Support : Leverage Accelerate for object detection/segmentation models (#28312)

* made changes for object detection models

* added support for segmentation models.

* Made changes for segmentaion models

* Changed import statements

* solving conflicts

* removed conflicts

* Resolving commits

* Removed conflicts

* Fix : Pixel_mask_value set to False

src/transformers/models/conditional_detr/modeling_conditional_detr.py

@@ -30,6 +30,7 @@ from ...utils import (
     ModelOutput,
     add_start_docstrings,
     add_start_docstrings_to_model_forward,
+    is_accelerate_available,
     is_scipy_available,
     is_timm_available,
     is_vision_available,
@@ -41,6 +42,10 @@ from ...utils.backbone_utils import load_backbone
 from .configuration_conditional_detr import ConditionalDetrConfig
 
 
+if is_accelerate_available():
+    from accelerate import PartialState
+    from accelerate.utils import reduce
+
 if is_scipy_available():
     from scipy.optimize import linear_sum_assignment
 
@@ -2507,11 +2512,12 @@ class ConditionalDetrLoss(nn.Module):
         # Compute the average number of target boxes across all nodes, for normalization purposes
         num_boxes = sum(len(t["class_labels"]) for t in targets)
         num_boxes = torch.as_tensor([num_boxes], dtype=torch.float, device=next(iter(outputs.values())).device)
-        # (Niels): comment out function below, distributed training to be added
-        # if is_dist_avail_and_initialized():
-        #     torch.distributed.all_reduce(num_boxes)
-        # (Niels) in original implementation, num_boxes is divided by get_world_size()
-        num_boxes = torch.clamp(num_boxes, min=1).item()
+
+        world_size = 1
+        if PartialState._shared_state != {}:
+            num_boxes = reduce(num_boxes)
+            world_size = PartialState().num_processes
+        num_boxes = torch.clamp(num_boxes / world_size, min=1).item()
 
         # Compute all the requested losses
         losses = {}

src/transformers/models/deformable_detr/modeling_deformable_detr.py

@@ -43,7 +43,7 @@ from ...modeling_attn_mask_utils import _prepare_4d_attention_mask
 from ...modeling_outputs import BaseModelOutput
 from ...modeling_utils import PreTrainedModel
 from ...pytorch_utils import meshgrid
-from ...utils import is_ninja_available, logging
+from ...utils import is_accelerate_available, is_ninja_available, logging
 from ...utils.backbone_utils import load_backbone
 from .configuration_deformable_detr import DeformableDetrConfig
 from .load_custom import load_cuda_kernels
@@ -65,6 +65,10 @@ else:
 if is_vision_available():
     from transformers.image_transforms import center_to_corners_format
 
+if is_accelerate_available():
+    from accelerate import PartialState
+    from accelerate.utils import reduce
+
 
 class MultiScaleDeformableAttentionFunction(Function):
     @staticmethod
@@ -2246,11 +2250,11 @@ class DeformableDetrLoss(nn.Module):
         # Compute the average number of target boxes accross all nodes, for normalization purposes
         num_boxes = sum(len(t["class_labels"]) for t in targets)
         num_boxes = torch.as_tensor([num_boxes], dtype=torch.float, device=next(iter(outputs.values())).device)
-        # (Niels): comment out function below, distributed training to be added
-        # if is_dist_avail_and_initialized():
-        #     torch.distributed.all_reduce(num_boxes)
-        # (Niels) in original implementation, num_boxes is divided by get_world_size()
-        num_boxes = torch.clamp(num_boxes, min=1).item()
+        world_size = 1
+        if PartialState._shared_state != {}:
+            num_boxes = reduce(num_boxes)
+            world_size = PartialState().num_processes
+        num_boxes = torch.clamp(num_boxes / world_size, min=1).item()
 
         # Compute all the requested losses
         losses = {}

src/transformers/models/detr/modeling_detr.py

@@ -30,6 +30,7 @@ from ...utils import (
     ModelOutput,
     add_start_docstrings,
     add_start_docstrings_to_model_forward,
+    is_accelerate_available,
     is_scipy_available,
     is_timm_available,
     is_vision_available,
@@ -41,6 +42,10 @@ from ...utils.backbone_utils import load_backbone
 from .configuration_detr import DetrConfig
 
 
+if is_accelerate_available():
+    from accelerate import PartialState
+    from accelerate.utils import reduce
+
 if is_scipy_available():
     from scipy.optimize import linear_sum_assignment
 
@@ -2204,11 +2209,11 @@ class DetrLoss(nn.Module):
         # Compute the average number of target boxes across all nodes, for normalization purposes
         num_boxes = sum(len(t["class_labels"]) for t in targets)
         num_boxes = torch.as_tensor([num_boxes], dtype=torch.float, device=next(iter(outputs.values())).device)
-        # (Niels): comment out function below, distributed training to be added
-        # if is_dist_avail_and_initialized():
-        #     torch.distributed.all_reduce(num_boxes)
-        # (Niels) in original implementation, num_boxes is divided by get_world_size()
-        num_boxes = torch.clamp(num_boxes, min=1).item()
+        world_size = 1
+        if PartialState._shared_state != {}:
+            num_boxes = reduce(num_boxes)
+            world_size = PartialState().num_processes
+        num_boxes = torch.clamp(num_boxes / world_size, min=1).item()
 
         # Compute all the requested losses
         losses = {}

src/transformers/models/mask2former/modeling_mask2former.py

@@ -34,7 +34,7 @@ from ...file_utils import (
 )
 from ...modeling_outputs import BaseModelOutput, BaseModelOutputWithCrossAttentions
 from ...modeling_utils import PreTrainedModel
-from ...utils import logging
+from ...utils import is_accelerate_available, logging
 from ...utils.backbone_utils import load_backbone
 from .configuration_mask2former import Mask2FormerConfig
 
@@ -42,6 +42,10 @@ from .configuration_mask2former import Mask2FormerConfig
 if is_scipy_available():
     from scipy.optimize import linear_sum_assignment
 
+if is_accelerate_available():
+    from accelerate import PartialState
+    from accelerate.utils import reduce
+
 logger = logging.get_logger(__name__)
 
 
@@ -788,6 +792,12 @@ class Mask2FormerLoss(nn.Module):
         """
         num_masks = sum([len(classes) for classes in class_labels])
         num_masks_pt = torch.as_tensor(num_masks, dtype=torch.float, device=device)
+        world_size = 1
+        if PartialState._shared_state != {}:
+            num_masks_pt = reduce(num_masks_pt)
+            world_size = PartialState().num_processes
+
+        num_masks_pt = torch.clamp(num_masks_pt / world_size, min=1)
         return num_masks_pt
 
 

src/transformers/models/maskformer/modeling_maskformer.py

@@ -31,6 +31,7 @@ from ...utils import (
     ModelOutput,
     add_start_docstrings,
     add_start_docstrings_to_model_forward,
+    is_accelerate_available,
     is_scipy_available,
     logging,
     replace_return_docstrings,
@@ -42,6 +43,10 @@ from .configuration_maskformer import MaskFormerConfig
 from .configuration_maskformer_swin import MaskFormerSwinConfig
 
 
+if is_accelerate_available():
+    from accelerate import PartialState
+    from accelerate.utils import reduce
+
 if is_scipy_available():
     from scipy.optimize import linear_sum_assignment
 
@@ -1194,6 +1199,12 @@ class MaskFormerLoss(nn.Module):
         """
         num_masks = sum([len(classes) for classes in class_labels])
         num_masks_pt = torch.as_tensor(num_masks, dtype=torch.float, device=device)
+        world_size = 1
+        if PartialState._shared_state != {}:
+            num_masks_pt = reduce(num_masks_pt)
+            world_size = PartialState().num_processes
+
+        num_masks_pt = torch.clamp(num_masks_pt / world_size, min=1)
         return num_masks_pt
 
 

src/transformers/models/oneformer/modeling_oneformer.py

@@ -31,6 +31,7 @@ from ...utils import (
     ModelOutput,
     add_start_docstrings,
     add_start_docstrings_to_model_forward,
+    is_accelerate_available,
     is_scipy_available,
     logging,
     replace_return_docstrings,
@@ -40,6 +41,10 @@ from ...utils.backbone_utils import load_backbone
 from .configuration_oneformer import OneFormerConfig
 
 
+if is_accelerate_available():
+    from accelerate import PartialState
+    from accelerate.utils import reduce
+
 logger = logging.get_logger(__name__)
 
 
@@ -723,6 +728,12 @@ class OneFormerLoss(nn.Module):
         """
         num_masks = sum([len(classes) for classes in class_labels])
         num_masks_pt = torch.as_tensor([num_masks], dtype=torch.float, device=device)
+        world_size = 1
+        if PartialState._shared_state != {}:
+            num_masks_pt = reduce(num_masks_pt)
+            world_size = PartialState().num_processes
+
+        num_masks_pt = torch.clamp(num_masks_pt / world_size, min=1)
         return num_masks_pt
 
 

src/transformers/models/table_transformer/modeling_table_transformer.py

@@ -30,6 +30,7 @@ from ...utils import (
     ModelOutput,
     add_start_docstrings,
     add_start_docstrings_to_model_forward,
+    is_accelerate_available,
     is_scipy_available,
     is_timm_available,
     is_vision_available,
@@ -50,6 +51,10 @@ if is_timm_available():
 if is_vision_available():
     from transformers.image_transforms import center_to_corners_format
 
+if is_accelerate_available():
+    from accelerate import PartialState
+    from accelerate.utils import reduce
+
 logger = logging.get_logger(__name__)
 
 _CONFIG_FOR_DOC = "TableTransformerConfig"
@@ -1751,11 +1756,11 @@ class TableTransformerLoss(nn.Module):
         # Compute the average number of target boxes across all nodes, for normalization purposes
         num_boxes = sum(len(t["class_labels"]) for t in targets)
         num_boxes = torch.as_tensor([num_boxes], dtype=torch.float, device=next(iter(outputs.values())).device)
-        # (Niels): comment out function below, distributed training to be added
-        # if is_dist_avail_and_initialized():
-        #     torch.distributed.all_reduce(num_boxes)
-        # (Niels) in original implementation, num_boxes is divided by get_world_size()
-        num_boxes = torch.clamp(num_boxes, min=1).item()
+        world_size = 1
+        if PartialState._shared_state != {}:
+            num_boxes = reduce(num_boxes)
+            world_size = PartialState().num_processes
+        num_boxes = torch.clamp(num_boxes / world_size, min=1).item()
 
         # Compute all the requested losses
         losses = {}

src/transformers/models/yolos/image_processing_yolos.py

@@ -1297,7 +1297,7 @@ class YolosImageProcessor(BaseImageProcessor):
             encoded_inputs = self.pad(
                 images,
                 annotations=annotations,
-                return_pixel_mask=True,
+                return_pixel_mask=False,
                 data_format=data_format,
                 input_data_format=input_data_format,
                 update_bboxes=do_convert_annotations,

src/transformers/models/yolos/modeling_yolos.py

@@ -33,6 +33,7 @@ from ...utils import (
     add_code_sample_docstrings,
     add_start_docstrings,
     add_start_docstrings_to_model_forward,
+    is_accelerate_available,
     is_scipy_available,
     is_vision_available,
     logging,
@@ -48,6 +49,9 @@ if is_scipy_available():
 if is_vision_available():
     from transformers.image_transforms import center_to_corners_format
 
+if is_accelerate_available():
+    from accelerate import PartialState
+    from accelerate.utils import reduce
 
 logger = logging.get_logger(__name__)
 
@@ -1074,11 +1078,11 @@ class YolosLoss(nn.Module):
         # Compute the average number of target boxes across all nodes, for normalization purposes
         num_boxes = sum(len(t["class_labels"]) for t in targets)
         num_boxes = torch.as_tensor([num_boxes], dtype=torch.float, device=next(iter(outputs.values())).device)
-        # (Niels): comment out function below, distributed training to be added
-        # if is_dist_avail_and_initialized():
-        #     torch.distributed.all_reduce(num_boxes)
-        # (Niels) in original implementation, num_boxes is divided by get_world_size()
-        num_boxes = torch.clamp(num_boxes, min=1).item()
+        world_size = 1
+        if PartialState._shared_state != {}:
+            num_boxes = reduce(num_boxes)
+            world_size = PartialState().num_processes
+        num_boxes = torch.clamp(num_boxes / world_size, min=1).item()
 
         # Compute all the requested losses
         losses = {}