Ulysses sequence parallelism shards a long sequence across GPUs: one all-to-all before attention trades the sequence shard for a head shard, a second trades it back. For long-sequence video DiT workloads those two collectives are the critical communication.

This ships that 4D all-to-all as torch.ops.fast_ulysses.all_to_all_4d, over NVLink, on one node.

Key features

• No SMs. The peer transfers are pitched cudaMemcpy2D/3DAsync straight into peers' symmetric-memory addresses, so they run on the copy engines while compute kernels hold every SM. (Measured: a peer copy overlaps a concurrent GEMM completely; a same-device copy does not, which is why out= from empty_output() — which removes the copy-out — is the faster path.)
• Relayout for free. The sequence/head permutation is expressed as source and destination strides on those copies, not as two permute kernels. That is roughly half of what the torch.distributed path spends.
• Uneven shards. Per-rank seq_splits / head_splits, which is what lets a caller drop sequence padding. Even splits are the special case, not a separate path.

Performance

8 GPUs, one exclusively allocated node per row, the same wheel and the same .so on all of them. base is torch.distributed permute + all_to_all_single + permute. Wan 720p, bf16, ms.

47–60% of the baseline is relayout that costs nothing here. "Of the fabric" is the transfer against what a flat peer copy achieves on the same machine — at 90–99% there is nothing left to schedule. out= from empty_output() removes the copy-out for a further 1.14–1.28×.

Every number was taken twice, on two different nodes except H100, whose two samples are the same node. Full tables, per-stage timings and the method: docs/benchmark.md.

Quick start

Save this as example.py and run torchrun --nproc_per_node=8 example.py:

import os

import torch
import torch.distributed as dist
from fast_ulysses import UlyssesGroup

dist.init_process_group("nccl")
torch.cuda.set_device(int(os.environ["LOCAL_RANK"]))
group = UlyssesGroup()

b, s_local, heads, d = 1, 4096, 8, 128
ws = dist.get_world_size()
x = torch.randn(b, s_local, heads * ws, d, dtype=torch.bfloat16, device="cuda")

y = group.all_to_all_4d(x, mode=0)        # (b, s_local * ws, heads, d)
z = group.all_to_all_4d(y, mode=1)        # back to x's shape

group.destroy()
dist.destroy_process_group()

Every rank must issue the same sequence of shapes. More: docs/quickstart.md.

A real one — a DiT attention block, sequence-parallel, forward and backward, checked against the same block without sequence parallelism:

torchrun --nproc_per_node=8 examples/dit_attention.py

The collective is differentiable, so that block is an ordinary nn.Module: no autograd.Function wrapper, no manual backward.

API

Shapes, splits and the collective contract: docs/api.md. Why the code is shaped this way, and what it rests on that is not guaranteed: docs/design.md. Coming from DeepSpeed-Ulysses, sglang's usp.py or yunchang — what each call becomes, and what this does not have: docs/migration.md.

Limits

• NVLink, one node, world_size in [1, 8], including odd sizes. Over PCIe — especially across a CPU socket — torch.distributed is faster, because the pitched copies this transport is built on degrade badly there. The constructor refuses such a group.
• float16 / bfloat16 / float32 / float8_e4m3fn / float8_e5m2 / int8 / uint8, and d * elem_size must be 16-byte aligned — so d % 8 at bfloat16, d % 16 at float8.
• Differentiable, and it propagates shapes under FakeTensor. Not traceable by torch.compile: the group is a torchbind object with no registered fake class, so Dynamo graph-breaks on it.
• Forward-mode AD works — torch.autograd.forward_ad, torch.func.jvp, torch.func.linearize — at one extra collective for the tangent. Reverse-mode torch.func does not: grad / vjp / jacrev raise, and vmap raises by an explicit check, since it would run one collective per batch element and ranks that disagree on the batch size hang.
• The async form is not differentiable and says so: its AsyncCollectiveTensor is a leaf, so a gradient would be dropped silently. Use all_to_all_4d when you need one.
• out= is not differentiable either, and cannot say so: it reaches a mutating op with no autograd formula, so the result is the caller's buffer, detached. It is a speed knob for inference and no-grad regions, not for a training graph.
• CUDA-graph capture covers the sync call on an already-warmed shape; see docs/design.md.

Install

Requires PyTorch 2.10+, CUDA 12.8+ or 13, and sm80 / sm90 / sm100 / sm120. torch is the only runtime dependency.

A wheel is built against one (torch minor, CUDA major) and embeds that torch's C++ ABI, so the pair has to match. Install torch first, then pick the index by the CUDA major it was built against:

pip install fast-ulysses --index-url https://triple-mu.github.io/fast-ulysses/whl/cu13/   # or cu12

That index carries fast-ulysses and nothing else, so pip cannot move your torch to satisfy it: it backtracks to the wheel pinned to the torch minor you already have. fast-ulysses wheel-url names the row for the environment running now and prints the same install as a fully pinned --find-links command against the release assets, when you want the file itself.

pip install fast-ulysses                                          # PyPI: torch 2.13.x + CUDA 13
pip install -e . --no-build-isolation                             # from source, all four arches
FAST_ULYSSES_CUDA_ARCH=90 pip install -e . --no-build-isolation   # one arch, much faster

PyPI carries that one pair, pinned torch==2.13.*. On any other torch the first command does not fail — pip upgrades torch to 2.13, a different CUDA build than the one you chose.

The one thing to know before reaching for --find-links: PEP 440 orders local versions, so an install that does not write +torch210cu128 out in full takes torch213cu130 whatever torch you have, silently. That, the per-row indexes and what to do when the import fails: docs/install.md.

Testing

pytest                  # the host-only plan tests always run; the GPU workers skip below 2 GPUs
pytest -m "not multigpu"                                   # host-only, no GPU needed
torchrun --nproc_per_node=8 test/distributed/correctness.py

Development setup: docs/develop.md.

License

Apache-2.0. See LICENSE.