573 lines
28 KiB
C++
573 lines
28 KiB
C++
/******************************************************************************
|
|
* Copyright (c) 2024, Tri Dao.
|
|
******************************************************************************/
|
|
|
|
#pragma once
|
|
|
|
#include <assert.h>
|
|
#include <stdint.h>
|
|
#include <stdlib.h>
|
|
|
|
#include <cuda_fp16.h>
|
|
|
|
#if defined(__CUDA_ARCH__) && __CUDA_ARCH__ >= 800
|
|
#include <cuda_bf16.h>
|
|
#endif
|
|
|
|
#include <cute/tensor.hpp>
|
|
|
|
#include <cutlass/cutlass.h>
|
|
#include <cutlass/array.h>
|
|
#include <cutlass/numeric_conversion.h>
|
|
#include <cutlass/numeric_types.h>
|
|
|
|
|
|
#define CHECK_CUDA(call) \
|
|
do { \
|
|
cudaError_t status_ = call; \
|
|
if (status_ != cudaSuccess) { \
|
|
fprintf(stderr, "CUDA error (%s:%d): %s\n", __FILE__, __LINE__, cudaGetErrorString(status_)); \
|
|
exit(1); \
|
|
} \
|
|
} while(0)
|
|
|
|
#define CHECK_CUDA_KERNEL_LAUNCH() CHECK_CUDA(cudaGetLastError())
|
|
|
|
namespace flash {
|
|
|
|
using namespace cute;
|
|
|
|
////////////////////////////////////////////////////////////////////////////////////////////////////
|
|
|
|
// A wrapper for the kernel that is used to guard against compilation on
|
|
// architectures that will never use the kernel. The purpose of this is to
|
|
// reduce the size of the compiled binary.
|
|
// Adapted from https://github.com/vllm-project/vllm/blob/4d29e91be84d27ca313d657eee92c067439a4c23/csrc/quantization/cutlass_w8a8/scaled_mm_c2x.cuh#L55
|
|
template <typename Kernel>
|
|
struct enable_sm90_or_later : Kernel {
|
|
template <typename... Args>
|
|
CUTLASS_DEVICE void operator()(Args&&... args) {
|
|
#if defined(__CUDA_ARCH__) && (__CUDA_ARCH__ >= 900)
|
|
Kernel::operator()(std::forward<Args>(args)...);
|
|
#endif
|
|
}
|
|
};
|
|
|
|
template <typename Kernel>
|
|
struct enable_sm80_to_sm89 : Kernel {
|
|
template <typename... Args>
|
|
CUTLASS_DEVICE void operator()(Args&&... args) {
|
|
#if defined(__CUDA_ARCH__) && (__CUDA_ARCH__ >= 800) && (__CUDA_ARCH__ <= 890)
|
|
Kernel::operator()(std::forward<Args>(args)...);
|
|
#endif
|
|
}
|
|
};
|
|
|
|
////////////////////////////////////////////////////////////////////////////////////////////////////
|
|
|
|
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;
|
|
}
|
|
};
|
|
|
|
////////////////////////////////////////////////////////////////////////////////////////////////////
|
|
|
|
// 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>
|
|
CUTLASS_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>
|
|
CUTLASS_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>
|
|
CUTLASS_DEVICE auto convert_type_unsafe(Tensor<Engine, Layout> const &tensor) {
|
|
using From_type = typename Engine::value_type;
|
|
static 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());
|
|
// Unsafe because we're returning a tensor with memory allocated on the stack. If the compiler does not
|
|
// inline this function, then the memory might not be valid.
|
|
}
|
|
|
|
////////////////////////////////////////////////////////////////////////////////////////////////////
|
|
|
|
template <typename Engine, typename Layout, typename EngineOut>
|
|
CUTLASS_DEVICE void convert_type_out(Tensor<Engine, Layout> const &tensor, Tensor<EngineOut, Layout> &out) {
|
|
// Somehow if we allocate out inside this function and return it, e2e is slower and the output can be wrong.
|
|
using From_type = typename Engine::value_type;
|
|
using To_type = typename EngineOut::value_type;
|
|
static constexpr int FragmentSize = std::max(sizeof(From_type) / sizeof(To_type), sizeof(To_type) / sizeof(From_type));
|
|
static_assert(CUTE_STATIC_V(size(tensor)) % FragmentSize == 0, "Fragment size does not vectorize properly");
|
|
Tensor frag = recast<cutlass::Array<From_type, FragmentSize> const>(tensor);
|
|
Tensor out_frg = recast<cutlass::Array<To_type, FragmentSize>>(out);
|
|
static_assert(size(frag) == size(out_frg));
|
|
cutlass::NumericArrayConverter<To_type, From_type, FragmentSize> convert_op;
|
|
#pragma unroll
|
|
for (int i = 0; i < size(frag); ++i) { out_frg[i] = convert_op(frag[i]); }
|
|
}
|
|
|
|
////////////////////////////////////////////////////////////////////////////////////////////////////
|
|
|
|
// 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 A, class Mma, class Tensor0>
|
|
CUTLASS_DEVICE
|
|
auto mma_partition_fragment_AB(Mma const& mma, Tensor0 const& tensor0) {
|
|
if constexpr (A) {
|
|
return mma.partition_fragment_A(tensor0);
|
|
} else {
|
|
return mma.partition_fragment_B(tensor0);
|
|
}
|
|
}
|
|
|
|
////////////////////////////////////////////////////////////////////////////////////////////////////
|
|
|
|
template <bool zero_init=false, int wg_wait=0, bool SwapAB=false, int M_slice=-1,
|
|
typename Tensor0, typename Tensor1, typename Tensor2, typename TiledMma>
|
|
CUTLASS_DEVICE void gemm(TiledMma& tiled_mma, Tensor0 const& tCrA, Tensor1 const& tCrB, Tensor2& tCrC) {
|
|
if constexpr (M_slice >= 0) {
|
|
static constexpr int MMA_M = decltype(size<1>(tCrC))::value;
|
|
static_assert(M_slice < MMA_M);
|
|
// After logical_divide, C has shape ((2,2,V), (MMA_M, 1), MMA_N)
|
|
Tensor tCrC_slice = cute::logical_divide(tCrC, Shape<cute::Underscore, Int<MMA_M>>{})(_, make_coord(Int<M_slice>{}, _), _);
|
|
if constexpr (!SwapAB) {
|
|
Tensor tCrA_slice = cute::logical_divide(tCrA, Shape<cute::Underscore, Int<MMA_M>>{})(_, make_coord(Int<M_slice>{}, _), _);
|
|
gemm<zero_init, wg_wait, SwapAB, /*M_slice=*/-1>(tiled_mma, tCrA_slice, tCrB, tCrC_slice);
|
|
} else {
|
|
Tensor tCrB_slice = cute::logical_divide(tCrB, Shape<cute::Underscore, Int<MMA_M>>{})(_, make_coord(Int<M_slice>{}, _), _);
|
|
gemm<zero_init, wg_wait, SwapAB, /*M_slice=*/-1>(tiled_mma, tCrA, tCrB_slice, tCrC_slice);
|
|
}
|
|
} else {
|
|
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) {
|
|
if constexpr (!SwapAB) {
|
|
warpgroup_fence_operand(const_cast<Tensor0 &>(tCrA));
|
|
} else {
|
|
warpgroup_fence_operand(const_cast<Tensor1 &>(tCrB));
|
|
}
|
|
}
|
|
warpgroup_fence_operand(tCrC);
|
|
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) {
|
|
if constexpr (!SwapAB) {
|
|
cute::gemm(tiled_mma, tCrA(_,_,k_block), tCrB(_,_,k_block), tCrC);
|
|
} else {
|
|
cute::gemm(tiled_mma, tCrB(_,_,k_block), tCrA(_,_,k_block), tCrC);
|
|
}
|
|
tiled_mma.accumulate_ = GMMA::ScaleOut::One;
|
|
}
|
|
warpgroup_commit_batch();
|
|
if constexpr (wg_wait >= 0) { warpgroup_wait<wg_wait>(); }
|
|
warpgroup_fence_operand(tCrC);
|
|
if constexpr (Is_RS) {
|
|
if constexpr (!SwapAB) {
|
|
warpgroup_fence_operand(const_cast<Tensor0 &>(tCrA));
|
|
} else {
|
|
warpgroup_fence_operand(const_cast<Tensor1 &>(tCrB));
|
|
}
|
|
}
|
|
}
|
|
}
|
|
|
|
////////////////////////////////////////////////////////////////////////////////////////////////////
|
|
|
|
template<bool A_in_regs=false, bool B_in_regs=false, bool SwapAB=false,
|
|
typename Tensor0, typename Tensor1,
|
|
typename Tensor2, typename Tensor3, typename Tensor4,
|
|
typename TiledMma, typename TiledCopyA, typename TiledCopyB,
|
|
typename ThrCopyA, typename ThrCopyB, typename Hook>
|
|
CUTLASS_DEVICE void gemm_sm80(Tensor0 &acc, Tensor1 &tCrA, Tensor2 &tCrB, Tensor3 const& tCsA,
|
|
Tensor4 const& tCsB, TiledMma tiled_mma,
|
|
TiledCopyA smem_tiled_copy_A, TiledCopyB smem_tiled_copy_B,
|
|
ThrCopyA smem_thr_copy_A, ThrCopyB smem_thr_copy_B, Hook fn) {
|
|
if constexpr (SwapAB) {
|
|
gemm_sm80<B_in_regs, A_in_regs>(acc, tCrB, tCrA, tCsB, tCsA, tiled_mma, smem_tiled_copy_B, smem_tiled_copy_A, smem_thr_copy_B, smem_thr_copy_A, fn);
|
|
} else {
|
|
CUTE_STATIC_ASSERT_V(size<1>(tCrA) == size<1>(acc)); // MMA_M
|
|
CUTE_STATIC_ASSERT_V(size<1>(tCrB) == size<2>(acc)); // MMA_N
|
|
CUTE_STATIC_ASSERT_V(size<2>(tCrA) == size<2>(tCrB)); // MMA_K
|
|
Tensor tCrA_copy_view = smem_thr_copy_A.retile_D(tCrA);
|
|
CUTE_STATIC_ASSERT_V(size<1>(tCsA) == size<1>(tCrA_copy_view)); // M
|
|
Tensor tCrB_copy_view = smem_thr_copy_B.retile_D(tCrB);
|
|
CUTE_STATIC_ASSERT_V(size<1>(tCsB) == size<1>(tCrB_copy_view)); // N
|
|
if (!A_in_regs) { cute::copy(smem_tiled_copy_A, tCsA(_, _, _0{}), tCrA_copy_view(_, _, _0{})); }
|
|
if (!B_in_regs) { cute::copy(smem_tiled_copy_B, tCsB(_, _, _0{}), tCrB_copy_view(_, _, _0{})); }
|
|
#pragma unroll
|
|
for (int i = 0; i < size<2>(tCrA); ++i) {
|
|
if (i < size<2>(tCrA) - 1) {
|
|
if (!A_in_regs) { cute::copy(smem_tiled_copy_A, tCsA(_, _, i + 1), tCrA_copy_view(_, _, i + 1)); }
|
|
if (!B_in_regs) { cute::copy(smem_tiled_copy_B, tCsB(_, _, i + 1), tCrB_copy_view(_, _, i + 1)); }
|
|
}
|
|
if constexpr (!std::is_same_v<Hook, std::nullptr_t>) {
|
|
if (i == 0) { fn(); }
|
|
}
|
|
cute::gemm(tiled_mma, tCrA(_, _, i), tCrB(_, _, i), acc);
|
|
}
|
|
}
|
|
}
|
|
|
|
////////////////////////////////////////////////////////////////////////////////////////////////////
|
|
|
|
template<typename Tensor0, typename Tensor1, typename Tensor2, typename Tensor3,
|
|
typename TiledMma, typename TiledCopy, typename ThrCopy>
|
|
CUTLASS_DEVICE void gemm_rs_sm80(Tensor0 &acc, Tensor1 &tCrA, Tensor2 &tCrB, Tensor3 const& tCsB,
|
|
TiledMma tiled_mma, TiledCopy smem_tiled_copy_B,
|
|
ThrCopy smem_thr_copy_B) {
|
|
CUTE_STATIC_ASSERT_V(size<1>(tCrA) == size<1>(acc)); // MMA_M
|
|
CUTE_STATIC_ASSERT_V(size<1>(tCrB) == size<2>(acc)); // MMA_N
|
|
CUTE_STATIC_ASSERT_V(size<2>(tCrA) == size<2>(tCrB)); // MMA_K
|
|
Tensor tCrB_copy_view = smem_thr_copy_B.retile_D(tCrB);
|
|
CUTE_STATIC_ASSERT_V(size<1>(tCsB) == size<1>(tCrB_copy_view)); // N
|
|
cute::copy(smem_tiled_copy_B, tCsB(_, _, _0{}), tCrB_copy_view(_, _, _0{}));
|
|
#pragma unroll
|
|
for (int i = 0; i < size<2>(tCrA); ++i) {
|
|
if (i < size<2>(tCrA) - 1) {
|
|
cute::copy(smem_tiled_copy_B, tCsB(_, _, i + 1), tCrB_copy_view(_, _, i + 1));
|
|
}
|
|
cute::gemm(tiled_mma, tCrA(_, _, i), tCrB(_, _, i), acc);
|
|
}
|
|
}
|
|
|
|
|
|
////////////////////////////////////////////////////////////////////////////////////////////////////
|
|
|
|
template <bool Is_even_MN=true, bool Is_even_K=true, bool Clear_OOB_MN=false, bool Clear_OOB_K=true,
|
|
class CopyAtom, class TV, class Tiler, typename Engine0, typename Layout0, typename Engine1, typename Layout1,
|
|
typename Engine2, typename Layout2, typename Engine3, typename Layout3>
|
|
CUTLASS_DEVICE void copy(TiledCopy<CopyAtom, TV, Tiler> const &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) {
|
|
// Decay TiledCopy to CopyAtom
|
|
auto copy_atom = static_cast<CopyAtom const&>(tiled_copy);
|
|
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));
|
|
auto has_with_bool = cute::is_valid([](auto t)->void_t<decltype(declval<typename decltype(t)::Traits>().with(true))>{}, copy_atom);
|
|
#pragma unroll
|
|
for (int m = 0; m < size<1>(S); ++m) {
|
|
bool predicate_mn = Is_even_MN || get<0>(identity_MN(_0{}, m, _0{})) < max_MN;
|
|
if constexpr (Is_even_MN || !Clear_OOB_MN) {
|
|
if (Is_even_MN || predicate_mn) {
|
|
#pragma unroll
|
|
for (int k = 0; k < size<2>(S); ++k) {
|
|
if constexpr (Is_even_K || !Clear_OOB_K) {
|
|
if (Is_even_K || predicate_K(k)) { cute::copy(copy_atom, S(_, m, k), D(_, m, k)); }
|
|
} else { // Clear_OOB_K == true && Is_even_K == false
|
|
// If copy traits can be transformed with a predicate value, do it, otherwise branch here
|
|
if constexpr (has_with_bool) {
|
|
cute::copy(copy_atom.with(predicate_K(k)), S(_, m, k), D(_, m, k));
|
|
} else {
|
|
if (predicate_K(k)) {
|
|
cute::copy(copy_atom, S(_, m, k), D(_, m, k));
|
|
} else {
|
|
cute::clear(D(_, m, k));
|
|
}
|
|
}
|
|
}
|
|
}
|
|
}
|
|
} else { // Clear_OOB_MN == true && Is_even_MN == false, also implies Clear_OOB_K == true
|
|
if constexpr (!has_with_bool) {
|
|
if (predicate_mn) {
|
|
#pragma unroll
|
|
for (int k = 0; k < size<2>(S); ++k) {
|
|
if (Is_even_K || predicate_K(k)) {
|
|
cute::copy(copy_atom, S(_, m, k), D(_, m, k));
|
|
} else if (Clear_OOB_K) {
|
|
cute::clear(D(_, m, k));
|
|
}
|
|
}
|
|
} else {
|
|
cute::clear(D(_, m, _));
|
|
}
|
|
} else { // combine the mn predicate with the k predicate
|
|
#pragma unroll
|
|
for (int k = 0; k < size<2>(S); ++k) {
|
|
cute::copy(copy_atom.with(predicate_mn && (Is_even_K || predicate_K(k))), S(_, m, k), D(_, m, k));
|
|
}
|
|
}
|
|
}
|
|
}
|
|
}
|
|
|
|
////////////////////////////////////////////////////////////////////////////////////////////////////
|
|
|
|
// Byte permute and shuffle to match register layout of
|
|
// (FP8 downcasted) accumulator of GEMM-I to FP8 operand A of GEMM-II.
|
|
template <typename Fragment>
|
|
CUTLASS_DEVICE void permute_Aregs_fp8(Fragment &frag) {
|
|
// frag has shape ((4, 2, 2), MMA_M, MMA_N), each element is 8 bits
|
|
static_assert(decltype(size<0, 0>(frag))::value == 4);
|
|
static_assert(decltype(size<0, 1>(frag))::value == 2);
|
|
static_assert(decltype(stride<0, 0>(frag))::value == 1);
|
|
static_assert(decltype(stride<0, 1>(frag))::value == 4);
|
|
static_assert(sizeof(typename Fragment::value_type) == 1);
|
|
|
|
int quad_idx = threadIdx.x % 4;
|
|
bool lane_03 = quad_idx == 0 || quad_idx == 3;
|
|
int selector_upper = lane_03 ? 0x5410 : 0x1054;
|
|
int selector_lower = lane_03 ? 0x7632 : 0x3276;
|
|
|
|
static constexpr int upper_map[4] = {0, 3, 1, 2};
|
|
// static constexpr int lower_map[4] = {1, 2, 0, 3};
|
|
|
|
Tensor frag_64b = recast<uint2>(frag); // ((1, 1, 2), MMA_M, MMA_N)
|
|
#pragma unroll
|
|
for (int i = 0; i < size(frag_64b); ++i) {
|
|
uint32_t upper = frag_64b[i].x;
|
|
uint32_t lower = frag_64b[i].y;
|
|
uint32_t upper0 = lane_03 ? upper : lower;
|
|
uint32_t lower0 = lane_03 ? lower : upper;
|
|
upper0 = __shfl_sync(uint32_t(-1), upper0, upper_map[quad_idx], 4);
|
|
// lower0 = __shfl_sync(uint32_t(-1), lower0, lower_map[quad_idx], 4);
|
|
lower0 = __shfl_sync(uint32_t(-1), lower0, upper_map[quad_idx] ^ 1, 4);
|
|
frag_64b[i].x = __byte_perm(upper0, lower0, selector_upper);
|
|
frag_64b[i].y = __byte_perm(upper0, lower0, selector_lower);
|
|
}
|
|
}
|
|
|
|
////////////////////////////////////////////////////////////////////////////////////////////////////
|
|
|
|
template <typename Fragment>
|
|
CUTLASS_DEVICE void permute_Cregs_fp8(Fragment &frag) {
|
|
// frag has shape ((2, 2, N / 8), MMA_M, MMA_N), each element is 32 bits
|
|
static_assert(decltype(size<0, 0>(frag))::value == 2);
|
|
static_assert(decltype(size<0, 1>(frag))::value == 2);
|
|
static_assert(decltype(size<0, 2>(frag))::value % 2 == 0);
|
|
static_assert(decltype(stride<0, 0>(frag))::value == 1);
|
|
static_assert(sizeof(typename Fragment::value_type) == 4);
|
|
Tensor frag_64b = group_modes<1, 3>(recast<uint2>(frag)); // ((1, 2, N / 8), (MMA_M, MMA_N))
|
|
#pragma unroll
|
|
for (int mi = 0; mi < size<1>(frag_64b); ++mi) {
|
|
#pragma unroll
|
|
for (int i = 0; i < size<0, 2>(frag_64b) / 2; ++i) {
|
|
cutlass::swap(frag_64b(make_coord(_0{}, _1{}, 2 * i), mi), frag_64b(make_coord(_0{}, _0{}, 2 * i + 1), mi));
|
|
}
|
|
}
|
|
}
|
|
|
|
////////////////////////////////////////////////////////////////////////////////////////////////////
|
|
|
|
template <typename Fragment>
|
|
CUTLASS_DEVICE void permute_output_fp8(Fragment &out) {
|
|
// out has shape ((2, 2, N / 8), MMA_M, MMA_N), each element is 32 bits
|
|
static_assert(decltype(size<0, 0>(out))::value == 2);
|
|
static_assert(decltype(size<0, 1>(out))::value == 2);
|
|
static_assert(decltype(size<0, 2>(out))::value % 2 == 0);
|
|
static_assert(decltype(stride<0, 0>(out))::value == 1);
|
|
static_assert(sizeof(typename Fragment::value_type) == 4);
|
|
Tensor frag = group_modes<1, 3>(out); // ((2, 2, N / 8), (MMA_M, MMA_N))
|
|
#pragma unroll
|
|
for (int mi = 0; mi < size<1>(frag); ++mi) {
|
|
#pragma unroll
|
|
for (int j = 0; j < size<0, 1>(frag); ++j) {
|
|
#pragma unroll
|
|
for (int i = 0; i < size<0, 2>(frag) / 2; ++i) {
|
|
cutlass::swap(frag(make_coord(_1{}, j, 2 * i), mi), frag(make_coord(_0{}, j, 2 * i + 1), mi));
|
|
}
|
|
}
|
|
}
|
|
}
|
|
|
|
////////////////////////////////////////////////////////////////////////////////////////////////////
|
|
|
|
template <typename Fragment>
|
|
CUTLASS_DEVICE void permute_output_fp8_Vcolmajor(Fragment &frag) {
|
|
// frag has shape ((2, 2, N / 8), MMA_M, MMA_N), each element is 16 bits
|
|
static_assert(decltype(size<0, 0>(frag))::value == 2);
|
|
static_assert(decltype(size<0, 1>(frag))::value == 2);
|
|
static_assert(decltype(stride<0, 0>(frag))::value == 1);
|
|
static_assert(sizeof(typename Fragment::value_type) == 2 || sizeof(typename Fragment::value_type) == 4);
|
|
|
|
int quad_idx = threadIdx.x % 4;
|
|
bool lane_03 = quad_idx == 0 || quad_idx == 3;
|
|
|
|
static constexpr int upper_map[4] = {0, 2, 3, 1};
|
|
// static constexpr int lower_map[4] = {2, 0, 1, 3};
|
|
|
|
// if (blockIdx.x == 0 && threadIdx.x == 128) { print_tensor(frag); }
|
|
using type2 = std::conditional_t<sizeof(typename Fragment::value_type) == 2, uint32_t, uint64_t>;
|
|
Tensor frag_2 = group_modes<1, 3>(recast<type2>(frag)); // ((1, 2, N / 8), (MMA_M, MMA_N))
|
|
// if (blockIdx.x == 0 && threadIdx.x == 128) { print(frag); printf("\n"); print(frag_2); }
|
|
#pragma unroll
|
|
for (int mi = 0; mi < size<1>(frag_2); ++mi) {
|
|
#pragma unroll
|
|
for (int j = 0; j < size<0, 1>(frag_2); ++j) {
|
|
#pragma unroll
|
|
for (int i = 0; i < size<0, 2>(frag_2) / 2; ++i) {
|
|
type2 upper = frag_2(make_coord(_0{}, j, 2 * i), mi);
|
|
type2 lower = frag_2(make_coord(_0{}, j, 2 * i + 1), mi);
|
|
type2 upper0 = lane_03 ? upper : lower;
|
|
type2 lower0 = lane_03 ? lower : upper;
|
|
upper0 = __shfl_sync(uint32_t(-1), upper0, upper_map[quad_idx], 4);
|
|
// lower0 = __shfl_sync(uint32_t(-1), lower0, lower_map[quad_idx], 4);
|
|
lower0 = __shfl_sync(uint32_t(-1), lower0, upper_map[quad_idx] ^ 2, 4);
|
|
frag_2(make_coord(_0{}, j, 2 * i), mi) = lane_03 ? upper0 : lower0;
|
|
frag_2(make_coord(_0{}, j, 2 * i + 1), mi) = lane_03 ? lower0 : upper0;
|
|
}
|
|
}
|
|
}
|
|
// if (blockIdx.x == 0 && threadIdx.x == 128) { print_tensor(frag); }
|
|
}
|
|
|
|
////////////////////////////////////////////////////////////////////////////////////////////////////
|
|
|
|
template <typename Engine, typename Layout>
|
|
CUTLASS_DEVICE void apply_softcap(Tensor<Engine, Layout> &tensor, float const softcap){
|
|
#pragma unroll
|
|
for (int i = 0; i < size(tensor); ++i) {
|
|
tensor(i) = cutlass::fast_tanh(tensor(i) * softcap);
|
|
}
|
|
}
|
|
|
|
template <typename Engine, typename Layout>
|
|
CUTLASS_DEVICE auto calculate_dtanh(Tensor<Engine, Layout> &tensor){
|
|
Tensor out = make_fragment_like<float>(tensor);
|
|
#pragma unroll
|
|
for (int i = 0; i < size(tensor); ++i) {
|
|
out(i) = 1.f - (tensor(i) * tensor(i));
|
|
}
|
|
return out;
|
|
}
|
|
|
|
////////////////////////////////////////////////////////////////////////////////////////////////////
|
|
|
|
CUTLASS_DEVICE
|
|
int canonical_warp_group_idx_nosync() {
|
|
return threadIdx.x / cutlass::NumThreadsPerWarpGroup;
|
|
}
|
|
|
|
////////////////////////////////////////////////////////////////////////////////////////////////////
|
|
|
|
} // namespace flash
|