83 lines
2.8 KiB
Plaintext
83 lines
2.8 KiB
Plaintext
#include "attn_decode_split_kv.cuh"
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#include "attn_entry_utils.cuh"
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#ifndef ASTRAI_NO_MMA
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#include "attn_decode_split_kv_mma.cuh"
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#endif
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// Scalar fallback: one warp per query head, split-KV across grid.z.
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static void launch_scalar_decode(AttentionParams<bf16>& p) {
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int group_size = p.q_head / p.kv_head;
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int chunks_total = (p.kv_len + DC_CHUNK - 1) / DC_CHUNK;
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p.num_splits = compute_num_splits(p.batch * p.kv_head, chunks_total);
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alloc_split_partials(p);
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size_t smem = DC_CHUNK * p.head_dim * sizeof(bf16);
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attn_decode_split_kv_kernel<<<dim3(p.batch * p.kv_head, 1, p.num_splits), dim3(32, group_size), smem>>>(p);
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attn_decode_combine_kernel<<<p.batch * p.q_head, p.head_dim>>>(p);
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}
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#ifndef ASTRAI_NO_MMA
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// MMA head-packing requires G <= 16 (BR=16 rows). sm_80+ tensor-core
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// + cp.async wins even at G=1 (decode is memory-bound, not compute-bound).
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// STAGES=2 (double-buffer) for D<=128 (smem 16 KB); STAGES=1 for D=256
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// (double-buffer would be 32 KB, near the 48 KB static cap — keep single
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// to preserve occupancy).
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template <int HEAD_DIM, int BC, int STAGES = (HEAD_DIM <= 128) ? 2 : 1>
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static void launch_mma_decode(AttentionParams<bf16>& p) {
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int tiles_total = (p.kv_len + BC - 1) / BC;
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p.num_splits = compute_num_splits(p.batch * p.kv_head, tiles_total);
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alloc_split_partials(p);
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attn_decode_split_kv_mma_kernel<HEAD_DIM, BC, STAGES><<<dim3(p.kv_head, p.batch, p.num_splits), 32>>>(p);
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attn_decode_combine_kernel<<<p.batch * p.q_head, p.head_dim>>>(p);
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}
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#endif
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template <int HEAD_DIM>
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static void dispatch_decode(AttentionParams<bf16>& p) {
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#ifndef ASTRAI_NO_MMA
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int G = p.q_head / p.kv_head;
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if (G >= 1 && G <= 16) {
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launch_mma_decode<HEAD_DIM, 32>(p);
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return;
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}
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#endif
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launch_scalar_decode(p);
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}
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torch::Tensor attn_decode(
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torch::Tensor q,
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torch::Tensor k,
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torch::Tensor v,
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c10::optional<torch::Tensor> mask,
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int64_t causal_offset,
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double scale,
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int64_t layout
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) {
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AttentionParams<bf16> p;
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attn_pack_params(q, k, v, mask, causal_offset, scale, layout, p);
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TORCH_CHECK(p.q_len == 1, "Q seq_len must be 1");
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TORCH_CHECK(p.head_dim % 32 == 0, "head_dim must be multiple of 32");
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// O matches Q's original layout
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auto O = torch::empty_strided(q.sizes(), q.strides(), q.options());
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auto O_view = (layout == 1) ? O.transpose(1, 2) : O;
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p.o = (bf16*)O_view.data_ptr();
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DISPATCH_HEAD_DIM(p.head_dim, dispatch_decode, p);
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return O;
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}
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PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) {
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m.def("attn_decode", &attn_decode,
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py::arg("q"),
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py::arg("k"),
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py::arg("v"),
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py::arg("mask") = py::none(),
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py::arg("causal_offset") = -1,
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py::arg("scale") = 0.0,
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py::arg("layout") = 0,
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"GQA decode (tensor-core head-packing on sm_80+, scalar fallback)");
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}
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