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[ROCm][Bugfix] Support FP8 KV cache for NoPE sparse MLA - #57134
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GSM8K on the FP8 KV / NoPE Triton pathFull 1,319-question GSM8K on GLM-5.3-Flash, TP=4, 0.9704 exact match (1,280 / 1,319), invalid rate 0.0. Config (in-tree That is the same chat / greedy / 1024 protocol as prior bf16 GSM8K on this machine (~96.9–97.2%). FP8 KV sits in that band. It is not the |
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Withdrawing the 0.9704 chat-completions GSM8K figure. That used Re-running now with the common recipe ( |
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Re-ran GSM8K on the protocol other GLM-5.3-Flash PRs use ( 1,319 questions, FP8 KV, |
Serving performance vs BF16 KV
Decode-heavy 1k points are within 1% of BF16. 8k/1k c=16 TTFT is queueing-dominated and the only point outside a few percent. |
… to run / 改用实测可跑的最大配置:524288 + bf16 KV The 1M context does not run on this recipe, at either KV dtype, and the two failures have different causes. Measured on 2x8 MI350X against this branch. fp8, which 19bea0b introduced: the prefill rank refuses at engine init with glm5_2: unexpected MLA cache stride 576 B/token; expected 656 (fp8_ds_mla) or 1152 (bf16) The layouts are not the same thing. fp8_ds_mla is 512 B fp8 kv_c + 16 B of four fp32 per-128-block scales + 128 B bf16 k_pe; upstream keeps the RoPE part in bf16 deliberately, "not quantized for accuracy". ROCm's plain fp8 is 512 B fp8 kv_c + 64 B fp8 k_pe with no inline scales at all -- vllm-project/vllm#57134 states the precompiled AITER sparse-MLA kernels assume "512 latent elements plus 64 RoPE elements" and dequantize "using the layer KV scale", a per-tensor scalar. So on NVIDIA `fp8` is an alias for fp8_ds_mla, and on ROCm it is a different record with coarser scaling and a quantized RoPE. TileRT's connector reads the prefill cache directly and rejects anything that is not 656 or 1152. bf16 at 1M: the connector's receive and staging buffers are sized from max_seq_len, because they are registered once for RDMA. At 99.1 KiB/token that is 99.06 GiB on each rank. Decode OOMs with 95.94 GiB free; on prefill the buffer sits outside vLLM's utilization budget, so it lands on top of 130.35 GiB of non-KV occupancy plus the 91.71 GiB of KV that a 1M sequence needs. 524288 with bf16 and gpu-memory-utilization 0.60 runs: 16/16 requests, TTFT p50 1010.3 ms, TPOT p50 2.04 ms, 334.8 tok/s -- within half a percent of the 202752 baseline this recipe was validated at. The utilization matters as much as the context: vLLM sizes its KV cache to fill the budget rather than to what max-model-len needs, so lowering the context alone frees nothing. At 0.70 both 512k and 768k allocate their two large buffers and then die on a 2 MiB activation with zero free memory. Serving the full 1M window needs the transfer buffers decoupled from max_seq_len -- streamed in chunks against a small registered region -- which is an engine change, not a recipe one. Capping the buffer at the corpus maximum would paper over it for this benchmark only. 1M 上下文在本配方下跑不起来，两种 KV dtype 各自的原因不同，均在 2x8 MI350X 上实测。 fp8（19bea0bd 引入）：prefill 侧在 engine init 即拒绝（错误原文见上）。两种布局 并非一回事：fp8_ds_mla 是 512 B fp8 kv_c + 16 B（4 个 fp32 的 per-128-block scale）+ 128 B bf16 k_pe，上游刻意让 RoPE 保持 bf16，"not quantized for accuracy"；ROCm 的普通 fp8 则是 512 B fp8 kv_c + 64 B fp8 k_pe，完全没有内联 scale——vllm-project/vllm#57134 说明预编译的 AITER 稀疏 MLA kernel 假定 "512 latent elements plus 64 RoPE elements"，并"using the layer KV scale" （per-tensor 标量）反量化。因此 NVIDIA 上 `fp8` 是 fp8_ds_mla 的别名，ROCm 上 它是另一种记录：更粗的缩放粒度，且 RoPE 也被量化。TileRT connector 直接读取 prefill 的 cache，非 656 或 1152 一律拒绝。 bf16 的 1M：connector 的接收与 staging 缓冲按 max_seq_len 预开，因为它们要为 RDMA 一次性注册。按 99.1 KiB/token 计，两侧各 99.06 GiB。decode 在空闲 95.94 GiB 时 OOM；prefill 侧该缓冲位于 vLLM 的 utilization 预算之外，叠加在 130.35 GiB 非 KV 占用与 1M 序列所需的 91.71 GiB KV 之上。 524288 + bf16 + gpu-memory-utilization 0.60 可跑：16/16 请求，TTFT p50 1010.3 ms、TPOT p50 2.04 ms、334.8 tok/s，与本配方验证所用的 202752 基线相差 不到 0.5%。utilization 与上下文同等重要：vLLM 按预算上限而非 max-model-len 的实际需要分配 KV，故单降上下文不腾显存。在 0.70 下，512k 与 768k 都能分配完 两块大缓冲，随后在一次 2 MiB 的激活分配上因零空闲而失败。 要服务完整的 1M 窗口，需要把传输缓冲与 max_seq_len 解耦——对一小块已注册区域 分片流式搬运——那是引擎改动，不是配方改动。把缓冲按语料最大长度封顶只能在本基准 上遮掩问题。
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Co-authored-by: Cursor <cursoragent@cursor.com> Signed-off-by: amd-dlimpus <257420672+amd-dlimpus@users.noreply.github.com>
Co-authored-by: Cursor <cursoragent@cursor.com> Signed-off-by: amd-dlimpus <257420672+amd-dlimpus@users.noreply.github.com>
The NoPE Triton path runs decode with one workgroup per request and head block, leaving most CUs idle at decode batch sizes. Port vllm#58584's split-K structure (per-split partials, existing DSv4 reduce and split heuristic) and give it an FP8 KV branch. The FP8 scale factors out of both products because K and V are the same latent row, so it is applied once to the QK scale and once to the partial accumulator. VLLM_ROCM_SPARSE_SPLIT_DECODE selects which caches take it: fp8 (default), all (also the model-dtype cache), or off. Pipelining defaults per dtype: two stages for FP8, one for bf16, each measured faster on MI355X. Co-authored-by: Cursor <cursoragent@cursor.com> Signed-off-by: Limpus, David <dlimpus@amd.com>
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simondanielsson
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Thanks for the fix!
This has a lot of overlap with #58584. Can you add fp8 kv support to the splitk kernel added there instead?
Suggestion: In terms of testing, can we just keep the correctness test of the kernel for fp8 kv vs a reference?
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| # Which KV caches take split-K decode on the NoPE Triton path: "fp8" | ||
| # (default), "all" (also the model-dtype cache), or "off". | ||
| _SPLIT_DECODE = os.getenv("VLLM_ROCM_SPARSE_SPLIT_DECODE", "fp8").strip().lower() |
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Suggestion: I don't think we need this, we should be running splitk for uniform decodes for all kv dtypes and no need to give users the option to disable it 👍
(For future reference, if we want to add new envvars we should add them to envs.py :) )
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Summary
Problem
AITER precompiled sparse MLA kernels assume the DeepSeek cache-row geometry: 512 latent elements plus 64 RoPE elements. GLM-5.3-Flash is rope-free and stores a 512-element row. FP8 NoPE requests currently reach the AITER path, whose 576-element assumption can read past the cache row and cause a ROCm GPU memory fault.
The existing Triton ragged path already handles the NoPE geometry. This change makes its routing geometry-based instead of BF16-only, adds scaled FP8 KV dequantization, and avoids FP8-quantizing Q for that path.
Duplicate-work check
No open PR found addresses ROCm NoPE FP8 sparse-attention KV rows. #39168 overlaps the ROCm ops file but changes the separate FP8 indexer-cache MQA path; the open GLM-5.3 NoPE FP8 PRs target NVIDIA backends. The previously bundled SharedTopkIndicesBuffer no-op was dropped because #57252 already landed ROCMAiterMLASparseImpl.record_logical_topk_ready().
Validation
Rebased onto main@63d9ad0a. Head: ae5cad3d.
Hardware validation used a full ROCm wheel from the previous head (73860da5) on the GLM-5.3-specific ROCm base image. The rebase kept the same kernel/backend/benchmark hunks and only resolved a header-constant conflict with the new upstream FP8_DTYPE plus dropped the now-redundant top-k no-op.
The serving matrix used
--max-num-batched-tokens 131072 --no-enable-chunked-prefillso each long prompt exercises the full-query FP8 MLA path. During diagnosis, default partial chunking on current main showed a separate GPU fault only after all instrumented sparse indexer and attention calls had synchronized and returned; it reproduced across both generic-nightly and GLM-specific runtime bases and is outside this FP8 MLA kernel change.GSM8K
Same recipe as other GLM-5.3-Flash PRs (#57590, #56960, #56176, #55738, #54925): EleutherAI
lm_evalagainst/v1/completions, 5-shot, greedy. Not chat completions.gen_kwargs:until=['Question:', '</s>', '<|im_end|>'],do_sample=False,temperature=0(lm_eval gsm8k.yaml defaults). Full 1,319 questions. No GPU faults.That matches the published GLM-5.3-Flash
lm_evalcompletions band: #54925 0.9158 / 0.9143 (model card 0.9128), #56176 0.9121 BF16 / 0.9219 MXFP4, #57546 0.9174 / 0.9166, #56960 0.9249 / 0.9234, #55738 93.03% ± 0.70.Serving performance (BF16 KV vs FP8 KV)
vllm bench serverandom dataset, same recipe as ROCm GLM-5.3-Flash PRs #57590 / #57979 (and NVIDIA #55736):--backend vllm --dataset-name random --ignore-eos --seed 5678 --random-range-ratio 0 --num-prompts $((4*CONC)) --num-warmups $CONC. Median TPOT and output tok/s as in #57979.Same image and overlay for both arms. Baseline is BF16 KV (main cannot boot FP8 NoPE). TP=4 on MI355X,
--attention-backend ROCM_AITER_MLA_SPARSE --max-model-len 16384 --max-num-seqs 64 --max-num-batched-tokens 16384 --no-enable-prefix-caching. 18/18 points, 0 failed requests.Decode-heavy 1k points are within 1% of BF16. 8k/1k is parity at c=1 and c=8; c=16 TTFT is queueing-dominated and the only point outside a few percent.
AI assistance
AI assistance was used to investigate, implement, test, rebase, and prepare this change.