Skip to content

[Context Parallel] fix: support PyTorch < 2.6 in distributed gather #13075

New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Open
akshatvishu wants to merge 3 commits into
base: main
Choose a base branch
from
+9 −1
Open

[Context Parallel] fix: support PyTorch < 2.6 in distributed gather #13075

Changes from 1 commit
Commits
Show all changes
3 commits
Select commit Hold shift + click to select a range
a46c350
[Context Parallel] fix: support PyTorch < 2.6 in distributed gather
akshatvishu Feb 3, 2026
f01c13c
[Context Parallel] fix: use get_device() and add fail-fast check
akshatvishu Feb 4, 2026
b300f05
fix: use torch.device with get_device and simplify error message
akshatvishu Feb 4, 2026
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
10 changes: 9 additions & 1 deletion src/diffusers/models/_modeling_parallel.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -290,7 +290,15 @@ def gather_size_by_comm(size: int, group: dist.ProcessGroup) -> List[int]:
# HACK: Use Gloo backend for all_gather to avoid H2D and D2H overhead
comm_backends = str(dist.get_backend(group=group))
# NOTE: e.g., dist.init_process_group(backend="cpu:gloo,cuda:nccl")
gather_device = "cpu" if "cpu" in comm_backends else torch.accelerator.current_accelerator()
if "cpu" in comm_backends:
gather_device = "cpu"
elif hasattr(torch, "accelerator"):
acc = torch.accelerator.current_accelerator()
# Fall back to CUDA when no accelerator is returned.
gather_device = acc if acc is not None else torch.device("cuda")
else:
gather_device = torch.device("cuda")

gathered_sizes = [torch.empty((1,), device=gather_device, dtype=torch.int64) for _ in range(world_size)]
dist.all_gather(
gathered_sizes,
Expand Down