add grouped_gemm_bias example

example/15_grouped_gemm/CMakeLists.txt

@@ -8,6 +8,7 @@ add_example_executable(example_grouped_gemm_multiple_d_dl_fp16 grouped_gemm_mult
 add_example_executable(example_grouped_gemm_xdl_splitk_fp16 grouped_gemm_xdl_splitk_fp16.cpp)
 
 add_example_executable(example_grouped_gemm_xdl_fixed_nk_fp16 grouped_gemm_xdl_fixed_nk_fp16.cpp)
+add_example_executable(example_grouped_gemm_xdl_fixed_nk_bias_fp16 grouped_gemm_xdl_fixed_nk_bias_fp16.cpp)
 
 
 add_dependencies(example_grouped_gemm_xdl
@@ -17,7 +18,7 @@ add_dependencies(example_grouped_gemm_xdl
                  example_grouped_gemm_xdl_int8
                  example_grouped_gemm_multiple_d_dl_fp16
                  example_grouped_gemm_xdl_splitk_fp16
-                 example_grouped_gemm_xdl_fixed_nk_fp16
+                 example_grouped_gemm_xdl_fixed_nk_bias_fp16
              )
 
 if(USE_BITINT_EXTENSION_INT4)

example/15_grouped_gemm/grouped_gemm_xdl_fixed_nk_bias_fp16.cpp

+// SPDX-License-Identifier: MIT
+// Copyright (c) 2018-2023, Advanced Micro Devices, Inc. All rights reserved.
+
+#include <iostream>
+#include <numeric>
+#include <initializer_list>
+#include <cstdlib>
+
+#include "ck/ck.hpp"
+#include "ck/tensor_operation/gpu/device/tensor_layout.hpp"
+#include "ck/tensor_operation/gpu/device/gemm_specialization.hpp"
+#include "ck/tensor_operation/gpu/device/impl/device_grouped_gemm_xdl_fixed_nk.hpp"
+#include "ck/tensor_operation/gpu/device/device_grouped_gemm.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/utility/literals.hpp"
+#include "ck/library/reference_tensor_operation/cpu/reference_gemm.hpp"
+
+template <ck::index_t... Is>
+using S = ck::Sequence<Is...>;
+
+using F16 = ck::half_t;
+using F32 = float;
+
+using Row = ck::tensor_layout::gemm::RowMajor;
+using Col = ck::tensor_layout::gemm::ColumnMajor;
+
+using PassThrough = ck::tensor_operation::element_wise::PassThrough;
+
+using ADataType        = F16;
+using BDataType        = F16;
+using AccDataType      = F32;
+using CShuffleDataType = F32;
+using D0DataType       = F32;
+using DsDataType       = ck::Tuple<D0DataType>;
+using EDataType        = F16;
+
+using ALayout  = Row;
+using BLayout  = Col;
+using D0Layout = Row;
+using DsLayout = ck::Tuple<D0Layout>;
+using ELayout  = Row;
+
+using AElementOp = PassThrough;
+using BElementOp = PassThrough;
+
+struct Add
+{
+    template <typename E, typename C, typename D0>
+    __host__ __device__ void operator()(E& e, const C& c, const D0& d0) const;
+
+    template <>
+    __host__ __device__ void
+    operator()<ck::half_t, float, float>(ck::half_t& e, const float& c, const float& d0) const
+    {
+        e = c + d0;
+    }
+
+    template <>
+    __host__ __device__ void operator()<ck::half_t, ck::half_t, float>(ck::half_t& e,
+                                                                       const ck::half_t& c,
+                                                                       const float& d0) const
+    {
+        e = c + d0;
+    }
+};
+
+using CDEElementOp = Add;
+
+static constexpr auto GemmDefault = ck::tensor_operation::device::GemmSpecialization::MNPadding;
+
+using DeviceGemmInstance = ck::tensor_operation::device::DeviceGroupedGemm_Xdl_Fixed_NK
+    // clang-format off
+//######| ALayout| BLayout| DsLayout| ELayout|     AData|     BData|     AccData|         CShuffle|     DsData|     EData|           A|           B|          CDE|           GEMM| NumGemmK| Block|  MPer|  NPer|  KPer| AK1| BK1| MPer| NPer| MXdl| NXdl|  ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockLds|  BBlockTransfer| BBlockTransfer| BBlockTransfer| BlockTransfer| BBlockTransfer| BBlockTransfer| BBlockLds|    CShuffle|    CShuffle| CBlockTransferClusterLengths|  CBlockTransfer|
+//######|        |        |         |        |      Type|      Type|        Type|         DataType|       Type|      Type| Elementwise| Elementwise|  Elementwise| Spacialization| Prefetch|  Size| Block| Block| Block|    |    |  XDL|  XDL|  Per|  Per|   ThreadCluster|  ThreadCluster| SrcAccessOrder|   SrcVectorDim|      SrcScalar|      DstScalar| AddExtraM|   ThreadCluster|  ThreadCluster| SrcAccessOrder|  SrcVectorDim|      SrcScalar|      DstScalar| AddExtraN| MXdlPerWave| NXdlPerWave|         _MBlock_MWaveMPerXdl| ScalarPerVector|
+//######|        |        |         |        |          |          |            |                 |           |          |   Operation|   Operation|    Operation|               |    Stage|      |      |      |      |    |    |     |     | Wave| Wave| Lengths_K0_M_K1|   ArrangeOrder|               |               |      PerVector|   PerVector_K1|          | Lengths_K0_N_K1|   ArrangeOrder|               |              |      PerVector|   PerVector_K1|          |  PerShuffle|  PerShuffle|         _NBlock_NWaveNPerXdl|   _NWaveNPerXdl|
+//######|        |        |         |        |          |          |            |                 |           |          |            |            |             |               |         |      |      |      |      |    |    |     |     |     |     |                |               |               |               |               |               |          |                |               |               |              |               |               |          |            |            |                             |                |
+        < ALayout, BLayout, DsLayout, ELayout, ADataType, BDataType, AccDataType, CShuffleDataType, DsDataType, EDataType,  AElementOp,  BElementOp, CDEElementOp,    GemmDefault,        1,   256,   256,   128,    32,   8,   8,   32,   32,    4,    2,     S<4, 64, 1>,     S<1, 0, 2>,     S<1, 0, 2>,              2,              8,              8,         1,     S<4, 64, 1>,     S<1, 0, 2>,     S<1, 0, 2>,             2,              8,              8,         1,           1,           1,               S<1, 32, 1, 8>,               8>;
+// clang-format on
+
+struct ProblemSize final
+{
+    std::vector<ck::index_t> Ms;
+    std::vector<ck::index_t> Ns;
+    std::vector<ck::index_t> Ks;
+
+    std::vector<ck::index_t> stride_As;
+    std::vector<ck::index_t> stride_Bs;
+    std::vector<ck::index_t> stride_Cs;
+
+    ck::index_t group_count;
+};
+
+struct ExecutionConfig final
+{
+    bool do_verification = true;
+    int init_method      = 1;
+    bool time_kernel     = false;
+};
+
+bool run_grouped_gemm(const ProblemSize& problem_size, const ExecutionConfig& config)
+{
+    auto group_count = problem_size.group_count;
+
+    // GEMM shape
+    std::vector<ck::tensor_operation::device::GemmDesc> gemm_descs;
+    std::vector<std::array<const void*, 1>> p_Ds;
+    std::vector<void*> p_Cs;
+
+    gemm_descs.reserve(group_count);
+
+    int sum_of_m = 0;
+
+    auto f_host_tensor_descriptor =
+        [](std::size_t row, std::size_t col, std::size_t stride, auto layout) {
+            using namespace ck::literals;
+
+            if(std::is_same<decltype(layout), ck::tensor_layout::gemm::RowMajor>::value)
+            {
+                return HostTensorDescriptor({row, col}, {stride, 1_uz});
+            }
+            else
+            {
+                return HostTensorDescriptor({row, col}, {1_uz, stride});
+            }
+        };
+
+    std::vector<Tensor<ADataType>> a_tensors;
+    std::vector<Tensor<BDataType>> b_tensors;
+    std::vector<Tensor<D0DataType>> d0_tensors;
+    std::vector<Tensor<EDataType>> c_host_tensors;
+    std::vector<Tensor<EDataType>> c_device_tensors;
+
+    a_tensors.reserve(group_count);
+    b_tensors.reserve(group_count);
+    d0_tensors.reserve(group_count);
+    c_host_tensors.reserve(group_count);
+    c_device_tensors.reserve(group_count);
+
+    using DeviceMemPtr = std::unique_ptr<DeviceMem>;
+
+    std::vector<DeviceMemPtr> a_tensors_device, b_tensors_device, d0_tensors_device,
+        c_tensors_device;
+
+    a_tensors_device.reserve(group_count);
+    b_tensors_device.reserve(group_count);
+    d0_tensors_device.reserve(group_count);
+    c_tensors_device.reserve(group_count);
+
+    std::size_t flop = 0, num_btype = 0;
+
+    for(int i = 0; i < group_count; i++)
+    {
+        sum_of_m += problem_size.Ms[i];
+        a_tensors.push_back(Tensor<ADataType>(f_host_tensor_descriptor(
+            problem_size.Ms[i], problem_size.Ks[i], problem_size.stride_As[i], ALayout{})));
+        b_tensors.push_back(Tensor<BDataType>(f_host_tensor_descriptor(
+            problem_size.Ks[i], problem_size.Ns[i], problem_size.stride_Bs[i], BLayout{})));
+        d0_tensors.push_back(Tensor<D0DataType>(
+            f_host_tensor_descriptor(problem_size.Ms[i], problem_size.Ns[i], 0, ELayout{})));
+        c_host_tensors.push_back(Tensor<EDataType>(f_host_tensor_descriptor(
+            problem_size.Ms[i], problem_size.Ns[i], problem_size.stride_Cs[i], ELayout{})));
+        c_device_tensors.push_back(Tensor<EDataType>(f_host_tensor_descriptor(
+            problem_size.Ms[i], problem_size.Ns[i], problem_size.stride_Cs[i], ELayout{})));
+        std::cout << "gemm[" << i << "] a_m_k: " << a_tensors[i].mDesc
+                  << " b_k_n: " << b_tensors[i].mDesc << " d_m_n: " << d0_tensors[i].mDesc
+                  << " c_m_n: " << c_device_tensors[i].mDesc << std::endl;
+
+        flop += std::size_t(2) * problem_size.Ms[i] * problem_size.Ks[i] * problem_size.Ns[i];
+        num_btype += sizeof(ADataType) * a_tensors[i].mDesc.GetElementSize() +
+                     sizeof(BDataType) * b_tensors[i].mDesc.GetElementSize() +
+                     sizeof(D0DataType) * d0_tensors[i].mDesc.GetElementSize() +
+                     sizeof(EDataType) * c_device_tensors[i].mDesc.GetElementSize();
+
+        switch(config.init_method)
+        {
+        case 0: break;
+        case 1:
+            a_tensors[i].GenerateTensorValue(GeneratorTensor_2<ADataType>{-5, 5});
+            b_tensors[i].GenerateTensorValue(GeneratorTensor_2<BDataType>{-5, 5});
+            break;
+        case 2:
+            a_tensors[i].GenerateTensorValue(GeneratorTensor_3<ADataType>{0.0, 1.0});
+            b_tensors[i].GenerateTensorValue(GeneratorTensor_3<BDataType>{-0.5, 0.5});
+            break;
+        default:
+            a_tensors[i].GenerateTensorValue(GeneratorTensor_Sequential<0>{});
+            b_tensors[i].GenerateTensorValue(GeneratorTensor_Sequential<1>{});
+        }
+
+        d0_tensors[i].GenerateTensorValue(GeneratorTensor_3<BDataType>{-0.5, 0.5});
+    }
+
+    using GroupedGemmKernelArgument = ck::tensor_operation::device::GroupedGemmKernelArgument<1>;
+
+    std::vector<GroupedGemmKernelArgument> grouped_gemm_kernel_args_;
+    grouped_gemm_kernel_args_.reserve(group_count);
+
+    for(int i = 0; i < group_count; i++)
+    {
+        a_tensors_device.emplace_back(
+            std::make_unique<DeviceMem>(sizeof(ADataType) * sum_of_m * problem_size.Ks[i]));
+
+        b_tensors_device.emplace_back(std::make_unique<DeviceMem>(
+            sizeof(BDataType) * problem_size.Ns[i] * problem_size.Ks[i]));
+
+        d0_tensors_device.emplace_back(
+            std::make_unique<DeviceMem>(sizeof(D0DataType) * problem_size.Ns[i]));
+
+        c_tensors_device.emplace_back(
+            std::make_unique<DeviceMem>(sizeof(EDataType) * sum_of_m * problem_size.Ns[i]));
+
+        a_tensors_device[i]->ToDevice(a_tensors[i].mData.data(),
+                                      a_tensors[i].mDesc.GetElementSpaceSize() * sizeof(ADataType));
+        b_tensors_device[i]->ToDevice(b_tensors[i].mData.data(),
+                                      b_tensors[i].mDesc.GetElementSpaceSize() * sizeof(BDataType));
+        d0_tensors_device[i]->ToDevice(d0_tensors[i].mData.data());
+        c_tensors_device[i]->SetZero();
+
+        p_Ds.push_back(std::array<const void*, 1>{d0_tensors_device[i]->GetDeviceBuffer()});
+        p_Cs.push_back(c_tensors_device[i]->GetDeviceBuffer());
+
+        gemm_descs.push_back({sum_of_m,
+                              problem_size.Ns[i],
+                              problem_size.Ks[i],
+                              problem_size.stride_As[i],
+                              problem_size.stride_Bs[i],
+                              problem_size.stride_Cs[i],
+                              {0}});
+
+        grouped_gemm_kernel_args_.push_back(
+            {a_tensors_device[i]->GetDeviceBuffer(),
+             b_tensors_device[i]->GetDeviceBuffer(),
+             std::array<const void*, 1>{d0_tensors_device[i]->GetDeviceBuffer()},
+             c_tensors_device[i]->GetDeviceBuffer(),
+             problem_size.Ms[i],
+             problem_size.Ns[i],
+             problem_size.Ks[i],
+             problem_size.stride_As[i],
+             problem_size.stride_Bs[i],
+             std::array<ck::index_t, 1>{0},
+             problem_size.stride_Cs[i]});
+    }
+
+    auto a_element_op   = AElementOp{};
+    auto b_element_op   = BElementOp{};
+    auto cde_element_op = CDEElementOp{};
+
+    auto gemm    = DeviceGemmInstance{};
+    auto invoker = gemm.MakeInvoker();
+
+    std::vector<const void*> p_As = {};
+    std::vector<const void*> p_Bs = {};
+
+    // do GEMM
+    auto argument = gemm.MakeArgument(
+        p_As, p_Bs, p_Ds, p_Cs, gemm_descs, a_element_op, b_element_op, cde_element_op);
+
+    DeviceMem gemm_desc_workspace(gemm.GetWorkSpaceSize(&argument));
+
+    hip_check_error(hipMemcpy(gemm_desc_workspace.GetDeviceBuffer(),
+                              grouped_gemm_kernel_args_.data(),
+                              gemm.GetWorkSpaceSize(&argument),
+                              hipMemcpyHostToDevice));
+
+    if(!gemm.IsSupportedArgument(argument))
+    {
+        throw std::runtime_error(
+            "wrong! device_gemm with the specified compilation parameters does "
+            "not support this GEMM problem");
+    }
+
+    gemm.SetDeviceKernelArgs(argument, gemm_desc_workspace.GetDeviceBuffer());
+
+    invoker.Run(argument, StreamConfig{nullptr, false});
+
+    bool pass = true;
+    if(config.do_verification)
+    {
+        using ReferenceGemmInstance = ck::tensor_operation::host::ReferenceGemm<ADataType,
+                                                                                BDataType,
+                                                                                EDataType,
+                                                                                AccDataType,
+                                                                                AElementOp,
+                                                                                BElementOp,
+                                                                                PassThrough>;
+
+        for(std::size_t i = 0; i < gemm_descs.size(); i++)
+        {
+            c_tensors_device[i]->FromDevice(c_device_tensors[i].mData.data(),
+                                            c_device_tensors[i].mDesc.GetElementSize() *
+                                                sizeof(EDataType));
+
+            auto ref_gemm    = ReferenceGemmInstance{};
+            auto ref_invoker = ref_gemm.MakeInvoker();
+
+            auto ref_argument = ref_gemm.MakeArgument(a_tensors[i],
+                                                      b_tensors[i],
+                                                      c_host_tensors[i],
+                                                      a_element_op,
+                                                      b_element_op,
+                                                      PassThrough{});
+
+            ref_invoker.Run(ref_argument);
+
+            for(int m = 0; m < problem_size.Ms[i]; ++m)
+            {
+                for(int n = 0; n < problem_size.Ns[i]; ++n)
+                {
+                    cde_element_op(
+                        c_host_tensors[i](m, n), c_host_tensors[i](m, n), d0_tensors[i](m, n));
+                }
+            }
+
+            pass &= ck::utils::check_err(c_device_tensors[i], c_host_tensors[i]);
+        }
+    }
+
+    if(config.time_kernel)
+    {
+        float ave_time   = invoker.Run(argument, StreamConfig{nullptr, config.time_kernel});
+        float tflops     = static_cast<float>(flop) / 1.E9 / ave_time;
+        float gb_per_sec = num_btype / 1.E6 / ave_time;
+
+        std::cout << "Perf: " << ave_time << " ms, " << tflops << " TFlops, " << gb_per_sec
+                  << " GB/s, " << gemm.GetTypeString() << std::endl;
+    }
+
+    return pass;
+}
+
+// int main(int argc, char* argv[]) { return !run_grouped_gemm_example(argc, argv); }
+int main(int argc, char* argv[])
+{
+    ProblemSize problem_size;
+    ExecutionConfig config;
+
+    problem_size.group_count = 16;
+
+    problem_size.Ms = {
+        167, 183, 177, 181, 153, 139, 156, 173, 163, 150, 204, 184, 168, 156, 168, 148};
+
+    for(int i = 0; i < problem_size.group_count; i++)
+    {
+        problem_size.Ns.push_back(768);
+        problem_size.Ks.push_back(4608);
+
+        problem_size.stride_As.push_back(problem_size.Ks[i]);
+        problem_size.stride_Bs.push_back(problem_size.Ks[i]);
+        problem_size.stride_Cs.push_back(problem_size.Ns[i]);
+    }
+
+    if(argc == 4)
+    {
+        config.do_verification = std::stoi(argv[1]);
+        config.init_method     = std::stoi(argv[2]);
+        config.time_kernel     = std::stoi(argv[3]);
+    }
+    else
+    {
+        printf("arg1: verification (0=no, 1=yes)\n");
+        printf("arg2: initialization (0=no init, 1=integer value, 2=decimal value)\n");
+        printf("arg3: time kernel (0=n0, 1=yes)\n");
+        exit(0);
+    }
+
+    return !run_grouped_gemm(problem_size, config);
+}

example/15_grouped_gemm/grouped_gemm_xdl_fixed_nk_fp16.cpp

@@ -34,7 +34,7 @@ using PassThrough = ck::tensor_operation::element_wise::PassThrough;
 using ADataType        = F16;
 using BDataType        = F16;
 using AccDataType      = F32;
-using CShuffleDataType = F16;
+using CShuffleDataType = F32;
 using DsDataType       = ck::Tuple<>;
 using EDataType        = F16;
 

include/ck/tensor_operation/gpu/device/impl/device_grouped_gemm_xdl_fixed_nk.hpp

@@ -29,6 +29,7 @@ template <typename GridwiseGemm,
           typename BLayout,
           typename DsLayout,
           typename ELayout,
+          typename DsDataType,
           typename Block2ETileMap,
           typename GroupedGemmBlock2ETileMap,
           typename AElementwiseOperation,
@@ -108,18 +109,6 @@ __global__ void
     const auto StrideDs = gemm_desc_ptr[group_id].StrideDs;
     const auto StrideE  = gemm_desc_ptr[group_id].StrideE;
 
-#if 0
-    using Row = ck::tensor_layout::gemm::RowMajor;
-    using Col = ck::tensor_layout::gemm::ColumnMajor;
-
-    using ALayout  = Row;
-    using BLayout  = Col;
-    using DsLayout = ck::Tuple<>;
-    using ELayout  = Row;
-#endif
-
-    using DsDataType = ck::Tuple<>;
-
     const auto e_grid_desc_m_n =
         GridwiseGemm::template MakeEGridDescriptor_M_N<ELayout, GemmSpec>(M, N, StrideE);
 
@@ -127,7 +116,7 @@ __global__ void
 
     const auto local_b2e_tile_map = Block2ETileMap{e_grid_desc_m_n};
 
-    constexpr auto NumDTensor = 0;
+    constexpr auto NumDTensor = DsDataType::Size();
 
     using DsGridPointer = decltype(GridwiseGemm::MakeDsGridPointer());
 
@@ -580,10 +569,9 @@ struct DeviceGroupedGemm_Xdl_Fixed_NK : public DeviceGroupedGemmFixedNK<ALayout,
                 throw std::runtime_error("wrong! group_count_ != p_Bs || 0 != p_Bs.size");
             }
 
-            if(!(group_count_ == ck::type_convert<ck::index_t>(p_Ds.size()) ||
-                 0 == ck::type_convert<ck::index_t>(p_Ds.size())))
+            if(!(group_count_ == ck::type_convert<ck::index_t>(p_Ds.size()) || NumDTensor == 0))
             {
-                throw std::runtime_error("wrong! group_count_ != p_Ds || 0 != p_Ds.size");
+                throw std::runtime_error("wrong! group_count_ != p_Ds");
             }
 
             if(!(group_count_ == ck::type_convert<ck::index_t>(p_Es.size())))
@@ -648,11 +636,17 @@ struct DeviceGroupedGemm_Xdl_Fixed_NK : public DeviceGroupedGemmFixedNK<ALayout,
 
                 const index_t grid_size_grp = local_b2c_tile_map.CalculateGridSize(e_grid_desc_m_n);
 
-                std::cout << "grp id: " << group_id << " grid_size: " << grid_size_grp << std::endl;
+                // std::cout << "grp id: " << group_id << " grid_size: " << grid_size_grp <<
+                // std::endl;
 
                 const index_t BlockStart = grid_size_;
                 const index_t BlockEnd   = grid_size_ + grid_size_grp;
 
+                if(group_id * grid_size_grp != grid_size_)
+                {
+                    throw std::runtime_error("wrong! grid_size_grp is not identical!");
+                }
+
                 grid_size_ += grid_size_grp;
 
                 if(GridwiseGemm::CheckValidity(a_grid_desc_m_k,
@@ -754,6 +748,14 @@ struct DeviceGroupedGemm_Xdl_Fixed_NK : public DeviceGroupedGemmFixedNK<ALayout,
                           << arg.gemm_desc_kernel_arg_[i].b_grid_desc_bk0_n_bk1_.GetLength(I2)
                           << "}";
 
+                static_for<0, NumDTensor, 1>{}([&](auto j) {
+                    std::cout << ", arg.d" << i << "_grid_desc_m_n_{"
+                              << arg.gemm_desc_kernel_arg_[i].ds_grid_desc_m_n_[j].GetLength(I0)
+                              << ", "
+                              << arg.gemm_desc_kernel_arg_[i].ds_grid_desc_m_n_[j].GetLength(I1)
+                              << "}";
+                });
+
                 std::cout << ", arg.e_grid_desc_m_n_{ "
                           << arg.gemm_desc_kernel_arg_[i].e_grid_desc_m_n_.GetLength(I0) << ", "
                           << arg.gemm_desc_kernel_arg_[i].e_grid_desc_m_n_.GetLength(I1) << "}"
@@ -805,6 +807,7 @@ struct DeviceGroupedGemm_Xdl_Fixed_NK : public DeviceGroupedGemmFixedNK<ALayout,
                                                      BLayout,
                                                      DsLayout,
                                                      ELayout,
+                                                     DsDataType,
                                                      Block2ETileMap,
                                                      GroupedGemmBlock2ETileMap,
                                                      AElementwiseOperation,

include/ck/tensor_operation/gpu/element/binary_element_wise_operation.hpp

@@ -10,72 +10,6 @@ namespace ck {
 namespace tensor_operation {
 namespace element_wise {
 
-struct Add
-{
-    template <typename Y, typename X0, typename X1>
-    __host__ __device__ constexpr void operator()(Y& y, const X0& x0, const X1& x1) const;
-
-    template <>
-    __host__ __device__ constexpr void
-    operator()<float>(float& y, const float& x0, const float& x1) const
-    {
-        y = x0 + x1;
-    };
-
-    template <>
-    __host__ __device__ constexpr void
-    operator()<double>(double& y, const double& x0, const double& x1) const
-    {
-        y = x0 + x1;
-    };
-
-    template <>
-    __host__ __device__ constexpr void
-    operator()<float>(float& y, const float& x0, const half_t& x1) const
-    {
-        y = x0 + type_convert<half_t>(x1);
-    };
-
-    template <>
-    __host__ __device__ constexpr void
-    operator()<half_t>(half_t& y, const float& x0, const half_t& x1) const
-    {
-        y = type_convert<half_t>(x0) + x1;
-    };
-
-    template <>
-    __host__ __device__ constexpr void
-    operator()<half_t>(half_t& y, const half_t& x0, const half_t& x1) const
-    {
-        y = x0 + x1;
-    };
-
-    template <>
-    __host__ __device__ constexpr void
-    operator()<float>(float& y, const float& x0, const bhalf_t& x1) const
-    {
-        const float x1_tmp = ck::type_convert<float>(x1);
-        y                  = x0 + x1_tmp;
-    }
-
-    template <>
-    __host__ __device__ constexpr void
-    operator()<bhalf_t>(bhalf_t& y, const bhalf_t& x0, const bhalf_t& x1) const
-    {
-        const float x1_tmp = ck::type_convert<float>(x0);
-        const float x2_tmp = ck::type_convert<float>(x1);
-        const float y_tmp  = x1_tmp + x2_tmp;
-        y                  = ck::type_convert<bhalf_t>(y_tmp);
-    }
-
-    template <>
-    __host__ __device__ constexpr void
-    operator()<int8_t>(int8_t& y, const int8_t& x0, const int8_t& x1) const
-    {
-        y = x0 + x1;
-    };
-};
-
 struct ScaleAdd
 {
     __host__ __device__ ScaleAdd(float scale) : scale_(scale) {}

include/ck/tensor_operation/gpu/element/unary_element_wise_operation.hpp

@@ -51,6 +51,12 @@ struct PassThrough
         y = x;
     }
 
+    template <>
+    __host__ __device__ void operator()<half_t, float>(half_t& y, const float& x) const
+    {
+        y = type_convert<half_t>(x);
+    }
+
     template <>
     __host__ __device__ void operator()<bhalf_t, float>(bhalf_t& y, const float& x) const
     {