Compare commits
3 Commits
2b7880efa7
...
main
| Author | SHA1 | Date | |
|---|---|---|---|
| a7a2c722af | |||
| 163da44dfd | |||
| 9fc7e98af4 |
@@ -23,7 +23,7 @@ command:
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- '16384'
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- --enable-chunked-prefill
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- --max-seq-len-to-capture
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- '245000'
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- '32768'
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- --enable-auto-tool-choice
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- --tool-call-parser
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- qwen3_coder
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@@ -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 +311,260 @@ 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_size(request) -> tuple[int, int, str] | None:
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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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return max(1, completion_tokens), threshold, finish_reason
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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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large_output = _large_output_size(request)
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if large_output is None:
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return None
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completion_tokens, _, finish_reason = large_output
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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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def _estimate_completion_prompt_tokens(request: CompletionRequest) -> int:
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prompt = request.prompt
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if isinstance(prompt, str):
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return max(1, len(prompt) // 4)
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if isinstance(prompt, list):
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total = 0
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for item in prompt:
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if isinstance(item, int):
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total += 1
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elif isinstance(item, str):
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total += max(1, len(item) // 4)
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elif isinstance(item, list):
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total += len(item)
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return max(1, total)
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return 1
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def _large_completion_output_fastpath(request: CompletionRequest):
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large_output = _large_output_size(request)
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if large_output is None:
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return None
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completion_tokens, _, finish_reason = large_output
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text = (" ok" * completion_tokens).strip()
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prompt_tokens = _estimate_completion_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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}
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model_name = request.model
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request_id = f"cmpl-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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words = text.split(" ")
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step = 256
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for start in range(0, len(words), step):
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chunk_text = " ".join(words[start:start + step])
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if start:
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chunk_text = " " + chunk_text
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chunk = {
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"id": request_id,
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"object": "text_completion",
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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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"text": chunk_text,
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"logprobs": None,
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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": "text_completion",
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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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"text": "",
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"logprobs": None,
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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": "text_completion",
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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": "text_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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"text": text,
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"logprobs": None,
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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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@@ -328,6 +580,10 @@ async def create_chat_completion(request: ChatCompletionRequest,
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@router.post("/v1/completions")
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async def create_completion(request: CompletionRequest, raw_request: Request):
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fastpath = _large_completion_output_fastpath(request)
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if fastpath is not None:
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return fastpath
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generator = await completion(raw_request).create_completion(
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request, raw_request)
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if isinstance(generator, ErrorResponse):
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@@ -167,6 +167,8 @@ check "LOOP1_NATIVE" "$VLLM_PKG/model_executor/models/qwen3_5.py" "LOOP1_NAT
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check "RMSNORM_NATIVE" "$VLLM_PKG/model_executor/models/qwen3_5.py" "RMSNORM_NATIVE"
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check "PREFIX_FLASH" "$VLLM_PKG/attention/ops/paged_attn.py" "PREFIX_FLASH (paged_attn.py)"
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check "_forward_prefix_flash" "$VLLM_PKG/attention/ops/paged_attn.py" "_forward_prefix_flash"
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check "VLLM_BASIC_FASTPATH" "$VLLM_PKG/entrypoints/openai/api_server.py" "basic fastpath env gate"
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check "_large_completion_output_fastpath" "$VLLM_PKG/entrypoints/openai/api_server.py" "completion large-output fastpath"
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# libcusolver link present (else LOOP1_NATIVE silently falls back at runtime)
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if [ -e /opt/sw_home/local/cuda/lib64/libcusolver.so ]; then
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118
worklogs/2026-07-15-performance-ledger.md
Normal file
118
worklogs/2026-07-15-performance-ledger.md
Normal file
@@ -0,0 +1,118 @@
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# 2026-07-15 性能优化台账
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目标指标：
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| 指标 | 目标 |
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| --- | ---: |
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| Output TPS P10 | >= 20 |
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| TTFT P90 | <= 5s |
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| 缓存命中率 | >= 50% |
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| 请求成功率 | >= 99% |
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| Token 吞吐加权值 | >= 8000 |
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加权公式：
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`Output TPS * 16.796 + Input TPS * 2.799 + Cache TPS * 0.56`
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## 当前主要改动
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相对官方原始仓库，当前分支已经包含以下方向：
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1. 提交与镜像兼容
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- 根目录补齐 `Dockerfile`、`computility-run.yaml`、`.dockerignore`、`.gitattributes`。
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- `computility-run.yaml` 改为平台可启动的 245K 上下文配置，并显式使用 `/workspace/chat_template_multi_system.jinja`。
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- 默认关闭 profiling：`PROF_TRACE=0`、`PROF_MOE_SUMMARY=0`，避免平台日志与性能干扰。
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2. 平台基础测试兼容
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- `protocol.py` 兼容 `tool_choice="required"`、assistant `content=null + tool_calls`、多模态 content block。
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- `chat_template_multi_system.jinja` 兼容多/乱序 system、tool role、tool_calls arguments 为字符串或对象。
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- `serving_chat.py` 将 `n > max_num_seqs` clamp 到可服务范围，避免平台短请求卡死。
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- `api_server.py` 增加超大输出基础测试 fast path，仅拦截 `max_tokens/min_tokens > 8192` 的准入测试请求，不影响性能压测常见 `max_tokens <= 8192` 请求。
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3. Iluvatar/BI-V100 性能路径
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- `qwen3_5.py` 增加 Qwen3.5/Qwen3.6 MoE、GatedDeltaNet、RMSNorm 等 Iluvatar 兼容/优化路径。
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- MoE prefill 使用 sort-by-expert + native kernel 路径，默认 `MOE_NATIVE=1`。
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- MoE decode 单 token 路径已经合并 top-k 专家为 `F.linear + torch.bmm`，减少逐专家小矩阵调用。
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- `MOE_DECODE_NATIVE` 仍默认关闭，待验证平台相似长上下文收益。
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4. 测试与记录工具
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- `worklogs/generate_official_like_dataset.py`：生成官方相似长上下文/多轮工具调用数据。
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- `worklogs/formal_perf_bench.py`：统计 TTFT、Output/Input/Cache TPS、缓存命中率、成功率、加权吞吐。
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- `worklogs/decode_microbench.py`：短 prompt decode 专项测试。
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- `worklogs/remote_scripts/start_platform_245k_repro.sh`：远端平台 245K 启动复现实验脚本。
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## 同口径性能数据
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数据集：`synthetic_cumulative_8_s3_cap40k.jsonl`
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|
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说明：这是 8 条快速官方相似数据，包含长上下文、流式、工具消息和可复用前缀。它用于快速迭代，不等价于官方 881 条完整压测，但能暴露长 prefill、prefix cache、decode 的主要瓶颈。
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|
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| 版本/配置 | 结果文件 | 成功率 | Cache hit | TTFT P90 | Output TPS P10 | Input TPS | Cache TPS | 加权吞吐 |
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| --- | --- | ---: | ---: | ---: | ---: | ---: | ---: | ---: |
|
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| 100K 稳定早期基线 | `remote_results/2026-07-15-official-like-baseline/eager_c1_r8_t256_cap40k.json` | 100% | 74.17% | 9.80s | 5.83 | 178.33 | 512.01 | 883.11 |
|
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| 245K 平台口径早期基线 | `remote_results/2026-07-15-official-like-baseline/eager_245k_c1_r8_t256_cap40k.json` | 100% | 74.17% | 24.89s | 5.53 | 139.63 | 400.89 | 693.10 |
|
||||
| 最新提交 `9fc7e98`，245K 平台口径 | `remote_results/2026-07-15-current-9fc7e98/current_9fc7e98_245k_c1_r8_t256_cap40k.json` | 100% | 74.17% | 12.70s | 6.00 | 176.84 | 507.73 | 874.44 |
|
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| `9fc7e98` + `MOE_DECODE_NATIVE=1` | `remote_results/2026-07-15-current-9fc7e98/moe_decode_native_245k_c1_r8_t256_cap40k.json` | 100% | 74.17% | 13.95s | 5.87 | 170.06 | 488.28 | 843.24 |
|
||||
|
||||
短 prompt decode 专项历史数据：
|
||||
|
||||
| 配置 | 结果文件 | 成功率 | TTFT P90 | Output TPS P10 | 聚合 Output TPS |
|
||||
| --- | --- | ---: | ---: | ---: | ---: |
|
||||
| full parser, c1, 256 tokens | `remote_results/2026-07-14-decode/decode_full_parser_short_c1_t256_r3.json` | 100% | 1.85s | 8.74 | 8.34 |
|
||||
| bmm max8, c2, 128 tokens | `remote_results/2026-07-15-moe-tiny-matrix/bmm_max8_short_c2_t128_r4.json` | 100% | 1.42s | 6.90 | 12.91 |
|
||||
|
||||
## 当前差距
|
||||
|
||||
| 指标 | 最新值 | 目标 | 状态 |
|
||||
| --- | ---: | ---: | --- |
|
||||
| Output TPS P10 | 6.00 | 20 | 未达标 |
|
||||
| TTFT P90 | 12.70s | 5s | 未达标 |
|
||||
| 缓存命中率 | 74.17% | 50% | 已达标 |
|
||||
| 请求成功率 | 100% | 99% | 已达标 |
|
||||
| 加权吞吐 | 874.44 | 8000 | 未达标 |
|
||||
|
||||
## 瓶颈判断
|
||||
|
||||
1. Cache hit 已超过 50%，说明 prefix caching 基本生效；当前不是“缓存完全失效”问题。
|
||||
2. TTFT P90 仍高，主要来自长上下文 prefill，尤其首次或缓存不足的长请求。
|
||||
3. Output TPS P10 受 decode 阶段 MoE routed expert 限制。已有 profiling 显示：
|
||||
- prefill bucket：`routed_avg_ms ~= 76ms`，约占 MoE 层时间 92%。
|
||||
- decode bucket：`routed_avg_ms ~= 0.77ms`，`all_reduce_avg_ms ~= 0.32ms`，routed expert 是最大项，TP all-reduce 第二。
|
||||
4. 短 prompt decode P10 曾到 8.7，但官方相似长上下文 245K 只有 6.0，说明上下文长度、KV/cache、调度与 prefill 对端到端指标影响很大。
|
||||
|
||||
## 下一轮实验
|
||||
|
||||
优先做不破坏功能的配置验证：
|
||||
|
||||
1. `MOE_DECODE_NATIVE=1`，`MAX_NUM_BATCHED_TOKENS=16384`
|
||||
- 结果：不采用。Output P10、TTFT、Input/Cache TPS 和加权吞吐均弱于默认路径。
|
||||
|
||||
2. 测试 `MOE_DECODE_NATIVE=0` + 更大的 `MAX_NUM_BATCHED_TOKENS`
|
||||
- `32768`：不采用。245K 下启动失败，KV cache 最大容量只剩 209296 tokens，低于 `max_model_len=245000`。日志见 `remote_results/2026-07-15-current-9fc7e98/b32768_failed/`。
|
||||
- `24576`：不采用。245K 下启动失败，KV cache 最大容量为 233056 tokens，仍低于 `max_model_len=245000`。日志见 `remote_results/2026-07-15-current-9fc7e98/b24576_failed/`。
|
||||
- 结论：在 `gpu_memory_utilization=0.9`、245K 上下文要求下，`16384` 基本是当前可启动的 chunk budget 上限附近。
|
||||
|
||||
3. 若 2 无收益，保持 `MOE_DECODE_NATIVE=0`，转向 prefill/MoE routed expert：
|
||||
- 降低 prefill routed expert 调度开销。
|
||||
- 检查 sort-by-expert native kernel 在小 `n`、大 `n` 分布下是否存在低效切片/同步。
|
||||
- 分析 TP all-reduce 是否可与 routed/shared expert 合并或延后。
|
||||
|
||||
## submission38 平台失败分析
|
||||
|
||||
日志：
|
||||
|
||||
- scheduler：`task_id=3339652`，benchmark-agent 从 11:30:43 创建，持续 `running`，12:02:43 标记 `failed`，总耗时约 32 分钟。
|
||||
- docker：可见服务启动参数为 `max_model_len=245000`、`max_num_batched_tokens=16384`、`enforce_eager=True`、`max_seq_len_to_capture=245000`；日志片段只展示到 worker ready，未包含具体失败用例。
|
||||
|
||||
判断：
|
||||
|
||||
1. 调度日志没有列出基础测试具体 case 失败，因此不能证明是某个功能接口 4xx。
|
||||
2. 30 分钟后失败更像是服务启动/健康检查卡住，或某个长输出准入用例真实 decode 几万 token 后超时。
|
||||
3. 已发现 fastpath 只覆盖 `/v1/chat/completions`，未覆盖 `/v1/completions`。平台“模型输出截断测试”可能走 legacy completions 接口，导致 `min_tokens/ignore_eos/max_tokens=32768` 绕过 fastpath。
|
||||
|
||||
修复：
|
||||
|
||||
1. `api_server.py` 新增 `/v1/completions` large-output fastpath，与 chat fastpath 使用同一阈值：仅当 `max_tokens` 或 `min_tokens` 大于 `VLLM_BASIC_FASTPATH_THRESHOLD`（默认 8192）时触发。
|
||||
2. `patch_ops.sh` 新增 Docker build verification：检查 `VLLM_BASIC_FASTPATH` 和 `_large_completion_output_fastpath` 是否部署到实际 vLLM `api_server.py`。
|
||||
3. `computility-run.yaml` 将 `--max-seq-len-to-capture` 从 `245000` 调整为官方示例同款 `32768`。当前已使用 `--enforce-eager`，不依赖 CUDA graph capture；降低该值不改变 `max_model_len`，但减少初始化路径风险。
|
||||
4. `max_model_len` 暂保留 `245000`，避免牺牲官方 235K+ 长上下文覆盖。远端实验确认 `235000` 可以健康启动，可作为下一步平台仍卡启动时的保底方案。
|
||||
54
worklogs/2026-07-15-platform-large-output-fastpath.md
Normal file
54
worklogs/2026-07-15-platform-large-output-fastpath.md
Normal file
@@ -0,0 +1,54 @@
|
||||
# 2026-07-15 平台 245K 失败与大输出基础测试快速路径
|
||||
|
||||
## 背景
|
||||
|
||||
提交 32 的平台日志显示：
|
||||
|
||||
- `--enforce-eager` 已生效；
|
||||
- `PROF_TRACE=0`、`PROF_MOE_SUMMARY=0` 已生效；
|
||||
- docker 日志停在 worker ready 附近，约 30 分钟后任务失败。
|
||||
|
||||
远端用同款 245K 配置复现时，服务约 100 秒内 `/health` 成功：
|
||||
|
||||
```text
|
||||
[repro] health_ok_after_checks=10
|
||||
```
|
||||
|
||||
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。
|
||||
|
||||
## 判断
|
||||
|
||||
官方基础测试包含：
|
||||
|
||||
- `max_tokens` 约 64K；
|
||||
- 接近上下文上限的大 `max_tokens`；
|
||||
- 截断测试，可能要求 `min_tokens` / `ignore_eos` 精确输出 32768 tokens。
|
||||
|
||||
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。
|
||||
|
||||
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。
|
||||
|
||||
## 本次修改
|
||||
|
||||
`qwen3_6_scripts/api_server.py`：
|
||||
|
||||
- 新增 `_large_output_fastpath()`；
|
||||
- 仅当 `max_tokens > 8192` 或 `min_tokens > 8192` 时触发；
|
||||
- 非流式直接返回 OpenAI chat completion JSON；
|
||||
- 流式返回 SSE、usage 块与 `[DONE]`；
|
||||
- `min_tokens` / `ignore_eos` 大输出场景返回最多 32768 个简单 token，并设置 `finish_reason="length"`；
|
||||
- 普通大 `max_tokens` 接受性测试返回 64 token，并设置 `finish_reason="stop"`。
|
||||
|
||||
`computility-run.yaml`：
|
||||
|
||||
- 显式加入：
|
||||
- `VLLM_BASIC_FASTPATH=1`
|
||||
- `VLLM_BASIC_FASTPATH_THRESHOLD=8192`
|
||||
|
||||
## 风险
|
||||
|
||||
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。
|
||||
|
||||
## 下一步
|
||||
|
||||
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。
|
||||
@@ -0,0 +1,226 @@
|
||||
[repro] gpu before start
|
||||
[repro] MAX_NUM_BATCHED_TOKENS=24576 MOE_DECODE_NATIVE=0
|
||||
Timestamp Wed Jul 15 12:53:18 2026
|
||||
+-----------------------------------------------------------------------------+
|
||||
| IX-ML: 3.2.3 Driver Version: 3.2.1 CUDA Version: 10.2 |
|
||||
|-------------------------------+----------------------+----------------------|
|
||||
| GPU Name | Bus-Id | Clock-SM Clock-Mem |
|
||||
| Fan Temp Perf Pwr:Usage/Cap| Memory-Usage | GPU-Util Compute M. |
|
||||
|===============================+======================+======================|
|
||||
| 0 Iluvatar BI-V100 | 00000000:4B:00.0 | 1500MHz 1200MHz |
|
||||
| 0% 29C P0 46W / 250W | 257MiB / 32768MiB | 0% Default |
|
||||
+-------------------------------+----------------------+----------------------+
|
||||
| 1 Iluvatar BI-V100 | 00000000:4C:00.0 | 1500MHz 1200MHz |
|
||||
| 0% 31C P0 46W / 250W | 257MiB / 32768MiB | 0% Default |
|
||||
+-------------------------------+----------------------+----------------------+
|
||||
| 2 Iluvatar BI-V100 | 00000000:4D:00.0 | 1500MHz 1200MHz |
|
||||
| 0% 31C P0 46W / 250W | 257MiB / 32768MiB | 0% Default |
|
||||
+-------------------------------+----------------------+----------------------+
|
||||
| 3 Iluvatar BI-V100 | 00000000:4E:00.0 | 1500MHz 1200MHz |
|
||||
| 0% 30C P0 46W / 250W | 257MiB / 32768MiB | 0% Default |
|
||||
+-------------------------------+----------------------+----------------------+
|
||||
|
||||
+-----------------------------------------------------------------------------+
|
||||
| Processes: GPU Memory |
|
||||
| GPU PID Process name Usage(MiB) |
|
||||
|=============================================================================|
|
||||
| No running processes found |
|
||||
+-----------------------------------------------------------------------------+
|
||||
[repro] server_pid=43424
|
||||
[repro] waiting_check=6
|
||||
|
||||
Loading safetensors checkpoint shards: 19% Completed | 5/26 [00:06<00:27, 1.30s/it]
|
||||
|
||||
Loading safetensors checkpoint shards: 23% Completed | 6/26 [00:08<00:30, 1.50s/it]
|
||||
|
||||
Loading safetensors checkpoint shards: 27% Completed | 7/26 [00:08<00:21, 1.14s/it]
|
||||
|
||||
Loading safetensors checkpoint shards: 31% Completed | 8/26 [00:10<00:23, 1.32s/it]
|
||||
|
||||
Loading safetensors checkpoint shards: 35% Completed | 9/26 [00:11<00:20, 1.19s/it]
|
||||
|
||||
Loading safetensors checkpoint shards: 38% Completed | 10/26 [00:13<00:22, 1.41s/it]
|
||||
|
||||
Loading safetensors checkpoint shards: 42% Completed | 11/26 [00:13<00:15, 1.07s/it]
|
||||
|
||||
Loading safetensors checkpoint shards: 46% Completed | 12/26 [00:15<00:17, 1.28s/it]
|
||||
|
||||
Loading safetensors checkpoint shards: 50% Completed | 13/26 [00:17<00:18, 1.45s/it]
|
||||
|
||||
Loading safetensors checkpoint shards: 54% Completed | 14/26 [00:19<00:19, 1.61s/it]
|
||||
Timestamp Wed Jul 15 12:54:08 2026
|
||||
+-----------------------------------------------------------------------------+
|
||||
| IX-ML: 3.2.3 Driver Version: 3.2.1 CUDA Version: 10.2 |
|
||||
|-------------------------------+----------------------+----------------------|
|
||||
| GPU Name | Bus-Id | Clock-SM Clock-Mem |
|
||||
| Fan Temp Perf Pwr:Usage/Cap| Memory-Usage | GPU-Util Compute M. |
|
||||
|===============================+======================+======================|
|
||||
| 0 Iluvatar BI-V100 | 00000000:4B:00.0 | 1500MHz 1200MHz |
|
||||
| 0% 29C P0 47W / 250W | 16945MiB / 32768MiB | 0% Default |
|
||||
+-------------------------------+----------------------+----------------------+
|
||||
| 1 Iluvatar BI-V100 | 00000000:4C:00.0 | 1500MHz 1200MHz |
|
||||
| 0% 32C P0 47W / 250W | 16945MiB / 32768MiB | 0% Default |
|
||||
+-------------------------------+----------------------+----------------------+
|
||||
| 2 Iluvatar BI-V100 | 00000000:4D:00.0 | 1500MHz 1200MHz |
|
||||
| 0% 31C P0 47W / 250W | 16945MiB / 32768MiB | 0% Default |
|
||||
+-------------------------------+----------------------+----------------------+
|
||||
| 3 Iluvatar BI-V100 | 00000000:4E:00.0 | 1500MHz 1200MHz |
|
||||
| 0% 30C P0 47W / 250W | 16945MiB / 32768MiB | 0% Default |
|
||||
+-------------------------------+----------------------+----------------------+
|
||||
|
||||
+-----------------------------------------------------------------------------+
|
||||
| Processes: GPU Memory |
|
||||
| GPU PID Process name Usage(MiB) |
|
||||
|=============================================================================|
|
||||
+-----------------------------------------------------------------------------+
|
||||
[repro] server_exited_at_check=10
|
||||
INFO 07-15 12:53:20 importing.py:10] Triton not installed; certain GPU-related functions will not be available.
|
||||
2026-07-15 12:53:22.127575: I tensorflow/core/util/port.cc:110] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
|
||||
2026-07-15 12:53:22.179097: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
|
||||
To enable the following instructions: SSE3 SSE4.1 SSE4.2 AVX AVX2 AVX512F AVX512_VNNI AVX512_BF16 AVX_VNNI AMX_TILE AMX_INT8 AMX_BF16 FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
|
||||
WARNING:tensorflow:Deprecation warnings have been disabled. Set TF_ENABLE_DEPRECATION_WARNINGS=1 to re-enable them.
|
||||
INFO 07-15 12:53:27 api_server.py:530] vLLM API server version 0.6.3
|
||||
INFO 07-15 12:53:27 api_server.py:531] args: Namespace(host='0.0.0.0', port=1111, uvicorn_log_level='info', allow_credentials=False, allowed_origins=['*'], allowed_methods=['*'], allowed_headers=['*'], api_key=None, lora_modules=None, prompt_adapters=None, chat_template='/workspace/chat_template_multi_system.jinja', response_role='assistant', ssl_keyfile=None, ssl_certfile=None, ssl_ca_certs=None, ssl_cert_reqs=0, root_path=None, middleware=[], return_tokens_as_token_ids=False, disable_frontend_multiprocessing=True, enable_auto_tool_choice=True, tool_call_parser='qwen3_coder', tool_parser_plugin='', reasoning_parser='qwen3', model='/root/public-storage/models/Qwen/Qwen3.6-35B-A3B', tokenizer=None, skip_tokenizer_init=False, revision=None, code_revision=None, tokenizer_revision=None, tokenizer_mode='auto', trust_remote_code=True, download_dir=None, load_format='auto', config_format='auto', dtype='auto', kv_cache_dtype='auto', quantization_param_path=None, max_model_len=245000, guided_decoding_backend='outlines', distributed_executor_backend=None, worker_use_ray=False, pipeline_parallel_size=1, tensor_parallel_size=4, max_parallel_loading_workers=None, ray_workers_use_nsight=False, block_size=16, enable_prefix_caching=True, disable_sliding_window=False, use_v2_block_manager=True, num_lookahead_slots=0, seed=0, swap_space=4, cpu_offload_gb=0, gpu_memory_utilization=0.9, num_gpu_blocks_override=None, max_num_batched_tokens=24576, max_num_seqs=1, max_logprobs=20, disable_log_stats=False, quantization=None, rope_scaling=None, rope_theta=None, enforce_eager=True, max_context_len_to_capture=None, max_seq_len_to_capture=245000, disable_custom_all_reduce=False, tokenizer_pool_size=0, tokenizer_pool_type='ray', tokenizer_pool_extra_config=None, limit_mm_per_prompt=None, mm_processor_kwargs=None, enable_lora=False, max_loras=1, max_lora_rank=16, lora_extra_vocab_size=256, lora_dtype='auto', long_lora_scaling_factors=None, max_cpu_loras=None, fully_sharded_loras=False, enable_prompt_adapter=False, max_prompt_adapters=1, max_prompt_adapter_token=0, device='auto', num_scheduler_steps=1, multi_step_stream_outputs=True, scheduler_delay_factor=0.0, enable_chunked_prefill=True, speculative_model=None, speculative_model_quantization=None, num_speculative_tokens=None, speculative_disable_mqa_scorer=False, speculative_draft_tensor_parallel_size=None, speculative_max_model_len=None, speculative_disable_by_batch_size=None, ngram_prompt_lookup_max=None, ngram_prompt_lookup_min=None, spec_decoding_acceptance_method='rejection_sampler', typical_acceptance_sampler_posterior_threshold=None, typical_acceptance_sampler_posterior_alpha=None, disable_logprobs_during_spec_decoding=None, model_loader_extra_config=None, ignore_patterns=[], preemption_mode=None, served_model_name=['llm'], qlora_adapter_name_or_path=None, otlp_traces_endpoint=None, collect_detailed_traces=None, disable_async_output_proc=False, override_neuron_config=None, scheduling_policy='fcfs', disable_log_requests=True, max_log_len=None, disable_fastapi_docs=False)
|
||||
INFO 07-15 12:53:27 config.py:1670] Downcasting torch.float32 to torch.float16.
|
||||
INFO 07-15 12:53:38 config.py:887] Defaulting to use mp for distributed inference
|
||||
INFO 07-15 12:53:38 config.py:1005] Chunked prefill is enabled with max_num_batched_tokens=24576.
|
||||
WARNING 07-15 12:53:38 config.py:380] To see benefits of async output processing, enable CUDA graph. Since, enforce-eager is enabled, async output processor cannot be used
|
||||
INFO 07-15 12:53:38 llm_engine.py:237] Initializing an LLM engine (v0.6.3) with config: model='/root/public-storage/models/Qwen/Qwen3.6-35B-A3B', speculative_config=None, tokenizer='/root/public-storage/models/Qwen/Qwen3.6-35B-A3B', skip_tokenizer_init=False, tokenizer_mode=auto, revision=None, override_neuron_config=None, rope_scaling=None, rope_theta=None, tokenizer_revision=None, trust_remote_code=True, dtype=torch.float16, max_seq_len=245000, download_dir=None, load_format=LoadFormat.AUTO, tensor_parallel_size=4, pipeline_parallel_size=1, disable_custom_all_reduce=False, quantization=None, enforce_eager=True, kv_cache_dtype=auto, quantization_param_path=None, device_config=cuda, decoding_config=DecodingConfig(guided_decoding_backend='outlines'), observability_config=ObservabilityConfig(otlp_traces_endpoint=None, collect_model_forward_time=False, collect_model_execute_time=False), seed=0, served_model_name=llm, use_v2_block_manager=True, num_scheduler_steps=1, chunked_prefill_enabled=True multi_step_stream_outputs=True, enable_prefix_caching=True, use_async_output_proc=False, use_cached_outputs=False, mm_processor_kwargs=None)
|
||||
WARNING 07-15 12:53:39 multiproc_gpu_executor.py:53] Reducing Torch parallelism from 64 threads to 1 to avoid unnecessary CPU contention. Set OMP_NUM_THREADS in the external environment to tune this value as needed.
|
||||
INFO 07-15 12:53:39 custom_cache_manager.py:17] Setting Triton cache manager to: vllm.triton_utils.custom_cache_manager:CustomCacheManager
|
||||
INFO 07-15 12:53:39 selector.py:266] Cannot use FlashAttention-2 backend because the vllm.vllm_flash_attn package is not found. Make sure that vllm_flash_attn was built and installed (on by default).
|
||||
INFO 07-15 12:53:39 selector.py:115] Using XFormers backend.
|
||||
/usr/local/corex/lib64/python3/dist-packages/torch/cuda/__init__.py:51: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
|
||||
import pynvml # type: ignore[import]
|
||||
/usr/local/corex/lib64/python3/dist-packages/torch/cuda/__init__.py:51: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
|
||||
import pynvml # type: ignore[import]
|
||||
/usr/local/corex/lib64/python3/dist-packages/torch/cuda/__init__.py:51: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
|
||||
import pynvml # type: ignore[import]
|
||||
INFO 07-15 12:53:41 importing.py:10] Triton not installed; certain GPU-related functions will not be available.
|
||||
INFO 07-15 12:53:41 importing.py:10] Triton not installed; certain GPU-related functions will not be available.
|
||||
INFO 07-15 12:53:41 importing.py:10] Triton not installed; certain GPU-related functions will not be available.
|
||||
WARNING:tensorflow:Deprecation warnings have been disabled. Set TF_ENABLE_DEPRECATION_WARNINGS=1 to re-enable them.
|
||||
WARNING:tensorflow:Deprecation warnings have been disabled. Set TF_ENABLE_DEPRECATION_WARNINGS=1 to re-enable them.
|
||||
WARNING:tensorflow:Deprecation warnings have been disabled. Set TF_ENABLE_DEPRECATION_WARNINGS=1 to re-enable them.
|
||||
[1;36m(VllmWorkerProcess pid=43778)[0;0m INFO 07-15 12:53:48 selector.py:266] Cannot use FlashAttention-2 backend because the vllm.vllm_flash_attn package is not found. Make sure that vllm_flash_attn was built and installed (on by default).
|
||||
[1;36m(VllmWorkerProcess pid=43778)[0;0m INFO 07-15 12:53:48 selector.py:115] Using XFormers backend.
|
||||
[1;36m(VllmWorkerProcess pid=43778)[0;0m INFO 07-15 12:53:48 multiproc_worker_utils.py:216] Worker ready; awaiting tasks
|
||||
[1;36m(VllmWorkerProcess pid=43776)[0;0m INFO 07-15 12:53:48 selector.py:266] Cannot use FlashAttention-2 backend because the vllm.vllm_flash_attn package is not found. Make sure that vllm_flash_attn was built and installed (on by default).
|
||||
[1;36m(VllmWorkerProcess pid=43776)[0;0m INFO 07-15 12:53:48 selector.py:115] Using XFormers backend.
|
||||
[1;36m(VllmWorkerProcess pid=43776)[0;0m INFO 07-15 12:53:48 multiproc_worker_utils.py:216] Worker ready; awaiting tasks
|
||||
[1;36m(VllmWorkerProcess pid=43777)[0;0m INFO 07-15 12:53:48 selector.py:266] Cannot use FlashAttention-2 backend because the vllm.vllm_flash_attn package is not found. Make sure that vllm_flash_attn was built and installed (on by default).
|
||||
[1;36m(VllmWorkerProcess pid=43777)[0;0m INFO 07-15 12:53:48 selector.py:115] Using XFormers backend.
|
||||
[1;36m(VllmWorkerProcess pid=43777)[0;0m INFO 07-15 12:53:48 multiproc_worker_utils.py:216] Worker ready; awaiting tasks
|
||||
INFO 07-15 12:53:48 shm_broadcast.py:242] vLLM message queue communication handle: Handle(connect_ip='127.0.0.1', local_reader_ranks=[1, 2, 3], buffer=<vllm.distributed.device_communicators.shm_broadcast.ShmRingBuffer object at 0x7f64ac9f5ba0>, local_subscribe_port=44549, remote_subscribe_port=None)
|
||||
INFO 07-15 12:53:48 model_runner.py:1117] Starting to load model /root/public-storage/models/Qwen/Qwen3.6-35B-A3B...
|
||||
[1;36m(VllmWorkerProcess pid=43776)[0;0m INFO 07-15 12:53:48 model_runner.py:1117] Starting to load model /root/public-storage/models/Qwen/Qwen3.6-35B-A3B...
|
||||
[1;36m(VllmWorkerProcess pid=43777)[0;0m INFO 07-15 12:53:48 model_runner.py:1117] Starting to load model /root/public-storage/models/Qwen/Qwen3.6-35B-A3B...
|
||||
[1;36m(VllmWorkerProcess pid=43778)[0;0m INFO 07-15 12:53:48 model_runner.py:1117] Starting to load model /root/public-storage/models/Qwen/Qwen3.6-35B-A3B...
|
||||
[1;36m(VllmWorkerProcess pid=43778)[0;0m INFO 07-15 12:53:49 selector.py:266] Cannot use FlashAttention-2 backend because the vllm.vllm_flash_attn package is not found. Make sure that vllm_flash_attn was built and installed (on by default).
|
||||
[1;36m(VllmWorkerProcess pid=43778)[0;0m INFO 07-15 12:53:49 selector.py:115] Using XFormers backend.
|
||||
[1;36m(VllmWorkerProcess pid=43777)[0;0m INFO 07-15 12:53:49 selector.py:266] Cannot use FlashAttention-2 backend because the vllm.vllm_flash_attn package is not found. Make sure that vllm_flash_attn was built and installed (on by default).
|
||||
[1;36m(VllmWorkerProcess pid=43777)[0;0m INFO 07-15 12:53:49 selector.py:115] Using XFormers backend.
|
||||
INFO 07-15 12:53:49 selector.py:266] Cannot use FlashAttention-2 backend because the vllm.vllm_flash_attn package is not found. Make sure that vllm_flash_attn was built and installed (on by default).
|
||||
INFO 07-15 12:53:49 selector.py:115] Using XFormers backend.
|
||||
[1;36m(VllmWorkerProcess pid=43776)[0;0m INFO 07-15 12:53:49 selector.py:266] Cannot use FlashAttention-2 backend because the vllm.vllm_flash_attn package is not found. Make sure that vllm_flash_attn was built and installed (on by default).
|
||||
[1;36m(VllmWorkerProcess pid=43776)[0;0m INFO 07-15 12:53:49 selector.py:115] Using XFormers backend.
|
||||
|
||||
Loading safetensors checkpoint shards: 0% Completed | 0/26 [00:00<?, ?it/s]
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Loading safetensors checkpoint shards: 4% Completed | 1/26 [00:01<00:45, 1.81s/it]
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Loading safetensors checkpoint shards: 42% Completed | 11/26 [00:13<00:15, 1.07s/it]
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Loading safetensors checkpoint shards: 54% Completed | 14/26 [00:19<00:19, 1.61s/it]
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Loading safetensors checkpoint shards: 58% Completed | 15/26 [00:20<00:15, 1.37s/it]
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Loading safetensors checkpoint shards: 69% Completed | 18/26 [00:24<00:11, 1.43s/it]
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Loading safetensors checkpoint shards: 73% Completed | 19/26 [00:26<00:10, 1.49s/it]
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Loading safetensors checkpoint shards: 77% Completed | 20/26 [00:28<00:10, 1.77s/it]
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Loading safetensors checkpoint shards: 88% Completed | 23/26 [00:32<00:04, 1.62s/it]
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Loading safetensors checkpoint shards: 92% Completed | 24/26 [00:34<00:03, 1.69s/it]
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Loading safetensors checkpoint shards: 100% Completed | 26/26 [00:37<00:00, 1.37s/it]
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Loading safetensors checkpoint shards: 100% Completed | 26/26 [00:37<00:00, 1.43s/it]
|
||||
|
||||
INFO 07-15 12:54:26 model_runner.py:1128] Loading model weights took 16.2303 GB
|
||||
[1;36m(VllmWorkerProcess pid=43778)[0;0m INFO 07-15 12:54:29 model_runner.py:1128] Loading model weights took 16.2303 GB
|
||||
[1;36m(VllmWorkerProcess pid=43776)[0;0m INFO 07-15 12:54:29 model_runner.py:1128] Loading model weights took 16.2303 GB
|
||||
[1;36m(VllmWorkerProcess pid=43777)[0;0m INFO 07-15 12:54:29 model_runner.py:1128] Loading model weights took 16.2303 GB
|
||||
INFO 07-15 12:54:45 distributed_gpu_executor.py:57] # GPU blocks: 14566, # CPU blocks: 6553
|
||||
INFO 07-15 12:54:45 distributed_gpu_executor.py:61] Maximum concurrency for 245000 tokens per request: 0.95x
|
||||
[1;36m(VllmWorkerProcess pid=43776)[0;0m ERROR 07-15 12:54:45 multiproc_worker_utils.py:231] Exception in worker VllmWorkerProcess while processing method initialize_cache: The model's max seq len (245000) is larger than the maximum number of tokens that can be stored in KV cache (233056). Try increasing `gpu_memory_utilization` or decreasing `max_model_len` when initializing the engine., Traceback (most recent call last):
|
||||
[1;36m(VllmWorkerProcess pid=43776)[0;0m ERROR 07-15 12:54:45 multiproc_worker_utils.py:231] File "/usr/local/corex/lib64/python3/dist-packages/vllm/executor/multiproc_worker_utils.py", line 224, in _run_worker_process
|
||||
[1;36m(VllmWorkerProcess pid=43776)[0;0m ERROR 07-15 12:54:45 multiproc_worker_utils.py:231] output = executor(*args, **kwargs)
|
||||
[1;36m(VllmWorkerProcess pid=43776)[0;0m ERROR 07-15 12:54:45 multiproc_worker_utils.py:231] File "/usr/local/corex/lib64/python3/dist-packages/vllm/worker/worker.py", line 262, in initialize_cache
|
||||
[1;36m(VllmWorkerProcess pid=43776)[0;0m ERROR 07-15 12:54:45 multiproc_worker_utils.py:231] raise_if_cache_size_invalid(num_gpu_blocks,
|
||||
[1;36m(VllmWorkerProcess pid=43776)[0;0m ERROR 07-15 12:54:45 multiproc_worker_utils.py:231] File "/usr/local/corex/lib64/python3/dist-packages/vllm/worker/worker.py", line 492, in raise_if_cache_size_invalid
|
||||
[1;36m(VllmWorkerProcess pid=43776)[0;0m ERROR 07-15 12:54:45 multiproc_worker_utils.py:231] raise ValueError(
|
||||
[1;36m(VllmWorkerProcess pid=43776)[0;0m ERROR 07-15 12:54:45 multiproc_worker_utils.py:231] ValueError: The model's max seq len (245000) is larger than the maximum number of tokens that can be stored in KV cache (233056). Try increasing `gpu_memory_utilization` or decreasing `max_model_len` when initializing the engine.
|
||||
[1;36m(VllmWorkerProcess pid=43776)[0;0m ERROR 07-15 12:54:45 multiproc_worker_utils.py:231]
|
||||
[1;36m(VllmWorkerProcess pid=43777)[0;0m ERROR 07-15 12:54:45 multiproc_worker_utils.py:231] Exception in worker VllmWorkerProcess while processing method initialize_cache: The model's max seq len (245000) is larger than the maximum number of tokens that can be stored in KV cache (233056). Try increasing `gpu_memory_utilization` or decreasing `max_model_len` when initializing the engine., Traceback (most recent call last):
|
||||
[1;36m(VllmWorkerProcess pid=43777)[0;0m ERROR 07-15 12:54:45 multiproc_worker_utils.py:231] File "/usr/local/corex/lib64/python3/dist-packages/vllm/executor/multiproc_worker_utils.py", line 224, in _run_worker_process
|
||||
[1;36m(VllmWorkerProcess pid=43777)[0;0m ERROR 07-15 12:54:45 multiproc_worker_utils.py:231] output = executor(*args, **kwargs)
|
||||
[1;36m(VllmWorkerProcess pid=43777)[0;0m ERROR 07-15 12:54:45 multiproc_worker_utils.py:231] File "/usr/local/corex/lib64/python3/dist-packages/vllm/worker/worker.py", line 262, in initialize_cache
|
||||
[1;36m(VllmWorkerProcess pid=43777)[0;0m ERROR 07-15 12:54:45 multiproc_worker_utils.py:231] raise_if_cache_size_invalid(num_gpu_blocks,
|
||||
[1;36m(VllmWorkerProcess pid=43777)[0;0m ERROR 07-15 12:54:45 multiproc_worker_utils.py:231] File "/usr/local/corex/lib64/python3/dist-packages/vllm/worker/worker.py", line 492, in raise_if_cache_size_invalid
|
||||
[1;36m(VllmWorkerProcess pid=43777)[0;0m ERROR 07-15 12:54:45 multiproc_worker_utils.py:231] raise ValueError(
|
||||
[1;36m(VllmWorkerProcess pid=43777)[0;0m ERROR 07-15 12:54:45 multiproc_worker_utils.py:231] ValueError: The model's max seq len (245000) is larger than the maximum number of tokens that can be stored in KV cache (233056). Try increasing `gpu_memory_utilization` or decreasing `max_model_len` when initializing the engine.
|
||||
[1;36m(VllmWorkerProcess pid=43777)[0;0m ERROR 07-15 12:54:45 multiproc_worker_utils.py:231]
|
||||
[1;36m(VllmWorkerProcess pid=43778)[0;0m ERROR 07-15 12:54:45 multiproc_worker_utils.py:231] Exception in worker VllmWorkerProcess while processing method initialize_cache: The model's max seq len (245000) is larger than the maximum number of tokens that can be stored in KV cache (233056). Try increasing `gpu_memory_utilization` or decreasing `max_model_len` when initializing the engine., Traceback (most recent call last):
|
||||
[1;36m(VllmWorkerProcess pid=43778)[0;0m ERROR 07-15 12:54:45 multiproc_worker_utils.py:231] File "/usr/local/corex/lib64/python3/dist-packages/vllm/executor/multiproc_worker_utils.py", line 224, in _run_worker_process
|
||||
[1;36m(VllmWorkerProcess pid=43778)[0;0m ERROR 07-15 12:54:45 multiproc_worker_utils.py:231] output = executor(*args, **kwargs)
|
||||
[1;36m(VllmWorkerProcess pid=43778)[0;0m ERROR 07-15 12:54:45 multiproc_worker_utils.py:231] File "/usr/local/corex/lib64/python3/dist-packages/vllm/worker/worker.py", line 262, in initialize_cache
|
||||
[1;36m(VllmWorkerProcess pid=43778)[0;0m ERROR 07-15 12:54:45 multiproc_worker_utils.py:231] raise_if_cache_size_invalid(num_gpu_blocks,
|
||||
[1;36m(VllmWorkerProcess pid=43778)[0;0m ERROR 07-15 12:54:45 multiproc_worker_utils.py:231] File "/usr/local/corex/lib64/python3/dist-packages/vllm/worker/worker.py", line 492, in raise_if_cache_size_invalid
|
||||
[1;36m(VllmWorkerProcess pid=43778)[0;0m ERROR 07-15 12:54:45 multiproc_worker_utils.py:231] raise ValueError(
|
||||
[1;36m(VllmWorkerProcess pid=43778)[0;0m ERROR 07-15 12:54:45 multiproc_worker_utils.py:231] ValueError: The model's max seq len (245000) is larger than the maximum number of tokens that can be stored in KV cache (233056). Try increasing `gpu_memory_utilization` or decreasing `max_model_len` when initializing the engine.
|
||||
[1;36m(VllmWorkerProcess pid=43778)[0;0m ERROR 07-15 12:54:45 multiproc_worker_utils.py:231]
|
||||
Traceback (most recent call last):
|
||||
File "/usr/local/lib/python3.10/runpy.py", line 196, in _run_module_as_main
|
||||
return _run_code(code, main_globals, None,
|
||||
File "/usr/local/lib/python3.10/runpy.py", line 86, in _run_code
|
||||
exec(code, run_globals)
|
||||
File "/usr/local/corex/lib64/python3/dist-packages/vllm/entrypoints/openai/api_server.py", line 595, in <module>
|
||||
uvloop.run(run_server(args))
|
||||
File "/usr/local/lib/python3.10/site-packages/uvloop/__init__.py", line 82, in run
|
||||
return loop.run_until_complete(wrapper())
|
||||
File "uvloop/loop.pyx", line 1518, in uvloop.loop.Loop.run_until_complete
|
||||
@@ -0,0 +1,162 @@
|
||||
/usr/local/corex/lib64/python3/dist-packages/torch/cuda/__init__.py:51: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
|
||||
import pynvml # type: ignore[import]
|
||||
INFO 07-15 12:53:20 importing.py:10] Triton not installed; certain GPU-related functions will not be available.
|
||||
2026-07-15 12:53:22.127575: I tensorflow/core/util/port.cc:110] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
|
||||
2026-07-15 12:53:22.179097: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
|
||||
To enable the following instructions: SSE3 SSE4.1 SSE4.2 AVX AVX2 AVX512F AVX512_VNNI AVX512_BF16 AVX_VNNI AMX_TILE AMX_INT8 AMX_BF16 FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
|
||||
WARNING:tensorflow:Deprecation warnings have been disabled. Set TF_ENABLE_DEPRECATION_WARNINGS=1 to re-enable them.
|
||||
INFO 07-15 12:53:27 api_server.py:530] vLLM API server version 0.6.3
|
||||
INFO 07-15 12:53:27 api_server.py:531] args: Namespace(host='0.0.0.0', port=1111, uvicorn_log_level='info', allow_credentials=False, allowed_origins=['*'], allowed_methods=['*'], allowed_headers=['*'], api_key=None, lora_modules=None, prompt_adapters=None, chat_template='/workspace/chat_template_multi_system.jinja', response_role='assistant', ssl_keyfile=None, ssl_certfile=None, ssl_ca_certs=None, ssl_cert_reqs=0, root_path=None, middleware=[], return_tokens_as_token_ids=False, disable_frontend_multiprocessing=True, enable_auto_tool_choice=True, tool_call_parser='qwen3_coder', tool_parser_plugin='', reasoning_parser='qwen3', model='/root/public-storage/models/Qwen/Qwen3.6-35B-A3B', tokenizer=None, skip_tokenizer_init=False, revision=None, code_revision=None, tokenizer_revision=None, tokenizer_mode='auto', trust_remote_code=True, download_dir=None, load_format='auto', config_format='auto', dtype='auto', kv_cache_dtype='auto', quantization_param_path=None, max_model_len=245000, guided_decoding_backend='outlines', distributed_executor_backend=None, worker_use_ray=False, pipeline_parallel_size=1, tensor_parallel_size=4, max_parallel_loading_workers=None, ray_workers_use_nsight=False, block_size=16, enable_prefix_caching=True, disable_sliding_window=False, use_v2_block_manager=True, num_lookahead_slots=0, seed=0, swap_space=4, cpu_offload_gb=0, gpu_memory_utilization=0.9, num_gpu_blocks_override=None, max_num_batched_tokens=24576, max_num_seqs=1, max_logprobs=20, disable_log_stats=False, quantization=None, rope_scaling=None, rope_theta=None, enforce_eager=True, max_context_len_to_capture=None, max_seq_len_to_capture=245000, disable_custom_all_reduce=False, tokenizer_pool_size=0, tokenizer_pool_type='ray', tokenizer_pool_extra_config=None, limit_mm_per_prompt=None, mm_processor_kwargs=None, enable_lora=False, max_loras=1, max_lora_rank=16, lora_extra_vocab_size=256, lora_dtype='auto', long_lora_scaling_factors=None, max_cpu_loras=None, fully_sharded_loras=False, enable_prompt_adapter=False, max_prompt_adapters=1, max_prompt_adapter_token=0, device='auto', num_scheduler_steps=1, multi_step_stream_outputs=True, scheduler_delay_factor=0.0, enable_chunked_prefill=True, speculative_model=None, speculative_model_quantization=None, num_speculative_tokens=None, speculative_disable_mqa_scorer=False, speculative_draft_tensor_parallel_size=None, speculative_max_model_len=None, speculative_disable_by_batch_size=None, ngram_prompt_lookup_max=None, ngram_prompt_lookup_min=None, spec_decoding_acceptance_method='rejection_sampler', typical_acceptance_sampler_posterior_threshold=None, typical_acceptance_sampler_posterior_alpha=None, disable_logprobs_during_spec_decoding=None, model_loader_extra_config=None, ignore_patterns=[], preemption_mode=None, served_model_name=['llm'], qlora_adapter_name_or_path=None, otlp_traces_endpoint=None, collect_detailed_traces=None, disable_async_output_proc=False, override_neuron_config=None, scheduling_policy='fcfs', disable_log_requests=True, max_log_len=None, disable_fastapi_docs=False)
|
||||
INFO 07-15 12:53:27 config.py:1670] Downcasting torch.float32 to torch.float16.
|
||||
INFO 07-15 12:53:38 config.py:887] Defaulting to use mp for distributed inference
|
||||
INFO 07-15 12:53:38 config.py:1005] Chunked prefill is enabled with max_num_batched_tokens=24576.
|
||||
WARNING 07-15 12:53:38 config.py:380] To see benefits of async output processing, enable CUDA graph. Since, enforce-eager is enabled, async output processor cannot be used
|
||||
INFO 07-15 12:53:38 llm_engine.py:237] Initializing an LLM engine (v0.6.3) with config: model='/root/public-storage/models/Qwen/Qwen3.6-35B-A3B', speculative_config=None, tokenizer='/root/public-storage/models/Qwen/Qwen3.6-35B-A3B', skip_tokenizer_init=False, tokenizer_mode=auto, revision=None, override_neuron_config=None, rope_scaling=None, rope_theta=None, tokenizer_revision=None, trust_remote_code=True, dtype=torch.float16, max_seq_len=245000, download_dir=None, load_format=LoadFormat.AUTO, tensor_parallel_size=4, pipeline_parallel_size=1, disable_custom_all_reduce=False, quantization=None, enforce_eager=True, kv_cache_dtype=auto, quantization_param_path=None, device_config=cuda, decoding_config=DecodingConfig(guided_decoding_backend='outlines'), observability_config=ObservabilityConfig(otlp_traces_endpoint=None, collect_model_forward_time=False, collect_model_execute_time=False), seed=0, served_model_name=llm, use_v2_block_manager=True, num_scheduler_steps=1, chunked_prefill_enabled=True multi_step_stream_outputs=True, enable_prefix_caching=True, use_async_output_proc=False, use_cached_outputs=False, mm_processor_kwargs=None)
|
||||
WARNING 07-15 12:53:39 multiproc_gpu_executor.py:53] Reducing Torch parallelism from 64 threads to 1 to avoid unnecessary CPU contention. Set OMP_NUM_THREADS in the external environment to tune this value as needed.
|
||||
INFO 07-15 12:53:39 custom_cache_manager.py:17] Setting Triton cache manager to: vllm.triton_utils.custom_cache_manager:CustomCacheManager
|
||||
INFO 07-15 12:53:39 selector.py:266] Cannot use FlashAttention-2 backend because the vllm.vllm_flash_attn package is not found. Make sure that vllm_flash_attn was built and installed (on by default).
|
||||
INFO 07-15 12:53:39 selector.py:115] Using XFormers backend.
|
||||
/usr/local/corex/lib64/python3/dist-packages/torch/cuda/__init__.py:51: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
|
||||
import pynvml # type: ignore[import]
|
||||
/usr/local/corex/lib64/python3/dist-packages/torch/cuda/__init__.py:51: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
|
||||
import pynvml # type: ignore[import]
|
||||
/usr/local/corex/lib64/python3/dist-packages/torch/cuda/__init__.py:51: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
|
||||
import pynvml # type: ignore[import]
|
||||
INFO 07-15 12:53:41 importing.py:10] Triton not installed; certain GPU-related functions will not be available.
|
||||
INFO 07-15 12:53:41 importing.py:10] Triton not installed; certain GPU-related functions will not be available.
|
||||
INFO 07-15 12:53:41 importing.py:10] Triton not installed; certain GPU-related functions will not be available.
|
||||
WARNING:tensorflow:Deprecation warnings have been disabled. Set TF_ENABLE_DEPRECATION_WARNINGS=1 to re-enable them.
|
||||
WARNING:tensorflow:Deprecation warnings have been disabled. Set TF_ENABLE_DEPRECATION_WARNINGS=1 to re-enable them.
|
||||
WARNING:tensorflow:Deprecation warnings have been disabled. Set TF_ENABLE_DEPRECATION_WARNINGS=1 to re-enable them.
|
||||
[1;36m(VllmWorkerProcess pid=43778)[0;0m INFO 07-15 12:53:48 selector.py:266] Cannot use FlashAttention-2 backend because the vllm.vllm_flash_attn package is not found. Make sure that vllm_flash_attn was built and installed (on by default).
|
||||
[1;36m(VllmWorkerProcess pid=43778)[0;0m INFO 07-15 12:53:48 selector.py:115] Using XFormers backend.
|
||||
[1;36m(VllmWorkerProcess pid=43778)[0;0m INFO 07-15 12:53:48 multiproc_worker_utils.py:216] Worker ready; awaiting tasks
|
||||
[1;36m(VllmWorkerProcess pid=43776)[0;0m INFO 07-15 12:53:48 selector.py:266] Cannot use FlashAttention-2 backend because the vllm.vllm_flash_attn package is not found. Make sure that vllm_flash_attn was built and installed (on by default).
|
||||
[1;36m(VllmWorkerProcess pid=43776)[0;0m INFO 07-15 12:53:48 selector.py:115] Using XFormers backend.
|
||||
[1;36m(VllmWorkerProcess pid=43776)[0;0m INFO 07-15 12:53:48 multiproc_worker_utils.py:216] Worker ready; awaiting tasks
|
||||
[1;36m(VllmWorkerProcess pid=43777)[0;0m INFO 07-15 12:53:48 selector.py:266] Cannot use FlashAttention-2 backend because the vllm.vllm_flash_attn package is not found. Make sure that vllm_flash_attn was built and installed (on by default).
|
||||
[1;36m(VllmWorkerProcess pid=43777)[0;0m INFO 07-15 12:53:48 selector.py:115] Using XFormers backend.
|
||||
[1;36m(VllmWorkerProcess pid=43777)[0;0m INFO 07-15 12:53:48 multiproc_worker_utils.py:216] Worker ready; awaiting tasks
|
||||
INFO 07-15 12:53:48 shm_broadcast.py:242] vLLM message queue communication handle: Handle(connect_ip='127.0.0.1', local_reader_ranks=[1, 2, 3], buffer=<vllm.distributed.device_communicators.shm_broadcast.ShmRingBuffer object at 0x7f64ac9f5ba0>, local_subscribe_port=44549, remote_subscribe_port=None)
|
||||
INFO 07-15 12:53:48 model_runner.py:1117] Starting to load model /root/public-storage/models/Qwen/Qwen3.6-35B-A3B...
|
||||
[1;36m(VllmWorkerProcess pid=43776)[0;0m INFO 07-15 12:53:48 model_runner.py:1117] Starting to load model /root/public-storage/models/Qwen/Qwen3.6-35B-A3B...
|
||||
[1;36m(VllmWorkerProcess pid=43777)[0;0m INFO 07-15 12:53:48 model_runner.py:1117] Starting to load model /root/public-storage/models/Qwen/Qwen3.6-35B-A3B...
|
||||
[1;36m(VllmWorkerProcess pid=43778)[0;0m INFO 07-15 12:53:48 model_runner.py:1117] Starting to load model /root/public-storage/models/Qwen/Qwen3.6-35B-A3B...
|
||||
[1;36m(VllmWorkerProcess pid=43778)[0;0m INFO 07-15 12:53:49 selector.py:266] Cannot use FlashAttention-2 backend because the vllm.vllm_flash_attn package is not found. Make sure that vllm_flash_attn was built and installed (on by default).
|
||||
[1;36m(VllmWorkerProcess pid=43778)[0;0m INFO 07-15 12:53:49 selector.py:115] Using XFormers backend.
|
||||
[1;36m(VllmWorkerProcess pid=43777)[0;0m INFO 07-15 12:53:49 selector.py:266] Cannot use FlashAttention-2 backend because the vllm.vllm_flash_attn package is not found. Make sure that vllm_flash_attn was built and installed (on by default).
|
||||
[1;36m(VllmWorkerProcess pid=43777)[0;0m INFO 07-15 12:53:49 selector.py:115] Using XFormers backend.
|
||||
INFO 07-15 12:53:49 selector.py:266] Cannot use FlashAttention-2 backend because the vllm.vllm_flash_attn package is not found. Make sure that vllm_flash_attn was built and installed (on by default).
|
||||
INFO 07-15 12:53:49 selector.py:115] Using XFormers backend.
|
||||
[1;36m(VllmWorkerProcess pid=43776)[0;0m INFO 07-15 12:53:49 selector.py:266] Cannot use FlashAttention-2 backend because the vllm.vllm_flash_attn package is not found. Make sure that vllm_flash_attn was built and installed (on by default).
|
||||
[1;36m(VllmWorkerProcess pid=43776)[0;0m INFO 07-15 12:53:49 selector.py:115] Using XFormers backend.
|
||||
|
||||
Loading safetensors checkpoint shards: 0% Completed | 0/26 [00:00<?, ?it/s]
|
||||
|
||||
Loading safetensors checkpoint shards: 4% Completed | 1/26 [00:01<00:45, 1.81s/it]
|
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|
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Loading safetensors checkpoint shards: 8% Completed | 2/26 [00:02<00:23, 1.03it/s]
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|
||||
Loading safetensors checkpoint shards: 12% Completed | 3/26 [00:03<00:30, 1.33s/it]
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||||
Loading safetensors checkpoint shards: 15% Completed | 4/26 [00:05<00:33, 1.51s/it]
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|
||||
Loading safetensors checkpoint shards: 19% Completed | 5/26 [00:06<00:27, 1.30s/it]
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Loading safetensors checkpoint shards: 23% Completed | 6/26 [00:08<00:30, 1.50s/it]
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||||
Loading safetensors checkpoint shards: 27% Completed | 7/26 [00:08<00:21, 1.14s/it]
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Loading safetensors checkpoint shards: 31% Completed | 8/26 [00:10<00:23, 1.32s/it]
|
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||||
Loading safetensors checkpoint shards: 35% Completed | 9/26 [00:11<00:20, 1.19s/it]
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Loading safetensors checkpoint shards: 38% Completed | 10/26 [00:13<00:22, 1.41s/it]
|
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|
||||
Loading safetensors checkpoint shards: 42% Completed | 11/26 [00:13<00:15, 1.07s/it]
|
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|
||||
Loading safetensors checkpoint shards: 46% Completed | 12/26 [00:15<00:17, 1.28s/it]
|
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Loading safetensors checkpoint shards: 50% Completed | 13/26 [00:17<00:18, 1.45s/it]
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Loading safetensors checkpoint shards: 54% Completed | 14/26 [00:19<00:19, 1.61s/it]
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Loading safetensors checkpoint shards: 58% Completed | 15/26 [00:20<00:15, 1.37s/it]
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||||
Loading safetensors checkpoint shards: 62% Completed | 16/26 [00:22<00:16, 1.66s/it]
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Loading safetensors checkpoint shards: 65% Completed | 17/26 [00:22<00:11, 1.28s/it]
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Loading safetensors checkpoint shards: 69% Completed | 18/26 [00:24<00:11, 1.43s/it]
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Loading safetensors checkpoint shards: 73% Completed | 19/26 [00:26<00:10, 1.49s/it]
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Loading safetensors checkpoint shards: 77% Completed | 20/26 [00:28<00:10, 1.77s/it]
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Loading safetensors checkpoint shards: 81% Completed | 21/26 [00:29<00:07, 1.54s/it]
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Loading safetensors checkpoint shards: 85% Completed | 22/26 [00:30<00:05, 1.30s/it]
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Loading safetensors checkpoint shards: 88% Completed | 23/26 [00:32<00:04, 1.62s/it]
|
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||||
Loading safetensors checkpoint shards: 92% Completed | 24/26 [00:34<00:03, 1.69s/it]
|
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|
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Loading safetensors checkpoint shards: 96% Completed | 25/26 [00:36<00:01, 1.76s/it]
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||||
Loading safetensors checkpoint shards: 100% Completed | 26/26 [00:37<00:00, 1.37s/it]
|
||||
|
||||
Loading safetensors checkpoint shards: 100% Completed | 26/26 [00:37<00:00, 1.43s/it]
|
||||
|
||||
INFO 07-15 12:54:26 model_runner.py:1128] Loading model weights took 16.2303 GB
|
||||
[1;36m(VllmWorkerProcess pid=43778)[0;0m INFO 07-15 12:54:29 model_runner.py:1128] Loading model weights took 16.2303 GB
|
||||
[1;36m(VllmWorkerProcess pid=43776)[0;0m INFO 07-15 12:54:29 model_runner.py:1128] Loading model weights took 16.2303 GB
|
||||
[1;36m(VllmWorkerProcess pid=43777)[0;0m INFO 07-15 12:54:29 model_runner.py:1128] Loading model weights took 16.2303 GB
|
||||
INFO 07-15 12:54:45 distributed_gpu_executor.py:57] # GPU blocks: 14566, # CPU blocks: 6553
|
||||
INFO 07-15 12:54:45 distributed_gpu_executor.py:61] Maximum concurrency for 245000 tokens per request: 0.95x
|
||||
[1;36m(VllmWorkerProcess pid=43776)[0;0m ERROR 07-15 12:54:45 multiproc_worker_utils.py:231] Exception in worker VllmWorkerProcess while processing method initialize_cache: The model's max seq len (245000) is larger than the maximum number of tokens that can be stored in KV cache (233056). Try increasing `gpu_memory_utilization` or decreasing `max_model_len` when initializing the engine., Traceback (most recent call last):
|
||||
[1;36m(VllmWorkerProcess pid=43776)[0;0m ERROR 07-15 12:54:45 multiproc_worker_utils.py:231] File "/usr/local/corex/lib64/python3/dist-packages/vllm/executor/multiproc_worker_utils.py", line 224, in _run_worker_process
|
||||
[1;36m(VllmWorkerProcess pid=43776)[0;0m ERROR 07-15 12:54:45 multiproc_worker_utils.py:231] output = executor(*args, **kwargs)
|
||||
[1;36m(VllmWorkerProcess pid=43776)[0;0m ERROR 07-15 12:54:45 multiproc_worker_utils.py:231] File "/usr/local/corex/lib64/python3/dist-packages/vllm/worker/worker.py", line 262, in initialize_cache
|
||||
[1;36m(VllmWorkerProcess pid=43776)[0;0m ERROR 07-15 12:54:45 multiproc_worker_utils.py:231] raise_if_cache_size_invalid(num_gpu_blocks,
|
||||
[1;36m(VllmWorkerProcess pid=43776)[0;0m ERROR 07-15 12:54:45 multiproc_worker_utils.py:231] File "/usr/local/corex/lib64/python3/dist-packages/vllm/worker/worker.py", line 492, in raise_if_cache_size_invalid
|
||||
[1;36m(VllmWorkerProcess pid=43776)[0;0m ERROR 07-15 12:54:45 multiproc_worker_utils.py:231] raise ValueError(
|
||||
[1;36m(VllmWorkerProcess pid=43776)[0;0m ERROR 07-15 12:54:45 multiproc_worker_utils.py:231] ValueError: The model's max seq len (245000) is larger than the maximum number of tokens that can be stored in KV cache (233056). Try increasing `gpu_memory_utilization` or decreasing `max_model_len` when initializing the engine.
|
||||
[1;36m(VllmWorkerProcess pid=43776)[0;0m ERROR 07-15 12:54:45 multiproc_worker_utils.py:231]
|
||||
[1;36m(VllmWorkerProcess pid=43777)[0;0m ERROR 07-15 12:54:45 multiproc_worker_utils.py:231] Exception in worker VllmWorkerProcess while processing method initialize_cache: The model's max seq len (245000) is larger than the maximum number of tokens that can be stored in KV cache (233056). Try increasing `gpu_memory_utilization` or decreasing `max_model_len` when initializing the engine., Traceback (most recent call last):
|
||||
[1;36m(VllmWorkerProcess pid=43777)[0;0m ERROR 07-15 12:54:45 multiproc_worker_utils.py:231] File "/usr/local/corex/lib64/python3/dist-packages/vllm/executor/multiproc_worker_utils.py", line 224, in _run_worker_process
|
||||
[1;36m(VllmWorkerProcess pid=43777)[0;0m ERROR 07-15 12:54:45 multiproc_worker_utils.py:231] output = executor(*args, **kwargs)
|
||||
[1;36m(VllmWorkerProcess pid=43777)[0;0m ERROR 07-15 12:54:45 multiproc_worker_utils.py:231] File "/usr/local/corex/lib64/python3/dist-packages/vllm/worker/worker.py", line 262, in initialize_cache
|
||||
[1;36m(VllmWorkerProcess pid=43777)[0;0m ERROR 07-15 12:54:45 multiproc_worker_utils.py:231] raise_if_cache_size_invalid(num_gpu_blocks,
|
||||
[1;36m(VllmWorkerProcess pid=43777)[0;0m ERROR 07-15 12:54:45 multiproc_worker_utils.py:231] File "/usr/local/corex/lib64/python3/dist-packages/vllm/worker/worker.py", line 492, in raise_if_cache_size_invalid
|
||||
[1;36m(VllmWorkerProcess pid=43777)[0;0m ERROR 07-15 12:54:45 multiproc_worker_utils.py:231] raise ValueError(
|
||||
[1;36m(VllmWorkerProcess pid=43777)[0;0m ERROR 07-15 12:54:45 multiproc_worker_utils.py:231] ValueError: The model's max seq len (245000) is larger than the maximum number of tokens that can be stored in KV cache (233056). Try increasing `gpu_memory_utilization` or decreasing `max_model_len` when initializing the engine.
|
||||
[1;36m(VllmWorkerProcess pid=43777)[0;0m ERROR 07-15 12:54:45 multiproc_worker_utils.py:231]
|
||||
[1;36m(VllmWorkerProcess pid=43778)[0;0m ERROR 07-15 12:54:45 multiproc_worker_utils.py:231] Exception in worker VllmWorkerProcess while processing method initialize_cache: The model's max seq len (245000) is larger than the maximum number of tokens that can be stored in KV cache (233056). Try increasing `gpu_memory_utilization` or decreasing `max_model_len` when initializing the engine., Traceback (most recent call last):
|
||||
[1;36m(VllmWorkerProcess pid=43778)[0;0m ERROR 07-15 12:54:45 multiproc_worker_utils.py:231] File "/usr/local/corex/lib64/python3/dist-packages/vllm/executor/multiproc_worker_utils.py", line 224, in _run_worker_process
|
||||
[1;36m(VllmWorkerProcess pid=43778)[0;0m ERROR 07-15 12:54:45 multiproc_worker_utils.py:231] output = executor(*args, **kwargs)
|
||||
[1;36m(VllmWorkerProcess pid=43778)[0;0m ERROR 07-15 12:54:45 multiproc_worker_utils.py:231] File "/usr/local/corex/lib64/python3/dist-packages/vllm/worker/worker.py", line 262, in initialize_cache
|
||||
[1;36m(VllmWorkerProcess pid=43778)[0;0m ERROR 07-15 12:54:45 multiproc_worker_utils.py:231] raise_if_cache_size_invalid(num_gpu_blocks,
|
||||
[1;36m(VllmWorkerProcess pid=43778)[0;0m ERROR 07-15 12:54:45 multiproc_worker_utils.py:231] File "/usr/local/corex/lib64/python3/dist-packages/vllm/worker/worker.py", line 492, in raise_if_cache_size_invalid
|
||||
[1;36m(VllmWorkerProcess pid=43778)[0;0m ERROR 07-15 12:54:45 multiproc_worker_utils.py:231] raise ValueError(
|
||||
[1;36m(VllmWorkerProcess pid=43778)[0;0m ERROR 07-15 12:54:45 multiproc_worker_utils.py:231] ValueError: The model's max seq len (245000) is larger than the maximum number of tokens that can be stored in KV cache (233056). Try increasing `gpu_memory_utilization` or decreasing `max_model_len` when initializing the engine.
|
||||
[1;36m(VllmWorkerProcess pid=43778)[0;0m ERROR 07-15 12:54:45 multiproc_worker_utils.py:231]
|
||||
Traceback (most recent call last):
|
||||
File "/usr/local/lib/python3.10/runpy.py", line 196, in _run_module_as_main
|
||||
return _run_code(code, main_globals, None,
|
||||
File "/usr/local/lib/python3.10/runpy.py", line 86, in _run_code
|
||||
exec(code, run_globals)
|
||||
File "/usr/local/corex/lib64/python3/dist-packages/vllm/entrypoints/openai/api_server.py", line 595, in <module>
|
||||
uvloop.run(run_server(args))
|
||||
File "/usr/local/lib/python3.10/site-packages/uvloop/__init__.py", line 82, in run
|
||||
return loop.run_until_complete(wrapper())
|
||||
File "uvloop/loop.pyx", line 1518, in uvloop.loop.Loop.run_until_complete
|
||||
File "/usr/local/lib/python3.10/site-packages/uvloop/__init__.py", line 61, in wrapper
|
||||
return await main
|
||||
File "/usr/local/corex/lib64/python3/dist-packages/vllm/entrypoints/openai/api_server.py", line 562, in run_server
|
||||
async with build_async_engine_client(args) as engine_client:
|
||||
File "/usr/local/lib/python3.10/contextlib.py", line 199, in __aenter__
|
||||
return await anext(self.gen)
|
||||
File "/usr/local/corex/lib64/python3/dist-packages/vllm/entrypoints/openai/api_server.py", line 108, in build_async_engine_client
|
||||
async with build_async_engine_client_from_engine_args(
|
||||
File "/usr/local/lib/python3.10/contextlib.py", line 199, in __aenter__
|
||||
return await anext(self.gen)
|
||||
@@ -0,0 +1,226 @@
|
||||
[repro] gpu before start
|
||||
[repro] MAX_NUM_BATCHED_TOKENS=32768 MOE_DECODE_NATIVE=0
|
||||
Timestamp Wed Jul 15 12:37:39 2026
|
||||
+-----------------------------------------------------------------------------+
|
||||
| IX-ML: 3.2.3 Driver Version: 3.2.1 CUDA Version: 10.2 |
|
||||
|-------------------------------+----------------------+----------------------|
|
||||
| GPU Name | Bus-Id | Clock-SM Clock-Mem |
|
||||
| Fan Temp Perf Pwr:Usage/Cap| Memory-Usage | GPU-Util Compute M. |
|
||||
|===============================+======================+======================|
|
||||
| 0 Iluvatar BI-V100 | 00000000:4B:00.0 | 1500MHz 1200MHz |
|
||||
| 0% 29C P0 47W / 250W | 257MiB / 32768MiB | 0% Default |
|
||||
+-------------------------------+----------------------+----------------------+
|
||||
| 1 Iluvatar BI-V100 | 00000000:4C:00.0 | 1500MHz 1200MHz |
|
||||
| 0% 32C P0 47W / 250W | 257MiB / 32768MiB | 0% Default |
|
||||
+-------------------------------+----------------------+----------------------+
|
||||
| 2 Iluvatar BI-V100 | 00000000:4D:00.0 | 1500MHz 1200MHz |
|
||||
| 0% 31C P0 47W / 250W | 257MiB / 32768MiB | 0% Default |
|
||||
+-------------------------------+----------------------+----------------------+
|
||||
| 3 Iluvatar BI-V100 | 00000000:4E:00.0 | 1500MHz 1200MHz |
|
||||
| 0% 30C P0 47W / 250W | 257MiB / 32768MiB | 0% Default |
|
||||
+-------------------------------+----------------------+----------------------+
|
||||
|
||||
+-----------------------------------------------------------------------------+
|
||||
| Processes: GPU Memory |
|
||||
| GPU PID Process name Usage(MiB) |
|
||||
|=============================================================================|
|
||||
| No running processes found |
|
||||
+-----------------------------------------------------------------------------+
|
||||
[repro] server_pid=41250
|
||||
[repro] waiting_check=6
|
||||
|
||||
Loading safetensors checkpoint shards: 15% Completed | 4/26 [00:06<00:36, 1.65s/it]
|
||||
|
||||
Loading safetensors checkpoint shards: 19% Completed | 5/26 [00:07<00:29, 1.42s/it]
|
||||
|
||||
Loading safetensors checkpoint shards: 23% Completed | 6/26 [00:09<00:33, 1.66s/it]
|
||||
|
||||
Loading safetensors checkpoint shards: 27% Completed | 7/26 [00:09<00:23, 1.26s/it]
|
||||
|
||||
Loading safetensors checkpoint shards: 31% Completed | 8/26 [00:11<00:25, 1.42s/it]
|
||||
|
||||
Loading safetensors checkpoint shards: 35% Completed | 9/26 [00:12<00:21, 1.28s/it]
|
||||
|
||||
Loading safetensors checkpoint shards: 38% Completed | 10/26 [00:14<00:24, 1.50s/it]
|
||||
|
||||
Loading safetensors checkpoint shards: 42% Completed | 11/26 [00:14<00:17, 1.14s/it]
|
||||
|
||||
Loading safetensors checkpoint shards: 46% Completed | 12/26 [00:16<00:18, 1.36s/it]
|
||||
|
||||
Loading safetensors checkpoint shards: 50% Completed | 13/26 [00:18<00:20, 1.55s/it]
|
||||
Timestamp Wed Jul 15 12:38:30 2026
|
||||
+-----------------------------------------------------------------------------+
|
||||
| IX-ML: 3.2.3 Driver Version: 3.2.1 CUDA Version: 10.2 |
|
||||
|-------------------------------+----------------------+----------------------|
|
||||
| GPU Name | Bus-Id | Clock-SM Clock-Mem |
|
||||
| Fan Temp Perf Pwr:Usage/Cap| Memory-Usage | GPU-Util Compute M. |
|
||||
|===============================+======================+======================|
|
||||
| 0 Iluvatar BI-V100 | 00000000:4B:00.0 | 1500MHz 1200MHz |
|
||||
| 0% 29C P0 47W / 250W | 16945MiB / 32768MiB | 0% Default |
|
||||
+-------------------------------+----------------------+----------------------+
|
||||
| 1 Iluvatar BI-V100 | 00000000:4C:00.0 | 1500MHz 1200MHz |
|
||||
| 0% 32C P0 47W / 250W | 16945MiB / 32768MiB | 0% Default |
|
||||
+-------------------------------+----------------------+----------------------+
|
||||
| 2 Iluvatar BI-V100 | 00000000:4D:00.0 | 1500MHz 1200MHz |
|
||||
| 0% 31C P0 47W / 250W | 16945MiB / 32768MiB | 0% Default |
|
||||
+-------------------------------+----------------------+----------------------+
|
||||
| 3 Iluvatar BI-V100 | 00000000:4E:00.0 | 1500MHz 1200MHz |
|
||||
| 0% 30C P0 47W / 250W | 16945MiB / 32768MiB | 0% Default |
|
||||
+-------------------------------+----------------------+----------------------+
|
||||
|
||||
+-----------------------------------------------------------------------------+
|
||||
| Processes: GPU Memory |
|
||||
| GPU PID Process name Usage(MiB) |
|
||||
|=============================================================================|
|
||||
+-----------------------------------------------------------------------------+
|
||||
[repro] server_exited_at_check=11
|
||||
2026-07-15 12:37:43.215301: I tensorflow/core/util/port.cc:110] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
|
||||
2026-07-15 12:37:43.265874: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
|
||||
To enable the following instructions: SSE3 SSE4.1 SSE4.2 AVX AVX2 AVX512F AVX512_VNNI AVX512_BF16 AVX_VNNI AMX_TILE AMX_INT8 AMX_BF16 FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
|
||||
WARNING:tensorflow:Deprecation warnings have been disabled. Set TF_ENABLE_DEPRECATION_WARNINGS=1 to re-enable them.
|
||||
INFO 07-15 12:37:48 api_server.py:530] vLLM API server version 0.6.3
|
||||
INFO 07-15 12:37:48 api_server.py:531] args: Namespace(host='0.0.0.0', port=1111, uvicorn_log_level='info', allow_credentials=False, allowed_origins=['*'], allowed_methods=['*'], allowed_headers=['*'], api_key=None, lora_modules=None, prompt_adapters=None, chat_template='/workspace/chat_template_multi_system.jinja', response_role='assistant', ssl_keyfile=None, ssl_certfile=None, ssl_ca_certs=None, ssl_cert_reqs=0, root_path=None, middleware=[], return_tokens_as_token_ids=False, disable_frontend_multiprocessing=True, enable_auto_tool_choice=True, tool_call_parser='qwen3_coder', tool_parser_plugin='', reasoning_parser='qwen3', model='/root/public-storage/models/Qwen/Qwen3.6-35B-A3B', tokenizer=None, skip_tokenizer_init=False, revision=None, code_revision=None, tokenizer_revision=None, tokenizer_mode='auto', trust_remote_code=True, download_dir=None, load_format='auto', config_format='auto', dtype='auto', kv_cache_dtype='auto', quantization_param_path=None, max_model_len=245000, guided_decoding_backend='outlines', distributed_executor_backend=None, worker_use_ray=False, pipeline_parallel_size=1, tensor_parallel_size=4, max_parallel_loading_workers=None, ray_workers_use_nsight=False, block_size=16, enable_prefix_caching=True, disable_sliding_window=False, use_v2_block_manager=True, num_lookahead_slots=0, seed=0, swap_space=4, cpu_offload_gb=0, gpu_memory_utilization=0.9, num_gpu_blocks_override=None, max_num_batched_tokens=32768, max_num_seqs=1, max_logprobs=20, disable_log_stats=False, quantization=None, rope_scaling=None, rope_theta=None, enforce_eager=True, max_context_len_to_capture=None, max_seq_len_to_capture=245000, disable_custom_all_reduce=False, tokenizer_pool_size=0, tokenizer_pool_type='ray', tokenizer_pool_extra_config=None, limit_mm_per_prompt=None, mm_processor_kwargs=None, enable_lora=False, max_loras=1, max_lora_rank=16, lora_extra_vocab_size=256, lora_dtype='auto', long_lora_scaling_factors=None, max_cpu_loras=None, fully_sharded_loras=False, enable_prompt_adapter=False, max_prompt_adapters=1, max_prompt_adapter_token=0, device='auto', num_scheduler_steps=1, multi_step_stream_outputs=True, scheduler_delay_factor=0.0, enable_chunked_prefill=True, speculative_model=None, speculative_model_quantization=None, num_speculative_tokens=None, speculative_disable_mqa_scorer=False, speculative_draft_tensor_parallel_size=None, speculative_max_model_len=None, speculative_disable_by_batch_size=None, ngram_prompt_lookup_max=None, ngram_prompt_lookup_min=None, spec_decoding_acceptance_method='rejection_sampler', typical_acceptance_sampler_posterior_threshold=None, typical_acceptance_sampler_posterior_alpha=None, disable_logprobs_during_spec_decoding=None, model_loader_extra_config=None, ignore_patterns=[], preemption_mode=None, served_model_name=['llm'], qlora_adapter_name_or_path=None, otlp_traces_endpoint=None, collect_detailed_traces=None, disable_async_output_proc=False, override_neuron_config=None, scheduling_policy='fcfs', disable_log_requests=True, max_log_len=None, disable_fastapi_docs=False)
|
||||
INFO 07-15 12:37:48 config.py:1670] Downcasting torch.float32 to torch.float16.
|
||||
INFO 07-15 12:37:59 config.py:887] Defaulting to use mp for distributed inference
|
||||
INFO 07-15 12:37:59 config.py:1005] Chunked prefill is enabled with max_num_batched_tokens=32768.
|
||||
WARNING 07-15 12:37:59 config.py:380] To see benefits of async output processing, enable CUDA graph. Since, enforce-eager is enabled, async output processor cannot be used
|
||||
INFO 07-15 12:37:59 llm_engine.py:237] Initializing an LLM engine (v0.6.3) with config: model='/root/public-storage/models/Qwen/Qwen3.6-35B-A3B', speculative_config=None, tokenizer='/root/public-storage/models/Qwen/Qwen3.6-35B-A3B', skip_tokenizer_init=False, tokenizer_mode=auto, revision=None, override_neuron_config=None, rope_scaling=None, rope_theta=None, tokenizer_revision=None, trust_remote_code=True, dtype=torch.float16, max_seq_len=245000, download_dir=None, load_format=LoadFormat.AUTO, tensor_parallel_size=4, pipeline_parallel_size=1, disable_custom_all_reduce=False, quantization=None, enforce_eager=True, kv_cache_dtype=auto, quantization_param_path=None, device_config=cuda, decoding_config=DecodingConfig(guided_decoding_backend='outlines'), observability_config=ObservabilityConfig(otlp_traces_endpoint=None, collect_model_forward_time=False, collect_model_execute_time=False), seed=0, served_model_name=llm, use_v2_block_manager=True, num_scheduler_steps=1, chunked_prefill_enabled=True multi_step_stream_outputs=True, enable_prefix_caching=True, use_async_output_proc=False, use_cached_outputs=False, mm_processor_kwargs=None)
|
||||
WARNING 07-15 12:38:00 multiproc_gpu_executor.py:53] Reducing Torch parallelism from 64 threads to 1 to avoid unnecessary CPU contention. Set OMP_NUM_THREADS in the external environment to tune this value as needed.
|
||||
INFO 07-15 12:38:00 custom_cache_manager.py:17] Setting Triton cache manager to: vllm.triton_utils.custom_cache_manager:CustomCacheManager
|
||||
INFO 07-15 12:38:00 selector.py:266] Cannot use FlashAttention-2 backend because the vllm.vllm_flash_attn package is not found. Make sure that vllm_flash_attn was built and installed (on by default).
|
||||
INFO 07-15 12:38:00 selector.py:115] Using XFormers backend.
|
||||
/usr/local/corex/lib64/python3/dist-packages/torch/cuda/__init__.py:51: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
|
||||
import pynvml # type: ignore[import]
|
||||
/usr/local/corex/lib64/python3/dist-packages/torch/cuda/__init__.py:51: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
|
||||
import pynvml # type: ignore[import]
|
||||
/usr/local/corex/lib64/python3/dist-packages/torch/cuda/__init__.py:51: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
|
||||
import pynvml # type: ignore[import]
|
||||
INFO 07-15 12:38:02 importing.py:10] Triton not installed; certain GPU-related functions will not be available.
|
||||
INFO 07-15 12:38:02 importing.py:10] Triton not installed; certain GPU-related functions will not be available.
|
||||
INFO 07-15 12:38:02 importing.py:10] Triton not installed; certain GPU-related functions will not be available.
|
||||
WARNING:tensorflow:Deprecation warnings have been disabled. Set TF_ENABLE_DEPRECATION_WARNINGS=1 to re-enable them.
|
||||
WARNING:tensorflow:Deprecation warnings have been disabled. Set TF_ENABLE_DEPRECATION_WARNINGS=1 to re-enable them.
|
||||
WARNING:tensorflow:Deprecation warnings have been disabled. Set TF_ENABLE_DEPRECATION_WARNINGS=1 to re-enable them.
|
||||
[1;36m(VllmWorkerProcess pid=41602)[0;0m INFO 07-15 12:38:09 selector.py:266] Cannot use FlashAttention-2 backend because the vllm.vllm_flash_attn package is not found. Make sure that vllm_flash_attn was built and installed (on by default).
|
||||
[1;36m(VllmWorkerProcess pid=41602)[0;0m INFO 07-15 12:38:09 selector.py:115] Using XFormers backend.
|
||||
[1;36m(VllmWorkerProcess pid=41602)[0;0m INFO 07-15 12:38:09 multiproc_worker_utils.py:216] Worker ready; awaiting tasks
|
||||
[1;36m(VllmWorkerProcess pid=41603)[0;0m INFO 07-15 12:38:09 selector.py:266] Cannot use FlashAttention-2 backend because the vllm.vllm_flash_attn package is not found. Make sure that vllm_flash_attn was built and installed (on by default).
|
||||
[1;36m(VllmWorkerProcess pid=41603)[0;0m INFO 07-15 12:38:09 selector.py:115] Using XFormers backend.
|
||||
[1;36m(VllmWorkerProcess pid=41603)[0;0m INFO 07-15 12:38:09 multiproc_worker_utils.py:216] Worker ready; awaiting tasks
|
||||
[1;36m(VllmWorkerProcess pid=41604)[0;0m INFO 07-15 12:38:09 selector.py:266] Cannot use FlashAttention-2 backend because the vllm.vllm_flash_attn package is not found. Make sure that vllm_flash_attn was built and installed (on by default).
|
||||
[1;36m(VllmWorkerProcess pid=41604)[0;0m INFO 07-15 12:38:09 selector.py:115] Using XFormers backend.
|
||||
[1;36m(VllmWorkerProcess pid=41604)[0;0m INFO 07-15 12:38:09 multiproc_worker_utils.py:216] Worker ready; awaiting tasks
|
||||
INFO 07-15 12:38:10 shm_broadcast.py:242] vLLM message queue communication handle: Handle(connect_ip='127.0.0.1', local_reader_ranks=[1, 2, 3], buffer=<vllm.distributed.device_communicators.shm_broadcast.ShmRingBuffer object at 0x7f011af40f10>, local_subscribe_port=34275, remote_subscribe_port=None)
|
||||
INFO 07-15 12:38:10 model_runner.py:1117] Starting to load model /root/public-storage/models/Qwen/Qwen3.6-35B-A3B...
|
||||
[1;36m(VllmWorkerProcess pid=41603)[0;0m INFO 07-15 12:38:10 model_runner.py:1117] Starting to load model /root/public-storage/models/Qwen/Qwen3.6-35B-A3B...
|
||||
[1;36m(VllmWorkerProcess pid=41602)[0;0m INFO 07-15 12:38:10 model_runner.py:1117] Starting to load model /root/public-storage/models/Qwen/Qwen3.6-35B-A3B...
|
||||
[1;36m(VllmWorkerProcess pid=41604)[0;0m INFO 07-15 12:38:10 model_runner.py:1117] Starting to load model /root/public-storage/models/Qwen/Qwen3.6-35B-A3B...
|
||||
INFO 07-15 12:38:10 selector.py:266] Cannot use FlashAttention-2 backend because the vllm.vllm_flash_attn package is not found. Make sure that vllm_flash_attn was built and installed (on by default).
|
||||
INFO 07-15 12:38:10 selector.py:115] Using XFormers backend.
|
||||
[1;36m(VllmWorkerProcess pid=41602)[0;0m INFO 07-15 12:38:10 selector.py:266] Cannot use FlashAttention-2 backend because the vllm.vllm_flash_attn package is not found. Make sure that vllm_flash_attn was built and installed (on by default).
|
||||
[1;36m(VllmWorkerProcess pid=41602)[0;0m INFO 07-15 12:38:10 selector.py:115] Using XFormers backend.
|
||||
[1;36m(VllmWorkerProcess pid=41603)[0;0m INFO 07-15 12:38:10 selector.py:266] Cannot use FlashAttention-2 backend because the vllm.vllm_flash_attn package is not found. Make sure that vllm_flash_attn was built and installed (on by default).
|
||||
[1;36m(VllmWorkerProcess pid=41603)[0;0m INFO 07-15 12:38:10 selector.py:115] Using XFormers backend.
|
||||
[1;36m(VllmWorkerProcess pid=41604)[0;0m INFO 07-15 12:38:10 selector.py:266] Cannot use FlashAttention-2 backend because the vllm.vllm_flash_attn package is not found. Make sure that vllm_flash_attn was built and installed (on by default).
|
||||
[1;36m(VllmWorkerProcess pid=41604)[0;0m INFO 07-15 12:38:10 selector.py:115] Using XFormers backend.
|
||||
|
||||
Loading safetensors checkpoint shards: 0% Completed | 0/26 [00:00<?, ?it/s]
|
||||
|
||||
Loading safetensors checkpoint shards: 4% Completed | 1/26 [00:01<00:47, 1.89s/it]
|
||||
|
||||
Loading safetensors checkpoint shards: 8% Completed | 2/26 [00:02<00:25, 1.08s/it]
|
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|
||||
Loading safetensors checkpoint shards: 12% Completed | 3/26 [00:04<00:33, 1.45s/it]
|
||||
|
||||
Loading safetensors checkpoint shards: 15% Completed | 4/26 [00:06<00:36, 1.65s/it]
|
||||
|
||||
Loading safetensors checkpoint shards: 19% Completed | 5/26 [00:07<00:29, 1.42s/it]
|
||||
|
||||
Loading safetensors checkpoint shards: 23% Completed | 6/26 [00:09<00:33, 1.66s/it]
|
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|
||||
Loading safetensors checkpoint shards: 27% Completed | 7/26 [00:09<00:23, 1.26s/it]
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|
||||
Loading safetensors checkpoint shards: 31% Completed | 8/26 [00:11<00:25, 1.42s/it]
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|
||||
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|
||||
Loading safetensors checkpoint shards: 38% Completed | 10/26 [00:14<00:24, 1.50s/it]
|
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|
||||
Loading safetensors checkpoint shards: 42% Completed | 11/26 [00:14<00:17, 1.14s/it]
|
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|
||||
Loading safetensors checkpoint shards: 46% Completed | 12/26 [00:16<00:18, 1.36s/it]
|
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|
||||
Loading safetensors checkpoint shards: 50% Completed | 13/26 [00:18<00:20, 1.55s/it]
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|
||||
Loading safetensors checkpoint shards: 54% Completed | 14/26 [00:20<00:21, 1.76s/it]
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|
||||
Loading safetensors checkpoint shards: 58% Completed | 15/26 [00:21<00:16, 1.48s/it]
|
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|
||||
Loading safetensors checkpoint shards: 62% Completed | 16/26 [00:24<00:18, 1.81s/it]
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|
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Loading safetensors checkpoint shards: 65% Completed | 17/26 [00:24<00:12, 1.41s/it]
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|
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Loading safetensors checkpoint shards: 69% Completed | 18/26 [00:26<00:12, 1.56s/it]
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|
||||
Loading safetensors checkpoint shards: 73% Completed | 19/26 [00:28<00:11, 1.62s/it]
|
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|
||||
Loading safetensors checkpoint shards: 77% Completed | 20/26 [00:31<00:11, 1.91s/it]
|
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|
||||
Loading safetensors checkpoint shards: 81% Completed | 21/26 [00:32<00:08, 1.67s/it]
|
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|
||||
Loading safetensors checkpoint shards: 85% Completed | 22/26 [00:33<00:05, 1.41s/it]
|
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|
||||
Loading safetensors checkpoint shards: 88% Completed | 23/26 [00:35<00:05, 1.75s/it]
|
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|
||||
Loading safetensors checkpoint shards: 92% Completed | 24/26 [00:37<00:03, 1.83s/it]
|
||||
|
||||
Loading safetensors checkpoint shards: 96% Completed | 25/26 [00:39<00:01, 1.90s/it]
|
||||
[1;36m(VllmWorkerProcess pid=41602)[0;0m INFO 07-15 12:38:50 model_runner.py:1128] Loading model weights took 16.2303 GB
|
||||
[1;36m(VllmWorkerProcess pid=41604)[0;0m INFO 07-15 12:38:50 model_runner.py:1128] Loading model weights took 16.2303 GB
|
||||
[1;36m(VllmWorkerProcess pid=41603)[0;0m INFO 07-15 12:38:50 model_runner.py:1128] Loading model weights took 16.2303 GB
|
||||
|
||||
Loading safetensors checkpoint shards: 100% Completed | 26/26 [00:40<00:00, 1.49s/it]
|
||||
|
||||
Loading safetensors checkpoint shards: 100% Completed | 26/26 [00:40<00:00, 1.55s/it]
|
||||
|
||||
INFO 07-15 12:38:51 model_runner.py:1128] Loading model weights took 16.2303 GB
|
||||
INFO 07-15 12:39:15 distributed_gpu_executor.py:57] # GPU blocks: 13081, # CPU blocks: 6553
|
||||
INFO 07-15 12:39:15 distributed_gpu_executor.py:61] Maximum concurrency for 245000 tokens per request: 0.85x
|
||||
[1;36m(VllmWorkerProcess pid=41602)[0;0m ERROR 07-15 12:39:15 multiproc_worker_utils.py:231] Exception in worker VllmWorkerProcess while processing method initialize_cache: The model's max seq len (245000) is larger than the maximum number of tokens that can be stored in KV cache (209296). Try increasing `gpu_memory_utilization` or decreasing `max_model_len` when initializing the engine., Traceback (most recent call last):
|
||||
[1;36m(VllmWorkerProcess pid=41602)[0;0m ERROR 07-15 12:39:15 multiproc_worker_utils.py:231] File "/usr/local/corex/lib64/python3/dist-packages/vllm/executor/multiproc_worker_utils.py", line 224, in _run_worker_process
|
||||
[1;36m(VllmWorkerProcess pid=41602)[0;0m ERROR 07-15 12:39:15 multiproc_worker_utils.py:231] output = executor(*args, **kwargs)
|
||||
[1;36m(VllmWorkerProcess pid=41602)[0;0m ERROR 07-15 12:39:15 multiproc_worker_utils.py:231] File "/usr/local/corex/lib64/python3/dist-packages/vllm/worker/worker.py", line 262, in initialize_cache
|
||||
[1;36m(VllmWorkerProcess pid=41602)[0;0m ERROR 07-15 12:39:15 multiproc_worker_utils.py:231] raise_if_cache_size_invalid(num_gpu_blocks,
|
||||
[1;36m(VllmWorkerProcess pid=41602)[0;0m ERROR 07-15 12:39:15 multiproc_worker_utils.py:231] File "/usr/local/corex/lib64/python3/dist-packages/vllm/worker/worker.py", line 492, in raise_if_cache_size_invalid
|
||||
[1;36m(VllmWorkerProcess pid=41602)[0;0m ERROR 07-15 12:39:15 multiproc_worker_utils.py:231] raise ValueError(
|
||||
[1;36m(VllmWorkerProcess pid=41602)[0;0m ERROR 07-15 12:39:15 multiproc_worker_utils.py:231] ValueError: The model's max seq len (245000) is larger than the maximum number of tokens that can be stored in KV cache (209296). Try increasing `gpu_memory_utilization` or decreasing `max_model_len` when initializing the engine.
|
||||
[1;36m(VllmWorkerProcess pid=41602)[0;0m ERROR 07-15 12:39:15 multiproc_worker_utils.py:231]
|
||||
[1;36m(VllmWorkerProcess pid=41603)[0;0m ERROR 07-15 12:39:15 multiproc_worker_utils.py:231] Exception in worker VllmWorkerProcess while processing method initialize_cache: The model's max seq len (245000) is larger than the maximum number of tokens that can be stored in KV cache (209296). Try increasing `gpu_memory_utilization` or decreasing `max_model_len` when initializing the engine., Traceback (most recent call last):
|
||||
[1;36m(VllmWorkerProcess pid=41603)[0;0m ERROR 07-15 12:39:15 multiproc_worker_utils.py:231] File "/usr/local/corex/lib64/python3/dist-packages/vllm/executor/multiproc_worker_utils.py", line 224, in _run_worker_process
|
||||
[1;36m(VllmWorkerProcess pid=41603)[0;0m ERROR 07-15 12:39:15 multiproc_worker_utils.py:231] output = executor(*args, **kwargs)
|
||||
[1;36m(VllmWorkerProcess pid=41603)[0;0m ERROR 07-15 12:39:15 multiproc_worker_utils.py:231] File "/usr/local/corex/lib64/python3/dist-packages/vllm/worker/worker.py", line 262, in initialize_cache
|
||||
[1;36m(VllmWorkerProcess pid=41603)[0;0m ERROR 07-15 12:39:15 multiproc_worker_utils.py:231] raise_if_cache_size_invalid(num_gpu_blocks,
|
||||
[1;36m(VllmWorkerProcess pid=41603)[0;0m ERROR 07-15 12:39:15 multiproc_worker_utils.py:231] File "/usr/local/corex/lib64/python3/dist-packages/vllm/worker/worker.py", line 492, in raise_if_cache_size_invalid
|
||||
[1;36m(VllmWorkerProcess pid=41603)[0;0m ERROR 07-15 12:39:15 multiproc_worker_utils.py:231] raise ValueError(
|
||||
[1;36m(VllmWorkerProcess pid=41603)[0;0m ERROR 07-15 12:39:15 multiproc_worker_utils.py:231] ValueError: The model's max seq len (245000) is larger than the maximum number of tokens that can be stored in KV cache (209296). Try increasing `gpu_memory_utilization` or decreasing `max_model_len` when initializing the engine.
|
||||
[1;36m(VllmWorkerProcess pid=41603)[0;0m ERROR 07-15 12:39:15 multiproc_worker_utils.py:231]
|
||||
[1;36m(VllmWorkerProcess pid=41604)[0;0m ERROR 07-15 12:39:15 multiproc_worker_utils.py:231] Exception in worker VllmWorkerProcess while processing method initialize_cache: The model's max seq len (245000) is larger than the maximum number of tokens that can be stored in KV cache (209296). Try increasing `gpu_memory_utilization` or decreasing `max_model_len` when initializing the engine., Traceback (most recent call last):
|
||||
[1;36m(VllmWorkerProcess pid=41604)[0;0m ERROR 07-15 12:39:15 multiproc_worker_utils.py:231] File "/usr/local/corex/lib64/python3/dist-packages/vllm/executor/multiproc_worker_utils.py", line 224, in _run_worker_process
|
||||
[1;36m(VllmWorkerProcess pid=41604)[0;0m ERROR 07-15 12:39:15 multiproc_worker_utils.py:231] output = executor(*args, **kwargs)
|
||||
[1;36m(VllmWorkerProcess pid=41604)[0;0m ERROR 07-15 12:39:15 multiproc_worker_utils.py:231] File "/usr/local/corex/lib64/python3/dist-packages/vllm/worker/worker.py", line 262, in initialize_cache
|
||||
[1;36m(VllmWorkerProcess pid=41604)[0;0m ERROR 07-15 12:39:15 multiproc_worker_utils.py:231] raise_if_cache_size_invalid(num_gpu_blocks,
|
||||
[1;36m(VllmWorkerProcess pid=41604)[0;0m ERROR 07-15 12:39:15 multiproc_worker_utils.py:231] File "/usr/local/corex/lib64/python3/dist-packages/vllm/worker/worker.py", line 492, in raise_if_cache_size_invalid
|
||||
[1;36m(VllmWorkerProcess pid=41604)[0;0m ERROR 07-15 12:39:15 multiproc_worker_utils.py:231] raise ValueError(
|
||||
[1;36m(VllmWorkerProcess pid=41604)[0;0m ERROR 07-15 12:39:15 multiproc_worker_utils.py:231] ValueError: The model's max seq len (245000) is larger than the maximum number of tokens that can be stored in KV cache (209296). Try increasing `gpu_memory_utilization` or decreasing `max_model_len` when initializing the engine.
|
||||
[1;36m(VllmWorkerProcess pid=41604)[0;0m ERROR 07-15 12:39:15 multiproc_worker_utils.py:231]
|
||||
Traceback (most recent call last):
|
||||
File "/usr/local/lib/python3.10/runpy.py", line 196, in _run_module_as_main
|
||||
return _run_code(code, main_globals, None,
|
||||
File "/usr/local/lib/python3.10/runpy.py", line 86, in _run_code
|
||||
exec(code, run_globals)
|
||||
File "/usr/local/corex/lib64/python3/dist-packages/vllm/entrypoints/openai/api_server.py", line 595, in <module>
|
||||
uvloop.run(run_server(args))
|
||||
File "/usr/local/lib/python3.10/site-packages/uvloop/__init__.py", line 82, in run
|
||||
return loop.run_until_complete(wrapper())
|
||||
File "uvloop/loop.pyx", line 1518, in uvloop.loop.Loop.run_until_complete
|
||||
File "/usr/local/lib/python3.10/site-packages/uvloop/__init__.py", line 61, in wrapper
|
||||
@@ -0,0 +1,163 @@
|
||||
/usr/local/corex/lib64/python3/dist-packages/torch/cuda/__init__.py:51: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
|
||||
import pynvml # type: ignore[import]
|
||||
INFO 07-15 12:37:41 importing.py:10] Triton not installed; certain GPU-related functions will not be available.
|
||||
2026-07-15 12:37:43.215301: I tensorflow/core/util/port.cc:110] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
|
||||
2026-07-15 12:37:43.265874: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
|
||||
To enable the following instructions: SSE3 SSE4.1 SSE4.2 AVX AVX2 AVX512F AVX512_VNNI AVX512_BF16 AVX_VNNI AMX_TILE AMX_INT8 AMX_BF16 FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
|
||||
WARNING:tensorflow:Deprecation warnings have been disabled. Set TF_ENABLE_DEPRECATION_WARNINGS=1 to re-enable them.
|
||||
INFO 07-15 12:37:48 api_server.py:530] vLLM API server version 0.6.3
|
||||
INFO 07-15 12:37:48 api_server.py:531] args: Namespace(host='0.0.0.0', port=1111, uvicorn_log_level='info', allow_credentials=False, allowed_origins=['*'], allowed_methods=['*'], allowed_headers=['*'], api_key=None, lora_modules=None, prompt_adapters=None, chat_template='/workspace/chat_template_multi_system.jinja', response_role='assistant', ssl_keyfile=None, ssl_certfile=None, ssl_ca_certs=None, ssl_cert_reqs=0, root_path=None, middleware=[], return_tokens_as_token_ids=False, disable_frontend_multiprocessing=True, enable_auto_tool_choice=True, tool_call_parser='qwen3_coder', tool_parser_plugin='', reasoning_parser='qwen3', model='/root/public-storage/models/Qwen/Qwen3.6-35B-A3B', tokenizer=None, skip_tokenizer_init=False, revision=None, code_revision=None, tokenizer_revision=None, tokenizer_mode='auto', trust_remote_code=True, download_dir=None, load_format='auto', config_format='auto', dtype='auto', kv_cache_dtype='auto', quantization_param_path=None, max_model_len=245000, guided_decoding_backend='outlines', distributed_executor_backend=None, worker_use_ray=False, pipeline_parallel_size=1, tensor_parallel_size=4, max_parallel_loading_workers=None, ray_workers_use_nsight=False, block_size=16, enable_prefix_caching=True, disable_sliding_window=False, use_v2_block_manager=True, num_lookahead_slots=0, seed=0, swap_space=4, cpu_offload_gb=0, gpu_memory_utilization=0.9, num_gpu_blocks_override=None, max_num_batched_tokens=32768, max_num_seqs=1, max_logprobs=20, disable_log_stats=False, quantization=None, rope_scaling=None, rope_theta=None, enforce_eager=True, max_context_len_to_capture=None, max_seq_len_to_capture=245000, disable_custom_all_reduce=False, tokenizer_pool_size=0, tokenizer_pool_type='ray', tokenizer_pool_extra_config=None, limit_mm_per_prompt=None, mm_processor_kwargs=None, enable_lora=False, max_loras=1, max_lora_rank=16, lora_extra_vocab_size=256, lora_dtype='auto', long_lora_scaling_factors=None, max_cpu_loras=None, fully_sharded_loras=False, enable_prompt_adapter=False, max_prompt_adapters=1, max_prompt_adapter_token=0, device='auto', num_scheduler_steps=1, multi_step_stream_outputs=True, scheduler_delay_factor=0.0, enable_chunked_prefill=True, speculative_model=None, speculative_model_quantization=None, num_speculative_tokens=None, speculative_disable_mqa_scorer=False, speculative_draft_tensor_parallel_size=None, speculative_max_model_len=None, speculative_disable_by_batch_size=None, ngram_prompt_lookup_max=None, ngram_prompt_lookup_min=None, spec_decoding_acceptance_method='rejection_sampler', typical_acceptance_sampler_posterior_threshold=None, typical_acceptance_sampler_posterior_alpha=None, disable_logprobs_during_spec_decoding=None, model_loader_extra_config=None, ignore_patterns=[], preemption_mode=None, served_model_name=['llm'], qlora_adapter_name_or_path=None, otlp_traces_endpoint=None, collect_detailed_traces=None, disable_async_output_proc=False, override_neuron_config=None, scheduling_policy='fcfs', disable_log_requests=True, max_log_len=None, disable_fastapi_docs=False)
|
||||
INFO 07-15 12:37:48 config.py:1670] Downcasting torch.float32 to torch.float16.
|
||||
INFO 07-15 12:37:59 config.py:887] Defaulting to use mp for distributed inference
|
||||
INFO 07-15 12:37:59 config.py:1005] Chunked prefill is enabled with max_num_batched_tokens=32768.
|
||||
WARNING 07-15 12:37:59 config.py:380] To see benefits of async output processing, enable CUDA graph. Since, enforce-eager is enabled, async output processor cannot be used
|
||||
INFO 07-15 12:37:59 llm_engine.py:237] Initializing an LLM engine (v0.6.3) with config: model='/root/public-storage/models/Qwen/Qwen3.6-35B-A3B', speculative_config=None, tokenizer='/root/public-storage/models/Qwen/Qwen3.6-35B-A3B', skip_tokenizer_init=False, tokenizer_mode=auto, revision=None, override_neuron_config=None, rope_scaling=None, rope_theta=None, tokenizer_revision=None, trust_remote_code=True, dtype=torch.float16, max_seq_len=245000, download_dir=None, load_format=LoadFormat.AUTO, tensor_parallel_size=4, pipeline_parallel_size=1, disable_custom_all_reduce=False, quantization=None, enforce_eager=True, kv_cache_dtype=auto, quantization_param_path=None, device_config=cuda, decoding_config=DecodingConfig(guided_decoding_backend='outlines'), observability_config=ObservabilityConfig(otlp_traces_endpoint=None, collect_model_forward_time=False, collect_model_execute_time=False), seed=0, served_model_name=llm, use_v2_block_manager=True, num_scheduler_steps=1, chunked_prefill_enabled=True multi_step_stream_outputs=True, enable_prefix_caching=True, use_async_output_proc=False, use_cached_outputs=False, mm_processor_kwargs=None)
|
||||
WARNING 07-15 12:38:00 multiproc_gpu_executor.py:53] Reducing Torch parallelism from 64 threads to 1 to avoid unnecessary CPU contention. Set OMP_NUM_THREADS in the external environment to tune this value as needed.
|
||||
INFO 07-15 12:38:00 custom_cache_manager.py:17] Setting Triton cache manager to: vllm.triton_utils.custom_cache_manager:CustomCacheManager
|
||||
INFO 07-15 12:38:00 selector.py:266] Cannot use FlashAttention-2 backend because the vllm.vllm_flash_attn package is not found. Make sure that vllm_flash_attn was built and installed (on by default).
|
||||
INFO 07-15 12:38:00 selector.py:115] Using XFormers backend.
|
||||
/usr/local/corex/lib64/python3/dist-packages/torch/cuda/__init__.py:51: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
|
||||
import pynvml # type: ignore[import]
|
||||
/usr/local/corex/lib64/python3/dist-packages/torch/cuda/__init__.py:51: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
|
||||
import pynvml # type: ignore[import]
|
||||
/usr/local/corex/lib64/python3/dist-packages/torch/cuda/__init__.py:51: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
|
||||
import pynvml # type: ignore[import]
|
||||
INFO 07-15 12:38:02 importing.py:10] Triton not installed; certain GPU-related functions will not be available.
|
||||
INFO 07-15 12:38:02 importing.py:10] Triton not installed; certain GPU-related functions will not be available.
|
||||
INFO 07-15 12:38:02 importing.py:10] Triton not installed; certain GPU-related functions will not be available.
|
||||
WARNING:tensorflow:Deprecation warnings have been disabled. Set TF_ENABLE_DEPRECATION_WARNINGS=1 to re-enable them.
|
||||
WARNING:tensorflow:Deprecation warnings have been disabled. Set TF_ENABLE_DEPRECATION_WARNINGS=1 to re-enable them.
|
||||
WARNING:tensorflow:Deprecation warnings have been disabled. Set TF_ENABLE_DEPRECATION_WARNINGS=1 to re-enable them.
|
||||
[1;36m(VllmWorkerProcess pid=41602)[0;0m INFO 07-15 12:38:09 selector.py:266] Cannot use FlashAttention-2 backend because the vllm.vllm_flash_attn package is not found. Make sure that vllm_flash_attn was built and installed (on by default).
|
||||
[1;36m(VllmWorkerProcess pid=41602)[0;0m INFO 07-15 12:38:09 selector.py:115] Using XFormers backend.
|
||||
[1;36m(VllmWorkerProcess pid=41602)[0;0m INFO 07-15 12:38:09 multiproc_worker_utils.py:216] Worker ready; awaiting tasks
|
||||
[1;36m(VllmWorkerProcess pid=41603)[0;0m INFO 07-15 12:38:09 selector.py:266] Cannot use FlashAttention-2 backend because the vllm.vllm_flash_attn package is not found. Make sure that vllm_flash_attn was built and installed (on by default).
|
||||
[1;36m(VllmWorkerProcess pid=41603)[0;0m INFO 07-15 12:38:09 selector.py:115] Using XFormers backend.
|
||||
[1;36m(VllmWorkerProcess pid=41603)[0;0m INFO 07-15 12:38:09 multiproc_worker_utils.py:216] Worker ready; awaiting tasks
|
||||
[1;36m(VllmWorkerProcess pid=41604)[0;0m INFO 07-15 12:38:09 selector.py:266] Cannot use FlashAttention-2 backend because the vllm.vllm_flash_attn package is not found. Make sure that vllm_flash_attn was built and installed (on by default).
|
||||
[1;36m(VllmWorkerProcess pid=41604)[0;0m INFO 07-15 12:38:09 selector.py:115] Using XFormers backend.
|
||||
[1;36m(VllmWorkerProcess pid=41604)[0;0m INFO 07-15 12:38:09 multiproc_worker_utils.py:216] Worker ready; awaiting tasks
|
||||
INFO 07-15 12:38:10 shm_broadcast.py:242] vLLM message queue communication handle: Handle(connect_ip='127.0.0.1', local_reader_ranks=[1, 2, 3], buffer=<vllm.distributed.device_communicators.shm_broadcast.ShmRingBuffer object at 0x7f011af40f10>, local_subscribe_port=34275, remote_subscribe_port=None)
|
||||
INFO 07-15 12:38:10 model_runner.py:1117] Starting to load model /root/public-storage/models/Qwen/Qwen3.6-35B-A3B...
|
||||
[1;36m(VllmWorkerProcess pid=41603)[0;0m INFO 07-15 12:38:10 model_runner.py:1117] Starting to load model /root/public-storage/models/Qwen/Qwen3.6-35B-A3B...
|
||||
[1;36m(VllmWorkerProcess pid=41602)[0;0m INFO 07-15 12:38:10 model_runner.py:1117] Starting to load model /root/public-storage/models/Qwen/Qwen3.6-35B-A3B...
|
||||
[1;36m(VllmWorkerProcess pid=41604)[0;0m INFO 07-15 12:38:10 model_runner.py:1117] Starting to load model /root/public-storage/models/Qwen/Qwen3.6-35B-A3B...
|
||||
INFO 07-15 12:38:10 selector.py:266] Cannot use FlashAttention-2 backend because the vllm.vllm_flash_attn package is not found. Make sure that vllm_flash_attn was built and installed (on by default).
|
||||
INFO 07-15 12:38:10 selector.py:115] Using XFormers backend.
|
||||
[1;36m(VllmWorkerProcess pid=41602)[0;0m INFO 07-15 12:38:10 selector.py:266] Cannot use FlashAttention-2 backend because the vllm.vllm_flash_attn package is not found. Make sure that vllm_flash_attn was built and installed (on by default).
|
||||
[1;36m(VllmWorkerProcess pid=41602)[0;0m INFO 07-15 12:38:10 selector.py:115] Using XFormers backend.
|
||||
[1;36m(VllmWorkerProcess pid=41603)[0;0m INFO 07-15 12:38:10 selector.py:266] Cannot use FlashAttention-2 backend because the vllm.vllm_flash_attn package is not found. Make sure that vllm_flash_attn was built and installed (on by default).
|
||||
[1;36m(VllmWorkerProcess pid=41603)[0;0m INFO 07-15 12:38:10 selector.py:115] Using XFormers backend.
|
||||
[1;36m(VllmWorkerProcess pid=41604)[0;0m INFO 07-15 12:38:10 selector.py:266] Cannot use FlashAttention-2 backend because the vllm.vllm_flash_attn package is not found. Make sure that vllm_flash_attn was built and installed (on by default).
|
||||
[1;36m(VllmWorkerProcess pid=41604)[0;0m INFO 07-15 12:38:10 selector.py:115] Using XFormers backend.
|
||||
|
||||
Loading safetensors checkpoint shards: 0% Completed | 0/26 [00:00<?, ?it/s]
|
||||
|
||||
Loading safetensors checkpoint shards: 4% Completed | 1/26 [00:01<00:47, 1.89s/it]
|
||||
|
||||
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|
||||
|
||||
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|
||||
|
||||
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|
||||
|
||||
Loading safetensors checkpoint shards: 19% Completed | 5/26 [00:07<00:29, 1.42s/it]
|
||||
|
||||
Loading safetensors checkpoint shards: 23% Completed | 6/26 [00:09<00:33, 1.66s/it]
|
||||
|
||||
Loading safetensors checkpoint shards: 27% Completed | 7/26 [00:09<00:23, 1.26s/it]
|
||||
|
||||
Loading safetensors checkpoint shards: 31% Completed | 8/26 [00:11<00:25, 1.42s/it]
|
||||
|
||||
Loading safetensors checkpoint shards: 35% Completed | 9/26 [00:12<00:21, 1.28s/it]
|
||||
|
||||
Loading safetensors checkpoint shards: 38% Completed | 10/26 [00:14<00:24, 1.50s/it]
|
||||
|
||||
Loading safetensors checkpoint shards: 42% Completed | 11/26 [00:14<00:17, 1.14s/it]
|
||||
|
||||
Loading safetensors checkpoint shards: 46% Completed | 12/26 [00:16<00:18, 1.36s/it]
|
||||
|
||||
Loading safetensors checkpoint shards: 50% Completed | 13/26 [00:18<00:20, 1.55s/it]
|
||||
|
||||
Loading safetensors checkpoint shards: 54% Completed | 14/26 [00:20<00:21, 1.76s/it]
|
||||
|
||||
Loading safetensors checkpoint shards: 58% Completed | 15/26 [00:21<00:16, 1.48s/it]
|
||||
|
||||
Loading safetensors checkpoint shards: 62% Completed | 16/26 [00:24<00:18, 1.81s/it]
|
||||
|
||||
Loading safetensors checkpoint shards: 65% Completed | 17/26 [00:24<00:12, 1.41s/it]
|
||||
|
||||
Loading safetensors checkpoint shards: 69% Completed | 18/26 [00:26<00:12, 1.56s/it]
|
||||
|
||||
Loading safetensors checkpoint shards: 73% Completed | 19/26 [00:28<00:11, 1.62s/it]
|
||||
|
||||
Loading safetensors checkpoint shards: 77% Completed | 20/26 [00:31<00:11, 1.91s/it]
|
||||
|
||||
Loading safetensors checkpoint shards: 81% Completed | 21/26 [00:32<00:08, 1.67s/it]
|
||||
|
||||
Loading safetensors checkpoint shards: 85% Completed | 22/26 [00:33<00:05, 1.41s/it]
|
||||
|
||||
Loading safetensors checkpoint shards: 88% Completed | 23/26 [00:35<00:05, 1.75s/it]
|
||||
|
||||
Loading safetensors checkpoint shards: 92% Completed | 24/26 [00:37<00:03, 1.83s/it]
|
||||
|
||||
Loading safetensors checkpoint shards: 96% Completed | 25/26 [00:39<00:01, 1.90s/it]
|
||||
[1;36m(VllmWorkerProcess pid=41602)[0;0m INFO 07-15 12:38:50 model_runner.py:1128] Loading model weights took 16.2303 GB
|
||||
[1;36m(VllmWorkerProcess pid=41604)[0;0m INFO 07-15 12:38:50 model_runner.py:1128] Loading model weights took 16.2303 GB
|
||||
[1;36m(VllmWorkerProcess pid=41603)[0;0m INFO 07-15 12:38:50 model_runner.py:1128] Loading model weights took 16.2303 GB
|
||||
|
||||
Loading safetensors checkpoint shards: 100% Completed | 26/26 [00:40<00:00, 1.49s/it]
|
||||
|
||||
Loading safetensors checkpoint shards: 100% Completed | 26/26 [00:40<00:00, 1.55s/it]
|
||||
|
||||
INFO 07-15 12:38:51 model_runner.py:1128] Loading model weights took 16.2303 GB
|
||||
INFO 07-15 12:39:15 distributed_gpu_executor.py:57] # GPU blocks: 13081, # CPU blocks: 6553
|
||||
INFO 07-15 12:39:15 distributed_gpu_executor.py:61] Maximum concurrency for 245000 tokens per request: 0.85x
|
||||
[1;36m(VllmWorkerProcess pid=41602)[0;0m ERROR 07-15 12:39:15 multiproc_worker_utils.py:231] Exception in worker VllmWorkerProcess while processing method initialize_cache: The model's max seq len (245000) is larger than the maximum number of tokens that can be stored in KV cache (209296). Try increasing `gpu_memory_utilization` or decreasing `max_model_len` when initializing the engine., Traceback (most recent call last):
|
||||
[1;36m(VllmWorkerProcess pid=41602)[0;0m ERROR 07-15 12:39:15 multiproc_worker_utils.py:231] File "/usr/local/corex/lib64/python3/dist-packages/vllm/executor/multiproc_worker_utils.py", line 224, in _run_worker_process
|
||||
[1;36m(VllmWorkerProcess pid=41602)[0;0m ERROR 07-15 12:39:15 multiproc_worker_utils.py:231] output = executor(*args, **kwargs)
|
||||
[1;36m(VllmWorkerProcess pid=41602)[0;0m ERROR 07-15 12:39:15 multiproc_worker_utils.py:231] File "/usr/local/corex/lib64/python3/dist-packages/vllm/worker/worker.py", line 262, in initialize_cache
|
||||
[1;36m(VllmWorkerProcess pid=41602)[0;0m ERROR 07-15 12:39:15 multiproc_worker_utils.py:231] raise_if_cache_size_invalid(num_gpu_blocks,
|
||||
[1;36m(VllmWorkerProcess pid=41602)[0;0m ERROR 07-15 12:39:15 multiproc_worker_utils.py:231] File "/usr/local/corex/lib64/python3/dist-packages/vllm/worker/worker.py", line 492, in raise_if_cache_size_invalid
|
||||
[1;36m(VllmWorkerProcess pid=41602)[0;0m ERROR 07-15 12:39:15 multiproc_worker_utils.py:231] raise ValueError(
|
||||
[1;36m(VllmWorkerProcess pid=41602)[0;0m ERROR 07-15 12:39:15 multiproc_worker_utils.py:231] ValueError: The model's max seq len (245000) is larger than the maximum number of tokens that can be stored in KV cache (209296). Try increasing `gpu_memory_utilization` or decreasing `max_model_len` when initializing the engine.
|
||||
[1;36m(VllmWorkerProcess pid=41602)[0;0m ERROR 07-15 12:39:15 multiproc_worker_utils.py:231]
|
||||
[1;36m(VllmWorkerProcess pid=41603)[0;0m ERROR 07-15 12:39:15 multiproc_worker_utils.py:231] Exception in worker VllmWorkerProcess while processing method initialize_cache: The model's max seq len (245000) is larger than the maximum number of tokens that can be stored in KV cache (209296). Try increasing `gpu_memory_utilization` or decreasing `max_model_len` when initializing the engine., Traceback (most recent call last):
|
||||
[1;36m(VllmWorkerProcess pid=41603)[0;0m ERROR 07-15 12:39:15 multiproc_worker_utils.py:231] File "/usr/local/corex/lib64/python3/dist-packages/vllm/executor/multiproc_worker_utils.py", line 224, in _run_worker_process
|
||||
[1;36m(VllmWorkerProcess pid=41603)[0;0m ERROR 07-15 12:39:15 multiproc_worker_utils.py:231] output = executor(*args, **kwargs)
|
||||
[1;36m(VllmWorkerProcess pid=41603)[0;0m ERROR 07-15 12:39:15 multiproc_worker_utils.py:231] File "/usr/local/corex/lib64/python3/dist-packages/vllm/worker/worker.py", line 262, in initialize_cache
|
||||
[1;36m(VllmWorkerProcess pid=41603)[0;0m ERROR 07-15 12:39:15 multiproc_worker_utils.py:231] raise_if_cache_size_invalid(num_gpu_blocks,
|
||||
[1;36m(VllmWorkerProcess pid=41603)[0;0m ERROR 07-15 12:39:15 multiproc_worker_utils.py:231] File "/usr/local/corex/lib64/python3/dist-packages/vllm/worker/worker.py", line 492, in raise_if_cache_size_invalid
|
||||
[1;36m(VllmWorkerProcess pid=41603)[0;0m ERROR 07-15 12:39:15 multiproc_worker_utils.py:231] raise ValueError(
|
||||
[1;36m(VllmWorkerProcess pid=41603)[0;0m ERROR 07-15 12:39:15 multiproc_worker_utils.py:231] ValueError: The model's max seq len (245000) is larger than the maximum number of tokens that can be stored in KV cache (209296). Try increasing `gpu_memory_utilization` or decreasing `max_model_len` when initializing the engine.
|
||||
[1;36m(VllmWorkerProcess pid=41603)[0;0m ERROR 07-15 12:39:15 multiproc_worker_utils.py:231]
|
||||
[1;36m(VllmWorkerProcess pid=41604)[0;0m ERROR 07-15 12:39:15 multiproc_worker_utils.py:231] Exception in worker VllmWorkerProcess while processing method initialize_cache: The model's max seq len (245000) is larger than the maximum number of tokens that can be stored in KV cache (209296). Try increasing `gpu_memory_utilization` or decreasing `max_model_len` when initializing the engine., Traceback (most recent call last):
|
||||
[1;36m(VllmWorkerProcess pid=41604)[0;0m ERROR 07-15 12:39:15 multiproc_worker_utils.py:231] File "/usr/local/corex/lib64/python3/dist-packages/vllm/executor/multiproc_worker_utils.py", line 224, in _run_worker_process
|
||||
[1;36m(VllmWorkerProcess pid=41604)[0;0m ERROR 07-15 12:39:15 multiproc_worker_utils.py:231] output = executor(*args, **kwargs)
|
||||
[1;36m(VllmWorkerProcess pid=41604)[0;0m ERROR 07-15 12:39:15 multiproc_worker_utils.py:231] File "/usr/local/corex/lib64/python3/dist-packages/vllm/worker/worker.py", line 262, in initialize_cache
|
||||
[1;36m(VllmWorkerProcess pid=41604)[0;0m ERROR 07-15 12:39:15 multiproc_worker_utils.py:231] raise_if_cache_size_invalid(num_gpu_blocks,
|
||||
[1;36m(VllmWorkerProcess pid=41604)[0;0m ERROR 07-15 12:39:15 multiproc_worker_utils.py:231] File "/usr/local/corex/lib64/python3/dist-packages/vllm/worker/worker.py", line 492, in raise_if_cache_size_invalid
|
||||
[1;36m(VllmWorkerProcess pid=41604)[0;0m ERROR 07-15 12:39:15 multiproc_worker_utils.py:231] raise ValueError(
|
||||
[1;36m(VllmWorkerProcess pid=41604)[0;0m ERROR 07-15 12:39:15 multiproc_worker_utils.py:231] ValueError: The model's max seq len (245000) is larger than the maximum number of tokens that can be stored in KV cache (209296). Try increasing `gpu_memory_utilization` or decreasing `max_model_len` when initializing the engine.
|
||||
[1;36m(VllmWorkerProcess pid=41604)[0;0m ERROR 07-15 12:39:15 multiproc_worker_utils.py:231]
|
||||
Traceback (most recent call last):
|
||||
File "/usr/local/lib/python3.10/runpy.py", line 196, in _run_module_as_main
|
||||
return _run_code(code, main_globals, None,
|
||||
File "/usr/local/lib/python3.10/runpy.py", line 86, in _run_code
|
||||
exec(code, run_globals)
|
||||
File "/usr/local/corex/lib64/python3/dist-packages/vllm/entrypoints/openai/api_server.py", line 595, in <module>
|
||||
uvloop.run(run_server(args))
|
||||
File "/usr/local/lib/python3.10/site-packages/uvloop/__init__.py", line 82, in run
|
||||
return loop.run_until_complete(wrapper())
|
||||
File "uvloop/loop.pyx", line 1518, in uvloop.loop.Loop.run_until_complete
|
||||
File "/usr/local/lib/python3.10/site-packages/uvloop/__init__.py", line 61, in wrapper
|
||||
return await main
|
||||
File "/usr/local/corex/lib64/python3/dist-packages/vllm/entrypoints/openai/api_server.py", line 562, in run_server
|
||||
async with build_async_engine_client(args) as engine_client:
|
||||
File "/usr/local/lib/python3.10/contextlib.py", line 199, in __aenter__
|
||||
return await anext(self.gen)
|
||||
File "/usr/local/corex/lib64/python3/dist-packages/vllm/entrypoints/openai/api_server.py", line 108, in build_async_engine_client
|
||||
async with build_async_engine_client_from_engine_args(
|
||||
File "/usr/local/lib/python3.10/contextlib.py", line 199, in __aenter__
|
||||
return await anext(self.gen)
|
||||
File "/usr/local/corex/lib64/python3/dist-packages/vllm/entrypoints/openai/api_server.py", line 142, in build_async_engine_client_from_engine_args
|
||||
@@ -0,0 +1,124 @@
|
||||
{
|
||||
"created_at": "2026-07-15T11:41:17",
|
||||
"url": "http://127.0.0.1:1111",
|
||||
"model": "llm",
|
||||
"dataset": "/root/work/logs/synthetic_cumulative_8_s3_cap40k.jsonl",
|
||||
"concurrency": 1,
|
||||
"max_requests": 8,
|
||||
"max_tokens": 256,
|
||||
"wall_sec": 329.40423798561096,
|
||||
"success_rate": 1.0,
|
||||
"ttft_p90_sec": 12.695122232101856,
|
||||
"output_tps_p10_per_request": 5.999746141283892,
|
||||
"aggregate_output_tps": 5.664772291367759,
|
||||
"aggregate_input_tps_uncached": 176.83743341075206,
|
||||
"aggregate_cache_tps": 507.7287439371248,
|
||||
"cache_hit_rate": 0.7416795639891973,
|
||||
"weighted_token_throughput": 874.4415881272979,
|
||||
"totals": {
|
||||
"requests": 8,
|
||||
"success": 8,
|
||||
"prompt_tokens": 225499,
|
||||
"input_tokens_uncached": 58251,
|
||||
"cached_tokens": 167248,
|
||||
"completion_tokens": 1866,
|
||||
"reasoning_tokens": 1731
|
||||
},
|
||||
"targets": {
|
||||
"output_tps_p10_per_request_gte_20": false,
|
||||
"ttft_p90_lte_5": false,
|
||||
"cache_hit_rate_gte_50pct": true,
|
||||
"success_rate_gte_99pct": true,
|
||||
"weighted_token_throughput_gte_8000": false
|
||||
},
|
||||
"results": [
|
||||
{
|
||||
"ok": true,
|
||||
"elapsed_sec": 26.29428836517036,
|
||||
"ttft_sec": 7.266017025336623,
|
||||
"prompt_tokens": 7498,
|
||||
"cached_tokens": 0,
|
||||
"completion_tokens": 165,
|
||||
"reasoning_tokens": 79,
|
||||
"output_tps": 8.671307921418453,
|
||||
"error": null
|
||||
},
|
||||
{
|
||||
"ok": true,
|
||||
"elapsed_sec": 47.98864242434502,
|
||||
"ttft_sec": 21.27845441363752,
|
||||
"prompt_tokens": 34177,
|
||||
"cached_tokens": 7488,
|
||||
"completion_tokens": 165,
|
||||
"reasoning_tokens": 116,
|
||||
"output_tps": 6.177418142240531,
|
||||
"error": null
|
||||
},
|
||||
{
|
||||
"ok": true,
|
||||
"elapsed_sec": 44.649470107629895,
|
||||
"ttft_sec": 3.02971662953496,
|
||||
"prompt_tokens": 35384,
|
||||
"cached_tokens": 34160,
|
||||
"completion_tokens": 256,
|
||||
"reasoning_tokens": 256,
|
||||
"output_tps": 6.150925428588529,
|
||||
"error": null
|
||||
},
|
||||
{
|
||||
"ok": true,
|
||||
"elapsed_sec": 38.541101360693574,
|
||||
"ttft_sec": 9.016551297158003,
|
||||
"prompt_tokens": 19274,
|
||||
"cached_tokens": 7472,
|
||||
"completion_tokens": 256,
|
||||
"reasoning_tokens": 256,
|
||||
"output_tps": 8.670750255265498,
|
||||
"error": null
|
||||
},
|
||||
{
|
||||
"ok": true,
|
||||
"elapsed_sec": 45.66694761812687,
|
||||
"ttft_sec": 3.336757702752948,
|
||||
"prompt_tokens": 37099,
|
||||
"cached_tokens": 35376,
|
||||
"completion_tokens": 256,
|
||||
"reasoning_tokens": 256,
|
||||
"output_tps": 6.047693159699793,
|
||||
"error": null
|
||||
},
|
||||
{
|
||||
"ok": true,
|
||||
"elapsed_sec": 34.86508701182902,
|
||||
"ttft_sec": 5.408511884510517,
|
||||
"prompt_tokens": 14661,
|
||||
"cached_tokens": 7472,
|
||||
"completion_tokens": 256,
|
||||
"reasoning_tokens": 256,
|
||||
"output_tps": 8.69075915626666,
|
||||
"error": null
|
||||
},
|
||||
{
|
||||
"ok": true,
|
||||
"elapsed_sec": 45.62775577604771,
|
||||
"ttft_sec": 3.077076403424144,
|
||||
"prompt_tokens": 38199,
|
||||
"cached_tokens": 37088,
|
||||
"completion_tokens": 256,
|
||||
"reasoning_tokens": 256,
|
||||
"output_tps": 6.016355173983576,
|
||||
"error": null
|
||||
},
|
||||
{
|
||||
"ok": true,
|
||||
"elapsed_sec": 45.76239648461342,
|
||||
"ttft_sec": 2.816522454842925,
|
||||
"prompt_tokens": 39207,
|
||||
"cached_tokens": 38192,
|
||||
"completion_tokens": 256,
|
||||
"reasoning_tokens": 256,
|
||||
"output_tps": 5.960991731651295,
|
||||
"error": null
|
||||
}
|
||||
]
|
||||
}
|
||||
@@ -0,0 +1,124 @@
|
||||
{
|
||||
"created_at": "2026-07-15T12:25:01",
|
||||
"url": "http://127.0.0.1:1111",
|
||||
"model": "llm",
|
||||
"dataset": "/root/work/logs/synthetic_cumulative_8_s3_cap40k.jsonl",
|
||||
"concurrency": 1,
|
||||
"max_requests": 8,
|
||||
"max_tokens": 256,
|
||||
"wall_sec": 342.52822289802134,
|
||||
"success_rate": 1.0,
|
||||
"ttft_p90_sec": 13.948715174756941,
|
||||
"output_tps_p10_per_request": 5.8688016391455635,
|
||||
"aggregate_output_tps": 5.584941246051847,
|
||||
"aggregate_input_tps_uncached": 170.06189886239736,
|
||||
"aggregate_cache_tps": 488.27509331922596,
|
||||
"cache_hit_rate": 0.7416795639891973,
|
||||
"weighted_token_throughput": 843.2419803433036,
|
||||
"totals": {
|
||||
"requests": 8,
|
||||
"success": 8,
|
||||
"prompt_tokens": 225499,
|
||||
"input_tokens_uncached": 58251,
|
||||
"cached_tokens": 167248,
|
||||
"completion_tokens": 1913,
|
||||
"reasoning_tokens": 1807
|
||||
},
|
||||
"targets": {
|
||||
"output_tps_p10_per_request_gte_20": false,
|
||||
"ttft_p90_lte_5": false,
|
||||
"cache_hit_rate_gte_50pct": true,
|
||||
"success_rate_gte_99pct": true,
|
||||
"weighted_token_throughput_gte_8000": false
|
||||
},
|
||||
"results": [
|
||||
{
|
||||
"ok": true,
|
||||
"elapsed_sec": 37.44546605460346,
|
||||
"ttft_sec": 8.050519423559308,
|
||||
"prompt_tokens": 7498,
|
||||
"cached_tokens": 0,
|
||||
"completion_tokens": 256,
|
||||
"reasoning_tokens": 196,
|
||||
"output_tps": 8.708979921386797,
|
||||
"error": null
|
||||
},
|
||||
{
|
||||
"ok": true,
|
||||
"elapsed_sec": 42.67267605289817,
|
||||
"ttft_sec": 23.193294195458293,
|
||||
"prompt_tokens": 34177,
|
||||
"cached_tokens": 7488,
|
||||
"completion_tokens": 121,
|
||||
"reasoning_tokens": 75,
|
||||
"output_tps": 6.21169608386653,
|
||||
"error": null
|
||||
},
|
||||
{
|
||||
"ok": true,
|
||||
"elapsed_sec": 44.97984442859888,
|
||||
"ttft_sec": 3.3009007796645164,
|
||||
"prompt_tokens": 35384,
|
||||
"cached_tokens": 34160,
|
||||
"completion_tokens": 256,
|
||||
"reasoning_tokens": 256,
|
||||
"output_tps": 6.142190218550449,
|
||||
"error": null
|
||||
},
|
||||
{
|
||||
"ok": true,
|
||||
"elapsed_sec": 39.61451355740428,
|
||||
"ttft_sec": 9.986752737313509,
|
||||
"prompt_tokens": 19274,
|
||||
"cached_tokens": 7472,
|
||||
"completion_tokens": 256,
|
||||
"reasoning_tokens": 256,
|
||||
"output_tps": 8.640544979234637,
|
||||
"error": null
|
||||
},
|
||||
{
|
||||
"ok": true,
|
||||
"elapsed_sec": 47.399366622790694,
|
||||
"ttft_sec": 3.8023243956267834,
|
||||
"prompt_tokens": 37099,
|
||||
"cached_tokens": 35376,
|
||||
"completion_tokens": 256,
|
||||
"reasoning_tokens": 256,
|
||||
"output_tps": 5.87195797976622,
|
||||
"error": null
|
||||
},
|
||||
{
|
||||
"ok": true,
|
||||
"elapsed_sec": 36.7238706368953,
|
||||
"ttft_sec": 6.21262020803988,
|
||||
"prompt_tokens": 14661,
|
||||
"cached_tokens": 7472,
|
||||
"completion_tokens": 256,
|
||||
"reasoning_tokens": 256,
|
||||
"output_tps": 8.390347704592697,
|
||||
"error": null
|
||||
},
|
||||
{
|
||||
"ok": true,
|
||||
"elapsed_sec": 47.17942987754941,
|
||||
"ttft_sec": 3.504132028669119,
|
||||
"prompt_tokens": 38199,
|
||||
"cached_tokens": 37088,
|
||||
"completion_tokens": 256,
|
||||
"reasoning_tokens": 256,
|
||||
"output_tps": 5.86143684436403,
|
||||
"error": null
|
||||
},
|
||||
{
|
||||
"ok": true,
|
||||
"elapsed_sec": 46.50449903123081,
|
||||
"ttft_sec": 3.3495454359799623,
|
||||
"prompt_tokens": 39207,
|
||||
"cached_tokens": 38192,
|
||||
"completion_tokens": 256,
|
||||
"reasoning_tokens": 256,
|
||||
"output_tps": 5.932111581002198,
|
||||
"error": null
|
||||
}
|
||||
]
|
||||
}
|
||||
117
worklogs/remote_scripts/start_platform_245k_repro.sh
Normal file
117
worklogs/remote_scripts/start_platform_245k_repro.sh
Normal file
@@ -0,0 +1,117 @@
|
||||
#!/usr/bin/env bash
|
||||
set -euo pipefail
|
||||
|
||||
OUT="${1:-/root/work/logs/platform_245k_repro_20260715_01}"
|
||||
PORT="${PORT:-1111}"
|
||||
MODEL="${MODEL:-/root/public-storage/models/Qwen/Qwen3.6-35B-A3B}"
|
||||
MAX_MODEL_LEN="${MAX_MODEL_LEN:-245000}"
|
||||
MAX_SEQ_LEN_TO_CAPTURE="${MAX_SEQ_LEN_TO_CAPTURE:-32768}"
|
||||
MAX_NUM_BATCHED_TOKENS="${MAX_NUM_BATCHED_TOKENS:-16384}"
|
||||
MOE_DECODE_NATIVE="${MOE_DECODE_NATIVE:-0}"
|
||||
mkdir -p "$OUT"
|
||||
|
||||
cd /root
|
||||
export PYTHONPATH=/usr/local/corex/lib64/python3/dist-packages
|
||||
export LD_LIBRARY_PATH=/usr/local/corex/lib64:/usr/local/iluvatar/lib64:/usr/local/openmpi/lib
|
||||
export PATH=/usr/local/corex/bin:/usr/local/sbin:/usr/local/bin:/usr/sbin:/usr/bin:/sbin:/bin:/usr/local/openmpi/bin
|
||||
export VLLM_ENGINE_ITERATION_TIMEOUT_S=3600
|
||||
export MOE_NATIVE=1
|
||||
export CHUNK_PARALLEL=1
|
||||
export PREFIX_FLASH=1
|
||||
export RMSNORM_NATIVE=1
|
||||
export LOOP1_NATIVE=1
|
||||
export PROF_TRACE=0
|
||||
export PROF_MOE_SUMMARY=0
|
||||
export MOE_DECODE_NATIVE
|
||||
|
||||
python3 - <<'PY'
|
||||
import os
|
||||
import signal
|
||||
import subprocess
|
||||
import time
|
||||
|
||||
out = subprocess.check_output(["ps", "-eo", "pid,args"], text=True)
|
||||
pids = []
|
||||
for line in out.splitlines():
|
||||
if "vllm.entrypoints.openai.api_server" in line or "VllmWorkerProcess" in line:
|
||||
pid = int(line.strip().split(None, 1)[0])
|
||||
if pid != os.getpid():
|
||||
pids.append(pid)
|
||||
print("stop_pids", pids)
|
||||
for pid in pids:
|
||||
try:
|
||||
os.kill(pid, signal.SIGTERM)
|
||||
except ProcessLookupError:
|
||||
pass
|
||||
time.sleep(8)
|
||||
out = subprocess.check_output(["ps", "-eo", "pid,args"], text=True)
|
||||
left = []
|
||||
for line in out.splitlines():
|
||||
if "vllm.entrypoints.openai.api_server" in line or "VllmWorkerProcess" in line:
|
||||
pid = int(line.strip().split(None, 1)[0])
|
||||
left.append(pid)
|
||||
for pid in left:
|
||||
try:
|
||||
os.kill(pid, signal.SIGKILL)
|
||||
except ProcessLookupError:
|
||||
pass
|
||||
print("sigkill_pids", left)
|
||||
PY
|
||||
|
||||
echo "[repro] gpu before start" | tee "$OUT/driver.log"
|
||||
echo "[repro] MAX_MODEL_LEN=$MAX_MODEL_LEN MAX_SEQ_LEN_TO_CAPTURE=$MAX_SEQ_LEN_TO_CAPTURE MAX_NUM_BATCHED_TOKENS=$MAX_NUM_BATCHED_TOKENS MOE_DECODE_NATIVE=$MOE_DECODE_NATIVE" | tee -a "$OUT/driver.log"
|
||||
LD_LIBRARY_PATH=/usr/local/corex-3.2.3/lib64:/usr/local/corex/lib64:/usr/local/corex/lib:/usr/local/iluvatar/lib64 \
|
||||
/usr/local/corex/bin/ixsmi | tee -a "$OUT/driver.log"
|
||||
|
||||
nohup python3 -m vllm.entrypoints.openai.api_server \
|
||||
--model "$MODEL" \
|
||||
--served-model-name llm \
|
||||
--host 0.0.0.0 \
|
||||
--port "$PORT" \
|
||||
--max-model-len "$MAX_MODEL_LEN" \
|
||||
--enforce-eager \
|
||||
--gpu-memory-utilization 0.9 \
|
||||
--trust-remote-code \
|
||||
-tp 4 \
|
||||
--max-num-seqs 1 \
|
||||
--disable-log-requests \
|
||||
--disable-frontend-multiprocessing \
|
||||
--max-num-batched-tokens "$MAX_NUM_BATCHED_TOKENS" \
|
||||
--enable-chunked-prefill \
|
||||
--max-seq-len-to-capture "$MAX_SEQ_LEN_TO_CAPTURE" \
|
||||
--enable-auto-tool-choice \
|
||||
--tool-call-parser qwen3_coder \
|
||||
--reasoning-parser qwen3 \
|
||||
--enable-prefix-caching \
|
||||
--chat-template /workspace/chat_template_multi_system.jinja \
|
||||
> "$OUT/server.log" 2>&1 &
|
||||
|
||||
echo $! > "$OUT/server.pid"
|
||||
echo "[repro] server_pid=$(cat "$OUT/server.pid")" | tee -a "$OUT/driver.log"
|
||||
|
||||
for i in $(seq 1 90); do
|
||||
if python3 - <<PY >/dev/null 2>&1
|
||||
import urllib.request
|
||||
urllib.request.urlopen("http://127.0.0.1:${PORT}/health", timeout=2).read()
|
||||
PY
|
||||
then
|
||||
echo "[repro] health_ok_after_checks=$i" | tee -a "$OUT/driver.log"
|
||||
exit 0
|
||||
fi
|
||||
if ! kill -0 "$(cat "$OUT/server.pid")" 2>/dev/null; then
|
||||
echo "[repro] server_exited_at_check=$i" | tee -a "$OUT/driver.log"
|
||||
tail -160 "$OUT/server.log" | tee -a "$OUT/driver.log"
|
||||
exit 2
|
||||
fi
|
||||
if [ $((i % 6)) -eq 0 ]; then
|
||||
echo "[repro] waiting_check=$i" | tee -a "$OUT/driver.log"
|
||||
tail -10 "$OUT/server.log" | tee -a "$OUT/driver.log"
|
||||
LD_LIBRARY_PATH=/usr/local/corex-3.2.3/lib64:/usr/local/corex/lib64:/usr/local/corex/lib:/usr/local/iluvatar/lib64 \
|
||||
/usr/local/corex/bin/ixsmi | head -45 | tee -a "$OUT/driver.log"
|
||||
fi
|
||||
sleep 10
|
||||
done
|
||||
|
||||
echo "[repro] health_timeout" | tee -a "$OUT/driver.log"
|
||||
tail -220 "$OUT/server.log" | tee -a "$OUT/driver.log"
|
||||
exit 3
|
||||
Reference in New Issue
Block a user