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llama:独立于kq_mask的形状构建k-pool稀疏选择(#31243)* llama:独立于kq_mask的形状构建k-pool稀疏选择 qwen4exp(QSA)和glm5-next 通过索引选取缓存单元,不再读取kq_mask的值,但仍像稠密的kq_mask那样对选择进行塑形。改为在缓存单元上以[n_kv, n_tokens]的维度构建选择,并在注意力计算前将其拆分为K视图的各个流。这样,稠密掩码可以改变其布局(例如按序列的行列表),而无需触及稀疏层。* 改进注释 共同作者:Georgi Gerganov * llama:删除未使用的build_fused_top_k_mask参数,审查代码中的小问题 --------- 共同作者:Georgi Gerganov **网站:** - **证明文件:** - **macOS/iOS:** - [macOS Apple Silico

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llama : build the k-pool sparse selection independently of the kq_mask shape (#31243)

* llama : build the k-pool sparse selection independently of the kq_mask shape

qwen4exp (QSA) and glm5-next pick cache cells by index and no longer read the kq_mask values, but they still shaped the selection like the dense kq_mask. Build the selection as [n_kv, n_tokens] over the cache cells instead, and split it into the streams of the K view right before the attention.

This lets the dense mask change its layout (e.g. per-sequence row lists) without touching the sparse layers.

* improve comment

Co-authored-by: Georgi Gerganov

* llama : drop the unused build_fused_top_k_mask args, review nits

---------

Co-authored-by: Georgi Gerganov

**Website:**

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**macOS/iOS:**

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- macOS Apple Silicon (arm64, KleidiAI enabled) [DISABLED](https://github.com/ggml-org/llama.cpp/pull/23780)

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