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Commit d0b9a467 authored by Chao Liu's avatar Chao Liu
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Merge remote-tracking branch 'origin/develop' into improve_pipeline

parents c8f6d5d1 8eca05a6
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library/src/tensor_operation_instance/gpu/gemm_reduce/device_gemm_reduce_xdl_cshuffle_f16_f16_f16_f32_f32_km_kn_mn_instance.cpp
View file @ d0b9a467
...@@ -2,7 +2,7 @@ ...@@ -2,7 +2,7 @@
#include "config.hpp" #include "config.hpp"
#include "device_gemm_reduce_xdl_cshuffle.hpp" #include "device_gemm_reduce_xdl_cshuffle.hpp"
#include "element_wise_operation.hpp" #include "element_wise_operation.hpp"
#include "element_wise_reduce_operation.hpp" #include "reduction_operator.hpp"
#include "device_operation_instance.hpp" #include "device_operation_instance.hpp"
namespace ck { namespace ck {
...@@ -19,9 +19,9 @@ using Col = ck::tensor_layout::gemm::ColumnMajor; ...@@ -19,9 +19,9 @@ using Col = ck::tensor_layout::gemm::ColumnMajor;
template <ck::index_t... Is> template <ck::index_t... Is>
using S = ck::Sequence<Is...>; using S = ck::Sequence<Is...>;
using PassThrough = ck::tensor_operation::element_wise::PassThrough; using PassThrough = ck::tensor_operation::element_wise::PassThrough;
using ReduceSum = ck::tensor_operation::element_wise::ReduceSum; using ReduceSum = ck::reduce::Add<F32>;
using ReduceSquareSum = ck::tensor_operation::element_wise::ReduceSquareSum; using Square = ck::tensor_operation::element_wise::UnarySquare<F32, F32, false>;
static constexpr auto GemmDefault = ck::tensor_operation::device::GemmSpecialization::Default; static constexpr auto GemmDefault = ck::tensor_operation::device::GemmSpecialization::Default;
...@@ -30,33 +30,31 @@ static constexpr auto GemmDefault = ck::tensor_operation::device::GemmSpecializa ...@@ -30,33 +30,31 @@ static constexpr auto GemmDefault = ck::tensor_operation::device::GemmSpecializa
// d1[m] = reduce1(c[m, n]) // d1[m] = reduce1(c[m, n])
using device_gemm_reduce_xdl_cshuffle_f16_f16_f16_f32_f32_km_kn_mn_instances = std::tuple< using device_gemm_reduce_xdl_cshuffle_f16_f16_f16_f32_f32_km_kn_mn_instances = std::tuple<
// clang-format off // clang-format off
//###########################| ALayout| BLayout| CLayout|AData| BData| CData| GemmAcc| CShuffle| ReduceAcc| DData| A| B| C| D0| D1| 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| CReduce| CReduceThreadLds2VGprCopy| CReduceThreadVgpr2GlobalCopy| //###########################| ALayout| BLayout| CLayout|AData| BData| CData| GemmAcc| CShuffle| ReduceAcc| DData| A| B| C| D0| D1| D1EleOp| 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| CReduce| CReduceThreadLds2VGprCopy| CReduceThreadVgpr2GlobalCopy|
//###########################| | | | Type| Type| Type| DataType| DataType| DataType| Type| Elementwise| Elementwise| Elementwise| Reduce| Reduce| Spacialization| 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_MPerBlock| ScalarPerVector| ThreadClusterLengths| SrcDstScalarPerVector| SrcDstScalarPerVector| //###########################| | | | Type| Type| Type| DataType| DataType| DataType| Type| Elementwise| Elementwise| Elementwise| Reduce| Reduce| Spacialization| 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_MPerBlock| ScalarPerVector| ThreadClusterLengths| SrcDstScalarPerVector| SrcDstScalarPerVector|
//###########################| | | | | | | | | | | Operation| Operation| 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_NPerBlock| _NPerBlock| _MPerBlock_NPerBlock| _NPerBlock| _MPerBlock| //###########################| | | | | | | | | | | Operation| Operation| 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_NPerBlock| _NPerBlock| _MPerBlock_NPerBlock| _NPerBlock| _MPerBlock|
//###########################| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | //###########################| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
DeviceGemmReduce_Xdl_CShuffle< Col, Row, Row, F16, F16, F16, F32, F32, F32, F32, PassThrough, PassThrough, PassThrough, ReduceSum, ReduceSquareSum, GemmDefault, 1, 256, 256, 128, 32, 2, 2, 32, 32, 4, 2, S<4, 64, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 4, 2, false, S<8, 32, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 4, 2, false, 1, 1, S<1, 32, 1, 8>, 8, S<64, 4>, 4, 1>, DeviceGemmReduce_Xdl_CShuffle< Col, Row, Row, F16, F16, F16, F32, F32, F32, F32, PassThrough, PassThrough, PassThrough, ReduceSum, ReduceSum, Square, GemmDefault, 1, 256, 256, 128, 32, 2, 2, 32, 32, 4, 2, S<4, 64, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 4, 2, false, S<8, 32, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 4, 2, false, 1, 1, S<1, 32, 1, 8>, 8, S<64, 4>, 4, 1>,
DeviceGemmReduce_Xdl_CShuffle< Col, Row, Row, F16, F16, F16, F32, F32, F32, F32, PassThrough, PassThrough, PassThrough, ReduceSum, ReduceSquareSum, GemmDefault, 1, 256, 256, 128, 32, 8, 8, 32, 32, 4, 2, S<4, 64, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 4, 8, true, S<4, 64, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 2, 8, true, 1, 1, S<1, 32, 1, 8>, 8, S<64, 4>, 4, 1>, DeviceGemmReduce_Xdl_CShuffle< Col, Row, Row, F16, F16, F16, F32, F32, F32, F32, PassThrough, PassThrough, PassThrough, ReduceSum, ReduceSum, Square, GemmDefault, 1, 256, 256, 128, 32, 8, 8, 32, 32, 4, 2, S<4, 64, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 4, 8, true, S<4, 64, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 2, 8, true, 1, 1, S<1, 32, 1, 8>, 8, S<64, 4>, 4, 1>,
DeviceGemmReduce_Xdl_CShuffle< Col, Row, Row, F16, F16, F16, F32, F32, F32, F32, PassThrough, PassThrough, PassThrough, ReduceSum, ReduceSquareSum, GemmDefault, 1, 256, 128, 256, 32, 2, 2, 32, 32, 2, 4, S<8, 32, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 4, 2, false, S<4, 64, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 4, 2, false, 1, 1, S<1, 32, 1, 8>, 8, S<64, 4>, 4, 1>, DeviceGemmReduce_Xdl_CShuffle< Col, Row, Row, F16, F16, F16, F32, F32, F32, F32, PassThrough, PassThrough, PassThrough, ReduceSum, ReduceSum, Square, GemmDefault, 1, 256, 128, 256, 32, 2, 2, 32, 32, 2, 4, S<8, 32, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 4, 2, false, S<4, 64, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 4, 2, false, 1, 1, S<1, 32, 1, 8>, 8, S<64, 4>, 4, 1>,
DeviceGemmReduce_Xdl_CShuffle< Col, Row, Row, F16, F16, F16, F32, F32, F32, F32, PassThrough, PassThrough, PassThrough, ReduceSum, ReduceSquareSum, GemmDefault, 1, 256, 128, 256, 32, 8, 8, 32, 32, 2, 4, S<4, 64, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 2, 8, true, S<4, 64, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 4, 8, true, 1, 1, S<1, 32, 1, 8>, 8, S<64, 4>, 4, 1>, DeviceGemmReduce_Xdl_CShuffle< Col, Row, Row, F16, F16, F16, F32, F32, F32, F32, PassThrough, PassThrough, PassThrough, ReduceSum, ReduceSum, Square, GemmDefault, 1, 256, 128, 256, 32, 8, 8, 32, 32, 2, 4, S<4, 64, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 2, 8, true, S<4, 64, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 4, 8, true, 1, 1, S<1, 32, 1, 8>, 8, S<64, 4>, 4, 1>,
DeviceGemmReduce_Xdl_CShuffle< Col, Row, Row, F16, F16, F16, F32, F32, F32, F32, PassThrough, PassThrough, PassThrough, ReduceSum, ReduceSquareSum, GemmDefault, 1, 128, 128, 128, 32, 2, 2, 32, 32, 4, 2, S<4, 32, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 4, 2, false, S<4, 32, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 4, 2, false, 1, 1, S<1, 16, 1, 8>, 8, S<32, 4>, 4, 1>, DeviceGemmReduce_Xdl_CShuffle< Col, Row, Row, F16, F16, F16, F32, F32, F32, F32, PassThrough, PassThrough, PassThrough, ReduceSum, ReduceSum, Square, GemmDefault, 1, 128, 128, 128, 32, 2, 2, 32, 32, 4, 2, S<4, 32, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 4, 2, false, S<4, 32, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 4, 2, false, 1, 1, S<1, 16, 1, 8>, 8, S<32, 4>, 4, 1>,
DeviceGemmReduce_Xdl_CShuffle< Col, Row, Row, F16, F16, F16, F32, F32, F32, F32, PassThrough, PassThrough, PassThrough, ReduceSum, ReduceSquareSum, GemmDefault, 1, 128, 128, 128, 32, 8, 8, 32, 32, 4, 2, S<4, 32, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 4, 8, true, S<4, 32, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 4, 8, true, 1, 1, S<1, 16, 1, 8>, 8, S<32, 4>, 4, 1>, DeviceGemmReduce_Xdl_CShuffle< Col, Row, Row, F16, F16, F16, F32, F32, F32, F32, PassThrough, PassThrough, PassThrough, ReduceSum, ReduceSum, Square, GemmDefault, 1, 128, 128, 128, 32, 8, 8, 32, 32, 4, 2, S<4, 32, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 4, 8, true, S<4, 32, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 4, 8, true, 1, 1, S<1, 16, 1, 8>, 8, S<32, 4>, 4, 1>,
DeviceGemmReduce_Xdl_CShuffle< Col, Row, Row, F16, F16, F16, F32, F32, F32, F32, PassThrough, PassThrough, PassThrough, ReduceSum, ReduceSquareSum, GemmDefault, 1, 256, 128, 128, 32, 2, 2, 32, 32, 2, 2, S<8, 32, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 4, 2, false, S<8, 32, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 4, 2, false, 1, 1, S<1, 32, 1, 8>, 8, S<64, 4>, 4, 1>, DeviceGemmReduce_Xdl_CShuffle< Col, Row, Row, F16, F16, F16, F32, F32, F32, F32, PassThrough, PassThrough, PassThrough, ReduceSum, ReduceSum, Square, GemmDefault, 1, 256, 128, 128, 32, 2, 2, 32, 32, 2, 2, S<8, 32, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 4, 2, false, S<8, 32, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 4, 2, false, 1, 1, S<1, 32, 1, 8>, 8, S<64, 4>, 4, 1>,
DeviceGemmReduce_Xdl_CShuffle< Col, Row, Row, F16, F16, F16, F32, F32, F32, F32, PassThrough, PassThrough, PassThrough, ReduceSum, ReduceSquareSum, GemmDefault, 1, 256, 128, 128, 32, 8, 8, 32, 32, 2, 2, S<4, 64, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 2, 8, true, S<4, 64, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 2, 8, true, 1, 1, S<1, 32, 1, 8>, 8, S<64, 4>, 4, 1>, DeviceGemmReduce_Xdl_CShuffle< Col, Row, Row, F16, F16, F16, F32, F32, F32, F32, PassThrough, PassThrough, PassThrough, ReduceSum, ReduceSum, Square, GemmDefault, 1, 256, 128, 128, 32, 8, 8, 32, 32, 2, 2, S<4, 64, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 2, 8, true, S<4, 64, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 2, 8, true, 1, 1, S<1, 32, 1, 8>, 8, S<64, 4>, 4, 1>,
DeviceGemmReduce_Xdl_CShuffle< Col, Row, Row, F16, F16, F16, F32, F32, F32, F32, PassThrough, PassThrough, PassThrough, ReduceSum, ReduceSquareSum, GemmDefault, 1, 128, 128, 64, 32, 2, 2, 32, 32, 2, 2, S<4, 32, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 4, 2, false, S<4, 16, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 4, 2, false, 1, 1, S<1, 32, 1, 4>, 8, S<64, 2>, 4, 1>, DeviceGemmReduce_Xdl_CShuffle< Col, Row, Row, F16, F16, F16, F32, F32, F32, F32, PassThrough, PassThrough, PassThrough, ReduceSum, ReduceSum, Square, GemmDefault, 1, 128, 128, 64, 32, 2, 2, 32, 32, 2, 2, S<4, 32, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 4, 2, false, S<4, 16, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 4, 2, false, 1, 1, S<1, 32, 1, 4>, 8, S<64, 2>, 4, 1>,
DeviceGemmReduce_Xdl_CShuffle< Col, Row, Row, F16, F16, F16, F32, F32, F32, F32, PassThrough, PassThrough, PassThrough, ReduceSum, ReduceSquareSum, GemmDefault, 1, 128, 128, 64, 32, 8, 8, 32, 32, 2, 2, S<4, 32, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 4, 8, true, S<4, 32, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 2, 8, true, 1, 1, S<1, 32, 1, 4>, 8, S<64, 2>, 4, 1>, DeviceGemmReduce_Xdl_CShuffle< Col, Row, Row, F16, F16, F16, F32, F32, F32, F32, PassThrough, PassThrough, PassThrough, ReduceSum, ReduceSum, Square, GemmDefault, 1, 128, 128, 64, 32, 8, 8, 32, 32, 2, 2, S<4, 32, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 4, 8, true, S<4, 32, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 2, 8, true, 1, 1, S<1, 32, 1, 4>, 8, S<64, 2>, 4, 1>,
DeviceGemmReduce_Xdl_CShuffle< Col, Row, Row, F16, F16, F16, F32, F32, F32, F32, PassThrough, PassThrough, PassThrough, ReduceSum, ReduceSquareSum, GemmDefault, 1, 128, 64, 128, 32, 2, 2, 32, 32, 2, 2, S<8, 16, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 4, 2, false, S<4, 32, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 4, 2, false, 1, 1, S<1, 16, 1, 8>, 8, S<32, 4>, 4, 1>, DeviceGemmReduce_Xdl_CShuffle< Col, Row, Row, F16, F16, F16, F32, F32, F32, F32, PassThrough, PassThrough, PassThrough, ReduceSum, ReduceSum, Square, GemmDefault, 1, 128, 64, 128, 32, 2, 2, 32, 32, 2, 2, S<8, 16, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 4, 2, false, S<4, 32, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 4, 2, false, 1, 1, S<1, 16, 1, 8>, 8, S<32, 4>, 4, 1>,
DeviceGemmReduce_Xdl_CShuffle< Col, Row, Row, F16, F16, F16, F32, F32, F32, F32, PassThrough, PassThrough, PassThrough, ReduceSum, ReduceSquareSum, GemmDefault, 1, 128, 64, 128, 32, 8, 8, 32, 32, 2, 2, S<4, 32, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 2, 8, true, S<4, 32, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 4, 8, true, 1, 1, S<1, 16, 1, 8>, 8, S<32, 4>, 4, 1>, DeviceGemmReduce_Xdl_CShuffle< Col, Row, Row, F16, F16, F16, F32, F32, F32, F32, PassThrough, PassThrough, PassThrough, ReduceSum, ReduceSum, Square, GemmDefault, 1, 128, 64, 128, 32, 8, 8, 32, 32, 2, 2, S<4, 32, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 2, 8, true, S<4, 32, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 4, 8, true, 1, 1, S<1, 16, 1, 8>, 8, S<32, 4>, 4, 1>,
DeviceGemmReduce_Xdl_CShuffle< Col, Row, Row, F16, F16, F16, F32, F32, F32, F32, PassThrough, PassThrough, PassThrough, ReduceSum, ReduceSquareSum, GemmDefault, 1, 256, 128, 64, 32, 2, 2, 32, 32, 2, 1, S<8, 32, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 4, 2, false, S<16,16, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 4, 2, false, 1, 1, S<1, 16, 1, 4>, 8, S<64, 4>, 4, 1>, DeviceGemmReduce_Xdl_CShuffle< Col, Row, Row, F16, F16, F16, F32, F32, F32, F32, PassThrough, PassThrough, PassThrough, ReduceSum, ReduceSum, Square, GemmDefault, 1, 256, 128, 64, 32, 2, 2, 32, 32, 2, 1, S<8, 32, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 4, 2, false, S<16,16, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 4, 2, false, 1, 1, S<1, 16, 1, 4>, 8, S<64, 4>, 4, 1>,
DeviceGemmReduce_Xdl_CShuffle< Col, Row, Row, F16, F16, F16, F32, F32, F32, F32, PassThrough, PassThrough, PassThrough, ReduceSum, ReduceSquareSum, GemmDefault, 1, 256, 128, 64, 32, 8, 8, 32, 32, 2, 1, S<4, 64, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 2, 8, true, S<4, 64, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 1, 8, true, 1, 1, S<1, 16, 1, 4>, 8, S<64, 4>, 4, 1>, DeviceGemmReduce_Xdl_CShuffle< Col, Row, Row, F16, F16, F16, F32, F32, F32, F32, PassThrough, PassThrough, PassThrough, ReduceSum, ReduceSum, Square, GemmDefault, 1, 256, 128, 64, 32, 8, 8, 32, 32, 2, 1, S<4, 64, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 2, 8, true, S<4, 64, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 1, 8, true, 1, 1, S<1, 16, 1, 4>, 8, S<64, 4>, 4, 1>,
DeviceGemmReduce_Xdl_CShuffle< Col, Row, Row, F16, F16, F16, F32, F32, F32, F32, PassThrough, PassThrough, PassThrough, ReduceSum, ReduceSquareSum, GemmDefault, 1, 256, 64, 128, 32, 2, 2, 32, 32, 1, 2, S<16,16, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 4, 2, false, S<8, 32, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 4, 2, false, 1, 1, S<1, 32, 1, 8>, 8, S<64, 4>, 4, 1>, DeviceGemmReduce_Xdl_CShuffle< Col, Row, Row, F16, F16, F16, F32, F32, F32, F32, PassThrough, PassThrough, PassThrough, ReduceSum, ReduceSum, Square, GemmDefault, 1, 256, 64, 128, 32, 2, 2, 32, 32, 1, 2, S<16,16, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 4, 2, false, S<8, 32, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 4, 2, false, 1, 1, S<1, 32, 1, 8>, 8, S<64, 4>, 4, 1>,
DeviceGemmReduce_Xdl_CShuffle< Col, Row, Row, F16, F16, F16, F32, F32, F32, F32, PassThrough, PassThrough, PassThrough, ReduceSum, ReduceSquareSum, GemmDefault, 1, 256, 64, 128, 32, 8, 8, 32, 32, 1, 2, S<4, 64, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 1, 8, true, S<4, 64, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 2, 8, true, 1, 1, S<1, 32, 1, 8>, 8, S<64, 4>, 4, 1> DeviceGemmReduce_Xdl_CShuffle< Col, Row, Row, F16, F16, F16, F32, F32, F32, F32, PassThrough, PassThrough, PassThrough, ReduceSum, ReduceSum, Square, GemmDefault, 1, 256, 64, 128, 32, 8, 8, 32, 32, 1, 2, S<4, 64, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 1, 8, true, S<4, 64, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 2, 8, true, 1, 1, S<1, 32, 1, 8>, 8, S<64, 4>, 4, 1>
// clang-format on // clang-format on
>; >;
void add_device_gemm_reduce_xdl_cshuffle_f16_f16_f16_f32_f32_km_kn_mn_instances( void add_device_gemm_reduce_xdl_cshuffle_f16_f16_f16_f32_f32_km_kn_mn_instances(
std::vector< std::vector<DeviceGemmReducePtr<PassThrough, PassThrough, PassThrough, Square>>& instances)
DeviceGemmReducePtr<PassThrough, PassThrough, PassThrough, ReduceSum, ReduceSquareSum>>&
instances)
{ {
add_device_operation_instances( add_device_operation_instances(
instances, device_gemm_reduce_xdl_cshuffle_f16_f16_f16_f32_f32_km_kn_mn_instances{}); instances, device_gemm_reduce_xdl_cshuffle_f16_f16_f16_f32_f32_km_kn_mn_instances{});
......
library/src/tensor_operation_instance/gpu/gemm_reduce/device_gemm_reduce_xdl_cshuffle_f16_f16_f16_f32_f32_km_nk_mn_instance.cpp
View file @ d0b9a467
...@@ -2,7 +2,7 @@ ...@@ -2,7 +2,7 @@
#include "config.hpp" #include "config.hpp"
#include "device_gemm_reduce_xdl_cshuffle.hpp" #include "device_gemm_reduce_xdl_cshuffle.hpp"
#include "element_wise_operation.hpp" #include "element_wise_operation.hpp"
#include "element_wise_reduce_operation.hpp" #include "reduction_operator.hpp"
#include "device_operation_instance.hpp" #include "device_operation_instance.hpp"
namespace ck { namespace ck {
...@@ -19,9 +19,9 @@ using Col = ck::tensor_layout::gemm::ColumnMajor; ...@@ -19,9 +19,9 @@ using Col = ck::tensor_layout::gemm::ColumnMajor;
template <ck::index_t... Is> template <ck::index_t... Is>
using S = ck::Sequence<Is...>; using S = ck::Sequence<Is...>;
using PassThrough = ck::tensor_operation::element_wise::PassThrough; using PassThrough = ck::tensor_operation::element_wise::PassThrough;
using ReduceSum = ck::tensor_operation::element_wise::ReduceSum; using ReduceSum = ck::reduce::Add<F32>;
using ReduceSquareSum = ck::tensor_operation::element_wise::ReduceSquareSum; using Square = ck::tensor_operation::element_wise::UnarySquare<F32, F32, false>;
static constexpr auto GemmDefault = ck::tensor_operation::device::GemmSpecialization::Default; static constexpr auto GemmDefault = ck::tensor_operation::device::GemmSpecialization::Default;
...@@ -30,33 +30,31 @@ static constexpr auto GemmDefault = ck::tensor_operation::device::GemmSpecializa ...@@ -30,33 +30,31 @@ static constexpr auto GemmDefault = ck::tensor_operation::device::GemmSpecializa
// d1[m] = reduce1(c[m, n]) // d1[m] = reduce1(c[m, n])
using device_gemm_reduce_xdl_cshuffle_f16_f16_f16_f32_f32_km_nk_mn_instances = std::tuple< using device_gemm_reduce_xdl_cshuffle_f16_f16_f16_f32_f32_km_nk_mn_instances = std::tuple<
// clang-format off // clang-format off
//###########################| ALayout| BLayout| CLayout|AData| BData| CData| GemmAcc| CShuffle| ReduceAcc| DData| A| B| C| D0| D1| 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| CReduce| CReduceThreadLds2VGprCopy| CReduceThreadVgpr2GlobalCopy| //###########################| ALayout| BLayout| CLayout|AData| BData| CData| GemmAcc| CShuffle| ReduceAcc| DData| A| B| C| D0| D1| D1EleOp| 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| CReduce| CReduceThreadLds2VGprCopy| CReduceThreadVgpr2GlobalCopy|
//###########################| | | | Type| Type| Type| DataType| DataType| DataType| Type| Elementwise| Elementwise| Elementwise| Reduce| Reduce| Spacialization| 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_MPerBlock| ScalarPerVector| ThreadClusterLengths| SrcDstScalarPerVector| SrcDstScalarPerVector| //###########################| | | | Type| Type| Type| DataType| DataType| DataType| Type| Elementwise| Elementwise| Elementwise| Reduce| Reduce| Spacialization| 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_MPerBlock| ScalarPerVector| ThreadClusterLengths| SrcDstScalarPerVector| SrcDstScalarPerVector|
//###########################| | | | | | | | | | | Operation| Operation| 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_NPerBlock| _NPerBlock| _MPerBlock_NPerBlock| _NPerBlock| _MPerBlock| //###########################| | | | | | | | | | | Operation| Operation| 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_NPerBlock| _NPerBlock| _MPerBlock_NPerBlock| _NPerBlock| _MPerBlock|
//###########################| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | //###########################| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
DeviceGemmReduce_Xdl_CShuffle< Col, Col, Row, F16, F16, F16, F32, F32, F32, F32, PassThrough, PassThrough, PassThrough, ReduceSum, ReduceSquareSum, GemmDefault, 1, 256, 256, 128, 32, 2, 8, 32, 32, 4, 2, S<4, 64, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 4, 2, false, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, true, 1, 1, S<1, 32, 1, 8>, 8, S<64, 4>, 4, 1>, DeviceGemmReduce_Xdl_CShuffle< Col, Col, Row, F16, F16, F16, F32, F32, F32, F32, PassThrough, PassThrough, PassThrough, ReduceSum, ReduceSum, Square, GemmDefault, 1, 256, 256, 128, 32, 2, 8, 32, 32, 4, 2, S<4, 64, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 4, 2, false, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, true, 1, 1, S<1, 32, 1, 8>, 8, S<64, 4>, 4, 1>,
DeviceGemmReduce_Xdl_CShuffle< Col, Col, Row, F16, F16, F16, F32, F32, F32, F32, PassThrough, PassThrough, PassThrough, ReduceSum, ReduceSquareSum, GemmDefault, 1, 256, 256, 128, 32, 8, 8, 32, 32, 4, 2, S<4, 64, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 4, 8, true, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, true, 1, 1, S<1, 32, 1, 8>, 8, S<64, 4>, 4, 1>, DeviceGemmReduce_Xdl_CShuffle< Col, Col, Row, F16, F16, F16, F32, F32, F32, F32, PassThrough, PassThrough, PassThrough, ReduceSum, ReduceSum, Square, GemmDefault, 1, 256, 256, 128, 32, 8, 8, 32, 32, 4, 2, S<4, 64, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 4, 8, true, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, true, 1, 1, S<1, 32, 1, 8>, 8, S<64, 4>, 4, 1>,
DeviceGemmReduce_Xdl_CShuffle< Col, Col, Row, F16, F16, F16, F32, F32, F32, F32, PassThrough, PassThrough, PassThrough, ReduceSum, ReduceSquareSum, GemmDefault, 1, 256, 128, 256, 32, 2, 8, 32, 32, 2, 4, S<8, 32, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 4, 2, false, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, true, 1, 1, S<1, 32, 1, 8>, 8, S<64, 4>, 4, 1>, DeviceGemmReduce_Xdl_CShuffle< Col, Col, Row, F16, F16, F16, F32, F32, F32, F32, PassThrough, PassThrough, PassThrough, ReduceSum, ReduceSum, Square, GemmDefault, 1, 256, 128, 256, 32, 2, 8, 32, 32, 2, 4, S<8, 32, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 4, 2, false, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, true, 1, 1, S<1, 32, 1, 8>, 8, S<64, 4>, 4, 1>,
DeviceGemmReduce_Xdl_CShuffle< Col, Col, Row, F16, F16, F16, F32, F32, F32, F32, PassThrough, PassThrough, PassThrough, ReduceSum, ReduceSquareSum, GemmDefault, 1, 256, 128, 256, 32, 8, 8, 32, 32, 2, 4, S<4, 64, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 2, 8, true, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, true, 1, 1, S<1, 32, 1, 8>, 8, S<64, 4>, 4, 1>, DeviceGemmReduce_Xdl_CShuffle< Col, Col, Row, F16, F16, F16, F32, F32, F32, F32, PassThrough, PassThrough, PassThrough, ReduceSum, ReduceSum, Square, GemmDefault, 1, 256, 128, 256, 32, 8, 8, 32, 32, 2, 4, S<4, 64, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 2, 8, true, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, true, 1, 1, S<1, 32, 1, 8>, 8, S<64, 4>, 4, 1>,
DeviceGemmReduce_Xdl_CShuffle< Col, Col, Row, F16, F16, F16, F32, F32, F32, F32, PassThrough, PassThrough, PassThrough, ReduceSum, ReduceSquareSum, GemmDefault, 1, 128, 128, 128, 32, 2, 8, 32, 32, 4, 2, S<4, 32, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 4, 2, false, S<4, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, true, 1, 1, S<1, 16, 1, 8>, 8, S<32, 4>, 4, 1>, DeviceGemmReduce_Xdl_CShuffle< Col, Col, Row, F16, F16, F16, F32, F32, F32, F32, PassThrough, PassThrough, PassThrough, ReduceSum, ReduceSum, Square, GemmDefault, 1, 128, 128, 128, 32, 2, 8, 32, 32, 4, 2, S<4, 32, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 4, 2, false, S<4, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, true, 1, 1, S<1, 16, 1, 8>, 8, S<32, 4>, 4, 1>,
DeviceGemmReduce_Xdl_CShuffle< Col, Col, Row, F16, F16, F16, F32, F32, F32, F32, PassThrough, PassThrough, PassThrough, ReduceSum, ReduceSquareSum, GemmDefault, 1, 128, 128, 128, 32, 8, 8, 32, 32, 4, 2, S<4, 32, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 4, 8, true, S<4, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, true, 1, 1, S<1, 16, 1, 8>, 8, S<32, 4>, 4, 1>, DeviceGemmReduce_Xdl_CShuffle< Col, Col, Row, F16, F16, F16, F32, F32, F32, F32, PassThrough, PassThrough, PassThrough, ReduceSum, ReduceSum, Square, GemmDefault, 1, 128, 128, 128, 32, 8, 8, 32, 32, 4, 2, S<4, 32, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 4, 8, true, S<4, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, true, 1, 1, S<1, 16, 1, 8>, 8, S<32, 4>, 4, 1>,
DeviceGemmReduce_Xdl_CShuffle< Col, Col, Row, F16, F16, F16, F32, F32, F32, F32, PassThrough, PassThrough, PassThrough, ReduceSum, ReduceSquareSum, GemmDefault, 1, 256, 128, 128, 32, 2, 8, 32, 32, 2, 2, S<8, 32, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 4, 2, false, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, true, 1, 1, S<1, 32, 1, 8>, 8, S<64, 4>, 4, 1>, DeviceGemmReduce_Xdl_CShuffle< Col, Col, Row, F16, F16, F16, F32, F32, F32, F32, PassThrough, PassThrough, PassThrough, ReduceSum, ReduceSum, Square, GemmDefault, 1, 256, 128, 128, 32, 2, 8, 32, 32, 2, 2, S<8, 32, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 4, 2, false, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, true, 1, 1, S<1, 32, 1, 8>, 8, S<64, 4>, 4, 1>,
DeviceGemmReduce_Xdl_CShuffle< Col, Col, Row, F16, F16, F16, F32, F32, F32, F32, PassThrough, PassThrough, PassThrough, ReduceSum, ReduceSquareSum, GemmDefault, 1, 256, 128, 128, 32, 8, 8, 32, 32, 2, 2, S<4, 64, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 2, 8, true, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, true, 1, 1, S<1, 32, 1, 8>, 8, S<64, 4>, 4, 1>, DeviceGemmReduce_Xdl_CShuffle< Col, Col, Row, F16, F16, F16, F32, F32, F32, F32, PassThrough, PassThrough, PassThrough, ReduceSum, ReduceSum, Square, GemmDefault, 1, 256, 128, 128, 32, 8, 8, 32, 32, 2, 2, S<4, 64, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 2, 8, true, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, true, 1, 1, S<1, 32, 1, 8>, 8, S<64, 4>, 4, 1>,
DeviceGemmReduce_Xdl_CShuffle< Col, Col, Row, F16, F16, F16, F32, F32, F32, F32, PassThrough, PassThrough, PassThrough, ReduceSum, ReduceSquareSum, GemmDefault, 1, 128, 128, 64, 32, 2, 8, 32, 32, 2, 2, S<4, 32, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 4, 2, false, S<4, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, true, 1, 1, S<1, 32, 1, 4>, 8, S<64, 2>, 4, 1>, DeviceGemmReduce_Xdl_CShuffle< Col, Col, Row, F16, F16, F16, F32, F32, F32, F32, PassThrough, PassThrough, PassThrough, ReduceSum, ReduceSum, Square, GemmDefault, 1, 128, 128, 64, 32, 2, 8, 32, 32, 2, 2, S<4, 32, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 4, 2, false, S<4, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, true, 1, 1, S<1, 32, 1, 4>, 8, S<64, 2>, 4, 1>,
DeviceGemmReduce_Xdl_CShuffle< Col, Col, Row, F16, F16, F16, F32, F32, F32, F32, PassThrough, PassThrough, PassThrough, ReduceSum, ReduceSquareSum, GemmDefault, 1, 128, 128, 64, 32, 8, 8, 32, 32, 2, 2, S<4, 32, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 4, 8, true, S<4, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, true, 1, 1, S<1, 32, 1, 4>, 8, S<64, 2>, 4, 1>, DeviceGemmReduce_Xdl_CShuffle< Col, Col, Row, F16, F16, F16, F32, F32, F32, F32, PassThrough, PassThrough, PassThrough, ReduceSum, ReduceSum, Square, GemmDefault, 1, 128, 128, 64, 32, 8, 8, 32, 32, 2, 2, S<4, 32, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 4, 8, true, S<4, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, true, 1, 1, S<1, 32, 1, 4>, 8, S<64, 2>, 4, 1>,
DeviceGemmReduce_Xdl_CShuffle< Col, Col, Row, F16, F16, F16, F32, F32, F32, F32, PassThrough, PassThrough, PassThrough, ReduceSum, ReduceSquareSum, GemmDefault, 1, 128, 64, 128, 32, 2, 8, 32, 32, 2, 2, S<8, 16, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 4, 2, false, S<4, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, true, 1, 1, S<1, 16, 1, 8>, 8, S<32, 4>, 4, 1>, DeviceGemmReduce_Xdl_CShuffle< Col, Col, Row, F16, F16, F16, F32, F32, F32, F32, PassThrough, PassThrough, PassThrough, ReduceSum, ReduceSum, Square, GemmDefault, 1, 128, 64, 128, 32, 2, 8, 32, 32, 2, 2, S<8, 16, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 4, 2, false, S<4, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, true, 1, 1, S<1, 16, 1, 8>, 8, S<32, 4>, 4, 1>,
DeviceGemmReduce_Xdl_CShuffle< Col, Col, Row, F16, F16, F16, F32, F32, F32, F32, PassThrough, PassThrough, PassThrough, ReduceSum, ReduceSquareSum, GemmDefault, 1, 128, 64, 128, 32, 8, 8, 32, 32, 2, 2, S<4, 32, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 2, 8, true, S<4, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, true, 1, 1, S<1, 16, 1, 8>, 8, S<32, 4>, 4, 1>, DeviceGemmReduce_Xdl_CShuffle< Col, Col, Row, F16, F16, F16, F32, F32, F32, F32, PassThrough, PassThrough, PassThrough, ReduceSum, ReduceSum, Square, GemmDefault, 1, 128, 64, 128, 32, 8, 8, 32, 32, 2, 2, S<4, 32, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 2, 8, true, S<4, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, true, 1, 1, S<1, 16, 1, 8>, 8, S<32, 4>, 4, 1>,
DeviceGemmReduce_Xdl_CShuffle< Col, Col, Row, F16, F16, F16, F32, F32, F32, F32, PassThrough, PassThrough, PassThrough, ReduceSum, ReduceSquareSum, GemmDefault, 1, 256, 128, 64, 32, 2, 8, 32, 32, 2, 1, S<8, 32, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 4, 2, false, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, true, 1, 1, S<1, 16, 1, 4>, 8, S<64, 4>, 4, 1>, DeviceGemmReduce_Xdl_CShuffle< Col, Col, Row, F16, F16, F16, F32, F32, F32, F32, PassThrough, PassThrough, PassThrough, ReduceSum, ReduceSum, Square, GemmDefault, 1, 256, 128, 64, 32, 2, 8, 32, 32, 2, 1, S<8, 32, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 4, 2, false, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, true, 1, 1, S<1, 16, 1, 4>, 8, S<64, 4>, 4, 1>,
DeviceGemmReduce_Xdl_CShuffle< Col, Col, Row, F16, F16, F16, F32, F32, F32, F32, PassThrough, PassThrough, PassThrough, ReduceSum, ReduceSquareSum, GemmDefault, 1, 256, 128, 64, 32, 8, 8, 32, 32, 2, 1, S<4, 64, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 2, 8, true, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, true, 1, 1, S<1, 16, 1, 4>, 8, S<64, 4>, 4, 1>, DeviceGemmReduce_Xdl_CShuffle< Col, Col, Row, F16, F16, F16, F32, F32, F32, F32, PassThrough, PassThrough, PassThrough, ReduceSum, ReduceSum, Square, GemmDefault, 1, 256, 128, 64, 32, 8, 8, 32, 32, 2, 1, S<4, 64, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 2, 8, true, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, true, 1, 1, S<1, 16, 1, 4>, 8, S<64, 4>, 4, 1>,
DeviceGemmReduce_Xdl_CShuffle< Col, Col, Row, F16, F16, F16, F32, F32, F32, F32, PassThrough, PassThrough, PassThrough, ReduceSum, ReduceSquareSum, GemmDefault, 1, 256, 64, 128, 32, 2, 8, 32, 32, 1, 2, S<16,16, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 4, 2, false, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, true, 1, 1, S<1, 32, 1, 8>, 8, S<64, 4>, 4, 1>, DeviceGemmReduce_Xdl_CShuffle< Col, Col, Row, F16, F16, F16, F32, F32, F32, F32, PassThrough, PassThrough, PassThrough, ReduceSum, ReduceSum, Square, GemmDefault, 1, 256, 64, 128, 32, 2, 8, 32, 32, 1, 2, S<16,16, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 4, 2, false, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, true, 1, 1, S<1, 32, 1, 8>, 8, S<64, 4>, 4, 1>,
DeviceGemmReduce_Xdl_CShuffle< Col, Col, Row, F16, F16, F16, F32, F32, F32, F32, PassThrough, PassThrough, PassThrough, ReduceSum, ReduceSquareSum, GemmDefault, 1, 256, 64, 128, 32, 8, 8, 32, 32, 1, 2, S<4, 64, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 1, 8, true, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, true, 1, 1, S<1, 32, 1, 8>, 8, S<64, 4>, 4, 1> DeviceGemmReduce_Xdl_CShuffle< Col, Col, Row, F16, F16, F16, F32, F32, F32, F32, PassThrough, PassThrough, PassThrough, ReduceSum, ReduceSum, Square, GemmDefault, 1, 256, 64, 128, 32, 8, 8, 32, 32, 1, 2, S<4, 64, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 1, 8, true, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, true, 1, 1, S<1, 32, 1, 8>, 8, S<64, 4>, 4, 1>
// clang-format on // clang-format on
>; >;
void add_device_gemm_reduce_xdl_cshuffle_f16_f16_f16_f32_f32_km_nk_mn_instances( void add_device_gemm_reduce_xdl_cshuffle_f16_f16_f16_f32_f32_km_nk_mn_instances(
std::vector< std::vector<DeviceGemmReducePtr<PassThrough, PassThrough, PassThrough, Square>>& instances)
DeviceGemmReducePtr<PassThrough, PassThrough, PassThrough, ReduceSum, ReduceSquareSum>>&
instances)
{ {
add_device_operation_instances( add_device_operation_instances(
instances, device_gemm_reduce_xdl_cshuffle_f16_f16_f16_f32_f32_km_nk_mn_instances{}); instances, device_gemm_reduce_xdl_cshuffle_f16_f16_f16_f32_f32_km_nk_mn_instances{});
......
library/src/tensor_operation_instance/gpu/gemm_reduce/device_gemm_reduce_xdl_cshuffle_f16_f16_f16_f32_f32_mk_kn_mn_instance.cpp
View file @ d0b9a467
...@@ -2,7 +2,7 @@ ...@@ -2,7 +2,7 @@
#include "config.hpp" #include "config.hpp"
#include "device_gemm_reduce_xdl_cshuffle.hpp" #include "device_gemm_reduce_xdl_cshuffle.hpp"
#include "element_wise_operation.hpp" #include "element_wise_operation.hpp"
#include "element_wise_reduce_operation.hpp" #include "reduction_operator.hpp"
#include "device_operation_instance.hpp" #include "device_operation_instance.hpp"
namespace ck { namespace ck {
...@@ -19,9 +19,9 @@ using Col = ck::tensor_layout::gemm::ColumnMajor; ...@@ -19,9 +19,9 @@ using Col = ck::tensor_layout::gemm::ColumnMajor;
template <ck::index_t... Is> template <ck::index_t... Is>
using S = ck::Sequence<Is...>; using S = ck::Sequence<Is...>;
using PassThrough = ck::tensor_operation::element_wise::PassThrough; using PassThrough = ck::tensor_operation::element_wise::PassThrough;
using ReduceSum = ck::tensor_operation::element_wise::ReduceSum; using ReduceSum = ck::reduce::Add<F32>;
using ReduceSquareSum = ck::tensor_operation::element_wise::ReduceSquareSum; using Square = ck::tensor_operation::element_wise::UnarySquare<F32, F32, false>;
static constexpr auto GemmDefault = ck::tensor_operation::device::GemmSpecialization::Default; static constexpr auto GemmDefault = ck::tensor_operation::device::GemmSpecialization::Default;
...@@ -30,33 +30,31 @@ static constexpr auto GemmDefault = ck::tensor_operation::device::GemmSpecializa ...@@ -30,33 +30,31 @@ static constexpr auto GemmDefault = ck::tensor_operation::device::GemmSpecializa
// d1[m] = reduce1(c[m, n]) // d1[m] = reduce1(c[m, n])
using device_gemm_reduce_xdl_cshuffle_f16_f16_f16_f32_f32_mk_kn_mn_instances = std::tuple< using device_gemm_reduce_xdl_cshuffle_f16_f16_f16_f32_f32_mk_kn_mn_instances = std::tuple<
// clang-format off // clang-format off
//###########################| ALayout| BLayout| CLayout| AData| BData| CData| GemmAcc| CShuffle| ReduceAcc| DData| A| B| C| D0| D1| 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| CReduce| CReduceThreadLds2VGprCopy| CReduceThreadVgpr2GlobalCopy| //###########################| ALayout| BLayout| CLayout| AData| BData| CData| GemmAcc| CShuffle| ReduceAcc| DData| A| B| C| D0| D1| D1EleOp| 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| CReduce| CReduceThreadLds2VGprCopy| CReduceThreadVgpr2GlobalCopy|
//###########################| | | | Type| Type| Type| DataType| DataType| DataType| Type| Elementwise| Elementwise| Elementwise| Reduce| Reduce| Spacialization| 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_MPerBlock| ScalarPerVector| ThreadClusterLengths| SrcDstScalarPerVector| SrcDstScalarPerVector| //###########################| | | | Type| Type| Type| DataType| DataType| DataType| Type| Elementwise| Elementwise| Elementwise| Reduce| Reduce| Spacialization| 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_MPerBlock| ScalarPerVector| ThreadClusterLengths| SrcDstScalarPerVector| SrcDstScalarPerVector|
//###########################| | | | | | | | | | | Operation| Operation| 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_NPerBlock| _NPerBlock| _MPerBlock_NPerBlock| _NPerBlock| _MPerBlock| //###########################| | | | | | | | | | | Operation| Operation| 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_NPerBlock| _NPerBlock| _MPerBlock_NPerBlock| _NPerBlock| _MPerBlock|
//###########################| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | //###########################| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
DeviceGemmReduce_Xdl_CShuffle< Row, Row, Row, F16, F16, F16, F32, F32, F32, F32, PassThrough, PassThrough, PassThrough, ReduceSum, ReduceSquareSum, GemmDefault, 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, true, S<8, 32, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 4, 2, false, 1, 1, S<1, 32, 1, 8>, 8, S<64, 4>, 4, 1>, DeviceGemmReduce_Xdl_CShuffle< Row, Row, Row, F16, F16, F16, F32, F32, F32, F32, PassThrough, PassThrough, PassThrough, ReduceSum, ReduceSum, Square, GemmDefault, 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, true, S<8, 32, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 4, 2, false, 1, 1, S<1, 32, 1, 8>, 8, S<64, 4>, 4, 1>,
DeviceGemmReduce_Xdl_CShuffle< Row, Row, Row, F16, F16, F16, F32, F32, F32, F32, PassThrough, PassThrough, PassThrough, ReduceSum, ReduceSquareSum, GemmDefault, 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, true, S<4, 64, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 2, 8, true, 1, 1, S<1, 32, 1, 8>, 8, S<64, 4>, 4, 1>, DeviceGemmReduce_Xdl_CShuffle< Row, Row, Row, F16, F16, F16, F32, F32, F32, F32, PassThrough, PassThrough, PassThrough, ReduceSum, ReduceSum, Square, GemmDefault, 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, true, S<4, 64, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 2, 8, true, 1, 1, S<1, 32, 1, 8>, 8, S<64, 4>, 4, 1>,
DeviceGemmReduce_Xdl_CShuffle< Row, Row, Row, F16, F16, F16, F32, F32, F32, F32, PassThrough, PassThrough, PassThrough, ReduceSum, ReduceSquareSum, GemmDefault, 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, true, S<4, 64, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 4, 2, false, 1, 1, S<1, 32, 1, 8>, 8, S<64, 4>, 4, 1>, DeviceGemmReduce_Xdl_CShuffle< Row, Row, Row, F16, F16, F16, F32, F32, F32, F32, PassThrough, PassThrough, PassThrough, ReduceSum, ReduceSum, Square, GemmDefault, 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, true, S<4, 64, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 4, 2, false, 1, 1, S<1, 32, 1, 8>, 8, S<64, 4>, 4, 1>,
DeviceGemmReduce_Xdl_CShuffle< Row, Row, Row, F16, F16, F16, F32, F32, F32, F32, PassThrough, PassThrough, PassThrough, ReduceSum, ReduceSquareSum, GemmDefault, 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, true, S<4, 64, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 4, 8, true, 1, 1, S<1, 32, 1, 8>, 8, S<64, 4>, 4, 1>, DeviceGemmReduce_Xdl_CShuffle< Row, Row, Row, F16, F16, F16, F32, F32, F32, F32, PassThrough, PassThrough, PassThrough, ReduceSum, ReduceSum, Square, GemmDefault, 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, true, S<4, 64, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 4, 8, true, 1, 1, S<1, 32, 1, 8>, 8, S<64, 4>, 4, 1>,
DeviceGemmReduce_Xdl_CShuffle< Row, Row, Row, F16, F16, F16, F32, F32, F32, F32, PassThrough, PassThrough, PassThrough, ReduceSum, ReduceSquareSum, GemmDefault, 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, true, S<4, 32, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 4, 2, false, 1, 1, S<1, 16, 1, 8>, 8, S<32, 4>, 4, 1>, DeviceGemmReduce_Xdl_CShuffle< Row, Row, Row, F16, F16, F16, F32, F32, F32, F32, PassThrough, PassThrough, PassThrough, ReduceSum, ReduceSum, Square, GemmDefault, 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, true, S<4, 32, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 4, 2, false, 1, 1, S<1, 16, 1, 8>, 8, S<32, 4>, 4, 1>,
DeviceGemmReduce_Xdl_CShuffle< Row, Row, Row, F16, F16, F16, F32, F32, F32, F32, PassThrough, PassThrough, PassThrough, ReduceSum, ReduceSquareSum, GemmDefault, 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, true, S<4, 32, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 4, 8, true, 1, 1, S<1, 16, 1, 8>, 8, S<32, 4>, 4, 1>, DeviceGemmReduce_Xdl_CShuffle< Row, Row, Row, F16, F16, F16, F32, F32, F32, F32, PassThrough, PassThrough, PassThrough, ReduceSum, ReduceSum, Square, GemmDefault, 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, true, S<4, 32, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 4, 8, true, 1, 1, S<1, 16, 1, 8>, 8, S<32, 4>, 4, 1>,
DeviceGemmReduce_Xdl_CShuffle< Row, Row, Row, F16, F16, F16, F32, F32, F32, F32, PassThrough, PassThrough, PassThrough, ReduceSum, ReduceSquareSum, GemmDefault, 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, true, S<8, 32, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 4, 2, false, 1, 1, S<1, 32, 1, 8>, 8, S<64, 4>, 4, 1>, DeviceGemmReduce_Xdl_CShuffle< Row, Row, Row, F16, F16, F16, F32, F32, F32, F32, PassThrough, PassThrough, PassThrough, ReduceSum, ReduceSum, Square, GemmDefault, 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, true, S<8, 32, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 4, 2, false, 1, 1, S<1, 32, 1, 8>, 8, S<64, 4>, 4, 1>,
DeviceGemmReduce_Xdl_CShuffle< Row, Row, Row, F16, F16, F16, F32, F32, F32, F32, PassThrough, PassThrough, PassThrough, ReduceSum, ReduceSquareSum, GemmDefault, 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, true, S<4, 64, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 2, 8, true, 1, 1, S<1, 32, 1, 8>, 8, S<64, 4>, 4, 1>, DeviceGemmReduce_Xdl_CShuffle< Row, Row, Row, F16, F16, F16, F32, F32, F32, F32, PassThrough, PassThrough, PassThrough, ReduceSum, ReduceSum, Square, GemmDefault, 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, true, S<4, 64, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 2, 8, true, 1, 1, S<1, 32, 1, 8>, 8, S<64, 4>, 4, 1>,
DeviceGemmReduce_Xdl_CShuffle< Row, Row, Row, F16, F16, F16, F32, F32, F32, F32, PassThrough, PassThrough, PassThrough, ReduceSum, ReduceSquareSum, GemmDefault, 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, true, S<8, 16, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 4, 2, false, 1, 1, S<1, 32, 1, 4>, 8, S<64, 2>, 4, 1>, DeviceGemmReduce_Xdl_CShuffle< Row, Row, Row, F16, F16, F16, F32, F32, F32, F32, PassThrough, PassThrough, PassThrough, ReduceSum, ReduceSum, Square, GemmDefault, 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, true, S<8, 16, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 4, 2, false, 1, 1, S<1, 32, 1, 4>, 8, S<64, 2>, 4, 1>,
DeviceGemmReduce_Xdl_CShuffle< Row, Row, Row, F16, F16, F16, F32, F32, F32, F32, PassThrough, PassThrough, PassThrough, ReduceSum, ReduceSquareSum, GemmDefault, 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, true, S<4, 32, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 2, 8, true, 1, 1, S<1, 32, 1, 4>, 8, S<64, 2>, 4, 1>, DeviceGemmReduce_Xdl_CShuffle< Row, Row, Row, F16, F16, F16, F32, F32, F32, F32, PassThrough, PassThrough, PassThrough, ReduceSum, ReduceSum, Square, GemmDefault, 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, true, S<4, 32, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 2, 8, true, 1, 1, S<1, 32, 1, 4>, 8, S<64, 2>, 4, 1>,
DeviceGemmReduce_Xdl_CShuffle< Row, Row, Row, F16, F16, F16, F32, F32, F32, F32, PassThrough, PassThrough, PassThrough, ReduceSum, ReduceSquareSum, GemmDefault, 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, true, S<4, 32, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 4, 2, false, 1, 1, S<1, 16, 1, 8>, 8, S<32, 4>, 4, 1>, DeviceGemmReduce_Xdl_CShuffle< Row, Row, Row, F16, F16, F16, F32, F32, F32, F32, PassThrough, PassThrough, PassThrough, ReduceSum, ReduceSum, Square, GemmDefault, 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, true, S<4, 32, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 4, 2, false, 1, 1, S<1, 16, 1, 8>, 8, S<32, 4>, 4, 1>,
DeviceGemmReduce_Xdl_CShuffle< Row, Row, Row, F16, F16, F16, F32, F32, F32, F32, PassThrough, PassThrough, PassThrough, ReduceSum, ReduceSquareSum, GemmDefault, 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, true, S<4, 32, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 4, 8, true, 1, 1, S<1, 16, 1, 8>, 8, S<32, 4>, 4, 1>, DeviceGemmReduce_Xdl_CShuffle< Row, Row, Row, F16, F16, F16, F32, F32, F32, F32, PassThrough, PassThrough, PassThrough, ReduceSum, ReduceSum, Square, GemmDefault, 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, true, S<4, 32, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 4, 8, true, 1, 1, S<1, 16, 1, 8>, 8, S<32, 4>, 4, 1>,
DeviceGemmReduce_Xdl_CShuffle< Row, Row, Row, F16, F16, F16, F32, F32, F32, F32, PassThrough, PassThrough, PassThrough, ReduceSum, ReduceSquareSum, GemmDefault, 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, true, S<16,16, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 4, 2, false, 1, 1, S<1, 16, 1, 4>, 8, S<64, 4>, 4, 1>, DeviceGemmReduce_Xdl_CShuffle< Row, Row, Row, F16, F16, F16, F32, F32, F32, F32, PassThrough, PassThrough, PassThrough, ReduceSum, ReduceSum, Square, GemmDefault, 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, true, S<16,16, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 4, 2, false, 1, 1, S<1, 16, 1, 4>, 8, S<64, 4>, 4, 1>,
DeviceGemmReduce_Xdl_CShuffle< Row, Row, Row, F16, F16, F16, F32, F32, F32, F32, PassThrough, PassThrough, PassThrough, ReduceSum, ReduceSquareSum, GemmDefault, 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, true, S<4, 64, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 1, 8, true, 1, 1, S<1, 16, 1, 4>, 8, S<64, 4>, 4, 1>, DeviceGemmReduce_Xdl_CShuffle< Row, Row, Row, F16, F16, F16, F32, F32, F32, F32, PassThrough, PassThrough, PassThrough, ReduceSum, ReduceSum, Square, GemmDefault, 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, true, S<4, 64, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 1, 8, true, 1, 1, S<1, 16, 1, 4>, 8, S<64, 4>, 4, 1>,
DeviceGemmReduce_Xdl_CShuffle< Row, Row, Row, F16, F16, F16, F32, F32, F32, F32, PassThrough, PassThrough, PassThrough, ReduceSum, ReduceSquareSum, GemmDefault, 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, true, S<8, 32, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 4, 2, false, 1, 1, S<1, 32, 1, 8>, 8, S<64, 4>, 4, 1>, DeviceGemmReduce_Xdl_CShuffle< Row, Row, Row, F16, F16, F16, F32, F32, F32, F32, PassThrough, PassThrough, PassThrough, ReduceSum, ReduceSum, Square, GemmDefault, 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, true, S<8, 32, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 4, 2, false, 1, 1, S<1, 32, 1, 8>, 8, S<64, 4>, 4, 1>,
DeviceGemmReduce_Xdl_CShuffle< Row, Row, Row, F16, F16, F16, F32, F32, F32, F32, PassThrough, PassThrough, PassThrough, ReduceSum, ReduceSquareSum, GemmDefault, 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, true, S<4, 64, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 2, 8, true, 1, 1, S<1, 32, 1, 8>, 8, S<64, 4>, 4, 1> DeviceGemmReduce_Xdl_CShuffle< Row, Row, Row, F16, F16, F16, F32, F32, F32, F32, PassThrough, PassThrough, PassThrough, ReduceSum, ReduceSum, Square, GemmDefault, 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, true, S<4, 64, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 2, 8, true, 1, 1, S<1, 32, 1, 8>, 8, S<64, 4>, 4, 1>
// clang-format on // clang-format on
>; >;
void add_device_gemm_reduce_xdl_cshuffle_f16_f16_f16_f32_f32_mk_kn_mn_instances( void add_device_gemm_reduce_xdl_cshuffle_f16_f16_f16_f32_f32_mk_kn_mn_instances(
std::vector< std::vector<DeviceGemmReducePtr<PassThrough, PassThrough, PassThrough, Square>>& instances)
DeviceGemmReducePtr<PassThrough, PassThrough, PassThrough, ReduceSum, ReduceSquareSum>>&
instances)
{ {
add_device_operation_instances( add_device_operation_instances(
instances, device_gemm_reduce_xdl_cshuffle_f16_f16_f16_f32_f32_mk_kn_mn_instances{}); instances, device_gemm_reduce_xdl_cshuffle_f16_f16_f16_f32_f32_mk_kn_mn_instances{});
......
library/src/tensor_operation_instance/gpu/gemm_reduce/device_gemm_reduce_xdl_cshuffle_f16_f16_f16_f32_f32_mk_nk_mn_instance.cpp
View file @ d0b9a467
...@@ -2,7 +2,7 @@ ...@@ -2,7 +2,7 @@
#include "config.hpp" #include "config.hpp"
#include "device_gemm_reduce_xdl_cshuffle.hpp" #include "device_gemm_reduce_xdl_cshuffle.hpp"
#include "element_wise_operation.hpp" #include "element_wise_operation.hpp"
#include "element_wise_reduce_operation.hpp" #include "reduction_operator.hpp"
#include "device_operation_instance.hpp" #include "device_operation_instance.hpp"
namespace ck { namespace ck {
...@@ -19,9 +19,9 @@ using Col = ck::tensor_layout::gemm::ColumnMajor; ...@@ -19,9 +19,9 @@ using Col = ck::tensor_layout::gemm::ColumnMajor;
template <ck::index_t... Is> template <ck::index_t... Is>
using S = ck::Sequence<Is...>; using S = ck::Sequence<Is...>;
using PassThrough = ck::tensor_operation::element_wise::PassThrough; using PassThrough = ck::tensor_operation::element_wise::PassThrough;
using ReduceSum = ck::tensor_operation::element_wise::ReduceSum; using ReduceSum = ck::reduce::Add<F32>;
using ReduceSquareSum = ck::tensor_operation::element_wise::ReduceSquareSum; using Square = ck::tensor_operation::element_wise::UnarySquare<F32, F32, false>;
static constexpr auto GemmDefault = ck::tensor_operation::device::GemmSpecialization::Default; static constexpr auto GemmDefault = ck::tensor_operation::device::GemmSpecialization::Default;
...@@ -30,30 +30,28 @@ static constexpr auto GemmDefault = ck::tensor_operation::device::GemmSpecializa ...@@ -30,30 +30,28 @@ static constexpr auto GemmDefault = ck::tensor_operation::device::GemmSpecializa
// d1[m] = reduce1(c[m, n]) // d1[m] = reduce1(c[m, n])
using device_gemm_reduce_xdl_cshuffle_f16_f16_f16_f32_f32_mk_nk_mn_instances = std::tuple< using device_gemm_reduce_xdl_cshuffle_f16_f16_f16_f32_f32_mk_nk_mn_instances = std::tuple<
// clang-format off // clang-format off
//###########################| ALayout| BLayout| CLayout|AData| BData| CData| GemmAcc| CShuffle| ReduceAcc| DData| A| B| C| D0| D1| 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| CReduce| CReduceThreadLds2VGprCopy| CReduceThreadVgpr2GlobalCopy| //###########################| ALayout| BLayout| CLayout|AData| BData| CData| GemmAcc| CShuffle| ReduceAcc| DData| A| B| C| D0| D1| D1EleOp| 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| CReduce| CReduceThreadLds2VGprCopy| CReduceThreadVgpr2GlobalCopy|
//###########################| | | | Type| Type| Type| DataType| DataType| DataType| Type| Elementwise| Elementwise| Elementwise| Reduce| Reduce| Spacialization| 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_MPerBlock| ScalarPerVector| ThreadClusterLengths| SrcDstScalarPerVector| SrcDstScalarPerVector| //###########################| | | | Type| Type| Type| DataType| DataType| DataType| Type| Elementwise| Elementwise| Elementwise| Reduce| Reduce| Spacialization| 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_MPerBlock| ScalarPerVector| ThreadClusterLengths| SrcDstScalarPerVector| SrcDstScalarPerVector|
//###########################| | | | | | | | | | | Operation| Operation| 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_NPerBlock| _NPerBlock| _MPerBlock_NPerBlock| _NPerBlock| _MPerBlock| //###########################| | | | | | | | | | | Operation| Operation| 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_NPerBlock| _NPerBlock| _MPerBlock_NPerBlock| _NPerBlock| _MPerBlock|
//###########################| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | //###########################| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
DeviceGemmReduce_Xdl_CShuffle< Row, Col, Row, F16, F16, F16, F32, F32, F32, F32, PassThrough, PassThrough, PassThrough, ReduceSum, ReduceSquareSum, GemmDefault, 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, true, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, true, 1, 1, S<1, 32, 1, 8>, 8, S<64, 4>, 4, 1>, DeviceGemmReduce_Xdl_CShuffle< Row, Col, Row, F16, F16, F16, F32, F32, F32, F32, PassThrough, PassThrough, PassThrough, ReduceSum, ReduceSum, Square, GemmDefault, 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, true, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, true, 1, 1, S<1, 32, 1, 8>, 8, S<64, 4>, 4, 1>,
DeviceGemmReduce_Xdl_CShuffle< Row, Col, Row, F16, F16, F16, F32, F32, F32, F32, PassThrough, PassThrough, PassThrough, ReduceSum, ReduceSquareSum, GemmDefault, 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, true, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, true, 1, 1, S<1, 32, 1, 8>, 8, S<64, 4>, 4, 1>, DeviceGemmReduce_Xdl_CShuffle< Row, Col, Row, F16, F16, F16, F32, F32, F32, F32, PassThrough, PassThrough, PassThrough, ReduceSum, ReduceSum, Square, GemmDefault, 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, true, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, true, 1, 1, S<1, 32, 1, 8>, 8, S<64, 4>, 4, 1>,
DeviceGemmReduce_Xdl_CShuffle< Row, Col, Row, F16, F16, F16, F32, F32, F32, F32, PassThrough, PassThrough, PassThrough, ReduceSum, ReduceSquareSum, GemmDefault, 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, true, S<4, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, true, 1, 1, S<1, 16, 1, 8>, 8, S<32, 4>, 4, 1>, DeviceGemmReduce_Xdl_CShuffle< Row, Col, Row, F16, F16, F16, F32, F32, F32, F32, PassThrough, PassThrough, PassThrough, ReduceSum, ReduceSum, Square, GemmDefault, 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, true, S<4, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, true, 1, 1, S<1, 16, 1, 8>, 8, S<32, 4>, 4, 1>,
DeviceGemmReduce_Xdl_CShuffle< Row, Col, Row, F16, F16, F16, F32, F32, F32, F32, PassThrough, PassThrough, PassThrough, ReduceSum, ReduceSquareSum, GemmDefault, 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, true, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, true, 1, 1, S<1, 32, 1, 8>, 8, S<64, 4>, 4, 1>, DeviceGemmReduce_Xdl_CShuffle< Row, Col, Row, F16, F16, F16, F32, F32, F32, F32, PassThrough, PassThrough, PassThrough, ReduceSum, ReduceSum, Square, GemmDefault, 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, true, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, true, 1, 1, S<1, 32, 1, 8>, 8, S<64, 4>, 4, 1>,
DeviceGemmReduce_Xdl_CShuffle< Row, Col, Row, F16, F16, F16, F32, F32, F32, F32, PassThrough, PassThrough, PassThrough, ReduceSum, ReduceSquareSum, GemmDefault, 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, true, S<4, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, true, 1, 1, S<1, 32, 1, 4>, 8, S<64, 2>, 4, 1>, DeviceGemmReduce_Xdl_CShuffle< Row, Col, Row, F16, F16, F16, F32, F32, F32, F32, PassThrough, PassThrough, PassThrough, ReduceSum, ReduceSum, Square, GemmDefault, 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, true, S<4, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, true, 1, 1, S<1, 32, 1, 4>, 8, S<64, 2>, 4, 1>,
DeviceGemmReduce_Xdl_CShuffle< Row, Col, Row, F16, F16, F16, F32, F32, F32, F32, PassThrough, PassThrough, PassThrough, ReduceSum, ReduceSquareSum, GemmDefault, 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, true, S<4, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, true, 1, 1, S<1, 16, 1, 8>, 8, S<32, 4>, 4, 1>, DeviceGemmReduce_Xdl_CShuffle< Row, Col, Row, F16, F16, F16, F32, F32, F32, F32, PassThrough, PassThrough, PassThrough, ReduceSum, ReduceSum, Square, GemmDefault, 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, true, S<4, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, true, 1, 1, S<1, 16, 1, 8>, 8, S<32, 4>, 4, 1>,
DeviceGemmReduce_Xdl_CShuffle< Row, Col, Row, F16, F16, F16, F32, F32, F32, F32, PassThrough, PassThrough, PassThrough, ReduceSum, ReduceSquareSum, GemmDefault, 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, true, S<4, 16, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, true, 1, 1, S<1, 16, 1, 4>, 8, S<32, 2>, 4, 1>, DeviceGemmReduce_Xdl_CShuffle< Row, Col, Row, F16, F16, F16, F32, F32, F32, F32, PassThrough, PassThrough, PassThrough, ReduceSum, ReduceSum, Square, GemmDefault, 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, true, S<4, 16, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, true, 1, 1, S<1, 16, 1, 4>, 8, S<32, 2>, 4, 1>,
DeviceGemmReduce_Xdl_CShuffle< Row, Col, Row, F16, F16, F16, F32, F32, F32, F32, PassThrough, PassThrough, PassThrough, ReduceSum, ReduceSquareSum, GemmDefault, 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, true, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, true, 1, 1, S<1, 32, 1, 8>, 8, S<64, 4>, 4, 1>, DeviceGemmReduce_Xdl_CShuffle< Row, Col, Row, F16, F16, F16, F32, F32, F32, F32, PassThrough, PassThrough, PassThrough, ReduceSum, ReduceSum, Square, GemmDefault, 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, true, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, true, 1, 1, S<1, 32, 1, 8>, 8, S<64, 4>, 4, 1>,
DeviceGemmReduce_Xdl_CShuffle< Row, Col, Row, F16, F16, F16, F32, F32, F32, F32, PassThrough, PassThrough, PassThrough, ReduceSum, ReduceSquareSum, GemmDefault, 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, true, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, true, 1, 1, S<1, 32, 1, 8>, 8, S<64, 4>, 4, 1>, DeviceGemmReduce_Xdl_CShuffle< Row, Col, Row, F16, F16, F16, F32, F32, F32, F32, PassThrough, PassThrough, PassThrough, ReduceSum, ReduceSum, Square, GemmDefault, 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, true, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, true, 1, 1, S<1, 32, 1, 8>, 8, S<64, 4>, 4, 1>,
DeviceGemmReduce_Xdl_CShuffle< Row, Col, Row, F16, F16, F16, F32, F32, F32, F32, PassThrough, PassThrough, PassThrough, ReduceSum, ReduceSquareSum, GemmDefault, 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, true, S<4, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, true, 1, 1, S<1, 32, 1, 4>, 8, S<64, 2>, 4, 1>, DeviceGemmReduce_Xdl_CShuffle< Row, Col, Row, F16, F16, F16, F32, F32, F32, F32, PassThrough, PassThrough, PassThrough, ReduceSum, ReduceSum, Square, GemmDefault, 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, true, S<4, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, true, 1, 1, S<1, 32, 1, 4>, 8, S<64, 2>, 4, 1>,
DeviceGemmReduce_Xdl_CShuffle< Row, Col, Row, F16, F16, F16, F32, F32, F32, F32, PassThrough, PassThrough, PassThrough, ReduceSum, ReduceSquareSum, GemmDefault, 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, true, S<4, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, true, 1, 1, S<1, 16, 1, 8>, 8, S<32, 4>, 4, 1>, DeviceGemmReduce_Xdl_CShuffle< Row, Col, Row, F16, F16, F16, F32, F32, F32, F32, PassThrough, PassThrough, PassThrough, ReduceSum, ReduceSum, Square, GemmDefault, 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, true, S<4, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, true, 1, 1, S<1, 16, 1, 8>, 8, S<32, 4>, 4, 1>,
DeviceGemmReduce_Xdl_CShuffle< Row, Col, Row, F16, F16, F16, F32, F32, F32, F32, PassThrough, PassThrough, PassThrough, ReduceSum, ReduceSquareSum, GemmDefault, 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, true, S<4, 16, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, true, 1, 1, S<1, 16, 1, 4>, 8, S<32, 2>, 4, 1>, DeviceGemmReduce_Xdl_CShuffle< Row, Col, Row, F16, F16, F16, F32, F32, F32, F32, PassThrough, PassThrough, PassThrough, ReduceSum, ReduceSum, Square, GemmDefault, 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, true, S<4, 16, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, true, 1, 1, S<1, 16, 1, 4>, 8, S<32, 2>, 4, 1>,
DeviceGemmReduce_Xdl_CShuffle< Row, Col, Row, F16, F16, F16, F32, F32, F32, F32, PassThrough, PassThrough, PassThrough, ReduceSum, ReduceSquareSum, GemmDefault, 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, true, S<4, 16, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, true, 1, 1, S<1, 16, 1, 4>, 8, S<32, 2>, 4, 1> DeviceGemmReduce_Xdl_CShuffle< Row, Col, Row, F16, F16, F16, F32, F32, F32, F32, PassThrough, PassThrough, PassThrough, ReduceSum, ReduceSum, Square, GemmDefault, 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, true, S<4, 16, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, true, 1, 1, S<1, 16, 1, 4>, 8, S<32, 2>, 4, 1>
// clang-format on // clang-format on
>; >;
void add_device_gemm_reduce_xdl_cshuffle_f16_f16_f16_f32_f32_mk_nk_mn_instances( void add_device_gemm_reduce_xdl_cshuffle_f16_f16_f16_f32_f32_mk_nk_mn_instances(
std::vector< std::vector<DeviceGemmReducePtr<PassThrough, PassThrough, PassThrough, Square>>& instances)
DeviceGemmReducePtr<PassThrough, PassThrough, PassThrough, ReduceSum, ReduceSquareSum>>&
instances)
{ {
add_device_operation_instances( add_device_operation_instances(
instances, device_gemm_reduce_xdl_cshuffle_f16_f16_f16_f32_f32_mk_nk_mn_instances{}); instances, device_gemm_reduce_xdl_cshuffle_f16_f16_f16_f32_f32_mk_nk_mn_instances{});
......
profiler/include/profile_batched_gemm_reduce_impl.hpp
View file @ d0b9a467
...@@ -8,7 +8,7 @@ ...@@ -8,7 +8,7 @@
#include "tensor_layout.hpp" #include "tensor_layout.hpp"
#include "device_tensor.hpp" #include "device_tensor.hpp"
#include "element_wise_operation.hpp" #include "element_wise_operation.hpp"
#include "element_wise_reduce_operation.hpp" #include "reduction_operator.hpp"
#include "device_gemm_reduce.hpp" #include "device_gemm_reduce.hpp"
#include "reference_batched_gemm.hpp" #include "reference_batched_gemm.hpp"
...@@ -21,8 +21,7 @@ using DeviceGemmReduceNoOpPtr = ck::tensor_operation::device::DeviceGemmReducePt ...@@ -21,8 +21,7 @@ using DeviceGemmReduceNoOpPtr = ck::tensor_operation::device::DeviceGemmReducePt
ck::tensor_operation::element_wise::PassThrough, ck::tensor_operation::element_wise::PassThrough,
ck::tensor_operation::element_wise::PassThrough, ck::tensor_operation::element_wise::PassThrough,
ck::tensor_operation::element_wise::PassThrough, ck::tensor_operation::element_wise::PassThrough,
ck::tensor_operation::element_wise::ReduceSum, ck::tensor_operation::element_wise::UnarySquare<float, float, false>>;
ck::tensor_operation::element_wise::ReduceSquareSum>;
void add_device_batched_gemm_reduce_xdl_cshuffle_f16_f16_f16_f32_f32_gmk_gkn_gmn_instances( void add_device_batched_gemm_reduce_xdl_cshuffle_f16_f16_f16_f32_f32_gmk_gkn_gmn_instances(
std::vector<DeviceGemmReduceNoOpPtr>&); std::vector<DeviceGemmReduceNoOpPtr>&);
...@@ -120,17 +119,19 @@ bool profile_batched_gemm_reduce_impl(int do_verification, ...@@ -120,17 +119,19 @@ bool profile_batched_gemm_reduce_impl(int do_verification,
b_g_k_n.GenerateTensorValue(GeneratorTensor_3<BDataType>{-0.5, 0.5}, num_thread); b_g_k_n.GenerateTensorValue(GeneratorTensor_3<BDataType>{-0.5, 0.5}, num_thread);
} }
using AElementOp = ck::tensor_operation::element_wise::PassThrough; using AElementOp = ck::tensor_operation::element_wise::PassThrough;
using BElementOp = ck::tensor_operation::element_wise::PassThrough; using BElementOp = ck::tensor_operation::element_wise::PassThrough;
using CElementOp = ck::tensor_operation::element_wise::PassThrough; using CElementOp = ck::tensor_operation::element_wise::PassThrough;
using D0ReduceOp = ck::tensor_operation::element_wise::ReduceSum; using D0ReduceOp = ck::reduce::Add<float>;
using D1ReduceOp = ck::tensor_operation::element_wise::ReduceSquareSum; using D1ReduceOp = ck::reduce::Add<float>;
using D1ElementOp = ck::tensor_operation::element_wise::UnarySquare<float, float, false>;
const auto a_element_op = AElementOp{}; const auto a_element_op = AElementOp{};
const auto b_element_op = BElementOp{}; const auto b_element_op = BElementOp{};
const auto c_element_op = CElementOp{}; const auto c_element_op = CElementOp{};
const auto d0_reduce_op = D0ReduceOp{}; const auto d0_reduce_op = D0ReduceOp{};
const auto d1_reduce_op = D1ReduceOp{}; const auto d1_reduce_op = D1ReduceOp{};
const auto d1_element_op = D1ElementOp{};
if(do_verification) if(do_verification)
{ {
...@@ -154,17 +155,21 @@ bool profile_batched_gemm_reduce_impl(int do_verification, ...@@ -154,17 +155,21 @@ bool profile_batched_gemm_reduce_impl(int do_verification,
{ {
for(int m = 0; m < M; ++m) for(int m = 0; m < M; ++m)
{ {
float d0_acc = d0_reduce_op.GetReduceZeroValue(); float d0_acc = d0_reduce_op.GetReductionZeroVal();
float d1_acc = d1_reduce_op.GetReduceZeroValue(); float d1_acc = d1_reduce_op.GetReductionZeroVal();
for(int n = 0; n < N; ++n) for(int n = 0; n < N; ++n)
{ {
d0_reduce_op.Reduce(d0_acc, c_g_m_n_host_result(batch, m, n)); float d0_val = ck::type_convert<float>(c_g_m_n_host_result(batch, m, n));
d1_reduce_op.Reduce(d1_acc, c_g_m_n_host_result(batch, m, n)); float d1_val;
d1_element_op(d1_val, d0_val);
d0_reduce_op(d0_acc, d0_val);
d1_reduce_op(d1_acc, d1_val);
} }
d0_g_m_host_result(batch, m) = d0_acc; d0_g_m_host_result(batch, m) = ck::type_convert<DDataType>(d0_acc);
d1_g_m_host_result(batch, m) = d1_acc; d1_g_m_host_result(batch, m) = ck::type_convert<DDataType>(d1_acc);
} }
} }
} }
...@@ -247,8 +252,7 @@ bool profile_batched_gemm_reduce_impl(int do_verification, ...@@ -247,8 +252,7 @@ bool profile_batched_gemm_reduce_impl(int do_verification,
a_element_op, a_element_op,
b_element_op, b_element_op,
c_element_op, c_element_op,
d0_reduce_op, d1_element_op,
d1_reduce_op,
BatchCount); BatchCount);
auto invoker_ptr = gemm_ptr->MakeInvokerPointer(); auto invoker_ptr = gemm_ptr->MakeInvokerPointer();
......
profiler/include/profile_gemm_reduce_impl.hpp
View file @ d0b9a467
...@@ -7,7 +7,7 @@ ...@@ -7,7 +7,7 @@
#include "tensor_layout.hpp" #include "tensor_layout.hpp"
#include "device_tensor.hpp" #include "device_tensor.hpp"
#include "element_wise_operation.hpp" #include "element_wise_operation.hpp"
#include "element_wise_reduce_operation.hpp" #include "reduction_operator.hpp"
#include "device_gemm_reduce.hpp" #include "device_gemm_reduce.hpp"
#include "reference_gemm.hpp" #include "reference_gemm.hpp"
...@@ -20,8 +20,7 @@ using DeviceGemmReduceNoOpPtr = ck::tensor_operation::device::DeviceGemmReducePt ...@@ -20,8 +20,7 @@ using DeviceGemmReduceNoOpPtr = ck::tensor_operation::device::DeviceGemmReducePt
ck::tensor_operation::element_wise::PassThrough, ck::tensor_operation::element_wise::PassThrough,
ck::tensor_operation::element_wise::PassThrough, ck::tensor_operation::element_wise::PassThrough,
ck::tensor_operation::element_wise::PassThrough, ck::tensor_operation::element_wise::PassThrough,
ck::tensor_operation::element_wise::ReduceSum, ck::tensor_operation::element_wise::UnarySquare<float, float, false>>;
ck::tensor_operation::element_wise::ReduceSquareSum>;
void add_device_gemm_reduce_xdl_cshuffle_f16_f16_f16_f32_f32_mk_kn_mn_instances( void add_device_gemm_reduce_xdl_cshuffle_f16_f16_f16_f32_f32_mk_kn_mn_instances(
std::vector<DeviceGemmReduceNoOpPtr>&); std::vector<DeviceGemmReduceNoOpPtr>&);
...@@ -113,17 +112,19 @@ bool profile_gemm_reduce_impl(int do_verification, ...@@ -113,17 +112,19 @@ bool profile_gemm_reduce_impl(int do_verification,
b_k_n.GenerateTensorValue(GeneratorTensor_3<BDataType>{-0.5, 0.5}, num_thread); b_k_n.GenerateTensorValue(GeneratorTensor_3<BDataType>{-0.5, 0.5}, num_thread);
} }
using AElementOp = ck::tensor_operation::element_wise::PassThrough; using AElementOp = ck::tensor_operation::element_wise::PassThrough;
using BElementOp = ck::tensor_operation::element_wise::PassThrough; using BElementOp = ck::tensor_operation::element_wise::PassThrough;
using CElementOp = ck::tensor_operation::element_wise::PassThrough; using CElementOp = ck::tensor_operation::element_wise::PassThrough;
using D0ReduceOp = ck::tensor_operation::element_wise::ReduceSum; using D0ReduceOp = ck::reduce::Add<float>;
using D1ReduceOp = ck::tensor_operation::element_wise::ReduceSquareSum; using D1ReduceOp = ck::reduce::Add<float>;
using D1ElementOp = ck::tensor_operation::element_wise::UnarySquare<float, float, false>;
const auto a_element_op = AElementOp{}; const auto a_element_op = AElementOp{};
const auto b_element_op = BElementOp{}; const auto b_element_op = BElementOp{};
const auto c_element_op = CElementOp{}; const auto c_element_op = CElementOp{};
const auto d0_reduce_op = D0ReduceOp{}; const auto d0_reduce_op = D0ReduceOp{};
const auto d1_reduce_op = D1ReduceOp{}; const auto d1_reduce_op = D1ReduceOp{};
const auto d1_element_op = D1ElementOp{};
if(do_verification) if(do_verification)
{ {
...@@ -140,17 +141,21 @@ bool profile_gemm_reduce_impl(int do_verification, ...@@ -140,17 +141,21 @@ bool profile_gemm_reduce_impl(int do_verification,
for(int m = 0; m < M; ++m) for(int m = 0; m < M; ++m)
{ {
float d0_acc = d0_reduce_op.GetReduceZeroValue(); float d0_acc = d0_reduce_op.GetReductionZeroVal();
float d1_acc = d1_reduce_op.GetReduceZeroValue(); float d1_acc = d1_reduce_op.GetReductionZeroVal();
for(int n = 0; n < N; ++n) for(int n = 0; n < N; ++n)
{ {
d0_reduce_op.Reduce(d0_acc, c_m_n_host_result(m, n)); float d0_val = ck::type_convert<float>(c_m_n_host_result(m, n));
d1_reduce_op.Reduce(d1_acc, c_m_n_host_result(m, n)); float d1_val;
d1_element_op(d1_val, d0_val);
d0_reduce_op(d0_acc, d0_val);
d1_reduce_op(d1_acc, d1_val);
} }
d0_m_host_result(m) = d0_acc; d0_m_host_result(m) = ck::type_convert<DDataType>(d0_acc);
d1_m_host_result(m) = d1_acc; d1_m_host_result(m) = ck::type_convert<DDataType>(d1_acc);
} }
} }
...@@ -232,8 +237,7 @@ bool profile_gemm_reduce_impl(int do_verification, ...@@ -232,8 +237,7 @@ bool profile_gemm_reduce_impl(int do_verification,
a_element_op, a_element_op,
b_element_op, b_element_op,
c_element_op, c_element_op,
d0_reduce_op, d1_element_op);
d1_reduce_op);
auto invoker_ptr = gemm_ptr->MakeInvokerPointer(); auto invoker_ptr = gemm_ptr->MakeInvokerPointer();
......
script/clang-format-overwrite.sh
View file @ d0b9a467
find . -name deps -prune -o -name build -prune -o -iname '*.h' -o -iname '*.hpp' -o -iname '*.cpp' -o -iname '*.h.in' -o -iname '*.hpp.in' -o -iname '*.cpp.in' -o -iname '*.cl' -o -iname '*.cuh' -o -iname '*.cu' | xargs -n 1 -P 16 -I{} -t sh -c 'clang-format-10 -i -style=file {}' #find . -name deps -prune -o -name build -prune -o -iname '*.h' -o -iname '*.hpp' -o -iname '*.cpp' -o -iname '*.h.in' -o -iname '*.hpp.in' -o -iname '*.cpp.in' -o -iname '*.cl' -o -iname '*.cuh' -o -iname '*.cu' | xargs -n 1 -P 16 -I{} -t sh -c 'clang-format-10 -i -style=file {}'
git status --porcelain | awk '$1 != "D" && (match($2, "\\.cpp|hpp")) {print $2}' | xargs -n 1 -P 16 -I{} -t sh -c 'clang-format-10 -i -style=file {}'
test/CMakeLists.txt
View file @ d0b9a467
...@@ -24,6 +24,7 @@ include_directories(BEFORE ...@@ -24,6 +24,7 @@ include_directories(BEFORE
add_custom_target(check COMMAND ${CMAKE_CTEST_COMMAND} --output-on-failure -C ${CMAKE_CFG_INTDIR}) add_custom_target(check COMMAND ${CMAKE_CTEST_COMMAND} --output-on-failure -C ${CMAKE_CFG_INTDIR})
add_custom_target(tests) add_custom_target(tests)
function(add_test_executable TEST_NAME) function(add_test_executable TEST_NAME)
message("adding test ${TEST_NAME}") message("adding test ${TEST_NAME}")
add_executable(${TEST_NAME} ${ARGN}) add_executable(${TEST_NAME} ${ARGN})
...@@ -32,6 +33,20 @@ function(add_test_executable TEST_NAME) ...@@ -32,6 +33,20 @@ function(add_test_executable TEST_NAME)
add_dependencies(check ${TEST_NAME}) add_dependencies(check ${TEST_NAME})
endfunction(add_test_executable TEST_NAME) endfunction(add_test_executable TEST_NAME)
include(GoogleTest)
function(add_gtest_executable TEST_NAME)
message("adding gtest ${TEST_NAME}")
add_executable(${TEST_NAME} ${ARGN})
add_dependencies(tests ${TEST_NAME})
add_dependencies(check ${TEST_NAME})
# suppress gtest warnings
target_compile_options(${TEST_NAME} PRIVATE -Wno-global-constructors)
target_link_libraries(${TEST_NAME} PRIVATE gtest_main)
gtest_discover_tests(${TEST_NAME})
endfunction(add_gtest_executable TEST_NAME)
add_subdirectory(magic_number_division) add_subdirectory(magic_number_division)
add_subdirectory(space_filling_curve) add_subdirectory(space_filling_curve)
add_subdirectory(conv_util) add_subdirectory(conv_util)
......
test/conv_util/CMakeLists.txt
View file @ d0b9a467
add_test_executable(test_conv_util conv_util.cpp) add_gtest_executable(test_conv_util conv_util.cpp)
target_link_libraries(test_conv_util PRIVATE host_tensor conv_fwd_util) target_link_libraries(test_conv_util PRIVATE host_tensor conv_fwd_util)
test/conv_util/conv_util.cpp
View file @ d0b9a467
#include <iostream> #include <iostream>
#include <string> #include <string>
#include <vector> #include <vector>
#include "gtest/gtest.h"
#include "config.hpp" #include "config.hpp"
#include "conv_fwd_util.hpp" #include "conv_fwd_util.hpp"
...@@ -9,196 +10,194 @@ ...@@ -9,196 +10,194 @@
namespace { namespace {
bool test_conv_params_get_output_spatial_lengths() class TestConvUtil : public ::testing::Test
{ {
bool res{true}; public:
// -------------------------- default 2D ------------------------------------ void SetNDParams(std::size_t ndims)
{
conv_params.num_dim_spatial = ndims;
conv_params.filter_spatial_lengths = std::vector<ck::index_t>(ndims, 3);
conv_params.input_spatial_lengths = std::vector<ck::index_t>(ndims, 71);
conv_params.conv_filter_strides = std::vector<ck::index_t>(ndims, 2);
conv_params.conv_filter_dilations = std::vector<ck::index_t>(ndims, 1);
conv_params.input_left_pads = std::vector<ck::index_t>(ndims, 1);
conv_params.input_right_pads = std::vector<ck::index_t>(ndims, 1);
}
protected:
// ------- default 2D -------
// input NCHW {128,192,71,71}, // input NCHW {128,192,71,71},
// weights KCYX {256,192,3,3}, // weights KCYX {256,192,3,3},
// stride {2,2}, // stride {2,2},
// dilations {1,1}, // dilations {1,1},
// padding {{1,1}, {1,1}} // padding {{1,1}, {1,1}}
ck::utils::conv::ConvParams conv_params; ck::utils::conv::ConvParams conv_params;
};
} // namespace
TEST_F(TestConvUtil, ConvParamsGetOutputSpatialLengths2D)
{
ck::utils::conv::ConvParams conv_params;
std::vector<ck::index_t> out_spatial_len = conv_params.GetOutputSpatialLengths(); std::vector<ck::index_t> out_spatial_len = conv_params.GetOutputSpatialLengths();
res = ck::utils::check_err(out_spatial_len, EXPECT_TRUE(ck::utils::check_err(out_spatial_len,
std::vector<ck::index_t>{36, 36}, std::vector<ck::index_t>{36, 36},
"Error: ConvParams 2D default constructor."); "Error: ConvParams 2D default constructor."));
conv_params.conv_filter_strides = std::vector<ck::index_t>{1, 1}; conv_params.conv_filter_strides = std::vector<ck::index_t>{1, 1};
out_spatial_len = conv_params.GetOutputSpatialLengths(); out_spatial_len = conv_params.GetOutputSpatialLengths();
res = ck::utils::check_err( EXPECT_TRUE(ck::utils::check_err(
out_spatial_len, std::vector<ck::index_t>{71, 71}, "Error: ConvParams 2D stride {1,1}."); out_spatial_len, std::vector<ck::index_t>{71, 71}, "Error: ConvParams 2D stride {1,1}."));
conv_params.conv_filter_strides = std::vector<ck::index_t>{2, 2}; conv_params.conv_filter_strides = std::vector<ck::index_t>{2, 2};
conv_params.input_left_pads = std::vector<ck::index_t>{2, 2}; conv_params.input_left_pads = std::vector<ck::index_t>{2, 2};
conv_params.input_right_pads = std::vector<ck::index_t>{2, 2}; conv_params.input_right_pads = std::vector<ck::index_t>{2, 2};
out_spatial_len = conv_params.GetOutputSpatialLengths(); out_spatial_len = conv_params.GetOutputSpatialLengths();
res = ck::utils::check_err(out_spatial_len, EXPECT_TRUE(ck::utils::check_err(out_spatial_len,
std::vector<ck::index_t>{37, 37}, std::vector<ck::index_t>{37, 37},
"Error: ConvParams 2D padding left/right {2,2}."); "Error: ConvParams 2D padding left/right {2,2}."));
conv_params.conv_filter_dilations = std::vector<ck::index_t>{2, 2}; conv_params.conv_filter_dilations = std::vector<ck::index_t>{2, 2};
out_spatial_len = conv_params.GetOutputSpatialLengths(); out_spatial_len = conv_params.GetOutputSpatialLengths();
res = ck::utils::check_err( EXPECT_TRUE(ck::utils::check_err(
out_spatial_len, std::vector<ck::index_t>{36, 36}, "Error: ConvParams 2D dilation {2,2}."); out_spatial_len, std::vector<ck::index_t>{36, 36}, "Error: ConvParams 2D dilation {2,2}."));
conv_params.conv_filter_strides = std::vector<ck::index_t>{3, 3}; conv_params.conv_filter_strides = std::vector<ck::index_t>{3, 3};
conv_params.input_left_pads = std::vector<ck::index_t>{1, 1}; conv_params.input_left_pads = std::vector<ck::index_t>{1, 1};
conv_params.input_right_pads = std::vector<ck::index_t>{1, 1}; conv_params.input_right_pads = std::vector<ck::index_t>{1, 1};
conv_params.conv_filter_dilations = std::vector<ck::index_t>{2, 2}; conv_params.conv_filter_dilations = std::vector<ck::index_t>{2, 2};
out_spatial_len = conv_params.GetOutputSpatialLengths(); out_spatial_len = conv_params.GetOutputSpatialLengths();
res = EXPECT_TRUE(
ck::utils::check_err(out_spatial_len, ck::utils::check_err(out_spatial_len,
std::vector<ck::index_t>{23, 23}, std::vector<ck::index_t>{23, 23},
"Error: ConvParams 2D strides{3,3}, padding {1,1}, dilations {2,2}."); "Error: ConvParams 2D strides{3,3}, padding {1,1}, dilations {2,2}."));
}
// -------------------------- 1D ------------------------------------ TEST_F(TestConvUtil, ConvParamsGetOutputSpatialLengths1D)
conv_params.num_dim_spatial = 1; {
conv_params.filter_spatial_lengths = std::vector<ck::index_t>{3}; SetNDParams(1);
conv_params.input_spatial_lengths = std::vector<ck::index_t>{71};
conv_params.conv_filter_strides = std::vector<ck::index_t>{2};
conv_params.conv_filter_dilations = std::vector<ck::index_t>{1};
conv_params.input_left_pads = std::vector<ck::index_t>{1};
conv_params.input_right_pads = std::vector<ck::index_t>{1};
out_spatial_len = conv_params.GetOutputSpatialLengths(); std::vector<ck::index_t> out_spatial_len = conv_params.GetOutputSpatialLengths();
res = ck::utils::check_err( EXPECT_TRUE(ck::utils::check_err(
out_spatial_len, std::vector<ck::index_t>{36}, "Error: ConvParams 1D."); out_spatial_len, std::vector<ck::index_t>{36}, "Error: ConvParams 1D."));
conv_params.conv_filter_strides = std::vector<ck::index_t>{1}; conv_params.conv_filter_strides = std::vector<ck::index_t>{1};
out_spatial_len = conv_params.GetOutputSpatialLengths(); out_spatial_len = conv_params.GetOutputSpatialLengths();
res = ck::utils::check_err( EXPECT_TRUE(ck::utils::check_err(
out_spatial_len, std::vector<ck::index_t>{71}, "Error: ConvParams 1D stride {1}."); out_spatial_len, std::vector<ck::index_t>{71}, "Error: ConvParams 1D stride {1}."));
conv_params.conv_filter_strides = std::vector<ck::index_t>{2}; conv_params.conv_filter_strides = std::vector<ck::index_t>{2};
conv_params.input_left_pads = std::vector<ck::index_t>{2}; conv_params.input_left_pads = std::vector<ck::index_t>{2};
conv_params.input_right_pads = std::vector<ck::index_t>{2}; conv_params.input_right_pads = std::vector<ck::index_t>{2};
out_spatial_len = conv_params.GetOutputSpatialLengths(); out_spatial_len = conv_params.GetOutputSpatialLengths();
res = ck::utils::check_err(out_spatial_len, EXPECT_TRUE(ck::utils::check_err(out_spatial_len,
std::vector<ck::index_t>{37}, std::vector<ck::index_t>{37},
"Error: ConvParams 1D padding left/right {2}."); "Error: ConvParams 1D padding left/right {2}."));
conv_params.conv_filter_dilations = std::vector<ck::index_t>{2}; conv_params.conv_filter_dilations = std::vector<ck::index_t>{2};
out_spatial_len = conv_params.GetOutputSpatialLengths(); out_spatial_len = conv_params.GetOutputSpatialLengths();
res = ck::utils::check_err( EXPECT_TRUE(ck::utils::check_err(
out_spatial_len, std::vector<ck::index_t>{36}, "Error: ConvParams 1D dilation {2}."); out_spatial_len, std::vector<ck::index_t>{36}, "Error: ConvParams 1D dilation {2}."));
conv_params.conv_filter_strides = std::vector<ck::index_t>{3}; conv_params.conv_filter_strides = std::vector<ck::index_t>{3};
conv_params.input_left_pads = std::vector<ck::index_t>{1}; conv_params.input_left_pads = std::vector<ck::index_t>{1};
conv_params.input_right_pads = std::vector<ck::index_t>{1}; conv_params.input_right_pads = std::vector<ck::index_t>{1};
conv_params.conv_filter_dilations = std::vector<ck::index_t>{2}; conv_params.conv_filter_dilations = std::vector<ck::index_t>{2};
out_spatial_len = conv_params.GetOutputSpatialLengths(); out_spatial_len = conv_params.GetOutputSpatialLengths();
res = ck::utils::check_err(out_spatial_len, EXPECT_TRUE(
std::vector<ck::index_t>{23}, ck::utils::check_err(out_spatial_len,
"Error: ConvParams 1D strides{3}, padding {1}, dilations {2}."); std::vector<ck::index_t>{23},
"Error: ConvParams 1D strides{3}, padding {1}, dilations {2}."));
// -------------------------- 3D ------------------------------------ }
conv_params.num_dim_spatial = 3;
conv_params.filter_spatial_lengths = std::vector<ck::index_t>{3, 3, 3}; TEST_F(TestConvUtil, ConvParamsGetOutputSpatialLengths3D)
conv_params.input_spatial_lengths = std::vector<ck::index_t>{71, 71, 71}; {
conv_params.conv_filter_strides = std::vector<ck::index_t>{2, 2, 2}; SetNDParams(3);
conv_params.conv_filter_dilations = std::vector<ck::index_t>{1, 1, 1};
conv_params.input_left_pads = std::vector<ck::index_t>{1, 1, 1}; std::vector<ck::index_t> out_spatial_len = conv_params.GetOutputSpatialLengths();
conv_params.input_right_pads = std::vector<ck::index_t>{1, 1, 1}; EXPECT_TRUE(ck::utils::check_err(
out_spatial_len, std::vector<ck::index_t>{36, 36, 36}, "Error: ConvParams 3D."));
out_spatial_len = conv_params.GetOutputSpatialLengths();
res = ck::utils::check_err(
out_spatial_len, std::vector<ck::index_t>{36, 36, 36}, "Error: ConvParams 3D.");
conv_params.conv_filter_strides = std::vector<ck::index_t>{1, 1, 1}; conv_params.conv_filter_strides = std::vector<ck::index_t>{1, 1, 1};
out_spatial_len = conv_params.GetOutputSpatialLengths(); out_spatial_len = conv_params.GetOutputSpatialLengths();
res = ck::utils::check_err(out_spatial_len, EXPECT_TRUE(ck::utils::check_err(out_spatial_len,
std::vector<ck::index_t>{71, 71, 71}, std::vector<ck::index_t>{71, 71, 71},
"Error: ConvParams 3D stride {1, 1, 1}."); "Error: ConvParams 3D stride {1, 1, 1}."));
conv_params.conv_filter_strides = std::vector<ck::index_t>{2, 2, 2}; conv_params.conv_filter_strides = std::vector<ck::index_t>{2, 2, 2};
conv_params.input_left_pads = std::vector<ck::index_t>{2, 2, 2}; conv_params.input_left_pads = std::vector<ck::index_t>{2, 2, 2};
conv_params.input_right_pads = std::vector<ck::index_t>{2, 2, 2}; conv_params.input_right_pads = std::vector<ck::index_t>{2, 2, 2};
out_spatial_len = conv_params.GetOutputSpatialLengths(); out_spatial_len = conv_params.GetOutputSpatialLengths();
res = ck::utils::check_err(out_spatial_len, EXPECT_TRUE(ck::utils::check_err(out_spatial_len,
std::vector<ck::index_t>{37, 37, 37}, std::vector<ck::index_t>{37, 37, 37},
"Error: ConvParams 3D padding left/right {2, 2, 2}."); "Error: ConvParams 3D padding left/right {2, 2, 2}."));
conv_params.conv_filter_dilations = std::vector<ck::index_t>{2, 2, 2}; conv_params.conv_filter_dilations = std::vector<ck::index_t>{2, 2, 2};
out_spatial_len = conv_params.GetOutputSpatialLengths(); out_spatial_len = conv_params.GetOutputSpatialLengths();
res = ck::utils::check_err(out_spatial_len, EXPECT_TRUE(ck::utils::check_err(out_spatial_len,
std::vector<ck::index_t>{36, 36, 36}, std::vector<ck::index_t>{36, 36, 36},
"Error: ConvParams 3D dilation {2, 2, 2}."); "Error: ConvParams 3D dilation {2, 2, 2}."));
conv_params.conv_filter_strides = std::vector<ck::index_t>{3, 3, 3}; conv_params.conv_filter_strides = std::vector<ck::index_t>{3, 3, 3};
conv_params.input_left_pads = std::vector<ck::index_t>{1, 1, 1}; conv_params.input_left_pads = std::vector<ck::index_t>{1, 1, 1};
conv_params.input_right_pads = std::vector<ck::index_t>{1, 1, 1}; conv_params.input_right_pads = std::vector<ck::index_t>{1, 1, 1};
conv_params.conv_filter_dilations = std::vector<ck::index_t>{2, 2, 2}; conv_params.conv_filter_dilations = std::vector<ck::index_t>{2, 2, 2};
out_spatial_len = conv_params.GetOutputSpatialLengths(); out_spatial_len = conv_params.GetOutputSpatialLengths();
res = ck::utils::check_err( EXPECT_TRUE(ck::utils::check_err(
out_spatial_len, out_spatial_len,
std::vector<ck::index_t>{23, 23, 23}, std::vector<ck::index_t>{23, 23, 23},
"Error: ConvParams 3D strides{3, 3, 3}, padding {1, 1, 1}, dilations {2, 2, 2}."); "Error: ConvParams 3D strides{3, 3, 3}, padding {1, 1, 1}, dilations {2, 2, 2}."));
return res;
} }
bool test_get_host_tensor_descriptor() TEST(ConvUtil, GetHostTensorDescriptor)
{ {
bool res{true};
namespace tl = ck::tensor_layout::convolution; namespace tl = ck::tensor_layout::convolution;
std::vector<std::size_t> dims{2, 3, 4, 5}; std::vector<std::size_t> dims{2, 3, 4, 5};
HostTensorDescriptor h = ck::utils::conv::get_host_tensor_descriptor(dims, tl::NHWC{}); HostTensorDescriptor h = ck::utils::conv::get_host_tensor_descriptor(dims, tl::NHWC{});
res = EXPECT_TRUE(ck::utils::check_err(
ck::utils::check_err(h.GetLengths(), {2, 3, 4, 5}, "Error: wrong NHWC dimensions lengths!"); h.GetLengths(), {2, 3, 4, 5}, "Error: wrong NHWC dimensions lengths!"));
res = ck::utils::check_err( EXPECT_TRUE(ck::utils::check_err(
h.GetStrides(), {3 * 4 * 5, 1, 3 * 5, 3}, "Error: wrong NHWC dimensions strides!"); h.GetStrides(), {3 * 4 * 5, 1, 3 * 5, 3}, "Error: wrong NHWC dimensions strides!"));
h = ck::utils::conv::get_host_tensor_descriptor(dims, tl::NCHW{}); h = ck::utils::conv::get_host_tensor_descriptor(dims, tl::NCHW{});
res = EXPECT_TRUE(ck::utils::check_err(
ck::utils::check_err(h.GetLengths(), {2, 3, 4, 5}, "Error: wrong NCHW dimensions lengths!"); h.GetLengths(), {2, 3, 4, 5}, "Error: wrong NCHW dimensions lengths!"));
res = ck::utils::check_err( EXPECT_TRUE(ck::utils::check_err(
h.GetStrides(), {3 * 4 * 5, 4 * 5, 5, 1}, "Error: wrong NCHW dimensions strides!"); h.GetStrides(), {3 * 4 * 5, 4 * 5, 5, 1}, "Error: wrong NCHW dimensions strides!"));
dims = std::vector<std::size_t>{2, 3, 4}; dims = std::vector<std::size_t>{2, 3, 4};
h = ck::utils::conv::get_host_tensor_descriptor(dims, tl::NWC{}); h = ck::utils::conv::get_host_tensor_descriptor(dims, tl::NWC{});
res = ck::utils::check_err(h.GetLengths(), {2, 3, 4}, "Error: wrong NWC dimensions lengths!"); EXPECT_TRUE(
res = ck::utils::check_err(h.GetLengths(), {2, 3, 4}, "Error: wrong NWC dimensions lengths!"));
ck::utils::check_err(h.GetStrides(), {3 * 4, 1, 3}, "Error: wrong NWC dimensions strides!"); EXPECT_TRUE(ck::utils::check_err(
h.GetStrides(), {3 * 4, 1, 3}, "Error: wrong NWC dimensions strides!"));
h = ck::utils::conv::get_host_tensor_descriptor(dims, tl::NCW{}); h = ck::utils::conv::get_host_tensor_descriptor(dims, tl::NCW{});
res = ck::utils::check_err(h.GetLengths(), {2, 3, 4}, "Error: wrong NCW dimensions lengths!"); EXPECT_TRUE(
res = ck::utils::check_err(h.GetLengths(), {2, 3, 4}, "Error: wrong NCW dimensions lengths!"));
ck::utils::check_err(h.GetStrides(), {3 * 4, 4, 1}, "Error: wrong NCW dimensions strides!"); EXPECT_TRUE(ck::utils::check_err(
h.GetStrides(), {3 * 4, 4, 1}, "Error: wrong NCW dimensions strides!"));
dims = std::vector<std::size_t>{2, 3, 4, 5, 6}; dims = std::vector<std::size_t>{2, 3, 4, 5, 6};
h = ck::utils::conv::get_host_tensor_descriptor(dims, tl::NDHWC{}); h = ck::utils::conv::get_host_tensor_descriptor(dims, tl::NDHWC{});
res = ck::utils::check_err(h.GetLengths(), dims, "Error: wrong NDHWC dimensions lengths!"); EXPECT_TRUE(
res = ck::utils::check_err(h.GetStrides(), ck::utils::check_err(h.GetLengths(), dims, "Error: wrong NDHWC dimensions lengths!"));
{3 * 4 * 5 * 6, // N EXPECT_TRUE(ck::utils::check_err(h.GetStrides(),
1, // C {3 * 4 * 5 * 6, // N
3 * 5 * 6, // D 1, // C
3 * 6, // H 3 * 5 * 6, // D
3}, // W 3 * 6, // H
"Error: wrong NDHWC dimensions strides!"); 3}, // W
"Error: wrong NDHWC dimensions strides!"));
h = ck::utils::conv::get_host_tensor_descriptor(dims, tl::NCDHW{});
res = ck::utils::check_err(h.GetLengths(), dims, "Error: wrong NCDHW dimensions lengths!"); h = ck::utils::conv::get_host_tensor_descriptor(dims, tl::NCDHW{});
res = ck::utils::check_err(h.GetStrides(), EXPECT_TRUE(
{3 * 4 * 5 * 6, // N ck::utils::check_err(h.GetLengths(), dims, "Error: wrong NCDHW dimensions lengths!"));
4 * 5 * 6, // C EXPECT_TRUE(ck::utils::check_err(h.GetStrides(),
5 * 6, // D {3 * 4 * 5 * 6, // N
6, // H 4 * 5 * 6, // C
1}, // W 5 * 6, // D
"Error: wrong NCDHW dimensions strides!"); 6, // H
1}, // W
return res; "Error: wrong NCDHW dimensions strides!"));
}
} // namespace
int main(void)
{
bool res = test_conv_params_get_output_spatial_lengths();
std::cout << "test_conv_params_get_output_spatial_lengths ..... "
<< (res ? "SUCCESS" : "FAILURE") << std::endl;
res = test_get_host_tensor_descriptor();
std::cout << "test_get_host_tensor_descriptor ..... " << (res ? "SUCCESS" : "FAILURE")
<< std::endl;
return res ? 0 : 1;
} }
test/convnd_fwd/CMakeLists.txt
View file @ d0b9a467
add_custom_target(test_convnd_fwd) add_custom_target(test_convnd_fwd)
add_test_executable(test_conv1d_fwd conv1d_fwd.cpp) add_gtest_executable(test_conv1d_fwd conv1d_fwd.cpp)
target_link_libraries(test_conv1d_fwd PRIVATE host_tensor device_conv1d_fwd_instance conv_fwd_util) target_link_libraries(test_conv1d_fwd PRIVATE host_tensor device_conv1d_fwd_instance conv_fwd_util)
target_link_libraries(test_conv1d_fwd PRIVATE )
add_dependencies(test_convnd_fwd test_conv1d_fwd) add_dependencies(test_convnd_fwd test_conv1d_fwd)
add_test_executable(test_conv2d_fwd conv2d_fwd.cpp) add_gtest_executable(test_conv2d_fwd conv2d_fwd.cpp)
target_link_libraries(test_conv2d_fwd PRIVATE host_tensor device_conv2d_fwd_instance conv_fwd_util) target_link_libraries(test_conv2d_fwd PRIVATE host_tensor device_conv2d_fwd_instance conv_fwd_util)
add_dependencies(test_convnd_fwd test_conv2d_fwd) add_dependencies(test_convnd_fwd test_conv2d_fwd)
add_test_executable(test_conv3d_fwd conv3d_fwd.cpp) add_gtest_executable(test_conv3d_fwd conv3d_fwd.cpp)
target_link_libraries(test_conv3d_fwd PRIVATE host_tensor device_conv3d_fwd_instance conv_fwd_util) target_link_libraries(test_conv3d_fwd PRIVATE host_tensor device_conv3d_fwd_instance conv_fwd_util)
add_dependencies(test_convnd_fwd test_conv3d_fwd) add_dependencies(test_convnd_fwd test_conv3d_fwd)
test/convnd_fwd/conv1d_fwd.cpp
View file @ d0b9a467
...@@ -2,6 +2,7 @@ ...@@ -2,6 +2,7 @@
#include <stdexcept> #include <stdexcept>
#include <tuple> #include <tuple>
#include <vector> #include <vector>
#include "gtest/gtest.h"
#include "data_type.hpp" #include "data_type.hpp"
#include "element_wise_operation.hpp" #include "element_wise_operation.hpp"
...@@ -10,7 +11,8 @@ ...@@ -10,7 +11,8 @@
namespace { namespace {
bool test_conv1D_nwc() template <typename T>
bool test_conv1d_nwc_instances(const std::vector<test::conv::DeviceConvFwdNoOpPtr>& conv_ptrs)
{ {
using namespace std::placeholders; using namespace std::placeholders;
using namespace ck::utils; using namespace ck::utils;
...@@ -18,31 +20,24 @@ bool test_conv1D_nwc() ...@@ -18,31 +20,24 @@ bool test_conv1D_nwc()
ck::utils::conv::ConvParams params; ck::utils::conv::ConvParams params;
params.num_dim_spatial = 1; params.num_dim_spatial = 1;
params.N = 2;
params.K = 16;
params.C = 4;
params.filter_spatial_lengths = std::vector<ck::index_t>{3}; params.filter_spatial_lengths = std::vector<ck::index_t>{3};
params.input_spatial_lengths = std::vector<ck::index_t>{16}; params.input_spatial_lengths = std::vector<ck::index_t>{71};
params.conv_filter_strides = std::vector<ck::index_t>{1}; params.conv_filter_strides = std::vector<ck::index_t>{2};
params.conv_filter_dilations = std::vector<ck::index_t>{1}; params.conv_filter_dilations = std::vector<ck::index_t>{1};
params.input_left_pads = std::vector<ck::index_t>{1}; params.input_left_pads = std::vector<ck::index_t>{1};
params.input_right_pads = std::vector<ck::index_t>{1}; params.input_right_pads = std::vector<ck::index_t>{1};
std::vector<test::conv::DeviceConvFwdNoOpPtr> conv_ptrs; conv::ConvFwdOpInstance<T, T, T, ctl::NWC, ctl::KCX, ctl::NWK> conv_instance(params);
test::conv::get_test_convolution_fwd_instance<1>(conv_ptrs);
conv::ConvFwdOpInstance<float, float, float, ctl::NWC, ctl::KCX, ctl::NWK> conv_instance(
params);
auto reference_conv_fwd_fun = std::bind( auto reference_conv_fwd_fun =
conv::run_reference_convolution_forward<1, float, float, float>, params, _1, _2, _3); std::bind(conv::run_reference_convolution_forward<1, T, T, T>, params, _1, _2, _3);
OpInstanceRunEngine<float, float, float> run_engine(conv_instance, reference_conv_fwd_fun); OpInstanceRunEngine<T, T, T> run_engine(conv_instance, reference_conv_fwd_fun);
run_engine.SetAtol(1e-5);
run_engine.SetRtol(1e-4);
return run_engine.Test(conv_ptrs); return run_engine.Test(conv_ptrs);
} }
template <typename T> } // anonymous namespace
bool test_conv1d_nwc_instances(const std::vector<test::conv::DeviceConvFwdNoOpPtr>& conv_ptrs)
TEST(Conv1DFwdNWC, TestConv1D)
{ {
using namespace std::placeholders; using namespace std::placeholders;
using namespace ck::utils; using namespace ck::utils;
...@@ -50,65 +45,49 @@ bool test_conv1d_nwc_instances(const std::vector<test::conv::DeviceConvFwdNoOpPt ...@@ -50,65 +45,49 @@ bool test_conv1d_nwc_instances(const std::vector<test::conv::DeviceConvFwdNoOpPt
ck::utils::conv::ConvParams params; ck::utils::conv::ConvParams params;
params.num_dim_spatial = 1; params.num_dim_spatial = 1;
params.N = 2;
params.K = 16;
params.C = 4;
params.filter_spatial_lengths = std::vector<ck::index_t>{3}; params.filter_spatial_lengths = std::vector<ck::index_t>{3};
params.input_spatial_lengths = std::vector<ck::index_t>{71}; params.input_spatial_lengths = std::vector<ck::index_t>{16};
params.conv_filter_strides = std::vector<ck::index_t>{2}; params.conv_filter_strides = std::vector<ck::index_t>{1};
params.conv_filter_dilations = std::vector<ck::index_t>{1}; params.conv_filter_dilations = std::vector<ck::index_t>{1};
params.input_left_pads = std::vector<ck::index_t>{1}; params.input_left_pads = std::vector<ck::index_t>{1};
params.input_right_pads = std::vector<ck::index_t>{1}; params.input_right_pads = std::vector<ck::index_t>{1};
conv::ConvFwdOpInstance<T, T, T, ctl::NWC, ctl::KCX, ctl::NWK> conv_instance(params); std::vector<test::conv::DeviceConvFwdNoOpPtr> conv_ptrs;
test::conv::get_test_convolution_fwd_instance<1>(conv_ptrs);
auto reference_conv_fwd_fun = conv::ConvFwdOpInstance<float, float, float, ctl::NWC, ctl::KCX, ctl::NWK> conv_instance(
std::bind(conv::run_reference_convolution_forward<1, T, T, T>, params, _1, _2, _3); params);
OpInstanceRunEngine<T, T, T> run_engine(conv_instance, reference_conv_fwd_fun);
return run_engine.Test(conv_ptrs);
}
bool test_conv1d_nwc_bf16_instances() auto reference_conv_fwd_fun = std::bind(
{ conv::run_reference_convolution_forward<1, float, float, float>, params, _1, _2, _3);
return test_conv1d_nwc_instances<ck::bhalf_t>( OpInstanceRunEngine<float, float, float> run_engine(conv_instance, reference_conv_fwd_fun);
ck::utils::conv::ConvolutionFwdInstances<ck::bhalf_t, ck::bhalf_t, ck::bhalf_t>::Get<1>()); run_engine.SetAtol(1e-5);
run_engine.SetRtol(1e-4);
EXPECT_TRUE(run_engine.Test(conv_ptrs));
} }
bool test_conv1d_nwc_f16_instances() TEST(Conv1DFwdNWC, Bf16Iinstances)
{ {
return test_conv1d_nwc_instances<ck::half_t>( EXPECT_TRUE(test_conv1d_nwc_instances<ck::bhalf_t>(
ck::utils::conv::ConvolutionFwdInstances<ck::half_t, ck::half_t, ck::half_t>::Get<1>()); ck::utils::conv::ConvolutionFwdInstances<ck::bhalf_t, ck::bhalf_t, ck::bhalf_t>::Get<1>()));
} }
bool test_conv1d_nwc_f32_instances() TEST(Conv1DFwdNWC, F16Instances)
{ {
return test_conv1d_nwc_instances<float>( EXPECT_TRUE(test_conv1d_nwc_instances<ck::half_t>(
ck::utils::conv::ConvolutionFwdInstances<float, float, float>::Get<1>()); ck::utils::conv::ConvolutionFwdInstances<ck::half_t, ck::half_t, ck::half_t>::Get<1>()));
} }
bool test_conv1d_nwc_int8_instances() TEST(Conv1DFwdNWC, F32Instances)
{ {
return test_conv1d_nwc_instances<int8_t>( EXPECT_TRUE(test_conv1d_nwc_instances<float>(
ck::utils::conv::ConvolutionFwdInstances<int8_t, int8_t, int8_t>::Get<1>()); ck::utils::conv::ConvolutionFwdInstances<float, float, float>::Get<1>()));
} }
} // anonymous namespace TEST(Conv1DFwdNWC, Int8Instances)
int main()
{ {
bool res{true}; EXPECT_TRUE(test_conv1d_nwc_instances<int8_t>(
res = test_conv1D_nwc(); ck::utils::conv::ConvolutionFwdInstances<int8_t, int8_t, int8_t>::Get<1>()));
std::cout << "test_conv1D_nwc ..... " << (res ? "SUCCESS" : "FAILURE") << std::endl;
res = test_conv1d_nwc_bf16_instances();
std::cout << "\nTestConv1DNWCBF16Instances ..... " << (res ? "SUCCESS" : "FAILURE")
<< std::endl;
res = test_conv1d_nwc_f16_instances();
std::cout << "\ntest_conv1d_nwc_f16_instances ..... " << (res ? "SUCCESS" : "FAILURE")
<< std::endl;
res = test_conv1d_nwc_f32_instances();
std::cout << "\ntest_conv1d_nwc_f32_instances ..... " << (res ? "SUCCESS" : "FAILURE")
<< std::endl;
res = test_conv1d_nwc_int8_instances();
std::cout << "\ntest_conv1d_nwc_int8_instances ..... " << (res ? "SUCCESS" : "FAILURE")
<< std::endl;
return res ? 0 : 1;
} }
test/convnd_fwd/conv2d_fwd.cpp
View file @ d0b9a467
...@@ -2,6 +2,7 @@ ...@@ -2,6 +2,7 @@
#include <iostream> #include <iostream>
#include <tuple> #include <tuple>
#include <vector> #include <vector>
#include "gtest/gtest.h"
#include "data_type.hpp" #include "data_type.hpp"
#include "element_wise_operation.hpp" #include "element_wise_operation.hpp"
...@@ -10,30 +11,6 @@ ...@@ -10,30 +11,6 @@
namespace { namespace {
bool test_conv2d_nhwc()
{
using namespace std::placeholders;
using namespace ck::utils;
ck::utils::conv::ConvParams params;
params.N = 2;
params.K = 16;
params.C = 4;
params.input_spatial_lengths = std::vector<ck::index_t>{16, 16};
params.conv_filter_strides = std::vector<ck::index_t>{1, 1};
std::vector<test::conv::DeviceConvFwdNoOpPtr> conv_ptrs;
test::conv::get_test_convolution_fwd_instance<2>(conv_ptrs);
conv::ConvFwdOpInstance<float, float, float> conv_instance(params);
auto reference_conv_fwd_fun = std::bind(
conv::run_reference_convolution_forward<2, float, float, float>, params, _1, _2, _3);
OpInstanceRunEngine<float, float, float> run_engine(conv_instance, reference_conv_fwd_fun);
run_engine.SetAtol(1e-5);
run_engine.SetRtol(1e-4);
return run_engine.Test(conv_ptrs);
}
template <typename T> template <typename T>
bool test_conv2d_nhwc_instances(const std::vector<test::conv::DeviceConvFwdNoOpPtr>& conv_ptrs) bool test_conv2d_nhwc_instances(const std::vector<test::conv::DeviceConvFwdNoOpPtr>& conv_ptrs)
{ {
...@@ -57,50 +34,58 @@ bool test_conv2d_nhwc_instances(const std::vector<test::conv::DeviceConvFwdNoOpP ...@@ -57,50 +34,58 @@ bool test_conv2d_nhwc_instances(const std::vector<test::conv::DeviceConvFwdNoOpP
return run_engine.Test(conv_ptrs); return run_engine.Test(conv_ptrs);
} }
bool test_conv2d_nhwc_bf16_instances() } // anonymous namespace
TEST(Conv2DFwdNHWC, TestConv2D)
{ {
return test_conv2d_nhwc_instances<ck::bhalf_t>( using namespace std::placeholders;
ck::utils::conv::ConvolutionFwdInstances<ck::bhalf_t, ck::bhalf_t, ck::bhalf_t>::Get<2>()); using namespace ck::utils;
ck::utils::conv::ConvParams params;
params.N = 2;
params.K = 16;
params.C = 4;
params.input_spatial_lengths = std::vector<ck::index_t>{16, 16};
params.conv_filter_strides = std::vector<ck::index_t>{1, 1};
std::vector<test::conv::DeviceConvFwdNoOpPtr> conv_ptrs;
test::conv::get_test_convolution_fwd_instance<2>(conv_ptrs);
conv::ConvFwdOpInstance<float, float, float> conv_instance(params);
auto reference_conv_fwd_fun = std::bind(
conv::run_reference_convolution_forward<2, float, float, float>, params, _1, _2, _3);
OpInstanceRunEngine<float, float, float> run_engine(conv_instance, reference_conv_fwd_fun);
run_engine.SetAtol(1e-5);
run_engine.SetRtol(1e-4);
EXPECT_TRUE(run_engine.Test(conv_ptrs));
} }
bool test_conv2d_nhwc_f16_instances() TEST(Conv2DFwdNHWC, Bf16Instances)
{ {
return test_conv2d_nhwc_instances<ck::half_t>( EXPECT_TRUE(test_conv2d_nhwc_instances<ck::bhalf_t>(
ck::utils::conv::ConvolutionFwdInstances<ck::half_t, ck::half_t, ck::half_t>::Get<2>()); ck::utils::conv::ConvolutionFwdInstances<ck::bhalf_t, ck::bhalf_t, ck::bhalf_t>::Get<2>()));
} }
bool test_conv2d_nhwc_f32_instances() TEST(Conv2DFwdNHWC, F16Instances)
{ {
return test_conv2d_nhwc_instances<float>( EXPECT_TRUE(test_conv2d_nhwc_instances<ck::half_t>(
ck::utils::conv::ConvolutionFwdInstances<float, float, float>::Get<2>()); ck::utils::conv::ConvolutionFwdInstances<ck::half_t, ck::half_t, ck::half_t>::Get<2>()));
} }
bool test_conv2d_nhwc_int8_instances() TEST(Conv2DFwdNHWC, BF32Instances)
{ {
return test_conv2d_nhwc_instances<int8_t>( EXPECT_TRUE(test_conv2d_nhwc_instances<float>(
ck::utils::conv::ConvolutionFwdInstances<int8_t, int8_t, int8_t>::Get<2>()); ck::utils::conv::ConvolutionFwdInstances<float, float, float>::Get<2>()));
} }
} // anonymous namespace TEST(Conv2DFwdNHWC, F32Instances)
{
EXPECT_TRUE(test_conv2d_nhwc_instances<float>(
ck::utils::conv::ConvolutionFwdInstances<float, float, float>::Get<2>()));
}
int main() TEST(Conv2DFwdNHWC, Int8Instances)
{ {
bool res{true}; EXPECT_TRUE(test_conv2d_nhwc_instances<int8_t>(
res = test_conv2d_nhwc(); ck::utils::conv::ConvolutionFwdInstances<int8_t, int8_t, int8_t>::Get<2>()));
std::cout << "test_conv2d_nhwc ..... " << (res ? "SUCCESS" : "FAILURE") << std::endl;
res = test_conv2d_nhwc_bf16_instances();
std::cout << "\ntest_conv2d_nhwc_bf16_instances ..... " << (res ? "SUCCESS" : "FAILURE")
<< std::endl;
res = test_conv2d_nhwc_f16_instances();
std::cout << "\ntest_conv2d_nhwc_f16_instances ....." << (res ? "SUCCESS" : "FAILURE")
<< std::endl;
res = test_conv2d_nhwc_f32_instances();
std::cout << "\ntest_conv2d_nhwc_f32_instances ..... " << (res ? "SUCCESS" : "FAILURE")
<< std::endl;
res = test_conv2d_nhwc_int8_instances();
std::cout << "\ntest_conv2d_nhwc_int8_instances ..... " << (res ? "SUCCESS" : "FAILURE")
<< std::endl;
return res ? 0 : 1;
} }
test/convnd_fwd/conv3d_fwd.cpp
View file @ d0b9a467
...@@ -3,6 +3,7 @@ ...@@ -3,6 +3,7 @@
#include <stdexcept> #include <stdexcept>
#include <tuple> #include <tuple>
#include <vector> #include <vector>
#include "gtest/gtest.h"
#include "data_type.hpp" #include "data_type.hpp"
#include "element_wise_operation.hpp" #include "element_wise_operation.hpp"
...@@ -11,7 +12,34 @@ ...@@ -11,7 +12,34 @@
namespace { namespace {
bool test_conv3d_ndhwc() template <typename T>
bool test_conv3d_ndhwc_instances(const std::vector<test::conv::DeviceConvFwdNoOpPtr>& conv_ptrs)
{
using namespace std::placeholders;
using namespace ck::utils;
namespace ctl = ck::tensor_layout::convolution;
conv::ConvParams params;
params.N = 64;
params.num_dim_spatial = 3;
params.filter_spatial_lengths = std::vector<ck::index_t>{3, 3, 2};
params.input_spatial_lengths = std::vector<ck::index_t>{32, 32, 2};
params.conv_filter_strides = std::vector<ck::index_t>{2, 2, 2};
params.conv_filter_dilations = std::vector<ck::index_t>{1, 1, 1};
params.input_left_pads = std::vector<ck::index_t>{1, 1, 1};
params.input_right_pads = std::vector<ck::index_t>{1, 1, 1};
conv::ConvFwdOpInstance<T, T, T, ctl::NDHWC, ctl::KZYXC, ctl::NDHWK> conv_instance(params);
auto reference_conv_fwd_fun =
std::bind(conv::run_reference_convolution_forward<3, T, T, T>, params, _1, _2, _3);
OpInstanceRunEngine<T, T, T> run_engine(conv_instance, reference_conv_fwd_fun);
return run_engine.Test(conv_ptrs);
}
} // anonymous namespace
TEST(Conv3DFwdNDHWC, TestConv3D)
{ {
using namespace std::placeholders; using namespace std::placeholders;
using namespace ck::utils; using namespace ck::utils;
...@@ -39,10 +67,10 @@ bool test_conv3d_ndhwc() ...@@ -39,10 +67,10 @@ bool test_conv3d_ndhwc()
OpInstanceRunEngine<float, float, float> run_engine(conv_instance, reference_conv_fwd_fun); OpInstanceRunEngine<float, float, float> run_engine(conv_instance, reference_conv_fwd_fun);
run_engine.SetAtol(1e-5); run_engine.SetAtol(1e-5);
run_engine.SetRtol(1e-4); run_engine.SetRtol(1e-4);
return run_engine.Test(conv_ptrs); EXPECT_TRUE(run_engine.Test(conv_ptrs));
} }
bool test_conv3d_ndhwc_2gb_input() TEST(Conv3DFwdNDHWC, InputOver2GB)
{ {
using PassThrough = ck::tensor_operation::element_wise::PassThrough; using PassThrough = ck::tensor_operation::element_wise::PassThrough;
using namespace ck::utils; using namespace ck::utils;
...@@ -79,10 +107,10 @@ bool test_conv3d_ndhwc_2gb_input() ...@@ -79,10 +107,10 @@ bool test_conv3d_ndhwc_2gb_input()
PassThrough{}, PassThrough{},
PassThrough{}, PassThrough{},
PassThrough{}); PassThrough{});
return !(conv_ptrs.back()->IsSupportedArgument(arg.get())); EXPECT_FALSE(conv_ptrs.back()->IsSupportedArgument(arg.get()));
} }
bool test_conv3d_ndhwc_2gb_filters() TEST(Conv3DFwdNDHWC, FiltersOver2GB)
{ {
using PassThrough = ck::tensor_operation::element_wise::PassThrough; using PassThrough = ck::tensor_operation::element_wise::PassThrough;
using namespace ck::utils; using namespace ck::utils;
...@@ -119,10 +147,10 @@ bool test_conv3d_ndhwc_2gb_filters() ...@@ -119,10 +147,10 @@ bool test_conv3d_ndhwc_2gb_filters()
PassThrough{}, PassThrough{},
PassThrough{}, PassThrough{},
PassThrough{}); PassThrough{});
return !(conv_ptrs.back()->IsSupportedArgument(arg.get())); EXPECT_FALSE(conv_ptrs.back()->IsSupportedArgument(arg.get()));
} }
bool test_conv3d_ndhwc_2gb_output() TEST(Conv3DFwdNDHWC, OutputOver2GB)
{ {
using PassThrough = ck::tensor_operation::element_wise::PassThrough; using PassThrough = ck::tensor_operation::element_wise::PassThrough;
using namespace ck::utils; using namespace ck::utils;
...@@ -158,88 +186,29 @@ bool test_conv3d_ndhwc_2gb_output() ...@@ -158,88 +186,29 @@ bool test_conv3d_ndhwc_2gb_output()
PassThrough{}, PassThrough{},
PassThrough{}, PassThrough{},
PassThrough{}); PassThrough{});
return !(conv_ptrs.back()->IsSupportedArgument(arg.get())); EXPECT_FALSE(conv_ptrs.back()->IsSupportedArgument(arg.get()));
}
template <typename T>
bool test_conv3d_ndhwc_instances(const std::vector<test::conv::DeviceConvFwdNoOpPtr>& conv_ptrs)
{
using namespace std::placeholders;
using namespace ck::utils;
namespace ctl = ck::tensor_layout::convolution;
conv::ConvParams params;
params.N = 64;
params.num_dim_spatial = 3;
params.filter_spatial_lengths = std::vector<ck::index_t>{3, 3, 2};
params.input_spatial_lengths = std::vector<ck::index_t>{32, 32, 2};
params.conv_filter_strides = std::vector<ck::index_t>{2, 2, 2};
params.conv_filter_dilations = std::vector<ck::index_t>{1, 1, 1};
params.input_left_pads = std::vector<ck::index_t>{1, 1, 1};
params.input_right_pads = std::vector<ck::index_t>{1, 1, 1};
conv::ConvFwdOpInstance<T, T, T, ctl::NDHWC, ctl::KZYXC, ctl::NDHWK> conv_instance(params);
auto reference_conv_fwd_fun =
std::bind(conv::run_reference_convolution_forward<3, T, T, T>, params, _1, _2, _3);
OpInstanceRunEngine<T, T, T> run_engine(conv_instance, reference_conv_fwd_fun);
return run_engine.Test(conv_ptrs);
} }
bool test_conv3d_ndhwc_bf16_instances() TEST(Conv3DFwdNDHWC, Bf16Instances)
{ {
return test_conv3d_ndhwc_instances<ck::bhalf_t>( EXPECT_TRUE(test_conv3d_ndhwc_instances<ck::bhalf_t>(
ck::utils::conv::ConvolutionFwdInstances<ck::bhalf_t, ck::bhalf_t, ck::bhalf_t>::Get<3>()); ck::utils::conv::ConvolutionFwdInstances<ck::bhalf_t, ck::bhalf_t, ck::bhalf_t>::Get<3>()));
} }
bool test_conv3d_ndhwc_f16_instances() TEST(Conv3DFwdNDHWC, F16Instances)
{ {
return test_conv3d_ndhwc_instances<ck::half_t>( EXPECT_TRUE(test_conv3d_ndhwc_instances<ck::half_t>(
ck::utils::conv::ConvolutionFwdInstances<ck::half_t, ck::half_t, ck::half_t>::Get<3>()); ck::utils::conv::ConvolutionFwdInstances<ck::half_t, ck::half_t, ck::half_t>::Get<3>()));
} }
bool test_conv3d_ndhwc_f32_instances() TEST(Conv3DFwdNDHWC, F32Instances)
{ {
return test_conv3d_ndhwc_instances<float>( EXPECT_TRUE(test_conv3d_ndhwc_instances<float>(
ck::utils::conv::ConvolutionFwdInstances<float, float, float>::Get<3>()); ck::utils::conv::ConvolutionFwdInstances<float, float, float>::Get<3>()));
} }
bool test_conv3d_ndhwc_int8_instances() TEST(Conv3DFwdNDHWC, Int8Instances)
{
return test_conv3d_ndhwc_instances<int8_t>(
ck::utils::conv::ConvolutionFwdInstances<int8_t, int8_t, int8_t>::Get<3>());
}
} // anonymous namespace
int main()
{ {
bool res{true}; EXPECT_TRUE(test_conv3d_ndhwc_instances<int8_t>(
res = test_conv3d_ndhwc(); ck::utils::conv::ConvolutionFwdInstances<int8_t, int8_t, int8_t>::Get<3>()));
std::cout << "test_conv3d_ndhwc ..... " << (res ? "SUCCESS" : "FAILURE") << std::endl;
res = test_conv3d_ndhwc_2gb_input();
std::cout << "\ntest_conv3d_ndhwc_2gb_input ..... " << (res ? "SUCCESS" : "FAILURE")
<< std::endl;
res = test_conv3d_ndhwc_2gb_filters();
std::cout << "\ntest_conv3d_ndhwc_2gb_filters ..... " << (res ? "SUCCESS" : "FAILURE")
<< std::endl;
res = test_conv3d_ndhwc_2gb_output();
std::cout << "\ntest_conv3d_ndhwc_2gb_output ..... " << (res ? "SUCCESS" : "FAILURE")
<< std::endl;
res = test_conv3d_ndhwc_bf16_instances();
std::cout << "\ntest_conv3d_ndhwc_bf16_instances ..... " << (res ? "SUCCESS" : "FAILURE")
<< std::endl;
res = test_conv3d_ndhwc_f16_instances();
std::cout << "\ntest_conv3d_ndhwc_f16_instances ..... " << (res ? "SUCCESS" : "FAILURE")
<< std::endl;
res = test_conv3d_ndhwc_f32_instances();
std::cout << "\ntest_conv3d_ndhwc_f32_instances ..... " << (res ? "SUCCESS" : "FAILURE")
<< std::endl;
res = test_conv3d_ndhwc_int8_instances();
std::cout << "\ntest_conv3d_ndhwc_int8_instances ..... " << (res ? "SUCCESS" : "FAILURE")
<< std::endl;
return res ? 0 : 1;
} }
test/gemm/gemm_util.hpp
View file @ d0b9a467
#ifndef GEMM_UTILS_HPP #ifndef GEMM_UTILS_HPP
#define GEMM_UTILS_HPP #define GEMM_UTILS_HPP
#include "check_err.hpp" #include "check_err.hpp"
#include "config.hpp" #include "config.hpp"
#include "device.hpp" #include "device.hpp"
#include "host_tensor.hpp" #include "host_tensor.hpp"
#include "host_tensor_generator.hpp" #include "host_tensor_generator.hpp"
#include "reference_gemm.hpp" #include "reference_gemm.hpp"
#include "tensor_layout.hpp" #include "tensor_layout.hpp"
namespace ck { namespace ck {
namespace gemm_util { namespace gemm_util {
struct GemmParams struct GemmParams
{ {
GemmParams() GemmParams()
: M(1024), N(1024), K(1024), StrideA(1024), StrideB(1024), StrideC(1024), alpha(1), beta(0) : M(1024), N(1024), K(1024), StrideA(1024), StrideB(1024), StrideC(1024), alpha(1), beta(0)
{ {
} }
ck::index_t M; ck::index_t M;
ck::index_t N; ck::index_t N;
ck::index_t K; ck::index_t K;
ck::index_t StrideA; ck::index_t StrideA;
ck::index_t StrideB; ck::index_t StrideB;
ck::index_t StrideC; ck::index_t StrideC;
float alpha; float alpha;
float beta; float beta;
}; };
template <typename GemmInstance, template <typename GemmInstance,
typename ADataType, typename ADataType,
typename BDataType, typename BDataType,
typename CDataType, typename CDataType,
typename AElementwiseOperation, typename AElementwiseOperation,
typename BElementwiseOperation, typename BElementwiseOperation,
typename CElementwiseOperation> typename CElementwiseOperation>
void RunHostGEMM(const Tensor<ADataType>& A, void RunHostGEMM(const Tensor<ADataType>& A,
const Tensor<BDataType>& B, const Tensor<BDataType>& B,
Tensor<CDataType>& C, Tensor<CDataType>& C,
AElementwiseOperation a_element_op, AElementwiseOperation a_element_op,
BElementwiseOperation b_element_op, BElementwiseOperation b_element_op,
CElementwiseOperation c_element_op) CElementwiseOperation c_element_op)
{ {
auto ref_gemm = GemmInstance{}; auto ref_gemm = GemmInstance{};
auto ref_invoker = ref_gemm.MakeInvoker(); auto ref_invoker = ref_gemm.MakeInvoker();
auto ref_argument = ref_gemm.MakeArgument(A, B, C, a_element_op, b_element_op, c_element_op); auto ref_argument = ref_gemm.MakeArgument(A, B, C, a_element_op, b_element_op, c_element_op);
ref_invoker.Run(ref_argument); ref_invoker.Run(ref_argument);
} }
template <typename DeviceGemmPtr_, template <typename DeviceGemmPtr_,
typename ADataType, typename ADataType,
typename BDataType, typename BDataType,
typename CDataType, typename CDataType,
typename AElementwiseOperation, typename AElementwiseOperation,
typename BElementwiseOperation, typename BElementwiseOperation,
typename CElementwiseOperation> typename CElementwiseOperation>
void RunDeviceGEMM(DeviceGemmPtr_& gemmPtr, void RunDeviceGEMM(DeviceGemmPtr_& gemmPtr,
const ck::gemm_util::GemmParams& params, const ck::gemm_util::GemmParams& params,
const Tensor<ADataType>& A, const Tensor<ADataType>& A,
const Tensor<BDataType>& B, const Tensor<BDataType>& B,
Tensor<CDataType>& C, Tensor<CDataType>& C,
AElementwiseOperation a_element_op, AElementwiseOperation a_element_op,
BElementwiseOperation b_element_op, BElementwiseOperation b_element_op,
CElementwiseOperation c_element_op) CElementwiseOperation c_element_op)
{ {
DeviceMem a_m_k_device_buf(sizeof(ADataType) * A.mDesc.GetElementSpace()); DeviceMem a_m_k_device_buf(sizeof(ADataType) * A.mDesc.GetElementSpace());
DeviceMem b_k_n_device_buf(sizeof(BDataType) * B.mDesc.GetElementSpace()); DeviceMem b_k_n_device_buf(sizeof(BDataType) * B.mDesc.GetElementSpace());
DeviceMem c_m_n_device_buf(sizeof(CDataType) * C.mDesc.GetElementSpace()); DeviceMem c_m_n_device_buf(sizeof(CDataType) * C.mDesc.GetElementSpace());
a_m_k_device_buf.ToDevice(A.mData.data()); a_m_k_device_buf.ToDevice(A.mData.data());
b_k_n_device_buf.ToDevice(B.mData.data()); b_k_n_device_buf.ToDevice(B.mData.data());
auto invoker_ptr = gemmPtr->MakeInvokerPointer(); auto invoker_ptr = gemmPtr->MakeInvokerPointer();
auto argument_ptr = auto argument_ptr =
gemmPtr->MakeArgumentPointer(static_cast<ADataType*>(a_m_k_device_buf.GetDeviceBuffer()), gemmPtr->MakeArgumentPointer(static_cast<ADataType*>(a_m_k_device_buf.GetDeviceBuffer()),
static_cast<BDataType*>(b_k_n_device_buf.GetDeviceBuffer()), static_cast<BDataType*>(b_k_n_device_buf.GetDeviceBuffer()),
static_cast<CDataType*>(c_m_n_device_buf.GetDeviceBuffer()), static_cast<CDataType*>(c_m_n_device_buf.GetDeviceBuffer()),
params.M, params.M,
params.N, params.N,
params.K, params.K,
params.StrideA, params.StrideA,
params.StrideB, params.StrideB,
params.StrideC, params.StrideC,
a_element_op, a_element_op,
b_element_op, b_element_op,
c_element_op); c_element_op);
if(!gemmPtr->IsSupportedArgument(argument_ptr.get())) if(!gemmPtr->IsSupportedArgument(argument_ptr.get()))
{ {
throw std::runtime_error( throw std::runtime_error(
"wrong! device_gemm with the specified compilation parameters does " "wrong! device_gemm with the specified compilation parameters does "
"not support this GEMM problem"); "not support this GEMM problem");
} }
invoker_ptr->Run(argument_ptr.get()); invoker_ptr->Run(argument_ptr.get());
c_m_n_device_buf.FromDevice(C.mData.data()); c_m_n_device_buf.FromDevice(C.mData.data());
} }
template <typename DeviceGemmPtr_, template <typename DeviceGemmPtr_,
typename ADataType, typename ADataType,
typename BDataType, typename BDataType,
typename CDataType, typename CDataType,
typename ALayout, typename ALayout,
typename BLayout, typename BLayout,
typename CLayout, typename CLayout,
typename AElementwiseOperation, typename AElementwiseOperation,
typename BElementwiseOperation, typename BElementwiseOperation,
typename CElementwiseOperation> typename CElementwiseOperation>
struct TestGemm struct TestGemm
{ {
auto PrepareGemmTensor(const ck::gemm_util::GemmParams& params) auto PrepareGemmTensor(const ck::gemm_util::GemmParams& params)
{ {
auto f_host_tensor_descriptor = auto f_host_tensor_descriptor =
[](std::size_t row, std::size_t col, std::size_t stride, auto layout) { [](std::size_t row, std::size_t col, std::size_t stride, auto layout) {
if(std::is_same<decltype(layout), ck::tensor_layout::gemm::RowMajor>::value) if(std::is_same<decltype(layout), ck::tensor_layout::gemm::RowMajor>::value)
{ {
return HostTensorDescriptor(std::vector<std::size_t>({row, col}), return HostTensorDescriptor(std::vector<std::size_t>({row, col}),
std::vector<std::size_t>({stride, 1})); std::vector<std::size_t>({stride, 1}));
} }
else else
{ {
return HostTensorDescriptor(std::vector<std::size_t>({row, col}), return HostTensorDescriptor(std::vector<std::size_t>({row, col}),
std::vector<std::size_t>({1, stride})); std::vector<std::size_t>({1, stride}));
} }
}; };
Tensor<ADataType> a_m_k( Tensor<ADataType> a_m_k(
f_host_tensor_descriptor(params.M, params.K, params.StrideA, ALayout{})); f_host_tensor_descriptor(params.M, params.K, params.StrideA, ALayout{}));
Tensor<BDataType> b_k_n( Tensor<BDataType> b_k_n(
f_host_tensor_descriptor(params.K, params.N, params.StrideB, BLayout{})); f_host_tensor_descriptor(params.K, params.N, params.StrideB, BLayout{}));
Tensor<CDataType> c_m_n_host_result( Tensor<CDataType> c_m_n_host_result(
f_host_tensor_descriptor(params.M, params.N, params.StrideC, CLayout{})); f_host_tensor_descriptor(params.M, params.N, params.StrideC, CLayout{}));
Tensor<CDataType> c_m_n_device_result( Tensor<CDataType> c_m_n_device_result(
f_host_tensor_descriptor(params.M, params.N, params.StrideC, CLayout{})); f_host_tensor_descriptor(params.M, params.N, params.StrideC, CLayout{}));
auto f_generate_tensor_value = [](auto desc, auto type) { auto f_generate_tensor_value = [](auto& tensor, auto type) {
using dataType = decltype(type); using dataType = decltype(type);
if(std::is_same<dataType, int8_t>::value) tensor.GenerateTensorValue(GeneratorTensor_2<dataType>{-5, 5});
{ };
desc.GenerateTensorValue(GeneratorTensor_2<int8_t>{-5, 5});
} f_generate_tensor_value(a_m_k, ADataType{});
else f_generate_tensor_value(b_k_n, BDataType{});
{
desc.GenerateTensorValue(GeneratorTensor_3<dataType>{-0.5, 0.5}); return std::make_tuple(a_m_k, b_k_n, c_m_n_host_result, c_m_n_device_result);
} }
};
auto operator()(DeviceGemmPtr_& gemmPtr)
f_generate_tensor_value(a_m_k, ADataType{}); {
f_generate_tensor_value(b_k_n, BDataType{}); std::cout << "ALayout = " << ALayout{}.name << ", BLayout = " << BLayout{}.name
<< ", CLayout = " << CLayout{}.name << std::endl;
return std::make_tuple(a_m_k, b_k_n, c_m_n_host_result, c_m_n_device_result); std::cout << gemmPtr->GetTypeString() << std::endl;
}
// Arrange
auto operator()(DeviceGemmPtr_& gemmPtr) ck::gemm_util::GemmParams params;
{ params.M = 1024;
std::cout << "ALayout = " << ALayout{}.name << ", BLayout = " << BLayout{}.name params.N = 1024;
<< ", CLayout = " << CLayout{}.name << std::endl; params.K = 1024;
std::cout << gemmPtr->GetTypeString() << std::endl; params.StrideA = 1024;
params.StrideB = 1024;
// Arrange params.StrideC = 1024;
ck::gemm_util::GemmParams params;
params.M = 1024; auto host_tensors = PrepareGemmTensor(params);
params.N = 1024;
params.K = 1024; const Tensor<ADataType>& a = std::get<0>(host_tensors);
params.StrideA = 1024; const Tensor<BDataType>& b = std::get<1>(host_tensors);
params.StrideB = 1024; Tensor<CDataType>& c_host = std::get<2>(host_tensors);
params.StrideC = 1024; Tensor<CDataType>& c_device = std::get<3>(host_tensors);
auto host_tensors = PrepareGemmTensor(params); auto a_element_op = AElementwiseOperation{};
auto b_element_op = BElementwiseOperation{};
const Tensor<ADataType>& a = std::get<0>(host_tensors); auto c_element_op = CElementwiseOperation{};
const Tensor<BDataType>& b = std::get<1>(host_tensors);
Tensor<CDataType>& c_host = std::get<2>(host_tensors); using ReferenceGemmInstance =
Tensor<CDataType>& c_device = std::get<3>(host_tensors); ck::tensor_operation::host::ReferenceGemm<ADataType,
BDataType,
auto a_element_op = AElementwiseOperation{}; CDataType,
auto b_element_op = BElementwiseOperation{}; AElementwiseOperation,
auto c_element_op = CElementwiseOperation{}; BElementwiseOperation,
CElementwiseOperation>;
using ReferenceGemmInstance = ck::gemm_util::RunHostGEMM<ReferenceGemmInstance>(
ck::tensor_operation::host::ReferenceGemm<ADataType, a, b, c_host, a_element_op, b_element_op, c_element_op);
BDataType,
CDataType, // Act
AElementwiseOperation, ck::gemm_util::RunDeviceGEMM(
BElementwiseOperation, gemmPtr, params, a, b, c_device, a_element_op, b_element_op, c_element_op);
CElementwiseOperation>;
ck::gemm_util::RunHostGEMM<ReferenceGemmInstance>( // Assert
a, b, c_host, a_element_op, b_element_op, c_element_op); bool res = false;
if(std::is_same<CDataType, float>::value)
// Act {
ck::gemm_util::RunDeviceGEMM( res = ck::utils::check_err(c_device.mData, c_host.mData);
gemmPtr, params, a, b, c_device, a_element_op, b_element_op, c_element_op); std::cout << (res ? "SUCCESS" : "FAILURE") << std::endl;
}
// Assert else if(std::is_same<CDataType, ck::half_t>::value)
bool res = false; {
if(std::is_same<CDataType, float>::value) res = ck::utils::check_err(c_device.mData, c_host.mData);
{ std::cout << (res ? "SUCCESS" : "FAILURE") << std::endl;
res = ck::utils::check_err(c_device.mData, c_host.mData); }
std::cout << (res ? "SUCCESS" : "FAILURE") << std::endl; else if(std::is_same<CDataType, int8_t>::value)
} {
else if(std::is_same<CDataType, ck::half_t>::value) res = ck::utils::check_err(c_device.mData, c_host.mData);
{ std::cout << (res ? "SUCCESS" : "FAILURE") << std::endl;
res = ck::utils::check_err(c_device.mData, c_host.mData); }
std::cout << (res ? "SUCCESS" : "FAILURE") << std::endl;
} return res;
else if(std::is_same<CDataType, int8_t>::value) }
{ };
res = ck::utils::check_err(c_device.mData, c_host.mData);
std::cout << (res ? "SUCCESS" : "FAILURE") << std::endl; template <typename DeviceGemmPtr_,
} typename ALayout,
typename BLayout,
return res; typename CLayout,
} typename AElementwiseOperation,
}; typename BElementwiseOperation,
typename CElementwiseOperation>
template <typename DeviceGemmPtr_, struct TestGemmBF16
typename ALayout, {
typename BLayout, using BF16 = ck::bhalf_t;
typename CLayout,
typename AElementwiseOperation, auto PrepareGemmTensorBF16(const ck::gemm_util::GemmParams& params)
typename BElementwiseOperation, {
typename CElementwiseOperation> auto f_host_tensor_descriptor =
struct TestGemmBF16 [](std::size_t row, std::size_t col, std::size_t stride, auto layout) {
{ if(std::is_same<decltype(layout), ck::tensor_layout::gemm::RowMajor>::value)
using BF16 = ck::bhalf_t; {
return HostTensorDescriptor(std::vector<std::size_t>({row, col}),
auto PrepareGemmTensorBF16(const ck::gemm_util::GemmParams& params) std::vector<std::size_t>({stride, 1}));
{ }
auto f_host_tensor_descriptor = else
[](std::size_t row, std::size_t col, std::size_t stride, auto layout) { {
if(std::is_same<decltype(layout), ck::tensor_layout::gemm::RowMajor>::value) return HostTensorDescriptor(std::vector<std::size_t>({row, col}),
{ std::vector<std::size_t>({1, stride}));
return HostTensorDescriptor(std::vector<std::size_t>({row, col}), }
std::vector<std::size_t>({stride, 1})); };
}
else // use fp32 host kernel to verify bf16 device kernel
{ Tensor<BF16> a_m_k_bf16(
return HostTensorDescriptor(std::vector<std::size_t>({row, col}), f_host_tensor_descriptor(params.M, params.K, params.StrideA, ALayout{}));
std::vector<std::size_t>({1, stride})); Tensor<BF16> b_k_n_bf16(
} f_host_tensor_descriptor(params.K, params.N, params.StrideB, BLayout{}));
}; Tensor<BF16> c_m_n_device_bf16(
f_host_tensor_descriptor(params.M, params.N, params.StrideC, CLayout{}));
// use fp32 host kernel to verify bf16 device kernel
Tensor<BF16> a_m_k_bf16( Tensor<float> a_m_k_fp32(
f_host_tensor_descriptor(params.M, params.K, params.StrideA, ALayout{})); f_host_tensor_descriptor(params.M, params.K, params.StrideA, ALayout{}));
Tensor<BF16> b_k_n_bf16( Tensor<float> b_k_n_fp32(
f_host_tensor_descriptor(params.K, params.N, params.StrideB, BLayout{})); f_host_tensor_descriptor(params.K, params.N, params.StrideB, BLayout{}));
Tensor<BF16> c_m_n_device_bf16( Tensor<float> c_m_n_host_fp32(
f_host_tensor_descriptor(params.M, params.N, params.StrideC, CLayout{})); f_host_tensor_descriptor(params.M, params.N, params.StrideC, CLayout{}));
Tensor<float> c_m_n_device_fp32(
Tensor<float> a_m_k_fp32( f_host_tensor_descriptor(params.M, params.N, params.StrideC, CLayout{}));
f_host_tensor_descriptor(params.M, params.K, params.StrideA, ALayout{}));
Tensor<float> b_k_n_fp32( a_m_k_bf16.GenerateTensorValue(GeneratorTensor_3<BF16>{-0.5, 0.5});
f_host_tensor_descriptor(params.K, params.N, params.StrideB, BLayout{})); b_k_n_bf16.GenerateTensorValue(GeneratorTensor_3<BF16>{-0.5, 0.5});
Tensor<float> c_m_n_host_fp32(
f_host_tensor_descriptor(params.M, params.N, params.StrideC, CLayout{})); bf16_to_f32_(a_m_k_bf16, a_m_k_fp32);
Tensor<float> c_m_n_device_fp32( bf16_to_f32_(b_k_n_bf16, b_k_n_fp32);
f_host_tensor_descriptor(params.M, params.N, params.StrideC, CLayout{}));
return std::make_tuple(a_m_k_bf16,
a_m_k_bf16.GenerateTensorValue(GeneratorTensor_3<BF16>{-0.5, 0.5}); b_k_n_bf16,
b_k_n_bf16.GenerateTensorValue(GeneratorTensor_3<BF16>{-0.5, 0.5}); c_m_n_device_bf16,
a_m_k_fp32,
bf16_to_f32_(a_m_k_bf16, a_m_k_fp32); b_k_n_fp32,
bf16_to_f32_(b_k_n_bf16, b_k_n_fp32); c_m_n_host_fp32,
c_m_n_device_fp32);
return std::make_tuple(a_m_k_bf16, }
b_k_n_bf16,
c_m_n_device_bf16, auto operator()(DeviceGemmPtr_& gemmPtr)
a_m_k_fp32, {
b_k_n_fp32, // Arrange
c_m_n_host_fp32, ck::gemm_util::GemmParams params;
c_m_n_device_fp32); params.M = 1024;
} params.N = 1024;
params.K = 1024;
auto operator()(DeviceGemmPtr_& gemmPtr) params.StrideA = 1024;
{ params.StrideB = 1024;
// Arrange params.StrideC = 1024;
ck::gemm_util::GemmParams params;
params.M = 1024; auto host_tensors = PrepareGemmTensorBF16(params);
params.N = 1024; const Tensor<BF16>& a_bf16 = std::get<0>(host_tensors);
params.K = 1024; const Tensor<BF16>& b_bf16 = std::get<1>(host_tensors);
params.StrideA = 1024; Tensor<BF16>& c_device_bf16 = std::get<2>(host_tensors);
params.StrideB = 1024; Tensor<float>& a_fp32 = std::get<3>(host_tensors);
params.StrideC = 1024; Tensor<float>& b_fp32 = std::get<4>(host_tensors);
Tensor<float>& c_host_fp32 = std::get<5>(host_tensors);
auto host_tensors = PrepareGemmTensorBF16(params); Tensor<float>& c_device_fp32 = std::get<6>(host_tensors);
const Tensor<BF16>& a_bf16 = std::get<0>(host_tensors);
const Tensor<BF16>& b_bf16 = std::get<1>(host_tensors); auto a_element_op = AElementwiseOperation{};
Tensor<BF16>& c_device_bf16 = std::get<2>(host_tensors); auto b_element_op = BElementwiseOperation{};
Tensor<float>& a_fp32 = std::get<3>(host_tensors); auto c_element_op = CElementwiseOperation{};
Tensor<float>& b_fp32 = std::get<4>(host_tensors);
Tensor<float>& c_host_fp32 = std::get<5>(host_tensors); // use fp32 host kernel to verify bf16 device kernel
Tensor<float>& c_device_fp32 = std::get<6>(host_tensors); using ReferenceGemmInstance =
ck::tensor_operation::host::ReferenceGemm<float,
auto a_element_op = AElementwiseOperation{}; float,
auto b_element_op = BElementwiseOperation{}; float,
auto c_element_op = CElementwiseOperation{}; AElementwiseOperation,
BElementwiseOperation,
// use fp32 host kernel to verify bf16 device kernel CElementwiseOperation>;
using ReferenceGemmInstance = ck::gemm_util::RunHostGEMM<ReferenceGemmInstance>(
ck::tensor_operation::host::ReferenceGemm<float, a_fp32, b_fp32, c_host_fp32, a_element_op, b_element_op, c_element_op);
float,
float, // Act
AElementwiseOperation, ck::gemm_util::RunDeviceGEMM(gemmPtr,
BElementwiseOperation, params,
CElementwiseOperation>; a_bf16,
ck::gemm_util::RunHostGEMM<ReferenceGemmInstance>( b_bf16,
a_fp32, b_fp32, c_host_fp32, a_element_op, b_element_op, c_element_op); c_device_bf16,
a_element_op,
// Act b_element_op,
ck::gemm_util::RunDeviceGEMM(gemmPtr, c_element_op);
params,
a_bf16, bf16_to_f32_(c_device_bf16, c_device_fp32);
b_bf16,
c_device_bf16, // Assert
a_element_op, bool res = ck::utils::check_err(
b_element_op, c_device_fp32.mData, c_host_fp32.mData, "Error: incorrect results!", 1e-2f, 1e-3f);
c_element_op); std::cout << (res ? "SUCCESS" : "FAILURE") << std::endl;
bf16_to_f32_(c_device_bf16, c_device_fp32); return res;
};
// Assert };
bool res = ck::utils::check_err(
c_device_fp32.mData, c_host_fp32.mData, "Error: incorrect results!", 1e-2f, 1e-3f); } // namespace gemm_util
std::cout << (res ? "SUCCESS" : "FAILURE") << std::endl; } // namespace ck
#endif
return res;
};
};
} // namespace gemm_util
} // namespace ck
#endif
test/reference_conv_fwd/CMakeLists.txt
View file @ d0b9a467
add_test_executable(test_reference_conv_fwd reference_conv_fwd.cpp) add_gtest_executable(test_reference_conv_fwd reference_conv_fwd.cpp)
target_link_libraries(test_reference_conv_fwd PRIVATE host_tensor conv_fwd_util) target_link_libraries(test_reference_conv_fwd PRIVATE host_tensor conv_fwd_util)
test/reference_conv_fwd/reference_conv_fwd.cpp
View file @ d0b9a467
...@@ -4,6 +4,7 @@ ...@@ -4,6 +4,7 @@
#include <numeric> #include <numeric>
#include <type_traits> #include <type_traits>
#include <vector> #include <vector>
#include "gtest/gtest.h"
#include "check_err.hpp" #include "check_err.hpp"
#include "config.hpp" #include "config.hpp"
...@@ -82,13 +83,13 @@ run_reference_convolution_forward(const ck::utils::conv::ConvParams& params, ...@@ -82,13 +83,13 @@ run_reference_convolution_forward(const ck::utils::conv::ConvParams& params,
OutElementOp{}); OutElementOp{});
ref_invoker.Run(ref_argument); ref_invoker.Run(ref_argument);
// std::cout <<"output: " << host_output.mDesc << std::endl << host_output.mData << std::endl;
return host_output; return host_output;
} }
bool test_conv2d_nhwc() } // anonymous namespace
TEST(ReferenceConvolutionFWD, Conv2DNHWC)
{ {
bool res{true};
ck::utils::conv::ConvParams params; ck::utils::conv::ConvParams params;
params.N = 1; params.N = 1;
params.K = 1; params.K = 1;
...@@ -118,11 +119,14 @@ bool test_conv2d_nhwc() ...@@ -118,11 +119,14 @@ bool test_conv2d_nhwc()
472.5, 472.5,
490.5, 490.5,
508.5}; 508.5};
res = res && ck::utils::check_err(out_tensor.mDesc.GetLengths(), EXPECT_TRUE(ck::utils::check_err(
ref_dims, out_tensor.mDesc.GetLengths(), ref_dims, "Error: wrong output tensor dimensions!"));
"Error: wrong output tensor dimensions!"); EXPECT_TRUE(ck::utils::check_err(out_tensor.mData, ref_data, "Error: incorrect results!"));
res = res && ck::utils::check_err(out_tensor.mData, ref_data, "Error: incorrect results!"); }
TEST(ReferenceConvolutionFWD, Conv2DNHWCStridesDilationsPadding)
{
ck::utils::conv::ConvParams params;
params.N = 1; params.N = 1;
params.K = 2; params.K = 2;
params.C = 2; params.C = 2;
...@@ -133,25 +137,21 @@ bool test_conv2d_nhwc() ...@@ -133,25 +137,21 @@ bool test_conv2d_nhwc()
params.input_left_pads = std::vector<ck::index_t>{1, 1}; params.input_left_pads = std::vector<ck::index_t>{1, 1};
params.input_right_pads = std::vector<ck::index_t>{1, 1}; params.input_right_pads = std::vector<ck::index_t>{1, 1};
out_tensor = run_reference_convolution_forward<2>(params); auto out_tensor = run_reference_convolution_forward<2>(params);
ref_dims = std::vector<std::size_t>{1, 2, 5, 5}; std::vector<std::size_t> ref_dims = std::vector<std::size_t>{1, 2, 5, 5};
ref_data = std::vector<float>{ std::vector<float> ref_data{
210., 210., 327., 327., 351., 351., 375., 375., 399., 399., 210., 210., 327., 327., 351., 351., 375., 375., 399., 399.,
459., 459., 706.5, 706.5, 742.5, 742.5, 778.5, 778.5, 814.5, 814.5, 459., 459., 706.5, 706.5, 742.5, 742.5, 778.5, 778.5, 814.5, 814.5,
747., 747., 1138.5, 1138.5, 1174.5, 1174.5, 1210.5, 1210.5, 1246.5, 1246.5, 747., 747., 1138.5, 1138.5, 1174.5, 1174.5, 1210.5, 1210.5, 1246.5, 1246.5,
1035., 1035., 1570.5, 1570.5, 1606.5, 1606.5, 1642.5, 1642.5, 1678.5, 1678.5, 1035., 1035., 1570.5, 1570.5, 1606.5, 1606.5, 1642.5, 1642.5, 1678.5, 1678.5,
1323., 1323., 2002.5, 2002.5, 2038.5, 2038.5, 2074.5, 2074.5, 2110.5, 2110.5}; 1323., 1323., 2002.5, 2002.5, 2038.5, 2038.5, 2074.5, 2074.5, 2110.5, 2110.5};
res = res && ck::utils::check_err(out_tensor.mDesc.GetLengths(), EXPECT_TRUE(ck::utils::check_err(
ref_dims, out_tensor.mDesc.GetLengths(), ref_dims, "Error: wrong output tensor dimensions!"));
"Error: wrong output tensor dimensions!"); EXPECT_TRUE(ck::utils::check_err(out_tensor.mData, ref_data, "Error: incorrect results!"));
res = res && ck::utils::check_err(out_tensor.mData, ref_data, "Error: incorrect results!");
return res;
} }
bool test_conv1d_nwc() TEST(ReferenceConvolutionFWD, Conv1DNWC)
{ {
bool res{true};
ck::utils::conv::ConvParams params; ck::utils::conv::ConvParams params;
params.num_dim_spatial = 1; params.num_dim_spatial = 1;
params.N = 1; params.N = 1;
...@@ -174,11 +174,14 @@ bool test_conv1d_nwc() ...@@ -174,11 +174,14 @@ bool test_conv1d_nwc()
ck::tensor_layout::convolution::NWK>(params); ck::tensor_layout::convolution::NWK>(params);
std::vector<std::size_t> ref_dims{1, 1, 4}; std::vector<std::size_t> ref_dims{1, 1, 4};
std::vector<float> ref_data{7.5, 13.5, 19.5, 25.5}; std::vector<float> ref_data{7.5, 13.5, 19.5, 25.5};
res = res && ck::utils::check_err(out_tensor.mDesc.GetLengths(), EXPECT_TRUE(ck::utils::check_err(
ref_dims, out_tensor.mDesc.GetLengths(), ref_dims, "Error: wrong output tensor dimensions!"));
"Error: wrong output tensor dimensions!"); EXPECT_TRUE(ck::utils::check_err(out_tensor.mData, ref_data, "Error: incorrect results!"));
res = res && ck::utils::check_err(out_tensor.mData, ref_data, "Error: incorrect results!"); }
TEST(ReferenceConvolutionFWD, Conv1DNWCStridesDilationsPadding)
{
ck::utils::conv::ConvParams params;
params.num_dim_spatial = 1; params.num_dim_spatial = 1;
params.N = 1; params.N = 1;
params.K = 2; params.K = 2;
...@@ -190,20 +193,24 @@ bool test_conv1d_nwc() ...@@ -190,20 +193,24 @@ bool test_conv1d_nwc()
params.input_left_pads = std::vector<ck::index_t>{1}; params.input_left_pads = std::vector<ck::index_t>{1};
params.input_right_pads = std::vector<ck::index_t>{1}; params.input_right_pads = std::vector<ck::index_t>{1};
out_tensor = run_reference_convolution_forward<1, auto out_tensor =
float, run_reference_convolution_forward<1,
float, float,
float, float,
ck::tensor_layout::convolution::NWC, float,
ck::tensor_layout::convolution::KXC, ck::tensor_layout::convolution::NWC,
ck::tensor_layout::convolution::NWK>(params); ck::tensor_layout::convolution::KXC,
ref_dims = std::vector<std::size_t>{1, 2, 5}; ck::tensor_layout::convolution::NWK>(params);
ref_data = std::vector<float>{9., 9., 19.5, 19.5, 31.5, 31.5, 43.5, 43.5, 55.5, 55.5}; std::vector<std::size_t> ref_dims{1, 2, 5};
res = res && ck::utils::check_err(out_tensor.mDesc.GetLengths(), std::vector<float> ref_data{9., 9., 19.5, 19.5, 31.5, 31.5, 43.5, 43.5, 55.5, 55.5};
ref_dims, EXPECT_TRUE(ck::utils::check_err(
"Error: wrong output tensor dimensions!"); out_tensor.mDesc.GetLengths(), ref_dims, "Error: wrong output tensor dimensions!"));
res = res && ck::utils::check_err(out_tensor.mData, ref_data, "Error: incorrect results!"); EXPECT_TRUE(ck::utils::check_err(out_tensor.mData, ref_data, "Error: incorrect results!"));
}
TEST(ReferenceConvolutionFWD, Conv1DNWCSameOutputSize)
{
ck::utils::conv::ConvParams params;
params.num_dim_spatial = 1; params.num_dim_spatial = 1;
params.N = 2; params.N = 2;
params.K = 16; params.K = 16;
...@@ -224,8 +231,8 @@ bool test_conv1d_nwc() ...@@ -224,8 +231,8 @@ bool test_conv1d_nwc()
ck::tensor_layout::convolution::NWK>( ck::tensor_layout::convolution::NWK>(
params, ck::utils::FillMonotonicSeq<float>{0.f, 0.1f}); params, ck::utils::FillMonotonicSeq<float>{0.f, 0.1f});
ref_dims = std::vector<std::size_t>{2, 16, 16}; std::vector<std::size_t> ref_dims{2, 16, 16};
ref_data = std::vector<float>{ std::vector<float> ref_data{
1.4, 1.4, 1.4, 1.4, 1.4, 1.4, 1.4, 1.4, 1.4, 1.4, 1.4, 1.4, 1.4, 1.4, 1.4, 1.4,
1.4, 1.4, 1.4, 1.4, 1.4, 1.4, 1.4, 1.4, 1.4, 1.4, 1.4, 1.4, 1.4, 1.4, 1.4, 1.4,
3.3, 3.3, 3.3, 3.3, 3.3, 3.3, 3.3, 3.3, 3.3, 3.3, 3.3, 3.3, 3.3, 3.3, 3.3, 3.3,
...@@ -290,17 +297,13 @@ bool test_conv1d_nwc() ...@@ -290,17 +297,13 @@ bool test_conv1d_nwc()
72.9, 72.9, 72.9, 72.9, 72.9, 72.9, 72.9, 72.9, 72.9, 72.9, 72.9, 72.9, 72.9, 72.9, 72.9, 72.9,
49.4, 49.4, 49.4, 49.4, 49.4, 49.4, 49.4, 49.4, 49.4, 49.4, 49.4, 49.4, 49.4, 49.4, 49.4, 49.4,
49.4, 49.4, 49.4, 49.4, 49.4, 49.4, 49.4, 49.4}; 49.4, 49.4, 49.4, 49.4, 49.4, 49.4, 49.4, 49.4};
res = res && ck::utils::check_err(out_tensor2.mDesc.GetLengths(), EXPECT_TRUE(ck::utils::check_err(
ref_dims, out_tensor2.mDesc.GetLengths(), ref_dims, "Error: wrong output tensor dimensions!"));
"Error: wrong output tensor dimensions!"); EXPECT_TRUE(ck::utils::check_err(out_tensor2.mData, ref_data, "Error: incorrect results!"));
res = res && ck::utils::check_err(out_tensor2.mData, ref_data, "Error: incorrect results!");
return res;
} }
bool test_conv3d_ncdhw() TEST(ReferenceConvolutionFWD, Conv3DNCDHW)
{ {
bool res{true};
ck::utils::conv::ConvParams params; ck::utils::conv::ConvParams params;
params.num_dim_spatial = 3; params.num_dim_spatial = 3;
params.N = 1; params.N = 1;
...@@ -331,12 +334,17 @@ bool test_conv3d_ncdhw() ...@@ -331,12 +334,17 @@ bool test_conv3d_ncdhw()
634.5, 637.2, 639.9, 642.60004, 650.7, 653.4, 656.10004, 658.8, 634.5, 637.2, 639.9, 642.60004, 650.7, 653.4, 656.10004, 658.8,
699.3, 702., 704.7, 707.4, 715.5, 718.2, 720.9, 723.60004, 699.3, 702., 704.7, 707.4, 715.5, 718.2, 720.9, 723.60004,
731.7, 734.4001, 737.10004, 739.8, 747.9001, 750.60004, 753.3, 756.}; 731.7, 734.4001, 737.10004, 739.8, 747.9001, 750.60004, 753.3, 756.};
res = res && ck::utils::check_err(out_tensor.mDesc.GetLengths(), EXPECT_TRUE(ck::utils::check_err(out_tensor.mDesc.GetLengths(),
ref_dims, ref_dims,
"Error [case 1]: wrong output tensor dimensions!"); "Error [case 1]: wrong output tensor dimensions!"));
res = res && EXPECT_TRUE(
ck::utils::check_err(out_tensor.mData, ref_data, "Error [case 1]: incorrect results!"); ck::utils::check_err(out_tensor.mData, ref_data, "Error [case 1]: incorrect results!"));
}
TEST(ReferenceConvolutionFWD, Conv3DNCDHWStridesDilations)
{
ck::utils::conv::ConvParams params;
params.num_dim_spatial = 3;
params.N = 1; params.N = 1;
params.K = 2; params.K = 2;
params.C = 2; params.C = 2;
...@@ -347,16 +355,16 @@ bool test_conv3d_ncdhw() ...@@ -347,16 +355,16 @@ bool test_conv3d_ncdhw()
params.input_left_pads = std::vector<ck::index_t>{0, 0, 0}; params.input_left_pads = std::vector<ck::index_t>{0, 0, 0};
params.input_right_pads = std::vector<ck::index_t>{0, 0, 0}; params.input_right_pads = std::vector<ck::index_t>{0, 0, 0};
out_tensor = run_reference_convolution_forward<3, auto out_tensor = run_reference_convolution_forward<3,
float, float,
float, float,
float, float,
ck::tensor_layout::convolution::NCDHW, ck::tensor_layout::convolution::NCDHW,
ck::tensor_layout::convolution::KCZYX, ck::tensor_layout::convolution::KCZYX,
ck::tensor_layout::convolution::NKDHW>( ck::tensor_layout::convolution::NKDHW>(
params, ck::utils::FillMonotonicSeq<float>{0.f, 0.1f}); params, ck::utils::FillMonotonicSeq<float>{0.f, 0.1f});
ref_dims = std::vector<std::size_t>{1, 2, 4, 4, 4}; std::vector<std::size_t> ref_dims{1, 2, 4, 4, 4};
ref_data = std::vector<float>{ std::vector<float> ref_data{
2756.7002, 2764.7998, 2772.9001, 2781., 2853.9001, 2862., 2870.1, 2878.2002, 2756.7002, 2764.7998, 2772.9001, 2781., 2853.9001, 2862., 2870.1, 2878.2002,
2951.1, 2959.2002, 2967.2998, 2975.4001, 3048.2998, 3056.4001, 3064.5, 3072.6, 2951.1, 2959.2002, 2967.2998, 2975.4001, 3048.2998, 3056.4001, 3064.5, 3072.6,
3923.1, 3931.2, 3939.2998, 3947.4, 4020.2998, 4028.4001, 4036.5002, 4044.5999, 3923.1, 3931.2, 3939.2998, 3947.4, 4020.2998, 4028.4001, 4036.5002, 4044.5999,
...@@ -373,26 +381,9 @@ bool test_conv3d_ncdhw() ...@@ -373,26 +381,9 @@ bool test_conv3d_ncdhw()
5283.9004, 5292., 5300.0996, 5308.2, 5381.0996, 5389.2, 5397.3, 5405.4004, 5283.9004, 5292., 5300.0996, 5308.2, 5381.0996, 5389.2, 5397.3, 5405.4004,
6255.9004, 6264.0005, 6272.1, 6280.2, 6353.1, 6361.2, 6369.301, 6377.4, 6255.9004, 6264.0005, 6272.1, 6280.2, 6353.1, 6361.2, 6369.301, 6377.4,
6450.301, 6458.4, 6466.5, 6474.6, 6547.5, 6555.6, 6563.699, 6571.801}; 6450.301, 6458.4, 6466.5, 6474.6, 6547.5, 6555.6, 6563.699, 6571.801};
res = res && ck::utils::check_err(out_tensor.mDesc.GetLengths(), EXPECT_TRUE(ck::utils::check_err(out_tensor.mDesc.GetLengths(),
ref_dims, ref_dims,
"Error [case 2]: wrong output tensor dimensions!"); "Error [case 2]: wrong output tensor dimensions!"));
res = EXPECT_TRUE(ck::utils::check_err(
res && ck::utils::check_err( out_tensor.mData, ref_data, "Error [case 2]: incorrect results!", 1e-4f, 1e-6f));
out_tensor.mData, ref_data, "Error [case 2]: incorrect results!", 1e-4f, 1e-6f);
return res;
}
} // anonymous namespace
int main(void)
{
bool res{true};
res = test_conv2d_nhwc();
std::cout << "test_conv2d_nhwc ..... " << (res ? "SUCCESS" : "FAILURE") << std::endl;
res = test_conv1d_nwc();
std::cout << "TestConv1DNHWC ..... " << (res ? "SUCCESS" : "FAILURE") << std::endl;
res = test_conv3d_ncdhw();
std::cout << "test_conv3d_ncdhw ..... " << (res ? "SUCCESS" : "FAILURE") << std::endl;
return res ? 0 : 1;
} }
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