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flash-mla / flash_mla /utils.h
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// Adapted from https://github.com/Dao-AILab/flash-attention/blob/main/hopper/utils.h
#pragma once
#include <assert.h>
#include <stdint.h>
#include <stdlib.h>
#include <cuda_bf16.h>
#include <cute/tensor.hpp>
#include <cutlass/array.h>
#include <cutlass/cutlass.h>
#include <cutlass/numeric_conversion.h>
#include <cutlass/numeric_types.h>
////////////////////////////////////////////////////////////////////////////////////////////////////
namespace flash {
////////////////////////////////////////////////////////////////////////////////////////////////////
template<typename T>
struct MaxOp {
__device__ __forceinline__ T operator()(T const & x, T const & y) { return x > y ? x : y; }
};
template <>
struct MaxOp<float> {
// This is slightly faster
__device__ __forceinline__ float operator()(float const &x, float const &y) { return max(x, y); }
};
////////////////////////////////////////////////////////////////////////////////////////////////////
template<typename T>
struct SumOp {
__device__ __forceinline__ T operator()(T const & x, T const & y) { return x + y; }
};
////////////////////////////////////////////////////////////////////////////////////////////////////
template<int THREADS>
struct Allreduce {
 static_assert(THREADS == 32 || THREADS == 16 || THREADS == 8 || THREADS == 4);
 template<typename T, typename Operator>
 static __device__ __forceinline__ T run(T x, Operator &op) {
 constexpr int OFFSET = THREADS / 2;
 x = op(x, __shfl_xor_sync(uint32_t(-1), x, OFFSET));
 return Allreduce<OFFSET>::run(x, op);
 }
};
////////////////////////////////////////////////////////////////////////////////////////////////////
template<>
struct Allreduce<2> {
template<typename T, typename Operator>
static __device__ __forceinline__ T run(T x, Operator &op) {
 x = op(x, __shfl_xor_sync(uint32_t(-1), x, 1));
 return x;
}
};
////////////////////////////////////////////////////////////////////////////////////////////////////
template <bool zero_init=false, int wg_wait=0, bool arrive=true, bool commit=true, typename Tensor0, typename Tensor1, typename Tensor2, typename TiledMma>
__forceinline__ __device__ void gemm(TiledMma &tiled_mma, Tensor0 const &tCrA, Tensor1 const &tCrB, Tensor2 &tCrC) {
 constexpr bool Is_RS = !cute::is_base_of<cute::GMMA::DescriptorIterator, typename TiledMma::FrgTypeA>::value;
 // Need to cast away const on tCrA since warpgroup_fence_operand doesn't take const
 if constexpr (Is_RS) { cute::warpgroup_fence_operand(const_cast<Tensor0 &>(tCrA)); }
 warpgroup_fence_operand(tCrC);
 if constexpr (arrive) {
 warpgroup_arrive();
 }
 if constexpr (zero_init) {
 tiled_mma.accumulate_ = GMMA::ScaleOut::Zero;
 // Unroll the K mode manually to set scale D to 1
 CUTLASS_PRAGMA_UNROLL
 for (int k_block = 0; k_block < size<2>(tCrA); ++k_block) {
 cute::gemm(tiled_mma, tCrA(_,_,k_block), tCrB(_,_,k_block), tCrC);
 tiled_mma.accumulate_ = GMMA::ScaleOut::One;
 }
 } else {
 // cute::gemm(tiled_mma, tCrA, tCrB, tCrC);
 // Unroll the K mode manually to set scale D to 1
 CUTLASS_PRAGMA_UNROLL
 for (int k_block = 0; k_block < size<2>(tCrA); ++k_block) {
 cute::gemm(tiled_mma, tCrA(_,_,k_block), tCrB(_,_,k_block), tCrC);
 tiled_mma.accumulate_ = GMMA::ScaleOut::One;
 }
 }
 if constexpr (commit) {
 warpgroup_commit_batch();
 }
 if constexpr (wg_wait >= 0) { warpgroup_wait<wg_wait>(); }
 warpgroup_fence_operand(tCrC);
 if constexpr (Is_RS) { warpgroup_fence_operand(const_cast<Tensor0 &>(tCrA)); }
}
////////////////////////////////////////////////////////////////////////////////////////////////////
// For SM80, convert acc_layout from (MMA=4, MMA_M, MMA_N) to (nrow=(2, MMA_M), ncol=(2, MMA_N))
// For SM90, convert acc_layout from ((2, 2, V), MMA_M, MMA_N) to (nrow=(2, MMA_M), ncol=(2, V, MMA_N))
template<bool Transposed=false, typename Layout0>
__forceinline__ __device__ auto convert_layout_acc_rowcol(Layout0 acc_layout) {
 if constexpr (decltype(rank<0>(acc_layout))::value == 3) { // SM90
 static_assert(decltype(size<0, 0>(acc_layout))::value == 2);
 static_assert(decltype(size<0, 1>(acc_layout))::value == 2);
 static_assert(decltype(rank(acc_layout))::value == 3);
 auto l = acc_layout;
 if constexpr (!Transposed) {
 return make_layout(make_layout(get<0, 1>(l), get<1>(l)), make_layout(get<0, 0>(l), get<0, 2>(l), get<2>(l)));
 } else {
 return make_layout(make_layout(get<0, 0>(l), get<0, 2>(l), get<2>(l)), make_layout(get<0, 1>(l), get<1>(l)));
 }
} else { // SM80
 static_assert(decltype(size<0>(acc_layout))::value == 4);
 static_assert(decltype(rank(acc_layout))::value == 3);
 auto l = logical_divide(acc_layout, Shape<_2>{}); // ((2, 2), MMA_M, MMA_N)
 if constexpr (!Transposed) {
 return make_layout(make_layout(get<0, 1>(l), get<1>(l)), make_layout(get<0, 0>(l), get<2>(l)));
 } else {
 return make_layout(make_layout(get<0, 0>(l), get<2>(l)), make_layout(get<0, 1>(l), get<1>(l)));
 }
 }
};
////////////////////////////////////////////////////////////////////////////////////////////////////
// For SM80, convert acc_layout from (MMA=4, MMA_M, MMA_N) to ((4, 2), MMA_M, MMA_N / 2)
// if using m16n8k16, or to (4, MMA_M, MMA_N) if using m16n8k8.
// For SM90, FP16/BF16, convert acc_layout from ((2, 2, N / 8), MMA_M, MMA_N) to ((2, 2, 2), MMA_M, (N / 16, MMA_N))
// For SM90, FP8, convert acc_layout from ((2, 2, N / 8), MMA_M, MMA_N) to ((4, 2, 2), MMA_M, (N / 32, MMA_N))
template<typename MMA_Traits, typename Layout0>
__forceinline__ __device__ auto convert_layout_acc_Aregs(Layout0 acc_layout) {
 using X = Underscore;
 if constexpr (decltype(rank<0>(acc_layout))::value == 3) { // SM90
 static_assert(decltype(size<0, 0>(acc_layout))::value == 2);
 static_assert(decltype(size<0, 1>(acc_layout))::value == 2);
 static_assert(decltype(rank(acc_layout))::value == 3);
 static_assert(decltype(rank(get<0>(acc_layout)))::value == 3);
 if constexpr (sizeof(typename MMA_Traits::ValTypeA) == 2) {
 auto l = logical_divide(get<0, 2>(acc_layout), Tile<_2>{}); // ((2, N / 16))
 return make_layout(make_layout(get<0, 0>(acc_layout), get<0, 1>(acc_layout), get<0, 0>(l)), get<1>(acc_layout), coalesce(make_layout(get<0, 1>(l), get<2>(acc_layout))));
 } else {
 static_assert(sizeof(typename MMA_Traits::ValTypeA) == 1);
 static_assert(decltype(stride<0, 0>(acc_layout))::value == 1);
 static_assert(decltype(stride<0, 1>(acc_layout))::value == 2);
 auto l = logical_divide(get<0, 2>(acc_layout), Tile<Layout<Shape<_2, _2>>>{}); // (((2, 2), N / 32))
 // This combines the first two modes (<0, 0> and <0, 1>) into one mode.
 // Will require register shuffling later to be correct.
 return make_layout(make_layout(Layout<_4>{}, get<0, 0, 0>(l), get<0, 0, 1>(l)),
 get<1>(acc_layout),
 coalesce(make_layout(get<0, 1>(l), get<2>(acc_layout)))); // ((4, 2, 2), MMA_M, N / 32 * MMA_N)
 // This combination is right but doesn't work with register shuffling.
 // return make_layout(make_layout(coalesce(make_layout(get<0, 0>(acc_layout), get<0, 0, 0>(l))), get<0, 1>(acc_layout), get<0, 0, 1>(l)),
 // get<1>(acc_layout),
 // coalesce(make_layout(get<0, 1>(l), get<2>(acc_layout))));
 }
 } else { // SM80
 static_assert(decltype(size<0>(acc_layout))::value == 4);
 static_assert(decltype(rank(acc_layout))::value == 3);
 constexpr int mma_shape_K = get<2>(typename MMA_Traits::Shape_MNK{});
 static_assert(mma_shape_K == 8 || mma_shape_K == 16);
 if constexpr (mma_shape_K == 8) {
 return acc_layout;
 } else {
 auto l = logical_divide(acc_layout, Shape<X, X, _2>{}); // (4, MMA_M, (2, MMA_N / 2)))
 return make_layout(make_layout(get<0>(l), get<2, 0>(l)), get<1>(l), get<2, 1>(l));
 }
 }
};
////////////////////////////////////////////////////////////////////////////////////////////////////
template <typename To_type, typename Engine, typename Layout>
__forceinline__ __device__ auto convert_type(Tensor<Engine, Layout> const &tensor) {
 using From_type = typename Engine::value_type;
 constexpr int numel = decltype(size(tensor))::value;
 cutlass::NumericArrayConverter<To_type, From_type, numel> convert_op;
 // HACK: this requires tensor to be "contiguous"
 auto frag = convert_op(*reinterpret_cast<const cutlass::Array<From_type, numel> *>(tensor.data()));
 return make_tensor(make_rmem_ptr<To_type>(&frag), tensor.layout());
}
////////////////////////////////////////////////////////////////////////////////////////////////////
// Blocks until all but N previous cp.async.commit_group operations have committed.
// This differs from cute::cp_async_wait in that when N = 0 we don't call cp.async.wait_all
// (which is equivalent to commit_group then wait_group 0).
// Instead we just call cp.async.wait_group 0, which is slightly faster.
// https://github.com/NVIDIA/cutlass/blob/master/include/cute/arch/copy_sm80.hpp#L113
template <int N>
CUTE_HOST_DEVICE
void cp_async_wait() {
#if defined(CUTE_ARCH_CP_ASYNC_SM80_ENABLED)
 asm volatile("cp.async.wait_group %0;\n" :: "n"(N));
#endif
}
////////////////////////////////////////////////////////////////////////////////////////////////////
template <bool Is_even_MN=true, bool Is_even_K=true, bool Clear_OOB_MN=false, bool Clear_OOB_K=true,
 typename TiledCopy, typename Engine0, typename Layout0, typename Engine1, typename Layout1,
 typename Engine2, typename Layout2, typename Engine3, typename Layout3>
__forceinline__ __device__ void copy(TiledCopy tiled_copy, Tensor<Engine0, Layout0> const &S,
 Tensor<Engine1, Layout1> &D, Tensor<Engine2, Layout2> const &identity_MN,
 Tensor<Engine3, Layout3> const &predicate_K, const int max_MN=0) {
 CUTE_STATIC_ASSERT_V(rank(S) == Int<3>{});
 CUTE_STATIC_ASSERT_V(rank(D) == Int<3>{});
 CUTE_STATIC_ASSERT_V(size<0>(S) == size<0>(D)); // MMA
 CUTE_STATIC_ASSERT_V(size<1>(S) == size<1>(D)); // MMA_M
 CUTE_STATIC_ASSERT_V(size<2>(S) == size<2>(D)); // MMA_K
 // There's no case where !Clear_OOB_K && Clear_OOB_MN
 static_assert(!(Clear_OOB_MN && !Clear_OOB_K));
 #pragma unroll
 for (int m = 0; m < size<1>(S); ++m) {
 if (Is_even_MN || get<0>(identity_MN(0, m, 0)) < max_MN) {
 #pragma unroll
 for (int k = 0; k < size<2>(S); ++k) {
 if (Is_even_K || predicate_K(k)) {
 cute::copy(tiled_copy, S(_, m, k), D(_, m, k));
 } else if (Clear_OOB_K) {
 cute::clear(D(_, m, k));
 }
 }
 } else if (Clear_OOB_MN) {
 cute::clear(D(_, m, _));
 }
 }
}
////////////////////////////////////////////////////////////////////////////////////////////////////
} // namespace flash