tools: warm-server FP8 vs BF16 benchmark + results doc
fp8_compare.py launches one xserv-server per model (same GPUs / TP for a fair comparison), gates readiness on a real generation (not /health), and streams GSM8K through /v1/chat/completions measuring per-request TTFT (time to first token) and TPOT (mean inter-token latency) plus exact-match accuracy. docs/benchmarks/fp8-quantization.md records the quantization scheme, the perf-bug fix, and the dash5 results. Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
This commit is contained in:
283
tools/fp8_compare.py
Normal file
283
tools/fp8_compare.py
Normal file
@@ -0,0 +1,283 @@
|
||||
#!/usr/bin/env python3
|
||||
"""Compare FP8-W8A8 vs BF16 gpt-oss on one box: GSM8K accuracy + TTFT/TPOT.
|
||||
|
||||
For each model it launches a warm xserv-server (same GPUs / same TP for a fair
|
||||
compute comparison), waits for a *real* generation to succeed (not /health),
|
||||
then streams N GSM8K problems through /v1/chat/completions measuring per-request
|
||||
TTFT (time to first token) and TPOT (mean inter-token latency). Accuracy is the
|
||||
exact-match rate on the extracted final number.
|
||||
|
||||
Run it ON the GPU box (it manages the servers itself):
|
||||
|
||||
python3 tools/fp8_compare.py \
|
||||
--fp8 /opt/wjh/models/gpt-oss-20b-fp8 \
|
||||
--bf16 /opt/wjh/models/gpt-oss-20b-bf16 \
|
||||
--gpus 0,1 --tp 2 --limit 150 --max-tokens 512
|
||||
"""
|
||||
|
||||
import argparse
|
||||
import json
|
||||
import os
|
||||
import re
|
||||
import signal
|
||||
import subprocess
|
||||
import sys
|
||||
import time
|
||||
import urllib.request
|
||||
import urllib.error
|
||||
from pathlib import Path
|
||||
|
||||
SCRIPT_DIR = Path(__file__).parent
|
||||
GSM8K = SCRIPT_DIR / "bench" / "data" / "gsm8k.json"
|
||||
SERVER_BIN = SCRIPT_DIR.parent / "target" / "release" / "xserv-server"
|
||||
SYSTEM = ("You are a careful math problem solver. Solve the problem step by step. "
|
||||
"Put your final numeric answer inside \\boxed{}.")
|
||||
|
||||
_BOXED_RE = re.compile(r"\\boxed\s*\{([^{}]*)\}")
|
||||
_NUM_RE = re.compile(r"-?\d+(?:,\d{3})*(?:\.\d+)?")
|
||||
|
||||
|
||||
def normalize_num(s):
|
||||
s = s.replace(",", "").strip()
|
||||
try:
|
||||
f = float(s)
|
||||
except ValueError:
|
||||
return None
|
||||
return str(int(f)) if f == int(f) else f"{f:g}"
|
||||
|
||||
|
||||
def extract_answer(text):
|
||||
if not text:
|
||||
return None
|
||||
boxed = _BOXED_RE.findall(text)
|
||||
if boxed:
|
||||
nums = _NUM_RE.findall(boxed[-1])
|
||||
if nums:
|
||||
return normalize_num(nums[-1])
|
||||
nums = _NUM_RE.findall(text)
|
||||
if nums:
|
||||
return normalize_num(nums[-1])
|
||||
return None
|
||||
|
||||
|
||||
def pct(vals, p):
|
||||
if not vals:
|
||||
return 0.0
|
||||
s = sorted(vals)
|
||||
i = max(0, min(len(s) - 1, int(round((p / 100.0) * (len(s) - 1)))))
|
||||
return s[i]
|
||||
|
||||
|
||||
# ---------- server lifecycle ----------
|
||||
|
||||
def gpu_mem_used_mb(gpus):
|
||||
out = subprocess.check_output(
|
||||
["nvidia-smi", "--query-gpu=index,memory.used", "--format=csv,noheader,nounits"],
|
||||
text=True)
|
||||
used = {}
|
||||
for line in out.strip().splitlines():
|
||||
idx, mem = [x.strip() for x in line.split(",")]
|
||||
used[int(idx)] = int(mem)
|
||||
return max(used.get(g, 0) for g in gpus)
|
||||
|
||||
|
||||
def start_server(model_dir, port, tp, gpus, log_path):
|
||||
env = dict(os.environ)
|
||||
env["CUDA_VISIBLE_DEVICES"] = ",".join(str(g) for g in gpus)
|
||||
cmd = [str(SERVER_BIN), str(model_dir), "--port", str(port),
|
||||
"--tp", str(tp), "--max-seq-len", "2048", "--max-batch", "8"]
|
||||
logf = open(log_path, "wb")
|
||||
# New session so we can kill the whole process tree without touching ours.
|
||||
p = subprocess.Popen(cmd, stdout=logf, stderr=subprocess.STDOUT,
|
||||
env=env, start_new_session=True)
|
||||
return p
|
||||
|
||||
|
||||
def stop_server(p, gpus, drain_to_mb=2000, timeout=120):
|
||||
if p.poll() is None:
|
||||
try:
|
||||
os.killpg(os.getpgid(p.pid), signal.SIGTERM)
|
||||
except ProcessLookupError:
|
||||
pass
|
||||
try:
|
||||
p.wait(timeout=30)
|
||||
except subprocess.TimeoutExpired:
|
||||
try:
|
||||
os.killpg(os.getpgid(p.pid), signal.SIGKILL)
|
||||
except ProcessLookupError:
|
||||
pass
|
||||
# Wait for VRAM to drain so the next server starts clean.
|
||||
t0 = time.time()
|
||||
while time.time() - t0 < timeout:
|
||||
if gpu_mem_used_mb(gpus) < drain_to_mb:
|
||||
return
|
||||
time.sleep(2)
|
||||
|
||||
|
||||
def wait_ready(base, model_id, timeout=900):
|
||||
"""Gate on a real 1-token generation, not /health (which lies during load)."""
|
||||
t0 = time.time()
|
||||
body = json.dumps({
|
||||
"model": model_id,
|
||||
"messages": [{"role": "user", "content": "hi"}],
|
||||
"max_tokens": 1, "temperature": 0.0, "stream": False,
|
||||
}).encode()
|
||||
while time.time() - t0 < timeout:
|
||||
try:
|
||||
req = urllib.request.Request(base + "/v1/chat/completions", data=body,
|
||||
headers={"Content-Type": "application/json"})
|
||||
with urllib.request.urlopen(req, timeout=120) as r:
|
||||
if r.status == 200:
|
||||
json.loads(r.read())
|
||||
return True
|
||||
except Exception:
|
||||
time.sleep(3)
|
||||
return False
|
||||
|
||||
|
||||
# ---------- one streamed request ----------
|
||||
|
||||
def stream_chat(base, model_id, user, max_tokens):
|
||||
body = json.dumps({
|
||||
"model": model_id,
|
||||
"messages": [{"role": "system", "content": SYSTEM},
|
||||
{"role": "user", "content": user}],
|
||||
"max_tokens": max_tokens, "temperature": 0.0, "stream": True,
|
||||
}).encode()
|
||||
req = urllib.request.Request(base + "/v1/chat/completions", data=body,
|
||||
headers={"Content-Type": "application/json"})
|
||||
t0 = time.perf_counter()
|
||||
ttft = None
|
||||
t_last = t0
|
||||
n = 0
|
||||
parts = []
|
||||
with urllib.request.urlopen(req, timeout=300) as resp:
|
||||
for raw in resp:
|
||||
line = raw.decode("utf-8", "ignore").strip()
|
||||
if not line.startswith("data:"):
|
||||
continue
|
||||
data = line[5:].strip()
|
||||
if data == "[DONE]":
|
||||
break
|
||||
try:
|
||||
obj = json.loads(data)
|
||||
except json.JSONDecodeError:
|
||||
continue
|
||||
delta = obj["choices"][0].get("delta", {})
|
||||
content = delta.get("content")
|
||||
if content:
|
||||
now = time.perf_counter()
|
||||
if ttft is None:
|
||||
ttft = now - t0
|
||||
n += 1
|
||||
t_last = now
|
||||
parts.append(content)
|
||||
ttft = ttft if ttft is not None else (time.perf_counter() - t0)
|
||||
decode_span = t_last - t0 - ttft
|
||||
tpot = decode_span / (n - 1) if n > 1 else 0.0
|
||||
return "".join(parts), ttft, tpot, n
|
||||
|
||||
|
||||
def run_eval(base, model_id, problems, max_tokens):
|
||||
correct = 0
|
||||
ttfts, tpots, toks = [], [], []
|
||||
n_scored = 0
|
||||
for i, prob in enumerate(problems):
|
||||
q = prob["problem"].replace("\n", " ")
|
||||
try:
|
||||
text, ttft, tpot, n = stream_chat(base, model_id, q, max_tokens)
|
||||
except Exception as e:
|
||||
print(f" [E] {i+1}/{len(problems)} {e}", flush=True)
|
||||
continue
|
||||
pred = extract_answer(text)
|
||||
gold = normalize_num(prob["answer"])
|
||||
ok = pred is not None and gold is not None and pred == gold
|
||||
correct += int(ok)
|
||||
n_scored += 1
|
||||
ttfts.append(ttft * 1000.0)
|
||||
if tpot > 0:
|
||||
tpots.append(tpot * 1000.0)
|
||||
toks.append(n)
|
||||
mark = "✓" if ok else "✗"
|
||||
print(f" [{mark}] {i+1:3d}/{len(problems)} gold={prob['answer']:>7s} "
|
||||
f"pred={str(pred):>7s} ttft={ttft*1000:6.1f}ms tpot={tpot*1000:5.1f}ms tok={n}",
|
||||
flush=True)
|
||||
return {
|
||||
"accuracy": correct / max(n_scored, 1),
|
||||
"correct": correct, "scored": n_scored,
|
||||
"ttft_ms_median": pct(ttfts, 50), "ttft_ms_p90": pct(ttfts, 90),
|
||||
"tpot_ms_median": pct(tpots, 50), "tpot_ms_p90": pct(tpots, 90),
|
||||
"tok_per_s_median": (1000.0 / pct(tpots, 50)) if pct(tpots, 50) > 0 else 0.0,
|
||||
"mean_tokens": sum(toks) / max(len(toks), 1),
|
||||
}
|
||||
|
||||
|
||||
def main():
|
||||
ap = argparse.ArgumentParser()
|
||||
ap.add_argument("--fp8", required=True)
|
||||
ap.add_argument("--bf16", required=True)
|
||||
ap.add_argument("--gpus", default="0,1")
|
||||
ap.add_argument("--tp", type=int, default=2)
|
||||
ap.add_argument("--limit", type=int, default=150)
|
||||
ap.add_argument("--max-tokens", type=int, default=512)
|
||||
ap.add_argument("--port", type=int, default=18080)
|
||||
ap.add_argument("--out", default=None)
|
||||
args = ap.parse_args()
|
||||
|
||||
gpus = [int(g) for g in args.gpus.split(",")]
|
||||
with open(GSM8K) as f:
|
||||
problems = json.load(f)[:args.limit]
|
||||
|
||||
base = f"http://127.0.0.1:{args.port}"
|
||||
results = {}
|
||||
for label, model_dir in [("FP8_W8A8", args.fp8), ("BF16", args.bf16)]:
|
||||
model_id = Path(model_dir).name
|
||||
log_path = f"/tmp/xserv_{label}.log"
|
||||
print(f"\n{'='*72}\n {label} ({model_dir}, tp={args.tp}, gpus={gpus})\n{'='*72}", flush=True)
|
||||
print(f" starting server (log: {log_path}) ...", flush=True)
|
||||
p = start_server(model_dir, args.port, args.tp, gpus, log_path)
|
||||
try:
|
||||
if not wait_ready(base, model_id):
|
||||
print(f" SERVER NOT READY — tail of log:", flush=True)
|
||||
print(subprocess.run(["tail", "-30", log_path], capture_output=True, text=True).stdout)
|
||||
stop_server(p, gpus)
|
||||
continue
|
||||
print(f" ready. running {len(problems)} GSM8K problems...", flush=True)
|
||||
t0 = time.time()
|
||||
r = run_eval(base, model_id, problems, args.max_tokens)
|
||||
r["wall_s"] = time.time() - t0
|
||||
results[label] = r
|
||||
print(f" -> acc={r['accuracy']*100:.1f}% ttft_med={r['ttft_ms_median']:.1f}ms "
|
||||
f"tpot_med={r['tpot_ms_median']:.1f}ms ({r['tok_per_s_median']:.1f} tok/s)", flush=True)
|
||||
finally:
|
||||
print(f" stopping server...", flush=True)
|
||||
stop_server(p, gpus)
|
||||
|
||||
print(f"\n{'='*72}\n SUMMARY (gpt-oss-20b, tp={args.tp}, GSM8K n={args.limit})\n{'='*72}")
|
||||
print(f"{'metric':<26s} {'FP8_W8A8':>14s} {'BF16':>14s}")
|
||||
print("-" * 56)
|
||||
f8, b6 = results.get("FP8_W8A8", {}), results.get("BF16", {})
|
||||
def row(name, key, fmt, scale=1.0):
|
||||
a = f8.get(key); b = b6.get(key)
|
||||
if a is None or b is None:
|
||||
return
|
||||
print(f"{name:<26s} {fmt.format(a*scale):>14s} {fmt.format(b*scale):>14s}")
|
||||
row("GSM8K accuracy (%)", "accuracy", "{:.1f}", 100.0)
|
||||
row("TTFT median (ms)", "ttft_ms_median", "{:.1f}")
|
||||
row("TTFT p90 (ms)", "ttft_ms_p90", "{:.1f}")
|
||||
row("TPOT median (ms)", "tpot_ms_median", "{:.2f}")
|
||||
row("TPOT p90 (ms)", "tpot_ms_p90", "{:.2f}")
|
||||
row("Throughput (tok/s)", "tok_per_s_median", "{:.1f}")
|
||||
row("Mean output tokens", "mean_tokens", "{:.0f}")
|
||||
if f8 and b6 and b6.get("tpot_ms_median"):
|
||||
sp = b6["tpot_ms_median"] / f8["tpot_ms_median"] if f8.get("tpot_ms_median") else 0
|
||||
print(f"\n FP8 decode speedup vs BF16: {sp:.2f}x")
|
||||
|
||||
out = args.out or f"/tmp/fp8_compare_{int(time.time())}.json"
|
||||
with open(out, "w") as f:
|
||||
json.dump({"args": vars(args), "results": results}, f, indent=2)
|
||||
print(f"\n saved: {out}")
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
Reference in New Issue
Block a user