Refresh DeepSeek-V4 B200 SGLang AgentX MTP

benchmarks/single_node/agentic/dsv4_fp4_b200_sglang_mtp.sh

@@ -0,0 +1,272 @@
+#!/usr/bin/env bash
+set -eo pipefail
+set -x
+
+# Agentic trace replay for DeepSeek-V4-Pro FP4 on B200 with native EAGLE MTP.
+# Throughput uses the committed golden synthetic AL; eval retains real target
+# verification.
+#
+# KV_OFFLOADING=dram requires KV_OFFLOAD_BACKEND=hicache.
+
+SCRIPT_DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)"
+INFERENCEX_ROOT="$(cd "$SCRIPT_DIR/../../.." && pwd)"
+export INFMAX_CONTAINER_WORKSPACE="${INFMAX_CONTAINER_WORKSPACE:-/workspace}"
+
+# The B200 DeepSeek-V4 Blackwell image installs SGLang editable under
+# /workspace, so its launcher mounts InferenceX at /ix instead. Resolve the
+# agentic tooling and results against the actual repository mount so the image
+# can keep its /workspace install and GitHub Actions can collect the outputs.
+if [[ ! -d "$INFMAX_CONTAINER_WORKSPACE/utils/aiperf" ]]; then
+    export INFMAX_CONTAINER_WORKSPACE="$INFERENCEX_ROOT"
+fi
+if [[ "${RESULT_DIR:-}" == /workspace/* && "$INFMAX_CONTAINER_WORKSPACE" != /workspace ]]; then
+    export RESULT_DIR="$INFMAX_CONTAINER_WORKSPACE/${RESULT_DIR#/workspace/}"
+fi
+source "$INFERENCEX_ROOT/benchmarks/benchmark_lib.sh"
+
+export AIPERF_REQUIRED_SERVER_METRIC_PREFIX="sglang:"
+
+check_env_vars MODEL TP CONC KV_OFFLOADING TOTAL_CPU_DRAM_GB RESULT_DIR DURATION EP_SIZE DP_ATTENTION
+
+if [[ -n "${SLURM_JOB_ID:-}" ]]; then
+    echo "JOB $SLURM_JOB_ID running on ${SLURMD_NODENAME:-unknown}"
+fi
+
+if [[ -n "${MODEL_PATH:-}" ]]; then
+    if [[ ! -d "$MODEL_PATH" || -z "$(ls -A "$MODEL_PATH" 2>/dev/null)" ]]; then
+        hf download "$MODEL" --local-dir "$MODEL_PATH"
+    fi
+else
+    hf download "$MODEL"
+    export MODEL_PATH="$MODEL"
+fi
+nvidia-smi
+
+resolve_trace_source
+
+# Keep AIPerf's Transformers-main dependency from replacing the older
+# Transformers build pinned by the B200-specialized SGLang image. The server
+# always launches with the image's original interpreter; AIPerf and result
+# processing use the isolated environment when InferenceX is mounted at /ix.
+SGLANG_PYTHON="$(command -v python3)"
+if [[ "$INFMAX_CONTAINER_WORKSPACE" != /workspace ]]; then
+    AGENTIC_VENV="${AGENTIC_VENV:-/tmp/inferencex-agentic-venv}"
+    "$SGLANG_PYTHON" -m venv "$AGENTIC_VENV"
+    export PATH="$AGENTIC_VENV/bin:$PATH"
+fi
+install_agentic_deps
+
+SERVER_LOG="$RESULT_DIR/server.log"
+mkdir -p "$RESULT_DIR"
+
+export SGLANG_ENABLE_UNIFIED_RADIX_TREE=1
+export SGLANG_OPT_UNIFIED_CACHE_FREE_OUT_OF_WINDOW_SLOTS=1
+
+CACHE_ARGS=()
+if require_agentic_kv_offload_backend hicache; then
+    # DeepSeek V4 HiCache currently rejects --hicache-size and supports
+    # capacity control only through a host/device token-capacity ratio.
+    # DSv4 exposes capacity as a host/device token ratio rather than bytes.
+    # B200 ratio=8 stays below the configured host-memory capacity for the
+    # currently supported TP8 shape.
+    DEFAULT_HICACHE_RATIO=8
+    HICACHE_RATIO="${HICACHE_RATIO:-$DEFAULT_HICACHE_RATIO}"
+    if [ "$HICACHE_RATIO" -gt "$DEFAULT_HICACHE_RATIO" ]; then
+        echo "Error: HICACHE_RATIO=$HICACHE_RATIO exceeds configured limit $DEFAULT_HICACHE_RATIO" >&2
+        exit 1
+    fi
+    HICACHE_WRITE_POLICY="${HICACHE_WRITE_POLICY:-write_through}"
+    HICACHE_IO_BACKEND="${HICACHE_IO_BACKEND:-direct}"
+    HICACHE_MEM_LAYOUT="${HICACHE_MEM_LAYOUT:-page_first_direct}"
+    CACHE_ARGS=(
+        --enable-hierarchical-cache
+        --hicache-ratio "$HICACHE_RATIO"
+        --hicache-write-policy "$HICACHE_WRITE_POLICY"
+        --hicache-io-backend "$HICACHE_IO_BACKEND"
+        --hicache-mem-layout "$HICACHE_MEM_LAYOUT"
+    )
+    echo "HiCache DSv4 CPU tier: ratio=$HICACHE_RATIO, capacity=${TOTAL_CPU_DRAM_GB} GB, write_policy=$HICACHE_WRITE_POLICY, io_backend=$HICACHE_IO_BACKEND, mem_layout=$HICACHE_MEM_LAYOUT"
+fi
+
+USE_SGLANG_ROUTER=false
+SGLANG_BACKEND_PORT="$PORT"
+ROUTER_LOG="$RESULT_DIR/router.log"
+if [ "$DP_ATTENTION" = "true" ]; then
+    USE_SGLANG_ROUTER=true
+    export AIPERF_HTTP_X_SMG_ROUTING_KEY_FROM_CORRELATION_ID=true
+    SGLANG_BACKEND_PORT=$((PORT + 1))
+    SGLANG_ROUTER_METRICS_PORT=$((PORT + 10000))
+    SGLANG_ROUTER_CMD=("$SGLANG_PYTHON" -m sglang_router.launch_router)
+fi
+
+PARALLEL_ARGS=(--tp "$TP")
+METRICS_ARGS=(--enable-metrics --enable-cache-report)
+CHUNKED_PREFILL_SIZE=8192
+if [ "$DP_ATTENTION" = "true" ]; then
+    DEEPEP_CONFIG='{"normal_dispatch":{"num_sms":96},"normal_combine":{"num_sms":96}}'
+    export SGLANG_OPT_USE_DEEPGEMM_MEGA_MOE=1
+    export SGLANG_OPT_FIX_HASH_MEGA_MOE=1
+    export SGLANG_OPT_USE_FAST_MASK_EP=1
+    export SGLANG_OPT_FIX_MEGA_MOE_MEMORY=1
+    export SGLANG_OPT_DEEPGEMM_MEGA_MOE_NUM_MAX_TOKENS_PER_RANK=4096
+    export SGLANG_OPT_FIX_NEXTN_MEGA_MOE=1
+    export SGLANG_DEEPEP_NUM_MAX_DISPATCH_TOKENS_PER_RANK=0
+    PARALLEL_ARGS+=(
+        --dp "$TP"
+        --tokenizer-worker-num "$TP"
+        --enable-dp-attention
+        --enable-dp-attention-local-control-broadcast
+        --incremental-streaming-output
+        --stream-interval 20
+        --dist-init-addr "127.0.0.1:$((PORT + 2000))"
+        --ep-size "$EP_SIZE"
+        --moe-a2a-backend deepep
+        --deepep-config "$DEEPEP_CONFIG"
+    )
+    CHUNKED_PREFILL_SIZE=32768
+else
+    PARALLEL_ARGS+=(
+        --moe-runner-backend flashinfer_mxfp4
+        --disable-flashinfer-autotune
+    )
+fi
+
+MODEL_ARGS=()
+# The B200-specialized image deadlocks immediately after weight loading when
+# forced through the B300 compressed-attention/page-size overrides.
+# DeepGEMM's DSv4 indexer needs a multi-GiB temporary allocation at long
+# contexts. Leave the same HBM headroom used by the B300 recipe so a nearly
+# full GPU KV cache does not OOM while HiCache is spilling to host memory.
+MEM_FRACTION_STATIC=0.88
+
+# AgentX concurrency counts live session trees, not individual requests.
+# Allow subagent fan-out to exceed CONC without clipping request bursts.
+MAX_RUNNING_REQUESTS=$((2 * CONC))
+CUDA_GRAPH_MAX_BS=$CONC
+[ "$CUDA_GRAPH_MAX_BS" -gt 64 ] && CUDA_GRAPH_MAX_BS=64
+
+export PYTHONNOUSERSITE=1
+export TORCH_CUDA_ARCH_LIST=10.0
+# Agentic warmup dispatches hundreds of large prompts at once. SGLang's
+# tokenizer process can leave request bytes unacknowledged for longer than
+# AIPerf's 30-second TCP_USER_TIMEOUT while it admits that initial burst,
+# causing Linux to abort otherwise-live localhost connections. Keep the
+# six-hour request timeout unchanged, but allow up to 15 minutes for TCP
+# progress before declaring the connection dead.
+export AIPERF_HTTP_TCP_USER_TIMEOUT=900000
+# Outlast AIPerf's pooled connections so an inter-turn idle gap cannot race
+# Uvicorn's five-second keep-alive closure.
+export SGLANG_TIMEOUT_KEEP_ALIVE=900
+export SGLANG_JIT_DEEPGEMM_FAST_WARMUP=1
+export SGLANG_OPT_SWA_SPLIT_LEAF_ON_INSERT=1
+export SGLANG_OPT_USE_JIT_NORM=1
+export SGLANG_OPT_USE_JIT_INDEXER_METADATA=1
+export SGLANG_OPT_USE_TOPK_V2=1
+export SGLANG_OPT_USE_CUSTOM_ALL_REDUCE_V2=1
+if [ "${EVAL_ONLY}" != "true" ]; then
+    export SGLANG_SIMULATE_ACC_LEN=2.49
+    export SGLANG_SIMULATE_ACC_METHOD=match-expected
+    export SGLANG_SIMULATE_ACC_TOKEN_MODE=real-draft-token
+fi
+TRITON_PTXAS_PATH=$(find \
+    /usr/local/cuda* \
+    /usr/local/lib/python*/dist-packages/nvidia \
+    /usr/local/lib/python*/site-packages/nvidia \
+    -type f -name ptxas -perm -u+x -print -quit 2>/dev/null || true)
+if [ -n "$TRITON_PTXAS_PATH" ]; then
+    export TRITON_PTXAS_PATH
+    echo "Using ptxas for Triton: $TRITON_PTXAS_PATH"
+fi
+SGLANG_CMD=(
+    "$SGLANG_PYTHON" -m sglang.launch_server
+    --model-path "$MODEL_PATH"
+    --served-model-name "$MODEL"
+    --host 0.0.0.0
+    --port "$SGLANG_BACKEND_PORT"
+    --trust-remote-code
+    "${PARALLEL_ARGS[@]}"
+    --mem-fraction-static "$MEM_FRACTION_STATIC"
+    --swa-full-tokens-ratio 0.1
+    --max-running-requests "$MAX_RUNNING_REQUESTS"
+    --cuda-graph-max-bs "$CUDA_GRAPH_MAX_BS"
+    --chunked-prefill-size "$CHUNKED_PREFILL_SIZE"
+    --tool-call-parser deepseekv4
+    --reasoning-parser deepseek-v4
+    --chat-template "$SCRIPT_DIR/../chat_templates/deepseek_v4_thinking.jinja"
+    --watchdog-timeout 1800
+    --speculative-algorithm EAGLE
+    --speculative-num-steps 3
+    --speculative-eagle-topk 1
+    --speculative-num-draft-tokens 4
+    # The B200 checkpoint lives on Lustre. Partition sequential prefetching
+    # across local ranks so post-load weight repacking reads from page cache
+    # instead of issuing redundant fragmented mmap faults from every rank.
+    --weight-loader-prefetch-checkpoints
+    "${MODEL_ARGS[@]}"
+    "${METRICS_ARGS[@]}"
+    "${CACHE_ARGS[@]}"
+)
+
+write_command "$RESULT_DIR/sglang_command.txt" "${SGLANG_CMD[@]}"
+
+{
+    echo "=== SGLANG_* env vars at launch ==="
+    env | grep -E '^SGLANG_' | sort
+    echo "==================================="
+} | tee "$SERVER_LOG"
+
+echo "Starting SGLang server for B200..."
+"${SGLANG_CMD[@]}" >> "$SERVER_LOG" 2>&1 &
+SERVER_PID=$!
+echo "Server PID: $SERVER_PID"
+
+capture_cache_metrics() {
+    {
+        echo "=== SGLang cache metrics snapshot $(date --iso-8601=seconds) ==="
+        curl -fsS "http://localhost:$SGLANG_BACKEND_PORT/metrics" 2>/dev/null \
+            | grep -E '^(sglang:(cache_hit_rate|cached_tokens_total|prompt_tokens_total|hicache_host_used_tokens|hicache_host_total_tokens|token_usage|num_requests_running|num_requests_waiting))' \
+            || true
+        echo "============================================================"
+    } >> "$SERVER_LOG"
+}
+
+wait_for_ready \
+    --endpoint "http://localhost:$SGLANG_BACKEND_PORT/health" \
+    --log "$SERVER_LOG" \
+    --pid "$SERVER_PID"
+
+if [ "$USE_SGLANG_ROUTER" = "true" ]; then
+    echo "Starting SGLang router on port $PORT for $TP DP ranks..."
+    "${SGLANG_ROUTER_CMD[@]}" \
+        --worker-urls "http://localhost:$SGLANG_BACKEND_PORT" \
+        --policy consistent_hashing \
+        --request-id-headers x-correlation-id \
+        --dp-aware \
+        --host 0.0.0.0 \
+        --port "$PORT" \
+        --prometheus-host 127.0.0.1 \
+        --prometheus-port "$SGLANG_ROUTER_METRICS_PORT" \
+        --connect-timeout-secs 900 \
+        --request-timeout-secs 14400 \
+        --disable-health-check \
+        --disable-retries > "$ROUTER_LOG" 2>&1 &
+    ROUTER_PID=$!
+    echo "Router PID: $ROUTER_PID"
+    wait_for_ready \
+        --endpoint "http://localhost:$PORT/health" \
+        --log "$ROUTER_LOG" \
+        --pid "$ROUTER_PID"
+fi
+
+if [ "${#METRICS_ARGS[@]}" -gt 0 ]; then
+    capture_cache_metrics
+    trap capture_cache_metrics EXIT
+fi
+
+if [ "${EVAL_ONLY}" = "true" ]; then
+    run_eval --port "$PORT"
+else
+    build_replay_cmd "$RESULT_DIR"
+    REPLAY_CMD+=" --server-metrics http://localhost:$SGLANG_BACKEND_PORT/metrics"
+    run_agentic_replay_and_write_outputs "$RESULT_DIR"
+fi

configs/nvidia-master.yaml

@@ -928,6 +928,23 @@ dsv4-fp4-b200-sglang:
       # DP-attention (DP_ATTENTION=true) — max-throughput CONC range
       - { tp: 8, ep: 8, dp-attn: true, conc-start: 256, conc-end: 1024 }
 
+dsv4-fp4-b200-sglang-agentic-hicache-mtp:
+  image: lmsysorg/sglang:v0.5.17-cu130
+  model: deepseek-ai/DeepSeek-V4-Pro
+  model-prefix: dsv4
+  runner: cluster:b200-dgxc
+  precision: fp4
+  framework: sglang
+  multinode: false
+  scenarios:
+    agentic-coding:
+    - dram-utilization: 0.80
+      search-space:
+      - { tp: 8, kv-offloading: none, spec-decoding: mtp, conc-list: [1, 2, 3, 4, 5] }
+      - { tp: 8, kv-offloading: dram, kv-offload-backend: { name: hicache }, spec-decoding: mtp, conc-list: [8, 10, 16, 32, 40, 44] }
+      - { tp: 8, ep: 8, dp-attn: true, kv-offloading: none, spec-decoding: mtp, conc-list: [16, 24, 32, 38, 44, 48, 50, 52], router: { name: sglang-router, version: "0.3.2" } }
+      - { tp: 8, ep: 8, dp-attn: true, kv-offloading: dram, kv-offload-backend: { name: hicache }, spec-decoding: mtp, conc-list: [16, 32, 38, 44, 50, 56, 64, 66, 68], router: { name: sglang-router, version: "0.3.2" } }
+
 dsv4-fp4-b200-vllm:
   image: vllm/vllm-openai:v0.25.0
   model: deepseek-ai/DeepSeek-V4-Pro

perf-changelog.yaml

@@ -5918,6 +5918,16 @@
     - "Add a TEP2 arm (tp 2, ep 2) to the qwen3.5-fp4-b200-sglang-mtp 8k/1k sweep at concurrency 16, 32, and 64"
     - "Rides on the NVFP4-V2 checkpoint switch from #2205"
   pr-link: https://github.com/SemiAnalysisAI/InferenceX/pull/2550
+
+- config-keys:
+    - dsv4-fp4-b200-sglang-agentic-hicache-mtp
+  scenario-type:
+    - agentic-coding
+  description:
+    - "Refresh the purged B200 DeepSeek-V4-Pro SGLang AgentX resident, DP-attention, and HiCache grid on SGLang v0.5.17."
+    - "Use native EAGLE MTP (3 steps, top-k 1, 4 draft tokens) and golden synthetic acceptance length 2.49 for throughput; eval retains real verification."
+    - "Follow the official SGLang DeepSeek-V4 Blackwell recipe, require nonempty SGLang server metrics, and keep pooled AgentX connections alive across inter-turn gaps."
+  pr-link: https://github.com/SemiAnalysisAI/InferenceX/pull/2578
 
 - config-keys:
     - dsv4-fp4-b300-sglang-agentic-hicache-mtp