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🔥Rework pipeline testing by removing PipelineTestCaseMeta 🚀 (#21516)

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* Add PipelineTesterMixin

* remove class PipelineTestCaseMeta

* move validate_test_components

* Add for ViT

* Add to SPECIAL_MODULE_TO_TEST_MAP

* style and quality

* Add feature-extraction

* update

* raise instead of skip

* add tiny_model_summary.json

* more explicit

* skip tasks not in mapping

* add availability check

* Add Copyright

* A way to diable irrelevant tests

* update with main

* remove disable_irrelevant_tests

* skip tests

* better skip message

* better skip message

* Add all pipeline task tests

* revert

* Import PipelineTesterMixin

* subclass test classes with PipelineTesterMixin

* Add pipieline_model_mapping

* Fix import after adding pipieline_model_mapping

* Fix style and quality after adding pipieline_model_mapping

* Fix one more import after adding pipieline_model_mapping

* Fix style and quality after adding pipieline_model_mapping

* Fix test issues

* Fix import requirements

* Fix mapping for MobileViTModelTest

* Update

* Better skip message

* pipieline_model_mapping could not be None

* Remove some PipelineTesterMixin

* Fix typo

* revert tests_fetcher.py

* update

* rename

* revert

* Remove PipelineTestCaseMeta from ZeroShotAudioClassificationPipelineTests

* style and quality

* test fetcher for all pipeline/model tests

---------

Co-authored-by: ydshieh <ydshieh@users.noreply.github.com>
This commit is contained in:
Yih-Dar
2023-02-28 19:40:57 +01:00
committed by GitHub
parent 4cb5ffa93d
commit 871c31a6f1
243 changed files with 5871 additions and 523 deletions
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16
tests/models/xlm/test_modeling_tf_xlm.py
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@@ -21,6 +21,7 @@ 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
from ...test_pipeline_mixin import PipelineTesterMixin
if is_tf_available():
@@ -276,7 +277,7 @@ class TFXLMModelTester:
@require_tf
class TFXLMModelTest(TFModelTesterMixin, unittest.TestCase):
class TFXLMModelTest(TFModelTesterMixin, PipelineTesterMixin, unittest.TestCase):
all_model_classes = (
(
TFXLMModel,
@@ -292,6 +293,19 @@ class TFXLMModelTest(TFModelTesterMixin, unittest.TestCase):
all_generative_model_classes = (
(TFXLMWithLMHeadModel,) if is_tf_available() else ()
) # TODO (PVP): Check other models whether language generation is also applicable
pipeline_model_mapping = (
{
"feature-extraction": TFXLMModel,
"fill-mask": TFXLMWithLMHeadModel,
"question-answering": TFXLMForQuestionAnsweringSimple,
"text-classification": TFXLMForSequenceClassification,
"text-generation": TFXLMWithLMHeadModel,
"token-classification": TFXLMForTokenClassification,
"zero-shot": TFXLMForSequenceClassification,
}
if is_tf_available()
else {}
)
test_head_masking = False
test_onnx = False

16
tests/models/xlm/test_modeling_xlm.py
View File

@@ -21,6 +21,7 @@ from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
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():
@@ -359,7 +360,7 @@ class XLMModelTester:
@require_torch
class XLMModelTest(ModelTesterMixin, GenerationTesterMixin, unittest.TestCase):
class XLMModelTest(ModelTesterMixin, GenerationTesterMixin, PipelineTesterMixin, unittest.TestCase):
all_model_classes = (
(
XLMModel,
@@ -376,6 +377,19 @@ class XLMModelTest(ModelTesterMixin, GenerationTesterMixin, unittest.TestCase):
all_generative_model_classes = (
(XLMWithLMHeadModel,) if is_torch_available() else ()
) # TODO (PVP): Check other models whether language generation is also applicable
pipeline_model_mapping = (
{
"feature-extraction": XLMModel,
"fill-mask": XLMWithLMHeadModel,
"question-answering": XLMForQuestionAnsweringSimple,
"text-classification": XLMForSequenceClassification,
"text-generation": XLMWithLMHeadModel,
"token-classification": XLMForTokenClassification,
"zero-shot": XLMForSequenceClassification,
}
if is_torch_available()
else {}
)
# XLM has 2 QA models -> need to manually set the correct labels for one of them here
def _prepare_for_class(self, inputs_dict, model_class, return_labels=False):