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Update tiny models and a few fixes (#22928)

Browse source 
* run_check_tiny_models

* update summary

* update mixin

* update pipeline_model_mapping

* update pipeline_model_mapping

* Update for gpt_bigcode

---------

Co-authored-by: ydshieh <ydshieh@users.noreply.github.com>
This commit is contained in:
Yih-Dar 2023-04-24 14:45:22 +02:00 committed by GitHub
parent 2fbd6df81c
commit 975159bb61
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GPG key ID: 4AEE18F83AFDEB23
9 changed files with 116 additions and 16 deletions
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14
tests/models/gpt_bigcode/test_modeling_gpt_bigcode.py
View file

@ -407,7 +407,7 @@ class GPTBigCodeModelTester:
@require_torch
class GPTBigCodeMQAModelTest(ModelTesterMixin, GenerationTesterMixin, PipelineTesterMixin, unittest.TestCase):
class GPTBigCodeModelTest(ModelTesterMixin, GenerationTesterMixin, PipelineTesterMixin, unittest.TestCase):
# TODO: Update the tests to use valid pretrained models.
all_model_classes = (
(
@ -420,11 +420,6 @@ class GPTBigCodeMQAModelTest(ModelTesterMixin, GenerationTesterMixin, PipelineTe
else ()
)
all_generative_model_classes = (GPTBigCodeForCausalLM,) if is_torch_available() else ()
fx_compatible = False
test_missing_keys = False
test_pruning = False
test_torchscript = False
multi_query = True
pipeline_model_mapping = (
{
"feature-extraction": GPTBigCodeModel,
@ -436,6 +431,11 @@ class GPTBigCodeMQAModelTest(ModelTesterMixin, GenerationTesterMixin, PipelineTe
if is_torch_available()
else {}
)
fx_compatible = False
test_missing_keys = False
test_pruning = False
test_torchscript = False
multi_query = True
# special case for DoubleHeads model
def _prepare_for_class(self, inputs_dict, model_class, return_labels=False):
@ -521,7 +521,7 @@ class GPTBigCodeMQAModelTest(ModelTesterMixin, GenerationTesterMixin, PipelineTe
@require_torch
class GPTBigCodeMHAModelTest(GPTBigCodeMQAModelTest):
class GPTBigCodeMHAModelTest(GPTBigCodeModelTest):
# `parameterized_class` breaks with mixins, so we use inheritance instead
multi_query = False

12
tests/models/sam/test_modeling_sam.py
View file

@ -26,6 +26,7 @@ from transformers.utils import is_torch_available, is_vision_available
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
@ -282,19 +283,28 @@ class SamModelTester:
@require_torch
class SamModelTest(ModelTesterMixin, unittest.TestCase):
class SamModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase):
"""
Here we also overwrite some of the tests of test_modeling_common.py, as SAM's vision encoder does not use input_ids, inputs_embeds,
attention_mask and seq_length.
"""
all_model_classes = (SamModel,) if is_torch_available() else ()
pipeline_model_mapping = (
{"feature-extraction": SamModel, "mask-generation": SamModel} if is_torch_available() else {}
)
fx_compatible = False
test_pruning = False
test_resize_embeddings = False
test_head_masking = False
test_torchscript = False
# TODO: Fix me @Arthur: `run_batch_test` in `tests/test_pipeline_mixin.py` not working
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 = SamModelTester(self)
self.vision_config_tester = ConfigTester(self, config_class=SamVisionConfig, has_text_modality=False)

10
tests/pipelines/test_pipelines_mask_generation.py
View file

@ -32,6 +32,12 @@ from transformers.testing_utils import (
if is_vision_available():
from PIL import Image
else:
class Image:
@staticmethod
def open(*args, **kwargs):
pass
def hashimage(image: Image) -> str:
@ -60,6 +66,10 @@ class MaskGenerationPipelineTests(unittest.TestCase):
"./tests/fixtures/tests_samples/COCO/000000039769.png",
]
# TODO: Fix me @Arthur
def run_pipeline_test(self, mask_generator, examples):
pass
@require_tf
@unittest.skip("Image segmentation not implemented in TF")
def test_small_model_tf(self):

6
tests/pipelines/test_pipelines_question_answering.py
View file

@ -44,9 +44,11 @@ class QAPipelineTests(unittest.TestCase):
tf_model_mapping = TF_MODEL_FOR_QUESTION_ANSWERING_MAPPING
if model_mapping is not None:
model_mapping = {config: model for config, model in model_mapping.items() if config.__name__ in _TO_SKIP}
model_mapping = {config: model for config, model in model_mapping.items() if config.__name__ not in _TO_SKIP}
if tf_model_mapping is not None:
tf_model_mapping = {config: model for config, model in tf_model_mapping.items() if config.__name__ in _TO_SKIP}
tf_model_mapping = {
config: model for config, model in tf_model_mapping.items() if config.__name__ not in _TO_SKIP
}
def get_test_pipeline(self, model, tokenizer, processor):
if isinstance(model.config, LxmertConfig):

6
tests/pipelines/test_pipelines_text_classification.py
View file

@ -35,9 +35,11 @@ class TextClassificationPipelineTests(unittest.TestCase):
tf_model_mapping = TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING
if model_mapping is not None:
model_mapping = {config: model for config, model in model_mapping.items() if config.__name__ in _TO_SKIP}
model_mapping = {config: model for config, model in model_mapping.items() if config.__name__ not in _TO_SKIP}
if tf_model_mapping is not None:
tf_model_mapping = {config: model for config, model in tf_model_mapping.items() if config.__name__ in _TO_SKIP}
tf_model_mapping = {
config: model for config, model in tf_model_mapping.items() if config.__name__ not in _TO_SKIP
}
@require_torch
def test_small_model_pt(self):

6
tests/pipelines/test_pipelines_token_classification.py
View file

@ -49,9 +49,11 @@ class TokenClassificationPipelineTests(unittest.TestCase):
tf_model_mapping = TF_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING
if model_mapping is not None:
model_mapping = {config: model for config, model in model_mapping.items() if config.__name__ in _TO_SKIP}
model_mapping = {config: model for config, model in model_mapping.items() if config.__name__ not in _TO_SKIP}
if tf_model_mapping is not None:
tf_model_mapping = {config: model for config, model in tf_model_mapping.items() if config.__name__ in _TO_SKIP}
tf_model_mapping = {
config: model for config, model in tf_model_mapping.items() if config.__name__ not in _TO_SKIP
}
def get_test_pipeline(self, model, tokenizer, processor):
token_classifier = TokenClassificationPipeline(model=model, tokenizer=tokenizer)

6
tests/pipelines/test_pipelines_zero_shot.py
View file

@ -36,9 +36,11 @@ class ZeroShotClassificationPipelineTests(unittest.TestCase):
tf_model_mapping = TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING
if model_mapping is not None:
model_mapping = {config: model for config, model in model_mapping.items() if config.__name__ in _TO_SKIP}
model_mapping = {config: model for config, model in model_mapping.items() if config.__name__ not in _TO_SKIP}
if tf_model_mapping is not None:
tf_model_mapping = {config: model for config, model in tf_model_mapping.items() if config.__name__ in _TO_SKIP}
tf_model_mapping = {
config: model for config, model in tf_model_mapping.items() if config.__name__ not in _TO_SKIP
}
def get_test_pipeline(self, model, tokenizer, processor):
classifier = ZeroShotClassificationPipeline(

8
tests/test_pipeline_mixin.py
View file

@ -40,6 +40,7 @@ from .pipelines.test_pipelines_fill_mask import FillMaskPipelineTests
from .pipelines.test_pipelines_image_classification import ImageClassificationPipelineTests
from .pipelines.test_pipelines_image_segmentation import ImageSegmentationPipelineTests
from .pipelines.test_pipelines_image_to_text import ImageToTextPipelineTests
from .pipelines.test_pipelines_mask_generation import MaskGenerationPipelineTests
from .pipelines.test_pipelines_object_detection import ObjectDetectionPipelineTests
from .pipelines.test_pipelines_question_answering import QAPipelineTests
from .pipelines.test_pipelines_summarization import SummarizationPipelineTests
@ -68,6 +69,7 @@ pipeline_test_mapping = {
"image-classification": {"test": ImageClassificationPipelineTests},
"image-segmentation": {"test": ImageSegmentationPipelineTests},
"image-to-text": {"test": ImageToTextPipelineTests},
"mask-generation": {"test": MaskGenerationPipelineTests},
"object-detection": {"test": ObjectDetectionPipelineTests},
"question-answering": {"test": QAPipelineTests},
"summarization": {"test": SummarizationPipelineTests},
@ -355,6 +357,12 @@ class PipelineTesterMixin:
def test_pipeline_image_to_text(self):
self.run_task_tests(task="image-to-text")
@is_pipeline_test
@require_vision
@require_torch
def test_pipeline_mask_generation(self):
self.run_task_tests(task="mask-generation")
@is_pipeline_test
@require_vision
@require_timm

64
tests/utils/tiny_model_summary.json
View file

@ -2294,6 +2294,50 @@
],
"sha": "d6694b0d8fe17978761c9305dc151780506b192e"
},
"GPTBigCodeForCausalLM": {
"tokenizer_classes": [
"GPT2Tokenizer",
"GPT2TokenizerFast"
],
"processor_classes": [],
"model_classes": [
"GPTBigCodeForCausalLM"
],
"sha": "99f7aaadf9c29669c63ef6c16f6bc5c07dbb9126"
},
"GPTBigCodeForSequenceClassification": {
"tokenizer_classes": [
"GPT2Tokenizer",
"GPT2TokenizerFast"
],
"processor_classes": [],
"model_classes": [
"GPTBigCodeForSequenceClassification"
],
"sha": "64a7398d5763161037b818314c60dd83d93d03e9"
},
"GPTBigCodeForTokenClassification": {
"tokenizer_classes": [
"GPT2Tokenizer",
"GPT2TokenizerFast"
],
"processor_classes": [],
"model_classes": [
"GPTBigCodeForTokenClassification"
],
"sha": "310537ecd22d45f71bf594b17922cf2abc338eaf"
},
"GPTBigCodeModel": {
"tokenizer_classes": [
"GPT2Tokenizer",
"GPT2TokenizerFast"
],
"processor_classes": [],
"model_classes": [
"GPTBigCodeModel"
],
"sha": "3069419084a9dc36802d47de9df3d314ccfc2f28"
},
"GPTJForCausalLM": {
"tokenizer_classes": [
"GPT2Tokenizer",
@ -2385,6 +2429,16 @@
],
"sha": "0229cfaaa843c6b492ac2abffabb00f1ff1936f8"
},
"GPTNeoXForSequenceClassification": {
"tokenizer_classes": [
"GPTNeoXTokenizerFast"
],
"processor_classes": [],
"model_classes": [
"GPTNeoXForSequenceClassification"
],
"sha": "17c4b845ee2e0bb780ca2dea2d59a3d9d5d3c651"
},
"GPTNeoXJapaneseForCausalLM": {
"tokenizer_classes": [
"GPTNeoXJapaneseTokenizer"
@ -4940,6 +4994,16 @@
],
"sha": "0a0fbb844eeefa0dce62bd05db30a2bb91e5dc88"
},
"SamModel": {
"tokenizer_classes": [],
"processor_classes": [
"SamImageProcessor"
],
"model_classes": [
"SamModel"
],
"sha": "eca8651bc84e5ac3b1b62e784b744a6bd1b82575"
},
"SegformerForImageClassification": {
"tokenizer_classes": [],
"processor_classes": [