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docs/source/en/model_doc/mctct.mdx

@@ -31,6 +31,11 @@ performance for many languages that also transfers well to LibriSpeech.*
 
 This model was contributed by [cwkeam](https://huggingface.co/cwkeam). The original code can be found [here](https://github.com/flashlight/wav2letter/tree/main/recipes/mling_pl).
 
+
+Tips:
+
+- The PyTorch version of this model is only available in torch 1.9 and higher.
+
 ## MCTCTConfig
 
 [[autodoc]] MCTCTConfig

src/transformers/models/mctct/modeling_mctct.py

@@ -33,12 +33,19 @@
     find_pruneable_heads_and_indices,
     prune_linear_layer,
 )
+from ...pytorch_utils import is_torch_less_than_1_9
 from ...utils import logging
 from .configuration_mctct import MCTCTConfig
 
 
 logger = logging.get_logger(__name__)
 
+if not is_torch_less_than_1_9:
+    logger.warning(
+        f"You are using torch=={torch.__version__}, but torch>=1.9.0 is required to use MCTCTModel. Please upgrade"
+        " torch."
+    )
+
 _HIDDEN_STATES_START_POSITION = 1
 
 _CONFIG_FOR_DOC = "MCTCTConfig"

src/transformers/models/perceiver/modeling_perceiver.py

@@ -3203,9 +3203,9 @@ def forward(self, inputs: torch.Tensor, pos: Optional[torch.Tensor] = None, netw
         if self.prep_type != "patches":
             # move channels to last dimension, as the _build_network_inputs method below expects this
             if inputs.ndim == 4:
-                inputs = torch.permute(inputs, (0, 2, 3, 1))
+                inputs = inputs.permute(0, 2, 3, 1)
             elif inputs.ndim == 5:
-                inputs = torch.permute(inputs, (0, 1, 3, 4, 2))
+                inputs = inputs.permute(0, 1, 3, 4, 2)
             else:
                 raise ValueError("Unsupported data format for conv1x1.")
 

src/transformers/models/realm/modeling_realm.py

@@ -1361,7 +1361,7 @@ def forward(
         # [batch_size, num_candidates, retriever_proj_size]
         candidate_score = candidate_score.view(-1, self.config.num_candidates, self.config.retriever_proj_size)
         # [batch_size, num_candidates]
-        relevance_score = torch.einsum("BD,BND->BN", query_score, candidate_score)
+        relevance_score = torch.einsum("bd,bnd->bn", query_score, candidate_score)
 
         if not return_dict:
             return relevance_score, query_score, candidate_score

src/transformers/pytorch_utils.py

@@ -28,6 +28,7 @@
 parsed_torch_version_base = version.parse(version.parse(torch.__version__).base_version)
 
 is_torch_less_than_1_8 = parsed_torch_version_base < version.parse("1.8.0")
+is_torch_less_than_1_9 = parsed_torch_version_base < version.parse("1.9.0")
 is_torch_greater_or_equal_than_1_10 = parsed_torch_version_base >= version.parse("1.10")
 is_torch_less_than_1_11 = parsed_torch_version_base < version.parse("1.11")
 

tests/models/bloom/test_modeling_bloom.py

@@ -37,7 +37,10 @@
         BloomModel,
         BloomTokenizerFast,
     )
-    from transformers.pytorch_utils import is_torch_greater_or_equal_than_1_10
+    from transformers.pytorch_utils import is_torch_greater_or_equal_than_1_10, is_torch_less_than_1_9
+else:
+    is_torch_greater_or_equal_than_1_10 = False
+    is_torch_less_than_1_9 = True
 
 
 @require_torch
@@ -502,7 +505,7 @@ def setUp(self):
         self.path_bigscience_model = "bigscience/bigscience-small-testing"
 
     @unittest.skipIf(
-        not is_torch_available() or not is_torch_greater_or_equal_than_1_10,
+        not is_torch_greater_or_equal_than_1_10,
         "Test failed with torch < 1.10 (`LayerNormKernelImpl` not implemented for `BFloat16`)",
     )
     @require_torch
@@ -737,6 +740,9 @@ def test_embeddings(self):
                 self.assertAlmostEqual(EMBEDDINGS_DS_AFTER_LN[key][idx], output_dict_norm[key][idx], places=1)
 
     @require_torch
+    @unittest.skipIf(
+        is_torch_less_than_1_9, reason="Test failed with torch < 1.9 (`min_cuda` not implemented for `BFloat16`)"
+    )
     def test_hidden_states_transformers(self):
         cuda_available = torch.cuda.is_available()
         model = BloomModel.from_pretrained(self.path_bigscience_model, use_cache=False, torch_dtype="auto").to(

tests/models/mctct/test_modeling_mctct.py

@@ -31,6 +31,9 @@
     import torch
 
     from transformers import MCTCTForCTC, MCTCTModel, MCTCTProcessor
+    from transformers.pytorch_utils import is_torch_less_than_1_9
+else:
+    is_torch_less_than_1_9 = True
 
 
 class MCTCTModelTester:
@@ -261,6 +264,7 @@ def prepare_config_and_inputs_for_common(self):
 
 
 @require_torch
+@unittest.skipIf(is_torch_less_than_1_9, "MCTCT is only available in torch v1.9+")
 class MCTCTModelTest(ModelTesterMixin, unittest.TestCase):
     all_model_classes = (MCTCTForCTC, MCTCTModel) if is_torch_available() else ()
     test_pruning = False

tests/models/swin/test_modeling_swin.py

@@ -32,6 +32,9 @@
 
     from transformers import SwinBackbone, SwinForImageClassification, SwinForMaskedImageModeling, SwinModel
     from transformers.models.swin.modeling_swin import SWIN_PRETRAINED_MODEL_ARCHIVE_LIST
+    from transformers.pytorch_utils import is_torch_less_than_1_9
+else:
+    is_torch_less_than_1_9 = True
 
 if is_vision_available():
     from PIL import Image
@@ -253,6 +256,10 @@ def test_model(self):
         config_and_inputs = self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_model(*config_and_inputs)
 
+    @unittest.skipIf(is_torch_less_than_1_9, reason="This test fails for SwinModel when torch < 1.9")
+    def test_training_gradient_checkpointing(self):
+        super().test_training_gradient_checkpointing()
+
     def test_backbone(self):
         config_and_inputs = self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_backbone(*config_and_inputs)

tests/models/trajectory_transformer/test_modeling_trajectory_transformer.py

@@ -35,6 +35,9 @@
     from transformers.models.trajectory_transformer.modeling_trajectory_transformer import (
         TRAJECTORY_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
     )
+    from transformers.pytorch_utils import is_torch_less_than_1_9
+else:
+    is_torch_less_than_1_9 = True
 
 
 class TrajectoryTransformerModelTester:
@@ -195,6 +198,7 @@ def test_training(self):
         ).loss
         loss.backward()
 
+    @unittest.skipIf(is_torch_less_than_1_9, reason="This test fails for TrajectoryTransformerModel when torch < 1.9")
     def test_training_gradient_checkpointing(self):
         if not self.model_tester.is_training:
             return

tests/models/wav2vec2/test_modeling_wav2vec2.py

@@ -70,6 +70,9 @@
         _compute_mask_indices,
         _sample_negative_indices,
     )
+    from transformers.pytorch_utils import is_torch_less_than_1_9
+else:
+    is_torch_less_than_1_9 = True
 
 
 if is_torchaudio_available():
@@ -1640,6 +1643,10 @@ def test_phoneme_recognition(self):
 
     @require_pyctcdecode
     @require_torchaudio
+    @unittest.skipIf(
+        is_torch_less_than_1_9,
+        reason="`torchaudio.functional.resample` needs torchaudio >= 0.9 which requires torch >= 0.9",
+    )
     def test_wav2vec2_with_lm(self):
         ds = load_dataset("common_voice", "es", split="test", streaming=True)
         sample = next(iter(ds))
@@ -1664,6 +1671,10 @@ def test_wav2vec2_with_lm(self):
 
     @require_pyctcdecode
     @require_torchaudio
+    @unittest.skipIf(
+        is_torch_less_than_1_9,
+        reason="`torchaudio.functional.resample` needs torchaudio >= 0.9 which requires torch >= 0.9",
+    )
     def test_wav2vec2_with_lm_pool(self):
         ds = load_dataset("common_voice", "es", split="test", streaming=True)
         sample = next(iter(ds))

tests/test_modeling_common.py

@@ -696,7 +696,9 @@ def clear_torch_jit_class_registry(self):
 
         torch._C._jit_clear_class_registry()
         torch.jit._recursive.concrete_type_store = torch.jit._recursive.ConcreteTypeStore()
-        torch.jit._state._clear_class_state()
+        # torch 1.8 has no `_clear_class_state` in `torch.jit._state`
+        if hasattr(torch.jit._state, "_clear_class_state"):
+            torch.jit._state._clear_class_state()
 
     def _create_and_check_torchscript(self, config, inputs_dict):
         if not self.test_torchscript: