ROCm/composable_kernel
1
0
Fork 0
mirror of https://github.com/ROCm/composable_kernel.git synced 2026-05-13 09:45:56 +00:00
Code Issues Packages Projects Releases Wiki Activity

upgrade from clang-format-12 to clang-format-18 (#2568)

Browse Source 
* upgrade to clang-format-18

* update to clang-format-18 in pre-commit-config
This commit is contained in:
Illia Silin
2025-07-28 11:34:07 -07:00
committed by GitHub
parent 9786087010
commit 504b101da3
373 changed files with 3351 additions and 3760 deletions
Download Patch File Download Diff File Expand all files Collapse all files

2
library/include/ck/library/reference_tensor_operation/cpu/reference_moe_gemm.hpp
View File

@@ -116,7 +116,7 @@ struct ReferenceMoeGemm : public device::BaseOperator
#if CK_USE_PK4_LAYOUT_SHUFFLE
v_a = i4_to_f32_gfx9(i4);
#else
v_a = i4 - 8;
v_a = i4 - 8;
#endif
}
else

2
library/include/ck/library/reference_tensor_operation/cpu/reference_moe_gemm1_blockscale.hpp
View File

@@ -110,7 +110,7 @@ struct ReferenceMoeGemm1BlockScale : public device::BaseOperator
#if CK_USE_PK4_LAYOUT_SHUFFLE
v_a = i4_to_f32_gfx9(i4);
#else
v_a = i4 - 8;
v_a = i4 - 8;
#endif
}
else

20
library/include/ck/library/reference_tensor_operation/gpu/reference_gemm.hpp
View File

@@ -25,17 +25,17 @@ template <typename ALayout,
typename ComputeTypeB>
__global__ void
#if CK_USE_LAUNCH_BOUNDS
__launch_bounds__(CK_MAX_THREAD_PER_BLOCK, CK_MIN_BLOCK_PER_CU)
__launch_bounds__(CK_MAX_THREAD_PER_BLOCK, CK_MIN_BLOCK_PER_CU)
#endif
naive_gemm_kernel(const ADataType* __restrict__ p_a_grid,
const BDataType* __restrict__ p_b_grid,
CDataType* __restrict__ p_c_grid,
index_t m,
index_t n,
index_t k,
const AElementwiseOperation a_element_op,
const BElementwiseOperation b_element_op,
const CDEElementwiseOperation c_element_op)
naive_gemm_kernel(const ADataType* __restrict__ p_a_grid,
const BDataType* __restrict__ p_b_grid,
CDataType* __restrict__ p_c_grid,
index_t m,
index_t n,
index_t k,
const AElementwiseOperation a_element_op,
const BElementwiseOperation b_element_op,
const CDEElementwiseOperation c_element_op)
{
using RowMajor = ck::tensor_layout::gemm::RowMajor;

36
library/include/ck/library/tensor_operation_instance/gpu/conv_tensor_rearrange/device_column_to_image_instance.hpp
View File

@@ -23,8 +23,9 @@ template <ck::index_t... Is>
using S = ck::Sequence<Is...>;
template <ck::index_t NDimSpatial, typename InLayout>
using device_column_to_image_bf16_instances = std::tuple<
// clang-format off
using device_column_to_image_bf16_instances =
std::tuple<
// clang-format off
//#####################| Num| InLayout| InDataType| OutDataType| Block| MPer| KPer| Thread| Scalar|
//#####################| Dim| | | | Size| Block| Block| Cluster| Per|
//#####################| Spatial| | | | | | | Lengths| Vector|
@@ -39,12 +40,13 @@ using device_column_to_image_bf16_instances = std::tuple<
DeviceColumnToImageImpl<NDimSpatial, InLayout, BF16, BF16, 256, 64, 64, S<16, 16>, 4>,
DeviceColumnToImageImpl<NDimSpatial, InLayout, BF16, BF16, 256, 128, 128, S<16, 16>, 4>,
DeviceColumnToImageImpl<NDimSpatial, InLayout, BF16, BF16, 256, 128, 128, S<16, 16>, 8>
// clang-format on
>;
// clang-format on
>;
template <ck::index_t NDimSpatial, typename InLayout>
using device_column_to_image_f16_instances = std::tuple<
// clang-format off
using device_column_to_image_f16_instances =
std::tuple<
// clang-format off
//#####################| Num| InLayout| InDataType| OutDataType| Block| MPer| KPer| Thread| Scalar|
//#####################| Dim| | | | Size| Block| Block| Cluster| Per|
//#####################| Spatial| | | | | | | Lengths| Vector|
@@ -59,12 +61,13 @@ using device_column_to_image_f16_instances = std::tuple<
DeviceColumnToImageImpl<NDimSpatial, InLayout, F16, F16, 256, 64, 64, S<16, 16>, 4>,
DeviceColumnToImageImpl<NDimSpatial, InLayout, F16, F16, 256, 128, 128, S<16, 16>, 4>,
DeviceColumnToImageImpl<NDimSpatial, InLayout, F16, F16, 256, 128, 128, S<16, 16>, 8>
// clang-format on
>;
// clang-format on
>;
template <ck::index_t NDimSpatial, typename InLayout>
using device_column_to_image_f32_instances = std::tuple<
// clang-format off
using device_column_to_image_f32_instances =
std::tuple<
// clang-format off
//#####################| Num| InLayout| InDataType| OutDataType| Block| MPer| KPer| Thread| Scalar|
//#####################| Dim| | | | Size| Block| Block| Cluster| Per|
//#####################| Spatial| | | | | | | Lengths| Vector|
@@ -76,12 +79,13 @@ using device_column_to_image_f32_instances = std::tuple<
DeviceColumnToImageImpl<NDimSpatial, InLayout, F32, F32, 128, 32, 64, S<8, 16>, 4>,
DeviceColumnToImageImpl<NDimSpatial, InLayout, F32, F32, 256, 64, 64, S<16, 16>, 4>,
DeviceColumnToImageImpl<NDimSpatial, InLayout, F32, F32, 256, 128, 128, S<16, 16>, 4>
// clang-format on
>;
// clang-format on
>;
template <ck::index_t NDimSpatial, typename InLayout>
using device_column_to_image_i8_instances = std::tuple<
// clang-format off
using device_column_to_image_i8_instances =
std::tuple<
// clang-format off
//#####################| Num| InLayout| InDataType| OutDataType| Block| MPer| KPer| Thread| Scalar|
//#####################| Dim| | | | Size| Block| Block| Cluster| Per|
//#####################| Spatial| | | | | | | Lengths| Vector|
@@ -97,8 +101,8 @@ using device_column_to_image_i8_instances = std::tuple<
DeviceColumnToImageImpl<NDimSpatial, InLayout, int8_t, int8_t, 256, 128, 128, S<16, 16>, 4>,
DeviceColumnToImageImpl<NDimSpatial, InLayout, int8_t, int8_t, 256, 128, 128, S<16, 16>, 8>,
DeviceColumnToImageImpl<NDimSpatial, InLayout, int8_t, int8_t, 256, 256, 256, S<16, 16>, 16>
// clang-format on
>;
// clang-format on
>;
} // namespace instance
} // namespace device

36
library/include/ck/library/tensor_operation_instance/gpu/conv_tensor_rearrange/device_image_to_column_instance.hpp
View File

@@ -23,8 +23,9 @@ template <ck::index_t... Is>
using S = ck::Sequence<Is...>;
template <ck::index_t NDimSpatial, typename InLayout>
using device_image_to_column_bf16_instances = std::tuple<
// clang-format off
using device_image_to_column_bf16_instances =
std::tuple<
// clang-format off
//#####################| Num| InLayout| InDataType| OutDataType| Block| MPer| KPer| Thread| Scalar|
//#####################| Dim| | | | Size| Block| Block| Cluster| Per|
//#####################| Spatial| | | | | | | Lengths| Vector|
@@ -38,12 +39,13 @@ using device_image_to_column_bf16_instances = std::tuple<
DeviceImageToColumnImpl<NDimSpatial, InLayout, BF16, BF16, 256, 64, 64, S<16, 16>, 4>,
DeviceImageToColumnImpl<NDimSpatial, InLayout, BF16, BF16, 256, 128, 128, S<16, 16>, 4>,
DeviceImageToColumnImpl<NDimSpatial, InLayout, BF16, BF16, 256, 128, 128, S<16, 16>, 8>
// clang-format on
>;
// clang-format on
>;
template <ck::index_t NDimSpatial, typename InLayout>
using device_image_to_column_f16_instances = std::tuple<
// clang-format off
using device_image_to_column_f16_instances =
std::tuple<
// clang-format off
//#####################| Num| InLayout| InDataType| OutDataType| Block| MPer| KPer| Thread| Scalar|
//#####################| Dim| | | | Size| Block| Block| Cluster| Per|
//#####################| Spatial| | | | | | | Lengths| Vector|
@@ -58,12 +60,13 @@ using device_image_to_column_f16_instances = std::tuple<
DeviceImageToColumnImpl<NDimSpatial, InLayout, F16, F16, 256, 64, 64, S<16, 16>, 4>,
DeviceImageToColumnImpl<NDimSpatial, InLayout, F16, F16, 256, 128, 128, S<16, 16>, 4>,
DeviceImageToColumnImpl<NDimSpatial, InLayout, F16, F16, 256, 128, 128, S<16, 16>, 8>
// clang-format on
>;
// clang-format on
>;
template <ck::index_t NDimSpatial, typename InLayout>
using device_image_to_column_f32_instances = std::tuple<
// clang-format off
using device_image_to_column_f32_instances =
std::tuple<
// clang-format off
//#####################| Num| InLayout| InDataType| OutDataType| Block| MPer| KPer| Thread| Scalar|
//#####################| Dim| | | | Size| Block| Block| Cluster| Per|
//#####################| Spatial| | | | | | | Lengths| Vector|
@@ -75,12 +78,13 @@ using device_image_to_column_f32_instances = std::tuple<
DeviceImageToColumnImpl<NDimSpatial, InLayout, F32, F32, 128, 32, 64, S<8, 16>, 4>,
DeviceImageToColumnImpl<NDimSpatial, InLayout, F32, F32, 256, 64, 64, S<16, 16>, 4>,
DeviceImageToColumnImpl<NDimSpatial, InLayout, F32, F32, 256, 128, 128, S<16, 16>, 4>
// clang-format on
>;
// clang-format on
>;
template <ck::index_t NDimSpatial, typename InLayout>
using device_image_to_column_i8_instances = std::tuple<
// clang-format off
using device_image_to_column_i8_instances =
std::tuple<
// clang-format off
//#####################| Num| InLayout| InDataType| OutDataType| Block| MPer| KPer| Thread| Scalar|
//#####################| Dim| | | | Size| Block| Block| Cluster| Per|
//#####################| Spatial| | | | | | | Lengths| Vector|
@@ -96,8 +100,8 @@ using device_image_to_column_i8_instances = std::tuple<
DeviceImageToColumnImpl<NDimSpatial, InLayout, int8_t, int8_t, 256, 128, 128, S<16, 16>, 4>,
DeviceImageToColumnImpl<NDimSpatial, InLayout, int8_t, int8_t, 256, 128, 128, S<16, 16>, 8>,
DeviceImageToColumnImpl<NDimSpatial, InLayout, int8_t, int8_t, 256, 256, 256, S<16, 16>, 16>
// clang-format on
>;
// clang-format on
>;
} // namespace instance
} // namespace device

9
library/include/ck/library/tensor_operation_instance/gpu/grouped_conv_bwd_weight/device_exp_gemm_xdl_universal_km_kn_mn_instance.hpp
View File

@@ -38,8 +38,9 @@ static constexpr auto Intrawave = BlockGemmPipelineScheduler::Intrawave;
static constexpr auto Interwave = BlockGemmPipelineScheduler::Interwave;
template <typename InOutDataType, GemmSpecialization GemmSpec>
using device_gemm_xdl_universal_km_kn_mn_comp_instances = std::tuple<
// clang-format off
using device_gemm_xdl_universal_km_kn_mn_comp_instances =
std::tuple<
// clang-format off
//#########################| ALayout| BLayout| CLayout|AData| BData| CData| AccData| Cshuffle| A| B| C| GEMM| Block| MPer| NPer| KPer| AK1| BK1|MPer| NPer| MXdl| NXdl| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockLds| BBlockTransfer| BBlockTransfer| BBlockTransfer| BlockTransfer| BBlockTransfer| BBlockTransfer| BBlockLds| CShuffle| CShuffle| CBlockTransferClusterLengths| CBlockTransfer| Block-wiseGemm| Block-wiseGemm|
//#########################| | | | Type| Type| Type| Type| Type| Elementwise| Elementwise| Elementwise|Specialization| Size| Block| Block| Block| | | XDL| XDL| Per| Per| ThreadCluster| ThreadCluster| SrcAccessOrder| SrcVectorDim| SrcScalar| DstScalar| AddExtraM| ThreadCluster| ThreadCluster| SrcAccessOrder| SrcVectorDim| SrcScalar| DstScalar| AddExtraN| MXdlPerWave| NXdlPerWave| _MBlock_MXdlPerWave_MWaveMPerXdl| ScalarPerVector| Pipeline| Pipeline|
//#########################| | | | | | | | | Operation| Operation| Operation| | | | | | | | | | Wave| Wave| Lengths_K0_M_K1| ArrangeOrder| | | PerVector| PerVector_K1| | Lengths_K0_N_K1| ArrangeOrder| | | PerVector| PerVector_K1| | PerShuffle| PerShuffle| _NBlock_NXdlPerWave_NWaveNPerXdl| _NWaveNPerXdl| Scheduler| Verision|
@@ -56,8 +57,8 @@ using device_gemm_xdl_universal_km_kn_mn_comp_instances = std::tuple<
DeviceBatchedGemmMultiD_Xdl_CShuffle_V3< Col, Row, Tuple<>, Row, InOutDataType, InOutDataType, Tuple<>, InOutDataType, F32, InOutDataType, PassThrough, PassThrough, PassThrough, GemmSpec, 256, 128, 128, 64, 4, 4, 32, 32, 2, 2, S<16, 16, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 8, 4, 0, S<16, 16, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 8, 4, 0, 1, 1, S<1, 16, 1, 16>, S<4>, BlockGemmPipelineScheduler::Intrawave, BlockGemmPipelineVersion::v3>,
DeviceBatchedGemmMultiD_Xdl_CShuffle_V3< Col, Row, Tuple<>, Row, InOutDataType, InOutDataType, Tuple<>, InOutDataType, F32, InOutDataType, PassThrough, PassThrough, PassThrough, GemmSpec, 256, 128, 128, 64, 4, 4, 32, 32, 2, 2, S<16, 16, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 8, 4, 0, S<16, 16, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 8, 4, 0, 1, 1, S<1, 16, 1, 16>, S<4>, BlockGemmPipelineScheduler::Intrawave, BlockGemmPipelineVersion::v5>,
DeviceBatchedGemmMultiD_Xdl_CShuffle_V3< Col, Row, Tuple<>, Row, InOutDataType, InOutDataType, Tuple<>, InOutDataType, F32, InOutDataType, PassThrough, PassThrough, PassThrough, GemmSpec, 256, 128, 128, 64, 4, 4, 32, 32, 2, 2, S<16, 16, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 8, 4, 0, S<16, 16, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 8, 4, 0, 1, 1, S<1, 16, 1, 16>, S<4>, BlockGemmPipelineScheduler::Interwave, BlockGemmPipelineVersion::v1>
// clang-format on
>;
// clang-format on
>;
template <typename InOutDataType,
BlockGemmPipelineScheduler BlkGemmPipeSched,

27
library/include/ck/library/tensor_operation_instance/gpu/grouped_conv_bwd_weight/device_grouped_conv_bwd_weight_dl_instance.hpp
View File

@@ -35,48 +35,51 @@ template <ck::index_t NDimSpatial,
typename BLayout,
typename ELayout,
ConvolutionBackwardWeightSpecialization ConvSpec>
using device_grouped_conv_bwd_weight_dl_f32_instances = std::tuple<
// clang-format off
using device_grouped_conv_bwd_weight_dl_f32_instances =
std::tuple<
// clang-format off
//############################| Num| InLayout| WeiLayout| OutLayout| InData| WeiData| OutData| AccData| In| Wei| Out| ConvBackward| Block| MPer| NPer| K0Per| K1| M1Per| N1Per| KPer| M1N1Thread| M1N1Thread| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| BBlockTransfer| BBlockTransfer| BBlockTransfer| BBlockTransfer| BBlockTransfer| BBlockTransfer| BBlockTransfer| CThreadTransfer| CThreadTransfer| CThreadTransfer|
//############################| Dim| | | | Type| Type| Type| Type| Elementwise| Elementwise| Elementwise| Weight| Size| Block| Block| Block| | Thread| Thread| Thread| ClusterM1Xs| ClusterN1Xs| ThreadSliceLengths| ThreadClusterLengths| ThreadCluster| SrcAccessOrder| SrcVectorTensorLengths| SrcVectorTensor| DstVectorTensorLengths| ThreadSliceLengths| ThreadClusterLengths| ThreadCluster| SrcAccessOrder| SrcVectorTensorLengths| SrcVectorTensor| DstVectorTensorLengths| SrcDstAccessOrder| SrcDstVectorDim| DstScalarPerVector|
//############################| Spatial| | | | | | | | Operation| Operation| Operation| Specialization| | | | | | | | | | | _K0_M0_M1_K1| _K0_M0_M1_K1| ArrangeOrder| | _K0_M0_M1_K1| ContiguousDimOrder| _K0_M0_M1_K1| _K0_N0_N1_K1| _K0_N0_N1_K1| ArrangeOrder| | _K0_N0_N1_K1| ContiguousDimOrder| _K0_N0_N1_K1| | | |
//############################| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
// generic instance
DeviceGroupedConvBwdWeight_Dl< NDimSpatial, ALayout, BLayout, ELayout, F32, F32, F32, F32, PassThrough, PassThrough, PassThrough, ConvSpec, 256, 128, 128, 16, 1, 4, 4, 1, S<8, 2>, S<8, 2>, S<1, 8, 1, 1, 1>, S<1, 2, 1, 128, 1>, S<0, 2, 3, 1, 4>, S<0, 2, 3, 1, 4>, S<1, 1, 1, 1, 1>, S<0, 2, 3, 1, 4>, S<1, 1, 1, 1, 1>, S<1, 1, 1, 8, 1>, S<1, 16, 1, 16, 1>, S<0, 1, 4, 2, 3>, S<0, 1, 4, 2, 3>, S<1, 1, 1, 1, 1>, S<0, 1, 4, 2, 3>, S<1, 1, 1, 1, 1>, S<0, 1, 2, 3, 4, 5>, 5, 1>
// clang-format on
>;
// clang-format on
>;
template <ck::index_t NDimSpatial,
typename ALayout,
typename BLayout,
typename ELayout,
ConvolutionBackwardWeightSpecialization ConvSpec>
using device_grouped_conv_bwd_weight_dl_f16_instances = std::tuple<
// clang-format off
using device_grouped_conv_bwd_weight_dl_f16_instances =
std::tuple<
// clang-format off
//############################| Num| InLayout| WeiLayout| OutLayout| InData| WeiData| OutData| AccData| In| Wei| Out| ConvBackward| Block| MPer| NPer| K0Per| K1| M1Per| N1Per| KPer| M1N1Thread| M1N1Thread| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| BBlockTransfer| BBlockTransfer| BBlockTransfer| BBlockTransfer| BBlockTransfer| BBlockTransfer| BBlockTransfer| CThreadTransfer| CThreadTransfer| CThreadTransfer|
//############################| Dim| | | | Type| Type| Type| Type| Elementwise| Elementwise| Elementwise| Weight| Size| Block| Block| Block| | Thread| Thread| Thread| ClusterM1Xs| ClusterN1Xs| ThreadSliceLengths| ThreadClusterLengths| ThreadCluster| SrcAccessOrder| SrcVectorTensorLengths| SrcVectorTensor| DstVectorTensorLengths| ThreadSliceLengths| ThreadClusterLengths| ThreadCluster| SrcAccessOrder| SrcVectorTensorLengths| SrcVectorTensor| DstVectorTensorLengths| SrcDstAccessOrder| SrcDstVectorDim| DstScalarPerVector|
//############################| Spatial| | | | | | | | Operation| Operation| Operation| Specialization| | | | | | | | | | | _K0_M0_M1_K1| _K0_M0_M1_K1| ArrangeOrder| | _K0_M0_M1_K1| ContiguousDimOrder| _K0_M0_M1_K1| _K0_N0_N1_K1| _K0_N0_N1_K1| ArrangeOrder| | _K0_N0_N1_K1| ContiguousDimOrder| _K0_N0_N1_K1| | | |
//############################| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
// generic instance
DeviceGroupedConvBwdWeight_Dl< NDimSpatial, ALayout, BLayout, ELayout, F16, F16, F16, F32, PassThrough, PassThrough, PassThrough, ConvSpec, 256, 128, 128, 16, 1, 4, 4, 1, S<8, 2>, S<8, 2>, S<1, 8, 1, 1, 1>, S<1, 2, 1, 128, 1>, S<0, 2, 3, 1, 4>, S<0, 2, 3, 1, 4>, S<1, 1, 1, 1, 1>, S<0, 2, 3, 1, 4>, S<1, 1, 1, 1, 1>, S<1, 1, 1, 8, 1>, S<1, 16, 1, 16, 1>, S<0, 1, 4, 2, 3>, S<0, 1, 4, 2, 3>, S<1, 1, 1, 1, 1>, S<0, 1, 4, 2, 3>, S<1, 1, 1, 1, 1>, S<0, 1, 2, 3, 4, 5>, 5, 1>
// clang-format on
>;
// clang-format on
>;
template <ck::index_t NDimSpatial,
typename ALayout,
typename BLayout,
typename ELayout,
ConvolutionBackwardWeightSpecialization ConvSpec>
using device_grouped_conv_bwd_weight_dl_bf16_instances = std::tuple<
// clang-format off
using device_grouped_conv_bwd_weight_dl_bf16_instances =
std::tuple<
// clang-format off
//############################| Num| InLayout| WeiLayout| OutLayout| InData| WeiData| OutData| AccData| In| Wei| Out| ConvBackward| Block| MPer| NPer| K0Per| K1| M1Per| N1Per| KPer| M1N1Thread| M1N1Thread| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| BBlockTransfer| BBlockTransfer| BBlockTransfer| BBlockTransfer| BBlockTransfer| BBlockTransfer| BBlockTransfer| CThreadTransfer| CThreadTransfer| CThreadTransfer|
//############################| Dim| | | | Type| Type| Type| Type| Elementwise| Elementwise| Elementwise| Weight| Size| Block| Block| Block| | Thread| Thread| Thread| ClusterM1Xs| ClusterN1Xs| ThreadSliceLengths| ThreadClusterLengths| ThreadCluster| SrcAccessOrder| SrcVectorTensorLengths| SrcVectorTensor| DstVectorTensorLengths| ThreadSliceLengths| ThreadClusterLengths| ThreadCluster| SrcAccessOrder| SrcVectorTensorLengths| SrcVectorTensor| DstVectorTensorLengths| SrcDstAccessOrder| SrcDstVectorDim| DstScalarPerVector|
//############################| Spatial| | | | | | | | Operation| Operation| Operation| Specialization| | | | | | | | | | | _K0_M0_M1_K1| _K0_M0_M1_K1| ArrangeOrder| | _K0_M0_M1_K1| ContiguousDimOrder| _K0_M0_M1_K1| _K0_N0_N1_K1| _K0_N0_N1_K1| ArrangeOrder| | _K0_N0_N1_K1| ContiguousDimOrder| _K0_N0_N1_K1| | | |
//############################| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
// generic instance
DeviceGroupedConvBwdWeight_Dl< NDimSpatial, ALayout, BLayout, ELayout, BF16, F32, BF16, F32, PassThrough, PassThrough, PassThrough, ConvSpec, 256, 128, 128, 16, 1, 4, 4, 1, S<8, 2>, S<8, 2>, S<1, 8, 1, 1, 1>, S<1, 2, 1, 128, 1>, S<0, 2, 3, 1, 4>, S<0, 2, 3, 1, 4>, S<1, 1, 1, 1, 1>, S<0, 2, 3, 1, 4>, S<1, 1, 1, 1, 1>, S<1, 1, 1, 8, 1>, S<1, 16, 1, 16, 1>, S<0, 1, 4, 2, 3>, S<0, 1, 4, 2, 3>, S<1, 1, 1, 1, 1>, S<0, 1, 4, 2, 3>, S<1, 1, 1, 1, 1>, S<0, 1, 2, 3, 4, 5>, 5, 1>
// clang-format on
>;
// clang-format on
>;
} // namespace instance
} // namespace device

18
library/include/ck/library/tensor_operation_instance/gpu/grouped_conv_bwd_weight/device_grouped_conv_bwd_weight_wmma_instance.hpp
View File

@@ -37,9 +37,8 @@ template <index_t NDSpatial,
typename BLayout,
typename CLayout,
ConvolutionBackwardWeightSpecialization ConvSpec>
using device_grouped_conv_bwd_weight_wmma_f16_instances =
std::tuple<
// clang-format off
using device_grouped_conv_bwd_weight_wmma_f16_instances = std::tuple<
// clang-format off
//#####################################| NumDim| A| B| C| AData| BData| CData| AccData| A| B| C| ConvForward| Block| MPer| NPer| KPer| K1| MPer| NPer| MRepeat| NRepeat| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockLds| BBlockTransfer| BBlockTransfer| BBlockTransfer| BlockTransfer| BBlockTransfer| BBlockTransfer| BBlockLds| CShuffle| CShuffle| CBlockTransferClusterLengths| CBlockTransfer|
//#####################################| Spatial| Layout| Layout| Layout| Type| Type| Type| Type| Elementwise| Elementwise| Elementwise| Specialization| Size| Block| Block| Block| | WMMA| WMMA| | | ThreadCluster| ThreadCluster| SrcAccessOrder| SrcVectorDim| SrcScalar| DstScalar| AddExtraM| ThreadCluster| ThreadCluster| SrcAccessOrder| SrcVectorDim| SrcScalar| DstScalar| AddExtraN| MRepeatPerWave| NRepeatPerWave| _MBlock_MPerBlock| ScalarPerVector|
//#####################################| | | | | | | | | Operation| Operation| Operation| | | | | | | | | | | Lengths_K0_M_K1| ArrangeOrder| | | PerVector| PerVector_K1| | Lengths_K0_N_K1| ArrangeOrder| | | PerVector| PerVector_K1| | PerShuffle| PerShuffle| _NBlock_NPerBlock| _NPerBlock|
@@ -71,17 +70,16 @@ using device_grouped_conv_bwd_weight_wmma_f16_instances =
DeviceGroupedConvBwdWeight_Wmma_CShuffle<NDSpatial, ALayout, BLayout, CLayout, F16, F16, F16, F32, PassThrough, PassThrough, PassThrough, ConvSpec, 32, 64, 32, 8, 8, 16, 16, 4, 2, S<8, 4, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 8, 8, 1, S<8, 4, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 8, 8, 1, 1, 1, S<1, 16, 1, 2>, 8>,
DeviceGroupedConvBwdWeight_Wmma_CShuffle<NDSpatial, ALayout, BLayout, CLayout, F16, F16, F16, F32, PassThrough, PassThrough, PassThrough, ConvSpec, 32, 64, 16, 8, 8, 16, 16, 4, 1, S<8, 4, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 8, 8, 1, S<8, 4, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 4, 8, 1, 1, 1, S<1, 16, 1, 2>, 8>,
DeviceGroupedConvBwdWeight_Wmma_CShuffle<NDSpatial, ALayout, BLayout, CLayout, F16, F16, F16, F32, PassThrough, PassThrough, PassThrough, ConvSpec, 32, 32, 16, 8, 8, 16, 16, 2, 1, S<8, 4, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 8, 8, 1, S<8, 4, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 4, 8, 1, 1, 1, S<1, 16, 1, 2>, 8>
// clang-format on
>;
// clang-format on
>;
template <index_t NDSpatial,
typename ALayout,
typename BLayout,
typename CLayout,
ConvolutionBackwardWeightSpecialization ConvSpec>
using device_grouped_conv_bwd_weight_wmma_i8_instances =
std::tuple<
// clang-format off
using device_grouped_conv_bwd_weight_wmma_i8_instances = std::tuple<
// clang-format off
//#####################################| NumDim| A| B| C| AData| BData| CData| AccData| A| B| C| ConvForward| Block| MPer| NPer| KPer| K1| MPer| NPer| MRepeat| NRepeat| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockLds| BBlockTransfer| BBlockTransfer| BBlockTransfer| BlockTransfer| BBlockTransfer| BBlockTransfer| BBlockLds| CShuffle| CShuffle| CBlockTransferClusterLengths| CBlockTransfer|
//#####################################| Spatial| Layout| Layout| Layout| Type| Type| Type| Type| Elementwise| Elementwise| Elementwise| Specialization| Size| Block| Block| Block| | WMMA| WMMA| | | ThreadCluster| ThreadCluster| SrcAccessOrder| SrcVectorDim| SrcScalar| DstScalar| AddExtraM| ThreadCluster| ThreadCluster| SrcAccessOrder| SrcVectorDim| SrcScalar| DstScalar| AddExtraN| MRepeatPerWave| NRepeatPerWave| _MBlock_MPerBlock| ScalarPerVector|
//#####################################| | | | | | | | | Operation| Operation| Operation| | | | | | | | | | | Lengths_K0_M_K1| ArrangeOrder| | | PerVector| PerVector_K1| | Lengths_K0_N_K1| ArrangeOrder| | | PerVector| PerVector_K1| | PerShuffle| PerShuffle| _NBlock_NPerBlock| _NPerBlock|
@@ -110,8 +108,8 @@ using device_grouped_conv_bwd_weight_wmma_i8_instances =
DeviceGroupedConvBwdWeight_Wmma_CShuffle<NDSpatial, ALayout, BLayout, CLayout, I8, I8, I8, I32, PassThrough, PassThrough, PassThrough, ConvSpec, 32, 64, 64, 8, 8, 16, 16, 4, 4, S<8, 4, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 16, 8, 1, S<8, 4, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 16, 8, 1, 1, 1, S<1, 16, 1, 2>, 8>,
DeviceGroupedConvBwdWeight_Wmma_CShuffle<NDSpatial, ALayout, BLayout, CLayout, I8, I8, I8, I32, PassThrough, PassThrough, PassThrough, ConvSpec, 32, 32, 32, 8, 8, 16, 16, 2, 2, S<8, 4, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 8, 8, 1, S<8, 4, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 8, 8, 1, 1, 1, S<1, 16, 1, 2>, 8>,
DeviceGroupedConvBwdWeight_Wmma_CShuffle<NDSpatial, ALayout, BLayout, CLayout, I8, I8, I8, I32, PassThrough, PassThrough, PassThrough, ConvSpec, 32, 64, 16, 8, 8, 16, 16, 4, 1, S<8, 4, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 16, 8, 1, S<8, 4, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 4, 8, 1, 1, 1, S<1, 16, 1, 2>, 8>
// clang-format on
>;
// clang-format on
>;
} // namespace instance
} // namespace device