Frank
55f87fcdce
The 2026-10-09 wire aliases become the service names everywhere: systemd units, serve scripts, env files and FRANK_* variables, deploy-plan/deploy-remote service keys, container names, Prometheus job names + alert rules, Grafana dashboard queries, bats seams, runbook filenames, homepage/AGENTS lore, and historical prose. Rollback tags renamed in lockstep. Residue in the tree: zero. Retired frank-medium/small/npu names are history and stay. Known casualty: scripts/patches/*.patch context lines were rewritten; those retired-era vLLM-fork patches no longer apply byte-exact.
151 lines
4.6 KiB
Bash
Executable File
151 lines
4.6 KiB
Bash
Executable File
#!/usr/bin/env bash
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# bench-frank-dragoon.sh — 64k-context benchmark for the frank-default-large stack.
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#
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# Measures generation throughput of Qwen3.6-27B Q8_K_P + DFlash + ngram-mod on
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# a single backend at 64k context. It starts llama-server on a scratch port,
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# drives one generation through the OpenAI-compatible API with a ~64k-token
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# prompt, and prints tokens/sec.
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#
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# Usage:
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# ./scripts/bench-frank-dragoon.sh <ROCm0|Vulkan0> [--generations N]
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#
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# Example:
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# ./scripts/bench-frank-dragoon.sh ROCm0
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# ./scripts/bench-frank-dragoon.sh Vulkan0 --generations 3
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set -euo pipefail
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SCRIPT_DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)"
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DEVICE="${1:?usage: bench-frank-dragoon.sh <ROCm0|Vulkan0> [--generations N]}"
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GENERATIONS=1
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if [ "${2:-}" = "--generations" ]; then
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GENERATIONS="${3:?--generations needs a count}"
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fi
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MODEL_DIR=/home/dank/models
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MODEL_FILE=Qwen3.6-27B-Uncensored-HauhauCS-Aggressive-Q8_K_P.gguf
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DRAFT_FILE=Qwen3.6-27B-DFlash-Q8_0.gguf
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LLAMA_SERVER=/home/dank/llama.cpp/build/bin/llama-server
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MODEL="$MODEL_DIR/$MODEL_FILE"
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DRAFT="$MODEL_DIR/$DRAFT_FILE"
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PORT=7890
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CTX=65536
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PARALLEL=1
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BATCH=2048
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UBATCH=512
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OUT_TOKENS=256
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for f in "$MODEL" "$DRAFT"; do
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if [[ ! -f "$f" ]]; then
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echo "missing required file: $f" >&2
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exit 1
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fi
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done
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echo "=== 64k benchmark: device=$DEVICE model=$MODEL_FILE ==="
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# --- start the server ----------------------------------------------------
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"$LLAMA_SERVER" \
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--model "$MODEL" \
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--spec-draft-model "$DRAFT" \
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--spec-type "draft-dflash,ngram-mod" \
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--spec-draft-n-max 8 \
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--spec-ngram-mod-n-min 48 \
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--spec-ngram-mod-n-max 64 \
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--spec-ngram-mod-n-match 24 \
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--device "$DEVICE" \
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--gpu-layers all \
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--cache-type-k q8_0 \
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--cache-type-v f16 \
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--ctx-size "$CTX" \
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--parallel "$PARALLEL" \
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--batch-size "$BATCH" \
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--ubatch-size "$UBATCH" \
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--alias bench \
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--host 127.0.0.1 \
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--port "$PORT" \
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> "/tmp/frankbench-$DEVICE.log" 2>&1 &
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SERVER_PID=$!
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echo "server pid $SERVER_PID on 127.0.0.1:$PORT"
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trap 'kill $SERVER_PID 2>/dev/null; wait $SERVER_PID 2>/dev/null' EXIT
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# --- wait for ready ------------------------------------------------------
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echo -n "waiting for server"
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ready=0
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for i in $(seq 1 400); do
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if curl -sf "http://127.0.0.1:$PORT/health" >/dev/null 2>&1; then
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echo " [ready]"
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ready=1
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break
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fi
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echo -n "."
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sleep 2
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done
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if [ "$ready" != "1" ]; then
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echo ""
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echo "FAIL: server did not become ready"
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tail -25 "/tmp/frankbench-$DEVICE.log"
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exit 1
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fi
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# --- drive N generations and measure --------------------------------------
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python3 - "$DEVICE" "$CTX" "$OUT_TOKENS" "$GENERATIONS" "$PORT" <<'PY'
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import sys, time, json, urllib.request, statistics
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device = sys.argv[1]
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ctx = int(sys.argv[2])
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out_tokens = int(sys.argv[3])
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generations = int(sys.argv[4])
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port = sys.argv[5]
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# ~64k-token prompt. One prose unit is ~110 chars ≈ 28 tokens for this model.
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# We aim for a prompt of ~60k tokens so generation stays within the 64k ctx.
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unit = ("The careful study of natural language reveals patterns of meaning "
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"that emerge from the deliberate arrangement of words and phrases. ")
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# approximate: unit is 160 chars, ~40 tokens; repeat to ~1500 units = ~60k tokens
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prompt = unit * 1500
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def req(prompt, n):
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body = json.dumps({
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"prompt": prompt,
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"max_tokens": n,
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"temperature": 0.0,
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"stream": False,
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}).encode()
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r = urllib.request.Request(f"http://127.0.0.1:{port}/v1/completions",
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data=body, headers={"Content-Type": "application/json"})
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with urllib.request.urlopen(r, timeout=1800) as resp:
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return json.load(resp)
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# prefill timing
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t0 = time.time()
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first = req(prompt, 0)
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t_pre = time.time() - t0
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used = first.get("usage", {})
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prompt_tokens = used.get("prompt_tokens", 0)
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print(f"prefill: {prompt_tokens} tokens in {t_pre:.2f}s "
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f"({prompt_tokens/t_pre:.1f} tok/s)")
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# generation timing
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rates = []
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total_gentok = 0
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total_time = 0.0
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for i in range(generations):
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t0 = time.time()
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out = req(prompt, out_tokens)
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dt = time.time() - t0
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uit = out.get("usage", {})
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comp = uit.get("completion_tokens", 0)
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gt = dt - t_pre # approximate generation time = total - prefill
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r = comp / gt if gt > 0 else 0
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rates.append(r)
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total_gentok += comp
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total_time += gt
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print(f"gen {i+1}: {comp} tokens in {gt:.2f}s ({r:.1f} tok/s)")
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avg = statistics.mean(rates) if rates else 0
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print(f"\nRESULT device={device} prompt_tokens={prompt_tokens} "
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f"avg_gen_tok_s={avg:.1f} over {generations} runs")
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PY
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