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Move is_pipeline_test_to_skip to specific model test classes (#21999)

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* Move `is_pipeline_test_to_skip` to specific model test classes

---------

Co-authored-by: ydshieh <ydshieh@users.noreply.github.com>
This commit is contained in:
Yih-Dar
2023-03-14 10:03:02 +01:00
committed by GitHub
parent 2beabd24f0
commit 6c2ad00c46
32 changed files with 506 additions and 108 deletions
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12
tests/models/ctrl/test_modeling_ctrl.py
View File

@@ -210,6 +210,18 @@ class CTRLModelTest(ModelTesterMixin, GenerationTesterMixin, PipelineTesterMixin
test_resize_embeddings = False
test_head_masking = False
# TODO: Fix the failed tests
def is_pipeline_test_to_skip(
self, pipeline_test_casse_name, config_class, model_architecture, tokenizer_name, processor_name
):
if pipeline_test_casse_name == "ZeroShotClassificationPipelineTests":
# Get `tokenizer does not have a padding token` error for both fast/slow tokenizers.
# `CTRLConfig` was never used in pipeline tests, either because of a missing checkpoint or because a tiny
# config could not be created.
return True
return False
def setUp(self):
self.model_tester = CTRLModelTester(self)
self.config_tester = ConfigTester(self, config_class=CTRLConfig, n_embd=37)

12
tests/models/ctrl/test_modeling_tf_ctrl.py
View File

@@ -185,6 +185,18 @@ class TFCTRLModelTest(TFModelTesterMixin, PipelineTesterMixin, unittest.TestCase
test_head_masking = False
test_onnx = False
# TODO: Fix the failed tests
def is_pipeline_test_to_skip(
self, pipeline_test_casse_name, config_class, model_architecture, tokenizer_name, processor_name
):
if pipeline_test_casse_name == "ZeroShotClassificationPipelineTests":
# Get `tokenizer does not have a padding token` error for both fast/slow tokenizers.
# `CTRLConfig` was never used in pipeline tests, either because of a missing checkpoint or because a tiny
# config could not be created.
return True
return False
def setUp(self):
self.model_tester = TFCTRLModelTester(self)
self.config_tester = ConfigTester(self, config_class=CTRLConfig, n_embd=37)

20
tests/models/flaubert/test_modeling_flaubert.py
View File

@@ -390,6 +390,26 @@ class FlaubertModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase
else {}
)
# TODO: Fix the failed tests
def is_pipeline_test_to_skip(
self, pipeline_test_casse_name, config_class, model_architecture, tokenizer_name, processor_name
):
if pipeline_test_casse_name == "FillMaskPipelineTests":
# Get `ValueError: AttributeError: 'NoneType' object has no attribute 'new_ones'` or `AssertionError`.
# `FlaubertConfig` was never used in pipeline tests: cannot create a simple tokenizer
return True
elif (
pipeline_test_casse_name == "QAPipelineTests"
and tokenizer_name is not None
and not tokenizer_name.endswith("Fast")
):
# `QAPipelineTests` fails for a few models when the slower tokenizer are used.
# (The slower tokenizers were never used for pipeline tests before the pipeline testing rework)
# TODO: check (and possibly fix) the `QAPipelineTests` with slower tokenizer
return True
return False
# 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):
inputs_dict = super()._prepare_for_class(inputs_dict, model_class, return_labels=return_labels)

20
tests/models/flaubert/test_modeling_tf_flaubert.py
View File

@@ -306,6 +306,26 @@ class TFFlaubertModelTest(TFModelTesterMixin, PipelineTesterMixin, unittest.Test
test_head_masking = False
test_onnx = False
# TODO: Fix the failed tests
def is_pipeline_test_to_skip(
self, pipeline_test_casse_name, config_class, model_architecture, tokenizer_name, processor_name
):
if pipeline_test_casse_name == "FillMaskPipelineTests":
# Get `ValueError: AttributeError: 'NoneType' object has no attribute 'new_ones'` or `AssertionError`.
# `FlaubertConfig` was never used in pipeline tests: cannot create a simple tokenizer
return True
elif (
pipeline_test_casse_name == "QAPipelineTests"
and tokenizer_name is not None
and not tokenizer_name.endswith("Fast")
):
# `QAPipelineTests` fails for a few models when the slower tokenizer are used.
# (The slower tokenizers were never used for pipeline tests before the pipeline testing rework)
# TODO: check (and possibly fix) the `QAPipelineTests` with slower tokenizer
return True
return False
def setUp(self):
self.model_tester = TFFlaubertModelTester(self)
self.config_tester = ConfigTester(self, config_class=FlaubertConfig, emb_dim=37)

9
tests/models/fnet/test_modeling_fnet.py
View File

@@ -299,6 +299,15 @@ class FNetModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase):
test_head_masking = False
test_pruning = False
# TODO: Fix the failed tests
def is_pipeline_test_to_skip(
self, pipeline_test_casse_name, config_class, model_architecture, tokenizer_name, processor_name
):
if pipeline_test_casse_name == "QAPipelineTests":
return True
return False
# special case for ForPreTraining 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)

16
tests/models/gptj/test_modeling_gptj.py
View File

@@ -385,6 +385,22 @@ class GPTJModelTest(ModelTesterMixin, GenerationTesterMixin, PipelineTesterMixin
test_model_parallel = False
test_head_masking = False
# TODO: Fix the failed tests
def is_pipeline_test_to_skip(
self, pipeline_test_casse_name, config_class, model_architecture, tokenizer_name, processor_name
):
if (
pipeline_test_casse_name == "QAPipelineTests"
and tokenizer_name is not None
and not tokenizer_name.endswith("Fast")
):
# `QAPipelineTests` fails for a few models when the slower tokenizer are used.
# (The slower tokenizers were never used for pipeline tests before the pipeline testing rework)
# TODO: check (and possibly fix) the `QAPipelineTests` with slower tokenizer
return True
return False
# special case for DoubleHeads 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)

16
tests/models/gptj/test_modeling_tf_gptj.py
View File

@@ -318,6 +318,22 @@ class TFGPTJModelTest(TFModelTesterMixin, TFCoreModelTesterMixin, PipelineTester
test_missing_keys = False
test_head_masking = False
# TODO: Fix the failed tests
def is_pipeline_test_to_skip(
self, pipeline_test_casse_name, config_class, model_architecture, tokenizer_name, processor_name
):
if (
pipeline_test_casse_name == "QAPipelineTests"
and tokenizer_name is not None
and not tokenizer_name.endswith("Fast")
):
# `QAPipelineTests` fails for a few models when the slower tokenizer are used.
# (The slower tokenizers were never used for pipeline tests before the pipeline testing rework)
# TODO: check (and possibly fix) the `QAPipelineTests` with slower tokenizer
return True
return False
def setUp(self):
self.model_tester = TFGPTJModelTester(self)
self.config_tester = ConfigTester(self, config_class=GPTJConfig, n_embd=37)

14
tests/models/layoutlm/test_modeling_layoutlm.py
View File

@@ -246,6 +246,20 @@ class LayoutLMModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase
)
fx_compatible = True
# TODO: Fix the failed tests
def is_pipeline_test_to_skip(
self, pipeline_test_casse_name, config_class, model_architecture, tokenizer_name, processor_name
):
if (
pipeline_test_casse_name == "DocumentQuestionAnsweringPipelineTests"
and tokenizer_name is not None
and not tokenizer_name.endswith("Fast")
):
# This pipeline uses `sequence_ids()` which is only available for fast tokenizers.
return True
return False
def setUp(self):
self.model_tester = LayoutLMModelTester(self)
self.config_tester = ConfigTester(self, config_class=LayoutLMConfig, hidden_size=37)

23
tests/models/layoutlmv2/test_modeling_layoutlmv2.py
View File

@@ -282,6 +282,29 @@ class LayoutLMv2ModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCa
else {}
)
# TODO: Fix the failed tests
def is_pipeline_test_to_skip(
self, pipeline_test_casse_name, config_class, model_architecture, tokenizer_name, processor_name
):
if pipeline_test_casse_name in [
"QAPipelineTests",
"TextClassificationPipelineTests",
"TokenClassificationPipelineTests",
"ZeroShotClassificationPipelineTests",
]:
# `LayoutLMv2Config` was never used in pipeline tests (`test_pt_LayoutLMv2Config_XXX`) due to lack of tiny
# config. With new tiny model creation, it is available, but we need to fix the failed tests.
return True
elif (
pipeline_test_casse_name == "DocumentQuestionAnsweringPipelineTests"
and tokenizer_name is not None
and not tokenizer_name.endswith("Fast")
):
# This pipeline uses `sequence_ids()` which is only available for fast tokenizers.
return True
return False
def setUp(self):
self.model_tester = LayoutLMv2ModelTester(self)
self.config_tester = ConfigTester(self, config_class=LayoutLMv2Config, hidden_size=37)

6
tests/models/layoutlmv3/test_modeling_layoutlmv3.py
View File

@@ -298,6 +298,12 @@ class LayoutLMv3ModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCa
else {}
)
# TODO: Fix the failed tests
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 = LayoutLMv3ModelTester(self)
self.config_tester = ConfigTester(self, config_class=LayoutLMv3Config, hidden_size=37)

6
tests/models/layoutlmv3/test_modeling_tf_layoutlmv3.py
View File

@@ -291,6 +291,12 @@ class TFLayoutLMv3ModelTest(TFModelTesterMixin, PipelineTesterMixin, unittest.Te
test_resize_embeddings = False
test_onnx = False
# TODO: Fix the failed tests
def is_pipeline_test_to_skip(
self, pipeline_test_casse_name, config_class, model_architecture, tokenizer_name, processor_name
):
return True
def _prepare_for_class(self, inputs_dict, model_class, return_labels=False) -> dict:
inputs_dict = copy.deepcopy(inputs_dict)

6
tests/models/lilt/test_modeling_lilt.py
View File

@@ -244,6 +244,12 @@ class LiltModelTest(ModelTesterMixin, GenerationTesterMixin, PipelineTesterMixin
fx_compatible = False
test_pruning = False
# TODO: Fix the failed tests
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 = LiltModelTester(self)
self.config_tester = ConfigTester(self, config_class=LiltConfig, hidden_size=37)

16
tests/models/longformer/test_modeling_longformer.py
View File

@@ -326,6 +326,22 @@ class LongformerModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCa
else {}
)
# TODO: Fix the failed tests
def is_pipeline_test_to_skip(
self, pipeline_test_casse_name, config_class, model_architecture, tokenizer_name, processor_name
):
if (
pipeline_test_casse_name == "QAPipelineTests"
and tokenizer_name is not None
and not tokenizer_name.endswith("Fast")
):
# `QAPipelineTests` fails for a few models when the slower tokenizer are used.
# (The slower tokenizers were never used for pipeline tests before the pipeline testing rework)
# TODO: check (and possibly fix) the `QAPipelineTests` with slower tokenizer
return True
return False
def setUp(self):
self.model_tester = LongformerModelTester(self)
self.config_tester = ConfigTester(self, config_class=LongformerConfig, hidden_size=37)

16
tests/models/longformer/test_modeling_tf_longformer.py
View File

@@ -299,6 +299,22 @@ class TFLongformerModelTest(TFModelTesterMixin, PipelineTesterMixin, unittest.Te
test_head_masking = False
test_onnx = False
# TODO: Fix the failed tests
def is_pipeline_test_to_skip(
self, pipeline_test_casse_name, config_class, model_architecture, tokenizer_name, processor_name
):
if (
pipeline_test_casse_name == "QAPipelineTests"
and tokenizer_name is not None
and not tokenizer_name.endswith("Fast")
):
# `QAPipelineTests` fails for a few models when the slower tokenizer are used.
# (The slower tokenizers were never used for pipeline tests before the pipeline testing rework)
# TODO: check (and possibly fix) the `QAPipelineTests` with slower tokenizer
return True
return False
def setUp(self):
self.model_tester = TFLongformerModelTester(self)
self.config_tester = ConfigTester(self, config_class=LongformerConfig, hidden_size=37)

11
tests/models/m2m_100/test_modeling_m2m_100.py
View File

@@ -246,6 +246,17 @@ class M2M100ModelTest(ModelTesterMixin, GenerationTesterMixin, PipelineTesterMix
test_pruning = False
test_missing_keys = False
# TODO: Fix the failed tests
def is_pipeline_test_to_skip(
self, pipeline_test_casse_name, config_class, model_architecture, tokenizer_name, processor_name
):
if pipeline_test_casse_name == "TranslationPipelineTests":
# Get `ValueError: Translation requires a `src_lang` and a `tgt_lang` for this model`.
# `M2M100Config` was never used in pipeline tests: cannot create a simple tokenizer.
return True
return False
def setUp(self):
self.model_tester = M2M100ModelTester(self)
self.config_tester = ConfigTester(self, config_class=M2M100Config)

16
tests/models/mvp/test_modeling_mvp.py
View File

@@ -433,6 +433,22 @@ class MvpModelTest(ModelTesterMixin, GenerationTesterMixin, PipelineTesterMixin,
test_pruning = False
test_missing_keys = False
# TODO: Fix the failed tests
def is_pipeline_test_to_skip(
self, pipeline_test_casse_name, config_class, model_architecture, tokenizer_name, processor_name
):
if (
pipeline_test_casse_name == "QAPipelineTests"
and tokenizer_name is not None
and not tokenizer_name.endswith("Fast")
):
# `QAPipelineTests` fails for a few models when the slower tokenizer are used.
# (The slower tokenizers were never used for pipeline tests before the pipeline testing rework)
# TODO: check (and possibly fix) the `QAPipelineTests` with slower tokenizer
return True
return False
def setUp(self):
self.model_tester = MvpModelTester(self)
self.config_tester = ConfigTester(self, config_class=MvpConfig)

12
tests/models/openai/test_modeling_openai.py
View File

@@ -210,6 +210,18 @@ class OpenAIGPTModelTest(ModelTesterMixin, GenerationTesterMixin, PipelineTester
else {}
)
# TODO: Fix the failed tests
def is_pipeline_test_to_skip(
self, pipeline_test_casse_name, config_class, model_architecture, tokenizer_name, processor_name
):
if pipeline_test_casse_name == "ZeroShotClassificationPipelineTests":
# Get `tokenizer does not have a padding token` error for both fast/slow tokenizers.
# `OpenAIGPTConfig` was never used in pipeline tests, either because of a missing checkpoint or because a
# tiny config could not be created.
return True
return False
# special case for DoubleHeads 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)

12
tests/models/openai/test_modeling_tf_openai.py
View File

@@ -214,6 +214,18 @@ class TFOpenAIGPTModelTest(TFModelTesterMixin, PipelineTesterMixin, unittest.Tes
test_head_masking = False
test_onnx = False
# TODO: Fix the failed tests
def is_pipeline_test_to_skip(
self, pipeline_test_casse_name, config_class, model_architecture, tokenizer_name, processor_name
):
if pipeline_test_casse_name == "ZeroShotClassificationPipelineTests":
# Get `tokenizer does not have a padding token` error for both fast/slow tokenizers.
# `OpenAIGPTConfig` was never used in pipeline tests, either because of a missing checkpoint or because a
# tiny config could not be created.
return True
return False
def setUp(self):
self.model_tester = TFOpenAIGPTModelTester(self)
self.config_tester = ConfigTester(self, config_class=OpenAIGPTConfig, n_embd=37)

16
tests/models/opt/test_modeling_opt.py
View File

@@ -207,6 +207,22 @@ class OPTModelTest(ModelTesterMixin, GenerationTesterMixin, PipelineTesterMixin,
test_pruning = False
test_missing_keys = False
# TODO: Fix the failed tests
def is_pipeline_test_to_skip(
self, pipeline_test_casse_name, config_class, model_architecture, tokenizer_name, processor_name
):
if (
pipeline_test_casse_name == "QAPipelineTests"
and tokenizer_name is not None
and not tokenizer_name.endswith("Fast")
):
# `QAPipelineTests` fails for a few models when the slower tokenizer are used.
# (The slower tokenizers were never used for pipeline tests before the pipeline testing rework)
# TODO: check (and possibly fix) the `QAPipelineTests` with slower tokenizer
return True
return False
def setUp(self):
self.model_tester = OPTModelTester(self)
self.config_tester = ConfigTester(self, config_class=OPTConfig)

11
tests/models/plbart/test_modeling_plbart.py
View File

@@ -237,6 +237,17 @@ class PLBartModelTest(ModelTesterMixin, GenerationTesterMixin, PipelineTesterMix
test_pruning = False
test_missing_keys = False
# TODO: Fix the failed tests
def is_pipeline_test_to_skip(
self, pipeline_test_casse_name, config_class, model_architecture, tokenizer_name, processor_name
):
if pipeline_test_casse_name == "TranslationPipelineTests":
# Get `ValueError: Translation requires a `src_lang` and a `tgt_lang` for this model`.
# `PLBartConfig` was never used in pipeline tests: cannot create a simple tokenizer.
return True
return False
def setUp(self):
self.model_tester = PLBartModelTester(self)
self.config_tester = ConfigTester(self, config_class=PLBartConfig)

12
tests/models/prophetnet/test_modeling_prophetnet.py
View File

@@ -904,6 +904,18 @@ class ProphetNetModelTest(ModelTesterMixin, GenerationTesterMixin, PipelineTeste
test_resize_embeddings = False
is_encoder_decoder = True
# TODO: Fix the failed tests
def is_pipeline_test_to_skip(
self, pipeline_test_casse_name, config_class, model_architecture, tokenizer_name, processor_name
):
if pipeline_test_casse_name == "TextGenerationPipelineTests":
# Get `ValueError: AttributeError: 'NoneType' object has no attribute 'new_ones'` or `AssertionError`.
# `ProphetNetConfig` was never used in pipeline tests: cannot create a simple
# tokenizer.
return True
return False
def setUp(self):
self.model_tester = ProphetNetModelTester(self)
self.config_tester = ConfigTester(self, config_class=ProphetNetConfig)

16
tests/models/reformer/test_modeling_reformer.py
View File

@@ -709,6 +709,22 @@ class ReformerLSHAttnModelTest(
test_headmasking = False
test_torchscript = False
# TODO: Fix the failed tests
def is_pipeline_test_to_skip(
self, pipeline_test_casse_name, config_class, model_architecture, tokenizer_name, processor_name
):
if (
pipeline_test_casse_name == "QAPipelineTests"
and tokenizer_name is not None
and not tokenizer_name.endswith("Fast")
):
# `QAPipelineTests` fails for a few models when the slower tokenizer are used.
# (The slower tokenizers were never used for pipeline tests before the pipeline testing rework)
# TODO: check (and possibly fix) the `QAPipelineTests` with slower tokenizer
return True
return False
def setUp(self):
self.model_tester = ReformerModelTester(
self,

18
tests/models/roc_bert/test_modeling_roc_bert.py
View File

@@ -586,6 +586,24 @@ class RoCBertModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase)
else {}
)
# TODO: Fix the failed tests when this model gets more usage
def is_pipeline_test_to_skip(
self, pipeline_test_casse_name, config_class, model_architecture, tokenizer_name, processor_name
):
if pipeline_test_casse_name in [
"FillMaskPipelineTests",
"FeatureExtractionPipelineTests",
"TextClassificationPipelineTests",
"TokenClassificationPipelineTests",
]:
# Get error: IndexError: index out of range in self.
# `word_shape_file` and `word_pronunciation_file` should be shrunk during tiny model creation,
# otherwise `IndexError` could occur in some embedding layers. Skip for now until this model has
# more usage.
return True
return False
# special case for ForPreTraining 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)

9
tests/models/roformer/test_modeling_tf_roformer.py
View File

@@ -271,6 +271,15 @@ class TFRoFormerModelTest(TFModelTesterMixin, PipelineTesterMixin, unittest.Test
test_head_masking = False
test_onnx = False
# TODO: add `prepare_inputs_for_generation` for `TFRoFormerForCausalLM`
def is_pipeline_test_to_skip(
self, pipeline_test_casse_name, config_class, model_architecture, tokenizer_name, processor_name
):
if pipeline_test_casse_name == "TextGenerationPipelineTests":
return True
return False
def setUp(self):
self.model_tester = TFRoFormerModelTester(self)
self.config_tester = ConfigTester(self, config_class=RoFormerConfig, hidden_size=37)

6
tests/models/tapas/test_modeling_tapas.py
View File

@@ -486,6 +486,12 @@ class TapasModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase):
)
return inputs_dict
# TODO: Fix the failed tests
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 = TapasModelTester(self)
self.config_tester = ConfigTester(self, config_class=TapasConfig, dim=37)

6
tests/models/tapas/test_modeling_tf_tapas.py
View File

@@ -443,6 +443,12 @@ class TFTapasModelTest(TFModelTesterMixin, PipelineTesterMixin, unittest.TestCas
test_head_masking = False
test_onnx = False
# TODO: Fix the failed tests
def is_pipeline_test_to_skip(
self, pipeline_test_casse_name, config_class, model_architecture, tokenizer_name, processor_name
):
return True
def _prepare_for_class(self, inputs_dict, model_class, return_labels=False) -> dict:
inputs_dict = copy.deepcopy(inputs_dict)

12
tests/models/transfo_xl/test_modeling_tf_transfo_xl.py
View File

@@ -177,6 +177,18 @@ class TFTransfoXLModelTest(TFModelTesterMixin, PipelineTesterMixin, unittest.Tes
test_onnx = False
test_mismatched_shapes = False
# TODO: Fix the failed tests
def is_pipeline_test_to_skip(
self, pipeline_test_casse_name, config_class, model_architecture, tokenizer_name, processor_name
):
if pipeline_test_casse_name == "TextGenerationPipelineTests":
# Get `ValueError: AttributeError: 'NoneType' object has no attribute 'new_ones'` or `AssertionError`.
# `TransfoXLConfig` was never used in pipeline tests: cannot create a simple
# tokenizer.
return True
return False
def setUp(self):
self.model_tester = TFTransfoXLModelTester(self)
self.config_tester = ConfigTester(self, config_class=TransfoXLConfig, d_embed=37)

12
tests/models/transfo_xl/test_modeling_transfo_xl.py
View File

@@ -271,6 +271,18 @@ class TransfoXLModelTest(ModelTesterMixin, GenerationTesterMixin, PipelineTester
test_resize_embeddings = True
test_mismatched_shapes = False
# TODO: Fix the failed tests
def is_pipeline_test_to_skip(
self, pipeline_test_casse_name, config_class, model_architecture, tokenizer_name, processor_name
):
if pipeline_test_casse_name == "TextGenerationPipelineTests":
# Get `ValueError: AttributeError: 'NoneType' object has no attribute 'new_ones'` or `AssertionError`.
# `TransfoXLConfig` was never used in pipeline tests: cannot create a simple
# tokenizer.
return True
return False
def check_cutoffs_and_n_token(
self, copied_cutoffs, layer, model_embed, model, model_class, resized_value, vocab_size
):

20
tests/models/xlm/test_modeling_tf_xlm.py
View File

@@ -309,6 +309,26 @@ class TFXLMModelTest(TFModelTesterMixin, PipelineTesterMixin, unittest.TestCase)
test_head_masking = False
test_onnx = False
# TODO: Fix the failed tests
def is_pipeline_test_to_skip(
self, pipeline_test_casse_name, config_class, model_architecture, tokenizer_name, processor_name
):
if pipeline_test_casse_name == "FillMaskPipelineTests":
# Get `ValueError: AttributeError: 'NoneType' object has no attribute 'new_ones'` or `AssertionError`.
# `XLMConfig` was never used in pipeline tests: cannot create a simple tokenizer
return True
elif (
pipeline_test_casse_name == "QAPipelineTests"
and tokenizer_name is not None
and not tokenizer_name.endswith("Fast")
):
# `QAPipelineTests` fails for a few models when the slower tokenizer are used.
# (The slower tokenizers were never used for pipeline tests before the pipeline testing rework)
# TODO: check (and possibly fix) the `QAPipelineTests` with slower tokenizer
return True
return False
def setUp(self):
self.model_tester = TFXLMModelTester(self)
self.config_tester = ConfigTester(self, config_class=XLMConfig, emb_dim=37)

20
tests/models/xlm/test_modeling_xlm.py
View File

@@ -391,6 +391,26 @@ class XLMModelTest(ModelTesterMixin, GenerationTesterMixin, PipelineTesterMixin,
else {}
)
# TODO: Fix the failed tests
def is_pipeline_test_to_skip(
self, pipeline_test_casse_name, config_class, model_architecture, tokenizer_name, processor_name
):
if pipeline_test_casse_name == "FillMaskPipelineTests":
# Get `ValueError: AttributeError: 'NoneType' object has no attribute 'new_ones'` or `AssertionError`.
# `XLMConfig` was never used in pipeline tests: cannot create a simple tokenizer
return True
elif (
pipeline_test_casse_name == "QAPipelineTests"
and tokenizer_name is not None
and not tokenizer_name.endswith("Fast")
):
# `QAPipelineTests` fails for a few models when the slower tokenizer are used.
# (The slower tokenizers were never used for pipeline tests before the pipeline testing rework)
# TODO: check (and possibly fix) the `QAPipelineTests` with slower tokenizer
return True
return False
# 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):
inputs_dict = super()._prepare_for_class(inputs_dict, model_class, return_labels=return_labels)