Merge branch 'amd-develop' into amd-master

codegen/src/device_gemm_multiple_d.cpp

+
+// SPDX-License-Identifier: MIT
+// Copyright (c) 2018-2023, Advanced Micro Devices, Inc. All rights reserved.
+
+#include "ck/host/device_gemm_multiple_d/problem.hpp"
+#include "ck/host/device_gemm_multiple_d/operation.hpp"
+#include "ck/host/utils.hpp"
+#include <algorithm>
+
+namespace ck {
+namespace host {
+namespace device_gemm_multiple_d {
+
+std::string Problem::GetIncludeHeader() const
+{
+    return "ck/tensor_operation/gpu/device/impl/device_gemm_multiple_d_xdl_cshuffle.hpp";
+}
+
+std::vector<Solution> Problem::GetSolutions(const std::string& arch) const
+{
+    if(get_xdlop_archs().count(arch) == 0)
+        return {};
+    auto ops = ck::host::device_gemm_multiple_d::Operation_Xdl_CShuffle::CreateOperations(*this);
+    std::vector<Solution> result;
+    std::transform(ops.begin(), ops.end(), std::back_inserter(result), [&](const auto& op) {
+        return op.ToSolution();
+    });
+    return result;
+}
+
+} // namespace device_gemm_multiple_d
+} // namespace host
+} // namespace ck
\ No newline at end of file

codegen/src/device_gemm_multiple_d_operation_xdl_cshuffle.cpp

+// SPDX-License-Identifier: MIT
+// Copyright (c) 2018-2024, Advanced Micro Devices, Inc. All rights reserved.
+
+#include "ck/host/device_gemm_multiple_d/operation.hpp"
+#include "ck/host/stringutils.hpp"
+#include "ck/host/utils.hpp"
+#include <cassert>
+
+namespace ck {
+namespace host {
+namespace device_gemm_multiple_d {
+
+static std::string GetGemmSpec(const std::size_t m,
+                               const std::size_t n,
+                               const std::size_t k,
+                               const std::size_t m_per_block,
+                               const std::size_t n_per_block,
+                               const std::size_t k_per_block)
+{
+    std::string spec = "";
+    if(integer_divide_ceil(m, m_per_block) * m_per_block - m != 0)
+        spec += "M";
+    if(integer_divide_ceil(n, n_per_block) * n_per_block - n != 0)
+        spec += "N";
+    if(integer_divide_ceil(k, k_per_block) * k_per_block - k != 0)
+        spec += "K";
+    if(spec == "")
+        return "ck::tensor_operation::device::GemmSpecialization::Default";
+
+    return "ck::tensor_operation::device::GemmSpecialization::" + spec + "Padding";
+}
+
+static Layout ToLayout(bool Trans) { return Trans ? Layout::Column : Layout::Row; }
+
+std::vector<Operation_Xdl_CShuffle> Operation_Xdl_CShuffle::CreateOperations(const Problem& prob)
+{
+    std::vector<Operation_Xdl_CShuffle> result;
+
+    std::vector<operation::TileDesc> tile_descriptions = {
+        // clang-format off
+//  Block|  MPer|  NPer|  KPer| AK1| BK1| MPer| NPer| MXdl| NXdl| NumGemmK|
+//   Size| Block| Block| Block|    |    |  XDL|  XDL|  Per|  Per| Prefetch|
+//       |      |      |      |    |    |     |     | Wave| Wave|    Stage|
+//       |      |      |      |    |    |     |     |     |     |         |
+  {   256,   256,   128,    32,   8,   8,   32,   32,    4,    2,        1},
+  {   256,   128,   256,    32,   8,   8,   32,   32,    2,    4,        1},
+  {   128,   128,   128,    32,   8,   8,   32,   32,    4,    2,        1},
+  {   256,   128,   128,    32,   8,   8,   32,   32,    2,    2,        1},
+  {   128,   128,    64,    32,   8,   8,   32,   32,    2,    2,        1},
+  {   128,    64,   128,    32,   8,   8,   32,   32,    2,    2,        1},
+  {   256,   128,    64,    32,   8,   8,   32,   32,    2,    1,        1},
+  {   256,    64,   128,    32,   8,   8,   32,   32,    1,    2,        1},
+        // clang-format on
+    };
+
+    std::vector<operation::BlockTransferDesc> a_block_descriptions_rowmajor = {
+        // clang-format off
+//  ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockLds|
+//   ThreadCluster|  ThreadCluster| SrcAccessOrder|   SrcVectorDim|      SrcScalar|      DstScalar| AddExtraM|
+// Lengths_K0_M_K1|   ArrangeOrder|               |               |      PerVector|   PerVector_K1|          |
+//                |               |               |               |               |               |          |
+  {    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},
+  {    S<4, 32, 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},
+  {    S<4, 32, 1>,     S<1, 0, 2>,     S<1, 0, 2>,              2,              8,              8,         1},
+  {    S<4, 32, 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},
+  {    S<4, 64, 1>,     S<1, 0, 2>,     S<1, 0, 2>,              2,              8,              8,         1},
+        // clang-format on
+    };
+
+    std::vector<operation::BlockTransferDesc> a_block_descriptions_colmajor = {
+        // clang-format off
+//  ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockLds|
+//   ThreadCluster|  ThreadCluster| SrcAccessOrder|   SrcVectorDim|      SrcScalar|      DstScalar| AddExtraM|
+// Lengths_K0_M_K1|   ArrangeOrder|               |               |      PerVector|   PerVector_K1|          |
+//                |               |               |               |               |               |          |
+        // clang-format on
+        {S<4, 64, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 4, 8, 1},
+        {S<4, 64, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 2, 8, 1},
+        {S<4, 32, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 4, 8, 1},
+        {S<4, 64, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 2, 8, 1},
+        {S<4, 32, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 4, 8, 1},
+        {S<4, 32, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 2, 8, 1},
+        {S<4, 64, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 2, 8, 1},
+        {S<4, 64, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 1, 8, 1},
+    };
+
+    std::vector<operation::BlockTransferDesc> b_block_descriptions_rowmajor = {
+        // clang-format off
+//  BBlockTransfer| BBlockTransfer| BBlockTransfer| BBlockTransfer| BBlockTransfer| BBlockTransfer| BBlockLds|
+//   ThreadCluster|  ThreadCluster| SrcAccessOrder|   SrcVectorDim|      SrcScalar|      DstScalar| AddExtraN|
+// Lengths_K0_N_K1|   ArrangeOrder|               |               |      PerVector|   PerVector_K1|          |
+//                |               |               |               |               |               |          |
+  {    S<4, 64, 1>,     S<0, 2, 1>,     S<0, 2, 1>,              1,              2,              8,         1},
+  {    S<4, 64, 1>,     S<0, 2, 1>,     S<0, 2, 1>,              1,              4,              8,         1},
+  {    S<4, 32, 1>,     S<0, 2, 1>,     S<0, 2, 1>,              1,              4,              8,         1},
+  {    S<4, 64, 1>,     S<0, 2, 1>,     S<0, 2, 1>,              1,              2,              8,         1},
+  {    S<4, 32, 1>,     S<0, 2, 1>,     S<0, 2, 1>,              1,              2,              8,         1},
+  {    S<4, 32, 1>,     S<0, 2, 1>,     S<0, 2, 1>,              1,              4,              8,         1},
+  {    S<4, 64, 1>,     S<0, 2, 1>,     S<0, 2, 1>,              1,              1,              8,         1},
+  {    S<4, 64, 1>,     S<0, 2, 1>,     S<0, 2, 1>,              1,              2,              8,         1},
+        // clang-format on
+    };
+
+    std::vector<operation::BlockTransferDesc> b_block_descriptions_colmajor = {
+        // clang-format off
+//  BBlockTransfer| BBlockTransfer| BBlockTransfer| BBlockTransfer| BBlockTransfer| BBlockTransfer| BBlockLds|
+//   ThreadCluster|  ThreadCluster| SrcAccessOrder|   SrcVectorDim|      SrcScalar|      DstScalar| AddExtraN|
+// Lengths_K0_N_K1|   ArrangeOrder|               |               |      PerVector|   PerVector_K1|          |
+//                |               |               |               |               |               |          |
+  {    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},
+  {    S<4, 32, 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},
+  {    S<4, 32, 1>,     S<1, 0, 2>,     S<1, 0, 2>,              2,              8,              8,         1},
+  {    S<4, 32, 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},
+  {    S<4, 64, 1>,     S<1, 0, 2>,     S<1, 0, 2>,              2,              8,              8,         1},
+        // clang-format on
+    };
+
+    std::vector<operation::CShuffleDesc> cshuffle_descriptions = {
+        // clang-format off
+//    CShuffle|    CShuffle|
+// MXdlPerWave| NXdlPerWave|
+//  PerShuffle|  PerShuffle|
+//            |            |
+  {          1,           1},
+  {          1,           1},
+  {          1,           1},
+  {          1,           1},
+  {          1,           1},
+  {          1,           1},
+  {          1,           1},
+  {          1,           1},
+        // clang-format on
+    };
+
+    std::vector<operation::CBlockTransferDesc> c_block_descriptions = {
+        // clang-format off
+// CBlockTransferClusterLengths|  CBlockTransfer
+//         _MBlock_MWaveMPerXdl| ScalarPerVector
+//         _NBlock_NWaveNPerXdl|   _NWaveNPerXdl
+//                             |                
+  {              S<1, 32, 1, 8>,               8},
+  {              S<1, 32, 1, 8>,               8},
+  {              S<1, 16, 1, 8>,               8},
+  {              S<1, 32, 1, 8>,               8},
+  {              S<1, 32, 1, 4>,               8},
+  {              S<1, 16, 1, 8>,               8},
+  {              S<1, 32, 1, 8>,               8},
+  {              S<1, 32, 1, 8>,               8},
+        // clang-format on
+    };
+
+    const auto a_block_descriptions =
+        prob.TransA ? a_block_descriptions_colmajor : a_block_descriptions_rowmajor;
+    const auto b_block_descriptions =
+        prob.TransB ? b_block_descriptions_colmajor : b_block_descriptions_rowmajor;
+
+    assert(tile_descriptions.size() == a_block_descriptions.size());
+    assert(tile_descriptions.size() == b_block_descriptions.size());
+    assert(tile_descriptions.size() == cshuffle_descriptions.size());
+    assert(tile_descriptions.size() == c_block_descriptions.size());
+
+    for(std::size_t i = 0; i < tile_descriptions.size(); i++)
+    {
+        Operation_Xdl_CShuffle x;
+        x.tile_desc           = tile_descriptions[i];
+        x.a_block_transfer    = a_block_descriptions[i];
+        x.b_block_transfer    = b_block_descriptions[i];
+        x.cshuffle            = cshuffle_descriptions[i];
+        x.c_block_transfer    = c_block_descriptions[i];
+        x.A                   = TensorDesc{prob.ADataType, ToLayout(prob.TransA)};
+        x.B                   = TensorDesc{prob.BDataType, ToLayout(prob.TransB)};
+        x.E                   = TensorDesc{prob.EDataType, ToLayout(prob.TransE)};
+        x.Ds                  = Transform(prob.DsTrans, prob.DsDataType, [](auto trans, auto dt) {
+            return TensorDesc{dt, ToLayout(trans)};
+        });
+        x.a_elem_op           = prob.AElementOp;
+        x.b_elem_op           = prob.BElementOp;
+        x.cde_elem_op         = prob.CDEElementOp;
+        x.gemm_specialization = GetGemmSpec(prob.M,
+                                            prob.N,
+                                            prob.K,
+                                            x.tile_desc.m_per_block,
+                                            x.tile_desc.n_per_block,
+                                            x.tile_desc.k_per_block);
+        result.push_back(x);
+    }
+    return result;
+}
+
+std::vector<std::vector<Operation_Xdl_CShuffle>> Operation_Xdl_CShuffle::CreateOperations()
+{
+    std::vector<Problem> problems;
+    for(bool TransA : {true, false})
+        for(bool TransB : {true, false})
+        {
+            Problem prob;
+            prob.TransA = TransA;
+            prob.TransB = TransB;
+            problems.push_back(prob);
+        }
+    return Transform(problems, [](const Problem& p) { return CreateOperations(p); });
+}
+
+static const char* const DeviceGemmMultipleD_Xdl_CShuffleTemplate =
+    "ck::tensor_operation::device::DeviceGemmMultipleD_Xdl_CShuffle<${LayoutA}, ${LayoutB}, "
+    "${LayoutDs}, ${LayoutE}, ${ADataType}, ${BDataType}, ${AccDataType}, ${CShuffleDataType}, "
+    "${DsDataType}, ${EDataType}, ${AElementwiseOperation}, ${BElementwiseOperation}, "
+    "${CDEElementwiseOperation}, ${GemmSpecialization}, ${NumGemmkPrefetchStage}, ${BlockSize}, "
+    "${MPerBlock}, ${NPerBlock}, ${KPerBlock}, ${AK1}, ${BK1}, ${MPerXDL}, ${NPerXDL}, "
+    "${MXdlPerWave}, ${NXdlPerWave}, ${ABlockTransferThreadClusterLengths_AK0_M_AK1}, "
+    "${ABlockTransferThreadClusterArrangeOrder}, ${ABlockTransferSrcAccessOrder}, "
+    "${ABlockTransferSrcVectorDim}, ${ABlockTransferSrcScalarPerVector}, "
+    "${ABlockTransferDstScalarPerVector_AK1}, ${ABlockLdsExtraM}, "
+    "${BBlockTransferThreadClusterLengths_BK0_N_BK1}, ${BBlockTransferThreadClusterArrangeOrder}, "
+    "${BBlockTransferSrcAccessOrder}, ${BBlockTransferSrcVectorDim}, "
+    "${BBlockTransferSrcScalarPerVector}, ${BBlockTransferDstScalarPerVector_BK1}, "
+    "${BBlockLdsExtraN}, ${CShuffleMXdlPerWavePerShuffle}, ${CShuffleNXdlPerWavePerShuffle}, "
+    "${CDEBlockTransferClusterLengths_MBlock_MPerBlock_NBlock_NPerBlock}, "
+    "${CDEBlockTransferScalarPerVector_NPerBlock}>";
+
+Solution Operation_Xdl_CShuffle::ToSolution() const
+{
+    std::unordered_map<std::string, std::string> values = {
+        {"LayoutA", ToString(this->A.layout)},
+        {"LayoutB", ToString(this->B.layout)},
+        {"LayoutDs",
+         MakeTuple(Transform(this->Ds, [](auto tensor) { return ToString(tensor.layout); }))},
+        {"LayoutE", ToString(this->E.layout)},
+        {"ADataType", ToString(this->A.element)},
+        {"BDataType", ToString(this->B.element)},
+        {"AccDataType", ToString(this->acc)},
+        {"CShuffleDataType", ToString(this->cs_type)},
+        {"DsDataType",
+         MakeTuple(Transform(this->Ds, [](auto tensor) { return ToString(tensor.element); }))},
+        {"EDataType", ToString(this->E.element)},
+        {"AElementwiseOperation", this->a_elem_op},
+        {"BElementwiseOperation", this->b_elem_op},
+        {"CDEElementwiseOperation", this->cde_elem_op},
+        {"GemmSpecialization", this->gemm_specialization},
+        {"NumGemmkPrefetchStage", std::to_string(this->tile_desc.num_gemmk_prefetch_stage)},
+        {"BlockSize", std::to_string(this->tile_desc.block_size)},
+        {"MPerBlock", std::to_string(this->tile_desc.m_per_block)},
+        {"NPerBlock", std::to_string(this->tile_desc.n_per_block)},
+        {"KPerBlock", std::to_string(this->tile_desc.k_per_block)},
+        {"AK1", std::to_string(this->tile_desc.ak1)},
+        {"BK1", std::to_string(this->tile_desc.bk1)},
+        {"MPerXDL", std::to_string(this->tile_desc.m_per_XDL)},
+        {"NPerXDL", std::to_string(this->tile_desc.n_per_XDL)},
+        {"MXdlPerWave", std::to_string(this->tile_desc.m_Xdl_per_wave)},
+        {"NXdlPerWave", std::to_string(this->tile_desc.n_Xdl_per_wave)},
+        {"ABlockTransferThreadClusterLengths_AK0_M_AK1",
+         this->a_block_transfer.thread_cluster_length},
+        {"ABlockTransferThreadClusterArrangeOrder",
+         this->a_block_transfer.thread_cluster_arrange_order},
+        {"ABlockTransferSrcAccessOrder", this->a_block_transfer.src_access_order},
+        {"ABlockTransferSrcVectorDim", std::to_string(this->a_block_transfer.src_vec_dim)},
+        {"ABlockTransferSrcScalarPerVector",
+         std::to_string(this->a_block_transfer.src_scalar_per_vector)},
+        {"ABlockTransferDstScalarPerVector_AK1",
+         std::to_string(this->a_block_transfer.dst_scalar_per_vector_k1)},
+        {"ABlockLdsExtraM", std::to_string(this->a_block_transfer.lds_add_extra_dim)},
+        {"BBlockTransferThreadClusterLengths_BK0_N_BK1",
+         this->b_block_transfer.thread_cluster_length},
+        {"BBlockTransferThreadClusterArrangeOrder",
+         this->b_block_transfer.thread_cluster_arrange_order},
+        {"BBlockTransferSrcAccessOrder", this->b_block_transfer.src_access_order},
+        {"BBlockTransferSrcVectorDim", std::to_string(this->b_block_transfer.src_vec_dim)},
+        {"BBlockTransferSrcScalarPerVector",
+         std::to_string(this->b_block_transfer.src_scalar_per_vector)},
+        {"BBlockTransferDstScalarPerVector_BK1",
+         std::to_string(this->b_block_transfer.dst_scalar_per_vector_k1)},
+        {"BBlockLdsExtraN", std::to_string(this->b_block_transfer.lds_add_extra_dim)},
+        {"CShuffleMXdlPerWavePerShuffle",
+         std::to_string(this->cshuffle.m_Xdl_per_wave_per_shuffle)},
+        {"CShuffleNXdlPerWavePerShuffle",
+         std::to_string(this->cshuffle.n_Xdl_per_wave_per_shuffle)},
+        {"CDEBlockTransferClusterLengths_MBlock_MPerBlock_NBlock_NPerBlock",
+         this->c_block_transfer.cluster_lengths_m_block_m_wave_m_per_Xdl_n_block_n_wave_n_per_Xdl},
+        {"CDEBlockTransferScalarPerVector_NPerBlock",
+         std::to_string(this->c_block_transfer.scalar_per_vector_n_wave_n_per_Xdl)},
+    };
+
+    return Solution{InterpolateString(DeviceGemmMultipleD_Xdl_CShuffleTemplate, values),
+                    std::move(values)};
+}
+
+} // namespace device_gemm_multiple_d
+} // namespace host
+} // namespace ck

codegen/src/headers.cpp

+#include "ck/host/headers.hpp"
+#include "ck_headers.hpp"
+
+namespace ck {
+namespace host {
+
+const std::string config_header = "";
+
+std::unordered_map<std::string_view, std::string_view> GetHeaders()
+{
+    auto headers = ck_headers();
+    headers.insert(std::make_pair("ck/config.h", config_header));
+    return headers;
+}
+
+} // namespace host
+} // namespace ck
\ No newline at end of file

codegen/src/types.cpp

+#include "ck/host/types.hpp"
+#include "ck/host/stringutils.hpp"
+#include <algorithm>
+#include <stdexcept>
+
+namespace ck {
+namespace host {
+
+Solution::Solution(std::string str, std::unordered_map<std::string, std::string> values)
+    : template_str(std::move(str)), template_values(std::move(values))
+{
+}
+
+std::string Solution::ToTemplateString() const { return this->template_str; }
+std::string Solution::GetTemplateParameter(const std::string& name) const
+{
+    return this->template_values.at(name);
+}
+
+std::string ToString(DataType dt)
+{
+    switch(dt)
+    {
+    case DataType::Float: return "float";
+    case DataType::Half: return "ck::half_t";
+    case DataType::Int8: return "int8_t";
+    case DataType::Int32: return "int32_t";
+    }
+    throw std::runtime_error("Incorrect data type");
+}
+
+std::string ToString(Layout dl)
+{
+    switch(dl)
+    {
+    case Layout::Row: return "ck::tensor_layout::gemm::RowMajor";
+    case Layout::Column: return "ck::tensor_layout::gemm::ColumnMajor";
+    }
+    throw std::runtime_error("Incorrect layout");
+}
+
+std::string ToString(GemmType gt)
+{
+    switch(gt)
+    {
+    case GemmType::Default: return "ck::tensor_operation::device::GemmSpecialization::Default";
+    }
+    throw std::runtime_error("Incorrect gemm type");
+}
+
+std::string SequenceStr(const std::vector<int>& v)
+{
+    return "ck::Sequence<" +
+           JoinStrings(Transform(v, [](int x) { return std::to_string(x); }), ", ") + ">";
+}
+
+std::string MakeTuple(const std::vector<std::string>& v)
+{
+    return "ck::Tuple<" + JoinStrings(v, ", ") + ">";
+}
+
+} // namespace host
+} // namespace ck

codegen/src/utils.cpp

+// SPDX-License-Identifier: MIT
+// Copyright (c) 2018-2023, Advanced Micro Devices, Inc. All rights reserved.
+
+#include "ck/host/utils.hpp"
+
+namespace ck {
+namespace host {
+
+std::size_t integer_divide_ceil(std::size_t x, std::size_t y)
+{
+    return (x + y - std::size_t{1}) / y;
+}
+
+const std::unordered_set<std::string>& get_xdlop_archs()
+{
+    static std::unordered_set<std::string> supported_archs{"gfx90a", "gfx908", "gfx940", "gfx942"};
+    return supported_archs;
+}
+
+} // namespace host
+} // namespace ck

codegen/test/CMakeLists.txt

+
+list(APPEND CMAKE_PREFIX_PATH /opt/rocm)
+add_subdirectory(rtc)
+
+file(GLOB TEST_SRCS CONFIGURE_DEPENDS *.cpp)
+foreach(TEST_SRC ${TEST_SRCS})
+get_filename_component(BASE_NAME ${TEST_SRC} NAME_WE)
+rocm_add_test_executable(test_host_${BASE_NAME} ${TEST_SRC})
+target_link_libraries(test_host_${BASE_NAME} ck_rtc ck_host)
+target_include_directories(test_host_${BASE_NAME} PUBLIC include())
+endforeach()

codegen/test/gemm_multiple_d.cpp

+#include "ck/host/device_gemm_multiple_d/problem.hpp"
+#include "ck/host/device_gemm_multiple_d/operation.hpp"
+#include "ck/host/headers.hpp"
+#include "ck/host/stringutils.hpp"
+#include "ck/host/utils.hpp"
+#include <algorithm>
+#include <cmath>
+#include <iterator>
+#include <random>
+#include <test.hpp>
+#include <rtc/compile_kernel.hpp>
+#include <rtc/hip.hpp>
+
+using half = _Float16;
+// using half = __fp16;
+
+std::vector<rtc::src_file> get_headers_for_test()
+{
+    std::vector<rtc::src_file> result;
+    auto hs = ck::host::GetHeaders();
+    std::transform(
+        hs.begin(), hs.end(), std::back_inserter(result), [&](const auto& p) -> rtc::src_file {
+            return {p.first, p.second};
+        });
+    return result;
+}
+
+template <class T>
+rtc::buffer<T> generate_buffer(std::size_t n, std::size_t seed = 0)
+{
+    rtc::buffer<T> result(n);
+    std::mt19937 gen(seed);
+    std::uniform_real_distribution<double> dis(-1.0);
+    std::generate(result.begin(), result.end(), [&] { return dis(gen); });
+    return result;
+}
+
+template <class T, class U>
+bool allclose(const T& a, const U& b, double atol = 0.01, double rtol = 0.01)
+{
+    return std::equal(a.begin(), a.end(), b.begin(), b.end(), [&](double x, double y) {
+        return fabs(x - y) < atol + rtol * fabs(y);
+    });
+}
+
+std::string classify(double x)
+{
+    switch(std::fpclassify(x))
+    {
+    case FP_INFINITE: return "inf";
+    case FP_NAN: return "nan";
+    case FP_NORMAL: return "normal";
+    case FP_SUBNORMAL: return "subnormal";
+    case FP_ZERO: return "zero";
+    default: return "unknown";
+    }
+}
+
+template <class Buffer>
+void print_classification(const Buffer& x)
+{
+    std::unordered_set<std::string> result;
+    for(const auto& i : x)
+        result.insert(classify(i));
+    for(const auto& c : result)
+        std::cout << c << ", ";
+    std::cout << std::endl;
+}
+
+template <class Buffer>
+void print_statistics(const Buffer& x)
+{
+    std::cout << "Min value: " << *std::min_element(x.begin(), x.end()) << ", ";
+    std::cout << "Max value: " << *std::max_element(x.begin(), x.end()) << ", ";
+    double num_elements = x.size();
+    auto mean =
+        std::accumulate(x.begin(), x.end(), double{0.0}, std::plus<double>{}) / num_elements;
+    auto stddev = std::sqrt(
+        std::accumulate(x.begin(),
+                        x.end(),
+                        double{0.0},
+                        [&](double r, double v) { return r + std::pow((v - mean), 2.0); }) /
+        num_elements);
+    std::cout << "Mean: " << mean << ", ";
+    std::cout << "StdDev: " << stddev << "\n";
+}
+
+template <class Buffer>
+void print_preview(const Buffer& x)
+{
+    if(x.size() <= 10)
+    {
+        std::for_each(x.begin(), x.end(), [&](double i) { std::cout << i << ", "; });
+    }
+    else
+    {
+        std::for_each(x.begin(), x.begin() + 5, [&](double i) { std::cout << i << ", "; });
+        std::cout << "..., ";
+        std::for_each(x.end() - 5, x.end(), [&](double i) { std::cout << i << ", "; });
+    }
+    std::cout << std::endl;
+}
+
+template <class T>
+struct check_all
+{
+    rtc::buffer<T> data{};
+    bool operator()(const rtc::buffer<T>& x)
+    {
+        if(data.empty())
+        {
+            data = x;
+            return true;
+        }
+        if(std::any_of(x.begin(), x.end(), [](double y) { return std::isnan(y); }))
+            return false;
+        return allclose(data, x);
+    }
+};
+
+template <class Solution>
+auto report(const Solution& solution, bool pass)
+{
+    return test::make_predicate(solution.ToTemplateString(), [=] { return pass; });
+}
+
+const std::string gemm_compile_check = R"__ck__(
+#include <${include}>
+
+extern "C" __global__ void f(const ck::half_t* a, const ck::half_t* b, ck::half_t* c) {
+    using G = ${template};
+    constexpr auto desc = ${template}::make_descriptor(ck::make_naive_tensor_descriptor_packed(ck::make_tuple(${m}, ${k})),
+                                             ck::make_naive_tensor_descriptor(ck::make_tuple(${n}, ${k}), ck::make_tuple(1, ${n})),
+                                             ck::make_tuple(),
+                                             ck::make_naive_tensor_descriptor_packed(ck::make_tuple(${m}, ${n})));
+
+    static_assert(desc.IsValid(), "Invalid ck gemm.");
+
+    if constexpr(desc.IsValid())
+    {
+        ${template}::Run(desc,
+               a,
+               b,
+               ck::make_tuple(),
+               c);
+    }
+}
+
+)__ck__";
+
+TEST_CASE(test_problem_kernel)
+{
+    ck::host::device_gemm_multiple_d::Problem prob;
+    prob.M = 1024;
+    prob.N = 1024;
+    prob.K = 1024;
+    check_all<half> check;
+    auto a = to_gpu(generate_buffer<half>(1024 * 1024, 0));
+    auto b = to_gpu(generate_buffer<half>(1024 * 1024, 1));
+    auto c = to_gpu(generate_buffer<half>(1024 * 1024, 2));
+
+    for(auto solution : prob.GetSolutions("gfx90a"))
+    {
+        auto src  = ck::host::InterpolateString(gemm_compile_check,
+                                               {{"include", prob.GetIncludeHeader()},
+                                                {"template", solution.ToTemplateString()},
+                                                {"m", std::to_string(prob.M)},
+                                                {"n", std::to_string(prob.N)},
+                                                {"k", std::to_string(prob.K)}});
+        auto srcs = get_headers_for_test();
+        srcs.push_back({"main.cpp", src});
+        rtc::compile_options options;
+        options.kernel_name = "f";
+        auto k              = rtc::compile_kernel(srcs, options);
+        auto block_size     = solution.GetTemplateParameter<std::size_t>("BlockSize");
+        auto m_per_block    = solution.GetTemplateParameter<std::size_t>("MPerBlock");
+        auto n_per_block    = solution.GetTemplateParameter<std::size_t>("NPerBlock");
+        auto grid_size      = ck::host::integer_divide_ceil(prob.M, m_per_block) *
+                         ck::host::integer_divide_ceil(prob.N, n_per_block);
+        k.launch(nullptr, grid_size * block_size, block_size)(a.data(), b.data(), c.data());
+        CHECK(report(solution, check(rtc::from_gpu(c))));
+    }
+}
+
+int main(int argc, const char* argv[]) { test::run(argc, argv); }

codegen/test/rtc/CMakeLists.txt

+
+find_package(hip)
+file(GLOB RTC_SOURCES CONFIGURE_DEPENDS src/*.cpp)
+add_library(ck_rtc ${RTC_SOURCES})
+target_include_directories(ck_rtc PUBLIC include)
+target_link_libraries(ck_rtc PUBLIC hip::host)

codegen/test/rtc/include/rtc/compile_kernel.hpp

+#ifndef GUARD_HOST_TEST_RTC_INCLUDE_RTC_COMPILE_KERNEL
+#define GUARD_HOST_TEST_RTC_INCLUDE_RTC_COMPILE_KERNEL
+
+#include <rtc/kernel.hpp>
+#include <filesystem>
+#include <string>
+
+namespace rtc {
+
+struct src_file
+{
+    std::filesystem::path path;
+    std::string_view content;
+};
+
+struct compile_options
+{
+    std::string flags       = "";
+    std::string kernel_name = "main";
+};
+
+kernel compile_kernel(const std::vector<src_file>& src,
+                      compile_options options = compile_options{});
+
+} // namespace rtc
+
+#endif

codegen/test/rtc/include/rtc/hip.hpp

+#ifndef GUARD_HOST_TEST_RTC_INCLUDE_RTC_HIP
+#define GUARD_HOST_TEST_RTC_INCLUDE_RTC_HIP
+
+#include <hip/hip_runtime_api.h>
+#include <memory>
+#include <string>
+
+namespace rtc {
+
+template <class T>
+struct buffer
+{
+    buffer() : ptr(), n(0) {}
+    buffer(std::shared_ptr<T> p, std::size_t sz) : ptr(p), n(sz) {}
+    buffer(std::shared_ptr<void> p, std::size_t sz)
+        : ptr(std::reinterpret_pointer_cast<T>(p)), n(sz)
+    {
+    }
+    explicit buffer(std::size_t sz) : ptr(new T[sz]), n(sz) {}
+    T* begin() { return data(); }
+    T* end() { return data() + size(); }
+    const T* begin() const { return data(); }
+    const T* end() const { return data() + size(); }
+
+    T& front() { return data()[0]; }
+    T& back() { return data()[size() - 1]; }
+    T& operator[](std::size_t i) { return data()[i]; }
+    T& at(std::size_t i)
+    {
+        if(i >= size())
+            throw std::runtime_error("Out of bounds");
+        return data()[i];
+    }
+
+    const T& front() const { return data()[0]; }
+    const T& back() const { return data()[size() - 1]; }
+    const T& operator[](std::size_t i) const { return data()[i]; }
+    const T& at(std::size_t i) const
+    {
+        if(i >= size())
+            throw std::runtime_error("Out of bounds");
+        return data()[i];
+    }
+    const T* data() const { return ptr.get(); }
+    T* data() { return ptr.get(); }
+
+    std::size_t size() const { return n; }
+    std::size_t bytes() const { return size() * sizeof(T); }
+
+    bool empty() const { return size() == 0; }
+
+    private:
+    std::shared_ptr<T> ptr;
+    std::size_t n;
+};
+
+std::string get_device_name();
+std::string hip_error(int error);
+
+std::shared_ptr<void> allocate_gpu(std::size_t sz, bool host = false);
+std::shared_ptr<void> write_to_gpu(const void* x, std::size_t sz, bool host = false);
+std::shared_ptr<void> read_from_gpu(const void* x, std::size_t sz);
+
+template <class T>
+buffer<T> to_gpu(const buffer<T>& input)
+{
+    return {write_to_gpu(input.data(), input.bytes()), input.size()};
+}
+
+template <class T>
+buffer<T> from_gpu(const buffer<T>& input)
+{
+    return {read_from_gpu(input.data(), input.bytes()), input.size()};
+}
+
+} // namespace rtc
+
+#endif

codegen/test/rtc/include/rtc/kernel.hpp

+#ifndef GUARD_HOST_TEST_RTC_INCLUDE_RTC_KERNEL
+#define GUARD_HOST_TEST_RTC_INCLUDE_RTC_KERNEL
+
+#include <hip/hip_runtime_api.h>
+#include <memory>
+#include <string>
+#include <vector>
+
+namespace rtc {
+
+struct kernel_argument
+{
+    template <class T,
+              class U = std::remove_reference_t<T>,
+              class   = std::enable_if_t<not std::is_base_of<kernel_argument, T>{}>>
+    kernel_argument(T&& x) : size(sizeof(U)), align(alignof(U)), data(&x) // NOLINT
+    {
+    }
+    std::size_t size;
+    std::size_t align;
+    void* data;
+};
+
+std::vector<char> pack_args(const std::vector<kernel_argument>& args);
+
+struct kernel_impl;
+
+struct kernel
+{
+    kernel() = default;
+    kernel(const char* image, const std::string& name);
+    template <class T>
+    kernel(const std::vector<T>& image, const std::string& name)
+        : kernel(reinterpret_cast<const char*>(image.data()), name)
+    {
+        static_assert(sizeof(T) == 1, "Only byte types");
+    }
+
+    void launch(hipStream_t stream,
+                std::size_t global,
+                std::size_t local,
+                const std::vector<kernel_argument>& args) const;
+
+    void launch(hipStream_t stream,
+                std::size_t global,
+                std::size_t local,
+                std::vector<void*> args) const;
+
+    template <class... Ts>
+    auto launch(hipStream_t stream, std::size_t global, std::size_t local, Ts... zs) const
+    {
+        return [=](auto&&... xs) {
+            launch(stream, global, local, std::vector<kernel_argument>{xs...}, zs...);
+        };
+    }
+
+    private:
+    std::shared_ptr<kernel_impl> impl;
+};
+} // namespace rtc
+
+#endif

codegen/test/rtc/include/rtc/manage_ptr.hpp

+#ifndef GUARD_HOST_TEST_RTC_INCLUDE_RTC_MANAGE_POINTER
+#define GUARD_HOST_TEST_RTC_INCLUDE_RTC_MANAGE_POINTER
+
+#include <type_traits>
+#include <memory>
+
+namespace rtc {
+template <class F, F f>
+struct manage_deleter
+{
+    template <class T>
+    void operator()(T* x) const
+    {
+        if(x != nullptr)
+        {
+            (void)f(x);
+        }
+    }
+};
+
+struct null_deleter
+{
+    template <class T>
+    void operator()(T*) const
+    {
+    }
+};
+
+template <class T, class F, F f>
+using manage_ptr = std::unique_ptr<T, manage_deleter<F, f>>;
+
+template <class T>
+struct element_type
+{
+    using type = typename T::element_type;
+};
+
+template <class T>
+using remove_ptr = typename std::
+    conditional_t<std::is_pointer<T>{}, std::remove_pointer<T>, element_type<T>>::type;
+
+template <class T>
+using shared = std::shared_ptr<remove_ptr<T>>;
+
+template <class T>
+shared<T> share(T p)
+{
+    return shared<T>{std::move(p)};
+}
+
+#define RTC_MANAGE_PTR(T, F) rtc::manage_ptr<std::remove_pointer_t<T>, decltype(&F), &F>
+
+} // namespace rtc
+
+#endif

codegen/test/rtc/include/rtc/tmp_dir.hpp

+#ifndef GUARD_HOST_TEST_RTC_INCLUDE_RTC_TMP_DIR
+#define GUARD_HOST_TEST_RTC_INCLUDE_RTC_TMP_DIR
+
+#include <string>
+#include <filesystem>
+
+namespace rtc {
+
+struct tmp_dir
+{
+    std::filesystem::path path;
+    tmp_dir(const std::string& prefix = "");
+
+    void execute(const std::string& cmd) const;
+
+    tmp_dir(tmp_dir const&) = delete;
+    tmp_dir& operator=(tmp_dir const&) = delete;
+
+    ~tmp_dir();
+};
+
+} // namespace rtc
+
+#endif

codegen/test/rtc/src/compile_kernel.cpp

+#include "rtc/hip.hpp"
+#include <rtc/compile_kernel.hpp>
+#include <rtc/tmp_dir.hpp>
+#include <stdexcept>
+#include <iostream>
+#include <fstream>
+#include <cassert>
+
+namespace rtc {
+
+template <class T>
+T generic_read_file(const std::string& filename, size_t offset = 0, size_t nbytes = 0)
+{
+    std::ifstream is(filename, std::ios::binary | std::ios::ate);
+    if(nbytes == 0)
+    {
+        // if there is a non-zero offset and nbytes is not set,
+        // calculate size of remaining bytes to read
+        nbytes = is.tellg();
+        if(offset > nbytes)
+            throw std::runtime_error("offset is larger than file size");
+        nbytes -= offset;
+    }
+    if(nbytes < 1)
+        throw std::runtime_error("Invalid size for: " + filename);
+    is.seekg(offset, std::ios::beg);
+
+    T buffer(nbytes, 0);
+    if(not is.read(&buffer[0], nbytes))
+        throw std::runtime_error("Error reading file: " + filename);
+    return buffer;
+}
+
+std::vector<char> read_buffer(const std::string& filename, size_t offset = 0, size_t nbytes = 0)
+{
+    return generic_read_file<std::vector<char>>(filename, offset, nbytes);
+}
+
+std::string read_string(const std::string& filename)
+{
+    return generic_read_file<std::string>(filename);
+}
+
+void write_buffer(const std::string& filename, const char* buffer, std::size_t size)
+{
+    std::ofstream os(filename);
+    os.write(buffer, size);
+}
+void write_buffer(const std::string& filename, const std::vector<char>& buffer)
+{
+    write_buffer(filename, buffer.data(), buffer.size());
+}
+void write_string(const std::string& filename, const std::string_view& buffer)
+{
+    write_buffer(filename, buffer.data(), buffer.size());
+}
+
+std::string compiler() { return "/opt/rocm/llvm/bin/clang++ -x hip --cuda-device-only"; }
+
+kernel compile_kernel(const std::vector<src_file>& srcs, compile_options options)
+{
+    assert(not srcs.empty());
+    tmp_dir td{"compile"};
+    options.flags += " -I. -O3";
+    options.flags += " -std=c++17";
+    options.flags += " --offload-arch=" + get_device_name();
+    std::string out;
+
+    for(const auto& src : srcs)
+    {
+        std::filesystem::path full_path   = td.path / src.path;
+        std::filesystem::path parent_path = full_path.parent_path();
+        std::filesystem::create_directories(parent_path);
+        write_string(full_path.string(), src.content);
+        if(src.path.extension().string() == ".cpp")
+        {
+            options.flags += " -c " + src.path.filename().string();
+            if(out.empty())
+                out = src.path.stem().string() + ".o";
+        }
+    }
+
+    options.flags += " -o " + out;
+    td.execute(compiler() + options.flags);
+
+    auto out_path = td.path / out;
+    if(not std::filesystem::exists(out_path))
+        throw std::runtime_error("Output file missing: " + out);
+
+    auto obj = read_buffer(out_path.string());
+
+    return kernel{obj.data(), options.kernel_name};
+}
+
+} // namespace rtc

codegen/test/rtc/src/hip.cpp

+#include <rtc/hip.hpp>
+#include <rtc/manage_ptr.hpp>
+#include <stdexcept>
+#include <cassert>
+
+namespace rtc {
+
+using hip_ptr = RTC_MANAGE_PTR(void, hipFree);
+
+std::string hip_error(int error) { return hipGetErrorString(static_cast<hipError_t>(error)); }
+
+int get_device_id()
+{
+    int device;
+    auto status = hipGetDevice(&device);
+    if(status != hipSuccess)
+        throw std::runtime_error("No device");
+    return device;
+}
+
+std::string get_device_name()
+{
+    hipDeviceProp_t props{};
+    auto status = hipGetDeviceProperties(&props, get_device_id());
+    if(status != hipSuccess)
+        throw std::runtime_error("Failed to get device properties");
+    return props.gcnArchName;
+}
+
+bool is_device_ptr(const void* ptr)
+{
+    hipPointerAttribute_t attr;
+    auto status = hipPointerGetAttributes(&attr, ptr);
+    if(status != hipSuccess)
+        return false;
+    return attr.type == hipMemoryTypeDevice;
+}
+
+void gpu_sync()
+{
+    auto status = hipDeviceSynchronize();
+    if(status != hipSuccess)
+        throw std::runtime_error("hip device synchronization failed: " + hip_error(status));
+}
+
+std::size_t get_available_gpu_memory()
+{
+    size_t free;
+    size_t total;
+    auto status = hipMemGetInfo(&free, &total);
+    if(status != hipSuccess)
+        throw std::runtime_error("Failed getting available memory: " + hip_error(status));
+    return free;
+}
+
+std::shared_ptr<void> allocate_gpu(std::size_t sz, bool host)
+{
+    if(sz > get_available_gpu_memory())
+        throw std::runtime_error("Memory not available to allocate buffer: " + std::to_string(sz));
+    void* alloc_ptr = nullptr;
+    auto status     = host ? hipHostMalloc(&alloc_ptr, sz) : hipMalloc(&alloc_ptr, sz);
+    if(status != hipSuccess)
+    {
+        if(host)
+            throw std::runtime_error("Gpu allocation failed: " + hip_error(status));
+        else
+            return allocate_gpu(sz, true);
+    }
+    assert(alloc_ptr != nullptr);
+    std::shared_ptr<void> result = share(hip_ptr{alloc_ptr});
+    return result;
+}
+
+std::shared_ptr<void> write_to_gpu(const void* x, std::size_t sz, bool host)
+{
+    gpu_sync();
+    auto result = allocate_gpu(sz, host);
+    assert(is_device_ptr(result.get()));
+    assert(not is_device_ptr(x));
+    auto status = hipMemcpy(result.get(), x, sz, hipMemcpyHostToDevice);
+    if(status != hipSuccess)
+        throw std::runtime_error("Copy to gpu failed: " + hip_error(status));
+    return result;
+}
+
+std::shared_ptr<void> read_from_gpu(const void* x, std::size_t sz)
+{
+    gpu_sync();
+    std::shared_ptr<char> result(new char[sz]);
+    assert(not is_device_ptr(result.get()));
+    if(not is_device_ptr(x))
+    {
+        throw std::runtime_error(
+            "read_from_gpu() requires Src buffer to be on the GPU, Copy from gpu failed\n");
+    }
+    auto status = hipMemcpy(result.get(), x, sz, hipMemcpyDeviceToHost);
+    if(status != hipSuccess)
+        throw std::runtime_error("Copy from gpu failed: " + hip_error(status)); // NOLINT
+    return std::static_pointer_cast<void>(result);
+}
+
+} // namespace rtc

codegen/test/rtc/src/kernel.cpp

+#include <rtc/kernel.hpp>
+#include <rtc/manage_ptr.hpp>
+#include <rtc/hip.hpp>
+#include <cassert>
+
+// extern declare the function since hip/hip_ext.h header is broken
+extern hipError_t hipExtModuleLaunchKernel(hipFunction_t, // NOLINT
+                                           uint32_t,
+                                           uint32_t,
+                                           uint32_t,
+                                           uint32_t,
+                                           uint32_t,
+                                           uint32_t,
+                                           size_t,
+                                           hipStream_t,
+                                           void**,
+                                           void**,
+                                           hipEvent_t = nullptr,
+                                           hipEvent_t = nullptr,
+                                           uint32_t   = 0);
+
+namespace rtc {
+
+std::vector<char> pack_args(const std::vector<kernel_argument>& args)
+{
+    std::vector<char> kernargs;
+    for(auto&& arg : args)
+    {
+        std::size_t n = arg.size;
+        const auto* p = static_cast<const char*>(arg.data);
+        // Insert padding
+        std::size_t padding = (arg.align - (kernargs.size() % arg.align)) % arg.align;
+        kernargs.insert(kernargs.end(), padding, 0);
+        kernargs.insert(kernargs.end(), p, p + n);
+    }
+    return kernargs;
+}
+
+using hip_module_ptr = RTC_MANAGE_PTR(hipModule_t, hipModuleUnload);
+
+struct kernel_impl
+{
+    hip_module_ptr module = nullptr;
+    hipFunction_t fun     = nullptr;
+};
+
+hip_module_ptr load_module(const char* image)
+{
+    hipModule_t raw_m;
+    auto status = hipModuleLoadData(&raw_m, image);
+    hip_module_ptr m{raw_m};
+    if(status != hipSuccess)
+        throw std::runtime_error("Failed to load module: " + hip_error(status));
+    return m;
+}
+
+kernel::kernel(const char* image, const std::string& name) : impl(std::make_shared<kernel_impl>())
+{
+    impl->module = load_module(image);
+    auto status  = hipModuleGetFunction(&impl->fun, impl->module.get(), name.c_str());
+    if(hipSuccess != status)
+        throw std::runtime_error("Failed to get function: " + name + ": " + hip_error(status));
+}
+
+void launch_kernel(hipFunction_t fun,
+                   hipStream_t stream,
+                   std::size_t global,
+                   std::size_t local,
+                   void* kernargs,
+                   std::size_t size)
+{
+    assert(global > 0);
+    assert(local > 0);
+    void* config[] = {HIP_LAUNCH_PARAM_BUFFER_POINTER,
+                      kernargs,
+                      HIP_LAUNCH_PARAM_BUFFER_SIZE,
+                      &size,
+                      HIP_LAUNCH_PARAM_END};
+
+    auto status = hipExtModuleLaunchKernel(fun,
+                                           global,
+                                           1,
+                                           1,
+                                           local,
+                                           1,
+                                           1,
+                                           0,
+                                           stream,
+                                           nullptr,
+                                           reinterpret_cast<void**>(&config),
+                                           nullptr,
+                                           nullptr);
+    if(status != hipSuccess)
+        throw std::runtime_error("Failed to launch kernel: " + hip_error(status));
+}
+
+void kernel::launch(hipStream_t stream,
+                    std::size_t global,
+                    std::size_t local,
+                    std::vector<void*> args) const
+{
+    assert(impl != nullptr);
+    void* kernargs   = args.data();
+    std::size_t size = args.size() * sizeof(void*);
+
+    launch_kernel(impl->fun, stream, global, local, kernargs, size);
+}
+
+void kernel::launch(hipStream_t stream,
+                    std::size_t global,
+                    std::size_t local,
+                    const std::vector<kernel_argument>& args) const
+{
+    assert(impl != nullptr);
+    std::vector<char> kernargs = pack_args(args);
+    std::size_t size           = kernargs.size();
+
+    launch_kernel(impl->fun, stream, global, local, kernargs.data(), size);
+}
+
+} // namespace rtc
\ No newline at end of file

codegen/test/rtc/src/tmp_dir.cpp

+#include <rtc/tmp_dir.hpp>
+#include <algorithm>
+#include <random>
+#include <thread>
+#include <unistd.h>
+
+namespace rtc {
+std::string random_string(std::string::size_type length)
+{
+    static const std::string& chars = "0123456789"
+                                      "abcdefghijklmnopqrstuvwxyz"
+                                      "ABCDEFGHIJKLMNOPQRSTUVWXYZ";
+
+    std::mt19937 rg{std::random_device{}()};
+    std::uniform_int_distribution<std::string::size_type> pick(0, chars.length() - 1);
+
+    std::string str(length, 0);
+    std::generate(str.begin(), str.end(), [&] { return chars[pick(rg)]; });
+
+    return str;
+}
+
+std::string unique_string(const std::string& prefix)
+{
+    auto pid = getpid();
+    auto tid = std::this_thread::get_id();
+    auto clk = std::chrono::steady_clock::now().time_since_epoch().count();
+    std::stringstream ss;
+    ss << std::hex << prefix << "-" << pid << "-" << tid << "-" << clk << "-" << random_string(16);
+    return ss.str();
+}
+
+tmp_dir::tmp_dir(const std::string& prefix)
+    : path(std::filesystem::temp_directory_path() /
+           unique_string(prefix.empty() ? "ck-rtc" : "ck-rtc-" + prefix))
+{
+    std::filesystem::create_directories(this->path);
+}
+
+void tmp_dir::execute(const std::string& cmd) const
+{
+    std::string s = "cd " + path.string() + "; " + cmd;
+    std::system(s.c_str());
+}
+
+tmp_dir::~tmp_dir() { std::filesystem::remove_all(this->path); }
+
+} // namespace rtc
\ No newline at end of file

docs/what-is-ck.rst → docs/conceptual/what-is-ck.rst

@@ -20,7 +20,7 @@ CK utilizes two concepts to achieve performance portability and code maintainabi
 * Algorithm complexity reduction for complex ML operators using an innovative technique called
   "Tensor Coordinate Transformation".
 
-.. image:: data/ck_component.png
+.. image:: ../data/ck_component.png
    :alt: CK Components
 
 
@@ -36,6 +36,6 @@ The CK library is structured into 4 layers:
 
 It also includes a simple wrapper component used to perform tensor transform operations more easily and with fewer lines of code.
 
-.. image:: data/ck_layer.png
+.. image:: ../data/ck_layer.png
    :alt: CK Layers
    
\ No newline at end of file

docs/conf.py

@@ -45,3 +45,5 @@ for sphinx_var in ROCmDocs.SPHINX_VARS:
 
 extensions += ['sphinxcontrib.bibtex']
 bibtex_bibfiles = ['refs.bib']
+
+cpp_id_attributes = ["__global__", "__device__", "__host__"]