191 lines
6.8 KiB
Bash
191 lines
6.8 KiB
Bash
#!/usr/bin/env bash
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# bench-spec-stack.sh — compare MTP-only vs MTP+ngram-mod spec on the v3
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# CIRU prompt-forge stack (Dankclaw, gfx1151), at 8k / 16k / 64k context.
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#
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# Reads authoritative prefill/generation tok/s from llama-server's own
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# `print_timing` lines (prompt eval time / eval time). Runs one server
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# instance per (spec, ctx) on a scratch port.
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#
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# Usage:
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# ./scripts/bench-spec-stack.sh <draft-mtp|draft-mtp,ngram-mod> <ctx> [out_tokens]
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# Example:
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# ./scripts/bench-spec-stack.sh draft-mtp,ngram-mod 16384 256
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set -euo pipefail
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SPEC="${1:?usage: bench-spec-stack.sh <spec> <ctx> [out_tokens]}"
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CTX="${2:?usage: bench-spec-stack.sh <spec> <ctx> [out_tokens]}"
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OUT_TOKENS="${3:-256}"
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ENV_FILE=/home/dank/vllmScripts/scripts/frank-default-large.env
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source "$ENV_FILE"
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# This harness benchmarks the retired v3 stack. Force the v3 values so the
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# harness works regardless of the live service env (now Kairic).
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MODEL_DIR=/home/dank/models/CIRU-Qwen3.8-27B-ActiveFPX-v3
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MODEL_FILE=Qwen3.8-27B-CIRU-ActiveFPX-PromptForge-v3-Q8-FFNEQ-LateQ6.gguf
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FFN_PFS=Qwen3.8-27B-CIRU-ActiveFPX-PromptForge-FFN.pfs
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GDN_PFS=Qwen3.8-27B-CIRU-ActiveFPX-PromptForge-GDN.pfs
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OUTPUT_K8_PFS=Qwen3.8-27B-CIRU-ActiveFPX-PromptForge-Output-K8.pfs
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MMPROJ_FILE=Qwen3.8-27B-mmproj-BF16.mmproj
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# Derive the PromptForge sidecar paths from the env base values.
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export PROMPTFORGE_SIDECAR="$MODEL_DIR/$FFN_PFS"
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export PROMPTFORGE_GDN_SIDECAR="$MODEL_DIR/$GDN_PFS"
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export PROMPTFORGE_MTP_OUTPUT_K8_PROXY="$MODEL_DIR/$OUTPUT_K8_PFS"
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MODEL="$MODEL_DIR/$MODEL_FILE"
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LLAMA_BIN=/home/dank/ciru-qwen38/ROCmFPX/build-promptforge/bin/llama-server
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PORT=7893
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PARALLEL=1
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BATCH=4096
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UBATCH=4096
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LOG="/tmp/specstack-$(echo "$SPEC" | tr ',' '-')-ctx$CTX.log"
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: > "$LOG"
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for f in "$MODEL" "$PROMPTFORGE_SIDECAR" "$PROMPTFORGE_GDN_SIDECAR" "$PROMPTFORGE_MTP_OUTPUT_K8_PROXY" "$MODEL_DIR/$MMPROJ_FILE"; do
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[[ -f "$f" ]] || { echo "missing: $f" >&2; exit 1; }
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done
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[[ -x "$LLAMA_BIN" ]] || { echo "missing: $LLAMA_BIN" >&2; exit 1; }
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export PROMPTFORGE_MODE="m2048_fused_tail1476"
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export GGML_CUDA_GRAPH_OPT=0
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export PROMPTFORGE_ENABLE_SMALLM_W8=1
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export PROMPTFORGE_MTP_OUTPUT_K8=1
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export PROMPTFORGE_MTP_OUTPUT_K8_VALIDATE=0
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export PROMPTFORGE_MTP_OUTPUT_K8_PROXY
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unset LLAMA_MTP_CPU_ARGMAX_FASTPATH LLAMA_MTP_BACKEND_GREEDY || true
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unset PROMPTFORGE_IU4_SIDECAR PROMPTFORGE_GDN_IU4_SIDECAR || true
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unset PROMPTFORGE_ENABLE_NGRAM_MOD PROMPTFORGE_ENABLE_NGRAM_M65_IU4 || true
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export ROCM_PATH="$ROCM_DEVEL"
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export LD_LIBRARY_PATH="${ROCM_LIB}/lib:${ROCM_CORE}/lib:${ROCM_DEVEL}/lib${LD_LIBRARY_PATH:+:$LD_LIBRARY_PATH}"
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echo "=== spec-stack bench: spec=$SPEC ctx=$CTX model=$MODEL_FILE ==="
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echo "=== env: OUTPUT_K8=$PROMPTFORGE_MTP_OUTPUT_K8 smallm=$PROMPTFORGE_ENABLE_SMALLM_W8 ==="
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# ngram-mod tuning matches the retired fallback line: n_min 48 / n_max 64 / n_match 24
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"$LLAMA_BIN" \
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--model "$MODEL" \
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--mmproj "$MODEL_DIR/$MMPROJ_FILE" \
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--alias bench \
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--host 127.0.0.1 \
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--port "$PORT" \
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-dev ROCm0 \
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-ngl 999 \
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-c "$CTX" \
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-b "$BATCH" \
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-ub "$UBATCH" \
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-fa on \
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-ctk "$CACHE_TYPE_K" \
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-ctv "$CACHE_TYPE_V" \
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-np "$PARALLEL" \
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-ctxcp "$CTX_CHECKPOINTS" \
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--cache-ram "$CACHE_RAM" \
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--cache-prompt \
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--no-mmap \
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--spec-type "$SPEC" \
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--spec-draft-device ROCm0 \
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--spec-draft-ngl 999 \
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--spec-draft-type-k f16 \
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--spec-draft-type-v f16 \
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--spec-draft-n-max 4 \
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--spec-draft-n-min 0 \
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--spec-draft-p-min 0.0 \
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--spec-draft-p-split 0.10 \
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--spec-draft-backend-sampling \
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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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--temp 0.7 \
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--top-p 0.95 \
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-lv 4 \
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>> "$LOG" 2>&1 &
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SERVER_PID=$!
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trap 'kill $SERVER_PID 2>/dev/null; wait $SERVER_PID 2>/dev/null' EXIT
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echo "server pid $SERVER_PID on 127.0.0.1:$PORT"
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echo -n "waiting for server"
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ready=0
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crashed=0
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for i in $(seq 1 900); do
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if ! kill -0 "$SERVER_PID" 2>/dev/null; then
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crashed=1
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break
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fi
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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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[ $((i % 10)) = 0 ] && echo -n .
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sleep 2
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done
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if [ "$crashed" = "1" ]; then
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echo " BACKEND_CRASHED (server died before ready)"
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tail -30 "$LOG"
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exit 2
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fi
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[ "$ready" = "1" ] || { echo " FAIL (timeout)"; tail -25 "$LOG"; exit 1; }
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# Print the implementations that initialized (feasibility evidence)
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grep -E "adding speculative implementation|ngram_mod|draft-mtp" "$LOG" | head -6
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python3 - "$SPEC" "$CTX" "$PORT" "$OUT_TOKENS" "$LOG" <<'PY'
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import sys, time, json, urllib.request
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spec, ctx, port, out_tokens, log = sys.argv[1], int(sys.argv[2]), sys.argv[3], int(sys.argv[4]), sys.argv[5]
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# Flowing prose (varied) at ~75% of ctx so MTP acceptance is realistic.
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VAR = (
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"narrative nonfiction blends observation with reflection, ",
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"and a careful writer lets each clause earn its place ",
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"in the sentence's rising line, so meaning accumulates slowly. ",
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"The reader is not hurried; the reader is led. Loose and periodic, ",
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"the style turns on concrete nouns and verbs that carry weight, ",
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"and it resists the abstract noun that hides a vague claim. ",
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"A paragraph is not a list. It is a single turn of thought that ",
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"opens, develops, and lands. This is the craft of prose. Choose, ",
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"arrange, and above all omit. Omission is the discipline that ",
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"keeps the whole clear, and clarity is the courtesy we owe the ",
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"willing reader."
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)
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n = len(VAR)
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parts = []
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t = 0
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while t * 76 < (ctx * 3) // 4:
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for k in range(5):
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parts.append(VAR[(t + k) % n])
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parts.append(f" This is cued by note {t}. ")
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t += 1
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prompt = "".join(parts)
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prompt += ("\n\nNow, in a detailed and flowing paragraph, continue this essay about "
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"the philosophy of language and its influence on modern computation. ")
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body = json.dumps({
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"prompt": prompt,
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"max_tokens": out_tokens,
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"temperature": 0.0,
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"stream": False,
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}).encode()
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req = 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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t0 = time.time()
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with urllib.request.urlopen(req, timeout=7200) as resp:
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out = json.load(resp)
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dt = time.time() - t0
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pt = out.get("usage", {}).get("prompt_tokens", 0)
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ct = out.get("usage", {}).get("completion_tokens", 0)
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print(f"request done: prompt_tokens={pt} completion_tokens={ct} wall={dt:.1f}s", flush=True)
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lines = open(log, encoding="utf-8", errors="ignore").read().splitlines()
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prompt_evals = [l for l in lines if "prompt eval time" in l]
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evals = [l for l in lines if "eval time" in l and "prompt eval" not in l]
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def grab(lines, label):
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if lines:
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print(label + ": " + lines[-1].strip())
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print(f"== RESULT spec={spec} ctx={ctx} ==")
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grab(prompt_evals, "PREFILL")
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grab(evals, "GENERATION")
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print(f"prompt_tokens={pt} completion_tokens={ct} wall={dt:.1f}s")
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PY |