Skip to content

GitLab

Commit f0224f2a authored by letaoqin's avatar letaoqin
Browse files

Merge branch 'develop' into dl_conv_multiple_d

parents befc2638 0e9c88ce
Hide whitespace changes
Inline Side-by-side
Showing with 1178 additions and 111 deletions
library/src/tensor_operation_instance/gpu/grouped_conv2d_bwd_data/device_grouped_conv2d_bwd_data_xdl_gnhwc_gkyxc_gnhwk_f16_instance.cpp
View file @ f0224f2a
...@@ -16,7 +16,7 @@ namespace instance { ...@@ -16,7 +16,7 @@ namespace instance {
using F16 = ck::half_t; using F16 = ck::half_t;
using F32 = float; using F32 = float;
using EmptyTuple = ck::Tuple<>; using Empty_Tuple = ck::Tuple<>;
template <ck::index_t... Is> template <ck::index_t... Is>
using S = ck::Sequence<Is...>; using S = ck::Sequence<Is...>;
...@@ -36,56 +36,58 @@ static constexpr auto ConvBwdDataFilter1x1Stride1Pad0 = ...@@ -36,56 +36,58 @@ static constexpr auto ConvBwdDataFilter1x1Stride1Pad0 =
using device_grouped_conv2d_bwd_data_xdl_gnhwc_gkyxc_gnhwk_f16_instances = std::tuple< using device_grouped_conv2d_bwd_data_xdl_gnhwc_gkyxc_gnhwk_f16_instances = std::tuple<
// clang-format off // clang-format off
// 1. Default // 1. Default
// ##############################################| NDim| ALayout| BLayout| DsLayout| ELayout| AData| BData| AccData| CShuffle| DsData| EData| AElementwise| BElementwise| CDEElementwise| ConvolutionBackward| DoPad| DoPad| NumGemmK| Block| MPer| NPer| KPer| AK1| BK1| MPer| NPer| MXdl| NXdl| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockLds| BBlockTransfer| BBlockTransfer| BBlockTransfer| BBlockTransfer| BBlockTransfer| BBlockTransfer| BBlockLds| CShuffleMXdl| CShuffleNXdl| CDEBlockTransfer| CDEBlockTransfer| // ##############################################| NDim| ALayout| BLayout| DsLayout| ELayout| AData| BData| AccData| CShuffle| DsData| EData| AElementwise| BElementwise| CDEElementwise| ConvolutionBackward| DoPad| DoPad| NumGemmK| Block| MPer| NPer| KPer| AK1| BK1| MPer| NPer| MXdl| NXdl| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockLds| BBlockTransfer| BBlockTransfer| BBlockTransfer| BBlockTransfer| BBlockTransfer| BBlockTransfer| BBlockLds| CShuffleMXdl| CShuffleNXdl| CDEBlockTransfer| CDEBlockTransfer|
// ##############################################| Spatial| | | | | Type| Type| Type| DataType| Type| Type| Operation| Operation| Operation| DataSpecialization| GemmM| GemmN| PrefetchStage| Size| Block| Block| Block| | | XDL| XDL| PerWave| PerWave| ThreadCluster| ThreadCluster| SrcAccessOrder| SrcVectorDim| SrcScalar| DstScalar| ExtraM| ThreadCluster| ThreadCluster| SrcAccessOrder| SrcVectorDim| SrcScalar| DstScalar| ExtraN| PerWave| PerWave| _MBlock_MPerBlock| ScalarPerVector| // ##############################################| Spatial| | | | | Type| Type| Type| DataType| Type| Type| Operation| Operation| Operation| DataSpecialization| GemmM| GemmN| PrefetchStage| Size| Block| Block| Block| | | XDL| XDL| PerWave| PerWave| ThreadCluster| ThreadCluster| SrcAccessOrder| SrcVectorDim| SrcScalar| DstScalar| ExtraM| ThreadCluster| ThreadCluster| SrcAccessOrder| SrcVectorDim| SrcScalar| DstScalar| ExtraN| PerWave| PerWave| _MBlock_MPerBlock| ScalarPerVector|
// ##############################################| | | | | | | | | | | | | | | | | | | | | | | | | | | | | Lengths_AK0_M_AK1| ArrangeOrder| | | PerVector| PerVector_AK1| | Lengths_BK0_N_BK1| ArrangeOrder| | | PerVector| PerVector_BK1| | PerShuffle| PerShuffle| _NBlock_NPerBlock| _NPerBlock| // ##############################################| | | | | | | | | | | | | | | | | | | | | | | | | | | | | Lengths_AK0_M_AK1| ArrangeOrder| | | PerVector| PerVector_AK1| | Lengths_BK0_N_BK1| ArrangeOrder| | | PerVector| PerVector_BK1| | PerShuffle| PerShuffle| _NBlock_NPerBlock| _NPerBlock|
// ##############################################| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | // ##############################################| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
DeviceGroupedConvBwdDataMultipleD_Xdl_CShuffle_v1< 2, GNHWK, GKYXC, EmptyTuple, GNHWC, F16, F16, F32, F16, EmptyTuple, F16, PassThrough, PassThrough, PassThrough, ConvBwdDataDefault, true, true, 1, 256, 256, 128, 32, 8, 8, 32, 32, 4, 2, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, 1, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 1, 8, 8, 1, 1, 1, S<1, 32, 1, 8>, 8>, DeviceGroupedConvBwdDataMultipleD_Xdl_CShuffle_v1< 2, GNHWK, GKYXC, Empty_Tuple, GNHWC, F16, F16, F32, F16, Empty_Tuple, F16, PassThrough, PassThrough, PassThrough, ConvBwdDataDefault, true, true, 1, 256, 256, 128, 32, 8, 8, 32, 32, 4, 2, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, 1, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 1, 8, 8, 1, 1, 1, S<1, 32, 1, 8>, 8>,
DeviceGroupedConvBwdDataMultipleD_Xdl_CShuffle_v1< 2, GNHWK, GKYXC, EmptyTuple, GNHWC, F16, F16, F32, F16, EmptyTuple, F16, PassThrough, PassThrough, PassThrough, ConvBwdDataDefault, true, true, 1, 256, 128, 256, 32, 8, 8, 32, 32, 2, 4, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, 1, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 1, 8, 8, 1, 1, 1, S<1, 32, 1, 8>, 8>, DeviceGroupedConvBwdDataMultipleD_Xdl_CShuffle_v1< 2, GNHWK, GKYXC, Empty_Tuple, GNHWC, F16, F16, F32, F16, Empty_Tuple, F16, PassThrough, PassThrough, PassThrough, ConvBwdDataDefault, true, true, 1, 256, 128, 256, 32, 8, 8, 32, 32, 2, 4, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, 1, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 1, 8, 8, 1, 1, 1, S<1, 32, 1, 8>, 8>,
DeviceGroupedConvBwdDataMultipleD_Xdl_CShuffle_v1< 2, GNHWK, GKYXC, EmptyTuple, GNHWC, F16, F16, F32, F16, EmptyTuple, F16, PassThrough, PassThrough, PassThrough, ConvBwdDataDefault, true, true, 1, 128, 128, 128, 32, 8, 8, 32, 32, 4, 2, S<4, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, 1, S<4, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 1, 8, 8, 1, 1, 1, S<1, 16, 1, 8>, 8>, DeviceGroupedConvBwdDataMultipleD_Xdl_CShuffle_v1< 2, GNHWK, GKYXC, Empty_Tuple, GNHWC, F16, F16, F32, F16, Empty_Tuple, F16, PassThrough, PassThrough, PassThrough, ConvBwdDataDefault, true, true, 1, 128, 128, 128, 32, 8, 8, 32, 32, 4, 2, S<4, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, 1, S<4, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 1, 8, 8, 1, 1, 1, S<1, 16, 1, 8>, 8>,
DeviceGroupedConvBwdDataMultipleD_Xdl_CShuffle_v1< 2, GNHWK, GKYXC, EmptyTuple, GNHWC, F16, F16, F32, F16, EmptyTuple, F16, PassThrough, PassThrough, PassThrough, ConvBwdDataDefault, true, true, 1, 256, 128, 128, 32, 8, 8, 32, 32, 2, 2, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, 1, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 1, 8, 8, 1, 1, 1, S<1, 32, 1, 8>, 8>, DeviceGroupedConvBwdDataMultipleD_Xdl_CShuffle_v1< 2, GNHWK, GKYXC, Empty_Tuple, GNHWC, F16, F16, F32, F16, Empty_Tuple, F16, PassThrough, PassThrough, PassThrough, ConvBwdDataDefault, true, true, 1, 256, 128, 128, 32, 8, 8, 32, 32, 2, 2, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, 1, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 1, 8, 8, 1, 1, 1, S<1, 32, 1, 8>, 8>,
DeviceGroupedConvBwdDataMultipleD_Xdl_CShuffle_v1< 2, GNHWK, GKYXC, EmptyTuple, GNHWC, F16, F16, F32, F16, EmptyTuple, F16, PassThrough, PassThrough, PassThrough, ConvBwdDataDefault, true, true, 1, 128, 128, 64, 32, 8, 8, 32, 32, 2, 2, S<4, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, 1, S<4, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 1, 8, 8, 1, 1, 1, S<1, 32, 1, 4>, 8>, DeviceGroupedConvBwdDataMultipleD_Xdl_CShuffle_v1< 2, GNHWK, GKYXC, Empty_Tuple, GNHWC, F16, F16, F32, F16, Empty_Tuple, F16, PassThrough, PassThrough, PassThrough, ConvBwdDataDefault, true, true, 1, 128, 128, 64, 32, 8, 8, 32, 32, 2, 2, S<4, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, 1, S<4, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 1, 8, 8, 1, 1, 1, S<1, 32, 1, 4>, 8>,
DeviceGroupedConvBwdDataMultipleD_Xdl_CShuffle_v1< 2, GNHWK, GKYXC, EmptyTuple, GNHWC, F16, F16, F32, F16, EmptyTuple, F16, PassThrough, PassThrough, PassThrough, ConvBwdDataDefault, true, true, 1, 128, 64, 128, 32, 8, 8, 32, 32, 2, 2, S<4, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, 1, S<4, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 1, 8, 8, 1, 1, 1, S<1, 16, 1, 8>, 8>, DeviceGroupedConvBwdDataMultipleD_Xdl_CShuffle_v1< 2, GNHWK, GKYXC, Empty_Tuple, GNHWC, F16, F16, F32, F16, Empty_Tuple, F16, PassThrough, PassThrough, PassThrough, ConvBwdDataDefault, true, true, 1, 128, 64, 128, 32, 8, 8, 32, 32, 2, 2, S<4, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, 1, S<4, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 1, 8, 8, 1, 1, 1, S<1, 16, 1, 8>, 8>,
DeviceGroupedConvBwdDataMultipleD_Xdl_CShuffle_v1< 2, GNHWK, GKYXC, EmptyTuple, GNHWC, F16, F16, F32, F16, EmptyTuple, F16, PassThrough, PassThrough, PassThrough, ConvBwdDataDefault, true, true, 1, 64, 64, 64, 32, 8, 8, 32, 32, 2, 2, S<4, 16, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, 1, S<4, 16, 1>, S<1, 0, 2>, S<1, 0, 2>, 1, 8, 8, 1, 1, 1, S<1, 16, 1, 4>, 8>, DeviceGroupedConvBwdDataMultipleD_Xdl_CShuffle_v1< 2, GNHWK, GKYXC, Empty_Tuple, GNHWC, F16, F16, F32, F16, Empty_Tuple, F16, PassThrough, PassThrough, PassThrough, ConvBwdDataDefault, true, true, 1, 64, 64, 64, 32, 8, 8, 32, 32, 2, 2, S<4, 16, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, 1, S<4, 16, 1>, S<1, 0, 2>, S<1, 0, 2>, 1, 8, 8, 1, 1, 1, S<1, 16, 1, 4>, 8>,
DeviceGroupedConvBwdDataMultipleD_Xdl_CShuffle_v1< 2, GNHWK, GKYXC, EmptyTuple, GNHWC, F16, F16, F32, F16, EmptyTuple, F16, PassThrough, PassThrough, PassThrough, ConvBwdDataDefault, true, true, 1, 256, 128, 64, 32, 8, 8, 32, 32, 2, 1, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, 1, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 1, 8, 8, 1, 1, 1, S<1, 32, 1, 8>, 8>, DeviceGroupedConvBwdDataMultipleD_Xdl_CShuffle_v1< 2, GNHWK, GKYXC, Empty_Tuple, GNHWC, F16, F16, F32, F16, Empty_Tuple, F16, PassThrough, PassThrough, PassThrough, ConvBwdDataDefault, true, true, 1, 256, 128, 64, 32, 8, 8, 32, 32, 2, 1, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, 1, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 1, 8, 8, 1, 1, 1, S<1, 32, 1, 8>, 8>,
DeviceGroupedConvBwdDataMultipleD_Xdl_CShuffle_v1< 2, GNHWK, GKYXC, EmptyTuple, GNHWC, F16, F16, F32, F16, EmptyTuple, F16, PassThrough, PassThrough, PassThrough, ConvBwdDataDefault, true, true, 1, 256, 64, 128, 32, 8, 8, 32, 32, 1, 2, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, 1, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 1, 8, 8, 1, 1, 1, S<1, 32, 1, 8>, 8>, DeviceGroupedConvBwdDataMultipleD_Xdl_CShuffle_v1< 2, GNHWK, GKYXC, Empty_Tuple, GNHWC, F16, F16, F32, F16, Empty_Tuple, F16, PassThrough, PassThrough, PassThrough, ConvBwdDataDefault, true, true, 1, 256, 64, 128, 32, 8, 8, 32, 32, 1, 2, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, 1, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 1, 8, 8, 1, 1, 1, S<1, 32, 1, 8>, 8>,
DeviceGroupedConvBwdDataMultipleD_Xdl_CShuffle_v1< 2, GNHWK, GKYXC, EmptyTuple, GNHWC, F16, F16, F32, F16, EmptyTuple, F16, PassThrough, PassThrough, PassThrough, ConvBwdDataDefault, true, true, 1, 128, 128, 32, 32, 8, 8, 32, 32, 2, 1, S<4, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, 1, S<4, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 1, 8, 8, 1, 1, 1, S<1, 32, 1, 4>, 8>, DeviceGroupedConvBwdDataMultipleD_Xdl_CShuffle_v1< 2, GNHWK, GKYXC, Empty_Tuple, GNHWC, F16, F16, F32, F16, Empty_Tuple, F16, PassThrough, PassThrough, PassThrough, ConvBwdDataDefault, true, true, 1, 128, 128, 32, 32, 8, 8, 32, 32, 2, 1, S<4, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, 1, S<4, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 1, 8, 8, 1, 1, 1, S<1, 32, 1, 4>, 8>,
DeviceGroupedConvBwdDataMultipleD_Xdl_CShuffle_v1< 2, GNHWK, GKYXC, EmptyTuple, GNHWC, F16, F16, F32, F16, EmptyTuple, F16, PassThrough, PassThrough, PassThrough, ConvBwdDataDefault, true, true, 1, 128, 32, 128, 32, 8, 8, 32, 32, 1, 2, S<4, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, 1, S<4, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 1, 8, 8, 1, 1, 1, S<1, 16, 1, 8>, 8>, DeviceGroupedConvBwdDataMultipleD_Xdl_CShuffle_v1< 2, GNHWK, GKYXC, Empty_Tuple, GNHWC, F16, F16, F32, F16, Empty_Tuple, F16, PassThrough, PassThrough, PassThrough, ConvBwdDataDefault, true, true, 1, 128, 32, 128, 32, 8, 8, 32, 32, 1, 2, S<4, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, 1, S<4, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 1, 8, 8, 1, 1, 1, S<1, 16, 1, 8>, 8>,
DeviceGroupedConvBwdDataMultipleD_Xdl_CShuffle_v1< 2, GNHWK, GKYXC, EmptyTuple, GNHWC, F16, F16, F32, F16, EmptyTuple, F16, PassThrough, PassThrough, PassThrough, ConvBwdDataDefault, true, true, 1, 64, 64, 32, 32, 8, 8, 32, 32, 2, 1, S<4, 16, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, 1, S<4, 16, 1>, S<1, 0, 2>, S<1, 0, 2>, 1, 8, 8, 1, 1, 1, S<1, 16, 1, 4>, 8>, DeviceGroupedConvBwdDataMultipleD_Xdl_CShuffle_v1< 2, GNHWK, GKYXC, Empty_Tuple, GNHWC, F16, F16, F32, F16, Empty_Tuple, F16, PassThrough, PassThrough, PassThrough, ConvBwdDataDefault, true, true, 1, 64, 64, 32, 32, 8, 8, 32, 32, 2, 1, S<4, 16, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, 1, S<4, 16, 1>, S<1, 0, 2>, S<1, 0, 2>, 1, 8, 8, 1, 1, 1, S<1, 16, 1, 4>, 8>,
DeviceGroupedConvBwdDataMultipleD_Xdl_CShuffle_v1< 2, GNHWK, GKYXC, EmptyTuple, GNHWC, F16, F16, F32, F16, EmptyTuple, F16, PassThrough, PassThrough, PassThrough, ConvBwdDataDefault, true, true, 1, 64, 32, 64, 32, 8, 8, 32, 32, 1, 2, S<4, 16, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, 1, S<4, 16, 1>, S<1, 0, 2>, S<1, 0, 2>, 1, 8, 8, 1, 1, 1, S<1, 16, 1, 4>, 8>, DeviceGroupedConvBwdDataMultipleD_Xdl_CShuffle_v1< 2, GNHWK, GKYXC, Empty_Tuple, GNHWC, F16, F16, F32, F16, Empty_Tuple, F16, PassThrough, PassThrough, PassThrough, ConvBwdDataDefault, true, true, 1, 64, 32, 64, 32, 8, 8, 32, 32, 1, 2, S<4, 16, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, 1, S<4, 16, 1>, S<1, 0, 2>, S<1, 0, 2>, 1, 8, 8, 1, 1, 1, S<1, 16, 1, 4>, 8>,
// 2. Filter1x1Stride1Pad0 // 2. Filter1x1Stride1Pad0
// ##############################################| NDim| ALayout| BLayout| DsLayout| ELayout| AData| BData| AccData| CShuffle| DsData| EData| AElementwise| BElementwise| CDEElementwise| ConvolutionBackward| DoPad| DoPad| NumGemmK| Block| MPer| NPer| KPer| AK1| BK1| MPer| NPer| MXdl| NXdl| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockLds| BBlockTransfer| BBlockTransfer| BBlockTransfer| BBlockTransfer| BBlockTransfer| BBlockTransfer| BBlockLds| CShuffleMXdl| CShuffleNXdl| CDEBlockTransfer| CDEBlockTransfer| // ##############################################| NDim| ALayout| BLayout| DsLayout| ELayout| AData| BData| AccData| CShuffle| DsData| EData| AElementwise| BElementwise| CDEElementwise| ConvolutionBackward| DoPad| DoPad| NumGemmK| Block| MPer| NPer| KPer| AK1| BK1| MPer| NPer| MXdl| NXdl| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockLds| BBlockTransfer| BBlockTransfer| BBlockTransfer| BBlockTransfer| BBlockTransfer| BBlockTransfer| BBlockLds| CShuffleMXdl| CShuffleNXdl| CDEBlockTransfer| CDEBlockTransfer|
// ##############################################| Spatial| | | | | Type| Type| Type| DataType| Type| Type| Operation| Operation| Operation| DataSpecialization| GemmM| GemmN| PrefetchStage| Size| Block| Block| Block| | | XDL| XDL| PerWave| PerWave| ThreadCluster| ThreadCluster| SrcAccessOrder| SrcVectorDim| SrcScalar| DstScalar| ExtraM| ThreadCluster| ThreadCluster| SrcAccessOrder| SrcVectorDim| SrcScalar| DstScalar| ExtraN| PerWave| PerWave| _MBlock_MPerBlock| ScalarPerVector| // ##############################################| Spatial| | | | | Type| Type| Type| DataType| Type| Type| Operation| Operation| Operation| DataSpecialization| GemmM| GemmN| PrefetchStage| Size| Block| Block| Block| | | XDL| XDL| PerWave| PerWave| ThreadCluster| ThreadCluster| SrcAccessOrder| SrcVectorDim| SrcScalar| DstScalar| ExtraM| ThreadCluster| ThreadCluster| SrcAccessOrder| SrcVectorDim| SrcScalar| DstScalar| ExtraN| PerWave| PerWave| _MBlock_MPerBlock| ScalarPerVector|
// ##############################################| | | | | | | | | | | | | | | | | | | | | | | | | | | | | Lengths_AK0_M_AK1| ArrangeOrder| | | PerVector| PerVector_AK1| | Lengths_BK0_N_BK1| ArrangeOrder| | | PerVector| PerVector_BK1| | PerShuffle| PerShuffle| _NBlock_NPerBlock| _NPerBlock| // ##############################################| | | | | | | | | | | | | | | | | | | | | | | | | | | | | Lengths_AK0_M_AK1| ArrangeOrder| | | PerVector| PerVector_AK1| | Lengths_BK0_N_BK1| ArrangeOrder| | | PerVector| PerVector_BK1| | PerShuffle| PerShuffle| _NBlock_NPerBlock| _NPerBlock|
// ##############################################| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | // ##############################################| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
DeviceGroupedConvBwdDataMultipleD_Xdl_CShuffle_v1< 2, GNHWK, GKYXC, EmptyTuple, GNHWC, F16, F16, F32, F16, EmptyTuple, F16, PassThrough, PassThrough, PassThrough, ConvBwdDataFilter1x1Stride1Pad0, true, true, 1, 256, 256, 128, 32, 8, 8, 32, 32, 4, 2, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, 1, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 1, 8, 8, 1, 1, 1, S<1, 32, 1, 8>, 8>, DeviceGroupedConvBwdDataMultipleD_Xdl_CShuffle_v1< 2, GNHWK, GKYXC, Empty_Tuple, GNHWC, F16, F16, F32, F16, Empty_Tuple, F16, PassThrough, PassThrough, PassThrough, ConvBwdDataFilter1x1Stride1Pad0, true, true, 1, 256, 256, 128, 32, 8, 8, 32, 32, 4, 2, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, 1, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 1, 8, 8, 1, 1, 1, S<1, 32, 1, 8>, 8>,
DeviceGroupedConvBwdDataMultipleD_Xdl_CShuffle_v1< 2, GNHWK, GKYXC, EmptyTuple, GNHWC, F16, F16, F32, F16, EmptyTuple, F16, PassThrough, PassThrough, PassThrough, ConvBwdDataFilter1x1Stride1Pad0, true, true, 1, 256, 128, 256, 32, 8, 8, 32, 32, 2, 4, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, 1, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 1, 8, 8, 1, 1, 1, S<1, 32, 1, 8>, 8>, DeviceGroupedConvBwdDataMultipleD_Xdl_CShuffle_v1< 2, GNHWK, GKYXC, Empty_Tuple, GNHWC, F16, F16, F32, F16, Empty_Tuple, F16, PassThrough, PassThrough, PassThrough, ConvBwdDataFilter1x1Stride1Pad0, true, true, 1, 256, 128, 256, 32, 8, 8, 32, 32, 2, 4, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, 1, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 1, 8, 8, 1, 1, 1, S<1, 32, 1, 8>, 8>,
DeviceGroupedConvBwdDataMultipleD_Xdl_CShuffle_v1< 2, GNHWK, GKYXC, EmptyTuple, GNHWC, F16, F16, F32, F16, EmptyTuple, F16, PassThrough, PassThrough, PassThrough, ConvBwdDataFilter1x1Stride1Pad0, true, true, 1, 128, 128, 128, 32, 8, 8, 32, 32, 4, 2, S<4, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, 1, S<4, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 1, 8, 8, 1, 1, 1, S<1, 16, 1, 8>, 8>, DeviceGroupedConvBwdDataMultipleD_Xdl_CShuffle_v1< 2, GNHWK, GKYXC, Empty_Tuple, GNHWC, F16, F16, F32, F16, Empty_Tuple, F16, PassThrough, PassThrough, PassThrough, ConvBwdDataFilter1x1Stride1Pad0, true, true, 1, 128, 128, 128, 32, 8, 8, 32, 32, 4, 2, S<4, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, 1, S<4, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 1, 8, 8, 1, 1, 1, S<1, 16, 1, 8>, 8>,
DeviceGroupedConvBwdDataMultipleD_Xdl_CShuffle_v1< 2, GNHWK, GKYXC, EmptyTuple, GNHWC, F16, F16, F32, F16, EmptyTuple, F16, PassThrough, PassThrough, PassThrough, ConvBwdDataFilter1x1Stride1Pad0, true, true, 1, 256, 128, 128, 32, 8, 8, 32, 32, 2, 2, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, 1, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 1, 8, 8, 1, 1, 1, S<1, 32, 1, 8>, 8>, DeviceGroupedConvBwdDataMultipleD_Xdl_CShuffle_v1< 2, GNHWK, GKYXC, Empty_Tuple, GNHWC, F16, F16, F32, F16, Empty_Tuple, F16, PassThrough, PassThrough, PassThrough, ConvBwdDataFilter1x1Stride1Pad0, true, true, 1, 256, 128, 128, 32, 8, 8, 32, 32, 2, 2, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, 1, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 1, 8, 8, 1, 1, 1, S<1, 32, 1, 8>, 8>,
DeviceGroupedConvBwdDataMultipleD_Xdl_CShuffle_v1< 2, GNHWK, GKYXC, EmptyTuple, GNHWC, F16, F16, F32, F16, EmptyTuple, F16, PassThrough, PassThrough, PassThrough, ConvBwdDataFilter1x1Stride1Pad0, true, true, 1, 128, 128, 64, 32, 8, 8, 32, 32, 2, 2, S<4, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, 1, S<4, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 1, 8, 8, 1, 1, 1, S<1, 32, 1, 4>, 8>, DeviceGroupedConvBwdDataMultipleD_Xdl_CShuffle_v1< 2, GNHWK, GKYXC, Empty_Tuple, GNHWC, F16, F16, F32, F16, Empty_Tuple, F16, PassThrough, PassThrough, PassThrough, ConvBwdDataFilter1x1Stride1Pad0, true, true, 1, 128, 128, 64, 32, 8, 8, 32, 32, 2, 2, S<4, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, 1, S<4, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 1, 8, 8, 1, 1, 1, S<1, 32, 1, 4>, 8>,
DeviceGroupedConvBwdDataMultipleD_Xdl_CShuffle_v1< 2, GNHWK, GKYXC, EmptyTuple, GNHWC, F16, F16, F32, F16, EmptyTuple, F16, PassThrough, PassThrough, PassThrough, ConvBwdDataFilter1x1Stride1Pad0, true, true, 1, 128, 64, 128, 32, 8, 8, 32, 32, 2, 2, S<4, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, 1, S<4, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 1, 8, 8, 1, 1, 1, S<1, 16, 1, 8>, 8>, DeviceGroupedConvBwdDataMultipleD_Xdl_CShuffle_v1< 2, GNHWK, GKYXC, Empty_Tuple, GNHWC, F16, F16, F32, F16, Empty_Tuple, F16, PassThrough, PassThrough, PassThrough, ConvBwdDataFilter1x1Stride1Pad0, true, true, 1, 128, 64, 128, 32, 8, 8, 32, 32, 2, 2, S<4, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, 1, S<4, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 1, 8, 8, 1, 1, 1, S<1, 16, 1, 8>, 8>,
DeviceGroupedConvBwdDataMultipleD_Xdl_CShuffle_v1< 2, GNHWK, GKYXC, EmptyTuple, GNHWC, F16, F16, F32, F16, EmptyTuple, F16, PassThrough, PassThrough, PassThrough, ConvBwdDataFilter1x1Stride1Pad0, true, true, 1, 64, 64, 64, 32, 8, 8, 32, 32, 2, 2, S<4, 16, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, 1, S<4, 16, 1>, S<1, 0, 2>, S<1, 0, 2>, 1, 8, 8, 1, 1, 1, S<1, 16, 1, 4>, 8>, DeviceGroupedConvBwdDataMultipleD_Xdl_CShuffle_v1< 2, GNHWK, GKYXC, Empty_Tuple, GNHWC, F16, F16, F32, F16, Empty_Tuple, F16, PassThrough, PassThrough, PassThrough, ConvBwdDataFilter1x1Stride1Pad0, true, true, 1, 64, 64, 64, 32, 8, 8, 32, 32, 2, 2, S<4, 16, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, 1, S<4, 16, 1>, S<1, 0, 2>, S<1, 0, 2>, 1, 8, 8, 1, 1, 1, S<1, 16, 1, 4>, 8>,
DeviceGroupedConvBwdDataMultipleD_Xdl_CShuffle_v1< 2, GNHWK, GKYXC, EmptyTuple, GNHWC, F16, F16, F32, F16, EmptyTuple, F16, PassThrough, PassThrough, PassThrough, ConvBwdDataFilter1x1Stride1Pad0, true, true, 1, 256, 128, 64, 32, 8, 8, 32, 32, 2, 1, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, 1, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 1, 8, 8, 1, 1, 1, S<1, 32, 1, 8>, 8>, DeviceGroupedConvBwdDataMultipleD_Xdl_CShuffle_v1< 2, GNHWK, GKYXC, Empty_Tuple, GNHWC, F16, F16, F32, F16, Empty_Tuple, F16, PassThrough, PassThrough, PassThrough, ConvBwdDataFilter1x1Stride1Pad0, true, true, 1, 256, 128, 64, 32, 8, 8, 32, 32, 2, 1, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, 1, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 1, 8, 8, 1, 1, 1, S<1, 32, 1, 8>, 8>,
DeviceGroupedConvBwdDataMultipleD_Xdl_CShuffle_v1< 2, GNHWK, GKYXC, EmptyTuple, GNHWC, F16, F16, F32, F16, EmptyTuple, F16, PassThrough, PassThrough, PassThrough, ConvBwdDataFilter1x1Stride1Pad0, true, true, 1, 256, 64, 128, 32, 8, 8, 32, 32, 1, 2, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, 1, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 1, 8, 8, 1, 1, 1, S<1, 32, 1, 8>, 8>, DeviceGroupedConvBwdDataMultipleD_Xdl_CShuffle_v1< 2, GNHWK, GKYXC, Empty_Tuple, GNHWC, F16, F16, F32, F16, Empty_Tuple, F16, PassThrough, PassThrough, PassThrough, ConvBwdDataFilter1x1Stride1Pad0, true, true, 1, 256, 64, 128, 32, 8, 8, 32, 32, 1, 2, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, 1, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 1, 8, 8, 1, 1, 1, S<1, 32, 1, 8>, 8>,
DeviceGroupedConvBwdDataMultipleD_Xdl_CShuffle_v1< 2, GNHWK, GKYXC, EmptyTuple, GNHWC, F16, F16, F32, F16, EmptyTuple, F16, PassThrough, PassThrough, PassThrough, ConvBwdDataFilter1x1Stride1Pad0, true, true, 1, 128, 128, 32, 32, 8, 8, 32, 32, 2, 1, S<4, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, 1, S<4, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 1, 8, 8, 1, 1, 1, S<1, 32, 1, 4>, 8>, DeviceGroupedConvBwdDataMultipleD_Xdl_CShuffle_v1< 2, GNHWK, GKYXC, Empty_Tuple, GNHWC, F16, F16, F32, F16, Empty_Tuple, F16, PassThrough, PassThrough, PassThrough, ConvBwdDataFilter1x1Stride1Pad0, true, true, 1, 128, 128, 32, 32, 8, 8, 32, 32, 2, 1, S<4, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, 1, S<4, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 1, 8, 8, 1, 1, 1, S<1, 32, 1, 4>, 8>,
DeviceGroupedConvBwdDataMultipleD_Xdl_CShuffle_v1< 2, GNHWK, GKYXC, EmptyTuple, GNHWC, F16, F16, F32, F16, EmptyTuple, F16, PassThrough, PassThrough, PassThrough, ConvBwdDataFilter1x1Stride1Pad0, true, true, 1, 128, 32, 128, 32, 8, 8, 32, 32, 1, 2, S<4, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, 1, S<4, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 1, 8, 8, 1, 1, 1, S<1, 16, 1, 8>, 8>, DeviceGroupedConvBwdDataMultipleD_Xdl_CShuffle_v1< 2, GNHWK, GKYXC, Empty_Tuple, GNHWC, F16, F16, F32, F16, Empty_Tuple, F16, PassThrough, PassThrough, PassThrough, ConvBwdDataFilter1x1Stride1Pad0, true, true, 1, 128, 32, 128, 32, 8, 8, 32, 32, 1, 2, S<4, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, 1, S<4, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 1, 8, 8, 1, 1, 1, S<1, 16, 1, 8>, 8>,
DeviceGroupedConvBwdDataMultipleD_Xdl_CShuffle_v1< 2, GNHWK, GKYXC, EmptyTuple, GNHWC, F16, F16, F32, F16, EmptyTuple, F16, PassThrough, PassThrough, PassThrough, ConvBwdDataFilter1x1Stride1Pad0, true, true, 1, 64, 64, 32, 32, 8, 8, 32, 32, 2, 1, S<4, 16, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, 1, S<4, 16, 1>, S<1, 0, 2>, S<1, 0, 2>, 1, 8, 8, 1, 1, 1, S<1, 16, 1, 4>, 8>, DeviceGroupedConvBwdDataMultipleD_Xdl_CShuffle_v1< 2, GNHWK, GKYXC, Empty_Tuple, GNHWC, F16, F16, F32, F16, Empty_Tuple, F16, PassThrough, PassThrough, PassThrough, ConvBwdDataFilter1x1Stride1Pad0, true, true, 1, 64, 64, 32, 32, 8, 8, 32, 32, 2, 1, S<4, 16, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, 1, S<4, 16, 1>, S<1, 0, 2>, S<1, 0, 2>, 1, 8, 8, 1, 1, 1, S<1, 16, 1, 4>, 8>,
DeviceGroupedConvBwdDataMultipleD_Xdl_CShuffle_v1< 2, GNHWK, GKYXC, EmptyTuple, GNHWC, F16, F16, F32, F16, EmptyTuple, F16, PassThrough, PassThrough, PassThrough, ConvBwdDataFilter1x1Stride1Pad0, true, true, 1, 64, 32, 64, 32, 8, 8, 32, 32, 1, 2, S<4, 16, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, 1, S<4, 16, 1>, S<1, 0, 2>, S<1, 0, 2>, 1, 8, 8, 1, 1, 1, S<1, 16, 1, 4>, 8> DeviceGroupedConvBwdDataMultipleD_Xdl_CShuffle_v1< 2, GNHWK, GKYXC, Empty_Tuple, GNHWC, F16, F16, F32, F16, Empty_Tuple, F16, PassThrough, PassThrough, PassThrough, ConvBwdDataFilter1x1Stride1Pad0, true, true, 1, 64, 32, 64, 32, 8, 8, 32, 32, 1, 2, S<4, 16, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, 1, S<4, 16, 1>, S<1, 0, 2>, S<1, 0, 2>, 1, 8, 8, 1, 1, 1, S<1, 16, 1, 4>, 8>
// clang-format on // clang-format on
>; >;
void add_device_grouped_conv2d_bwd_data_xdl_gnhwc_gkyxc_gnhwk_f16_instances( void add_device_grouped_conv2d_bwd_data_xdl_gnhwc_gkyxc_gnhwk_f16_instances(
std::vector<std::unique_ptr<DeviceGroupedConvBwdData<2, std::vector<std::unique_ptr<DeviceGroupedConvBwdDataMultipleD<2,
GNHWC, GNHWK,
GKYXC, GKYXC,
GNHWK, Empty_Tuple,
F16, GNHWC,
F16, F16,
F16, F16,
PassThrough, Empty_Tuple,
PassThrough, F16,
PassThrough>>>& instances) PassThrough,
PassThrough,
PassThrough>>>& instances)
{ {
add_device_operation_instances( add_device_operation_instances(
instances, device_grouped_conv2d_bwd_data_xdl_gnhwc_gkyxc_gnhwk_f16_instances{}); instances, device_grouped_conv2d_bwd_data_xdl_gnhwc_gkyxc_gnhwk_f16_instances{});
......
library/src/tensor_operation_instance/gpu/grouped_conv2d_bwd_weight/CMakeLists.txt 0 → 100644
View file @ f0224f2a
add_instance_library(device_grouped_conv2d_bwd_weight_instance
device_grouped_conv2d_bwd_weight_xdl_gnhwc_gkyxc_gnhwk_f16_instance.cpp
device_grouped_conv2d_bwd_weight_xdl_gnhwc_gkyxc_gnhwk_f32_instance.cpp
device_grouped_conv2d_bwd_weight_xdl_gnhwc_gkyxc_gnhwk_bf16_instance.cpp
)
library/src/tensor_operation_instance/gpu/grouped_conv2d_bwd_weight/device_grouped_conv2d_bwd_weight_xdl_gnhwc_gkyxc_gnhwk_bf16_instance.cpp 0 → 100644
View file @ f0224f2a
// SPDX-License-Identifier: MIT
// Copyright (c) 2018-2022, Advanced Micro Devices, Inc. All rights reserved.
#include <cstdlib>
#include "ck/ck.hpp"
#include "ck/tensor_operation/gpu/device/tensor_layout.hpp"
#include "ck/tensor_operation/gpu/device/impl/device_grouped_conv_bwd_weight_gnwc_gkxc_gnwk_xdl_cshuffle.hpp"
#include "ck/tensor_operation/gpu/element/element_wise_operation.hpp"
#include "ck/library/tensor_operation_instance/add_device_operation_instance.hpp"
namespace ck {
namespace tensor_operation {
namespace device {
namespace instance {
using BF16 = bhalf_t;
using F32 = float;
template <ck::index_t... Is>
using S = ck::Sequence<Is...>;
using GNHWC = ck::tensor_layout::convolution::GNHWC;
using GKYXC = ck::tensor_layout::convolution::GKYXC;
using GNHWK = ck::tensor_layout::convolution::GNHWK;
using PassThrough = ck::tensor_operation::element_wise::PassThrough;
static constexpr auto ConvBwdWeightDefault =
ck::tensor_operation::device::ConvolutionBackwardWeightSpecialization::Default;
static constexpr auto ConvBwdWeightFilter1x1Stride1Pad0 =
ck::tensor_operation::device::ConvolutionBackwardWeightSpecialization::Filter1x1Stride1Pad0;
// Compilation parameters for in[n, hi, wi, c] * wei[k, y, x, c] = out[n, ho, wo, k]
using device_grouped_conv2d_bwd_weight_xdl_c_shuffle_gnhwc_gkyxc_gnhwk_bf16_f32_bf16_instances =
std::tuple<
// clang-format off
//#########################################| Num| InData| WeiData| OutData| AccData| In| Wei| Out| ConvBackward| Block| MPer| NPer| K0Per| K1| MPer| NPer| MXdl| NXdl| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockLds| BBlockTransfer| BBlockTransfer| BBlockTransfer| BlockTransfer| BBlockTransfer| BBlockTransfer| BBlockLds| CShuffle| CShuffle| CBlockTransfer| CBlockTransfer|
//#########################################| Dim| Type| Type| Type| Type| Elementwise| Elementwise| Elementwise| Weight| Size| Block| Block| Block| | XDL| XDL| Per| Per| ThreadCluster| ThreadCluster| SrcAccessOrder| SrcVectorDim| SrcScalar| DstScalar| AddExtraM| ThreadCluster| ThreadCluster| SrcAccessOrder| SrcVectorDim| SrcScalar| DstScalar| AddExtraN| MXdlPerWave| NXdlPerWave| ClusterLengths| ScalarPerVector|
//#########################################| Spatial| | | | | Operation| Operation| Operation| Specialization| | | | | | | | Wave| Wave| Lengths_K0_M_K1| ArrangeOrder| | | PerVector| PerVector_K1| | Lengths_K0_N_K1| ArrangeOrder| | | PerVector| PerVector_K1| | PerShuffle| PerShuffle| MBlock_MPerBlock| NWaveNPerXdl|
//#########################################| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | NBlock_NPerBlock| |
DeviceGroupedConvBwdWeightGnwcGkxcGnwk_Xdl_CShuffle< 2, BF16, F32, BF16, F32, PassThrough, PassThrough, PassThrough, ConvBwdWeightDefault, 256, 256, 128, 4, 8, 32, 32, 4, 2, S<1, 4, 32, 2>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 8, 4, true, S<1, 4, 16, 4>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 8, 2, true, 1, 1, S<1, 32, 1, 8>, 4>,
DeviceGroupedConvBwdWeightGnwcGkxcGnwk_Xdl_CShuffle< 2, BF16, F32, BF16, F32, PassThrough, PassThrough, PassThrough, ConvBwdWeightDefault, 256, 128, 256, 4, 8, 32, 32, 2, 4, S<1, 4, 16, 4>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 8, 2, true, S<1, 4, 32, 2>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 8, 4, true, 1, 1, S<1, 32, 1, 8>, 4>,
DeviceGroupedConvBwdWeightGnwcGkxcGnwk_Xdl_CShuffle< 2, BF16, F32, BF16, F32, PassThrough, PassThrough, PassThrough, ConvBwdWeightDefault, 128, 128, 128, 4, 8, 32, 32, 4, 2, S<1, 4, 16, 2>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 8, 4, true, S<1, 4, 16, 2>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 8, 4, true, 1, 1, S<1, 32, 1, 4>, 4>,
DeviceGroupedConvBwdWeightGnwcGkxcGnwk_Xdl_CShuffle< 2, BF16, F32, BF16, F32, PassThrough, PassThrough, PassThrough, ConvBwdWeightDefault, 256, 128, 128, 4, 8, 32, 32, 2, 2, S<1, 4, 16, 4>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 8, 2, true, S<1, 4, 16, 4>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 8, 2, true, 1, 1, S<1, 32, 1, 4>, 4>,
DeviceGroupedConvBwdWeightGnwcGkxcGnwk_Xdl_CShuffle< 2, BF16, F32, BF16, F32, PassThrough, PassThrough, PassThrough, ConvBwdWeightDefault, 128, 128, 64, 4, 8, 32, 32, 2, 2, S<1, 4, 16, 2>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 8, 4, true, S<1, 4, 8, 4>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 8, 2, true, 1, 1, S<1, 32, 1, 4>, 4>,
DeviceGroupedConvBwdWeightGnwcGkxcGnwk_Xdl_CShuffle< 2, BF16, F32, BF16, F32, PassThrough, PassThrough, PassThrough, ConvBwdWeightDefault, 128, 64, 128, 4, 8, 32, 32, 2, 2, S<1, 4, 8, 4>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 8, 2, true, S<1, 4, 16, 2>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 8, 4, true, 1, 1, S<1, 32, 1, 4>, 4>,
DeviceGroupedConvBwdWeightGnwcGkxcGnwk_Xdl_CShuffle< 2, BF16, F32, BF16, F32, PassThrough, PassThrough, PassThrough, ConvBwdWeightDefault, 64, 64, 64, 4, 8, 32, 32, 2, 2, S<1, 4, 8, 2>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 8, 4, true, S<1, 4, 8, 2>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 8, 4, true, 1, 1, S<1, 16, 1, 4>, 4>,
DeviceGroupedConvBwdWeightGnwcGkxcGnwk_Xdl_CShuffle< 2, BF16, F32, BF16, F32, PassThrough, PassThrough, PassThrough, ConvBwdWeightDefault, 256, 128, 64, 4, 8, 32, 32, 2, 1, S<1, 4, 16, 4>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 8, 2, true, S<1, 4, 8, 8>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 8, 1, true, 1, 1, S<1, 32, 1, 4>, 4>,
DeviceGroupedConvBwdWeightGnwcGkxcGnwk_Xdl_CShuffle< 2, BF16, F32, BF16, F32, PassThrough, PassThrough, PassThrough, ConvBwdWeightDefault, 256, 64, 128, 4, 8, 32, 32, 1, 2, S<1, 4, 8, 8>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 8, 1, true, S<1, 4, 16, 4>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 8, 2, true, 1, 1, S<1, 32, 1, 4>, 4>,
DeviceGroupedConvBwdWeightGnwcGkxcGnwk_Xdl_CShuffle< 2, BF16, F32, BF16, F32, PassThrough, PassThrough, PassThrough, ConvBwdWeightDefault, 128, 128, 32, 4, 8, 32, 32, 2, 1, S<1, 4, 16, 2>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 8, 4, true, S<1, 4, 4, 8>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 8, 1, true, 1, 1, S<1, 32, 1, 4>, 4>,
DeviceGroupedConvBwdWeightGnwcGkxcGnwk_Xdl_CShuffle< 2, BF16, F32, BF16, F32, PassThrough, PassThrough, PassThrough, ConvBwdWeightDefault, 128, 32, 128, 4, 8, 32, 32, 1, 2, S<1, 4, 4, 8>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 8, 1, true, S<1, 4, 16, 2>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 8, 4, true, 1, 1, S<1, 32, 1, 4>, 4>,
DeviceGroupedConvBwdWeightGnwcGkxcGnwk_Xdl_CShuffle< 2, BF16, F32, BF16, F32, PassThrough, PassThrough, PassThrough, ConvBwdWeightDefault, 64, 64, 32, 4, 8, 32, 32, 2, 1, S<1, 4, 8, 2>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 8, 4, true, S<1, 4, 4, 4>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 8, 2, true, 1, 1, S<1, 16, 1, 4>, 4>,
DeviceGroupedConvBwdWeightGnwcGkxcGnwk_Xdl_CShuffle< 2, BF16, F32, BF16, F32, PassThrough, PassThrough, PassThrough, ConvBwdWeightDefault, 64, 32, 64, 4, 8, 32, 32, 1, 2, S<1, 4, 4, 4>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 8, 2, true, S<1, 4, 8, 2>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 8, 4, true, 1, 1, S<1, 16, 1, 4>, 4>
// clang-format on
>;
using device_grouped_conv2d_bwd_weight_xdl_gnhwc_gkyxc_gnhwk_1x1_s1_p0_bf16_f32_bf16_instances =
std::tuple<
// clang-format off
//#########################################| Num| InData| WeiData| OutData| AccData| In| Wei| Out| ConvBackward| Block| MPer| NPer| K0Per| K1| MPer| NPer| MXdl| NXdl| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockLds| BBlockTransfer| BBlockTransfer| BBlockTransfer| BlockTransfer| BBlockTransfer| BBlockTransfer| BBlockLds| CShuffle| CShuffle| CBlockTransfer| CBlockTransfer|
//#########################################| Dim| Type| Type| Type| Type| Elementwise| Elementwise| Elementwise| Weight| Size| Block| Block| Block| | XDL| XDL| Per| Per| ThreadCluster| ThreadCluster| SrcAccessOrder| SrcVectorDim| SrcScalar| DstScalar| AddExtraM| ThreadCluster| ThreadCluster| SrcAccessOrder| SrcVectorDim| SrcScalar| DstScalar| AddExtraN| MXdlPerWave| NXdlPerWave| ClusterLengths| ScalarPerVector|
//#########################################| Spatial| | | | | Operation| Operation| Operation| Specialization| | | | | | | | Wave| Wave| Lengths_K0_M_K1| ArrangeOrder| | | PerVector| PerVector_K1| | Lengths_K0_N_K1| ArrangeOrder| | | PerVector| PerVector_K1| | PerShuffle| PerShuffle| MBlock_MPerBlock| NWaveNPerXdl|
//#########################################| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | NBlock_NPerBlock| |
DeviceGroupedConvBwdWeightGnwcGkxcGnwk_Xdl_CShuffle< 2, BF16, F32, BF16, F32, PassThrough, PassThrough, PassThrough, ConvBwdWeightFilter1x1Stride1Pad0, 256, 256, 128, 4, 8, 32, 32, 4, 2, S<1, 4, 32, 2>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 8, 4, true, S<1, 4, 16, 4>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 8, 2, true, 1, 1, S<1, 32, 1, 8>, 4>,
DeviceGroupedConvBwdWeightGnwcGkxcGnwk_Xdl_CShuffle< 2, BF16, F32, BF16, F32, PassThrough, PassThrough, PassThrough, ConvBwdWeightFilter1x1Stride1Pad0, 256, 128, 256, 4, 8, 32, 32, 2, 4, S<1, 4, 16, 4>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 8, 2, true, S<1, 4, 32, 2>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 8, 4, true, 1, 1, S<1, 32, 1, 8>, 4>,
DeviceGroupedConvBwdWeightGnwcGkxcGnwk_Xdl_CShuffle< 2, BF16, F32, BF16, F32, PassThrough, PassThrough, PassThrough, ConvBwdWeightFilter1x1Stride1Pad0, 128, 128, 128, 4, 8, 32, 32, 4, 2, S<1, 4, 16, 2>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 8, 4, true, S<1, 4, 16, 2>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 8, 4, true, 1, 1, S<1, 32, 1, 4>, 4>,
DeviceGroupedConvBwdWeightGnwcGkxcGnwk_Xdl_CShuffle< 2, BF16, F32, BF16, F32, PassThrough, PassThrough, PassThrough, ConvBwdWeightFilter1x1Stride1Pad0, 256, 128, 128, 4, 8, 32, 32, 2, 2, S<1, 4, 16, 4>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 8, 2, true, S<1, 4, 16, 4>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 8, 2, true, 1, 1, S<1, 32, 1, 4>, 4>,
DeviceGroupedConvBwdWeightGnwcGkxcGnwk_Xdl_CShuffle< 2, BF16, F32, BF16, F32, PassThrough, PassThrough, PassThrough, ConvBwdWeightFilter1x1Stride1Pad0, 128, 128, 64, 4, 8, 32, 32, 2, 2, S<1, 4, 16, 2>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 8, 4, true, S<1, 4, 8, 4>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 8, 2, true, 1, 1, S<1, 32, 1, 4>, 4>,
DeviceGroupedConvBwdWeightGnwcGkxcGnwk_Xdl_CShuffle< 2, BF16, F32, BF16, F32, PassThrough, PassThrough, PassThrough, ConvBwdWeightFilter1x1Stride1Pad0, 128, 64, 128, 4, 8, 32, 32, 2, 2, S<1, 4, 8, 4>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 8, 2, true, S<1, 4, 16, 2>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 8, 4, true, 1, 1, S<1, 32, 1, 4>, 4>,
DeviceGroupedConvBwdWeightGnwcGkxcGnwk_Xdl_CShuffle< 2, BF16, F32, BF16, F32, PassThrough, PassThrough, PassThrough, ConvBwdWeightFilter1x1Stride1Pad0, 64, 64, 64, 4, 8, 32, 32, 2, 2, S<1, 4, 8, 2>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 8, 4, true, S<1, 4, 8, 2>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 8, 4, true, 1, 1, S<1, 16, 1, 4>, 4>,
DeviceGroupedConvBwdWeightGnwcGkxcGnwk_Xdl_CShuffle< 2, BF16, F32, BF16, F32, PassThrough, PassThrough, PassThrough, ConvBwdWeightFilter1x1Stride1Pad0, 256, 128, 64, 4, 8, 32, 32, 2, 1, S<1, 4, 16, 4>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 8, 2, true, S<1, 4, 8, 8>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 8, 1, true, 1, 1, S<1, 32, 1, 4>, 4>,
DeviceGroupedConvBwdWeightGnwcGkxcGnwk_Xdl_CShuffle< 2, BF16, F32, BF16, F32, PassThrough, PassThrough, PassThrough, ConvBwdWeightFilter1x1Stride1Pad0, 256, 64, 128, 4, 8, 32, 32, 1, 2, S<1, 4, 8, 8>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 8, 1, true, S<1, 4, 16, 4>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 8, 2, true, 1, 1, S<1, 32, 1, 4>, 4>,
DeviceGroupedConvBwdWeightGnwcGkxcGnwk_Xdl_CShuffle< 2, BF16, F32, BF16, F32, PassThrough, PassThrough, PassThrough, ConvBwdWeightFilter1x1Stride1Pad0, 128, 128, 32, 4, 8, 32, 32, 2, 1, S<1, 4, 16, 2>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 8, 4, true, S<1, 4, 4, 8>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 8, 1, true, 1, 1, S<1, 32, 1, 4>, 4>,
DeviceGroupedConvBwdWeightGnwcGkxcGnwk_Xdl_CShuffle< 2, BF16, F32, BF16, F32, PassThrough, PassThrough, PassThrough, ConvBwdWeightFilter1x1Stride1Pad0, 128, 32, 128, 4, 8, 32, 32, 1, 2, S<1, 4, 4, 8>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 8, 1, true, S<1, 4, 16, 2>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 8, 4, true, 1, 1, S<1, 32, 1, 4>, 4>,
DeviceGroupedConvBwdWeightGnwcGkxcGnwk_Xdl_CShuffle< 2, BF16, F32, BF16, F32, PassThrough, PassThrough, PassThrough, ConvBwdWeightFilter1x1Stride1Pad0, 64, 64, 32, 4, 8, 32, 32, 2, 1, S<1, 4, 8, 2>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 8, 4, true, S<1, 4, 4, 4>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 8, 2, true, 1, 1, S<1, 16, 1, 4>, 4>,
DeviceGroupedConvBwdWeightGnwcGkxcGnwk_Xdl_CShuffle< 2, BF16, F32, BF16, F32, PassThrough, PassThrough, PassThrough, ConvBwdWeightFilter1x1Stride1Pad0, 64, 32, 64, 4, 8, 32, 32, 1, 2, S<1, 4, 4, 4>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 8, 2, true, S<1, 4, 8, 2>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 8, 4, true, 1, 1, S<1, 16, 1, 4>, 4>
// clang-format on
>;
void add_device_grouped_conv2d_bwd_weight_xdl_gnhwc_gkyxc_gnhwk_bf16_f32_bf16_instances(
std::vector<std::unique_ptr<DeviceGroupedConvBwdWeight<2,
GNHWC,
GKYXC,
GNHWK,
BF16,
F32,
BF16,
PassThrough,
PassThrough,
PassThrough>>>& instances)
{
add_device_operation_instances(
instances,
device_grouped_conv2d_bwd_weight_xdl_c_shuffle_gnhwc_gkyxc_gnhwk_bf16_f32_bf16_instances{});
add_device_operation_instances(
instances,
device_grouped_conv2d_bwd_weight_xdl_gnhwc_gkyxc_gnhwk_1x1_s1_p0_bf16_f32_bf16_instances{});
}
} // namespace instance
} // namespace device
} // namespace tensor_operation
} // namespace ck
library/src/tensor_operation_instance/gpu/grouped_conv2d_bwd_weight/device_grouped_conv2d_bwd_weight_xdl_gnhwc_gkyxc_gnhwk_f16_instance.cpp 0 → 100644
View file @ f0224f2a
// SPDX-License-Identifier: MIT
// Copyright (c) 2018-2022, Advanced Micro Devices, Inc. All rights reserved.
#include <cstdlib>
#include "ck/ck.hpp"
#include "ck/tensor_operation/gpu/device/tensor_layout.hpp"
#include "ck/tensor_operation/gpu/device/impl/device_grouped_conv_bwd_weight_gnwc_gkxc_gnwk_xdl_cshuffle.hpp"
#include "ck/tensor_operation/gpu/element/element_wise_operation.hpp"
#include "ck/library/tensor_operation_instance/add_device_operation_instance.hpp"
namespace ck {
namespace tensor_operation {
namespace device {
namespace instance {
using F16 = ck::half_t;
using F32 = float;
template <ck::index_t... Is>
using S = ck::Sequence<Is...>;
using GNHWC = ck::tensor_layout::convolution::GNHWC;
using GKYXC = ck::tensor_layout::convolution::GKYXC;
using GNHWK = ck::tensor_layout::convolution::GNHWK;
using PassThrough = ck::tensor_operation::element_wise::PassThrough;
static constexpr auto ConvBwdWeightDefault =
ck::tensor_operation::device::ConvolutionBackwardWeightSpecialization::Default;
static constexpr auto ConvBwdWeightFilter1x1Stride1Pad0 =
ck::tensor_operation::device::ConvolutionBackwardWeightSpecialization::Filter1x1Stride1Pad0;
// Compilation parameters for in[n, hi, wi, c] * wei[k, y, x, c] = out[n, ho, wo, k]
using device_grouped_conv2d_bwd_weight_xdl_c_shuffle_gnhwc_gkyxc_gnhwk_f16_default_instances =
std::tuple<
// clang-format off
//#########################################| Num| InData| WeiData| OutData| AccData| In| Wei| Out| ConvBackward| Block| MPer| NPer| K0Per| K1| MPer| NPer| MXdl| NXdl| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockLds| BBlockTransfer| BBlockTransfer| BBlockTransfer| BlockTransfer| BBlockTransfer| BBlockTransfer| BBlockLds| CShuffle| CShuffle| CBlockTransfer| CBlockTransfer|
//#########################################| Dim| Type| Type| Type| Type| Elementwise| Elementwise| Elementwise| Weight| Size| Block| Block| Block| | XDL| XDL| Per| Per| ThreadCluster| ThreadCluster| SrcAccessOrder| SrcVectorDim| SrcScalar| DstScalar| AddExtraM| ThreadCluster| ThreadCluster| SrcAccessOrder| SrcVectorDim| SrcScalar| DstScalar| AddExtraN| MXdlPerWave| NXdlPerWave| ClusterLengths| ScalarPerVector|
//#########################################| Spatial| | | | | Operation| Operation| Operation| Specialization| | | | | | | | Wave| Wave| Lengths_K0_M_K1| ArrangeOrder| | | PerVector| PerVector_K1| | Lengths_K0_N_K1| ArrangeOrder| | | PerVector| PerVector_K1| | PerShuffle| PerShuffle| MBlock_MPerBlock| NWaveNPerXdl|
//#########################################| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | NBlock_NPerBlock| |
DeviceGroupedConvBwdWeightGnwcGkxcGnwk_Xdl_CShuffle< 2, F16, F16, F16, F32, PassThrough, PassThrough, PassThrough, ConvBwdWeightDefault, 256, 256, 128, 4, 8, 32, 32, 4, 2, S<1, 4, 32, 2>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 8, 4, true, S<1, 4, 16, 4>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 8, 2, true, 1, 1, S<1, 32, 1, 8>, 8>,
DeviceGroupedConvBwdWeightGnwcGkxcGnwk_Xdl_CShuffle< 2, F16, F16, F16, F32, PassThrough, PassThrough, PassThrough, ConvBwdWeightDefault, 256, 128, 256, 4, 8, 32, 32, 2, 4, S<1, 4, 16, 4>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 8, 2, true, S<1, 4, 32, 2>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 8, 4, true, 1, 1, S<1, 32, 1, 8>, 8>,
DeviceGroupedConvBwdWeightGnwcGkxcGnwk_Xdl_CShuffle< 2, F16, F16, F16, F32, PassThrough, PassThrough, PassThrough, ConvBwdWeightDefault, 128, 128, 128, 4, 8, 32, 32, 4, 2, S<1, 4, 16, 2>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 8, 4, true, S<1, 4, 16, 2>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 8, 4, true, 1, 1, S<1, 32, 1, 4>, 8>,
DeviceGroupedConvBwdWeightGnwcGkxcGnwk_Xdl_CShuffle< 2, F16, F16, F16, F32, PassThrough, PassThrough, PassThrough, ConvBwdWeightDefault, 256, 128, 128, 4, 8, 32, 32, 2, 2, S<1, 4, 16, 4>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 8, 2, true, S<1, 4, 16, 4>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 8, 2, true, 1, 1, S<1, 32, 1, 4>, 8>,
DeviceGroupedConvBwdWeightGnwcGkxcGnwk_Xdl_CShuffle< 2, F16, F16, F16, F32, PassThrough, PassThrough, PassThrough, ConvBwdWeightDefault, 128, 128, 64, 4, 8, 32, 32, 2, 2, S<1, 4, 16, 2>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 8, 4, true, S<1, 4, 8, 4>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 8, 2, true, 1, 1, S<1, 32, 1, 4>, 8>,
DeviceGroupedConvBwdWeightGnwcGkxcGnwk_Xdl_CShuffle< 2, F16, F16, F16, F32, PassThrough, PassThrough, PassThrough, ConvBwdWeightDefault, 128, 64, 128, 4, 8, 32, 32, 2, 2, S<1, 4, 8, 4>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 8, 2, true, S<1, 4, 16, 2>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 8, 4, true, 1, 1, S<1, 32, 1, 4>, 8>,
DeviceGroupedConvBwdWeightGnwcGkxcGnwk_Xdl_CShuffle< 2, F16, F16, F16, F32, PassThrough, PassThrough, PassThrough, ConvBwdWeightDefault, 64, 64, 64, 4, 8, 32, 32, 2, 2, S<1, 4, 8, 2>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 8, 4, true, S<1, 4, 8, 2>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 8, 4, true, 1, 1, S<1, 16, 1, 4>, 8>,
DeviceGroupedConvBwdWeightGnwcGkxcGnwk_Xdl_CShuffle< 2, F16, F16, F16, F32, PassThrough, PassThrough, PassThrough, ConvBwdWeightDefault, 256, 128, 64, 4, 8, 32, 32, 2, 1, S<1, 4, 16, 4>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 8, 2, true, S<1, 4, 8, 8>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 8, 1, true, 1, 1, S<1, 32, 1, 4>, 8>,
DeviceGroupedConvBwdWeightGnwcGkxcGnwk_Xdl_CShuffle< 2, F16, F16, F16, F32, PassThrough, PassThrough, PassThrough, ConvBwdWeightDefault, 256, 64, 128, 4, 8, 32, 32, 1, 2, S<1, 4, 8, 8>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 8, 1, true, S<1, 4, 16, 4>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 8, 2, true, 1, 1, S<1, 32, 1, 4>, 8>,
DeviceGroupedConvBwdWeightGnwcGkxcGnwk_Xdl_CShuffle< 2, F16, F16, F16, F32, PassThrough, PassThrough, PassThrough, ConvBwdWeightDefault, 128, 128, 32, 4, 8, 32, 32, 2, 1, S<1, 4, 16, 2>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 8, 4, true, S<1, 4, 4, 8>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 8, 1, true, 1, 1, S<1, 32, 1, 4>, 8>,
DeviceGroupedConvBwdWeightGnwcGkxcGnwk_Xdl_CShuffle< 2, F16, F16, F16, F32, PassThrough, PassThrough, PassThrough, ConvBwdWeightDefault, 128, 32, 128, 4, 8, 32, 32, 1, 2, S<1, 4, 4, 8>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 8, 1, true, S<1, 4, 16, 2>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 8, 4, true, 1, 1, S<1, 32, 1, 4>, 8>,
DeviceGroupedConvBwdWeightGnwcGkxcGnwk_Xdl_CShuffle< 2, F16, F16, F16, F32, PassThrough, PassThrough, PassThrough, ConvBwdWeightDefault, 64, 64, 32, 4, 8, 32, 32, 2, 1, S<1, 4, 8, 2>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 8, 4, true, S<1, 4, 4, 4>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 8, 2, true, 1, 1, S<1, 16, 1, 4>, 8>,
DeviceGroupedConvBwdWeightGnwcGkxcGnwk_Xdl_CShuffle< 2, F16, F16, F16, F32, PassThrough, PassThrough, PassThrough, ConvBwdWeightDefault, 64, 32, 64, 4, 8, 32, 32, 1, 2, S<1, 4, 4, 4>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 8, 2, true, S<1, 4, 8, 2>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 8, 4, true, 1, 1, S<1, 16, 1, 4>, 8>
// clang-format on
>;
using device_grouped_conv2d_bwd_weight_xdl_gnhwc_gkyxc_gnhwk_1x1_s1_p0_f16_instances = std::tuple<
// clang-format off
//#########################################| Num| InData| WeiData| OutData| AccData| In| Wei| Out| ConvBackward| Block| MPer| NPer| K0Per| K1| MPer| NPer| MXdl| NXdl| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockLds| BBlockTransfer| BBlockTransfer| BBlockTransfer| BlockTransfer| BBlockTransfer| BBlockTransfer| BBlockLds| CShuffle| CShuffle| CBlockTransfer| CBlockTransfer|
//#########################################| Dim| Type| Type| Type| Type| Elementwise| Elementwise| Elementwise| Weight| Size| Block| Block| Block| | XDL| XDL| Per| Per| ThreadCluster| ThreadCluster| SrcAccessOrder| SrcVectorDim| SrcScalar| DstScalar| AddExtraM| ThreadCluster| ThreadCluster| SrcAccessOrder| SrcVectorDim| SrcScalar| DstScalar| AddExtraN| MXdlPerWave| NXdlPerWave| ClusterLengths| ScalarPerVector|
//#########################################| Spatial| | | | | Operation| Operation| Operation| Specialization| | | | | | | | Wave| Wave| Lengths_K0_M_K1| ArrangeOrder| | | PerVector| PerVector_K1| | Lengths_K0_N_K1| ArrangeOrder| | | PerVector| PerVector_K1| | PerShuffle| PerShuffle| MBlock_MPerBlock| NWaveNPerXdl|
//#########################################| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | NBlock_NPerBlock| |
DeviceGroupedConvBwdWeightGnwcGkxcGnwk_Xdl_CShuffle< 2, F16, F16, F16, F32, PassThrough, PassThrough, PassThrough, ConvBwdWeightFilter1x1Stride1Pad0, 256, 256, 128, 4, 8, 32, 32, 4, 2, S<1, 4, 32, 2>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 8, 4, true, S<1, 4, 16, 4>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 8, 2, true, 1, 1, S<1, 32, 1, 8>, 8>,
DeviceGroupedConvBwdWeightGnwcGkxcGnwk_Xdl_CShuffle< 2, F16, F16, F16, F32, PassThrough, PassThrough, PassThrough, ConvBwdWeightFilter1x1Stride1Pad0, 256, 128, 256, 4, 8, 32, 32, 2, 4, S<1, 4, 16, 4>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 8, 2, true, S<1, 4, 32, 2>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 8, 4, true, 1, 1, S<1, 32, 1, 8>, 8>,
DeviceGroupedConvBwdWeightGnwcGkxcGnwk_Xdl_CShuffle< 2, F16, F16, F16, F32, PassThrough, PassThrough, PassThrough, ConvBwdWeightFilter1x1Stride1Pad0, 128, 128, 128, 4, 8, 32, 32, 4, 2, S<1, 4, 16, 2>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 8, 4, true, S<1, 4, 16, 2>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 8, 4, true, 1, 1, S<1, 32, 1, 4>, 8>,
DeviceGroupedConvBwdWeightGnwcGkxcGnwk_Xdl_CShuffle< 2, F16, F16, F16, F32, PassThrough, PassThrough, PassThrough, ConvBwdWeightFilter1x1Stride1Pad0, 256, 128, 128, 4, 8, 32, 32, 2, 2, S<1, 4, 16, 4>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 8, 2, true, S<1, 4, 16, 4>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 8, 2, true, 1, 1, S<1, 32, 1, 4>, 8>,
DeviceGroupedConvBwdWeightGnwcGkxcGnwk_Xdl_CShuffle< 2, F16, F16, F16, F32, PassThrough, PassThrough, PassThrough, ConvBwdWeightFilter1x1Stride1Pad0, 128, 128, 64, 4, 8, 32, 32, 2, 2, S<1, 4, 16, 2>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 8, 4, true, S<1, 4, 8, 4>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 8, 2, true, 1, 1, S<1, 32, 1, 4>, 8>,
DeviceGroupedConvBwdWeightGnwcGkxcGnwk_Xdl_CShuffle< 2, F16, F16, F16, F32, PassThrough, PassThrough, PassThrough, ConvBwdWeightFilter1x1Stride1Pad0, 128, 64, 128, 4, 8, 32, 32, 2, 2, S<1, 4, 8, 4>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 8, 2, true, S<1, 4, 16, 2>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 8, 4, true, 1, 1, S<1, 32, 1, 4>, 8>,
DeviceGroupedConvBwdWeightGnwcGkxcGnwk_Xdl_CShuffle< 2, F16, F16, F16, F32, PassThrough, PassThrough, PassThrough, ConvBwdWeightFilter1x1Stride1Pad0, 64, 64, 64, 4, 8, 32, 32, 2, 2, S<1, 4, 8, 2>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 8, 4, true, S<1, 4, 8, 2>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 8, 4, true, 1, 1, S<1, 16, 1, 4>, 8>,
DeviceGroupedConvBwdWeightGnwcGkxcGnwk_Xdl_CShuffle< 2, F16, F16, F16, F32, PassThrough, PassThrough, PassThrough, ConvBwdWeightFilter1x1Stride1Pad0, 256, 128, 64, 4, 8, 32, 32, 2, 1, S<1, 4, 16, 4>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 8, 2, true, S<1, 4, 8, 8>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 8, 1, true, 1, 1, S<1, 32, 1, 4>, 8>,
DeviceGroupedConvBwdWeightGnwcGkxcGnwk_Xdl_CShuffle< 2, F16, F16, F16, F32, PassThrough, PassThrough, PassThrough, ConvBwdWeightFilter1x1Stride1Pad0, 256, 64, 128, 4, 8, 32, 32, 1, 2, S<1, 4, 8, 8>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 8, 1, true, S<1, 4, 16, 4>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 8, 2, true, 1, 1, S<1, 32, 1, 4>, 8>,
DeviceGroupedConvBwdWeightGnwcGkxcGnwk_Xdl_CShuffle< 2, F16, F16, F16, F32, PassThrough, PassThrough, PassThrough, ConvBwdWeightFilter1x1Stride1Pad0, 128, 128, 32, 4, 8, 32, 32, 2, 1, S<1, 4, 16, 2>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 8, 4, true, S<1, 4, 4, 8>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 8, 1, true, 1, 1, S<1, 32, 1, 4>, 8>,
DeviceGroupedConvBwdWeightGnwcGkxcGnwk_Xdl_CShuffle< 2, F16, F16, F16, F32, PassThrough, PassThrough, PassThrough, ConvBwdWeightFilter1x1Stride1Pad0, 128, 32, 128, 4, 8, 32, 32, 1, 2, S<1, 4, 4, 8>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 8, 1, true, S<1, 4, 16, 2>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 8, 4, true, 1, 1, S<1, 32, 1, 4>, 8>,
DeviceGroupedConvBwdWeightGnwcGkxcGnwk_Xdl_CShuffle< 2, F16, F16, F16, F32, PassThrough, PassThrough, PassThrough, ConvBwdWeightFilter1x1Stride1Pad0, 64, 64, 32, 4, 8, 32, 32, 2, 1, S<1, 4, 8, 2>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 8, 4, true, S<1, 4, 4, 4>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 8, 2, true, 1, 1, S<1, 16, 1, 4>, 8>,
DeviceGroupedConvBwdWeightGnwcGkxcGnwk_Xdl_CShuffle< 2, F16, F16, F16, F32, PassThrough, PassThrough, PassThrough, ConvBwdWeightFilter1x1Stride1Pad0, 64, 32, 64, 4, 8, 32, 32, 1, 2, S<1, 4, 4, 4>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 8, 2, true, S<1, 4, 8, 2>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 8, 4, true, 1, 1, S<1, 16, 1, 4>, 8>
// clang-format on
>;
void add_device_grouped_conv2d_bwd_weight_xdl_gnhwc_gkyxc_gnhwk_f16_instances(
std::vector<std::unique_ptr<DeviceGroupedConvBwdWeight<2,
GNHWC,
GKYXC,
GNHWK,
F16,
F16,
F16,
PassThrough,
PassThrough,
PassThrough>>>& instances)
{
add_device_operation_instances(
instances,
device_grouped_conv2d_bwd_weight_xdl_c_shuffle_gnhwc_gkyxc_gnhwk_f16_default_instances{});
add_device_operation_instances(
instances,
device_grouped_conv2d_bwd_weight_xdl_gnhwc_gkyxc_gnhwk_1x1_s1_p0_f16_instances{});
}
} // namespace instance
} // namespace device
} // namespace tensor_operation
} // namespace ck
library/src/tensor_operation_instance/gpu/grouped_conv2d_bwd_weight/device_grouped_conv2d_bwd_weight_xdl_gnhwc_gkyxc_gnhwk_f32_instance.cpp 0 → 100644
View file @ f0224f2a
// SPDX-License-Identifier: MIT
// Copyright (c) 2018-2022, Advanced Micro Devices, Inc. All rights reserved.
#include <cstdlib>
#include "ck/ck.hpp"
#include "ck/tensor_operation/gpu/device/tensor_layout.hpp"
#include "ck/tensor_operation/gpu/device/impl/device_grouped_conv_bwd_weight_gnwc_gkxc_gnwk_xdl_cshuffle.hpp"
#include "ck/tensor_operation/gpu/element/element_wise_operation.hpp"
#include "ck/library/tensor_operation_instance/add_device_operation_instance.hpp"
namespace ck {
namespace tensor_operation {
namespace device {
namespace instance {
using F32 = float;
template <ck::index_t... Is>
using S = ck::Sequence<Is...>;
using GNHWC = ck::tensor_layout::convolution::GNHWC;
using GKYXC = ck::tensor_layout::convolution::GKYXC;
using GNHWK = ck::tensor_layout::convolution::GNHWK;
using PassThrough = ck::tensor_operation::element_wise::PassThrough;
static constexpr auto ConvBwdWeightDefault =
ck::tensor_operation::device::ConvolutionBackwardWeightSpecialization::Default;
static constexpr auto ConvBwdWeightFilter1x1Stride1Pad0 =
ck::tensor_operation::device::ConvolutionBackwardWeightSpecialization::Filter1x1Stride1Pad0;
// Compilation parameters for in[n, hi, wi, c] * wei[k, y, x, c] = out[n, ho, wo, k]
using device_grouped_conv2d_bwd_weight_xdl_c_shuffle_gnhwc_gkyxc_gnhwk_f32_default_instances =
std::tuple<
// clang-format off
//#########################################| Num| InData| WeiData| OutData| AccData| In| Wei| Out| ConvBackward| Block| MPer| NPer| K0Per| K1| MPer| NPer| MXdl| NXdl| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockLds| BBlockTransfer| BBlockTransfer| BBlockTransfer| BlockTransfer| BBlockTransfer| BBlockTransfer| BBlockLds| CShuffle| CShuffle| CBlockTransfer| CBlockTransfer|
//#########################################| Dim| Type| Type| Type| Type| Elementwise| Elementwise| Elementwise| Weight| Size| Block| Block| Block| | XDL| XDL| Per| Per| ThreadCluster| ThreadCluster| SrcAccessOrder| SrcVectorDim| SrcScalar| DstScalar| AddExtraM| ThreadCluster| ThreadCluster| SrcAccessOrder| SrcVectorDim| SrcScalar| DstScalar| AddExtraN| MXdlPerWave| NXdlPerWave| ClusterLengths| ScalarPerVector|
//#########################################| Spatial| | | | | Operation| Operation| Operation| Specialization| | | | | | | | Wave| Wave| Lengths_K0_M_K1| ArrangeOrder| | | PerVector| PerVector_K1| | Lengths_K0_N_K1| ArrangeOrder| | | PerVector| PerVector_K1| | PerShuffle| PerShuffle| MBlock_MPerBlock| NWaveNPerXdl|
//#########################################| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | NBlock_NPerBlock| |
DeviceGroupedConvBwdWeightGnwcGkxcGnwk_Xdl_CShuffle< 2, F32, F32, F32, F32, PassThrough, PassThrough, PassThrough, ConvBwdWeightDefault, 256, 256, 128, 4, 4, 32, 32, 4, 2, S<1, 4, 64, 1>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 4, 4, true, S<1, 4, 32, 2>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 4, 2, true, 1, 1, S<1, 32, 1, 8>, 4>,
DeviceGroupedConvBwdWeightGnwcGkxcGnwk_Xdl_CShuffle< 2, F32, F32, F32, F32, PassThrough, PassThrough, PassThrough, ConvBwdWeightDefault, 256, 128, 256, 4, 4, 32, 32, 2, 4, S<1, 4, 32, 2>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 4, 2, true, S<1, 4, 64, 1>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 4, 4, true, 1, 1, S<1, 32, 1, 8>, 4>,
DeviceGroupedConvBwdWeightGnwcGkxcGnwk_Xdl_CShuffle< 2, F32, F32, F32, F32, PassThrough, PassThrough, PassThrough, ConvBwdWeightDefault, 128, 128, 128, 4, 4, 32, 32, 4, 2, S<1, 4, 32, 1>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 4, 4, true, S<1, 4, 32, 1>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 4, 4, true, 1, 1, S<1, 32, 1, 4>, 4>,
DeviceGroupedConvBwdWeightGnwcGkxcGnwk_Xdl_CShuffle< 2, F32, F32, F32, F32, PassThrough, PassThrough, PassThrough, ConvBwdWeightDefault, 256, 128, 128, 4, 4, 32, 32, 2, 2, S<1, 4, 32, 2>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 4, 2, true, S<1, 4, 32, 2>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 4, 2, true, 1, 1, S<1, 32, 1, 4>, 4>,
DeviceGroupedConvBwdWeightGnwcGkxcGnwk_Xdl_CShuffle< 2, F32, F32, F32, F32, PassThrough, PassThrough, PassThrough, ConvBwdWeightDefault, 128, 128, 64, 4, 4, 32, 32, 2, 2, S<1, 4, 32, 1>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 4, 4, true, S<1, 4, 16, 2>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 4, 2, true, 1, 1, S<1, 32, 1, 4>, 4>,
DeviceGroupedConvBwdWeightGnwcGkxcGnwk_Xdl_CShuffle< 2, F32, F32, F32, F32, PassThrough, PassThrough, PassThrough, ConvBwdWeightDefault, 128, 64, 128, 4, 4, 32, 32, 2, 2, S<1, 4, 16, 2>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 4, 2, true, S<1, 4, 32, 1>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 4, 4, true, 1, 1, S<1, 32, 1, 4>, 4>,
DeviceGroupedConvBwdWeightGnwcGkxcGnwk_Xdl_CShuffle< 2, F32, F32, F32, F32, PassThrough, PassThrough, PassThrough, ConvBwdWeightDefault, 64, 64, 64, 4, 4, 32, 32, 2, 2, S<1, 4, 16, 1>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 4, 4, true, S<1, 4, 16, 1>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 4, 4, true, 1, 1, S<1, 16, 1, 4>, 4>,
DeviceGroupedConvBwdWeightGnwcGkxcGnwk_Xdl_CShuffle< 2, F32, F32, F32, F32, PassThrough, PassThrough, PassThrough, ConvBwdWeightDefault, 256, 128, 64, 4, 4, 32, 32, 2, 1, S<1, 4, 32, 2>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 4, 2, true, S<1, 4, 16, 4>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 4, 1, true, 1, 1, S<1, 32, 1, 4>, 4>,
DeviceGroupedConvBwdWeightGnwcGkxcGnwk_Xdl_CShuffle< 2, F32, F32, F32, F32, PassThrough, PassThrough, PassThrough, ConvBwdWeightDefault, 256, 64, 128, 4, 4, 32, 32, 1, 2, S<1, 4, 16, 4>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 4, 1, true, S<1, 4, 32, 2>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 4, 2, true, 1, 1, S<1, 32, 1, 4>, 4>,
DeviceGroupedConvBwdWeightGnwcGkxcGnwk_Xdl_CShuffle< 2, F32, F32, F32, F32, PassThrough, PassThrough, PassThrough, ConvBwdWeightDefault, 128, 128, 32, 4, 4, 32, 32, 2, 1, S<1, 4, 32, 1>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 4, 4, true, S<1, 4, 8, 4>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 4, 1, true, 1, 1, S<1, 32, 1, 4>, 4>,
DeviceGroupedConvBwdWeightGnwcGkxcGnwk_Xdl_CShuffle< 2, F32, F32, F32, F32, PassThrough, PassThrough, PassThrough, ConvBwdWeightDefault, 128, 32, 128, 4, 4, 32, 32, 1, 2, S<1, 4, 8, 4>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 4, 1, true, S<1, 4, 32, 1>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 4, 4, true, 1, 1, S<1, 32, 1, 4>, 4>,
DeviceGroupedConvBwdWeightGnwcGkxcGnwk_Xdl_CShuffle< 2, F32, F32, F32, F32, PassThrough, PassThrough, PassThrough, ConvBwdWeightDefault, 64, 64, 32, 4, 4, 32, 32, 2, 1, S<1, 4, 16, 1>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 4, 4, true, S<1, 4, 8, 2>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 4, 2, true, 1, 1, S<1, 16, 1, 4>, 4>,
DeviceGroupedConvBwdWeightGnwcGkxcGnwk_Xdl_CShuffle< 2, F32, F32, F32, F32, PassThrough, PassThrough, PassThrough, ConvBwdWeightDefault, 64, 32, 64, 4, 4, 32, 32, 1, 2, S<1, 4, 8, 2>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 4, 2, true, S<1, 4, 16, 1>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 4, 4, true, 1, 1, S<1, 16, 1, 4>, 4>
// clang-format on
>;
using device_grouped_conv2d_bwd_weight_xdl_gnhwc_gkyxc_gnhwk_1x1_s1_p0_f32_instances = std::tuple<
// clang-format off
//#########################################| Num| InData| WeiData| OutData| AccData| In| Wei| Out| ConvBackward| Block| MPer| NPer| K0Per| K1| MPer| NPer| MXdl| NXdl| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockLds| BBlockTransfer| BBlockTransfer| BBlockTransfer| BlockTransfer| BBlockTransfer| BBlockTransfer| BBlockLds| CShuffle| CShuffle| CBlockTransfer| CBlockTransfer|
//#########################################| Dim| Type| Type| Type| Type| Elementwise| Elementwise| Elementwise| Weight| Size| Block| Block| Block| | XDL| XDL| Per| Per| ThreadCluster| ThreadCluster| SrcAccessOrder| SrcVectorDim| SrcScalar| DstScalar| AddExtraM| ThreadCluster| ThreadCluster| SrcAccessOrder| SrcVectorDim| SrcScalar| DstScalar| AddExtraN| MXdlPerWave| NXdlPerWave| ClusterLengths| ScalarPerVector|
//#########################################| Spatial| | | | | Operation| Operation| Operation| Specialization| | | | | | | | Wave| Wave| Lengths_K0_M_K1| ArrangeOrder| | | PerVector| PerVector_K1| | Lengths_K0_N_K1| ArrangeOrder| | | PerVector| PerVector_K1| | PerShuffle| PerShuffle| MBlock_MPerBlock| NWaveNPerXdl|
//#########################################| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | NBlock_NPerBlock| |
DeviceGroupedConvBwdWeightGnwcGkxcGnwk_Xdl_CShuffle< 2, F32, F32, F32, F32, PassThrough, PassThrough, PassThrough, ConvBwdWeightFilter1x1Stride1Pad0, 256, 256, 128, 4, 4, 32, 32, 4, 2, S<1, 4, 64, 1>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 4, 4, true, S<1, 4, 32, 2>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 4, 2, true, 1, 1, S<1, 32, 1, 8>, 4>,
DeviceGroupedConvBwdWeightGnwcGkxcGnwk_Xdl_CShuffle< 2, F32, F32, F32, F32, PassThrough, PassThrough, PassThrough, ConvBwdWeightFilter1x1Stride1Pad0, 256, 128, 256, 4, 4, 32, 32, 2, 4, S<1, 4, 32, 2>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 4, 2, true, S<1, 4, 64, 1>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 4, 4, true, 1, 1, S<1, 32, 1, 8>, 4>,
DeviceGroupedConvBwdWeightGnwcGkxcGnwk_Xdl_CShuffle< 2, F32, F32, F32, F32, PassThrough, PassThrough, PassThrough, ConvBwdWeightFilter1x1Stride1Pad0, 128, 128, 128, 4, 4, 32, 32, 4, 2, S<1, 4, 32, 1>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 4, 4, true, S<1, 4, 32, 1>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 4, 4, true, 1, 1, S<1, 32, 1, 4>, 4>,
DeviceGroupedConvBwdWeightGnwcGkxcGnwk_Xdl_CShuffle< 2, F32, F32, F32, F32, PassThrough, PassThrough, PassThrough, ConvBwdWeightFilter1x1Stride1Pad0, 256, 128, 128, 4, 4, 32, 32, 2, 2, S<1, 4, 32, 2>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 4, 2, true, S<1, 4, 32, 2>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 4, 2, true, 1, 1, S<1, 32, 1, 4>, 4>,
DeviceGroupedConvBwdWeightGnwcGkxcGnwk_Xdl_CShuffle< 2, F32, F32, F32, F32, PassThrough, PassThrough, PassThrough, ConvBwdWeightFilter1x1Stride1Pad0, 128, 128, 64, 4, 4, 32, 32, 2, 2, S<1, 4, 32, 1>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 4, 4, true, S<1, 4, 16, 2>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 4, 2, true, 1, 1, S<1, 32, 1, 4>, 4>,
DeviceGroupedConvBwdWeightGnwcGkxcGnwk_Xdl_CShuffle< 2, F32, F32, F32, F32, PassThrough, PassThrough, PassThrough, ConvBwdWeightFilter1x1Stride1Pad0, 128, 64, 128, 4, 4, 32, 32, 2, 2, S<1, 4, 16, 2>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 4, 2, true, S<1, 4, 32, 1>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 4, 4, true, 1, 1, S<1, 32, 1, 4>, 4>,
DeviceGroupedConvBwdWeightGnwcGkxcGnwk_Xdl_CShuffle< 2, F32, F32, F32, F32, PassThrough, PassThrough, PassThrough, ConvBwdWeightFilter1x1Stride1Pad0, 64, 64, 64, 4, 4, 32, 32, 2, 2, S<1, 4, 16, 1>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 4, 4, true, S<1, 4, 16, 1>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 4, 4, true, 1, 1, S<1, 16, 1, 4>, 4>,
DeviceGroupedConvBwdWeightGnwcGkxcGnwk_Xdl_CShuffle< 2, F32, F32, F32, F32, PassThrough, PassThrough, PassThrough, ConvBwdWeightFilter1x1Stride1Pad0, 256, 128, 64, 4, 4, 32, 32, 2, 1, S<1, 4, 32, 2>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 4, 2, true, S<1, 4, 16, 4>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 4, 1, true, 1, 1, S<1, 32, 1, 4>, 4>,
DeviceGroupedConvBwdWeightGnwcGkxcGnwk_Xdl_CShuffle< 2, F32, F32, F32, F32, PassThrough, PassThrough, PassThrough, ConvBwdWeightFilter1x1Stride1Pad0, 256, 64, 128, 4, 4, 32, 32, 1, 2, S<1, 4, 16, 4>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 4, 1, true, S<1, 4, 32, 2>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 4, 2, true, 1, 1, S<1, 32, 1, 4>, 4>,
DeviceGroupedConvBwdWeightGnwcGkxcGnwk_Xdl_CShuffle< 2, F32, F32, F32, F32, PassThrough, PassThrough, PassThrough, ConvBwdWeightFilter1x1Stride1Pad0, 128, 128, 32, 4, 4, 32, 32, 2, 1, S<1, 4, 32, 1>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 4, 4, true, S<1, 4, 8, 4>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 4, 1, true, 1, 1, S<1, 32, 1, 4>, 4>,
DeviceGroupedConvBwdWeightGnwcGkxcGnwk_Xdl_CShuffle< 2, F32, F32, F32, F32, PassThrough, PassThrough, PassThrough, ConvBwdWeightFilter1x1Stride1Pad0, 128, 32, 128, 4, 4, 32, 32, 1, 2, S<1, 4, 8, 4>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 4, 1, true, S<1, 4, 32, 1>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 4, 4, true, 1, 1, S<1, 32, 1, 4>, 4>,
DeviceGroupedConvBwdWeightGnwcGkxcGnwk_Xdl_CShuffle< 2, F32, F32, F32, F32, PassThrough, PassThrough, PassThrough, ConvBwdWeightFilter1x1Stride1Pad0, 64, 64, 32, 4, 4, 32, 32, 2, 1, S<1, 4, 16, 1>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 4, 4, true, S<1, 4, 8, 2>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 4, 2, true, 1, 1, S<1, 16, 1, 4>, 4>,
DeviceGroupedConvBwdWeightGnwcGkxcGnwk_Xdl_CShuffle< 2, F32, F32, F32, F32, PassThrough, PassThrough, PassThrough, ConvBwdWeightFilter1x1Stride1Pad0, 64, 32, 64, 4, 4, 32, 32, 1, 2, S<1, 4, 8, 2>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 4, 2, true, S<1, 4, 16, 1>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 4, 4, true, 1, 1, S<1, 16, 1, 4>, 4>
// clang-format on
>;
void add_device_grouped_conv2d_bwd_weight_xdl_gnhwc_gkyxc_gnhwk_f32_instances(
std::vector<std::unique_ptr<DeviceGroupedConvBwdWeight<2,
GNHWC,
GKYXC,
GNHWK,
F32,
F32,
F32,
PassThrough,
PassThrough,
PassThrough>>>& instances)
{
add_device_operation_instances(
instances,
device_grouped_conv2d_bwd_weight_xdl_c_shuffle_gnhwc_gkyxc_gnhwk_f32_default_instances{});
add_device_operation_instances(
instances,
device_grouped_conv2d_bwd_weight_xdl_gnhwc_gkyxc_gnhwk_1x1_s1_p0_f32_instances{});
}
} // namespace instance
} // namespace device
} // namespace tensor_operation
} // namespace ck
library/src/tensor_operation_instance/gpu/grouped_conv3d_bwd_weight/CMakeLists.txt 0 → 100644
View file @ f0224f2a
add_instance_library(device_grouped_conv3d_bwd_weight_instance
device_grouped_conv3d_bwd_weight_xdl_gndhwc_gkzyxc_gndhwk_f16_instance.cpp
device_grouped_conv3d_bwd_weight_xdl_gndhwc_gkzyxc_gndhwk_f32_instance.cpp
device_grouped_conv3d_bwd_weight_xdl_gndhwc_gkzyxc_gndhwk_bf16_instance.cpp
)
library/src/tensor_operation_instance/gpu/grouped_conv3d_bwd_weight/device_grouped_conv3d_bwd_weight_xdl_gndhwc_gkzyxc_gndhwk_bf16_instance.cpp 0 → 100644
View file @ f0224f2a
// SPDX-License-Identifier: MIT
// Copyright (c) 2018-2022, Advanced Micro Devices, Inc. All rights reserved.
#include <cstdlib>
#include "ck/ck.hpp"
#include "ck/tensor_operation/gpu/device/tensor_layout.hpp"
#include "ck/tensor_operation/gpu/device/impl/device_grouped_conv_bwd_weight_gnwc_gkxc_gnwk_xdl_cshuffle.hpp"
#include "ck/tensor_operation/gpu/element/element_wise_operation.hpp"
#include "ck/library/tensor_operation_instance/add_device_operation_instance.hpp"
namespace ck {
namespace tensor_operation {
namespace device {
namespace instance {
using BF16 = bhalf_t;
using F32 = float;
template <ck::index_t... Is>
using S = ck::Sequence<Is...>;
using GNDHWC = ck::tensor_layout::convolution::GNDHWC;
using GKZYXC = ck::tensor_layout::convolution::GKZYXC;
using GNDHWK = ck::tensor_layout::convolution::GNDHWK;
using PassThrough = ck::tensor_operation::element_wise::PassThrough;
static constexpr auto ConvBwdWeightDefault =
ck::tensor_operation::device::ConvolutionBackwardWeightSpecialization::Default;
static constexpr auto ConvBwdWeightFilter1x1Stride1Pad0 =
ck::tensor_operation::device::ConvolutionBackwardWeightSpecialization::Filter1x1Stride1Pad0;
// Compilation parameters for in[n, di, hi, wi, c] * wei[k, z, y, x, c] = out[n, do, ho, wo, k]
using device_grouped_conv3d_bwd_weight_xdl_c_shuffle_gndhwc_gkzyxc_gndhwk_bf16_f32_bf16_instances =
std::tuple<
// clang-format off
//#########################################| Num| InData| WeiData| OutData| AccData| In| Wei| Out| ConvBackward| Block| MPer| NPer| K0Per| K1| MPer| NPer| MXdl| NXdl| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockLds| BBlockTransfer| BBlockTransfer| BBlockTransfer| BlockTransfer| BBlockTransfer| BBlockTransfer| BBlockLds| CShuffle| CShuffle| CBlockTransfer| CBlockTransfer|
//#########################################| Dim| Type| Type| Type| Type| Elementwise| Elementwise| Elementwise| Weight| Size| Block| Block| Block| | XDL| XDL| Per| Per| ThreadCluster| ThreadCluster| SrcAccessOrder| SrcVectorDim| SrcScalar| DstScalar| AddExtraM| ThreadCluster| ThreadCluster| SrcAccessOrder| SrcVectorDim| SrcScalar| DstScalar| AddExtraN| MXdlPerWave| NXdlPerWave| ClusterLengths| ScalarPerVector|
//#########################################| Spatial| | | | | Operation| Operation| Operation| Specialization| | | | | | | | Wave| Wave| Lengths_K0_M_K1| ArrangeOrder| | | PerVector| PerVector_K1| | Lengths_K0_N_K1| ArrangeOrder| | | PerVector| PerVector_K1| | PerShuffle| PerShuffle| MBlock_MPerBlock| NWaveNPerXdl|
//#########################################| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | NBlock_NPerBlock| |
DeviceGroupedConvBwdWeightGnwcGkxcGnwk_Xdl_CShuffle< 3, BF16, F32, BF16, F32, PassThrough, PassThrough, PassThrough, ConvBwdWeightDefault, 256, 256, 128, 4, 8, 32, 32, 4, 2, S<1, 4, 32, 2>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 8, 4, true, S<1, 4, 16, 4>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 8, 2, true, 1, 1, S<1, 32, 1, 8>, 4>,
DeviceGroupedConvBwdWeightGnwcGkxcGnwk_Xdl_CShuffle< 3, BF16, F32, BF16, F32, PassThrough, PassThrough, PassThrough, ConvBwdWeightDefault, 256, 128, 256, 4, 8, 32, 32, 2, 4, S<1, 4, 16, 4>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 8, 2, true, S<1, 4, 32, 2>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 8, 4, true, 1, 1, S<1, 32, 1, 8>, 4>,
DeviceGroupedConvBwdWeightGnwcGkxcGnwk_Xdl_CShuffle< 3, BF16, F32, BF16, F32, PassThrough, PassThrough, PassThrough, ConvBwdWeightDefault, 128, 128, 128, 4, 8, 32, 32, 4, 2, S<1, 4, 16, 2>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 8, 4, true, S<1, 4, 16, 2>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 8, 4, true, 1, 1, S<1, 32, 1, 4>, 4>,
DeviceGroupedConvBwdWeightGnwcGkxcGnwk_Xdl_CShuffle< 3, BF16, F32, BF16, F32, PassThrough, PassThrough, PassThrough, ConvBwdWeightDefault, 256, 128, 128, 4, 8, 32, 32, 2, 2, S<1, 4, 16, 4>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 8, 2, true, S<1, 4, 16, 4>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 8, 2, true, 1, 1, S<1, 32, 1, 4>, 4>,
DeviceGroupedConvBwdWeightGnwcGkxcGnwk_Xdl_CShuffle< 3, BF16, F32, BF16, F32, PassThrough, PassThrough, PassThrough, ConvBwdWeightDefault, 128, 128, 64, 4, 8, 32, 32, 2, 2, S<1, 4, 16, 2>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 8, 4, true, S<1, 4, 8, 4>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 8, 2, true, 1, 1, S<1, 32, 1, 4>, 4>,
DeviceGroupedConvBwdWeightGnwcGkxcGnwk_Xdl_CShuffle< 3, BF16, F32, BF16, F32, PassThrough, PassThrough, PassThrough, ConvBwdWeightDefault, 128, 64, 128, 4, 8, 32, 32, 2, 2, S<1, 4, 8, 4>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 8, 2, true, S<1, 4, 16, 2>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 8, 4, true, 1, 1, S<1, 32, 1, 4>, 4>,
DeviceGroupedConvBwdWeightGnwcGkxcGnwk_Xdl_CShuffle< 3, BF16, F32, BF16, F32, PassThrough, PassThrough, PassThrough, ConvBwdWeightDefault, 64, 64, 64, 4, 8, 32, 32, 2, 2, S<1, 4, 8, 2>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 8, 4, true, S<1, 4, 8, 2>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 8, 4, true, 1, 1, S<1, 16, 1, 4>, 4>,
DeviceGroupedConvBwdWeightGnwcGkxcGnwk_Xdl_CShuffle< 3, BF16, F32, BF16, F32, PassThrough, PassThrough, PassThrough, ConvBwdWeightDefault, 256, 128, 64, 4, 8, 32, 32, 2, 1, S<1, 4, 16, 4>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 8, 2, true, S<1, 4, 8, 8>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 8, 1, true, 1, 1, S<1, 32, 1, 4>, 4>,
DeviceGroupedConvBwdWeightGnwcGkxcGnwk_Xdl_CShuffle< 3, BF16, F32, BF16, F32, PassThrough, PassThrough, PassThrough, ConvBwdWeightDefault, 256, 64, 128, 4, 8, 32, 32, 1, 2, S<1, 4, 8, 8>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 8, 1, true, S<1, 4, 16, 4>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 8, 2, true, 1, 1, S<1, 32, 1, 4>, 4>,
DeviceGroupedConvBwdWeightGnwcGkxcGnwk_Xdl_CShuffle< 3, BF16, F32, BF16, F32, PassThrough, PassThrough, PassThrough, ConvBwdWeightDefault, 128, 128, 32, 4, 8, 32, 32, 2, 1, S<1, 4, 16, 2>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 8, 4, true, S<1, 4, 4, 8>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 8, 1, true, 1, 1, S<1, 32, 1, 4>, 4>,
DeviceGroupedConvBwdWeightGnwcGkxcGnwk_Xdl_CShuffle< 3, BF16, F32, BF16, F32, PassThrough, PassThrough, PassThrough, ConvBwdWeightDefault, 128, 32, 128, 4, 8, 32, 32, 1, 2, S<1, 4, 4, 8>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 8, 1, true, S<1, 4, 16, 2>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 8, 4, true, 1, 1, S<1, 32, 1, 4>, 4>,
DeviceGroupedConvBwdWeightGnwcGkxcGnwk_Xdl_CShuffle< 3, BF16, F32, BF16, F32, PassThrough, PassThrough, PassThrough, ConvBwdWeightDefault, 64, 64, 32, 4, 8, 32, 32, 2, 1, S<1, 4, 8, 2>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 8, 4, true, S<1, 4, 4, 4>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 8, 2, true, 1, 1, S<1, 16, 1, 4>, 4>,
DeviceGroupedConvBwdWeightGnwcGkxcGnwk_Xdl_CShuffle< 3, BF16, F32, BF16, F32, PassThrough, PassThrough, PassThrough, ConvBwdWeightDefault, 64, 32, 64, 4, 8, 32, 32, 1, 2, S<1, 4, 4, 4>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 8, 2, true, S<1, 4, 8, 2>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 8, 4, true, 1, 1, S<1, 16, 1, 4>, 4>
// clang-format on
>;
using device_grouped_conv3d_bwd_weight_xdl_gndhwc_gkzyxc_gndhwk_1x1_s1_p0_bf16_f32_bf16_instances =
std::tuple<
// clang-format off
//#########################################| Num| InData| WeiData| OutData| AccData| In| Wei| Out| ConvBackward| Block| MPer| NPer| K0Per| K1| MPer| NPer| MXdl| NXdl| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockLds| BBlockTransfer| BBlockTransfer| BBlockTransfer| BlockTransfer| BBlockTransfer| BBlockTransfer| BBlockLds| CShuffle| CShuffle| CBlockTransfer| CBlockTransfer|
//#########################################| Dim| Type| Type| Type| Type| Elementwise| Elementwise| Elementwise| Weight| Size| Block| Block| Block| | XDL| XDL| Per| Per| ThreadCluster| ThreadCluster| SrcAccessOrder| SrcVectorDim| SrcScalar| DstScalar| AddExtraM| ThreadCluster| ThreadCluster| SrcAccessOrder| SrcVectorDim| SrcScalar| DstScalar| AddExtraN| MXdlPerWave| NXdlPerWave| ClusterLengths| ScalarPerVector|
//#########################################| Spatial| | | | | Operation| Operation| Operation| Specialization| | | | | | | | Wave| Wave| Lengths_K0_M_K1| ArrangeOrder| | | PerVector| PerVector_K1| | Lengths_K0_N_K1| ArrangeOrder| | | PerVector| PerVector_K1| | PerShuffle| PerShuffle| MBlock_MPerBlock| NWaveNPerXdl|
//#########################################| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | NBlock_NPerBlock| |
DeviceGroupedConvBwdWeightGnwcGkxcGnwk_Xdl_CShuffle< 3, BF16, F32, BF16, F32, PassThrough, PassThrough, PassThrough, ConvBwdWeightFilter1x1Stride1Pad0, 256, 256, 128, 4, 8, 32, 32, 4, 2, S<1, 4, 32, 2>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 8, 4, true, S<1, 4, 16, 4>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 8, 2, true, 1, 1, S<1, 32, 1, 8>, 4>,
DeviceGroupedConvBwdWeightGnwcGkxcGnwk_Xdl_CShuffle< 3, BF16, F32, BF16, F32, PassThrough, PassThrough, PassThrough, ConvBwdWeightFilter1x1Stride1Pad0, 256, 128, 256, 4, 8, 32, 32, 2, 4, S<1, 4, 16, 4>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 8, 2, true, S<1, 4, 32, 2>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 8, 4, true, 1, 1, S<1, 32, 1, 8>, 4>,
DeviceGroupedConvBwdWeightGnwcGkxcGnwk_Xdl_CShuffle< 3, BF16, F32, BF16, F32, PassThrough, PassThrough, PassThrough, ConvBwdWeightFilter1x1Stride1Pad0, 128, 128, 128, 4, 8, 32, 32, 4, 2, S<1, 4, 16, 2>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 8, 4, true, S<1, 4, 16, 2>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 8, 4, true, 1, 1, S<1, 32, 1, 4>, 4>,
DeviceGroupedConvBwdWeightGnwcGkxcGnwk_Xdl_CShuffle< 3, BF16, F32, BF16, F32, PassThrough, PassThrough, PassThrough, ConvBwdWeightFilter1x1Stride1Pad0, 256, 128, 128, 4, 8, 32, 32, 2, 2, S<1, 4, 16, 4>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 8, 2, true, S<1, 4, 16, 4>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 8, 2, true, 1, 1, S<1, 32, 1, 4>, 4>,
DeviceGroupedConvBwdWeightGnwcGkxcGnwk_Xdl_CShuffle< 3, BF16, F32, BF16, F32, PassThrough, PassThrough, PassThrough, ConvBwdWeightFilter1x1Stride1Pad0, 128, 128, 64, 4, 8, 32, 32, 2, 2, S<1, 4, 16, 2>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 8, 4, true, S<1, 4, 8, 4>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 8, 2, true, 1, 1, S<1, 32, 1, 4>, 4>,
DeviceGroupedConvBwdWeightGnwcGkxcGnwk_Xdl_CShuffle< 3, BF16, F32, BF16, F32, PassThrough, PassThrough, PassThrough, ConvBwdWeightFilter1x1Stride1Pad0, 128, 64, 128, 4, 8, 32, 32, 2, 2, S<1, 4, 8, 4>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 8, 2, true, S<1, 4, 16, 2>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 8, 4, true, 1, 1, S<1, 32, 1, 4>, 4>,
DeviceGroupedConvBwdWeightGnwcGkxcGnwk_Xdl_CShuffle< 3, BF16, F32, BF16, F32, PassThrough, PassThrough, PassThrough, ConvBwdWeightFilter1x1Stride1Pad0, 64, 64, 64, 4, 8, 32, 32, 2, 2, S<1, 4, 8, 2>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 8, 4, true, S<1, 4, 8, 2>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 8, 4, true, 1, 1, S<1, 16, 1, 4>, 4>,
DeviceGroupedConvBwdWeightGnwcGkxcGnwk_Xdl_CShuffle< 3, BF16, F32, BF16, F32, PassThrough, PassThrough, PassThrough, ConvBwdWeightFilter1x1Stride1Pad0, 256, 128, 64, 4, 8, 32, 32, 2, 1, S<1, 4, 16, 4>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 8, 2, true, S<1, 4, 8, 8>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 8, 1, true, 1, 1, S<1, 32, 1, 4>, 4>,
DeviceGroupedConvBwdWeightGnwcGkxcGnwk_Xdl_CShuffle< 3, BF16, F32, BF16, F32, PassThrough, PassThrough, PassThrough, ConvBwdWeightFilter1x1Stride1Pad0, 256, 64, 128, 4, 8, 32, 32, 1, 2, S<1, 4, 8, 8>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 8, 1, true, S<1, 4, 16, 4>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 8, 2, true, 1, 1, S<1, 32, 1, 4>, 4>,
DeviceGroupedConvBwdWeightGnwcGkxcGnwk_Xdl_CShuffle< 3, BF16, F32, BF16, F32, PassThrough, PassThrough, PassThrough, ConvBwdWeightFilter1x1Stride1Pad0, 128, 128, 32, 4, 8, 32, 32, 2, 1, S<1, 4, 16, 2>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 8, 4, true, S<1, 4, 4, 8>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 8, 1, true, 1, 1, S<1, 32, 1, 4>, 4>,
DeviceGroupedConvBwdWeightGnwcGkxcGnwk_Xdl_CShuffle< 3, BF16, F32, BF16, F32, PassThrough, PassThrough, PassThrough, ConvBwdWeightFilter1x1Stride1Pad0, 128, 32, 128, 4, 8, 32, 32, 1, 2, S<1, 4, 4, 8>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 8, 1, true, S<1, 4, 16, 2>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 8, 4, true, 1, 1, S<1, 32, 1, 4>, 4>,
DeviceGroupedConvBwdWeightGnwcGkxcGnwk_Xdl_CShuffle< 3, BF16, F32, BF16, F32, PassThrough, PassThrough, PassThrough, ConvBwdWeightFilter1x1Stride1Pad0, 64, 64, 32, 4, 8, 32, 32, 2, 1, S<1, 4, 8, 2>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 8, 4, true, S<1, 4, 4, 4>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 8, 2, true, 1, 1, S<1, 16, 1, 4>, 4>,
DeviceGroupedConvBwdWeightGnwcGkxcGnwk_Xdl_CShuffle< 3, BF16, F32, BF16, F32, PassThrough, PassThrough, PassThrough, ConvBwdWeightFilter1x1Stride1Pad0, 64, 32, 64, 4, 8, 32, 32, 1, 2, S<1, 4, 4, 4>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 8, 2, true, S<1, 4, 8, 2>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 8, 4, true, 1, 1, S<1, 16, 1, 4>, 4>
// clang-format on
>;
void add_device_grouped_conv3d_bwd_weight_xdl_gndhwc_gkzyxc_gndhwk_bf16_f32_bf16_instances(
std::vector<std::unique_ptr<DeviceGroupedConvBwdWeight<3,
GNDHWC,
GKZYXC,
GNDHWK,
BF16,
F32,
BF16,
PassThrough,
PassThrough,
PassThrough>>>& instances)
{
add_device_operation_instances(
instances,
device_grouped_conv3d_bwd_weight_xdl_c_shuffle_gndhwc_gkzyxc_gndhwk_bf16_f32_bf16_instances{});
add_device_operation_instances(
instances,
device_grouped_conv3d_bwd_weight_xdl_gndhwc_gkzyxc_gndhwk_1x1_s1_p0_bf16_f32_bf16_instances{});
}
} // namespace instance
} // namespace device
} // namespace tensor_operation
} // namespace ck
library/src/tensor_operation_instance/gpu/grouped_conv3d_bwd_weight/device_grouped_conv3d_bwd_weight_xdl_gndhwc_gkzyxc_gndhwk_f16_instance.cpp 0 → 100644
View file @ f0224f2a
// SPDX-License-Identifier: MIT
// Copyright (c) 2018-2022, Advanced Micro Devices, Inc. All rights reserved.
#include <cstdlib>
#include "ck/ck.hpp"
#include "ck/tensor_operation/gpu/device/tensor_layout.hpp"
#include "ck/tensor_operation/gpu/device/impl/device_grouped_conv_bwd_weight_gnwc_gkxc_gnwk_xdl_cshuffle.hpp"
#include "ck/tensor_operation/gpu/element/element_wise_operation.hpp"
#include "ck/library/tensor_operation_instance/add_device_operation_instance.hpp"
namespace ck {
namespace tensor_operation {
namespace device {
namespace instance {
using F16 = ck::half_t;
using F32 = float;
template <ck::index_t... Is>
using S = ck::Sequence<Is...>;
using GNDHWC = ck::tensor_layout::convolution::GNDHWC;
using GKZYXC = ck::tensor_layout::convolution::GKZYXC;
using GNDHWK = ck::tensor_layout::convolution::GNDHWK;
using PassThrough = ck::tensor_operation::element_wise::PassThrough;
static constexpr auto ConvBwdWeightDefault =
ck::tensor_operation::device::ConvolutionBackwardWeightSpecialization::Default;
static constexpr auto ConvBwdWeightFilter1x1Stride1Pad0 =
ck::tensor_operation::device::ConvolutionBackwardWeightSpecialization::Filter1x1Stride1Pad0;
// Compilation parameters for in[n, di, hi, wi, c] * wei[k, z, y, x, c] = out[n, do, ho, wo, k]
using device_grouped_conv3d_bwd_weight_xdl_c_shuffle_gndhwc_gkzyxc_gndhwk_f16_default_instances =
std::tuple<
// clang-format off
//#########################################| Num| InData| WeiData| OutData| AccData| In| Wei| Out| ConvBackward| Block| MPer| NPer| K0Per| K1| MPer| NPer| MXdl| NXdl| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockLds| BBlockTransfer| BBlockTransfer| BBlockTransfer| BlockTransfer| BBlockTransfer| BBlockTransfer| BBlockLds| CShuffle| CShuffle| CBlockTransfer| CBlockTransfer|
//#########################################| Dim| Type| Type| Type| Type| Elementwise| Elementwise| Elementwise| Weight| Size| Block| Block| Block| | XDL| XDL| Per| Per| ThreadCluster| ThreadCluster| SrcAccessOrder| SrcVectorDim| SrcScalar| DstScalar| AddExtraM| ThreadCluster| ThreadCluster| SrcAccessOrder| SrcVectorDim| SrcScalar| DstScalar| AddExtraN| MXdlPerWave| NXdlPerWave| ClusterLengths| ScalarPerVector|
//#########################################| Spatial| | | | | Operation| Operation| Operation| Specialization| | | | | | | | Wave| Wave| Lengths_K0_M_K1| ArrangeOrder| | | PerVector| PerVector_K1| | Lengths_K0_N_K1| ArrangeOrder| | | PerVector| PerVector_K1| | PerShuffle| PerShuffle| MBlock_MPerBlock| NWaveNPerXdl|
//#########################################| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | NBlock_NPerBlock| |
DeviceGroupedConvBwdWeightGnwcGkxcGnwk_Xdl_CShuffle< 3, F16, F16, F16, F32, PassThrough, PassThrough, PassThrough, ConvBwdWeightDefault, 256, 256, 128, 4, 8, 32, 32, 4, 2, S<1, 4, 32, 2>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 8, 4, true, S<1, 4, 16, 4>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 8, 2, true, 1, 1, S<1, 32, 1, 8>, 8>,
DeviceGroupedConvBwdWeightGnwcGkxcGnwk_Xdl_CShuffle< 3, F16, F16, F16, F32, PassThrough, PassThrough, PassThrough, ConvBwdWeightDefault, 256, 128, 256, 4, 8, 32, 32, 2, 4, S<1, 4, 16, 4>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 8, 2, true, S<1, 4, 32, 2>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 8, 4, true, 1, 1, S<1, 32, 1, 8>, 8>,
DeviceGroupedConvBwdWeightGnwcGkxcGnwk_Xdl_CShuffle< 3, F16, F16, F16, F32, PassThrough, PassThrough, PassThrough, ConvBwdWeightDefault, 128, 128, 128, 4, 8, 32, 32, 4, 2, S<1, 4, 16, 2>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 8, 4, true, S<1, 4, 16, 2>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 8, 4, true, 1, 1, S<1, 32, 1, 4>, 8>,
DeviceGroupedConvBwdWeightGnwcGkxcGnwk_Xdl_CShuffle< 3, F16, F16, F16, F32, PassThrough, PassThrough, PassThrough, ConvBwdWeightDefault, 256, 128, 128, 4, 8, 32, 32, 2, 2, S<1, 4, 16, 4>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 8, 2, true, S<1, 4, 16, 4>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 8, 2, true, 1, 1, S<1, 32, 1, 4>, 8>,
DeviceGroupedConvBwdWeightGnwcGkxcGnwk_Xdl_CShuffle< 3, F16, F16, F16, F32, PassThrough, PassThrough, PassThrough, ConvBwdWeightDefault, 128, 128, 64, 4, 8, 32, 32, 2, 2, S<1, 4, 16, 2>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 8, 4, true, S<1, 4, 8, 4>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 8, 2, true, 1, 1, S<1, 32, 1, 4>, 8>,
DeviceGroupedConvBwdWeightGnwcGkxcGnwk_Xdl_CShuffle< 3, F16, F16, F16, F32, PassThrough, PassThrough, PassThrough, ConvBwdWeightDefault, 128, 64, 128, 4, 8, 32, 32, 2, 2, S<1, 4, 8, 4>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 8, 2, true, S<1, 4, 16, 2>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 8, 4, true, 1, 1, S<1, 32, 1, 4>, 8>,
DeviceGroupedConvBwdWeightGnwcGkxcGnwk_Xdl_CShuffle< 3, F16, F16, F16, F32, PassThrough, PassThrough, PassThrough, ConvBwdWeightDefault, 64, 64, 64, 4, 8, 32, 32, 2, 2, S<1, 4, 8, 2>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 8, 4, true, S<1, 4, 8, 2>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 8, 4, true, 1, 1, S<1, 16, 1, 4>, 8>,
DeviceGroupedConvBwdWeightGnwcGkxcGnwk_Xdl_CShuffle< 3, F16, F16, F16, F32, PassThrough, PassThrough, PassThrough, ConvBwdWeightDefault, 256, 128, 64, 4, 8, 32, 32, 2, 1, S<1, 4, 16, 4>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 8, 2, true, S<1, 4, 8, 8>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 8, 1, true, 1, 1, S<1, 32, 1, 4>, 8>,
DeviceGroupedConvBwdWeightGnwcGkxcGnwk_Xdl_CShuffle< 3, F16, F16, F16, F32, PassThrough, PassThrough, PassThrough, ConvBwdWeightDefault, 256, 64, 128, 4, 8, 32, 32, 1, 2, S<1, 4, 8, 8>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 8, 1, true, S<1, 4, 16, 4>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 8, 2, true, 1, 1, S<1, 32, 1, 4>, 8>,
DeviceGroupedConvBwdWeightGnwcGkxcGnwk_Xdl_CShuffle< 3, F16, F16, F16, F32, PassThrough, PassThrough, PassThrough, ConvBwdWeightDefault, 128, 128, 32, 4, 8, 32, 32, 2, 1, S<1, 4, 16, 2>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 8, 4, true, S<1, 4, 4, 8>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 8, 1, true, 1, 1, S<1, 32, 1, 4>, 8>,
DeviceGroupedConvBwdWeightGnwcGkxcGnwk_Xdl_CShuffle< 3, F16, F16, F16, F32, PassThrough, PassThrough, PassThrough, ConvBwdWeightDefault, 128, 32, 128, 4, 8, 32, 32, 1, 2, S<1, 4, 4, 8>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 8, 1, true, S<1, 4, 16, 2>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 8, 4, true, 1, 1, S<1, 32, 1, 4>, 8>,
DeviceGroupedConvBwdWeightGnwcGkxcGnwk_Xdl_CShuffle< 3, F16, F16, F16, F32, PassThrough, PassThrough, PassThrough, ConvBwdWeightDefault, 64, 64, 32, 4, 8, 32, 32, 2, 1, S<1, 4, 8, 2>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 8, 4, true, S<1, 4, 4, 4>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 8, 2, true, 1, 1, S<1, 16, 1, 4>, 8>,
DeviceGroupedConvBwdWeightGnwcGkxcGnwk_Xdl_CShuffle< 3, F16, F16, F16, F32, PassThrough, PassThrough, PassThrough, ConvBwdWeightDefault, 64, 32, 64, 4, 8, 32, 32, 1, 2, S<1, 4, 4, 4>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 8, 2, true, S<1, 4, 8, 2>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 8, 4, true, 1, 1, S<1, 16, 1, 4>, 8>
// clang-format on
>;
using device_grouped_conv3d_bwd_weight_xdl_gndhwc_gkzyxc_gndhwk_1x1_s1_p0_f16_instances =
std::tuple<
// clang-format off
//#########################################| Num| InData| WeiData| OutData| AccData| In| Wei| Out| ConvBackward| Block| MPer| NPer| K0Per| K1| MPer| NPer| MXdl| NXdl| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockLds| BBlockTransfer| BBlockTransfer| BBlockTransfer| BlockTransfer| BBlockTransfer| BBlockTransfer| BBlockLds| CShuffle| CShuffle| CBlockTransfer| CBlockTransfer|
//#########################################| Dim| Type| Type| Type| Type| Elementwise| Elementwise| Elementwise| Weight| Size| Block| Block| Block| | XDL| XDL| Per| Per| ThreadCluster| ThreadCluster| SrcAccessOrder| SrcVectorDim| SrcScalar| DstScalar| AddExtraM| ThreadCluster| ThreadCluster| SrcAccessOrder| SrcVectorDim| SrcScalar| DstScalar| AddExtraN| MXdlPerWave| NXdlPerWave| ClusterLengths| ScalarPerVector|
//#########################################| Spatial| | | | | Operation| Operation| Operation| Specialization| | | | | | | | Wave| Wave| Lengths_K0_M_K1| ArrangeOrder| | | PerVector| PerVector_K1| | Lengths_K0_N_K1| ArrangeOrder| | | PerVector| PerVector_K1| | PerShuffle| PerShuffle| MBlock_MPerBlock| NWaveNPerXdl|
//#########################################| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | NBlock_NPerBlock| |
DeviceGroupedConvBwdWeightGnwcGkxcGnwk_Xdl_CShuffle< 3, F16, F16, F16, F32, PassThrough, PassThrough, PassThrough, ConvBwdWeightFilter1x1Stride1Pad0, 256, 256, 128, 4, 8, 32, 32, 4, 2, S<1, 4, 32, 2>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 8, 4, true, S<1, 4, 16, 4>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 8, 2, true, 1, 1, S<1, 32, 1, 8>, 8>,
DeviceGroupedConvBwdWeightGnwcGkxcGnwk_Xdl_CShuffle< 3, F16, F16, F16, F32, PassThrough, PassThrough, PassThrough, ConvBwdWeightFilter1x1Stride1Pad0, 256, 128, 256, 4, 8, 32, 32, 2, 4, S<1, 4, 16, 4>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 8, 2, true, S<1, 4, 32, 2>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 8, 4, true, 1, 1, S<1, 32, 1, 8>, 8>,
DeviceGroupedConvBwdWeightGnwcGkxcGnwk_Xdl_CShuffle< 3, F16, F16, F16, F32, PassThrough, PassThrough, PassThrough, ConvBwdWeightFilter1x1Stride1Pad0, 128, 128, 128, 4, 8, 32, 32, 4, 2, S<1, 4, 16, 2>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 8, 4, true, S<1, 4, 16, 2>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 8, 4, true, 1, 1, S<1, 32, 1, 4>, 8>,
DeviceGroupedConvBwdWeightGnwcGkxcGnwk_Xdl_CShuffle< 3, F16, F16, F16, F32, PassThrough, PassThrough, PassThrough, ConvBwdWeightFilter1x1Stride1Pad0, 256, 128, 128, 4, 8, 32, 32, 2, 2, S<1, 4, 16, 4>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 8, 2, true, S<1, 4, 16, 4>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 8, 2, true, 1, 1, S<1, 32, 1, 4>, 8>,
DeviceGroupedConvBwdWeightGnwcGkxcGnwk_Xdl_CShuffle< 3, F16, F16, F16, F32, PassThrough, PassThrough, PassThrough, ConvBwdWeightFilter1x1Stride1Pad0, 128, 128, 64, 4, 8, 32, 32, 2, 2, S<1, 4, 16, 2>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 8, 4, true, S<1, 4, 8, 4>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 8, 2, true, 1, 1, S<1, 32, 1, 4>, 8>,
DeviceGroupedConvBwdWeightGnwcGkxcGnwk_Xdl_CShuffle< 3, F16, F16, F16, F32, PassThrough, PassThrough, PassThrough, ConvBwdWeightFilter1x1Stride1Pad0, 128, 64, 128, 4, 8, 32, 32, 2, 2, S<1, 4, 8, 4>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 8, 2, true, S<1, 4, 16, 2>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 8, 4, true, 1, 1, S<1, 32, 1, 4>, 8>,
DeviceGroupedConvBwdWeightGnwcGkxcGnwk_Xdl_CShuffle< 3, F16, F16, F16, F32, PassThrough, PassThrough, PassThrough, ConvBwdWeightFilter1x1Stride1Pad0, 64, 64, 64, 4, 8, 32, 32, 2, 2, S<1, 4, 8, 2>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 8, 4, true, S<1, 4, 8, 2>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 8, 4, true, 1, 1, S<1, 16, 1, 4>, 8>,
DeviceGroupedConvBwdWeightGnwcGkxcGnwk_Xdl_CShuffle< 3, F16, F16, F16, F32, PassThrough, PassThrough, PassThrough, ConvBwdWeightFilter1x1Stride1Pad0, 256, 128, 64, 4, 8, 32, 32, 2, 1, S<1, 4, 16, 4>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 8, 2, true, S<1, 4, 8, 8>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 8, 1, true, 1, 1, S<1, 32, 1, 4>, 8>,
DeviceGroupedConvBwdWeightGnwcGkxcGnwk_Xdl_CShuffle< 3, F16, F16, F16, F32, PassThrough, PassThrough, PassThrough, ConvBwdWeightFilter1x1Stride1Pad0, 256, 64, 128, 4, 8, 32, 32, 1, 2, S<1, 4, 8, 8>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 8, 1, true, S<1, 4, 16, 4>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 8, 2, true, 1, 1, S<1, 32, 1, 4>, 8>,
DeviceGroupedConvBwdWeightGnwcGkxcGnwk_Xdl_CShuffle< 3, F16, F16, F16, F32, PassThrough, PassThrough, PassThrough, ConvBwdWeightFilter1x1Stride1Pad0, 128, 128, 32, 4, 8, 32, 32, 2, 1, S<1, 4, 16, 2>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 8, 4, true, S<1, 4, 4, 8>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 8, 1, true, 1, 1, S<1, 32, 1, 4>, 8>,
DeviceGroupedConvBwdWeightGnwcGkxcGnwk_Xdl_CShuffle< 3, F16, F16, F16, F32, PassThrough, PassThrough, PassThrough, ConvBwdWeightFilter1x1Stride1Pad0, 128, 32, 128, 4, 8, 32, 32, 1, 2, S<1, 4, 4, 8>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 8, 1, true, S<1, 4, 16, 2>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 8, 4, true, 1, 1, S<1, 32, 1, 4>, 8>,
DeviceGroupedConvBwdWeightGnwcGkxcGnwk_Xdl_CShuffle< 3, F16, F16, F16, F32, PassThrough, PassThrough, PassThrough, ConvBwdWeightFilter1x1Stride1Pad0, 64, 64, 32, 4, 8, 32, 32, 2, 1, S<1, 4, 8, 2>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 8, 4, true, S<1, 4, 4, 4>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 8, 2, true, 1, 1, S<1, 16, 1, 4>, 8>,
DeviceGroupedConvBwdWeightGnwcGkxcGnwk_Xdl_CShuffle< 3, F16, F16, F16, F32, PassThrough, PassThrough, PassThrough, ConvBwdWeightFilter1x1Stride1Pad0, 64, 32, 64, 4, 8, 32, 32, 1, 2, S<1, 4, 4, 4>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 8, 2, true, S<1, 4, 8, 2>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 8, 4, true, 1, 1, S<1, 16, 1, 4>, 8>
// clang-format on
>;
void add_device_grouped_conv3d_bwd_weight_xdl_gndhwc_gkzyxc_gndhwk_f16_instances(
std::vector<std::unique_ptr<DeviceGroupedConvBwdWeight<3,
GNDHWC,
GKZYXC,
GNDHWK,
F16,
F16,
F16,
PassThrough,
PassThrough,
PassThrough>>>& instances)
{
add_device_operation_instances(
instances,
device_grouped_conv3d_bwd_weight_xdl_c_shuffle_gndhwc_gkzyxc_gndhwk_f16_default_instances{});
add_device_operation_instances(
instances,
device_grouped_conv3d_bwd_weight_xdl_gndhwc_gkzyxc_gndhwk_1x1_s1_p0_f16_instances{});
}
} // namespace instance
} // namespace device
} // namespace tensor_operation
} // namespace ck
library/src/tensor_operation_instance/gpu/grouped_conv3d_bwd_weight/device_grouped_conv3d_bwd_weight_xdl_gndhwc_gkzyxc_gndhwk_f32_instance.cpp 0 → 100644
View file @ f0224f2a
// SPDX-License-Identifier: MIT
// Copyright (c) 2018-2022, Advanced Micro Devices, Inc. All rights reserved.
#include <cstdlib>
#include "ck/ck.hpp"
#include "ck/tensor_operation/gpu/device/tensor_layout.hpp"
#include "ck/tensor_operation/gpu/device/impl/device_grouped_conv_bwd_weight_gnwc_gkxc_gnwk_xdl_cshuffle.hpp"
#include "ck/tensor_operation/gpu/element/element_wise_operation.hpp"
#include "ck/library/tensor_operation_instance/add_device_operation_instance.hpp"
namespace ck {
namespace tensor_operation {
namespace device {
namespace instance {
using F32 = float;
template <ck::index_t... Is>
using S = ck::Sequence<Is...>;
using GNDHWC = ck::tensor_layout::convolution::GNDHWC;
using GKZYXC = ck::tensor_layout::convolution::GKZYXC;
using GNDHWK = ck::tensor_layout::convolution::GNDHWK;
using PassThrough = ck::tensor_operation::element_wise::PassThrough;
static constexpr auto ConvBwdWeightDefault =
ck::tensor_operation::device::ConvolutionBackwardWeightSpecialization::Default;
static constexpr auto ConvBwdWeightFilter1x1Stride1Pad0 =
ck::tensor_operation::device::ConvolutionBackwardWeightSpecialization::Filter1x1Stride1Pad0;
// Compilation parameters for in[n, di, hi, wi, c] * wei[k, z, y, x, c] = out[n, do, ho, wo, k]
using device_grouped_conv3d_bwd_weight_xdl_c_shuffle_gndhwc_gkzyxc_gndhwk_f32_default_instances =
std::tuple<
// clang-format off
//#########################################| Num| InData| WeiData| OutData| AccData| In| Wei| Out| ConvBackward| Block| MPer| NPer| K0Per| K1| MPer| NPer| MXdl| NXdl| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockLds| BBlockTransfer| BBlockTransfer| BBlockTransfer| BlockTransfer| BBlockTransfer| BBlockTransfer| BBlockLds| CShuffle| CShuffle| CBlockTransfer| CBlockTransfer|
//#########################################| Dim| Type| Type| Type| Type| Elementwise| Elementwise| Elementwise| Weight| Size| Block| Block| Block| | XDL| XDL| Per| Per| ThreadCluster| ThreadCluster| SrcAccessOrder| SrcVectorDim| SrcScalar| DstScalar| AddExtraM| ThreadCluster| ThreadCluster| SrcAccessOrder| SrcVectorDim| SrcScalar| DstScalar| AddExtraN| MXdlPerWave| NXdlPerWave| ClusterLengths| ScalarPerVector|
//#########################################| Spatial| | | | | Operation| Operation| Operation| Specialization| | | | | | | | Wave| Wave| Lengths_K0_M_K1| ArrangeOrder| | | PerVector| PerVector_K1| | Lengths_K0_N_K1| ArrangeOrder| | | PerVector| PerVector_K1| | PerShuffle| PerShuffle| MBlock_MPerBlock| NWaveNPerXdl|
//#########################################| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | NBlock_NPerBlock| |
DeviceGroupedConvBwdWeightGnwcGkxcGnwk_Xdl_CShuffle< 3, F32, F32, F32, F32, PassThrough, PassThrough, PassThrough, ConvBwdWeightDefault, 256, 256, 128, 4, 4, 32, 32, 4, 2, S<1, 4, 64, 1>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 4, 4, true, S<1, 4, 32, 2>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 4, 2, true, 1, 1, S<1, 32, 1, 8>, 4>,
DeviceGroupedConvBwdWeightGnwcGkxcGnwk_Xdl_CShuffle< 3, F32, F32, F32, F32, PassThrough, PassThrough, PassThrough, ConvBwdWeightDefault, 256, 128, 256, 4, 4, 32, 32, 2, 4, S<1, 4, 32, 2>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 4, 2, true, S<1, 4, 64, 1>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 4, 4, true, 1, 1, S<1, 32, 1, 8>, 4>,
DeviceGroupedConvBwdWeightGnwcGkxcGnwk_Xdl_CShuffle< 3, F32, F32, F32, F32, PassThrough, PassThrough, PassThrough, ConvBwdWeightDefault, 128, 128, 128, 4, 4, 32, 32, 4, 2, S<1, 4, 32, 1>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 4, 4, true, S<1, 4, 32, 1>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 4, 4, true, 1, 1, S<1, 32, 1, 4>, 4>,
DeviceGroupedConvBwdWeightGnwcGkxcGnwk_Xdl_CShuffle< 3, F32, F32, F32, F32, PassThrough, PassThrough, PassThrough, ConvBwdWeightDefault, 256, 128, 128, 4, 4, 32, 32, 2, 2, S<1, 4, 32, 2>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 4, 2, true, S<1, 4, 32, 2>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 4, 2, true, 1, 1, S<1, 32, 1, 4>, 4>,
DeviceGroupedConvBwdWeightGnwcGkxcGnwk_Xdl_CShuffle< 3, F32, F32, F32, F32, PassThrough, PassThrough, PassThrough, ConvBwdWeightDefault, 128, 128, 64, 4, 4, 32, 32, 2, 2, S<1, 4, 32, 1>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 4, 4, true, S<1, 4, 16, 2>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 4, 2, true, 1, 1, S<1, 32, 1, 4>, 4>,
DeviceGroupedConvBwdWeightGnwcGkxcGnwk_Xdl_CShuffle< 3, F32, F32, F32, F32, PassThrough, PassThrough, PassThrough, ConvBwdWeightDefault, 128, 64, 128, 4, 4, 32, 32, 2, 2, S<1, 4, 16, 2>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 4, 2, true, S<1, 4, 32, 1>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 4, 4, true, 1, 1, S<1, 32, 1, 4>, 4>,
DeviceGroupedConvBwdWeightGnwcGkxcGnwk_Xdl_CShuffle< 3, F32, F32, F32, F32, PassThrough, PassThrough, PassThrough, ConvBwdWeightDefault, 64, 64, 64, 4, 4, 32, 32, 2, 2, S<1, 4, 16, 1>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 4, 4, true, S<1, 4, 16, 1>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 4, 4, true, 1, 1, S<1, 16, 1, 4>, 4>,
DeviceGroupedConvBwdWeightGnwcGkxcGnwk_Xdl_CShuffle< 3, F32, F32, F32, F32, PassThrough, PassThrough, PassThrough, ConvBwdWeightDefault, 256, 128, 64, 4, 4, 32, 32, 2, 1, S<1, 4, 32, 2>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 4, 2, true, S<1, 4, 16, 4>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 4, 1, true, 1, 1, S<1, 32, 1, 4>, 4>,
DeviceGroupedConvBwdWeightGnwcGkxcGnwk_Xdl_CShuffle< 3, F32, F32, F32, F32, PassThrough, PassThrough, PassThrough, ConvBwdWeightDefault, 256, 64, 128, 4, 4, 32, 32, 1, 2, S<1, 4, 16, 4>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 4, 1, true, S<1, 4, 32, 2>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 4, 2, true, 1, 1, S<1, 32, 1, 4>, 4>,
DeviceGroupedConvBwdWeightGnwcGkxcGnwk_Xdl_CShuffle< 3, F32, F32, F32, F32, PassThrough, PassThrough, PassThrough, ConvBwdWeightDefault, 128, 128, 32, 4, 4, 32, 32, 2, 1, S<1, 4, 32, 1>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 4, 4, true, S<1, 4, 8, 4>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 4, 1, true, 1, 1, S<1, 32, 1, 4>, 4>,
DeviceGroupedConvBwdWeightGnwcGkxcGnwk_Xdl_CShuffle< 3, F32, F32, F32, F32, PassThrough, PassThrough, PassThrough, ConvBwdWeightDefault, 128, 32, 128, 4, 4, 32, 32, 1, 2, S<1, 4, 8, 4>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 4, 1, true, S<1, 4, 32, 1>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 4, 4, true, 1, 1, S<1, 32, 1, 4>, 4>,
DeviceGroupedConvBwdWeightGnwcGkxcGnwk_Xdl_CShuffle< 3, F32, F32, F32, F32, PassThrough, PassThrough, PassThrough, ConvBwdWeightDefault, 64, 64, 32, 4, 4, 32, 32, 2, 1, S<1, 4, 16, 1>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 4, 4, true, S<1, 4, 8, 2>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 4, 2, true, 1, 1, S<1, 16, 1, 4>, 4>,
DeviceGroupedConvBwdWeightGnwcGkxcGnwk_Xdl_CShuffle< 3, F32, F32, F32, F32, PassThrough, PassThrough, PassThrough, ConvBwdWeightDefault, 64, 32, 64, 4, 4, 32, 32, 1, 2, S<1, 4, 8, 2>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 4, 2, true, S<1, 4, 16, 1>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 4, 4, true, 1, 1, S<1, 16, 1, 4>, 4>
// clang-format on
>;
using device_grouped_conv3d_bwd_weight_xdl_gndhwc_gkzyxc_gndhwk_1x1_s1_p0_f32_instances =
std::tuple<
// clang-format off
//#########################################| Num| InData| WeiData| OutData| AccData| In| Wei| Out| ConvBackward| Block| MPer| NPer| K0Per| K1| MPer| NPer| MXdl| NXdl| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockLds| BBlockTransfer| BBlockTransfer| BBlockTransfer| BlockTransfer| BBlockTransfer| BBlockTransfer| BBlockLds| CShuffle| CShuffle| CBlockTransfer| CBlockTransfer|
//#########################################| Dim| Type| Type| Type| Type| Elementwise| Elementwise| Elementwise| Weight| Size| Block| Block| Block| | XDL| XDL| Per| Per| ThreadCluster| ThreadCluster| SrcAccessOrder| SrcVectorDim| SrcScalar| DstScalar| AddExtraM| ThreadCluster| ThreadCluster| SrcAccessOrder| SrcVectorDim| SrcScalar| DstScalar| AddExtraN| MXdlPerWave| NXdlPerWave| ClusterLengths| ScalarPerVector|
//#########################################| Spatial| | | | | Operation| Operation| Operation| Specialization| | | | | | | | Wave| Wave| Lengths_K0_M_K1| ArrangeOrder| | | PerVector| PerVector_K1| | Lengths_K0_N_K1| ArrangeOrder| | | PerVector| PerVector_K1| | PerShuffle| PerShuffle| MBlock_MPerBlock| NWaveNPerXdl|
//#########################################| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | NBlock_NPerBlock| |
DeviceGroupedConvBwdWeightGnwcGkxcGnwk_Xdl_CShuffle< 3, F32, F32, F32, F32, PassThrough, PassThrough, PassThrough, ConvBwdWeightFilter1x1Stride1Pad0, 256, 256, 128, 4, 4, 32, 32, 4, 2, S<1, 4, 64, 1>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 4, 4, true, S<1, 4, 32, 2>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 4, 2, true, 1, 1, S<1, 32, 1, 8>, 4>,
DeviceGroupedConvBwdWeightGnwcGkxcGnwk_Xdl_CShuffle< 3, F32, F32, F32, F32, PassThrough, PassThrough, PassThrough, ConvBwdWeightFilter1x1Stride1Pad0, 256, 128, 256, 4, 4, 32, 32, 2, 4, S<1, 4, 32, 2>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 4, 2, true, S<1, 4, 64, 1>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 4, 4, true, 1, 1, S<1, 32, 1, 8>, 4>,
DeviceGroupedConvBwdWeightGnwcGkxcGnwk_Xdl_CShuffle< 3, F32, F32, F32, F32, PassThrough, PassThrough, PassThrough, ConvBwdWeightFilter1x1Stride1Pad0, 128, 128, 128, 4, 4, 32, 32, 4, 2, S<1, 4, 32, 1>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 4, 4, true, S<1, 4, 32, 1>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 4, 4, true, 1, 1, S<1, 32, 1, 4>, 4>,
DeviceGroupedConvBwdWeightGnwcGkxcGnwk_Xdl_CShuffle< 3, F32, F32, F32, F32, PassThrough, PassThrough, PassThrough, ConvBwdWeightFilter1x1Stride1Pad0, 256, 128, 128, 4, 4, 32, 32, 2, 2, S<1, 4, 32, 2>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 4, 2, true, S<1, 4, 32, 2>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 4, 2, true, 1, 1, S<1, 32, 1, 4>, 4>,
DeviceGroupedConvBwdWeightGnwcGkxcGnwk_Xdl_CShuffle< 3, F32, F32, F32, F32, PassThrough, PassThrough, PassThrough, ConvBwdWeightFilter1x1Stride1Pad0, 128, 128, 64, 4, 4, 32, 32, 2, 2, S<1, 4, 32, 1>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 4, 4, true, S<1, 4, 16, 2>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 4, 2, true, 1, 1, S<1, 32, 1, 4>, 4>,
DeviceGroupedConvBwdWeightGnwcGkxcGnwk_Xdl_CShuffle< 3, F32, F32, F32, F32, PassThrough, PassThrough, PassThrough, ConvBwdWeightFilter1x1Stride1Pad0, 128, 64, 128, 4, 4, 32, 32, 2, 2, S<1, 4, 16, 2>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 4, 2, true, S<1, 4, 32, 1>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 4, 4, true, 1, 1, S<1, 32, 1, 4>, 4>,
DeviceGroupedConvBwdWeightGnwcGkxcGnwk_Xdl_CShuffle< 3, F32, F32, F32, F32, PassThrough, PassThrough, PassThrough, ConvBwdWeightFilter1x1Stride1Pad0, 64, 64, 64, 4, 4, 32, 32, 2, 2, S<1, 4, 16, 1>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 4, 4, true, S<1, 4, 16, 1>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 4, 4, true, 1, 1, S<1, 16, 1, 4>, 4>,
DeviceGroupedConvBwdWeightGnwcGkxcGnwk_Xdl_CShuffle< 3, F32, F32, F32, F32, PassThrough, PassThrough, PassThrough, ConvBwdWeightFilter1x1Stride1Pad0, 256, 128, 64, 4, 4, 32, 32, 2, 1, S<1, 4, 32, 2>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 4, 2, true, S<1, 4, 16, 4>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 4, 1, true, 1, 1, S<1, 32, 1, 4>, 4>,
DeviceGroupedConvBwdWeightGnwcGkxcGnwk_Xdl_CShuffle< 3, F32, F32, F32, F32, PassThrough, PassThrough, PassThrough, ConvBwdWeightFilter1x1Stride1Pad0, 256, 64, 128, 4, 4, 32, 32, 1, 2, S<1, 4, 16, 4>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 4, 1, true, S<1, 4, 32, 2>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 4, 2, true, 1, 1, S<1, 32, 1, 4>, 4>,
DeviceGroupedConvBwdWeightGnwcGkxcGnwk_Xdl_CShuffle< 3, F32, F32, F32, F32, PassThrough, PassThrough, PassThrough, ConvBwdWeightFilter1x1Stride1Pad0, 128, 128, 32, 4, 4, 32, 32, 2, 1, S<1, 4, 32, 1>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 4, 4, true, S<1, 4, 8, 4>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 4, 1, true, 1, 1, S<1, 32, 1, 4>, 4>,
DeviceGroupedConvBwdWeightGnwcGkxcGnwk_Xdl_CShuffle< 3, F32, F32, F32, F32, PassThrough, PassThrough, PassThrough, ConvBwdWeightFilter1x1Stride1Pad0, 128, 32, 128, 4, 4, 32, 32, 1, 2, S<1, 4, 8, 4>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 4, 1, true, S<1, 4, 32, 1>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 4, 4, true, 1, 1, S<1, 32, 1, 4>, 4>,
DeviceGroupedConvBwdWeightGnwcGkxcGnwk_Xdl_CShuffle< 3, F32, F32, F32, F32, PassThrough, PassThrough, PassThrough, ConvBwdWeightFilter1x1Stride1Pad0, 64, 64, 32, 4, 4, 32, 32, 2, 1, S<1, 4, 16, 1>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 4, 4, true, S<1, 4, 8, 2>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 4, 2, true, 1, 1, S<1, 16, 1, 4>, 4>,
DeviceGroupedConvBwdWeightGnwcGkxcGnwk_Xdl_CShuffle< 3, F32, F32, F32, F32, PassThrough, PassThrough, PassThrough, ConvBwdWeightFilter1x1Stride1Pad0, 64, 32, 64, 4, 4, 32, 32, 1, 2, S<1, 4, 8, 2>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 4, 2, true, S<1, 4, 16, 1>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 4, 4, true, 1, 1, S<1, 16, 1, 4>, 4>
// clang-format on
>;
void add_device_grouped_conv3d_bwd_weight_xdl_gndhwc_gkzyxc_gndhwk_f32_instances(
std::vector<std::unique_ptr<DeviceGroupedConvBwdWeight<3,
GNDHWC,
GKZYXC,
GNDHWK,
F32,
F32,
F32,
PassThrough,
PassThrough,
PassThrough>>>& instances)
{
add_device_operation_instances(
instances,
device_grouped_conv3d_bwd_weight_xdl_c_shuffle_gndhwc_gkzyxc_gndhwk_f32_default_instances{});
add_device_operation_instances(
instances,
device_grouped_conv3d_bwd_weight_xdl_gndhwc_gkzyxc_gndhwk_1x1_s1_p0_f32_instances{});
}
} // namespace instance
} // namespace device
} // namespace tensor_operation
} // namespace ck
library/src/utility/convolution_parameter.cpp
View file @ f0224f2a
...@@ -72,14 +72,10 @@ std::size_t ConvParam::GetFlops() const ...@@ -72,14 +72,10 @@ std::size_t ConvParam::GetFlops() const
{ {
// 2 * G * N * K * C * <output spatial lengths product> * <filter spatial lengths product> // 2 * G * N * K * C * <output spatial lengths product> * <filter spatial lengths product>
return static_cast<std::size_t>(2) * G_ * N_ * K_ * C_ * return static_cast<std::size_t>(2) * G_ * N_ * K_ * C_ *
std::accumulate(std::begin(output_spatial_lengths_), ck::accumulate_n<std::size_t>(
std::begin(output_spatial_lengths_) + num_dim_spatial_, std::begin(output_spatial_lengths_), num_dim_spatial_, 1, std::multiplies<>()) *
static_cast<std::size_t>(1), ck::accumulate_n<std::size_t>(
std::multiplies<std::size_t>()) * std::begin(filter_spatial_lengths_), num_dim_spatial_, 1, std::multiplies<>());
std::accumulate(std::begin(filter_spatial_lengths_),
std::begin(filter_spatial_lengths_) + num_dim_spatial_,
static_cast<std::size_t>(1),
std::multiplies<std::size_t>());
} }
std::string get_conv_param_parser_helper_msg() std::string get_conv_param_parser_helper_msg()
......
profiler/CMakeLists.txt
View file @ f0224f2a
...@@ -20,12 +20,13 @@ set(PROFILER_SOURCE ...@@ -20,12 +20,13 @@ set(PROFILER_SOURCE
src/profile_conv_fwd_bias_relu.cpp src/profile_conv_fwd_bias_relu.cpp
src/profile_conv_fwd_bias_relu_add.cpp src/profile_conv_fwd_bias_relu_add.cpp
src/profile_conv_bwd_data.cpp src/profile_conv_bwd_data.cpp
src/profile_conv_bwd_weight.cpp
src/profile_grouped_conv_fwd.cpp src/profile_grouped_conv_fwd.cpp
src/profile_grouped_conv_bwd_weight.cpp
src/profile_reduce.cpp src/profile_reduce.cpp
src/profile_groupnorm.cpp src/profile_groupnorm.cpp
src/profile_layernorm.cpp src/profile_layernorm.cpp
src/profile_softmax.cpp src/profile_softmax.cpp
src/profile_batchnorm_fwd.cpp
) )
add_executable(ckProfiler ${PROFILER_SOURCE}) add_executable(ckProfiler ${PROFILER_SOURCE})
...@@ -49,11 +50,14 @@ target_link_libraries(ckProfiler PRIVATE device_grouped_conv3d_fwd_instance) ...@@ -49,11 +50,14 @@ target_link_libraries(ckProfiler PRIVATE device_grouped_conv3d_fwd_instance)
target_link_libraries(ckProfiler PRIVATE device_conv1d_bwd_data_instance) target_link_libraries(ckProfiler PRIVATE device_conv1d_bwd_data_instance)
target_link_libraries(ckProfiler PRIVATE device_conv2d_bwd_data_instance) target_link_libraries(ckProfiler PRIVATE device_conv2d_bwd_data_instance)
target_link_libraries(ckProfiler PRIVATE device_conv3d_bwd_data_instance) target_link_libraries(ckProfiler PRIVATE device_conv3d_bwd_data_instance)
target_link_libraries(ckProfiler PRIVATE device_conv1d_bwd_weight_instance) target_link_libraries(ckProfiler PRIVATE device_grouped_conv1d_bwd_weight_instance)
target_link_libraries(ckProfiler PRIVATE device_conv2d_bwd_weight_instance) target_link_libraries(ckProfiler PRIVATE device_grouped_conv2d_bwd_weight_instance)
target_link_libraries(ckProfiler PRIVATE device_conv3d_bwd_weight_instance) target_link_libraries(ckProfiler PRIVATE device_grouped_conv3d_bwd_weight_instance)
target_link_libraries(ckProfiler PRIVATE device_conv2d_fwd_bias_relu_instance) target_link_libraries(ckProfiler PRIVATE device_conv2d_fwd_bias_relu_instance)
target_link_libraries(ckProfiler PRIVATE device_conv2d_fwd_bias_relu_add_instance) target_link_libraries(ckProfiler PRIVATE device_conv2d_fwd_bias_relu_add_instance)
target_link_libraries(ckProfiler PRIVATE device_normalization_instance) target_link_libraries(ckProfiler PRIVATE device_normalization_instance)
target_link_libraries(ckProfiler PRIVATE device_softmax_instance) target_link_libraries(ckProfiler PRIVATE device_softmax_instance)
target_link_libraries(ckProfiler PRIVATE device_reduce_instance) target_link_libraries(ckProfiler PRIVATE device_reduce_instance)
target_link_libraries(ckProfiler PRIVATE device_batchnorm_instance)
rocm_install(TARGETS ckProfiler COMPONENT profiler)
profiler/include/profile_batched_gemm_add_relu_gemm_add_impl.hpp
View file @ f0224f2a
...@@ -14,6 +14,7 @@ ...@@ -14,6 +14,7 @@
#include "ck/library/utility/device_memory.hpp" #include "ck/library/utility/device_memory.hpp"
#include "ck/library/utility/host_tensor.hpp" #include "ck/library/utility/host_tensor.hpp"
#include "ck/library/utility/host_tensor_generator.hpp" #include "ck/library/utility/host_tensor_generator.hpp"
#include "ck/library/utility/literals.hpp"
#include "ck/library/reference_tensor_operation/cpu/reference_batched_gemm.hpp" #include "ck/library/reference_tensor_operation/cpu/reference_batched_gemm.hpp"
namespace ck { namespace ck {
...@@ -111,15 +112,15 @@ bool profile_batched_gemm_add_relu_gemm_add_impl(bool do_verification, ...@@ -111,15 +112,15 @@ bool profile_batched_gemm_add_relu_gemm_add_impl(bool do_verification,
std::size_t stride, std::size_t stride,
std::size_t batch_stride, std::size_t batch_stride,
auto layout) { auto layout) {
using namespace ck::literals;
if(std::is_same<decltype(layout), Row>::value) if(std::is_same<decltype(layout), Row>::value)
{ {
return HostTensorDescriptor(std::vector<std::size_t>({batch_count, row, col}), return HostTensorDescriptor({batch_count, row, col}, {batch_stride, stride, 1_uz});
std::vector<std::size_t>({batch_stride, stride, 1}));
} }
else else
{ {
return HostTensorDescriptor(std::vector<std::size_t>({batch_count, row, col}), return HostTensorDescriptor({batch_count, row, col}, {batch_stride, 1_uz, stride});
std::vector<std::size_t>({batch_stride, 1, stride}));
} }
}; };
...@@ -330,8 +331,7 @@ bool profile_batched_gemm_add_relu_gemm_add_impl(bool do_verification, ...@@ -330,8 +331,7 @@ bool profile_batched_gemm_add_relu_gemm_add_impl(bool do_verification,
{ {
e1_g_m_o_device_buf.FromDevice(e1_g_m_o_device_result.mData.data()); e1_g_m_o_device_buf.FromDevice(e1_g_m_o_device_result.mData.data());
pass = pass & ck::utils::check_err(e1_g_m_o_device_result.mData, pass = pass & ck::utils::check_err(e1_g_m_o_device_result, e1_g_m_o_host_result);
e1_g_m_o_host_result.mData);
if(do_log) if(do_log)
{ {
......
profiler/include/profile_batched_gemm_gemm_impl.hpp
View file @ f0224f2a
...@@ -16,6 +16,7 @@ ...@@ -16,6 +16,7 @@
#include "ck/library/utility/device_memory.hpp" #include "ck/library/utility/device_memory.hpp"
#include "ck/library/utility/host_tensor.hpp" #include "ck/library/utility/host_tensor.hpp"
#include "ck/library/utility/host_tensor_generator.hpp" #include "ck/library/utility/host_tensor_generator.hpp"
#include "ck/library/utility/literals.hpp"
#include "ck/library/reference_tensor_operation/cpu/reference_batched_gemm.hpp" #include "ck/library/reference_tensor_operation/cpu/reference_batched_gemm.hpp"
namespace ck { namespace ck {
...@@ -105,15 +106,15 @@ bool profile_batched_gemm_gemm_impl(bool do_verification, ...@@ -105,15 +106,15 @@ bool profile_batched_gemm_gemm_impl(bool do_verification,
std::size_t stride, std::size_t stride,
std::size_t batch_stride, std::size_t batch_stride,
auto layout) { auto layout) {
using namespace ck::literals;
if(std::is_same<decltype(layout), Row>::value) if(std::is_same<decltype(layout), Row>::value)
{ {
return HostTensorDescriptor(std::vector<std::size_t>({batch_count, row, col}), return HostTensorDescriptor({batch_count, row, col}, {batch_stride, stride, 1_uz});
std::vector<std::size_t>({batch_stride, stride, 1}));
} }
else else
{ {
return HostTensorDescriptor(std::vector<std::size_t>({batch_count, row, col}), return HostTensorDescriptor({batch_count, row, col}, {batch_stride, 1_uz, stride});
std::vector<std::size_t>({batch_stride, 1, stride}));
} }
}; };
...@@ -283,8 +284,7 @@ bool profile_batched_gemm_gemm_impl(bool do_verification, ...@@ -283,8 +284,7 @@ bool profile_batched_gemm_gemm_impl(bool do_verification,
{ {
c_g_m_o_device_buf.FromDevice(c_g_m_o_device_result.mData.data()); c_g_m_o_device_buf.FromDevice(c_g_m_o_device_result.mData.data());
pass = pass & pass = pass & ck::utils::check_err(c_g_m_o_device_result, c_g_m_o_host_result);
ck::utils::check_err(c_g_m_o_device_result.mData, c_g_m_o_host_result.mData);
if(do_log) if(do_log)
{ {
......
profiler/include/profile_batched_gemm_impl.hpp
View file @ f0224f2a
...@@ -16,6 +16,7 @@ ...@@ -16,6 +16,7 @@
#include "ck/library/utility/device_memory.hpp" #include "ck/library/utility/device_memory.hpp"
#include "ck/library/utility/host_tensor.hpp" #include "ck/library/utility/host_tensor.hpp"
#include "ck/library/utility/host_tensor_generator.hpp" #include "ck/library/utility/host_tensor_generator.hpp"
#include "ck/library/utility/literals.hpp"
#include "ck/library/reference_tensor_operation/cpu/reference_batched_gemm.hpp" #include "ck/library/reference_tensor_operation/cpu/reference_batched_gemm.hpp"
namespace ck { namespace ck {
...@@ -50,15 +51,15 @@ bool profile_batched_gemm_impl(int do_verification, ...@@ -50,15 +51,15 @@ bool profile_batched_gemm_impl(int do_verification,
std::size_t stride, std::size_t stride,
std::size_t batch_stride, std::size_t batch_stride,
auto layout) { auto layout) {
using namespace ck::literals;
if(is_same<decltype(layout), tensor_layout::gemm::RowMajor>::value) if(is_same<decltype(layout), tensor_layout::gemm::RowMajor>::value)
{ {
return HostTensorDescriptor(std::vector<std::size_t>({batch_count, row, col}), return HostTensorDescriptor({batch_count, row, col}, {batch_stride, stride, 1_uz});
std::vector<std::size_t>({batch_stride, stride, 1}));
} }
else else
{ {
return HostTensorDescriptor(std::vector<std::size_t>({batch_count, row, col}), return HostTensorDescriptor({batch_count, row, col}, {batch_stride, 1_uz, stride});
std::vector<std::size_t>({batch_stride, 1, stride}));
} }
}; };
...@@ -202,8 +203,7 @@ bool profile_batched_gemm_impl(int do_verification, ...@@ -202,8 +203,7 @@ bool profile_batched_gemm_impl(int do_verification,
{ {
c_device_buf.FromDevice(c_g_m_n_device_result.mData.data()); c_device_buf.FromDevice(c_g_m_n_device_result.mData.data());
pass = pass & pass = pass & ck::utils::check_err(c_g_m_n_device_result, c_g_m_n_host_result);
ck::utils::check_err(c_g_m_n_device_result.mData, c_g_m_n_host_result.mData);
if(do_log) if(do_log)
{ {
......
profiler/include/profile_batched_gemm_reduce_impl.hpp
View file @ f0224f2a
...@@ -14,6 +14,7 @@ ...@@ -14,6 +14,7 @@
#include "ck/library/utility/device_memory.hpp" #include "ck/library/utility/device_memory.hpp"
#include "ck/library/utility/host_tensor.hpp" #include "ck/library/utility/host_tensor.hpp"
#include "ck/library/utility/host_tensor_generator.hpp" #include "ck/library/utility/host_tensor_generator.hpp"
#include "ck/library/utility/literals.hpp"
#include "ck/library/reference_tensor_operation/cpu/reference_batched_gemm.hpp" #include "ck/library/reference_tensor_operation/cpu/reference_batched_gemm.hpp"
namespace ck { namespace ck {
...@@ -78,15 +79,15 @@ bool profile_batched_gemm_reduce_impl(int do_verification, ...@@ -78,15 +79,15 @@ bool profile_batched_gemm_reduce_impl(int do_verification,
std::size_t col, std::size_t col,
std::size_t stride, std::size_t stride,
auto layout) { auto layout) {
using namespace ck::literals;
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>({batch_count, row, col}), return HostTensorDescriptor({batch_count, row, col}, {row * stride, stride, 1_uz});
std::vector<std::size_t>({row * stride, stride, 1}));
} }
else else
{ {
return HostTensorDescriptor(std::vector<std::size_t>({batch_count, row, col}), return HostTensorDescriptor({batch_count, row, col}, {col * stride, 1_uz, stride});
std::vector<std::size_t>({col * stride, 1, stride}));
} }
}; };
...@@ -95,17 +96,13 @@ bool profile_batched_gemm_reduce_impl(int do_verification, ...@@ -95,17 +96,13 @@ bool profile_batched_gemm_reduce_impl(int do_verification,
Tensor<CDataType> c_g_m_n_host_result( Tensor<CDataType> c_g_m_n_host_result(
f_host_tensor_descriptor(BatchCount, M, N, StrideC, CLayout{})); f_host_tensor_descriptor(BatchCount, M, N, StrideC, CLayout{}));
Tensor<ReduceDataType> d0_g_m_host_result(HostTensorDescriptor(std::vector<std::size_t>( Tensor<ReduceDataType> d0_g_m_host_result({BatchCount, M});
{static_cast<std::size_t>(BatchCount), static_cast<std::size_t>(M)}))); Tensor<ReduceDataType> d1_g_m_host_result({BatchCount, M});
Tensor<ReduceDataType> d1_g_m_host_result(HostTensorDescriptor(std::vector<std::size_t>(
{static_cast<std::size_t>(BatchCount), static_cast<std::size_t>(M)})));
Tensor<CDataType> c_g_m_n_device_result( Tensor<CDataType> c_g_m_n_device_result(
f_host_tensor_descriptor(BatchCount, M, N, StrideC, CLayout{})); f_host_tensor_descriptor(BatchCount, M, N, StrideC, CLayout{}));
Tensor<ReduceDataType> d0_g_m_device_result(HostTensorDescriptor(std::vector<std::size_t>( Tensor<ReduceDataType> d0_g_m_device_result({BatchCount, M});
{static_cast<std::size_t>(BatchCount), static_cast<std::size_t>(M)}))); Tensor<ReduceDataType> d1_g_m_device_result({BatchCount, M});
Tensor<ReduceDataType> d1_g_m_device_result(HostTensorDescriptor(std::vector<std::size_t>(
{static_cast<std::size_t>(BatchCount), static_cast<std::size_t>(M)})));
std::cout << "a_g_m_k: " << a_g_m_k.mDesc << std::endl; std::cout << "a_g_m_k: " << a_g_m_k.mDesc << std::endl;
std::cout << "b_g_k_n: " << b_g_k_n.mDesc << std::endl; std::cout << "b_g_k_n: " << b_g_k_n.mDesc << std::endl;
...@@ -319,12 +316,9 @@ bool profile_batched_gemm_reduce_impl(int do_verification, ...@@ -319,12 +316,9 @@ bool profile_batched_gemm_reduce_impl(int do_verification,
reduce0_device_buf.FromDevice(d0_g_m_device_result.mData.data()); reduce0_device_buf.FromDevice(d0_g_m_device_result.mData.data());
reduce1_device_buf.FromDevice(d1_g_m_device_result.mData.data()); reduce1_device_buf.FromDevice(d1_g_m_device_result.mData.data());
bool c_error = bool c_error = ck::utils::check_err(c_g_m_n_device_result, c_g_m_n_host_result);
ck::utils::check_err(c_g_m_n_device_result.mData, c_g_m_n_host_result.mData); bool d0_error = ck::utils::check_err(d0_g_m_device_result, d0_g_m_host_result);
bool d0_error = bool d1_error = ck::utils::check_err(d1_g_m_device_result, d1_g_m_host_result);
ck::utils::check_err(d0_g_m_device_result.mData, d0_g_m_host_result.mData);
bool d1_error =
ck::utils::check_err(d1_g_m_device_result.mData, d1_g_m_host_result.mData);
pass = pass && (c_error == true); pass = pass && (c_error == true);
pass = pass && (d0_error == true); pass = pass && (d0_error == true);
......
profiler/include/profile_batched_gemm_softmax_gemm_impl.hpp
View file @ f0224f2a
...@@ -16,6 +16,7 @@ ...@@ -16,6 +16,7 @@
#include "ck/library/utility/device_memory.hpp" #include "ck/library/utility/device_memory.hpp"
#include "ck/library/utility/host_tensor.hpp" #include "ck/library/utility/host_tensor.hpp"
#include "ck/library/utility/host_tensor_generator.hpp" #include "ck/library/utility/host_tensor_generator.hpp"
#include "ck/library/utility/literals.hpp"
#include "ck/library/reference_tensor_operation/cpu/reference_batched_gemm.hpp" #include "ck/library/reference_tensor_operation/cpu/reference_batched_gemm.hpp"
#include "ck/library/reference_tensor_operation/cpu/reference_softmax.hpp" #include "ck/library/reference_tensor_operation/cpu/reference_softmax.hpp"
...@@ -113,15 +114,15 @@ bool profile_batched_gemm_softmax_gemm_impl(bool do_verification, ...@@ -113,15 +114,15 @@ bool profile_batched_gemm_softmax_gemm_impl(bool do_verification,
std::size_t stride, std::size_t stride,
std::size_t batch_stride, std::size_t batch_stride,
auto layout) { auto layout) {
using namespace ck::literals;
if(std::is_same<decltype(layout), Row>::value) if(std::is_same<decltype(layout), Row>::value)
{ {
return HostTensorDescriptor(std::vector<std::size_t>({batch_count, row, col}), return HostTensorDescriptor({batch_count, row, col}, {batch_stride, stride, 1_uz});
std::vector<std::size_t>({batch_stride, stride, 1}));
} }
else else
{ {
return HostTensorDescriptor(std::vector<std::size_t>({batch_count, row, col}), return HostTensorDescriptor({batch_count, row, col}, {batch_stride, 1_uz, stride});
std::vector<std::size_t>({batch_stride, 1, stride}));
} }
}; };
...@@ -307,8 +308,7 @@ bool profile_batched_gemm_softmax_gemm_impl(bool do_verification, ...@@ -307,8 +308,7 @@ bool profile_batched_gemm_softmax_gemm_impl(bool do_verification,
{ {
c_g_m_o_device_buf.FromDevice(c_g_m_o_device_result.mData.data()); c_g_m_o_device_buf.FromDevice(c_g_m_o_device_result.mData.data());
pass = pass & pass = pass & ck::utils::check_err(c_g_m_o_device_result, c_g_m_o_host_result);
ck::utils::check_err(c_g_m_o_device_result.mData, c_g_m_o_host_result.mData);
if(do_log) if(do_log)
{ {
......
profiler/include/profile_batched_gemm_softmax_gemm_permute_impl.hpp
View file @ f0224f2a
...@@ -16,6 +16,7 @@ ...@@ -16,6 +16,7 @@
#include "ck/library/utility/device_memory.hpp" #include "ck/library/utility/device_memory.hpp"
#include "ck/library/utility/host_tensor.hpp" #include "ck/library/utility/host_tensor.hpp"
#include "ck/library/utility/host_tensor_generator.hpp" #include "ck/library/utility/host_tensor_generator.hpp"
#include "ck/library/utility/literals.hpp"
#include "ck/library/reference_tensor_operation/cpu/reference_batched_gemm.hpp" #include "ck/library/reference_tensor_operation/cpu/reference_batched_gemm.hpp"
#include "ck/library/reference_tensor_operation/cpu/reference_softmax.hpp" #include "ck/library/reference_tensor_operation/cpu/reference_softmax.hpp"
...@@ -308,8 +309,25 @@ bool profile_batched_gemm_softmax_gemm_permute_impl(bool do_verification, ...@@ -308,8 +309,25 @@ bool profile_batched_gemm_softmax_gemm_permute_impl(bool do_verification,
{ {
c_device_buf.FromDevice(c_gs_ms_os_device_result.mData.data()); c_device_buf.FromDevice(c_gs_ms_os_device_result.mData.data());
pass = pass & ck::utils::check_err(c_gs_ms_os_device_result.mData, // default absolute error and relative error is 0.001
c_gs_ms_os_host_result.mData); double rtol = 1e-3;
double atol = 1e-3;
// when BF16 is taken, set absolute error and relative error to 0.01
if(std::is_same_v<ADataType, ck::bhalf_t> &&
std::is_same_v<B0DataType, ck::bhalf_t> &&
std::is_same_v<B1DataType, ck::bhalf_t> &&
std::is_same_v<CDataType, ck::bhalf_t>)
{
rtol = 1e-2;
atol = 1e-2;
}
pass = pass & ck::utils::check_err(c_gs_ms_os_device_result,
c_gs_ms_os_host_result,
"Error: Incorrect results!",
rtol,
atol);
if(do_log) if(do_log)
{ {
......
profiler/include/profile_batchnorm_forward_impl.hpp 0 → 100644
View file @ f0224f2a
// SPDX-License-Identifier: MIT
// Copyright (c) 2018-2022, Advanced Micro Devices, Inc. All rights reserved.
#pragma once
#include <iomanip>
#include <stdexcept>
#include "ck/ck.hpp"
#include "ck/tensor_operation/gpu/element/element_wise_operation.hpp"
#include "ck/library/utility/check_err.hpp"
#include "ck/library/utility/device_memory.hpp"
#include "ck/library/utility/host_tensor.hpp"
#include "ck/library/utility/host_tensor_generator.hpp"
#include "ck/library/tensor_operation_instance/gpu/batchnorm_forward.hpp"
#include "ck/library/reference_tensor_operation/cpu/reference_batchnorm_forward.hpp"
namespace ck {
namespace profiler {
template <typename XDataType,
typename YDataType,
typename AccDataType,
typename ScaleDataType,
typename BiasDataType,
typename MeanVarDataType,
index_t Rank,
index_t NumBatchNormReduceDim>
bool profile_batchnorm_forward_impl(int do_verification,
int init_method,
bool do_dumpout,
bool time_kernel,
const std::vector<size_t> inOutLengths,
const std::vector<int> reduceDims,
bool updateMovingAverage,
bool saveMeanAndInvVariance,
double averageFactor,
double epsilon)
{
if(inOutLengths.size() != Rank || reduceDims.size() != NumBatchNormReduceDim)
{
throw std::runtime_error("Invalid tensor lengths or number of reduce dimensions!");
};
std::vector<size_t> scaleBiasMeanVarLengths;
// used for calculating the effective transferred bytes by each operation
size_t total_length;
size_t invariant_length = 1;
total_length =
std::accumulate(inOutLengths.begin(), inOutLengths.end(), 1, std::multiplies<size_t>{});
if(std::any_of(reduceDims.begin(), reduceDims.end(), [](int d) { return d < 0 || d >= Rank; }))
throw std::runtime_error("Invalid reduce dimensions!");
for(int dim = 0; dim < Rank; dim++)
{
if(std::none_of(reduceDims.begin(), reduceDims.end(), [&](int d) { return dim == d; }))
{
scaleBiasMeanVarLengths.push_back(inOutLengths[dim]);
invariant_length *= inOutLengths[dim];
};
}
// input data of the batchnorm forward algorithm
Tensor<XDataType> x(inOutLengths);
Tensor<ScaleDataType> bnScale(scaleBiasMeanVarLengths);
Tensor<BiasDataType> bnBias(scaleBiasMeanVarLengths);
// output data of the batchnorm forward algorithm
Tensor<YDataType> y_ref(inOutLengths);
Tensor<YDataType> y(inOutLengths);
Tensor<MeanVarDataType> resultSaveMean_ref(scaleBiasMeanVarLengths);
Tensor<MeanVarDataType> resultSaveInvVariance_ref(scaleBiasMeanVarLengths);
Tensor<MeanVarDataType> resultRunningMean_ref(scaleBiasMeanVarLengths);
Tensor<MeanVarDataType> resultRunningVariance_ref(scaleBiasMeanVarLengths);
auto inOutStrides = x.mDesc.GetStrides();
auto scaleBiasMeanVarStrides = bnScale.mDesc.GetStrides();
std::size_t num_thread = std::thread::hardware_concurrency();
if(updateMovingAverage)
{
const float x_mean = 0.0f;
const float x_stddev = 1.0f;
const float noise_stddev = 0.04f;
// input data in normal distribution
x.GenerateTensorValue(GeneratorTensor_4<XDataType>{x_mean, x_stddev}, num_thread);
// initialize the runningMean to be values with tiny variation to the mean of the x
// values
resultRunningMean_ref.GenerateTensorValue(
GeneratorTensor_4<MeanVarDataType>{x_mean, noise_stddev}, num_thread);
// initialize the runningVariance to be values with tiny variation to the variance of
// the x values
resultRunningVariance_ref.GenerateTensorValue(
GeneratorTensor_4<MeanVarDataType>{x_stddev * x_stddev, noise_stddev}, num_thread);
}
else
{
if constexpr(ck::is_same_v<XDataType, int8_t>)
x.GenerateTensorValue(GeneratorTensor_2<XDataType>{-5, 5}, num_thread);
else
x.GenerateTensorValue(GeneratorTensor_3<XDataType>{-1.0f, 1.0f}, num_thread);
};
if(do_verification)
{
switch(init_method)
{
case 0:
bnScale.GenerateTensorValue(GeneratorTensor_0<ScaleDataType>{}, num_thread);
bnBias.GenerateTensorValue(GeneratorTensor_0<BiasDataType>{}, num_thread);
break;
case 1:
bnScale.GenerateTensorValue(GeneratorTensor_1<ScaleDataType>{1}, num_thread);
bnBias.GenerateTensorValue(GeneratorTensor_1<BiasDataType>{0}, num_thread);
break;
case 2:
bnScale.GenerateTensorValue(GeneratorTensor_2<ScaleDataType>{-5, 5}, num_thread);
bnBias.GenerateTensorValue(GeneratorTensor_2<BiasDataType>{-5, 5}, num_thread);
break;
default:
bnScale.GenerateTensorValue(GeneratorTensor_3<ScaleDataType>{-1.0f, 1.0f}, num_thread);
bnBias.GenerateTensorValue(GeneratorTensor_3<BiasDataType>{-1.0f, 1.0f}, num_thread);
}
};
// these buffers are usually provided by the user application
DeviceMem x_dev(sizeof(XDataType) * x.mDesc.GetElementSpaceSize());
DeviceMem y_dev(sizeof(XDataType) * y.mDesc.GetElementSpaceSize());
DeviceMem bnScale_dev(sizeof(ScaleDataType) * bnScale.mDesc.GetElementSpaceSize());
DeviceMem bnBias_dev(sizeof(BiasDataType) * bnBias.mDesc.GetElementSpaceSize());
// mean_dev or resultSaveMean_dev
DeviceMem resultSaveMean_dev(sizeof(MeanVarDataType) *
resultSaveMean_ref.mDesc.GetElementSpaceSize());
// meansquare_dev or resultSaveInvVariance_dev
DeviceMem resultSaveInvVariance_dev(sizeof(MeanVarDataType) *
resultSaveInvVariance_ref.mDesc.GetElementSpaceSize());
// resultRunningMean_dev
DeviceMem resultRunningMean_dev(sizeof(MeanVarDataType) *
resultRunningMean_ref.mDesc.GetElementSpaceSize());
// resultRunningVariance_dev
DeviceMem resultRunningVariance_dev(sizeof(MeanVarDataType) *
resultRunningVariance_ref.mDesc.GetElementSpaceSize());
x_dev.ToDevice(x.mData.data());
bnScale_dev.ToDevice(bnScale.mData.data());
bnBias_dev.ToDevice(bnBias.mData.data());
if(updateMovingAverage)
{
resultRunningMean_dev.ToDevice(resultRunningMean_ref.mData.data());
resultRunningVariance_dev.ToDevice(resultRunningVariance_ref.mData.data());
};
// used for storing the device result for verification when updateMovingAverage is enabled
Tensor<MeanVarDataType> resultRunningMean(scaleBiasMeanVarLengths);
Tensor<MeanVarDataType> resultRunningVariance(scaleBiasMeanVarLengths);
// used for storing the device result for verification when saveMeanAndInvVariance is enabled
Tensor<MeanVarDataType> resultSaveMean(scaleBiasMeanVarLengths);
Tensor<MeanVarDataType> resultSaveInvVariance(scaleBiasMeanVarLengths);
std::array<index_t, Rank> arrInOutLengths;
std::array<index_t, Rank> arrInOutStrides;
std::array<index_t, Rank - NumBatchNormReduceDim> arrScaleBiasMeanVarLengths;
std::array<index_t, Rank - NumBatchNormReduceDim> arrScaleBiasMeanVarStrides;
std::array<int, NumBatchNormReduceDim> arrReduceDims;
std::copy(inOutLengths.begin(), inOutLengths.end(), arrInOutLengths.begin());
std::copy(inOutStrides.begin(), inOutStrides.end(), arrInOutStrides.begin());
std::copy(scaleBiasMeanVarLengths.begin(),
scaleBiasMeanVarLengths.end(),
arrScaleBiasMeanVarLengths.begin());
std::copy(scaleBiasMeanVarStrides.begin(),
scaleBiasMeanVarStrides.end(),
arrScaleBiasMeanVarStrides.begin());
std::copy(reduceDims.begin(), reduceDims.end(), arrReduceDims.begin());
using PassThroughOp = ck::tensor_operation::element_wise::PassThrough;
// add device batchnorm-forward instances
using DeviceOp = ck::tensor_operation::device::DeviceBatchNormFwd<XDataType,
YDataType,
AccDataType,
ScaleDataType,
BiasDataType,
MeanVarDataType,
PassThroughOp,
Rank,
NumBatchNormReduceDim>;
// get device op instances
const auto instance_ptrs =
ck::tensor_operation::device::instance::DeviceOperationInstanceFactory<
DeviceOp>::GetInstances();
std::cout << "found " << instance_ptrs.size() << " instances" << std::endl;
std::string best_instance_name;
float best_avg_time = std::numeric_limits<float>::max();
float best_gb_per_sec = 0;
if(do_verification)
{
using ReferenceBatchNormFwdInstance =
ck::tensor_operation::host::ReferenceBatchNormFwd<XDataType,
YDataType,
AccDataType,
ScaleDataType,
BiasDataType,
MeanVarDataType,
PassThroughOp,
Rank,
NumBatchNormReduceDim>;
auto batchNormFwd_ref = ReferenceBatchNormFwdInstance{};
auto argument_ptr_ref = batchNormFwd_ref.MakeArgumentPointer(
arrInOutLengths,
arrInOutStrides,
arrInOutStrides,
arrReduceDims,
arrScaleBiasMeanVarLengths,
arrScaleBiasMeanVarStrides,
arrScaleBiasMeanVarStrides,
arrScaleBiasMeanVarStrides,
x.mData.data(),
bnScale.mData.data(),
bnBias.mData.data(),
epsilon,
PassThroughOp{},
y_ref.mData.data(),
saveMeanAndInvVariance ? resultSaveMean_ref.mData.data() : nullptr,
saveMeanAndInvVariance ? resultSaveInvVariance_ref.mData.data() : nullptr,
averageFactor,
updateMovingAverage ? resultRunningMean_ref.mData.data() : nullptr,
updateMovingAverage ? resultRunningVariance_ref.mData.data() : nullptr);
if(!batchNormFwd_ref.IsSupportedArgument(argument_ptr_ref.get()))
{
std::cout << "The runtime parameters not supported by the reference instance, exiting!"
<< std::endl;
return (false);
};
auto invoker_ptr_ref = batchNormFwd_ref.MakeInvokerPointer();
(void)invoker_ptr_ref->Run(argument_ptr_ref.get());
}
int num_kernel = 0;
bool pass = true;
for(auto& inst_ptr : instance_ptrs)
{
auto argument_ptr = inst_ptr->MakeArgumentPointer(
arrInOutLengths,
arrInOutStrides,
arrInOutStrides,
arrReduceDims,
arrScaleBiasMeanVarLengths,
arrScaleBiasMeanVarStrides,
arrScaleBiasMeanVarStrides,
arrScaleBiasMeanVarStrides,
x_dev.GetDeviceBuffer(),
bnScale_dev.GetDeviceBuffer(),
bnBias_dev.GetDeviceBuffer(),
epsilon,
PassThroughOp{},
y_dev.GetDeviceBuffer(),
saveMeanAndInvVariance ? resultSaveMean_dev.GetDeviceBuffer() : nullptr,
saveMeanAndInvVariance ? resultSaveInvVariance_dev.GetDeviceBuffer() : nullptr,
averageFactor,
updateMovingAverage ? resultRunningMean_dev.GetDeviceBuffer() : nullptr,
updateMovingAverage ? resultRunningVariance_dev.GetDeviceBuffer() : nullptr);
if(inst_ptr->IsSupportedArgument(argument_ptr.get()))
{
num_kernel++;
}
else
{
if(time_kernel)
{
std::cout << inst_ptr->GetTypeString()
<< " skipped due to unsupported argument: " << std::endl;
}
continue;
};
size_t workspace_sz = inst_ptr->GetWorkSpaceSize(argument_ptr.get());
DeviceMem workspace_dev(workspace_sz);
inst_ptr->SetWorkSpacePointer(argument_ptr.get(), workspace_dev.GetDeviceBuffer());
auto invoker_ptr = inst_ptr->MakeInvokerPointer();
float avg_time = invoker_ptr->Run(argument_ptr.get(), StreamConfig{nullptr, time_kernel});
size_t num_bytes = 0;
// inputing of x, scale, bias, outputing of y
num_bytes += total_length * (sizeof(XDataType) + sizeof(YDataType)) +
invariant_length * (sizeof(ScaleDataType) + sizeof(BiasDataType));
// outputing of mean, inv-variance
num_bytes += saveMeanAndInvVariance ? invariant_length * sizeof(MeanVarDataType) * 2 : 0;
// updating of moving mean, variance
num_bytes += updateMovingAverage ? invariant_length * sizeof(MeanVarDataType) * 4 : 0;
float gb_per_sec = num_bytes / 1.E6 / avg_time;
if(time_kernel)
std::cout << "Perf: " << avg_time << " ms, " << gb_per_sec << " GB/s, "
<< inst_ptr->GetTypeString() << std::endl;
if(avg_time < best_avg_time)
{
best_instance_name = inst_ptr->GetTypeString();
best_avg_time = avg_time;
best_gb_per_sec = gb_per_sec;
}
if(do_verification)
{
using ck::utils::check_err;
bool single_pass;
y_dev.FromDevice(y.mData.data());
if constexpr(ck::is_same_v<YDataType, ck::bhalf_t>)
single_pass = check_err(y.mData, y_ref.mData, "y results", 1e-2, 1e-2);
else
single_pass = check_err(y.mData, y_ref.mData, "y results", 4e-3, 4e-3);
if(updateMovingAverage)
{
resultRunningMean_dev.FromDevice(resultRunningMean.mData.data());
resultRunningVariance_dev.FromDevice(resultRunningVariance.mData.data());
// clang-format off
single_pass = single_pass && check_err(resultRunningMean.mData, resultRunningMean_ref.mData, "average mean results", 1.5e-5, 1.5e-5);
single_pass = single_pass && check_err(resultRunningVariance.mData, resultRunningVariance_ref.mData, "average variance results", 1e-5, 1e-5);
// clang-format on
};
if(saveMeanAndInvVariance)
{
resultSaveMean_dev.FromDevice(resultSaveMean.mData.data());
resultSaveInvVariance_dev.FromDevice(resultSaveInvVariance.mData.data());
// clang-format off
single_pass = single_pass && check_err(resultSaveMean.mData, resultSaveMean_ref.mData, "mean results", 3e-5, 3e-5);
single_pass = single_pass && check_err(resultSaveInvVariance.mData, resultSaveInvVariance_ref.mData, "inv-variance results", 7e-5, 7e-5);
// clang-format on
};
pass = pass && single_pass;
};
if(do_dumpout)
{
using ck::host_common::dumpBufferToFile;
// clang-format off
dumpBufferToFile("dump_x.bin", x.mData.data(), x.mDesc.GetElementSize());
dumpBufferToFile("dump_y.bin", y.mData.data(), y.mDesc.GetElementSize());
dumpBufferToFile("dump_y_ref.bin", y_ref.mData.data(), y_ref.mDesc.GetElementSize());
// clang-format off
if(saveMeanAndInvVariance)
{
// clang-format off
dumpBufferToFile("dump_mean.bin", resultSaveMean.mData.data(), resultSaveMean.mDesc.GetElementSize());
dumpBufferToFile("dump_mean_ref.bin", resultSaveMean_ref.mData.data(), resultSaveMean_ref.mDesc.GetElementSize());
dumpBufferToFile("dump_invvar.bin", resultSaveInvVariance.mData.data(), resultSaveInvVariance.mDesc.GetElementSize());
dumpBufferToFile("dump_invvar_ref.bin", resultSaveInvVariance_ref.mData.data(), resultSaveInvVariance_ref.mDesc.GetElementSize());
// clang-format on
};
};
}
if(time_kernel)
{
std::cout << "best perf = " << best_avg_time << " ms, " << best_gb_per_sec << " GB/s, "
<< best_instance_name << std::endl;
}
if(num_kernel == 0)
{
std::cout << "Error: No kernel is applicable" << std::endl;
return false;
}
return pass;
}
} // namespace profiler
} // namespace ck
profiler/include/profile_conv_bwd_data_impl.hpp
View file @ f0224f2a
...@@ -209,8 +209,7 @@ bool profile_conv_bwd_data_impl(int do_verification, ...@@ -209,8 +209,7 @@ bool profile_conv_bwd_data_impl(int do_verification,
{ {
in_device_buf.FromDevice(input_device_result.mData.data()); in_device_buf.FromDevice(input_device_result.mData.data());
pass = pass = pass & ck::utils::check_err(input_device_result, input_host_result);
pass & ck::utils::check_err(input_device_result.mData, input_host_result.mData);
if(do_log) if(do_log)
{ {
......
profiler/include/profile_conv_fwd_bias_relu_add_impl.hpp
View file @ f0224f2a
...@@ -12,6 +12,7 @@ ...@@ -12,6 +12,7 @@
#include "ck/library/utility/device_memory.hpp" #include "ck/library/utility/device_memory.hpp"
#include "ck/library/utility/host_tensor.hpp" #include "ck/library/utility/host_tensor.hpp"
#include "ck/library/utility/host_tensor_generator.hpp" #include "ck/library/utility/host_tensor_generator.hpp"
#include "ck/library/utility/literals.hpp"
#include "ck/library/reference_tensor_operation/cpu/reference_conv_fwd_bias_activation_add.hpp" #include "ck/library/reference_tensor_operation/cpu/reference_conv_fwd_bias_activation_add.hpp"
namespace ck { namespace ck {
...@@ -68,19 +69,19 @@ void profile_conv_fwd_bias_relu_add_impl(int do_verification, ...@@ -68,19 +69,19 @@ void profile_conv_fwd_bias_relu_add_impl(int do_verification,
auto f_host_tensor_descriptor = auto f_host_tensor_descriptor =
[](std::size_t N_, std::size_t C_, std::size_t H, std::size_t W, auto layout) { [](std::size_t N_, std::size_t C_, std::size_t H, std::size_t W, auto layout) {
using namespace ck::literals;
if constexpr(is_same<decltype(layout), ck::tensor_layout::convolution::NCHW>::value || if constexpr(is_same<decltype(layout), ck::tensor_layout::convolution::NCHW>::value ||
is_same<decltype(layout), ck::tensor_layout::convolution::KCYX>::value || is_same<decltype(layout), ck::tensor_layout::convolution::KCYX>::value ||
is_same<decltype(layout), ck::tensor_layout::convolution::NKHW>::value) is_same<decltype(layout), ck::tensor_layout::convolution::NKHW>::value)
{ {
return HostTensorDescriptor(std::vector<std::size_t>({N_, C_, H, W}), return HostTensorDescriptor({N_, C_, H, W}, {C_ * H * W, H * W, W, 1_uz});
std::vector<std::size_t>({C_ * H * W, H * W, W, 1}));
} }
else if constexpr(is_same<decltype(layout), tensor_layout::convolution::NHWC>::value || else if constexpr(is_same<decltype(layout), tensor_layout::convolution::NHWC>::value ||
is_same<decltype(layout), tensor_layout::convolution::KYXC>::value || is_same<decltype(layout), tensor_layout::convolution::KYXC>::value ||
is_same<decltype(layout), tensor_layout::convolution::NHWK>::value) is_same<decltype(layout), tensor_layout::convolution::NHWK>::value)
{ {
return HostTensorDescriptor(std::vector<std::size_t>({N_, C_, H, W}), return HostTensorDescriptor({N_, C_, H, W}, {C_ * H * W, 1_uz, W * C_, C_});
std::vector<std::size_t>({C_ * H * W, 1, W * C_, C_}));
} }
}; };
...@@ -92,8 +93,7 @@ void profile_conv_fwd_bias_relu_add_impl(int do_verification, ...@@ -92,8 +93,7 @@ void profile_conv_fwd_bias_relu_add_impl(int do_verification,
f_host_tensor_descriptor(N, K, Ho, Wo, OutLayout{})); f_host_tensor_descriptor(N, K, Ho, Wo, OutLayout{}));
// bias: assume contiguous 1d vector // bias: assume contiguous 1d vector
Tensor<OutDataType> bias_k( Tensor<OutDataType> bias_k({K});
HostTensorDescriptor(std::vector<std::size_t>({static_cast<std::size_t>(K)})));
// residual: assume same layout as output tensor // residual: assume same layout as output tensor
Tensor<OutDataType> resi_n_k_ho_wo(f_host_tensor_descriptor(N, K, Ho, Wo, OutLayout{})); Tensor<OutDataType> resi_n_k_ho_wo(f_host_tensor_descriptor(N, K, Ho, Wo, OutLayout{}));
...@@ -251,8 +251,7 @@ void profile_conv_fwd_bias_relu_add_impl(int do_verification, ...@@ -251,8 +251,7 @@ void profile_conv_fwd_bias_relu_add_impl(int do_verification,
{ {
out_device_buf.FromDevice(out_n_k_ho_wo_device_result.mData.data()); out_device_buf.FromDevice(out_n_k_ho_wo_device_result.mData.data());
ck::utils::check_err(out_n_k_ho_wo_device_result.mData, ck::utils::check_err(out_n_k_ho_wo_device_result, out_n_k_ho_wo_host_result);
out_n_k_ho_wo_host_result.mData);
if(do_log) if(do_log)
{ {
......
Markdown is supported
0% or .
You are about to add 0 people to the discussion. Proceed with caution.
Finish editing this message first!
Please register or to comment