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[NV] minimax-m3-b200/b300-mtp: update fp8 MTP to vLLM nightly / [NV] minimax-m3-b200/b300-mtp：将 FP8 MTP 更新至 vLLM nightly #2337

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Ankur-singh merged 5 commits into from
Jul 29, 2026
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[NV] minimax-m3-b200/b300-mtp: update fp8 MTP to vLLM nightly / [NV] minimax-m3-b200/b300-mtp：将 FP8 MTP 更新至 vLLM nightly #2337

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7148fb9
[NV] minimax-m3-b200/b300-mtp: vLLM nightly image, GQA draft head, FL…
xinli-sw Jul 26, 2026
1274cd2
[NV] minimax-m3-b200/b300-mtp: add perf-changelog pr-link
xinli-sw Jul 26, 2026
924e327
Merge remote-tracking branch 'origin/main' into minimaxm3-fp8-b200-b3…
xinli-sw Jul 26, 2026
315b9a0
[NV] minimax-m3-b200/b300-mtp: drop tp4-ep4-DEP (dp-attn) arm from th…
xinli-sw Jul 26, 2026
6ba556e
Merge remote-tracking branch 'origin/main' into pr-2337-reuse-50331
Ankur-singh Jul 29, 2026
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48 changes: 7 additions & 41 deletions benchmarks/single_node/fixed_seq_len/minimaxm3_fp8_b200_mtp.sh
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Original file line number Diff line number Diff line change
Expand Up @@ -3,15 +3,15 @@
# MiniMax-M3 MXFP8 B200 single-node vLLM recipe with EAGLE3 speculative
# decoding — the repo's spec-decoding=mtp variant of minimaxm3_fp8_b200.sh
# (https://recipes.vllm.ai/MiniMaxAI/MiniMax-M3). Adds the
# Inferact/MiniMax-M3-EAGLE3 draft head via --speculative-config with 3
# Inferact/MiniMax-M3-EAGLE3-GQA draft head via --speculative-config with 3
# speculative tokens. Everything else keeps the non-MTP serve shape:
# --block-size 128 is mandatory (MSA sparse_block_size is 128; the default 16
# misaligns sparse indexing), and --language-model-only skips the vision
# encoder for the text-only benchmark. dp-attn=true maps to DP×EP (DEP);
# ep>1 maps to TP+EP (TEP).
#
# The target uses the FlashInfer TRT-LLM attention path. The EAGLE3 drafter is
# pinned separately to TRITON_ATTN.
# The target uses the FlashInfer TRT-LLM attention path. The EAGLE3-GQA drafter
# is pinned separately to FLASH_ATTN.

source "$(dirname "$0")/../../benchmark_lib.sh"

Expand All @@ -27,40 +27,7 @@ check_env_vars \
RANDOM_RANGE_RATIO \
RESULT_FILENAME

# The 0618 image keeps MiniMax M3 top-k indices in a persistent
# [head_kv, max_tokens, topK] buffer for CUDA graphs. Slicing that buffer to
# the actual prefill length is non-contiguous when TP leaves multiple local KV
# heads, and the MSA CSR builder rejects it. Materialize the slice until the
# image includes this fix.
python3 - <<'PYEOF' || { echo "MiniMax M3 MSA contiguity patch failed" >&2; exit 1; }
import importlib.util
import pathlib

spec = importlib.util.find_spec("vllm")
if spec is None or not spec.submodule_search_locations:
raise RuntimeError("Could not locate the installed vllm package")

target = (
pathlib.Path(next(iter(spec.submodule_search_locations)))
/ "models"
/ "minimax_m3"
/ "nvidia"
/ "sparse_attention_msa.py"
)
src = target.read_text()
old = " prefill_topk = topk[:, nd:num_tokens, :]\n"
new = " prefill_topk = topk[:, nd:num_tokens, :].contiguous()\n"

if new in src:
print(f"[minimax-m3-msa-patch] already applied: {target}")
elif src.count(old) == 1:
target.write_text(src.replace(old, new, 1))
print(f"[minimax-m3-msa-patch] patched: {target}")
else:
raise RuntimeError(f"Expected exactly one patch anchor in {target}")
PYEOF

DRAFT_MODEL="Inferact/MiniMax-M3-EAGLE3"
DRAFT_MODEL="Inferact/MiniMax-M3-EAGLE3-GQA"

if [[ -n "$SLURM_JOB_ID" ]]; then
echo "JOB $SLURM_JOB_ID running on $SLURMD_NODENAME"
Expand Down Expand Up @@ -116,14 +83,13 @@ $PARALLEL_ARGS \
--gpu-memory-utilization 0.90 \
--max-model-len $MAX_MODEL_LEN \
--block-size 128 \
--attention-config '{"backend": "FLASHINFER", "use_trtllm_attention": true}' \
--attention-config.indexer_kv_dtype "fp8" \
--attention-config '{"backend": "FLASHINFER", "use_trtllm_attention": true, "indexer_kv_dtype": "fp8"}' \
--kv-cache-dtype fp8 \
--language-model-only \
--max-cudagraph-capture-size 2048 \
--max-num-batched-tokens "$((ISL * 2 ))" \
--speculative-config "{\"method\": \"eagle3\", \"model\": \"$DRAFT_MODEL_PATH\", \"num_speculative_tokens\": $NUM_SPEC_TOKENS, \"attention_backend\": \"TRITON_ATTN\"}" \
--stream-interval 20 --no-enable-prefix-caching \
--speculative-config "{\"method\": \"eagle3\", \"model\": \"$DRAFT_MODEL_PATH\", \"num_speculative_tokens\": $NUM_SPEC_TOKENS, \"attention_backend\": \"FLASH_ATTN\"}" \
--stream-interval 32 --no-enable-prefix-caching \
--trust-remote-code > $SERVER_LOG 2>&1 &

SERVER_PID=$!
Expand Down
48 changes: 7 additions & 41 deletions benchmarks/single_node/fixed_seq_len/minimaxm3_fp8_b300_mtp.sh
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Original file line number Diff line number Diff line change
Expand Up @@ -3,13 +3,13 @@
# MiniMax-M3 MXFP8 B300 single-node vLLM recipe with EAGLE3 speculative
# decoding — the repo's spec-decoding=mtp variant of minimaxm3_fp8_b300.sh
# (https://recipes.vllm.ai/MiniMaxAI/MiniMax-M3). Adds the
# Inferact/MiniMax-M3-EAGLE3 draft head via --speculative-config with 3
# Inferact/MiniMax-M3-EAGLE3-GQA draft head via --speculative-config with 3
# speculative tokens. Everything else keeps the non-MTP serve shape:
# --block-size 128 is mandatory (MSA sparse/index cache); the benchmark is
# text-only, so --language-model-only frees the vision encoder's VRAM.
#
# The target uses the FlashInfer TRT-LLM attention path. The EAGLE3 drafter is
# pinned separately to TRITON_ATTN.
# The target uses the FlashInfer TRT-LLM attention path. The EAGLE3-GQA drafter
# is pinned separately to FLASH_ATTN.

source "$(dirname "$0")/../../benchmark_lib.sh"

Expand All @@ -25,40 +25,7 @@ check_env_vars \
RANDOM_RANGE_RATIO \
RESULT_FILENAME

# The 0618 image keeps MiniMax M3 top-k indices in a persistent
# [head_kv, max_tokens, topK] buffer for CUDA graphs. Slicing that buffer to
# the actual prefill length is non-contiguous when TP leaves multiple local KV
# heads, and the MSA CSR builder rejects it. Materialize the slice until the
# image includes this fix.
python3 - <<'PYEOF' || { echo "MiniMax M3 MSA contiguity patch failed" >&2; exit 1; }
import importlib.util
import pathlib

spec = importlib.util.find_spec("vllm")
if spec is None or not spec.submodule_search_locations:
raise RuntimeError("Could not locate the installed vllm package")

target = (
pathlib.Path(next(iter(spec.submodule_search_locations)))
/ "models"
/ "minimax_m3"
/ "nvidia"
/ "sparse_attention_msa.py"
)
src = target.read_text()
old = " prefill_topk = topk[:, nd:num_tokens, :]\n"
new = " prefill_topk = topk[:, nd:num_tokens, :].contiguous()\n"

if new in src:
print(f"[minimax-m3-msa-patch] already applied: {target}")
elif src.count(old) == 1:
target.write_text(src.replace(old, new, 1))
print(f"[minimax-m3-msa-patch] patched: {target}")
else:
raise RuntimeError(f"Expected exactly one patch anchor in {target}")
PYEOF

DRAFT_MODEL="Inferact/MiniMax-M3-EAGLE3"
DRAFT_MODEL="Inferact/MiniMax-M3-EAGLE3-GQA"

# `hf download` creates the target dir if missing and is itself idempotent.
# When MODEL_PATH is unset (stand-alone runs), fall back to the HF_HUB_CACHE.
Expand Down Expand Up @@ -118,14 +85,13 @@ $PARALLEL_ARGS \
--gpu-memory-utilization 0.90 \
--max-model-len $MAX_MODEL_LEN \
--block-size 128 \
--attention-config '{"backend": "FLASHINFER", "use_trtllm_attention": true}' \
--attention-config.indexer_kv_dtype "fp8" \
--attention-config '{"backend": "FLASHINFER", "use_trtllm_attention": true, "indexer_kv_dtype": "fp8"}' \
--kv-cache-dtype fp8 \
--language-model-only \
--max-cudagraph-capture-size 2048 \
--max-num-batched-tokens "$((ISL * 2 ))" \
--speculative-config "{\"method\": \"eagle3\", \"model\": \"$DRAFT_MODEL_PATH\", \"num_speculative_tokens\": $NUM_SPEC_TOKENS, \"attention_backend\": \"TRITON_ATTN\"}" \
--stream-interval 20 --no-enable-prefix-caching \
--speculative-config "{\"method\": \"eagle3\", \"model\": \"$DRAFT_MODEL_PATH\", \"num_speculative_tokens\": $NUM_SPEC_TOKENS, \"attention_backend\": \"FLASH_ATTN\"}" \
--stream-interval 32 --no-enable-prefix-caching \
--trust-remote-code > $SERVER_LOG 2>&1 &

SERVER_PID=$!
Expand Down
6 changes: 2 additions & 4 deletions configs/nvidia-master.yaml
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Original file line number Diff line number Diff line change
Expand Up @@ -7729,7 +7729,7 @@ minimaxm3-fp4-b200-vllm:
# precedent: spec decode pays off at low/mid concurrency while acceptance
# dilutes in big batches, and the draft weights + draft KV shave headroom.
minimaxm3-fp8-b200-vllm-mtp:
image: vllm/vllm-openai:minimax-m3-0618-x86_64-cu130
image: vllm/vllm-openai:nightly-4080263bb2c5d10deac17aaeb88e0823bc35bca9
model: MiniMaxAI/MiniMax-M3-MXFP8
model-prefix: minimaxm3
runner: b200-dgxc
Comment on lines 7729 to 7735

@claude claude Bot Jul 26, 2026

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🟡 The config comments above minimaxm3-fp8-b200-vllm-mtp (~line 7458-7459) and minimaxm3-fp8-b300-vllm-mtp (~line 7513) still say the drafter is 'Inferact/MiniMax-M3-EAGLE3' pinned to 'TRITON_ATTN', but this PR switches both recipes to 'Inferact/MiniMax-M3-EAGLE3-GQA' with 'FLASH_ATTN'. Please update these two comment blocks to match, similar to how the h200/h100 mtp entries already describe the FLASH_ATTN/GQA rationale.

Extended reasoning...

configs/nvidia-master.yaml contains a hand-written comment block directly above each minimaxm3-fp8-*-vllm-mtp config entry explaining the EAGLE3 speculative-decoding setup for that recipe. For minimaxm3-fp8-b200-vllm-mtp the comment (around lines 7456-7459) reads: "...adds the Inferact/MiniMax-M3-EAGLE3 draft head... The target uses the FlashInfer TRT-LLM attention path. The EAGLE3 drafter is pinned separately to TRITON_ATTN." For minimaxm3-fp8-b300-vllm-mtp the comment (around lines 7511-7513) similarly references the plain Inferact/MiniMax-M3-EAGLE3 draft head with no attention-backend caveat.

This PR changes exactly what those comments describe: the draft head becomes Inferact/MiniMax-M3-EAGLE3-GQA and the speculative attention_backend moves from TRITON_ATTN to FLASH_ATTN (visible in the matching .sh recipe diffs, where the header comments were correctly updated: "Inferact/MiniMax-M3-EAGLE3-GQA draft head" and "The EAGLE3-GQA drafter is pinned separately to FLASH_ATTN."). However, the diff hunk touching configs/nvidia-master.yaml only replaces the image: line inside each block (lines 7461-7467 for b200, 7517-7523 for b300) — the descriptive comment lines sitting just above those keys were left untouched.

The result is that after this PR merges, the YAML comments for the b200 and b300 mtp entries misdescribe the actual recipe: they name the wrong draft-head repo (missing "-GQA") and, for b200, explicitly claim the drafter is "pinned to TRITON_ATTN" when it is now pinned to FLASH_ATTN. This is a real inconsistency the PR introduces/worsens, not a pre-existing one, since the PR's own script changes are what make the comments wrong. Contrast this with the sibling h200/h100 mtp entries further down in the same file (~lines 7539-7546 and 7565-7570), which already correctly explain the FLASH_ATTN/GQA rationale — those are the template this PR's two comment blocks should be brought in line with.

Step-by-step proof: (1) Before this PR, b200 mtp comment says draft head = Inferact/MiniMax-M3-EAGLE3, backend = TRITON_ATTN, matching the old script's DRAFT_MODEL="Inferact/MiniMax-M3-EAGLE3" and "attention_backend": "TRITON_ATTN". (2) This PR's diff to minimaxm3_fp8_b200_mtp.sh changes DRAFT_MODEL to "Inferact/MiniMax-M3-EAGLE3-GQA" and attention_backend to "FLASH_ATTN", and updates that script's own header comment to match. (3) The configs/nvidia-master.yaml diff for the same recipe only touches the image: line — the comment block above it is byte-for-byte identical to before. (4) Therefore post-merge, the YAML comment and the actual recipe behavior diverge: reading the config comment alone, a maintainer would believe the drafter still uses TRITON_ATTN with the non-GQA head, which is now false. The same divergence applies to the b300 comment (missing "-GQA" and no FLASH_ATTN mention).

This is comment-only and has zero effect on runtime behavior — the scripts themselves (the actual source of truth for how the benchmark runs) are correct. So while it's a genuine, PR-introduced inconsistency worth fixing to keep the config self-documenting, it doesn't block anything at merge. Fix: update the two comment blocks in configs/nvidia-master.yaml to say "Inferact/MiniMax-M3-EAGLE3-GQA draft head" and "pinned separately to FLASH_ATTN" (b200) / add the same GQA/FLASH_ATTN mention (b300), mirroring the phrasing already used in the h200/h100 mtp comments.

Expand All @@ -7745,7 +7745,6 @@ minimaxm3-fp8-b200-vllm-mtp:
- { tp: 8, ep: 8, conc-start: 1, conc-end: 256, spec-decoding: mtp }
- { tp: 4, conc-start: 1, conc-end: 64, spec-decoding: mtp }
- { tp: 4, ep: 4, conc-start: 64, conc-end: 256, spec-decoding: mtp }
- { tp: 4, ep: 4, dp-attn: true, conc-start: 64, conc-end: 128, spec-decoding: mtp }
- { tp: 8, ep: 8, dp-attn: true, conc-start: 128, conc-end: 256, spec-decoding: mtp }

# EAGLE3 speculative-decoding (spec-decoding: mtp) variant of MiniMax-M3 NVFP4
Expand Down Expand Up @@ -7785,7 +7784,7 @@ minimaxm3-fp4-b200-vllm-mtp:
# big batches, and the draft weights + draft KV shave headroom — tp2-ep2 is
# dropped entirely since its KV headroom was already thin without a draft.
minimaxm3-fp8-b300-vllm-mtp:
image: vllm/vllm-openai:minimax-m3-0618-x86_64-cu130
image: vllm/vllm-openai:nightly-4080263bb2c5d10deac17aaeb88e0823bc35bca9
model: MiniMaxAI/MiniMax-M3-MXFP8
model-prefix: minimaxm3
runner: b300
Expand All @@ -7801,7 +7800,6 @@ minimaxm3-fp8-b300-vllm-mtp:
- { tp: 8, ep: 8, conc-start: 1, conc-end: 256, spec-decoding: mtp }
- { tp: 4, conc-start: 1, conc-end: 64, spec-decoding: mtp }
- { tp: 4, ep: 4, conc-start: 64, conc-end: 256, spec-decoding: mtp }
- { tp: 4, ep: 4, dp-attn: true, conc-start: 64, conc-end: 128, spec-decoding: mtp }
- { tp: 8, ep: 8, dp-attn: true, conc-start: 128, conc-end: 256, spec-decoding: mtp }

# EAGLE3 speculative-decoding (spec-decoding: mtp) variant of
Expand Down
8 changes: 8 additions & 0 deletions perf-changelog.yaml
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Original file line number Diff line number Diff line change
Expand Up @@ -5230,3 +5230,11 @@
- "Bump image to vllm/vllm-openai:nightly-4080263bb2c5d10deac17aaeb88e0823bc35bca9 and dynamo to 1.3.0.dev20260710; drop the minimax-m3-gb300-vllm-fixes.sh setup patch (shipped in the nightly image)"
- "Collapse the 2xDEP8 decode into a single DEP16 worker (decode_workers 2->1, gpus_per_decode 8->16, data-parallel-size 8->16); recipe renamed 3p2d-dep2-dep8 -> 3p1d-dep2-dep16 (conc 512). The remaining decodes are 1xDEP8 or TEP and are unchanged."
pr-link: https://github.com/SemiAnalysisAI/InferenceX/pull/2340

- config-keys:
- minimaxm3-fp8-b200-vllm-mtp
- minimaxm3-fp8-b300-vllm-mtp
description:
- "Bump image to vllm/vllm-openai:nightly-4080263bb2c5d10deac17aaeb88e0823bc35bca9; set --stream-interval 32; consolidate --attention-config with indexer_kv_dtype fp8; drop the sparse_attention_msa contiguity patch (shipped in the nightly image)"
- "EAGLE3 draft head Inferact/MiniMax-M3-EAGLE3 -> Inferact/MiniMax-M3-EAGLE3-GQA; speculative attention_backend TRITON_ATTN -> FLASH_ATTN; num_speculative_tokens kept at 3"
pr-link: https://github.com/SemiAnalysisAI/InferenceX/pull/2337