qwen qwq 32b is it using sparse, flash or dense attention?

[sprappcom]

as titled

[jklj077]

that's a confusing question.

QwQ-32B-Preview is based on Qwen2.5-32B-Instruct, which adopts the qwen2 arch as defined in https://github.com/huggingface/transformers/blob/main/src/transformers/models/qwen2/modeling_qwen2.py. it uses Grouped Query Attention (GQA).

• is it sparse? I think so, in a certain way. compared to the original multihead attention (MLA), the key and the value is indeed sparse. it is also why GQA can save space compared to MLA.
• is it flash? I thinks so. FlashAttention or FlashAttention v2 is efficient kernel implementation of the general QKV attention making use of GPU features, which increases throughput and saves space. FlashAttention does support GQA.
• is it dense? I think so, in a certain way. GQA does not make use of sparse matrix operations and it only relies on dense operators.