[Core] Add Ring Attention Primitives for Context Parallelism

[knitcapcat-amd]

Purpose

Add Ring Attention primitives as a building block for Prefill Context Parallelism (PCP), delivering the "Ring-CP style attention backend algorithm" listed in the PCP roadmap (RFC #25749, ref RFC #26133).

Ring Attention partitions the sequence across CP ranks: Q stays local while K/V circulate through a P2P ring. Each step computes a partial attention block and incrementally merges the result via online softmax correction (sigmoid/logsigmoid), enabling communication-computation overlap without the post-hoc all-gather + LSE correction required by the existing DCP approach (cp_lse_ag_out_rs).

This PR provides the communication + computation framework only. It does not enable PCP end-to-end; PCP integration (KV cache management, ModelRunner token sharding, supports_pcp=True) will follow in a subsequent PR. This follows the pattern of #28718 (PCP infra merged separately from attention backend) and #34883 (A2A comm backend for DCP added as a separate PR).

New files

File	Lines	Description
vllm/distributed/ring_comm.py	174	Async ring P2P communicator with dedicated CUDA stream, event-based sync, and rank-parity deadlock avoidance)
vllm/v1/attention/ops/ring_attn.py	236	ring_flash_attn_varlen_func — Ring Attention for packed variable-length sequences, with online softmax merge, KV packing (halves P2P ops per step), double buffering (zero per-step allocation), and cached CUDA stream. Uses fa_utils.flash_attn_varlen_func for platform-agnostic FA dispatch.
tests/distributed/test_ring_attn.py	293	14 correctness configs × CP={2,4}, covering bidirectional + causal, MHA + GQA, bf16 + fp16, single + multi-request packed batches. Supports both pytest+ray (CI) and standalone torchrun.

Key design choices

• Communication-computation overlap: RingComm runs P2P on a dedicated CUDA stream while FlashAttention runs on the compute stream. Profiled 94% of P2P time hidden behind FA compute on MI300X at S=8192.
• KV packing: K and V are concatenated into a single contiguous buffer before P2P, reducing ops per ring step from 4 to 2.
• Double buffering: Two pre-allocated receive buffers alternate across steps, eliminating per-step torch.empty_like allocation.
• Cached CUDA stream: hipStreamCreate costs ~2ms on ROCm; a module-level cache avoids this on every call (TODO: move to backend init in PCP integration PR).
• Uses fa_utils for FA dispatch: No custom backend selection — platform adaptation (CUDA/ROCm/XPU) is handled by vLLM's existing fa_utils.flash_attn_varlen_func.

Known limitations

• Sequence sharding assumes contiguous partitioning (no stripe/zigzag). Dual-chunk partitioning with per-step position-aware masking is planned for the PCP integration PR.
• Each request's seq_len must be divisible by cp_size.
• Not compatible with torch.compile (Python ring loop + dist.batch_isend_irecv + explicit CUDA stream management). This is consistent with existing DCP code which also runs in eager mode.
• Does not manage KV cache (caller's responsibility, same as other ops in vllm/v1/attention/ops/).

Test Plan

# Correctness — torchrun (standalone, 2 GPU)
torchrun --nproc_per_node=2 tests/distributed/test_ring_attn.py

# Correctness — torchrun (standalone, 4 GPU)
torchrun --nproc_per_node=4 tests/distributed/test_ring_attn.py

# Correctness — pytest (CI mode, requires ray)
pytest tests/distributed/test_ring_attn.py -v

Test Result

Tested on 8× AMD MI300X (ROCm 7.1.1, PyTorch 2.9.1, flash_attn 2.8.3).

Correctness: CP=2 (14/14 PASS), CP=4 (14/14 PASS) — all max_diff ≤ 0.003906 at ATOL=5e-3.

=== Ring Attention varlen tests (CP=2) ===
  [PASS] single_bidir_MHA       [PASS] multi_eq_bidir        [PASS] multi_diff_bidir
  [PASS] single_causal_MHA      [PASS] multi_eq_causal       [PASS] multi_diff_causal
  [PASS] single_bidir_GQA       [PASS] single_causal_GQA     [PASS] multi_bidir_GQA
  [PASS] multi_causal_GQA       [PASS] multi_bidir_fp16      [PASS] single_GQA_fp16
  [PASS] long_causal_GQA        [PASS] multi_long_causal_GQA

Latency benchmark (1 request, GQA 32q/8kv heads, D=128, bf16, MI300X):

Bidirectional (perfectly balanced across ranks):

seq_len	FA 1GPU	CP=2	Speedup	CP=4	Speedup	CP=8	Speedup
4K	0.92ms	0.88ms	1.04x	1.20ms	0.77x	2.51ms	0.37x
8K	3.25ms	2.25ms	1.44x	1.91ms	1.71x	2.47ms	1.32x
16K	13.81ms	7.47ms	1.85x	4.90ms	2.82x	3.96ms	3.51x
32K	56.36ms	29.12ms	1.94x	15.40ms	3.69x	10.87ms	5.22x

Causal (contiguous partition, load imbalanced):

seq_len	FA 1GPU	CP=2	Speedup	CP=4	Speedup	CP=8	Speedup
8K	1.73ms	1.84ms	0.94x	1.75ms	1.02x	2.28ms	0.78x
16K	6.83ms	5.73ms	1.19x	4.45ms	1.54x	3.57ms	1.92x
32K	26.76ms	22.11ms	1.21x	13.48ms	2.03x	9.39ms	2.91x

Note: Causal results use contiguous partitioning without load balancing. Dual-chunk/zigzag partitioning (per-step position-aware masking, as in TransformerEngine) would improve causal scaling and is planned for the PCP integration PR.

Overlap profiling (S=8192, CP=2, torch.profiler trace):

compute stream:  |███████ step0 FA 4129us ██████|█|███████ step1 FA 3511us ██████|
comm stream:        |████ NCCL P2P 3769us ██████|
                 → 94% of P2P hidden behind FA compute

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[gemini-code-assist]

Code Review

This pull request implements Ring Attention for Context Parallelism, enabling distributed attention across multiple GPUs by circulating Key and Value tensors in a ring topology. It introduces a RingComm utility for asynchronous P2P communication with CUDA stream overlap and a ring_flash_attn_varlen_func that handles variable-length sequences and online softmax merging. Feedback suggests improving performance and memory efficiency by removing redundant .contiguous() calls before tensor concatenation and optimizing the pre-allocation of receive buffers for smaller world sizes to reduce memory pressure.

[gemini-code-assist]

Calling .contiguous() on k and v here is redundant because they are immediately passed to torch.cat at line 191. torch.cat handles non-contiguous inputs and produces a contiguous output tensor anyway. Explicitly calling .contiguous() on the inputs before cat can lead to unnecessary memory allocations and copies if the tensors are already views, or it's a no-op if they are already contiguous. Removing these calls improves performance in the hot path.

Suggested change

	k = k.contiguous()
	v = v.contiguous()
	k = k
	v = v

[gemini-code-assist]

The allocation of two receive buffers for double buffering is unnecessary when the world_size is small. Specifically:

• If world_size == 1, no communication happens and 0 buffers are needed.
• If world_size == 2, only 1 receive buffer is needed as there is only one P2P step.

Since Ring Attention is often used with very large tensors for long contexts, avoiding these extra allocations can significantly reduce memory pressure and prevent potential OOMs.

Suggested change

	recv_bufs = (torch.empty_like(kv), torch.empty_like(kv))
	num_recv_bufs = min(2, comm.world_size - 1) if comm.world_size > 1 else 0
	recv_bufs = tuple(torch.empty_like(kv) for _ in range(num_recv_bufs))

[knitcapcat-amd]

@LucasWilkinson @MatthewBonanni Hi, could you add the ready label to trigger CI? This is my first PR to vllm. Thank you!