Compile for gfx908 and gfx90a (#130)

* adding compilation for multiple targets

* fix build

* clean

* update Jekinsfile

* update readme

* update Jenkins

* use ck::half_t instead of ushort for bf16

* rename enum classes

* clean

* rename

* clean

library/src/tensor_operation_instance/gpu/gemm/device_gemm_xdl_splitk_f32_f32_f32_km_kn_mn_instance.cpp

@@ -20,7 +20,7 @@ using S = ck::Sequence<Is...>;
 
 using PassThrough = ck::tensor_operation::element_wise::PassThrough;
 
-static constexpr auto GemmDefault = ck::tensor_operation::device::GemmSpecialization_t::Default;
+static constexpr auto GemmDefault = ck::tensor_operation::device::GemmSpecialization::Default;
 
 // Compilation parameters for a[k, m] * b[k, n] = c[m, n]
 using device_gemm_xdl_splitk_f32_f32_f32_km_kn_mn_instances = std::tuple<

library/src/tensor_operation_instance/gpu/gemm/device_gemm_xdl_splitk_f32_f32_f32_km_nk_mn_instance.cpp

@@ -20,7 +20,7 @@ using S = ck::Sequence<Is...>;
 
 using PassThrough = ck::tensor_operation::element_wise::PassThrough;
 
-static constexpr auto GemmDefault = ck::tensor_operation::device::GemmSpecialization_t::Default;
+static constexpr auto GemmDefault = ck::tensor_operation::device::GemmSpecialization::Default;
 
 // Compilation parameters for a[k, m] * b[n, k] = c[m, n]
 using device_gemm_xdl_splitk_f32_f32_f32_km_nk_mn_instances = std::tuple<

library/src/tensor_operation_instance/gpu/gemm/device_gemm_xdl_splitk_f32_f32_f32_mk_kn_mn_instance.cpp

@@ -20,7 +20,7 @@ using S = ck::Sequence<Is...>;
 
 using PassThrough = ck::tensor_operation::element_wise::PassThrough;
 
-static constexpr auto GemmMNPadding = ck::tensor_operation::device::GemmSpecialization_t::MNPadding;
+static constexpr auto GemmMNPadding = ck::tensor_operation::device::GemmSpecialization::MNPadding;
 
 // Compilation parameters for a[m, k] * b[k, n] = c[m, n]
 using device_gemm_xdl_splitk_f32_f32_f32_mk_kn_mn_instances = std::tuple<

library/src/tensor_operation_instance/gpu/gemm/device_gemm_xdl_splitk_f32_f32_f32_mk_nk_mn_instance.cpp

@@ -20,7 +20,7 @@ using S = ck::Sequence<Is...>;
 
 using PassThrough = ck::tensor_operation::element_wise::PassThrough;
 
-static constexpr auto GemmDefault = ck::tensor_operation::device::GemmSpecialization_t::Default;
+static constexpr auto GemmDefault = ck::tensor_operation::device::GemmSpecialization::Default;
 
 // Compilation parameters for a[m, k] * b[n, k] = c[m, n]
 using device_gemm_xdl_splitk_f32_f32_f32_mk_nk_mn_instances = std::tuple<

library/src/tensor_operation_instance/gpu/gemm_reduce/device_gemm_reduce_xdl_cshuffle_f16_f16_f16_f32_f32_km_kn_mn_instance.cpp

@@ -23,7 +23,7 @@ using PassThrough     = ck::tensor_operation::element_wise::PassThrough;
 using ReduceSum       = ck::tensor_operation::element_wise::ReduceSum;
 using ReduceSquareSum = ck::tensor_operation::element_wise::ReduceSquareSum;
 
-static constexpr auto GemmDefault = ck::tensor_operation::device::GemmSpecialization_t::Default;
+static constexpr auto GemmDefault = ck::tensor_operation::device::GemmSpecialization::Default;
 
 // c[m, n] = a[k, m] * b[k, n]
 // d0[m] = reduce0(c[m, n])

library/src/tensor_operation_instance/gpu/gemm_reduce/device_gemm_reduce_xdl_cshuffle_f16_f16_f16_f32_f32_km_nk_mn_instance.cpp

@@ -23,7 +23,7 @@ using PassThrough     = ck::tensor_operation::element_wise::PassThrough;
 using ReduceSum       = ck::tensor_operation::element_wise::ReduceSum;
 using ReduceSquareSum = ck::tensor_operation::element_wise::ReduceSquareSum;
 
-static constexpr auto GemmDefault = ck::tensor_operation::device::GemmSpecialization_t::Default;
+static constexpr auto GemmDefault = ck::tensor_operation::device::GemmSpecialization::Default;
 
 // c[m, n] = a[k, m] * b[n, k]
 // d0[m] = reduce0(c[m, n])

library/src/tensor_operation_instance/gpu/gemm_reduce/device_gemm_reduce_xdl_cshuffle_f16_f16_f16_f32_f32_mk_kn_mn_instance.cpp

@@ -23,7 +23,7 @@ using PassThrough     = ck::tensor_operation::element_wise::PassThrough;
 using ReduceSum       = ck::tensor_operation::element_wise::ReduceSum;
 using ReduceSquareSum = ck::tensor_operation::element_wise::ReduceSquareSum;
 
-static constexpr auto GemmDefault = ck::tensor_operation::device::GemmSpecialization_t::Default;
+static constexpr auto GemmDefault = ck::tensor_operation::device::GemmSpecialization::Default;
 
 // c[m, n] = a[m, k] * b[n, k]
 // d0[m] = reduce0(c[m, n])

library/src/tensor_operation_instance/gpu/gemm_reduce/device_gemm_reduce_xdl_cshuffle_f16_f16_f16_f32_f32_mk_nk_mn_instance.cpp

@@ -23,7 +23,7 @@ using PassThrough     = ck::tensor_operation::element_wise::PassThrough;
 using ReduceSum       = ck::tensor_operation::element_wise::ReduceSum;
 using ReduceSquareSum = ck::tensor_operation::element_wise::ReduceSquareSum;
 
-static constexpr auto GemmDefault = ck::tensor_operation::device::GemmSpecialization_t::Default;
+static constexpr auto GemmDefault = ck::tensor_operation::device::GemmSpecialization::Default;
 
 // c[m, n] = a[m, k] * b[n, k]
 // d0[m] = reduce0(c[m, n])

library/src/tensor_operation_instance/gpu/grouped_gemm/device_grouped_gemm_xdl_f16_f16_f16_km_kn_mn_instance.cpp

@@ -20,7 +20,7 @@ using S = ck::Sequence<Is...>;
 
 using PassThrough = ck::tensor_operation::element_wise::PassThrough;
 
-static constexpr auto GemmDefault = ck::tensor_operation::device::GemmSpecialization_t::Default;
+static constexpr auto GemmDefault = ck::tensor_operation::device::GemmSpecialization::Default;
 
 // Compilation parameters for a[k, m] * b[k, n] = c[m, n]
 using device_grouped_gemm_xdl_f16_f16_f16_km_kn_mn_instances = std::tuple<

library/src/tensor_operation_instance/gpu/grouped_gemm/device_grouped_gemm_xdl_f16_f16_f16_km_nk_mn_instance.cpp

@@ -20,7 +20,7 @@ using S = ck::Sequence<Is...>;
 
 using PassThrough = ck::tensor_operation::element_wise::PassThrough;
 
-static constexpr auto GemmDefault = ck::tensor_operation::device::GemmSpecialization_t::Default;
+static constexpr auto GemmDefault = ck::tensor_operation::device::GemmSpecialization::Default;
 
 // Compilation parameters for a[k, m] * b[n, k] = c[m, n]
 using device_grouped_gemm_xdl_f16_f16_f16_km_nk_mn_instances = std::tuple<

library/src/tensor_operation_instance/gpu/grouped_gemm/device_grouped_gemm_xdl_f16_f16_f16_mk_kn_mn_instance.cpp

@@ -20,7 +20,7 @@ using S = ck::Sequence<Is...>;
 
 using PassThrough = ck::tensor_operation::element_wise::PassThrough;
 
-static constexpr auto GemmDefault = ck::tensor_operation::device::GemmSpecialization_t::Default;
+static constexpr auto GemmDefault = ck::tensor_operation::device::GemmSpecialization::Default;
 
 // Compilation parameters for a[m, k] * b[k, n] = c[m, n]
 using device_grouped_gemm_xdl_f16_f16_f16_mk_kn_mn_instances = std::tuple<

library/src/tensor_operation_instance/gpu/grouped_gemm/device_grouped_gemm_xdl_f16_f16_f16_mk_nk_mn_instance.cpp

@@ -20,8 +20,8 @@ using S = ck::Sequence<Is...>;
 
 using PassThrough = ck::tensor_operation::element_wise::PassThrough;
 
-static constexpr auto GemmDefault   = ck::tensor_operation::device::GemmSpecialization_t::Default;
-static constexpr auto GemmMNPadding = ck::tensor_operation::device::GemmSpecialization_t::MNPadding;
+static constexpr auto GemmDefault   = ck::tensor_operation::device::GemmSpecialization::Default;
+static constexpr auto GemmMNPadding = ck::tensor_operation::device::GemmSpecialization::MNPadding;
 
 // Compilation parameters for a[m, k] * b[n, k] = c[m, n]
 using device_grouped_gemm_xdl_f16_f16_f16_mk_nk_mn_instances = std::tuple<

profiler/src/README.md → profiler/README.md

-## Docker script
-```bash
-docker run                                                                   \
--it                                                                          \
---rm                                                                         \
---privileged                                                                 \
---group-add sudo                                                             \
--w /root/workspace                                                           \
--v ${PATH_TO_LOCAL_WORKSPACE}:/root/workspace                                \
-rocm/tensorflow:rocm4.3.1-tf2.6-dev                                          \
-/bin/bash
-```
-
-## Build ```ckProfiler```
-```bash
-mkdir build && cd build
-```
-
-```bash
-# Need to Specify target ID, example below is gfx908
-cmake                                                                  \
--D BUILD_DEV=OFF                                                       \
--D CMAKE_BUILD_TYPE=Release                                            \
--D CMAKE_CXX_FLAGS="-DCK_AMD_GPU_GFX908 --amdgpu-target=gfx908 -O3 "   \
--D CMAKE_CXX_COMPILER=/opt/rocm/bin/hipcc                              \
--D CMAKE_PREFIX_PATH=/opt/rocm                                         \
-..
-```
-
-```bash
- make -j ckProfiler
-```
-
 ## Profile GEMM kernels
 ```bash
 #arg1: tensor operation (gemm=GEMM)
@@ -42,8 +9,8 @@ cmake                                                                  \
 #arg7: run kernel # of times (>1)
 #arg8 to 13: M, N, K, StrideA, StrideB, StrideC
 
-#####################   op  datatype  layout  verify  init  log  repeat  M___ N___ K___  StrideA StrideB StrideC
-./profiler/ckProfiler gemm         1       1       1     1    0       5  3840 4096 4096     4096    4096    4096
+################        op  datatype  layout  verify  init  log  repeat  M___ N___ K___  StrideA StrideB StrideC
+./bin/ckProfiler      gemm         1       1       1     1    0       5  3840 4096 4096     4096    4096    4096
 ```
 
 Result (MI100 @ 1087Mhz, 133.5TFlops peak FP16)
@@ -55,7 +22,7 @@ c_m_n: dim 2, lengths {3840, 4096}, strides {4096, 1}
 Best Perf: 1.1933 ms, 107.977 TFlops, 79.0848 GB/s
 ```
 
-## Profile forward convolution kernels
+## Profile 2d forward convolution kernels
 ```bash
 #arg1: tensor operation (conv=Convolution)
 #arg2: data type (0=fp32, 1=fp16)
@@ -67,8 +34,8 @@ Best Perf: 1.1933 ms, 107.977 TFlops, 79.0848 GB/s
 #arg8: print matrix value (0=no, 1=yes)
 #arg9: run kernel # of times (>1)
 #arg10 to 24: N, K, C, Y, X, Hi, Wi, Sy, Sx, Dy, Dx, LeftPy, LeftPx, RightPy, RightPx
- #####################   op     datatype  in_layout   wei_layout  out_layout  verify  init  log  repeat  N__ K___ C___ Y X Hi__ Wi__ Strides Dilations LeftPads RightPads
- ./profiler/ckProfiler conv_fwd        1          1            1           1       1     1    0       5  128  256  192 3 3   71   71     2 2       1 1      1 1       1 1
+ ################          op datatype  in_layout   wei_layout  out_layout  verify  init  log  repeat  N__ K___ C___ Y X Hi__ Wi__ Strides Dilations LeftPads RightPads
+ ./bin/ckProfiler  conv2d_fwd        1          1            1           1       1     1    0       5  128  256  192 3 3   71   71     2 2       1 1      1 1       1 1
 ```
 
 Result (MI100 @ 1087Mhz, 133.5TFlops peak FP16)

profiler/include/profile_convnd_bwd_data_impl.hpp

@@ -12,7 +12,7 @@
 
 using F16  = ck::half_t;
 using F32  = float;
-using BF16 = ushort;
+using BF16 = ck::bhalf_t;
 using INT8 = int8_t;
 namespace ck {
 namespace tensor_operation {

profiler/include/profile_reduce_impl.hpp

@@ -64,9 +64,9 @@ template <typename DescriptionType>
 bool description_match(const DescriptionType& description,
                        int Rank,
                        const std::vector<int>& reduceDims,
-                       ReduceTensorOp_t ReduceOpId,
-                       NanPropagation_t NanOpt,
-                       ReduceTensorIndices_t IndicesOpt)
+                       ReduceTensorOp ReduceOpId,
+                       NanPropagation NanOpt,
+                       ReduceTensorIndices IndicesOpt)
 {
     if(description.Rank_ != Rank || description.ReduceOpId_ != static_cast<int>(ReduceOpId) ||
        description.NanOpt_ != static_cast<int>(NanOpt) ||
@@ -148,9 +148,9 @@ template <typename InDataType,
           typename OutDataType,
           int Rank,
           int NumReduceDim,
-          ReduceTensorOp_t ReduceOpId,
-          NanPropagation_t NanOpt,
-          ReduceTensorIndices_t IndicesOpt>
+          ReduceTensorOp ReduceOpId,
+          NanPropagation NanOpt,
+          ReduceTensorIndices IndicesOpt>
 void profile_reduce_impl_impl(bool do_verification,
                               int init_method,
                               bool do_log,
@@ -166,17 +166,17 @@ void profile_reduce_impl_impl(bool do_verification,
     using namespace ck::host_reduce;
 
     constexpr bool op_support_indices =
-        (ReduceOpId == ReduceTensorOp_t::MIN || ReduceOpId == ReduceTensorOp_t::MAX ||
-         ReduceOpId == ReduceTensorOp_t::AMAX);
+        (ReduceOpId == ReduceTensorOp::MIN || ReduceOpId == ReduceTensorOp::MAX ||
+         ReduceOpId == ReduceTensorOp::AMAX);
 
     constexpr bool NeedIndices =
-        (op_support_indices && (IndicesOpt != ReduceTensorIndices_t::NO_INDICES));
+        (op_support_indices && (IndicesOpt != ReduceTensorIndices::NO_INDICES));
 
-    constexpr bool PropagateNan = (NanOpt == NanPropagation_t::PROPAGATE_NAN);
+    constexpr bool PropagateNan = (NanOpt == NanPropagation::PROPAGATE_NAN);
 
     constexpr bool out_support_atomic_add = std::is_same<OutDataType, float>::value;
     constexpr bool op_support_atomic_add =
-        !op_support_indices && ReduceOpId != ReduceTensorOp_t::NORM2;
+        !op_support_indices && ReduceOpId != ReduceTensorOp::NORM2;
     constexpr bool use_atomic_add = (out_support_atomic_add && op_support_atomic_add);
 
     // 1) If InDataType is half_t, must use half_t as AccDataType for indexable reduction operations
@@ -194,7 +194,7 @@ void profile_reduce_impl_impl(bool do_verification,
 
     // 1) The indices can only be used when the reduction operation is indexable
     constexpr bool invalid_reduce_3 =
-        (!op_support_indices && IndicesOpt != ReduceTensorIndices_t::NO_INDICES);
+        (!op_support_indices && IndicesOpt != ReduceTensorIndices::NO_INDICES);
 
     // 1) If InDataType is int8_t, must use int8_t as AccDataType for indexable reduction operations
     // 2) If InDataType is int8_t, must use int32_t as AccDataType for non-indexable reduction
@@ -207,8 +207,8 @@ void profile_reduce_impl_impl(bool do_verification,
     // 1) If InDataType is int8_t, the supported operation must be either indexable operations or
     // ADD/AVG
     constexpr bool invalid_reduce_5 = std::is_same<InDataType, int8_t>::value &&
-                                      (!op_support_indices && ReduceOpId != ReduceTensorOp_t::ADD &&
-                                       ReduceOpId != ReduceTensorOp_t::AVG);
+                                      (!op_support_indices && ReduceOpId != ReduceTensorOp::ADD &&
+                                       ReduceOpId != ReduceTensorOp::AVG);
 
     // 1) If InDataType is bhalf_t, must use float as AccDataType for all reduction operations
     constexpr bool invalid_reduce_6 =
@@ -631,9 +631,9 @@ void profile_reduce_impl(bool do_verification,
                          int nrepeat,
                          const std::vector<size_t>& inLengths,
                          const std::vector<int>& reduceDims,
-                         ReduceTensorOp_t ReduceOpId,
-                         NanPropagation_t NanOpt,
-                         ReduceTensorIndices_t IndicesOpt,
+                         ReduceTensorOp ReduceOpId,
+                         NanPropagation NanOpt,
+                         ReduceTensorIndices IndicesOpt,
                          float alpha,
                          float beta)
 {
@@ -659,9 +659,9 @@ void profile_reduce_impl(bool do_verification,
                                  OutDataType,
                                  descType::Rank_,
                                  descType::NumReduceDim_,
-                                 static_cast<ReduceTensorOp_t>(descType::ReduceOpId_),
-                                 static_cast<NanPropagation_t>(descType::NanOpt_),
-                                 static_cast<ReduceTensorIndices_t>(descType::IndicesOpt_)>(
+                                 static_cast<ReduceTensorOp>(descType::ReduceOpId_),
+                                 static_cast<NanPropagation>(descType::NanOpt_),
+                                 static_cast<ReduceTensorIndices>(descType::IndicesOpt_)>(
             do_verification,
             init_method,
             do_log,

profiler/src/profile_batched_gemm_reduce.cpp

@@ -9,7 +9,7 @@
 
 int profile_batched_gemm_reduce(int argc, char* argv[])
 {
-    enum struct GemmMatrixLayout_t
+    enum struct GemmMatrixLayout
     {
         MK_KN_MN, // 0
         MK_NK_MN, // 1
@@ -17,7 +17,7 @@ int profile_batched_gemm_reduce(int argc, char* argv[])
         KM_NK_MN, // 3
     };
 
-    enum struct GemmReduceDataType_t
+    enum struct GemmReduceDataType
     {
         F32_F32_F32_F32_F32, // 0
         F16_F16_F16_F32_F32, // 1
@@ -40,8 +40,8 @@ int profile_batched_gemm_reduce(int argc, char* argv[])
         exit(1);
     }
 
-    const auto data_type       = static_cast<GemmReduceDataType_t>(std::stoi(argv[2]));
-    const auto layout          = static_cast<GemmMatrixLayout_t>(std::stoi(argv[3]));
+    const auto data_type       = static_cast<GemmReduceDataType>(std::stoi(argv[2]));
+    const auto layout          = static_cast<GemmMatrixLayout>(std::stoi(argv[3]));
     const bool do_verification = std::stoi(argv[4]);
     const int init_method      = std::stoi(argv[5]);
     const bool do_log          = std::stoi(argv[6]);
@@ -57,8 +57,7 @@ int profile_batched_gemm_reduce(int argc, char* argv[])
 
     const int BatchCount = std::stoi(argv[14]);
 
-    if(data_type == GemmReduceDataType_t::F16_F16_F16_F32_F32 &&
-       layout == GemmMatrixLayout_t::MK_KN_MN)
+    if(data_type == GemmReduceDataType::F16_F16_F16_F32_F32 && layout == GemmMatrixLayout::MK_KN_MN)
     {
         ck::profiler::profile_batched_gemm_reduce_impl<ck::half_t,
                                                        ck::half_t,
@@ -79,8 +78,8 @@ int profile_batched_gemm_reduce(int argc, char* argv[])
             (StrideC < 0) ? N : StrideC,
             BatchCount);
     }
-    else if(data_type == GemmReduceDataType_t::F16_F16_F16_F32_F32 &&
-            layout == GemmMatrixLayout_t::MK_NK_MN)
+    else if(data_type == GemmReduceDataType::F16_F16_F16_F32_F32 &&
+            layout == GemmMatrixLayout::MK_NK_MN)
     {
         ck::profiler::profile_batched_gemm_reduce_impl<ck::half_t,
                                                        ck::half_t,
@@ -101,8 +100,8 @@ int profile_batched_gemm_reduce(int argc, char* argv[])
             (StrideC < 0) ? N : StrideC,
             BatchCount);
     }
-    else if(data_type == GemmReduceDataType_t::F16_F16_F16_F32_F32 &&
-            layout == GemmMatrixLayout_t::KM_KN_MN)
+    else if(data_type == GemmReduceDataType::F16_F16_F16_F32_F32 &&
+            layout == GemmMatrixLayout::KM_KN_MN)
     {
         ck::profiler::profile_batched_gemm_reduce_impl<ck::half_t,
                                                        ck::half_t,
@@ -123,8 +122,8 @@ int profile_batched_gemm_reduce(int argc, char* argv[])
             (StrideC < 0) ? N : StrideC,
             BatchCount);
     }
-    else if(data_type == GemmReduceDataType_t::F16_F16_F16_F32_F32 &&
-            layout == GemmMatrixLayout_t::KM_NK_MN)
+    else if(data_type == GemmReduceDataType::F16_F16_F16_F32_F32 &&
+            layout == GemmMatrixLayout::KM_NK_MN)
     {
         ck::profiler::profile_batched_gemm_reduce_impl<ck::half_t,
                                                        ck::half_t,

profiler/src/profile_convnd_bwd_data.cpp

@@ -7,7 +7,7 @@
 
 #include "profile_convnd_bwd_data_impl.hpp"
 
-enum ConvDataType
+enum struct ConvDataType
 {
     F32_F32_F32,    // 0
     F16_F16_F16,    // 1
@@ -15,19 +15,19 @@ enum ConvDataType
     INT8_INT8_INT8, // 3
 };
 
-enum ConvInputLayout
+enum struct ConvInputLayout
 {
     NCHW, // 0
     NHWC, // 1
 };
 
-enum ConvWeightLayout
+enum struct ConvWeightLayout
 {
     KCYX, // 0
     KYXC, // 1
 };
 
-enum ConvOutputLayout
+enum struct ConvOutputLayout
 {
     NKHW, // 0
     NHWK, // 1
@@ -97,10 +97,10 @@ int profile_convnd_bwd_data(int argc, char* argv[], int num_dim_spatial)
         return 1;
     }
 
-    const int data_type        = static_cast<ConvDataType>(std::stoi(argv[2]));
-    const int in_layout        = static_cast<ConvInputLayout>(std::stoi(argv[3]));
-    const int wei_layout       = static_cast<ConvWeightLayout>(std::stoi(argv[4]));
-    const int out_layout       = static_cast<ConvOutputLayout>(std::stoi(argv[5]));
+    const auto data_type       = static_cast<ConvDataType>(std::stoi(argv[2]));
+    const auto in_layout       = static_cast<ConvInputLayout>(std::stoi(argv[3]));
+    const auto wei_layout      = static_cast<ConvWeightLayout>(std::stoi(argv[4]));
+    const auto out_layout      = static_cast<ConvOutputLayout>(std::stoi(argv[5]));
     const bool do_verification = std::stoi(argv[6]);
     const int init_method      = std::stoi(argv[7]);
     const bool do_log          = std::stoi(argv[8]);

profiler/src/profile_gemm_reduce.cpp

@@ -8,7 +8,7 @@
 
 int profile_gemm_reduce(int argc, char* argv[])
 {
-    enum struct GemmMatrixLayout_t
+    enum struct GemmMatrixLayout
     {
         MK_KN_MN, // 0
         MK_NK_MN, // 1
@@ -16,7 +16,7 @@ int profile_gemm_reduce(int argc, char* argv[])
         KM_NK_MN, // 3
     };
 
-    enum struct GemmReduceDataType_t
+    enum struct GemmReduceDataType
     {
         F32_F32_F32_F32_F32, // 0
         F16_F16_F16_F32_F32, // 1
@@ -39,8 +39,8 @@ int profile_gemm_reduce(int argc, char* argv[])
         exit(1);
     }
 
-    const auto data_type       = static_cast<GemmReduceDataType_t>(std::stoi(argv[2]));
-    const auto layout          = static_cast<GemmMatrixLayout_t>(std::stoi(argv[3]));
+    const auto data_type       = static_cast<GemmReduceDataType>(std::stoi(argv[2]));
+    const auto layout          = static_cast<GemmMatrixLayout>(std::stoi(argv[3]));
     const bool do_verification = std::stoi(argv[4]);
     const int init_method      = std::stoi(argv[5]);
     const bool do_log          = std::stoi(argv[6]);
@@ -54,8 +54,7 @@ int profile_gemm_reduce(int argc, char* argv[])
     const int StrideB = std::stoi(argv[12]);
     const int StrideC = std::stoi(argv[13]);
 
-    if(data_type == GemmReduceDataType_t::F16_F16_F16_F32_F32 &&
-       layout == GemmMatrixLayout_t::MK_KN_MN)
+    if(data_type == GemmReduceDataType::F16_F16_F16_F32_F32 && layout == GemmMatrixLayout::MK_KN_MN)
     {
         ck::profiler::profile_gemm_reduce_impl<ck::half_t,
                                                ck::half_t,
@@ -75,8 +74,8 @@ int profile_gemm_reduce(int argc, char* argv[])
             (StrideB < 0) ? N : StrideB,
             (StrideC < 0) ? N : StrideC);
     }
-    else if(data_type == GemmReduceDataType_t::F16_F16_F16_F32_F32 &&
-            layout == GemmMatrixLayout_t::MK_NK_MN)
+    else if(data_type == GemmReduceDataType::F16_F16_F16_F32_F32 &&
+            layout == GemmMatrixLayout::MK_NK_MN)
     {
         ck::profiler::profile_gemm_reduce_impl<ck::half_t,
                                                ck::half_t,
@@ -96,8 +95,8 @@ int profile_gemm_reduce(int argc, char* argv[])
             (StrideB < 0) ? K : StrideB,
             (StrideC < 0) ? N : StrideC);
     }
-    else if(data_type == GemmReduceDataType_t::F16_F16_F16_F32_F32 &&
-            layout == GemmMatrixLayout_t::KM_KN_MN)
+    else if(data_type == GemmReduceDataType::F16_F16_F16_F32_F32 &&
+            layout == GemmMatrixLayout::KM_KN_MN)
     {
         ck::profiler::profile_gemm_reduce_impl<ck::half_t,
                                                ck::half_t,
@@ -117,8 +116,8 @@ int profile_gemm_reduce(int argc, char* argv[])
             (StrideB < 0) ? N : StrideB,
             (StrideC < 0) ? N : StrideC);
     }
-    else if(data_type == GemmReduceDataType_t::F16_F16_F16_F32_F32 &&
-            layout == GemmMatrixLayout_t::KM_NK_MN)
+    else if(data_type == GemmReduceDataType::F16_F16_F16_F32_F32 &&
+            layout == GemmMatrixLayout::KM_NK_MN)
     {
         ck::profiler::profile_gemm_reduce_impl<ck::half_t,
                                                ck::half_t,

profiler/src/profile_grouped_gemm.cpp

@@ -6,7 +6,7 @@
 #include <half.hpp>
 #include "profile_grouped_gemm_impl.hpp"
 
-enum GemmMatrixLayout
+enum struct GemmMatrixLayout
 {
     MK_KN_MN, // 0
     MK_NK_MN, // 1
@@ -18,7 +18,7 @@ enum GemmMatrixLayout
     KM_NK_NM, // 7
 };
 
-enum GemmDataType
+enum struct GemmDataType
 {
     F32_F32_F32,    // 0
     F16_F16_F16,    // 1
@@ -61,8 +61,8 @@ int profile_grouped_gemm(int argc, char* argv[])
         exit(1);
     }
 
-    const int data_type        = static_cast<GemmDataType>(std::stoi(argv[2]));
-    const int layout           = static_cast<GemmMatrixLayout>(std::stoi(argv[3]));
+    const auto data_type       = static_cast<GemmDataType>(std::stoi(argv[2]));
+    const auto layout          = static_cast<GemmMatrixLayout>(std::stoi(argv[3]));
     const bool do_verification = std::stoi(argv[4]);
     const int init_method      = std::stoi(argv[5]);
     const bool do_log          = std::stoi(argv[6]);

profiler/src/profile_reduce.cpp

@@ -20,9 +20,9 @@
 
 using namespace std;
 
-using ck::NanPropagation_t;
-using ck::ReduceTensorIndices_t;
-using ck::ReduceTensorOp_t;
+using ck::NanPropagation;
+using ck::ReduceTensorIndices;
+using ck::ReduceTensorOp;
 
 static struct option long_options[] = {{"inLengths", required_argument, nullptr, 'D'},
                                        {"reduceDims", required_argument, nullptr, 'R'},
@@ -84,7 +84,7 @@ static std::vector<T> getTypeValuesFromString(const char* cstr_values)
     return (values);
 }
 
-enum struct appDataType_t
+enum struct AppDataType
 {
     appHalf     = 0,
     appFloat    = 1,
@@ -130,15 +130,15 @@ class AppArgs
 
     std::vector<float> scales;
 
-    ReduceTensorOp_t reduceOp = ReduceTensorOp_t::ADD;
-    appDataType_t compTypeId  = appDataType_t::appFloat;
-    appDataType_t outTypeId   = appDataType_t::appFloat;
+    ReduceTensorOp reduceOp = ReduceTensorOp::ADD;
+    AppDataType compTypeId  = AppDataType::appFloat;
+    AppDataType outTypeId   = AppDataType::appFloat;
 
     bool compType_assigned = false;
     bool outType_assigned  = false;
 
-    NanPropagation_t nanOpt          = NanPropagation_t::NOT_PROPAGATE_NAN;
-    ReduceTensorIndices_t indicesOpt = ReduceTensorIndices_t::NO_INDICES;
+    NanPropagation nanOpt          = NanPropagation::NOT_PROPAGATE_NAN;
+    ReduceTensorIndices indicesOpt = ReduceTensorIndices::NO_INDICES;
     bool do_log                    = false;
     bool do_verification           = false;
     bool do_dumpout                = false;
@@ -213,33 +213,33 @@ class AppArgs
                 if(!optarg)
                     throw std::runtime_error("Invalid option format!");
 
-                reduceOp = static_cast<ReduceTensorOp_t>(std::atoi(optarg));
+                reduceOp = static_cast<ReduceTensorOp>(std::atoi(optarg));
                 break;
             case 'C':
                 if(!optarg)
                     throw std::runtime_error("Invalid option format!");
 
-                compTypeId        = static_cast<appDataType_t>(std::atoi(optarg));
+                compTypeId        = static_cast<AppDataType>(std::atoi(optarg));
                 compType_assigned = true;
                 break;
             case 'W':
                 if(!optarg)
                     throw std::runtime_error("Invalid option format!");
 
-                outTypeId        = static_cast<appDataType_t>(std::atoi(optarg));
+                outTypeId        = static_cast<AppDataType>(std::atoi(optarg));
                 outType_assigned = true;
                 break;
             case 'N':
                 if(!optarg)
                     throw std::runtime_error("Invalid option format!");
 
-                nanOpt = static_cast<NanPropagation_t>(std::atoi(optarg));
+                nanOpt = static_cast<NanPropagation>(std::atoi(optarg));
                 break;
             case 'I':
                 if(!optarg)
                     throw std::runtime_error("Invalid option format!");
 
-                indicesOpt = static_cast<ReduceTensorIndices_t>(std::atoi(optarg));
+                indicesOpt = static_cast<ReduceTensorIndices>(std::atoi(optarg));
                 break;
             case 'S':
                 if(!optarg)
@@ -303,10 +303,10 @@ class AppArgs
             scales.push_back(0.0f);
         };
 
-        if(reduceOp == ReduceTensorOp_t::MIN || reduceOp == ReduceTensorOp_t::MAX ||
-           reduceOp == ReduceTensorOp_t::AMAX)
+        if(reduceOp == ReduceTensorOp::MIN || reduceOp == ReduceTensorOp::MAX ||
+           reduceOp == ReduceTensorOp::AMAX)
         {
-            if(indicesOpt != ReduceTensorIndices_t::NO_INDICES)
+            if(indicesOpt != ReduceTensorIndices::NO_INDICES)
                 need_indices = true;
 
             // for indexable operations, no need to assign compType and outType, just let them be
@@ -333,22 +333,22 @@ int profile_reduce(int argc, char* argv[])
 
     check_reduce_dims(rank, args.reduceDims);
 
-    if(args.reduceOp == ReduceTensorOp_t::MUL || args.reduceOp == ReduceTensorOp_t::NORM1)
+    if(args.reduceOp == ReduceTensorOp::MUL || args.reduceOp == ReduceTensorOp::NORM1)
         throw std::runtime_error("MUL and NORM1 are not supported by composable kernel!");
 
     if(args.use_half)
     {
         if(!args.compType_assigned)
-            args.compTypeId = appDataType_t::appHalf;
+            args.compTypeId = AppDataType::appHalf;
 
         if(args.outType_assigned &&
-           (args.outTypeId != appDataType_t::appHalf && args.outTypeId != appDataType_t::appFloat))
-            args.outTypeId = appDataType_t::appFloat;
+           (args.outTypeId != AppDataType::appHalf && args.outTypeId != AppDataType::appFloat))
+            args.outTypeId = AppDataType::appFloat;
 
         if(!args.outType_assigned)
-            args.outTypeId = appDataType_t::appHalf;
+            args.outTypeId = AppDataType::appHalf;
 
-        if(args.compTypeId == appDataType_t::appHalf)
+        if(args.compTypeId == AppDataType::appHalf)
         {
             profile_reduce_impl<ck::half_t, ck::half_t, ck::half_t>(args.do_verification,
                                                                     args.init_method,
@@ -363,7 +363,7 @@ int profile_reduce(int argc, char* argv[])
                                                                     args.scales[0],
                                                                     args.scales[1]);
         }
-        else if(args.compTypeId == appDataType_t::appFloat)
+        else if(args.compTypeId == AppDataType::appFloat)
         {
             profile_reduce_impl<ck::half_t, float, ck::half_t>(args.do_verification,
                                                                args.init_method,
@@ -399,16 +399,16 @@ int profile_reduce(int argc, char* argv[])
     else if(args.use_int8)
     {
         if(!args.compType_assigned)
-            args.compTypeId = appDataType_t::appInt8;
+            args.compTypeId = AppDataType::appInt8;
 
         if(args.outType_assigned &&
-           (args.outTypeId != appDataType_t::appInt8 && args.outTypeId != appDataType_t::appInt32))
-            args.outTypeId = appDataType_t::appInt32;
+           (args.outTypeId != AppDataType::appInt8 && args.outTypeId != AppDataType::appInt32))
+            args.outTypeId = AppDataType::appInt32;
 
         if(!args.outType_assigned)
-            args.outTypeId = appDataType_t::appInt8;
+            args.outTypeId = AppDataType::appInt8;
 
-        if(args.compTypeId == appDataType_t::appInt8)
+        if(args.compTypeId == AppDataType::appInt8)
         {
             profile_reduce_impl<int8_t, int8_t, int8_t>(args.do_verification,
                                                         args.init_method,
@@ -423,7 +423,7 @@ int profile_reduce(int argc, char* argv[])
                                                         args.scales[0],
                                                         args.scales[1]);
         }
-        else if(args.compTypeId == appDataType_t::appInt32)
+        else if(args.compTypeId == AppDataType::appInt32)
         {
             profile_reduce_impl<int8_t, int32_t, int8_t>(args.do_verification,
                                                          args.init_method,
@@ -443,12 +443,12 @@ int profile_reduce(int argc, char* argv[])
     }
     else if(args.use_bf16)
     {
-        if(args.outType_assigned && (args.outTypeId != appDataType_t::appBFloat16 &&
-                                     args.outTypeId != appDataType_t::appFloat))
-            args.outTypeId = appDataType_t::appFloat;
+        if(args.outType_assigned &&
+           (args.outTypeId != AppDataType::appBFloat16 && args.outTypeId != AppDataType::appFloat))
+            args.outTypeId = AppDataType::appFloat;
 
         if(!args.outType_assigned)
-            args.outTypeId = appDataType_t::appBFloat16;
+            args.outTypeId = AppDataType::appBFloat16;
 
         profile_reduce_impl<ck::bhalf_t, float, ck::bhalf_t>(args.do_verification,
                                                              args.init_method,
@@ -465,7 +465,7 @@ int profile_reduce(int argc, char* argv[])
     }
     else
     {
-        if(args.compTypeId == appDataType_t::appFloat)
+        if(args.compTypeId == AppDataType::appFloat)
         {
             profile_reduce_impl<float, float, float>(args.do_verification,
                                                      args.init_method,
@@ -480,7 +480,7 @@ int profile_reduce(int argc, char* argv[])
                                                      args.scales[0],
                                                      args.scales[1]);
         }
-        else if(args.compTypeId == appDataType_t::appDouble)
+        else if(args.compTypeId == AppDataType::appDouble)
         {
             profile_reduce_impl<float, double, float>(args.do_verification,
                                                       args.init_method,