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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,67 @@ | ||
| #include <cmath> | ||
| #include <cute/tensor.hpp> | ||
| #include <cutlass/cutlass.h> | ||
| #include <cutlass/array.h> | ||
| #include "utils.h" | ||
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| namespace onnxruntime { | ||
| namespace flash { | ||
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| using namespace cute; | ||
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| //////////////////////////////////////////////////////////////////////////////////////////////////// | ||
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| template <bool Is_causal> | ||
| struct Alibi { | ||
| const float alibi_slope; | ||
| 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) | ||
| : alibi_slope(alibi_slope), max_seqlen_k(max_seqlen_k), max_seqlen_q(max_seqlen_q){}; | ||
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| template <typename Engine, typename Layout> | ||
| __forceinline__ __device__ void apply_alibi(Tensor<Engine, Layout>& tensor, | ||
| const int col_idx_offset_, | ||
| const int row_idx_offset, | ||
| const int warp_row_stride) { | ||
| // tensor has shape (ncol=(2, MMA_M), nrow=(2, MMA_N)) | ||
| static_assert(Layout::rank == 2, "Only support 2D Tensor"); | ||
| const int lane_id = threadIdx.x % 32; | ||
| const int col_idx_offset = col_idx_offset_ + (lane_id % 4) * 2; | ||
| if constexpr (Is_causal) { // Simpler, we add the same bias vector to all rows | ||
| #pragma unroll | ||
| for (int nj = 0; nj < size<1, 1>(tensor); ++nj) { | ||
| const int col_idx_base = col_idx_offset + nj * 8; | ||
| #pragma unroll | ||
| for (int j = 0; j < size<1, 0>(tensor); ++j) { | ||
| const int col_idx = col_idx_base + j; | ||
| #pragma unroll | ||
| for (int mi = 0; mi < size<0>(tensor); ++mi) { | ||
| tensor(mi, make_coord(j, nj)) += alibi_slope * col_idx; | ||
| } | ||
| } | ||
| } | ||
| } else { // Bias depends on both row_idx and col_idx | ||
| #pragma unroll | ||
| for (int mi = 0; mi < size<0, 1>(tensor); ++mi) { | ||
| const int row_idx_base = row_idx_offset + mi * warp_row_stride; | ||
| #pragma unroll | ||
| for (int i = 0; i < size<0, 0>(tensor); ++i) { | ||
| const int row_idx = row_idx_base + i * 8; | ||
| #pragma unroll | ||
| for (int nj = 0; nj < size<1, 1>(tensor); ++nj) { | ||
| const int col_idx_base = col_idx_offset + nj * 8; | ||
| #pragma unroll | ||
| for (int j = 0; j < size<1, 0>(tensor); ++j) { | ||
| const int col_idx = col_idx_base + j; | ||
| 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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| } // namespace flash | ||
| } // namespace onnxruntime |
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