dsv4-fp4-b200-sglang: revert b200 portion of #1158

.github/configs/nvidia-master.yaml

@@ -1670,7 +1670,7 @@ dsr1-fp4-b200-sglang:
     - { tp: 8, ep: 8, conc-start: 4, conc-end: 16 }
 
 dsv4-fp4-b200-sglang:
-  image: lmsysorg/sglang:deepseek-v4-blackwell@sha256:df18bfc4aa9ecf59451002b49ba00cae58042de9e2a96378bbd21b404dd62c7b
+  image: lmsysorg/sglang:deepseek-v4-blackwell
   model: deepseek-ai/DeepSeek-V4-Pro
   model-prefix: dsv4
   runner: b200-dsv4
@@ -1690,7 +1690,7 @@ dsv4-fp4-b200-sglang:
     osl: 1024
     search-space:
     # low-latency
-    - { tp: 8, ep: 1, conc-start: 1, conc-end: 32 }
+    - { tp: 8, ep: 1, conc-start: 4, conc-end: 32 }
     # balanced
     - { tp: 8, ep: 8, dp-attn: true, conc-start: 64, conc-end: 128 }
     # max-throughput
@@ -1699,7 +1699,7 @@ dsv4-fp4-b200-sglang:
     osl: 1024
     search-space:
     # low-latency
-    - { tp: 8, ep: 1, conc-start: 1, conc-end: 32 }
+    - { tp: 8, ep: 1, conc-start: 4, conc-end: 32 }
     # balanced
     - { tp: 8, ep: 8, dp-attn: true, conc-start: 64, conc-end: 128 }
     # max-throughput

benchmarks/single_node/dsv4_fp4_b200.sh

@@ -5,7 +5,6 @@ source "$(dirname "$0")/../benchmark_lib.sh"
 check_env_vars \
     MODEL \
     TP \
-    DP_ATTENTION \
     CONC \
     ISL \
     OSL \
@@ -20,13 +19,7 @@ hf download "$MODEL"
 
 nvidia-smi
 
-# Common SGLANG env vars (apply to every config).
 export SGLANG_JIT_DEEPGEMM_PRECOMPILE=0
-export SGLANG_OPT_SWA_SPLIT_LEAF_ON_INSERT=1
-export SGLANG_OPT_USE_JIT_NORM=1
-export SGLANG_OPT_USE_JIT_INDEXER_METADATA=1
-export SGLANG_OPT_USE_TOPK_V2=1
-export SGLANG_OPT_USE_CUSTOM_ALL_REDUCE_V2=1
 
 # TODO(Cam): the lmsysorg/sglang:deepseek-v4-blackwell image installs sglang
 # editable at /workspace/sglang/python; prior sglang tags used /sgl-workspace/sglang.
@@ -37,7 +30,7 @@ export SGLANG_OPT_USE_CUSTOM_ALL_REDUCE_V2=1
 SERVER_LOG="$PWD/server.log"
 PORT=${PORT:-8888}
 
-echo "TP: $TP, DP_ATTENTION: $DP_ATTENTION, CONC: $CONC, ISL: $ISL, OSL: $OSL"
+echo "TP: $TP, CONC: $CONC, ISL: $ISL, OSL: $OSL"
 
 EVAL_CONTEXT_ARGS=""
 if [ "${EVAL_ONLY}" = "true" ]; then
@@ -47,41 +40,47 @@ fi
 
 start_gpu_monitor --output "$PWD/gpu_metrics.csv"
 
-# Pick the parallelism + MoE backend based on DP_ATTENTION (mirrors the vllm
-# script's pattern). DP-attention turns on EP-MoE (deepep) and the related
-# mega_moe optimizations; single-instance uses flashinfer_mxfp4.
+# Three recipes from https://docs.sglang.io/cookbook/autoregressive/DeepSeek/DeepSeek-V4
+# (spec-decoding / MTP and prefix-caching flags dropped for the baseline):
+#   - low-latency    (CONC <= 32):        TP-only, chunked-prefill, disable autotune
+#   - balanced       (32 < CONC <= 128):  + DP-attn, max-running-requests=128
+#   - max-throughput (CONC > 128):        + DP-attn, max-running-requests=256
 DEEPEP_CONFIG='{"normal_dispatch":{"num_sms":96},"normal_combine":{"num_sms":96}}'
 
-if [ "${DP_ATTENTION}" = "true" ]; then
-    export SGLANG_OPT_USE_DEEPGEMM_MEGA_MOE=1
-    export SGLANG_OPT_FIX_HASH_MEGA_MOE=1
-    export SGLANG_OPT_USE_FAST_MASK_EP=1
-    export SGLANG_OPT_FIX_MEGA_MOE_MEMORY=1
-    export SGLANG_OPT_DEEPGEMM_MEGA_MOE_NUM_MAX_TOKENS_PER_RANK=4096
-    export SGLANG_OPT_FIX_NEXTN_MEGA_MOE=1
-    export SGLANG_DEEPEP_NUM_MAX_DISPATCH_TOKENS_PER_RANK=0
-    PARALLEL_ARGS=(
+if [[ $CONC -le 32 ]]; then
+    RECIPE=low-latency
+    RECIPE_FLAGS=(
+        --moe-runner-backend flashinfer_mxfp4
+        --chunked-prefill-size 4096
+        --disable-flashinfer-autotune
+        --mem-fraction-static 0.82
+    )
+elif [[ $CONC -le 128 ]]; then
+    RECIPE=balanced
+    export SGLANG_DEEPEP_NUM_MAX_DISPATCH_TOKENS_PER_RANK=256
+    RECIPE_FLAGS=(
         --dp-size "$TP"
         --enable-dp-attention
         --moe-a2a-backend deepep
         --deepep-config "$DEEPEP_CONFIG"
-        --chunked-prefill-size 32768
+        --mem-fraction-static 0.82
+        --cuda-graph-max-bs 64
+        --max-running-requests 128
     )
 else
-    PARALLEL_ARGS=(
-        --moe-runner-backend flashinfer_mxfp4
-        --chunked-prefill-size 8192
-        --disable-flashinfer-autotune
+    RECIPE=max-throughput
+    export SGLANG_DEEPEP_NUM_MAX_DISPATCH_TOKENS_PER_RANK=256
+    RECIPE_FLAGS=(
+        --dp-size "$TP"
+        --enable-dp-attention
+        --moe-a2a-backend deepep
+        --deepep-config "$DEEPEP_CONFIG"
+        --mem-fraction-static 0.82
+        --cuda-graph-max-bs 64
+        --max-running-requests 256
     )
 fi
-
-# Print all SGLANG_* env vars to both the CI step log and server.log so the
-# launch config is auditable from the result artifact alone.
-{
-    echo "=== SGLANG_* env vars at launch ==="
-    env | grep -E '^SGLANG_' | sort
-    echo "==================================="
-} | tee "$SERVER_LOG"
+echo "Recipe: $RECIPE (CONC=$CONC)"
 
 set -x
 PYTHONNOUSERSITE=1 sglang serve \
@@ -90,10 +89,8 @@ PYTHONNOUSERSITE=1 sglang serve \
     --port $PORT \
     --trust-remote-code \
     --tp $TP \
-    --max-running-requests "$((CONC * 3 / 2))" \
-    --mem-fraction-static 0.90 \
-    --swa-full-tokens-ratio 0.1 \
-    "${PARALLEL_ARGS[@]}" $EVAL_CONTEXT_ARGS >> $SERVER_LOG 2>&1 &
+    --disable-radix-cache \
+    "${RECIPE_FLAGS[@]}" $EVAL_CONTEXT_ARGS > $SERVER_LOG 2>&1 &
 
 SERVER_PID=$!