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qwen3.5-122B-A10B-on-spark/scripts/bench_decode.py
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6.0 KiB
Python

#!/usr/bin/env python3
"""Single-stream (c=1) decode-rate benchmark for an OpenAI-compatible vLLM server.
Measures *decode* tok/s (excludes TTFT/prefill) over N sequential requests, and
optionally scrapes vLLM /metrics for speculative-decode acceptance length.
stdlib only — runs anywhere with python3, no pip installs.
python3 bench_decode.py --base-url http://127.0.0.1:8000 \
--model Intel/Qwen3.5-122B-A10B-int4-AutoRound \
--max-tokens 256 --runs 5 --label "int4 baseline"
"""
import argparse, json, statistics, sys, time, urllib.request, urllib.error
PROMPT = ("You are a careful writer. Write a detailed, continuous explanation of how "
"a modern mixture-of-experts transformer performs autoregressive decoding, "
"covering routing, KV cache, and memory bandwidth. Begin now:\n\n")
def post_stream(base_url, model, prompt, max_tokens, timeout):
"""Stream /v1/completions; return (completion_tokens, t_first, t_last)."""
body = json.dumps({
"model": model, "prompt": prompt, "max_tokens": max_tokens,
"temperature": 0.0, "stream": True,
"stream_options": {"include_usage": True},
# force the full token budget so we measure steady-state decode
"ignore_eos": True, "min_tokens": max_tokens,
}).encode()
req = urllib.request.Request(base_url.rstrip("/") + "/v1/completions",
data=body, headers={"Content-Type": "application/json",
"Authorization": "Bearer x"})
t_first = t_last = None
completion_tokens = 0
chunks = 0
with urllib.request.urlopen(req, timeout=timeout) as r:
for raw in r:
line = raw.decode("utf-8", "replace").strip()
if not line.startswith("data:"):
continue
data = line[5:].strip()
if data == "[DONE]":
break
try:
obj = json.loads(data)
except json.JSONDecodeError:
continue
usage = obj.get("usage")
if usage and usage.get("completion_tokens"):
completion_tokens = usage["completion_tokens"]
choices = obj.get("choices") or []
if choices and choices[0].get("text"):
now = time.perf_counter()
if t_first is None:
t_first = now
t_last = now
chunks += 1
if completion_tokens == 0:
completion_tokens = chunks # fallback: 1 chunk ~= 1 token
return completion_tokens, t_first, t_last
def scrape_metrics(base_url, timeout=10):
"""Return dict of spec-decode counters from vLLM /metrics, if present."""
keys = ("num_accepted_tokens", "num_draft_tokens", "num_drafts",
"accepted_tokens", "draft_tokens")
out = {}
try:
with urllib.request.urlopen(base_url.rstrip("/") + "/metrics", timeout=timeout) as r:
for line in r.read().decode("utf-8", "replace").splitlines():
if line.startswith("#"):
continue
if "spec_decode" in line or "speculat" in line:
name, _, val = line.partition(" ")
try:
out[name] = out.get(name, 0.0) + float(val)
except ValueError:
pass
except (urllib.error.URLError, OSError):
pass
return out
def main():
ap = argparse.ArgumentParser()
ap.add_argument("--base-url", default="http://127.0.0.1:8000")
ap.add_argument("--model", required=True)
ap.add_argument("--max-tokens", type=int, default=256)
ap.add_argument("--runs", type=int, default=5)
ap.add_argument("--warmup", type=int, default=1)
ap.add_argument("--timeout", type=float, default=600)
ap.add_argument("--label", default="")
ap.add_argument("--prompt", default=PROMPT)
args = ap.parse_args()
m_before = scrape_metrics(args.base_url)
for _ in range(args.warmup):
try:
post_stream(args.base_url, args.model, args.prompt, 32, args.timeout)
except Exception as e:
print(f"warmup failed: {e}", file=sys.stderr); sys.exit(2)
decode_tps, ttfts, e2e_tps = [], [], []
for i in range(args.runs):
t0 = time.perf_counter()
toks, tf, tl = post_stream(args.base_url, args.model, args.prompt, args.max_tokens, args.timeout)
t1 = time.perf_counter()
if not toks or tf is None or tl is None or tl <= tf:
print(f" run {i}: degenerate (toks={toks})", file=sys.stderr); continue
dec = (toks - 1) / (tl - tf)
decode_tps.append(dec); ttfts.append((tf - t0) * 1000); e2e_tps.append(toks / (t1 - t0))
print(f" run {i}: {toks} tok decode={dec:6.1f} tok/s ttft={ (tf-t0)*1000:6.0f} ms")
m_after = scrape_metrics(args.base_url)
if not decode_tps:
print("no successful runs", file=sys.stderr); sys.exit(1)
print(f"\n=== {args.label or args.model} ===")
print(f"decode tok/s : median {statistics.median(decode_tps):.1f} "
f"mean {statistics.mean(decode_tps):.1f} min {min(decode_tps):.1f} max {max(decode_tps):.1f}")
print(f"ttft ms : median {statistics.median(ttfts):.0f}")
print(f"e2e tok/s : median {statistics.median(e2e_tps):.1f}")
# spec-decode acceptance length, if counters moved
def delta(k):
return m_after.get(k, 0.0) - m_before.get(k, 0.0)
acc = next((delta(k) for k in m_after if "accepted" in k), 0.0)
drafts = next((delta(k) for k in m_after if "num_drafts" in k or ("draft" in k and "tokens" not in k)), 0.0)
dtoks = next((delta(k) for k in m_after if "draft_tokens" in k or "num_draft_tokens" in k), 0.0)
if acc or dtoks:
al = (acc / drafts) if drafts else float("nan")
rate = (acc / dtoks) if dtoks else float("nan")
print(f"spec accept : +{acc:.0f} accepted, +{dtoks:.0f} drafted, "
f"mean accept len ~{al:.2f}, accept rate ~{rate:.1%}")
else:
print("spec accept : (no spec-decode counters — baseline/no drafter)")
if __name__ == "__main__":
main()