Fix deprecation warnings for int div (#15180)

* Fix deprecation warnings for int div

Co-authored-by: mgoldey <matthew.goldey@gmail.com>

* Fix import

* ensure that tensor output is python scalar

* make backward compatible

* make code more readable

* adapt test functions

Co-authored-by: mgoldey <matthew.goldey@gmail.com>
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>

examples/pytorch/speech-pretraining/run_wav2vec2_pretraining_no_trainer.py

@@ -302,6 +302,8 @@ class DataCollatorForWav2Vec2Pretraining:
         batch_size = batch["input_values"].shape[0]
 
         mask_indices_seq_length = self.model._get_feat_extract_output_lengths(batch["input_values"].shape[-1])
+        # make sure masked sequence length is a Python scalar
+        mask_indices_seq_length = int(mask_indices_seq_length)
 
         # make sure that no loss is computed on padded inputs
         if batch.get("attention_mask") is not None:

src/transformers/modeling_utils.py

@@ -23,6 +23,7 @@ from functools import partial
 from typing import Any, Callable, Dict, List, Optional, Set, Tuple, Union
 
 import torch
+from packaging import version
 from torch import Tensor, device, nn
 from torch.nn import CrossEntropyLoss
 
@@ -2362,3 +2363,13 @@ def apply_chunking_to_forward(
         return torch.cat(output_chunks, dim=chunk_dim)
 
     return forward_fn(*input_tensors)
+
+
+def torch_int_div(tensor1, tensor2):
+    """
+    A function that performs integer division across different versions of PyTorch.
+    """
+    if version.parse(torch.__version__) < version.parse("1.8.0"):
+        return tensor1 // tensor2
+    else:
+        return torch.div(tensor1, tensor2, rounding_mode="floor")

src/transformers/models/hubert/modeling_hubert.py

@@ -33,7 +33,7 @@ from ...file_utils import (
     replace_return_docstrings,
 )
 from ...modeling_outputs import BaseModelOutput, CausalLMOutput, SequenceClassifierOutput
-from ...modeling_utils import PreTrainedModel
+from ...modeling_utils import PreTrainedModel, torch_int_div
 from ...utils import logging
 from .configuration_hubert import HubertConfig
 
@@ -829,7 +829,7 @@ class HubertPreTrainedModel(PreTrainedModel):
         def _conv_out_length(input_length, kernel_size, stride):
             # 1D convolutional layer output length formula taken
             # from https://pytorch.org/docs/stable/generated/torch.nn.Conv1d.html
-            return (input_length - kernel_size) // stride + 1
+            return torch_int_div(input_length - kernel_size, stride) + 1
 
         for kernel_size, stride in zip(self.config.conv_kernel, self.config.conv_stride):
             input_lengths = _conv_out_length(input_lengths, kernel_size, stride)

src/transformers/models/sew/modeling_sew.py

@@ -29,7 +29,7 @@ from transformers.deepspeed import is_deepspeed_zero3_enabled
 from ...activations import ACT2FN
 from ...file_utils import add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward
 from ...modeling_outputs import BaseModelOutput, CausalLMOutput, SequenceClassifierOutput
-from ...modeling_utils import PreTrainedModel
+from ...modeling_utils import PreTrainedModel, torch_int_div
 from ...utils import logging
 from .configuration_sew import SEWConfig
 
@@ -735,7 +735,7 @@ class SEWPreTrainedModel(PreTrainedModel):
         def _conv_out_length(input_length, kernel_size, stride):
             # 1D convolutional layer output length formula taken
             # from https://pytorch.org/docs/stable/generated/torch.nn.Conv1d.html
-            return (input_length - kernel_size) // stride + 1
+            return torch_int_div(input_length - kernel_size, stride) + 1
 
         for kernel_size, stride in zip(self.config.conv_kernel, self.config.conv_stride):
             input_lengths = _conv_out_length(input_lengths, kernel_size, stride)

src/transformers/models/sew_d/modeling_sew_d.py

@@ -30,7 +30,7 @@ from transformers.deepspeed import is_deepspeed_zero3_enabled
 from ...activations import ACT2FN
 from ...file_utils import add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward
 from ...modeling_outputs import BaseModelOutput, CausalLMOutput, SequenceClassifierOutput
-from ...modeling_utils import PreTrainedModel
+from ...modeling_utils import PreTrainedModel, torch_int_div
 from ...utils import logging
 from .configuration_sew_d import SEWDConfig
 
@@ -1266,7 +1266,7 @@ class SEWDPreTrainedModel(PreTrainedModel):
         def _conv_out_length(input_length, kernel_size, stride):
             # 1D convolutional layer output length formula taken
             # from https://pytorch.org/docs/stable/generated/torch.nn.Conv1d.html
-            return (input_length - kernel_size) // stride + 1
+            return torch_int_div(input_length - kernel_size, stride) + 1
 
         for kernel_size, stride in zip(self.config.conv_kernel, self.config.conv_stride):
             input_lengths = _conv_out_length(input_lengths, kernel_size, stride)

src/transformers/models/unispeech/modeling_unispeech.py

@@ -35,7 +35,7 @@ from ...file_utils import (
     replace_return_docstrings,
 )
 from ...modeling_outputs import BaseModelOutput, CausalLMOutput, SequenceClassifierOutput
-from ...modeling_utils import PreTrainedModel
+from ...modeling_utils import PreTrainedModel, torch_int_div
 from ...utils import logging
 from .configuration_unispeech import UniSpeechConfig
 
@@ -969,7 +969,7 @@ class UniSpeechPreTrainedModel(PreTrainedModel):
         def _conv_out_length(input_length, kernel_size, stride):
             # 1D convolutional layer output length formula taken
             # from https://pytorch.org/docs/stable/generated/torch.nn.Conv1d.html
-            return (input_length - kernel_size) // stride + 1
+            return torch_int_div(input_length - kernel_size, stride) + 1
 
         for kernel_size, stride in zip(self.config.conv_kernel, self.config.conv_stride):
             input_lengths = _conv_out_length(input_lengths, kernel_size, stride)

src/transformers/models/unispeech_sat/modeling_unispeech_sat.py

@@ -35,7 +35,7 @@ from ...file_utils import (
     replace_return_docstrings,
 )
 from ...modeling_outputs import BaseModelOutput, CausalLMOutput, SequenceClassifierOutput, TokenClassifierOutput
-from ...modeling_utils import PreTrainedModel
+from ...modeling_utils import PreTrainedModel, torch_int_div
 from ...utils import logging
 from .configuration_unispeech_sat import UniSpeechSatConfig
 
@@ -1003,7 +1003,7 @@ class UniSpeechSatPreTrainedModel(PreTrainedModel):
         def _conv_out_length(input_length, kernel_size, stride):
             # 1D convolutional layer output length formula taken
             # from https://pytorch.org/docs/stable/generated/torch.nn.Conv1d.html
-            return (input_length - kernel_size) // stride + 1
+            return torch_int_div(input_length - kernel_size, stride) + 1
 
         for kernel_size, stride in zip(self.config.conv_kernel, self.config.conv_stride):
             input_lengths = _conv_out_length(input_lengths, kernel_size, stride)

src/transformers/models/wav2vec2/modeling_wav2vec2.py

@@ -41,7 +41,7 @@ from ...modeling_outputs import (
     SequenceClassifierOutput,
     TokenClassifierOutput,
 )
-from ...modeling_utils import PreTrainedModel
+from ...modeling_utils import PreTrainedModel, torch_int_div
 from ...utils import logging
 from .configuration_wav2vec2 import Wav2Vec2Config
 
@@ -1104,7 +1104,7 @@ class Wav2Vec2PreTrainedModel(PreTrainedModel):
         def _conv_out_length(input_length, kernel_size, stride):
             # 1D convolutional layer output length formula taken
             # from https://pytorch.org/docs/stable/generated/torch.nn.Conv1d.html
-            return (input_length - kernel_size) // stride + 1
+            return torch_int_div(input_length - kernel_size, stride) + 1
 
         for kernel_size, stride in zip(self.config.conv_kernel, self.config.conv_stride):
             input_lengths = _conv_out_length(input_lengths, kernel_size, stride)

src/transformers/models/wavlm/modeling_wavlm.py

@@ -35,7 +35,7 @@ from ...file_utils import (
     add_start_docstrings_to_model_forward,
 )
 from ...modeling_outputs import BaseModelOutput, CausalLMOutput, SequenceClassifierOutput, TokenClassifierOutput
-from ...modeling_utils import PreTrainedModel
+from ...modeling_utils import PreTrainedModel, torch_int_div
 from ...utils import logging
 from .configuration_wavlm import WavLMConfig
 
@@ -1057,7 +1057,7 @@ class WavLMPreTrainedModel(PreTrainedModel):
         def _conv_out_length(input_length, kernel_size, stride):
             # 1D convolutional layer output length formula taken
             # from https://pytorch.org/docs/stable/generated/torch.nn.Conv1d.html
-            return (input_length - kernel_size) // stride + 1
+            return torch_int_div(input_length - kernel_size, stride) + 1
 
         for kernel_size, stride in zip(self.config.conv_kernel, self.config.conv_stride):
             input_lengths = _conv_out_length(input_lengths, kernel_size, stride)

tests/test_modeling_wav2vec2.py

@@ -794,10 +794,10 @@ class Wav2Vec2RobustModelTest(ModelTesterMixin, unittest.TestCase):
         config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
         model = Wav2Vec2ForPreTraining(config).to(torch_device)
 
-        features_shape = (
-            inputs_dict["input_values"].shape[0],
-            model._get_feat_extract_output_lengths(inputs_dict["input_values"].shape[1]),
-        )
+        batch_size = inputs_dict["input_values"].shape[0]
+        feature_seq_length = int(model._get_feat_extract_output_lengths(inputs_dict["input_values"].shape[1]))
+
+        features_shape = (batch_size, feature_seq_length)
 
         mask_time_indices = _compute_mask_indices(
             features_shape,
@@ -1158,10 +1158,10 @@ class Wav2Vec2ModelIntegrationTest(unittest.TestCase):
 
         inputs_dict = feature_extractor(input_speech, return_tensors="pt", padding=True)
 
-        features_shape = (
-            inputs_dict["input_values"].shape[0],
-            model._get_feat_extract_output_lengths(torch.tensor(inputs_dict["input_values"].shape[1])),
-        )
+        batch_size = inputs_dict["input_values"].shape[0]
+        feature_seq_length = int(model._get_feat_extract_output_lengths(inputs_dict["input_values"].shape[1]))
+
+        features_shape = (batch_size, feature_seq_length)
 
         np.random.seed(4)
         mask_time_indices = _compute_mask_indices(
@@ -1208,10 +1208,10 @@ class Wav2Vec2ModelIntegrationTest(unittest.TestCase):
 
         inputs_dict = feature_extractor(input_speech, return_tensors="pt", padding=True)
 
-        features_shape = (
-            inputs_dict["input_values"].shape[0],
-            model._get_feat_extract_output_lengths(torch.tensor(inputs_dict["input_values"].shape[1])),
-        )
+        batch_size = inputs_dict["input_values"].shape[0]
+        feature_seq_length = int(model._get_feat_extract_output_lengths(inputs_dict["input_values"].shape[1]))
+
+        features_shape = (batch_size, feature_seq_length)
 
         torch.manual_seed(0)
         mask_time_indices = _compute_mask_indices(
@@ -1279,10 +1279,10 @@ class Wav2Vec2ModelIntegrationTest(unittest.TestCase):
 
         inputs_dict = feature_extractor(input_speech, return_tensors="pt", padding=True)
 
-        features_shape = (
-            inputs_dict["input_values"].shape[0],
-            model._get_feat_extract_output_lengths(inputs_dict["input_values"].shape[1]),
-        )
+        batch_size = inputs_dict["input_values"].shape[0]
+        feature_seq_length = int(model._get_feat_extract_output_lengths(inputs_dict["input_values"].shape[1]))
+
+        features_shape = (batch_size, feature_seq_length)
 
         torch.manual_seed(0)
         np.random.seed(0)