Use MiniMax-M3 updated GB300 image and fix MNNVL workspace (#1888)

* chore: use MiniMax-M3 GB300 performance image

* fix(vllm): size MNNVL workspace for one-shot all-reduce

Author: Oseltamivir

.github/configs/nvidia-master.yaml

@@ -11835,7 +11835,7 @@ minimaxm3-fp8-b300-dynamo-vllm:
 # DEP8, DEP4. 4 GPU/node (GB300 NVL72). 4p3d (3 decode workers) skipped.
 # kv-cache-dtype=fp8 added. srun_options mem=0 required.
 minimaxm3-fp8-gb300-dynamo-vllm:
-  image: vllm/vllm-openai:nightly-aarch64
+  image: vllm/vllm-openai:minimax-m3-perf-arm64-13.0.1-7a67223
   model: MiniMaxAI/MiniMax-M3-MXFP8
   model-prefix: minimaxm3
   runner: gb300-nv

benchmarks/multi_node/srt-slurm-recipes/configs/minimax-m3-gb300-vllm-fixes.sh

@@ -0,0 +1,38 @@
+#!/usr/bin/env bash
+set -euo pipefail
+
+python3 - <<'PYEOF'
+from importlib.util import find_spec
+from pathlib import Path
+
+spec = find_spec("vllm")
+if not spec or not spec.origin:
+    raise RuntimeError("vllm is not installed")
+root = Path(spec.origin).parent
+patches = {
+    root / "distributed/device_communicators/flashinfer_all_reduce.py": [
+        (
+            "            comm_backend=comm_backend,\n"
+            "            group=group,\n",
+            "            comm_backend=comm_backend,\n"
+            '            force_oneshot_support=backend == "mnnvl",\n'
+            "            group=group,\n",
+        ),
+    ],
+    root / "models/minimax_m3/nvidia/sparse_attention_msa.py": [
+        (
+            "            prefill_topk = topk[:, nd:num_tokens, :]\n",
+            "            prefill_topk = topk[:, nd:num_tokens, :].contiguous()\n",
+        ),
+    ],
+}
+for path, edits in patches.items():
+    source = path.read_text()
+    for old, new in edits:
+        if new in source:
+            continue
+        if source.count(old) != 1:
+            raise RuntimeError(f"missing or ambiguous patch anchor in {path}")
+        source = source.replace(old, new, 1)
+    path.write_text(source)
+PYEOF

benchmarks/multi_node/srt-slurm-recipes/vllm/minimax-m3-gb300-fp8/1k1k/disagg-gb300-1p1d-dep2-tep8-3n.yaml

@@ -5,7 +5,7 @@ name: "minimax-m3-vllm-disagg-gb300-1p1d-dep2-tep8-fp8-1k1k"
 
 model:
   path: "minimax-m3-mxfp8"
-  container: "vllm/vllm-openai:nightly-aarch64"
+  container: "vllm/vllm-openai:minimax-m3-perf-arm64-13.0.1-7a67223"
   precision: "fp8"
 
 dynamo:

benchmarks/multi_node/srt-slurm-recipes/vllm/minimax-m3-gb300-fp8/1k1k/disagg-gb300-1p1d-dep2-tp4-marlin-2n.yaml

@@ -5,7 +5,7 @@ name: "minimax-m3-vllm-disagg-gb300-1p1d-dep2-tp4-marlin-fp8-1k1k"
 
 model:
   path: "minimax-m3-mxfp8"
-  container: "vllm/vllm-openai:nightly-aarch64"
+  container: "vllm/vllm-openai:minimax-m3-perf-arm64-13.0.1-7a67223"
   precision: "fp8"
 
 dynamo:

benchmarks/multi_node/srt-slurm-recipes/vllm/minimax-m3-gb300-fp8/1k1k/disagg-gb300-1p2d-dep2-dep4-3n.yaml

@@ -5,7 +5,7 @@ name: "minimax-m3-vllm-disagg-gb300-1p2d-dep2-dep4-fp8-1k1k"
 
 model:
   path: "minimax-m3-mxfp8"
-  container: "vllm/vllm-openai:nightly-aarch64"
+  container: "vllm/vllm-openai:minimax-m3-perf-arm64-13.0.1-7a67223"
   precision: "fp8"
 
 dynamo:

benchmarks/multi_node/srt-slurm-recipes/vllm/minimax-m3-gb300-fp8/1k1k/disagg-gb300-2p1d-dep2-dep8-3n.yaml

@@ -5,7 +5,7 @@ name: "minimax-m3-vllm-disagg-gb300-2p1d-dep2-dep8-fp8-1k1k"
 
 model:
   path: "minimax-m3-mxfp8"
-  container: "vllm/vllm-openai:nightly-aarch64"
+  container: "vllm/vllm-openai:minimax-m3-perf-arm64-13.0.1-7a67223"
   precision: "fp8"
 
 dynamo:

benchmarks/multi_node/srt-slurm-recipes/vllm/minimax-m3-gb300-fp8/1k1k/disagg-gb300-2p1d-dep2-tep8-3n.yaml

@@ -5,7 +5,7 @@ name: "minimax-m3-vllm-disagg-gb300-2p1d-dep2-tep8-fp8-1k1k"
 
 model:
   path: "minimax-m3-mxfp8"
-  container: "vllm/vllm-openai:nightly-aarch64"
+  container: "vllm/vllm-openai:minimax-m3-perf-arm64-13.0.1-7a67223"
   precision: "fp8"
 
 dynamo:

benchmarks/multi_node/srt-slurm-recipes/vllm/minimax-m3-gb300-fp8/1k1k/disagg-gb300-2p2d-dep2-tep8-5n.yaml

@@ -5,7 +5,7 @@ name: "minimax-m3-vllm-disagg-gb300-2p2d-dep2-tep8-fp8-1k1k"
 
 model:
   path: "minimax-m3-mxfp8"
-  container: "vllm/vllm-openai:nightly-aarch64"
+  container: "vllm/vllm-openai:minimax-m3-perf-arm64-13.0.1-7a67223"
   precision: "fp8"
 
 dynamo:

benchmarks/multi_node/srt-slurm-recipes/vllm/minimax-m3-gb300-fp8/1k1k/disagg-gb300-3p2d-dep2-tep8-6n.yaml

@@ -5,7 +5,7 @@ name: "minimax-m3-vllm-disagg-gb300-3p2d-dep2-tep8-fp8-1k1k"
 
 model:
   path: "minimax-m3-mxfp8"
-  container: "vllm/vllm-openai:nightly-aarch64"
+  container: "vllm/vllm-openai:minimax-m3-perf-arm64-13.0.1-7a67223"
   precision: "fp8"
 
 dynamo:

benchmarks/multi_node/srt-slurm-recipes/vllm/minimax-m3-gb300-fp8/8k1k/disagg-gb300-1p1d-dep2-tp4-marlin-2n.yaml

@@ -5,7 +5,7 @@ name: "minimax-m3-vllm-disagg-gb300-1p1d-dep2-tp4-marlin-fp8-8k1k"
 
 model:
   path: "minimax-m3-mxfp8"
-  container: "vllm/vllm-openai:nightly-aarch64"
+  container: "vllm/vllm-openai:minimax-m3-perf-arm64-13.0.1-7a67223"
   precision: "fp8"
 
 dynamo: