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[AgentX] vLLM DeepSeek-V4 B200 aggregate / vLLM DeepSeek-V4 B200 聚合配置#2224

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[AgentX] vLLM DeepSeek-V4 B200 aggregate / vLLM DeepSeek-V4 B200 聚合配置#2224
cquil11 merged 7 commits into
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agent/split-pr-2202-b200

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@cquil11 cquil11 commented Jul 15, 2026

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Summary

  • Split the B200 portion of [AgentX] vLLM Deepseek-V4 B200/B300 agg #2202 into an independent PR.
  • Preserve the B200 launcher, vLLM image, KV-offload backends, sparse DSV4 attention, mega-MoE settings, CUDA graph configuration, and concurrency search space from the original PR.
  • Keep the change hardware-scoped to the B200 launcher, the B200 config block, and its appended changelog entry.
  • This replacement is merged with the latest origin/main.

Original PR: #2202
Earlier source PR referenced by #2202: #2188

Testing

  • bash -n benchmarks/single_node/agentic/dsv4_fp4_b200_vllm.sh
  • Generated the DSV4 vLLM B200 full-sweep matrix successfully.
  • python -m pytest utils/matrix_logic/ -q — 221 passed.

中文说明

  • [AgentX] vLLM Deepseek-V4 B200/B300 agg #2202 中的 B200 部分拆分为独立 PR。
  • 完整保留原 PR 的 B200 启动器、vLLM 镜像、KV 卸载后端、稀疏 DSV4 attention、mega-MoE 配置、CUDA Graph 配置和并发搜索空间。
  • 变更范围仅包含 B200 启动器、B200 配置块以及追加的变更日志条目。
  • 此替代 PR 已合并最新的 origin/main

原 PR:#2202
#2202 引用的更早来源 PR:#2188

测试

  • bash -n benchmarks/single_node/agentic/dsv4_fp4_b200_vllm.sh
  • 已成功生成 DSV4 vLLM B200 完整扫描矩阵。
  • python -m pytest utils/matrix_logic/ -q — 221 项测试通过。

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Thanks for the contribution! Please reach out to respective companies' CODEOWNER to fill in the latest PR_REVIEW_CHECKLIST.md before pinging core maintainer on Slack for review. In order for the signoff PR check bot to trigger, you must follow the PR_REVIEW_CHECKLIST.md template correctly, including the phrase As a PR reviewer and CODEOWNER, I have reviewed this and have.

For PR verification, add the full-sweep-fail-fast label (strongly recommended) to this PR — the benchmark sweep only runs on labeled PRs. Use full-sweep-enabled only if you need matrix jobs to keep running past a failure.

PR authors are responsible for ensuring that after merging, all GitHub Action jobs fully pass. A lot of the time, failures are just flakes and simply re-running the failed jobs will fix it. See GitHub's docs on re-running failed jobs


感谢你的贡献!请联系相应公司的 CODEOWNER 填写最新的 PR_REVIEW_CHECKLIST.md,然后再在 Slack 上联系核心维护者进行审阅。为了触发 signoff PR 检查机器人,你必须正确遵循 PR_REVIEW_CHECKLIST.md 模板,包括保留英文语句 As a PR reviewer and CODEOWNER, I have reviewed this and have

如需进行 PR 验证,请为此 PR 添加 full-sweep-fail-fast 标签(强烈推荐)— 基准测试 sweep 仅在带有标签的 PR 上运行。仅当需要矩阵任务在失败后继续运行时才使用 full-sweep-enabled

PR 作者有责任确保合并后所有 GitHub Action 任务完全通过。 很多时候失败只是偶发抖动(flake),重新运行失败的任务即可解决。参见 GitHub 关于重新运行失败任务的文档

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Thanks for the contribution! Please reach out to respective companies' CODEOWNER to fill in the latest PR_REVIEW_CHECKLIST.md before pinging core maintainer on Slack for review. In order for the signoff PR check bot to trigger, you must follow the PR_REVIEW_CHECKLIST.md template correctly, including the phrase As a PR reviewer and CODEOWNER, I have reviewed this and have.

For PR verification, add the full-sweep-fail-fast label (strongly recommended) to this PR — the benchmark sweep only runs on labeled PRs. Use full-sweep-enabled only if you need matrix jobs to keep running past a failure.

PR authors are responsible for ensuring that after merging, all GitHub Action jobs fully pass. A lot of the time, failures are just flakes and simply re-running the failed jobs will fix it. See GitHub's docs on re-running failed jobs


感谢你的贡献!请联系相应公司的 CODEOWNER 填写最新的 PR_REVIEW_CHECKLIST.md,然后再在 Slack 上联系核心维护者进行审阅。为了触发 signoff PR 检查机器人,你必须正确遵循 PR_REVIEW_CHECKLIST.md 模板,包括保留英文语句 As a PR reviewer and CODEOWNER, I have reviewed this and have

如需进行 PR 验证,请为此 PR 添加 full-sweep-fail-fast 标签(强烈推荐)— 基准测试 sweep 仅在带有标签的 PR 上运行。仅当需要矩阵任务在失败后继续运行时才使用 full-sweep-enabled

PR 作者有责任确保合并后所有 GitHub Action 任务完全通过。 很多时候失败只是偶发抖动(flake),重新运行失败的任务即可解决。参见 GitHub 关于重新运行失败任务的文档

cquil11 added a commit that referenced this pull request Jul 15, 2026
Point the split B200 changelog entry to PR #2224.

中文:将拆分后的 B200 变更日志条目链接到 PR #2224
@cquil11 cquil11 added NVIDIA full-sweep-enabled agentx AgentX benchmarks, recipes, and infrastructure labels Jul 15, 2026
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@@ -1,5 +1,5 @@
#!/usr/bin/env bash
set -euo pipefail
set -eo pipefail

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🟡 Line 2 changed set -euo pipefail to set -eo pipefail, dropping nounset — this is the only script among all 24 in benchmarks/single_node/agentic/ that doesn't use -u. The rewrite replaced every ${VAR:-default}-guarded reference ($DCP_SIZE, $SLURM_JOB_ID, $SLURMD_NODENAME, $MODEL_PATH, $KV_OFFLOAD_BACKEND) with bare $VAR forms, which is why -u had to go; the near-identical dsv4_fp4_b300_vllm.sh shows the same optional-var pattern works fine with -u kept and ${VAR:-} guards. Recommend restoring set -euo pipefail and guarding the new bare references, to keep this script's safety net consistent with its siblings.

Extended reasoning...

The bug: Line 2 of dsv4_fp4_b200_vllm.sh was changed from set -euo pipefail to set -eo pipefail, silently dropping the nounset (-u) flag. Every other script in benchmarks/single_node/agentic/ — including the near-identical dsv4_fp4_b300_vllm.sh, plus dsv4_fp4_mi355x_vllm.sh, dsv4_fp4_b200_sglang.sh, dsv4_fp8_h200.sh, and all the kimik2.5_*/qwen3.5_*/minimaxm3_* variants — still uses set -euo pipefail. This script is now the sole outlier in the directory.\n\nWhy it happened: The diff rewrote several previously-guarded optional-variable references into bare, unguarded forms: DCP_SIZE=${DCP_SIZE:-1} became an if [ -z "$DCP_SIZE" ]; then DCP_SIZE=1; fi block (which still reads the bare $DCP_SIZE first), ${SLURM_JOB_ID:-}/${SLURMD_NODENAME:-unknown} became bare $SLURM_JOB_ID/$SLURMD_NODENAME, ${MODEL_PATH:-} became bare $MODEL_PATH, and the new case "$KV_OFFLOAD_BACKEND" in "") branch reads $KV_OFFLOAD_BACKEND unguarded (note it isn't even part of check_env_vars, so it can legitimately be unset). None of these would survive set -u if the variable is genuinely unset, so -u had to be dropped to make the rewritten code work as written.\n\nWhy this is avoidable: The sibling dsv4_fp4_b300_vllm.sh proves -u and this exact optional-variable pattern coexist fine: it keeps set -euo pipefail while using DCP_SIZE="${DCP_SIZE:-1}", declare -p SLURM_JOB_ID >/dev/null 2>&1 && [ -n "$SLURM_JOB_ID" ], and similar guards for MODEL_PATH. The new case statement here could equally have matched on "${KV_OFFLOAD_BACKEND:-}" instead of the bare variable. So dropping -u wasn't required by the case-statement refactor — it was a side effect of un-guarding several other references at the same time.\n\nImpact: With -u disabled, a future typo in a variable name anywhere in this script (e.g. RESULT_DIR misspelled as RESUT_DIR) will silently expand to an empty string and continue executing rather than aborting immediately with an "unbound variable" error — exactly the failure mode -u exists to catch. As written today the script runs correctly (all currently-referenced optional vars are handled, and required vars are separately validated by check_env_vars), so there's no concrete runtime failure right now. This is a latent robustness/consistency regression rather than an active bug.\n\nProof by example: Suppose a future edit adds echo "Writing to $RESUT_DIR" (a typo of $RESULT_DIR) somewhere in this script. With set -eo pipefail (current state), $RESUT_DIR expands to "", the echo prints "Writing to ", and the script continues — the typo goes unnoticed until output is inspected downstream. With set -euo pipefail (every sibling script's setting), the same line would immediately abort with RESUT_DIR: unbound variable, catching the bug at the point of introduction. This is precisely why the convention exists across all 23 other scripts in the directory.\n\nFix: Restore set -euo pipefail on line 2 and guard the newly-unguarded references the same way dsv4_fp4_b300_vllm.sh does: DCP_SIZE="${DCP_SIZE:-1}"/PCP_SIZE="${PCP_SIZE:-1}", [[ -n "${SLURM_JOB_ID:-}" ]] / ${SLURMD_NODENAME:-unknown}, [[ -n "${MODEL_PATH:-}" ]], and case "${KV_OFFLOAD_BACKEND:-}" in.

cquil11 and others added 3 commits July 15, 2026 19:41
Carry the B200-only launcher, config search space, and changelog scope from PR #2202.

中文:拆分 DeepSeek-V4 B200 vLLM AgentX 配方,仅保留 PR #2202 中的 B200 启动器、配置搜索空间和变更日志范围。
Point the split B200 changelog entry to PR #2224.

中文:将拆分后的 B200 变更日志条目链接到 PR #2224
@ivanium
ivanium force-pushed the agent/split-pr-2202-b200 branch from 53cf054 to 455eabc Compare July 15, 2026 19:48
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@ivanium

ivanium commented Jul 16, 2026

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vLLM recipe PR: vllm-project/recipes#645

cquil11 commented Jul 16, 2026

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/reuse-sweep-run 29445892486

@SemiAnalysisAI SemiAnalysisAI deleted a comment from Klaud-Cold Jul 16, 2026

cquil11 commented Jul 16, 2026

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As a PR reviewer and CODEOWNER, I have reviewed this and have:

  • Verified that as of the moment of typing this, this is the latest version of PR_REVIEW_CHECKLIST.md
  • Verified that the general code quality meets the InferenceX standard and does not make the code quality any worse.
  • Verified that this PR has passed PR validation: https://github.com/SemiAnalysisAI/InferenceX/actions/runs/29516237062
  • Verified that this PR passes evals. N/A for AgentX: AgentX has no separate eval jobs; all 28 AgentX benchmark jobs passed in https://github.com/SemiAnalysisAI/InferenceX/actions/runs/29445892486
  • Verified that speculative decoding PRs uses chat templates to align the AL distribution to real world
  • For agentic workloads: verified that speculative-decoding configs (EAGLE / MTP / draft models) run with simulated synthetic acceptance, with the acceptance-length value taken from the committed golden AL curve in golden_al_distribution/ for that model, thinking mode, and draft length. A submission may choose any supported draft length, but it may not substitute a different acceptance target.
  • Verified that the model architecture isn't changed with benchmark hacks like using --hf-overrides to skipping indexer for every x layers on models that don't natively support this. As a general rule, we won't accept optimizations that reduces the number of model architecture FLOPs. Anything that makes that same computation run faster is fair game; FLOPs at lower precisions is fine, given that the config passes private evals. As an general north star princple, we should only use optimizations which is used in production by customers that care about accuracy
  • If an company claims that they support vLLM/SGLang as first class LLM inference engines on their hardware, I have verified that the respective vLLM submission made using upstream https://hub.docker.com/u/vllm docker repo, upstream SGLang https://hub.docker.com/u/lmsysorg docker repo. The only exceptions are for new hardware, such as MI455X UALoE72, Vera Rubin NVL72, Rubin NVL8, etc., and for new model architectures where there is an actual reason why vLLM/SGLang does not fundamentally support them yet as supported by vLLM/SGLang community maintainers
  • If an company claims that they support vLLM/SGLang as first class upstream in-tree LLM inference engines on their hardware, I have have verified that the respective vLLM/SGLang submission has been made before additional frameworks (TRT-LLM, ATOM, etc.). The only exceptions are for new hardware, such as MI455X UALoE72, Vera Rubin NVL72, Rubin NVL8, etc., and for new model architectures where there is an actual reason why vLLM/SGLang does not fundamentally support them yet.
  • Verified that every single-node vLLM/SGLang recipe in this PR is documented in the official vLLM recipes and/or the SGLang cookbook:
    • I linked the corresponding upstream PR in the vLLM recipe repo or SGLang repo and verified that it is MERGED before this InferenceX PR merges. An opened, draft, or closed-without-merge upstream PR does not satisfy this requirement. If the matching recipe was already published, I linked the published recipe/cookbook page in the additional detail section below.
  • Verified that this PR does not patch the inference engine or serving stack — the pinned image must run as shipped. This covers .patch files / git apply / patch, inline patches embedded in benchmark scripts (e.g. a python3/sed heredoc that rewrites installed engine sources before serving), in-place edits of site-packages, monkey-patching, overwriting container files, and installing forked/rebuilt engine wheels on top of the pinned image. The only exception is a patch covered by a filled-out waiver at docs/waiver/<PR_NUMBER>.md — named after the PR that introduces the patch and filed in that same PR, stating what is patched, why the unmodified upstream image cannot run this benchmark, the upstream PR/issue link, and the removal plan — which I have linked below in the additional detail section.
  • If any of the above criteria cannot reasonably be satisfied, I have provided additional reasoning below.

Additional detail section:

Signed: cquil11

@Klaud-Cold

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❌❌❌ REJECTED ❌❌❌

@cquil11 — Check 3 fails: two deployment-defining kernel-selection args in this PR are not in the merged upstream recipe (vllm-project/recipes#645 / published DeepSeek-V4-Pro recipe): --moe-backend deep_gemm_amxf4_mega_moe (recipe's Blackwell override pins deep_gemm_mega_moe; the amxf4 variant is not a valid backend on public vLLM main) and --attention-config backend FLASHINFER_MLA_SPARSE_DSV4 (recipe pins no attention backend; vLLM's B200 default for DSV4 is FlashMLA sparse). Update the upstream recipe to document these backends (then re-sign), or align the config with the published recipe.

✅ Check 0 (CODEOWNER): PASS — all changed paths are owned by @InferenceX/core (incl. configs/nvidia-master.yaml per #2245); signer cquil11 is an org MEMBER acting as core maintainer (team membership API not readable by this token — not a failure).
✅ Check 1 (passing sweep on in-PR commit): PASS — in-PR commit 455eabc has all 28 agentic / benchmark check-runs green in run 29445892486; this PR's only config is AgentX, so the single-node */ / eval / lanes are empty-by-design skips, and the reuse gate on the signed head (run 29516237062) accepted that sweep.
➖ Check 2 (evals pass): N/A — AgentX configs have no eval jobs by design (utils/matrix_logic/generate_sweep_configs.py skips agentic entries: "do not support lm-eval"), matching the sign-off's stated reasoning; the green run used this PR's exact image (vllm/vllm-openai:nightly-dev-x86_64-cu13.0.1-904e4ec).
❌ Check 3 (recipe linked, merged, complete): FAIL — link present and MERGED (recipes#645, 2026-07-16), and both KV-offload connectors are documented, but MAJOR kernel backends diverge: --moe-backend deep_gemm_amxf4_mega_moe contradicts the recipe's deep_gemm_mega_moe, and the FLASHINFER_MLA_SPARSE_DSV4 attention backend (+ use_prefill_query_quantization) is absent from the recipe and is not the B200 default. Informational only (harness tuning, not blockers): FULL_DECODE_ONLY cudagraphs vs recipe's FULL_AND_PIECEWISE, autotune off, --numa-bind, --enable-ep-weight-filter, --prefill-schedule-interval, VLLM_* env toggles.
✅ Check 4 (reuse command): PASS — /reuse-sweep-run 29445892486 posted by cquil11 (MEMBER).
✅ Check 5 (latest checklist template): PASS — every current-template item present and checked.
✅ Check 6 (upstream image / engine-first): PASS — vllm/vllm-openai:nightly-dev-x86_64-cu13.0.1-904e4ec verified live on the upstream vLLM Docker Hub org (pushed by vllmbot 2026-07-14); framework is vLLM itself, so engine-first ordering is trivially satisfied.
✅ Check 7 (no architecture hacks): PASS — no --hf-overrides/model-config edits; sparse DSV4 attention and the FP4 indexer cache are the model's native architecture.
➖ Check 8 (spec-decode chat templates): N/A — no speculative-decoding changes in this PR.
✅ Check 9 (no engine patches): PASS — no patching introduced; the PR actually removes the previous nvidia-cutlass-dsl-libs force-reinstall, and the pinned mooncake / vllm-router / aiperf installs are declared harness/connector deps, not engine rebuilds.
➖ Check 10 (agentic golden AL): N/A — agentic changes but no speculative decoding, and no synthetic-acceptance knobs present.

@cquil11
cquil11 merged commit 4bbb2ac into main Jul 16, 2026
25 checks passed
@cquil11
cquil11 deleted the agent/split-pr-2202-b200 branch July 16, 2026 17:25
ApostaC added a commit that referenced this pull request Jul 16, 2026
…sweep

Rebased onto main after PR #2224 merged the tuned B200 recipe. The
LMCache points now live directly in the official
dsv4-fp4-b200-vllm-agentic search space alongside the vllm-simple and
Mooncake arms (no standalone config section). LMCache 0.5.1 MP server
(lmcache_driven transfer mode) + LMCacheMPConnector per PR #2153; the
lmcache arm drops --enable-cumem-allocator because cuMem/VMM
allocations cannot be CUDA-IPC-exported to the LMCache server. Ladder
validated in the PR #2231 bring-up sweep: peak ~25.7k total tok/s/GPU
at DEP8 conc 72 with 96-98% cache hit.

中文:在 PR #2224 的调优配方合并后 rebase 到 main。LMCache 测试点直接并入
官方 dsv4-fp4-b200-vllm-agentic 搜索空间,与 vllm-simple、Mooncake 分支
并列(不再使用独立配置段)。LMCache 0.5.1 MP server(lmcache_driven
传输模式)+ LMCacheMPConnector(沿用 PR #2153);lmcache 分支去掉
--enable-cumem-allocator(cuMem/VMM 分配无法通过 CUDA IPC 导出给
LMCache server)。测试点阶梯已在 PR #2231 调试扫描中验证:DEP8 并发 72
达到峰值约 25.7k total tok/s/GPU,缓存命中率 96–98%。

Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
majunze2001 added a commit that referenced this pull request Jul 17, 2026
Restructure so the sweep runs only the new MTP work, not the existing aggregate:
- Revert dsv4-fp4-b200-vllm-agentic to its main (#2224) search space -- unchanged
  vs main, so it is not re-run.
- Add dsv4-fp4-b200-vllm-agentic-mtp: MTP twins (num_speculative_tokens=3) of the
  aggregate arms (TP8 GPU-resident, TP8 SimpleCPU, DEP8 SimpleCPU, DEP8 Mooncake),
  each mirroring its non-MTP conc-list.
- Point the perf-changelog entry at only the new key, so only it sweeps.

(No separate TP8 key: B200's TP8 arms already exist in the #2224 aggregate.)

Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
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