diff --git a/include/ck/tensor_operation/gpu/warp/wmma_gemm.hpp b/include/ck/tensor_operation/gpu/warp/wmma_gemm.hpp index 429df2413f..f1baf223a1 100644 --- a/include/ck/tensor_operation/gpu/warp/wmma_gemm.hpp +++ b/include/ck/tensor_operation/gpu/warp/wmma_gemm.hpp @@ -16,11 +16,13 @@ enum struct WmmaInstr wmma_f32_16x16x16_bf16, wmma_f16_16x16x16_f16, wmma_bf16_16x16x16_bf16, + wmma_i32_16x16x16_iu16, wmma_i32_16x16x16_iu8, wmma_i32_16x16x16_iu4, // gfx12 wmma_f32_16x16x16_f16_gfx12, wmma_f32_16x16x16_bf16_gfx12, + wmma_i32_16x16x16_iu16_gfx12, wmma_i32_16x16x16_iu8_gfx12, wmma_f32_16x16x16_f8f8_gfx12, wmma_f32_16x16x16_f8bf8_gfx12, @@ -590,6 +592,16 @@ struct WmmaSelector return WmmaInstr::wmma_bf16_16x16x16_bf16; } + template <> + constexpr auto GetWmma() + { +#ifdef __gfx12__ + return WmmaInstr::wmma_i32_16x16x16_iu16_gfx12; +#else + return WmmaInstr::wmma_i32_16x16x16_iu16; +#endif + } + template <> constexpr auto GetWmma() { diff --git a/library/include/ck/library/tensor_operation_instance/gpu/gemm_add.hpp b/library/include/ck/library/tensor_operation_instance/gpu/gemm_add.hpp index 12583f8d1a..985cf2b1ec 100644 --- a/library/include/ck/library/tensor_operation_instance/gpu/gemm_add.hpp +++ b/library/include/ck/library/tensor_operation_instance/gpu/gemm_add.hpp @@ -43,7 +43,7 @@ void add_device_gemm_add_xdl_c_shuffle_bf16_i8_bf16_bf16_mk_kn_mn_mn_instances( Add>>>&); #elif defined(CK_USE_WMMA) -void add_device_gemm_add_wmma_c_shuffle_f16_i8_f16_f16_mk_kn_mn_mn_instances( +void add_device_gemm_add_wmma_c_shuffle_f16_f16_f16_f16_mk_kn_mn_mn_instances( std::vector>>&); -void add_device_gemm_add_wmma_c_shuffle_bf16_i8_bf16_bf16_mk_kn_mn_mn_instances( +void add_device_gemm_add_wmma_c_shuffle_bf16_f16_bf16_bf16_mk_kn_mn_mn_instances( std::vector && is_same_v && +// TODO: +// here for WMMA, currently BDataType and ADataType must be the same +#if defined(CK_ENABLE_FP16) + if constexpr(is_same_v && is_same_v && is_same_v && is_same_v) { if constexpr(is_same_v && is_same_v && is_same_v && is_same_v) { - add_device_gemm_add_wmma_c_shuffle_f16_i8_f16_f16_mk_kn_mn_mn_instances(op_ptrs); + add_device_gemm_add_wmma_c_shuffle_f16_f16_f16_f16_mk_kn_mn_mn_instances(op_ptrs); } } #endif -#if defined(CK_ENABLE_INT8) && defined(CK_ENABLE_BF16) - if constexpr(is_same_v && is_same_v && +#if defined(CK_ENABLE_BF16) +// TODO: +// here for WMMA, currently BDataType and ADataType must be the same + if constexpr(is_same_v && is_same_v && is_same_v && is_same_v) { if constexpr(is_same_v && is_same_v && is_same_v && is_same_v) { - add_device_gemm_add_wmma_c_shuffle_bf16_i8_bf16_bf16_mk_kn_mn_mn_instances(op_ptrs); + add_device_gemm_add_wmma_c_shuffle_bf16_f16_bf16_bf16_mk_kn_mn_mn_instances(op_ptrs); } } #endif diff --git a/library/src/tensor_operation_instance/gpu/gemm_add/CMakeLists.txt b/library/src/tensor_operation_instance/gpu/gemm_add/CMakeLists.txt index 371f47bf96..5f8a418b4a 100644 --- a/library/src/tensor_operation_instance/gpu/gemm_add/CMakeLists.txt +++ b/library/src/tensor_operation_instance/gpu/gemm_add/CMakeLists.txt @@ -2,6 +2,6 @@ add_instance_library(device_gemm_add_instance device_gemm_add_xdl_c_shuffle_f16_i8_f16_f16_mk_kn_mn_mn_instance.cpp device_gemm_add_xdl_c_shuffle_bf16_i8_bf16_bf16_mk_kn_mn_mn_instance.cpp - device_gemm_add_wmma_c_shuffle_f16_i8_f16_f16_mk_kn_mn_mn_instance.cpp - device_gemm_add_wmma_c_shuffle_bf16_i8_bf16_bf16_mk_kn_mn_mn_instance.cpp + device_gemm_add_wmma_c_shuffle_f16_f16_f16_f16_mk_kn_mn_mn_instance.cpp + device_gemm_add_wmma_c_shuffle_bf16_f16_bf16_bf16_mk_kn_mn_mn_instance.cpp ) diff --git a/library/src/tensor_operation_instance/gpu/gemm_add/device_gemm_add_wmma_c_shuffle_bf16_i8_bf16_bf16_mk_kn_mn_mn_instance.cpp b/library/src/tensor_operation_instance/gpu/gemm_add/device_gemm_add_wmma_c_shuffle_bf16_f16_bf16_bf16_mk_kn_mn_mn_instance.cpp similarity index 90% rename from library/src/tensor_operation_instance/gpu/gemm_add/device_gemm_add_wmma_c_shuffle_bf16_i8_bf16_bf16_mk_kn_mn_mn_instance.cpp rename to library/src/tensor_operation_instance/gpu/gemm_add/device_gemm_add_wmma_c_shuffle_bf16_f16_bf16_bf16_mk_kn_mn_mn_instance.cpp index f1c571e2f0..4db8af6554 100644 --- a/library/src/tensor_operation_instance/gpu/gemm_add/device_gemm_add_wmma_c_shuffle_bf16_i8_bf16_bf16_mk_kn_mn_mn_instance.cpp +++ b/library/src/tensor_operation_instance/gpu/gemm_add/device_gemm_add_wmma_c_shuffle_bf16_f16_bf16_bf16_mk_kn_mn_mn_instance.cpp @@ -20,7 +20,7 @@ static constexpr auto GemmMNKPadding = ck::tensor_operation::device::GemmSpecial // e = elementwise((a * b), d0, d1) // outout: e[m, n] // input: a[m, k], b[k, n], d0[m, n], d1[m, n] -using device_gemm_add_wmma_c_shuffle_bf16_i8_bf16_bf16_mk_kn_mn_mn_generic_instances = std::tuple< +using device_gemm_add_wmma_c_shuffle_bf16_f16_bf16_bf16_mk_kn_mn_mn_generic_instances = std::tuple< // clang-format off // M/N/K padding //################################| A| B| Ds| E| AData| BData| AccData| CShuffle| DsData| EData| A| B| CDE| GEMM| Prefetch| Block| MPer| NPer| K0Per| K1| MPer| NPer| MRepeat| NRepeat| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockLds| BBlockTransfer| BBlockTransfer| BBlockTransfer| BlockTransfer| BBlockTransfer| BBlockTransfer| BBlockLds| CShuffle| CShuffle| CBlockTransferClusterLengths| CBlockTransfer| @@ -28,11 +28,11 @@ using device_gemm_add_wmma_c_shuffle_bf16_i8_bf16_bf16_mk_kn_mn_mn_generic_insta //################################| | | | | | | | | | | Operation| Operation| Operation| | | | | | | | | | | | Lengths_K0_M_K1| ArrangeOrder| | | PerVector| PerVector_K1| | Lengths_K0_N_K1| ArrangeOrder| | | PerVector| PerVector_K1| | PerShuffle| PerShuffle| _NBlock_NWaveNPerXdl| _NWaveNPerXdl| //################################| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | // TODO: these template variables need to be adjusted - DeviceGemmMultipleD_Wmma_CShuffle< Row, Row, Row_Tuple, Row, BF16, I8, I32, F32, BF16_Tuple, BF16, PassThrough, PassThrough, Add, GemmMNKPadding, 1, 256, 64, 128, 32, 8, 32, 32, 1, 2, S<4, 64, 1>, S<0, 2, 1>, S<1, 0, 2>, 2, 1, 8, 1, S<4, 64, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 1, 8, 1, 1, 1, S<1, 32, 1, 8>, 1> + DeviceGemmMultipleD_Wmma_CShuffle< Row, Row, Row_Tuple, Row, BF16, BF16, I32, F32, BF16_Tuple, BF16, PassThrough, PassThrough, Add, GemmMNKPadding, 1, 256, 64, 128, 32, 8, 16, 16, 1, 2, S<4, 64, 1>, S<0, 2, 1>, S<1, 0, 2>, 2, 1, 8, 1, S<4, 64, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 1, 8, 1, 1, 1, S<1, 32, 1, 8>, 1> // clang-format on >; -using device_gemm_add_wmma_c_shuffle_bf16_i8_bf16_bf16_mk_kn_mn_mn_instances = std::tuple< +using device_gemm_add_wmma_c_shuffle_bf16_f16_bf16_bf16_mk_kn_mn_mn_instances = std::tuple< // clang-format off // M/N/K padding //################################| A| B| Ds| E| AData| BData| AccData| CShuffle| DsData| EData| A| B| CDE| GEMM| Prefetch| Block| MPer| NPer| K0Per| K1| MPer| NPer| MRepeat| NRepeat| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockLds| BBlockTransfer| BBlockTransfer| BBlockTransfer| BlockTransfer| BBlockTransfer| BBlockTransfer| BBlockLds| CShuffle| CShuffle| CBlockTransferClusterLengths| CBlockTransfer| @@ -40,19 +40,19 @@ using device_gemm_add_wmma_c_shuffle_bf16_i8_bf16_bf16_mk_kn_mn_mn_instances = s //################################| | | | | | | | | | | Operation| Operation| Operation| | | | | | | | | | | | Lengths_K0_M_K1| ArrangeOrder| | | PerVector| PerVector_K1| | Lengths_K0_N_K1| ArrangeOrder| | | PerVector| PerVector_K1| | PerShuffle| PerShuffle| _NBlock_NWaveNPerXdl| _NWaveNPerXdl| //################################| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | // TODO: these template variables need to be adjusted - DeviceGemmMultipleD_Wmma_CShuffle< Row, Row, Row_Tuple, Row, BF16, I8, F32, F32, BF16_Tuple, BF16, PassThrough, PassThrough, Add, GemmMNKPadding, 1, 256, 16, 128, 32, 8, 16, 16, 1, 2, S<4, 16, 4>, S<1, 0, 2>, S<1, 0, 2>, 2, 2, 2, 1, S<4, 64, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 2, 8, 1, 1, 1, S<1, 16, 1, 4>, 1, LoopScheduler::Default, PipelineVersion::v1>, - DeviceGemmMultipleD_Wmma_CShuffle< Row, Row, Row_Tuple, Row, BF16, I8, F32, F32, BF16_Tuple, BF16, PassThrough, PassThrough, Add, GemmMNKPadding, 1, 64, 16, 16, 32, 8, 16, 16, 1, 1, S<4, 16, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, 1, S<4, 16, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 1, 1, 1, 1, 1, S<1, 16, 1, 4>, 1, LoopScheduler::Default, PipelineVersion::v1>, - DeviceGemmMultipleD_Wmma_CShuffle< Row, Row, Row_Tuple, Row, BF16, I8, F32, F32, BF16_Tuple, BF16, PassThrough, PassThrough, Add, GemmMNKPadding, 1, 64, 16, 16, 64, 8, 16, 16, 1, 1, S<4, 16, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, 1, S<4, 16, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 1, 1, 1, 1, 1, S<1, 16, 1, 4>, 1, LoopScheduler::Default, PipelineVersion::v1> + DeviceGemmMultipleD_Wmma_CShuffle< Row, Row, Row_Tuple, Row, BF16, BF16, F32, F32, BF16_Tuple, BF16, PassThrough, PassThrough, Add, GemmMNKPadding, 1, 256, 16, 128, 32, 8, 16, 16, 1, 2, S<4, 16, 4>, S<1, 0, 2>, S<1, 0, 2>, 2, 2, 2, 1, S<4, 64, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 2, 8, 1, 1, 1, S<1, 16, 1, 4>, 1, LoopScheduler::Default, PipelineVersion::v1>, + DeviceGemmMultipleD_Wmma_CShuffle< Row, Row, Row_Tuple, Row, BF16, BF16, F32, F32, BF16_Tuple, BF16, PassThrough, PassThrough, Add, GemmMNKPadding, 1, 64, 16, 16, 32, 8, 16, 16, 1, 1, S<4, 16, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, 1, S<4, 16, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 1, 1, 1, 1, 1, S<1, 16, 1, 4>, 1, LoopScheduler::Default, PipelineVersion::v1>, + DeviceGemmMultipleD_Wmma_CShuffle< Row, Row, Row_Tuple, Row, BF16, BF16, F32, F32, BF16_Tuple, BF16, PassThrough, PassThrough, Add, GemmMNKPadding, 1, 64, 16, 16, 64, 8, 16, 16, 1, 1, S<4, 16, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, 1, S<4, 16, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 1, 1, 1, 1, 1, S<1, 16, 1, 4>, 1, LoopScheduler::Default, PipelineVersion::v1> // clang-format on >; -void add_device_gemm_add_wmma_c_shuffle_bf16_i8_bf16_bf16_mk_kn_mn_mn_instances( +void add_device_gemm_add_wmma_c_shuffle_bf16_f16_bf16_bf16_mk_kn_mn_mn_instances( std::vector>>& instances) { add_device_operation_instances( - instances, device_gemm_add_wmma_c_shuffle_bf16_i8_bf16_bf16_mk_kn_mn_mn_generic_instances{}); + instances, device_gemm_add_wmma_c_shuffle_bf16_f16_bf16_bf16_mk_kn_mn_mn_generic_instances{}); add_device_operation_instances( - instances, device_gemm_add_wmma_c_shuffle_bf16_i8_bf16_bf16_mk_kn_mn_mn_instances{}); + instances, device_gemm_add_wmma_c_shuffle_bf16_f16_bf16_bf16_mk_kn_mn_mn_instances{}); } } // namespace instance diff --git a/library/src/tensor_operation_instance/gpu/gemm_add/device_gemm_add_wmma_c_shuffle_f16_i8_f16_f16_mk_kn_mn_mn_instance.cpp b/library/src/tensor_operation_instance/gpu/gemm_add/device_gemm_add_wmma_c_shuffle_f16_f16_f16_f16_mk_kn_mn_mn_instance.cpp similarity index 71% rename from library/src/tensor_operation_instance/gpu/gemm_add/device_gemm_add_wmma_c_shuffle_f16_i8_f16_f16_mk_kn_mn_mn_instance.cpp rename to library/src/tensor_operation_instance/gpu/gemm_add/device_gemm_add_wmma_c_shuffle_f16_f16_f16_f16_mk_kn_mn_mn_instance.cpp index f29f9720f4..368d17002a 100644 --- a/library/src/tensor_operation_instance/gpu/gemm_add/device_gemm_add_wmma_c_shuffle_f16_i8_f16_f16_mk_kn_mn_mn_instance.cpp +++ b/library/src/tensor_operation_instance/gpu/gemm_add/device_gemm_add_wmma_c_shuffle_f16_f16_f16_f16_mk_kn_mn_mn_instance.cpp @@ -20,7 +20,7 @@ static constexpr auto GemmMNKPadding = ck::tensor_operation::device::GemmSpecial // e = elementwise((a * b), d0, d1) // outout: e[m, n] // input: a[m, k], b[k, n], d0[m, n], d1[m, n] -using device_gemm_add_wmma_c_shuffle_f16_i8_f16_f16_mk_kn_mn_mn_generic_instances = std::tuple< +using device_gemm_add_wmma_c_shuffle_f16_f16_f16_f16_mk_kn_mn_mn_generic_instances = std::tuple< // clang-format off // M/N/K padding //################################| A| B| Ds| E| AData| BData| AccData| CShuffle| DsData| EData| A| B| CDE| GEMM| Prefetch| Block| MPer| NPer| K0Per| K1| MPer| NPer| MRepeat| NRepeat| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockLds| BBlockTransfer| BBlockTransfer| BBlockTransfer| BlockTransfer| BBlockTransfer| BBlockTransfer| BBlockLds| CShuffle| CShuffle| CBlockTransferClusterLengths| CBlockTransfer| @@ -28,11 +28,11 @@ using device_gemm_add_wmma_c_shuffle_f16_i8_f16_f16_mk_kn_mn_mn_generic_instance //################################| | | | | | | | | | | Operation| Operation| Operation| | | | | | | | | | | | Lengths_K0_M_K1| ArrangeOrder| | | PerVector| PerVector_K1| | Lengths_K0_N_K1| ArrangeOrder| | | PerVector| PerVector_K1| | PerShuffle| PerShuffle| _NBlock_NWaveNPerXdl| _NWaveNPerXdl| //################################| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | // TODO: these template variables need to be adjusted - DeviceGemmMultipleD_Wmma_CShuffle< Row, Row, Row_Tuple, Row, F16, I8, F32, F32,F16_Tuple, F16, PassThrough, PassThrough, Add, GemmMNKPadding, 1, 256, 64, 128, 32, 8, 32, 32, 1, 2, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 1, 8, 1, S<4, 64, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 1, 8, 1, 1, 1, S<1, 32, 1, 8>, 1> + DeviceGemmMultipleD_Wmma_CShuffle< Row, Row, Row_Tuple, Row, F16, F16, F32, F32,F16_Tuple, F16, PassThrough, PassThrough, Add, GemmMNKPadding, 1, 256, 64, 128, 32, 8, 16, 16, 1, 2, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 1, 8, 1, S<4, 64, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 1, 8, 1, 1, 1, S<1, 32, 1, 8>, 1> // clang-format on >; -using device_gemm_add_wmma_c_shuffle_f16_i8_f16_f16_mk_kn_mn_mn_instances = std::tuple< +using device_gemm_add_wmma_c_shuffle_f16_f16_f16_f16_mk_kn_mn_mn_instances = std::tuple< // clang-format off // M/N/K padding //################################| A| B| Ds| E| AData| BData| AccData| CShuffle| DsData| EData| A| B| CDE| GEMM| Prefetch| Block| MPer| NPer| K0Per| K1| MPer| NPer| MRepeat| NRepeat| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockLds| BBlockTransfer| BBlockTransfer| BBlockTransfer| BlockTransfer| BBlockTransfer| BBlockTransfer| BBlockLds| CShuffle| CShuffle| CBlockTransferClusterLengths| CBlockTransfer| @@ -40,19 +40,19 @@ using device_gemm_add_wmma_c_shuffle_f16_i8_f16_f16_mk_kn_mn_mn_instances = std: //################################| | | | | | | | | | | Operation| Operation| Operation| | | | | | | | | | | | Lengths_K0_M_K1| ArrangeOrder| | | PerVector| PerVector_K1| | Lengths_K0_N_K1| ArrangeOrder| | | PerVector| PerVector_K1| | PerShuffle| PerShuffle| _NBlock_NWaveNPerXdl| _NWaveNPerXdl| //################################| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | // TODO: these template variables need to be adjusted - DeviceGemmMultipleD_Wmma_CShuffle< Row, Row, Row_Tuple, Row, F16, I8, F32, F32,F16_Tuple, F16, PassThrough, PassThrough, Add, GemmMNKPadding, 1, 256, 16, 128, 32, 8, 16, 16, 1, 2, S<4, 16, 4>, S<1, 0, 2>, S<1, 0, 2>, 2, 2, 2, 1, S<4, 64, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 1, 8, 1, 1, 1, S<1, 16, 1, 4>, 1, LoopScheduler::Default, PipelineVersion::v1>, - DeviceGemmMultipleD_Wmma_CShuffle< Row, Row, Row_Tuple, Row, F16, I8, F32, F32,F16_Tuple, F16, PassThrough, PassThrough, Add, GemmMNKPadding, 1, 64, 16, 16, 32, 8, 16, 16, 1, 1, S<4, 16, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, 1, S<4, 64, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 1, 1, 1, 1, 1, S<1, 16, 1, 4>, 1, LoopScheduler::Default, PipelineVersion::v1>, - DeviceGemmMultipleD_Wmma_CShuffle< Row, Row, Row_Tuple, Row, F16, I8, F32, F32,F16_Tuple, F16, PassThrough, PassThrough, Add, GemmMNKPadding, 1, 64, 16, 16, 64, 8, 16, 16, 1, 1, S<4, 16, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, 1, S<4, 64, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 1, 1, 1, 1, 1, S<1, 16, 1, 4>, 1, LoopScheduler::Default, PipelineVersion::v1> + DeviceGemmMultipleD_Wmma_CShuffle< Row, Row, Row_Tuple, Row, F16, F16, F32, F32,F16_Tuple, F16, PassThrough, PassThrough, Add, GemmMNKPadding, 1, 256, 16, 128, 32, 8, 16, 16, 1, 2, S<4, 16, 4>, S<1, 0, 2>, S<1, 0, 2>, 2, 2, 2, 1, S<4, 64, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 1, 8, 1, 1, 1, S<1, 16, 1, 4>, 1, LoopScheduler::Default, PipelineVersion::v1>, + DeviceGemmMultipleD_Wmma_CShuffle< Row, Row, Row_Tuple, Row, F16, F16, F32, F32,F16_Tuple, F16, PassThrough, PassThrough, Add, GemmMNKPadding, 1, 64, 16, 16, 32, 8, 16, 16, 1, 1, S<4, 16, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, 1, S<4, 64, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 1, 1, 1, 1, 1, S<1, 16, 1, 4>, 1, LoopScheduler::Default, PipelineVersion::v1>, + DeviceGemmMultipleD_Wmma_CShuffle< Row, Row, Row_Tuple, Row, F16, F16, F32, F32,F16_Tuple, F16, PassThrough, PassThrough, Add, GemmMNKPadding, 1, 64, 16, 16, 64, 8, 16, 16, 1, 1, S<4, 16, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, 1, S<4, 64, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 1, 1, 1, 1, 1, S<1, 16, 1, 4>, 1, LoopScheduler::Default, PipelineVersion::v1> // clang-format on >; -void add_device_gemm_add_wmma_c_shuffle_f16_i8_f16_f16_mk_kn_mn_mn_instances( +void add_device_gemm_add_wmma_c_shuffle_f16_f16_f16_f16_mk_kn_mn_mn_instances( std::vector>>& instances) { add_device_operation_instances( - instances, device_gemm_add_wmma_c_shuffle_f16_i8_f16_f16_mk_kn_mn_mn_generic_instances{}); + instances, device_gemm_add_wmma_c_shuffle_f16_f16_f16_f16_mk_kn_mn_mn_generic_instances{}); add_device_operation_instances( - instances, device_gemm_add_wmma_c_shuffle_f16_i8_f16_f16_mk_kn_mn_mn_instances{}); + instances, device_gemm_add_wmma_c_shuffle_f16_f16_f16_f16_mk_kn_mn_mn_instances{}); } } // namespace instance