Merge branch 'develop' into lib_gemm_softmax_gemm_type

.gitignore

@@ -48,6 +48,11 @@ build*
 .gdb_history
 install.dir*
 
-# directories containing generated documentation
-docs/source/_build/
-docs/docBin/
+# documentation artifacts
+build/
+_build/
+_images/
+_static/
+_templates/
+_toc.yml
+docBin/

.readthedocs.yaml

+# Read the Docs configuration file
+# See https://docs.readthedocs.io/en/stable/config-file/v2.html for details
+
+version: 2
+
+build:
+   os: ubuntu-22.04
+   tools:
+      python: "3.8"
+
+sphinx:
+   configuration: docs/conf.py
+
+formats: [htmlzip]
+
+python:
+   install:
+   - requirements: docs/.sphinx/requirements.txt

CMakeLists.txt

@@ -22,6 +22,7 @@ include(TargetFlags)
 list(APPEND CMAKE_PREFIX_PATH ${CMAKE_INSTALL_PREFIX} ${CMAKE_INSTALL_PREFIX}/llvm ${CMAKE_INSTALL_PREFIX}/hip /opt/rocm /opt/rocm/llvm /opt/rocm/hip)
 
 option(USE_BITINT_EXTENSION_INT4, "Whether to enable clang's BitInt extension to provide int4 data type." OFF)
+option(USE_OPT_NAVI3X, "Whether to enable LDS cumode and Wavefront32 mode for NAVI3X silicons." OFF)
 
 if(USE_BITINT_EXTENSION_INT4)
     add_compile_definitions(CK_EXPERIMENTAL_BIT_INT_EXTENSION_INT4)
@@ -29,6 +30,12 @@ if(USE_BITINT_EXTENSION_INT4)
     message("CK compiled with USE_BITINT_EXTENSION_INT4 set to ${USE_BITINT_EXTENSION_INT4}")
 endif()
 
+if(USE_OPT_NAVI3X)
+    add_compile_options(-mcumode)
+    add_compile_options(-mno-wavefrontsize64)
+    message("CK compiled with USE_OPT_NAVI3X set to ${USE_OPT_NAVI3X}")
+endif()
+
 ## Threads
 set(THREADS_PREFER_PTHREAD_FLAG ON)
 find_package(Threads REQUIRED)

README.md

@@ -7,7 +7,7 @@ CK utilizes two concepts to achieve performance portability and code maintainabi
 * A tile-based programming model
 * Algorithm complexity reduction for complex ML operators, using innovative technique we call "Tensor Coordinate Transformation".
 
-![ALT](/doc/image/ck_component.png "CK Components")
+![ALT](/docs/data/ck_component.png "CK Components")
 
 ## Code Structure
 Current CK library are structured into 4 layers:
@@ -16,7 +16,17 @@ Current CK library are structured into 4 layers:
 * "Instantiated Kernel and Invoker" layer
 * "Client API" layer
 
-![ALT](/doc/image/ck_layer.png "CK Layers")
+![ALT](/docs/data/ck_layer.png "CK Layers")
+
+## Documentation
+
+Run the steps below to build documentation locally.
+
+```
+cd docs
+pip3 install -r .sphinx/requirements.txt
+python3 -m sphinx -T -E -b html -d _build/doctrees -D language=en . _build/html
+```
 
 ## Contributors
 The list of developers and contributors is here: [Contributors](/CONTRIBUTORS.md)

client_example/04_contraction/CMakeLists.txt

-add_executable(client_contraction_scale contraction_scale.cpp)
-target_link_libraries(client_contraction_scale PRIVATE composable_kernel::device_operations)
+add_executable(client_contraction_scale_fp32 contraction_scale_fp32.cpp)
+target_link_libraries(client_contraction_scale_fp32 PRIVATE composable_kernel::device_operations)
 
-add_executable(client_contraction_bilinear contraction_bilinear.cpp)
-target_link_libraries(client_contraction_bilinear PRIVATE composable_kernel::device_operations)
+add_executable(client_contraction_bilinear_fp32 contraction_bilinear_fp32.cpp)
+target_link_libraries(client_contraction_bilinear_fp32 PRIVATE composable_kernel::device_operations)
+
+add_executable(client_contraction_scale_fp64 contraction_scale_fp64.cpp)
+target_link_libraries(client_contraction_scale_fp64 PRIVATE composable_kernel::device_operations)
+
+add_executable(client_contraction_bilinear_fp64 contraction_bilinear_fp64.cpp)
+target_link_libraries(client_contraction_bilinear_fp64 PRIVATE composable_kernel::device_operations)
 
 add_executable(contraction_g1m2n3k1_add_xdl_fp16 contraction_g1m2n3k1_add_xdl_fp16.cpp)
 target_link_libraries(contraction_g1m2n3k1_add_xdl_fp16 PRIVATE composable_kernel::device_operations)

client_example/04_contraction/contraction_bilinear_fp64.cpp

+// SPDX-License-Identifier: MIT
+// Copyright (c) 2018-2022, Advanced Micro Devices, Inc. All rights reserved.
+
+#include <iomanip>
+#include <numeric>
+#include <vector>
+#include <iostream>
+
+#include "ck/ck.hpp"
+#include "ck/tensor_operation/gpu/device/tensor_layout.hpp"
+#include "ck/tensor_operation/gpu/device/device_contraction_multiple_d.hpp"
+#include "ck/tensor_operation/gpu/element/element_wise_operation.hpp"
+
+#include "ck/library/tensor_operation_instance/gpu/contraction_bilinear.hpp"
+#include "ck/library/utility/numeric.hpp"
+
+using F64 = double;
+
+using PassThrough = ck::tensor_operation::element_wise::PassThrough;
+using Bilinear    = ck::tensor_operation::element_wise::Bilinear;
+
+using AElementOp   = PassThrough;
+using BElementOp   = PassThrough;
+using CDEElementOp = Bilinear;
+
+using ADataType        = F64;
+using BDataType        = F64;
+using AccDataType      = F64;
+using CShuffleDataType = F64;
+using DDataType        = F64;
+using DsDataType       = ck::Tuple<DDataType>;
+using EDataType        = F64;
+
+static constexpr ck::index_t NumDimM = 2;
+static constexpr ck::index_t NumDimN = 2;
+static constexpr ck::index_t NumDimK = 2;
+
+struct SimpleDeviceMem
+{
+    SimpleDeviceMem() = delete;
+
+    SimpleDeviceMem(std::size_t mem_size) : p_mem_{}
+    {
+        (void)hipMalloc(static_cast<void**>(&p_mem_), mem_size);
+    }
+
+    void* GetDeviceBuffer() { return p_mem_; }
+
+    ~SimpleDeviceMem() { (void)hipFree(p_mem_); }
+
+    void* p_mem_;
+};
+
+int main(int argc, char* argv[])
+{
+// kknn
+#if 1
+    // A[M0, M1, K0, K1]
+    std::vector<ck::index_t> a_ms_ks_lengths{30, 128, 32, 64};
+    std::vector<ck::index_t> a_ms_ks_strides{524288, 4096, 128, 1};
+    // B[N0, N1, K0, K1]
+    std::vector<ck::index_t> b_ns_ks_lengths{32, 64, 32, 64};
+    std::vector<ck::index_t> b_ns_ks_strides{524288, 4096, 128, 1};
+    // D[M0, M1, N0, N1]
+    std::vector<ck::index_t> d_ms_ns_lengths{30, 128, 32, 64};
+    std::vector<ck::index_t> d_ms_ns_strides{524288, 4096, 128, 1};
+    // E[M0, M1, N0, N1]
+    std::vector<ck::index_t> e_ms_ns_lengths{30, 128, 32, 64};
+    std::vector<ck::index_t> e_ms_ns_strides{524288, 4096, 128, 1};
+// knnn
+#elif 0
+    // A[M0, M1, K0, K1]
+    std::vector<ck::index_t> a_ms_ks_lengths{30, 128, 32, 64};
+    std::vector<ck::index_t> a_ms_ks_strides{524288, 4096, 128, 1};
+    // B[N0, N1, K0, K1]
+    std::vector<ck::index_t> b_ns_ks_lengths{32, 64, 32, 64};
+    std::vector<ck::index_t> b_ns_ks_strides{64, 1, 131072, 2048};
+    // D[M0, M1, N0, N1]
+    std::vector<ck::index_t> d_ms_ns_lengths{30, 128, 32, 64};
+    std::vector<ck::index_t> d_ms_ns_strides{524288, 4096, 128, 1};
+    // E[M0, M1, N0, N1]
+    std::vector<ck::index_t> e_ms_ns_lengths{30, 128, 32, 64};
+    std::vector<ck::index_t> e_ms_ns_strides{524288, 4096, 128, 1};
+// mknn
+#elif 0
+    // A[M0, M1, K0, K1]
+    std::vector<ck::index_t> a_ms_ks_lengths{30, 128, 32, 64};
+    std::vector<ck::index_t> a_ms_ks_strides{128, 1, 245760, 3840};
+    // B[N0, N1, K0, K1]
+    std::vector<ck::index_t> b_ns_ks_lengths{32, 64, 32, 64};
+    std::vector<ck::index_t> b_ns_ks_strides{524288, 4096, 128, 1};
+    // D[M0, M1, N0, N1]
+    std::vector<ck::index_t> d_ms_ns_lengths{30, 128, 32, 64};
+    std::vector<ck::index_t> d_ms_ns_strides{524288, 4096, 128, 1};
+    // E[M0, M1, N0, N1]
+    std::vector<ck::index_t> e_ms_ns_lengths{30, 128, 32, 64};
+    std::vector<ck::index_t> e_ms_ns_strides{524288, 4096, 128, 1};
+// mnnn
+#elif 0
+    // A[M0, M1, K0, K1]
+    std::vector<ck::index_t> a_ms_ks_lengths{30, 128, 32, 64};
+    std::vector<ck::index_t> a_ms_ks_strides{128, 1, 245760, 3840};
+    // B[N0, N1, K0, K1]
+    std::vector<ck::index_t> b_ns_ks_lengths{32, 64, 32, 64};
+    std::vector<ck::index_t> b_ns_ks_strides{64, 1, 131072, 2048};
+    // D[M0, M1, N0, N1]
+    std::vector<ck::index_t> d_ms_ns_lengths{30, 128, 32, 64};
+    std::vector<ck::index_t> d_ms_ns_strides{524288, 4096, 128, 1};
+    // E[M0, M1, N0, N1]
+    std::vector<ck::index_t> e_ms_ns_lengths{30, 128, 32, 64};
+    std::vector<ck::index_t> e_ms_ns_strides{524288, 4096, 128, 1};
+#endif
+
+    float alpha = 1.f;
+    float beta  = 1.f;
+
+    if(argc == 1)
+    {
+        // use default case
+    }
+    else if(argc == 25)
+    {
+        const ck::index_t M0 = std::stoi(argv[1]);
+        const ck::index_t M1 = std::stoi(argv[2]);
+
+        const ck::index_t N0 = std::stoi(argv[3]);
+        const ck::index_t N1 = std::stoi(argv[4]);
+
+        const ck::index_t K0 = std::stoi(argv[5]);
+        const ck::index_t K1 = std::stoi(argv[6]);
+
+        a_ms_ks_lengths = {M0, M1, K0, K1};
+        a_ms_ks_strides = {
+            std::stoi(argv[7]), std::stoi(argv[8]), std::stoi(argv[9]), std::stoi(argv[10])};
+
+        b_ns_ks_lengths = {N0, N1, K0, K1};
+        b_ns_ks_strides = {
+            std::stoi(argv[11]), std::stoi(argv[12]), std::stoi(argv[13]), std::stoi(argv[14])};
+
+        d_ms_ns_lengths = {M0, M1, N0, N1};
+        d_ms_ns_strides = {
+            std::stoi(argv[15]), std::stoi(argv[16]), std::stoi(argv[17]), std::stoi(argv[18])};
+
+        e_ms_ns_lengths = {M0, M1, N0, N1};
+        e_ms_ns_strides = {
+            std::stoi(argv[19]), std::stoi(argv[20]), std::stoi(argv[21]), std::stoi(argv[22])};
+
+        alpha = std::stof(argv[23]);
+        beta  = std::stof(argv[24]);
+    }
+    else
+    {
+        printf("arg1 to 6: M0, M1, N0, N1, K0, K1\n");
+        printf("arg7 to 10: Stride_A_M0, Stride_A_M1, Stride_A_K0, Stride_A_K1\n");
+        printf("arg11 to 14: Stride_B_N0, Stride_B_N1, Stride_B_K0, Stride_B_K1\n");
+        printf("arg15 to 18: Stride_D_M0, Stride_D_M1, Stride_D_N0, Stride_D_N1\n");
+        printf("arg19 to 22: Stride_E_M0, Stride_E_M1, Stride_E_N0, Stride_E_N1\n");
+        printf("arg23 to 24: alpha, beta\n");
+        exit(0);
+    }
+
+    auto f_tensor_space_size = [](auto lengths, auto strides) {
+        std::size_t space_size = 1;
+        for(std::size_t i = 0; i < lengths.size(); ++i)
+        {
+            space_size += (lengths[i] - 1) * strides[i];
+        }
+        return space_size;
+    };
+
+    SimpleDeviceMem a_device_buf(sizeof(ADataType) *
+                                 f_tensor_space_size(a_ms_ks_lengths, a_ms_ks_strides));
+    SimpleDeviceMem b_device_buf(sizeof(BDataType) *
+                                 f_tensor_space_size(b_ns_ks_lengths, b_ns_ks_strides));
+    SimpleDeviceMem d_device_buf(sizeof(DDataType) *
+                                 f_tensor_space_size(d_ms_ns_lengths, d_ms_ns_strides));
+    SimpleDeviceMem e_device_buf(sizeof(EDataType) *
+                                 f_tensor_space_size(e_ms_ns_lengths, e_ms_ns_strides));
+
+    using DeviceOp = ck::tensor_operation::device::DeviceContractionMultipleD<
+        NumDimM,
+        NumDimN,
+        NumDimK,
+        ADataType,
+        BDataType,
+        ck::Tuple<DDataType>,
+        EDataType,
+        ck::tensor_operation::element_wise::PassThrough,
+        ck::tensor_operation::element_wise::PassThrough,
+        ck::tensor_operation::element_wise::Bilinear>;
+
+    // get device op instances
+    const auto op_ptrs = ck::tensor_operation::device::instance::DeviceOperationInstanceFactory<
+        DeviceOp>::GetInstances();
+
+    std::cout << "found " << op_ptrs.size() << " instances" << std::endl;
+
+    const auto a_element_op   = AElementOp{};
+    const auto b_element_op   = BElementOp{};
+    const auto cde_element_op = CDEElementOp{alpha, beta};
+
+    std::string best_op_name;
+    bool found            = false;
+    int best_op_id        = -1;
+    float best_ave_time   = 0;
+    float best_tflops     = 0;
+    float best_gb_per_sec = 0;
+
+    // profile device operation instances
+    std::cout << "Run all instances and do timing" << std::endl;
+
+    for(int i = 0; i < op_ptrs.size(); ++i)
+    {
+        auto& op_ptr = op_ptrs[i];
+
+        auto argument_ptr =
+            op_ptr->MakeArgumentPointer(a_device_buf.GetDeviceBuffer(),
+                                        b_device_buf.GetDeviceBuffer(),
+                                        std::array<const void*, 1>{d_device_buf.GetDeviceBuffer()},
+                                        e_device_buf.GetDeviceBuffer(),
+                                        a_ms_ks_lengths,
+                                        a_ms_ks_strides,
+                                        b_ns_ks_lengths,
+                                        b_ns_ks_strides,
+                                        std::array<std::vector<ck::index_t>, 1>{d_ms_ns_lengths},
+                                        std::array<std::vector<ck::index_t>, 1>{d_ms_ns_strides},
+                                        e_ms_ns_lengths,
+                                        e_ms_ns_strides,
+                                        a_element_op,
+                                        b_element_op,
+                                        cde_element_op);
+
+        auto invoker_ptr = op_ptr->MakeInvokerPointer();
+
+        std::string op_name = op_ptr->GetTypeString();
+
+        if(op_ptr->IsSupportedArgument(argument_ptr.get()))
+        {
+            float ave_time = invoker_ptr->Run(argument_ptr.get(), StreamConfig{nullptr, true});
+
+            ck::index_t M = ck::accumulate_n<ck::index_t>(
+                e_ms_ns_lengths.begin(), NumDimM, 1, std::multiplies<>{});
+
+            ck::index_t N = ck::accumulate_n<ck::index_t>(
+                e_ms_ns_lengths.begin() + NumDimM, NumDimN, 1, std::multiplies<>{});
+
+            ck::index_t K = ck::accumulate_n<ck::index_t>(
+                a_ms_ks_lengths.begin() + NumDimM, NumDimK, 1, std::multiplies<>{});
+
+            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(DDataType) * M * N + sizeof(EDataType) * M * N;
+
+            float tflops = static_cast<float>(flop) / 1.E9 / ave_time;
+
+            float gb_per_sec = num_btype / 1.E6 / ave_time;
+
+            std::cout << "Perf: " << std::setw(10) << ave_time << " ms, " << tflops << " TFlops, "
+                      << gb_per_sec << " GB/s, " << op_name << std::endl;
+
+            if(tflops > best_tflops)
+            {
+                found           = true;
+                best_op_id      = i;
+                best_op_name    = op_name;
+                best_tflops     = tflops;
+                best_ave_time   = ave_time;
+                best_gb_per_sec = gb_per_sec;
+            }
+        }
+        else
+        {
+            std::cout << op_name << " does not support this problem" << std::endl;
+        }
+    }
+
+    std::cout << "Best Perf: " << best_ave_time << " ms, " << best_tflops << " TFlops, "
+              << best_gb_per_sec << " GB/s, " << best_op_name << std::endl;
+
+    return 0;
+}

client_example/04_contraction/contraction_scale_fp64.cpp

+// SPDX-License-Identifier: MIT
+// Copyright (c) 2018-2022, Advanced Micro Devices, Inc. All rights reserved.
+
+#include <iomanip>
+#include <numeric>
+#include <vector>
+#include <iostream>
+
+#include "ck/ck.hpp"
+#include "ck/tensor_operation/gpu/device/tensor_layout.hpp"
+#include "ck/tensor_operation/gpu/device/device_contraction_multiple_d.hpp"
+#include "ck/tensor_operation/gpu/element/element_wise_operation.hpp"
+
+#include "ck/library/tensor_operation_instance/gpu/contraction_scale.hpp"
+#include "ck/library/utility/numeric.hpp"
+
+using F64 = double;
+
+using PassThrough = ck::tensor_operation::element_wise::PassThrough;
+using Scale       = ck::tensor_operation::element_wise::Scale;
+
+using AElementOp   = PassThrough;
+using BElementOp   = PassThrough;
+using CDEElementOp = Scale;
+
+using ADataType        = F64;
+using BDataType        = F64;
+using AccDataType      = F64;
+using CShuffleDataType = F64;
+using DsDataType       = ck::Tuple<>;
+using EDataType        = F64;
+
+static constexpr ck::index_t NumDimM = 2;
+static constexpr ck::index_t NumDimN = 2;
+static constexpr ck::index_t NumDimK = 2;
+
+struct SimpleDeviceMem
+{
+    SimpleDeviceMem() = delete;
+
+    SimpleDeviceMem(std::size_t mem_size) : p_mem_{}
+    {
+        (void)hipMalloc(static_cast<void**>(&p_mem_), mem_size);
+    }
+
+    void* GetDeviceBuffer() { return p_mem_; }
+
+    ~SimpleDeviceMem() { (void)hipFree(p_mem_); }
+
+    void* p_mem_;
+};
+
+int main(int argc, char* argv[])
+{
+// kkn
+#if 1
+    // A[M0, M1, K0, K1]
+    std::vector<ck::index_t> a_ms_ks_lengths{30, 128, 32, 64};
+    std::vector<ck::index_t> a_ms_ks_strides{524288, 4096, 128, 1};
+    // B[N0, N1, K0, K1]
+    std::vector<ck::index_t> b_ns_ks_lengths{32, 64, 32, 64};
+    std::vector<ck::index_t> b_ns_ks_strides{524288, 4096, 128, 1};
+    // D[M0, M1, N0, N1]
+    std::vector<ck::index_t> d_ms_ns_lengths{30, 128, 32, 64};
+    std::vector<ck::index_t> d_ms_ns_strides{524288, 4096, 128, 1};
+    // E[M0, M1, N0, N1]
+    std::vector<ck::index_t> e_ms_ns_lengths{30, 128, 32, 64};
+    std::vector<ck::index_t> e_ms_ns_strides{524288, 4096, 128, 1};
+// knn
+#elif 0
+    // A[M0, M1, K0, K1]
+    std::vector<ck::index_t> a_ms_ks_lengths{30, 128, 32, 64};
+    std::vector<ck::index_t> a_ms_ks_strides{524288, 4096, 128, 1};
+    // B[N0, N1, K0, K1]
+    std::vector<ck::index_t> b_ns_ks_lengths{32, 64, 32, 64};
+    std::vector<ck::index_t> b_ns_ks_strides{64, 1, 131072, 2048};
+    // D[M0, M1, N0, N1]
+    std::vector<ck::index_t> d_ms_ns_lengths{30, 128, 32, 64};
+    std::vector<ck::index_t> d_ms_ns_strides{524288, 4096, 128, 1};
+    // E[M0, M1, N0, N1]
+    std::vector<ck::index_t> e_ms_ns_lengths{30, 128, 32, 64};
+    std::vector<ck::index_t> e_ms_ns_strides{524288, 4096, 128, 1};
+// mkn
+#elif 0
+    // A[M0, M1, K0, K1]
+    std::vector<ck::index_t> a_ms_ks_lengths{30, 128, 32, 64};
+    std::vector<ck::index_t> a_ms_ks_strides{128, 1, 245760, 3840};
+    // B[N0, N1, K0, K1]
+    std::vector<ck::index_t> b_ns_ks_lengths{32, 64, 32, 64};
+    std::vector<ck::index_t> b_ns_ks_strides{524288, 4096, 128, 1};
+    // D[M0, M1, N0, N1]
+    std::vector<ck::index_t> d_ms_ns_lengths{30, 128, 32, 64};
+    std::vector<ck::index_t> d_ms_ns_strides{524288, 4096, 128, 1};
+    // E[M0, M1, N0, N1]
+    std::vector<ck::index_t> e_ms_ns_lengths{30, 128, 32, 64};
+    std::vector<ck::index_t> e_ms_ns_strides{524288, 4096, 128, 1};
+// mnn
+#elif 0
+    // A[M0, M1, K0, K1]
+    std::vector<ck::index_t> a_ms_ks_lengths{30, 128, 32, 64};
+    std::vector<ck::index_t> a_ms_ks_strides{128, 1, 245760, 3840};
+    // B[N0, N1, K0, K1]
+    std::vector<ck::index_t> b_ns_ks_lengths{32, 64, 32, 64};
+    std::vector<ck::index_t> b_ns_ks_strides{64, 1, 131072, 2048};
+    // D[M0, M1, N0, N1]
+    std::vector<ck::index_t> d_ms_ns_lengths{30, 128, 32, 64};
+    std::vector<ck::index_t> d_ms_ns_strides{524288, 4096, 128, 1};
+    // E[M0, M1, N0, N1]
+    std::vector<ck::index_t> e_ms_ns_lengths{30, 128, 32, 64};
+    std::vector<ck::index_t> e_ms_ns_strides{524288, 4096, 128, 1};
+#endif
+
+    float scale = 1.f;
+
+    if(argc == 1)
+    {
+        // use default case
+    }
+    else if(argc == 20)
+    {
+        const ck::index_t M0 = std::stoi(argv[1]);
+        const ck::index_t M1 = std::stoi(argv[2]);
+
+        const ck::index_t N0 = std::stoi(argv[3]);
+        const ck::index_t N1 = std::stoi(argv[4]);
+
+        const ck::index_t K0 = std::stoi(argv[5]);
+        const ck::index_t K1 = std::stoi(argv[6]);
+
+        a_ms_ks_lengths = {M0, M1, K0, K1};
+        a_ms_ks_strides = {
+            std::stoi(argv[7]), std::stoi(argv[8]), std::stoi(argv[9]), std::stoi(argv[10])};
+
+        b_ns_ks_lengths = {N0, N1, K0, K1};
+        b_ns_ks_strides = {
+            std::stoi(argv[11]), std::stoi(argv[12]), std::stoi(argv[13]), std::stoi(argv[14])};
+
+        e_ms_ns_lengths = {M0, M1, N0, N1};
+        e_ms_ns_strides = {
+            std::stoi(argv[15]), std::stoi(argv[16]), std::stoi(argv[17]), std::stoi(argv[18])};
+
+        scale = std::stof(argv[19]);
+    }
+    else
+    {
+        printf("arg1 to 6: M0, M1, N0, N1, K0, K1\n");
+        printf("arg7 to 10: Stride_A_M0, Stride_A_M1, Stride_A_K0, Stride_A_K1\n");
+        printf("arg11 to 14: Stride_B_N0, Stride_B_N1, Stride_B_K0, Stride_B_K1\n");
+        printf("arg15 to 18: Stride_E_M0, Stride_E_M1, Stride_E_N0, Stride_E_N1\n");
+        printf("arg19: scale\n");
+        exit(0);
+    }
+
+    auto f_tensor_space_size = [](auto lengths, auto strides) {
+        std::size_t space_size = 1;
+        for(std::size_t i = 0; i < lengths.size(); ++i)
+        {
+            space_size += (lengths[i] - 1) * strides[i];
+        }
+        return space_size;
+    };
+
+    SimpleDeviceMem a_device_buf(sizeof(ADataType) *
+                                 f_tensor_space_size(a_ms_ks_lengths, a_ms_ks_strides));
+    SimpleDeviceMem b_device_buf(sizeof(BDataType) *
+                                 f_tensor_space_size(b_ns_ks_lengths, b_ns_ks_strides));
+    SimpleDeviceMem e_device_buf(sizeof(EDataType) *
+                                 f_tensor_space_size(e_ms_ns_lengths, e_ms_ns_strides));
+
+    using DeviceOp = ck::tensor_operation::device::DeviceContractionMultipleD<
+        NumDimM,
+        NumDimN,
+        NumDimK,
+        ADataType,
+        BDataType,
+        ck::Tuple<>,
+        EDataType,
+        ck::tensor_operation::element_wise::PassThrough,
+        ck::tensor_operation::element_wise::PassThrough,
+        ck::tensor_operation::element_wise::Scale>;
+
+    // get device op instances
+    const auto op_ptrs = ck::tensor_operation::device::instance::DeviceOperationInstanceFactory<
+        DeviceOp>::GetInstances();
+
+    std::cout << "found " << op_ptrs.size() << " instances" << std::endl;
+
+    const auto a_element_op   = AElementOp{};
+    const auto b_element_op   = BElementOp{};
+    const auto cde_element_op = CDEElementOp{scale};
+
+    std::string best_op_name;
+    bool found            = false;
+    int best_op_id        = -1;
+    float best_ave_time   = 0;
+    float best_tflops     = 0;
+    float best_gb_per_sec = 0;
+
+    // profile device operation instances
+    std::cout << "Run all instances and do timing" << std::endl;
+
+    for(int i = 0; i < op_ptrs.size(); ++i)
+    {
+        auto& op_ptr = op_ptrs[i];
+
+        auto argument_ptr = op_ptr->MakeArgumentPointer(a_device_buf.GetDeviceBuffer(),
+                                                        b_device_buf.GetDeviceBuffer(),
+                                                        std::array<const void*, 0>{},
+                                                        e_device_buf.GetDeviceBuffer(),
+                                                        a_ms_ks_lengths,
+                                                        a_ms_ks_strides,
+                                                        b_ns_ks_lengths,
+                                                        b_ns_ks_strides,
+                                                        std::array<std::vector<ck::index_t>, 0>{},
+                                                        std::array<std::vector<ck::index_t>, 0>{},
+                                                        e_ms_ns_lengths,
+                                                        e_ms_ns_strides,
+                                                        a_element_op,
+                                                        b_element_op,
+                                                        cde_element_op);
+
+        auto invoker_ptr = op_ptr->MakeInvokerPointer();
+
+        std::string op_name = op_ptr->GetTypeString();
+
+        if(op_ptr->IsSupportedArgument(argument_ptr.get()))
+        {
+            float ave_time = invoker_ptr->Run(argument_ptr.get(), StreamConfig{nullptr, true});
+
+            ck::index_t M = ck::accumulate_n<ck::index_t>(
+                e_ms_ns_lengths.begin(), NumDimM, 1, std::multiplies<>{});
+
+            ck::index_t N = ck::accumulate_n<ck::index_t>(
+                e_ms_ns_lengths.begin() + NumDimM, NumDimN, 1, std::multiplies<>{});
+
+            ck::index_t K = ck::accumulate_n<ck::index_t>(
+                a_ms_ks_lengths.begin() + NumDimM, NumDimK, 1, std::multiplies<>{});
+
+            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(EDataType) * M * N;
+
+            float tflops = static_cast<float>(flop) / 1.E9 / ave_time;
+
+            float gb_per_sec = num_btype / 1.E6 / ave_time;
+
+            std::cout << "Perf: " << std::setw(10) << ave_time << " ms, " << tflops << " TFlops, "
+                      << gb_per_sec << " GB/s, " << op_name << std::endl;
+
+            if(tflops > best_tflops)
+            {
+                found           = true;
+                best_op_id      = i;
+                best_op_name    = op_name;
+                best_tflops     = tflops;
+                best_ave_time   = ave_time;
+                best_gb_per_sec = gb_per_sec;
+            }
+        }
+        else
+        {
+            std::cout << op_name << " does not support this problem" << std::endl;
+        }
+    }
+
+    std::cout << "Best Perf: " << best_ave_time << " ms, " << best_tflops << " TFlops, "
+              << best_gb_per_sec << " GB/s, " << best_op_name << std::endl;
+
+    return 0;
+}

client_example/09_quantization/CMakeLists.txt

+add_executable(client_conv2d_fwd_bias_tanh_perchannel_quantization conv2d_fwd_bias_tanh_perchannel_quantization.cpp)
+target_link_libraries(client_conv2d_fwd_bias_tanh_perchannel_quantization PRIVATE composable_kernel::device_operations)
+
 add_executable(client_conv2d_fwd_bias_relu_perchannel_quantization conv2d_fwd_bias_relu_perchannel_quantization.cpp)
 target_link_libraries(client_conv2d_fwd_bias_relu_perchannel_quantization PRIVATE composable_kernel::device_operations)
 
+add_executable(client_conv2d_fwd_bias_tanh_perlayer_quantization conv2d_fwd_bias_tanh_perlayer_quantization.cpp)
+target_link_libraries(client_conv2d_fwd_bias_tanh_perlayer_quantization PRIVATE composable_kernel::device_operations)
+
 add_executable(client_conv2d_fwd_bias_relu_perlayer_quantization conv2d_fwd_bias_relu_perlayer_quantization.cpp)
 target_link_libraries(client_conv2d_fwd_bias_relu_perlayer_quantization PRIVATE composable_kernel::device_operations)
 

client_example/09_quantization/conv2d_fwd_bias_relu_perlayer_quantization.cpp

@@ -26,15 +26,16 @@ using OutElementOp = ck::tensor_operation::element_wise::Add_Activation_Mul_Clam
 
 static constexpr ck::index_t NumDimSpatial = 2;
 static constexpr ck::index_t G             = 1;
-static constexpr ck::index_t N             = 4;   // batch size
-static constexpr ck::index_t K             = 64;  // output channel
-static constexpr ck::index_t C             = 192; // input channel
-static constexpr ck::index_t Y             = 3;   // filter H
-static constexpr ck::index_t X             = 3;   // filter W
-static constexpr ck::index_t Hi            = 71;  // input H
-static constexpr ck::index_t Wi            = 71;  // input W
-static constexpr ck::index_t Ho            = 36;  // output H
-static constexpr ck::index_t Wo            = 36;  // output W
+static constexpr ck::index_t N             = 4;    // batch size
+static constexpr ck::index_t K             = 64;   // output channel
+static constexpr ck::index_t C             = 192;  // input channel
+static constexpr ck::index_t Y             = 3;    // filter H
+static constexpr ck::index_t X             = 3;    // filter W
+static constexpr ck::index_t Hi            = 71;   // input H
+static constexpr ck::index_t Wi            = 71;   // input W
+static constexpr ck::index_t Ho            = 36;   // output H
+static constexpr ck::index_t Wo            = 36;   // output W
+static constexpr float requant_scale       = 0.5f; // requantize qAcc to qz
 
 struct SimpleDeviceMem
 {
@@ -102,26 +103,27 @@ int main(int argc, char* argv[])
 
     for(int i = 0; i < op_ptrs.size(); ++i)
     {
-        auto& op_ptr      = op_ptrs[i];
-        auto argument_ptr = op_ptr->MakeArgumentPointer(in.GetDeviceBuffer(),
-                                                        wei.GetDeviceBuffer(),
-                                                        {bias.GetDeviceBuffer()},
-                                                        out.GetDeviceBuffer(),
-                                                        in_lengths,
-                                                        in_strides,
-                                                        weight_lengths,
-                                                        weight_strides,
-                                                        {bias_lengths},
-                                                        {bias_strides},
-                                                        out_lengths,
-                                                        out_strides,
-                                                        conv_strides,
-                                                        conv_dilations,
-                                                        in_left_pad,
-                                                        in_right_pad,
-                                                        PassThrough{},
-                                                        PassThrough{},
-                                                        OutElementOp{0.5f, ActivationOp{}});
+        auto& op_ptr = op_ptrs[i];
+        auto argument_ptr =
+            op_ptr->MakeArgumentPointer(in.GetDeviceBuffer(),
+                                        wei.GetDeviceBuffer(),
+                                        {bias.GetDeviceBuffer()},
+                                        out.GetDeviceBuffer(),
+                                        in_lengths,
+                                        in_strides,
+                                        weight_lengths,
+                                        weight_strides,
+                                        {bias_lengths},
+                                        {bias_strides},
+                                        out_lengths,
+                                        out_strides,
+                                        conv_strides,
+                                        conv_dilations,
+                                        in_left_pad,
+                                        in_right_pad,
+                                        PassThrough{},
+                                        PassThrough{},
+                                        OutElementOp{requant_scale, ActivationOp{}});
 
         auto invoker_ptr    = op_ptr->MakeInvokerPointer();
         std::string op_name = op_ptr->GetTypeString();
@@ -165,25 +167,26 @@ int main(int argc, char* argv[])
         auto& op_ptr = op_ptrs[best_op_id];
         std::cout << "Run the best instance without timing: " << op_ptr->GetTypeString()
                   << std::endl;
-        auto argument_ptr = op_ptr->MakeArgumentPointer(in.GetDeviceBuffer(),
-                                                        wei.GetDeviceBuffer(),
-                                                        {bias.GetDeviceBuffer()},
-                                                        out.GetDeviceBuffer(),
-                                                        in_lengths,
-                                                        in_strides,
-                                                        weight_lengths,
-                                                        weight_strides,
-                                                        {bias_lengths},
-                                                        {bias_strides},
-                                                        out_lengths,
-                                                        out_strides,
-                                                        conv_strides,
-                                                        conv_dilations,
-                                                        in_left_pad,
-                                                        in_right_pad,
-                                                        PassThrough{},
-                                                        PassThrough{},
-                                                        OutElementOp{0.5f, ActivationOp{}});
+        auto argument_ptr =
+            op_ptr->MakeArgumentPointer(in.GetDeviceBuffer(),
+                                        wei.GetDeviceBuffer(),
+                                        {bias.GetDeviceBuffer()},
+                                        out.GetDeviceBuffer(),
+                                        in_lengths,
+                                        in_strides,
+                                        weight_lengths,
+                                        weight_strides,
+                                        {bias_lengths},
+                                        {bias_strides},
+                                        out_lengths,
+                                        out_strides,
+                                        conv_strides,
+                                        conv_dilations,
+                                        in_left_pad,
+                                        in_right_pad,
+                                        PassThrough{},
+                                        PassThrough{},
+                                        OutElementOp{requant_scale, ActivationOp{}});
 
         auto invoker_ptr = op_ptr->MakeInvokerPointer();
 

client_example/09_quantization/conv2d_fwd_bias_tanh_perchannel_quantization.cpp

+// SPDX-License-Identifier: MIT
+// Copyright (c) 2018-2022, Advanced Micro Devices, Inc. All rights reserved.
+
+#include <iomanip>
+#include <iostream>
+#include <vector>
+
+#include "ck/ck.hpp"
+#include "ck/library/tensor_operation_instance/gpu/quantization/grouped_convolution_bias_forward_perchannel_quantization.hpp"
+#include "ck/tensor_operation/gpu/device/tensor_layout.hpp"
+#include "ck/tensor_operation/gpu/device/device_conv_fwd.hpp"
+#include "ck/tensor_operation/gpu/element/element_wise_operation.hpp"
+
+using InDataType           = int8_t;
+using WeiDataType          = int8_t;
+using BiasDataType         = int32_t;
+using RequantScaleDataType = float;
+using OutDataType          = int8_t;
+
+using InLayout           = ck::tensor_layout::convolution::GNHWC;
+using WeiLayout          = ck::tensor_layout::convolution::GKYXC;
+using BiasLayout         = ck::tensor_layout::convolution::G_K;
+using RequantScaleLayout = ck::tensor_layout::convolution::G_K;
+using OutLayout          = ck::tensor_layout::convolution::GNHWK;
+using PassThrough        = ck::tensor_operation::element_wise::PassThrough;
+using ActivationOp       = ck::tensor_operation::element_wise::TanH;
+using OutElementOp =
+    ck::tensor_operation::element_wise::Add_Mul2_Activation_Mul_Clamp<ActivationOp>;
+
+static constexpr ck::index_t NumDimSpatial = 2;
+static constexpr ck::index_t G             = 1;
+static constexpr ck::index_t N             = 4;    // batch size
+static constexpr ck::index_t K             = 64;   // output channel
+static constexpr ck::index_t C             = 192;  // input channel
+static constexpr ck::index_t Y             = 3;    // filter H
+static constexpr ck::index_t X             = 3;    // filter W
+static constexpr ck::index_t Hi            = 71;   // input H
+static constexpr ck::index_t Wi            = 71;   // input W
+static constexpr ck::index_t Ho            = 36;   // output H
+static constexpr ck::index_t Wo            = 36;   // output W
+static constexpr float sz_inv              = 0.5f; // inverse of scale_z
+
+struct SimpleDeviceMem
+{
+    SimpleDeviceMem() = delete;
+
+    SimpleDeviceMem(std::size_t mem_size) : p_mem_{}
+    {
+        (void)hipMalloc(static_cast<void**>(&p_mem_), mem_size);
+    }
+
+    void* GetDeviceBuffer() { return p_mem_; }
+
+    ~SimpleDeviceMem() { (void)hipFree(p_mem_); }
+
+    void* p_mem_;
+};
+
+int main(int argc, char* argv[])
+{
+    std::array<ck::index_t, 5> in_lengths{G, N, C, Hi, Wi};
+    std::array<ck::index_t, 5> in_strides{N * Hi * Wi * C, Hi * Wi * C, 1, Wi * C, C};
+    std::array<ck::index_t, 5> weight_lengths{G, K, C, Y, X};
+    std::array<ck::index_t, 5> weight_strides{K * Y * X * C, Y * X * C, 1, X * C, C};
+    std::array<ck::index_t, 5> bias_lengths{G, N, K, Ho, Wo};
+    std::array<ck::index_t, 5> bias_strides{K, 0, 1, 0, 0};
+    std::array<ck::index_t, 5> requant_scale_lengths{G, N, K, Ho, Wo};
+    std::array<ck::index_t, 5> requant_scale_strides{K, 0, 1, 0, 0};
+    std::array<ck::index_t, 5> out_lengths{G, N, K, Ho, Wo};
+    std::array<ck::index_t, 5> out_strides{N * Ho * Wo * K, Ho * Wo * K, 1, Wo * K, K};
+    std::array<ck::index_t, 2> in_left_pad{1, 1};
+    std::array<ck::index_t, 2> in_right_pad{1, 1};
+    std::array<ck::index_t, 2> conv_strides{2, 2};
+    std::array<ck::index_t, 2> conv_dilations{1, 1};
+
+    SimpleDeviceMem in(sizeof(InDataType) * N * Hi * Wi * C);
+    SimpleDeviceMem wei(sizeof(WeiDataType) * K * Y * X * C);
+    SimpleDeviceMem bias(sizeof(BiasDataType) * K * Y * X * C);
+    SimpleDeviceMem requant_scale(sizeof(RequantScaleDataType) * K * Y * X * C);
+    SimpleDeviceMem out(sizeof(OutDataType) * N * Ho * Wo * K);
+
+    using DeviceOp = ck::tensor_operation::device::DeviceGroupedConvFwdMultipleD<
+        NumDimSpatial,
+        InLayout,
+        WeiLayout,
+        ck::Tuple<BiasLayout, RequantScaleLayout>,
+        OutLayout,
+        InDataType,
+        WeiDataType,
+        ck::Tuple<BiasDataType, RequantScaleDataType>,
+        OutDataType,
+        PassThrough,
+        PassThrough,
+        OutElementOp>;
+    // get device op instances
+    const auto op_ptrs = ck::tensor_operation::device::instance::DeviceOperationInstanceFactory<
+        DeviceOp>::GetInstances();
+
+    std::cout << "found " << op_ptrs.size() << " instances" << std::endl;
+
+    std::string best_op_name;
+    int best_op_id        = -1;
+    float best_avg_time   = std::numeric_limits<float>::max();
+    float best_gb_per_sec = 0;
+    float best_tflops     = 0;
+
+    // profile device operation instances
+    std::cout << "Run all instances and do timing" << std::endl;
+
+    for(int i = 0; i < op_ptrs.size(); ++i)
+    {
+        auto& op_ptr = op_ptrs[i];
+        auto argument_ptr =
+            op_ptr->MakeArgumentPointer(in.GetDeviceBuffer(),
+                                        wei.GetDeviceBuffer(),
+                                        {bias.GetDeviceBuffer(), requant_scale.GetDeviceBuffer()},
+                                        out.GetDeviceBuffer(),
+                                        in_lengths,
+                                        in_strides,
+                                        weight_lengths,
+                                        weight_strides,
+                                        {bias_lengths, requant_scale_lengths},
+                                        {bias_strides, requant_scale_strides},
+                                        out_lengths,
+                                        out_strides,
+                                        conv_strides,
+                                        conv_dilations,
+                                        in_left_pad,
+                                        in_right_pad,
+                                        PassThrough{},
+                                        PassThrough{},
+                                        OutElementOp{sz_inv, ActivationOp{}});
+
+        auto invoker_ptr    = op_ptr->MakeInvokerPointer();
+        std::string op_name = op_ptr->GetTypeString();
+
+        if(op_ptr->IsSupportedArgument(argument_ptr.get()))
+        {
+            float avg_time = invoker_ptr->Run(argument_ptr.get(), StreamConfig{nullptr, true});
+
+            std::size_t flop = G * 2 * N * K * C * Ho * Wo * Y * X;
+            std::size_t num_bytes =
+                G * sizeof(InDataType) * N * Hi * Wi * C + G * sizeof(WeiDataType) * K * Y * X * C +
+                G * sizeof(BiasDataType) * K + G * sizeof(RequantScaleDataType) * K +
+                G * sizeof(OutDataType) * N * Ho * Wo * K;
+
+            float tflops     = static_cast<float>(flop) / 1.E9 / avg_time;
+            float gb_per_sec = num_bytes / 1.E6 / avg_time;
+
+            std::cout << "Perf: " << std::setw(10) << avg_time << " ms, " << tflops << " TFlops, "
+                      << gb_per_sec << " GB/s, " << op_name << std::endl;
+
+            if(tflops > best_tflops)
+            {
+                best_op_id      = i;
+                best_op_name    = op_name;
+                best_avg_time   = avg_time;
+                best_gb_per_sec = gb_per_sec;
+                best_tflops     = tflops;
+            }
+        }
+        else
+        {
+            std::cout << op_name << " does not support this problem" << std::endl;
+        }
+    }
+
+    // run the best intance
+    if(best_op_id != -1)
+    {
+        std::cout << "Best Perf: " << std::setw(10) << best_avg_time << " ms, " << best_tflops
+                  << " TFlops, " << best_gb_per_sec << " GB/s, " << best_op_name << std::endl;
+
+        auto& op_ptr = op_ptrs[best_op_id];
+        std::cout << "Run the best instance without timing: " << op_ptr->GetTypeString()
+                  << std::endl;
+        auto argument_ptr =
+            op_ptr->MakeArgumentPointer(in.GetDeviceBuffer(),
+                                        wei.GetDeviceBuffer(),
+                                        {bias.GetDeviceBuffer(), requant_scale.GetDeviceBuffer()},
+                                        out.GetDeviceBuffer(),
+                                        in_lengths,
+                                        in_strides,
+                                        weight_lengths,
+                                        weight_strides,
+                                        {bias_lengths, requant_scale_lengths},
+                                        {bias_strides, requant_scale_strides},
+                                        out_lengths,
+                                        out_strides,
+                                        conv_strides,
+                                        conv_dilations,
+                                        in_left_pad,
+                                        in_right_pad,
+                                        PassThrough{},
+                                        PassThrough{},
+                                        OutElementOp{sz_inv, ActivationOp{}});
+
+        auto invoker_ptr = op_ptr->MakeInvokerPointer();
+
+        if(op_ptr->IsSupportedArgument(argument_ptr.get()))
+        {
+            invoker_ptr->Run(argument_ptr.get(), StreamConfig{nullptr, false});
+        }
+
+        std::cout << "Done" << std::endl;
+    }
+
+    return 0;
+}
\ No newline at end of file

client_example/09_quantization/conv2d_fwd_bias_tanh_perlayer_quantization.cpp

+// SPDX-License-Identifier: MIT
+// Copyright (c) 2018-2022, Advanced Micro Devices, Inc. All rights reserved.
+
+#include <iomanip>
+#include <iostream>
+#include <vector>
+
+#include "ck/ck.hpp"
+#include "ck/library/tensor_operation_instance/gpu/quantization/grouped_convolution_bias_forward_perlayer_quantization.hpp"
+#include "ck/tensor_operation/gpu/device/tensor_layout.hpp"
+#include "ck/tensor_operation/gpu/device/device_conv_fwd.hpp"
+#include "ck/tensor_operation/gpu/element/element_wise_operation.hpp"
+
+using InDataType   = int8_t;
+using WeiDataType  = int8_t;
+using BiasDataType = int32_t;
+using OutDataType  = int8_t;
+
+using InLayout     = ck::tensor_layout::convolution::GNHWC;
+using WeiLayout    = ck::tensor_layout::convolution::GKYXC;
+using BiasLayout   = ck::tensor_layout::convolution::G_K;
+using OutLayout    = ck::tensor_layout::convolution::GNHWK;
+using PassThrough  = ck::tensor_operation::element_wise::PassThrough;
+using ActivationOp = ck::tensor_operation::element_wise::TanH;
+using OutElementOp = ck::tensor_operation::element_wise::Add_Mul_Activation_Mul_Clamp<ActivationOp>;
+
+static constexpr ck::index_t NumDimSpatial = 2;
+static constexpr ck::index_t G             = 1;
+static constexpr ck::index_t N             = 4;    // batch size
+static constexpr ck::index_t K             = 64;   // output channel
+static constexpr ck::index_t C             = 192;  // input channel
+static constexpr ck::index_t Y             = 3;    // filter H
+static constexpr ck::index_t X             = 3;    // filter W
+static constexpr ck::index_t Hi            = 71;   // input H
+static constexpr ck::index_t Wi            = 71;   // input W
+static constexpr ck::index_t Ho            = 36;   // output H
+static constexpr ck::index_t Wo            = 36;   // output W
+static constexpr float sacc                = 0.5f; //  scale of acc
+static constexpr float sz_inv              = 0.5f; // inverse of scale_z
+
+struct SimpleDeviceMem
+{
+    SimpleDeviceMem() = delete;
+
+    SimpleDeviceMem(std::size_t mem_size) : p_mem_{}
+    {
+        (void)hipMalloc(static_cast<void**>(&p_mem_), mem_size);
+    }
+
+    void* GetDeviceBuffer() { return p_mem_; }
+
+    ~SimpleDeviceMem() { (void)hipFree(p_mem_); }
+
+    void* p_mem_;
+};
+
+int main(int argc, char* argv[])
+{
+    std::array<ck::index_t, 5> in_lengths{G, N, C, Hi, Wi};
+    std::array<ck::index_t, 5> in_strides{N * Hi * Wi * C, Hi * Wi * C, 1, Wi * C, C};
+    std::array<ck::index_t, 5> weight_lengths{G, K, C, Y, X};
+    std::array<ck::index_t, 5> weight_strides{K * Y * X * C, Y * X * C, 1, X * C, C};
+    std::array<ck::index_t, 5> bias_lengths{G, N, K, Ho, Wo};
+    std::array<ck::index_t, 5> bias_strides{K, 0, 1, 0, 0};
+    std::array<ck::index_t, 5> out_lengths{G, N, K, Ho, Wo};
+    std::array<ck::index_t, 5> out_strides{N * Ho * Wo * K, Ho * Wo * K, 1, Wo * K, K};
+    std::array<ck::index_t, 2> in_left_pad{1, 1};
+    std::array<ck::index_t, 2> in_right_pad{1, 1};
+    std::array<ck::index_t, 2> conv_strides{2, 2};
+    std::array<ck::index_t, 2> conv_dilations{1, 1};
+
+    SimpleDeviceMem in(sizeof(InDataType) * N * Hi * Wi * C);
+    SimpleDeviceMem wei(sizeof(WeiDataType) * K * Y * X * C);
+    SimpleDeviceMem bias(sizeof(BiasDataType) * K * Y * X * C);
+    SimpleDeviceMem out(sizeof(OutDataType) * N * Ho * Wo * K);
+
+    using DeviceOp =
+        ck::tensor_operation::device::DeviceGroupedConvFwdMultipleD<NumDimSpatial,
+                                                                    InLayout,
+                                                                    WeiLayout,
+                                                                    ck::Tuple<BiasLayout>,
+                                                                    OutLayout,
+                                                                    InDataType,
+                                                                    WeiDataType,
+                                                                    ck::Tuple<BiasDataType>,
+                                                                    OutDataType,
+                                                                    PassThrough,
+                                                                    PassThrough,
+                                                                    OutElementOp>;
+    // get device op instances
+    const auto op_ptrs = ck::tensor_operation::device::instance::DeviceOperationInstanceFactory<
+        DeviceOp>::GetInstances();
+
+    std::cout << "found " << op_ptrs.size() << " instances" << std::endl;
+
+    std::string best_op_name;
+    int best_op_id        = -1;
+    float best_avg_time   = std::numeric_limits<float>::max();
+    float best_gb_per_sec = 0;
+    float best_tflops     = 0;
+
+    // profile device operation instances
+    std::cout << "Run all instances and do timing" << std::endl;
+
+    for(int i = 0; i < op_ptrs.size(); ++i)
+    {
+        auto& op_ptr      = op_ptrs[i];
+        auto argument_ptr = op_ptr->MakeArgumentPointer(in.GetDeviceBuffer(),
+                                                        wei.GetDeviceBuffer(),
+                                                        {bias.GetDeviceBuffer()},
+                                                        out.GetDeviceBuffer(),
+                                                        in_lengths,
+                                                        in_strides,
+                                                        weight_lengths,
+                                                        weight_strides,
+                                                        {bias_lengths},
+                                                        {bias_strides},
+                                                        out_lengths,
+                                                        out_strides,
+                                                        conv_strides,
+                                                        conv_dilations,
+                                                        in_left_pad,
+                                                        in_right_pad,
+                                                        PassThrough{},
+                                                        PassThrough{},
+                                                        OutElementOp{sacc, sz_inv, ActivationOp{}});
+
+        auto invoker_ptr    = op_ptr->MakeInvokerPointer();
+        std::string op_name = op_ptr->GetTypeString();
+
+        if(op_ptr->IsSupportedArgument(argument_ptr.get()))
+        {
+            float avg_time = invoker_ptr->Run(argument_ptr.get(), StreamConfig{nullptr, true});
+
+            std::size_t flop = G * 2 * N * K * C * Ho * Wo * Y * X;
+            std::size_t num_bytes =
+                G * sizeof(InDataType) * N * Hi * Wi * C + G * sizeof(WeiDataType) * K * Y * X * C +
+                G * sizeof(BiasDataType) * K + G * sizeof(OutDataType) * N * Ho * Wo * K;
+
+            float tflops     = static_cast<float>(flop) / 1.E9 / avg_time;
+            float gb_per_sec = num_bytes / 1.E6 / avg_time;
+
+            std::cout << "Perf: " << std::setw(10) << avg_time << " ms, " << tflops << " TFlops, "
+                      << gb_per_sec << " GB/s, " << op_name << std::endl;
+
+            if(tflops > best_tflops)
+            {
+                best_op_id      = i;
+                best_op_name    = op_name;
+                best_avg_time   = avg_time;
+                best_gb_per_sec = gb_per_sec;
+                best_tflops     = tflops;
+            }
+        }
+        else
+        {
+            std::cout << op_name << " does not support this problem" << std::endl;
+        }
+    }
+
+    // run the best intance
+    if(best_op_id != -1)
+    {
+        std::cout << "Best Perf: " << std::setw(10) << best_avg_time << " ms, " << best_tflops
+                  << " TFlops, " << best_gb_per_sec << " GB/s, " << best_op_name << std::endl;
+
+        auto& op_ptr = op_ptrs[best_op_id];
+        std::cout << "Run the best instance without timing: " << op_ptr->GetTypeString()
+                  << std::endl;
+        auto argument_ptr = op_ptr->MakeArgumentPointer(in.GetDeviceBuffer(),
+                                                        wei.GetDeviceBuffer(),
+                                                        {bias.GetDeviceBuffer()},
+                                                        out.GetDeviceBuffer(),
+                                                        in_lengths,
+                                                        in_strides,
+                                                        weight_lengths,
+                                                        weight_strides,
+                                                        {bias_lengths},
+                                                        {bias_strides},
+                                                        out_lengths,
+                                                        out_strides,
+                                                        conv_strides,
+                                                        conv_dilations,
+                                                        in_left_pad,
+                                                        in_right_pad,
+                                                        PassThrough{},
+                                                        PassThrough{},
+                                                        OutElementOp{sacc, sz_inv, ActivationOp{}});
+
+        auto invoker_ptr = op_ptr->MakeInvokerPointer();
+
+        if(op_ptr->IsSupportedArgument(argument_ptr.get()))
+        {
+            invoker_ptr->Run(argument_ptr.get(), StreamConfig{nullptr, false});
+        }
+
+        std::cout << "Done" << std::endl;
+    }
+
+    return 0;
+}
\ No newline at end of file

client_example/09_quantization/conv2d_fwd_perlayer_quantization.cpp

@@ -24,15 +24,16 @@ using OutElementOp = ck::tensor_operation::element_wise::Activation_Mul_Clamp<Ac
 
 static constexpr ck::index_t NumDimSpatial = 2;
 static constexpr ck::index_t G             = 1;
-static constexpr ck::index_t N             = 4;   // batch size
-static constexpr ck::index_t K             = 64;  // output channel
-static constexpr ck::index_t C             = 192; // input channel
-static constexpr ck::index_t Y             = 3;   // filter H
-static constexpr ck::index_t X             = 3;   // filter W
-static constexpr ck::index_t Hi            = 71;  // input H
-static constexpr ck::index_t Wi            = 71;  // input W
-static constexpr ck::index_t Ho            = 36;  // output H
-static constexpr ck::index_t Wo            = 36;  // output W
+static constexpr ck::index_t N             = 4;    // batch size
+static constexpr ck::index_t K             = 64;   // output channel
+static constexpr ck::index_t C             = 192;  // input channel
+static constexpr ck::index_t Y             = 3;    // filter H
+static constexpr ck::index_t X             = 3;    // filter W
+static constexpr ck::index_t Hi            = 71;   // input H
+static constexpr ck::index_t Wi            = 71;   // input W
+static constexpr ck::index_t Ho            = 36;   // output H
+static constexpr ck::index_t Wo            = 36;   // output W
+static constexpr float requant_scale       = 0.5f; // requantize qAcc to qY
 
 struct SimpleDeviceMem
 {
@@ -96,26 +97,27 @@ int main(int argc, char* argv[])
 
     for(int i = 0; i < op_ptrs.size(); ++i)
     {
-        auto& op_ptr      = op_ptrs[i];
-        auto argument_ptr = op_ptr->MakeArgumentPointer(in.GetDeviceBuffer(),
-                                                        wei.GetDeviceBuffer(),
-                                                        {},
-                                                        out.GetDeviceBuffer(),
-                                                        in_lengths,
-                                                        in_strides,
-                                                        weight_lengths,
-                                                        weight_strides,
-                                                        {},
-                                                        {},
-                                                        out_lengths,
-                                                        out_strides,
-                                                        conv_strides,
-                                                        conv_dilations,
-                                                        in_left_pad,
-                                                        in_right_pad,
-                                                        PassThrough{},
-                                                        PassThrough{},
-                                                        OutElementOp{0.5f, ActivationOp{}});
+        auto& op_ptr = op_ptrs[i];
+        auto argument_ptr =
+            op_ptr->MakeArgumentPointer(in.GetDeviceBuffer(),
+                                        wei.GetDeviceBuffer(),
+                                        {},
+                                        out.GetDeviceBuffer(),
+                                        in_lengths,
+                                        in_strides,
+                                        weight_lengths,
+                                        weight_strides,
+                                        {},
+                                        {},
+                                        out_lengths,
+                                        out_strides,
+                                        conv_strides,
+                                        conv_dilations,
+                                        in_left_pad,
+                                        in_right_pad,
+                                        PassThrough{},
+                                        PassThrough{},
+                                        OutElementOp{requant_scale, ActivationOp{}});
 
         auto invoker_ptr    = op_ptr->MakeInvokerPointer();
         std::string op_name = op_ptr->GetTypeString();
@@ -158,25 +160,26 @@ int main(int argc, char* argv[])
         auto& op_ptr = op_ptrs[best_op_id];
         std::cout << "Run the best instance without timing: " << op_ptr->GetTypeString()
                   << std::endl;
-        auto argument_ptr = op_ptr->MakeArgumentPointer(in.GetDeviceBuffer(),
-                                                        wei.GetDeviceBuffer(),
-                                                        {},
-                                                        out.GetDeviceBuffer(),
-                                                        in_lengths,
-                                                        in_strides,
-                                                        weight_lengths,
-                                                        weight_strides,
-                                                        {},
-                                                        {},
-                                                        out_lengths,
-                                                        out_strides,
-                                                        conv_strides,
-                                                        conv_dilations,
-                                                        in_left_pad,
-                                                        in_right_pad,
-                                                        PassThrough{},
-                                                        PassThrough{},
-                                                        OutElementOp{0.5f, ActivationOp{}});
+        auto argument_ptr =
+            op_ptr->MakeArgumentPointer(in.GetDeviceBuffer(),
+                                        wei.GetDeviceBuffer(),
+                                        {},
+                                        out.GetDeviceBuffer(),
+                                        in_lengths,
+                                        in_strides,
+                                        weight_lengths,
+                                        weight_strides,
+                                        {},
+                                        {},
+                                        out_lengths,
+                                        out_strides,
+                                        conv_strides,
+                                        conv_dilations,
+                                        in_left_pad,
+                                        in_right_pad,
+                                        PassThrough{},
+                                        PassThrough{},
+                                        OutElementOp{requant_scale, ActivationOp{}});
 
         auto invoker_ptr = op_ptr->MakeInvokerPointer();
 

doc/markdown/dockerhub.md

-## CK docker hub
-
-[Docker hub](https://hub.docker.com/r/rocm/composable_kernel)
-
-## Why do I need this?
-
-To make our lives easier and bring Composable Kernel dependencies together, we recommend using docker images.
-
-## So what is Composable Kernel?
-
-Composable Kernel (CK) library aims to provide a programming model for writing performance critical kernels for machine learning workloads across multiple architectures including GPUs, CPUs, etc, through general purpose kernel languages, like HIP C++.
-
-To get the CK library
-
-```
-git clone https://github.com/ROCmSoftwarePlatform/composable_kernel.git
-```
-
-run a docker container 
-
-```
-docker run                                                            \
--it                                                                   \
---privileged                                                          \
---group-add sudo                                                      \
--w /root/workspace                                                    \
--v ${PATH_TO_LOCAL_WORKSPACE}:/root/workspace                         \
-rocm/composable_kernel:ck_ub20.04_rocm5.3_release                     \
-/bin/bash
-```
-
-and build the CK
-
-```
-mkdir build && cd build
-
-# Need to specify target ID, example below is for gfx908 and gfx90a
-cmake                                                                                             \
--D CMAKE_PREFIX_PATH=/opt/rocm                                                                    \
--D CMAKE_CXX_COMPILER=/opt/rocm/bin/hipcc                                                         \
--D CMAKE_CXX_FLAGS="-O3"                                                                          \
--D CMAKE_BUILD_TYPE=Release                                                                       \
--D GPU_TARGETS="gfx908;gfx90a"                                                                    \
-..
-```
-
-and 
-
-```
-make -j examples tests
-```
-
-To run all the test cases including tests and examples run
-
-```
-make test
-```
-
-We can also run specific examples or tests like
-
-```
-./bin/example_gemm_xdl_fp16
-./bin/test_gemm_fp16
-```
-
-For more details visit [CK github repo](https://github.com/ROCmSoftwarePlatform/composable_kernel), [CK examples](https://github.com/ROCmSoftwarePlatform/composable_kernel/tree/develop/example), [even more CK examples](https://github.com/ROCmSoftwarePlatform/composable_kernel/tree/develop/client_example).
-
-## And what is inside?
-
-The docker images have everything you need for running CK including:
-
-* [ROCm](https://www.amd.com/en/graphics/servers-solutions-rocm)
-* [CMake](https://cmake.org/)
-* [Compiler](https://github.com/RadeonOpenCompute/llvm-project)
-
-## Which image is right for me?
-
-Let's take a look at the image naming, for example "ck_ub20.04_rocm5.4_release". The image specs are:
-
-* "ck" - made for running Composable Kernel
-* "ub20.04" - based on Ubuntu 20.04
-* "rocm5.4" - ROCm platform version 5.4
-* "release" - compiler version is release
-
-So just pick the right image for your project dependencies and you're all set.
-
-## DIY starts here
-
-If you need to customize a docker image or just can't stop tinkering, feel free to adjust the [Dockerfile](https://github.com/ROCmSoftwarePlatform/composable_kernel/blob/develop/Dockerfile) for your needs.
-
-## License
-
-CK is released under the MIT [license](https://github.com/ROCmSoftwarePlatform/composable_kernel/blob/develop/LICENSE).

doc/markdown/tutorial_hello_world.md

-## CK Hello world
-
-## Motivation
-
-This tutorial is aimed at engineers dealing with artificial intelligence and machine learning who would like to optimize their pipelines and squeeze every performance drop by adding Composable Kernel (CK) library to their projects. We would like to make the CK library approachable so the tutorial is not based on the latest release and doesn't have all the bleeding edge features, but it will be reproducible now and forever.
-
-During this tutorial we will have an introduction to the CK library, we will build it and run some examples and tests, so to say we will run a "Hello world" example. In future tutorials we will go in depth and breadth and get familiar with other tools and ways to integrate CK into your project.
-
-## Description
-
-Modern AI technology solves more and more problems in all imaginable fields, but crafting fast and efficient workflows is still challenging. CK is one of the tools to make AI heavy lifting as fast and efficient as possible. CK is a collection of optimized AI operator kernels and tools to create new ones. The library has components required for majority of modern neural networks architectures including matrix multiplication, convolution, contraction, reduction, attention modules, variety of activation functions, fused operators and many more.
-
-So how do we (almost) reach the speed of light? CK acceleration abilities are based on:
-
-* Layered structure.
-* Tile-based computation model.
-* Tensor coordinate transformation.
-* Hardware acceleration use.
-* Support of low precision data types including fp16, bf16, int8 and int4.
-
-If you are excited and need more technical details and benchmarking results - read this awesome blog [post](https://community.amd.com/t5/instinct-accelerators/amd-composable-kernel-library-efficient-fused-kernels-for-ai/ba-p/553224). 
-
-For more details visit our [github repo](https://github.com/ROCmSoftwarePlatform/composable_kernel).
-
-## Hardware targets
-
-CK library fully supports "gfx908" and "gfx90a" GPU architectures and only some operators are supported for "gfx1030". Let's check the hardware you have at hand and decide on the target GPU architecture 
-
-GPU Target	AMD GPU
-gfx908 	Radeon Instinct MI100
-gfx90a 	Radeon Instinct MI210, MI250, MI250X
-gfx1030 	Radeon PRO V620, W6800, W6800X, W6800X Duo, W6900X, RX 6800, RX 6800 XT, RX 6900 XT, RX 6900 XTX, RX 6950 XT
-
-There are also [cloud options](https://aws.amazon.com/ec2/instance-types/g4/) you can find if you don't have an AMD GPU at hand.
-
-## Build the library
-
-First let's clone the library and rebase to the tested version:
-
-```
-git clone https://github.com/ROCmSoftwarePlatform/composable_kernel.git
-cd composable_kernel/
-git checkout tutorial_hello_world
-```
-
-To make our lives easier we prepared [docker images](https://hub.docker.com/r/rocm/composable_kernel) with all the necessary dependencies. Pick the right image and create a container. In this tutorial we use "rocm/composable_kernel:ck_ub20.04_rocm5.3_release" image, it is based on Ubuntu 20.04, ROCm v5.3, compiler release version.
-
-If your current folder is ${HOME}, start the docker container with
-
-```
-docker run  \
--it  \
---privileged  \
---group-add sudo  \
--w /root/workspace  \
--v ${HOME}:/root/workspace  \
-rocm/composable_kernel:ck_ub20.04_rocm5.3_release  \
-/bin/bash
-```
-
-If your current folder is different from ${HOME}, adjust the line `-v ${HOME}:/root/workspace` to fit your folder structure.
-
-Inside the docker container current folder is "~/workspace", library path is "~/workspace/composable_kernel", navigate to the library
-
-```
-cd composable_kernel/
-```
-
-Create and go to the "build" directory
-
-```
-mkdir build && cd build
-```
-
-In the previous section we talked about target GPU architecture. Once you decide which one is right for you, run cmake using the right GPU_TARGETS flag
-
-```
-cmake  \
--D CMAKE_PREFIX_PATH=/opt/rocm  \
--D CMAKE_CXX_COMPILER=/opt/rocm/bin/hipcc  \
--D CMAKE_CXX_FLAGS="-O3"  \
--D CMAKE_BUILD_TYPE=Release  \
--D BUILD_DEV=OFF  \
--D GPU_TARGETS="gfx908;gfx90a;gfx1030" ..
-```
-
-If everything went well the cmake run will end up with:
-
-```
--- Configuring done
--- Generating done
--- Build files have been written to: "/root/workspace/composable_kernel/build"
-```
-
-Finally, we can build examples and tests
-
-```
-make -j examples tests
-```
-
-If everything is smooth, you'll see
-
-```
-Scanning dependencies of target tests
-[100%] Built target tests
-```
-
-## Run examples and tests
-
-Examples are listed as test cases as well, so we can run all examples and tests with
-
-```
-ctest
-```
-
-You can check the list of all tests by running
-
-```
-ctest -N
-```
-
-We can also run them separately, here is a separate example execution. 
-
-```
-./bin/example_gemm_xdl_fp16 1 1 1
-```
-
-The arguments "1 1 1" mean that we want to run this example in the mode: verify results with CPU, initialize matrices with integers and benchmark the kernel execution. You can play around with these parameters and see how output and execution results change.
-
-If everything goes well and you have a device based on gfx908 or gfx90a architecture you should see something like
-
-```
-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}
-launch_and_time_kernel: grid_dim {480, 1, 1}, block_dim {256, 1, 1}
-Warm up 1 time
-Start running 10 times...
-Perf: 1.10017 ms, 117.117 TFlops, 87.6854 GB/s, DeviceGemmXdl<256, 256, 128, 4, 8, 32, 32, 4, 2> NumPrefetch: 1, LoopScheduler: Default, PipelineVersion: v1
-```
-
-Meanwhile, running it on a gfx1030 device should result in
-
-```
-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}
-DeviceGemmXdl<256, 256, 128, 4, 8, 32, 32, 4, 2> NumPrefetch: 1, LoopScheduler: Default, PipelineVersion: v1 does not support this problem
-```
-
-But don't panic, some of the operators are supported on gfx1030 architecture, so you can run a separate example like
-
-```
-./bin/example_gemm_dl_fp16 1 1 1
-```
-
-and it should result in something nice similar to
-
-```
-a_m_k: dim 2, lengths {3840, 4096}, strides {1, 4096}
-b_k_n: dim 2, lengths {4096, 4096}, strides {4096, 1}
-c_m_n: dim 2, lengths {3840, 4096}, strides {4096, 1}
-arg.a_grid_desc_k0_m0_m1_k1_{2048, 3840, 2}
-arg.b_grid_desc_k0_n0_n1_k1_{2048, 4096, 2}
-arg.c_grid_desc_m_n_{ 3840, 4096}
-launch_and_time_kernel: grid_dim {960, 1, 1}, block_dim {256, 1, 1}
-Warm up 1 time
-Start running 10 times...
-Perf: 3.65695 ms, 35.234 TFlops, 26.3797 GB/s, DeviceGemmDl<256, 128, 128, 16, 2, 4, 4, 1>
-```
-
-Or we can run a separate test
-
-```
-ctest -R test_gemm_fp16
-```
-
-If everything goes well you should see something like
-
-```
-Start 121: test_gemm_fp16
-1/1 Test #121: test_gemm_fp16 ...................   Passed   51.81 sec
-
-100% tests passed, 0 tests failed out of 1
-```
-
-## Summary
-
-In this tutorial we took the first look at the Composable Kernel library, built it on your system and ran some examples and tests. Stay tuned, in the next tutorial we will run kernels with different configs to find out the best one for your hardware and task.
-
-P.S.: Don't forget to switch out the cloud instance if you have launched one, you can find better ways to spend your money for sure!

docs/Doxyfile → docs/.doxygen/Doxyfile

@@ -51,7 +51,7 @@ PROJECT_BRIEF          = "prototype interfaces compatible with ROCm platform and
 # pixels and the maximum width should not exceed 200 pixels. Doxygen will copy
 # the logo to the output directory.
 
-PROJECT_LOGO           = ./rocm.jpg
+PROJECT_LOGO           = 
 
 # The OUTPUT_DIRECTORY tag is used to specify the (relative or absolute) path
 # into which the generated documentation will be written. If a relative path is
@@ -775,10 +775,10 @@ WARN_LOGFILE           =
 # spaces. See also FILE_PATTERNS and EXTENSION_MAPPING
 # Note: If this tag is empty the current directory is searched.
 
-INPUT                  = ../include/ck/tensor_operation/gpu/grid \
-                         ../include/ck/tensor_operation/gpu/block \
-                         ../include/ck/tensor_operation/gpu/thread \
-                         ../library/include/ck/library/utility
+INPUT                  = ../../include/ck/tensor_operation/gpu/grid \
+                         ../../include/ck/tensor_operation/gpu/block \
+                         ../../include/ck/tensor_operation/gpu/thread \
+                         ../../library/include/ck/library/utility
 
 # This tag can be used to specify the character encoding of the source files
 # that doxygen parses. Internally doxygen uses the UTF-8 encoding. Doxygen uses

docs/.sphinx/_toc.yml.in

+root: index

docs/.sphinx/requirements.in

+git+https://github.com/RadeonOpenCompute/rocm-docs-core.git
+sphinxcontrib-bibtex==2.5.0

docs/.sphinx/requirements.txt

+#
+# This file is autogenerated by pip-compile with Python 3.10
+# by the following command:
+#
+#    pip-compile requirements.in
+#
+accessible-pygments==0.0.4
+    # via pydata-sphinx-theme
+alabaster==0.7.13
+    # via sphinx
+asttokens==2.2.1
+    # via stack-data
+attrs==22.2.0
+    # via
+    #   jsonschema
+    #   jupyter-cache
+babel==2.12.1
+    # via
+    #   pydata-sphinx-theme
+    #   sphinx
+backcall==0.2.0
+    # via ipython
+beautifulsoup4==4.12.0
+    # via pydata-sphinx-theme
+breathe==4.34.0
+    # via rocm-docs-core
+certifi==2022.12.7
+    # via requests
+cffi==1.15.1
+    # via pynacl
+charset-normalizer==3.1.0
+    # via requests
+click==8.1.3
+    # via
+    #   jupyter-cache
+    #   sphinx-external-toc
+comm==0.1.3
+    # via ipykernel
+debugpy==1.6.6
+    # via ipykernel
+decorator==5.1.1
+    # via ipython
+deprecated==1.2.13
+    # via pygithub
+docutils==0.16
+    # via
+    #   breathe
+    #   myst-parser
+    #   pybtex-docutils
+    #   pydata-sphinx-theme
+    #   rocm-docs-core
+    #   sphinx
+    #   sphinxcontrib-bibtex
+executing==1.2.0
+    # via stack-data
+fastjsonschema==2.16.3
+    # via nbformat
+gitdb==4.0.10
+    # via gitpython
+gitpython==3.1.31
+    # via rocm-docs-core
+greenlet==2.0.2
+    # via sqlalchemy
+idna==3.4
+    # via requests
+imagesize==1.4.1
+    # via sphinx
+importlib-metadata==6.1.0
+    # via
+    #   jupyter-cache
+    #   myst-nb
+importlib-resources==5.10.4
+    # via rocm-docs-core
+ipykernel==6.22.0
+    # via myst-nb
+ipython==8.11.0
+    # via
+    #   ipykernel
+    #   myst-nb
+jedi==0.18.2
+    # via ipython
+jinja2==3.1.2
+    # via
+    #   myst-parser
+    #   sphinx
+jsonschema==4.17.3
+    # via nbformat
+jupyter-cache==0.5.0
+    # via myst-nb
+jupyter-client==8.1.0
+    # via
+    #   ipykernel
+    #   nbclient
+jupyter-core==5.3.0
+    # via
+    #   ipykernel
+    #   jupyter-client
+    #   nbformat
+latexcodec==2.0.1
+    # via pybtex
+linkify-it-py==1.0.3
+    # via myst-parser
+markdown-it-py==2.2.0
+    # via
+    #   mdit-py-plugins
+    #   myst-parser
+markupsafe==2.1.2
+    # via jinja2
+matplotlib-inline==0.1.6
+    # via
+    #   ipykernel
+    #   ipython
+mdit-py-plugins==0.3.5
+    # via myst-parser
+mdurl==0.1.2
+    # via markdown-it-py
+myst-nb==0.17.1
+    # via rocm-docs-core
+myst-parser[linkify]==0.18.1
+    # via
+    #   myst-nb
+    #   rocm-docs-core
+nbclient==0.5.13
+    # via
+    #   jupyter-cache
+    #   myst-nb
+nbformat==5.8.0
+    # via
+    #   jupyter-cache
+    #   myst-nb
+    #   nbclient
+nest-asyncio==1.5.6
+    # via
+    #   ipykernel
+    #   nbclient
+packaging==23.0
+    # via
+    #   ipykernel
+    #   pydata-sphinx-theme
+    #   sphinx
+parso==0.8.3
+    # via jedi
+pexpect==4.8.0
+    # via ipython
+pickleshare==0.7.5
+    # via ipython
+platformdirs==3.1.1
+    # via jupyter-core
+prompt-toolkit==3.0.38
+    # via ipython
+psutil==5.9.4
+    # via ipykernel
+ptyprocess==0.7.0
+    # via pexpect
+pure-eval==0.2.2
+    # via stack-data
+pybtex==0.24.0
+    # via
+    #   pybtex-docutils
+    #   sphinxcontrib-bibtex
+pybtex-docutils==1.0.2
+    # via sphinxcontrib-bibtex
+pycparser==2.21
+    # via cffi
+pydata-sphinx-theme==0.13.1
+    # via sphinx-book-theme
+pygithub==1.57
+    # via rocm-docs-core
+pygments==2.14.0
+    # via
+    #   accessible-pygments
+    #   ipython
+    #   pydata-sphinx-theme
+    #   sphinx
+pyjwt==2.6.0
+    # via pygithub
+pynacl==1.5.0
+    # via pygithub
+pyrsistent==0.19.3
+    # via jsonschema
+python-dateutil==2.8.2
+    # via jupyter-client
+pyyaml==6.0
+    # via
+    #   jupyter-cache
+    #   myst-nb
+    #   myst-parser
+    #   pybtex
+    #   sphinx-external-toc
+pyzmq==25.0.2
+    # via
+    #   ipykernel
+    #   jupyter-client
+requests==2.28.2
+    # via
+    #   pygithub
+    #   sphinx
+rocm-docs-core @ git+https://github.com/RadeonOpenCompute/rocm-docs-core.git
+    # via -r requirements.in
+six==1.16.0
+    # via
+    #   asttokens
+    #   latexcodec
+    #   pybtex
+    #   python-dateutil
+smmap==5.0.0
+    # via gitdb
+snowballstemmer==2.2.0
+    # via sphinx
+soupsieve==2.4
+    # via beautifulsoup4
+sphinx==4.3.1
+    # via
+    #   breathe
+    #   myst-nb
+    #   myst-parser
+    #   pydata-sphinx-theme
+    #   rocm-docs-core
+    #   sphinx-book-theme
+    #   sphinx-copybutton
+    #   sphinx-design
+    #   sphinx-external-toc
+    #   sphinx-notfound-page
+    #   sphinxcontrib-bibtex
+sphinx-book-theme==1.0.0rc2
+    # via rocm-docs-core
+sphinx-copybutton==0.5.1
+    # via rocm-docs-core
+sphinx-design==0.3.0
+    # via rocm-docs-core
+sphinx-external-toc==0.3.1
+    # via rocm-docs-core
+sphinx-notfound-page==0.8.3
+    # via rocm-docs-core
+sphinxcontrib-applehelp==1.0.4
+    # via sphinx
+sphinxcontrib-bibtex==2.5.0
+    # via
+    #   -r requirements.in
+    #   rocm-docs-core
+sphinxcontrib-devhelp==1.0.2
+    # via sphinx
+sphinxcontrib-htmlhelp==2.0.1
+    # via sphinx
+sphinxcontrib-jsmath==1.0.1
+    # via sphinx
+sphinxcontrib-qthelp==1.0.3
+    # via sphinx
+sphinxcontrib-serializinghtml==1.1.5
+    # via sphinx
+sqlalchemy==1.4.47
+    # via jupyter-cache
+stack-data==0.6.2
+    # via ipython
+tabulate==0.9.0
+    # via jupyter-cache
+tornado==6.2
+    # via
+    #   ipykernel
+    #   jupyter-client
+traitlets==5.9.0
+    # via
+    #   comm
+    #   ipykernel
+    #   ipython
+    #   jupyter-client
+    #   jupyter-core
+    #   matplotlib-inline
+    #   nbclient
+    #   nbformat
+typing-extensions==4.5.0
+    # via
+    #   myst-nb
+    #   myst-parser
+uc-micro-py==1.0.1
+    # via linkify-it-py
+urllib3==1.26.15
+    # via requests
+wcwidth==0.2.6
+    # via prompt-toolkit
+wrapt==1.15.0
+    # via deprecated
+zipp==3.15.0
+    # via importlib-metadata
+
+# The following packages are considered to be unsafe in a requirements file:
+# setuptools