prepare for "__floordiv__ is deprecated and its behavior will change in a future version of pytorch" (#20211)

* rounding_mode = "floor"  instead of // to prevent behavioral change

* add other TODO

* use `torch_int_div` from pytrch_utils

* same for tests

* fix copies

* style

* use relative imports when needed

* Co-authored-by: sgugger <sylvain.gugger@gmail.com>

src/transformers/models/big_bird/modeling_big_bird.py

@@ -37,7 +37,7 @@ from ...modeling_outputs import (
     TokenClassifierOutput,
 )
 from ...modeling_utils import PreTrainedModel
-from ...pytorch_utils import apply_chunking_to_forward
+from ...pytorch_utils import apply_chunking_to_forward, torch_int_div
 from ...utils import (
     ModelOutput,
     add_code_sample_docstrings,
@@ -971,11 +971,8 @@ class BigBirdBlockSparseAttention(nn.Module):
         num_indices_to_gather = indices.shape[-2] * indices.shape[-1]
         num_indices_to_pick_from = params.shape[2]
 
-        indices_shift = (
-            torch.arange(indices.shape[0] * indices.shape[1] * num_indices_to_gather, device=indices.device)
-            // num_indices_to_gather
-            * num_indices_to_pick_from
-        )
+        shift = torch.arange(indices.shape[0] * indices.shape[1] * num_indices_to_gather, device=indices.device)
+        indices_shift = torch_int_div(shift, num_indices_to_gather) * num_indices_to_pick_from
 
         flattened_indices = indices.view(-1) + indices_shift
         flattened_params = params.reshape(-1, params.shape[-2], params.shape[-1])

src/transformers/models/bigbird_pegasus/modeling_bigbird_pegasus.py

@@ -36,6 +36,7 @@ from ...modeling_outputs import (
     Seq2SeqSequenceClassifierOutput,
 )
 from ...modeling_utils import PreTrainedModel
+from ...pytorch_utils import torch_int_div
 from ...utils import (
     add_code_sample_docstrings,
     add_end_docstrings,
@@ -789,11 +790,8 @@ class BigBirdPegasusBlockSparseAttention(nn.Module):
         num_indices_to_gather = indices.shape[-2] * indices.shape[-1]
         num_indices_to_pick_from = params.shape[2]
 
-        indices_shift = (
-            torch.arange(indices.shape[0] * indices.shape[1] * num_indices_to_gather, device=indices.device)
-            // num_indices_to_gather
-            * num_indices_to_pick_from
-        )
+        shift = torch.arange(indices.shape[0] * indices.shape[1] * num_indices_to_gather, device=indices.device)
+        indices_shift = torch_int_div(shift, num_indices_to_gather) * num_indices_to_pick_from
 
         flattened_indices = indices.view(-1) + indices_shift
         flattened_params = params.reshape(-1, params.shape[-2], params.shape[-1])

src/transformers/models/conditional_detr/image_processing_conditional_detr.py

@@ -67,6 +67,8 @@ if is_torch_available():
     import torch
     from torch import nn
 
+    from transformers.pytorch_utils import torch_int_div
+
 
 if is_vision_available():
     import PIL
@@ -1311,7 +1313,7 @@ class ConditionalDetrImageProcessor(BaseImageProcessor):
         prob = out_logits.sigmoid()
         topk_values, topk_indexes = torch.topk(prob.view(out_logits.shape[0], -1), 300, dim=1)
         scores = topk_values
-        topk_boxes = topk_indexes // out_logits.shape[2]
+        topk_boxes = torch_int_div(topk_indexes, out_logits.shape[2])
         labels = topk_indexes % out_logits.shape[2]
         boxes = center_to_corners_format(out_bbox)
         boxes = torch.gather(boxes, 1, topk_boxes.unsqueeze(-1).repeat(1, 1, 4))
@@ -1357,7 +1359,7 @@ class ConditionalDetrImageProcessor(BaseImageProcessor):
         prob = out_logits.sigmoid()
         topk_values, topk_indexes = torch.topk(prob.view(out_logits.shape[0], -1), 100, dim=1)
         scores = topk_values
-        topk_boxes = topk_indexes // out_logits.shape[2]
+        topk_boxes = torch_int_div(topk_indexes, out_logits.shape[2])
         labels = topk_indexes % out_logits.shape[2]
         boxes = center_to_corners_format(out_bbox)
         boxes = torch.gather(boxes, 1, topk_boxes.unsqueeze(-1).repeat(1, 1, 4))

src/transformers/models/conditional_detr/modeling_conditional_detr.py

@@ -504,7 +504,7 @@ def build_position_encoding(config):
 def gen_sine_position_embeddings(pos_tensor):
     scale = 2 * math.pi
     dim_t = torch.arange(128, dtype=torch.float32, device=pos_tensor.device)
-    dim_t = 10000 ** (2 * (dim_t // 2) / 128)
+    dim_t = 10000 ** (2 * torch_int_div(dim_t, 2) / 128)
     x_embed = pos_tensor[:, :, 0] * scale
     y_embed = pos_tensor[:, :, 1] * scale
     pos_x = x_embed[:, :, None] / dim_t

src/transformers/models/deformable_detr/image_processing_deformable_detr.py

@@ -67,6 +67,8 @@ if is_torch_available():
     import torch
     from torch import nn
 
+    from ...pytorch_utils import torch_int_div
+
 
 if is_vision_available():
     import PIL
@@ -1309,7 +1311,7 @@ class DeformableDetrImageProcessor(BaseImageProcessor):
         prob = out_logits.sigmoid()
         topk_values, topk_indexes = torch.topk(prob.view(out_logits.shape[0], -1), 100, dim=1)
         scores = topk_values
-        topk_boxes = topk_indexes // out_logits.shape[2]
+        topk_boxes = torch_int_div(topk_indexes, out_logits.shape[2])
         labels = topk_indexes % out_logits.shape[2]
         boxes = center_to_corners_format(out_bbox)
         boxes = torch.gather(boxes, 1, topk_boxes.unsqueeze(-1).repeat(1, 1, 4))
@@ -1354,7 +1356,7 @@ class DeformableDetrImageProcessor(BaseImageProcessor):
         prob = out_logits.sigmoid()
         topk_values, topk_indexes = torch.topk(prob.view(out_logits.shape[0], -1), 100, dim=1)
         scores = topk_values
-        topk_boxes = topk_indexes // out_logits.shape[2]
+        topk_boxes = torch_int_div(topk_indexes, out_logits.shape[2])
         labels = topk_indexes % out_logits.shape[2]
         boxes = center_to_corners_format(out_bbox)
         boxes = torch.gather(boxes, 1, topk_boxes.unsqueeze(-1).repeat(1, 1, 4))

src/transformers/models/deformable_detr/modeling_deformable_detr.py

@@ -41,7 +41,7 @@ from ...file_utils import (
 )
 from ...modeling_outputs import BaseModelOutput
 from ...modeling_utils import PreTrainedModel
-from ...pytorch_utils import meshgrid
+from ...pytorch_utils import meshgrid, torch_int_div
 from ...utils import is_ninja_available, logging
 from ..auto import AutoBackbone
 from .configuration_deformable_detr import DeformableDetrConfig
@@ -497,7 +497,7 @@ class DeformableDetrSinePositionEmbedding(nn.Module):
             x_embed = (x_embed - 0.5) / (x_embed[:, :, -1:] + eps) * self.scale
 
         dim_t = torch.arange(self.embedding_dim, dtype=torch.float32, device=pixel_values.device)
-        dim_t = self.temperature ** (2 * (dim_t // 2) / self.embedding_dim)
+        dim_t = self.temperature ** (2 * torch_int_div(dim_t, 2 / self.embedding_dim))
 
         pos_x = x_embed[:, :, :, None] / dim_t
         pos_y = y_embed[:, :, :, None] / dim_t
@@ -1552,7 +1552,7 @@ class DeformableDetrModel(DeformableDetrPreTrainedModel):
         scale = 2 * math.pi
 
         dim_t = torch.arange(num_pos_feats, dtype=torch.float32, device=proposals.device)
-        dim_t = temperature ** (2 * (dim_t // 2) / num_pos_feats)
+        dim_t = temperature ** (2 * torch.div(dim_t, 2) / num_pos_feats)
         # batch_size, num_queries, 4
         proposals = proposals.sigmoid() * scale
         # batch_size, num_queries, 4, 128

src/transformers/models/deta/image_processing_deta.py

@@ -63,6 +63,8 @@ from ...utils.generic import ExplicitEnum, TensorType
 if is_torch_available():
     import torch
 
+    from ...pytorch_utils import torch_int_div
+
 if is_torchvision_available():
     from torchvision.ops.boxes import batched_nms
 
@@ -965,7 +967,7 @@ class DetaImageProcessor(BaseImageProcessor):
 
         all_scores = prob.view(batch_size, num_queries * num_labels).to(out_logits.device)
         all_indexes = torch.arange(num_queries * num_labels)[None].repeat(batch_size, 1).to(out_logits.device)
-        all_boxes = all_indexes // out_logits.shape[2]
+        all_boxes = torch_int_div(all_indexes, out_logits.shape[2])
         all_labels = all_indexes % out_logits.shape[2]
 
         boxes = center_to_corners_format(out_bbox)

src/transformers/models/deta/modeling_deta.py

@@ -36,7 +36,7 @@ from ...file_utils import (
 )
 from ...modeling_outputs import BaseModelOutput
 from ...modeling_utils import PreTrainedModel
-from ...pytorch_utils import meshgrid
+from ...pytorch_utils import meshgrid, torch_int_div
 from ...utils import is_torchvision_available, logging, requires_backends
 from ..auto import AutoBackbone
 from .configuration_deta import DetaConfig
@@ -399,7 +399,7 @@ class DetaSinePositionEmbedding(nn.Module):
             x_embed = (x_embed - 0.5) / (x_embed[:, :, -1:] + eps) * self.scale
 
         dim_t = torch.arange(self.embedding_dim, dtype=torch.float32, device=pixel_values.device)
-        dim_t = self.temperature ** (2 * (dim_t // 2) / self.embedding_dim)
+        dim_t = self.temperature ** (2 * torch_int_div(dim_t, 2 / self.embedding_dim))
 
         pos_x = x_embed[:, :, :, None] / dim_t
         pos_y = y_embed[:, :, :, None] / dim_t
@@ -1463,7 +1463,7 @@ class DetaModel(DetaPreTrainedModel):
         scale = 2 * math.pi
 
         dim_t = torch.arange(num_pos_feats, dtype=torch.float32, device=proposals.device)
-        dim_t = temperature ** (2 * (dim_t // 2) / num_pos_feats)
+        dim_t = temperature ** (2 * torch.div(dim_t, 2) / num_pos_feats)
         # batch_size, num_queries, 4
         proposals = proposals.sigmoid() * scale
         # batch_size, num_queries, 4, 128

src/transformers/models/mask2former/image_processing_mask2former.py

@@ -57,6 +57,8 @@ if is_torch_available():
     import torch
     from torch import nn
 
+    from ...pytorch_utils import torch_int_div
+
 
 # Copied from transformers.models.detr.image_processing_detr.max_across_indices
 def max_across_indices(values: Iterable[Any]) -> List[Any]:
@@ -1007,7 +1009,7 @@ class Mask2FormerImageProcessor(BaseImageProcessor):
             scores_per_image, topk_indices = scores.flatten(0, 1).topk(num_queries, sorted=False)
             labels_per_image = labels[topk_indices]
 
-            topk_indices = topk_indices // num_classes
+            topk_indices = torch_int_div(topk_indices, num_classes)
             mask_pred = mask_pred[topk_indices]
             pred_masks = (mask_pred > 0).float()
 

src/transformers/models/maskformer/image_processing_maskformer.py

@@ -61,6 +61,8 @@ if is_torch_available():
     import torch
     from torch import nn
 
+    from ...pytorch_utils import torch_int_div
+
 
 # Copied from transformers.models.detr.image_processing_detr.max_across_indices
 def max_across_indices(values: Iterable[Any]) -> List[Any]:
@@ -1075,7 +1077,7 @@ class MaskFormerImageProcessor(BaseImageProcessor):
             scores_per_image, topk_indices = scores.flatten(0, 1).topk(num_queries, sorted=False)
             labels_per_image = labels[topk_indices]
 
-            topk_indices = topk_indices // num_classes
+            topk_indices = torch_int_div(topk_indices, num_classes)
             mask_pred = mask_pred[topk_indices]
             pred_masks = (mask_pred > 0).float()
 

src/transformers/models/oneformer/image_processing_oneformer.py

@@ -58,6 +58,8 @@ if is_torch_available():
     import torch
     from torch import nn
 
+    from ...pytorch_utils import torch_int_div
+
 
 # Copied from transformers.models.detr.image_processing_detr.max_across_indices
 def max_across_indices(values: Iterable[Any]) -> List[Any]:
@@ -1120,7 +1122,7 @@ class OneFormerImageProcessor(BaseImageProcessor):
             scores_per_image, topk_indices = scores.flatten(0, 1).topk(num_queries, sorted=False)
             labels_per_image = labels[topk_indices]
 
-            topk_indices = topk_indices // num_classes
+            topk_indices = torch_int_div(topk_indices, num_classes)
             # mask_pred = mask_pred.unsqueeze(1).repeat(1, self.sem_seg_head.num_classes, 1).flatten(0, 1)
             mask_pred = masks_queries_logits[i][topk_indices]
 

src/transformers/models/reformer/modeling_reformer.py

@@ -909,10 +909,9 @@ class LSHSelfAttention(nn.Module, EfficientAttentionMixin):
         relevant_bucket_idx_chunk = bucket_idx[tuple(relevant_bucket_idx_chunk.transpose(0, 1))]
 
         # adapt bucket_idx for batch and hidden states for index select
+        offset = torch.arange(relevant_bucket_idx_chunk.shape[-1], device=hidden_states.device, dtype=torch.long)
         bucket_idx_batch_offset = sequence_length * (
-            batch_size
-            * torch.arange(relevant_bucket_idx_chunk.shape[-1], device=hidden_states.device, dtype=torch.long)
-            // relevant_bucket_idx_chunk.shape[-1]
+            batch_size * torch.div(offset, relevant_bucket_idx_chunk.shape[-1], rounding_mode="floor")
         )
 
         # add batch offset

src/transformers/models/tapas/modeling_tapas.py

@@ -29,7 +29,12 @@ from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
 from ...activations import ACT2FN
 from ...modeling_outputs import BaseModelOutput, BaseModelOutputWithPooling, MaskedLMOutput, SequenceClassifierOutput
 from ...modeling_utils import PreTrainedModel
-from ...pytorch_utils import apply_chunking_to_forward, find_pruneable_heads_and_indices, prune_linear_layer
+from ...pytorch_utils import (
+    apply_chunking_to_forward,
+    find_pruneable_heads_and_indices,
+    prune_linear_layer,
+    torch_int_div,
+)
 from ...utils import (
     ModelOutput,
     add_start_docstrings,
@@ -1632,11 +1637,8 @@ class ProductIndexMap(IndexMap):
 
     def project_outer(self, index):
         """Projects an index with the same index set onto the outer components."""
-        return IndexMap(
-            indices=(index.indices // self.inner_index.num_segments).type(torch.float).floor().type(torch.long),
-            num_segments=self.outer_index.num_segments,
-            batch_dims=index.batch_dims,
-        )
+        indices = torch_int_div(index.indices, self.inner_index.num_segments).type(torch.long)
+        return IndexMap(indices=indices, num_segments=self.outer_index.num_segments, batch_dims=index.batch_dims)
 
     def project_inner(self, index):
         """Projects an index with the same index set onto the inner components."""

tests/generation/test_beam_search.py

@@ -32,6 +32,7 @@ if is_torch_available():
         DisjunctiveConstraint,
         PhrasalConstraint,
     )
+    from transformers.pytorch_utils import torch_int_div
 
 
 class BeamSearchTester:
@@ -160,10 +161,8 @@ class BeamSearchTester:
         expected_output_scores = cut_expected_tensor(next_scores)
 
         # add num_beams * batch_idx
-        expected_output_indices = (
-            cut_expected_tensor(next_indices)
-            + (torch.arange(self.num_beams * self.batch_size, device=torch_device) // self.num_beams) * self.num_beams
-        )
+        offset = torch_int_div(torch.arange(self.num_beams * self.batch_size, device=torch_device), self.num_beams)
+        expected_output_indices = cut_expected_tensor(next_indices) + offset * self.num_beams
 
         self.parent.assertListEqual(expected_output_tokens.tolist(), output_tokens.tolist())
         self.parent.assertListEqual(expected_output_indices.tolist(), output_indices.tolist())
@@ -399,10 +398,8 @@ class ConstrainedBeamSearchTester:
         expected_output_scores = cut_expected_tensor(next_scores)
 
         # add num_beams * batch_idx
-        expected_output_indices = (
-            cut_expected_tensor(next_indices)
-            + (torch.arange(self.num_beams * self.batch_size, device=torch_device) // self.num_beams) * self.num_beams
-        )
+        offset = torch_int_div(torch.arange(self.num_beams * self.batch_size, device=torch_device), self.num_beams)
+        expected_output_indices = cut_expected_tensor(next_indices) + offset * self.num_beams
 
         self.parent.assertListEqual(expected_output_tokens.tolist(), output_tokens.tolist())
         self.parent.assertListEqual(expected_output_indices.tolist(), output_indices.tolist())

tests/models/wav2vec2/test_modeling_wav2vec2.py

@@ -71,7 +71,7 @@ if is_torch_available():
         _compute_mask_indices,
         _sample_negative_indices,
     )
-    from transformers.pytorch_utils import is_torch_less_than_1_9
+    from transformers.pytorch_utils import is_torch_less_than_1_9, torch_int_div
 else:
     is_torch_less_than_1_9 = True
 
@@ -1217,10 +1217,8 @@ class Wav2Vec2UtilsTest(unittest.TestCase):
         sequence_length = 10
         hidden_size = 4
         num_negatives = 3
-
-        features = (torch.arange(sequence_length * hidden_size, device=torch_device) // hidden_size).view(
-            sequence_length, hidden_size
-        )  # each value in vector consits of same value
+        sequence = torch_int_div(torch.arange(sequence_length * hidden_size, device=torch_device), hidden_size)
+        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()
 
         # sample negative indices
@@ -1247,9 +1245,8 @@ class Wav2Vec2UtilsTest(unittest.TestCase):
         mask = torch.ones((batch_size, sequence_length), dtype=torch.long, device=torch_device)
         mask[-1, sequence_length // 2 :] = 0
 
-        features = (torch.arange(sequence_length * hidden_size, device=torch_device) // hidden_size).view(
-            sequence_length, hidden_size
-        )  # each value in vector consits of same value
+        sequence = torch_int_div(torch.arange(sequence_length * hidden_size, device=torch_device), hidden_size)
+        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()
 
         # replace masked feature vectors with -100 to test that those are not sampled