Time to Say Goodbye, torch 1.7 and 1.8 (#22291)

* time to say goodbye, torch 1.7 and 1.8

* clean up torch_int_div

* clean up is_torch_less_than_1_8-9

* update

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

tests/models/wav2vec2/test_modeling_wav2vec2.py

@@ -71,9 +71,6 @@ if is_torch_available():
         _compute_mask_indices,
         _sample_negative_indices,
     )
-    from transformers.pytorch_utils import is_torch_less_than_1_9, torch_int_div
-else:
-    is_torch_less_than_1_9 = True
 
 
 if is_torchaudio_available():
@@ -1217,7 +1214,9 @@ class Wav2Vec2UtilsTest(unittest.TestCase):
         sequence_length = 10
         hidden_size = 4
         num_negatives = 3
-        sequence = torch_int_div(torch.arange(sequence_length * hidden_size, device=torch_device), hidden_size)
+        sequence = torch.div(
+            torch.arange(sequence_length * hidden_size, device=torch_device), hidden_size, rounding_mode="floor"
+        )
         features = sequence.view(sequence_length, hidden_size)  # each value in vector consits of same value
         features = features[None, :].expand(batch_size, sequence_length, hidden_size).contiguous()
 
@@ -1245,7 +1244,9 @@ class Wav2Vec2UtilsTest(unittest.TestCase):
         mask = torch.ones((batch_size, sequence_length), dtype=torch.long, device=torch_device)
         mask[-1, sequence_length // 2 :] = 0
 
-        sequence = torch_int_div(torch.arange(sequence_length * hidden_size, device=torch_device), hidden_size)
+        sequence = torch.div(
+            torch.arange(sequence_length * hidden_size, device=torch_device), hidden_size, rounding_mode="floor"
+        )
         features = sequence.view(sequence_length, hidden_size)  # each value in vector consits of same value
         features = features[None, :].expand(batch_size, sequence_length, hidden_size).contiguous()
 
@@ -1651,10 +1652,6 @@ class Wav2Vec2ModelIntegrationTest(unittest.TestCase):
 
     @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))
@@ -1679,10 +1676,6 @@ class Wav2Vec2ModelIntegrationTest(unittest.TestCase):
 
     @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/onnx/test_onnx_v2.py

@@ -14,7 +14,6 @@ from transformers.onnx import (
     OnnxConfig,
     OnnxConfigWithPast,
     ParameterFormat,
-    export,
     validate_model_outputs,
 )
 from transformers.onnx.utils import (
@@ -40,15 +39,6 @@ class OnnxUtilsTestCaseV2(TestCase):
     Cover all the utilities involved to export ONNX models
     """
 
-    @require_torch
-    @patch("transformers.onnx.convert.is_torch_onnx_dict_inputs_support_available", return_value=False)
-    def test_ensure_pytorch_version_ge_1_8_0(self, mock_is_torch_onnx_dict_inputs_support_available):
-        """
-        Ensure we raise an Exception if the pytorch version is unsupported (< 1.8.0)
-        """
-        self.assertRaises(AssertionError, export, None, None, None, None, None)
-        mock_is_torch_onnx_dict_inputs_support_available.assert_called()
-
     def test_compute_effective_axis_dimension(self):
         """
         When exporting ONNX model with dynamic axis (batch or sequence) we set batch_size and/or sequence_length = -1.