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https://github.com/deepseek-ai/FlashMLA
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use 64x64 transpose_v
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chenhongmin.will
parent
d1689ab64f
commit
855c985b00
@ -12,6 +12,7 @@ using namespace cute;
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#include "softmax.h"
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#include "static_switch.h"
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#include "flash_mla.h"
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#include "fp8_transpose_v.h"
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template<typename PrecType, int DIM, int DIM2 = DIM, cute::GMMA::Major major = GMMA::Major::K>
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@ -86,20 +87,11 @@ struct Flash_fwd_kernel_traits_mla {
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getSmemLayoutK<Element, kHeadDim, kHeadDimV>(),
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Shape<Int<kBlockN>, Int<kHeadDim>>{}));
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// ------ for f16 ------
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using SmemLayoutV = decltype(tile_to_shape(
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getSmemLayoutK<Element, kHeadDim, kHeadDimV>(),
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Shape<Int<kBlockN>, Int<kHeadDimV>>{}));
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using SmemLayoutVtransposed = decltype(composition(SmemLayoutV{}, make_layout(Shape<Int<kHeadDimV>, Int<kBlockN>>{}, GenRowMajor{})));
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// ------ for f8 ------
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using SmemLayoutVtLoad = decltype(tile_to_shape(
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getSmemLayoutK<Element, kHeadDim, kHeadDimV, GMMA::Major::MN>(),
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Shape<Int<kHeadDimV>, Int<kBlockN>>{}));
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using SmemLayoutVtMMa = decltype(tile_to_shape(
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getSmemLayoutK<Element, kHeadDim, kHeadDimV>(),
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Shape<Int<kHeadDimV>, Int<kBlockN> >{}));
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using SmemLayoutP = std::conditional_t<
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Is_FP8,
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Layout<Shape<Shape<_4, _2>, Int<kNThreadsS>, _1, _2, Int<kBlockN / 32>>>,
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@ -155,6 +147,13 @@ struct Flash_fwd_kernel_traits_mla {
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Copy_Atom<AutoVectorizingCopyWithAssumedAlignment<128>, ElementAccum>{},
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GmemLayoutAtomOaccum{},
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Layout<Shape<_1, Int<kGmemElemsPerLoadAccum>>>{})); // Val layout, 4 vals per store
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// ------ for f8 ------
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using SmemLayoutVtMMa = decltype(tile_to_shape(
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getSmemLayoutK<Element, kHeadDim, kHeadDimV>(),
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Shape<Int<kHeadDimV>, Int<kBlockN> >{}));
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using SmemFp8Tranpose = SmemTransposeFp8_64x64<kBlockN, kHeadDimV, Element>;
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};
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namespace flash {
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@ -292,10 +291,7 @@ __forceinline__ __device__ void compute_attn_1rowblock_splitkv_mla(const Flash_f
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Tensor sQ = make_tensor(make_smem_ptr(shared_storage.smem_q.data()), typename Kernel_traits::SmemLayoutQ{});
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Tensor sK = make_tensor(make_smem_ptr(shared_storage.smem_k.data()), typename Kernel_traits::SmemLayoutK{});
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auto sV = cute::conditional_return<Kernel_traits::Is_FP8>(
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cute::as_position_independent_swizzle_tensor(make_tensor(make_smem_ptr(shared_storage.smem_k.data()), typename Kernel_traits::SmemLayoutVtLoad{})),
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make_tensor(make_smem_ptr(shared_storage.smem_k.data()), typename Kernel_traits::SmemLayoutV{}));
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auto sV = make_tensor(make_smem_ptr(shared_storage.smem_k.data()), typename Kernel_traits::SmemLayoutV{});
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auto sVt = cute::conditional_return<Kernel_traits::Is_FP8>(
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make_tensor(make_smem_ptr(shared_storage.smem_vt.data()), typename Kernel_traits::SmemLayoutVtMMa{}),
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make_tensor(make_smem_ptr(shared_storage.smem_k.data()), typename Kernel_traits::SmemLayoutVtransposed{}));
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@ -438,9 +434,6 @@ __forceinline__ __device__ void compute_attn_1rowblock_splitkv_mla(const Flash_f
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if constexpr (!Kernel_traits::Is_FP8) {
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tOrVt.data() = tOrVt.data() + sK_offset / 8;
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}
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else {
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sV.data() = sV.data() + sK_offset;
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}
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}
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// We need to clear the sK smem tiles because K is V.
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@ -474,53 +467,23 @@ __forceinline__ __device__ void compute_attn_1rowblock_splitkv_mla(const Flash_f
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if constexpr (Kernel_traits::Is_FP8) {
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auto TransV = [&]() {
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// refer to fa3's TransV: https://github.com/Dao-AILab/flash-attention/blob/main/hopper/mainloop_fwd_sm90_tma_gmma_ws.hpp#L697
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using LDSM_divide_shape = Shape<_64, _8>;
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using S2RTiledCopyVt = decltype(make_tiled_copy(
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Copy_Atom<SM75_U16x8_LDSM_T, Element>{},
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Layout<Shape<_32, _4, _1, _1>, Stride<_4, _1, _0, _0>>{}, // thread layout
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Layout<Shape<_2, _2, _1, _4>, Stride<_1, _2, _16, _4>>{} // val layout
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));
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using SmemFp8Tranpose = typename Kernel_traits::SmemFp8Tranpose;
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SmemFp8Tranpose smem_transpose_V;
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Tensor sV_divide = as_position_independent_swizzle_tensor(
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make_tensor(make_smem_ptr(shared_storage.smem_k.data()), typename SmemFp8Tranpose::SmemLayoutTransposeV{}));
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Tensor sVt_divide = as_position_independent_swizzle_tensor(
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make_tensor(make_smem_ptr(shared_storage.smem_vt.data()), typename SmemFp8Tranpose::SmemLayoutTransposeVt{}));
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using STSM_divide_shape = Shape<_8, _16>;
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using R2STiledCopyV = decltype(make_tiled_copy(
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Copy_Atom<SM90_U32x4_STSM_N, Element>{},
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Layout<Shape<_8, _4, _4, _1>, Stride<_4, _1, _32, _0>>{}, // thread layout
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Layout<Shape<_1, _4, _2, _2>, Stride<_0, _1, _4, _8>>{} // val layout
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));
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if (n_block % 2 == 1) {
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sV_divide.data() = sV_divide.data() + size(sK);
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}
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S2RTiledCopyVt s2r_tiled_copy_vt;
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R2STiledCopyV r2s_tiled_copy_v;
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auto s2r_thr_copy_vt = s2r_tiled_copy_vt.get_thread_slice(warp_group_thread_idx);
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auto r2s_thr_copy_v = r2s_tiled_copy_v.get_thread_slice(warp_group_thread_idx);
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// flat_divide(sVt, LDSM_divide_shape{}): (64, 8, kHeadDim / 64, kBlockN / 8)
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Tensor tTranssVt_ = s2r_thr_copy_vt.partition_S(flat_divide(sV, LDSM_divide_shape{})); // ((16, 1), 1, 1, kHeadDim / 64, kBlockN / 32)
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// flat_divide(sV, STSM_divide_shape{}): (8, 16, kHeadDim / 8, (4, kBlockN / 64))
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Tensor tTranssV_ = r2s_thr_copy_v.partition_D(flat_divide(sVt, STSM_divide_shape{})); // ((16, 1), 1, 1, kHeadDim / 64, (2, kBlockN / 64))
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CUTE_STATIC_ASSERT_V(rank(tTranssVt_) == rank(tTranssV_));
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CUTE_STATIC_ASSERT_V(size<0>(tTranssVt_) == size<0>(tTranssV_));
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CUTE_STATIC_ASSERT_V(size<1>(tTranssVt_) == size<1>(tTranssV_));
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CUTE_STATIC_ASSERT_V(size<2>(tTranssVt_) == size<2>(tTranssV_));
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CUTE_STATIC_ASSERT_V(size<3>(tTranssVt_) == size<3>(tTranssV_));
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CUTE_STATIC_ASSERT_V(size<4>(tTranssVt_) == size<4>(tTranssV_));
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static constexpr int Transpose_ILP = (size<2>(tTranssVt_) * size<3>(tTranssVt_)) % 2 == 0 ? 2 : 1;
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Tensor tTranssVt = logical_divide(group_modes<1, rank(tTranssVt_)>(tTranssVt_), Shape<Underscore, Int<Transpose_ILP>>{}); // ((16, 1), (2, kHeadDim / 64 * kBlockN / 32 / 2))
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Tensor tTranssV = logical_divide(group_modes<1, rank(tTranssV_)>(tTranssV_), Shape<Underscore, Int<Transpose_ILP>>{}); // ((16, 1), (2, kHeadDim / 64 * kBlockN / 32 / 2))
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#pragma unroll
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for (int i = 0; i < size<1, 1>(tTranssVt); ++i) {
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Tensor tTransrV = make_fragment_like(tTranssV(_, make_coord(_, _0{})));
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static_assert(size<0>(tTransrV) == 16);
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Tensor tTransrV_64 = recast<uint2>(tTransrV);
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cute::copy(s2r_tiled_copy_vt, tTranssVt(_, make_coord(_, i)), tTransrV);
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#pragma unroll
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for (int j = 0; j < size(tTransrV_64); ++j) {
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uint32_t upper = tTransrV_64[j].x;
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uint32_t lower = tTransrV_64[j].y;
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tTransrV_64[j].x = __byte_perm(upper, lower, 0x6420);
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tTransrV_64[j].y = __byte_perm(upper, lower, 0x7531);
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CUTLASS_PRAGMA_UNROLL
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for (int j = 0; j < shape<2>(typename SmemFp8Tranpose::SmemLayoutTransposeV{}); ++j) {
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CUTLASS_PRAGMA_UNROLL
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for (int i = 0; i < shape<1>(typename SmemFp8Tranpose::SmemLayoutTransposeV{}); ++i) {
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smem_transpose_V.transpose(flatten(sV_divide(_, i, j)), flatten(sVt_divide(_, i, j)));
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}
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cute::copy(r2s_tiled_copy_v, tTransrV, tTranssV(_, make_coord(_, i)));
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}
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};
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@ -548,8 +511,6 @@ __forceinline__ __device__ void compute_attn_1rowblock_splitkv_mla(const Flash_f
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const int sK_offset = n_block % 2 == 0 ? size(sK) : -size(sK);
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if constexpr (!Kernel_traits::Is_FP8) {
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tOrVt.data() = tOrVt.data() + sK_offset / 8;
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} else {
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sV.data() = sV.data() + sK_offset;
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}
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}
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82
csrc/fp8_transpose_v.h
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82
csrc/fp8_transpose_v.h
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@ -0,0 +1,82 @@
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#pragma once
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template <int kBlockN, int kHeadDim, typename Element>
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struct SmemTransposeFp8_64x64 {
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static_assert(sizeof(Element) == 1);
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static_assert((kBlockN % 64 == 0) && (kHeadDim % 64 == 0));
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using SmemLayoutK = decltype(tile_to_shape(
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GMMA::Layout_K_SW64_Atom<Element>{},
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Shape<Int<kBlockN>, Int<kHeadDim>>{}));
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using SmemLayoutV = decltype(composition(
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SmemLayoutK{},
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Layout<Shape<Int<kBlockN>, Int<kHeadDim>>, Stride<_1, Int<kBlockN>>>{}));
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using TransposeShapeAtomV = Shape<_64, _64>;
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// for fp8 in-kernel transpose -- src layout
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using SmemLayoutDivideV = decltype(tiled_divide(SmemLayoutV{}, TransposeShapeAtomV{}));
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using SmemShapeLDSM = Shape<Shape<_8, _8>, Shape<_16, _4>>;
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using FactoringShapeV = decltype(make_shape(SmemShapeLDSM{},
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shape<1>(SmemLayoutDivideV{}), shape<2>(SmemLayoutDivideV{}), shape<3>(SmemLayoutDivideV{})));
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using SmemLayoutTransposeV = decltype(composition(SmemLayoutDivideV{}, make_layout(FactoringShapeV{})));
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// For fp8, this is the memory transpose.
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using SmemLayoutAtomVt = decltype(tile_to_shape(GMMA::Layout_K_SW64_Atom<Element>{}, TransposeShapeAtomV{}));
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using SmemLayoutVt = decltype(tile_to_shape(
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SmemLayoutAtomVt{},
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Shape<Int<kHeadDim>, Int<kBlockN>>{}));
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// for fp8 in-kernel transpose -- dst layout
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using SmemLayoutVtTrans = decltype(composition(
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SmemLayoutVt{}, make_ordered_layout(product_each(shape(SmemLayoutV{})), Step<_2, _1, _3>{})));
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using SmemLayoutDivideVt = decltype(tiled_divide(SmemLayoutVtTrans{}, TransposeShapeAtomV{}));
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using SmemShapeSTSM = Shape<Shape<_16, _4>, Shape<_8, _8>>;
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using FactoringShapeVt = decltype(make_shape(SmemShapeSTSM{}, shape<1>(SmemLayoutDivideVt{}),
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shape<2>(SmemLayoutDivideVt{}), shape<3>(SmemLayoutDivideVt{})));
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using SmemLayoutTransposeVt = decltype(composition(SmemLayoutDivideVt{}, make_layout(FactoringShapeVt{})));
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using ldsm_thread_shape = Shape<_4, _1, _8, _4>;
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using ldsm_value_shape = Shape<_2, _8, _2, _1>;
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using ldsm_value_stride = Stride<_2, _4, _1, _0>;
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using TiledCopyLDSM = decltype(make_tiled_copy(Copy_Atom<SM75_U16x8_LDSM_T, Element>{}, Layout<ldsm_thread_shape>{},
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Layout<ldsm_value_shape, ldsm_value_stride>{}));
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TiledCopyLDSM tiled_copy_ldsm;
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using stsm_thread_shape = Shape<_4, _1, _8, _4>;
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// using stsm_thread_stride = Stride<_1, _0, _4, _32>;
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using stsm_value_shape = Shape<_4, _4, _1, _2>;
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using stsm_value_stride = Stride<_1, _8, _0, _4>;
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using TiledCopySTSM = decltype(make_tiled_copy(Copy_Atom<SM90_U32x4_STSM_N, Element>{}, Layout<stsm_thread_shape>{},
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Layout<stsm_value_shape, stsm_value_stride>{}));
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TiledCopySTSM tiled_copy_stsm;
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template <class SmemTensor, class SmemTensorOut>
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CUTLASS_DEVICE void transpose(SmemTensor &&s_in, SmemTensorOut &&s_out) {
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using namespace cute;
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auto tid = threadIdx.x;
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auto thr_copy_ldsm = tiled_copy_ldsm.get_thread_slice(tid);
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auto thr_copy_stsm = tiled_copy_stsm.get_thread_slice(tid);
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auto tXsX = thr_copy_ldsm.partition_S(s_in);
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auto tXrX = make_tensor<Element>(shape(tXsX));
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auto tXsX_out = thr_copy_stsm.partition_D(s_out);
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cute::copy(tiled_copy_ldsm, tXsX, tXrX);
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auto data = tXrX.data();
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CUTLASS_PRAGMA_UNROLL
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for (int n = 0; n < size(tXrX); n += 8) {
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uint32_t *data_32bit = reinterpret_cast<uint32_t *>(&data[n]);
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auto upper = data_32bit[0];
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auto lower = data_32bit[1];
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data_32bit[0] = __byte_perm(upper, lower, 0x6420);
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data_32bit[1] = __byte_perm(upper, lower, 0x7531);
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}
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cute::copy(tiled_copy_stsm, tXrX, tXsX_out);
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}
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};
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