ViperEkura
a01e8bbe98
- Introduce KernelTraits<HEAD_DIM, BC, WARPS, STAGES> compile-time config bundle, replacing scattered <KD, NC8, KT2, ...> template params - Template all MMA and scalar kernels on IsCausal/HasMask bools to eliminate inner-loop runtime branches - Dispatch to 4-path IsCausal/HasMask kernel variants at entry points based on p.causal_offset and p.use_mask - Update standalone test files with new kernel signatures, add causal test cases - Fix duplicate using bf16 in MMA kernels that include attn_mma_utils.cuh
83 lines
2.8 KiB
Plaintext
83 lines
2.8 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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template <int HEAD_DIM, bool IsCausal, bool HasMask>
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static void launch_mma_prefill(AttentionParams<bf16>& p) {
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constexpr int WARPS = 4;
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constexpr int BC = (HEAD_DIM <= 128) ? 32 : 16;
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using Traits = KernelTraits<HEAD_DIM, BC, WARPS, 2>;
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dim3 grid((p.q_len + Traits::BR * WARPS - 1) / (Traits::BR * WARPS),
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p.q_head, p.batch);
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dim3 block(Traits::NUM_THREADS, 1, 1);
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attn_prefill_split_q_mma_kernel<Traits, IsCausal, HasMask><<<grid, block>>>(p);
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}
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#endif
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template <int HEAD_DIM, bool IsCausal, bool HasMask>
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static void launch_scalar_prefill(AttentionParams<bf16>& p) {
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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, IsCausal, HasMask><<<grid, block>>>(p);
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}
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template <int HEAD_DIM>
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static void dispatch_prefill(AttentionParams<bf16>& p) {
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bool is_causal = (p.causal_offset >= 0);
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bool has_mask = (p.use_mask && p.mask);
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#ifndef ASTRAI_NO_MMA
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if (is_causal) {
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if (has_mask) launch_mma_prefill<HEAD_DIM, true, true>(p);
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else launch_mma_prefill<HEAD_DIM, true, false>(p);
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} else {
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if (has_mask) launch_mma_prefill<HEAD_DIM, false, true>(p);
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else launch_mma_prefill<HEAD_DIM, false, false>(p);
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}
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#else
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if (is_causal) {
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if (has_mask) launch_scalar_prefill<HEAD_DIM, true, true>(p);
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else launch_scalar_prefill<HEAD_DIM, true, false>(p);
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} else {
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if (has_mask) launch_scalar_prefill<HEAD_DIM, false, true>(p);
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else launch_scalar_prefill<HEAD_DIM, false, false>(p);
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}
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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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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.head_dim % 16 == 0, "head_dim must be multiple of 16");
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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_prefill, 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_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("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 prefill (tensor-core mma on sm_80+, scalar fallback)");
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}
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