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Unverified Commit 31ea132a authored by zjing14's avatar zjing14 Committed by GitHub
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Fp16/fp8 mixed-precision Gemm with multiply+add fusion (#865) 



* add compute_type

* add multiply_add ckProfiler

* add f8_fp16 support

* clean

* clean

* fixed lds size calc

* format

---------
Co-authored-by: default avatarJing Zhang <jizha@amd.com>
parent c8a8385f
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Showing with 1927 additions and 27 deletions
include/ck/tensor_operation/gpu/device/impl/device_batched_contraction_multiple_d_xdl_cshuffle.hpp
View file @ 31ea132a
...@@ -543,9 +543,13 @@ struct DeviceBatchedContractionMultipleD_Xdl_CShuffle ...@@ -543,9 +543,13 @@ struct DeviceBatchedContractionMultipleD_Xdl_CShuffle
EGridDesc_G_M_N e_grid_desc_g_m_n_; EGridDesc_G_M_N e_grid_desc_g_m_n_;
}; };
using ComputeDataType = ADataType;
// GridwiseGemm // GridwiseGemm
using GridwiseGemm = GridwiseGemmMultipleD_xdl_cshuffle< using GridwiseGemm = GridwiseGemmMultipleD_xdl_cshuffle<
ADataType, // TODO: distinguish A/B datatype ADataType,
BDataType,
ComputeDataType,
AccDataType, AccDataType,
CShuffleDataType, CShuffleDataType,
DsDataType, DsDataType,
......
include/ck/tensor_operation/gpu/device/impl/device_batched_gemm_e_permute_xdl.hpp
View file @ 31ea132a
...@@ -331,8 +331,13 @@ struct DeviceBatchedGemmEPermuteXdl : public DeviceBatchedGemmEPermute<ALayout, ...@@ -331,8 +331,13 @@ struct DeviceBatchedGemmEPermuteXdl : public DeviceBatchedGemmEPermute<ALayout,
EGridDesc_G0_G1_M_N e_grid_desc_g0_g1_m_n_; EGridDesc_G0_G1_M_N e_grid_desc_g0_g1_m_n_;
}; };
using ComputeDataType = ADataType;
// GridwiseGemm
using GridwiseGemm = GridwiseGemmMultipleD_xdl_cshuffle< using GridwiseGemm = GridwiseGemmMultipleD_xdl_cshuffle<
ADataType, // TODO: distinguish A/B datatype ADataType,
BDataType,
ComputeDataType,
AccDataType, AccDataType,
CShuffleDataType, CShuffleDataType,
ck::Tuple<>, // DsDataType, ck::Tuple<>, // DsDataType,
......
include/ck/tensor_operation/gpu/device/impl/device_batched_gemm_multi_d_xdl.hpp
View file @ 31ea132a
...@@ -324,8 +324,12 @@ struct DeviceBatchedGemmMultiD_Xdl : public DeviceBatchedGemmMultiD<ALayout, ...@@ -324,8 +324,12 @@ struct DeviceBatchedGemmMultiD_Xdl : public DeviceBatchedGemmMultiD<ALayout,
index_t BatchStrideE_; index_t BatchStrideE_;
}; };
using ComputeDataType = ADataType;
using GridwiseGemm = GridwiseGemmMultipleD_xdl_cshuffle< using GridwiseGemm = GridwiseGemmMultipleD_xdl_cshuffle<
ADataType, // TODO: distinguish A/B datatype ADataType, // TODO: distinguish A/B datatype
BDataType,
ComputeDataType,
AccDataType, AccDataType,
CShuffleDataType, CShuffleDataType,
DsDataType, DsDataType,
......
include/ck/tensor_operation/gpu/device/impl/device_contraction_multiple_d_xdl_cshuffle.hpp
View file @ 31ea132a
...@@ -310,9 +310,13 @@ struct DeviceContractionMultipleD_Xdl_CShuffle ...@@ -310,9 +310,13 @@ struct DeviceContractionMultipleD_Xdl_CShuffle
using DsGridDesc_M_N = remove_cvref_t<decltype(MakeDsGridDescriptor_M_N({{}}, {{}}))>; using DsGridDesc_M_N = remove_cvref_t<decltype(MakeDsGridDescriptor_M_N({{}}, {{}}))>;
using EGridDesc_M_N = decltype(MakeEGridDescriptor_M_N({}, {})); using EGridDesc_M_N = decltype(MakeEGridDescriptor_M_N({}, {}));
using ComputeDataType = ADataType;
// GridwiseGemm // GridwiseGemm
using GridwiseGemm = GridwiseGemmMultipleD_xdl_cshuffle< using GridwiseGemm = GridwiseGemmMultipleD_xdl_cshuffle<
ADataType, // TODO: distinguish A/B datatype ADataType, // TODO: distinguish A/B datatype
BDataType,
ComputeDataType,
AccDataType, AccDataType,
CShuffleDataType, CShuffleDataType,
DsDataType, DsDataType,
......
include/ck/tensor_operation/gpu/device/impl/device_gemm_multiple_d_xdl_cshuffle.hpp
View file @ 31ea132a
...@@ -20,7 +20,8 @@ ...@@ -20,7 +20,8 @@
namespace ck { namespace ck {
template <typename GridwiseGemm, template <typename GridwiseGemm,
typename ABDataType, typename ADataType,
typename BDataType,
typename DsPointer, typename DsPointer,
typename EDataType, typename EDataType,
typename AElementwiseOperation, typename AElementwiseOperation,
...@@ -36,8 +37,8 @@ __global__ void ...@@ -36,8 +37,8 @@ __global__ void
#if CK_USE_LAUNCH_BOUNDS #if CK_USE_LAUNCH_BOUNDS
__launch_bounds__(CK_MAX_THREAD_PER_BLOCK, CK_MIN_BLOCK_PER_CU) __launch_bounds__(CK_MAX_THREAD_PER_BLOCK, CK_MIN_BLOCK_PER_CU)
#endif #endif
kernel_gemm_multiple_d_xdl_cshuffle(const ABDataType* __restrict__ p_a_grid, kernel_gemm_multiple_d_xdl_cshuffle(const ADataType* __restrict__ p_a_grid,
const ABDataType* __restrict__ p_b_grid, const BDataType* __restrict__ p_b_grid,
DsPointer p_ds_grid, DsPointer p_ds_grid,
EDataType* __restrict__ p_e_grid, EDataType* __restrict__ p_e_grid,
const AElementwiseOperation a_element_op, const AElementwiseOperation a_element_op,
...@@ -242,9 +243,13 @@ struct DeviceGemmMultipleD_Xdl_CShuffle : public DeviceGemmMultipleD<ALayout, ...@@ -242,9 +243,13 @@ struct DeviceGemmMultipleD_Xdl_CShuffle : public DeviceGemmMultipleD<ALayout,
using DsGridDesc_M_N = remove_cvref_t<decltype(MakeDsGridDescriptor_M_N({}, {}, {}))>; using DsGridDesc_M_N = remove_cvref_t<decltype(MakeDsGridDescriptor_M_N({}, {}, {}))>;
using EGridDesc_M_N = decltype(MakeEGridDescriptor_M_N<ELayout>(1, 1, 1)); using EGridDesc_M_N = decltype(MakeEGridDescriptor_M_N<ELayout>(1, 1, 1));
using ComputeDataType = EDataType;
// GridwiseGemm // GridwiseGemm
using GridwiseGemm = GridwiseGemmMultipleD_xdl_cshuffle< using GridwiseGemm = GridwiseGemmMultipleD_xdl_cshuffle<
ADataType, // TODO: distinguish A/B datatype ADataType, // TODO: distinguish A/B datatype
BDataType,
ComputeDataType,
AccDataType, AccDataType,
CShuffleDataType, CShuffleDataType,
DsDataType, DsDataType,
...@@ -442,6 +447,7 @@ struct DeviceGemmMultipleD_Xdl_CShuffle : public DeviceGemmMultipleD<ALayout, ...@@ -442,6 +447,7 @@ struct DeviceGemmMultipleD_Xdl_CShuffle : public DeviceGemmMultipleD<ALayout,
const auto kernel = kernel_gemm_multiple_d_xdl_cshuffle< const auto kernel = kernel_gemm_multiple_d_xdl_cshuffle<
GridwiseGemm, GridwiseGemm,
ADataType, // TODO: distiguish A/B datatype ADataType, // TODO: distiguish A/B datatype
BDataType, // TODO: distiguish A/B datatype
typename GridwiseGemm::DsGridPointer, typename GridwiseGemm::DsGridPointer,
EDataType, EDataType,
AElementwiseOperation, AElementwiseOperation,
......
include/ck/tensor_operation/gpu/device/impl/device_grouped_contraction_multiple_d_xdl_cshuffle.hpp
View file @ 31ea132a
...@@ -355,9 +355,13 @@ struct DeviceGroupedContractionMultipleD_Xdl_CShuffle ...@@ -355,9 +355,13 @@ struct DeviceGroupedContractionMultipleD_Xdl_CShuffle
using DsGridDesc_M_N = remove_cvref_t<decltype(MakeDsGridDescriptor_M_N({{}}, {{}}))>; using DsGridDesc_M_N = remove_cvref_t<decltype(MakeDsGridDescriptor_M_N({{}}, {{}}))>;
using EGridDesc_M_N = decltype(MakeEGridDescriptor_M_N({}, {})); using EGridDesc_M_N = decltype(MakeEGridDescriptor_M_N({}, {}));
using ComputeDataType = ADataType;
// GridwiseGemm // GridwiseGemm
using GridwiseGemm = GridwiseGemmMultipleD_xdl_cshuffle< using GridwiseGemm = GridwiseGemmMultipleD_xdl_cshuffle<
ADataType, // TODO: distinguish A/B datatype ADataType, // TODO: distinguish A/B datatype
BDataType,
ComputeDataType,
AccDataType, AccDataType,
CShuffleDataType, CShuffleDataType,
DsDataType, DsDataType,
......
include/ck/tensor_operation/gpu/device/impl/device_grouped_conv_bwd_data_multiple_d_xdl_cshuffle_v1.hpp
View file @ 31ea132a
...@@ -355,6 +355,8 @@ struct DeviceGroupedConvBwdDataMultipleD_Xdl_CShuffle_v1 ...@@ -355,6 +355,8 @@ struct DeviceGroupedConvBwdDataMultipleD_Xdl_CShuffle_v1
// GridwiseGemm // GridwiseGemm
using GridwiseGemm = GridwiseGemmMultipleD_xdl_cshuffle< using GridwiseGemm = GridwiseGemmMultipleD_xdl_cshuffle<
ABDataType, // TODO: distinguish A/B datatype
ABDataType, // TODO: distinguish A/B datatype
ABDataType, // TODO: distinguish A/B datatype ABDataType, // TODO: distinguish A/B datatype
AccDataType, AccDataType,
CShuffleDataType, CShuffleDataType,
......
include/ck/tensor_operation/gpu/device/impl/device_grouped_conv_fwd_multiple_d_xdl_cshuffle.hpp
View file @ 31ea132a
...@@ -367,9 +367,13 @@ struct DeviceGroupedConvFwdMultipleD_Xdl_CShuffle ...@@ -367,9 +367,13 @@ struct DeviceGroupedConvFwdMultipleD_Xdl_CShuffle
using DsGridDesc_M_N = remove_cvref_t<decltype(MakeDsGridDescriptor_M_N({}, {}))>; using DsGridDesc_M_N = remove_cvref_t<decltype(MakeDsGridDescriptor_M_N({}, {}))>;
using EGridDesc_M_N = remove_cvref_t<decltype(MakeEGridDescriptor_M_N<ELayout>({}, {}))>; using EGridDesc_M_N = remove_cvref_t<decltype(MakeEGridDescriptor_M_N<ELayout>({}, {}))>;
using ComputeDataType = ADataType;
// GridwiseGemm // GridwiseGemm
using GridwiseGemm = GridwiseGemmMultipleD_xdl_cshuffle< using GridwiseGemm = GridwiseGemmMultipleD_xdl_cshuffle<
ADataType, // TODO: distinguish A/B datatype ADataType, // TODO: distinguish A/B datatype
BDataType,
ComputeDataType,
AccDataType, AccDataType,
CShuffleDataType, CShuffleDataType,
DsDataType, DsDataType,
......
include/ck/tensor_operation/gpu/device/impl/device_grouped_gemm_xdl.hpp
View file @ 31ea132a
...@@ -228,9 +228,13 @@ struct DeviceGroupedGemm_Xdl : public DeviceGroupedGemm<ALayout, ...@@ -228,9 +228,13 @@ struct DeviceGroupedGemm_Xdl : public DeviceGroupedGemm<ALayout,
using DsGridDesc_M_N = remove_cvref_t<decltype(MakeDsGridDescriptor_M_N({}, {}, {}))>; using DsGridDesc_M_N = remove_cvref_t<decltype(MakeDsGridDescriptor_M_N({}, {}, {}))>;
using EGridDesc_M_N = decltype(MakeEGridDescriptor_M_N<ELayout>(1, 1, 1)); using EGridDesc_M_N = decltype(MakeEGridDescriptor_M_N<ELayout>(1, 1, 1));
using ComputeDataType = ADataType;
// GridwiseGemm // GridwiseGemm
using GridwiseGemm = GridwiseGemmMultipleD_xdl_cshuffle< using GridwiseGemm = GridwiseGemmMultipleD_xdl_cshuffle<
ADataType, // TODO: distinguish A/B datatype ADataType, // TODO: distinguish A/B datatype
BDataType,
ComputeDataType,
AccDataType, AccDataType,
CShuffleDataType, CShuffleDataType,
DsDataType, DsDataType,
......
include/ck/tensor_operation/gpu/element/element_wise_operation.hpp
View file @ 31ea132a
...@@ -195,6 +195,51 @@ struct AddMultiply ...@@ -195,6 +195,51 @@ struct AddMultiply
} }
}; };
// C = A * B
// E = C x D0 + D1
struct MultiplyAdd
{
template <typename E, typename C, typename D0, typename D1>
__host__ __device__ void operator()(E& e, const C& c, const D0& d0, const D1& d1) const;
template <>
__host__ __device__ void operator()<half_t, half_t, half_t, half_t>(half_t& e,
const half_t& c,
const half_t& d0,
const half_t& d1) const
{
const half_t y = (c * d0) + d1;
e = y;
}
template <>
__host__ __device__ void operator()<half_t, float, half_t, half_t>(half_t& e,
const float& c,
const half_t& d0,
const half_t& d1) const
{
const half_t y = type_convert<half_t>(c) * d0 + d1;
e = y;
}
template <>
__host__ __device__ void operator()<float, float, half_t, half_t>(float& e,
const float& c,
const half_t& d0,
const half_t& d1) const
{
const float y = c * d0 + d1;
e = y;
}
template <>
__host__ __device__ void operator()<half_t, float, float, float>(half_t& e,
const float& c,
const float& d0,
const float& d1) const
{
const float y = c * d0 + d1;
e = y;
}
};
// E = FastGelu(C + D0 + D1) // E = FastGelu(C + D0 + D1)
struct AddAddFastGelu struct AddAddFastGelu
{ {
......
include/ck/tensor_operation/gpu/grid/gridwise_gemm_multiple_d_xdl_cshuffle.hpp
View file @ 31ea132a
...@@ -26,7 +26,9 @@ namespace ck { ...@@ -26,7 +26,9 @@ namespace ck {
// E = cde_op(C, D0, D1, ...) // E = cde_op(C, D0, D1, ...)
// Assume: // Assume:
// D0, D1, ... and E have the same layout // D0, D1, ... and E have the same layout
template <typename ABDataType, // FIXME: don't assume A/B have same datatype template <typename ADataType,
typename BDataType,
typename ComputeDataType_,
typename AccDataType, typename AccDataType,
typename CShuffleDataType, typename CShuffleDataType,
typename DsDataType, typename DsDataType,
...@@ -92,15 +94,11 @@ struct GridwiseGemmMultipleD_xdl_cshuffle ...@@ -92,15 +94,11 @@ struct GridwiseGemmMultipleD_xdl_cshuffle
using GridwiseGemmPipe = remove_cvref_t< using GridwiseGemmPipe = remove_cvref_t<
decltype(GridwiseGemmPipeline_Selector<PipelineVer, NumGemmKPrefetchStage, LoopSched>())>; decltype(GridwiseGemmPipeline_Selector<PipelineVer, NumGemmKPrefetchStage, LoopSched>())>;
// denorm test fix, required to work around fp16 mfma issue
// we convert fp16->fp32->bf16 and execute bf16 mfma instruction
// when mfma if fixed, remove this section and update
// ABDataTypeAdjusted -> ABDataType throughout this file
#if CK_WORKAROUND_DENORM_FIX #if CK_WORKAROUND_DENORM_FIX
using ABDataTypeAdjusted = using ComputeDataType =
conditional_t<is_same_v<ABDataType, ck::half_t>, ck::bhalf_t, ABDataType>; conditional_t<is_same_v<ComputeDataType_, ck::half_t>, ck::bhalf_t, ComputeDataType_>;
#else #else
using ABDataTypeAdjusted = ABDataType; using ComputeDataType = ComputeDataType_;
#endif #endif
__host__ __device__ static constexpr auto GetABlockDescriptor_AK0PerBlock_MPerBlock_AK1() __host__ __device__ static constexpr auto GetABlockDescriptor_AK0PerBlock_MPerBlock_AK1()
...@@ -170,7 +168,7 @@ struct GridwiseGemmMultipleD_xdl_cshuffle ...@@ -170,7 +168,7 @@ struct GridwiseGemmMultipleD_xdl_cshuffle
c_shuffle_block_desc_mblock_mperblock_nblock_nperblock.GetElementSpaceSize(); c_shuffle_block_desc_mblock_mperblock_nblock_nperblock.GetElementSpaceSize();
return math::max((a_block_space_size_aligned + b_block_space_size_aligned) * return math::max((a_block_space_size_aligned + b_block_space_size_aligned) *
sizeof(ABDataType), sizeof(ComputeDataType),
c_block_size * sizeof(CShuffleDataType)); c_block_size * sizeof(CShuffleDataType));
} }
...@@ -313,8 +311,8 @@ struct GridwiseGemmMultipleD_xdl_cshuffle ...@@ -313,8 +311,8 @@ struct GridwiseGemmMultipleD_xdl_cshuffle
// check tensor size: cannot be larger than 2GB each // check tensor size: cannot be larger than 2GB each
constexpr long_index_t TwoGB = (long_index_t{1} << 31); constexpr long_index_t TwoGB = (long_index_t{1} << 31);
if(!(a_grid_desc_m_k.GetElementSpaceSize() * sizeof(ABDataType) <= TwoGB && if(!(a_grid_desc_m_k.GetElementSpaceSize() * sizeof(ADataType) <= TwoGB &&
b_grid_desc_n_k.GetElementSpaceSize() * sizeof(ABDataType) <= TwoGB && b_grid_desc_n_k.GetElementSpaceSize() * sizeof(BDataType) <= TwoGB &&
e_grid_desc_m_n.GetElementSpaceSize() * sizeof(EDataType) <= TwoGB)) e_grid_desc_m_n.GetElementSpaceSize() * sizeof(EDataType) <= TwoGB))
{ {
return false; return false;
...@@ -338,8 +336,8 @@ struct GridwiseGemmMultipleD_xdl_cshuffle ...@@ -338,8 +336,8 @@ struct GridwiseGemmMultipleD_xdl_cshuffle
typename DsGridDesc_MBlock_MPerBlock_NBlock_NPerBlock, typename DsGridDesc_MBlock_MPerBlock_NBlock_NPerBlock,
typename EGridDesc_MBlock_MPerBlock_NBlock_NPerBlock, typename EGridDesc_MBlock_MPerBlock_NBlock_NPerBlock,
typename Block2ETileMap> typename Block2ETileMap>
__device__ static void Run(const ABDataType* __restrict__ p_a_grid, __device__ static void Run(const ADataType* __restrict__ p_a_grid,
const ABDataType* __restrict__ p_b_grid, const BDataType* __restrict__ p_b_grid,
DsGridPointer p_ds_grid, DsGridPointer p_ds_grid,
EDataType* __restrict__ p_e_grid, EDataType* __restrict__ p_e_grid,
void* __restrict__ p_shared, void* __restrict__ p_shared,
...@@ -408,8 +406,8 @@ struct GridwiseGemmMultipleD_xdl_cshuffle ...@@ -408,8 +406,8 @@ struct GridwiseGemmMultipleD_xdl_cshuffle
Sequence<AK0PerBlock, MPerBlock, AK1>, Sequence<AK0PerBlock, MPerBlock, AK1>,
ABlockTransferThreadClusterLengths_AK0_M_AK1, ABlockTransferThreadClusterLengths_AK0_M_AK1,
ABlockTransferThreadClusterArrangeOrder, ABlockTransferThreadClusterArrangeOrder,
ABDataType, ADataType,
ABDataTypeAdjusted, ComputeDataType,
decltype(a_grid_desc_ak0_m_ak1), decltype(a_grid_desc_ak0_m_ak1),
decltype(a_block_desc_ak0_m_ak1), decltype(a_block_desc_ak0_m_ak1),
ABlockTransferSrcAccessOrder, ABlockTransferSrcAccessOrder,
...@@ -439,8 +437,8 @@ struct GridwiseGemmMultipleD_xdl_cshuffle ...@@ -439,8 +437,8 @@ struct GridwiseGemmMultipleD_xdl_cshuffle
Sequence<BK0PerBlock, NPerBlock, BK1>, Sequence<BK0PerBlock, NPerBlock, BK1>,
BBlockTransferThreadClusterLengths_BK0_N_BK1, BBlockTransferThreadClusterLengths_BK0_N_BK1,
BBlockTransferThreadClusterArrangeOrder, BBlockTransferThreadClusterArrangeOrder,
ABDataType, BDataType,
ABDataTypeAdjusted, ComputeDataType,
decltype(b_grid_desc_bk0_n_bk1), decltype(b_grid_desc_bk0_n_bk1),
decltype(b_block_desc_bk0_n_bk1), decltype(b_block_desc_bk0_n_bk1),
BBlockTransferSrcAccessOrder, BBlockTransferSrcAccessOrder,
...@@ -470,11 +468,11 @@ struct GridwiseGemmMultipleD_xdl_cshuffle ...@@ -470,11 +468,11 @@ struct GridwiseGemmMultipleD_xdl_cshuffle
// sanity check // sanity check
constexpr index_t KPack = constexpr index_t KPack =
math::max(math::lcm(AK1, BK1), math::max(math::lcm(AK1, BK1),
MfmaSelector<ABDataTypeAdjusted, MPerXdl, NPerXdl>::selected_mfma.k_per_blk); MfmaSelector<ComputeDataType, MPerXdl, NPerXdl>::selected_mfma.k_per_blk);
auto blockwise_gemm = BlockwiseGemmXdlops_k0mk1_k0nk1_m0n0m1n1m2m3m4n2_Selector< auto blockwise_gemm = BlockwiseGemmXdlops_k0mk1_k0nk1_m0n0m1n1m2m3m4n2_Selector<
BlockSize, BlockSize,
ABDataTypeAdjusted, ComputeDataType,
AccDataType, AccDataType,
decltype(a_block_desc_ak0_m_ak1), decltype(a_block_desc_ak0_m_ak1),
decltype(b_block_desc_bk0_n_bk1), decltype(b_block_desc_bk0_n_bk1),
...@@ -492,11 +490,10 @@ struct GridwiseGemmMultipleD_xdl_cshuffle ...@@ -492,11 +490,10 @@ struct GridwiseGemmMultipleD_xdl_cshuffle
a_block_desc_ak0_m_ak1.GetElementSpaceSize(), max_lds_align); a_block_desc_ak0_m_ak1.GetElementSpaceSize(), max_lds_align);
auto a_block_buf = make_dynamic_buffer<AddressSpaceEnum::Lds>( auto a_block_buf = make_dynamic_buffer<AddressSpaceEnum::Lds>(
static_cast<ABDataTypeAdjusted*>(p_shared), static_cast<ComputeDataType*>(p_shared), a_block_desc_ak0_m_ak1.GetElementSpaceSize());
a_block_desc_ak0_m_ak1.GetElementSpaceSize());
auto b_block_buf = make_dynamic_buffer<AddressSpaceEnum::Lds>( auto b_block_buf = make_dynamic_buffer<AddressSpaceEnum::Lds>(
static_cast<ABDataTypeAdjusted*>(p_shared) + a_block_space_size_aligned, static_cast<ComputeDataType*>(p_shared) + a_block_space_size_aligned,
b_block_desc_bk0_n_bk1.GetElementSpaceSize()); b_block_desc_bk0_n_bk1.GetElementSpaceSize());
constexpr auto a_block_slice_copy_step = make_multi_index(KPerBlock / AK1, 0, 0); constexpr auto a_block_slice_copy_step = make_multi_index(KPerBlock / AK1, 0, 0);
......
library/include/ck/library/tensor_operation_instance/device_operation_instance_factory.hpp
View file @ 31ea132a
...@@ -32,6 +32,8 @@ using F32_Tuple = ck::Tuple<F32>; ...@@ -32,6 +32,8 @@ using F32_Tuple = ck::Tuple<F32>;
using I32_Tuple = ck::Tuple<I32>; using I32_Tuple = ck::Tuple<I32>;
using I32_F32_Tuple = ck::Tuple<I32, F32>; using I32_F32_Tuple = ck::Tuple<I32, F32>;
using F32_F32_Tuple = ck::Tuple<F32, F32>;
// GEMM layout // GEMM layout
using Row = ck::tensor_layout::gemm::RowMajor; using Row = ck::tensor_layout::gemm::RowMajor;
using Col = ck::tensor_layout::gemm::ColumnMajor; using Col = ck::tensor_layout::gemm::ColumnMajor;
...@@ -95,6 +97,7 @@ using AddFastGelu = ck::tensor_operation::element_wise::AddFastGelu; ...@@ -95,6 +97,7 @@ using AddFastGelu = ck::tensor_operation::element_wise::AddFastGelu;
using AddReluAdd = ck::tensor_operation::element_wise::AddReluAdd; using AddReluAdd = ck::tensor_operation::element_wise::AddReluAdd;
using FastGelu = ck::tensor_operation::element_wise::FastGelu; using FastGelu = ck::tensor_operation::element_wise::FastGelu;
using AddMultiply = ck::tensor_operation::element_wise::AddMultiply; using AddMultiply = ck::tensor_operation::element_wise::AddMultiply;
using MultiplyAdd = ck::tensor_operation::element_wise::MultiplyAdd;
using ScaleAdd = ck::tensor_operation::element_wise::ScaleAdd; using ScaleAdd = ck::tensor_operation::element_wise::ScaleAdd;
using Gelu = ck::tensor_operation::element_wise::Gelu; using Gelu = ck::tensor_operation::element_wise::Gelu;
using Swish = ck::tensor_operation::element_wise::Swish; using Swish = ck::tensor_operation::element_wise::Swish;
......
library/include/ck/library/tensor_operation_instance/gpu/gemm_multiply_add.hpp 0 → 100644
View file @ 31ea132a
// SPDX-License-Identifier: MIT
// Copyright (c) 2018-2023, Advanced Micro Devices, Inc. All rights reserved.
#pragma once
#include <cstdlib>
#include <vector>
#include <memory>
#include "ck/ck.hpp"
#include "ck/tensor_operation/gpu/device/tensor_layout.hpp"
#include "ck/tensor_operation/gpu/device/device_gemm_multiple_d.hpp"
#include "ck/tensor_operation/gpu/element/element_wise_operation.hpp"
#include "ck/library/tensor_operation_instance/device_operation_instance_factory.hpp"
namespace ck {
namespace tensor_operation {
namespace device {
namespace instance {
void add_device_gemm_multiply_add_xdl_c_shuffle_f16_f16_f16_f16_f16_mk_kn_mn_mn_mn_instances(
std::vector<std::unique_ptr<DeviceGemmMultipleD<Row,
Row,
Row_Row_Tuple,
Row,
F16,
F16,
F16_F16_Tuple,
F16,
PassThrough,
PassThrough,
MultiplyAdd>>>&);
void add_device_gemm_multiply_add_xdl_c_shuffle_f16_f16_f16_f16_f16_mk_nk_mn_mn_mn_instances(
std::vector<std::unique_ptr<DeviceGemmMultipleD<Row,
Col,
Row_Row_Tuple,
Row,
F16,
F16,
F16_F16_Tuple,
F16,
PassThrough,
PassThrough,
MultiplyAdd>>>&);
void add_device_gemm_multiply_add_xdl_c_shuffle_f16_f8_f32_f32_f16_mk_kn_mn_mn_mn_instances(
std::vector<std::unique_ptr<DeviceGemmMultipleD<Row,
Row,
Row_Row_Tuple,
Row,
F16,
F8,
F32_F32_Tuple,
F16,
PassThrough,
PassThrough,
MultiplyAdd>>>&);
void add_device_gemm_multiply_add_xdl_c_shuffle_f16_f8_f32_f32_f16_mk_nk_mn_mn_mn_instances(
std::vector<std::unique_ptr<DeviceGemmMultipleD<Row,
Col,
Row_Row_Tuple,
Row,
F16,
F8,
F32_F32_Tuple,
F16,
PassThrough,
PassThrough,
MultiplyAdd>>>&);
// GEMM + Multiply + Add
template <typename ALayout,
typename BLayout,
typename D0Layout,
typename D1Layout,
typename ELayout,
typename ADataType,
typename BDataType,
typename D0DataType,
typename D1DataType,
typename EDataType>
struct DeviceOperationInstanceFactory<ck::tensor_operation::device::DeviceGemmMultipleD<
ALayout,
BLayout,
ck::Tuple<D0Layout, D1Layout>,
ELayout,
ADataType,
BDataType,
ck::Tuple<D0DataType, D1DataType>,
EDataType,
ck::tensor_operation::element_wise::PassThrough,
ck::tensor_operation::element_wise::PassThrough,
ck::tensor_operation::element_wise::MultiplyAdd>>
{
using DeviceOp = DeviceGemmMultipleD<ALayout,
BLayout,
ck::Tuple<D0Layout, D1Layout>,
ELayout,
ADataType,
BDataType,
ck::Tuple<D0DataType, D1DataType>,
EDataType,
ck::tensor_operation::element_wise::PassThrough,
ck::tensor_operation::element_wise::PassThrough,
ck::tensor_operation::element_wise::MultiplyAdd>;
static auto GetInstances()
{
std::vector<std::unique_ptr<DeviceOp>> op_ptrs;
if constexpr(is_same_v<ADataType, half_t> && is_same_v<BDataType, half_t> &&
is_same_v<D0DataType, half_t> && is_same_v<D1DataType, half_t> &&
is_same_v<EDataType, half_t>)
{
if constexpr(is_same_v<ALayout, Row> && is_same_v<BLayout, Row> &&
is_same_v<D0Layout, Row> && is_same_v<D1Layout, Row> &&
is_same_v<ELayout, Row>)
{
add_device_gemm_multiply_add_xdl_c_shuffle_f16_f16_f16_f16_f16_mk_kn_mn_mn_mn_instances(
op_ptrs);
}
else if constexpr(is_same_v<ALayout, Row> && is_same_v<BLayout, Col> &&
is_same_v<D0Layout, Row> && is_same_v<D1Layout, Row> &&
is_same_v<ELayout, Row>)
{
add_device_gemm_multiply_add_xdl_c_shuffle_f16_f16_f16_f16_f16_mk_nk_mn_mn_mn_instances(
op_ptrs);
}
}
if constexpr(is_same_v<ADataType, half_t> && is_same_v<BDataType, f8_t> &&
is_same_v<D0DataType, float> && is_same_v<D1DataType, float> &&
is_same_v<EDataType, half_t>)
{
if constexpr(is_same_v<ALayout, Row> && is_same_v<BLayout, Row> &&
is_same_v<D0Layout, Row> && is_same_v<D1Layout, Row> &&
is_same_v<ELayout, Row>)
{
add_device_gemm_multiply_add_xdl_c_shuffle_f16_f8_f32_f32_f16_mk_kn_mn_mn_mn_instances(
op_ptrs);
}
else if constexpr(is_same_v<ALayout, Row> && is_same_v<BLayout, Col> &&
is_same_v<D0Layout, Row> && is_same_v<D1Layout, Row> &&
is_same_v<ELayout, Row>)
{
add_device_gemm_multiply_add_xdl_c_shuffle_f16_f8_f32_f32_f16_mk_nk_mn_mn_mn_instances(
op_ptrs);
}
}
return op_ptrs;
}
};
} // namespace instance
} // namespace device
} // namespace tensor_operation
} // namespace ck
library/src/tensor_operation_instance/gpu/gemm_multiply_add/CMakeLists.txt 0 → 100644
View file @ 31ea132a
add_instance_library(device_gemm_multiply_add_instance
device_gemm_multiply_add_xdl_c_shuffle_f16_f16_f16_f16_f16_mk_kn_mn_mn_mn_instance.cpp
device_gemm_multiply_add_xdl_c_shuffle_f16_f16_f16_f16_f16_mk_nk_mn_mn_mn_instance.cpp
device_gemm_multiply_add_xdl_c_shuffle_f16_f8_f32_f32_f16_mk_kn_mn_mn_mn_instance.cpp
device_gemm_multiply_add_xdl_c_shuffle_f16_f8_f32_f32_f16_mk_nk_mn_mn_mn_instance.cpp
)
library/src/tensor_operation_instance/gpu/gemm_multiply_add/device_gemm_multiply_add_xdl_c_shuffle_f16_f16_f16_f16_f16_mk_kn_mn_mn_mn_instance.cpp 0 → 100644
View file @ 31ea132a
// SPDX-License-Identifier: MIT
// Copyright (c) 2018-2023, Advanced Micro Devices, Inc. All rights reserved.
#include <cstdlib>
#include "ck/ck.hpp"
#include "ck/tensor_operation/gpu/device/tensor_layout.hpp"
#include "ck/tensor_operation/gpu/device/gemm_specialization.hpp"
#include "ck/tensor_operation/gpu/device/impl/device_gemm_multiple_d_xdl_cshuffle.hpp"
#include "ck/tensor_operation/gpu/element/element_wise_operation.hpp"
#include "ck/library/tensor_operation_instance/add_device_operation_instance.hpp"
namespace ck {
namespace tensor_operation {
namespace device {
namespace instance {
using F16 = ck::half_t;
using F32 = float;
using F16_Tuple = ck::Tuple<F16, F16>;
using Row = ck::tensor_layout::gemm::RowMajor;
using Col = ck::tensor_layout::gemm::ColumnMajor;
using Row_Tuple = ck::Tuple<Row, Row>;
template <ck::index_t... Is>
using S = ck::Sequence<Is...>;
using PassThrough = ck::tensor_operation::element_wise::PassThrough;
using MultiplyAdd = ck::tensor_operation::element_wise::MultiplyAdd;
static constexpr auto GemmMNKPadding = ck::tensor_operation::device::GemmSpecialization::MNKPadding;
using device_gemm_multiply_add_xdl_c_shuffle_f16_f16_f16_f16_f16_mk_kn_mn_mn_mn_instances =
std::tuple<
// clang-format off
// M/N/K padding
//##############################| A| B| Ds| E| AData| BData| AccData| CShuffle| DsData| EData| A| B| CDE| GEMM| NumGemmK| Block| MPer| NPer| KPer| AK1| BK1| MPer| NPer| MXdl| NXdl| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockLds| BBlockTransfer| BBlockTransfer| BBlockTransfer| BlockTransfer| BBlockTransfer| BBlockTransfer| BBlockLds| CShuffle| CShuffle| CBlockTransferClusterLengths| CBlockTransfer|
//##############################| Layout| Layout| Layout| Layout| Type| Type| Type| DataType| Type| Type| Elementwise| Elementwise| Elementwise| Specialization| Prefetch| Size| Block| Block| Block| | | XDL| XDL| Per| Per| ThreadCluster| ThreadCluster| SrcAccessOrder| SrcVectorDim| SrcScalar| DstScalar| AddExtraM| ThreadCluster| ThreadCluster| SrcAccessOrder| SrcVectorDim| SrcScalar| DstScalar| AddExtraN| MXdlPerWave| NXdlPerWave| _MBlock_MWaveMPerXdl| ScalarPerVector|
//##############################| | | | | | | | | | | Operation| Operation| Operation| | Stage| | | | | | | | | Wave| Wave| Lengths_K0_M_K1| ArrangeOrder| | | PerVector| PerVector_K1| | Lengths_K0_N_K1| ArrangeOrder| | | PerVector| PerVector_K1| | PerShuffle| PerShuffle| _NBlock_NWaveNPerXdl| _NWaveNPerXdl|
//##############################| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
DeviceGemmMultipleD_Xdl_CShuffle< Row, Row, Row_Tuple, Row, F16, F16, F32, F16, F16_Tuple, F16, PassThrough, PassThrough, MultiplyAdd, GemmMNKPadding, 1, 256, 256, 128, 32, 8, 2, 32, 32, 4, 2, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, 1, S<8, 32, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 4, 2, 0, 1, 1, S<1, 32, 1, 8>, 8>,
DeviceGemmMultipleD_Xdl_CShuffle< Row, Row, Row_Tuple, Row, F16, F16, F32, F16, F16_Tuple, F16, PassThrough, PassThrough, MultiplyAdd, GemmMNKPadding, 1, 256, 256, 128, 32, 8, 8, 32, 32, 4, 2, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, 1, S<4, 64, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 2, 8, 1, 1, 1, S<1, 32, 1, 8>, 8>,
DeviceGemmMultipleD_Xdl_CShuffle< Row, Row, Row_Tuple, Row, F16, F16, F32, F16, F16_Tuple, F16, PassThrough, PassThrough, MultiplyAdd, GemmMNKPadding, 1, 256, 128, 256, 32, 8, 2, 32, 32, 2, 4, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, 1, S<4, 64, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 4, 2, 0, 1, 1, S<1, 32, 1, 8>, 8>,
DeviceGemmMultipleD_Xdl_CShuffle< Row, Row, Row_Tuple, Row, F16, F16, F32, F16, F16_Tuple, F16, PassThrough, PassThrough, MultiplyAdd, GemmMNKPadding, 1, 256, 128, 256, 32, 8, 8, 32, 32, 2, 4, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, 1, S<4, 64, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 4, 8, 1, 1, 1, S<1, 32, 1, 8>, 8>,
DeviceGemmMultipleD_Xdl_CShuffle< Row, Row, Row_Tuple, Row, F16, F16, F32, F16, F16_Tuple, F16, PassThrough, PassThrough, MultiplyAdd, GemmMNKPadding, 1, 128, 128, 128, 32, 8, 2, 32, 32, 4, 2, S<4, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, 1, S<4, 32, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 4, 2, 0, 1, 1, S<1, 16, 1, 8>, 8>,
DeviceGemmMultipleD_Xdl_CShuffle< Row, Row, Row_Tuple, Row, F16, F16, F32, F16, F16_Tuple, F16, PassThrough, PassThrough, MultiplyAdd, GemmMNKPadding, 1, 128, 128, 128, 32, 8, 8, 32, 32, 4, 2, S<4, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, 1, S<4, 32, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 4, 8, 1, 1, 1, S<1, 16, 1, 8>, 8>,
DeviceGemmMultipleD_Xdl_CShuffle< Row, Row, Row_Tuple, Row, F16, F16, F32, F16, F16_Tuple, F16, PassThrough, PassThrough, MultiplyAdd, GemmMNKPadding, 1, 256, 128, 128, 32, 8, 2, 32, 32, 2, 2, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, 1, S<8, 32, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 4, 2, 0, 1, 1, S<1, 32, 1, 8>, 8>,
DeviceGemmMultipleD_Xdl_CShuffle< Row, Row, Row_Tuple, Row, F16, F16, F32, F16, F16_Tuple, F16, PassThrough, PassThrough, MultiplyAdd, GemmMNKPadding, 1, 256, 128, 128, 32, 8, 8, 32, 32, 2, 2, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, 1, S<4, 64, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 2, 8, 1, 1, 1, S<1, 32, 1, 8>, 8>,
DeviceGemmMultipleD_Xdl_CShuffle< Row, Row, Row_Tuple, Row, F16, F16, F32, F16, F16_Tuple, F16, PassThrough, PassThrough, MultiplyAdd, GemmMNKPadding, 1, 128, 128, 64, 32, 8, 2, 32, 32, 2, 2, S<4, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, 1, S<8, 16, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 4, 2, 0, 1, 1, S<1, 32, 1, 4>, 8>,
DeviceGemmMultipleD_Xdl_CShuffle< Row, Row, Row_Tuple, Row, F16, F16, F32, F16, F16_Tuple, F16, PassThrough, PassThrough, MultiplyAdd, GemmMNKPadding, 1, 128, 128, 64, 32, 8, 8, 32, 32, 2, 2, S<4, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, 1, S<4, 32, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 2, 8, 1, 1, 1, S<1, 32, 1, 4>, 8>,
DeviceGemmMultipleD_Xdl_CShuffle< Row, Row, Row_Tuple, Row, F16, F16, F32, F16, F16_Tuple, F16, PassThrough, PassThrough, MultiplyAdd, GemmMNKPadding, 1, 128, 64, 128, 32, 8, 2, 32, 32, 2, 2, S<4, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, 1, S<4, 32, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 4, 2, 0, 1, 1, S<1, 16, 1, 8>, 8>,
DeviceGemmMultipleD_Xdl_CShuffle< Row, Row, Row_Tuple, Row, F16, F16, F32, F16, F16_Tuple, F16, PassThrough, PassThrough, MultiplyAdd, GemmMNKPadding, 1, 128, 64, 128, 32, 8, 8, 32, 32, 2, 2, S<4, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, 1, S<4, 32, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 4, 8, 1, 1, 1, S<1, 16, 1, 8>, 8>,
DeviceGemmMultipleD_Xdl_CShuffle< Row, Row, Row_Tuple, Row, F16, F16, F32, F16, F16_Tuple, F16, PassThrough, PassThrough, MultiplyAdd, GemmMNKPadding, 1, 256, 128, 64, 32, 8, 2, 32, 32, 2, 1, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, 1, S<16,16, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 4, 2, 0, 1, 1, S<1, 32, 1, 8>, 8>,
DeviceGemmMultipleD_Xdl_CShuffle< Row, Row, Row_Tuple, Row, F16, F16, F32, F16, F16_Tuple, F16, PassThrough, PassThrough, MultiplyAdd, GemmMNKPadding, 1, 256, 128, 64, 32, 8, 8, 32, 32, 2, 1, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, 1, S<4, 64, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 1, 8, 1, 1, 1, S<1, 32, 1, 8>, 8>,
DeviceGemmMultipleD_Xdl_CShuffle< Row, Row, Row_Tuple, Row, F16, F16, F32, F16, F16_Tuple, F16, PassThrough, PassThrough, MultiplyAdd, GemmMNKPadding, 1, 256, 64, 128, 32, 8, 2, 32, 32, 1, 2, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, 1, S<8, 32, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 4, 2, 0, 1, 1, S<1, 32, 1, 8>, 8>,
DeviceGemmMultipleD_Xdl_CShuffle< Row, Row, Row_Tuple, Row, F16, F16, F32, F16, F16_Tuple, F16, PassThrough, PassThrough, MultiplyAdd, GemmMNKPadding, 1, 256, 64, 128, 32, 8, 8, 32, 32, 1, 2, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, 1, S<4, 64, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 2, 8, 1, 1, 1, S<1, 32, 1, 8>, 8>
// clang-format on
>;
void add_device_gemm_multiply_add_xdl_c_shuffle_f16_f16_f16_f16_f16_mk_kn_mn_mn_mn_instances(
std::vector<std::unique_ptr<DeviceGemmMultipleD<Row,
Row,
Row_Tuple,
Row,
F16,
F16,
F16_Tuple,
F16,
PassThrough,
PassThrough,
MultiplyAdd>>>& instances)
{
add_device_operation_instances(
instances,
device_gemm_multiply_add_xdl_c_shuffle_f16_f16_f16_f16_f16_mk_kn_mn_mn_mn_instances{});
}
} // namespace instance
} // namespace device
} // namespace tensor_operation
} // namespace ck
library/src/tensor_operation_instance/gpu/gemm_multiply_add/device_gemm_multiply_add_xdl_c_shuffle_f16_f16_f16_f16_f16_mk_nk_mn_mn_mn_instance.cpp 0 → 100644
View file @ 31ea132a
// SPDX-License-Identifier: MIT
// Copyright (c) 2018-2023, Advanced Micro Devices, Inc. All rights reserved.
#include <cstdlib>
#include "ck/ck.hpp"
#include "ck/tensor_operation/gpu/device/tensor_layout.hpp"
#include "ck/tensor_operation/gpu/device/gemm_specialization.hpp"
#include "ck/tensor_operation/gpu/device/impl/device_gemm_multiple_d_xdl_cshuffle.hpp"
#include "ck/tensor_operation/gpu/element/element_wise_operation.hpp"
#include "ck/library/tensor_operation_instance/add_device_operation_instance.hpp"
namespace ck {
namespace tensor_operation {
namespace device {
namespace instance {
using F16 = ck::half_t;
using F32 = float;
using F16_Tuple = ck::Tuple<F16, F16>;
using Row = ck::tensor_layout::gemm::RowMajor;
using Col = ck::tensor_layout::gemm::ColumnMajor;
using Row_Tuple = ck::Tuple<Row, Row>;
template <ck::index_t... Is>
using S = ck::Sequence<Is...>;
using PassThrough = ck::tensor_operation::element_wise::PassThrough;
using MultiplyAdd = ck::tensor_operation::element_wise::MultiplyAdd;
static constexpr auto GemmMNKPadding = ck::tensor_operation::device::GemmSpecialization::MNKPadding;
using device_gemm_multiply_add_xdl_c_shuffle_f16_f16_f16_f16_f16_mk_nk_mn_mn_mn_instances =
std::tuple<
// clang-format off
// M/N/K padding
// N % 8 == 0 && K % 1 == 0
//##############################| A| B| Ds| E| AData| BData| AccData| CShuffle| DsData| EData| A| B| CDE| GEMM| NumGemmK| Block| MPer| NPer| KPer| AK1| BK1| MPer| NPer| MXdl| NXdl| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockLds| BBlockTransfer| BBlockTransfer| BBlockTransfer| BlockTransfer| BBlockTransfer| BBlockTransfer| BBlockLds| CShuffle| CShuffle| CBlockTransferClusterLengths| CBlockTransfer|
//##############################| Layout| Layout| Layout| Layout| Type| Type| Type| DataType| Type| Type| Elementwise| Elementwise| Elementwise| Specialization| Prefetch| Size| Block| Block| Block| | | XDL| XDL| Per| Per| ThreadCluster| ThreadCluster| SrcAccessOrder| SrcVectorDim| SrcScalar| DstScalar| AddExtraM| ThreadCluster| ThreadCluster| SrcAccessOrder| SrcVectorDim| SrcScalar| DstScalar| AddExtraN| MXdlPerWave| NXdlPerWave| _MBlock_MWaveMPerXdl| ScalarPerVector|
//##############################| | | | | | | | | | | Operation| Operation| Operation| | Stage| | | | | | | | | Wave| Wave| Lengths_K0_M_K1| ArrangeOrder| | | PerVector| PerVector_K1| | Lengths_K0_N_K1| ArrangeOrder| | | PerVector| PerVector_K1| | PerShuffle| PerShuffle| _NBlock_NWaveNPerXdl| _NWaveNPerXdl|
//##############################| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
DeviceGemmMultipleD_Xdl_CShuffle< Row, Col, Row_Tuple, Row, F16, F16, F32, F16, F16_Tuple, F16, PassThrough, PassThrough, MultiplyAdd, GemmMNKPadding, 1, 256, 256, 128, 32, 8, 8, 32, 32, 4, 2, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, 1, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, 1, 1, 1, S<1, 4, 1, 64>, 1>,
DeviceGemmMultipleD_Xdl_CShuffle< Row, Col, Row_Tuple, Row, F16, F16, F32, F16, F16_Tuple, F16, PassThrough, PassThrough, MultiplyAdd, GemmMNKPadding, 1, 256, 128, 256, 32, 8, 8, 32, 32, 2, 4, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, 1, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, 1, 1, 1, S<1, 4, 1, 64>, 1>,
DeviceGemmMultipleD_Xdl_CShuffle< Row, Col, Row_Tuple, Row, F16, F16, F32, F16, F16_Tuple, F16, PassThrough, PassThrough, MultiplyAdd, GemmMNKPadding, 1, 128, 128, 128, 32, 8, 8, 32, 32, 4, 2, S<4, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, 1, S<4, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, 1, 1, 1, S<1, 2, 1, 64>, 1>,
DeviceGemmMultipleD_Xdl_CShuffle< Row, Col, Row_Tuple, Row, F16, F16, F32, F16, F16_Tuple, F16, PassThrough, PassThrough, MultiplyAdd, GemmMNKPadding, 1, 256, 128, 128, 32, 8, 8, 32, 32, 2, 2, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, 1, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, 1, 1, 1, S<1, 4, 1, 64>, 1>,
DeviceGemmMultipleD_Xdl_CShuffle< Row, Col, Row_Tuple, Row, F16, F16, F32, F16, F16_Tuple, F16, PassThrough, PassThrough, MultiplyAdd, GemmMNKPadding, 1, 128, 128, 64, 32, 8, 8, 32, 32, 2, 2, S<4, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, 1, S<4, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, 1, 1, 1, S<1, 4, 1, 32>, 1>,
DeviceGemmMultipleD_Xdl_CShuffle< Row, Col, Row_Tuple, Row, F16, F16, F32, F16, F16_Tuple, F16, PassThrough, PassThrough, MultiplyAdd, GemmMNKPadding, 1, 128, 64, 128, 32, 8, 8, 32, 32, 2, 2, S<4, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, 1, S<4, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, 1, 1, 1, S<1, 2, 1, 64>, 1>,
DeviceGemmMultipleD_Xdl_CShuffle< Row, Col, Row_Tuple, Row, F16, F16, F32, F16, F16_Tuple, F16, PassThrough, PassThrough, MultiplyAdd, GemmMNKPadding, 1, 64, 64, 64, 32, 8, 8, 32, 32, 2, 2, S<4, 16, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, 1, S<4, 16, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, 1, 1, 1, S<1, 2, 1, 32>, 1>,
DeviceGemmMultipleD_Xdl_CShuffle< Row, Col, Row_Tuple, Row, F16, F16, F32, F16, F16_Tuple, F16, PassThrough, PassThrough, MultiplyAdd, GemmMNKPadding, 1, 256, 128, 64, 32, 8, 8, 32, 32, 2, 1, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, 1, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, 1, 1, 1, S<1, 4, 1, 64>, 1>,
DeviceGemmMultipleD_Xdl_CShuffle< Row, Col, Row_Tuple, Row, F16, F16, F32, F16, F16_Tuple, F16, PassThrough, PassThrough, MultiplyAdd, GemmMNKPadding, 1, 256, 64, 128, 32, 8, 8, 32, 32, 1, 2, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, 1, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, 1, 1, 1, S<1, 4, 1, 64>, 1>,
DeviceGemmMultipleD_Xdl_CShuffle< Row, Col, Row_Tuple, Row, F16, F16, F32, F16, F16_Tuple, F16, PassThrough, PassThrough, MultiplyAdd, GemmMNKPadding, 1, 128, 128, 32, 32, 8, 8, 32, 32, 2, 1, S<4, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, 1, S<4, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, 1, 1, 1, S<1, 4, 1, 32>, 1>,
DeviceGemmMultipleD_Xdl_CShuffle< Row, Col, Row_Tuple, Row, F16, F16, F32, F16, F16_Tuple, F16, PassThrough, PassThrough, MultiplyAdd, GemmMNKPadding, 1, 128, 32, 128, 32, 8, 8, 32, 32, 1, 2, S<4, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, 1, S<4, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, 1, 1, 1, S<1, 2, 1, 64>, 1>,
DeviceGemmMultipleD_Xdl_CShuffle< Row, Col, Row_Tuple, Row, F16, F16, F32, F16, F16_Tuple, F16, PassThrough, PassThrough, MultiplyAdd, GemmMNKPadding, 1, 64, 64, 32, 32, 8, 8, 32, 32, 2, 1, S<4, 16, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, 1, S<4, 16, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, 1, 1, 1, S<1, 2, 1, 32>, 1>,
DeviceGemmMultipleD_Xdl_CShuffle< Row, Col, Row_Tuple, Row, F16, F16, F32, F16, F16_Tuple, F16, PassThrough, PassThrough, MultiplyAdd, GemmMNKPadding, 1, 64, 32, 64, 32, 8, 8, 32, 32, 1, 2, S<4, 16, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, 1, S<4, 16, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, 1, 1, 1, S<1, 2, 1, 32>, 1>
// clang-format on
>;
void add_device_gemm_multiply_add_xdl_c_shuffle_f16_f16_f16_f16_f16_mk_nk_mn_mn_mn_instances(
std::vector<std::unique_ptr<DeviceGemmMultipleD<Row,
Col,
Row_Tuple,
Row,
F16,
F16,
F16_Tuple,
F16,
PassThrough,
PassThrough,
MultiplyAdd>>>& instances)
{
add_device_operation_instances(
instances,
device_gemm_multiply_add_xdl_c_shuffle_f16_f16_f16_f16_f16_mk_nk_mn_mn_mn_instances{});
}
} // namespace instance
} // namespace device
} // namespace tensor_operation
} // namespace ck
library/src/tensor_operation_instance/gpu/gemm_multiply_add/device_gemm_multiply_add_xdl_c_shuffle_f16_f8_f32_f32_f16_mk_kn_mn_mn_mn_instance.cpp 0 → 100644
View file @ 31ea132a
// SPDX-License-Identifier: MIT
// Copyright (c) 2018-2023, Advanced Micro Devices, Inc. All rights reserved.
#include <cstdlib>
#include "ck/ck.hpp"
#include "ck/tensor_operation/gpu/device/tensor_layout.hpp"
#include "ck/tensor_operation/gpu/device/gemm_specialization.hpp"
#include "ck/tensor_operation/gpu/device/impl/device_gemm_multiple_d_xdl_cshuffle.hpp"
#include "ck/tensor_operation/gpu/element/element_wise_operation.hpp"
#include "ck/library/tensor_operation_instance/add_device_operation_instance.hpp"
namespace ck {
namespace tensor_operation {
namespace device {
namespace instance {
using F8 = ck::f8_t;
using F16 = ck::half_t;
using F32 = float;
using F32_Tuple = ck::Tuple<F32, F32>;
using Row = ck::tensor_layout::gemm::RowMajor;
using Col = ck::tensor_layout::gemm::ColumnMajor;
using Row_Tuple = ck::Tuple<Row, Row>;
template <ck::index_t... Is>
using S = ck::Sequence<Is...>;
using PassThrough = ck::tensor_operation::element_wise::PassThrough;
using MultiplyAdd = ck::tensor_operation::element_wise::MultiplyAdd;
static constexpr auto GemmMNKPadding = ck::tensor_operation::device::GemmSpecialization::MNKPadding;
using device_gemm_multiply_add_xdl_c_shuffle_f16_f8_f32_f32_f16_mk_kn_mn_mn_mn_instances =
std::tuple<
// clang-format off
// M/N/K padding
//##############################| A| B| Ds| E| AData| BData| AccData| CShuffle| DsData| EData| A| B| CDE| GEMM| NumGemmK| Block| MPer| NPer| KPer| AK1| BK1| MPer| NPer| MXdl| NXdl| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockLds| BBlockTransfer| BBlockTransfer| BBlockTransfer| BlockTransfer| BBlockTransfer| BBlockTransfer| BBlockLds| CShuffle| CShuffle| CBlockTransferClusterLengths| CBlockTransfer|
//##############################| Layout| Layout| Layout| Layout| Type| Type| Type| DataType| Type| Type| Elementwise| Elementwise| Elementwise| Specialization| Prefetch| Size| Block| Block| Block| | | XDL| XDL| Per| Per| ThreadCluster| ThreadCluster| SrcAccessOrder| SrcVectorDim| SrcScalar| DstScalar| AddExtraM| ThreadCluster| ThreadCluster| SrcAccessOrder| SrcVectorDim| SrcScalar| DstScalar| AddExtraN| MXdlPerWave| NXdlPerWave| _MBlock_MWaveMPerXdl| ScalarPerVector|
//##############################| | | | | | | | | | | Operation| Operation| Operation| | Stage| | | | | | | | | Wave| Wave| Lengths_K0_M_K1| ArrangeOrder| | | PerVector| PerVector_K1| | Lengths_K0_N_K1| ArrangeOrder| | | PerVector| PerVector_K1| | PerShuffle| PerShuffle| _NBlock_NWaveNPerXdl| _NWaveNPerXdl|
//##############################| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
DeviceGemmMultipleD_Xdl_CShuffle< Row, Row, Row_Tuple, Row, F16, F8, F32, F32, F32_Tuple, F16, PassThrough, PassThrough, MultiplyAdd, GemmMNKPadding, 1, 256, 256, 128, 32, 8, 2, 32, 32, 4, 2, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, 1, S<8, 32, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 4, 2, 0, 1, 1, S<1, 32, 1, 8>, 8>,
DeviceGemmMultipleD_Xdl_CShuffle< Row, Row, Row_Tuple, Row, F16, F8, F32, F32, F32_Tuple, F16, PassThrough, PassThrough, MultiplyAdd, GemmMNKPadding, 1, 256, 256, 128, 32, 8, 8, 32, 32, 4, 2, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, 1, S<4, 64, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 2, 8, 1, 1, 1, S<1, 32, 1, 8>, 8>,
DeviceGemmMultipleD_Xdl_CShuffle< Row, Row, Row_Tuple, Row, F16, F8, F32, F32, F32_Tuple, F16, PassThrough, PassThrough, MultiplyAdd, GemmMNKPadding, 1, 256, 128, 256, 32, 8, 2, 32, 32, 2, 4, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, 1, S<4, 64, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 4, 2, 0, 1, 1, S<1, 32, 1, 8>, 8>,
DeviceGemmMultipleD_Xdl_CShuffle< Row, Row, Row_Tuple, Row, F16, F8, F32, F32, F32_Tuple, F16, PassThrough, PassThrough, MultiplyAdd, GemmMNKPadding, 1, 256, 128, 256, 32, 8, 8, 32, 32, 2, 4, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, 1, S<4, 64, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 4, 8, 1, 1, 1, S<1, 32, 1, 8>, 8>,
DeviceGemmMultipleD_Xdl_CShuffle< Row, Row, Row_Tuple, Row, F16, F8, F32, F32, F32_Tuple, F16, PassThrough, PassThrough, MultiplyAdd, GemmMNKPadding, 1, 128, 128, 128, 32, 8, 2, 32, 32, 4, 2, S<4, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, 1, S<4, 32, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 4, 2, 0, 1, 1, S<1, 16, 1, 8>, 8>,
DeviceGemmMultipleD_Xdl_CShuffle< Row, Row, Row_Tuple, Row, F16, F8, F32, F32, F32_Tuple, F16, PassThrough, PassThrough, MultiplyAdd, GemmMNKPadding, 1, 128, 128, 128, 32, 8, 8, 32, 32, 4, 2, S<4, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, 1, S<4, 32, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 4, 8, 1, 1, 1, S<1, 16, 1, 8>, 8>,
DeviceGemmMultipleD_Xdl_CShuffle< Row, Row, Row_Tuple, Row, F16, F8, F32, F32, F32_Tuple, F16, PassThrough, PassThrough, MultiplyAdd, GemmMNKPadding, 1, 256, 128, 128, 32, 8, 2, 32, 32, 2, 2, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, 1, S<8, 32, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 4, 2, 0, 1, 1, S<1, 32, 1, 8>, 8>,
DeviceGemmMultipleD_Xdl_CShuffle< Row, Row, Row_Tuple, Row, F16, F8, F32, F32, F32_Tuple, F16, PassThrough, PassThrough, MultiplyAdd, GemmMNKPadding, 1, 256, 128, 128, 32, 8, 8, 32, 32, 2, 2, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, 1, S<4, 64, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 2, 8, 1, 1, 1, S<1, 32, 1, 8>, 8>,
DeviceGemmMultipleD_Xdl_CShuffle< Row, Row, Row_Tuple, Row, F16, F8, F32, F32, F32_Tuple, F16, PassThrough, PassThrough, MultiplyAdd, GemmMNKPadding, 1, 128, 128, 64, 32, 8, 2, 32, 32, 2, 2, S<4, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, 1, S<8, 16, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 4, 2, 0, 1, 1, S<1, 32, 1, 4>, 8>,
DeviceGemmMultipleD_Xdl_CShuffle< Row, Row, Row_Tuple, Row, F16, F8, F32, F32, F32_Tuple, F16, PassThrough, PassThrough, MultiplyAdd, GemmMNKPadding, 1, 128, 128, 64, 32, 8, 8, 32, 32, 2, 2, S<4, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, 1, S<4, 32, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 2, 8, 1, 1, 1, S<1, 32, 1, 4>, 8>,
DeviceGemmMultipleD_Xdl_CShuffle< Row, Row, Row_Tuple, Row, F16, F8, F32, F32, F32_Tuple, F16, PassThrough, PassThrough, MultiplyAdd, GemmMNKPadding, 1, 128, 64, 128, 32, 8, 2, 32, 32, 2, 2, S<4, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, 1, S<4, 32, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 4, 2, 0, 1, 1, S<1, 16, 1, 8>, 8>,
DeviceGemmMultipleD_Xdl_CShuffle< Row, Row, Row_Tuple, Row, F16, F8, F32, F32, F32_Tuple, F16, PassThrough, PassThrough, MultiplyAdd, GemmMNKPadding, 1, 128, 64, 128, 32, 8, 8, 32, 32, 2, 2, S<4, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, 1, S<4, 32, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 4, 8, 1, 1, 1, S<1, 16, 1, 8>, 8>,
DeviceGemmMultipleD_Xdl_CShuffle< Row, Row, Row_Tuple, Row, F16, F8, F32, F32, F32_Tuple, F16, PassThrough, PassThrough, MultiplyAdd, GemmMNKPadding, 1, 256, 128, 64, 32, 8, 2, 32, 32, 2, 1, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, 1, S<16,16, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 4, 2, 0, 1, 1, S<1, 32, 1, 8>, 8>,
DeviceGemmMultipleD_Xdl_CShuffle< Row, Row, Row_Tuple, Row, F16, F8, F32, F32, F32_Tuple, F16, PassThrough, PassThrough, MultiplyAdd, GemmMNKPadding, 1, 256, 128, 64, 32, 8, 8, 32, 32, 2, 1, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, 1, S<4, 64, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 1, 8, 1, 1, 1, S<1, 32, 1, 8>, 8>,
DeviceGemmMultipleD_Xdl_CShuffle< Row, Row, Row_Tuple, Row, F16, F8, F32, F32, F32_Tuple, F16, PassThrough, PassThrough, MultiplyAdd, GemmMNKPadding, 1, 256, 64, 128, 32, 8, 2, 32, 32, 1, 2, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, 1, S<8, 32, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 4, 2, 0, 1, 1, S<1, 32, 1, 8>, 8>,
DeviceGemmMultipleD_Xdl_CShuffle< Row, Row, Row_Tuple, Row, F16, F8, F32, F32, F32_Tuple, F16, PassThrough, PassThrough, MultiplyAdd, GemmMNKPadding, 1, 256, 64, 128, 32, 8, 8, 32, 32, 1, 2, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, 1, S<4, 64, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 2, 8, 1, 1, 1, S<1, 32, 1, 8>, 8>
// clang-format on
>;
void add_device_gemm_multiply_add_xdl_c_shuffle_f16_f8_f32_f32_f16_mk_kn_mn_mn_mn_instances(
std::vector<std::unique_ptr<DeviceGemmMultipleD<Row,
Row,
Row_Tuple,
Row,
F16,
F8,
F32_Tuple,
F16,
PassThrough,
PassThrough,
MultiplyAdd>>>& instances)
{
add_device_operation_instances(
instances,
device_gemm_multiply_add_xdl_c_shuffle_f16_f8_f32_f32_f16_mk_kn_mn_mn_mn_instances{});
}
} // namespace instance
} // namespace device
} // namespace tensor_operation
} // namespace ck
library/src/tensor_operation_instance/gpu/gemm_multiply_add/device_gemm_multiply_add_xdl_c_shuffle_f16_f8_f32_f32_f16_mk_nk_mn_mn_mn_instance.cpp 0 → 100644
View file @ 31ea132a
// SPDX-License-Identifier: MIT
// Copyright (c) 2018-2023, Advanced Micro Devices, Inc. All rights reserved.
#include <cstdlib>
#include "ck/ck.hpp"
#include "ck/tensor_operation/gpu/device/tensor_layout.hpp"
#include "ck/tensor_operation/gpu/device/gemm_specialization.hpp"
#include "ck/tensor_operation/gpu/device/impl/device_gemm_multiple_d_xdl_cshuffle.hpp"
#include "ck/tensor_operation/gpu/element/element_wise_operation.hpp"
#include "ck/library/tensor_operation_instance/add_device_operation_instance.hpp"
namespace ck {
namespace tensor_operation {
namespace device {
namespace instance {
using F8 = ck::f8_t;
using F16 = ck::half_t;
using F32 = float;
using F32_Tuple = ck::Tuple<F32, F32>;
using Row = ck::tensor_layout::gemm::RowMajor;
using Col = ck::tensor_layout::gemm::ColumnMajor;
using Row_Tuple = ck::Tuple<Row, Row>;
template <ck::index_t... Is>
using S = ck::Sequence<Is...>;
using PassThrough = ck::tensor_operation::element_wise::PassThrough;
using MultiplyAdd = ck::tensor_operation::element_wise::MultiplyAdd;
static constexpr auto GemmMNKPadding = ck::tensor_operation::device::GemmSpecialization::MNKPadding;
using device_gemm_multiply_add_xdl_c_shuffle_f16_f8_f32_f32_f16_mk_nk_mn_mn_mn_instances =
std::tuple<
// clang-format off
// M/N/K padding
// N % 8 == 0 && K % 1 == 0
//##############################| A| B| Ds| E| AData| BData| AccData| CShuffle| DsData| EData| A| B| CDE| GEMM| NumGemmK| Block| MPer| NPer| KPer| AK1| BK1| MPer| NPer| MXdl| NXdl| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockLds| BBlockTransfer| BBlockTransfer| BBlockTransfer| BlockTransfer| BBlockTransfer| BBlockTransfer| BBlockLds| CShuffle| CShuffle| CBlockTransferClusterLengths| CBlockTransfer|
//##############################| Layout| Layout| Layout| Layout| Type| Type| Type| DataType| Type| Type| Elementwise| Elementwise| Elementwise| Specialization| Prefetch| Size| Block| Block| Block| | | XDL| XDL| Per| Per| ThreadCluster| ThreadCluster| SrcAccessOrder| SrcVectorDim| SrcScalar| DstScalar| AddExtraM| ThreadCluster| ThreadCluster| SrcAccessOrder| SrcVectorDim| SrcScalar| DstScalar| AddExtraN| MXdlPerWave| NXdlPerWave| _MBlock_MWaveMPerXdl| ScalarPerVector|
//##############################| | | | | | | | | | | Operation| Operation| Operation| | Stage| | | | | | | | | Wave| Wave| Lengths_K0_M_K1| ArrangeOrder| | | PerVector| PerVector_K1| | Lengths_K0_N_K1| ArrangeOrder| | | PerVector| PerVector_K1| | PerShuffle| PerShuffle| _NBlock_NWaveNPerXdl| _NWaveNPerXdl|
//##############################| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
DeviceGemmMultipleD_Xdl_CShuffle< Row, Col, Row_Tuple, Row, F16, F8, F32, F32, F32_Tuple, F16, PassThrough, PassThrough, MultiplyAdd, GemmMNKPadding, 1, 256, 256, 128, 32, 8, 8, 32, 32, 4, 2, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, 1, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, 1, 1, 1, S<1, 4, 1, 64>, 1>,
DeviceGemmMultipleD_Xdl_CShuffle< Row, Col, Row_Tuple, Row, F16, F8, F32, F32, F32_Tuple, F16, PassThrough, PassThrough, MultiplyAdd, GemmMNKPadding, 1, 256, 128, 256, 32, 8, 8, 32, 32, 2, 4, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, 1, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, 1, 1, 1, S<1, 4, 1, 64>, 1>,
DeviceGemmMultipleD_Xdl_CShuffle< Row, Col, Row_Tuple, Row, F16, F8, F32, F32, F32_Tuple, F16, PassThrough, PassThrough, MultiplyAdd, GemmMNKPadding, 1, 128, 128, 128, 32, 8, 8, 32, 32, 4, 2, S<4, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, 1, S<4, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, 1, 1, 1, S<1, 2, 1, 64>, 1>,
DeviceGemmMultipleD_Xdl_CShuffle< Row, Col, Row_Tuple, Row, F16, F8, F32, F32, F32_Tuple, F16, PassThrough, PassThrough, MultiplyAdd, GemmMNKPadding, 1, 256, 128, 128, 32, 8, 8, 32, 32, 2, 2, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, 1, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, 1, 1, 1, S<1, 4, 1, 64>, 1>,
DeviceGemmMultipleD_Xdl_CShuffle< Row, Col, Row_Tuple, Row, F16, F8, F32, F32, F32_Tuple, F16, PassThrough, PassThrough, MultiplyAdd, GemmMNKPadding, 1, 128, 128, 64, 32, 8, 8, 32, 32, 2, 2, S<4, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, 1, S<4, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, 1, 1, 1, S<1, 4, 1, 32>, 1>,
DeviceGemmMultipleD_Xdl_CShuffle< Row, Col, Row_Tuple, Row, F16, F8, F32, F32, F32_Tuple, F16, PassThrough, PassThrough, MultiplyAdd, GemmMNKPadding, 1, 128, 64, 128, 32, 8, 8, 32, 32, 2, 2, S<4, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, 1, S<4, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, 1, 1, 1, S<1, 2, 1, 64>, 1>,
DeviceGemmMultipleD_Xdl_CShuffle< Row, Col, Row_Tuple, Row, F16, F8, F32, F32, F32_Tuple, F16, PassThrough, PassThrough, MultiplyAdd, GemmMNKPadding, 1, 64, 64, 64, 32, 8, 8, 32, 32, 2, 2, S<4, 16, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, 1, S<4, 16, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, 1, 1, 1, S<1, 2, 1, 32>, 1>,
DeviceGemmMultipleD_Xdl_CShuffle< Row, Col, Row_Tuple, Row, F16, F8, F32, F32, F32_Tuple, F16, PassThrough, PassThrough, MultiplyAdd, GemmMNKPadding, 1, 256, 128, 64, 32, 8, 8, 32, 32, 2, 1, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, 1, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, 1, 1, 1, S<1, 4, 1, 64>, 1>,
DeviceGemmMultipleD_Xdl_CShuffle< Row, Col, Row_Tuple, Row, F16, F8, F32, F32, F32_Tuple, F16, PassThrough, PassThrough, MultiplyAdd, GemmMNKPadding, 1, 256, 64, 128, 32, 8, 8, 32, 32, 1, 2, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, 1, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, 1, 1, 1, S<1, 4, 1, 64>, 1>,
DeviceGemmMultipleD_Xdl_CShuffle< Row, Col, Row_Tuple, Row, F16, F8, F32, F32, F32_Tuple, F16, PassThrough, PassThrough, MultiplyAdd, GemmMNKPadding, 1, 128, 128, 32, 32, 8, 8, 32, 32, 2, 1, S<4, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, 1, S<4, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, 1, 1, 1, S<1, 4, 1, 32>, 1>,
DeviceGemmMultipleD_Xdl_CShuffle< Row, Col, Row_Tuple, Row, F16, F8, F32, F32, F32_Tuple, F16, PassThrough, PassThrough, MultiplyAdd, GemmMNKPadding, 1, 128, 32, 128, 32, 8, 8, 32, 32, 1, 2, S<4, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, 1, S<4, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, 1, 1, 1, S<1, 2, 1, 64>, 1>,
DeviceGemmMultipleD_Xdl_CShuffle< Row, Col, Row_Tuple, Row, F16, F8, F32, F32, F32_Tuple, F16, PassThrough, PassThrough, MultiplyAdd, GemmMNKPadding, 1, 64, 64, 32, 32, 8, 8, 32, 32, 2, 1, S<4, 16, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, 1, S<4, 16, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, 1, 1, 1, S<1, 2, 1, 32>, 1>,
DeviceGemmMultipleD_Xdl_CShuffle< Row, Col, Row_Tuple, Row, F16, F8, F32, F32, F32_Tuple, F16, PassThrough, PassThrough, MultiplyAdd, GemmMNKPadding, 1, 64, 32, 64, 32, 8, 8, 32, 32, 1, 2, S<4, 16, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, 1, S<4, 16, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, 1, 1, 1, S<1, 2, 1, 32>, 1>
// clang-format on
>;
void add_device_gemm_multiply_add_xdl_c_shuffle_f16_f8_f32_f32_f16_mk_nk_mn_mn_mn_instances(
std::vector<std::unique_ptr<DeviceGemmMultipleD<Row,
Col,
Row_Tuple,
Row,
F16,
F8,
F32_Tuple,
F16,
PassThrough,
PassThrough,
MultiplyAdd>>>& instances)
{
add_device_operation_instances(
instances,
device_gemm_multiply_add_xdl_c_shuffle_f16_f8_f32_f32_f16_mk_nk_mn_mn_mn_instances{});
}
} // namespace instance
} // namespace device
} // namespace tensor_operation
} // namespace ck
profiler/include/profiler/profile_gemm_multiply_add_impl.hpp 0 → 100644
View file @ 31ea132a
// SPDX-License-Identifier: MIT
// Copyright (c) 2018-2023, Advanced Micro Devices, Inc. All rights reserved.
#pragma once
#include <iomanip>
#include "ck/ck.hpp"
#include "ck/tensor_operation/gpu/device/tensor_layout.hpp"
#include "ck/tensor_operation/gpu/device/device_gemm_multiple_d.hpp"
#include "ck/tensor_operation/gpu/element/element_wise_operation.hpp"
#include "ck/library/tensor_operation_instance/gpu/gemm_multiply_add.hpp"
#include "ck/library/utility/check_err.hpp"
#include "ck/library/utility/device_memory.hpp"
#include "ck/library/utility/host_tensor.hpp"
#include "ck/library/utility/host_tensor_generator.hpp"
#include "ck/library/utility/literals.hpp"
#include "ck/library/reference_tensor_operation/cpu/reference_gemm.hpp"
namespace ck {
namespace profiler {
template <typename ADataType,
typename BDataType,
typename AccDataType,
typename D0DataType,
typename D1DataType,
typename EDataType,
typename ALayout,
typename BLayout,
typename D0Layout,
typename D1Layout,
typename ELayout>
bool profile_gemm_multiply_add_impl(int do_verification,
int init_method,
bool /*do_log*/,
bool time_kernel,
int M,
int N,
int K,
int StrideA,
int StrideB,
int StrideD0,
int StrideD1,
int StrideE)
{
auto f_host_tensor_descriptor =
[](std::size_t row, std::size_t col, std::size_t stride, auto layout) {
using namespace ck::literals;
if(is_same<decltype(layout), tensor_layout::gemm::RowMajor>::value)
{
return HostTensorDescriptor({row, col}, {stride, 1_uz});
}
else
{
return HostTensorDescriptor({row, col}, {1_uz, stride});
}
};
Tensor<ADataType> a_m_k(f_host_tensor_descriptor(M, K, StrideA, ALayout{}));
Tensor<BDataType> b_k_n(f_host_tensor_descriptor(K, N, StrideB, BLayout{}));
Tensor<D0DataType> d0_m_n(f_host_tensor_descriptor(M, N, StrideD0, D0Layout{}));
Tensor<D1DataType> d1_m_n(f_host_tensor_descriptor(M, N, StrideD1, D1Layout{}));
Tensor<EDataType> e_m_n_device_result(f_host_tensor_descriptor(M, N, StrideE, ELayout{}));
Tensor<EDataType> e_m_n_host_result(f_host_tensor_descriptor(M, N, StrideE, ELayout{}));
std::cout << "a_m_k: " << a_m_k.mDesc << std::endl;
std::cout << "b_k_n: " << b_k_n.mDesc << std::endl;
std::cout << "d0_m_n: " << d0_m_n.mDesc << std::endl;
std::cout << "d1_m_n: " << d1_m_n.mDesc << std::endl;
std::cout << "e_m_n: " << e_m_n_device_result.mDesc << std::endl;
switch(init_method)
{
case 0: break;
case 1:
a_m_k.GenerateTensorValue(GeneratorTensor_2<ADataType>{-5, 5});
b_k_n.GenerateTensorValue(GeneratorTensor_2<BDataType>{-5, 5});
d0_m_n.GenerateTensorValue(GeneratorTensor_2<D0DataType>{-5, 5});
d1_m_n.GenerateTensorValue(GeneratorTensor_2<D1DataType>{-1, 1});
break;
default:
a_m_k.GenerateTensorValue(GeneratorTensor_3<ADataType>{0.0, 1.0});
b_k_n.GenerateTensorValue(GeneratorTensor_3<BDataType>{-0.5, 0.5});
d0_m_n.GenerateTensorValue(GeneratorTensor_3<D0DataType>{0.0, 1.0});
d1_m_n.GenerateTensorValue(GeneratorTensor_3<D1DataType>{0.0, 1.0});
}
using PassThrough = ck::tensor_operation::element_wise::PassThrough;
using MultiplyAdd = ck::tensor_operation::element_wise::MultiplyAdd;
using AElementOp = PassThrough;
using BElementOp = PassThrough;
using CDEElementOp = MultiplyAdd;
const auto a_element_op = AElementOp{};
const auto b_element_op = BElementOp{};
const auto cde_element_op = CDEElementOp{};
using DeviceOp =
ck::tensor_operation::device::DeviceGemmMultipleD<ALayout,
BLayout,
ck::Tuple<D0Layout, D1Layout>,
ELayout,
ADataType,
BDataType,
ck::Tuple<D0DataType, D1DataType>,
EDataType,
PassThrough,
PassThrough,
CDEElementOp>;
// get device op instances
const auto op_ptrs = ck::tensor_operation::device::instance::DeviceOperationInstanceFactory<
DeviceOp>::GetInstances();
std::cout << "found " << op_ptrs.size() << " instances" << std::endl;
// run reference
if(do_verification)
{
Tensor<AccDataType> c_m_n({M, N});
using ReferenceGemmInstance = ck::tensor_operation::host::ReferenceGemm<ADataType,
BDataType,
AccDataType,
AccDataType,
AElementOp,
BElementOp,
PassThrough>;
auto ref_gemm = ReferenceGemmInstance{};
auto ref_invoker = ref_gemm.MakeInvoker();
auto ref_argument =
ref_gemm.MakeArgument(a_m_k, b_k_n, c_m_n, a_element_op, b_element_op, PassThrough{});
ref_invoker.Run(ref_argument);
for(int m = 0; m < M; ++m)
{
for(int n = 0; n < N; ++n)
{
cde_element_op(e_m_n_host_result(m, n), c_m_n(m, n), d0_m_n(m, n), d1_m_n(m, n));
}
}
}
DeviceMem a_device_buf(sizeof(ADataType) * a_m_k.mDesc.GetElementSpaceSize());
DeviceMem b_device_buf(sizeof(BDataType) * b_k_n.mDesc.GetElementSpaceSize());
DeviceMem d0_m_n_device_buf(sizeof(D0DataType) * d0_m_n.mDesc.GetElementSpaceSize());
DeviceMem d1_m_n_device_buf(sizeof(D1DataType) * d1_m_n.mDesc.GetElementSpaceSize());
DeviceMem e_device_buf(sizeof(EDataType) * e_m_n_device_result.mDesc.GetElementSpaceSize());
a_device_buf.ToDevice(a_m_k.mData.data());
b_device_buf.ToDevice(b_k_n.mData.data());
d0_m_n_device_buf.ToDevice(d0_m_n.mData.data());
d1_m_n_device_buf.ToDevice(d1_m_n.mData.data());
std::string best_op_name;
float best_ave_time = 0;
float best_tflops = 0;
float best_gb_per_sec = 0;
bool pass = true;
// profile device operation instances
for(auto& op_ptr : op_ptrs)
{
auto argument_ptr = op_ptr->MakeArgumentPointer(
a_device_buf.GetDeviceBuffer(),
b_device_buf.GetDeviceBuffer(),
std::array<const void*, 2>{d0_m_n_device_buf.GetDeviceBuffer(),
d1_m_n_device_buf.GetDeviceBuffer()},
e_device_buf.GetDeviceBuffer(),
M,
N,
K,
StrideA,
StrideB,
std::array<ck::index_t, 2>{StrideD0, StrideD1},
StrideE,
a_element_op,
b_element_op,
cde_element_op);
auto invoker_ptr = op_ptr->MakeInvokerPointer();
std::string op_name = op_ptr->GetTypeString();
if(op_ptr->IsSupportedArgument(argument_ptr.get()))
{
// re-init E to zero before profiling a kernel
e_device_buf.SetZero();
float ave_time =
invoker_ptr->Run(argument_ptr.get(), StreamConfig{nullptr, time_kernel});
std::size_t flop = std::size_t(2) * M * N * K;
std::size_t num_btype =
sizeof(ADataType) * M * K + sizeof(BDataType) * K * N + sizeof(EDataType) * M * N;
float tflops = static_cast<float>(flop) / 1.E9 / ave_time;
float gb_per_sec = num_btype / 1.E6 / ave_time;
std::cout << "Perf: " << std::setw(10) << ave_time << " ms, " << tflops << " TFlops, "
<< gb_per_sec << " GB/s, " << op_name << std::endl;
if(tflops > best_tflops)
{
best_op_name = op_name;
best_tflops = tflops;
best_ave_time = ave_time;
best_gb_per_sec = gb_per_sec;
}
if(do_verification)
{
e_device_buf.FromDevice(e_m_n_device_result.mData.data());
pass = pass && ck::utils::check_err(e_m_n_device_result, e_m_n_host_result);
}
}
else
{
std::cout << op_name << " does not support this problem" << std::endl;
}
}
std::cout << "Best Perf: " << best_ave_time << " ms, " << best_tflops << " TFlops, "
<< best_gb_per_sec << " GB/s, " << best_op_name << std::endl;
return pass;
}
} // namespace profiler
} // namespace ck
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