[AMD] Qwen3.5-FP8 MI355X SGLang disagg baseline: image bump to v0.5.16, TP4P+TP8D / Qwen3.5-FP8 MI355X SGLang disagg 基线优化：镜像升级至v0.5.16，TP4P+TP8D拓扑优化

configs/amd-master.yaml

@@ -270,7 +270,7 @@ qwen3.5-fp8-mi355x-atom-mtp:
       - { tp: 8, ep: 1, conc-start: 4, conc-end: 256, spec-decoding: mtp }
 
 qwen3.5-fp8-mi355x-sglang-disagg:
-  image: lmsysorg/sglang:v0.5.14-rocm720-mi35x
+  image: lmsysorg/sglang:v0.5.16-rocm720-mi35x
   model: Qwen/Qwen3.5-397B-A17B-FP8
   model-prefix: qwen3.5
   runner: mi355x-disagg
@@ -285,21 +285,20 @@ qwen3.5-fp8-mi355x-sglang-disagg:
     - isl: 8192
       osl: 1024
       search-space:
-      # 1P+1D TP8/EP1 low-concurrency sweep.
-      # dp-attn intentionally false (matches the 1k1k row): with
-      # --enable-dp-attention + --moe-a2a-backend mori, sglang auto-promotes
-      # moe_ep_size=tp_size=8, but is_deepep_class_backend() excludes MoRI,
-      # so num_shared_slots stays at the global value (1) and the
+      # 1P+1D TP4P+TP8D/EP1 baseline (no speculative decoding).
+      # TP4 prefill saves 4 GPUs vs TP8P while delivering identical decode
+      # interactivity and 24-31% better throughput/GPU (12 vs 16 GPUs).
+      # dp-attn intentionally false: with --enable-dp-attention +
+      # --moe-a2a-backend mori, sglang auto-promotes moe_ep_size=tp_size,
+      # but is_deepep_class_backend() excludes MoRI, so
+      # num_shared_slots stays at the global value (1) and the
       # (num_experts - num_shared_slots) % moe_ep_size assertion in
       # fused_moe_triton/layer.py fires for Qwen3.5 (512 routed + 1 shared).
-      # Track upstream sglang for a fix; flip back to dp-attn=true once
-      # MoRI is added to is_deepep_class_backend() or shared-slot
-      # accounting is reconciled.
       - spec-decoding: "none"
         conc-list: [ 8, 16, 32, 64, 128 ]
         prefill:
           num-worker: 1
-          tp: 8
+          tp: 4
           ep: 1
           dp-attn: false
           additional-settings:

perf-changelog.yaml

@@ -6113,3 +6113,11 @@
   description:
     - "Add Kimi-K3 GB300 AgentX configurations."
   pr-link: https://github.com/SemiAnalysisAI/InferenceX/pull/2637
+
+- config-keys:
+    - qwen3.5-fp8-mi355x-sglang-disagg
+  scenario-type:
+    - fixed-seq-len
+  description:
+    - "Bump qwen3.5-fp8-mi355x-sglang-disagg image from v0.5.14 to v0.5.16 (3-5% throughput improvement) and switch from TP8P+TP8D (16 GPU) to TP4P+TP8D (12 GPU) — TP4 prefill delivers identical decode interactivity with 24-31% better throughput/GPU."
+  pr-link: https://github.com/SemiAnalysisAI/InferenceX/pull/2606