[peymanr]

Purpose

Adds context-parallel decode scoring for MiniMax-M3's sparse lightning indexer on ROCm (TP>1, bf16 index cache, opt-in via --attention-config '{"minimax_m3_indexer_cp": true}').

Problem

MiniMax-M3 selects a sparse set of KV blocks per query token with a lightweight indexer that scores every block in the request's context. Index heads shard like KV heads (sparse_num_index_heads == num_key_value_heads == 4), and the index-K cache is a single shared head replicated on every rank. So with TP>1 every rank streams the whole index cache — the same seq_len/128 blocks — once per decode step, just to score its own head(s). That read is duplicated world_size times and at long context and large decode batches it is a large term in the step.

Solution

Shard the blocks instead of duplicating the read. Because each rank owns different index heads, every rank has to score every head on its shard:

1. All-gather the index queries — heads * 128 bf16 per token (1 KB at M3's 4 heads).
2. Each rank scores all global heads on its 1/world_size round-robin block shard (block = local * world + rank).
3. For each head, take the local top-k of the full k over the shard, pack each candidate into one int64 (score, global block id) key, and all-gather the keys — P * heads * k * 8 bytes per token, independent of context length.
4. Merge per global head, keeping only the heads this rank owns.

Index-cache traffic per rank drops to 1/P; scoring compute per rank is unchanged (heads heads over 1/P of the blocks). The merge is exact: for a given head, a block in that head's global top-k is necessarily in its own shard's top-k for the same head. Forced init/sink and sliding-window blocks are pinned on both the shard and the merge.

Default: OFF (AttentionConfig.minimax_m3_indexer_cp = False); nothing enables it automatically. Turning it on is a startup error unless the index cache is bf16 (indexer_kv_dtype "auto" or "bf16") — the score kernel reads the cache without a scale; the fp8 path is #57909 — and when the fused decode path (#59653) is also enabled, since that kernel computes indexer top-k itself. It is also a startup error on non-ROCm platforms or with tensor_parallel_size == 1.

When to enable: long-context workloads. CP adds a fixed per-layer cost (two small all-gathers, scoring every index head, the merge), so it slows decode on short contexts and speeds it up on long ones. On MI350X at TP=4 (vllm bench serve, OSL 1024), mean TPOT changes by +15% / +7% at 4k ISL (concurrency 8 / 32), breaks even around 64k / 32k, and improves by 9% / 16–27% at 128k. TTFT is unaffected. Agentic traces (AgentX, ISL ~74k) fall on the winning side.

CUDA graph capture

Two properties are required for CP to work under cudagraph_mode=FULL_DECODE_ONLY.

CP must be what gets captured. vLLM v2 records FULL decode graphs with cudagraph_runtime_mode=NONE (see ModelCudaGraphManager.capture), so a CUDAGraphMode.FULL guard never fires. Guarding on torch.cuda.is_current_stream_capturing() would record the non-CP indexer into the decode graph that is then replayed. The only guard is torch.compiler.is_compiling(), because Dynamo tracing cannot capture the collectives. Persistent buffers (in IndexerCPDecode) keep the decode path allocation-free so CP is what gets captured.

Kernel shapes must be capture-stable. Capture goes through _dummy_run, which sets seq_lens = max_query_len. Shapes derived from the batch's max_seq_len would specialize a one-block context into every decode graph. All kernel shapes come from max_model_len, and block counts and strides are runtime arguments rather than tl.constexpr. test_indexer_context_scores_independent_of_shape_bound asserts scores are identical whether the grid is sized for the batch or for a bound 8x larger.

--enforce-eager is not required.

MiniMax-M3 sparse attention pipeline

Stage	Backend	Index cache dtype	When selected
Indexer	Triton (this PR)	bf16	indexer_kv_dtype=bf16; no AITER dependency
Indexer	AITER (#57909)	fp8_e4m3	indexer_kv_dtype=fp8
Attention	AITER_SPARSE_PA	—	SHUFFLE_KV_CACHE_LAYOUT=1 + ROCM_AITER_UNIFIED_ATTN
Attention	Triton	—	Fallback when AITER unavailable

Changes

File	Type
vllm/models/minimax_m3/amd/indexer_context_parallel.py	New — IndexerCPDecode (Q all-gather, per-head score/exchange/merge) and MiniMaxM3IndexerTritonCPImpl
vllm/models/minimax_m3/amd/ops/indexer_context_parallel.py	New — Triton _context_score shard kernel (bf16 cache only)
vllm/models/minimax_m3/amd/ops/indexer_cp_exchange.py	New — packed-key local top-k, all-gather (AITER IPC, RCCL fallback), per-head merge
vllm/config/attention.py	Modified — minimax_m3_indexer_cp: bool = False; rejects non-bf16 index caches and fused decode at startup
vllm/models/minimax_m3/common/indexer.py	Modified — CP dispatch in select_indexer_impl_cls (ROCm, TP>1)
tests/kernels/attention/test_minimax_m3_indexer_cp.py	New — shard scores, capture-shape invariance, per-head top-k merge (1 and 4 heads), head ownership
tests/distributed/test_minimax_m3_indexer_cp_tp.py	New — multi-GPU CP vs non-CP top-k per global head
.buildkite/test-amd.yaml	Runs the multi-GPU test in the AMD 4-GPU distributed job

With minimax_m3_indexer_cp unset (the default) no existing code path changes.

Test Plan

Hardware and image

8x AMD Instinct MI355X (gfx950). vllm/vllm-openai-rocm:nightly-rocm100-92044241a02f05de51420d654daf669de20b4691 with this PR's files applied on top of that exact commit.

Correctness

pytest tests/kernels/attention/test_minimax_m3_indexer_cp.py -v
pytest tests/distributed/test_minimax_m3_indexer_cp_tp.py -v

The distributed test runs the production CP decode across real TP ranks and compares each rank's top-k with the non-CP decode kernel (minimax_m3_index_decode) on the full context, per global index head, using M3's config (4 heads, top-16, init 0, local 1) on 32k–82k token contexts. It is parametrized over TP 2/4/8 (TP=8 covers head replication), query_len 1/4 (spec-decode verify), and RCCL vs AITER IPC all-gather. It also asserts the four heads select different blocks on ≥90% of rows, so a merge that mixed heads would fail.

Contexts are deliberately long: at top-16 x 128-token blocks plus a forced local block, any prompt under ~2k tokens selects every block, so short-context evals cannot detect a wrong selection.

Server (MXFP4 weights, Triton bf16 indexer)

Baseline is the same command without "minimax_m3_indexer_cp":true.

export VLLM_ROCM_USE_AITER=1
export VLLM_ROCM_USE_AITER_MOE=1
export VLLM_ROCM_USE_AITER_FUSION_SHARED_EXPERTS=1
export VLLM_ROCM_SHUFFLE_KV_CACHE_LAYOUT=1
export VLLM_ROCM_QUICK_REDUCE_QUANTIZATION=INT4
export VLLM_ROCM_QUICK_REDUCE_CAST_BF16_TO_FP16=0
export VLLM_ROCM_QUICK_REDUCE_QUANTIZATION_MIN_SIZE_KB=256

vllm serve /model/MiniMax-M3-MXFP4 \
  --served-model-name amd/MiniMax-M3-MXFP4 --port 8779 \
  --tensor-parallel-size 4 --trust-remote-code \
  --block-size 128 --gpu-memory-utilization 0.90 \
  --max-num-seqs 128 --max-num-batched-tokens 65536 \
  --enable-chunked-prefill --language-model-only --enable-prefix-caching \
  --attention-backend ROCM_AITER_UNIFIED_ATTN --moe-backend aiter \
  --kv-cache-dtype fp8 --attention-config '{"indexer_kv_dtype":"bf16","minimax_m3_indexer_cp":true}' \
  --tool-call-parser minimax_m3 --reasoning-parser minimax_m3 --enable-auto-tool-choice \
  --default-chat-template-kwargs '{"thinking_mode":"enabled"}' --stream-interval 20 \
  --compilation-config '{"cudagraph_mode":"FULL_DECODE_ONLY"}'

AgentX benchmark

aiperf profile --scenario inferencex-agentx-mvp \
  --url http://localhost:8779 --endpoint-type chat \
  --public-dataset semianalysis_cc_traces_weka_062126_256k \
  --concurrency 32 --benchmark-duration 900 \
  --streaming --use-server-token-count \
  --trajectory-start-min-ratio 0.0 --trajectory-start-max-ratio 0.0 \
  --model amd/MiniMax-M3-MXFP4 --tokenizer /model/MiniMax-M3-MXFP4 \
  --tokenizer-trust-remote-code --apply-chat-template

aiperf 0.12.0, 900 s per point after a 90 s warmup.

Test Result

Correctness

• tests/kernels/attention/test_minimax_m3_indexer_cp.py: 16 passed (including config validation).
• Server started with only --attention-config '{"indexer_kv_dtype":"bf16","minimax_m3_indexer_cp":true}' selects the CP indexer; the same flag with "indexer_kv_dtype":"fp8" exits at startup with the bf16 error.
• tests/distributed/test_minimax_m3_indexer_cp_tp.py: 12 passed (TP 2/4/8 x query_len 1/4 x RCCL/AITER).

AgentX sweep (MXFP4, TP=4, no speculative decoding)

Same node, same image, same PR code in both arms; only the CP switch differs. (These runs used the earlier environment-variable switch; it has since moved to attention_config.minimax_m3_indexer_cp with no change to the CP code path.) CP ran first, baseline second, then CP conc=15 was repeated on a fresh server. Every point has 0 failed requests.

Concurrency	Baseline (tok/s/GPU)	CP (tok/s/GPU)	Throughput	Requests	ITL baseline → CP (ms)	ISL baseline	ISL CP
15	12,652	12,997	+2.7%	+1.6%	16.2 → 14.7 (−9.5%)	73,900	74,737
15 (repeat)	12,652	13,320	+5.3%	+3.3%	16.2 → 13.8 (−14.9%)	73,900	75,319
20	15,245	15,957	+4.7%	+3.9%	19.3 → 16.9 (−12.2%)	75,343	75,935
24	15,385	17,115	+11.2%	+7.7%	22.7 → 19.7 (−12.9%)	74,606	76,245
32	20,125	23,175	+15.2%	+14.4%	37.6 → 28.6 (−23.9%)	73,741	74,232

The gain grows with decode batch size, as expected when the term being divided by P is per-sequence index-cache traffic. At concurrency 15 the throughput delta is about the size of run-to-run variation (baseline conc=15 measured 12,936 and 12,652 in two passes); ITL improves at every point. ISL is within 3% across arms with CP's the higher, so the delta is not a shorter-trajectory artifact.

One earlier pass on the same node was discarded: its baseline engine (CP off) exited partway through conc=32 (42% failed requests), and its CP conc=15 point ran at half speed with no errors. Neither reproduced in the pass above, which also logs both servers in full.

Withdrawn: the previous version of this PR scored only each rank's own index head on its shard and merged across ranks, which mixes different heads' candidates at TP=4. The AgentX, EAGLE3 and GSM8K results previously reported here were measured on that code and are removed. EAGLE3 has not been re-measured. GSM8K is not repeated because it cannot distinguish a correct selection from a wrong one at its context lengths (see Test Plan); the per-head equivalence test replaces it.

Hardware note: these are MI355X numbers; absolute tok/s/GPU is not comparable to the MI350X figures in earlier revisions. Every comparison is baseline vs CP on the same node.

AI assistance

This PR was developed with AI assistance, including the per-head CP design, the CUDA-graph capture analysis, the multi-GPU equivalence test, and running the benchmarks above on our hardware, and was prepared with assistance from PR Pundit. I have reviewed every changed line and the results.

This does not duplicate #57909, which implements indexer CP for the AITER fp8 path (indexer_kv_dtype=fp8). This PR covers the Triton bf16 path. The two select different MiniMaxM3Indexer implementations and do not overlap in code.

Related

• [ROCm][PERF] Enable MiniMax-M3 Indexer CP #57909 — CP indexer for the AITER fp8 path.
• [ROCm][Perf][MiniMax-M3] Fused sparse-layer decode via AITER #59653 — fused sparse-layer decode; CP is refused when it is enabled.

[claude]

Claude Code Review

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[github-actions]

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[ChuanLi1101]

• _decode_score_chunks is called in amd/ops/indexer_context_parallel.py but never defined.
• VLLM_ROCM_MINIMAX_INDEXER_CP is not in vllm/envs.py (PR body claims it is).
• DCO is red because all 5 commits are missing Signed-off-by. Author is already Peyman Razaghi <prazaghi@amd.com> — add that trailer (git rebase HEAD~5 --signoff) and force-push. Not a name/email mismatch.
• AgentX A/B is on the PR; still missing a comparison to the public MiniMax M3 InferenceX table. Conc=20 ITL/tput regresses — please say whether CP is default-off until AITER ([ROCm][Model][MiniMax-M3] Context-parallel AITER sparse indexer (fp8, opt-in) #57840) lands.
• Per-step torch.full + full-matrix MAX allreduce: cudagraph / comm volume?

[claude]

Claude Code Review

This pull request is from a fork — automated review is disabled. A repository maintainer can comment @claude review to run a one-time review.

[simondanielsson]

Thanks for the work!

[simondanielsson]

Suggestion: I think we'd need to properly check for graph capture here

[peymanr]

You were right that capture needed attention here, though the failure turned out to be the opposite of a missing guard.

An is_current_stream_capturing() skip is what this had originally, and it is actively unsafe: vLLM v2 records FULL decode graphs with cudagraph_runtime_mode=NONE (see ModelCudaGraphManager.capture), so skipping during capture does not fall back for one warmup step — it bakes the non-CP indexer into the replayed decode graph and CP silently never runs.

The real bug was shape specialization. _capture_cudagraphs goes through _dummy_run, which sets seq_lens to the query length:

elif dcp_dummy_context_len > 0:
    seq_lens = max_query_len + dcp_dummy_context_len
else:
    seq_lens = max_query_len

so at capture d.max_seq_len is 4 with 3 draft tokens. The score kernel was sized off the batch, which baked LOCAL_BLOCKS = GLOBAL_BLOCKS = CHUNK = 1 and a (batch, 1) grid into every decode graph. On replay at 79k context each rank scored only block rank and the top-k saw one candidate per shard, so the indexer returned the first few blocks plus the forced sink and sliding-window pins regardless of context length. Throughput was down 46%, and output degraded enough to shorten the trace trajectories themselves (ISL 65.7k against the baseline's 79.4k).

Fixed in 9fd6ca3 by taking every kernel shape from max_model_len and making the block counts and strides runtime arguments, so nothing shape-dependent can be specialized into a graph again. The per-request causal bound clips the real work at runtime and chunks past the end return immediately. test_indexer_context_scores_independent_of_shape_bound locks this in: it asserts the scores are identical whether the grid is sized for the batch or for a bound 8x larger.

With that fixed, CP is a real win once it is not hidden behind EAGLE3's 2.78 acceptance length. AgentX on the Weka trace, TP=4 MXFP4, no spec decode, FULL_DECODE_ONLY, baseline and CP back-to-back on the same server config:

conc	baseline tok/s/GPU	CP tok/s/GPU	throughput	ITL
15	12,248	13,266	+8.3%	−20.5%
20	13,661	16,145	+18.2%	−20.6%
32	17,858	21,996	+23.2%	−28.0%

On your other suggestion in this file — yes, an all-gather is the right call and that is what it does now. The exchange all-gathers packed (score, block id) int64 keys, so the payload is P * k * 8 bytes per token and does not grow with context.

This investigation and fix were AI-assisted; I have reviewed every changed line and run the benchmarks above myself.

[simondanielsson]

Question: Will this re-run the decode part when in mixed batches?

[peymanr]

Good catch on the version you reviewed — at that point prefill went through super().forward(), and the base Triton impl runs both halves, so a mixed batch would have scored the decode tokens twice: once through CP, then again through the non-CP path.

That is no longer the structure. The prefill branch now calls the score and top-k kernels directly:

if index_md.num_prefills > 0:
    p = index_md.prefill
    score = minimax_m3_index_score(iq[nd:], kv, p.block_table, ...)
    prefill_topk = minimax_m3_index_topk(score, p.cu_seqlens_q, ...)

so a mixed batch runs CP decode over iq[:nd] and prefill over iq[nd:] exactly once each, writing into disjoint slices of the shared top-k buffer.

The one remaining super().forward() is the decode fallback (Dynamo tracing, or an unbound/empty index cache during the profiling run). It returns immediately, so it also handles both halves in a single call rather than falling through into the prefill branch.

Prefill deliberately stays on the existing non-CP path. Prefill already amortizes the index-cache read across many query tokens, so sharding blocks there would not recover enough traffic to pay for the exchange — CP only targets decode, where each step re-streams the whole cache for a handful of query tokens.

[github-actions]

✅ @peymanr, CI is now available for this PR.

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[peymanr]

/amd-ci run

[simondanielsson]

I think the implementation looks good, although I didn't read through every single line of triton here. Would be good if someone could also have a look 👍

Suggestion: can we please attach gpqa and niah accuracy results vs main?

[dllehr-amd]

Thanks @peymanr I think this is good to go in now!

[dllehr-amd]

I approved, but while Ci is running @peymanr can you confirm accuracy as Simon mentioned?

[dllehr-amd]

/ci run

[github-actions]

✅ Triggered Buildkite CI #95471 for commit 161acee03025.

ℹ️ This PR is 1 commit behind upstream main. Merged upstream main into this branch as 161acee03025 before starting CI. Pull the branch before pushing more changes.

[dllehr-amd]

/ci run

[github-actions]

✅ Triggered Buildkite CI #95503 for commit 4b8df7e1eac3.

[peymanr]

@simondanielsson accuracy vs main, as requested. MI355X, TP=4, MXFP4, bf16 index cache. main = unmodified nightly-rocm100-df3bd094 on GPUs 0–3; CP = same image + this PR with "minimax_m3_indexer_cp": true on GPUs 4–7. Same server args otherwise (thinking enabled, FULL_DECODE_ONLY graphs).

GPQA-diamond (tests/evals/gpt_oss harness, GPQAEval(n_repeats=8) via the Responses API, 1,584 samples per arm): main 67.05%, CP 67.17%.

NIAH (lm-eval RULER, niah_single_1/2/3, 100 of 500 samples per task at each length, one run per length):

length	niah_single_1 main / CP	niah_single_2	niah_single_3
8k	1.00 / 1.00	1.00 / 1.00	1.00 / 1.00
32k	1.00 / 0.98	1.00 / 1.00	1.00 / 1.00
64k	0.99 / 0.98	1.00 / 1.00	1.00 / 1.00
128k	0.98 / 0.98	1.00 / 1.00	1.00 / 1.00

Both are within noise of main. The only differences are 1–2 samples on niah_single_1, where main itself also misses 1–2 samples at 64k and 128k.

NIAH command (per length L): lm_eval --model local-completions --model_args model=amd/MiniMax-M3-MXFP4,base_url=…/v1/completions,tokenizer=<model dir>,max_length=163840,tokenized_requests=False --tasks niah_single_1,niah_single_2,niah_single_3 --metadata '{"max_seq_lengths":[L]}' --limit 100 --seed 0

[shen-shanshan]

/ci run

[github-actions]

✅ Triggered Buildkite CI #96351 for commit 60d0d967cb18.

ℹ️ This PR is 1 commit behind upstream main. Merged upstream main into this branch as 60d0d967cb18 before starting CI. Pull the branch before pushing more changes.

[shen-shanshan]

The acc test results look good.