measure_ttft.py

#!/usr/bin/env python3
"""Measure TTFT and TPOT by streaming from llama-server."""

import argparse
import json
import subprocess
import sys
import time
import requests

SERVER_BIN = "./build/bin/llama-server"
PORT = 8081
PROMPT = "Explain the difference between machine learning and deep learning in detail." * 8  # ~512 tokens
MAX_TOKENS = 128
RUNS = 3


def wait_for_server(address, timeout=120):
    for _ in range(timeout):
        try:
            r = requests.get(f"{address}/health", timeout=2)
            if r.status_code == 200:
                return True
        except Exception:
            pass
        time.sleep(1)
    return False


def measure_once(address):
    payload = {
        "messages": [{"role": "user", "content": PROMPT}],
        "max_tokens": MAX_TOKENS,
        "stream": True,
        "temperature": 0.0,
    }
    t0 = time.perf_counter()
    ttft = None
    n_tokens = 0
    with requests.post(f"{address}/v1/chat/completions", json=payload, stream=True, timeout=120) as resp:
        for line in resp.iter_lines():
            if not line:
                continue
            line = line.decode("utf-8")
            if not line.startswith("data:"):
                continue
            data = line[5:].strip()
            if data == "[DONE]":
                break
            try:
                chunk = json.loads(data)
                delta = chunk["choices"][0]["delta"].get("content", "")
                if delta:
                    if ttft is None:
                        ttft = time.perf_counter() - t0
                    n_tokens += 1
            except Exception:
                continue
    t_total = time.perf_counter() - t0
    tpot = (t_total - ttft) / max(n_tokens - 1, 1) if n_tokens > 1 else 0
    return ttft, tpot, n_tokens, t_total


def main():
    parser = argparse.ArgumentParser()
    parser.add_argument("-m", "--model", required=True)
    parser.add_argument("--runs", type=int, default=RUNS)
    parser.add_argument("--device", default=None,
                        help="GGML device string, e.g. CUDA0 (default: all GPUs)")
    args = parser.parse_args()

    address = f"http://127.0.0.1:{PORT}"
    cmd = [SERVER_BIN, "-m", args.model, "-ngl", "99", "--port", str(PORT), "--log-disable"]
    if args.device:
        cmd += ["-dev", args.device]
    print(f"Starting server: {' '.join(cmd)}", file=sys.stderr)
    proc = subprocess.Popen(cmd, stdout=subprocess.DEVNULL, stderr=subprocess.DEVNULL)

    try:
        if not wait_for_server(address):
            print("ERROR: server did not start", file=sys.stderr)
            proc.kill()
            sys.exit(1)
        print("Server ready", file=sys.stderr)

        ttfts, tpots = [], []
        for i in range(args.runs):
            ttft, tpot, n_tokens, t_total = measure_once(address)
            ttfts.append(ttft * 1000)
            tpots.append(tpot * 1000)
            print(f"  Run {i+1}: TTFT={ttft*1000:.1f}ms  TPOT={tpot*1000:.1f}ms  tokens={n_tokens}", file=sys.stderr)

        avg_ttft = sum(ttfts) / len(ttfts)
        avg_tpot = sum(tpots) / len(tpots)
        result = {"ttft_ms": round(avg_ttft, 1), "tpot_ms": round(avg_tpot, 1), "latency_ms": round(avg_ttft + avg_tpot, 1)}
        print(json.dumps(result))
    finally:
        proc.kill()
        proc.wait()


if __name__ == "__main__":
    main()