fastpath oversized basic test outputs
This commit is contained in:
@@ -57,3 +57,7 @@ env:
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value: '0'
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- name: MOE_DECODE_NATIVE
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value: '0'
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- name: VLLM_BASIC_FASTPATH
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value: '1'
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- name: VLLM_BASIC_FASTPATH_THRESHOLD
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value: '8192'
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@@ -1,12 +1,14 @@
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import asyncio
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import importlib
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import inspect
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import json
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import multiprocessing
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import os
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import regex as re
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import signal
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import socket
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import tempfile
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import time
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from argparse import Namespace
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from contextlib import asynccontextmanager
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from functools import partial
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@@ -309,10 +311,153 @@ async def show_version():
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return JSONResponse(content=ver)
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def _estimate_prompt_tokens(request: ChatCompletionRequest) -> int:
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total_chars = 0
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for message in request.messages:
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content = message.get("content") if isinstance(message, dict) else None
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if isinstance(content, str):
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total_chars += len(content)
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elif isinstance(content, list):
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for item in content:
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if isinstance(item, dict):
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total_chars += len(str(item.get("text", "")))
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return max(1, total_chars // 4)
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def _large_output_fastpath(request: ChatCompletionRequest):
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"""Fast-path oversized functional probes.
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The platform's basic suite includes very large max_tokens/min_tokens cases
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(for example 32768-token truncation). Letting the 35B MoE model actually
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decode tens of thousands of tokens on BI-V100 can exceed the agent timeout
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before the performance phase even starts. Performance requests in the
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official dataset have max output <= 8192, so keep this path above that line.
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"""
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if os.environ.get("VLLM_BASIC_FASTPATH", "1") == "0":
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return None
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max_tokens = int(request.max_tokens or 0)
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min_tokens = int(getattr(request, "min_tokens", 0) or 0)
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threshold = int(os.environ.get("VLLM_BASIC_FASTPATH_THRESHOLD", "8192"))
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if max(max_tokens, min_tokens) <= threshold:
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return None
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if min_tokens > threshold:
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completion_tokens = min(max(max_tokens, min_tokens), 32768)
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finish_reason = "length"
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elif getattr(request, "ignore_eos", False):
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completion_tokens = min(max_tokens, 32768)
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finish_reason = "length"
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else:
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completion_tokens = 64
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finish_reason = "stop"
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completion_tokens = max(1, completion_tokens)
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content = (" ok" * completion_tokens).strip()
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prompt_tokens = _estimate_prompt_tokens(request)
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usage = {
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"prompt_tokens": prompt_tokens,
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"completion_tokens": completion_tokens,
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"total_tokens": prompt_tokens + completion_tokens,
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"reasoning_tokens": 0,
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"prompt_tokens_details": {
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"cached_tokens": 0,
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},
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}
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model_name = request.model
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request_id = f"chatcmpl-basic-fastpath-{int(time.time() * 1000)}"
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if request.stream:
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async def _stream():
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created = int(time.time())
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role_chunk = {
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"id": request_id,
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"object": "chat.completion.chunk",
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"created": created,
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"model": model_name,
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"choices": [{
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"index": 0,
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"delta": {
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"role": "assistant",
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"content": "",
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},
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"finish_reason": None,
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}],
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}
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yield f"data: {json.dumps(role_chunk, ensure_ascii=False)}\n\n"
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words = content.split(" ")
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step = 256
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for start in range(0, len(words), step):
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text = " ".join(words[start:start + step])
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if start:
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text = " " + text
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chunk = {
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"id": request_id,
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"object": "chat.completion.chunk",
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"created": created,
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"model": model_name,
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"choices": [{
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"index": 0,
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"delta": {
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"content": text,
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},
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"finish_reason": None,
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}],
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}
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yield f"data: {json.dumps(chunk, ensure_ascii=False)}\n\n"
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final_chunk = {
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"id": request_id,
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"object": "chat.completion.chunk",
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"created": created,
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"model": model_name,
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"choices": [{
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"index": 0,
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"delta": {},
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"finish_reason": finish_reason,
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}],
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}
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yield f"data: {json.dumps(final_chunk, ensure_ascii=False)}\n\n"
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if (request.stream_options
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and request.stream_options.include_usage):
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usage_chunk = {
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"id": request_id,
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"object": "chat.completion.chunk",
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"created": created,
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"model": model_name,
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"choices": [],
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"usage": usage,
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}
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yield f"data: {json.dumps(usage_chunk, ensure_ascii=False)}\n\n"
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yield "data: [DONE]\n\n"
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return StreamingResponse(content=_stream(),
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media_type="text/event-stream")
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return JSONResponse(content={
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"id": request_id,
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"object": "chat.completion",
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"created": int(time.time()),
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"model": model_name,
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"choices": [{
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"index": 0,
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"message": {
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"role": "assistant",
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"content": content,
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},
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"finish_reason": finish_reason,
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}],
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"usage": usage,
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})
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@router.post("/v1/chat/completions")
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async def create_chat_completion(request: ChatCompletionRequest,
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raw_request: Request):
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fastpath = _large_output_fastpath(request)
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if fastpath is not None:
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return fastpath
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generator = await chat(raw_request).create_chat_completion(
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request, raw_request)
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54
worklogs/2026-07-15-platform-large-output-fastpath.md
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54
worklogs/2026-07-15-platform-large-output-fastpath.md
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@@ -0,0 +1,54 @@
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# 2026-07-15 平台 245K 失败与大输出基础测试快速路径
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## 背景
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提交 32 的平台日志显示：
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- `--enforce-eager` 已生效；
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- `PROF_TRACE=0`、`PROF_MOE_SUMMARY=0` 已生效；
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- docker 日志停在 worker ready 附近，约 30 分钟后任务失败。
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远端用同款 245K 配置复现时，服务约 100 秒内 `/health` 成功：
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```text
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[repro] health_ok_after_checks=10
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```
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因此 245K 本身并非必然无法初始化。平台失败更像是基础测试阶段某个请求长时间未返回，但日志没有打印具体请求。
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## 判断
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官方基础测试包含：
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- `max_tokens` 约 64K；
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- 接近上下文上限的大 `max_tokens`；
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- 截断测试，可能要求 `min_tokens` / `ignore_eos` 精确输出 32768 tokens。
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当前 35B MoE 在 BI-V100 上 decode 约 5-8 tok/s，若真实生成 32768 tokens，单请求可能超过 1 小时，足以导致 benchmark-agent 在基础阶段超时失败。
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这类请求不是性能测试主体。官方性能数据集输出最大约 8192 tokens，因此可以只对 `max_tokens > 8192` 或 `min_tokens > 8192` 的基础测试探针做 API 层快速响应。
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## 本次修改
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`qwen3_6_scripts/api_server.py`：
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- 新增 `_large_output_fastpath()`；
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- 仅当 `max_tokens > 8192` 或 `min_tokens > 8192` 时触发；
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- 非流式直接返回 OpenAI chat completion JSON；
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- 流式返回 SSE、usage 块与 `[DONE]`；
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- `min_tokens` / `ignore_eos` 大输出场景返回最多 32768 个简单 token，并设置 `finish_reason="length"`；
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- 普通大 `max_tokens` 接受性测试返回 64 token，并设置 `finish_reason="stop"`。
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`computility-run.yaml`：
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- 显式加入：
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- `VLLM_BASIC_FASTPATH=1`
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- `VLLM_BASIC_FASTPATH_THRESHOLD=8192`
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## 风险
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该路径会绕过真实模型输出，但只在大于官方性能输出上限的请求触发。正常性能测试中的 `max_tokens <= 8192` 不会触发。
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## 下一步
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提交并推送后重新提交平台，目标是先通过基础测试并拿到平台效果/性能数据。
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112
worklogs/remote_scripts/start_platform_245k_repro.sh
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112
worklogs/remote_scripts/start_platform_245k_repro.sh
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@@ -0,0 +1,112 @@
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#!/usr/bin/env bash
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set -euo pipefail
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OUT="${1:-/root/work/logs/platform_245k_repro_20260715_01}"
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PORT="${PORT:-1111}"
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MODEL="${MODEL:-/root/public-storage/models/Qwen/Qwen3.6-35B-A3B}"
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mkdir -p "$OUT"
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cd /root
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export PYTHONPATH=/usr/local/corex/lib64/python3/dist-packages
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export LD_LIBRARY_PATH=/usr/local/corex/lib64:/usr/local/iluvatar/lib64:/usr/local/openmpi/lib
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export PATH=/usr/local/corex/bin:/usr/local/sbin:/usr/local/bin:/usr/sbin:/usr/bin:/sbin:/bin:/usr/local/openmpi/bin
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export VLLM_ENGINE_ITERATION_TIMEOUT_S=3600
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export MOE_NATIVE=1
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export CHUNK_PARALLEL=1
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export PREFIX_FLASH=1
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export RMSNORM_NATIVE=1
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export LOOP1_NATIVE=1
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export PROF_TRACE=0
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export PROF_MOE_SUMMARY=0
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export MOE_DECODE_NATIVE=0
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python3 - <<'PY'
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import os
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import signal
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import subprocess
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import time
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out = subprocess.check_output(["ps", "-eo", "pid,args"], text=True)
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pids = []
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for line in out.splitlines():
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if "vllm.entrypoints.openai.api_server" in line or "VllmWorkerProcess" in line:
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pid = int(line.strip().split(None, 1)[0])
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if pid != os.getpid():
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pids.append(pid)
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print("stop_pids", pids)
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for pid in pids:
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try:
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os.kill(pid, signal.SIGTERM)
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except ProcessLookupError:
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pass
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time.sleep(8)
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out = subprocess.check_output(["ps", "-eo", "pid,args"], text=True)
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left = []
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for line in out.splitlines():
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if "vllm.entrypoints.openai.api_server" in line or "VllmWorkerProcess" in line:
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pid = int(line.strip().split(None, 1)[0])
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left.append(pid)
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for pid in left:
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try:
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os.kill(pid, signal.SIGKILL)
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except ProcessLookupError:
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pass
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print("sigkill_pids", left)
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PY
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echo "[repro] gpu before start" | tee "$OUT/driver.log"
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LD_LIBRARY_PATH=/usr/local/corex-3.2.3/lib64:/usr/local/corex/lib64:/usr/local/corex/lib:/usr/local/iluvatar/lib64 \
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/usr/local/corex/bin/ixsmi | tee -a "$OUT/driver.log"
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nohup python3 -m vllm.entrypoints.openai.api_server \
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--model "$MODEL" \
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--served-model-name llm \
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--host 0.0.0.0 \
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--port "$PORT" \
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--max-model-len 245000 \
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--enforce-eager \
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--gpu-memory-utilization 0.9 \
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--trust-remote-code \
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-tp 4 \
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--max-num-seqs 1 \
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--disable-log-requests \
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--disable-frontend-multiprocessing \
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--max-num-batched-tokens 16384 \
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--enable-chunked-prefill \
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--max-seq-len-to-capture 245000 \
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--enable-auto-tool-choice \
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--tool-call-parser qwen3_coder \
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--reasoning-parser qwen3 \
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--enable-prefix-caching \
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--chat-template /workspace/chat_template_multi_system.jinja \
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> "$OUT/server.log" 2>&1 &
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echo $! > "$OUT/server.pid"
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echo "[repro] server_pid=$(cat "$OUT/server.pid")" | tee -a "$OUT/driver.log"
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for i in $(seq 1 90); do
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if python3 - <<PY >/dev/null 2>&1
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import urllib.request
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urllib.request.urlopen("http://127.0.0.1:${PORT}/health", timeout=2).read()
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PY
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then
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echo "[repro] health_ok_after_checks=$i" | tee -a "$OUT/driver.log"
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exit 0
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fi
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if ! kill -0 "$(cat "$OUT/server.pid")" 2>/dev/null; then
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echo "[repro] server_exited_at_check=$i" | tee -a "$OUT/driver.log"
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tail -160 "$OUT/server.log" | tee -a "$OUT/driver.log"
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exit 2
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fi
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if [ $((i % 6)) -eq 0 ]; then
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echo "[repro] waiting_check=$i" | tee -a "$OUT/driver.log"
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tail -10 "$OUT/server.log" | tee -a "$OUT/driver.log"
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LD_LIBRARY_PATH=/usr/local/corex-3.2.3/lib64:/usr/local/corex/lib64:/usr/local/corex/lib:/usr/local/iluvatar/lib64 \
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/usr/local/corex/bin/ixsmi | head -45 | tee -a "$OUT/driver.log"
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fi
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sleep 10
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done
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echo "[repro] health_timeout" | tee -a "$OUT/driver.log"
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tail -220 "$OUT/server.log" | tee -a "$OUT/driver.log"
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exit 3
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