[Bugfix] Disable custom all-reduce on consumer Blackwell (sm_12x)#47751
[Bugfix] Disable custom all-reduce on consumer Blackwell (sm_12x)#47751NicholausAShupe wants to merge 1 commit into
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The custom all-reduce CUDA kernels are only built and tuned for sm_90 and sm_100 (see CUSTOM_ALL_REDUCE_MAX_SIZES, which lists 9.0/10.0/10.3 but no 12.x). On consumer Blackwell (sm_120 / sm_121, e.g. RTX PRO 6000 and the RTX 50-series) the kernel's IPC / peer-mapping path aborts with `custom_all_reduce.cuh 'invalid argument'` during cudagraph capture, killing the TP worker. The existing topology guards do not catch this: they only disable custom all-reduce for more than two PCIe-only GPUs, so a 2-GPU tensor-parallel setup passes the checks, enables the kernel, and then crashes at capture. Disable custom all-reduce for the whole sm_12x family via current_platform.is_device_capability_family(120) (extracted into CustomAllreduce._is_unsupported_arch for testability) and fall back to NCCL all-reduce, so users no longer have to pass disable_custom_all_reduce=True manually. The early return leaves the communicator in its default-disabled state (self.disabled = True) before any buffer allocation or ops.init_custom_ar call. Add a unit test covering the sm_12x family (disabled), other CUDA archs (enabled), and non-CUDA platforms (not gated). Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com> Signed-off-by: Nicholaus Shupe <nicshupe@glpwd.com>
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Purpose
Auto-disable vLLM's custom all-reduce on consumer Blackwell (sm_12x) and fall back to NCCL all-reduce.
The custom all-reduce CUDA kernels are only built and tuned for sm_90 / sm_100 (see
CUSTOM_ALL_REDUCE_MAX_SIZES, which lists 9.0/10.0/10.3 but no 12.x). On consumer Blackwell (sm_120 / sm_121 — RTX PRO 6000, RTX 50-series, GB10/DGX Spark) the kernel's IPC / peer-mapping path aborts withcustom_all_reduce.cuh:455 'invalid argument'during cudagraph capture, killing the TP worker.The existing topology guards don't catch this: they only disable custom all-reduce for more than two PCIe-only GPUs, so a 2-GPU tensor-parallel setup passes the checks, enables the kernel, and then crashes at capture. Today users must pass
--disable-custom-all-reducemanually.This gates on the whole 12.x family via the existing
current_platform.is_device_capability_family(120)helper (extracted intoCustomAllreduce._is_unsupported_arch()for testability). sm_100 (datacenter Blackwell), Hopper, Ada, and Ampere are unaffected.Fixes the "Issue 9a — Custom all-reduce kernel crash" blocker reported in #47266. (That report also lists 9b/9c as further, independent cudagraph-capture blockers on sm_120; this PR addresses 9a only.)
Test Plan
pytest tests/distributed/test_custom_all_reduce.py -k is_unsupported_arch— asserts the sm_12x family is disabled, other CUDA archs stay enabled, and non-CUDA platforms are not gated.--disable-custom-all-reduce:vllm serve <model> --tensor-parallel-size 2and confirm the warning fires, FULL cudagraph capture completes without thecustom_all_reduce.cuh:455crash, and the server serves.Test Result
ruff check/ruff formatclean (v0.14.0, repo config);mypy(3.10 and 3.12) pass.disable_custom_all_reduce=Falsein engine config):Custom allreduce is disabled because it is not supported on consumer Blackwell (sm_12x ...); falling back to NCCL all-reduce.Capturing CUDA graphs (mixed prefill-decode, PIECEWISE)and(decode, FULL)both completed;Graph capturing finished— nocustom_all_reduce.cuh:455crash (the failure this PR fixes).Application startup complete;/health→ 200; a/v1/completionsrequest returned a correct response over the NCCL fallback path.Notes
custom_all_reduce.pyfor sm_12x (the SM12x enablement PR [New Model][Nvidia] Add SM12x support for DeepSeek V4 Flash with essential fixes #41834 does not touch it); this is orthogonal to the DeepSeek-V4fp8_ds_mlawork in [Bugfix]Fix DeepSeek-V4 fp8_ds_mla KV cache reshape #47716.