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

* 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>

tests/models/electra/test_modeling_electra.py

@@ -22,6 +22,7 @@ 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, random_attention_mask
+from ...test_pipeline_mixin import PipelineTesterMixin
 
 
 if is_torch_available():
@@ -374,7 +375,7 @@ class ElectraModelTester:
 
 
 @require_torch
-class ElectraModelTest(ModelTesterMixin, unittest.TestCase):
+class ElectraModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase):
     all_model_classes = (
         (
             ElectraModel,
@@ -389,6 +390,19 @@ class ElectraModelTest(ModelTesterMixin, unittest.TestCase):
         if is_torch_available()
         else ()
     )
+    pipeline_model_mapping = (
+        {
+            "feature-extraction": ElectraModel,
+            "fill-mask": ElectraForMaskedLM,
+            "question-answering": ElectraForQuestionAnswering,
+            "text-classification": ElectraForSequenceClassification,
+            "text-generation": ElectraForCausalLM,
+            "token-classification": ElectraForTokenClassification,
+            "zero-shot": ElectraForSequenceClassification,
+        }
+        if is_torch_available()
+        else {}
+    )
     fx_compatible = True
 
     # special case for ForPreTraining model

tests/models/electra/test_modeling_tf_electra.py

@@ -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, floats_tensor, ids_tensor, random_attention_mask
+from ...test_pipeline_mixin import PipelineTesterMixin
 
 
 if is_tf_available():
@@ -487,7 +488,7 @@ class TFElectraModelTester:
 
 
 @require_tf
-class TFElectraModelTest(TFModelTesterMixin, unittest.TestCase):
+class TFElectraModelTest(TFModelTesterMixin, PipelineTesterMixin, unittest.TestCase):
     all_model_classes = (
         (
             TFElectraModel,
@@ -501,6 +502,18 @@ class TFElectraModelTest(TFModelTesterMixin, unittest.TestCase):
         if is_tf_available()
         else ()
     )
+    pipeline_model_mapping = (
+        {
+            "feature-extraction": TFElectraModel,
+            "fill-mask": TFElectraForMaskedLM,
+            "question-answering": TFElectraForQuestionAnswering,
+            "text-classification": TFElectraForSequenceClassification,
+            "token-classification": TFElectraForTokenClassification,
+            "zero-shot": TFElectraForSequenceClassification,
+        }
+        if is_tf_available()
+        else {}
+    )
     test_head_masking = False
     test_onnx = False
 

tests/models/ernie/test_modeling_ernie.py

@@ -23,6 +23,7 @@ from transformers.testing_utils import require_torch, require_torch_gpu, slow, t
 from ...generation.test_utils import GenerationTesterMixin
 from ...test_configuration_common import ConfigTester
 from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
+from ...test_pipeline_mixin import PipelineTesterMixin
 
 
 if is_torch_available():
@@ -426,7 +427,7 @@ class ErnieModelTester:
 
 
 @require_torch
-class ErnieModelTest(ModelTesterMixin, GenerationTesterMixin, unittest.TestCase):
+class ErnieModelTest(ModelTesterMixin, GenerationTesterMixin, PipelineTesterMixin, unittest.TestCase):
     all_model_classes = (
         (
             ErnieModel,
@@ -443,6 +444,19 @@ class ErnieModelTest(ModelTesterMixin, GenerationTesterMixin, unittest.TestCase)
         else ()
     )
     all_generative_model_classes = (ErnieForCausalLM,) if is_torch_available() else ()
+    pipeline_model_mapping = (
+        {
+            "feature-extraction": ErnieModel,
+            "fill-mask": ErnieForMaskedLM,
+            "question-answering": ErnieForQuestionAnswering,
+            "text-classification": ErnieForSequenceClassification,
+            "text-generation": ErnieForCausalLM,
+            "token-classification": ErnieForTokenClassification,
+            "zero-shot": ErnieForSequenceClassification,
+        }
+        if is_torch_available()
+        else {}
+    )
     fx_compatible = False
 
     # special case for ForPreTraining model

tests/models/ernie_m/test_modeling_ernie_m.py

@@ -22,6 +22,7 @@ from transformers.testing_utils import require_torch, slow, torch_device
 
 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():
@@ -223,7 +224,7 @@ class ErnieMModelTester:
 
 
 @require_torch
-class ErnieMModelTest(ModelTesterMixin, unittest.TestCase):
+class ErnieMModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase):
     all_model_classes = (
         (
             ErnieMModel,
@@ -236,6 +237,17 @@ class ErnieMModelTest(ModelTesterMixin, unittest.TestCase):
         else ()
     )
     all_generative_model_classes = ()
+    pipeline_model_mapping = (
+        {
+            "feature-extraction": ErnieMModel,
+            "question-answering": ErnieMForQuestionAnswering,
+            "text-classification": ErnieMForSequenceClassification,
+            "token-classification": ErnieMForTokenClassification,
+            "zero-shot": ErnieMForSequenceClassification,
+        }
+        if is_torch_available()
+        else {}
+    )
     test_torchscript = False
 
     def setUp(self):

tests/models/esm/test_modeling_esm.py

@@ -22,6 +22,7 @@ from transformers.testing_utils import TestCasePlus, require_torch, slow, torch_
 
 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():
@@ -165,7 +166,7 @@ class EsmModelTester:
 
 
 @require_torch
-class EsmModelTest(ModelTesterMixin, unittest.TestCase):
+class EsmModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase):
     test_mismatched_shapes = False
 
     all_model_classes = (
@@ -179,6 +180,17 @@ class EsmModelTest(ModelTesterMixin, unittest.TestCase):
         else ()
     )
     all_generative_model_classes = ()
+    pipeline_model_mapping = (
+        {
+            "feature-extraction": EsmModel,
+            "fill-mask": EsmForMaskedLM,
+            "text-classification": EsmForSequenceClassification,
+            "token-classification": EsmForTokenClassification,
+            "zero-shot": EsmForSequenceClassification,
+        }
+        if is_torch_available()
+        else {}
+    )
     test_sequence_classification_problem_types = True
 
     def setUp(self):

tests/models/esm/test_modeling_esmfold.py

@@ -22,6 +22,7 @@ from transformers.testing_utils import TestCasePlus, require_torch, slow, torch_
 
 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():
@@ -143,11 +144,12 @@ class EsmFoldModelTester:
 
 
 @require_torch
-class EsmFoldModelTest(ModelTesterMixin, unittest.TestCase):
+class EsmFoldModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase):
     test_mismatched_shapes = False
 
     all_model_classes = (EsmForProteinFolding,) if is_torch_available() else ()
     all_generative_model_classes = ()
+    pipeline_model_mapping = {} if is_torch_available() else {}
     test_sequence_classification_problem_types = False
 
     def setUp(self):

tests/models/esm/test_modeling_tf_esm.py

@@ -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, floats_tensor, ids_tensor, random_attention_mask
+from ...test_pipeline_mixin import PipelineTesterMixin
 
 
 if is_tf_available():
@@ -194,7 +195,7 @@ class TFEsmModelTester:
 
 
 @require_tf
-class TFEsmModelTest(TFModelTesterMixin, unittest.TestCase):
+class TFEsmModelTest(TFModelTesterMixin, PipelineTesterMixin, unittest.TestCase):
     all_model_classes = (
         (
             TFEsmModel,
@@ -205,6 +206,17 @@ class TFEsmModelTest(TFModelTesterMixin, unittest.TestCase):
         if is_tf_available()
         else ()
     )
+    pipeline_model_mapping = (
+        {
+            "feature-extraction": TFEsmModel,
+            "fill-mask": TFEsmForMaskedLM,
+            "text-classification": TFEsmForSequenceClassification,
+            "token-classification": TFEsmForTokenClassification,
+            "zero-shot": TFEsmForSequenceClassification,
+        }
+        if is_tf_available()
+        else {}
+    )
     test_head_masking = False
     test_onnx = False
 

tests/models/flaubert/test_modeling_flaubert.py

@@ -21,6 +21,7 @@ from transformers.testing_utils import require_torch, require_torch_gpu, slow, t
 
 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():
@@ -362,7 +363,7 @@ class FlaubertModelTester(object):
 
 
 @require_torch
-class FlaubertModelTest(ModelTesterMixin, unittest.TestCase):
+class FlaubertModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase):
     all_model_classes = (
         (
             FlaubertModel,
@@ -376,6 +377,18 @@ class FlaubertModelTest(ModelTesterMixin, unittest.TestCase):
         if is_torch_available()
         else ()
     )
+    pipeline_model_mapping = (
+        {
+            "feature-extraction": FlaubertModel,
+            "fill-mask": FlaubertWithLMHeadModel,
+            "question-answering": FlaubertForQuestionAnsweringSimple,
+            "text-classification": FlaubertForSequenceClassification,
+            "token-classification": FlaubertForTokenClassification,
+            "zero-shot": FlaubertForSequenceClassification,
+        }
+        if is_torch_available()
+        else {}
+    )
 
     # Flaubert 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):

tests/models/flaubert/test_modeling_tf_flaubert.py

@@ -20,6 +20,7 @@ from transformers.testing_utils import require_sentencepiece, require_tf, requir
 
 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():
@@ -274,7 +275,7 @@ class TFFlaubertModelTester:
 
 
 @require_tf
-class TFFlaubertModelTest(TFModelTesterMixin, unittest.TestCase):
+class TFFlaubertModelTest(TFModelTesterMixin, PipelineTesterMixin, unittest.TestCase):
     all_model_classes = (
         (
             TFFlaubertModel,
@@ -290,6 +291,18 @@ class TFFlaubertModelTest(TFModelTesterMixin, unittest.TestCase):
     all_generative_model_classes = (
         (TFFlaubertWithLMHeadModel,) if is_tf_available() else ()
     )  # TODO (PVP): Check other models whether language generation is also applicable
+    pipeline_model_mapping = (
+        {
+            "feature-extraction": TFFlaubertModel,
+            "fill-mask": TFFlaubertWithLMHeadModel,
+            "question-answering": TFFlaubertForQuestionAnsweringSimple,
+            "text-classification": TFFlaubertForSequenceClassification,
+            "token-classification": TFFlaubertForTokenClassification,
+            "zero-shot": TFFlaubertForSequenceClassification,
+        }
+        if is_tf_available()
+        else {}
+    )
     test_head_masking = False
     test_onnx = False
 

tests/models/flava/test_modeling_flava.py

@@ -42,6 +42,7 @@ from ...test_modeling_common import (
     ids_tensor,
     random_attention_mask,
 )
+from ...test_pipeline_mixin import PipelineTesterMixin
 
 
 if is_torch_available():
@@ -856,8 +857,9 @@ class FlavaModelTester:
 
 
 @require_torch
-class FlavaModelTest(ModelTesterMixin, unittest.TestCase):
+class FlavaModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase):
     all_model_classes = (FlavaModel,) if is_torch_available() else ()
+    pipeline_model_mapping = {"feature-extraction": FlavaModel} if is_torch_available() else {}
     class_for_tester = FlavaModelTester
     test_head_masking = False
     test_pruning = False

tests/models/fnet/test_modeling_fnet.py

@@ -24,6 +24,7 @@ from transformers.testing_utils import require_tokenizers, require_torch, slow,
 
 from ...test_configuration_common import ConfigTester
 from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
+from ...test_pipeline_mixin import PipelineTesterMixin
 
 
 if is_torch_available():
@@ -265,7 +266,7 @@ class FNetModelTester:
 
 
 @require_torch
-class FNetModelTest(ModelTesterMixin, unittest.TestCase):
+class FNetModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase):
     all_model_classes = (
         (
             FNetModel,
@@ -280,6 +281,18 @@ class FNetModelTest(ModelTesterMixin, unittest.TestCase):
         if is_torch_available()
         else ()
     )
+    pipeline_model_mapping = (
+        {
+            "feature-extraction": FNetModel,
+            "fill-mask": FNetForMaskedLM,
+            "question-answering": FNetForQuestionAnswering,
+            "text-classification": FNetForSequenceClassification,
+            "token-classification": FNetForTokenClassification,
+            "zero-shot": FNetForSequenceClassification,
+        }
+        if is_torch_available()
+        else {}
+    )
 
     # Skip Tests
     test_pruning = False

tests/models/fsmt/test_modeling_fsmt.py

@@ -26,6 +26,7 @@ from transformers.utils import cached_property
 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():
@@ -153,9 +154,19 @@ def prepare_fsmt_inputs_dict(
 
 
 @require_torch
-class FSMTModelTest(ModelTesterMixin, GenerationTesterMixin, unittest.TestCase):
+class FSMTModelTest(ModelTesterMixin, GenerationTesterMixin, PipelineTesterMixin, unittest.TestCase):
     all_model_classes = (FSMTModel, FSMTForConditionalGeneration) if is_torch_available() else ()
     all_generative_model_classes = (FSMTForConditionalGeneration,) if is_torch_available() else ()
+    pipeline_model_mapping = (
+        {
+            "conversational": FSMTForConditionalGeneration,
+            "feature-extraction": FSMTModel,
+            "summarization": FSMTForConditionalGeneration,
+            "text2text-generation": FSMTForConditionalGeneration,
+        }
+        if is_torch_available()
+        else {}
+    )
     is_encoder_decoder = True
     test_pruning = False
     test_missing_keys = False

tests/models/funnel/test_modeling_funnel.py

@@ -22,6 +22,7 @@ from transformers.testing_utils import require_sentencepiece, require_tokenizers
 
 from ...test_configuration_common import ConfigTester
 from ...test_modeling_common import ModelTesterMixin, ids_tensor
+from ...test_pipeline_mixin import PipelineTesterMixin
 
 
 if is_torch_available():
@@ -351,7 +352,7 @@ class FunnelModelTester:
 
 
 @require_torch
-class FunnelModelTest(ModelTesterMixin, unittest.TestCase):
+class FunnelModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase):
     test_head_masking = False
     test_pruning = False
     all_model_classes = (
@@ -365,6 +366,18 @@ class FunnelModelTest(ModelTesterMixin, unittest.TestCase):
         if is_torch_available()
         else ()
     )
+    pipeline_model_mapping = (
+        {
+            "feature-extraction": (FunnelBaseModel, FunnelModel),
+            "fill-mask": FunnelForMaskedLM,
+            "question-answering": FunnelForQuestionAnswering,
+            "text-classification": FunnelForSequenceClassification,
+            "token-classification": FunnelForTokenClassification,
+            "zero-shot": FunnelForSequenceClassification,
+        }
+        if is_torch_available()
+        else {}
+    )
 
     # special case for ForPreTraining model
     def _prepare_for_class(self, inputs_dict, model_class, return_labels=False):

tests/models/funnel/test_modeling_tf_funnel.py

@@ -21,6 +21,7 @@ from transformers.testing_utils import require_tf, tooslow
 
 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():
@@ -329,7 +330,7 @@ class TFFunnelModelTester:
 
 
 @require_tf
-class TFFunnelModelTest(TFModelTesterMixin, unittest.TestCase):
+class TFFunnelModelTest(TFModelTesterMixin, PipelineTesterMixin, unittest.TestCase):
     all_model_classes = (
         (
             TFFunnelModel,
@@ -341,6 +342,18 @@ class TFFunnelModelTest(TFModelTesterMixin, unittest.TestCase):
         if is_tf_available()
         else ()
     )
+    pipeline_model_mapping = (
+        {
+            "feature-extraction": (TFFunnelBaseModel, TFFunnelModel),
+            "fill-mask": TFFunnelForMaskedLM,
+            "question-answering": TFFunnelForQuestionAnswering,
+            "text-classification": TFFunnelForSequenceClassification,
+            "token-classification": TFFunnelForTokenClassification,
+            "zero-shot": TFFunnelForSequenceClassification,
+        }
+        if is_tf_available()
+        else {}
+    )
     test_head_masking = False
     test_onnx = False
 

tests/models/git/test_modeling_git.py

@@ -25,6 +25,7 @@ from transformers.testing_utils import require_torch, require_vision, slow, torc
 from ...generation.test_utils import GenerationTesterMixin
 from ...test_configuration_common import ConfigTester
 from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
+from ...test_pipeline_mixin import PipelineTesterMixin
 
 
 if is_torch_available():
@@ -378,9 +379,12 @@ class GitModelTester:
 
 
 @require_torch
-class GitModelTest(ModelTesterMixin, GenerationTesterMixin, unittest.TestCase):
+class GitModelTest(ModelTesterMixin, GenerationTesterMixin, PipelineTesterMixin, unittest.TestCase):
     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 {}
+    )
     fx_compatible = False
     test_torchscript = False
 

tests/models/glpn/test_modeling_glpn.py

@@ -24,6 +24,7 @@ from transformers.testing_utils import require_torch, require_vision, slow, torc
 
 from ...test_configuration_common import ConfigTester
 from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
+from ...test_pipeline_mixin import PipelineTesterMixin
 
 
 if is_torch_available():
@@ -143,8 +144,11 @@ class GLPNModelTester:
 
 
 @require_torch
-class GLPNModelTest(ModelTesterMixin, unittest.TestCase):
+class GLPNModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase):
     all_model_classes = (GLPNModel, GLPNForDepthEstimation) if is_torch_available() else ()
+    pipeline_model_mapping = (
+        {"depth-estimation": GLPNForDepthEstimation, "feature-extraction": GLPNModel} if is_torch_available() else {}
+    )
 
     test_head_masking = False
     test_pruning = False

tests/models/gpt2/test_modeling_gpt2.py

@@ -24,6 +24,7 @@ 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, floats_tensor, ids_tensor, random_attention_mask
+from ...test_pipeline_mixin import PipelineTesterMixin
 
 
 if is_torch_available():
@@ -429,13 +430,24 @@ class GPT2ModelTester:
 
 
 @require_torch
-class GPT2ModelTest(ModelTesterMixin, GenerationTesterMixin, unittest.TestCase):
+class GPT2ModelTest(ModelTesterMixin, GenerationTesterMixin, PipelineTesterMixin, unittest.TestCase):
     all_model_classes = (
         (GPT2Model, GPT2LMHeadModel, GPT2DoubleHeadsModel, GPT2ForSequenceClassification, GPT2ForTokenClassification)
         if is_torch_available()
         else ()
     )
     all_generative_model_classes = (GPT2LMHeadModel, GPT2DoubleHeadsModel) if is_torch_available() else ()
+    pipeline_model_mapping = (
+        {
+            "feature-extraction": GPT2Model,
+            "text-classification": GPT2ForSequenceClassification,
+            "text-generation": GPT2LMHeadModel,
+            "token-classification": GPT2ForTokenClassification,
+            "zero-shot": GPT2ForSequenceClassification,
+        }
+        if is_torch_available()
+        else {}
+    )
     all_parallelizable_model_classes = (GPT2LMHeadModel, GPT2DoubleHeadsModel) if is_torch_available() else ()
     fx_compatible = True
     test_missing_keys = False

tests/models/gpt2/test_modeling_tf_gpt2.py

@@ -20,6 +20,7 @@ from transformers.testing_utils import require_tf, require_tf2onnx, slow
 
 from ...test_configuration_common import ConfigTester
 from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
+from ...test_pipeline_mixin import PipelineTesterMixin
 from ...utils.test_modeling_tf_core import TFCoreModelTesterMixin
 
 
@@ -361,13 +362,23 @@ class TFGPT2ModelTester:
 
 
 @require_tf
-class TFGPT2ModelTest(TFModelTesterMixin, TFCoreModelTesterMixin, unittest.TestCase):
+class TFGPT2ModelTest(TFModelTesterMixin, TFCoreModelTesterMixin, PipelineTesterMixin, unittest.TestCase):
     all_model_classes = (
         (TFGPT2Model, TFGPT2LMHeadModel, TFGPT2ForSequenceClassification, TFGPT2DoubleHeadsModel)
         if is_tf_available()
         else ()
     )
     all_generative_model_classes = (TFGPT2LMHeadModel,) if is_tf_available() else ()
+    pipeline_model_mapping = (
+        {
+            "feature-extraction": TFGPT2Model,
+            "text-classification": TFGPT2ForSequenceClassification,
+            "text-generation": TFGPT2LMHeadModel,
+            "zero-shot": TFGPT2ForSequenceClassification,
+        }
+        if is_tf_available()
+        else {}
+    )
     test_head_masking = False
     test_onnx = True
     onnx_min_opset = 10

tests/models/gpt_neo/test_modeling_gpt_neo.py

@@ -24,6 +24,7 @@ from transformers.utils import cached_property
 from ...generation.test_utils import GenerationTesterMixin
 from ...test_configuration_common import ConfigTester
 from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
+from ...test_pipeline_mixin import PipelineTesterMixin
 
 
 if is_torch_available():
@@ -371,11 +372,21 @@ class GPTNeoModelTester:
 
 
 @require_torch
-class GPTNeoModelTest(ModelTesterMixin, GenerationTesterMixin, unittest.TestCase):
+class GPTNeoModelTest(ModelTesterMixin, GenerationTesterMixin, PipelineTesterMixin, unittest.TestCase):
     all_model_classes = (
         (GPTNeoModel, GPTNeoForCausalLM, GPTNeoForSequenceClassification) if is_torch_available() else ()
     )
     all_generative_model_classes = (GPTNeoForCausalLM,) if is_torch_available() else ()
+    pipeline_model_mapping = (
+        {
+            "feature-extraction": GPTNeoModel,
+            "text-classification": GPTNeoForSequenceClassification,
+            "text-generation": GPTNeoForCausalLM,
+            "zero-shot": GPTNeoForSequenceClassification,
+        }
+        if is_torch_available()
+        else {}
+    )
     fx_compatible = True
     test_missing_keys = False
     test_pruning = False

tests/models/gpt_neox/test_modeling_gpt_neox.py

@@ -22,6 +22,7 @@ from transformers.testing_utils import require_torch, torch_device
 
 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():
@@ -185,9 +186,12 @@ class GPTNeoXModelTester:
 
 
 @require_torch
-class GPTNeoXModelTest(ModelTesterMixin, unittest.TestCase):
+class GPTNeoXModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase):
     all_model_classes = (GPTNeoXModel, GPTNeoXForCausalLM) if is_torch_available() else ()
     all_generative_model_classes = (GPTNeoXForCausalLM,) if is_torch_available() else ()
+    pipeline_model_mapping = (
+        {"feature-extraction": GPTNeoXModel, "text-generation": GPTNeoXForCausalLM} if is_torch_available() else {}
+    )
     test_pruning = False
     test_missing_keys = False
     test_model_parallel = False