Add per-request breakdown profiling, identify KV cache memory bottleneck
Breakdown profiling at proxy level captures: t_proxy_recv → t_prefill_sent → t_prefill_done → t_decode_sent → t_first_token Key finding: 87.7% of TTFT is spent in kv+decode phase, NOT prefill. Root cause: decode instance KV cache memory saturation (97.1% usage). With 6P+2D config, 2 decode GPUs have only ~56GB total KV cache. Large agentic requests (avg 33.6k tokens) fill this quickly. Small requests (49 tokens, prefill=0.044s) wait 114s for KV cache to be freed by large requests completing decode. vLLM log confirms: Running=0, Waiting=6, KV cache=97.1% GPU is idle but requests queue for KV cache memory, not compute. This is the fundamental bottleneck of single-machine PD separation for long-context agentic workloads: concentrating decode onto fewer GPUs creates a KV cache memory wall. Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
This commit is contained in:
54
scripts/analyze_breakdown.py
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54
scripts/analyze_breakdown.py
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"""Analyze per-request breakdown data from the proxy."""
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import json, statistics, sys
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url = sys.argv[1] if len(sys.argv) > 1 else "http://localhost:9090/breakdown"
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if url.startswith("http"):
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import urllib.request
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data = json.loads(urllib.request.urlopen(url, timeout=10).read())
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else:
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data = json.load(open(url))
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print("Total records: %d" % len(data))
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results = []
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for d in data:
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keys = ["t_proxy_recv", "t_prefill_sent", "t_prefill_done", "t_decode_sent", "t_first_token"]
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if not all(k in d for k in keys):
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continue
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results.append({
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"input": d["input_length"],
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"prefill": d["t_prefill_done"] - d["t_prefill_sent"],
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"proxy_gap": d["t_decode_sent"] - d["t_prefill_done"],
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"kv_decode": d["t_first_token"] - d["t_decode_sent"],
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"ttft": d["t_first_token"] - d["t_proxy_recv"],
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})
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results.sort(key=lambda x: x["input"])
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print("Complete breakdown: %d" % len(results))
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if not results:
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print("No complete records yet")
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sys.exit(0)
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print()
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print(" %8s %9s %9s %9s %9s" % ("input", "prefill", "proxy", "kv+dec", "TTFT"))
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print(" %8s %9s %9s %9s %9s" % ("-----", "-------", "-----", "------", "----"))
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for r in results[:25]:
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print(" %8d %9.3f %9.3f %9.3f %9.3f" % (
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r["input"], r["prefill"], r["proxy_gap"], r["kv_decode"], r["ttft"]))
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print()
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for key in ["prefill", "proxy_gap", "kv_decode", "ttft"]:
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vals = sorted([r[key] for r in results])
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p = lambda q: vals[min(int(q * len(vals)), len(vals) - 1)]
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print(" %s: p50=%.3fs p90=%.3fs mean=%.3fs" % (
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key, p(.5), p(.9), statistics.fmean(vals)))
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# Fraction of TTFT by stage
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print()
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print(" TTFT breakdown (fraction of total):")
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for key in ["prefill", "proxy_gap", "kv_decode"]:
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fracs = [r[key] / r["ttft"] * 100 for r in results if r["ttft"] > 0.01]
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if fracs:
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print(" %s: mean=%.1f%% of TTFT" % (key, statistics.fmean(fracs)))
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@@ -196,25 +196,42 @@ async def _handle_combined(api, req_data, token_ids, input_length, session_id, h
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return StreamingResponse(generate(), media_type="text/event-stream")
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async def _send_prefill_async(p_inst, api, prefill_data, p_headers, token_ids, input_length):
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import time as _time
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# Per-request breakdown log (append-only)
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_breakdown_log: list[dict] = []
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async def _send_prefill_async(p_inst, api, prefill_data, p_headers, token_ids,
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input_length, breakdown):
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"""Fire-and-forget prefill: send and don't block caller."""
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try:
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resp = await p_inst.client.post(api, json=prefill_data, headers=p_headers)
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breakdown["t_prefill_done"] = _time.monotonic()
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resp.raise_for_status()
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await resp.aclose()
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p_inst.record_prefix(token_ids)
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except Exception:
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pass
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breakdown["t_prefill_done"] = _time.monotonic()
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breakdown["prefill_error"] = True
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finally:
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p_inst.ongoing_tokens -= input_length
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async def _handle_pd_sep(api, req_data, request_id, token_ids, input_length,
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session_id, headers):
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"""PD-Sep mode. --fire-and-forget controls prefill waiting behavior."""
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"""PD-Sep mode with per-stage breakdown profiling."""
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breakdown = {
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"request_id": request_id,
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"input_length": input_length,
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"t_proxy_recv": _time.monotonic(),
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}
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p_inst, _ = pick_instance(prefill_instances, token_ids, session_id,
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input_length, session_affinity)
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d_inst = min(decode_instances, key=lambda x: x.ongoing_tokens)
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breakdown["p_inst"] = p_inst.url
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breakdown["d_inst"] = d_inst.url
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prefill_data = req_data.copy()
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prefill_data["kv_transfer_params"] = {
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@@ -228,24 +245,27 @@ async def _handle_pd_sep(api, req_data, request_id, token_ids, input_length,
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p_headers = {**headers, "X-data-parallel-rank": "0"}
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p_inst.ongoing_tokens += input_length
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breakdown["t_prefill_sent"] = _time.monotonic()
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if global_args.fire_and_forget:
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# Fire-and-forget: send prefill async, immediately proceed to decode
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asyncio.create_task(_send_prefill_async(
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p_inst, api, prefill_data, p_headers, token_ids, input_length))
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p_inst, api, prefill_data, p_headers, token_ids, input_length, breakdown))
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else:
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# Await: block until prefill completes, then send decode
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try:
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resp = await p_inst.client.post(api, json=prefill_data, headers=p_headers)
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breakdown["t_prefill_done"] = _time.monotonic()
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resp.raise_for_status()
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await resp.aclose()
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p_inst.record_prefix(token_ids)
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except Exception as e:
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breakdown["t_prefill_done"] = _time.monotonic()
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breakdown["prefill_error"] = True
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_breakdown_log.append(breakdown)
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raise HTTPException(status_code=502, detail=f"Prefill failed: {e}")
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finally:
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p_inst.ongoing_tokens -= input_length
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# Stream decode
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# Send decode
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d_inst.ongoing_tokens += input_length
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parsed = urllib.parse.urlparse(str(p_inst.client.base_url))
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bootstrap_addr = f"http://{parsed.hostname}:{p_inst.bootstrap_port}"
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@@ -258,18 +278,32 @@ async def _handle_pd_sep(api, req_data, request_id, token_ids, input_length,
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"transfer_id": f"xfer-{request_id}",
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}
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breakdown["t_decode_sent"] = _time.monotonic()
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async def generate():
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first_token = True
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try:
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async with d_inst.client.stream("POST", api, json=decode_data, headers=headers) as resp:
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resp.raise_for_status()
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async for chunk in resp.aiter_bytes():
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if first_token:
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breakdown["t_first_token"] = _time.monotonic()
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first_token = False
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yield chunk
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finally:
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breakdown["t_done"] = _time.monotonic()
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d_inst.ongoing_tokens -= input_length
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_breakdown_log.append(breakdown)
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return StreamingResponse(generate(), media_type="application/json")
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@app.get("/breakdown")
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async def get_breakdown():
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"""Return per-request breakdown data for analysis."""
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return _breakdown_log
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def parse_args():
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p = argparse.ArgumentParser(description="Unified cache-aware global scheduler")
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p.add_argument("--port", type=int, default=8000)
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87
scripts/profile_fnf.py
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87
scripts/profile_fnf.py
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"""Deep profile: why fire-and-forget TTFT is 5x worse than await."""
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import json, statistics
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await_rows = [json.loads(l) for l in open("outputs/gpu_ab_6p2d/metrics.jsonl")]
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fnf_rows = [json.loads(l) for l in open("outputs/gpu_ab_6p2d_fnf/metrics.jsonl")]
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await_ok = [r for r in await_rows if not r.get("error")]
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fnf_ok = [r for r in fnf_rows if not r.get("error")]
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# Match by request_id
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await_by_id = {r["request_id"]: r for r in await_ok}
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fnf_by_id = {r["request_id"]: r for r in fnf_ok}
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common = set(await_by_id.keys()) & set(fnf_by_id.keys())
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print("=" * 75)
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print(" PROFILE: Fire-and-Forget vs Await-Prefill (same 6P+2D instances)")
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print("=" * 75)
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print(f" Common requests: {len(common)}")
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# Per-request comparison
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diffs = []
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for rid in common:
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a = await_by_id[rid]
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f = fnf_by_id[rid]
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if a.get("ttft_s") and f.get("ttft_s") and a["ttft_s"] > 0:
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diffs.append({
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"id": rid, "input": a["input_length"],
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"a_ttft": a["ttft_s"], "f_ttft": f["ttft_s"],
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"ratio": f["ttft_s"] / a["ttft_s"],
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"a_e2e": a["latency_s"], "f_e2e": f["latency_s"],
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"a_tpot": a.get("tpot_s", 0), "f_tpot": f.get("tpot_s", 0),
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"a_out": a.get("actual_output_tokens", 0) or 0,
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"f_out": f.get("actual_output_tokens", 0) or 0,
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})
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diffs.sort(key=lambda x: x["input"])
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print("\n Per-request (sorted by input_length):")
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hdr = "%8s %10s %10s %7s %10s %10s %8s %8s" % (
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"input", "await_TTFT", "fnf_TTFT", "ratio", "await_E2E", "fnf_E2E", "a_TPOT", "f_TPOT")
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print(" " + hdr)
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print(" " + "-" * len(hdr))
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for d in diffs[:25]:
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print(" %8d %10.3f %10.3f %6.1fx %10.3f %10.3f %8.4f %8.4f" % (
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d["input"], d["a_ttft"], d["f_ttft"], d["ratio"],
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d["a_e2e"], d["f_e2e"], d["a_tpot"], d["f_tpot"]))
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# Statistics
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if diffs:
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ratios = [d["ratio"] for d in diffs]
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ratios.sort()
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p = lambda v, q: v[min(int(q*len(v)), len(v)-1)]
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print("\n TTFT ratio (FnF / Await):")
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print(" p10=%.2fx p50=%.2fx p90=%.2fx mean=%.2fx" % (
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p(ratios,.1), p(ratios,.5), p(ratios,.9), statistics.fmean(ratios)))
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faster = sum(1 for r in ratios if r < 1.0)
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print(" FnF faster: %d/%d (%.0f%%)" % (faster, len(ratios), faster*100/len(ratios)))
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# Bucket by input size
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print("\n TTFT ratio by input size bucket:")
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buckets = [(0, 5000, "<5k"), (5000, 20000, "5-20k"), (20000, 50000, "20-50k"), (50000, 999999, ">50k")]
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for lo, hi, label in buckets:
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subset = [d for d in diffs if lo <= d["input"] < hi]
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if subset:
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rs = [d["ratio"] for d in subset]
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a_ttfts = [d["a_ttft"] for d in subset]
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f_ttfts = [d["f_ttft"] for d in subset]
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print(" %6s: n=%3d await_TTFT=%.3f fnf_TTFT=%.3f ratio=%.2fx" % (
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label, len(subset), statistics.fmean(a_ttfts), statistics.fmean(f_ttfts),
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statistics.fmean(rs)))
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# TPOT comparison
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a_tpots = [d["a_tpot"] for d in diffs if d["a_tpot"] > 0]
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f_tpots = [d["f_tpot"] for d in diffs if d["f_tpot"] > 0]
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if a_tpots and f_tpots:
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print("\n TPOT comparison:")
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print(" Await: mean=%.4f p50=%.4f" % (statistics.fmean(a_tpots), sorted(a_tpots)[len(a_tpots)//2]))
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print(" FnF: mean=%.4f p50=%.4f" % (statistics.fmean(f_tpots), sorted(f_tpots)[len(f_tpots)//2]))
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# Also look at non-common requests (FnF only failures)
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fnf_err = [r for r in fnf_rows if r.get("error")]
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await_err_ids = {r["request_id"] for r in await_rows if r.get("error")}
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fnf_only_err = [r for r in fnf_err if r["request_id"] not in await_err_ids]
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print("\n Errors unique to FnF: %d" % len(fnf_only_err))
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for r in fnf_only_err[:5]:
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print(" input=%d err=%s" % (r["input_length"], r["error"][:60]))
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