Merge branch 'develop' of https://github.com/ROCmSoftwarePlatform/composable_kernel into wmma_op

client_example/09_quantization/CMakeLists.txt

+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_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)
 
+add_executable(client_conv2d_fwd_perchannel_quantization conv2d_fwd_perchannel_quantization.cpp)
+target_link_libraries(client_conv2d_fwd_perchannel_quantization PRIVATE composable_kernel::device_operations)
+
 add_executable(client_conv2d_fwd_perlayer_quantization conv2d_fwd_perlayer_quantization.cpp)
 target_link_libraries(client_conv2d_fwd_perlayer_quantization PRIVATE composable_kernel::device_operations)

client_example/09_quantization/conv2d_fwd_bias_relu_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::Relu;
+using OutElementOp = ck::tensor_operation::element_wise::Add_Activation_Mul2_Clamp<ActivationOp>;
+
+static constexpr ck::index_t NumDimSpatial = 2;
+static constexpr ck::index_t G             = 1;
+static constexpr ck::index_t N             = 4;
+static constexpr ck::index_t K             = 64;
+static constexpr ck::index_t C             = 32;
+static constexpr ck::index_t Y             = 3;
+static constexpr ck::index_t X             = 3;
+static constexpr ck::index_t Hi            = 71;
+static constexpr ck::index_t Wi            = 71;
+static constexpr ck::index_t Ho            = 36;
+static constexpr ck::index_t Wo            = 36;
+
+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, C, Ho, Wo};
+    std::array<ck::index_t, 5> out_strides{N * Ho * Wo * C, Ho * Wo * C, 1, Wo * C, C};
+    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{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(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;
+        }
+    }
+
+    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;
+
+    // run the best intance
+    {
+        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{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_relu_perlayer_quantization.cpp

@@ -6,7 +6,7 @@
 #include <vector>
 
 #include "ck/ck.hpp"
-#include "ck/library/tensor_operation_instance/gpu/grouped_convolution_bias_forward_perlayer_quantization.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"

client_example/09_quantization/conv2d_fwd_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_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 RequantScaleDataType = float;
+using OutDataType          = int8_t;
+
+using InLayout           = ck::tensor_layout::convolution::GNHWC;
+using WeiLayout          = ck::tensor_layout::convolution::GKYXC;
+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       = PassThrough;
+using OutElementOp       = ck::tensor_operation::element_wise::Activation_Mul2_Clamp<ActivationOp>;
+
+static constexpr ck::index_t NumDimSpatial = 2;
+static constexpr ck::index_t G             = 1;
+static constexpr ck::index_t N             = 4;
+static constexpr ck::index_t K             = 64;
+static constexpr ck::index_t C             = 32;
+static constexpr ck::index_t Y             = 3;
+static constexpr ck::index_t X             = 3;
+static constexpr ck::index_t Hi            = 71;
+static constexpr ck::index_t Wi            = 71;
+static constexpr ck::index_t Ho            = 36;
+static constexpr ck::index_t Wo            = 36;
+
+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> 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, C, Ho, Wo};
+    std::array<ck::index_t, 5> out_strides{N * Ho * Wo * C, Ho * Wo * C, 1, Wo * C, C};
+    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 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<RequantScaleLayout>,
+                                                                    OutLayout,
+                                                                    InDataType,
+                                                                    WeiDataType,
+                                                                    ck::Tuple<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(),
+                                                        {requant_scale.GetDeviceBuffer()},
+                                                        out.GetDeviceBuffer(),
+                                                        in_lengths,
+                                                        in_strides,
+                                                        weight_lengths,
+                                                        weight_strides,
+                                                        {requant_scale_lengths},
+                                                        {requant_scale_strides},
+                                                        out_lengths,
+                                                        out_strides,
+                                                        conv_strides,
+                                                        conv_dilations,
+                                                        in_left_pad,
+                                                        in_right_pad,
+                                                        PassThrough{},
+                                                        PassThrough{},
+                                                        OutElementOp{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(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;
+        }
+    }
+
+    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;
+
+    // run the best intance
+    {
+        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{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

@@ -6,7 +6,7 @@
 #include <vector>
 
 #include "ck/ck.hpp"
-#include "ck/library/tensor_operation_instance/gpu/grouped_convolution_forward_perlayer_quantization.hpp"
+#include "ck/library/tensor_operation_instance/gpu/quantization/grouped_convolution_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"

client_example/13_batchnorm/CMakeLists.txt

 add_executable(client_batchnorm_fwd_nhwc batchnorm_fwd_nhwc.cpp)
+add_executable(client_batchnorm_bwd_nhwc batchnorm_bwd_nhwc.cpp)
 target_link_libraries(client_batchnorm_fwd_nhwc PRIVATE composable_kernel::device_operations)
+target_link_libraries(client_batchnorm_bwd_nhwc PRIVATE composable_kernel::device_operations)

client_example/13_batchnorm/batchnorm_bwd_nhwc.cpp

+// SPDX-License-Identifier: MIT
+// Copyright (c) 2018-2022, Advanced Micro Devices, Inc. All rights reserved.
+
+#include <functional>
+#include <numeric>
+#include <iomanip>
+#include <iostream>
+#include <vector>
+
+#include "ck/ck.hpp"
+#include "ck/tensor_operation/gpu/device/device_reduce.hpp"
+#include "ck/tensor_operation/gpu/element/element_wise_operation.hpp"
+
+#include "ck/library/tensor_operation_instance/gpu/batchnorm_backward.hpp"
+
+using XDataType           = ck::half_t;
+using DxDataType          = float;
+using DyDataType          = float;
+using AccDataType         = float;
+using ScaleDataType       = ck::half_t;
+using DscaleDbiasDataType = float;
+using MeanVarDataType     = float;
+
+using PassThrough = ck::tensor_operation::element_wise::PassThrough;
+
+constexpr int Rank                  = 4;
+constexpr int NumBatchNormReduceDim = 3;
+
+const double epsilon = std::numeric_limits<float>::epsilon();
+
+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, Rank> xyLengths{16, 8, 128, 256};
+    std::array<ck::index_t, Rank> xyStrides{8 * 128 * 256, 128 * 256, 256, 1};
+    std::array<ck::index_t, Rank - NumBatchNormReduceDim> scaleBiasMeanVarLengths{256};
+    std::array<ck::index_t, Rank - NumBatchNormReduceDim> scaleBiasMeanVarStrides{1};
+    std::array<int, NumBatchNormReduceDim> reduceDims{0, 1, 2};
+
+    ck::index_t numXYElement =
+        std::accumulate(xyLengths.begin(), xyLengths.end(), 1, std::multiplies<ck::index_t>());
+
+    ck::index_t numScaleBiasMeanVarElement = std::accumulate(scaleBiasMeanVarLengths.begin(),
+                                                             scaleBiasMeanVarLengths.end(),
+                                                             1,
+                                                             std::multiplies<ck::index_t>());
+
+    SimpleDeviceMem x(sizeof(XDataType) * numXYElement);
+    SimpleDeviceMem dy(sizeof(DyDataType) * numXYElement);
+    SimpleDeviceMem scale(sizeof(ScaleDataType) * numScaleBiasMeanVarElement);
+    SimpleDeviceMem mean(sizeof(MeanVarDataType) * numScaleBiasMeanVarElement);
+    SimpleDeviceMem invVariance(sizeof(MeanVarDataType) * numScaleBiasMeanVarElement);
+    SimpleDeviceMem dx(sizeof(DxDataType) * numXYElement);
+    SimpleDeviceMem dscale(sizeof(DscaleDbiasDataType) * numScaleBiasMeanVarElement);
+    SimpleDeviceMem dbias(sizeof(DscaleDbiasDataType) * numScaleBiasMeanVarElement);
+
+    using DeviceOp = ck::tensor_operation::device::DeviceBatchNormBwd<XDataType,
+                                                                      DxDataType,
+                                                                      DyDataType,
+                                                                      AccDataType,
+                                                                      ScaleDataType,
+                                                                      DscaleDbiasDataType,
+                                                                      MeanVarDataType,
+                                                                      PassThrough,
+                                                                      Rank,
+                                                                      NumBatchNormReduceDim>;
+
+    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;
+    bool found            = false;
+    int best_op_id        = -1;
+    float best_ave_time   = std::numeric_limits<float>::max();
+    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(xyLengths,
+                                                        xyStrides,
+                                                        xyStrides,
+                                                        xyStrides,
+                                                        reduceDims,
+                                                        scaleBiasMeanVarLengths,
+                                                        scaleBiasMeanVarStrides,
+                                                        scaleBiasMeanVarStrides,
+                                                        scaleBiasMeanVarStrides,
+                                                        x.GetDeviceBuffer(),
+                                                        dy.GetDeviceBuffer(),
+                                                        scale.GetDeviceBuffer(),
+                                                        mean.GetDeviceBuffer(),
+                                                        invVariance.GetDeviceBuffer(),
+                                                        epsilon,
+                                                        PassThrough{},
+                                                        dx.GetDeviceBuffer(),
+                                                        dscale.GetDeviceBuffer(),
+                                                        dbias.GetDeviceBuffer());
+
+        auto invoker_ptr    = op_ptr->MakeInvokerPointer();
+        std::string op_name = op_ptr->GetTypeString();
+
+        if(op_ptr->IsSupportedArgument(argument_ptr.get()))
+        {
+            size_t workspace_sz = op_ptr->GetWorkSpaceSize(argument_ptr.get());
+
+            SimpleDeviceMem workspace(workspace_sz);
+
+            op_ptr->SetWorkSpacePointer(argument_ptr.get(), workspace.GetDeviceBuffer());
+
+            float ave_time = invoker_ptr->Run(argument_ptr.get(), StreamConfig{nullptr, true});
+
+            std::size_t num_bytes =
+                numXYElement * (sizeof(XDataType) + sizeof(DyDataType) + sizeof(DxDataType)) +
+                numScaleBiasMeanVarElement *
+                    (sizeof(ScaleDataType) + sizeof(DscaleDbiasDataType) * 2 +
+                     sizeof(MeanVarDataType) * 2);
+
+            float gb_per_sec = num_bytes / 1.E6 / ave_time;
+
+            std::cout << "Perf: " << std::setw(10) << ave_time << " ms, " << gb_per_sec << " GB/s, "
+                      << op_name << std::endl;
+
+            if(ave_time < best_ave_time)
+            {
+                found           = true;
+                best_op_id      = i;
+                best_op_name    = op_name;
+                best_ave_time   = ave_time;
+                best_gb_per_sec = gb_per_sec;
+            }
+        }
+        else
+        {
+            std::cout << op_name << " does not support this problem" << std::endl;
+        }
+    }
+
+    if(found)
+    {
+        std::cout << "Best Perf: " << best_ave_time << " ms, " << best_gb_per_sec << " GB/s, "
+                  << best_op_name << std::endl;
+
+        // run the best intance
+        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(xyLengths,
+                                                        xyStrides,
+                                                        xyStrides,
+                                                        xyStrides,
+                                                        reduceDims,
+                                                        scaleBiasMeanVarLengths,
+                                                        scaleBiasMeanVarStrides,
+                                                        scaleBiasMeanVarStrides,
+                                                        scaleBiasMeanVarStrides,
+                                                        x.GetDeviceBuffer(),
+                                                        dy.GetDeviceBuffer(),
+                                                        scale.GetDeviceBuffer(),
+                                                        mean.GetDeviceBuffer(),
+                                                        invVariance.GetDeviceBuffer(),
+                                                        epsilon,
+                                                        PassThrough{},
+                                                        dx.GetDeviceBuffer(),
+                                                        dscale.GetDeviceBuffer(),
+                                                        dbias.GetDeviceBuffer());
+
+        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;
+}

example/14_gemm_quantization/CMakeLists.txt

+add_example_executable(example_gemm_xdl_bias_relu_quantization_int8 gemm_xdl_bias_relu_quantization_int8.cpp)
+add_example_executable(example_gemm_xdl_quantization_int8 gemm_xdl_quantization_int8.cpp)
\ No newline at end of file

example/14_gemm_quantization/gemm_xdl_bias_relu_quantization_int8.cpp

+// SPDX-License-Identifier: MIT
+// Copyright (c) 2018-2022, Advanced Micro Devices, Inc. All rights reserved.
+
+#include <iostream>
+#include <numeric>
+#include <initializer_list>
+#include <cstdlib>
+
+#include "ck/ck.hpp"
+#include "ck/tensor_operation/gpu/device/tensor_layout.hpp"
+#include "ck/tensor_operation/gpu/device/gemm_specialization.hpp"
+#include "ck/tensor_operation/gpu/device/impl/device_gemm_multiple_d_xdl_cshuffle.hpp"
+#include "ck/tensor_operation/gpu/element/element_wise_operation.hpp"
+
+#include "ck/library/utility/device_memory.hpp"
+#include "ck/library/utility/host_tensor.hpp"
+#include "ck/library/utility/host_tensor_generator.hpp"
+#include "ck/library/utility/literals.hpp"
+#include "ck/library/reference_tensor_operation/cpu/reference_gemm.hpp"
+#include "ck/library/utility/check_err.hpp"
+
+template <ck::index_t... Is>
+using S = ck::Sequence<Is...>;
+
+using I8  = int8_t;
+using I32 = int32_t;
+using Row = ck::tensor_layout::gemm::RowMajor;
+using Col = ck::tensor_layout::gemm::ColumnMajor;
+
+using PassThrough  = ck::tensor_operation::element_wise::PassThrough;
+using ActivationOp = ck::tensor_operation::element_wise::Relu;
+using CDEElementOp = ck::tensor_operation::element_wise::Add_Activation_Mul_Clamp<ActivationOp>;
+
+using ADataType        = I8;
+using BDataType        = I8;
+using AccDataType      = I32;
+using CShuffleDataType = I32;
+using BiasDataType     = I32;
+using DsDataType       = ck::Tuple<BiasDataType>;
+using EDataType        = I8;
+
+using ALayout    = Row;
+using BLayout    = Col;
+using BiasLayout = Row;
+using DsLayout   = ck::Tuple<BiasLayout>;
+using ELayout    = Row;
+
+static constexpr auto GemmDefault = ck::tensor_operation::device::GemmSpecialization::Default;
+
+// clang-format off
+using DeviceGemmInstance = ck::tensor_operation::device::DeviceGemmMultipleD_Xdl_CShuffle<
+     ALayout,
+     BLayout,
+     DsLayout,
+     ELayout,
+     ADataType,
+     BDataType,
+     AccDataType,
+     CShuffleDataType,
+     DsDataType,
+     EDataType,
+     PassThrough,                // AElementwiseOperation,
+     PassThrough,                // BElementwiseOperation,
+     CDEElementOp,               // CDEElementwiseOperation,
+     GemmDefault,                // GemmSpecialization GemmSpec,
+     1,                          // NumGemmKPrefetchStage,
+     256,                        // BlockSize,
+     256,                        // MPerBlock,
+     128,                        // NPerBlock,
+     64,                         // KPerBlock,
+     16,                         // AK1,
+     16,                         // BK1,
+     32,                         // MPerXDL,
+     32,                         // NPerXDL,
+     4,                          // MXdlPerWave,
+     2,                          // NXdlPerWave,
+     S<4, 64, 1>,                // ABlockTransferThreadClusterLengths_AK0_M_AK1,
+     S<1, 0, 2>,                 // ABlockTransferThreadClusterArrangeOrder,
+     S<1, 0, 2>,                 // ABlockTransferSrcAccessOrder,
+     2,                          // index_t ABlockTransferSrcVectorDim,
+     16,                         // index_t ABlockTransferSrcScalarPerVector,
+     16,                         // index_t ABlockTransferDstScalarPerVector_AK1,
+     1,                          // bool ABlockLdsExtraM,
+     S<4, 64, 1>,                // typename BBlockTransferThreadClusterLengths_BK0_N_BK1,
+     S<1, 0, 2>,                 // typename BBlockTransferThreadClusterArrangeOrder,
+     S<1, 0, 2>,                 // typename BBlockTransferSrcAccessOrder,
+     2,                          // index_t BBlockTransferSrcVectorDim,
+     8,                          // index_t BBlockTransferSrcScalarPerVector,
+     8,                          // index_t BBlockTransferDstScalarPerVector_BK1,
+     1,                          // bool BBlockLdsExtraN,
+     1,                          // index_t CShuffleMXdlPerWavePerShuffle,
+     1,                          // index_t CShuffleNXdlPerWavePerShuffle,
+     S<1, 64, 1, 4>,             // typename CShuffleBlockTransferClusterLengths_MBlock_MPerBlock_NBlock_NPerBlock,
+     8>;                         // index_t CShuffleBlockTransferScalarPerVector_NPerBlock>
+// clang-format on
+
+using ReferenceGemmInstance = ck::tensor_operation::host::ReferenceGemm<ADataType,
+                                                                        BDataType,
+                                                                        AccDataType,
+                                                                        AccDataType,
+                                                                        PassThrough,
+                                                                        PassThrough,
+                                                                        PassThrough>;
+
+int main()
+{
+    bool do_verification = true;
+    bool time_kernel     = false;
+
+    // GEMM shape
+    ck::index_t M = 1024;
+    ck::index_t N = 1024;
+    ck::index_t K = 1024;
+
+    ck::index_t StrideA    = 1024;
+    ck::index_t StrideB    = 1024;
+    ck::index_t StrideBias = 0;
+    ck::index_t StrideE    = 1024;
+
+    float requant_scale = 0.03;
+
+    auto f_host_tensor_descriptor2d =
+        [](std::size_t row, std::size_t col, std::size_t stride, auto layout) {
+            using namespace ck::literals;
+
+            if(std::is_same<decltype(layout), ck::tensor_layout::gemm::RowMajor>::value)
+            {
+                return HostTensorDescriptor(std::vector<std::size_t>({row, col}),
+                                            std::vector<std::size_t>({stride, 1_uz}));
+            }
+            else
+            {
+                return HostTensorDescriptor(std::vector<std::size_t>({row, col}),
+                                            std::vector<std::size_t>({1_uz, stride}));
+            }
+        };
+
+    auto f_host_tensor_descriptor1d = [](std::size_t len, std::size_t stride) {
+        return HostTensorDescriptor(std::vector<std::size_t>({len}),
+                                    std::vector<std::size_t>({stride}));
+    };
+
+    Tensor<ADataType> a_m_k(f_host_tensor_descriptor2d(M, K, StrideA, ALayout{}));
+    Tensor<BDataType> b_k_n(f_host_tensor_descriptor2d(K, N, StrideB, BLayout{}));
+    Tensor<BiasDataType> bias_n(f_host_tensor_descriptor1d(N, 1));
+    Tensor<EDataType> e_m_n_host_result(f_host_tensor_descriptor2d(M, N, StrideE, ELayout{}));
+    Tensor<EDataType> e_m_n_device_result(f_host_tensor_descriptor2d(M, N, StrideE, ELayout{}));
+
+    std::cout << "a_m_k: " << a_m_k.mDesc << std::endl;
+    std::cout << "b_k_n: " << b_k_n.mDesc << std::endl;
+    std::cout << "bias_n: " << bias_n.mDesc << std::endl;
+    std::cout << "e_m_n: " << e_m_n_host_result.mDesc << std::endl;
+
+    a_m_k.GenerateTensorValue(GeneratorTensor_2<ADataType>{-128, 127});
+    b_k_n.GenerateTensorValue(GeneratorTensor_2<BDataType>{-128, 127});
+    bias_n.GenerateTensorValue(GeneratorTensor_2<BiasDataType>{-128, 127});
+
+    DeviceMem a_device_buf(sizeof(ADataType) * a_m_k.mDesc.GetElementSpaceSize());
+    DeviceMem b_device_buf(sizeof(BDataType) * b_k_n.mDesc.GetElementSpaceSize());
+    DeviceMem bias_device_buf(sizeof(BiasDataType) * bias_n.mDesc.GetElementSpaceSize());
+    DeviceMem e_device_buf(sizeof(EDataType) * e_m_n_device_result.mDesc.GetElementSpaceSize());
+
+    a_device_buf.ToDevice(a_m_k.mData.data());
+    b_device_buf.ToDevice(b_k_n.mData.data());
+    bias_device_buf.ToDevice(bias_n.mData.data());
+
+    auto a_element_op   = PassThrough{};
+    auto b_element_op   = PassThrough{};
+    auto cde_element_op = CDEElementOp{requant_scale, ActivationOp{}};
+
+    // do GEMM
+    auto gemm     = DeviceGemmInstance{};
+    auto invoker  = gemm.MakeInvoker();
+    auto argument = gemm.MakeArgument(a_device_buf.GetDeviceBuffer(),
+                                      b_device_buf.GetDeviceBuffer(),
+                                      {bias_device_buf.GetDeviceBuffer()},
+                                      e_device_buf.GetDeviceBuffer(),
+                                      M,
+                                      N,
+                                      K,
+                                      StrideA,
+                                      StrideB,
+                                      {StrideBias},
+                                      StrideE,
+                                      a_element_op,
+                                      b_element_op,
+                                      cde_element_op);
+
+    if(!gemm.IsSupportedArgument(argument))
+    {
+        throw std::runtime_error(
+            "wrong! device_gemm with the specified compilation parameters does "
+            "not support this GEMM problem");
+    }
+
+    float ave_time = invoker.Run(argument, StreamConfig{nullptr, time_kernel});
+
+    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: " << ave_time << " ms, " << tflops << " TFlops, " << gb_per_sec << " GB/s, "
+              << gemm.GetTypeString() << std::endl;
+
+    e_device_buf.FromDevice(e_m_n_device_result.mData.data());
+
+    if(do_verification)
+    {
+        Tensor<AccDataType> c_m_n(HostTensorDescriptor{M, N});
+
+        auto ref_gemm    = ReferenceGemmInstance{};
+        auto ref_invoker = ref_gemm.MakeInvoker();
+
+        auto ref_argument =
+            ref_gemm.MakeArgument(a_m_k, b_k_n, c_m_n, a_element_op, b_element_op, PassThrough{});
+
+        ref_invoker.Run(ref_argument);
+
+        for(int m = 0; m < M; ++m)
+        {
+            for(int n = 0; n < N; ++n)
+            {
+                cde_element_op(e_m_n_host_result(m, n), c_m_n(m, n), bias_n(n));
+            }
+        }
+
+        return ck::utils::check_err(e_m_n_device_result, e_m_n_host_result) ? 0 : 1;
+    }
+
+    return 0;
+}

example/14_gemm_xdl_quantization/gemm_xdl_relu_quantization_int8.cpp → example/14_gemm_quantization/gemm_xdl_quantization_int8.cpp

@@ -9,7 +9,7 @@
 #include "ck/ck.hpp"
 #include "ck/tensor_operation/gpu/device/tensor_layout.hpp"
 #include "ck/tensor_operation/gpu/device/gemm_specialization.hpp"
-#include "ck/tensor_operation/gpu/device/impl/device_gemm_xdl_cshuffle.hpp"
+#include "ck/tensor_operation/gpu/device/impl/device_gemm_multiple_d_xdl_cshuffle.hpp"
 #include "ck/tensor_operation/gpu/element/element_wise_operation.hpp"
 
 #include "ck/library/utility/device_memory.hpp"
@@ -22,50 +22,59 @@
 template <ck::index_t... Is>
 using S = ck::Sequence<Is...>;
 
+using I8  = int8_t;
+using I32 = int32_t;
+using Row = ck::tensor_layout::gemm::RowMajor;
+using Col = ck::tensor_layout::gemm::ColumnMajor;
+
 using PassThrough  = ck::tensor_operation::element_wise::PassThrough;
-using ActivationOp = ck::tensor_operation::element_wise::Relu;
-using CElementOp   = ck::tensor_operation::element_wise::Activation_Mul_Clamp<ActivationOp>;
+using ActivationOp = PassThrough;
+using CDEElementOp = ck::tensor_operation::element_wise::Activation_Mul_Clamp<ActivationOp>;
 
-using ADataType        = int8_t;
-using BDataType        = int8_t;
-using CDataType        = int8_t;
-using AccDataType      = int32_t;
-using CShuffleDataType = float;
+using ADataType        = I8;
+using BDataType        = I8;
+using AccDataType      = I32;
+using CShuffleDataType = I32;
+using DsDataType       = ck::Tuple<>;
+using EDataType        = I8;
 
-using ALayout = ck::tensor_layout::gemm::RowMajor;
-using BLayout = ck::tensor_layout::gemm::ColumnMajor;
-using CLayout = ck::tensor_layout::gemm::RowMajor;
+using ALayout  = Row;
+using BLayout  = Col;
+using DsLayout = ck::Tuple<>;
+using ELayout  = Row;
 
 static constexpr auto GemmDefault = ck::tensor_operation::device::GemmSpecialization::Default;
 
 // clang-format off
-using DeviceGemmInstance = ck::tensor_operation::device::DeviceGemm_Xdl_CShuffle<
-     ALayout,                    // typename ALayout,
-     BLayout,                    // typename BLayout,
-     CLayout,                    // typename CLayout,
-     ADataType,                  // typename ADataType,
-     BDataType,                  // typename BDataType,
-     CDataType,                  // typename CDataType,
-     AccDataType,                // typename GemmAccDataType,
-     CShuffleDataType,           // typename CShuffleDataType,
-     PassThrough,                // typename AElementwiseOperation,
-     PassThrough,                // typename BElementwiseOperation,
-     CElementOp,         // typename CElementwiseOperation,
+using DeviceGemmInstance = ck::tensor_operation::device::DeviceGemmMultipleD_Xdl_CShuffle<
+     ALayout,
+     BLayout,
+     DsLayout,
+     ELayout,
+     ADataType,
+     BDataType,
+     AccDataType,
+     CShuffleDataType,
+     DsDataType,
+     EDataType,
+     PassThrough,                // AElementwiseOperation,
+     PassThrough,                // BElementwiseOperation,
+     CDEElementOp,               // CDEElementwiseOperation,
      GemmDefault,                // GemmSpecialization GemmSpec,
-     1,                          // index_t NumGemmKPrefetchStage,
-     256,                        // index_t BlockSize,
-     256,                        // index_t MPerBlock,
-     128,                        // index_t NPerBlock,
-     64,                         // index_t KPerBlock,
-     16,                         // index_t AK1,
-     16,                         // index_t BK1,
-     32,                         // index_t MPerXDL,
-     32,                         // index_t NPerXDL,
-     4,                          // index_t MXdlPerWave,
-     2,                          // index_t NXdlPerWave,
-     S<4, 64, 1>,                // typename ABlockTransferThreadClusterLengths_AK0_M_AK1,
-     S<1, 0, 2>,                 // typename ABlockTransferThreadClusterArrangeOrder,
-     S<1, 0, 2>,                 // typename ABlockTransferSrcAccessOrder,
+     1,                          // NumGemmKPrefetchStage,
+     256,                        // BlockSize,
+     256,                        // MPerBlock,
+     128,                        // NPerBlock,
+     64,                         // KPerBlock,
+     16,                         // AK1,
+     16,                         // BK1,
+     32,                         // MPerXDL,
+     32,                         // NPerXDL,
+     4,                          // MXdlPerWave,
+     2,                          // NXdlPerWave,
+     S<4, 64, 1>,                // ABlockTransferThreadClusterLengths_AK0_M_AK1,
+     S<1, 0, 2>,                 // ABlockTransferThreadClusterArrangeOrder,
+     S<1, 0, 2>,                 // ABlockTransferSrcAccessOrder,
      2,                          // index_t ABlockTransferSrcVectorDim,
      16,                         // index_t ABlockTransferSrcScalarPerVector,
      16,                         // index_t ABlockTransferDstScalarPerVector_AK1,
@@ -84,53 +93,23 @@ using DeviceGemmInstance = ck::tensor_operation::device::DeviceGemm_Xdl_CShuffle
 // clang-format on
 
 using ReferenceGemmInstance = ck::tensor_operation::host::
-    ReferenceGemm<ADataType, BDataType, CDataType, float, PassThrough, PassThrough, CElementOp>;
+    ReferenceGemm<ADataType, BDataType, EDataType, float, PassThrough, PassThrough, CDEElementOp>;
 
-int main(int argc, char* argv[])
+int main()
 {
     bool do_verification = true;
-    int init_method      = 1;
     bool time_kernel     = false;
 
     // GEMM shape
-    ck::index_t M = 3840;
-    ck::index_t N = 4096;
-    ck::index_t K = 4096;
-
-    ck::index_t StrideA = 4096;
-    ck::index_t StrideB = 4096;
-    ck::index_t StrideC = 4096;
-
-    float quant_multiplier = 0.03;
-
-    if(argc == 4)
-    {
-        do_verification = std::stoi(argv[1]);
-        init_method     = std::stoi(argv[2]);
-        time_kernel     = std::stoi(argv[3]);
-    }
-    else if(argc == 10)
-    {
-        do_verification = std::stoi(argv[1]);
-        init_method     = std::stoi(argv[2]);
-        time_kernel     = std::stoi(argv[3]);
+    ck::index_t M = 1024;
+    ck::index_t N = 1024;
+    ck::index_t K = 1024;
 
-        M = std::stoi(argv[4]);
-        N = std::stoi(argv[5]);
-        K = std::stoi(argv[6]);
+    ck::index_t StrideA = 1024;
+    ck::index_t StrideB = 1024;
+    ck::index_t StrideE = 1024;
 
-        StrideA = std::stoi(argv[7]);
-        StrideB = std::stoi(argv[8]);
-        StrideC = std::stoi(argv[9]);
-    }
-    else
-    {
-        printf("arg1: verification (0=no, 1=yes)\n");
-        printf("arg2: initialization (0=no init, 1=integer value, 2=decimal value)\n");
-        printf("arg3: time kernel (0=n0, 1=yes)\n");
-        printf("arg4 to 9: M (256x), N(128x), K(32x), StrideA, StrideB, StrideC\n");
-        exit(0);
-    }
+    float requant_scale = 0.03;
 
     auto f_host_tensor_descriptor =
         [](std::size_t row, std::size_t col, std::size_t stride, auto layout) {
@@ -138,61 +117,56 @@ int main(int argc, char* argv[])
 
             if(std::is_same<decltype(layout), ck::tensor_layout::gemm::RowMajor>::value)
             {
-                return HostTensorDescriptor({row, col}, {stride, 1_uz});
+                return HostTensorDescriptor(std::vector<std::size_t>({row, col}),
+                                            std::vector<std::size_t>({stride, 1_uz}));
             }
             else
             {
-                return HostTensorDescriptor({row, col}, {1_uz, stride});
+                return HostTensorDescriptor(std::vector<std::size_t>({row, col}),
+                                            std::vector<std::size_t>({1_uz, stride}));
             }
         };
 
     Tensor<ADataType> a_m_k(f_host_tensor_descriptor(M, K, StrideA, ALayout{}));
     Tensor<BDataType> b_k_n(f_host_tensor_descriptor(K, N, StrideB, BLayout{}));
-    Tensor<CDataType> c_m_n_host_result(f_host_tensor_descriptor(M, N, StrideC, CLayout{}));
-    Tensor<CDataType> c_m_n_device_result(f_host_tensor_descriptor(M, N, StrideC, CLayout{}));
+    Tensor<EDataType> e_m_n_host_result(f_host_tensor_descriptor(M, N, StrideE, ELayout{}));
+    Tensor<EDataType> e_m_n_device_result(f_host_tensor_descriptor(M, N, StrideE, ELayout{}));
 
     std::cout << "a_m_k: " << a_m_k.mDesc << std::endl;
     std::cout << "b_k_n: " << b_k_n.mDesc << std::endl;
-    std::cout << "c_m_n: " << c_m_n_host_result.mDesc << std::endl;
+    std::cout << "e_m_n: " << e_m_n_host_result.mDesc << std::endl;
 
-    switch(init_method)
-    {
-    case 0: break;
-    case 1:
-        a_m_k.GenerateTensorValue(GeneratorTensor_2<ADataType>{-5, 5});
-        b_k_n.GenerateTensorValue(GeneratorTensor_2<BDataType>{-5, 5});
-        break;
-    default:
-        a_m_k.GenerateTensorValue(GeneratorTensor_3<ADataType>{0.0, 1.0});
-        b_k_n.GenerateTensorValue(GeneratorTensor_3<BDataType>{-0.5, 0.5});
-    }
+    a_m_k.GenerateTensorValue(GeneratorTensor_2<ADataType>{-128, 127});
+    b_k_n.GenerateTensorValue(GeneratorTensor_2<BDataType>{-128, 127});
 
-    DeviceMem a_m_k_device_buf(sizeof(ADataType) * a_m_k.mDesc.GetElementSpaceSize());
-    DeviceMem b_k_n_device_buf(sizeof(BDataType) * b_k_n.mDesc.GetElementSpaceSize());
-    DeviceMem c_m_n_device_buf(sizeof(CDataType) * c_m_n_device_result.mDesc.GetElementSpaceSize());
+    DeviceMem a_device_buf(sizeof(ADataType) * a_m_k.mDesc.GetElementSpaceSize());
+    DeviceMem b_device_buf(sizeof(BDataType) * b_k_n.mDesc.GetElementSpaceSize());
+    DeviceMem e_device_buf(sizeof(EDataType) * e_m_n_device_result.mDesc.GetElementSpaceSize());
 
-    a_m_k_device_buf.ToDevice(a_m_k.mData.data());
-    b_k_n_device_buf.ToDevice(b_k_n.mData.data());
+    a_device_buf.ToDevice(a_m_k.mData.data());
+    b_device_buf.ToDevice(b_k_n.mData.data());
 
-    auto a_element_op = PassThrough{};
-    auto b_element_op = PassThrough{};
-    auto c_element_op = CElementOp{quant_multiplier, ActivationOp{}};
+    auto a_element_op   = PassThrough{};
+    auto b_element_op   = PassThrough{};
+    auto cde_element_op = CDEElementOp{requant_scale, ActivationOp{}};
 
     // do GEMM
     auto gemm     = DeviceGemmInstance{};
     auto invoker  = gemm.MakeInvoker();
-    auto argument = gemm.MakeArgument(static_cast<ADataType*>(a_m_k_device_buf.GetDeviceBuffer()),
-                                      static_cast<BDataType*>(b_k_n_device_buf.GetDeviceBuffer()),
-                                      static_cast<CDataType*>(c_m_n_device_buf.GetDeviceBuffer()),
+    auto argument = gemm.MakeArgument(a_device_buf.GetDeviceBuffer(),
+                                      b_device_buf.GetDeviceBuffer(),
+                                      {},
+                                      e_device_buf.GetDeviceBuffer(),
                                       M,
                                       N,
                                       K,
                                       StrideA,
                                       StrideB,
-                                      StrideC,
+                                      {},
+                                      StrideE,
                                       a_element_op,
                                       b_element_op,
-                                      c_element_op);
+                                      cde_element_op);
 
     if(!gemm.IsSupportedArgument(argument))
     {
@@ -205,7 +179,7 @@ int main(int argc, char* argv[])
 
     std::size_t flop = std::size_t(2) * M * N * K;
     std::size_t num_btype =
-        sizeof(ADataType) * M * K + sizeof(BDataType) * K * N + sizeof(CDataType) * M * N;
+        sizeof(ADataType) * M * K + sizeof(BDataType) * K * N + sizeof(EDataType) * M * N;
 
     float tflops = static_cast<float>(flop) / 1.E9 / ave_time;
 
@@ -214,7 +188,7 @@ int main(int argc, char* argv[])
     std::cout << "Perf: " << ave_time << " ms, " << tflops << " TFlops, " << gb_per_sec << " GB/s, "
               << gemm.GetTypeString() << std::endl;
 
-    c_m_n_device_buf.FromDevice(c_m_n_device_result.mData.data());
+    e_device_buf.FromDevice(e_m_n_device_result.mData.data());
 
     if(do_verification)
     {
@@ -222,11 +196,11 @@ int main(int argc, char* argv[])
         auto ref_invoker = ref_gemm.MakeInvoker();
 
         auto ref_argument = ref_gemm.MakeArgument(
-            a_m_k, b_k_n, c_m_n_host_result, a_element_op, b_element_op, c_element_op);
+            a_m_k, b_k_n, e_m_n_host_result, a_element_op, b_element_op, cde_element_op);
 
         ref_invoker.Run(ref_argument);
 
-        return ck::utils::check_err(c_m_n_device_result, c_m_n_host_result) ? 0 : 1;
+        return ck::utils::check_err(e_m_n_device_result, e_m_n_host_result) ? 0 : 1;
     }
 
     return 0;

example/14_gemm_xdl_quantization/CMakeLists.txt

-add_example_executable(example_gemm_xdl_relu_quantization_int8 gemm_xdl_relu_quantization_int8.cpp)
\ No newline at end of file

example/34_batchnorm/batchnorm_backward_nhwc.cpp

@@ -11,7 +11,7 @@
 #include "ck/library/utility/host_tensor.hpp"
 #include "ck/library/utility/host_tensor_generator.hpp"
 #include "ck/library/utility/host_common_util.hpp"
-#include "ck/library/reference_tensor_operation/cpu/reference_batchnorm_backward_nhwc_c.hpp"
+#include "ck/library/reference_tensor_operation/cpu/reference_batchnorm_backward.hpp"
 #include "ck/tensor_operation/gpu/device/impl/device_batchnorm_backward_impl.hpp"
 
 static struct option long_options[] = {{"inOutLengths", required_argument, nullptr, 'D'},
@@ -106,7 +106,7 @@ class BatchNormBwdArg
 
 using namespace ck;
 
-template <typename InOutDataType, typename AccDataType, bool UseMultiblockInK>
+template <typename XDataType, typename AccDataType, bool UseMultiblockInK>
 bool bnorm_bwd_nhwc_test(bool do_verification,
                          int init_method,
                          bool time_kernel,
@@ -118,13 +118,15 @@ bool bnorm_bwd_nhwc_test(bool do_verification,
     constexpr index_t Rank         = 4;
     constexpr index_t NumReduceDim = 3;
 
+    using ScaleDataType = XDataType;
+
     const std::vector<size_t> scaleBiasMeanVarLengths = {inOutLengths[3]};
 
     // input data of the batchnorm backward algorithm
-    Tensor<InOutDataType> x(inOutLengths);
-    Tensor<InOutDataType> dy(inOutLengths);
+    Tensor<XDataType> x(inOutLengths);
+    Tensor<AccDataType> dy(inOutLengths);
 
-    Tensor<AccDataType> bnScale(scaleBiasMeanVarLengths);
+    Tensor<ScaleDataType> bnScale(scaleBiasMeanVarLengths);
 
     Tensor<AccDataType> savedMean(scaleBiasMeanVarLengths);
     Tensor<AccDataType> savedInvVar(scaleBiasMeanVarLengths);
@@ -132,8 +134,8 @@ bool bnorm_bwd_nhwc_test(bool do_verification,
     Tensor<AccDataType> savedVariance(scaleBiasMeanVarLengths);
 
     // output data of the batchnorm backward algorithm
-    Tensor<InOutDataType> dx_ref(inOutLengths);
-    Tensor<InOutDataType> dx(inOutLengths);
+    Tensor<AccDataType> dx_ref(inOutLengths);
+    Tensor<AccDataType> dx(inOutLengths);
 
     Tensor<AccDataType> dscale(scaleBiasMeanVarLengths);
     Tensor<AccDataType> dbias(scaleBiasMeanVarLengths);
@@ -153,7 +155,7 @@ bool bnorm_bwd_nhwc_test(bool do_verification,
         const float noise_stddev = 0.0001f;
 
         // input data in normal distribution
-        x.GenerateTensorValue(GeneratorTensor_4<InOutDataType>{x_mean, x_stddev}, num_thread);
+        x.GenerateTensorValue(GeneratorTensor_4<XDataType>{x_mean, x_stddev}, num_thread);
 
         // initialize the savedMean to be values with tiny variation to the mean of the x values
         savedMean.GenerateTensorValue(GeneratorTensor_4<AccDataType>{x_mean, noise_stddev},
@@ -182,7 +184,7 @@ bool bnorm_bwd_nhwc_test(bool do_verification,
         const float x_stddev = 1.0f;
 
         // input data in normal distribution
-        x.GenerateTensorValue(GeneratorTensor_4<InOutDataType>{x_mean, x_stddev}, num_thread);
+        x.GenerateTensorValue(GeneratorTensor_4<XDataType>{x_mean, x_stddev}, num_thread);
     };
 
     if(do_verification)
@@ -190,34 +192,34 @@ bool bnorm_bwd_nhwc_test(bool do_verification,
         switch(init_method)
         {
         case 0:
-            dy.GenerateTensorValue(GeneratorTensor_0<InOutDataType>{}, num_thread);
-            bnScale.GenerateTensorValue(GeneratorTensor_0<InOutDataType>{}, num_thread);
+            dy.GenerateTensorValue(GeneratorTensor_0<AccDataType>{}, num_thread);
+            bnScale.GenerateTensorValue(GeneratorTensor_0<ScaleDataType>{}, num_thread);
             break;
         case 1:
-            dy.GenerateTensorValue(GeneratorTensor_1<InOutDataType>{1}, num_thread);
-            bnScale.GenerateTensorValue(GeneratorTensor_1<InOutDataType>{1}, num_thread);
+            dy.GenerateTensorValue(GeneratorTensor_1<AccDataType>{1}, num_thread);
+            bnScale.GenerateTensorValue(GeneratorTensor_1<ScaleDataType>{1}, num_thread);
             break;
         case 2:
-            dy.GenerateTensorValue(GeneratorTensor_2<InOutDataType>{-5, 5}, num_thread);
-            bnScale.GenerateTensorValue(GeneratorTensor_2<InOutDataType>{-5, 5}, num_thread);
+            dy.GenerateTensorValue(GeneratorTensor_2<AccDataType>{-2, 2}, num_thread);
+            bnScale.GenerateTensorValue(GeneratorTensor_2<ScaleDataType>{-5, 5}, num_thread);
             break;
         default:
-            dy.GenerateTensorValue(GeneratorTensor_3<InOutDataType>{-0.2f, 0.2f}, num_thread);
-            bnScale.GenerateTensorValue(GeneratorTensor_3<InOutDataType>{-0.5f, 0.5f}, num_thread);
+            dy.GenerateTensorValue(GeneratorTensor_3<AccDataType>{-0.2f, 0.2f}, num_thread);
+            bnScale.GenerateTensorValue(GeneratorTensor_3<ScaleDataType>{-0.5f, 0.5f}, num_thread);
         }
     };
 
     // input data of the batchnorm backward algorithm
-    DeviceMem x_dev(sizeof(InOutDataType) * x.mDesc.GetElementSpaceSize());
-    DeviceMem dy_dev(sizeof(InOutDataType) * dy.mDesc.GetElementSpaceSize());
+    DeviceMem x_dev(sizeof(XDataType) * x.mDesc.GetElementSpaceSize());
+    DeviceMem dy_dev(sizeof(AccDataType) * dy.mDesc.GetElementSpaceSize());
 
-    DeviceMem bnScale_dev(sizeof(AccDataType) * bnScale.mDesc.GetElementSpaceSize());
+    DeviceMem bnScale_dev(sizeof(ScaleDataType) * bnScale.mDesc.GetElementSpaceSize());
 
     DeviceMem savedMean_dev(sizeof(AccDataType) * savedMean.mDesc.GetElementSpaceSize());
     DeviceMem savedInvVar_dev(sizeof(AccDataType) * savedInvVar.mDesc.GetElementSpaceSize());
 
     // output data of the batchnorm backward algorithm
-    DeviceMem dx_dev(sizeof(InOutDataType) * dx.mDesc.GetElementSpaceSize());
+    DeviceMem dx_dev(sizeof(AccDataType) * dx.mDesc.GetElementSpaceSize());
 
     DeviceMem dscale_dev(sizeof(AccDataType) * dscale.mDesc.GetElementSpaceSize());
     DeviceMem dbias_dev(sizeof(AccDataType) * dbias.mDesc.GetElementSpaceSize());
@@ -249,13 +251,13 @@ bool bnorm_bwd_nhwc_test(bool do_verification,
     using PassThroughOp = ck::tensor_operation::element_wise::PassThrough;
 
     using DeviceBatchNormBwdInstance =
-        ck::tensor_operation::device::DeviceBatchNormBwdImpl<InOutDataType,
-                                                             InOutDataType,
-                                                             InOutDataType,
+        ck::tensor_operation::device::DeviceBatchNormBwdImpl<XDataType,
+                                                             AccDataType,
+                                                             AccDataType,
                                                              AccDataType,
-                                                             AccDataType, // ScaleDataType
-                                                             AccDataType, // BiasDataType
-                                                             AccDataType, // MeanVarDataType
+                                                             ScaleDataType, // ScaleDataType
+                                                             AccDataType,   // DscaleDbiasDataType
+                                                             AccDataType,   // MeanVarDataType
                                                              PassThroughOp,
                                                              Rank,
                                                              NumReduceDim,
@@ -269,8 +271,8 @@ bool bnorm_bwd_nhwc_test(bool do_verification,
                                                              1,  // XSrcVectorSize
                                                              1,  // DySrcVectorSize
                                                              1,  // DxDstVectorSize
-                                                             1,  // ScaleSrcDstVectorSize
-                                                             1,  // BiasDstVectorSize
+                                                             1,  // ScaleSrcVectorSize
+                                                             1,  // DscaleDbiasDstVectorSize
                                                              1>; // MeanVarSrcVectorSize
 
     auto batchnorm_bwd = DeviceBatchNormBwdInstance{};
@@ -324,7 +326,7 @@ bool bnorm_bwd_nhwc_test(bool do_verification,
 
         // inputing of x, dy, scale, outputing of dx, dscale, dbias
         num_bytes +=
-            total_length * sizeof(InOutDataType) * 3 + invariant_length * sizeof(AccDataType) * 3;
+            total_length * sizeof(XDataType) * 3 + invariant_length * sizeof(AccDataType) * 3;
 
         // outputing of mean, inv-variance
         num_bytes += haveSavedMeanInvVar ? invariant_length * sizeof(AccDataType) * 2 : 0;
@@ -341,14 +343,16 @@ bool bnorm_bwd_nhwc_test(bool do_verification,
     if(do_verification)
     {
         using ReferenceBatchNormBwdInstance =
-            ck::tensor_operation::host::ReferenceBatchNormBwd_Input_N_H_W_C_Output_C<InOutDataType,
-                                                                                     InOutDataType,
-                                                                                     InOutDataType,
-                                                                                     AccDataType,
-                                                                                     AccDataType,
-                                                                                     AccDataType,
-                                                                                     AccDataType,
-                                                                                     PassThroughOp>;
+            ck::tensor_operation::host::ReferenceBatchNormBwd<XDataType,
+                                                              AccDataType,
+                                                              AccDataType,
+                                                              AccDataType,
+                                                              ScaleDataType, // ScaleDataType
+                                                              AccDataType,
+                                                              AccDataType,
+                                                              PassThroughOp,
+                                                              Rank,
+                                                              NumReduceDim>;
 
         auto batchNormBwd_ref = ReferenceBatchNormBwdInstance{};
 
@@ -390,8 +394,8 @@ bool bnorm_bwd_nhwc_test(bool do_verification,
         dbias_dev.FromDevice(dbias.data());
 
         // clang-format off
-        pass = pass && ck::utils::check_err(dbias.mData, dbias_ref.mData, "dBias result:", 1e-5, 1e-5);
-        pass = pass && ck::utils::check_err(dscale.mData, dscale_ref.mData, "dScale result:", 1e-5, 2e-4);
+        pass = pass && ck::utils::check_err(dbias.mData, dbias_ref.mData, "dBias result:", 2e-4, 2e-4);
+        pass = pass && ck::utils::check_err(dscale.mData, dscale_ref.mData, "dScale result:", 2e-4, 2e-4);
         pass = pass && ck::utils::check_err(dx.mData, dx_ref.mData, "dx result:");
         // clang-format on
     };

example/44_conv2d_fwd_quant/CMakeLists.txt → example/44_conv2d_fwd_quantization/CMakeLists.txt

+add_example_executable(example_conv2d_fwd_xdl_perchannel_quantization_int8 conv2d_fwd_xdl_bias_relu_perchannel_quantization_int8.cpp)
 add_example_executable(example_conv2d_fwd_xdl_perlayer_quantization_int8 conv2d_fwd_xdl_perlayer_quantization_int8.cpp)
 add_example_executable(example_conv2d_fwd_xdl_bias_relu_perlayer_quantization_int8 conv2d_fwd_xdl_bias_relu_perlayer_quantization_int8.cpp)

example/44_conv2d_fwd_quantization/conv2d_fwd_xdl_bias_relu_perchannel_quantization_int8.cpp

+// SPDX-License-Identifier: MIT
+// Copyright (c) 2018-2022, Advanced Micro Devices, Inc. All rights reserved.
+
+#include "ck/ck.hpp"
+#include "ck/tensor_operation/gpu/device/tensor_layout.hpp"
+#include "ck/tensor_operation/gpu/device/impl/device_grouped_conv_fwd_multiple_d_xdl_cshuffle.hpp"
+#include "ck/tensor_operation/gpu/element/element_wise_operation.hpp"
+
+#include "ck/library/utility/algorithm.hpp"
+#include "ck/library/utility/check_err.hpp"
+#include "ck/library/utility/device_memory.hpp"
+#include "ck/library/utility/host_tensor.hpp"
+#include "ck/library/utility/host_tensor_generator.hpp"
+#include "ck/library/utility/literals.hpp"
+#include "ck/library/utility/convolution_parameter.hpp"
+#include "ck/library/utility/convolution_host_tensor_descriptor_helper.hpp"
+#include "ck/library/reference_tensor_operation/cpu/reference_conv_fwd.hpp"
+
+using InDataType           = int8_t;
+using WeiDataType          = int8_t;
+using BiasDataType         = int32_t;
+using RequantScaleDataType = float;
+using AccDataType          = int32_t;
+using CShuffleDataType     = int32_t;
+using OutDataType          = int8_t;
+
+template <ck::index_t... Is>
+using S = ck::Sequence<Is...>;
+
+using PassThrough  = ck::tensor_operation::element_wise::PassThrough;
+using InElementOp  = PassThrough;
+using WeiElementOp = PassThrough;
+using ActivationOp = ck::tensor_operation::element_wise::Relu;
+using OutElementOp = ck::tensor_operation::element_wise::Add_Activation_Mul2_Clamp<ActivationOp>;
+
+static constexpr auto ConvSpec =
+    ck::tensor_operation::device::ConvolutionForwardSpecialization::Default;
+
+static constexpr auto GemmSpec = ck::tensor_operation::device::GemmSpecialization::MNKPadding;
+
+template <ck::index_t NDimSpatial,
+          typename InLayout,
+          typename WeiLayout,
+          typename BiasLayout,
+          typename RequantScaleLayout,
+          typename OutLayout>
+using DeviceGroupedConvNDFwdInstance =
+    ck::tensor_operation::device::DeviceGroupedConvFwdMultipleD_Xdl_CShuffle<
+        NDimSpatial,
+        InLayout,
+        WeiLayout,
+        ck::Tuple<BiasLayout, RequantScaleLayout>,
+        OutLayout,
+        InDataType,
+        WeiDataType,
+        AccDataType,
+        CShuffleDataType,
+        ck::Tuple<BiasDataType, RequantScaleDataType>,
+        OutDataType,
+        InElementOp,
+        WeiElementOp,
+        OutElementOp,
+        ConvSpec,    // ConvForwardSpecialization
+        GemmSpec,    // GemmSpecialization
+        1,           //
+        256,         // BlockSize
+        128,         // MPerBlock
+        256,         // NPerBlock
+        64,          // KPerBlock
+        16,          // AK1
+        16,          // BK1
+        32,          // MPerXdl
+        32,          // NPerXdl
+        2,           // MXdlPerWave
+        4,           // NXdlPerWave
+        S<4, 64, 1>, // ABlockTransferThreadClusterLengths_AK0_M_AK1
+        S<1, 0, 2>,  // ABlockTransferThreadClusterArrangeOrder
+        S<1, 0, 2>,  // ABlockTransferSrcAccessOrder
+        2,           // ABlockTransferSrcVectorDim
+        16,          // ABlockTransferSrcScalarPerVector
+        16,          // ABlockTransferDstScalarPerVector_AK1
+        1,           // ABlockLdsExtraM
+        S<4, 64, 1>, // BBlockTransferThreadClusterLengths_BK0_N_BK1
+        S<1, 0, 2>,  // BBlockTransferThreadClusterArrangeOrder
+        S<1, 0, 2>,  // BBlockTransferSrcAccessOrder
+        2,           // BBlockTransferSrcVectorDim
+        16,          // BBlockTransferSrcScalarPerVector
+        16,          // BBlockTransferDstScalarPerVector_BK1
+        1,           // BBlockLdsExtraN
+        1,
+        1,
+        S<1, 64, 1, 4>,
+        8>;
+
+template <ck::index_t NDimSpatial,
+          typename InDataType,
+          typename WeiDataType,
+          typename OutDataType,
+          typename InElementOp,
+          typename WeiElementOp,
+          typename OutElementOp,
+          typename DeviceConvNDFwdInstance>
+bool run_grouped_conv_fwd(bool do_verification,
+                          bool time_kernel,
+                          const ck::utils::conv::ConvParam& conv_param,
+                          const HostTensorDescriptor& in_g_n_c_wis_desc,
+                          const HostTensorDescriptor& wei_g_k_c_xs_desc,
+                          const HostTensorDescriptor& bias_g_k_desc,
+                          const HostTensorDescriptor& requant_scale_g_k_desc,
+                          const HostTensorDescriptor& out_g_n_k_wos_desc,
+                          const InElementOp& in_element_op,
+                          const WeiElementOp& wei_element_op,
+                          const OutElementOp& out_element_op)
+{
+    Tensor<InDataType> in(in_g_n_c_wis_desc);
+    Tensor<WeiDataType> wei(wei_g_k_c_xs_desc);
+    Tensor<BiasDataType> bias(bias_g_k_desc);
+    Tensor<RequantScaleDataType> requant_scale(requant_scale_g_k_desc);
+    Tensor<OutDataType> out_host(out_g_n_k_wos_desc);
+    Tensor<OutDataType> out_device(out_g_n_k_wos_desc);
+
+    std::cout << "in: " << in.mDesc << std::endl;
+    std::cout << "wei: " << wei.mDesc << std::endl;
+    std::cout << "bias: " << bias.mDesc << std::endl;
+    std::cout << "requant_scale: " << requant_scale.mDesc << std::endl;
+    std::cout << "out: " << out_host.mDesc << std::endl;
+
+    in.GenerateTensorValue(GeneratorTensor_2<InDataType>{-128, 127});
+    wei.GenerateTensorValue(GeneratorTensor_2<WeiDataType>{-128, 127});
+    bias.GenerateTensorValue(GeneratorTensor_2<BiasDataType>{-128, 127});
+    requant_scale.GenerateTensorValue(GeneratorTensor_2<RequantScaleDataType>{0, 1});
+
+    DeviceMem in_device_buf(sizeof(InDataType) * in.mDesc.GetElementSpaceSize());
+    DeviceMem wei_device_buf(sizeof(WeiDataType) * wei.mDesc.GetElementSpaceSize());
+    DeviceMem bias_device_buf(sizeof(BiasDataType) * bias.mDesc.GetElementSpaceSize());
+    DeviceMem requant_scale_device_buf(sizeof(RequantScaleDataType) *
+                                       requant_scale.mDesc.GetElementSpaceSize());
+    DeviceMem out_device_buf(sizeof(OutDataType) * out_device.mDesc.GetElementSpaceSize());
+
+    in_device_buf.ToDevice(in.mData.data());
+    wei_device_buf.ToDevice(wei.mData.data());
+    bias_device_buf.ToDevice(bias.mData.data());
+    requant_scale_device_buf.ToDevice(requant_scale.mData.data());
+
+    std::array<ck::index_t, NDimSpatial + 3> a_g_n_c_wis_lengths{};
+    std::array<ck::index_t, NDimSpatial + 3> a_g_n_c_wis_strides{};
+    std::array<ck::index_t, NDimSpatial + 3> b_g_k_c_xs_lengths{};
+    std::array<ck::index_t, NDimSpatial + 3> b_g_k_c_xs_strides{};
+    std::array<ck::index_t, NDimSpatial + 3> d0_g_n_k_wos_lengths{};
+    std::array<ck::index_t, NDimSpatial + 3> d0_g_n_k_wos_strides{};
+    std::array<ck::index_t, NDimSpatial + 3> d1_g_n_k_wos_lengths{};
+    std::array<ck::index_t, NDimSpatial + 3> d1_g_n_k_wos_strides{};
+    std::array<ck::index_t, NDimSpatial + 3> e_g_n_k_wos_lengths{};
+    std::array<ck::index_t, NDimSpatial + 3> e_g_n_k_wos_strides{};
+    std::array<ck::index_t, NDimSpatial> conv_filter_strides{};
+    std::array<ck::index_t, NDimSpatial> conv_filter_dilations{};
+    std::array<ck::index_t, NDimSpatial> input_left_pads{};
+    std::array<ck::index_t, NDimSpatial> input_right_pads{};
+
+    auto copy = [](const auto& x, auto& y) { ck::ranges::copy(x, y.begin()); };
+
+    copy(in_g_n_c_wis_desc.GetLengths(), a_g_n_c_wis_lengths);
+    copy(in_g_n_c_wis_desc.GetStrides(), a_g_n_c_wis_strides);
+    copy(wei_g_k_c_xs_desc.GetLengths(), b_g_k_c_xs_lengths);
+    copy(wei_g_k_c_xs_desc.GetStrides(), b_g_k_c_xs_strides);
+    copy(bias_g_k_desc.GetLengths(), d0_g_n_k_wos_lengths);
+    copy(bias_g_k_desc.GetStrides(), d0_g_n_k_wos_strides);
+    copy(requant_scale_g_k_desc.GetLengths(), d1_g_n_k_wos_lengths);
+    copy(requant_scale_g_k_desc.GetStrides(), d1_g_n_k_wos_strides);
+    copy(out_g_n_k_wos_desc.GetLengths(), e_g_n_k_wos_lengths);
+    copy(out_g_n_k_wos_desc.GetStrides(), e_g_n_k_wos_strides);
+    copy(conv_param.conv_filter_strides_, conv_filter_strides);
+    copy(conv_param.conv_filter_dilations_, conv_filter_dilations);
+    copy(conv_param.input_left_pads_, input_left_pads);
+    copy(conv_param.input_right_pads_, input_right_pads);
+
+    // do Conv
+    auto conv     = DeviceConvNDFwdInstance{};
+    auto invoker  = conv.MakeInvoker();
+    auto argument = conv.MakeArgument(
+        in_device_buf.GetDeviceBuffer(),
+        wei_device_buf.GetDeviceBuffer(),
+        {bias_device_buf.GetDeviceBuffer(), requant_scale_device_buf.GetDeviceBuffer()},
+        out_device_buf.GetDeviceBuffer(),
+        a_g_n_c_wis_lengths,
+        a_g_n_c_wis_strides,
+        b_g_k_c_xs_lengths,
+        b_g_k_c_xs_strides,
+        {d0_g_n_k_wos_lengths, d1_g_n_k_wos_lengths},
+        {d0_g_n_k_wos_strides, d1_g_n_k_wos_strides},
+        e_g_n_k_wos_lengths,
+        e_g_n_k_wos_strides,
+        conv_filter_strides,
+        conv_filter_dilations,
+        input_left_pads,
+        input_right_pads,
+        in_element_op,
+        wei_element_op,
+        out_element_op);
+
+    if(!conv.IsSupportedArgument(argument))
+    {
+        throw std::runtime_error(
+            "wrong! device_conv with the specified compilation parameters does "
+            "not support this Conv problem");
+    }
+
+    float avg_time = invoker.Run(argument, StreamConfig{nullptr, time_kernel});
+
+    std::size_t flop      = conv_param.GetFlops();
+    std::size_t num_btype = conv_param.GetByte<InDataType, WeiDataType, OutDataType>();
+
+    float tflops     = static_cast<float>(flop) / 1.E9 / avg_time;
+    float gb_per_sec = num_btype / 1.E6 / avg_time;
+    std::cout << "Perf: " << avg_time << " ms, " << tflops << " TFlops, " << gb_per_sec << " GB/s, "
+              << conv.GetTypeString() << std::endl;
+
+    bool pass = true;
+
+    if(do_verification)
+    {
+        Tensor<CShuffleDataType> c_host(out_g_n_k_wos_desc);
+
+        auto ref_conv = ck::tensor_operation::host::ReferenceConvFwd<NDimSpatial,
+                                                                     InDataType,
+                                                                     WeiDataType,
+                                                                     CShuffleDataType,
+                                                                     InElementOp,
+                                                                     WeiElementOp,
+                                                                     PassThrough>();
+
+        auto ref_invoker  = ref_conv.MakeInvoker();
+        auto ref_argument = ref_conv.MakeArgument(in,
+                                                  wei,
+                                                  c_host,
+                                                  conv_param.conv_filter_strides_,
+                                                  conv_param.conv_filter_dilations_,
+                                                  conv_param.input_left_pads_,
+                                                  conv_param.input_right_pads_,
+                                                  in_element_op,
+                                                  wei_element_op,
+                                                  PassThrough{});
+
+        ref_invoker.Run(ref_argument);
+
+        // TODO: implement elementwise operation for host
+        out_host.ForEach([&](auto&, auto idx) {
+            out_element_op(out_host(idx), c_host(idx), bias(idx), requant_scale(idx));
+        });
+
+        out_device_buf.FromDevice(out_device.mData.data());
+
+        pass &=
+            ck::utils::check_err(out_device, out_host, "Error: incorrect results!", 1e-5f, 1e-4f);
+    }
+
+    return (pass ? 0 : 1);
+}
+
+int main()
+{
+    bool do_verification           = true;
+    bool time_kernel               = true;
+    const ck::index_t ndim_spatial = 2;
+
+    ck::utils::conv::ConvParam conv_param{
+        ndim_spatial, // n_dim
+        1,            // group
+        4,            // batch
+        64,           // output channels
+        32,           // input chanels
+        {3, 3},       // weight HW
+        {71, 71},     // x HW
+        {2, 2},       // strides
+        {1, 1},       // dilations
+        {1, 1},       // left_pads
+        {1, 1}        // right_pads
+    };
+
+    const auto in_element_op  = InElementOp{};
+    const auto wei_element_op = WeiElementOp{};
+    const auto out_element_op = OutElementOp{ActivationOp{}};
+
+    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;
+
+    const auto in_g_n_c_wis_desc =
+        ck::utils::conv::make_input_host_tensor_descriptor_g_n_c_wis_packed<InLayout>(conv_param);
+
+    const auto wei_g_k_c_xs_desc =
+        ck::utils::conv::make_weight_host_tensor_descriptor_g_k_c_xs_packed<WeiLayout>(conv_param);
+
+    // TODO - make_bias_host_tensor_descriptor_g_n_k_wos_packed()
+    const auto bias_g_k_desc = HostTensorDescriptor({conv_param.G_,
+                                                     conv_param.N_,
+                                                     conv_param.K_,
+                                                     conv_param.output_spatial_lengths_[0],
+                                                     conv_param.output_spatial_lengths_[1]},
+                                                    {
+                                                        conv_param.K_, // g
+                                                        0,             // n
+                                                        1,             // k
+                                                        0,             // ho
+                                                        0              // wo
+                                                    });
+
+    const auto requant_scale_g_k_desc = bias_g_k_desc;
+
+    const auto out_g_n_k_wos_desc =
+        ck::utils::conv::make_output_host_tensor_descriptor_g_n_k_wos_packed<OutLayout>(conv_param);
+
+    std::cout << out_g_n_k_wos_desc << std::endl;
+
+    using deviceOp = DeviceGroupedConvNDFwdInstance<ndim_spatial,
+                                                    InLayout,
+                                                    WeiLayout,
+                                                    BiasLayout,
+                                                    RequantScaleLayout,
+                                                    OutLayout>;
+
+    return run_grouped_conv_fwd<ndim_spatial,
+                                InDataType,
+                                WeiDataType,
+                                OutDataType,
+                                InElementOp,
+                                WeiElementOp,
+                                OutElementOp,
+                                deviceOp>(do_verification,
+                                          time_kernel,
+                                          conv_param,
+                                          in_g_n_c_wis_desc,
+                                          wei_g_k_c_xs_desc,
+                                          bias_g_k_desc,
+                                          requant_scale_g_k_desc,
+                                          out_g_n_k_wos_desc,
+                                          in_element_op,
+                                          wei_element_op,
+                                          out_element_op);
+}

example/44_conv2d_fwd_quant/conv2d_fwd_xdl_bias_relu_perlayer_quantization_int8.cpp → example/44_conv2d_fwd_quantization/conv2d_fwd_xdl_bias_relu_perlayer_quantization_int8.cpp

@@ -11,6 +11,7 @@
 #include "ck/library/utility/device_memory.hpp"
 #include "ck/library/utility/host_tensor.hpp"
 #include "ck/library/utility/host_tensor_generator.hpp"
+#include "ck/library/utility/literals.hpp"
 #include "ck/library/utility/convolution_parameter.hpp"
 #include "ck/library/utility/convolution_host_tensor_descriptor_helper.hpp"
 #include "ck/library/reference_tensor_operation/cpu/reference_conv_fwd.hpp"
@@ -163,26 +164,25 @@ bool run_grouped_conv_fwd(bool do_verification,
     // do Conv
     auto conv     = DeviceConvNDFwdInstance{};
     auto invoker  = conv.MakeInvoker();
-    auto argument = conv.MakeArgument(
-        in_device_buf.GetDeviceBuffer(),
-        wei_device_buf.GetDeviceBuffer(),
-        std::array<const void*, 1>{bias_device_buf.GetDeviceBuffer()},
-        out_device_buf.GetDeviceBuffer(),
-        a_g_n_c_wis_lengths,
-        a_g_n_c_wis_strides,
-        b_g_k_c_xs_lengths,
-        b_g_k_c_xs_strides,
-        std::array<std::array<ck::index_t, NDimSpatial + 3>, 1>{{d0_g_n_k_wos_lengths}},
-        std::array<std::array<ck::index_t, NDimSpatial + 3>, 1>{{d0_g_n_k_wos_strides}},
-        e_g_n_k_wos_lengths,
-        e_g_n_k_wos_strides,
-        conv_filter_strides,
-        conv_filter_dilations,
-        input_left_pads,
-        input_right_pads,
-        in_element_op,
-        wei_element_op,
-        out_element_op);
+    auto argument = conv.MakeArgument(in_device_buf.GetDeviceBuffer(),
+                                      wei_device_buf.GetDeviceBuffer(),
+                                      {bias_device_buf.GetDeviceBuffer()},
+                                      out_device_buf.GetDeviceBuffer(),
+                                      a_g_n_c_wis_lengths,
+                                      a_g_n_c_wis_strides,
+                                      b_g_k_c_xs_lengths,
+                                      b_g_k_c_xs_strides,
+                                      {d0_g_n_k_wos_lengths},
+                                      {d0_g_n_k_wos_strides},
+                                      e_g_n_k_wos_lengths,
+                                      e_g_n_k_wos_strides,
+                                      conv_filter_strides,
+                                      conv_filter_dilations,
+                                      input_left_pads,
+                                      input_right_pads,
+                                      in_element_op,
+                                      wei_element_op,
+                                      out_element_op);
 
     if(!conv.IsSupportedArgument(argument))
     {
@@ -235,8 +235,8 @@ bool run_grouped_conv_fwd(bool do_verification,
 
         out_device_buf.FromDevice(out_device.mData.data());
 
-        pass &= ck::utils::check_err(
-            out_device.mData, out_host.mData, "Error: incorrect results!", 1e-5f, 1e-4f);
+        pass &=
+            ck::utils::check_err(out_device, out_host, "Error: incorrect results!", 1e-5f, 1e-4f);
     }
 
     return (pass ? 0 : 1);

example/44_conv2d_fwd_quant/conv2d_fwd_xdl_perlayer_quantization_int8.cpp → example/44_conv2d_fwd_quantization/conv2d_fwd_xdl_perlayer_quantization_int8.cpp

@@ -11,6 +11,7 @@
 #include "ck/library/utility/device_memory.hpp"
 #include "ck/library/utility/host_tensor.hpp"
 #include "ck/library/utility/host_tensor_generator.hpp"
+#include "ck/library/utility/literals.hpp"
 #include "ck/library/utility/convolution_parameter.hpp"
 #include "ck/library/utility/convolution_host_tensor_descriptor_helper.hpp"
 #include "ck/library/reference_tensor_operation/cpu/reference_conv_fwd.hpp"
@@ -150,14 +151,14 @@ bool run_grouped_conv_fwd(bool do_verification,
     auto invoker  = conv.MakeInvoker();
     auto argument = conv.MakeArgument(in_device_buf.GetDeviceBuffer(),
                                       wei_device_buf.GetDeviceBuffer(),
-                                      std::array<const void*, 0>{},
+                                      {},
                                       out_device_buf.GetDeviceBuffer(),
                                       a_g_n_c_wis_lengths,
                                       a_g_n_c_wis_strides,
                                       b_g_k_c_xs_lengths,
                                       b_g_k_c_xs_strides,
-                                      std::array<std::array<ck::index_t, NDimSpatial + 3>, 0>{{}},
-                                      std::array<std::array<ck::index_t, NDimSpatial + 3>, 0>{{}},
+                                      {},
+                                      {},
                                       e_g_n_k_wos_lengths,
                                       e_g_n_k_wos_strides,
                                       conv_filter_strides,
@@ -213,8 +214,8 @@ bool run_grouped_conv_fwd(bool do_verification,
 
         out_device_buf.FromDevice(out_device.mData.data());
 
-        pass &= ck::utils::check_err(
-            out_device.mData, out_host.mData, "Error: incorrect results!", 1e-5f, 1e-4f);
+        pass &=
+            ck::utils::check_err(out_device, out_host, "Error: incorrect results!", 1e-5f, 1e-4f);
     }
 
     return (pass ? 0 : 1);

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

@@ -13,7 +13,16 @@ namespace ck {
 namespace tensor_operation {
 namespace device {
 
-template <index_t Rank, index_t NumBatchNormReduceDim, typename DyElementwiseOp>
+template <typename XDataType,
+          typename DxDataType,
+          typename DyDataType,
+          typename AccDataType,
+          typename ScaleDataType,
+          typename DscaleDbiasDataType,
+          typename MeanVarDataType,
+          typename DyElementwiseOp,
+          index_t Rank,
+          index_t NumBatchNormReduceDim>
 struct DeviceBatchNormBwd : public BaseOperator
 {
     static constexpr index_t NumInvariantDim = Rank - NumBatchNormReduceDim;
@@ -26,7 +35,7 @@ struct DeviceBatchNormBwd : public BaseOperator
                         const std::array<int, NumBatchNormReduceDim> reduceDims,
                         const std::array<ck::index_t, NumInvariantDim> bnScaleBiasMeanVarLengths,
                         const std::array<ck::index_t, NumInvariantDim> bnScaleStrides,
-                        const std::array<ck::index_t, NumInvariantDim> bnBiasStrides,
+                        const std::array<ck::index_t, NumInvariantDim> bnDscaleDbiasStrides,
                         const std::array<ck::index_t, NumInvariantDim> bnMeanVarStrides,
                         const void* p_x,
                         const void* p_dy,
@@ -42,9 +51,26 @@ struct DeviceBatchNormBwd : public BaseOperator
     virtual std::unique_ptr<BaseInvoker> MakeInvokerPointer() = 0;
 };
 
-template <index_t Rank, index_t NumBatchNormReduceDim, typename DyElementwiseOp>
-using DeviceBatchNormBwdPtr =
-    std::unique_ptr<DeviceBatchNormBwd<Rank, NumBatchNormReduceDim, DyElementwiseOp>>;
+template <typename XDataType,
+          typename DxDataType,
+          typename DyDataType,
+          typename AccDataType,
+          typename ScaleDataType,
+          typename DscaleDbiasDataType,
+          typename MeanVarDataType,
+          typename DyElementwiseOp,
+          index_t Rank,
+          index_t NumBatchNormReduceDim>
+using DeviceBatchNormBwdPtr = std::unique_ptr<DeviceBatchNormBwd<XDataType,
+                                                                 DxDataType,
+                                                                 DyDataType,
+                                                                 AccDataType,
+                                                                 ScaleDataType,
+                                                                 DscaleDbiasDataType,
+                                                                 MeanVarDataType,
+                                                                 DyElementwiseOp,
+                                                                 Rank,
+                                                                 NumBatchNormReduceDim>>;
 
 } // namespace device
 } // namespace tensor_operation

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

@@ -27,7 +27,7 @@ template <typename XDataType,
           typename DyDataType,
           typename AccDataType,
           typename ScaleDataType,
-          typename BiasDataType,
+          typename DscaleDbiasDataType,
           typename MeanVarDataType,
           typename DyElementwiseOp,
           index_t Rank,
@@ -42,11 +42,19 @@ template <typename XDataType,
           index_t XSrcVectorSize,
           index_t DySrcVectorSize,
           index_t DxDstVectorSize,
-          index_t ScaleSrcDstVectorSize,
-          index_t BiasDstVectorSize,
+          index_t ScaleSrcVectorSize,
+          index_t DscaleDbiasDstVectorSize,
           index_t MeanVarSrcVectorSize>
-struct DeviceBatchNormBwdImpl
-    : public DeviceBatchNormBwd<Rank, NumBatchNormReduceDim, DyElementwiseOp>
+struct DeviceBatchNormBwdImpl : public DeviceBatchNormBwd<XDataType,
+                                                          DxDataType,
+                                                          DyDataType,
+                                                          AccDataType,
+                                                          ScaleDataType,
+                                                          DscaleDbiasDataType,
+                                                          MeanVarDataType,
+                                                          DyElementwiseOp,
+                                                          Rank,
+                                                          NumBatchNormReduceDim>
 {
     static_assert(Rank <= 6, "Bigger Rank size is not supported!");
     static_assert(BlockSize == MThreadClusterSize * KThreadClusterSize,
@@ -194,7 +202,7 @@ struct DeviceBatchNormBwdImpl
                  const std::array<int, NumBatchNormReduceDim> reduceDims,
                  const std::array<ck::index_t, NumInvariantDim> bnScaleBiasMeanVarLengths,
                  const std::array<ck::index_t, NumInvariantDim> bnScaleStrides,
-                 const std::array<ck::index_t, NumInvariantDim> bnBiasStrides,
+                 const std::array<ck::index_t, NumInvariantDim> bnDscaleDbiasStrides,
                  const std::array<ck::index_t, NumInvariantDim> bnMeanVarStrides,
                  const XDataType* p_x,
                  const DyDataType* p_dy,
@@ -204,11 +212,11 @@ struct DeviceBatchNormBwdImpl
                  const DyElementwiseOp dy_elementwise_op,
                  double epsilon,
                  DxDataType* p_dx,
-                 ScaleDataType* p_dscale,
-                 BiasDataType* p_dbias)
+                 DscaleDbiasDataType* p_dscale,
+                 DscaleDbiasDataType* p_dbias)
             : bnScaleBiasMeanVarLengths_(bnScaleBiasMeanVarLengths),
               bnScaleStrides_(bnScaleStrides),
-              bnBiasStrides_(bnBiasStrides),
+              bnDscaleDbiasStrides_(bnDscaleDbiasStrides),
               bnMeanVarStrides_(bnMeanVarStrides),
               p_x_(p_x),
               p_dy_(p_dy),
@@ -272,8 +280,8 @@ struct DeviceBatchNormBwdImpl
                 MakeXY2dDescriptor(xyLengths_, dxStrides_, blkGroupSize, numBlockTileIteration);
             scale_grid_desc_m =
                 MakeScaleBiasMeanVar1dDescriptor(bnScaleBiasMeanVarLengths, bnScaleStrides);
-            bias_grid_desc_m =
-                MakeScaleBiasMeanVar1dDescriptor(bnScaleBiasMeanVarLengths, bnBiasStrides);
+            dscale_dbias_grid_desc_m =
+                MakeScaleBiasMeanVar1dDescriptor(bnScaleBiasMeanVarLengths, bnDscaleDbiasStrides);
             mean_var_grid_desc_m =
                 MakeScaleBiasMeanVar1dDescriptor(bnScaleBiasMeanVarLengths, bnMeanVarStrides);
         }
@@ -289,7 +297,7 @@ struct DeviceBatchNormBwdImpl
 
         std::array<index_t, Rank - NumBatchNormReduceDim> bnScaleBiasMeanVarLengths_;
         std::array<index_t, Rank - NumBatchNormReduceDim> bnScaleStrides_;
-        std::array<index_t, Rank - NumBatchNormReduceDim> bnBiasStrides_;
+        std::array<index_t, Rank - NumBatchNormReduceDim> bnDscaleDbiasStrides_;
         std::array<index_t, Rank - NumBatchNormReduceDim> bnMeanVarStrides_;
 
         const XDataType* p_x_;
@@ -299,8 +307,8 @@ struct DeviceBatchNormBwdImpl
         const MeanVarDataType* p_savedInvVar_;
         const DyElementwiseOp dy_elementwise_op_;
         DxDataType* p_dx_;
-        ScaleDataType* p_dscale_;
-        BiasDataType* p_dbias_;
+        DscaleDbiasDataType* p_dscale_;
+        DscaleDbiasDataType* p_dbias_;
 
         long_index_t invariant_length;
         long_index_t reduce_length;
@@ -313,7 +321,7 @@ struct DeviceBatchNormBwdImpl
         XYGridDesc_M_K dy_grid_desc_m_k;
         XYGridDesc_M_K dx_grid_desc_m_k;
         ScaleBiasGridDesc_M scale_grid_desc_m;
-        ScaleBiasGridDesc_M bias_grid_desc_m;
+        ScaleBiasGridDesc_M dscale_dbias_grid_desc_m;
         MeanVarGridDesc_M mean_var_grid_desc_m;
 
         void* workspace_mean;
@@ -337,11 +345,11 @@ struct DeviceBatchNormBwdImpl
         {
             // workspace for the partial reduced result for dscale
             workspace_size +=
-                pArg_->invariant_length * pArg_->blkGroupSize * sizeof(ScaleDataType) + 64;
+                pArg_->invariant_length * pArg_->blkGroupSize * sizeof(DscaleDbiasDataType) + 64;
 
             // workspace for the partial reduced result for dbias
             workspace_size +=
-                pArg_->invariant_length * pArg_->blkGroupSize * sizeof(BiasDataType) + 64;
+                pArg_->invariant_length * pArg_->blkGroupSize * sizeof(DscaleDbiasDataType) + 64;
 
             if(!pArg_->haveSavedMeanInvVar_)
             {
@@ -379,7 +387,7 @@ struct DeviceBatchNormBwdImpl
         // setup buffer for the partial reduced result for dscale
         pArg_->workspace_reduce_dscale = pArg_->p_workspace_;
 
-        space_sz = pArg_->invariant_length * pArg_->blkGroupSize * sizeof(ScaleDataType);
+        space_sz = pArg_->invariant_length * pArg_->blkGroupSize * sizeof(DscaleDbiasDataType);
         space_sz = math::integer_least_multiple(space_sz, 64);
 
         // setup buffer for the partial reduced result for dbias
@@ -388,7 +396,7 @@ struct DeviceBatchNormBwdImpl
 
         if(UseMultiblockInK && pArg_->blkGroupSize > 1)
         {
-            space_sz = pArg_->invariant_length * pArg_->blkGroupSize * sizeof(BiasDataType);
+            space_sz = pArg_->invariant_length * pArg_->blkGroupSize * sizeof(DscaleDbiasDataType);
             space_sz = math::integer_least_multiple(space_sz, 64);
 
             // setup buffer for welford intermediate mean
@@ -454,7 +462,7 @@ struct DeviceBatchNormBwdImpl
                                                          DyDataType,
                                                          AccDataType,
                                                          ScaleDataType,
-                                                         BiasDataType,
+                                                         DscaleDbiasDataType,
                                                          MeanVarDataType,
                                                          DyElementwiseOp,
                                                          XYGridDesc_M_K,
@@ -477,7 +485,7 @@ struct DeviceBatchNormBwdImpl
                                                                DxDataType,
                                                                AccDataType,
                                                                ScaleDataType,
-                                                               BiasDataType,
+                                                               DscaleDbiasDataType,
                                                                MeanVarDataType,
                                                                DyElementwiseOp,
                                                                XYGridDesc_M_K,
@@ -493,8 +501,8 @@ struct DeviceBatchNormBwdImpl
                                                                XSrcVectorSize,
                                                                DySrcVectorSize,
                                                                DxDstVectorSize,
-                                                               ScaleSrcDstVectorSize,
-                                                               BiasDstVectorSize,
+                                                               ScaleSrcVectorSize,
+                                                               DscaleDbiasDstVectorSize,
                                                                MeanVarSrcVectorSize>;
 
             if(UseMultiblockInK && arg.blkGroupSize > 1)
@@ -553,7 +561,7 @@ struct DeviceBatchNormBwdImpl
                         DyDataType,
                         AccDataType,
                         ScaleDataType,
-                        BiasDataType,
+                        DscaleDbiasDataType,
                         MeanVarDataType,
                         DyElementwiseOp,
                         XYGridDesc_M_K,
@@ -568,7 +576,7 @@ struct DeviceBatchNormBwdImpl
                         DyDataType,
                         DxDataType,
                         ScaleDataType,
-                        BiasDataType,
+                        DscaleDbiasDataType,
                         MeanVarDataType,
                         DyElementwiseOp,
                         XYGridDesc_M_K,
@@ -614,8 +622,8 @@ struct DeviceBatchNormBwdImpl
                         : static_cast<MeanVarDataType*>(arg.workspace_savedInvVar),
                     arg.p_x_,
                     arg.p_dy_,
-                    static_cast<ScaleDataType*>(arg.workspace_reduce_dscale),
-                    static_cast<BiasDataType*>(arg.workspace_reduce_dbias));
+                    static_cast<DscaleDbiasDataType*>(arg.workspace_reduce_dscale),
+                    static_cast<DscaleDbiasDataType*>(arg.workspace_reduce_dbias));
 
                 avg_time += launch_and_time_kernel(
                     stream_config,
@@ -629,13 +637,13 @@ struct DeviceBatchNormBwdImpl
                     dscale_dbias_grid_desc_m_k,
                     arg.mean_var_grid_desc_m,
                     arg.scale_grid_desc_m,
-                    arg.bias_grid_desc_m,
+                    arg.dscale_dbias_grid_desc_m,
                     arg.blkGroupSize,
                     arg.reduce_length,
                     arg.numBlockTileIteration,
                     numDscaleDbiasBlockTileIteration,
-                    static_cast<const ScaleDataType*>(arg.workspace_reduce_dscale),
-                    static_cast<const BiasDataType*>(arg.workspace_reduce_dbias),
+                    static_cast<const DscaleDbiasDataType*>(arg.workspace_reduce_dscale),
+                    static_cast<const DscaleDbiasDataType*>(arg.workspace_reduce_dbias),
                     arg.haveSavedMeanInvVar_
                         ? arg.p_savedMean_
                         : static_cast<const MeanVarDataType*>(arg.workspace_savedMean),
@@ -664,7 +672,7 @@ struct DeviceBatchNormBwdImpl
                                                                   DxDataType,
                                                                   AccDataType,
                                                                   ScaleDataType,
-                                                                  BiasDataType,
+                                                                  DscaleDbiasDataType,
                                                                   MeanVarDataType,
                                                                   DyElementwiseOp,
                                                                   XYGridDesc_M_K,
@@ -680,8 +688,8 @@ struct DeviceBatchNormBwdImpl
                                                                   XSrcVectorSize,
                                                                   DySrcVectorSize,
                                                                   DxDstVectorSize,
-                                                                  ScaleSrcDstVectorSize,
-                                                                  BiasDstVectorSize,
+                                                                  ScaleSrcVectorSize,
+                                                                  DscaleDbiasDstVectorSize,
                                                                   MeanVarSrcVectorSize>;
 
                 const auto kern_batchnorm_bwd = kernel_batchnorm_backward_with_blockwise_welford<
@@ -691,7 +699,7 @@ struct DeviceBatchNormBwdImpl
                     DxDataType,
                     AccDataType,
                     ScaleDataType,
-                    BiasDataType,
+                    DscaleDbiasDataType,
                     MeanVarDataType,
                     DyElementwiseOp,
                     XYGridDesc_M_K,
@@ -708,7 +716,7 @@ struct DeviceBatchNormBwdImpl
                                                    arg.dy_grid_desc_m_k,
                                                    arg.dx_grid_desc_m_k,
                                                    arg.scale_grid_desc_m,
-                                                   arg.bias_grid_desc_m,
+                                                   arg.dscale_dbias_grid_desc_m,
                                                    arg.mean_var_grid_desc_m,
                                                    get_reduce_count_per_thread,
                                                    arg.reduce_length,
@@ -764,16 +772,16 @@ struct DeviceBatchNormBwdImpl
                 return false;
         };
 
-        if(pArg_->bnScaleStrides_[NumInvariantDim - 1] != 1 && ScaleSrcDstVectorSize != 1)
+        if(pArg_->bnScaleStrides_[NumInvariantDim - 1] != 1 && ScaleSrcVectorSize != 1)
             return false;
 
-        if(pArg_->bnBiasStrides_[NumInvariantDim - 1] != 1 && BiasDstVectorSize != 1)
+        if(pArg_->bnDscaleDbiasStrides_[NumInvariantDim - 1] != 1 && DscaleDbiasDstVectorSize != 1)
             return false;
 
-        if(pArg_->bnScaleBiasMeanVarLengths_[NumInvariantDim - 1] % ScaleSrcDstVectorSize != 0)
+        if(pArg_->bnScaleBiasMeanVarLengths_[NumInvariantDim - 1] % ScaleSrcVectorSize != 0)
             return false;
 
-        if(pArg_->bnScaleBiasMeanVarLengths_[NumInvariantDim - 1] % BiasDstVectorSize != 0)
+        if(pArg_->bnScaleBiasMeanVarLengths_[NumInvariantDim - 1] % DscaleDbiasDstVectorSize != 0)
             return false;
 
         if(pArg_->haveSavedMeanInvVar_)
@@ -806,7 +814,7 @@ struct DeviceBatchNormBwdImpl
                         const std::array<int, NumBatchNormReduceDim> reduceDims,
                         const std::array<ck::index_t, NumInvariantDim> bnScaleBiasMeanVarLengths,
                         const std::array<ck::index_t, NumInvariantDim> bnScaleStrides,
-                        const std::array<ck::index_t, NumInvariantDim> bnBiasStrides,
+                        const std::array<ck::index_t, NumInvariantDim> bnDscaleDbiasStrides,
                         const std::array<ck::index_t, NumInvariantDim> bnMeanVarStrides,
                         const void* p_x,
                         const void* p_dy,
@@ -826,7 +834,7 @@ struct DeviceBatchNormBwdImpl
                                           reduceDims,
                                           bnScaleBiasMeanVarLengths,
                                           bnScaleStrides,
-                                          bnBiasStrides,
+                                          bnDscaleDbiasStrides,
                                           bnMeanVarStrides,
                                           static_cast<const XDataType*>(p_x),
                                           static_cast<const DyDataType*>(p_dy),
@@ -836,8 +844,8 @@ struct DeviceBatchNormBwdImpl
                                           dy_elementwise_op,
                                           epsilon,
                                           static_cast<DxDataType*>(p_dx),
-                                          static_cast<ScaleDataType*>(p_dscale),
-                                          static_cast<BiasDataType*>(p_dbias));
+                                          static_cast<DscaleDbiasDataType*>(p_dscale),
+                                          static_cast<DscaleDbiasDataType*>(p_dbias));
     };
 
     std::unique_ptr<BaseInvoker> MakeInvokerPointer() override
@@ -854,7 +862,7 @@ struct DeviceBatchNormBwdImpl
         str << "M_C" << MThreadClusterSize << "_S" << MThreadSliceSize << ",";
         str << "K_C" << KThreadClusterSize << "_S" << KThreadSliceSize << ",";
         str << "XDyDxVectorDim_" << XDyDxVectorDim  << ",";
-        str << "VectorSize_X" << XSrcVectorSize << "_scale_" << ScaleSrcDstVectorSize << "_bias_" << BiasDstVectorSize << "_mean_var_" << MeanVarSrcVectorSize << "_Dx_" << DxDstVectorSize << ">";
+        str << "VectorSize_X" << XSrcVectorSize << "_scale_" << ScaleSrcVectorSize << "_bias_" << DscaleDbiasDstVectorSize << "_mean_var_" << MeanVarSrcVectorSize << "_Dx_" << DxDstVectorSize << ">";
         // clang-format on
 
         return str.str();

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

@@ -10,8 +10,8 @@ namespace element_wise {
 template <typename Activation>
 struct Activation_Mul_Clamp
 {
-    Activation_Mul_Clamp(float multiplier, Activation activationOp)
-        : multiplier_(multiplier), activationOp_(activationOp)
+    Activation_Mul_Clamp(float requantScale, Activation activationOp)
+        : requantScale_(requantScale), activationOp_(activationOp)
     {
     }
 
@@ -19,7 +19,7 @@ struct Activation_Mul_Clamp
     {
         float x_fp32 = ck::type_convert<float>(x);
         activationOp_(x_fp32, x_fp32);
-        float y_fp32 = math::clamp(multiplier_ * x_fp32, -128.f, 127.f);
+        float y_fp32 = math::clamp(requantScale_ * x_fp32, -128.f, 127.f);
         y            = ck::type_convert<int8_t>(y_fp32);
     }
 
@@ -28,10 +28,29 @@ struct Activation_Mul_Clamp
         // We might type_convert to int8 after lambda in someplace
         float x_fp32 = ck::type_convert<float>(x);
         activationOp_(x_fp32, x_fp32);
-        y = math::clamp(multiplier_ * x_fp32, -128.f, 127.f);
+        y = math::clamp(requantScale_ * x_fp32, -128.f, 127.f);
+    }
+
+    float requantScale_;
+    Activation activationOp_;
+};
+
+// Conv Perchannel quantization + Activation function which is piecewise linear function, such as
+// relu, leaky relu ...etc
+template <typename Activation>
+struct Activation_Mul2_Clamp
+{
+    Activation_Mul2_Clamp(Activation activationOp) : activationOp_(activationOp) {}
+
+    __host__ __device__ constexpr void
+    operator()(int8_t& y, const int32_t& x, const float& requantScale) const
+    {
+        float y_fp32 = ck::type_convert<float>(x);
+        activationOp_(y_fp32, y_fp32);
+        y_fp32 = math::clamp(requantScale * y_fp32, -128.f, 127.f);
+        y      = ck::type_convert<int8_t>(y_fp32);
     }
 
-    float multiplier_;
     Activation activationOp_;
 };
 
@@ -39,21 +58,40 @@ struct Activation_Mul_Clamp
 template <typename Activation>
 struct Add_Activation_Mul_Clamp
 {
-    Add_Activation_Mul_Clamp(float multiplier, Activation activationOp)
-        : multiplier_(multiplier), activationOp_(activationOp)
+    Add_Activation_Mul_Clamp(float requantScale, Activation activationOp)
+        : requantScale_(requantScale), activationOp_(activationOp)
     {
     }
 
     __host__ __device__ constexpr void
-    operator()(int8_t& y, const int32_t& x1, const int32_t& x2) const
+    operator()(int8_t& y, const int32_t& x, const int32_t& bias) const
+    {
+        float y_fp32 = ck::type_convert<float>(x + bias);
+        activationOp_(y_fp32, y_fp32);
+        y_fp32 = math::clamp(requantScale_ * y_fp32, -128.f, 127.f);
+        y      = ck::type_convert<int8_t>(y_fp32);
+    }
+
+    float requantScale_;
+    Activation activationOp_;
+};
+
+// Conv Perchannel quantization + Activation function which is piecewise linear function, such as
+// relu, leaky relu ...etc
+template <typename Activation>
+struct Add_Activation_Mul2_Clamp
+{
+    Add_Activation_Mul2_Clamp(Activation activationOp) : activationOp_(activationOp) {}
+
+    __host__ __device__ constexpr void
+    operator()(int8_t& y, const int32_t& x, const int32_t& bias, const float& requantScale) const
     {
-        float y_fp32 = ck::type_convert<float>(x1 + x2);
+        float y_fp32 = ck::type_convert<float>(x + bias);
         activationOp_(y_fp32, y_fp32);
-        y_fp32 = math::clamp(multiplier_ * y_fp32, -128.f, 127.f);
+        y_fp32 = math::clamp(requantScale * y_fp32, -128.f, 127.f);
         y      = ck::type_convert<int8_t>(y_fp32);
     }
 
-    float multiplier_;
     Activation activationOp_;
 };
 
@@ -61,23 +99,23 @@ struct Add_Activation_Mul_Clamp
 template <typename Activation>
 struct Add_Mul_Activation_Mul_Clamp
 {
-    Add_Mul_Activation_Mul_Clamp(float multiplier1, float multiplier2, Activation activationOp)
-        : multiplier1_(multiplier1), multiplier2_(multiplier2), activationOp_(activationOp)
+    Add_Mul_Activation_Mul_Clamp(float requantScale1, float requantScale2, Activation activationOp)
+        : requantScale1_(requantScale1), requantScale2_(requantScale2), activationOp_(activationOp)
     {
     }
 
     __host__ __device__ constexpr void
-    operator()(int8_t& y, const int32_t& x1, const int32_t& x2) const
+    operator()(int8_t& y, const int32_t& x, const int32_t& bias) const
     {
-        float y_fp32 = ck::type_convert<float>(x1 + x2);
-        y_fp32       = multiplier1_ * y_fp32;
+        float y_fp32 = ck::type_convert<float>(x + bias);
+        y_fp32       = requantScale1_ * y_fp32;
         activationOp_(y_fp32, y_fp32);
-        y_fp32 = math::clamp(multiplier2_ * y_fp32, -128.f, 127.f);
+        y_fp32 = math::clamp(requantScale2_ * y_fp32, -128.f, 127.f);
         y      = ck::type_convert<int8_t>(y_fp32);
     }
 
-    float multiplier1_;
-    float multiplier2_;
+    float requantScale1_;
+    float requantScale2_;
     Activation activationOp_;
 };
 

include/ck/tensor_operation/gpu/grid/batchnorm_multiblock/gridwise_multiblock_reduce_second_half_batchnorm_backward_final.hpp

@@ -16,7 +16,7 @@ template <typename GridwiseReduceSecondHalfBatchNormBackwardFinal_,
           typename DyDataType,
           typename DxDataType,
           typename ScaleDataType,
-          typename BiasDataType,
+          typename DscaleDbiasDataType,
           typename MeanVarDataType,
           typename DyElementwiseOp,
           typename XYGridDesc_M_K,
@@ -35,8 +35,8 @@ __global__ void kernel_reduce_second_half_batchnorm_backward_final(
     long_index_t reduce_size,
     index_t num_xy_k_block_tile_iteration,
     index_t num_dscale_dbias_k_block_tile_iteration,
-    const ScaleDataType* const __restrict__ p_reduce_dscale,
-    const BiasDataType* const __restrict__ p_reduce_dbias,
+    const DscaleDbiasDataType* const __restrict__ p_reduce_dscale,
+    const DscaleDbiasDataType* const __restrict__ p_reduce_dbias,
     const MeanVarDataType* const __restrict__ p_mean,
     const MeanVarDataType* const __restrict__ p_inv_var,
     const XDataType* const __restrict__ p_x,
@@ -44,8 +44,8 @@ __global__ void kernel_reduce_second_half_batchnorm_backward_final(
     const ScaleDataType* const __restrict__ p_scale,
     const DyElementwiseOp dy_elementwise_op,
     DxDataType* const __restrict__ p_dx,
-    ScaleDataType* const __restrict__ p_dscale,
-    BiasDataType* const __restrict__ p_dbias)
+    DscaleDbiasDataType* const __restrict__ p_dscale,
+    DscaleDbiasDataType* const __restrict__ p_dbias)
 {
     GridwiseReduceSecondHalfBatchNormBackwardFinal_::Run(x_grid_desc_m_k,
                                                          dy_grid_desc_m_k,
@@ -76,7 +76,7 @@ template <typename XDataType,
           typename DxDataType,
           typename AccDataType,
           typename ScaleDataType,
-          typename BiasDataType,
+          typename DscaleDbiasDataType,
           typename MeanVarDataType,
           typename DyElementwiseOp,
           typename XYGridDesc_M_K,
@@ -92,8 +92,8 @@ template <typename XDataType,
           index_t XSrcVectorSize,
           index_t DySrcVectorSize,
           index_t DxDstVectorSize,
-          index_t ScaleSrcDstVectorSize,
-          index_t BiasDstVectorSize,
+          index_t ScaleSrcVectorSize,
+          index_t DscaleDbiasDstVectorSize,
           index_t MeanVarSrcVectorSize>
 struct GridwiseReduceSecondHalfBatchNormBackwardFinal
 {
@@ -155,13 +155,13 @@ struct GridwiseReduceSecondHalfBatchNormBackwardFinal
                                const DscaleDbiasGridDesc_M_K& dscale_dbias_grid_desc_m_k,
                                const MeanVarGridDesc_M& mean_var_grid_desc_m,
                                const ScaleBiasGridDesc_M& scale_grid_desc_m,
-                               const ScaleBiasGridDesc_M& bias_grid_desc_m,
+                               const ScaleBiasGridDesc_M& dscale_dbias_grid_desc_m,
                                index_t blkgroup_size,
                                long_index_t reduce_size,
                                index_t num_xy_k_block_tile_iteration,
                                index_t num_dscale_dbias_k_block_tile_iteration,
-                               const ScaleDataType* const __restrict__ p_reduce_dscale,
-                               const BiasDataType* const __restrict__ p_reduce_dbias,
+                               const DscaleDbiasDataType* const __restrict__ p_reduce_dscale,
+                               const DscaleDbiasDataType* const __restrict__ p_reduce_dbias,
                                const MeanVarDataType* const __restrict__ p_mean,
                                const MeanVarDataType* const __restrict__ p_inv_var,
                                const XDataType* const __restrict__ p_x,
@@ -169,8 +169,8 @@ struct GridwiseReduceSecondHalfBatchNormBackwardFinal
                                const ScaleDataType* const __restrict__ p_scale,
                                const DyElementwiseOp dy_elementwise_op,
                                DxDataType* const __restrict__ p_dx,
-                               ScaleDataType* const __restrict__ p_dscale,
-                               BiasDataType* const __restrict__ p_dbias)
+                               DscaleDbiasDataType* const __restrict__ p_dscale,
+                               DscaleDbiasDataType* const __restrict__ p_dbias)
     {
         __shared__ AccDataType p_reduce_work_buffer[BlockSize];
 
@@ -222,24 +222,8 @@ struct GridwiseReduceSecondHalfBatchNormBackwardFinal
         // Step 1: do final reduction of dbias = sum(dy), dscale = sum(dy * (x-mean) * inv-variance)
         // clang-format on
 
-        auto threadwise_dscale_load_m_k =
-            ThreadwiseTensorSliceTransfer_v2<ScaleDataType,
-                                             AccDataType,
-                                             DscaleDbiasGridDesc_M_K,
-                                             decltype(thread_buffer_desc_m_1),
-                                             ThreadBufferLengths_M_1,
-                                             Sequence<0, 1>,
-                                             1,
-                                             1,
-                                             1,
-                                             true>(
-                dscale_dbias_grid_desc_m_k,
-                make_multi_index(blkgroup_id * M_BlockTileSize +
-                                     thread_m_cluster_id * MThreadSliceSize,
-                                 thread_k_cluster_id * 1));
-
-        auto threadwise_dbias_load_m_k =
-            ThreadwiseTensorSliceTransfer_v2<BiasDataType,
+        auto threadwise_dscale_dbias_load_m_k =
+            ThreadwiseTensorSliceTransfer_v2<DscaleDbiasDataType,
                                              AccDataType,
                                              DscaleDbiasGridDesc_M_K,
                                              decltype(thread_buffer_desc_m_1),
@@ -254,38 +238,20 @@ struct GridwiseReduceSecondHalfBatchNormBackwardFinal
                                      thread_m_cluster_id * MThreadSliceSize,
                                  thread_k_cluster_id * 1));
 
-        auto threadwise_dscale_store_m =
+        auto threadwise_dscale_dbias_store_m =
             ThreadwiseTensorSliceTransfer_v1r3<AccDataType,
-                                               ScaleDataType,
+                                               DscaleDbiasDataType,
                                                decltype(thread_buffer_desc_m),
                                                ScaleBiasGridDesc_M,
                                                PassThroughOp,
                                                ThreadBufferLengths_M,
                                                Sequence<0>,
                                                0,
-                                               ScaleSrcDstVectorSize,
+                                               DscaleDbiasDstVectorSize,
                                                InMemoryDataOperationEnum::Set,
                                                1,
                                                true>(
-                scale_grid_desc_m,
-                make_multi_index(blkgroup_id * M_BlockTileSize +
-                                 thread_m_cluster_id * MThreadSliceSize),
-                PassThroughOp{});
-
-        auto threadwise_dbias_store_m =
-            ThreadwiseTensorSliceTransfer_v1r3<AccDataType,
-                                               BiasDataType,
-                                               decltype(thread_buffer_desc_m),
-                                               ScaleBiasGridDesc_M,
-                                               PassThroughOp,
-                                               ThreadBufferLengths_M,
-                                               Sequence<0>,
-                                               0,
-                                               BiasDstVectorSize,
-                                               InMemoryDataOperationEnum::Set,
-                                               1,
-                                               true>(
-                bias_grid_desc_m,
+                dscale_dbias_grid_desc_m,
                 make_multi_index(blkgroup_id * M_BlockTileSize +
                                  thread_m_cluster_id * MThreadSliceSize),
                 PassThroughOp{});
@@ -297,10 +263,10 @@ struct GridwiseReduceSecondHalfBatchNormBackwardFinal
             p_reduce_dbias, dscale_dbias_grid_desc_m_k.GetElementSpaceSize());
 
         auto dscale_global_buf = make_dynamic_buffer<AddressSpaceEnum::Global>(
-            p_dscale, scale_grid_desc_m.GetElementSpaceSize());
+            p_dscale, dscale_dbias_grid_desc_m.GetElementSpaceSize());
 
         auto dbias_global_buf = make_dynamic_buffer<AddressSpaceEnum::Global>(
-            p_dbias, bias_grid_desc_m.GetElementSpaceSize());
+            p_dbias, dscale_dbias_grid_desc_m.GetElementSpaceSize());
 
         constexpr auto dscale_dbias_thread_copy_step_m_k =
             make_multi_index(0, KThreadClusterSize * 1);
@@ -313,25 +279,23 @@ struct GridwiseReduceSecondHalfBatchNormBackwardFinal
         for(index_t reducedTiles = 0; reducedTiles < num_dscale_dbias_k_block_tile_iteration;
             ++reducedTiles)
         {
-            threadwise_dscale_load_m_k.Run(dscale_dbias_grid_desc_m_k,
-                                           reduce_dscale_global_buf,
-                                           thread_buffer_desc_m_1,
-                                           make_tuple(I0, I0),
-                                           reduce_dscale_thread_buf);
-
-            threadwise_dbias_load_m_k.Run(dscale_dbias_grid_desc_m_k,
-                                          reduce_dbias_global_buf,
-                                          thread_buffer_desc_m_1,
-                                          make_tuple(I0, I0),
-                                          reduce_dbias_thread_buf);
+            threadwise_dscale_dbias_load_m_k.Run(dscale_dbias_grid_desc_m_k,
+                                                 reduce_dscale_global_buf,
+                                                 thread_buffer_desc_m_1,
+                                                 make_tuple(I0, I0),
+                                                 reduce_dscale_thread_buf);
+
+            threadwise_dscale_dbias_load_m_k.Run(dscale_dbias_grid_desc_m_k,
+                                                 reduce_dbias_global_buf,
+                                                 thread_buffer_desc_m_1,
+                                                 make_tuple(I0, I0),
+                                                 reduce_dbias_thread_buf);
 
             ThreadwiseReduce::Reduce(reduce_dscale_thread_buf, dscale_thread_buf);
             ThreadwiseReduce::Reduce(reduce_dbias_thread_buf, dbias_thread_buf);
 
-            threadwise_dscale_load_m_k.MoveSrcSliceWindow(dscale_dbias_grid_desc_m_k,
-                                                          dscale_dbias_thread_copy_step_m_k);
-            threadwise_dbias_load_m_k.MoveSrcSliceWindow(dscale_dbias_grid_desc_m_k,
-                                                         dscale_dbias_thread_copy_step_m_k);
+            threadwise_dscale_dbias_load_m_k.MoveSrcSliceWindow(dscale_dbias_grid_desc_m_k,
+                                                                dscale_dbias_thread_copy_step_m_k);
         }
 
         static_for<0, MThreadSliceSize, 1>{}([&](auto I) {
@@ -343,17 +307,17 @@ struct GridwiseReduceSecondHalfBatchNormBackwardFinal
             BlockwiseReduce::Reduce(reduce_work_buf, dbias_thread_buf(I));
         });
 
-        threadwise_dscale_store_m.Run(thread_buffer_desc_m,
-                                      make_tuple(I0),
-                                      dscale_thread_buf,
-                                      scale_grid_desc_m,
-                                      dscale_global_buf);
+        threadwise_dscale_dbias_store_m.Run(thread_buffer_desc_m,
+                                            make_tuple(I0),
+                                            dscale_thread_buf,
+                                            dscale_dbias_grid_desc_m,
+                                            dscale_global_buf);
 
-        threadwise_dbias_store_m.Run(thread_buffer_desc_m,
-                                     make_tuple(I0),
-                                     dbias_thread_buf,
-                                     bias_grid_desc_m,
-                                     dbias_global_buf);
+        threadwise_dscale_dbias_store_m.Run(thread_buffer_desc_m,
+                                            make_tuple(I0),
+                                            dbias_thread_buf,
+                                            dscale_dbias_grid_desc_m,
+                                            dbias_global_buf);
 
         // clang-format off
         // Step 2: calculate dx = 1/N * inv-variance * scale * (N * dy - dbias - dscale * (x - mean) * inv-variance)
@@ -418,7 +382,7 @@ struct GridwiseReduceSecondHalfBatchNormBackwardFinal
                                              ThreadBufferLengths_M,
                                              Sequence<0>,
                                              0,
-                                             ScaleSrcDstVectorSize,
+                                             ScaleSrcVectorSize,
                                              1,
                                              true>(
                 scale_grid_desc_m,