Fix MiDashengLM TP>1 crash in audio encoder attention

[mganczarenko]

Purpose

Fix TP>1 crash in mispeech/midashenglm-7b audio encoder.

DashengAttention.forward() uses full embed_dim (C) in tensor reshapes instead of TP-aware self.head_dim and self.q_size, causing shape mismatch runtime errors when tensor_parallel_size > 1.

Test Plan

# Set up environment
uv venv --python 3.12
source .venv/bin/activate
uv pip install -r requirements/lint.txt
VLLM_USE_PRECOMPILED=1 uv pip install -e . --torch-backend=auto

# Test TP=2 (previously crashed, now works)
vllm serve mispeech/midashenglm-7b \
  --dtype bfloat16 \
  --tensor-parallel-size 2 \
  --max-model-len 8192 \
  --gpu-memory-utilization 0.85 \
  --trust-remote-code \
  --limit-mm-per-prompt '{"image": 1}'

# Test inference
 curl http://localhost:8000/v1/completions \
  -H "Content-Type: application/json" \
  -d '{
    "model": "mispeech/midashenglm-7b",
    "prompt": "Hello",
    "max_tokens": 10
  }'

Test Result

Before fix (TP=2):
RuntimeError: shape '[1, 1568, 3, 8, 64]' is invalid for input of size 602112
Server crashes during first inference request.

After fix (TP=2):
✓ Server starts successfully
✓ Health check passes
✓ Inference completes (generates audio tokens 151872 as expected for audio model)
✓ Clean shutdown

TP=1 (unchanged): Works before and after — fix is no-op when not sharded.

serve_succeed.log
serve_failed.log

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[Copilot]

Pull request overview

Fixes a tensor-parallelism (TP>1) shape mismatch crash in the MiDashengLM audio encoder attention by making the attention tensor reshapes use TP-aware dimensions instead of the full (unsharded) embedding dimension.

Changes:

• Update DashengAttention.forward() to reshape QKV using self.head_dim (per-head dim) rather than C // self.num_heads.
• Update the post-attention reshape to use self.q_size (per-partition Q hidden size) rather than the full C.

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[mergify]

Hi @mganczarenko, the pre-commit checks have failed. Please run:

uv pip install pre-commit>=4.5.1
pre-commit install
pre-commit run --all-files

Then, commit the changes and push to your branch.

For future commits, pre-commit will run automatically on changed files before each commit.