Entrpi
72badaf39a
A forum user hit HTTP 400 "'auto' tool choice requires --enable-auto-tool-choice
and --tool-call-parser to be set" — the server shipped without tool-calling
enabled, which is exactly what agent/tool workloads (the whole point) need.
- serve.sh + mtp_serve.sh now pass --enable-auto-tool-choice --tool-call-parser
qwen3_xml --reasoning-parser qwen3 by default (overridable/disable via
TOOL_PARSER= / REASONING_PARSER=).
- Parser matters: Qwen3.5 emits the XML tool format
(<function=name><parameter=k>v</parameter></function>), so hermes returns 200
but with EMPTY tool_calls (the call lands in content). qwen3_xml parses it.
Verified on the box: tool_choice=auto -> tool_calls=[get_weather {city:Paris}];
plain chat unaffected; <think> -> reasoning_content.
- README: document tool-calling defaults + that the server binds 0.0.0.0
(LAN-reachable at http://<spark-ip>:8000; no auth).
93 lines
4.8 KiB
Bash
Executable File
93 lines
4.8 KiB
Bash
Executable File
#!/bin/bash
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# serve.sh — runs INSIDE the sm121 vLLM container (mounted at /host). Applies the
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# runtime monkeypatches, then `vllm serve`s the Qwen3.5-122B-A10B INT4 target with
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# the DFlash drafter. Driven by install.sh; can also be run by hand.
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#
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# args: $1 = num_speculative_tokens (0 = no-spec baseline)
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# $2 = target attention backend (flash_attn | FLASHINFER)
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# env: MODEL target path/repo (default Intel INT4; /model for hybrid)
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# INC_HYBRID=1 apply the hybrid INT4+FP8 dense-expert dispatch patch
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# INT8_LMHEAD_V3=1 apply the int8 lm-head GEMV patch
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# MAX_MODEL_LEN GPU_MEM PORT
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#
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# Stack rationale: the DFlash drafter is non-causal -> needs FLASH_ATTN (FA2). The
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# hybrid GDN+mamba+MoE target's KV page geometry won't absorb the drafter's
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# attention spec without patch_unify2 (scale-block unify) + prefix-caching OFF
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# (NoPrefixCache coordinator, dodges the hash assert). See docs/FINDINGS.md.
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set -euo pipefail
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NSPEC="${1:-12}"
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BACKEND="${2:-flash_attn}"
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MODEL="${MODEL:-Intel/Qwen3.5-122B-A10B-int4-AutoRound}"
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DRAFT="${DRAFT:-z-lab/Qwen3.5-122B-A10B-DFlash}"
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MAX_MODEL_LEN="${MAX_MODEL_LEN:-262144}" # model native max; KV is ~24 KiB/token so it fits
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GPU_MEM="${GPU_MEM:-0.82}" # VALIDATED: ~14 GiB free on 128 GB (119 GiB) GB10 (0.88+ over-subscribes -> swap)
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MAX_NUM_SEQS="${MAX_NUM_SEQS:-3}" # 3 concurrent streams; KV pool ~427k (dense) / ~457k (dflash) tokens at full 262144
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MAX_BATCHED_TOKENS="${MAX_BATCHED_TOKENS:-8192}" # chunked-prefill chunk (NOT = max-model-len)
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PORT="${PORT:-8000}"
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# Read straight to the device (no mmap, no host staging) — the slow default safetensors
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# read+copy is ~8 min on Spark; fastsafetensors cuts it to ~1 min. Falls back to nogds
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# automatically. Override LOAD_FORMAT=auto|safetensors if the pkg is absent (or set
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# --safetensors-load-strategy eager via SAFETENSORS_STRATEGY).
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LOAD_FORMAT="${LOAD_FORMAT:-fastsafetensors}"
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# Reclaim vLLM's CUDA-graph memory OVER-estimate back to the KV pool. The profiler
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# reserves ~0.7 GiB for the graph pool but capture actually uses ~0.14 GiB; disabling
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# the estimate gives the difference (~0.6 GiB / ~9.5k tokens) to KV. The real capture
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# then comes out of the (1 - gpu-mem) headroom, which is ~21 GiB at 0.82 -> no OOM
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# risk at the shipped util. Set ESTIMATE_CUDAGRAPHS=1 to restore vLLM's default if you
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# push gpu-mem very high (small headroom).
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export VLLM_MEMORY_PROFILER_ESTIMATE_CUDAGRAPHS="${VLLM_MEMORY_PROFILER_ESTIMATE_CUDAGRAPHS:-0}"
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# FLA sm121 big-tile shmem fix (prefill/TTFT only on sm121; harmless, free).
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echo "[serve] FLA sm121 big-tile shmem patch"
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python3 /host/patch_fla_shmem.py || true
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if [ "${INC_HYBRID:-0}" = "1" ]; then
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echo "[serve] hybrid INT4+FP8 dispatch patch (inc.py)"
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python3 /host/patch_inc_hybrid.py
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fi
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if [ "${INT8_LMHEAD_V3:-0}" = "1" ]; then
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echo "[serve] int8 lm-head v3 patch (batched w8a16 GEMV)"
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python3 /host/patch_int8_lmhead_v3.py
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fi
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if [ "$NSPEC" = "0" ]; then
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SPEC_ARG=()
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echo "[serve] NO-SPEC baseline (identical flags, prefix-off)"
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else
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SPEC_ARG=(--speculative-config "{\"method\":\"dflash\",\"model\":\"$DRAFT\",\"num_speculative_tokens\":$NSPEC,\"attention_backend\":\"FLASH_ATTN\"}")
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echo "[serve] DFlash n=$NSPEC, target-backend=$BACKEND, drafter=FLASH_ATTN, model=$MODEL"
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fi
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python3 /host/patch_unify2.py || { [ "$NSPEC" = "0" ] && true; }
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# OpenAI automatic tool-calling + reasoning split. Without --enable-auto-tool-choice
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# + --tool-call-parser, any client that sends `tools` with tool_choice="auto" gets
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# HTTP 400 — so agent/tool workloads (the whole point here) need these on. The tool
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# parser only activates when a request carries `tools`, so leaving it on is free for
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# plain chat. Qwen3.5 emits the XML tool format
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# (<tool_call><function=name><parameter=k>v</parameter></function></tool_call>) -> the
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# qwen3_xml parser (NOT hermes, which only reads the JSON format -> empty tool_calls),
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# and <think> reasoning the qwen3 parser splits into `reasoning_content`. Verified:
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# tool_choice="auto" -> tool_calls=[get_weather {"city":"Paris"}]. Disable either with
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# TOOL_PARSER="" / REASONING_PARSER="", or override the name (e.g. qwen3_coder).
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TOOL_PARSER="${TOOL_PARSER:-qwen3_xml}"
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REASONING_PARSER="${REASONING_PARSER:-qwen3}"
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TOOL_ARG=()
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[ -n "$TOOL_PARSER" ] && TOOL_ARG+=(--enable-auto-tool-choice --tool-call-parser "$TOOL_PARSER")
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[ -n "$REASONING_PARSER" ] && TOOL_ARG+=(--reasoning-parser "$REASONING_PARSER")
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exec vllm serve "$MODEL" \
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--served-model-name qwen \
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--host 0.0.0.0 --port "$PORT" \
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--max-model-len "$MAX_MODEL_LEN" \
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--max-num-seqs "$MAX_NUM_SEQS" \
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--max-num-batched-tokens "$MAX_BATCHED_TOKENS" \
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--gpu-memory-utilization "$GPU_MEM" \
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--no-enable-prefix-caching \
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--enable-chunked-prefill \
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--trust-remote-code \
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--load-format "$LOAD_FORMAT" \
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--attention-backend "$BACKEND" \
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"${TOOL_ARG[@]}" \
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"${SPEC_ARG[@]}"
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