147 lines
4.9 KiB
Python
147 lines
4.9 KiB
Python
"""用例8：并发压力测试 —— 测量高并发下的 TPS 保持率、P95/P99 延迟与错误率
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并发数取 config.DEFAULT_CONCURRENCY（默认 100），repeats 控制并发批次数量。
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"""
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import concurrent.futures
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import logging
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from .. import config
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from ..stats import percentile
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from .base import case_meta, make_result
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logger = logging.getLogger(__name__)
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CONCURRENT_PROMPT = "用一句话回答：1+1等于几？"
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BASELINE_SAMPLES = 5 # 基准串行采样次数
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def _run_one(client) -> dict:
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"""单次并发请求，失败时返回 None 标记"""
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try:
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infer = client.generate(
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CONCURRENT_PROMPT,
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options={"temperature": 0.0, "num_predict": 32},
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)
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return {
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"ok": True,
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"infer": infer,
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"elapsed_ms": infer.e2e_ms,
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}
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except Exception as e:
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logger.debug("并发请求失败: %s", e)
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return {"ok": False, "error": str(e)}
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def _collect_serial(client, n) -> list:
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"""串行基准采样"""
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entries = []
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for i in range(n):
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r = _run_one(client)
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if r["ok"]:
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entries.append(r)
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return entries
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def _collect_concurrent(client, n, stop_event=None) -> list:
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"""并发采集（线程池）"""
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entries = []
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completed = 0
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def _wrapped():
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return _run_one(client)
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with concurrent.futures.ThreadPoolExecutor(max_workers=n) as executor:
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futures = [executor.submit(_wrapped) for _ in range(n)]
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for fut in concurrent.futures.as_completed(futures):
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try:
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r = fut.result(timeout=300)
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except Exception as e:
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r = {"ok": False, "error": str(e)}
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entries.append(r)
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completed += 1
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if stop_event and stop_event.is_set():
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for f in futures:
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f.cancel()
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break
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return entries
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def run_test(client, repeats: int = 1, stop_event=None) -> dict:
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"""执行并发压力测试"""
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if repeats > 3:
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repeats = 3 # 并发批次过多会非常耗时，限制上限
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n_concurrent = config.DEFAULT_CONCURRENCY
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# ---- Step 1: 串行基准 ----
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logger.info("[并发] 计算基准 TPS (串行 %d 次)...", BASELINE_SAMPLES)
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baseline = _collect_serial(client, BASELINE_SAMPLES)
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valid_base = [e for e in baseline if e["ok"]]
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if not valid_base:
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return case_meta(
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"case_08_concurrency", "并发压力测试",
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"基准测试全部失败", "压力", 30,
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) | {"status": "failed", "error": "基准测试全部失败", "results": []}
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base_tps = sum(e["infer"].decode_speed_tok_s for e in valid_base) / len(valid_base)
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base_e2e = sorted(e["elapsed_ms"] for e in valid_base)
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base_p50 = percentile(base_e2e, 50)
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logger.info("[并发] 基准 TPS: %.2f tok/s, E2E P50: %.0fms", base_tps, base_p50)
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# ---- Step 2: 并发测试 ----
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logger.info("[并发] 启动 %d 并发请求...", n_concurrent)
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all_concurrent = []
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for batch in range(repeats):
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if stop_event and stop_event.is_set():
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break
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batch_results = _collect_concurrent(client, n_concurrent, stop_event)
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all_concurrent.extend(batch_results)
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ok_count = sum(1 for r in batch_results if r["ok"])
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logger.info("[并发] 批次 %d 完成: %d/%d 成功", batch + 1, ok_count, len(batch_results))
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# ---- Step 3: 汇总 ----
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valid_con = [e for e in all_concurrent if e["ok"]]
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failed_count = len(all_concurrent) - len(valid_con)
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con_tps = (
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sum(e["infer"].decode_speed_tok_s for e in valid_con) / len(valid_con)
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if valid_con else 0.0
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)
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e2e_vals = sorted(e["elapsed_ms"] for e in valid_con)
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e2e_p50 = percentile(e2e_vals, 50)
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e2e_p95 = percentile(e2e_vals, 95)
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e2e_p99 = percentile(e2e_vals, 99)
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tps_keep = (con_tps / base_tps * 100) if base_tps > 0 else 0.0
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total = len(all_concurrent) or 1
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error_rate = failed_count / total * 100
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# 构造标准结果条目（供引擎统计/前端展示），并附加并发摘要
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results = [
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make_result(
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e["infer"], i + 1,
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batch="concurrent",
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error=("" if e["ok"] else e.get("error", "unknown")),
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)
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for i, e in enumerate(all_concurrent)
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if e["ok"]
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]
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meta = case_meta(
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"case_08_concurrency", "并发压力测试",
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"高并发下的 TPS 保持率、P95/P99 延迟、错误率", "压力", 30,
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)
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meta["results"] = results
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meta["concurrency"] = n_concurrent
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meta["baseline_tps"] = round(base_tps, 2)
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meta["concurrent_tps"] = round(con_tps, 2)
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meta["tps_keep_rate_pct"] = round(tps_keep, 2)
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meta["e2e_p50_ms"] = round(e2e_p50, 2)
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meta["e2e_p95_ms"] = round(e2e_p95, 2)
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meta["e2e_p99_ms"] = round(e2e_p99, 2)
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meta["success_count"] = len(valid_con)
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meta["failed_count"] = failed_count
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meta["error_rate_pct"] = round(error_rate, 2)
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return meta
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