214 lines
11 KiB
C++
214 lines
11 KiB
C++
/******************************************************************************
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* Copyright (c) 2024, Tri Dao.
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******************************************************************************/
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#pragma once
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#include <cute/tensor.hpp>
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namespace flash {
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using namespace cute;
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template <typename Engine, typename Layout>
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__forceinline__ __device__ void apply_mask(Tensor<Engine, Layout> &tensor, const int max_seqlen_k,
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const int col_idx_offset_ = 0) {
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// tensor has shape (nrow=(2, MMA_M), ncol=(2, MMA_N))
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static_assert(Layout::rank == 2, "Only support 2D Tensor");
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const int lane_id = threadIdx.x % 32;
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const int col_idx_offset = col_idx_offset_ + (lane_id % 4) * 2;
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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 * 8;
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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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if (col_idx >= max_seqlen_k) {
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// Without the "make_coord" we get wrong results
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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)) = -INFINITY;
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}
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}
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}
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}
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}
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template <bool HasWSLeft=true, typename Engine, typename Layout>
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__forceinline__ __device__ void apply_mask_local(Tensor<Engine, Layout> &tensor, const int col_idx_offset_,
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const int max_seqlen_k, const int row_idx_offset,
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const int max_seqlen_q, const int warp_row_stride,
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const int window_size_left, const int window_size_right) {
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// tensor has shape (nrow=(2, MMA_M), ncol=(2, MMA_N))
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static_assert(Layout::rank == 2, "Only support 2D Tensor");
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const int lane_id = threadIdx.x % 32;
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const int col_idx_offset = col_idx_offset_ + (lane_id % 4) * 2;
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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_base = row_idx_offset + mi * warp_row_stride;
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#pragma unroll
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for (int i = 0; i < size<0, 0>(tensor); ++i) {
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const int row_idx = row_idx_base + i * 8;
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const int col_idx_limit_left = std::max(0, row_idx + max_seqlen_k - max_seqlen_q - window_size_left);
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const int col_idx_limit_right = std::min(max_seqlen_k, row_idx + 1 + max_seqlen_k - max_seqlen_q + window_size_right);
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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 * 8;
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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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if (col_idx >= col_idx_limit_right || (HasWSLeft && col_idx < col_idx_limit_left)) {
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tensor(make_coord(i, mi), make_coord(j, nj)) = -INFINITY;
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}
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}
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}
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// if (cute::thread0()) {
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// printf("mi = %d, i = %d, row_idx = %d, max_seqlen_k = %d\n", mi, i, row_idx, max_seqlen_k);
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// print(tensor(make_coord(i, mi), _));
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// // print(tensor(_, j + nj * size<1, 0>(tensor)));
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// }
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}
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}
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}
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template <typename Engine, typename Layout>
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__forceinline__ __device__ void apply_mask_causal(Tensor<Engine, Layout> &tensor, const int col_idx_offset_,
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const int max_seqlen_k, const int row_idx_offset,
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const int max_seqlen_q, const int warp_row_stride) {
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// Causal masking is equivalent to local masking with window_size_left = infinity and window_size_right = 0
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apply_mask_local</*HasWSLeft=*/false>(tensor, col_idx_offset_, max_seqlen_k, row_idx_offset,
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max_seqlen_q, warp_row_stride, -1, 0);
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}
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template <typename Engine0, typename Layout0, typename Engine1, typename Layout1>
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__forceinline__ __device__ void apply_mask_causal_w_idx(
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Tensor<Engine0, Layout0> &tensor, Tensor<Engine1, Layout1> const &idx_rowcol,
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const int col_idx_offset_, const int max_seqlen_k, const int row_idx_offset)
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{
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// tensor has shape (nrow=(2, MMA_M), ncol=(2, MMA_N))
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static_assert(Layout0::rank == 2, "Only support 2D Tensor");
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static_assert(Layout1::rank == 2, "Only support 2D Tensor");
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CUTE_STATIC_ASSERT_V(size<0>(tensor) == size<0>(idx_rowcol));
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CUTE_STATIC_ASSERT_V(size<1>(tensor) == size<1>(idx_rowcol));
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#pragma unroll
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for (int mi = 0; mi < size<0>(tensor); ++mi) {
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const int col_idx_limit = std::min(max_seqlen_k, 1 + row_idx_offset + get<0>(idx_rowcol(mi, 0)));
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#pragma unroll
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for (int ni = 0; ni < size<1, 1>(tensor); ++ni) {
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if (col_idx_offset_ + get<1>(idx_rowcol(0, ni)) >= col_idx_limit) {
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tensor(mi, ni) = -INFINITY;
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}
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}
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// if (cute::thread0()) {
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// printf("ni = %d, j = %d, col_idx = %d, max_seqlen_k = %d\n", ni, j, col_idx, max_seqlen_k);
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// print(tensor(_, make_coord(j, ni)));
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// // print(tensor(_, j + ni * size<1, 0>(tensor)));
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// }
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}
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}
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template <bool Is_causal, bool Is_local, bool Has_alibi>
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struct Mask {
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const int max_seqlen_k, max_seqlen_q;
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const int window_size_left, window_size_right;
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const float alibi_slope;
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__forceinline__ __device__ Mask(const int max_seqlen_k, const int max_seqlen_q,
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const int window_size_left, const int window_size_right,
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const float alibi_slope=0.f)
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: max_seqlen_k(max_seqlen_k)
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, max_seqlen_q(max_seqlen_q)
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, window_size_left(window_size_left)
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, window_size_right(window_size_right)
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, alibi_slope(!Has_alibi ? 0.0 : alibi_slope) {
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};
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// Causal_mask: whether this particular iteration needs causal masking
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template <bool Causal_mask=false, bool Is_even_MN=true, typename Engine, typename Layout>
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__forceinline__ __device__ void apply_mask(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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static_assert(!(Causal_mask && Is_local), "Cannot be both causal and local");
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static_assert(Layout::rank == 3, "Only support 3D Tensor");
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static_assert(decltype(size<0>(tensor_))::value == 4, "First dimension must be 4");
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static constexpr bool Need_masking = Has_alibi || Causal_mask || Is_local || !Is_even_MN;
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// if (cute::thread0()) { printf("Has_alibi = %d, Causal_mask=%d, Is_local=%d, Is_even_MN = %d, Need_masking = %d\n", Has_alibi, Causal_mask, Is_local, Is_even_MN, Need_masking); }
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if constexpr (Need_masking) {
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// Reshape tensor_ from (MMA=4, MMA_M, MMA_N) to (nrow=(2, MMA_M), ncol=(2, MMA_N))
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Tensor tensor = make_tensor(tensor_.data(), flash::convert_layout_acc_rowcol(tensor_.layout()));
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// Do we need both row and column indices, or just column incides?
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static constexpr bool Col_idx_only = !(Has_alibi && !Is_causal) && !Is_local && !Causal_mask;
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const int lane_id = threadIdx.x % 32;
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const int col_idx_offset = col_idx_offset_ + (lane_id % 4) * 2;
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if constexpr (Col_idx_only) {
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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 * 8;
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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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// No causal, no local
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if constexpr (Has_alibi) {
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tensor(mi, make_coord(j, nj)) += alibi_slope * col_idx;
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}
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if constexpr (!Is_even_MN) {
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if (col_idx >= max_seqlen_k) { tensor(mi, make_coord(j, nj)) = -INFINITY; }
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}
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}
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}
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}
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} else {
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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_base = row_idx_offset + mi * warp_row_stride;
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#pragma unroll
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for (int i = 0; i < size<0, 0>(tensor); ++i) {
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const int row_idx = row_idx_base + i * 8;
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const int col_idx_limit_left = std::max(0, row_idx + max_seqlen_k - max_seqlen_q - window_size_left);
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const int col_idx_limit_right = std::min(max_seqlen_k, row_idx + 1 + max_seqlen_k - max_seqlen_q + window_size_right);
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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 * 8;
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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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if constexpr (Has_alibi) {
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if constexpr (Is_causal) {
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tensor(make_coord(i, mi), make_coord(j, nj)) += alibi_slope * col_idx;
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} else {
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tensor(make_coord(i, 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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if constexpr (Causal_mask) {
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if (col_idx >= col_idx_limit_right) {
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tensor(make_coord(i, mi), make_coord(j, nj)) = -INFINITY;
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}
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}
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if constexpr (Is_local) {
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if (col_idx >= col_idx_limit_right || col_idx < col_idx_limit_left) {
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tensor(make_coord(i, mi), make_coord(j, nj)) = -INFINITY;
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}
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}
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if constexpr (!Causal_mask && !Is_local && !Is_even_MN) {
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// Causal and Local already handles MN masking
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if (col_idx >= max_seqlen_k) {
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tensor(make_coord(i, mi), make_coord(j, nj)) = -INFINITY;
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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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}
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}
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};
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};
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} // namespace flash
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