77 lines
2.8 KiB
C++
77 lines
2.8 KiB
C++
// Adapted from Dao-AILab/flash-attention (https://github.com/Dao-AILab/flash-attention/tree/v2.6.3)
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#pragma once
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#include <cmath>
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#include <cute/tensor.hpp>
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#include <mctlass/mctlass.h>
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#include <mctlass/array.h>
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#include "utils.h"
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namespace flash {
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using namespace cute;
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////////////////////////////////////////////////////////////////////////////////////////////////////
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template <bool Is_causal>
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struct Alibi {
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const float alibi_slope;
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const int max_seqlen_k, max_seqlen_q;
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__forceinline__ __device__ Alibi(const float alibi_slope, const int max_seqlen_k, const int max_seqlen_q)
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: alibi_slope(alibi_slope)
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, max_seqlen_k(max_seqlen_k)
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, max_seqlen_q(max_seqlen_q) {
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};
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template <typename Engine, typename Layout>
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__forceinline__ __device__ void apply_alibi(Tensor<Engine, Layout> &tensor,
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const int col_idx_offset_,
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const int row_idx_offset,
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const int warp_row_stride,
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const int warp_col_stride = 16) {
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// tensor has shape (nrow=(1, MMA_M), ncol=(4, MMA_N))
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static_assert(Layout::rank == 2, "Only support 2D Tensor");
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static_assert(decltype(size<0, 0>(tensor))::value == 1);
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static_assert(decltype(size<1, 0>(tensor))::value == 4);
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const int col_idx_offset = col_idx_offset_ + ((__lane_id() >> 4) << 2);
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if constexpr (Is_causal) { // Simpler, we add the same bias vector to all rows
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#pragma unroll
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for (int nj = 0; nj < size<1, 1>(tensor); ++nj) {
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const int col_idx_base = col_idx_offset + nj * warp_col_stride;
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#pragma unroll
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for (int j = 0; j < size<1, 0>(tensor); ++j) {
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const int col_idx = col_idx_base + j;
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#pragma unroll
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for (int mi = 0; mi < size<0>(tensor); ++mi) {
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tensor(mi, make_coord(j, nj)) += alibi_slope * col_idx;
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}
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}
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}
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} else { // Bias depends on both row_idx and col_idx
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#pragma unroll
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for (int mi = 0; mi < size<0, 1>(tensor); ++mi) {
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const int row_idx = row_idx_offset + mi * warp_row_stride;
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#pragma unroll
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for (int nj = 0; nj < size<1, 1>(tensor); ++nj) {
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const int col_idx_base = col_idx_offset + nj * warp_col_stride;
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#pragma unroll
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for (int j = 0; j < size<1, 0>(tensor); ++j) {
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const int col_idx = col_idx_base + j;
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tensor(make_coord(0, mi), make_coord(j, nj)) -= alibi_slope * abs(row_idx + max_seqlen_k - max_seqlen_q - col_idx);
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}
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}
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}
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}
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}
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};
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} // namespace flash
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