80 lines
2.7 KiB
Plaintext
80 lines
2.7 KiB
Plaintext
#include "attn_prefill_split_q.cuh"
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#include "attn_entry_utils.cuh"
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#ifndef ASTRAI_NO_MMA
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#include "attn_prefill_split_q_mma.cuh"
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#endif
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template <int HEAD_DIM>
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static void dispatch_prefill(AttentionParams<bf16>& p) {
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#ifndef ASTRAI_NO_MMA
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constexpr int WARPS = 4, BR = 16;
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// KV tile: bigger tiles amortize the per-tile cp.async wait + barrier +
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// loop overhead over more tensor-core work (this kernel is latency-bound,
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// not compute/bandwidth-bound), so BC=32 wins ~6-8% over BC=16 for
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// D<=128. D=256 stays at 16: BC=32 double-buffered would need 64KB smem,
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// over the 48KB static cap.
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constexpr int BC = (HEAD_DIM <= 128) ? 32 : 16;
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dim3 grid((p.q_len + BR * WARPS - 1) / (BR * WARPS), p.q_head, p.batch);
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dim3 block(WARPS * 32, 1, 1);
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// Static shared memory — double-buffered K/V only (no sQ: Q goes direct
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// to registers). 2*BC*LD bf16 each for sK and sV → 4*BC*HEAD_DIM*2 bytes.
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// Occupancy is smem-capped: D=64→3 blocks/SM (16KB), D=128→1 (32KB),
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// D=256→1 (32KB, BC=16).
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attn_prefill_split_q_mma_kernel<HEAD_DIM, WARPS, BC><<<grid, block>>>(p);
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#else
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constexpr int G = 8, ROWS = 32, P_BC = 32;
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dim3 grid((p.q_len + ROWS - 1) / ROWS, p.q_head, p.batch);
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dim3 block(G, ROWS, 1);
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attn_prefill_split_q_kernel_t<HEAD_DIM, G, ROWS, P_BC><<<grid, block>>>(p);
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#endif
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}
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torch::Tensor attn_prefill(
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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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bool is_causal = false,
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int64_t causal_offset = 0,
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c10::optional<double> scale = c10::nullopt
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) {
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AttentionParams<bf16> p;
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attn_pack_params(q, k, v, mask, is_causal, causal_offset, scale, p);
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TORCH_CHECK(p.head_dim % 16 == 0, "head_dim must be multiple of 16");
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auto O = torch::empty_like(q);
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p.o = (bf16*)O.data_ptr();
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switch (p.head_dim) {
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case 32:
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dispatch_prefill<32>(p);
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break;
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case 64:
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dispatch_prefill<64>(p);
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break;
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case 128:
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dispatch_prefill<128>(p);
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break;
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case 256:
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dispatch_prefill<256>(p);
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break;
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default:
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TORCH_CHECK(false, "prefill: unsupported head_dim ", p.head_dim,
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" (supported: 32,64,128,256)");
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}
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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_prefill", &attn_prefill,
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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("is_causal") = false,
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py::arg("causal_offset") = 0,
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py::arg("scale") = py::none(),
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"GQA prefill (tensor-core mma on sm_80+, scalar fallback)");
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}
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