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Commit 0629870d authored by Jun Liu's avatar Jun Liu
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Merge branch 'amd-develop' into amd-master

parents 48fe8532 d4ad52d6
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Showing with 247 additions and 182 deletions
include/ck/tensor_operation/gpu/grid/gridwise_gemm_xdlops_v3r2.hpp
View file @ 0629870d
......@@ -142,8 +142,8 @@ struct GridwiseGemm_k0mk1_k0nk1_mn_xdlops_v3r2
using ThisThreadBlock = ThisThreadBlock<BlockSize>;
using GridwiseGemmPipe = remove_cvref_t<decltype(
GridwiseGemmPipeline_Selector<PipelineVer, NumGemmKPrefetchStage>())>;
using GridwiseGemmPipe = remove_cvref_t<
decltype(GridwiseGemmPipeline_Selector<PipelineVer, NumGemmKPrefetchStage>())>;
__host__ __device__ static constexpr auto GetABlockDescriptor_K0PerBlock_MPerBlock_K1()
{
......@@ -323,13 +323,13 @@ struct GridwiseGemm_k0mk1_k0nk1_mn_xdlops_v3r2
}
using CGridDescriptor_MBlock_MXdlPerWave_MWaveMPerXdl_NBlock_NXdlPerWave_NWaveNPerXdl =
remove_cvref_t<decltype(
MakeCGridDescriptor_MBlock_MXdlPerWave_MWaveMPerXdl_NBlock_NXdlPerWave_NWaveNPerXdl(
remove_cvref_t<
decltype(MakeCGridDescriptor_MBlock_MXdlPerWave_MWaveMPerXdl_NBlock_NXdlPerWave_NWaveNPerXdl(
CGridDesc_M_N{}))>;
using C0GridDescriptor_MBlock_MXdlPerWave_MWaveMPerXdl_NBlock_NXdlPerWave_NWaveNPerXdl =
remove_cvref_t<decltype(
MakeCGridDescriptor_MBlock_MXdlPerWave_MWaveMPerXdl_NBlock_NXdlPerWave_NWaveNPerXdl(
remove_cvref_t<
decltype(MakeCGridDescriptor_MBlock_MXdlPerWave_MWaveMPerXdl_NBlock_NXdlPerWave_NWaveNPerXdl(
C0GridDesc_M_N{}))>;
using DefaultBlock2CTileMap =
......@@ -654,12 +654,9 @@ struct GridwiseGemm_k0mk1_k0nk1_mn_xdlops_v3r2
FloatC, // typename Src0Data,
FloatC, // typename Src1Data,
FloatC, // typename DstData,
decltype(
c_block_desc_mblock_mxdlperwave_mwavemperxdl_nblock_nxdlperwave_nwavenperxdl),
decltype(
c0_grid_desc_mblock_mxdlperwave_mwavemperxdl_nblock_nxdlperwave_nwavenperxdl),
decltype(
c_grid_desc_mblock_mxdlperwave_mwavemperxdl_nblock_nxdlperwave_nwavenperxdl),
decltype(c_block_desc_mblock_mxdlperwave_mwavemperxdl_nblock_nxdlperwave_nwavenperxdl),
decltype(c0_grid_desc_mblock_mxdlperwave_mwavemperxdl_nblock_nxdlperwave_nwavenperxdl),
decltype(c_grid_desc_mblock_mxdlperwave_mwavemperxdl_nblock_nxdlperwave_nwavenperxdl),
Sequence<0, 1, 2, 3, 4, 5>, // typename DimAccessOrder,
5, // index_t VectorDim,
CBlockTransferScalarPerVector_NWaveNPerXdl, // index_t ScalarPerVector,
......
include/ck/tensor_operation/gpu/grid/gridwise_gemm_xdlops_v3r3.hpp
View file @ 0629870d
......@@ -151,8 +151,8 @@ struct GridwiseGemm_k0mk1_k0nk1_mn_xdlops_v3r3
using ThisThreadBlock = ThisThreadBlock<BlockSize>;
using GridwiseGemmPipe = remove_cvref_t<decltype(
GridwiseGemmPipeline_Selector<PipelineVer, NumGemmKPrefetchStage>())>;
using GridwiseGemmPipe = remove_cvref_t<
decltype(GridwiseGemmPipeline_Selector<PipelineVer, NumGemmKPrefetchStage>())>;
__host__ __device__ static constexpr auto GetABlockDescriptor_K0PerBlock_MPerBlock_K1()
{
......@@ -331,18 +331,18 @@ struct GridwiseGemm_k0mk1_k0nk1_mn_xdlops_v3r3
c_grid_desc_m_n);
}
using CGridDescriptor_MBlock_MXdlPerWave_MWaveMPerXdl_NBlock_NXdlPerWave_NWaveNPerXdl =
remove_cvref_t<decltype(
MakeCGridDescriptor_MBlock_MXdlPerWave_MWaveMPerXdl_NBlock_NXdlPerWave_NWaveNPerXdl(
remove_cvref_t<
decltype(MakeCGridDescriptor_MBlock_MXdlPerWave_MWaveMPerXdl_NBlock_NXdlPerWave_NWaveNPerXdl(
CGridDesc_M_N{}))>;
using C0GridDescriptor_MBlock_MXdlPerWave_MWaveMPerXdl_NBlock_NXdlPerWave_NWaveNPerXdl =
remove_cvref_t<decltype(
MakeCGridDescriptor_MBlock_MXdlPerWave_MWaveMPerXdl_NBlock_NXdlPerWave_NWaveNPerXdl(
remove_cvref_t<
decltype(MakeCGridDescriptor_MBlock_MXdlPerWave_MWaveMPerXdl_NBlock_NXdlPerWave_NWaveNPerXdl(
C0GridDesc_M_N{}))>;
using C1GridDescriptor_MBlock_MXdlPerWave_MWaveMPerXdl_NBlock_NXdlPerWave_NWaveNPerXdl =
remove_cvref_t<decltype(
MakeCGridDescriptor_MBlock_MXdlPerWave_MWaveMPerXdl_NBlock_NXdlPerWave_NWaveNPerXdl(
remove_cvref_t<
decltype(MakeCGridDescriptor_MBlock_MXdlPerWave_MWaveMPerXdl_NBlock_NXdlPerWave_NWaveNPerXdl(
C1GridDesc_M_N{}))>;
using DefaultBlock2CTileMap =
......@@ -674,14 +674,10 @@ struct GridwiseGemm_k0mk1_k0nk1_mn_xdlops_v3r3
FloatC, // typename Src1Data,
FloatC, // typename Src2Data,
FloatC, // typename DstData,
decltype(
c_block_desc_mblock_mxdlperwave_mwavemperxdl_nblock_nxdlperwave_nwavenperxdl),
decltype(
c0_grid_desc_mblock_mxdlperwave_mwavemperxdl_nblock_nxdlperwave_nwavenperxdl),
decltype(
c1_grid_desc_mblock_mxdlperwave_mwavemperxdl_nblock_nxdlperwave_nwavenperxdl),
decltype(
c_grid_desc_mblock_mxdlperwave_mwavemperxdl_nblock_nxdlperwave_nwavenperxdl),
decltype(c_block_desc_mblock_mxdlperwave_mwavemperxdl_nblock_nxdlperwave_nwavenperxdl),
decltype(c0_grid_desc_mblock_mxdlperwave_mwavemperxdl_nblock_nxdlperwave_nwavenperxdl),
decltype(c1_grid_desc_mblock_mxdlperwave_mwavemperxdl_nblock_nxdlperwave_nwavenperxdl),
decltype(c_grid_desc_mblock_mxdlperwave_mwavemperxdl_nblock_nxdlperwave_nwavenperxdl),
Sequence<0, 1, 2, 3, 4, 5>, // typename DimAccessOrder,
5, // index_t VectorDim,
CBlockTransferScalarPerVector_NWaveNPerXdl, // index_t ScalarPerVector,
......
include/ck/tensor_operation/gpu/grid/gridwise_sparse_embeddings_forward_layernorm.hpp
View file @ 0629870d
......@@ -78,8 +78,8 @@ struct GridwiseSparseEmbeddingsForwardLayernorm
using ThreadwiseWolfordDesc2D = decltype(make_naive_tensor_descriptor_packed(make_tuple(
Number<DimSubBlocks * DimThreadSize>{}, Number<RowSubBlocks * RowVectorSize>{})));
using ThreadwiseWolfordDescReduce = decltype(
make_naive_tensor_descriptor_packed(make_tuple(Number<DimSubBlocks * DimThreadSize>{})));
using ThreadwiseWolfordDescReduce = decltype(make_naive_tensor_descriptor_packed(
make_tuple(Number<DimSubBlocks * DimThreadSize>{})));
using ThreadwiseWelford =
ThreadwiseWelford<AccDataType, ThreadwiseWolfordDesc2D, ThreadwiseWolfordDescReduce>;
......
include/ck/tensor_operation/gpu/grid/normalization/gridwise_normalization_splitk_1st.hpp
View file @ 0629870d
......@@ -87,9 +87,9 @@ struct GridwiseNormalizationSplitK1st
int left_kPerBlock = math::integer_divide_ceil(k, kGridSize);
int kRightmostBlock = kRaw - left_kPerBlock * (kGridSize - 1);
int kPerThread = kRightmostBlock < K_BlockTileSize
? 0
: KThreadSliceSize * (kRightmostBlock / K_BlockTileSize);
int kPerBlockTail = kRightmostBlock - kPerThread * KThreadClusterSize;
? 0
: KThreadSliceSize * (kRightmostBlock / K_BlockTileSize);
int kPerBlockTail = kRightmostBlock - kPerThread * KThreadClusterSize;
if(kPerBlockTail > 0)
{
......
include/ck/tensor_operation/gpu/thread/threadwise_tensor_slice_transfer_v3r1.hpp
View file @ 0629870d
......@@ -129,6 +129,9 @@ struct ThreadwiseTensorSliceTransfer_v3r1
constexpr auto src_access_lengths = SliceLengths{} / src_scalar_per_access;
static_assert(SliceLengths::At(SrcVectorDim) % SrcScalarPerVector == 0,
"SliceLengths[SrcVectorDim] must be divisible by SrcScalarPerVector");
constexpr auto src_dim_access_order = SrcDimAccessOrder{};
constexpr auto ordered_src_access_lengths =
......
include/ck/tensor_operation/operator_transform/transform_conv_bwd_data_to_gemm_v1.hpp
View file @ 0629870d
......@@ -236,8 +236,6 @@ struct TransformConvBwdDataToGemm_v1
const index_t ConvDilationH = conv_filter_dilations[HIdx - NonSpatialDimsNum];
const index_t ConvDilationW = conv_filter_dilations[WIdx - NonSpatialDimsNum];
const index_t AK0 = K / AK1;
// n_do_ho_wo_k for 3d or n_ho_wo_k for 2d
const auto out_grid_desc =
make_out_grid_desc<NDimSpatial, ALayout, ConvBwdDataSpecialization>(
......@@ -247,6 +245,8 @@ struct TransformConvBwdDataToGemm_v1
ck::tensor_operation::device::ConvolutionBackwardDataSpecialization::
Filter1x1Stride1Pad0)
{
const index_t AK0 = math::integer_divide_ceil(K, AK1);
// A: output tensor
const auto out_gemmak0_gemmmraw_gemmak1_grid_desc = transform_tensor_descriptor(
out_grid_desc,
......@@ -308,6 +308,9 @@ struct TransformConvBwdDataToGemm_v1
const auto YDotSlice = math::integer_divide_ceil(Y - i_ytilde, YTilde);
const auto XDotSlice = math::integer_divide_ceil(X - i_xtilde, XTilde);
const index_t AK0 =
math::integer_divide_ceil(ZDotSlice * YDotSlice * XDotSlice * K, AK1);
if constexpr(NDimSpatial == 2)
{
// A: output tensor
......@@ -332,7 +335,7 @@ struct TransformConvBwdDataToGemm_v1
make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2>{}, Sequence<3>{}),
make_tuple(Sequence<0>{}, Sequence<1, 2>{}, Sequence<3, 4>{}, Sequence<5>{}));
const auto out_n_ydotslice_htildeslice_xdotslice_wtildeslice_ak0_ak1_grid_desc =
const auto out_n_ydotslice_htildeslice_xdotslice_wtildeslice_k_grid_desc =
transform_tensor_descriptor(
out_n_ydot_htilde_xdot_wtilde_k_grid_desc,
make_tuple(make_pass_through_transform(N),
......@@ -340,7 +343,7 @@ struct TransformConvBwdDataToGemm_v1
make_slice_transform(HTilde, IHTildeSliceBegin, HTildeSlice),
make_slice_transform(XDot, I0, XDotSlice),
make_slice_transform(WTilde, IWTildeSliceBegin, WTildeSlice),
make_unmerge_transform(make_tuple(AK0, AK1))),
make_pass_through_transform(K)),
make_tuple(Sequence<0>{},
Sequence<1>{},
Sequence<2>{},
......@@ -352,21 +355,28 @@ struct TransformConvBwdDataToGemm_v1
Sequence<2>{},
Sequence<3>{},
Sequence<4>{},
Sequence<5, 6>{}));
Sequence<5>{}));
const auto out_gemmak0_gemmmraw_gemmak1_grid_desc = transform_tensor_descriptor(
out_n_ydotslice_htildeslice_xdotslice_wtildeslice_ak0_ak1_grid_desc,
make_tuple(make_merge_transform(make_tuple(YDotSlice, XDotSlice, AK0)),
make_merge_transform(make_tuple(N, HTildeSlice, WTildeSlice)),
make_pass_through_transform(AK1)),
make_tuple(Sequence<1, 3, 5>{}, Sequence<0, 2, 4>{}, Sequence<6>{}),
make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2>{}));
const auto out_gemmk_gemmmraw_grid_desc = transform_tensor_descriptor(
out_n_ydotslice_htildeslice_xdotslice_wtildeslice_k_grid_desc,
make_tuple(make_merge_transform(make_tuple(YDotSlice, XDotSlice, K)),
make_merge_transform(make_tuple(N, HTildeSlice, WTildeSlice))),
make_tuple(Sequence<1, 3, 5>{}, Sequence<0, 2, 4>{}),
make_tuple(Sequence<0>{}, Sequence<1>{}));
const auto out_gemmak0_gemmm_gemmak1_grid_desc =
const auto out_gemmk_gemmm_padded_grid_desc =
ck::tensor_operation::device::PadTensorDescriptor(
out_gemmak0_gemmmraw_gemmak1_grid_desc,
make_tuple(AK0, GemmMPerBlock, AK1),
Sequence<false, DoPadGemmM, false>{});
out_gemmk_gemmmraw_grid_desc,
make_tuple(AK1, GemmMPerBlock),
Sequence<true, DoPadGemmM>{});
const auto out_gemmak0_gemmm_gemmak1_grid_desc = transform_tensor_descriptor(
out_gemmk_gemmm_padded_grid_desc,
make_tuple(make_unmerge_transform(make_tuple(AK0, AK1)),
make_pass_through_transform(
out_gemmk_gemmm_padded_grid_desc.GetLength(I1))),
make_tuple(Sequence<0>{}, Sequence<1>{}),
make_tuple(Sequence<0, 2>{}, Sequence<1>{}));
return out_gemmak0_gemmm_gemmak1_grid_desc;
}
......@@ -411,7 +421,7 @@ struct TransformConvBwdDataToGemm_v1
Sequence<7>{}));
const auto
out_n_zdotslice_dtildeslice_ydotslice_htildeslice_xdotslice_wtildeslice_ak0_ak1_grid_desc =
out_n_zdotslice_dtildeslice_ydotslice_htildeslice_xdotslice_wtildeslice_k_grid_desc =
transform_tensor_descriptor(
out_n_zdot_dtilde_ydot_htilde_xdot_wtilde_k_grid_desc,
make_tuple(make_pass_through_transform(N),
......@@ -421,7 +431,7 @@ struct TransformConvBwdDataToGemm_v1
make_slice_transform(HTilde, IHTildeSliceBegin, HTildeSlice),
make_slice_transform(XDot, I0, XDotSlice),
make_slice_transform(WTilde, IWTildeSliceBegin, WTildeSlice),
make_unmerge_transform(make_tuple(AK0, AK1))),
make_pass_through_transform(K)),
make_tuple(Sequence<0>{},
Sequence<1>{},
Sequence<2>{},
......@@ -437,22 +447,29 @@ struct TransformConvBwdDataToGemm_v1
Sequence<4>{},
Sequence<5>{},
Sequence<6>{},
Sequence<7, 8>{}));
Sequence<7>{}));
const auto out_gemmak0_gemmmraw_gemmak1_grid_desc = transform_tensor_descriptor(
out_n_zdotslice_dtildeslice_ydotslice_htildeslice_xdotslice_wtildeslice_ak0_ak1_grid_desc,
const auto out_gemmk_gemmmraw_grid_desc = transform_tensor_descriptor(
out_n_zdotslice_dtildeslice_ydotslice_htildeslice_xdotslice_wtildeslice_k_grid_desc,
make_tuple(
make_merge_transform(make_tuple(ZDotSlice, YDotSlice, XDotSlice, AK0)),
make_merge_transform(make_tuple(N, DTildeSlice, HTildeSlice, WTildeSlice)),
make_pass_through_transform(AK1)),
make_tuple(Sequence<1, 3, 5, 7>{}, Sequence<0, 2, 4, 6>{}, Sequence<8>{}),
make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2>{}));
make_merge_transform(make_tuple(ZDotSlice, YDotSlice, XDotSlice, K)),
make_merge_transform(make_tuple(N, DTildeSlice, HTildeSlice, WTildeSlice))),
make_tuple(Sequence<1, 3, 5, 7>{}, Sequence<0, 2, 4, 6>{}),
make_tuple(Sequence<0>{}, Sequence<1>{}));
const auto out_gemmak0_gemmm_gemmak1_grid_desc =
const auto out_gemmk_gemmm_padded_grid_desc =
ck::tensor_operation::device::PadTensorDescriptor(
out_gemmak0_gemmmraw_gemmak1_grid_desc,
make_tuple(AK0, GemmMPerBlock, AK1),
Sequence<false, DoPadGemmM, false>{});
out_gemmk_gemmmraw_grid_desc,
make_tuple(AK1, GemmMPerBlock),
Sequence<true, DoPadGemmM>{});
const auto out_gemmak0_gemmm_gemmak1_grid_desc = transform_tensor_descriptor(
out_gemmk_gemmm_padded_grid_desc,
make_tuple(make_unmerge_transform(make_tuple(AK0, AK1)),
make_pass_through_transform(
out_gemmk_gemmm_padded_grid_desc.GetLength(I1))),
make_tuple(Sequence<0>{}, Sequence<1>{}),
make_tuple(Sequence<0, 2>{}, Sequence<1>{}));
return out_gemmak0_gemmm_gemmak1_grid_desc;
}
......@@ -505,8 +522,6 @@ struct TransformConvBwdDataToGemm_v1
const index_t ConvDilationH = conv_filter_dilations[HIdx - NonSpatialDimsNum];
const index_t ConvDilationW = conv_filter_dilations[WIdx - NonSpatialDimsNum];
const index_t BK0 = K / BK1;
// assume packed
// k_y_x_c for 2d or k_z_y_x_c for 3d
const auto wei_grid_desc = make_wei_grid_desc<BLayout>(K, Z, Y, X, C);
......@@ -515,6 +530,8 @@ struct TransformConvBwdDataToGemm_v1
ck::tensor_operation::device::ConvolutionBackwardDataSpecialization::
Filter1x1Stride1Pad0)
{
const index_t BK0 = math::integer_divide_ceil(K, BK1);
// B: weight tensor
const auto wei_gemmbk0_gemmnraw_gemmbk1_grid_desc =
transform_tensor_descriptor(make_naive_tensor_descriptor_packed(make_tuple(K, C)),
......@@ -551,6 +568,9 @@ struct TransformConvBwdDataToGemm_v1
const auto YDotSlice = math::integer_divide_ceil(Y - i_ytilde, YTilde);
const auto XDotSlice = math::integer_divide_ceil(X - i_xtilde, XTilde);
const index_t BK0 =
math::integer_divide_ceil(ZDotSlice * YDotSlice * XDotSlice * K, BK1);
// B weight tensor
if constexpr(NDimSpatial == 2)
{
......@@ -566,43 +586,47 @@ struct TransformConvBwdDataToGemm_v1
make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2>{}, Sequence<3>{}),
make_tuple(Sequence<0>{}, Sequence<1, 2>{}, Sequence<3, 4>{}, Sequence<5>{}));
const auto wei_bk0_bk1_ydotslice_xdotslice_c_grid_desc =
transform_tensor_descriptor(
wei_k_ydot_ytilde_xdot_xtilde_c_grid_desc,
make_tuple(make_unmerge_transform(make_tuple(BK0, BK1)),
make_slice_transform(YDot, I0, YDotSlice),
make_slice_transform(XDot, I0, XDotSlice),
make_freeze_transform(i_ytilde),
make_freeze_transform(i_xtilde),
make_pass_through_transform(C)),
make_tuple(Sequence<0>{},
Sequence<1>{},
Sequence<3>{},
Sequence<2>{},
Sequence<4>{},
Sequence<5>{}),
make_tuple(Sequence<0, 1>{},
Sequence<2>{},
Sequence<3>{},
Sequence<>{},
Sequence<>{},
Sequence<4>{}));
const auto wei_k_ydotslice_xdotslice_c_grid_desc = transform_tensor_descriptor(
wei_k_ydot_ytilde_xdot_xtilde_c_grid_desc,
make_tuple(make_pass_through_transform(K),
make_slice_transform(YDot, I0, YDotSlice),
make_slice_transform(XDot, I0, XDotSlice),
make_freeze_transform(i_ytilde),
make_freeze_transform(i_xtilde),
make_pass_through_transform(C)),
make_tuple(Sequence<0>{},
Sequence<1>{},
Sequence<3>{},
Sequence<2>{},
Sequence<4>{},
Sequence<5>{}),
make_tuple(Sequence<0>{},
Sequence<1>{},
Sequence<2>{},
Sequence<>{},
Sequence<>{},
Sequence<3>{}));
const auto wei_gemmbk0_gemmnraw_gemmbk1_grid_desc = transform_tensor_descriptor(
wei_bk0_bk1_ydotslice_xdotslice_c_grid_desc,
make_tuple(make_merge_transform(make_tuple(YDotSlice, XDotSlice, BK0)),
make_pass_through_transform(C),
make_pass_through_transform(BK1)),
make_tuple(Sequence<2, 3, 0>{}, Sequence<4>{}, Sequence<1>{}),
make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2>{}));
const auto wei_gemmk_gemmnraw_grid_desc = transform_tensor_descriptor(
wei_k_ydotslice_xdotslice_c_grid_desc,
make_tuple(make_merge_transform(make_tuple(YDotSlice, XDotSlice, K)),
make_pass_through_transform(C)),
make_tuple(Sequence<1, 2, 0>{}, Sequence<3>{}),
make_tuple(Sequence<0>{}, Sequence<1>{}));
const auto wei_gemmbk0_gemmn_gemmbk1_grid_desc =
const auto wei_gemmk_gemmn_padded_grid_desc =
ck::tensor_operation::device::PadTensorDescriptor(
wei_gemmbk0_gemmnraw_gemmbk1_grid_desc,
make_tuple(wei_gemmbk0_gemmnraw_gemmbk1_grid_desc.GetLength(I0),
GemmNPerBlock,
BK1),
Sequence<false, DoPadGemmN, false>{});
wei_gemmk_gemmnraw_grid_desc,
make_tuple(BK1, GemmNPerBlock),
Sequence<true, DoPadGemmN>{});
const auto wei_gemmbk0_gemmn_gemmbk1_grid_desc = transform_tensor_descriptor(
wei_gemmk_gemmn_padded_grid_desc,
make_tuple(make_unmerge_transform(make_tuple(BK0, BK1)),
make_pass_through_transform(
wei_gemmk_gemmn_padded_grid_desc.GetLength(I1))),
make_tuple(Sequence<0>{}, Sequence<1>{}),
make_tuple(Sequence<0, 2>{}, Sequence<1>{}));
return wei_gemmbk0_gemmn_gemmbk1_grid_desc;
}
......@@ -631,10 +655,10 @@ struct TransformConvBwdDataToGemm_v1
Sequence<5, 6>{},
Sequence<7>{}));
const auto wei_bk0_bk1_zdotslice_ydotslice_xdotslice_c_grid_desc =
const auto wei_gemmk_zdotslice_ydotslice_xdotslice_c_grid_desc =
transform_tensor_descriptor(
wei_k_zdot_ztilde_ydot_ytilde_xdot_xtilde_c_grid_desc,
make_tuple(make_unmerge_transform(make_tuple(BK0, BK1)),
make_tuple(make_pass_through_transform(K),
make_slice_transform(ZDot, I0, ZDotSlice),
make_slice_transform(YDot, I0, YDotSlice),
make_slice_transform(XDot, I0, XDotSlice),
......@@ -650,33 +674,37 @@ struct TransformConvBwdDataToGemm_v1
Sequence<4>{},
Sequence<6>{},
Sequence<7>{}),
make_tuple(Sequence<0, 1>{},
make_tuple(Sequence<0>{},
Sequence<1>{},
Sequence<2>{},
Sequence<3>{},
Sequence<4>{},
Sequence<>{},
Sequence<>{},
Sequence<>{},
Sequence<5>{}));
Sequence<4>{}));
const auto wei_gemmbk0_gemmnraw_gemmbk1_grid_desc = transform_tensor_descriptor(
wei_bk0_bk1_zdotslice_ydotslice_xdotslice_c_grid_desc,
make_tuple(
make_merge_transform(make_tuple(ZDotSlice, YDotSlice, XDotSlice, BK0)),
make_pass_through_transform(C),
make_pass_through_transform(BK1)),
make_tuple(Sequence<2, 3, 4, 0>{}, Sequence<5>{}, Sequence<1>{}),
make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2>{}));
const auto wei_gemmk_gemmnraw_grid_desc = transform_tensor_descriptor(
wei_gemmk_zdotslice_ydotslice_xdotslice_c_grid_desc,
make_tuple(make_merge_transform(make_tuple(ZDotSlice, YDotSlice, XDotSlice, K)),
make_pass_through_transform(C)),
make_tuple(Sequence<1, 2, 3, 0>{}, Sequence<4>{}),
make_tuple(Sequence<0>{}, Sequence<1>{}));
const auto wei_gemmbk0_gemmn_gemmbk1_grid_desc =
const auto wei_gemmk_gemm_padded_grid_desc =
ck::tensor_operation::device::PadTensorDescriptor(
wei_gemmbk0_gemmnraw_gemmbk1_grid_desc,
make_tuple(wei_gemmbk0_gemmnraw_gemmbk1_grid_desc.GetLength(I0),
GemmNPerBlock,
BK1),
Sequence<false, DoPadGemmN, false>{});
wei_gemmk_gemmnraw_grid_desc,
make_tuple(BK1, GemmNPerBlock),
Sequence<true, DoPadGemmN>{});
return wei_gemmbk0_gemmn_gemmbk1_grid_desc;
const auto wei_gemmbk0_gemm_gemmbk1_grid_desc = transform_tensor_descriptor(
wei_gemmk_gemm_padded_grid_desc,
make_tuple(
make_unmerge_transform(make_tuple(BK0, BK1)),
make_pass_through_transform(wei_gemmk_gemm_padded_grid_desc.GetLength(I1))),
make_tuple(Sequence<0>{}, Sequence<1>{}),
make_tuple(Sequence<0, 2>{}, Sequence<1>{}));
return wei_gemmbk0_gemm_gemmbk1_grid_desc;
}
else
{
......
include/ck/version.h.in 0 → 100644
View file @ 0629870d
/*******************************************************************************
*
* MIT License
*
* Copyright (c) 2023 Advanced Micro Devices, Inc.
*
* Permission is hereby granted, free of charge, to any person obtaining a copy
* of this software and associated documentation files (the "Software"), to deal
* in the Software without restriction, including without limitation the rights
* to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
* copies of the Software, and to permit persons to whom the Software is
* furnished to do so, subject to the following conditions:
*
* The above copyright notice and this permission notice shall be included in all
* copies or substantial portions of the Software.
*
* THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
* IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
* FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
* AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
* LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
* OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
* SOFTWARE.
*
*******************************************************************************/
/* the configured version and settings for miopen- Composable Kernel */
#ifndef CK_VERSION_H_
#define CK_VERSION_H_
// clang-format off
#define CK_VERSION @CMAKE_PROJECT_VERSION@
#define CK_VERSION_MAJOR @CMAKE_PROJECT_VERSION_MAJOR@
#define CK_VERSION_MINOR @CMAKE_PROJECT_VERSION_MINOR@
#define CK_VERSION_PATCH @CMAKE_PROJECT_VERSION_PATCH@
#define CK_COMMIT_ID @COMMIT_ID@
// clang-format on
#endif
library/include/ck/library/tensor_operation_instance/device_operation_instance_factory.hpp
View file @ 0629870d
......@@ -17,6 +17,7 @@ namespace instance {
using F64 = double;
using F32 = float;
using F16 = ck::half_t;
using F8 = ck::f8_t;
using BF16 = ck::bhalf_t;
using I8 = int8_t;
using I32 = int32_t;
......
library/include/ck/library/tensor_operation_instance/gpu/batched_gemm.hpp
View file @ 0629870d
......@@ -16,7 +16,7 @@ namespace ck {
namespace tensor_operation {
namespace device {
namespace instance {
#ifdef __bf16__
#ifdef CK_ENABLE_BF16
void add_device_batched_gemm_xdl_bf16_bf16_bf16_gkm_gkn_gmn_instances(
std::vector<std::unique_ptr<
DeviceBatchedGemm<Col, Row, Row, BF16, BF16, BF16, PassThrough, PassThrough, PassThrough>>>&
......@@ -37,7 +37,7 @@ void add_device_batched_gemm_xdl_bf16_bf16_bf16_gmk_gnk_gmn_instances(
DeviceBatchedGemm<Row, Col, Row, BF16, BF16, BF16, PassThrough, PassThrough, PassThrough>>>&
instances);
#endif
#ifdef __fp16__
#ifdef CK_ENABLE_FP16
void add_device_batched_gemm_xdl_f16_f16_f16_gkm_gkn_gmn_instances(
std::vector<std::unique_ptr<
DeviceBatchedGemm<Col, Row, Row, F16, F16, F16, PassThrough, PassThrough, PassThrough>>>&
......@@ -58,7 +58,7 @@ void add_device_batched_gemm_xdl_f16_f16_f16_gmk_gnk_gmn_instances(
DeviceBatchedGemm<Row, Col, Row, F16, F16, F16, PassThrough, PassThrough, PassThrough>>>&
instances);
#endif
#ifdef __fp32__
#ifdef CK_ENABLE_FP32
void add_device_batched_gemm_xdl_f32_f32_f32_gkm_gkn_gmn_instances(
std::vector<std::unique_ptr<
DeviceBatchedGemm<Col, Row, Row, F32, F32, F32, PassThrough, PassThrough, PassThrough>>>&
......@@ -79,7 +79,7 @@ void add_device_batched_gemm_xdl_f32_f32_f32_gmk_gnk_gmn_instances(
DeviceBatchedGemm<Row, Col, Row, F32, F32, F32, PassThrough, PassThrough, PassThrough>>>&
instances);
#endif
#ifdef __int8__
#ifdef CK_ENABLE_INT8
void add_device_batched_gemm_xdl_int8_int8_int8_gkm_gkn_gmn_instances(
std::vector<std::unique_ptr<DeviceBatchedGemm<Col,
Row,
......@@ -154,7 +154,7 @@ struct DeviceOperationInstanceFactory<ck::tensor_operation::device::DeviceBatche
static auto GetInstances()
{
std::vector<std::unique_ptr<DeviceOp>> op_ptrs;
#ifdef __fp32__
#ifdef CK_ENABLE_FP32
if constexpr(is_same_v<ADataType, float> && is_same_v<BDataType, float> &&
is_same_v<CDataType, float>)
{
......@@ -180,7 +180,7 @@ struct DeviceOperationInstanceFactory<ck::tensor_operation::device::DeviceBatche
}
}
#endif
#ifdef __fp16__
#ifdef CK_ENABLE_FP16
if constexpr(is_same_v<ADataType, half_t> && is_same_v<BDataType, half_t> &&
is_same_v<CDataType, half_t>)
{
......@@ -206,7 +206,7 @@ struct DeviceOperationInstanceFactory<ck::tensor_operation::device::DeviceBatche
}
}
#endif
#ifdef __bf16__
#ifdef CK_ENABLE_BF16
if constexpr(is_same_v<ADataType, bhalf_t> && is_same_v<BDataType, bhalf_t> &&
is_same_v<CDataType, bhalf_t>)
{
......@@ -232,7 +232,7 @@ struct DeviceOperationInstanceFactory<ck::tensor_operation::device::DeviceBatche
}
}
#endif
#ifdef __int8__
#ifdef CK_ENABLE_INT8
if constexpr(is_same_v<ADataType, int8_t> && is_same_v<BDataType, int8_t> &&
is_same_v<CDataType, int8_t>)
{
......
library/include/ck/library/tensor_operation_instance/gpu/batched_gemm_add_relu_gemm_add.hpp
View file @ 0629870d
......@@ -14,7 +14,7 @@
using CDE0ElementOp = ck::tensor_operation::element_wise::AddRelu;
using CDE1ElementOp = ck::tensor_operation::element_wise::Add;
#ifdef __fp16__
#ifdef CK_ENABLE_FP16
namespace ck {
namespace tensor_operation {
namespace device {
......
library/include/ck/library/tensor_operation_instance/gpu/batched_gemm_bias_permute.hpp
View file @ 0629870d
......@@ -13,7 +13,7 @@
#include "ck/tensor_operation/gpu/element/element_wise_operation.hpp"
#include "ck/library/tensor_operation_instance/device_operation_instance_factory.hpp"
#ifdef __fp16__
#ifdef CK_ENABLE_FP16
namespace ck {
namespace tensor_operation {
namespace device {
......
library/include/ck/library/tensor_operation_instance/gpu/batched_gemm_bias_softmax_gemm_permute.hpp
View file @ 0629870d
......@@ -16,7 +16,7 @@ namespace ck {
namespace tensor_operation {
namespace device {
namespace instance {
#ifdef __fp16__
#ifdef CK_ENABLE_FP16
void add_device_batched_gemm_bias_masking_softmax_gemm_permute_xdl_cshuffle_f16_f16_f16_f16_gmk_gnk_gno_gmo_instances(
std::vector<std::unique_ptr<
DeviceBatchedGemmSoftmaxGemmPermute<2,
......@@ -59,7 +59,7 @@ void add_device_batched_gemm_bias_softmax_gemm_permute_xdl_cshuffle_f16_f16_f16_
MaskingSpecialization::MaskDisabled>>>&
instances);
#endif
#ifdef __bf16__
#ifdef CK_ENABLE_BF16
void add_device_batched_gemm_bias_masking_softmax_gemm_permute_xdl_cshuffle_bf16_bf16_bf16_bf16_gmk_gnk_gno_gmo_instances(
std::vector<std::unique_ptr<
DeviceBatchedGemmSoftmaxGemmPermute<2,
......@@ -148,7 +148,7 @@ struct DeviceOperationInstanceFactory<
static auto GetInstances()
{
std::vector<std::unique_ptr<DeviceOp>> op_ptrs;
#ifdef __fp16__
#ifdef CK_ENABLE_FP16
if constexpr(is_same_v<ADataType, half_t> && is_same_v<B0DataType, half_t> &&
is_same_v<B1DataType, half_t> && is_same_v<CDataType, half_t> &&
Acc0BiasDataType::Size() == 1 &&
......@@ -166,7 +166,7 @@ struct DeviceOperationInstanceFactory<
}
}
#endif
#ifdef __bf16__
#ifdef CK_ENABLE_BF16
else if constexpr(is_same_v<ADataType, BF16> && is_same_v<B0DataType, BF16> &&
is_same_v<B1DataType, BF16> && is_same_v<CDataType, BF16> &&
Acc0BiasDataType::Size() == 1 &&
......
library/include/ck/library/tensor_operation_instance/gpu/batched_gemm_gemm.hpp
View file @ 0629870d
......@@ -16,7 +16,7 @@ namespace ck {
namespace tensor_operation {
namespace device {
namespace instance {
#ifdef __fp16__
#ifdef CK_ENABLE_FP16
void add_device_batched_gemm_gemm_xdl_cshuffle_f16_f16_f16_f16_gmk_gnk_gno_gmo_instance(
std::vector<std::unique_ptr<DeviceBatchedGemmGemm<Row,
Col,
......
library/include/ck/library/tensor_operation_instance/gpu/batched_gemm_multi_d.hpp
View file @ 0629870d
......@@ -19,7 +19,7 @@ namespace ck {
namespace tensor_operation {
namespace device {
namespace instance {
#ifdef __fp16__
#ifdef CK_ENABLE_FP16
void add_device_batched_gemm_multi_d_dl_f16_f16_f16_gkm_gkn_gmn_instances(
std::vector<std::unique_ptr<DeviceBatchedGemmMultiD<Col,
Row,
......@@ -124,7 +124,7 @@ void add_device_batched_gemm_multi_d_dl_f16_f16_f16_gmk_gnk_gmn_irregular_instan
PassThrough,
PassThrough>>>& instances);
#endif
#ifdef __int8__
#ifdef CK_ENABLE_INT8
void add_device_batched_gemm_multi_d_dl_i8_i8_i8_gkm_gkn_gmn_instances(
std::vector<std::unique_ptr<DeviceBatchedGemmMultiD<Col,
Row,
......@@ -263,7 +263,7 @@ struct DeviceOperationInstanceFactory<ck::tensor_operation::device::DeviceBatche
static auto GetInstances()
{
std::vector<std::unique_ptr<DeviceOp>> op_ptrs;
#ifdef __fp16__
#ifdef CK_ENABLE_FP16
if constexpr(is_same_v<ADataType, half_t> && is_same_v<BDataType, half_t> &&
is_same_v<EDataType, half_t>)
{
......@@ -297,7 +297,7 @@ struct DeviceOperationInstanceFactory<ck::tensor_operation::device::DeviceBatche
}
}
#endif
#ifdef __int8__
#ifdef CK_ENABLE_INT8
else if constexpr(is_same_v<ADataType, int8_t> && is_same_v<BDataType, int8_t> &&
is_same_v<EDataType, int8_t>)
{
......
library/include/ck/library/tensor_operation_instance/gpu/batched_gemm_softmax_gemm.hpp
View file @ 0629870d
......@@ -11,7 +11,7 @@
#include "ck/tensor_operation/gpu/element/element_wise_operation.hpp"
#include "ck/library/tensor_operation_instance/device_operation_instance_factory.hpp"
#ifdef __fp16__
#ifdef CK_ENABLE_FP16
namespace ck {
namespace tensor_operation {
namespace device {
......
library/include/ck/library/tensor_operation_instance/gpu/batched_gemm_softmax_gemm_permute.hpp
View file @ 0629870d
......@@ -16,7 +16,7 @@ namespace ck {
namespace tensor_operation {
namespace device {
namespace instance {
#ifdef __fp16__
#ifdef CK_ENABLE_FP16
void add_device_batched_gemm_masking_softmax_gemm_permute_xdl_cshuffle_f16_f16_f16_f16_gmk_gnk_gno_gmo_instances(
std::vector<std::unique_ptr<
DeviceBatchedGemmSoftmaxGemmPermute<2,
......@@ -59,7 +59,7 @@ void add_device_batched_gemm_softmax_gemm_permute_xdl_cshuffle_f16_f16_f16_f16_g
MaskingSpecialization::MaskDisabled>>>&
instances);
#endif
#ifdef __bf16__
#ifdef CK_ENABLE_BF16
void add_device_batched_gemm_masking_softmax_gemm_permute_xdl_cshuffle_bf16_bf16_bf16_bf16_gmk_gnk_gno_gmo_instances(
std::vector<std::unique_ptr<
DeviceBatchedGemmSoftmaxGemmPermute<2,
......@@ -148,7 +148,7 @@ struct DeviceOperationInstanceFactory<
static auto GetInstances()
{
std::vector<std::unique_ptr<DeviceOp>> op_ptrs;
#ifdef __fp16__
#ifdef CK_ENABLE_FP16
if constexpr(is_same_v<ADataType, half_t> && is_same_v<B0DataType, half_t> &&
is_same_v<B1DataType, half_t> && is_same_v<CDataType, half_t>)
{
......@@ -164,7 +164,7 @@ struct DeviceOperationInstanceFactory<
}
}
#endif
#ifdef __bf16__
#ifdef CK_ENABLE_BF16
else if constexpr(is_same_v<ADataType, BF16> && is_same_v<B0DataType, BF16> &&
is_same_v<B1DataType, BF16> && is_same_v<CDataType, BF16>)
{
......
library/include/ck/library/tensor_operation_instance/gpu/contraction_bilinear.hpp
View file @ 0629870d
......@@ -16,7 +16,7 @@ namespace ck {
namespace tensor_operation {
namespace device {
namespace instance {
#ifdef __fp32__
#ifdef CK_ENABLE_FP32
// float
void add_device_contraction_bilinear_m2_n2_k2_xdl_c_shuffle_f32_f32_f32_f32_kknn_instance(
std::vector<std::unique_ptr<DeviceContractionMultipleD<2,
......@@ -66,7 +66,7 @@ void add_device_contraction_bilinear_m2_n2_k2_xdl_c_shuffle_f32_f32_f32_f32_mnnn
PassThrough,
Bilinear>>>& instances);
#endif
#ifdef __fp64__
#ifdef CK_ENABLE_FP64
// double
void add_device_contraction_bilinear_m2_n2_k2_xdl_c_shuffle_f64_f64_f64_f64_kknn_instance(
std::vector<std::unique_ptr<DeviceContractionMultipleD<2,
......@@ -150,7 +150,7 @@ struct DeviceOperationInstanceFactory<ck::tensor_operation::device::DeviceContra
static auto GetInstances()
{
std::vector<std::unique_ptr<DeviceOp>> op_ptrs;
#ifdef __fp32__
#ifdef CK_ENABLE_FP32
if constexpr(is_same_v<ADataType, float> && is_same_v<BDataType, float> &&
is_same_v<DDataType, float> && is_same_v<EDataType, float>)
{
......@@ -167,7 +167,7 @@ struct DeviceOperationInstanceFactory<ck::tensor_operation::device::DeviceContra
}
}
#endif
#ifdef __fp64__
#ifdef CK_ENABLE_FP64
if constexpr(is_same_v<ADataType, double> && is_same_v<BDataType, double> &&
is_same_v<DDataType, double> && is_same_v<EDataType, double>)
{
......
library/include/ck/library/tensor_operation_instance/gpu/contraction_scale.hpp
View file @ 0629870d
......@@ -16,7 +16,7 @@ namespace ck {
namespace tensor_operation {
namespace device {
namespace instance {
#ifdef __fp32__
#ifdef CK_ENABLE_FP32
// float
void add_device_contraction_scale_m2_n2_k2_xdl_c_shuffle_f32_f32_f32_kkn_instance(
std::vector<std::unique_ptr<DeviceContractionMultipleD<2,
......@@ -66,7 +66,7 @@ void add_device_contraction_scale_m2_n2_k2_xdl_c_shuffle_f32_f32_f32_mnn_instanc
PassThrough,
Scale>>>& instances);
#endif
#ifdef __fp64__
#ifdef CK_ENABLE_FP64
// double
void add_device_contraction_scale_m2_n2_k2_xdl_c_shuffle_f64_f64_f64_kkn_instance(
std::vector<std::unique_ptr<DeviceContractionMultipleD<2,
......@@ -149,7 +149,7 @@ struct DeviceOperationInstanceFactory<ck::tensor_operation::device::DeviceContra
static auto GetInstances()
{
std::vector<std::unique_ptr<DeviceOp>> op_ptrs;
#ifdef __fp32__
#ifdef CK_ENABLE_FP32
if constexpr(is_same_v<ADataType, float> && is_same_v<BDataType, float> &&
is_same_v<EDataType, float>)
{
......@@ -166,7 +166,7 @@ struct DeviceOperationInstanceFactory<ck::tensor_operation::device::DeviceContra
}
}
#endif
#ifdef __fp64__
#ifdef CK_ENABLE_FP64
if constexpr(is_same_v<ADataType, double> && is_same_v<BDataType, double> &&
is_same_v<EDataType, double>)
{
......
library/include/ck/library/tensor_operation_instance/gpu/convolution_backward_data.hpp
View file @ 0629870d
......@@ -16,7 +16,7 @@ namespace ck {
namespace tensor_operation {
namespace device {
namespace instance {
#ifdef __bf16__
#ifdef CK_ENABLE_BF16
// conv1d backward data
void add_device_conv1d_bwd_data_xdl_nwc_kxc_nwk_bf16_instances(
std::vector<std::unique_ptr<DeviceConvBwdData<1,
......@@ -30,19 +30,19 @@ void add_device_conv1d_bwd_data_xdl_nwc_kxc_nwk_bf16_instances(
PassThrough,
PassThrough>>>& instances);
#endif
#ifdef __fp16__
#ifdef CK_ENABLE_FP16
void add_device_conv1d_bwd_data_xdl_nwc_kxc_nwk_f16_instances(
std::vector<std::unique_ptr<
DeviceConvBwdData<1, NWC, KXC, NWK, F16, F16, F16, PassThrough, PassThrough, PassThrough>>>&
instances);
#endif
#ifdef __fp32__
#ifdef CK_ENABLE_FP32
void add_device_conv1d_bwd_data_xdl_nwc_kxc_nwk_f32_instances(
std::vector<std::unique_ptr<
DeviceConvBwdData<1, NWC, KXC, NWK, F32, F32, F32, PassThrough, PassThrough, PassThrough>>>&
instances);
#endif
#ifdef __int8__
#ifdef CK_ENABLE_INT8
void add_device_conv1d_bwd_data_xdl_nwc_kxc_nwk_int8_instances(
std::vector<std::unique_ptr<DeviceConvBwdData<1,
NWC,
......@@ -55,7 +55,7 @@ void add_device_conv1d_bwd_data_xdl_nwc_kxc_nwk_int8_instances(
PassThrough,
PassThrough>>>& instances);
#endif
#ifdef __bf16__
#ifdef CK_ENABLE_BF16
// conv2d backward data
void add_device_conv2d_bwd_data_xdl_nhwc_kyxc_nhwk_bf16_instances(
std::vector<std::unique_ptr<DeviceConvBwdData<2,
......@@ -69,7 +69,7 @@ void add_device_conv2d_bwd_data_xdl_nhwc_kyxc_nhwk_bf16_instances(
PassThrough,
PassThrough>>>& instances);
#endif
#ifdef __fp16__
#ifdef CK_ENABLE_FP16
void add_device_conv2d_bwd_data_xdl_nhwc_kyxc_nhwk_f16_instances(
std::vector<std::unique_ptr<DeviceConvBwdData<2,
NHWC,
......@@ -82,7 +82,7 @@ void add_device_conv2d_bwd_data_xdl_nhwc_kyxc_nhwk_f16_instances(
PassThrough,
PassThrough>>>& instances);
#endif
#ifdef __fp32__
#ifdef CK_ENABLE_FP32
void add_device_conv2d_bwd_data_xdl_nhwc_kyxc_nhwk_f32_instances(
std::vector<std::unique_ptr<DeviceConvBwdData<2,
NHWC,
......@@ -95,7 +95,7 @@ void add_device_conv2d_bwd_data_xdl_nhwc_kyxc_nhwk_f32_instances(
PassThrough,
PassThrough>>>& instances);
#endif
#ifdef __int8__
#ifdef CK_ENABLE_INT8
void add_device_conv2d_bwd_data_xdl_nhwc_kyxc_nhwk_int8_instances(
std::vector<std::unique_ptr<DeviceConvBwdData<2,
NHWC,
......@@ -109,7 +109,7 @@ void add_device_conv2d_bwd_data_xdl_nhwc_kyxc_nhwk_int8_instances(
PassThrough>>>& instances);
#endif
#ifdef DL_KERNELS
#ifdef __fp16__
#ifdef CK_ENABLE_FP16
// conv2d dl
void add_device_conv2d_bwd_data_dl_nhwc_kyxc_nhwk_f16_instances(
std::vector<std::unique_ptr<DeviceConvBwdData<2,
......@@ -123,7 +123,7 @@ void add_device_conv2d_bwd_data_dl_nhwc_kyxc_nhwk_f16_instances(
PassThrough,
PassThrough>>>& instances);
#endif
#ifdef __fp32__
#ifdef CK_ENABLE_FP32
void add_device_conv2d_bwd_data_dl_nhwc_kyxc_nhwk_f32_instances(
std::vector<std::unique_ptr<DeviceConvBwdData<2,
NHWC,
......@@ -136,7 +136,7 @@ void add_device_conv2d_bwd_data_dl_nhwc_kyxc_nhwk_f32_instances(
PassThrough,
PassThrough>>>& instances);
#endif
#ifdef __int8__
#ifdef CK_ENABLE_INT8
void add_device_conv2d_bwd_data_dl_nhwc_kyxc_nhwk_int8_instances(
std::vector<std::unique_ptr<DeviceConvBwdData<2,
NHWC,
......@@ -150,7 +150,7 @@ void add_device_conv2d_bwd_data_dl_nhwc_kyxc_nhwk_int8_instances(
PassThrough>>>& instances);
#endif
#endif
#ifdef __bf16__
#ifdef CK_ENABLE_BF16
// conv3d backward data
void add_device_conv3d_bwd_data_xdl_ndhwc_kzyxc_ndhwk_bf16_instances(
std::vector<std::unique_ptr<DeviceConvBwdData<3,
......@@ -164,7 +164,7 @@ void add_device_conv3d_bwd_data_xdl_ndhwc_kzyxc_ndhwk_bf16_instances(
PassThrough,
PassThrough>>>& instances);
#endif
#ifdef __fp16__
#ifdef CK_ENABLE_FP16
void add_device_conv3d_bwd_data_xdl_ndhwc_kzyxc_ndhwk_f16_instances(
std::vector<std::unique_ptr<DeviceConvBwdData<3,
NDHWC,
......@@ -177,7 +177,7 @@ void add_device_conv3d_bwd_data_xdl_ndhwc_kzyxc_ndhwk_f16_instances(
PassThrough,
PassThrough>>>& instances);
#endif
#ifdef __fp32__
#ifdef CK_ENABLE_FP32
void add_device_conv3d_bwd_data_xdl_ndhwc_kzyxc_ndhwk_f32_instances(
std::vector<std::unique_ptr<DeviceConvBwdData<3,
NDHWC,
......@@ -190,7 +190,7 @@ void add_device_conv3d_bwd_data_xdl_ndhwc_kzyxc_ndhwk_f32_instances(
PassThrough,
PassThrough>>>& instances);
#endif
#ifdef __int8__
#ifdef CK_ENABLE_INT8
void add_device_conv3d_bwd_data_xdl_ndhwc_kzyxc_ndhwk_int8_instances(
std::vector<std::unique_ptr<DeviceConvBwdData<3,
NDHWC,
......@@ -245,21 +245,21 @@ struct DeviceOperationInstanceFactory<ck::tensor_operation::device::DeviceConvBw
{
add_device_conv1d_bwd_data_xdl_nwc_kxc_nwk_f32_instances(op_ptrs);
}
#ifdef __fp16__
#ifdef CK_ENABLE_FP16
if constexpr(is_same_v<InDataType, half_t> && is_same_v<WeiDataType, half_t> &&
is_same_v<OutDataType, half_t>)
{
add_device_conv1d_bwd_data_xdl_nwc_kxc_nwk_f16_instances(op_ptrs);
}
#endif
#ifdef __bf16__
#ifdef CK_ENABLE_BF16
if constexpr(is_same_v<InDataType, ck::bhalf_t> &&
is_same_v<WeiDataType, ck::bhalf_t> && is_same_v<OutDataType, ck::bhalf_t>)
{
add_device_conv1d_bwd_data_xdl_nwc_kxc_nwk_bf16_instances(op_ptrs);
}
#endif
#ifdef __int8__
#ifdef CK_ENABLE_INT8
if constexpr(is_same_v<InDataType, int8_t> && is_same_v<WeiDataType, int8_t> &&
is_same_v<OutDataType, int8_t>)
{
......@@ -278,7 +278,7 @@ struct DeviceOperationInstanceFactory<ck::tensor_operation::device::DeviceConvBw
add_device_conv2d_bwd_data_dl_nhwc_kyxc_nhwk_f32_instances(op_ptrs);
#endif
}
#ifdef __fp16__
#ifdef CK_ENABLE_FP16
if constexpr(is_same_v<InDataType, half_t> && is_same_v<WeiDataType, half_t> &&
is_same_v<OutDataType, half_t>)
{
......@@ -288,14 +288,14 @@ struct DeviceOperationInstanceFactory<ck::tensor_operation::device::DeviceConvBw
#endif
}
#endif
#ifdef __bf16__
#ifdef CK_ENABLE_BF16
if constexpr(is_same_v<InDataType, ck::bhalf_t> &&
is_same_v<WeiDataType, ck::bhalf_t> && is_same_v<OutDataType, ck::bhalf_t>)
{
add_device_conv2d_bwd_data_xdl_nhwc_kyxc_nhwk_bf16_instances(op_ptrs);
}
#endif
#ifdef __int8__
#ifdef CK_ENABLE_INT8
if constexpr(is_same_v<InDataType, int8_t> && is_same_v<WeiDataType, int8_t> &&
is_same_v<OutDataType, int8_t>)
{
......@@ -314,21 +314,21 @@ struct DeviceOperationInstanceFactory<ck::tensor_operation::device::DeviceConvBw
{
add_device_conv3d_bwd_data_xdl_ndhwc_kzyxc_ndhwk_f32_instances(op_ptrs);
}
#ifdef __fp16__
#ifdef CK_ENABLE_FP16
if constexpr(is_same_v<InDataType, half_t> && is_same_v<WeiDataType, half_t> &&
is_same_v<OutDataType, half_t>)
{
add_device_conv3d_bwd_data_xdl_ndhwc_kzyxc_ndhwk_f16_instances(op_ptrs);
}
#endif
#ifdef __bf16__
#ifdef CK_ENABLE_BF16
if constexpr(is_same_v<InDataType, ck::bhalf_t> &&
is_same_v<WeiDataType, ck::bhalf_t> && is_same_v<OutDataType, ck::bhalf_t>)
{
add_device_conv3d_bwd_data_xdl_ndhwc_kzyxc_ndhwk_bf16_instances(op_ptrs);
}
#endif
#ifdef __int8__
#ifdef CK_ENABLE_INT8
if constexpr(is_same_v<InDataType, int8_t> && is_same_v<WeiDataType, int8_t> &&
is_same_v<OutDataType, int8_t>)
{
......
library/include/ck/library/tensor_operation_instance/gpu/convolution_forward.hpp
View file @ 0629870d
......@@ -18,7 +18,7 @@ namespace device {
namespace instance {
// conv2d forward
#ifdef __fp16__
#ifdef CK_ENABLE_FP16
void add_device_conv2d_fwd_xdl_c_shuffle_nhwc_kyxc_nhwk_f16_instances(
std::vector<std::unique_ptr<
DeviceConvFwd<2, NHWC, KYXC, NHWK, F16, F16, F16, PassThrough, PassThrough, PassThrough>>>&
......@@ -28,7 +28,7 @@ void add_device_conv2d_fwd_xdl_nhwc_kyxc_nhwk_f16_instances(
DeviceConvFwd<2, NHWC, KYXC, NHWK, F16, F16, F16, PassThrough, PassThrough, PassThrough>>>&
instances);
#endif
#ifdef __bf16__
#ifdef CK_ENABLE_BF16
void add_device_conv2d_fwd_xdl_nhwc_kyxc_nhwk_bf16_instances(
std::vector<std::unique_ptr<DeviceConvFwd<2,
NHWC,
......@@ -41,13 +41,13 @@ void add_device_conv2d_fwd_xdl_nhwc_kyxc_nhwk_bf16_instances(
PassThrough,
PassThrough>>>& instances);
#endif
#ifdef __fp32__
#ifdef CK_ENABLE_FP32
void add_device_conv2d_fwd_xdl_nhwc_kyxc_nhwk_f32_instances(
std::vector<std::unique_ptr<
DeviceConvFwd<2, NHWC, KYXC, NHWK, F32, F32, F32, PassThrough, PassThrough, PassThrough>>>&
instances);
#endif
#ifdef __int8__
#ifdef CK_ENABLE_INT8
void add_device_conv2d_fwd_xdl_nhwc_kyxc_nhwk_int8_instances(
std::vector<std::unique_ptr<DeviceConvFwd<2,
NHWC,
......@@ -103,7 +103,7 @@ struct DeviceOperationInstanceFactory<
{
add_device_conv2d_fwd_xdl_nhwc_kyxc_nhwk_f32_instances(op_ptrs);
}
#ifdef __fp16__
#ifdef CK_ENABLE_FP16
else if constexpr(is_same_v<InDataType, half_t> && is_same_v<WeiDataType, half_t> &&
is_same_v<OutDataType, half_t>)
{
......@@ -111,7 +111,7 @@ struct DeviceOperationInstanceFactory<
add_device_conv2d_fwd_xdl_c_shuffle_nhwc_kyxc_nhwk_f16_instances(op_ptrs);
}
#endif
#ifdef __bf16__
#ifdef CK_ENABLE_BF16
else if constexpr(is_same_v<InDataType, ck::bhalf_t> &&
is_same_v<WeiDataType, ck::bhalf_t> &&
is_same_v<OutDataType, ck::bhalf_t>)
......@@ -119,7 +119,7 @@ struct DeviceOperationInstanceFactory<
add_device_conv2d_fwd_xdl_nhwc_kyxc_nhwk_bf16_instances(op_ptrs);
}
#endif
#ifdef __int8__
#ifdef CK_ENABLE_INT8
else if constexpr(is_same_v<InDataType, int8_t> && is_same_v<WeiDataType, int8_t> &&
is_same_v<OutDataType, int8_t>)
{
......
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