354 lines
16 KiB
C++
354 lines
16 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 <assert.h>
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#include <stdint.h>
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#include <stdlib.h>
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#include <cuda_fp16.h>
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#if defined(__CUDA_ARCH__) && __CUDA_ARCH__ >= 800
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#include <cuda_bf16.h>
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#endif
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#include <cute/tensor.hpp>
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#include <cute/arch/cluster_sm90.hpp> // For cute::elect_one_sync()
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#include <cutlass/array.h>
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#include <cutlass/cutlass.h>
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#include <cutlass/numeric_conversion.h>
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#include <cutlass/numeric_types.h>
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#define CHECK_CUDA(call) \
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do { \
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cudaError_t status_ = call; \
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if (status_ != cudaSuccess) { \
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fprintf(stderr, "CUDA error (%s:%d): %s\n", __FILE__, __LINE__, cudaGetErrorString(status_)); \
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exit(1); \
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} \
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} while(0)
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#define CHECK_CUDA_KERNEL_LAUNCH() CHECK_CUDA(cudaGetLastError())
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namespace flash {
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using namespace cute;
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////////////////////////////////////////////////////////////////////////////////////////////////////
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template<typename T>
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struct MaxOp {
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__device__ __forceinline__ T operator()(T const & x, T const & y) { return x > y ? x : y; }
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};
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template <>
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struct MaxOp<float> {
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// This is slightly faster
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__device__ __forceinline__ float operator()(float const &x, float const &y) { return max(x, y); }
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};
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////////////////////////////////////////////////////////////////////////////////////////////////////
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template<typename T>
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struct SumOp {
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__device__ __forceinline__ T operator()(T const & x, T const & y) { return x + y; }
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};
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////////////////////////////////////////////////////////////////////////////////////////////////////
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template<int THREADS>
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struct Allreduce {
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static_assert(THREADS == 32 || THREADS == 16 || THREADS == 8 || THREADS == 4);
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template<typename T, typename Operator>
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static __device__ __forceinline__ T run(T x, Operator &op) {
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constexpr int OFFSET = THREADS / 2;
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x = op(x, __shfl_xor_sync(uint32_t(-1), x, OFFSET));
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return Allreduce<OFFSET>::run(x, op);
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}
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};
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////////////////////////////////////////////////////////////////////////////////////////////////////
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template<>
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struct Allreduce<2> {
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template<typename T, typename Operator>
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static __device__ __forceinline__ T run(T x, Operator &op) {
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x = op(x, __shfl_xor_sync(uint32_t(-1), x, 1));
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return x;
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}
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};
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////////////////////////////////////////////////////////////////////////////////////////////////////
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// For SM80, convert acc_layout from (MMA=4, MMA_M, MMA_N) to (nrow=(2, MMA_M), ncol=(2, MMA_N))
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// For SM90, convert acc_layout from ((2, 2, V), MMA_M, MMA_N) to (nrow=(2, MMA_M), ncol=(2, V, MMA_N))
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template<typename Layout>
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__forceinline__ __device__ auto convert_layout_acc_rowcol(Layout acc_layout) {
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if constexpr (decltype(rank<0>(acc_layout))::value == 3) { // SM90
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static_assert(decltype(size<0, 0>(acc_layout))::value == 2);
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static_assert(decltype(size<0, 1>(acc_layout))::value == 2);
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static_assert(decltype(rank(acc_layout))::value == 3);
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auto l = acc_layout;
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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)));
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} else { // SM80
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static_assert(decltype(size<0>(acc_layout))::value == 4);
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static_assert(decltype(rank(acc_layout))::value == 3);
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auto l = logical_divide(acc_layout, Shape<_2>{}); // ((2, 2), MMA_M, MMA_N)
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return make_layout(make_layout(get<0, 1>(l), get<1>(l)), make_layout(get<0, 0>(l), get<2>(l)));
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}
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};
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////////////////////////////////////////////////////////////////////////////////////////////////////
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// For SM90, convert acc_layout from ((2, 2, V), MMA_N, MMA_M) to (nrow=(2, V, MMA_M), ncol=(2, MMA_N))
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template<typename Layout>
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__forceinline__ __device__ auto convert_layout_acc_transposed_rowcol(Layout acc_layout) {
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static_assert(decltype(size<0, 0>(acc_layout))::value == 2);
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static_assert(decltype(size<0, 1>(acc_layout))::value == 2);
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static_assert(decltype(rank(acc_layout))::value == 3);
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auto l = acc_layout;
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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)));
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};
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////////////////////////////////////////////////////////////////////////////////////////////////////
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// For SM80, convert acc_layout from (MMA=4, MMA_M, MMA_N) to ((4, 2), MMA_M, MMA_N / 2)
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// if using m16n8k16, or to (4, MMA_M, MMA_N) if using m16n8k8.
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// For SM90, convert acc_layout from ((2, 2, N / 8), MMA_M, MMA_N) to ((2, 2, 2), MMA_M, (N / 16, MMA_N))
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template<typename MMA_traits, typename Layout>
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__forceinline__ __device__ auto convert_layout_acc_Aregs(Layout acc_layout) {
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using X = Underscore;
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if constexpr (decltype(rank<0>(acc_layout))::value == 3) { // SM90
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static_assert(decltype(size<0, 0>(acc_layout))::value == 2);
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static_assert(decltype(size<0, 1>(acc_layout))::value == 2);
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static_assert(decltype(rank(acc_layout))::value == 3);
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static_assert(decltype(rank(get<0>(acc_layout)))::value == 3);
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auto l = logical_divide(get<0>(acc_layout), Shape<X, X, _2>{}); // (2, 2, (2, N / 16)))
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return make_layout(make_layout(get<0>(l), get<1>(l), get<2, 0>(l)), get<1>(acc_layout), make_layout(get<2, 1>(l), get<2>(acc_layout)));
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} else { // SM80
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static_assert(decltype(size<0>(acc_layout))::value == 4);
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static_assert(decltype(rank(acc_layout))::value == 3);
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constexpr int mma_shape_K = get<2>(typename MMA_traits::Shape_MNK{});
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static_assert(mma_shape_K == 8 || mma_shape_K == 16);
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if constexpr (mma_shape_K == 8) {
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return acc_layout;
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} else {
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auto l = logical_divide(acc_layout, Shape<X, X, _2>{}); // (4, MMA_M, (2, MMA_N / 2)))
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return make_layout(make_layout(get<0>(l), get<2, 0>(l)), get<1>(l), get<2, 1>(l));
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}
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}
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};
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// Convert acc_layout from ((2, 2, N / 8), MMA_M, MMA_N) to ((4, 2, 2), MMA_M, (N / 32, MMA_N))
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template<typename Layout>
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__forceinline__ __device__ auto convert_layout_acc_Aregs_fp8(Layout acc_layout) {
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using X = Underscore;
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static_assert(decltype(size<0, 0>(acc_layout))::value == 2);
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static_assert(decltype(size<0, 1>(acc_layout))::value == 2);
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static_assert(decltype(rank(acc_layout))::value == 3);
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static_assert(decltype(rank(get<0>(acc_layout)))::value == 3);
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auto l = logical_divide(get<0>(acc_layout), Shape<X, X, _4>{}); // (2, 2, (2, N / 32)))
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return make_layout(make_layout(Shape<_4, _2, _2>{}),
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get<1>(acc_layout),
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make_layout(get<2, 1>(l), get<2>(acc_layout)));
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};
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////////////////////////////////////////////////////////////////////////////////////////////////////
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// Byte permute for fp8 kernel
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template <typename Fragment>
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CUTLASS_DEVICE void permute_regs_A_to_C(Fragment &accum) {
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auto data = accum.data();
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#pragma unroll
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for (int n = 0; n < size(accum); 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, 0x5410);
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data_32bit[1] = __byte_perm(upper, lower, 0x7632);
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}
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}
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////////////////////////////////////////////////////////////////////////////////////////////////////
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template <typename To_type, typename Engine, typename Layout>
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__forceinline__ __device__ auto convert_type(Tensor<Engine, Layout> const &tensor) {
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using From_type = typename Engine::value_type;
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constexpr int numel = decltype(size(tensor))::value;
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cutlass::NumericArrayConverter<To_type, From_type, numel> convert_op;
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// HACK: this requires tensor to be "contiguous"
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auto frag = convert_op(*reinterpret_cast<const cutlass::Array<From_type, numel> *>(tensor.data()));
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return make_tensor(make_rmem_ptr<To_type>(&frag), tensor.layout());
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// Tensor out = make_tensor_like<To_type>(tensor);
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// cute::copy(make_tensor(make_rmem_ptr<To_type>(&frag), tensor.layout()), out);
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// return out;
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}
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////////////////////////////////////////////////////////////////////////////////////////////////////
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template <bool zero_init=false, int wg_wait=0, bool arrive=true, bool commit=true, typename Tensor0, typename Tensor1, typename Tensor2,
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typename TiledMma>
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__forceinline__ __device__ void gemm(TiledMma &tiled_mma, Tensor0 const &tCrA, Tensor1 const &tCrB, Tensor2 &tCrC) {
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constexpr bool Is_RS = !cute::is_base_of<cute::GMMA::DescriptorIterator, typename TiledMma::FrgTypeA>::value;
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// Need to cast away const on tCrA since warpgroup_fence_operand doesn't take const
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if constexpr (Is_RS) { warpgroup_fence_operand(const_cast<Tensor0 &>(tCrA)); }
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warpgroup_fence_operand(tCrC);
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if constexpr (arrive) {
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warpgroup_arrive();
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}
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if constexpr (zero_init) {
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tiled_mma.accumulate_ = GMMA::ScaleOut::Zero;
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// Unroll the K mode manually to set scale D to 1
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CUTLASS_PRAGMA_UNROLL
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for (int k_block = 0; k_block < size<2>(tCrA); ++k_block) {
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cute::gemm(tiled_mma, tCrA(_,_,k_block), tCrB(_,_,k_block), tCrC);
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tiled_mma.accumulate_ = GMMA::ScaleOut::One;
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}
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} else {
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// cute::gemm(tiled_mma, tCrA, tCrB, tCrC);
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// Unroll the K mode manually to set scale D to 1
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CUTLASS_PRAGMA_UNROLL
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for (int k_block = 0; k_block < size<2>(tCrA); ++k_block) {
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cute::gemm(tiled_mma, tCrA(_,_,k_block), tCrB(_,_,k_block), tCrC);
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tiled_mma.accumulate_ = GMMA::ScaleOut::One;
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}
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}
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if constexpr (commit) {
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warpgroup_commit_batch();
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}
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if constexpr (wg_wait >= 0) { warpgroup_wait<wg_wait>(); }
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warpgroup_fence_operand(tCrC);
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if constexpr (Is_RS) { warpgroup_fence_operand(const_cast<Tensor0 &>(tCrA)); }
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}
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////////////////////////////////////////////////////////////////////////////////////////////////////
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template <bool Is_even_MN=true, bool Is_even_K=true, bool Clear_OOB_MN=false, bool Clear_OOB_K=true,
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typename TiledCopy, typename Engine0, typename Layout0, typename Engine1, typename Layout1,
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typename Engine2, typename Layout2, typename Engine3, typename Layout3>
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__forceinline__ __device__ void copy(TiledCopy tiled_copy, Tensor<Engine0, Layout0> const &S,
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Tensor<Engine1, Layout1> &D, Tensor<Engine2, Layout2> const &identity_MN,
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Tensor<Engine3, Layout3> const &predicate_K, const int max_MN=0) {
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CUTE_STATIC_ASSERT_V(rank(S) == Int<3>{});
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CUTE_STATIC_ASSERT_V(rank(D) == Int<3>{});
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CUTE_STATIC_ASSERT_V(size<0>(S) == size<0>(D)); // MMA
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CUTE_STATIC_ASSERT_V(size<1>(S) == size<1>(D)); // MMA_M
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CUTE_STATIC_ASSERT_V(size<2>(S) == size<2>(D)); // MMA_K
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// There's no case where !Clear_OOB_K && Clear_OOB_MN
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static_assert(!(Clear_OOB_MN && !Clear_OOB_K));
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#pragma unroll
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for (int m = 0; m < size<1>(S); ++m) {
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if (Is_even_MN || get<0>(identity_MN(0, m, 0)) < max_MN) {
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#pragma unroll
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for (int k = 0; k < size<2>(S); ++k) {
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if (Is_even_K || predicate_K(k)) {
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cute::copy(tiled_copy, S(_, m, k), D(_, m, k));
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} else if (Clear_OOB_K) {
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cute::clear(D(_, m, k));
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}
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}
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} else if (Clear_OOB_MN) {
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cute::clear(D(_, m, _));
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}
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}
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}
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////////////////////////////////////////////////////////////////////////////////////////////////////
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template <int NumCopyThreads, typename ElemO, typename TMACopyO, typename LayoutO,
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typename TileShapeO, typename SMemO, typename SeqLenTraits>
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__forceinline__ __device__ void write_tma(
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ElemO* O, const TMACopyO& tma_store_O,
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const LayoutO& layout_O, const TileShapeO& tile_shape_O,
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const SMemO& sO, int m_block, int bidh, int bidb,
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const SeqLenTraits& seqlen_traits_o, int write_warp_idx) {
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Tensor mO = tma_store_O.get_tma_tensor(layout_O.shape());
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Tensor gO = seqlen_traits_o.get_local_tile_tensor(
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mO, tile_shape_O, bidh, bidb
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)(_, _, m_block); // (M, K)
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auto block_tma_O = tma_store_O.get_slice(_0{});
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Tensor tOgO = block_tma_O.partition_D(gO); // (TMA, TMA_M, TMA_K)
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Tensor tOsO = block_tma_O.partition_S(sO); // (TMA, TMA_M, TMA_K)
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int const lane_predicate = cute::elect_one_sync();
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int const warp_idx = cutlass::canonical_warp_idx_sync();
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if (warp_idx == write_warp_idx && lane_predicate) {
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cute::copy(tma_store_O, tOsO, tOgO);
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tma_store_arrive();
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}
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// Note: no wait here.
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// tma_store_wait<0>();
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}
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template <int NumCopyThreads, typename ElemO, typename TiledCopyO, typename LayoutO,
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typename TileShapeO, typename SMemO, typename SeqLenTraits>
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__forceinline__ __device__ void write_tiled(
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ElemO* O, const TiledCopyO& tiled_copy_O,
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const LayoutO& layout_O, const TileShapeO& tile_shape_O,
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const SMemO& sO, int m_block, int bidh, int bidb,
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const SeqLenTraits& seqlen_traits_o) {
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Tensor mO = make_tensor(make_gmem_ptr(O), layout_O);
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Tensor gO = seqlen_traits_o.get_local_tile_tensor(
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mO, tile_shape_O, bidh, bidb
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)(_, _, m_block); // (M, K)
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ThrCopy thr_copy_O = tiled_copy_O.get_slice(threadIdx.x - NumCopyThreads);
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Tensor tOgO = thr_copy_O.partition_D(gO); // (CPY,CPY_M,CPY_K,k)
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Tensor tOsO = thr_copy_O.partition_S(sO); // (CPY,CPY_M,CPY_K)
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// Prepare for TiledCopy.
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// Grouping is needed because cute::copy_if() does group_modes<1, R> for src and dst.
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// After grouping, the first dim is number of elements to read together.
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Tensor tOsOFlatten = cute::flatten(tOsO);
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Tensor tOsOGroup = cute::group_modes<1, rank(tOsOFlatten)>(tOsOFlatten);
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Tensor tOgOFlatten = cute::flatten(tOgO);
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Tensor tOgOGroup = cute::group_modes<1, rank(tOgOFlatten)>(tOgOFlatten);
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// Get thread coords to global index mapping.
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Tensor gOCounting = cute::make_identity_tensor(gO.shape());
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Tensor tSgOCounting = thr_copy_O.partition_D(gOCounting);
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Tensor tSgOCountingFlatten = cute::flatten(tSgOCounting);
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Tensor tSgOCountingGrouped =
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cute::group_modes<1, rank(tSgOCountingFlatten)>(tSgOCountingFlatten);
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// Write out to GMEM.
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const int kNumMsPerTile = get<0>(tile_shape_O);
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int cta_m = std::min(
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seqlen_traits_o.actual_seq_len - m_block * kNumMsPerTile, kNumMsPerTile
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);
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if (cta_m == kNumMsPerTile) {
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copy(tiled_copy_O, tOsOGroup, tOgOGroup);
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} else {
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auto predicate_fn = [&](auto coords) {
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auto s_coords = tSgOCountingGrouped(_0{}, coords);
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return elem_less(get<0>(s_coords), cta_m);
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};
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copy_if(tiled_copy_O, predicate_fn, tOsOGroup, tOgOGroup);
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}
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}
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template <bool IsTMACopy, int NumCopyThreads, typename ElemO,
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typename TMACopyO, typename TiledCopyO, typename LayoutO,
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typename TileShapeO, typename SMemO, typename SeqLenTraits>
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__forceinline__ __device__ void write_O(
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ElemO* O, const TMACopyO& tma_copy_O, const TiledCopyO& tiled_copy_O,
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const LayoutO& layout_O, const TileShapeO& tile_shape_O,
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const SMemO& sO, int m_block, int bidh, int bidb,
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const SeqLenTraits& seqlen_traits_o, int write_warp_idx) {
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if constexpr (IsTMACopy) {
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write_tma<NumCopyThreads>(O, tma_copy_O, layout_O, tile_shape_O, sO, m_block, bidh, bidb, seqlen_traits_o, write_warp_idx);
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} else {
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write_tiled<NumCopyThreads>(O, tiled_copy_O, layout_O, tile_shape_O, sO, m_block, bidh, bidb, seqlen_traits_o);
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}
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}
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////////////////////////////////////////////////////////////////////////////////////////////////////
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} // namespace flash
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