Gemm+Bilinear (#316)

* refactor

* update example

* update example

* gemm bilinear

* clean

* update

example/02_gemm_alpha_beta/CMakeLists.txt

-add_example_executable(example_gemm_xdl_alpha_beta gemm_xdl_alpha_beta.cpp)

example/02_gemm_bilinear/CMakeLists.txt

+add_example_executable(example_gemm_bilinear_xdl_fp16 gemm_bilinear_xdl_fp16.cpp)

example/02_gemm_alpha_beta/README.md → example/02_gemm_bilinear/README.md

-# Instructions for ```example_gemm_xdl_alpha_beta```
+# Instructions for ```example_gemm_bilinear_xdl_fp16```
 
-## Run ```example_gemm_xdl_alpha_beta```
+## Run ```example_gemm_bilinear_xdl_fp16```
 ```bash
 #arg1: verification (0=no, 1=yes)
 #arg2: initialization (0=no init, 1=integer value, 2=decimal value)
-#arg3: run kernel # of times (>1)
-./bin/example_gemm_xdl_alpha_beta 1 1 1 0.5 0.5
+#arg3: time kernel (0=no, 1=yes)
+#arg4 to 10: M (256x), N(128x), K(32x), StrideA, StrideB, StrideD, StrideE
+#arg11 to 12: alpha, beta
+./bin/example_gemm_bilinear_xdl_fp16 1 1 1 3840 4096 4096 4096 4096 4096 4096 0.5 0.5
 ```
 Result (MI100 @ 1502Mhz, 184.6TFlops peak FP16)
 ```

example/02_gemm_alpha_beta/gemm_xdl_alpha_beta.cpp → example/02_gemm_bilinear/gemm_bilinear_xdl_fp16.cpp

@@ -8,80 +8,105 @@
 
 #include "ck/ck.hpp"
 #include "ck/tensor_operation/gpu/device/gemm_specialization.hpp"
-#include "ck/tensor_operation/gpu/device/device_gemm_xdl_c_shuffle_bias_2d.hpp"
+#include "ck/tensor_operation/gpu/device/device_gemm_multiple_d_xdl_cshuffle.hpp"
 #include "ck/tensor_operation/gpu/element/element_wise_operation.hpp"
 
-#include "ck/library/utility/check_err.hpp"
 #include "ck/library/host_tensor/device_memory.hpp"
 #include "ck/library/host_tensor/host_tensor.hpp"
 #include "ck/library/host_tensor/host_tensor_generator.hpp"
-#include "ck/library/reference_tensor_operation/cpu/reference_gemm_bias_2d.hpp"
+#include "ck/library/reference_tensor_operation/cpu/reference_gemm.hpp"
+#include "ck/library/utility/check_err.hpp"
+
+struct AlphaBetaAdd
+{
+    AlphaBetaAdd(float alpha, float beta) : alpha_(alpha), beta_(beta){};
+
+    template <typename E, typename C, typename D>
+    __host__ __device__ constexpr void operator()(E& e, const C& c, const D& d) const;
+
+    template <>
+    __host__ __device__ constexpr void operator()<ck::half_t, float, ck::half_t>(
+        ck::half_t& e, const float& c, const ck::half_t& d) const
+    {
+        e = ck::type_convert<ck::half_t>(alpha_ * c + beta_ * ck::type_convert<float>(d));
+    };
+
+    float alpha_;
+    float beta_;
+};
 
 template <ck::index_t... Is>
 using S = ck::Sequence<Is...>;
 
-using ADataType   = ck::half_t;
-using BDataType   = ck::half_t;
-using CDataType   = ck::half_t;
-using AccDataType = float;
-
-using ALayout = ck::tensor_layout::gemm::RowMajor;
-using BLayout = ck::tensor_layout::gemm::ColumnMajor;
-using CLayout = ck::tensor_layout::gemm::RowMajor;
-
-using AElementOp = ck::tensor_operation::element_wise::PassThrough;
-using BElementOp = ck::tensor_operation::element_wise::PassThrough;
-using CElementOp = ck::tensor_operation::element_wise::AlphaBetaAdd;
-
-// clang-format off
-using DeviceGemmInstance = ck::tensor_operation::device::DeviceGemmXdl_C_Shuffle_Bias_2d<
-    ADataType,              // ADataType
-    BDataType,              // BDataType
-    CDataType,              // CDataType
-    AccDataType,            // AccDataType
-    ALayout,                // ALayout
-    BLayout,                // BLayout
-    CLayout,                // CLayout
-    AElementOp,             // AElementwiseOperation
-    BElementOp,             // BElementwiseOperation
-    CElementOp,             // CElementwiseOperation
-    256,                    // BlockSize
-    256,                    // MPerBlock
-    128,                    // NPerBlock
-    4,                      // K0PerBlock
-    8,                      // K1
-    32,                     // MPerXDL
-    32,                     // NPerXDL
-    4,                      // MXdlPerWave
-    2,                      // NXdlPerWave
-    S<4, 64, 1>,            // ABlockTransferThreadClusterLengths_K0_M_K1
-    S<1, 0, 2>,             // ABlockTransferThreadClusterArrangeOrder
-    S<1, 0, 2>,             // ABlockTransferSrcAccessOrder
-    2,                      // ABlockTransferSrcVectorDim
-    8,                      // ABlockTransferSrcScalarPerVector
-    8,                      // ABlockTransferDstScalarPerVector_K1
-    true,                   // ABlockLdsAddExtraM
-    S<4, 64, 1>,            // BBlockTransferThreadClusterLengths_K0_N_K1
-    S<1, 0, 2>,             // BBlockTransferThreadClusterArrangeOrder
-    S<1, 0, 2>,             // BBlockTransferSrcAccessOrder
-    2,                      // BBlockTransferSrcVectorDim
-    8,                      // BBlockTransferSrcScalarPerVector
-    8,                      // BBlockTransferDstScalarPerVector_K1
-    true,                   // BBlockLdsAddExtraN
-    1,                      // CShuffleMXdlPerWavePerShuffle
-    1,                      // CShuffleNXdlPerWavePerShuffle
-    S<1, 1, 32, 1, 1, 8>,   // CBlockTransferClusterLengths_MBlock_MXdlPerWave_MWaveMPerXdl_NBlock_NXdlPerWave_NWaveNPerXdl
-    8>;                     // CBlockTransferScalarPerVector_NWaveNPerXdl
-// clang-format on
-
-using ReferenceGemmInstance = ck::tensor_operation::host::ReferenceGemmBias2D<ADataType,
-                                                                              BDataType,
-                                                                              CDataType,
-                                                                              CDataType,
-                                                                              AccDataType,
-                                                                              AElementOp,
-                                                                              BElementOp,
-                                                                              CElementOp>;
+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 DDataType        = F16;
+using DsDataType       = ck::Tuple<DDataType>;
+using EDataType        = F16;
+
+using ALayout  = Row;
+using BLayout  = Col;
+using DELayout = Row;
+
+using AElementOp   = PassThrough;
+using BElementOp   = PassThrough;
+using CDEElementOp = AlphaBetaAdd;
+
+static constexpr auto GemmDefault = ck::tensor_operation::device::GemmSpecialization::MNKPadding;
+
+using DeviceOpInstance =
+    ck::tensor_operation::device::DeviceGemmMultipleD_Xdl_CShuffle<ALayout,
+                                                                   BLayout,
+                                                                   DELayout,
+                                                                   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>;
 
 int main(int argc, char* argv[])
 {
@@ -96,12 +121,17 @@ int main(int argc, char* argv[])
 
     ck::index_t StrideA = 4096;
     ck::index_t StrideB = 4096;
-    ck::index_t StrideC = 4096;
+    ck::index_t StrideD = 4096;
+    ck::index_t StrideE = 4096;
 
     float alpha = 1.0f;
     float beta  = 1.0f;
 
-    if(argc == 4)
+    if(argc == 1)
+    {
+        // use default case
+    }
+    else if(argc == 4)
     {
         do_verification = std::stoi(argv[1]);
         init_method     = std::stoi(argv[2]);
@@ -116,7 +146,7 @@ int main(int argc, char* argv[])
         alpha = std::stof(argv[4]);
         beta  = std::stof(argv[5]);
     }
-    else if(argc == 12)
+    else if(argc == 13)
     {
         do_verification = std::stoi(argv[1]);
         init_method     = std::stoi(argv[2]);
@@ -128,17 +158,19 @@ int main(int argc, char* argv[])
 
         StrideA = std::stoi(argv[7]);
         StrideB = std::stoi(argv[8]);
-        StrideC = std::stoi(argv[9]);
+        StrideD = std::stoi(argv[9]);
+        StrideE = std::stoi(argv[10]);
 
-        alpha = std::stof(argv[10]);
-        beta  = std::stof(argv[11]);
+        alpha = std::stof(argv[11]);
+        beta  = std::stof(argv[12]);
     }
     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");
-        printf("arg4 to 9: M (256x), N(128x), K(32x), StrideA, StrideB, StrideC, alpha, beta\n");
+        printf("arg3: time kernel (0=no, 1=yes)\n");
+        printf("arg4 to 9: M (256x), N(128x), K(32x), StrideA, StrideB, StrideD, StrideE, alpha, "
+               "beta\n");
         exit(0);
     }
 
@@ -158,14 +190,14 @@ int main(int argc, char* argv[])
 
     Tensor<ADataType> a_m_k(f_host_tensor_descriptor(M, K, StrideA, ALayout{}));
     Tensor<BDataType> b_k_n(f_host_tensor_descriptor(K, N, StrideB, BLayout{}));
-    Tensor<CDataType> c0_m_n(f_host_tensor_descriptor(M, N, StrideC, CLayout{}));
-    Tensor<CDataType> c_m_n_host_result(f_host_tensor_descriptor(M, N, StrideC, CLayout{}));
-    Tensor<CDataType> c_m_n_device_result(f_host_tensor_descriptor(M, N, StrideC, CLayout{}));
+    Tensor<DDataType> d_m_n(f_host_tensor_descriptor(M, N, StrideD, DELayout{}));
+    Tensor<EDataType> e_m_n_host_result(f_host_tensor_descriptor(M, N, StrideE, DELayout{}));
+    Tensor<EDataType> e_m_n_device_result(f_host_tensor_descriptor(M, N, StrideE, DELayout{}));
 
     std::cout << "a_m_k: " << a_m_k.mDesc << std::endl;
     std::cout << "b_k_n: " << b_k_n.mDesc << std::endl;
-    std::cout << "c0_m_n: " << c0_m_n.mDesc << std::endl;
-    std::cout << "c_m_n: " << c_m_n_host_result.mDesc << std::endl;
+    std::cout << "d_m_n: " << d_m_n.mDesc << std::endl;
+    std::cout << "e_m_n: " << e_m_n_host_result.mDesc << std::endl;
 
     switch(init_method)
     {
@@ -173,42 +205,48 @@ int main(int argc, char* argv[])
     case 1:
         a_m_k.GenerateTensorValue(GeneratorTensor_2<ADataType>{-5, 5});
         b_k_n.GenerateTensorValue(GeneratorTensor_2<BDataType>{-5, 5});
-        c0_m_n.GenerateTensorValue(GeneratorTensor_2<CDataType>{-5, 5});
+        d_m_n.GenerateTensorValue(GeneratorTensor_2<DDataType>{-5, 5});
         break;
     default:
         a_m_k.GenerateTensorValue(GeneratorTensor_3<ADataType>{0.0, 1.0});
         b_k_n.GenerateTensorValue(GeneratorTensor_3<BDataType>{-0.5, 0.5});
-        c0_m_n.GenerateTensorValue(GeneratorTensor_3<CDataType>{-0.5, 0.5});
+        d_m_n.GenerateTensorValue(GeneratorTensor_3<DDataType>{-0.5, 0.5});
     }
 
     DeviceMem a_m_k_device_buf(sizeof(ADataType) * a_m_k.mDesc.GetElementSpace());
     DeviceMem b_k_n_device_buf(sizeof(BDataType) * b_k_n.mDesc.GetElementSpace());
-    DeviceMem c0_m_n_device_buf(sizeof(CDataType) * c0_m_n.mDesc.GetElementSpace());
-    DeviceMem c_m_n_device_buf(sizeof(CDataType) * c_m_n_device_result.mDesc.GetElementSpace());
+    DeviceMem d_m_n_device_buf(sizeof(DDataType) * d_m_n.mDesc.GetElementSpace());
+    DeviceMem e_m_n_device_buf(sizeof(EDataType) * e_m_n_device_result.mDesc.GetElementSpace());
 
     a_m_k_device_buf.ToDevice(a_m_k.mData.data());
     b_k_n_device_buf.ToDevice(b_k_n.mData.data());
-    c0_m_n_device_buf.ToDevice(c0_m_n.mData.data());
-    c_m_n_device_buf.ToDevice(c_m_n_device_result.mData.data());
+    d_m_n_device_buf.ToDevice(d_m_n.mData.data());
+    e_m_n_device_buf.ToDevice(e_m_n_device_result.mData.data());
+
+    auto a_element_op   = AElementOp{};
+    auto b_element_op   = BElementOp{};
+    auto cde_element_op = CDEElementOp{alpha, beta};
 
     // do GEMM
-    auto gemm     = DeviceGemmInstance{};
-    auto invoker  = gemm.MakeInvoker();
-    auto argument = gemm.MakeArgument(static_cast<ADataType*>(a_m_k_device_buf.GetDeviceBuffer()),
-                                      static_cast<BDataType*>(b_k_n_device_buf.GetDeviceBuffer()),
-                                      static_cast<CDataType*>(c0_m_n_device_buf.GetDeviceBuffer()),
-                                      static_cast<CDataType*>(c_m_n_device_buf.GetDeviceBuffer()),
-                                      M,
-                                      N,
-                                      K,
-                                      StrideA,
-                                      StrideB,
-                                      StrideC,
-                                      AElementOp{},
-                                      BElementOp{},
-                                      CElementOp{alpha, beta});
-
-    if(!gemm.IsSupportedArgument(argument))
+    auto device_op = DeviceOpInstance{};
+    auto invoker   = device_op.MakeInvoker();
+    auto argument =
+        device_op.MakeArgument(a_m_k_device_buf.GetDeviceBuffer(),
+                               b_k_n_device_buf.GetDeviceBuffer(),
+                               std::array<const void*, 1>{d_m_n_device_buf.GetDeviceBuffer()},
+                               e_m_n_device_buf.GetDeviceBuffer(),
+                               M,
+                               N,
+                               K,
+                               StrideA,
+                               StrideB,
+                               std::array<ck::index_t, 1>{StrideD},
+                               StrideE,
+                               a_element_op,
+                               b_element_op,
+                               cde_element_op);
+
+    if(!device_op.IsSupportedArgument(argument))
     {
         throw std::runtime_error(
             "wrong! device_gemm with the specified compilation parameters does "
@@ -219,7 +257,7 @@ int main(int argc, char* argv[])
 
     std::size_t flop = std::size_t(2) * M * N * K;
     std::size_t num_btype =
-        sizeof(ADataType) * M * K + sizeof(BDataType) * K * N + sizeof(CDataType) * M * N;
+        sizeof(ADataType) * M * K + sizeof(BDataType) * K * N + sizeof(EDataType) * M * N;
 
     float tflops = static_cast<float>(flop) / 1.E9 / ave_time;
 
@@ -228,24 +266,39 @@ int main(int argc, char* argv[])
     std::cout << "Perf: " << ave_time << " ms, " << tflops << " TFlops, " << gb_per_sec << " GB/s"
               << std::endl;
 
-    c_m_n_device_buf.FromDevice(c_m_n_device_result.mData.data());
+    e_m_n_device_buf.FromDevice(e_m_n_device_result.mData.data());
 
     if(do_verification)
     {
-        auto ref_gemm    = ReferenceGemmInstance{};
-        auto ref_invoker = ref_gemm.MakeInvoker();
-
-        auto ref_argument = ref_gemm.MakeArgument(a_m_k,
-                                                  b_k_n,
-                                                  c0_m_n,
-                                                  c_m_n_host_result,
-                                                  AElementOp{},
-                                                  BElementOp{},
-                                                  CElementOp{alpha, beta});
+        Tensor<CShuffleDataType> c_m_n(HostTensorDescriptor(
+            std::vector<std::size_t>{static_cast<std::size_t>(M), static_cast<std::size_t>(N)}));
+
+        using ReferenceGemmInstance = ck::tensor_operation::host::ReferenceGemm<ADataType,
+                                                                                BDataType,
+                                                                                CShuffleDataType,
+                                                                                AccDataType,
+                                                                                AElementOp,
+                                                                                BElementOp,
+                                                                                PassThrough>;
+        auto ref_gemm               = ReferenceGemmInstance{};
+        auto ref_invoker            = ref_gemm.MakeInvoker();
+
+        auto ref_argument =
+            ref_gemm.MakeArgument(a_m_k, b_k_n, c_m_n, a_element_op, b_element_op, PassThrough{});
 
         ref_invoker.Run(ref_argument);
 
-        return ck::utils::check_err(c_m_n_device_result.mData, c_m_n_host_result.mData) ? 0 : 1;
+        for(int m = 0; m < M; ++m)
+        {
+            for(int n = 0; n < N; ++n)
+            {
+                cde_element_op(e_m_n_host_result(m, n), c_m_n(m, n), d_m_n(m, n));
+            }
+        }
+
+        e_m_n_device_buf.FromDevice(e_m_n_device_result.mData.data());
+
+        return ck::utils::check_err(e_m_n_device_result.mData, e_m_n_host_result.mData) ? 0 : 1;
     }
 
     return 0;

example/03_gemm_bias_relu/CMakeLists.txt

-add_example_executable(example_gemm_xdl_bias_relu gemm_xdl_bias_relu.cpp)
+add_example_executable(example_gemm_bias_relu_xdl_fp16 gemm_bias_relu_xdl_fp16.cpp)

example/03_gemm_bias_relu/README.md

-# Instructions for ```example_gemm_xdl_bias_relu_add```
+# Instructions for ```example_gemm_bias_relu_xdl_fp16```
 
-## Run ```example_gemm_xdl_bias_relu_add```
+## Run ```example_gemm_bias_relu_xdl_fp16```
 ```bash
 #arg1: verification (0=no, 1=yes)
 #arg2: initialization (0=no init, 1=integer value, 2=decimal value)
-#arg3: run kernel # of times (>1)
-#arg4 to 9: M (256x), N(128x), K(32x), StrideA, StrideB, StrideC
-./bin/example_gemm_xdl_bias_relu_add 0 1 5 3840 4096 4096 4096 4096 4096
-```
-
-Result (MI100 @ 1087Mhz, 133.5TFlops peak FP16)
-```
-a_m_k: dim 2, lengths {3840, 4096}, strides {4096, 1}
-b_k_n: dim 2, lengths {4096, 4096}, strides {1, 4096}
-c_m_n: dim 2, lengths {3840, 4096}, strides {4096, 1}
-c0_m_n: dim 2, lengths {3840, 4096}, strides {4096, 1}
-c1_m_n: dim 2, lengths {3840, 4096}, strides {1, 0}
-arg.a_grid_desc_k0_m_k1_{512, 3840, 8}
-arg.b_grid_desc_k0_n_k1_{512, 4096, 8}
-arg.c_grid_desc_m_n_{ 3840, 4096}
-arg.c0_grid_desc_m_n_{ 3840, 4096}
-arg.c1_grid_desc_m_n_{ 3840, 4096}
-launch_and_time_kernel: grid_dim {480, 1, 1}, block_dim {256, 1, 1}
-Warm up
-Start running 5 times...
-Perf: 1.27583 ms, 100.992 TFlops, 73.9688 GB/s
+#arg3: time kernel (0=no, 1=yes)
+#arg4 to 9: M (256x), N(128x), K(32x), StrideA, StrideB, StrideE
+./bin/example_gemm_bias_relu_xdl_fp16 1 1 1 3840 4096 4096 4096 4096 4096
 ```

example/03_gemm_bias_relu/gemm_xdl_bias_relu.cpp → example/03_gemm_bias_relu/gemm_bias_relu_xdl_fp16.cpp

@@ -58,7 +58,7 @@ using AElementOp   = PassThrough;
 using BElementOp   = PassThrough;
 using CDEElementOp = AddRelu;
 
-static constexpr auto GemmDefault = ck::tensor_operation::device::GemmSpecialization::Default;
+static constexpr auto GemmDefault = ck::tensor_operation::device::GemmSpecialization::MNKPadding;
 
 using DeviceOpInstance =
     ck::tensor_operation::device::DeviceGemmMultipleD_Xdl_CShuffle<ALayout,

example/CMakeLists.txt

@@ -22,7 +22,7 @@ function(add_example_executable_no_testing EXAMPLE_NAME FILE_NAME)
 endfunction(add_example_executable_no_testing EXAMPLE_NAME)
 
 add_subdirectory(01_gemm)
-add_subdirectory(02_gemm_alpha_beta)
+add_subdirectory(02_gemm_bilinear)
 add_subdirectory(03_gemm_bias_relu)
 add_subdirectory(04_gemm_add_add_fastgelu)
 add_subdirectory(06_conv2d_fwd_bias_relu)

include/ck/tensor_operation/gpu/device/convolution_forward_specialization.hpp

@@ -18,7 +18,7 @@ enum struct ConvolutionForwardSpecialization
     OddC,
 };
 
-inline std::string getConvFwdSpecializationStr(const ConvolutionForwardSpecialization& s)
+inline std::string getConvForwardSpecializationString(const ConvolutionForwardSpecialization& s)
 {
     switch(s)
     {

include/ck/tensor_operation/gpu/device/device_batched_gemm.hpp

@@ -23,22 +23,23 @@ template <typename ALayout,
           typename CElementwiseOperation>
 struct DeviceBatchedGemm : public BaseOperator
 {
-    virtual std::unique_ptr<BaseArgument> MakeArgumentPointer(const void* p_a,
-                                                              const void* p_b,
-                                                              void* p_c,
-                                                              ck::index_t M,
-                                                              ck::index_t N,
-                                                              ck::index_t K,
-                                                              ck::index_t StrideA,
-                                                              ck::index_t StrideB,
-                                                              ck::index_t StrideC,
-                                                              ck::index_t BatchStrideA,
-                                                              ck::index_t BatchStrideB,
-                                                              ck::index_t BatchStrideC,
-                                                              AElementwiseOperation a_element_op,
-                                                              BElementwiseOperation b_element_op,
-                                                              CElementwiseOperation c_element_op,
-                                                              ck::index_t Batch) = 0;
+    virtual std::unique_ptr<BaseArgument>
+    MakeArgumentPointer(const void* p_a,
+                        const void* p_b,
+                        void* p_c,
+                        ck::index_t M,
+                        ck::index_t N,
+                        ck::index_t K,
+                        ck::index_t StrideA,
+                        ck::index_t StrideB,
+                        ck::index_t StrideC,
+                        ck::index_t BatchStrideA,
+                        ck::index_t BatchStrideB,
+                        ck::index_t BatchStrideC,
+                        ck::index_t Batch,
+                        AElementwiseOperation a_element_op,
+                        BElementwiseOperation b_element_op,
+                        CElementwiseOperation c_element_op) = 0;
 
     virtual std::unique_ptr<BaseInvoker> MakeInvokerPointer() = 0;
 };

include/ck/tensor_operation/gpu/device/device_batched_gemm_xdl.hpp

@@ -344,12 +344,12 @@ struct DeviceBatchedGemmXdl : public DeviceBatchedGemm<ALayout,
                  index_t BatchStrideA,
                  index_t BatchStrideB,
                  index_t BatchStrideC,
+                 index_t Batch,
                  index_t M01,
                  index_t N01,
                  AElementwiseOperation a_element_op,
                  BElementwiseOperation b_element_op,
-                 CElementwiseOperation c_element_op,
-                 index_t Batch)
+                 CElementwiseOperation c_element_op)
             : p_a_grid_{p_a_grid},
               p_b_grid_{p_b_grid},
               p_c_grid_{p_c_grid},
@@ -546,10 +546,10 @@ struct DeviceBatchedGemmXdl : public DeviceBatchedGemm<ALayout,
                              index_t BatchStrideA,
                              index_t BatchStrideB,
                              index_t BatchStrideC,
+                             index_t Batch,
                              AElementwiseOperation a_element_op,
                              BElementwiseOperation b_element_op,
-                             CElementwiseOperation c_element_op,
-                             index_t Batch)
+                             CElementwiseOperation c_element_op)
     {
         return Argument{p_a,
                         p_b,
@@ -563,12 +563,12 @@ struct DeviceBatchedGemmXdl : public DeviceBatchedGemm<ALayout,
                         BatchStrideA,
                         BatchStrideB,
                         BatchStrideC,
+                        Batch,
                         1,
                         1,
                         a_element_op,
                         b_element_op,
-                        c_element_op,
-                        Batch};
+                        c_element_op};
     }
 
     static auto MakeInvoker() { return Invoker{}; }
@@ -586,10 +586,10 @@ struct DeviceBatchedGemmXdl : public DeviceBatchedGemm<ALayout,
                                                       index_t BatchStrideA,
                                                       index_t BatchStrideB,
                                                       index_t BatchStrideC,
+                                                      index_t Batch,
                                                       AElementwiseOperation a_element_op,
                                                       BElementwiseOperation b_element_op,
-                                                      CElementwiseOperation c_element_op,
-                                                      index_t Batch) override
+                                                      CElementwiseOperation c_element_op) override
     {
         return std::make_unique<Argument>(static_cast<const ADataType*>(p_a),
                                           static_cast<const BDataType*>(p_b),
@@ -603,12 +603,12 @@ struct DeviceBatchedGemmXdl : public DeviceBatchedGemm<ALayout,
                                           BatchStrideA,
                                           BatchStrideB,
                                           BatchStrideC,
+                                          Batch,
                                           1,
                                           1,
                                           a_element_op,
                                           b_element_op,
-                                          c_element_op,
-                                          Batch);
+                                          c_element_op);
     }
 
     // polymorphic

include/ck/tensor_operation/gpu/device/device_conv2d_fwd_xdl_c_shuffle_nhwc_kyxc_nhwk.hpp

@@ -871,7 +871,7 @@ struct DeviceConv2dFwdXdl_C_Shuffle_Input_N_Hi_Wi_C_Weight_K_Y_X_C_Output_N_Ho_W
             << MPerBlock << ", "
             << NPerBlock << ", "
             << K0PerBlock << ", "
-            << getConvFwdSpecializationStr(ConvForwardSpecialization)
+            << getConvForwardSpecializationString(ConvForwardSpecialization)
             << ">";
         // clang-format on
 

include/ck/tensor_operation/gpu/device/device_conv2d_fwd_xdl_nhwc_kyxc_nhwk.hpp

@@ -711,7 +711,7 @@ struct DeviceConv2dFwdXdl_Input_N_Hi_Wi_C_Weight_K_Y_X_C_Output_N_Ho_Wo_K
             << MPerBlock << ", "
             << NPerBlock << ", "
             << K0PerBlock << ", "
-            << getConvFwdSpecializationStr(ConvForwardSpecialization)
+            << getConvForwardSpecializationString(ConvForwardSpecialization)
             << ">";
         // clang-format on
 

include/ck/tensor_operation/gpu/device/device_convnd_fwd_xdl_nhwc_kyxc_nhwk.hpp

@@ -1033,7 +1033,7 @@ struct DeviceConvNDFwdXdl_Input_N_Hi_Wi_C_Weight_K_Y_X_C_Output_N_Ho_Wo_K
             << MPerBlock << ", "
             << NPerBlock << ", "
             << K0PerBlock << ", "
-            << getConvFwdSpecializationStr(ConvForwardSpecialization)
+            << getConvForwardSpecializationString(ConvForwardSpecialization)
             << ">";
         // clang-format on
 

include/ck/tensor_operation/gpu/device/device_gemm_bias.hpp

-// SPDX-License-Identifier: MIT
-// Copyright (c) 2018-2022, Advanced Micro Devices, Inc. All rights reserved.
-
-#pragma once
-
-#include <iostream>
-
-#include "ck/tensor_operation/gpu/device/device_base.hpp"
-
-namespace ck {
-namespace tensor_operation {
-namespace device {
-
-template <typename AElementwiseOperation,
-          typename BElementwiseOperation,
-          typename CElementwiseOperation>
-struct DeviceGemmBias : public BaseOperator
-{
-    virtual std::unique_ptr<BaseArgument>
-    MakeArgumentPointer(const void* p_a,
-                        const void* p_b,
-                        const void* p_bias,
-                        void* p_c,
-                        ck::index_t M,
-                        ck::index_t N,
-                        ck::index_t K,
-                        ck::index_t StrideA,
-                        ck::index_t StrideB,
-                        ck::index_t StrideC,
-                        AElementwiseOperation a_element_op,
-                        BElementwiseOperation b_element_op,
-                        CElementwiseOperation c_element_op) = 0;
-
-    virtual std::unique_ptr<BaseInvoker> MakeInvokerPointer() = 0;
-};
-
-template <typename AElementwiseOperation,
-          typename BElementwiseOperation,
-          typename CElementwiseOperation>
-using DeviceGemmBiasPtr = std::unique_ptr<
-    DeviceGemmBias<AElementwiseOperation, BElementwiseOperation, CElementwiseOperation>>;
-
-} // namespace device
-} // namespace tensor_operation
-} // namespace ck

include/ck/tensor_operation/gpu/device/device_gemm_bias_activation.hpp

-// SPDX-License-Identifier: MIT
-// Copyright (c) 2018-2022, Advanced Micro Devices, Inc. All rights reserved.
-
-#pragma once
-
-#include <iostream>
-
-#include "ck/tensor_operation/gpu/device/device_base.hpp"
-
-namespace ck {
-namespace tensor_operation {
-namespace device {
-
-template <typename AElementwiseOperation,
-          typename BElementwiseOperation,
-          typename CElementwiseOperation>
-struct DeviceGemmBiasActivation : public BaseOperator
-{
-    virtual std::unique_ptr<BaseArgument> MakeArgumentPointer(const void* p_a,
-                                                              const void* p_b,
-                                                              void* p_c,
-                                                              const void* p_c0,
-                                                              ck::index_t M,
-                                                              ck::index_t N,
-                                                              ck::index_t K,
-                                                              ck::index_t StrideA,
-                                                              ck::index_t StrideB,
-                                                              ck::index_t StrideC,
-                                                              AElementwiseOperation a_element_op,
-                                                              BElementwiseOperation b_element_op,
-                                                              CElementwiseOperation c_element_op,
-                                                              ck::index_t KBatch = 1) = 0;
-
-    virtual std::unique_ptr<BaseInvoker> MakeInvokerPointer() = 0;
-};
-
-template <typename AElementwiseOperation,
-          typename BElementwiseOperation,
-          typename CElementwiseOperation>
-using DeviceGemmBiasActivationPtr = std::unique_ptr<
-    DeviceGemmBiasActivation<AElementwiseOperation, BElementwiseOperation, CElementwiseOperation>>;
-
-} // namespace device
-} // namespace tensor_operation
-} // namespace ck

include/ck/tensor_operation/gpu/device/device_gemm_bias_activation_add.hpp

-// SPDX-License-Identifier: MIT
-// Copyright (c) 2018-2022, Advanced Micro Devices, Inc. All rights reserved.
-
-#ifndef DEVICE_GEMM_BIAS_ACTIVATION_ADD_HPP
-#define DEVICE_GEMM_BIAS_ACTIVATION_ADD_HPP
-
-#include <iostream>
-#include "device_base.hpp"
-
-namespace ck {
-namespace tensor_operation {
-namespace device {
-
-template <typename AElementwiseOperation,
-          typename BElementwiseOperation,
-          typename CElementwiseOperation>
-struct DeviceGemmBiasActivationAdd : public BaseOperator
-{
-    virtual std::unique_ptr<BaseArgument> MakeArgumentPointer(const void* p_a,
-                                                              const void* p_b,
-                                                              void* p_c,
-                                                              const void* p_c0,
-                                                              const void* p_c1,
-                                                              ck::index_t M,
-                                                              ck::index_t N,
-                                                              ck::index_t K,
-                                                              ck::index_t StrideA,
-                                                              ck::index_t StrideB,
-                                                              ck::index_t StrideC,
-                                                              ck::index_t StrideC1,
-                                                              AElementwiseOperation a_element_op,
-                                                              BElementwiseOperation b_element_op,
-                                                              CElementwiseOperation c_element_op,
-                                                              ck::index_t KBatch = 1) = 0;
-
-    virtual std::unique_ptr<BaseInvoker> MakeInvokerPointer() = 0;
-};
-
-template <typename AElementwiseOperation,
-          typename BElementwiseOperation,
-          typename CElementwiseOperation>
-using DeviceGemmBiasActivationAddPtr =
-    std::unique_ptr<DeviceGemmBiasActivationAdd<AElementwiseOperation,
-                                                BElementwiseOperation,
-                                                CElementwiseOperation>>;
-
-} // namespace device
-} // namespace tensor_operation
-} // namespace ck
-#endif

include/ck/tensor_operation/gpu/device/device_gemm_multiple_d_xdl_cshuffle.hpp

@@ -746,7 +746,8 @@ struct DeviceGemmMultipleD_Xdl_CShuffle : public DeviceGemmMultipleD<ALayout,
             << NPerBlock << ", "
             << KPerBlock << ", "
             << AK1 << ", "
-            << BK1
+            << BK1 << ", "
+            << getGemmSpecializationString(GemmSpec)
             << ">";
         // clang-format on