Add minimax M3 MXFP8 MI355X vLLM EAGLE3 (related PR for upstreaming patch https://github.com/vllm-project/vllm/pull/45546)

.github/configs/amd-master.yaml

@@ -2810,3 +2810,39 @@ minimaxm3-fp8-mi355x-vllm:
       - { tp: 8, ep: 8, conc-start: 1, conc-end: 512 }
       - { tp: 4, conc-start: 1, conc-end: 128 }
       - { tp: 8, ep: 8, dp-attn: true, conc-start: 128, conc-end: 512 }
+
+# EAGLE3 speculative-decoding (spec-decoding: mtp) variant of
+# minimaxm3-fp8-mi355x-vllm, pairing MiniMaxAI/MiniMax-M3-MXFP8 with the
+# Inferact/MiniMax-M3-EAGLE3 draft head (3 speculative tokens). No
+# attention_backend override is needed — the server runs on TRITON_ATTN, so
+# the FlashInfer page-128/MHA limitation that forced FLASH_ATTN on Blackwell
+# does not apply here. Search space mirrors the non-MTP entry trimmed at the
+# extreme-concurrency end, identical to the minimaxm3-fp8-b300-vllm-mtp /
+# b200-vllm-mtp precedent: spec decode pays off at low/mid concurrency while
+# acceptance dilutes in big batches, and the draft weights + draft KV shave
+# headroom — tp2-ep2 is dropped since its KV headroom was already thin.
+minimaxm3-fp8-mi355x-vllm-mtp:
+  image: vllm/vllm-openai-rocm:minimax-m3
+  model: MiniMaxAI/MiniMax-M3-MXFP8
+  model-prefix: minimaxm3
+  runner: mi355x
+  precision: fp8
+  framework: vllm
+  multinode: false
+  scenarios:
+    fixed-seq-len:
+    - isl: 1024
+      osl: 1024
+      search-space:
+      - { tp: 8, conc-start: 1, conc-end: 64, spec-decoding: 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: 8, ep: 8, dp-attn: true, conc-start: 256, conc-end: 512, spec-decoding: mtp }
+    - isl: 8192
+      osl: 1024
+      search-space:
+      - { tp: 8, conc-start: 1, conc-end: 64, spec-decoding: 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: 8, ep: 8, dp-attn: true, conc-start: 128, conc-end: 256, spec-decoding: mtp }

benchmarks/single_node/fixed_seq_len/minimaxm3_fp8_mi355x_mtp.sh

@@ -0,0 +1,208 @@
+#!/usr/bin/env bash
+
+# MiniMax-M3 MXFP8 MI355X (gfx950) single-node vLLM recipe with EAGLE3
+# speculative decoding — the spec-decoding=mtp variant of
+# minimaxm3_fp8_mi355x.sh. Adds the Inferact/MiniMax-M3-EAGLE3 draft head via
+# --speculative-config with 3 speculative tokens. Everything else mirrors the
+# non-MTP recipe: MXFP8 from TP=4 on gfx950, mandatory --block-size 128,
+# --language-model-only for the text-only benchmark, FP8 KV cache,
+# --attention-backend TRITON_ATTN, and --enforce-eager.
+#
+# Unlike the CUDA recipes, the drafter needs no attention_backend override:
+# the FlashInfer "page size 128 requires GQA/MQA" limitation that forced
+# FLASH_ATTN for the EAGLE3 MHA head on Blackwell is FlashInfer/CUDA-specific.
+# Here the whole server runs on TRITON_ATTN (set globally below), which serves
+# the MHA draft fine.
+#
+# [AI generated draft test] The shipped vllm/vllm-openai-rocm:minimax-m3 image
+# does NOT implement SupportsEagle3 on the AMD MiniMax-M3 model, so EAGLE3
+# engine init fails with "Model does not support EAGLE3 interface but
+# aux_hidden_state_outputs was requested". This recipe applies that fix
+# (functionstackx/vllm#1 — ported from nvidia/model.py) in-place to the
+# installed vllm before serving, so we can validate EAGLE3 on real MI355X
+# hardware ahead of an image rebuild. The patch is idempotent and fails the
+# job loudly if the installed amd/model.py has drifted from the expected base.
+
+source "$(dirname "$0")/../../benchmark_lib.sh"
+
+check_env_vars \
+    MODEL \
+    TP \
+    EP_SIZE \
+    DP_ATTENTION \
+    CONC \
+    ISL \
+    OSL \
+    MAX_MODEL_LEN \
+    RANDOM_RANGE_RATIO \
+    RESULT_FILENAME
+
+DRAFT_MODEL="Inferact/MiniMax-M3-EAGLE3"
+
+if [[ -n "$SLURM_JOB_ID" ]]; then
+  echo "JOB $SLURM_JOB_ID running on $SLURMD_NODENAME"
+fi
+
+# MODEL stays a bare HF id on the mi355x single-node runner (weights are
+# pre-staged in the mounted NFS HF cache, so this is a fast cache hit). The
+# EAGLE3 draft is not staged; fetch it into the same cache.
+if [[ "$MODEL" != /* ]]; then
+  hf download "$MODEL"
+  hf download "$DRAFT_MODEL"
+fi
+
+if [ -n "$ROCR_VISIBLE_DEVICES" ]; then
+    export HIP_VISIBLE_DEVICES="$ROCR_VISIBLE_DEVICES"
+fi
+
+SERVER_LOG=/workspace/server.log
+export VLLM_ENGINE_READY_TIMEOUT_S=3600
+
+if [ "${EVAL_ONLY}" = "true" ]; then
+    setup_eval_context
+fi
+
+PARALLEL_ARGS=(--tensor-parallel-size "$TP")
+if [ "${DP_ATTENTION}" = "true" ]; then
+    PARALLEL_ARGS=(
+        --tensor-parallel-size 1
+        --data-parallel-size "$TP"
+        --enable-expert-parallel
+    )
+elif [ "$EP_SIZE" -gt 1 ]; then
+    PARALLEL_ARGS+=(--enable-expert-parallel)
+fi
+
+# use 3 speculative tokens for all configs for now
+NUM_SPEC_TOKENS=3
+
+# [AI generated draft test] Patch the installed AMD MiniMax-M3 model to add the
+# SupportsEagle3 interface (functionstackx/vllm#1). Mirrors nvidia/model.py:
+# adds EagleModelMixin to the inner model + aux-hidden-state emission, and
+# SupportsEagle3 to the two outer classes. Idempotent; hard-fails if the
+# installed file has drifted from the expected base (so we never silently run
+# unpatched and mislabel the result).
+python3 - <<'PYEOF' || { echo "EAGLE3 in-place patch failed" >&2; exit 1; }
+import ast, importlib.util, pathlib, sys
+
+spec = importlib.util.find_spec("vllm")
+root = pathlib.Path(spec.submodule_search_locations[0])
+target = root / "models" / "minimax_m3" / "amd" / "model.py"
+src = target.read_text()
+
+if "EagleModelMixin" in src and "class MiniMaxM3Model(nn.Module, EagleModelMixin):" in src:
+    print(f"[eagle3-patch] already applied: {target}")
+    sys.exit(0)
+
+edits = [
+    (
+        "from vllm.model_executor.models.interfaces import (\n"
+        "    MultiModalEmbeddings,\n"
+        "    SupportsMultiModal,\n"
+        ")",
+        "from vllm.model_executor.models.interfaces import (\n"
+        "    EagleModelMixin,\n"
+        "    MultiModalEmbeddings,\n"
+        "    SupportsEagle3,\n"
+        "    SupportsMultiModal,\n"
+        ")",
+    ),
+    (
+        "class MiniMaxM3Model(nn.Module):",
+        "class MiniMaxM3Model(nn.Module, EagleModelMixin):",
+    ),
+    (
+        "        inputs_embeds: torch.Tensor | None = None,\n"
+        "    ) -> torch.Tensor:\n"
+        "        if inputs_embeds is not None:",
+        "        inputs_embeds: torch.Tensor | None = None,\n"
+        "    ) -> torch.Tensor | tuple[torch.Tensor, list[torch.Tensor]]:\n"
+        "        if inputs_embeds is not None:",
+    ),
+    (
+        "        residual = None\n\n"
+        "        for layer in self.layers[self.start_layer : self.end_layer]:\n"
+        "            hidden_states, residual = layer(positions, hidden_states, residual)\n\n"
+        "        hidden_states, _ = self.norm(hidden_states, residual)\n"
+        "        return hidden_states",
+        "        residual = None\n\n"
+        "        # EAGLE3 is not yet compatible with pipeline parallel\n"
+        "        aux_hidden_states = self._maybe_add_hidden_state([], 0, hidden_states, residual)\n"
+        "        for idx, layer in enumerate(self.layers[self.start_layer : self.end_layer]):\n"
+        "            hidden_states, residual = layer(positions, hidden_states, residual)\n"
+        "            self._maybe_add_hidden_state(\n"
+        "                aux_hidden_states, idx + 1, hidden_states, residual\n"
+        "            )\n\n"
+        "        hidden_states, _ = self.norm(hidden_states, residual)\n\n"
+        "        if len(aux_hidden_states) > 0:\n"
+        "            return hidden_states, aux_hidden_states\n"
+        "        return hidden_states",
+    ),
+    (
+        "class MiniMaxM3SparseForCausalLM(nn.Module):",
+        "class MiniMaxM3SparseForCausalLM(nn.Module, SupportsEagle3):",
+    ),
+    (
+        "class MiniMaxM3SparseForConditionalGeneration(nn.Module, SupportsMultiModal):",
+        "class MiniMaxM3SparseForConditionalGeneration(nn.Module, SupportsMultiModal, SupportsEagle3):",
+    ),
+]
+
+for old, new in edits:
+    count = src.count(old)
+    if count != 1:
+        sys.exit(
+            f"[eagle3-patch] anchor matched {count} times (expected 1); "
+            f"installed {target} has drifted from the expected base — aborting"
+        )
+    src = src.replace(old, new)
+
+ast.parse(src)
+target.write_text(src)
+print(f"[eagle3-patch] applied EAGLE3 support to {target}")
+PYEOF
+
+start_gpu_monitor
+
+set -x
+vllm serve "$MODEL" --port "$PORT" \
+    "${PARALLEL_ARGS[@]}" \
+    --block-size 128 \
+    --language-model-only \
+    --max-model-len "$MAX_MODEL_LEN" \
+    --kv-cache-dtype fp8 \
+    --attention-backend TRITON_ATTN \
+    --enforce-eager \
+    --speculative-config "{\"method\": \"eagle3\", \"model\": \"$DRAFT_MODEL\", \"num_speculative_tokens\": $NUM_SPEC_TOKENS}" \
+    --tool-call-parser minimax_m3 \
+    --reasoning-parser minimax_m3 \
+    --enable-auto-tool-choice > "$SERVER_LOG" 2>&1 &
+
+SERVER_PID=$!
+wait_for_server_ready --port "$PORT" --server-log "$SERVER_LOG" --server-pid "$SERVER_PID"
+
+pip install -q datasets pandas
+
+# Spec-decode acceptance rate degrades on raw random tokens; route prompts
+# through the chat template as the other MTP recipes do.
+run_benchmark_serving \
+    --model "$MODEL" \
+    --port "$PORT" \
+    --backend vllm \
+    --input-len "$ISL" \
+    --output-len "$OSL" \
+    --random-range-ratio "$RANDOM_RANGE_RATIO" \
+    --num-prompts "$((CONC * 10))" \
+    --max-concurrency "$CONC" \
+    --result-filename "$RESULT_FILENAME" \
+    --result-dir /workspace/ \
+    --trust-remote-code \
+    --use-chat-template
+
+if [ "${RUN_EVAL}" = "true" ]; then
+    run_eval --framework lm-eval --port "$PORT"
+    append_lm_eval_summary
+fi
+
+stop_gpu_monitor
+set +x

perf-changelog.yaml

@@ -3724,3 +3724,14 @@
   description:
     - "Start the TP-only latency rows of the MiniMax-M3 EAGLE3 MTP sweeps (H200, H100) at concurrency 1 instead of 4, matching the conc-1 start used on the non-MTP day-zero recipes — captures the single-request latency point. TEP/DEP rows keep their higher concurrency starts."
   pr-link: https://github.com/SemiAnalysisAI/InferenceX/pull/1743
+
+- config-keys:
+    - minimaxm3-fp8-mi355x-vllm-mtp
+  description:
+    - "Initial submission: MiniMax-M3 MXFP8 MI355X (gfx950) vLLM benchmark with EAGLE3 speculative decoding (target: MiniMaxAI/MiniMax-M3-MXFP8, draft: Inferact/MiniMax-M3-EAGLE3, 3 speculative tokens) — spec-decoding=mtp variant of the MI355X day-zero recipe"
+    - "Image: vllm/vllm-openai-rocm:minimax-m3 (same day-zero ROCm build as the non-MTP entry)"
+    - "Serve shape follows minimaxm3-fp8-mi355x-vllm (--block-size 128, --language-model-only, --kv-cache-dtype fp8, --attention-backend TRITON_ATTN, --enforce-eager, minimax_m3 parsers); prompts routed through the chat template for realistic acceptance"
+    - "No attention_backend override on the drafter: the server runs on TRITON_ATTN, so the FlashInfer page-128/MHA limitation that forced FLASH_ATTN on the CUDA recipes does not apply on ROCm"
+    - "Layouts: TP8 / TP4 (latency), TP8+EP8 / TP4+EP4 (TEP), TP8+EP8 dp-attn (DEP) across 1k1k and 8k1k — non-MTP search space trimmed at the extreme-concurrency end, tp2-ep2 dropped, mirroring the minimaxm3-fp8-b300-vllm-mtp search space"
+    - "[AI generated draft test] The shipped ROCm image's AMD MiniMax-M3 model lacks SupportsEagle3, so the recipe patches it in-place at runtime (functionstackx/vllm#1, ported from nvidia/model.py) before serving — validates EAGLE3 on MI355X ahead of an image rebuild"
+  pr-link: https://github.com/SemiAnalysisAI/InferenceX/pull/1745