Repository: huggingface/transformers
Pull request: [Trainer] use deepspeed SP process group when Accelerate doesn’t build a mesh

# What does this PR do?

With this change: https://github.com/huggingface/accelerate/pull/3916 there is no torch device mesh when sp_backend="deepspeed", and transformers currently assumes it exists.
  That’s why you get:

```
  sp_group = self.accelerator.torch_device_mesh["sp"].get_group()
  TypeError: 'NoneType' object is not subscriptable
```

With this change, it uses DeepSpeed’s SP process group when Accelerate doesn’t build a mesh.

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Changed files:
filename: src/transformers/trainer.py
patch: @@ -3922,8 +3922,20 @@ def _deepspeed_sp_compute_loss(self, model, inputs, return_outputs, pc):
         outputs = model(**inputs)
         loss = outputs.loss
 
-        sp_group = self.accelerator.torch_device_mesh["sp"].get_group()
-        sp_world_size = pc.sp_size
+        # Prefer DeepSpeed SP groups when using Ulysses; otherwise fall back to torch device mesh.
+        if pc.sp_backend == "deepspeed" and pc.sp_size > 1:
+            from deepspeed.utils import groups
+
+            sp_group = groups._get_sequence_parallel_group()
+            sp_world_size = groups._get_sequence_parallel_world_size()
+        elif self.accelerator.torch_device_mesh is not None:
+            sp_group = self.accelerator.torch_device_mesh["sp"].get_group()
+            sp_world_size = pc.sp_size
+        else:
+            raise ValueError(
+                "Sequence parallelism is enabled but no SP process group is available. "
+                "Ensure torch_device_mesh is initialized or sp_backend='deepspeed' with sp_size > 1."
+            )
         # differentiable weighted per-shard-loss aggregation across ranks
         losses_per_rank = torch.distributed.nn.functional.all_gather(loss, group=sp_group)
         # special dealing with SFT that has prompt tokens that aren't used in loss computation