[Bugfix][CPU] Voxtral realtime: scale attention metadata out of place

[alex-chaiko]

This is a follow up PR to #53921 which introduces CPU support for mistralai/Voxtral-Mini-4B-Realtime-2602 model.
Now Voxtral-Mini-4B-Realtime-2602 crashes with Segfault on vLLM CPU backend if run natively while it works on docker.

Reproducer

Full vLLM build from source according to https://docs.vllm.ai/en/latest/getting_started/installation/cpu/#build-wheel-from-source.

pip install mistral-common[soundfile]
vllm serve mistralai/Voxtral-Mini-4B-Realtime-2602
. . .
(APIServer pid=519002) INFO 09-03 10:50:58 [model.py:688] Resolved architecture: VoxtralRealtimeGeneration
. . .
(APIServer pid=519002) WARNING 09-03 10:51:00 [vllm.py:667] Model Runner V2 requires Triton; using the V1 model runner instead.
WARNING 09-03 10:51:04 [importing.py:45] Triton is installed, but doesn't include CPU backend. Disabling Triton.
. . .
(APIServer pid=520457) INFO:     Application startup complete.

Testing

python examples/speech_to_text/realtime/openai_realtime_client.py 
WARNING 09-03 11:00:27 [importing.py:45] Triton is installed, but doesn't include CPU backend. Disabling Triton.
INFO 09-03 11:00:27 [importing.py:74] Triton is installed but 0 active driver(s) found (expected 1). Disabling Triton to prevent runtime errors.
INFO 09-03 11:00:27 [importing.py:98] Triton not installed or not compatible; certain GPU-related functions will not be available.
No audio path provided, using default: /home/user/.cache/vllm/assets/vllm_public_assets/mary_had_lamb.ogg
Session created: sess-9d3300c858ac57f8
Loading audio from: /home/user/.cache/vllm/assets/vllm_public_assets/mary_had_lamb.ogg
Sending 125 audio chunks...
Audio sent. Waiting for transcription...

Transcription: 
Error: EngineCore encountered an issue. See stack trace (above) for the root cause.

Error on serving side

(APIServer pid=522058) INFO:     127.0.0.1:49736 - "WebSocket /v1/realtime" [accepted]
(APIServer pid=522058) INFO:     connection open
!!!!!!! Segfault encountered !!!!!!!
  File "<unknown>", line 0, in tcmalloc::ThreadCache::ReleaseToCentralCache(tcmalloc::ThreadCache::FreeList*, unsigned int, int)
  File "<unknown>", line 0, in tcmalloc::ThreadCache::Scavenge()
. . .
(EngineCore pid=522260) ERROR 09-03 11:05:46 [core.py:1376] EngineCore encountered a fatal error.

Root cause

The problem is that WhisperCausalAttentionWithBlockPoolingBuilder.build() scaled the attention metadata by block_pool_size in place on a copy.deepcopy. On the V1 CPU model runner, query_start_loc and query_start_loc_cpu are .gpu / .cpu slices of one CpuGpuBuffer that CPUModelRunner._postprocess_tensors aliases (v.gpu = v.cpu), and deepcopy preserves that sharing - so the two statements compound to block_pool_size ** 2.

The fix is to apply scaling out of place.

Purpose

Scale attention metadata out of place to avoid double scaling and heap corruption.

Test Plan

vllm serve mistralai/Voxtral-Mini-4B-Realtime-2602
python examples/speech_to_text/realtime/openai_realtime_client.py 

Test Result

python examples/speech_to_text/realtime/openai_realtime_client.py 
WARNING 09-03 11:46:03 [importing.py:45] Triton is installed, but doesn't include CPU backend. Disabling Triton.
INFO 09-03 11:46:03 [importing.py:74] Triton is installed but 0 active driver(s) found (expected 1). Disabling Triton to prevent runtime errors.
INFO 09-03 11:46:03 [importing.py:98] Triton not installed or not compatible; certain GPU-related functions will not be available.
No audio path provided, using default: /home/sdp/.cache/vllm/assets/vllm_public_assets/mary_had_lamb.ogg
Session created: sess-8667690e98689ad0
Loading audio from: /home/sdp/.cache/vllm/assets/vllm_public_assets/mary_had_lamb.ogg
Sending 125 audio chunks...
Audio sent. Waiting for transcription...

Transcription:  First words I spoke in the original phonograph. A little piece of practical poetry. Mary had a little lamb, it squeaked with quite a flow, and everywhere that Mary went, the lamb was sure to go.

Final transcription:  First words I spoke in the original phonograph. A little piece of practical poetry. Mary had a little lamb, it squeaked with quite a flow, and everywhere that Mary went, the lamb was sure to go.
Usage: {'prompt_tokens': 39, 'total_tokens': 249, 'completion_tokens': 210, 'prompt_tokens_details': None, 'completion_tokens_details': None}

---

Details

- [ ] Bugfix for mistralai/Voxtral-Mini-4B-Realtime-2602 - [ ] python examples/speech_to_text/realtime/openai_realtime_client.py - [ ] The test results, such as pasting the results comparison before and after, or e2e results

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[alex-chaiko]

The model of interest works on CPU via the vLLM Docker image because the Docker environment includes the Triton CPU backend and uses the V2 model runner, which follows a different code path. This is the preferred option, with the expectation that Triton CPU support will soon be available in the native environment as well.

Currently, Triton-CPU can be built and installed in the same way as shown in the Dockerfile.cpu:

git clone --recurse-submodules --shallow-submodules --filter=blob:none https://github.com/triton-lang/triton-cpu.git
cd triton-cpu
git checkout 270e696d
export MAX_JOBS=16
uv build --wheel --out-dir=../dist
. . .
Successfully built ../dist/triton-3.7.0+git270e696d-cp312-cp312-linux_x86_64.whl
pip install triton-3.7.0+git270e696d-cp312-cp312-linux_x86_64.whl

This PR will be closed in favor of the V2 model runner with the Triton CPU backend.

[alex-chaiko]

The issue has been fixed by #51167.