Gahow Wang
24c49c31c2
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>
284 lines
10 KiB
Python
284 lines
10 KiB
Python
#!/usr/bin/env python3
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"""Compare FP8-W8A8 vs BF16 gpt-oss on one box: GSM8K accuracy + TTFT/TPOT.
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For each model it launches a warm xserv-server (same GPUs / same TP for a fair
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compute comparison), waits for a *real* generation to succeed (not /health),
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then streams N GSM8K problems through /v1/chat/completions measuring per-request
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TTFT (time to first token) and TPOT (mean inter-token latency). Accuracy is the
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exact-match rate on the extracted final number.
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Run it ON the GPU box (it manages the servers itself):
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python3 tools/fp8_compare.py \
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--fp8 /opt/wjh/models/gpt-oss-20b-fp8 \
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--bf16 /opt/wjh/models/gpt-oss-20b-bf16 \
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--gpus 0,1 --tp 2 --limit 150 --max-tokens 512
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"""
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import argparse
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import json
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import os
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import re
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import signal
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import subprocess
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import sys
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import time
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import urllib.request
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import urllib.error
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from pathlib import Path
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SCRIPT_DIR = Path(__file__).parent
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GSM8K = SCRIPT_DIR / "bench" / "data" / "gsm8k.json"
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SERVER_BIN = SCRIPT_DIR.parent / "target" / "release" / "xserv-server"
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SYSTEM = ("You are a careful math problem solver. Solve the problem step by step. "
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"Put your final numeric answer inside \\boxed{}.")
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_BOXED_RE = re.compile(r"\\boxed\s*\{([^{}]*)\}")
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_NUM_RE = re.compile(r"-?\d+(?:,\d{3})*(?:\.\d+)?")
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def normalize_num(s):
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s = s.replace(",", "").strip()
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try:
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f = float(s)
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except ValueError:
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return None
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return str(int(f)) if f == int(f) else f"{f:g}"
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def extract_answer(text):
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if not text:
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return None
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boxed = _BOXED_RE.findall(text)
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if boxed:
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nums = _NUM_RE.findall(boxed[-1])
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if nums:
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return normalize_num(nums[-1])
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nums = _NUM_RE.findall(text)
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if nums:
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return normalize_num(nums[-1])
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return None
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def pct(vals, p):
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if not vals:
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return 0.0
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s = sorted(vals)
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i = max(0, min(len(s) - 1, int(round((p / 100.0) * (len(s) - 1)))))
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return s[i]
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# ---------- server lifecycle ----------
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def gpu_mem_used_mb(gpus):
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out = subprocess.check_output(
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["nvidia-smi", "--query-gpu=index,memory.used", "--format=csv,noheader,nounits"],
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text=True)
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used = {}
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for line in out.strip().splitlines():
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idx, mem = [x.strip() for x in line.split(",")]
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used[int(idx)] = int(mem)
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return max(used.get(g, 0) for g in gpus)
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def start_server(model_dir, port, tp, gpus, log_path):
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env = dict(os.environ)
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env["CUDA_VISIBLE_DEVICES"] = ",".join(str(g) for g in gpus)
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cmd = [str(SERVER_BIN), str(model_dir), "--port", str(port),
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"--tp", str(tp), "--max-seq-len", "2048", "--max-batch", "8"]
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logf = open(log_path, "wb")
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# New session so we can kill the whole process tree without touching ours.
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p = subprocess.Popen(cmd, stdout=logf, stderr=subprocess.STDOUT,
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env=env, start_new_session=True)
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return p
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def stop_server(p, gpus, drain_to_mb=2000, timeout=120):
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if p.poll() is None:
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try:
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os.killpg(os.getpgid(p.pid), signal.SIGTERM)
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except ProcessLookupError:
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pass
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try:
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p.wait(timeout=30)
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except subprocess.TimeoutExpired:
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try:
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os.killpg(os.getpgid(p.pid), signal.SIGKILL)
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except ProcessLookupError:
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pass
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# Wait for VRAM to drain so the next server starts clean.
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t0 = time.time()
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while time.time() - t0 < timeout:
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if gpu_mem_used_mb(gpus) < drain_to_mb:
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return
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time.sleep(2)
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def wait_ready(base, model_id, timeout=900):
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"""Gate on a real 1-token generation, not /health (which lies during load)."""
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t0 = time.time()
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body = json.dumps({
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"model": model_id,
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"messages": [{"role": "user", "content": "hi"}],
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"max_tokens": 1, "temperature": 0.0, "stream": False,
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}).encode()
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while time.time() - t0 < timeout:
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try:
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req = urllib.request.Request(base + "/v1/chat/completions", data=body,
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headers={"Content-Type": "application/json"})
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with urllib.request.urlopen(req, timeout=120) as r:
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if r.status == 200:
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json.loads(r.read())
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return True
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except Exception:
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time.sleep(3)
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return False
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# ---------- one streamed request ----------
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def stream_chat(base, model_id, user, max_tokens):
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body = json.dumps({
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"model": model_id,
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"messages": [{"role": "system", "content": SYSTEM},
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{"role": "user", "content": user}],
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"max_tokens": max_tokens, "temperature": 0.0, "stream": True,
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}).encode()
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req = urllib.request.Request(base + "/v1/chat/completions", data=body,
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headers={"Content-Type": "application/json"})
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t0 = time.perf_counter()
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ttft = None
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t_last = t0
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n = 0
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parts = []
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with urllib.request.urlopen(req, timeout=300) as resp:
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for raw in resp:
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line = raw.decode("utf-8", "ignore").strip()
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if not line.startswith("data:"):
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continue
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data = line[5:].strip()
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if data == "[DONE]":
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break
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try:
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obj = json.loads(data)
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except json.JSONDecodeError:
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continue
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delta = obj["choices"][0].get("delta", {})
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content = delta.get("content")
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if content:
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now = time.perf_counter()
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if ttft is None:
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ttft = now - t0
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n += 1
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t_last = now
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parts.append(content)
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ttft = ttft if ttft is not None else (time.perf_counter() - t0)
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decode_span = t_last - t0 - ttft
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tpot = decode_span / (n - 1) if n > 1 else 0.0
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return "".join(parts), ttft, tpot, n
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def run_eval(base, model_id, problems, max_tokens):
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correct = 0
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ttfts, tpots, toks = [], [], []
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n_scored = 0
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for i, prob in enumerate(problems):
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q = prob["problem"].replace("\n", " ")
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try:
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text, ttft, tpot, n = stream_chat(base, model_id, q, max_tokens)
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except Exception as e:
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print(f" [E] {i+1}/{len(problems)} {e}", flush=True)
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continue
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pred = extract_answer(text)
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gold = normalize_num(prob["answer"])
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ok = pred is not None and gold is not None and pred == gold
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correct += int(ok)
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n_scored += 1
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ttfts.append(ttft * 1000.0)
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if tpot > 0:
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tpots.append(tpot * 1000.0)
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toks.append(n)
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mark = "✓" if ok else "✗"
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print(f" [{mark}] {i+1:3d}/{len(problems)} gold={prob['answer']:>7s} "
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f"pred={str(pred):>7s} ttft={ttft*1000:6.1f}ms tpot={tpot*1000:5.1f}ms tok={n}",
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flush=True)
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return {
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"accuracy": correct / max(n_scored, 1),
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"correct": correct, "scored": n_scored,
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"ttft_ms_median": pct(ttfts, 50), "ttft_ms_p90": pct(ttfts, 90),
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"tpot_ms_median": pct(tpots, 50), "tpot_ms_p90": pct(tpots, 90),
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"tok_per_s_median": (1000.0 / pct(tpots, 50)) if pct(tpots, 50) > 0 else 0.0,
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"mean_tokens": sum(toks) / max(len(toks), 1),
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}
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def main():
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ap = argparse.ArgumentParser()
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ap.add_argument("--fp8", required=True)
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ap.add_argument("--bf16", required=True)
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ap.add_argument("--gpus", default="0,1")
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ap.add_argument("--tp", type=int, default=2)
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ap.add_argument("--limit", type=int, default=150)
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ap.add_argument("--max-tokens", type=int, default=512)
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ap.add_argument("--port", type=int, default=18080)
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ap.add_argument("--out", default=None)
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args = ap.parse_args()
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gpus = [int(g) for g in args.gpus.split(",")]
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with open(GSM8K) as f:
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problems = json.load(f)[:args.limit]
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base = f"http://127.0.0.1:{args.port}"
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results = {}
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for label, model_dir in [("FP8_W8A8", args.fp8), ("BF16", args.bf16)]:
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model_id = Path(model_dir).name
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log_path = f"/tmp/xserv_{label}.log"
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print(f"\n{'='*72}\n {label} ({model_dir}, tp={args.tp}, gpus={gpus})\n{'='*72}", flush=True)
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print(f" starting server (log: {log_path}) ...", flush=True)
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p = start_server(model_dir, args.port, args.tp, gpus, log_path)
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try:
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if not wait_ready(base, model_id):
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print(f" SERVER NOT READY — tail of log:", flush=True)
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print(subprocess.run(["tail", "-30", log_path], capture_output=True, text=True).stdout)
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stop_server(p, gpus)
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continue
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print(f" ready. running {len(problems)} GSM8K problems...", flush=True)
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t0 = time.time()
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r = run_eval(base, model_id, problems, args.max_tokens)
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r["wall_s"] = time.time() - t0
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results[label] = r
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print(f" -> acc={r['accuracy']*100:.1f}% ttft_med={r['ttft_ms_median']:.1f}ms "
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f"tpot_med={r['tpot_ms_median']:.1f}ms ({r['tok_per_s_median']:.1f} tok/s)", flush=True)
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finally:
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print(f" stopping server...", flush=True)
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stop_server(p, gpus)
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print(f"\n{'='*72}\n SUMMARY (gpt-oss-20b, tp={args.tp}, GSM8K n={args.limit})\n{'='*72}")
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print(f"{'metric':<26s} {'FP8_W8A8':>14s} {'BF16':>14s}")
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print("-" * 56)
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f8, b6 = results.get("FP8_W8A8", {}), results.get("BF16", {})
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def row(name, key, fmt, scale=1.0):
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a = f8.get(key); b = b6.get(key)
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if a is None or b is None:
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return
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print(f"{name:<26s} {fmt.format(a*scale):>14s} {fmt.format(b*scale):>14s}")
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row("GSM8K accuracy (%)", "accuracy", "{:.1f}", 100.0)
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row("TTFT median (ms)", "ttft_ms_median", "{:.1f}")
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row("TTFT p90 (ms)", "ttft_ms_p90", "{:.1f}")
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row("TPOT median (ms)", "tpot_ms_median", "{:.2f}")
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row("TPOT p90 (ms)", "tpot_ms_p90", "{:.2f}")
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row("Throughput (tok/s)", "tok_per_s_median", "{:.1f}")
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row("Mean output tokens", "mean_tokens", "{:.0f}")
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if f8 and b6 and b6.get("tpot_ms_median"):
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sp = b6["tpot_ms_median"] / f8["tpot_ms_median"] if f8.get("tpot_ms_median") else 0
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print(f"\n FP8 decode speedup vs BF16: {sp:.2f}x")
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out = args.out or f"/tmp/fp8_compare_{int(time.time())}.json"
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with open(out, "w") as f:
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json.dump({"args": vars(args), "results": results}, f, indent=2)
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print(f"\n saved: {out}")
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if __name__ == "__main__":
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main()
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