CollectiveX: experimental cross-vendor collective/EP benchmark#1896
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Oseltamivir wants to merge 185 commits into
Open
CollectiveX: experimental cross-vendor collective/EP benchmark#1896Oseltamivir wants to merge 185 commits into
Oseltamivir wants to merge 185 commits into
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Per-SKU launch adapters (launch_<sku>.sh) that run any benchmark via a CX_BENCH selector through a shared run_in_container.sh; multi-arch digest-pinned sglang container; NCCL-primitive + DeepEP dispatch/combine benchmarks with provenance + correctness gating; and an on:push workflow (GB200 NCCL smoke; workflow_dispatch for B200/DeepEP/larger sweeps). Validated on hardware: NCCL primitives on B200 (8x NVLink) and GB200 (4x NVL72 MNNVL); DeepEP dispatch/combine on GB200 (correctness-gated).
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The GB200 on:push smoke hung 25 min in enroot import: a bare digest ref (repo@sha256:) can't form an anonymous Docker Hub token scope, so enroot prompted for a password and blocked in non-interactive CI. Import by the multi-arch TAG instead (anonymous auth works, same as the serving launchers) and add </dev/null so a missing token fails fast rather than hanging. Use v0.5.11-cu130 (multi-arch amd64+arm64, index sha256:061fb71f…): v0.5.12-cu130's 62 layers overflow enroot's overlay-based squash creation on these nodes (failed to mount overlay … Invalid argument). v0.5.11-cu130 imports cleanly and is pre-staged on GB200.
On the GB200 Actions path, CX_STAGE_DIR makes the launcher rsync the tree to compute-visible Lustre and the container writes results/ there; upload-artifact reads the checkout's results/ (empty), so the green smoke produced no artifact. Add cx_collect_results to copy result JSONs from the stage dir back to the checkout after the run (no-op when no staging was used).
Add summarize.py (compact NCCL/DeepEP results table, printed at end of every job) and make it the result gate. Fix review findings: benchmark failures/skipped-deepep now fail the job instead of reporting green (#1); DeepEP nodes from SLURM_NNODES not world_size//8 (#3); apply Buffer.set_num_sms so num_comm_sms is real (#8); nccl-tests -c 1 with a missing check footer is now invalid (#7); use context managers for file reads (#4,#5); launchers export COLLECTIVEX_IMAGE/_DIGEST for provenance (#9); trim workflow_dispatch sku options to launcher-backed pools (#2). Artifact-path finding (#6) already fixed via cx_collect_results.
| is_token_in_rank=is_token_in_rank, | ||
| num_tokens_per_expert=num_tokens_per_expert, | ||
| ) | ||
| combined_x, _, _ = buffer.combine(recv_x, handle, topk_weights=recv_topk_weights) |
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Dispatch dtype not applied
Medium Severity
The --dispatch-dtype / CX_DISPATCH_DTYPE value is stored in result metadata but never used when building inputs or calling buffer.dispatch. Runs always use bfloat16 token tensors regardless of fp8 vs bf16, so provenance and comparison keys can describe a different shape than what was measured.
Additional Locations (1)
Reviewed by Cursor Bugbot for commit b384171. Configure here.
summarize.py --markdown emits GitHub-flavored markdown tables (NCCL + DeepEP); a per-job 'Results summary' workflow step appends it to $GITHUB_STEP_SUMMARY so the run page shows a rendered table (per the GitHub job-summaries feature). Plain-text mode still drives the in-container result gate.
| --timestamp "$TS" || cx_log "WARN: parse $op failed" | ||
| done | ||
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| cx_log "done — JSON artifacts under $CX_DIR/results/" |
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Multinode launcher ignores failures
High Severity
The B200 multinode adapter logs warnings when srun or run_nccl.py fail but always exits successfully. Unlike run_in_container.sh, it never runs summarize.py as a non-zero gate, so workflow_dispatch on b200-multinode can finish green with no valid NCCL results.
Reviewed by Cursor Bugbot for commit f48daed. Configure here.
| run: bash "experimental/CollectiveX/launchers/launch_${RUNNER_NAME%%_*}.sh" | ||
| - name: Results summary | ||
| if: always() | ||
| run: python3 experimental/CollectiveX/summarize.py --results-dir experimental/CollectiveX/results --markdown >> "$GITHUB_STEP_SUMMARY" |
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Workflow skips result failure gate
Medium Severity
Both jobs only run summarize.py --markdown, which is documented to always exit 0. The workflow never runs the plain summarize.py gate on the checkout’s results/ after launch, so a successful Launch step can stay green when the checkout has no valid JSON (e.g. staged runs where copy-back failed).
Additional Locations (1)
Reviewed by Cursor Bugbot for commit f48daed. Configure here.
| dst="$repo_root/experimental/CollectiveX/results" | ||
| mkdir -p "$dst" | ||
| cp "$mount_src/experimental/CollectiveX/results/"*.json "$dst/" 2>/dev/null || true | ||
| cx_log "copied results from stage dir -> $dst (for artifact upload)" |
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Result copy errors ignored
Medium Severity
cx_collect_results wraps the staged-to-checkout cp in 2>/dev/null || true and always logs success, so a failed or empty copy does not affect the launcher exit code and the workflow can pass without uploadable JSON.
Reviewed by Cursor Bugbot for commit f48daed. Configure here.
First AMD / cross-vendor reach, scaffolded ahead of Milestone 1: - run_mori.py: MoRI dispatch+combine (normal mode), correctness-gated, mirroring ROCm/mori's dispatch_combine example — int32 routing indices, (n,0) fp8 scales, the zero-copy registered-combine-input-buffer staging step, and expected = input x (#unique destination ranks). Emits the same flat JSON shape (family=moe, backend=mori) with CUDA-event timing. - launchers/launch_mi355x-amds.sh: AMD adapter — partition compute, no account, --cpus-per-task=128, node-local /var/lib/squash imported via srun on the allocated node, --container-writable --container-remap-root, forces CX_BENCH=mori, mounts the (compute-visible) checkout at /ix. - launchers/run_in_container.sh: run_mori_suite + mori case (nccl|deepep|mori|all). - launchers/common.sh: ROCm MoRI image (rocm/sgl-dev:...-mori-0227-2) in cx_default_image for mi355x*/mi350x*/mi325x*/mi300x*. - workflow: mi355x sku + mori benchmark options for workflow_dispatch. - docs: CONTAINERS.md AMD section, README files/run/risks, plan.md status. Not yet hardware-validated (no MI355X access) — MoRI's Python API is version-sensitive (marked ADAPT HERE); the first runner job is the validation, as GB200 was for DeepEP. The ROCm image isn't digest-pinned yet.
- workflow: replace the on:push GB200 NCCL smoke with the MI355X MoRI dispatch/combine run (runs-on: mi355x, CX_BENCH=mori), and name the job "CollectiveX Experimental" (no longer "smoke"). GB200/B200 NCCL + DeepEP remain on workflow_dispatch. - launch_mi355x-amds.sh: adapt more faithfully to runners/launch_mi355x-amds.sh — squeue by job-name only (no -u), flock -w 600, and clear ROCm gpucore.* dumps after the run so the next checkout is clean. Bump default CX_TIME to 60 for a cold ROCm-image import. - summarize.py: drop the "N/N results valid." footer from both the job-summary (markdown) and plain output; the failure gate still reports invalid results. Relabel the MoE section "MoE dispatch+combine (DeepEP / MoRI)". - docs: README/plan describe push -> MI355X MoRI.
| rm -f \"$SQUASH_FILE\" | ||
| enroot import -o \"$SQUASH_FILE\" \"docker://$IMAGE\" </dev/null | ||
| fi | ||
| " |
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MI355X import errors ignored
High Severity
The node-local enroot import runs inside an srun bash snippet without set -e and with no check after import. A failed import still yields exit 0 from that snippet, so the job continues into pyxis with a missing or corrupt squash file.
Reviewed by Cursor Bugbot for commit d8ee9bf. Configure here.
| - name: Launch ${{ inputs.sku }} / ${{ inputs.benchmark }} | ||
| env: | ||
| RUNNER_NAME: ${{ runner.name }} | ||
| run: bash "experimental/CollectiveX/launchers/launch_${RUNNER_NAME%%_*}.sh" |
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Workflow skips multinode staging
Medium Severity
CX_STAGE_DIR is set only when inputs.sku is gb200. The b200-multinode dispatch target uses launch_b200-dgxc-slurm.sh, which documents the same compute-visible checkout requirement but leaves staging unset, so Slurm jobs may not see the repo mount.
Reviewed by Cursor Bugbot for commit d8ee9bf. Configure here.
… default) First MI355X run reached the MoRI dispatch kernel — salloc, ROCm-image import, mount, torchrun, 8-rank Gloo + shmem init, and EpDispatchCombineConfig/op/dispatch all worked, confirming the API signatures. It OOM'd MoRI's default 2 GiB static symmetric heap (hidden=7168 dispatch/combine buffers across 8 ranks request ~0.9 GiB each). run_mori.py now sets MORI_SHMEM_HEAP_SIZE before `import mori` (default 16 GiB, override CX_MORI_HEAP_BYTES). Docstring + CONTAINERS.md record the finding; correctness/timing validated by the heap-sized re-run.
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| salloc --partition="$PARTITION" --exclude="$EXCLUDE_NODES" --gres=gpu:"$NGPUS" \ | ||
| --exclusive --cpus-per-task=128 --time="$TIME_MIN" --no-shell --job-name="$RUNNER_NAME" | ||
| JOB_ID="$(squeue --name="$RUNNER_NAME" -h -o %A | head -n1)" |
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Slurm job ID not scoped
Medium Severity
launch_mi355x-amds.sh resolves JOB_ID with squeue --name="$RUNNER_NAME" and no -u "$USER", while the other CollectiveX NVIDIA launchers filter by user. On a shared cluster, the first matching job name may belong to another account, so subsequent srun/scancel can target the wrong allocation.
Additional Locations (1)
Reviewed by Cursor Bugbot for commit ac3f1b9. Configure here.
The heap-bump run cleared the 2 GiB OOM but then failed registering the 16 GiB symmetric heap as an RDMA memory region (errno 22 EINVAL, size=17179869184). ROCm/mori's reference test uses MORI_SHMEM_HEAP_SIZE="6G" single-node — big enough for the hidden=7168 dispatch/combine buffers, small enough to register. Match it: default "6G" (override CX_MORI_HEAP_SIZE). The rest of the config already matches the reference (max_num_inp_token_per_rank=4096, hidden=7168, backend cpu:gloo,cuda:nccl), so this lands on the proven single-node setup.
Drove run_mori.py to a correct run on 8x MI355X (on-node via salloc+srun): dispatch+combine numerically correct (combine within tol, max_rel ~2e-3), ~85us round-trip at the decode shape. The first runs surfaced four issues, all fixed and re-validated: - RDMA MR ceiling: MoRI registers the WHOLE symmetric heap as one RDMA MR at init (even single-node; no disable-RDMA knob). The ionic_rdma NICs cap GPU MRs at ~4 GiB — a 6 GiB heap fails (RegisterRdmaMemoryRegion errno 22), 2 GiB registers. Hold heap at MORI_SHMEM_HEAP_SIZE=2G (override CX_MORI_HEAP_SIZE). - Buffer sizing: max_num_inp_token_per_rank 4096 -> max(512, n) so the buffers fit the 2 GiB heap (4096 was inherited from the reference test). - Correctness shape: combine returns the full max-token buffer; compare only combined[:n] against expected. - recv count: read total_recv BEFORE combine (combine resets recv_num, which made recv_nonzero a false negative). - Teardown: MoRI's shmem teardown asserts (CheckStatusValid -> SIGABRT) when the op is destroyed after shmem_finalize(); hard-exit after writing results. Docs (README/plan/CONTAINERS) updated from "scaffolded" to validated, with the fabric constraints recorded.
…CH=nccl) Adds the AMD collective-primitive path so all_reduce/reduce_scatter/all_gather/ alltoall run on MI355X, not just MoRI: - common.sh: cx_build_rccl_tests — clones ROCm/rccl-tests and builds with `make` against /opt/rocm (amdclang++/librccl). It's a nccl-tests fork producing the same <op>_perf binaries and output format, so run_nccl.py parses it unchanged. Validated building + running all 4 ops in-container on MI355X (correctness OK). - run_in_container.sh: run_nccl_suite picks rccl-tests on ROCm (/opt/rocm or hipcc), nccl-tests otherwise; identical op loop + run_nccl.py invocation. - launch_mi355x-amds.sh: honor CX_BENCH (mori default | nccl) instead of forcing mori; same -g N single-node 8-GPU launch. - docs: README/CONTAINERS note the rccl path. B200 already has the nccl path; this makes primitives available on all three SKUs via workflow_dispatch.
…t cancel each other
| if name: | ||
| devices.append(name) | ||
| elif _run(["ibstat", "-l"]): | ||
| devices = [d.strip() for d in _run(["ibstat", "-l"]).splitlines() if d.strip()] |
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ibstat fallback may crash capture
Low Severity
In _rdma, the ibstat -l branch calls _run twice. If the first call succeeds but the second returns None, None.splitlines() raises and env_capture.py aborts before writing provenance JSON for that run.
Reviewed by Cursor Bugbot for commit 2b23573. Configure here.
…on-node launch_gb200-nv.sh now branches on CX_NODES: 1 (default) keeps the single-tray 4-GPU dispatcher path; >1 runs across the NVL72 NVLink fabric (e.g. CX_NODES=2 = 8 GPU) by building nccl-tests MPI=1, running each op across WORLD ranks via `srun --mpi=pmix` (1 GPU/rank) with the MNNVL env, and parsing on the login node — mirroring launch_b200-dgxc-slurm but staying on NVLink instead of IB. Validated on GB200 (2x watchtower-navy trays, 8 GPU): all 4 ops valid, peak busbw all_reduce 822.8 / reduce_scatter 670.6 / all_gather 651.2 / alltoall 625.0 GB/s — ~30% over single-tray and on par with B200 8-GPU NVLink, i.e. MNNVL engaged (not an IB fallback). - common.sh: cx_build_nccl_tests auto-detects MPI_HOME for MPI=1 (Debian OpenMPI headers live under /usr/lib/<arch>/openmpi/include; MPI_HOME=/usr fails). Works x86_64 + aarch64. - launch_b200-dgxc-slurm.sh: fix BUILD_IN_CTR path (.nccl-tests/nccl-tests/build). - workflow: add `nodes` dispatch input -> CX_NODES.
…er config) Formalizes the 'newest-good-per-config kept; superseded moved aside' the .gitignore references: groups results/ by comparison_key, keeps the newest 3 usable runs per config (preserves repeat-run aggregation), moves older/superseded/stale-failed to results/.superseded (out of the plot glob, recoverable). Genuinely-failed configs with no valid counterpart are kept (preserve-failed-cases deliverable).
…torch rendezvous) Both cross-node attempts failed at the torch.distributed rendezvous, not the EP backend: MI355X gloo 'connect refused remote=[127.0.1.1]' (hostname loopback-aliased in /etc/hosts) and H200 'connect to worker-1:29561 timed out' (hostname not routable cross-node). Resolve MASTER_ADDR via scontrol NodeAddr (the routable IP) in both multi-node launchers, fall back to hostname. GB200/GB300 worked because their hostnames are routable.
Cross-node EP (goal 182/183) failed at torch's gloo connectFullMesh with remote=[127.0.1.1] despite MASTER_ADDR being the routable NodeAddr IP: the per-rank mesh advertises each rank's hostname, which the MI355X/H200 /etc/hosts aliases to loopback. Add runtime/_xnode_net.sh (sourced per-rank) to auto-pin GLOO_SOCKET_IFNAME/NCCL_SOCKET_IFNAME to the routable 10.x NIC, and wire it into the MI355X multi-srun WRAP and run_in_container's multi-node torchrun path.
probe_deepep_caps.py / probe_deepep_ll.py were one-off read-only DeepEP capability probes from the earliest FP8/LL commit. The capability surface they sampled is now owned canonically by tests/capability.py (+ ep_mori.py); the probes have zero inbound references anywhere (code, docs, workflows). Remove them as dead scaffolding.
prune_results.py is the canonical results-hygiene tool (newest-3-per-comparison_key with publication_status gating + repeat-run preservation). tools/_keep_newest.py was the older newest-1 variant; 0 inbound references, and its own docstring path (launchers/_keep_newest.py) is stale. Remove the duplicate.
…fallback Harden the cross-node bootstrap helper: always print the container's hostname + every visible IPv4 (so a cross-node GHA log self-documents what each rank's network namespace sees), and tolerate minimal images without iproute2. Clarify that the iface pin cannot fix an unreachable MASTER_ADDR (a cluster/container-net property), only the per-rank gloo connectFullMesh advertisement.
…ILE) The env:// TCPStore rendezvous (MASTER_ADDR:PORT) times out cross-node on the H100/H200/MI355X fleets because the rank-0 management-subnet NodeAddr is not reachable from a peer rank's enroot container net namespace. When CX_RDZV_FILE points at a path on the compute-visible shared mount, init the PG via a FileStore instead: ranks exchange the store + NCCL unique-id through the shared file, and NCCL connects peers over the IB fabric (routable cross-node) rather than the unreachable management TCP. Default-off; single-node path is byte-identical.
Replace the one-container-per-node + torchrun path (whose elastic-agent TCPStore timed out 900s on the unreachable management-subnet master-addr) with the proven multi-srun shape used by MI355X/GB300: Slurm places NODES*NGPUS ranks directly (RANK/LOCAL_RANK from SLURM_*), no torchrun agent. Ranks rendezvous via a FileStore on the shared mount (CX_RDZV_FILE) so NCCL bootstraps over IB instead of the unreachable management TCP. Parses CX_TIMING; sources _xnode_net.sh.
The FileStore rendezvous (CX_RDZV_FILE) got past the management-subnet TCPStore wall, but the multi-srun-per-rank shape lacked uccl (pip-installed by cx_build_uccl in run_in_container, which per-rank ephemeral containers skip). Fix: keep one-container-per-node so run_in_container builds uccl once per node, then when CX_NNODES>1 spawn NGPUS local ranks directly (global RANK = CX_NODE_RANK*NGPUS+local) rendezvousing via the shared-mount FileStore instead of torchrun — torchrun's elastic agent ran its own unreachable cross-node TCPStore. run_ep_suite refactored to a shared EPARGS array driving both paths.
…vendors) The canonical token-shuffle EP on pure torch.distributed all_to_all_single: the ONLY EP backend that survives cross-node without GPUDirect-RDMA. UCCL's ibv_reg_mr fails EINVAL->SIGSEGV and MoRI's RDMA registration aborts (both after the rendezvous now forms via FileStore), but NCCL/RCCL host-stage the all-to-all over IB. tests/ep_nccl.py (bf16/normal/layout-and-dispatch); run_ep + run_in_container (run_nccl_ep_suite, no build) + capability (both vendors) + workflow choice wired. MI355X multi-srun also gets CX_RDZV_FILE (nccl-ep uses pure rccl PG + FileStore, sidestepping the gloo connectFullMesh 127.0.1.1 alias entirely).
The backend dispatch elif was added but the argparse choices list still rejected 'nccl-ep' (run 28326942401: 'invalid choice: nccl-ep'). Add it to choices.
…RDMA root cause) Rewrite the cross-node section: goal 182 (H100/H200) is DONE via nccl-ep over IB (H200 world=16, run 28327088942, correct=True). Document the two-layer root cause: (1) rendezvous wall (management-subnet store unreachable from container netns) solved by shared-mount FileStore + local-spawn; (2) custom-RDMA data-path wall (UCCL ibv_reg_mr EINVAL→SIGSEGV, MoRI SIGABRT, DeepEP asserts) needs GPUDirect-RDMA the HCAs lack, so NCCL/RCCL host-staged all-to-all is the portable cross-node EP. MI355X (183) validation in flight on rccl.
The MI355X AMD-bench allowlist didn't include nccl-ep, so CX_BENCH=nccl-ep silently fell back to mori — run 28327089664 ran MoRI cross-node (SIGABRT) instead of the intended rccl all-to-all EP. nccl-ep IS AMD-supported (pure RCCL all_to_all_single); add it to the allowlist so goal-183 cross-node runs on rccl.
…ep/rccl MI355X nodes=2/world=16 over RoCE/IB, run 28328718973 correct=True T=1-8. Both cross-node EP points (182 H200, 183 MI355X) now done via the unified nccl-ep path; the custom-RDMA GPUDirect wall is documented + routed around.
mooncake (HOST_GPU_BENCH amd-capable) wasn't in the MI355X bench allowlist, so it silently fell back to mori (run 28340951096). Add it so run_mooncake_suite can attempt the ROCm transfer-engine on MI355X (documents the wall if the wheel lacks HIP support).
MoonCake on MI355X = evidenced ROCm wall (engine inits on rdma0 but the wheel has no transfer_write_on_hip, only _on_cuda; run 28342781762 invalid/0 groups) — needs an upstream Mooncake ROCm build. MI355X rccl-tests (All-reduce/All-gather tab) keeps failing in the runner checkout/setup step (shared with the agentic fleet) — a runner-contention infra flake, not an rccl limitation. mori-io (28.2), copy- engine/SDMA, and rccl-kv (71.7 GB/s) backfilled successfully.
…ests) The persistent MI355X rccl-primitives failure was capability.py rejecting benchmark=nccl on amd (exit 3 in the Validate-capability step, before the launcher ran) — masked earlier by the gharunner06 root-LOGS EACCES. But the nccl BENCHMARK runs on both vendors: run_nccl_suite auto-picks rccl-tests on ROCm. Make COLLECTIVE nccl valid on amd so the All-reduce/All-gather tabs get an MI355X line.
…l-parity sweeps Thread deepep_v2=true (kernel_gen=v2 from-source) and a --backend override that remaps the deepep suite matrix onto uccl/flashinfer/deepep-hybrid/nccl-ep, with a capability pre-filter (resolve() per case) so no doomed dispatch is fired. Enables per-backend full-matrix parity: deepep-v2 242 / uccl 242 / flashinfer 162 / deepep-hybrid 156 NVIDIA cases across H100/H200/B300.
…nodes Add b200 (8x NVLink, sibling of b300) + gb200 (NVL72, sibling of gb300) to platforms.yaml + every relevant suite's platform list (mirroring b300/gb300 coverage). Un-drop gb300 in _gha_suite.sh (runners online now) + map gb200/b200 in the SKU dict. Thread nodes for the rack-scale SKUs (gb200/gb300 = 4 GPU/tray, so EP8 = 2 trays/nodes). Enables full-parity sweeps across all 7 SKUs.
…gate Replace the thousands-of-individual-dispatches model with the InferenceX CI shape: ONE run = setup (resolve suites into shard matrix via sweep_matrix.py) -> sweep (a MATRIX job, one cell per SHARD = sku×backend×mode×resource, each sweeping its cases in ONE allocation via run_in_container SHARD mode) -> aggregate (collect every shard into ONE results/aggregate/*.ndjson via aggregate_results.py). Collapses ~534 deepep dispatches into ~45 cells + 1 aggregated file. run_in_container gains a CX_SHARD_FILE loop (per-case CX_TS keeps outputs unique); sweep_matrix resolves/ chunks/capability-filters shards + emits slim matrix (cases via artifact).
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| runs-on: ubuntu-latest | ||
| outputs: | ||
| matrix: ${{ steps.gen.outputs.matrix }} | ||
| n: ${{ steps.gen.outputs.n }} | ||
| steps: | ||
| - uses: actions/checkout@de0fac2e4500dabe0009e67214ff5f5447ce83dd # v5.0.0 | ||
| with: { clean: true } | ||
| - run: pip install --quiet pyyaml | ||
| - id: gen | ||
| working-directory: experimental/CollectiveX | ||
| run: | | ||
| set -euo pipefail | ||
| ov=""; [ "${{ inputs.backend }}" != "deepep" ] && ov="--backend ${{ inputs.backend }}" | ||
| v2=""; [ "${{ inputs.deepep_v2 }}" = "true" ] && v2="--deepep-v2" | ||
| os=""; [ -n "${{ inputs.only_sku }}" ] && os="--only-sku ${{ inputs.only_sku }}" | ||
| # full matrix (with cases) -> artifact for the cells; slim (no cases) -> the strategy output. | ||
| python3 sweep_matrix.py --suites "${{ inputs.suites }}" --max-cases "${{ inputs.max_cases }}" $ov $v2 $os --out matrix_full.json >/dev/null | ||
| SLIM=$(python3 -c "import json;m=json.load(open('matrix_full.json'));print(json.dumps({'include':[{k:v for k,v in x.items() if k!='cases'} for x in m['include']]}))") | ||
| echo "matrix=$SLIM" >> "$GITHUB_OUTPUT" | ||
| echo "n=$(python3 -c "import json;print(len(json.load(open('matrix_full.json'))['include']))")" >> "$GITHUB_OUTPUT" | ||
| python3 -c "import json;m=json.load(open('matrix_full.json'));print('shard-cells:',len(m['include']),'cases:',sum(x['n'] for x in m['include']))" | ||
| - uses: actions/upload-artifact@043fb46d1a93c77aae656e7c1c64a875d1fc6a0a # v7.0.1 | ||
| with: | ||
| name: cxsweep-matrix-${{ github.run_id }} | ||
| path: experimental/CollectiveX/matrix_full.json | ||
| if-no-files-found: error | ||
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| # ---- sweep: ONE matrix cell per shard (the parent job with child jobs) ---- | ||
| sweep: |
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| needs: setup | ||
| if: ${{ fromJSON(needs.setup.outputs.n) > 0 }} | ||
| strategy: | ||
| fail-fast: false | ||
| max-parallel: 10 # don't saturate the ~20-runner fleet; cells queue as slots free | ||
| matrix: ${{ fromJSON(needs.setup.outputs.matrix) }} | ||
| # h200 label spans two clusters; pin to the validated dgxc pool (mirrors collectivex-experimental). | ||
| runs-on: ${{ matrix.sku == 'h200' && 'h200-dgxc' || matrix.sku }} | ||
| timeout-minutes: 350 | ||
| env: | ||
| CX_BENCH: ${{ matrix.backend }} | ||
| CX_DEEPEP_V2: ${{ matrix.deepep_v2 && '1' || '' }} | ||
| CX_NODES: ${{ matrix.nodes }} | ||
| CX_SHARD_FILE: results/.shard_${{ matrix.id }}.json | ||
| COLLECTIVEX_SOURCE_SHA: ${{ github.sha }} | ||
| CX_NODELIST: ${{ matrix.sku == 'mi355x' && 'mia1-p01-g10,mia1-p01-g15' || '' }} | ||
| CX_STAGE_DIR: ${{ matrix.sku == 'gb200' && '/mnt/lustre01/users-public/sa-shared/cx-stage' || '' }} | ||
| steps: | ||
| - uses: actions/checkout@de0fac2e4500dabe0009e67214ff5f5447ce83dd # v5.0.0 | ||
| with: { clean: true } | ||
| - uses: actions/download-artifact@d3f86a106a0bac45b974a628896c90dbdf5c8093 # v4.3.0 | ||
| with: | ||
| name: cxsweep-matrix-${{ github.run_id }} | ||
| path: experimental/CollectiveX | ||
| - name: Extract this shard's cases (stdlib only — no runner deps) | ||
| working-directory: experimental/CollectiveX | ||
| run: | | ||
| set -euo pipefail | ||
| python3 -c " | ||
| import json | ||
| m=json.load(open('matrix_full.json')) | ||
| s=[x for x in m['include'] if x['id']=='${{ matrix.id }}'] | ||
| assert s, 'shard ${{ matrix.id }} not in matrix' | ||
| s=s[0] | ||
| json.dump({'id':s['id'],'sku':s['sku'],'backend':s['backend'],'nodes':s['nodes'],'deepep_v2':s['deepep_v2'],'cases':s['cases']}, open('results/.shard_${{ matrix.id }}.json','w')) | ||
| print('shard ${{ matrix.id }}:', len(s['cases']), 'cases') | ||
| " | ||
| - name: Sweep shard ${{ matrix.id }} (${{ matrix.n }} cases, one allocation) | ||
| env: | ||
| RUNNER_NAME: ${{ runner.name }} | ||
| run: bash "experimental/CollectiveX/launchers/launch_${RUNNER_NAME%%_*}.sh" | ||
| - name: Shard summary | ||
| if: always() | ||
| run: python3 experimental/CollectiveX/summarize.py --results-dir experimental/CollectiveX/results --markdown >> "$GITHUB_STEP_SUMMARY" || true | ||
| - name: Upload shard results | ||
| if: always() | ||
| uses: actions/upload-artifact@043fb46d1a93c77aae656e7c1c64a875d1fc6a0a # v7.0.1 | ||
| with: | ||
| name: cxshard-${{ matrix.id }}-${{ github.run_id }} | ||
| path: experimental/CollectiveX/results/*.json # glob skips the hidden .shard_*.json | ||
| if-no-files-found: warn | ||
|
|
||
| # ---- aggregate: collect every shard into ONE ndjson (the "result aggregator at the end") ---- | ||
| aggregate: |
Comment on lines
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| needs: sweep | ||
| if: always() | ||
| runs-on: ubuntu-latest | ||
| steps: | ||
| - uses: actions/checkout@de0fac2e4500dabe0009e67214ff5f5447ce83dd # v5.0.0 | ||
| with: { clean: true } | ||
| - uses: actions/download-artifact@d3f86a106a0bac45b974a628896c90dbdf5c8093 # v4.3.0 | ||
| with: | ||
| pattern: cxshard-*-${{ github.run_id }} | ||
| path: _shards | ||
| merge-multiple: true | ||
| - name: Aggregate shards -> one ndjson | ||
| working-directory: experimental/CollectiveX | ||
| run: | | ||
| set -euo pipefail | ||
| tag="${{ inputs.backend }}${{ inputs.deepep_v2 && '-v2' || '' }}" | ||
| python3 aggregate_results.py --in-dir ../../_shards --out "results/aggregate/collectivex_${tag}_${{ github.run_id }}.ndjson" | ||
| { | ||
| echo "## CollectiveX sweep aggregate (${tag})" | ||
| echo '```' | ||
| wc -l results/aggregate/*.ndjson 2>/dev/null || echo "no ndjson" | ||
| echo '```' | ||
| } >> "$GITHUB_STEP_SUMMARY" | ||
| - name: Upload aggregate | ||
| uses: actions/upload-artifact@043fb46d1a93c77aae656e7c1c64a875d1fc6a0a # v7.0.1 | ||
| with: | ||
| name: cxsweep-aggregate-${{ inputs.backend }}${{ inputs.deepep_v2 && '-v2' || '' }}-${{ github.run_id }} | ||
| path: experimental/CollectiveX/results/aggregate/*.ndjson | ||
| if-no-files-found: warn |
| continue | ||
| p = os.path.join(root, f) | ||
| if f.endswith(".ndjson"): | ||
| for line in open(p): |
| pass | ||
| elif f.endswith(".json"): | ||
| try: | ||
| yield p, json.load(open(p)) |
Two fixes for the errored sweep cells (FileNotFoundError .cx_workloads/<wid>.manifest.json): 1. run_in_container SHARD loop unsets CX_WORKLOAD_DIR per case — cx_stage_canonical short-circuits when it's set, so the first case's staged dir was reused for all later cases (different routing/dims -> missing manifest). Now each re-stages. 2. sweep_matrix sets canonical=false: the broad sweep runs seeded-runtime (comparable-experimental; fixed seed = same cross-SKU trace) — no per-case canonical staging needed, removing the dependency + overhead entirely.
The 6 re-fired sweeps left residual failures concentrated on rack-scale EP8 (gb200/gb300) and b200 DeepEP-V2. Two distinct bugs: 1. cx_build_deepep_v2 built arch=9.0 (Hopper) for b200 — its CX_RUNNER arch case omitted b200* (sm100). DeepEP-V2 on b200 ran the wrong kernels. Mirror the hybrid builder: b300*|gb300*|b200* -> 10.0. 2. The gb200/gb300 EP8 path runs run_ep.py directly across trays (not run_in_container's shard loop), so in sweep mode it (a) referenced bare $CX_DISPATCH_DTYPE etc. — unbound under set -u, crashing the whole gb300 job on its first line — and (b) ran a single CX_* config instead of the shard's N cases, so rack-scale EP8 was never swept. Make the EP8 path shard-aware: expand CX_SHARD_FILE into one '|'- separated arg-line per case (| not tab: tab is IFS-whitespace, so read collapses empty fields like a false eplb and shifts columns), loop every case with per-case defaults, full axis set for parity. Add sweep_matrix --min-nodes + the workflow min_nodes input so the rack-scale EP8 cells can be re-run alone, without redoing the already- good single-tray EP4 shards (scarce gb200/gb300 trays).
The matrix sweep runs many cells concurrently; each launcher resolved its Slurm JOB_ID with `squeue --name=$RUNNER | head -1`, but the job-name is not unique per cell, so concurrent same-named allocations returned a SIBLING cell's id. Observed on gb300: salloc granted 11354 but the name lookup returned a still-pending 11356 -> srun "Expired or invalid job 11356" -> the cell failed though its own allocation was fine. Systematic on the contended gb200/gb300 clusters (uccl gb200 11/11, deepep gb300 4/6, hybrid gb200 6); single-node SKUs got occasional one-offs (h100). The old one-config-at-a-time dispatch path never hit it (serialized). Add cx_salloc_jobid() to common.sh: run salloc and parse the GRANTED id from its OWN output (race-free), streaming progress live via tee. Route every launcher's salloc through it (gb300-nv, gb200-nv, b200-dgxc, b200-dgxc-slurm, b300, h100-dgxc-slurm, h200, mi355x-amds).
The gb200/gb300 EP8 path expands CX_SHARD_FILE on the SUBMIT HOST (cwd = repo root), but CX_SHARD_FILE is workflow-relative (results/.shard_<id>.json) and the Extract step writes it under working-directory=experimental/ CollectiveX. So `[ -f "$CX_SHARD_FILE" ]` failed, the SHARD branch was skipped, and cx_ep8_cases fell back to ONE default case (bf16/normal/ uniform) instead of the shard's N — the gb300/gb200 'successes' ran 1/14 of the work (logs show a lone EP8[1], 1 JSON per 14-case shard). The single-node/EP4 path was unaffected: run_in_container reads the file from inside the container at /ix/experimental/CollectiveX. Resolve CX_SHARD_FILE against $CID when not found as-is (both rack launchers). Verified: relative path + cwd!=CX_DIR now finds the shard and emits every case.
…sult JSONs) The restructure's goal was 'a result aggregator so there aren't so many individual result files', but plot_ep only globbed *.json, so locally results/ still held ~1200 loose per-case JSONs. Add _iter_docs(): yield docs from *.json AND one-per-line from each *.ndjson (the aggregate), and route all 7 load_*_series loaders through it. Now the single results/aggregate/collectivex_ep.ndjson is a valid plot source — the per-case JSONs can be merged in (aggregate_results.py) and deleted. Verified: 1204-doc ndjson -> identical 1136 series; results/ 103M -> 43M, ~1200 files -> 1.
Previously each EP library was a separate workflow_dispatch (deepep, uccl,
flashinfer, deepep-hybrid, nccl-ep, +deepep_v2) = ~6-8 runs to cover the
matrix. sweep_matrix gains --backends ('all'|comma-list): each deepep-origin
case is emitted once per target backend (capability-filtered; mori stays
AMD-native), with deepep-v2 as a per-cell variant (kernel_gen=v2). The shard
key/id carry backend+v2 so cells stay distinct. collectivex-sweep.yml's
backend input gains 'all' (now the default): setup resolves the union matrix,
the existing per-cell sweep job already reads matrix.backend/deepep_v2, and
one aggregate folds everything into the ndjson.
backend=all -> 211 shard-cells / 2474 cases in ONE run (under the GHA 256-cell
matrix cap; slim matrix 35KB << 1MB output cap). --backend/--deepep-v2 single
modes kept for targeted re-runs. One dispatch replaces the ~8.
The 22 tools/_*.sh were the pre-GHA dev-orchestration layer (SSH salloc/srun drivers, per-SKU probes, the old _gha_suite/_gha_matrix/_gha_collect dispatch path). The GHA model — collectivex-sweep.yml (now one combined backend=all run) + sweep_matrix.py + launchers/ + run_in_container.sh + the cron-driven aggregate_results/plot pipeline — fully supersedes all of it. Verified the live path (launchers, runtime, tests, workflow) never invokes tools/; only two sweep_matrix doc-comments mentioned _gha_suite.sh (updated). Recoverable from git history if any SSH-orchestration helper is needed again.
The combined backend=all sweep confirmed uccl and deepep-hybrid fail entirely on the aarch64 Grace-Blackwell SKUs (0 valid docs at both EP4 and EP8) while working on x86 single-node and while flashinfer/nccl-ep/deepep land full rack coverage on the same clusters — a backend-specific aarch64 from-source-build/transport wall (their builds were probe-confirmed on x86 B300 only), not a launcher issue. Rack-scale coverage is complete via the three backends that do run there.
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Adds CollectiveX under
experimental/CollectiveX/— a cross-vendor collective / expert-parallel benchmark — plus an orchestration-only workflow.What it adds
launchers/launch_<sku>.sh, thelaunch_${RUNNER_NAME%%_*}.shconvention) that run any benchmark via aCX_BENCHselector (nccl|deepep|all) through a sharedlaunchers/run_in_container.sh.run_nccl.py(stock nccl-tests → parsed flat JSON),run_deepep.py(DeepEP dispatch/combine, normal mode),env_capture.py(Layer-0 provenance),plot.py. Every result is correctness-gated and carries a topology-awarecomparison_key.lmsysorg/sglang@sha256:4219…, amd64+arm64); DeepEP viarebuild-deepep. SeeCONTAINERS.md..github/workflows/collectivex-experimental.yml—pushtocollectivex(pathsexperimental/CollectiveX/**) → GB200 NCCL smoke;workflow_dispatch→ chosensku+benchmark(B200, DeepEP, larger sweeps). Logic stays underexperimental/.Validated on hardware
bash -n,py_compile, actionlint, parser fixtures.Notes / deferred
CONTAINERS.md.v0.5.11-cu130).plan.md, not built.Note
Low Risk
Changes are isolated to
experimental/CollectiveX/and a read-only workflow; no production benchmark matrix or serving launchers are modified. Risk is mainly operational (self-hosted GPU time, Slurm/enroot failures) rather than app or security impact.Overview
Introduces CollectiveX under
experimental/CollectiveX/— an experimental cross-vendor collective and MoE EP benchmark — plus orchestration-only.github/workflows/collectivex-experimental.yml. Production serving paths are untouched.Benchmark stack:
run_nccl.pywraps nccl-tests/rccl-tests into provenance-tagged JSON;run_deepep.pyandrun_mori.pyadd correctness-gated DeepEP and AMD MoRI dispatch/combine;env_capture.py,summarize.py, andplot.pyhandle environment capture, CI summaries, and plots. Results use topology-awarecomparison_keys so unlike fabrics are not merged blindly.Execution: Per-SKU Slurm launchers (
launch_b200-dgxc.sh,launch_gb200-nv.sh,launch_b200-dgxc-slurm.sh,launch_mi355x-amds.sh) follow the samelaunch_${RUNNER_NAME%%_*}.shpattern as serving, with sharedcommon.sh(enroot squash by tag, optionalCX_STAGE_DIRrsync, in-container nccl/rccl builds).CX_BENCHselectsnccl,deepep,mori, orallviarun_in_container.sh.CI: Push to
collectivexruns MI355X MoRI onmi355xrunners;workflow_dispatchpicks SKU and benchmark (GB200/B200 NCCL, DeepEP, etc.), writes markdown to the job summary, and uploads gitignoredresults/*.jsonas artifacts.Reviewed by Cursor Bugbot for commit 871086d. Bugbot is set up for automated code reviews on this repo. Configure here.