[Model] Fix hunyuan-vl shape mismatch

[Potabk]

Purpose

After mmencoderattn, we got the out shaple like :[B, S, N, D], We should merge the multi-head before entering the O matrix.

Test Plan

Test Result

---

Essential Elements of an Effective PR Description Checklist

• The purpose of the PR, such as "Fix some issue (link existing issues this PR will resolve)".
• The test plan, such as providing test command.
• The test results, such as pasting the results comparison before and after, or e2e results
• (Optional) The necessary documentation update, such as updating supported_models.md and examples for a new model.
• (Optional) Release notes update. If your change is user facing, please update the release notes draft in the Google Doc.

[gemini-code-assist]

Code Review

This pull request addresses a shape mismatch in HunYuanVisionAttention by correctly reshaping the attention output before the final projection. The fix appears to be correct given the context of a 4D tensor output from the attention layer. I have one suggestion to make the implementation more maintainable and robust.

[gemini-code-assist]

For better maintainability and readability, you can simplify the calculation of the last dimension in the view operation. Instead of re-calculating the partitioned hidden size from the number of heads and head size, you can directly use the shape of the weight from the subsequent projection layer o_proj. This makes the code more robust to future changes as it directly references the expected input dimension of the next layer.

        out = out.view(
            x.size(0),
            -1,
            self.o_proj.weight.shape[1],
        )

[Isotr0py]

After mmencoderattn, we got the out shaple like :[B, S, N, D], We should merge the multi-head before entering the O matrix.

Not really, we will automatically reshape output to make sure it align with q, k, v input:

vllm/vllm/attention/layers/mm_encoder_attention.py

Lines 160 to 177 in 8711b21

	is_reshaped = query.dim() != 4
	query, key, value = self.maybe_reshape_qkv_to_4d(
	query, key, value, bsz, q_len, kv_len
	)
	output = vit_flash_attn_wrapper(
	q=query,
	k=key,
	v=value,
	cu_seqlens=cu_seqlens,
	max_seqlen=max_seqlen,
	batch_size=bsz,
	is_rocm_aiter=(self.attn_backend == AttentionBackendEnum.ROCM_AITER_FA),
	fa_version=self._fa_version,
	)
	if is_reshaped:
	output = output.reshape(bsz, q_len, -1)

So I think we won't hit the shape mismatch issue here. 🤔

[Potabk]

So, it seems the issue for ascend, I'll have a further check

[Potabk]

Thx, Confirmed it is the issue for forward_oot vllm-project/vllm-ascend#5443