[Build][SM70] Make TokenSpeed MLA optional for V100 builds

[ga-it]

[Build][SM70] Make TokenSpeed MLA optional for V100 builds

AI-assisted draft. AI assistance was used for upstream comparison,
implementation, dependency resolution, live build validation, and this
write-up. The human submitter reviewed.

Purpose

The CUDA requirement set simultaneously pins:

• apache-tvm-ffi==0.1.10 for the current TileLang stack; and
• tokenspeed-mla==0.2.5, which requires apache-tvm-ffi==0.1.13.

The set is unsatisfiable. TokenSpeed MLA is already imported as an optional
backend and its decode and prefill implementations accept only compute
capability major 10 (Blackwell). It is not usable on the target SM70/V100
build.

This PR removes TokenSpeed from the mandatory CUDA set, documents it as
optional, and replaces the unconditional install hint with a compatibility
warning. Blackwell backend code, selection, and kernels are unchanged; a
Blackwell user may install a TokenSpeed package once its TVM-FFI requirement is
compatible with the installed TileLang stack.

Base SHA: 62ad1e02693f4c857f3b7547cef1860ee54e8053.

Duplicate-work audit

The complete open PR list and TokenSpeed searches were refreshed on 2026-08-28.
No open PR fixes this resolver conflict.
PR 358 changes Torchaudio only
in requirements/cuda.txt. Closed dependency-refresh PRs retain the
conflicting TokenSpeed and TVM-FFI pins. Existing TokenSpeed-related issues
concern runtime or model behavior rather than this exact dependency
contradiction.

Test Plan

Command used for both states:

uv pip compile requirements/cuda.txt \
  --python-version 3.12 \
  --python-platform x86_64-manylinux_2_28 \
  --no-cache \
  --output-file <validated-temporary-path>

Run that command before and after the change, then run Ruff formatting/lint,
Python byte-compilation, patch hygiene, an integrated CUDA 12.8.1 SM70 image
build, import checks, and TP4 V100 health/runtime smoke requests.

Test Result

Unpatched current main:

exit 1
No solution found when resolving dependencies:
Because tokenspeed-mla==0.2.5 depends on apache-tvm-ffi==0.1.13 and you
require apache-tvm-ffi==0.1.10, ... your requirements and
tokenspeed-mla==0.2.5 are incompatible.

Patched SM70 candidate:

exit 0
Resolved 192 packages in 26.18s
apache-tvm-ffi==0.1.10
tilelang==0.1.10
tokenspeed-mla absent

On gazasrv16, an isolated precompiled editable install from unpatched current
main reproduced the same resolver failure. Applying this candidate allowed the
Linux environment to resolve 191 packages and install both the editable source
and full CUDA test dependency set. This was a disposable worktree; production
containers, GPUs, and host packages were not changed.

Additional checks:

uvx --from ruff==0.14.0 ruff format --check \
  vllm/v1/attention/backends/mla/tokenspeed_mla.py \
  vllm/v1/attention/backends/mla/prefill/tokenspeed_mla.py
2 files already formatted

uvx --from ruff==0.14.0 ruff check <same files>
All checks passed!

uv run --no-project --python 3.12 python -m py_compile <same files>
exit 0

git diff --check
exit 0

git apply --check 0003-upstream-sm70-optional-tokenspeed.patch
exit 0 against 62ad1e0

Integrated V100 build and runtime smoke

The patched dependency set was used in a successful CUDA 12.8.1 SM70 image
build for four V100-SXM2-32GB GPUs. vLLM core, MoE, the SM70 sampler,
FlashAttention-V100, and FlashQLA imported successfully. HTTP /health,
/v1/models, and /metrics returned 200; the container remained healthy with
zero restarts.

These runtime figures are smoke evidence for the resulting V100 image, not
performance attributed to dependency removal:

Request	TTFT	Decode	MTP4 acceptance	Total
6,316 prompt / 512 output	2.9538 s	87.0357 tok/s	73.2824%	8.8249 s
564,577 cold / 512 output	690.9660 s	31.2186 tok/s	79.7131%	707.3345 s
564,577 identical replay / 512 output	9.2287 s	31.1965 tok/s	79.7131%	25.6088 s

The service exposed 2,557,299 FP8 E5M2 KV-cache tokens and reused 99.6087% of
the long identical prefix. Backend tokenization processed the approximately
564.6k-token prompt at 224k-228k tokens/s.

Blackwell boundary

This change was validated only for SM70/V100. It does not claim TokenSpeed
correctness or performance on Blackwell. The current mandatory pins cannot
install TokenSpeed alongside the repository's TileLang/TVM-FFI stack; the
preferred long-term Blackwell solution is a mutually compatible published
dependency set, after which TokenSpeed can be offered as an explicit optional
extra or architecture-specific image dependency.

Risk and rollback

SM70 behavior is unchanged because the backend rejects capability 7.0. The
risk is that a Blackwell image no longer receives TokenSpeed transitively; its
documented fallback backends remain available, and explicit installation is
possible when dependencies are compatible. Rollback restores the mandatory
pin but also restores the resolver failure.

---

Essential Elements of an Effective PR Description Checklist

• The purpose of the PR, including the exact TokenSpeed/TVM-FFI conflict and the open dependency PRs checked for duplicates.
• The test plan, including before/after dependency resolution, Ruff, byte-compilation, patch hygiene, SM70 image build/imports, and V100 runtime smoke.
• The test results, including the reproduced resolver failure, successful patched resolution/install, image/runtime health, decode performance, MTP acceptance, KV-cache capacity, prefix-cache reuse, tokenization rate, and restart status.
• (Optional) The necessary documentation update is included in docs/design/attention_backends.md, documenting TokenSpeed as optional and requiring TileLang-compatible apache-tvm-ffi.

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[yangzhuxinyzx]

按最新 main 9ed8697 复核：requirements/cuda.txt 和两个 TokenSpeed backend 文件已由 8ab1bf6 纳入同等修复，三文件与本 PR 完全相同。目前模拟合并只剩 attention_backends.md 的 3 行文档，而 attention-backend-docs 生成器会将其恢复，导致本 PR 的静态检查失败。实际依赖冲突已解决，本 PR 暂留 Open 记录来源；无需重引入已集成的源码。若继续文档补充，应修改对应生成模板。