CollectiveX: add FlashInfer one-sided NVLink EP for the GB SKUs / CollectiveX：为 GB SKU 新增 FlashInfer 单边 NVLink EP 后端

.github/workflows/collectivex-sweep.yml

@@ -10,7 +10,7 @@ on:
         description: "EP library to sweep — 'all' runs every EP backend in one matrix"
         type: choice
         default: all
-        options: [all, deepep-v2, mori, uccl-ep, nccl-ep]
+        options: [all, deepep-v2, mori, uccl-ep, nccl-ep, flashinfer-ep]
       only_sku:
         description: Restrict to one GHA runner pool; blank = all
         type: string

.github/workflows/test-collectivex.yml

@@ -0,0 +1,50 @@
+name: Test CollectiveX
+
+# The sweep exercises the benchmark on real hardware, but nothing ran CollectiveX's own
+# unit tests, so the oracle contracts they pin were unverified on every PR. Torch is
+# installed CPU-only: the torch-dependent tests are combine-oracle arithmetic checks that
+# never touch a device, and without it they skip silently rather than fail.
+
+on:
+  pull_request:
+    # ready_for_review is not in the default set (opened/synchronize/reopened), so without
+    # it a PR developed as a draft has every run skipped by the draft gate below and gets
+    # no run at all on the transition — the suite would never see the merge candidate.
+    types: [opened, synchronize, reopened, ready_for_review]
+    paths:
+      - 'experimental/CollectiveX/**'
+      - '.github/workflows/test-collectivex.yml'
+
+permissions:
+  contents: read
+
+jobs:
+  test:
+    if: github.event.pull_request.draft != true
+    runs-on: ubuntu-latest
+    permissions:
+      contents: read
+
+    steps:
+      - name: Checkout code
+        uses: actions/checkout@9c091bb21b7c1c1d1991bb908d89e4e9dddfe3e0 # v7.0.0
+
+      - name: Set up Python
+        uses: actions/setup-python@5fda3b95a4ea91299a34e894583c3862153e4b97 # v7.0.0
+        with:
+          python-version: '3.12'
+
+      - name: Install dependencies
+        run: |
+          python -m pip install --upgrade pip
+          pip install --index-url https://download.pytorch.org/whl/cpu torch
+
+      # Every skip in this suite is torch-gated, so a missing torch turns the oracle
+      # checks into silent passes. Fail here instead, where the cause is obvious.
+      - name: Verify torch is importable
+        run: python -c "import torch; print(torch.__version__)"
+
+      - name: Run unit tests
+        run: |
+          cd experimental/CollectiveX
+          python -m unittest discover -s tests -p "test_*.py" -v

experimental/CollectiveX/README.md

@@ -75,6 +75,7 @@ scale-up domain.
 | MoRI | `production` — vLLM `--all2all-backend mori_*`, SGLang `--moe-a2a-backend mori` | `normal` mode uses the direct `IntraNode` kernel for scale-up EP8 on every CDNA SKU and pins `InterNodeV1` for EP16 over 2x8 XGMI + RDMA. `low-latency` mode selects the `IntraNodeLL` decode kernel (single-call, pure-intranode, same compact layout and unweighted combine as `IntraNode`), decode/EP8 only. FP8 dispatch is caller-prequantized (per-SKU e4m3fnuz on gfx942, e4m3fn on gfx950); combine stays BF16 (`quant_type=none`) alongside BF16 dispatch |
 | UCCL-EP | `candidate` — no engine exposes a UCCL-EP selector | [UCCL](https://github.com/uccl-project/uccl) EP: a drop-in, API-identical DeepEP replacement whose CPU proxies issue GPUDirect RDMA over plain `libibverbs` (no NVSHMEM/IBGDA), with software message ordering, atomics, and flow control; scale-up is single-node `cudaIpc` over NVLink/XGMI (never MNNVL). `normal` mode is the legacy `Buffer` `dispatch`/`combine` (unweighted rank-sum); `low-latency` reuses the legacy `low_latency_dispatch`/`low_latency_combine` decode kernels (weighted combine), decode/EP8 only. FP8 dispatch is caller-prequantized in `normal` mode (blockwise e4m3fn, per-SKU e4m3fnuz on gfx942); in `low-latency` mode the caller sends BF16 and the decode kernel quantizes to e4m3 internally (`use_fp8`). Combine is BF16. Runs on NVIDIA and AMD (H100/H200/B200 + MI300X/MI325X/MI355X), EP8 scale-up. Cross-node EP16 is functional (the internode RDMA path connects and the light case passes correctness) but its CPU-proxy throughput overruns the standardized per-case wall-clock budget on heavy token counts, so EP16 is an unsupported coverage row for now |
 | NCCL EP | `candidate` — NVIDIA's own library, but no engine exposes an NCCL-EP selector | [NCCL EP](https://github.com/NVIDIA/nccl/tree/master/contrib/nccl_ep): NVIDIA's native MoE dispatch/combine on the NCCL Device API — LSA (NVLink load/store) intra-node, GIN (GPU-Initiated Networking) inter-node — driven through the `nccl4py` bindings. `normal` mode selects the `HIGH_THROUGHPUT` algorithm (FLAT `[N, hidden]` receive, unweighted rank-sum combine); the `LOW_LATENCY` algorithm carries an EP8 `ll_backends` row on all six NVIDIA SKUs, restored once the single-handle fix removed the NVIDIA/nccl#2303 signal aliasing. BF16 only: NCCL EP's FP8 machinery exists upstream but its RELEASE.md lists it unsupported/untested, so no FP8 case is emitted. NVIDIA-only and CUDA 13 only. EP8 scale-up on H100/H200/B200/B300 plus EP8 and EP16 on GB200/GB300, where EP16 stays inside the MNNVL scale-up domain. x86 EP16 scale-out is an unsupported coverage row: the cross-node GIN path faults inside `nccl_ep.cc` identically on RoCE and IB across four SKUs, a GDAKI limit rather than a fabric-selection one |
+| FlashInfer EP | `production` — vLLM `--all2all-backend flashinfer_nvlink_one_sided` | [FlashInfer](https://github.com/flashinfer-ai/flashinfer) `MoeAlltoAll`: TensorRT-LLM's one-sided MNNVL all-to-all, where each rank writes tokens straight into its peers' workspace windows and combine reads them back — no send/recv pairing and no NVSHMEM. `normal` mode only (there is one kernel family; no separate decode path), BF16 only, and GB200/GB300 only, since the transport is MNNVL. EP8 and EP16, both inside the scale-up domain. Unlike every other backend here, its combine accumulates in the PAYLOAD dtype rather than FP32: wheels before 0.6.16 reduce the top-k contributions with a pairwise BF16 tree that rounds at every level, so the oracle models that reduction directly (`combine_reduction = "topk-slot-tree"`) instead of widening the tolerance. 0.6.16 moved the accumulator to FP32, and the adapter switches models on the installed version |
 
 DeepEP V2 means the `ElasticBuffer` implementation introduced by
 [DeepEP PR #605](https://github.com/deepseek-ai/DeepEP/pull/605), not a newer legacy `Buffer` build.