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[Bugfix][Distributed] Preserve symmetric collective outputs - #58154
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The symmetric-memory AllGather and ReduceScatter paths returned their cached output scratch directly, so a later same-shape collective could overwrite an earlier live result. Return owned clones while retaining the registered scratch allocations for the collectives. Extend both distributed tests to keep one result live across a same-key call through the companion public entry point, then verify distinct storage and both numerical results. Co-authored-by: OpenAI Codex <noreply@openai.com> Signed-off-by: jeffnvidia <jmahou@nvidia.com>
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@kaijunli-infr @WoosukKwon @samnordmann can u please review? |
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Approved. The "clone-out" approach looks correct and well scoped: persistent symmetric scratch buffers remain reusable without exposing it as caller-owned output
Two non-blocking comment below
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could we add CUDA-graph coverage for both operations? Capture two same-key calls, retain both outputs, then replay with changed inputs and verify both results.
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Added CUDA graph coverage in cfaf005: both AllGather and ReduceScatter capture two same-key calls, retain both outputs, and verify them after two replays with changed inputs. The exact PR head passed both tests on 2xGB200 with no skips. NCCL logs confirmed the symmetric paths.
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In the PR's description: “does not repeat allocation” should refer specifically to symmetric scratch allocation. clone() still creates fresh result storage. Suggested wording: smth like“Preserves cached symmetric scratch and window registration, adding fresh result storage and one output-sized D2D copy per affected call.”
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Updated the PR description to clarify that cached symmetric scratch and window registration are reused, while each clone() allocates fresh result storage and makes an output-sized device-to-device copy. Thanks for catching the imprecise wording.
Capture two same-key AllGather and ReduceScatter calls, then replay with changed inputs and verify both retained results. Co-authored-by: OpenAI Codex <noreply@openai.com> Signed-off-by: jeffnvidia <jmahou@nvidia.com>
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@tlrmchlsmth @bnellnm @tjtanaa @Rohan138 @ZJY0516 Can you please review and approve? |
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Thanks for fixing this. cc @GirasoleY
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✅ @jeffnvidia, CI is now available for this PR.
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Hi @jeffnvidia, the pre-commit checks have failed. Please run: uv pip install pre-commit>=4.5.1
pre-commit install
pre-commit run --all-filesThen, commit the changes and push to your branch. For future commits, |
Assisted-by: OpenAI Codex Signed-off-by: jeffnvidia <jmahou@nvidia.com>
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/ci run |
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❌ This PR is 1 commit behind upstream |
Assisted-by: OpenAI Codex Signed-off-by: jeffnvidia <jmahou@nvidia.com>
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/ci run |
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✅ Triggered Buildkite CI #92593 for commit |
CI selector (shadow): 0 test steps (0 jobs) instead of 66 (86 jobs)Shadow mode: this changes nothing about what CI runs. It shows what the evidence-based selector would pick for this PR, next to today's rules. How it works. Feedback welcome: reply here if it would skip a step this change needs, or runs something unrelated.
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/ci retry |
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✅ No failed, timed-out, or expired jobs need retrying: https://buildkite.com/vllm/ci/builds/92593 |
| pynccl_comm.all_gather(symm_output, input_) | ||
| return symm_output | ||
| return symm_output.clone() |
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Thanks for catching the issue. However this means we will lose the zero copy behavior.
To allow different use cases, let's add view: bool = False in the argument:
- return symmem buffer as tensor view when
view == true - return a clone when
view == false
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Hi @GirasoleY, thanks.
I agree the clone has a measurable cost. In a two-GPU probe, returning the cached buffer reduced AllGather time from 68 to 36 µs and ReduceScatter from 107 to 88 µs.
My concern with adding view=True to the generic API is that the next same-key collective overwrites any earlier result that is still live. This is a correctness problem, and I don't think it's safe to let this option to users. I haven’t found an in-tree caller with a lifetime guarantee that would make this opt-in safe.
Could we keep this PR focused on the ownership fix and pursue the copy cost separately?
I can work on a separate PR to improve the fix.
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@jeffnvidia can you check and explain if the patch is still needed after #49194 ? |
I checked #49194. Its MoE combine path supplies a final output to reduce_scatterv_into_output, so that symmetric path writes directly into caller-owned storage and needs no clone. Generic symmetric dim-0 reduce_scatter/reduce_scatterv still return cached rs_out; single-tensor all_gather/all_gatherv return cached ag_out. A later same-key call can overwrite a retained result, which I reproduced on #49194 current head. So #58154 is still needed for generic calls. When integrating the PRs, I'll clone only scratch-backed results, preserving #49194 direct MoE output. The current #58154 patch needs this adjustment because it clones the helper result unconditionally. |
OK sounds good. #49194 has just been merged so just make sure there's no conflict, |
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This pull request has merge conflicts that must be resolved before it can be |
Resolve the vllm-project#49194 integration by cloning only generic scratch-backed ReduceScatter results while retaining direct MoE output. Keep AllGather ownership and both regression suites. Co-authored-by: OpenAI Codex <codex@openai.com> Signed-off-by: jeffnvidia <jmahou@nvidia.com>
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/ci run |
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❌ This PR is 24 commits behind upstream |
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/ci run --allow-stale |
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✅ Triggered Buildkite CI #93278 for commit
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Co-authored-by: OpenAI Codex <noreply@openai.com> Signed-off-by: jeffnvidia <jmahou@nvidia.com>
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/ci run |
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✅ Triggered Buildkite CI #93296 for commit |
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@tlrmchlsmth @bnellnm @tjtanaa @Rohan138 @ZJY0516 @GirasoleY Can you please review and merge? |
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@tlrmchlsmth @ZJY0516 Could you help with final review and merge? CI #93296 has 143 passed steps and no hard failures (one non-blocking Ascend NPU failure). All six symmetric-memory tests, including CUDA graph coverage, passed locally on 2xGB200 at head |
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@tlrmchlsmth @bnellnm @tjtanaa @Rohan138 @ZJY0516 @GirasoleY can u please merge? |
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Purpose
Fix an output-ownership bug in NCCL symmetric-memory AllGather and
ReduceScatter.
#46703 changed these paths to reuse persistent registered scratch buffers, but
returned those buffers directly. A later collective with the same shape and
dtype can therefore overwrite an earlier result that is still live. The
collective itself computes the correct value, but the returned tensor does not
have independent storage.
This can silently produce incorrect model outputs. It was reproduced in a
TP16 Qwen2.5-72B run, where the affected ReduceScatter path disagreed with the
ordinary collective path for all 16 test prompts.
Changes
have distinct storage and preserve both values.
Batched AllGather and current AllReduce are unchanged because they already
return fresh output storage.
The fix preserves cached symmetric scratch and window registration. Each
affected generic call allocates fresh result storage and makes one output-sized
device-to-device copy.
Related: #49194 provides caller-owned output for a specialized MoE
ReduceScatter path, while #55211 bounds scratch growth and handles AllReduce
ownership. Neither fixes generic symmetric ReduceScatter or single-tensor
AllGather.
Test plan
This test file is already included in vLLM's optional 2xB200 distributed CI
job.
Test results
without skips. NCCL logs confirmed that the symmetric algorithms were used.
operations; clone-out returned distinct storage and preserved both results.
agreement with the ordinary collective path.
cfaf005b6on 2xGB200 (Blyris job47152): the AllGatherand ReduceScatter CUDA graph tests passed with no skips (
2 passed, 1 deselected). Each retained two same-key outputs across two changed-inputreplays. NCCL logs selected symmetric
AllGather_STandReduceScatter_LD.Runtime: PyTorch 2.14.0a0 (NV 26.09), CUDA 13.4, NCCL 2.31.2. Upstream
pre-commit remains gated before lint/tests by its author/label pre-run check.
AI assistance
OpenAI Codex assisted with investigation, implementation, testing, and draft
preparation. The human submitter must review and understand the change before
marking the PR ready.