Byebye pytorch 1.9 (#24080)

byebye

---------

Co-authored-by: ydshieh <ydshieh@users.noreply.github.com>

tests/models/bloom/test_modeling_bloom.py

@@ -38,9 +38,6 @@ if is_torch_available():
         BloomModel,
         BloomTokenizerFast,
     )
-    from transformers.pytorch_utils import is_torch_greater_or_equal_than_1_10
-else:
-    is_torch_greater_or_equal_than_1_10 = False
 
 
 @require_torch
@@ -518,10 +515,6 @@ class BloomEmbeddingTest(unittest.TestCase):
         super().setUp()
         self.path_bigscience_model = "bigscience/bigscience-small-testing"
 
-    @unittest.skipIf(
-        not is_torch_greater_or_equal_than_1_10,
-        "Test failed with torch < 1.10 (`LayerNormKernelImpl` not implemented for `BFloat16`)",
-    )
     @require_torch
     def test_embeddings(self):
         # The config in this checkpoint has `bfloat16` as `torch_dtype` -> model in `bfloat16`

tests/models/bridgetower/test_modeling_bridgetower.py

@@ -50,9 +50,6 @@ if is_torch_available():
         BridgeTowerModel,
     )
     from transformers.models.bridgetower.modeling_bridgetower import BRIDGETOWER_PRETRAINED_MODEL_ARCHIVE_LIST
-    from transformers.pytorch_utils import is_torch_greater_or_equal_than_1_10
-else:
-    is_torch_greater_or_equal_than_1_10 = False
 
 if is_vision_available():
     from PIL import Image
@@ -298,7 +295,6 @@ class BridgeTowerModelTester:
 
 
 @require_torch
-@unittest.skipIf(not is_torch_greater_or_equal_than_1_10, "BridgeTower is only available in torch v1.10+")
 class BridgeTowerModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase):
     all_model_classes = (
         (
@@ -516,7 +512,6 @@ def prepare_img():
 
 @require_torch
 @require_vision
-@unittest.skipIf(not is_torch_greater_or_equal_than_1_10, "BridgeTower is only available in torch v1.10+")
 class BridgeTowerModelIntegrationTest(unittest.TestCase):
     @cached_property
     def default_processor(self):
@@ -601,7 +596,6 @@ class BridgeTowerModelIntegrationTest(unittest.TestCase):
 
 @slow
 @require_torch
-@unittest.skipIf(not is_torch_greater_or_equal_than_1_10, "BridgeTower is only available in torch v1.10+")
 class BridgeTowerModelTrainingTest(unittest.TestCase):
     all_training_supported_model_classes = (
         (BridgeTowerForImageAndTextRetrieval, BridgeTowerForMaskedLM, BridgeTowerForContrastiveLearning)

tests/models/levit/test_modeling_levit.py

@@ -20,8 +20,6 @@ import unittest
 import warnings
 from math import ceil, floor
 
-from packaging import version
-
 from transformers import LevitConfig
 from transformers.file_utils import cached_property, is_torch_available, is_vision_available
 from transformers.models.auto import get_values
@@ -346,10 +344,6 @@ class LevitModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase):
             loss.backward()
 
     def test_problem_types(self):
-        parsed_torch_version_base = version.parse(version.parse(torch.__version__).base_version)
-        if parsed_torch_version_base.base_version.startswith("1.9"):
-            self.skipTest(reason="This test fails with PyTorch 1.9.x: some CUDA issue")
-
         config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
 
         problem_types = [

tests/models/tvlt/test_modeling_tvlt.py

@@ -42,9 +42,6 @@ if is_torch_available():
 
     from transformers import TvltForAudioVisualClassification, TvltForPreTraining, TvltModel
     from transformers.models.tvlt.modeling_tvlt import TVLT_PRETRAINED_MODEL_ARCHIVE_LIST
-    from transformers.pytorch_utils import is_torch_greater_or_equal_than_1_10
-else:
-    is_torch_greater_or_equal_than_1_10 = False
 
 
 if is_datasets_available():
@@ -322,7 +319,6 @@ class TvltModelTester:
 
 
 @require_torch
-@unittest.skipIf(not is_torch_greater_or_equal_than_1_10, "TVLT is only available in torch v1.10+")
 class TvltModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase):
     all_model_classes = (
         (TvltModel, TvltForPreTraining, TvltForAudioVisualClassification) if is_torch_available() else ()

tests/models/vilt/test_modeling_vilt.py

@@ -42,9 +42,6 @@ if is_torch_available():
         ViltModel,
     )
     from transformers.models.vilt.modeling_vilt import VILT_PRETRAINED_MODEL_ARCHIVE_LIST
-    from transformers.pytorch_utils import is_torch_greater_or_equal_than_1_10
-else:
-    is_torch_greater_or_equal_than_1_10 = False
 
 if is_vision_available():
     import PIL
@@ -218,7 +215,6 @@ class ViltModelTester:
 
 
 @require_torch
-@unittest.skipIf(not is_torch_greater_or_equal_than_1_10, "Vilt is only available in torch v1.10+")
 class ViltModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase):
     all_model_classes = (
         (
@@ -520,7 +516,6 @@ class ViltModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase):
 
 
 @require_torch
-@unittest.skipIf(not is_torch_greater_or_equal_than_1_10, "Vilt is only available in torch v1.10+")
 class ViltForImagesAndTextClassificationModelTest(ViltModelTest, unittest.TestCase):
     all_model_classes = (ViltForImagesAndTextClassification,) if is_torch_available() else ()
 
@@ -545,7 +540,6 @@ def prepare_img():
 
 @require_torch
 @require_vision
-@unittest.skipIf(not is_torch_greater_or_equal_than_1_10, "Vilt is only available in torch v1.10+")
 class ViltModelIntegrationTest(unittest.TestCase):
     @cached_property
     def default_processor(self):