF_MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING = None
TF_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING = None
TF_MODEL_FOR_MASK_GENERATION_MAPPING = None
TF_MODEL_FOR_MASKED_IMAGE_MODELING_MAPPING = None

4
tests/models/autoformer/test_modeling_autoformer.py
View File

@ -25,6 +25,7 @@ from transformers.testing_utils import require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
TOLERANCE = 1e-4
@ -201,9 +202,10 @@ class AutoformerModelTester:
@require_torch
class AutoformerModelTest(ModelTesterMixin, unittest.TestCase):
class AutoformerModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase):
all_model_classes = (AutoformerModel, AutoformerForPrediction) if is_torch_available() else ()
all_generative_model_classes = (AutoformerForPrediction,) if is_torch_available() else ()
pipeline_model_mapping = {"feature-extraction": AutoformerModel} if is_torch_available() else {}
test_pruning = False
test_head_masking = False
test_missing_keys = False

2
tests/models/encodec/test_modeling_encodec.py
View File

@ -117,7 +117,7 @@ class EncodecModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase)
test_pruning = False
test_headmasking = False
test_resize_embeddings = False
pipeline_model_mapping = {}
pipeline_model_mapping = {"feature-extraction": EncodecModel} if is_torch_available() else {}
input_name = "input_values"
def _prepare_for_class(self, inputs_dict, model_class, return_labels=False):

13
tests/models/git/test_modeling_git.py
View File

@ -383,11 +383,22 @@ class GitModelTest(ModelTesterMixin, GenerationTesterMixin, PipelineTesterMixin,
all_model_classes = (GitModel, GitForCausalLM) if is_torch_available() else ()
all_generative_model_classes = (GitForCausalLM,) if is_torch_available() else ()
pipeline_model_mapping = (
{"feature-extraction": GitModel, "text-generation": GitForCausalLM} if is_torch_available() else {}
{"feature-extraction": GitModel, "image-to-text": GitForCausalLM, "text-generation": GitForCausalLM}
if is_torch_available()
else {}
)
fx_compatible = False
test_torchscript = False
# `GitForCausalLM` doesn't fit into image-to-text pipeline. We might need to overwrite its `generate` function.
def is_pipeline_test_to_skip(
self, pipeline_test_casse_name, config_class, model_architecture, tokenizer_name, processor_name
):
if pipeline_test_casse_name == "ImageToTextPipelineTests":
return True
return False
# special case for GitForCausalLM model
def _prepare_for_class(self, inputs_dict, model_class, return_labels=False):
inputs_dict = super()._prepare_for_class(inputs_dict, model_class, return_labels=return_labels)

5
tests/models/layoutlmv2/test_modeling_layoutlmv2.py
View File

@ -270,10 +270,7 @@ class LayoutLMv2ModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCa
else ()
)
pipeline_model_mapping = (
{
"document-question-answering": LayoutLMv2ForQuestionAnswering,
"feature-extraction": LayoutLMv2Model,
}
{"document-question-answering": LayoutLMv2ForQuestionAnswering, "feature-extraction": LayoutLMv2Model}
if is_torch_available()
else {}
)

5
tests/models/layoutlmv3/test_modeling_layoutlmv3.py
View File

@ -286,10 +286,7 @@ class LayoutLMv3ModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCa
else ()
)
pipeline_model_mapping = (
{
"document-question-answering": LayoutLMv3ForQuestionAnswering,
"feature-extraction": LayoutLMv3Model,
}
{"document-question-answering": LayoutLMv3ForQuestionAnswering, "feature-extraction": LayoutLMv3Model}
if is_torch_available()
else {}
)

8
tests/models/layoutlmv3/test_modeling_tf_layoutlmv3.py
View File

@ -278,13 +278,7 @@ class TFLayoutLMv3ModelTest(TFModelTesterMixin, PipelineTesterMixin, unittest.Te
else ()
)
pipeline_model_mapping = (
{
"feature-extraction": TFLayoutLMv3Model,
"question-answering": TFLayoutLMv3ForQuestionAnswering,
"text-classification": TFLayoutLMv3ForSequenceClassification,
"token-classification": TFLayoutLMv3ForTokenClassification,
"zero-shot": TFLayoutLMv3ForSequenceClassification,
}
{"document-question-answering": TFLayoutLMv3ForQuestionAnswering, "feature-extraction": TFLayoutLMv3Model}
if is_tf_available()
else {}
)

5
tests/models/timm_backbone/test_modeling_timm_backbone.py
View File

@ -32,6 +32,8 @@ if is_torch_available():
from transformers import TimmBackbone, TimmBackboneConfig
from ...test_pipeline_mixin import PipelineTesterMixin
class TimmBackboneModelTester:
def __init__(
@ -95,8 +97,9 @@ class TimmBackboneModelTester:
@require_torch
@require_timm
class TimmBackboneModelTest(ModelTesterMixin, BackboneTesterMixin, unittest.TestCase):
class TimmBackboneModelTest(ModelTesterMixin, BackboneTesterMixin, PipelineTesterMixin, unittest.TestCase):
all_model_classes = (TimmBackbone,) if is_torch_available() else ()
pipeline_model_mapping = {"feature-extraction": TimmBackbone} if is_torch_available() else {}
test_resize_embeddings = False
test_head_masking = False
test_pruning = False

2
tests/models/wav2vec2/test_modeling_tf_wav2vec2.py
View File

@ -322,7 +322,7 @@ class TFWav2Vec2ModelTest(TFModelTesterMixin, PipelineTesterMixin, unittest.Test
(TFWav2Vec2Model, TFWav2Vec2ForCTC, TFWav2Vec2ForSequenceClassification) if is_tf_available() else ()
)
pipeline_model_mapping = (
{"feature-extraction": TFWav2Vec2Model, "audio-classification": TFWav2Vec2ForSequenceClassification}
{"audio-classification": TFWav2Vec2ForSequenceClassification, "feature-extraction": TFWav2Vec2Model}
if is_tf_available()
else {}
)

4
tests/pipelines/test_pipelines_audio_classification.py
View File

@ -16,7 +16,7 @@ import unittest
import numpy as np
from transformers import MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING
from transformers import MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING, TF_MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING
from transformers.pipelines import AudioClassificationPipeline, pipeline
from transformers.testing_utils import (
is_pipeline_test,
@ -31,9 +31,9 @@ from .test_pipelines_common import ANY
@is_pipeline_test
@require_torch
class AudioClassificationPipelineTests(unittest.TestCase):
model_mapping = MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING
tf_model_mapping = TF_MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING
def get_test_pipeline(self, model, tokenizer, processor):
audio_classifier = AudioClassificationPipeline(model=model, feature_extractor=processor)

12
tests/pipelines/test_pipelines_mask_generation.py
View File

@ -18,7 +18,12 @@ from typing import Dict
import numpy as np
from transformers import MODEL_FOR_MASK_GENERATION_MAPPING, is_vision_available, pipeline
from transformers import (
MODEL_FOR_MASK_GENERATION_MAPPING,
TF_MODEL_FOR_MASK_GENERATION_MAPPING,
is_vision_available,
pipeline,
)
from transformers.pipelines import MaskGenerationPipeline
from transformers.testing_utils import (
is_pipeline_test,
@ -58,6 +63,9 @@ class MaskGenerationPipelineTests(unittest.TestCase):
model_mapping = dict(
(list(MODEL_FOR_MASK_GENERATION_MAPPING.items()) if MODEL_FOR_MASK_GENERATION_MAPPING else [])
)
tf_model_mapping = dict(
(list(TF_MODEL_FOR_MASK_GENERATION_MAPPING.items()) if TF_MODEL_FOR_MASK_GENERATION_MAPPING else [])
)
def get_test_pipeline(self, model, tokenizer, processor):
image_segmenter = MaskGenerationPipeline(model=model, image_processor=processor)
@ -66,7 +74,7 @@ class MaskGenerationPipelineTests(unittest.TestCase):
"./tests/fixtures/tests_samples/COCO/000000039769.png",
]
# TODO: Fix me @Arthur
# TODO: Implement me @Arthur
def run_pipeline_test(self, mask_generator, examples):
pass

3
tests/test_pipeline_mixin.py
View File

@ -17,6 +17,7 @@ import copy
import json
import os
import random
import unittest
from pathlib import Path
from transformers.testing_utils import (
@ -314,7 +315,6 @@ class PipelineTesterMixin:
run_batch_test(pipeline, examples)
@is_pipeline_test
@require_torch
def test_pipeline_audio_classification(self):
self.run_task_tests(task="audio-classification")
@ -366,6 +366,7 @@ class PipelineTesterMixin:
def test_pipeline_image_to_text(self):
self.run_task_tests(task="image-to-text")
@unittest.skip(reason="`run_pipeline_test` is currently not implemented.")
@is_pipeline_test
@require_vision
@require_torch

62
tests/utils/tiny_model_summary.json
View File

@ -1597,7 +1597,8 @@
"EfficientFormerImageProcessor"
],
"model_classes": [
"EfficientFormerForImageClassification"
"EfficientFormerForImageClassification",
"TFEfficientFormerForImageClassification"
],
"sha": "ebadb628e12f268e321fcc756fa4606f7b5b3178"
},
@ -1607,7 +1608,8 @@
"EfficientFormerImageProcessor"
],
"model_classes": [
"EfficientFormerForImageClassificationWithTeacher"
"EfficientFormerForImageClassificationWithTeacher",
"TFEfficientFormerForImageClassificationWithTeacher"
],
"sha": "1beabce6da9cb4ebbeafcd1ef23fac36b4a269e2"
},
@ -1617,7 +1619,8 @@
"EfficientFormerImageProcessor"
],
"model_classes": [
"EfficientFormerModel"
"EfficientFormerModel",
"TFEfficientFormerModel"
],
"sha": "200fae5b875844d09c8a91d1c155b72b06a517f6"
},
@ -1736,6 +1739,16 @@
],
"sha": "312b532cbef26610d80f2bd008650160cae4f7a1"
},
"EncodecModel": {
"tokenizer_classes": [],
"processor_classes": [
"EncodecFeatureExtractor"
],
"model_classes": [
"EncodecModel"
],
"sha": "e14c5a2fd6529c85cd4ac5a05ee9e550ced6a006"
},
"EncoderDecoderModel": {
"tokenizer_classes": [
"BertTokenizer",
@ -3888,6 +3901,36 @@
],
"sha": "b3a1452e7cb44b600b21ee14f3d5382366855a46"
},
"MobileViTV2ForImageClassification": {
"tokenizer_classes": [],
"processor_classes": [
"MobileViTImageProcessor"
],
"model_classes": [
"MobileViTV2ForImageClassification"
],
"sha": "25752b0967ad594341d1b685401450d7f698433c"
},
"MobileViTV2ForSemanticSegmentation": {
"tokenizer_classes": [],
"processor_classes": [
"MobileViTImageProcessor"
],
"model_classes": [
"MobileViTV2ForSemanticSegmentation"
],
"sha": "13b953f50be33219d55a12f1098be38b88000897"
},
"MobileViTV2Model": {
"tokenizer_classes": [],
"processor_classes": [
"MobileViTImageProcessor"
],
"model_classes": [
"MobileViTV2Model"
],
"sha": "2f46357659db2d6d54d870e28073deeea1c8cb64"
},
"MvpForCausalLM": {
"tokenizer_classes": [
"MvpTokenizer",
@ -4452,6 +4495,16 @@
],
"sha": "83ec4d2d61ed62525ee033e13d144817beb29d19"
},
"Pix2StructForConditionalGeneration": {
"tokenizer_classes": [
"T5TokenizerFast"
],
"processor_classes": [
"Pix2StructImageProcessor"
],
"model_classes": [],
"sha": "42b3de00ad535076c4893e4ac5ae2d2748cc4ccb"
},
"PoolFormerForImageClassification": {
"tokenizer_classes": [],
"processor_classes": [
@ -5123,7 +5176,8 @@
"SamImageProcessor"
],
"model_classes": [
"SamModel"
"SamModel",
"TFSamModel"
],
"sha": "eca8651bc84e5ac3b1b62e784b744a6bd1b82575"
},