perf: precompute ragged Q tile scheduling
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@@ -13,7 +13,7 @@ enum TensorLayout : int {
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// - Contiguous K/V: dense [batch, kv_head, kv_len, head_dim] tensors (k/v).
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// - Paged (SGLang-style): flat pool [size, kv_head, head_dim] + req_to_token.
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// Each kernel selects the addressing via a KVSource policy (see
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// attn_kv_source.cuh); a given call only touches the fields of one mode, so
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// attn_layout_policies.cuh); a given call only touches the fields of one mode, so
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// this is a POD shared by both paths rather than two parallel structs that
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// drift out of sync.
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template<typename T, typename AT = float>
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@@ -59,6 +59,9 @@ struct AttentionParams {
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const int* __restrict__ req_pool_indices; // [batch]
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const int* __restrict__ kv_indptr; // [batch + 1]
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const int* __restrict__ qo_indptr; // [batch + 1] or nullptr for decode
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const int* __restrict__ q_tile_to_batch; // [num_q_tiles], prefill only
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const int* __restrict__ q_tile_to_index; // [num_q_tiles], prefill only
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int num_q_tiles;
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int max_context_len; // req_to_token stride (dim 1)
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// Decode split-KV workspace
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