ViperEkura
2667b8116d
- merge AttentionParams and PagedAttentionParams into one struct - add attn_kv_source.cuh with ContigKV/PagedKV addressing policies - template prefill/decode kernels (MMA + scalar) on the KV policy, deleting the four duplicated attn_paged_*.cuh variants - template dispatcher launchers on KV; single combine kernel - verify: all correctness tests pass and SASS matches baseline (no perf regression)
67 lines
2.6 KiB
C++
67 lines
2.6 KiB
C++
#pragma once
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// Tensor layout for Q/K/V tensors passed to attention kernels.
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// Internally, kernels always operate on BHLD [batch, n_heads, seq_len, head_dim].
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// When the caller passes BLHD, dims 1 and 2 are transposed at entry.
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enum TensorLayout : int {
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BHLD = 0, // [batch, n_heads, seq_len, head_dim]
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BLHD = 1, // [batch, seq_len, n_heads, head_dim]
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};
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// Unified attention params covering BOTH addressing modes:
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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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// 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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struct AttentionParams {
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// ---- shared across all paths ----
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int batch;
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int q_head;
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int kv_head;
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int head_dim;
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int use_mask;
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int causal_offset; // -1 = non-causal; >=0 = absolute position of first Q token
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int num_splits;
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float scale;
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// Q strides (element offsets for each dim — layout-agnostic)
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int q_stride_b, q_stride_h, q_stride_l, q_stride_d;
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// Mask: 2D [batch, kv_len], 3D [batch, q_len, kv_len],
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// or 4D [batch, n_heads, q_len, kv_len] (head dim broadcasts when stride=0)
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int mask_b_stride; // batch stride
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int mask_h_stride; // head stride (0 = broadcast across heads)
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int mask_q_stride; // q stride (0 = all q rows share)
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const bool* __restrict__ mask;
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const T* __restrict__ q;
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T* __restrict__ o;
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AT* __restrict__ o_part;
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AT* __restrict__ ml_part;
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// ---- contiguous K/V mode ----
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int q_len;
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int kv_len;
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int kv_stride_b, kv_stride_h, kv_stride_l, kv_stride_d;
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const T* __restrict__ k;
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const T* __restrict__ v;
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// ---- paged (SGLang flat pool) mode ----
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const T* __restrict__ k_cache;
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const T* __restrict__ v_cache;
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// Indexing
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const int64_t* __restrict__ req_to_token; // [num_reqs, max_context_len]
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const int64_t* __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 (decode)
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int max_context_len; // req_to_token stride (dim 1)
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int max_seq_len; // max per-request seq_len (host-side, for split computation)
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int total_q; // total Q tokens across all requests (host-side, for grid)
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int max_q_len; // max per-request q_len (host-side, for prefill grid)
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};
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