Rename ported functions

Rename CpuPooling to CpuPool2d
Rename CpuPoolingKernel to CpuPool2dKernel
Rename CpuPoolingAssemblyWrapperKernel to CpuPool2dAssemblyWrapperKernel
Move CpuPool2dAssemblyWrapperKernel in internal subfolder
Rename CpuDepthwiseConvolutionNativeKernel to CpuDepthwiseConv2dNativeKernel
Rename CpuDepthwiseConvolutionAssemblyDispatch to CpuDepthwiseConv2dAssemblyDispatch
Rename CpuDepthwiseConvolution to CpuDepthwiseConv2d
Rename CpuDirectConvolutionKernel to CpuDirectConv2dKernel
Rename CpuDirectConvolutionOutputStageKernel to CpuDirectConv2dOutputStageKernel
Rename CpuDirectConvolution to CpuDirectConv2d
Rename ClPoolingKernel to ClPool2dKernel
Rename ClPooling to ClPool2d
Rename ClDirectConvolutionKernel to ClDirectConv2dKernel

Resolves: COMPMID-4405

Change-Id: I8e48f015e4e492a76a7512f5679cb3eb0cd028f6
Signed-off-by: Manuel Bottini <manuel.bottini@arm.com>
Reviewed-on: https://review.mlplatform.org/c/ml/ComputeLibrary/+/5708
Reviewed-by: Georgios Pinitas <georgios.pinitas@arm.com>
Tested-by: Arm Jenkins <bsgcomp@arm.com>
Comments-Addressed: Arm Jenkins <bsgcomp@arm.com>

1 files changed, 665 insertions, 0 deletions

diff --git a/src/core/gpu/cl/kernels/ClDirectConv2dKernel.cpp b/src/core/gpu/cl/kernels/ClDirectConv2dKernel.cpp
new file mode 100644
index 0000000000..2c9a4f301b
--- /dev/null
+++ b/src/core/gpu/cl/kernels/ClDirectConv2dKernel.cpp
@@ -0,0 +1,665 @@
+/*
+ * Copyright (c) 2017-2021 Arm Limited.
+ *
+ * SPDX-License-Identifier: MIT
+ *
+ * Permission is hereby granted, free of charge, to any person obtaining a copy
+ * of this software and associated documentation files (the "Software"), to
+ * deal in the Software without restriction, including without limitation the
+ * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or
+ * sell copies of the Software, and to permit persons to whom the Software is
+ * furnished to do so, subject to the following conditions:
+ *
+ * The above copyright notice and this permission notice shall be included in all
+ * copies or substantial portions of the Software.
+ *
+ * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+ * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+ * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+ * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+ * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+ * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+ * SOFTWARE.
+ */
+#include "src/core/gpu/cl/kernels/ClDirectConv2dKernel.h"
+
+#include "arm_compute/core/CL/CLHelpers.h"
+#include "arm_compute/core/CL/CLKernelLibrary.h"
+#include "arm_compute/core/CL/ICLTensor.h"
+#include "arm_compute/core/Helpers.h"
+#include "arm_compute/core/ITensor.h"
+#include "arm_compute/core/PixelValue.h"
+#include "arm_compute/core/Utils.h"
+#include "arm_compute/core/utils/misc/ShapeCalculator.h"
+#include "arm_compute/core/utils/quantization/AsymmHelpers.h"
+#include "src/core/AccessWindowStatic.h"
+#include "src/core/CL/CLUtils.h"
+#include "src/core/CL/CLValidate.h"
+#include "src/core/gpu/cl/kernels/gemm/ClGemmHelpers.h"
+#include "src/core/helpers/AutoConfiguration.h"
+#include "src/core/helpers/WindowHelpers.h"
+#include "support/Cast.h"
+#include "support/StringSupport.h"
+namespace arm_compute
+{
+namespace opencl
+{
+namespace kernels
+{
+namespace
+{
+Status validate_arguments(const ITensorInfo *src, const ITensorInfo *weights, const ITensorInfo *biases, const ITensorInfo *dst, const PadStrideInfo &conv_info)
+{
+    ARM_COMPUTE_RETURN_ERROR_ON_F16_UNSUPPORTED(src);
+    ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(src, 1, DataType::QASYMM8_SIGNED, DataType::QASYMM8, DataType::F16, DataType::F32);
+    ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(src, weights);
+
+    const DataLayout data_layout = src->data_layout();
+    const int        width_idx   = get_data_layout_dimension_index(data_layout, DataLayoutDimension::WIDTH);
+    const int        height_idx  = get_data_layout_dimension_index(data_layout, DataLayoutDimension::HEIGHT);
+    const int        channel_idx = get_data_layout_dimension_index(data_layout, DataLayoutDimension::CHANNEL);
+
+    ARM_COMPUTE_RETURN_ERROR_ON_MSG(weights->dimension(width_idx) != weights->dimension(height_idx), "Weights should have same width and height");
+    ARM_COMPUTE_RETURN_ERROR_ON_MSG(weights->dimension(channel_idx) != src->dimension(channel_idx),
+                                    "Weights feature map dimension should match the respective src's one");
+    ARM_COMPUTE_RETURN_ERROR_ON_MSG(weights->num_dimensions() > 4, "Weights can be at most 4 dimensional");
+    ARM_COMPUTE_RETURN_ERROR_ON_MSG((weights->dimension(width_idx) == 1) && std::get<0>(conv_info.stride()) > 3, "Strides larger than 3 not supported for 1x1 convolution.");
+    ARM_COMPUTE_RETURN_ERROR_ON_MSG((weights->dimension(width_idx) == 3 || weights->dimension(width_idx) == 5 || weights->dimension(width_idx) == 9)
+                                    && std::get<0>(conv_info.stride()) > 2,
+                                    "Strides larger than 2 not supported for 3x3, 5x5, 9x9 convolution.");
+
+    if(data_layout == DataLayout::NCHW)
+    {
+        if(is_data_type_quantized(src->data_type()))
+        {
+            ARM_COMPUTE_RETURN_ERROR_ON_MSG(weights->dimension(width_idx) != 1 && weights->dimension(width_idx) != 3 && weights->dimension(width_idx) != 5 && weights->dimension(width_idx) != 9,
+                                            "Kernel sizes other than 1x1, 3x3, 5x5 or 9x9 are not supported with quantized data types");
+        }
+        else
+        {
+            ARM_COMPUTE_RETURN_ERROR_ON_MSG(weights->dimension(width_idx) != 1 && weights->dimension(width_idx) != 3 && weights->dimension(width_idx) != 5,
+                                            "Kernel sizes other than 1x1, 3x3 or 5x5 are not supported with float data types");
+        }
+    }
+
+    if(biases != nullptr)
+    {
+        if(is_data_type_quantized_asymmetric(src->data_type()))
+        {
+            ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(biases, 1, DataType::S32);
+        }
+        else
+        {
+            ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(weights, biases);
+        }
+        ARM_COMPUTE_RETURN_ERROR_ON_MSG(biases->dimension(0) != weights->dimension(3),
+                                        "Biases size and number of src feature maps should match");
+        ARM_COMPUTE_RETURN_ERROR_ON_MSG(biases->num_dimensions() > 1,
+                                        "Biases should be one dimensional");
+    }
+
+    // Checks performed when dst is configured
+    if(dst->total_size() != 0)
+    {
+        ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DIMENSIONS(dst->tensor_shape(),
+                                                           misc::shape_calculator::compute_deep_convolution_shape(*src, *weights, conv_info));
+        ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(src, dst);
+    }
+
+    const auto data_type = src->data_type();
+    if(is_data_type_quantized(data_type))
+    {
+        const UniformQuantizationInfo iqinfo = src->quantization_info().uniform();
+        const UniformQuantizationInfo wqinfo = weights->quantization_info().uniform();
+        const UniformQuantizationInfo oqinfo = dst->quantization_info().uniform();
+
+        float multiplier        = iqinfo.scale * wqinfo.scale / oqinfo.scale;
+        int   output_multiplier = 0;
+        int   output_shift      = 0;
+        ARM_COMPUTE_RETURN_ON_ERROR(quantization::calculate_quantized_multiplier(multiplier, &output_multiplier, &output_shift));
+    }
+    return Status{};
+}
+
+inline bool can_run_optimized_kernel_for_bifrost_nchw(GPUTarget gpu_target, unsigned int conv_stride_x, unsigned int conv_stride_y, unsigned int kernel_size,
+                                                      DataType data_type, DataLayout data_layout)
+{
+    return gpu_target_is_in(gpu_target,
+                            GPUTarget::G71, GPUTarget::G72, GPUTarget::G76,
+                            GPUTarget::G51, GPUTarget::G51BIG, GPUTarget::G51LIT,
+                            GPUTarget::G52, GPUTarget::G52LIT)
+           && (kernel_size <= 5)
+           && (conv_stride_x == 1) && (conv_stride_y == 1)
+           && (data_type == DataType::F32)
+           && (data_layout == DataLayout::NCHW);
+}
+
+inline void setup_num_elems_nchw(unsigned int &num_elems_read_per_iteration_x, unsigned int &num_elems_read_per_iteration_y,
+                                 unsigned int &num_elems_written_per_iteration_x, unsigned int &num_elems_written_per_iteration_y,
+                                 unsigned int kernel_size, const PadStrideInfo &conv_info, const GPUTarget target, ITensorInfo *src)
+{
+    const DataType   data_type     = src->data_type();
+    const DataLayout data_layout   = src->data_layout();
+    unsigned int     conv_stride_x = std::get<0>(conv_info.stride());
+    unsigned int     conv_stride_y = std::get<1>(conv_info.stride());
+
+    const bool run_optimized_bifrost = can_run_optimized_kernel_for_bifrost_nchw(target, conv_stride_x, conv_stride_y, kernel_size, data_type, data_layout);
+
+    if(run_optimized_bifrost)
+    {
+        // Configure kernel window
+        switch(kernel_size)
+        {
+            case 1:
+            {
+                num_elems_read_per_iteration_x    = 4;
+                num_elems_read_per_iteration_y    = 4;
+                num_elems_written_per_iteration_x = 4;
+                num_elems_written_per_iteration_y = 4;
+                break;
+            }
+            case 3:
+            {
+                num_elems_read_per_iteration_x    = 6;
+                num_elems_read_per_iteration_y    = 5;
+                num_elems_written_per_iteration_x = 4;
+                num_elems_written_per_iteration_y = 3;
+                break;
+            }
+            case 5:
+            {
+                num_elems_read_per_iteration_x    = 8;
+                num_elems_read_per_iteration_y    = 6;
+                num_elems_written_per_iteration_x = 4;
+                num_elems_written_per_iteration_y = 2;
+                break;
+            }
+            default:
+            {
+                ARM_COMPUTE_ERROR("Kernel size not optimized for Bifrost");
+            }
+        }
+    }
+    else
+    {
+        num_elems_read_per_iteration_y    = kernel_size;
+        num_elems_written_per_iteration_x = 8;
+        num_elems_written_per_iteration_y = 1;
+        switch(kernel_size)
+        {
+            case 1:
+                switch(conv_stride_x)
+                {
+                    case 1:
+                        num_elems_read_per_iteration_x = 8;
+                        break;
+                    case 2:
+                        num_elems_read_per_iteration_x = 16;
+                        break;
+                    case 3:
+                        switch(src->element_size())
+                        {
+                            case 1:
+                                num_elems_read_per_iteration_x = 28;
+                                break;
+                            case 2:
+                                num_elems_read_per_iteration_x = 24;
+                                break;
+                            case 4:
+                                num_elems_read_per_iteration_x = 22;
+                                break;
+                            default:
+                                ARM_COMPUTE_ERROR("Invalid data size");
+                        }
+                        break;
+                    default:
+                        ARM_COMPUTE_ERROR("Invalid convolution stride X");
+                }
+                break;
+            case 3:
+                switch(conv_stride_x)
+                {
+                    case 1:
+                        num_elems_read_per_iteration_x = 10;
+                        break;
+                    case 2:
+                        num_elems_read_per_iteration_x = 17;
+                        break;
+                    default:
+                        ARM_COMPUTE_ERROR("Invalid convolution stride X");
+                }
+                break;
+            case 5:
+                switch(conv_stride_x)
+                {
+                    case 1:
+                        num_elems_read_per_iteration_x = 12;
+                        break;
+                    case 2:
+                        num_elems_read_per_iteration_x = 20;
+                        break;
+                    default:
+                        ARM_COMPUTE_ERROR("Invalid convolution stride X");
+                }
+                break;
+            case 9:
+                switch(conv_stride_x)
+                {
+                    case 1:
+                        num_elems_read_per_iteration_x = 16;
+                        break;
+                    case 2:
+                        num_elems_read_per_iteration_x = 24;
+                        break;
+                    default:
+                        ARM_COMPUTE_ERROR("Invalid convolution stride X");
+                }
+                break;
+            default:
+                ARM_COMPUTE_ERROR("Invalid direct convolution size");
+        }
+    }
+}
+
+std::pair<Status, Window> validate_and_configure_window(ITensorInfo *src, ITensorInfo *weights, ITensorInfo *dst, const PadStrideInfo &conv_info, const GPUTarget target)
+{
+    const DataLayout data_layout = src->data_layout();
+
+    // Get dst shape
+    TensorShape output_shape = misc::shape_calculator::compute_deep_convolution_shape(*src, *weights, conv_info);
+
+    // Output auto inizialitation if not yet initialized
+    auto_init_if_empty(*dst, output_shape,
+                       1,
+                       src->data_type(),
+                       src->quantization_info());
+
+    if(data_layout == DataLayout::NHWC)
+    {
+        const unsigned int vec_size = std::min(static_cast<unsigned int>(dst->tensor_shape()[0]), 4u);
+        unsigned int       num_rows = 1U;
+        if(dst->tensor_shape()[0] > 16)
+        {
+            num_rows = src->data_type() == DataType::F32 ? 2U : 4U;
+        }
+
+        // Create window and update padding
+        Window win = calculate_max_window(output_shape, Steps(vec_size, num_rows));
+        return std::make_pair(Status{}, win);
+    }
+    else if(data_layout == DataLayout::NCHW)
+    {
+        const int          width_idx   = get_data_layout_dimension_index(data_layout, DataLayoutDimension::WIDTH);
+        const unsigned int kernel_size = weights->dimension(width_idx);
+
+        unsigned int num_elems_read_per_iteration_x    = 0;
+        unsigned int num_elems_read_per_iteration_y    = 0;
+        unsigned int num_elems_written_per_iteration_x = 0;
+        unsigned int num_elems_written_per_iteration_y = 0;
+
+        unsigned int conv_pad_left = conv_info.pad_left();
+        unsigned int conv_pad_top  = conv_info.pad_top();
+        unsigned int conv_stride_x = std::get<0>(conv_info.stride());
+        unsigned int conv_stride_y = std::get<1>(conv_info.stride());
+
+        setup_num_elems_nchw(num_elems_read_per_iteration_x, num_elems_read_per_iteration_y,
+                             num_elems_written_per_iteration_x, num_elems_written_per_iteration_y,
+                             kernel_size, conv_info, target, src);
+
+        // Create window and update padding
+        bool   window_changed = false;
+        Window win            = calculate_max_window(*dst, Steps(num_elems_written_per_iteration_x, num_elems_written_per_iteration_y));
+
+        AccessWindowRectangle input_access(src, -conv_pad_left, -conv_pad_top, num_elems_read_per_iteration_x, num_elems_read_per_iteration_y, conv_stride_x, conv_stride_y);
+        AccessWindowStatic    weights_access(weights, 0, 0, kernel_size, kernel_size);
+        AccessWindowRectangle output_access(dst, 0, 0, num_elems_written_per_iteration_x, num_elems_written_per_iteration_y);
+        window_changed = update_window_and_padding(win, input_access, weights_access, output_access);
+        output_access.set_valid_region(win, ValidRegion(Coordinates(), dst->tensor_shape()));
+        Status err = (window_changed) ? ARM_COMPUTE_CREATE_ERROR(ErrorCode::RUNTIME_ERROR, "Insufficient Padding!") : Status{};
+        return std::make_pair(err, win);
+    }
+    else
+    {
+        ARM_COMPUTE_ERROR("Not supported");
+    }
+}
+
+bool export_to_cl_image_support(ITensorInfo *tensor, GPUTarget gpu_target, DataLayout data_layout)
+{
+    if(tensor->tensor_shape()[0] % 4 || (data_layout != DataLayout::NHWC))
+    {
+        return false;
+    }
+
+    // If not floating point
+    if(!is_data_type_float(tensor->data_type()))
+    {
+        return false;
+    }
+
+    if(gpu_target == GPUTarget::G71 || get_arch_from_target(gpu_target) == GPUTarget::MIDGARD)
+    {
+        return false;
+    }
+
+    // Check if the cl_khr_image2d_from_buffer extension is supported on the target platform
+    if(!image2d_from_buffer_supported(CLKernelLibrary::get().get_device()))
+    {
+        return false;
+    }
+
+    // Check cl image pitch alignment
+    if(get_cl_image_pitch_alignment(CLKernelLibrary::get().get_device()) == 0)
+    {
+        return false;
+    }
+
+    const size_t image_w     = tensor->tensor_shape()[0] / 4;
+    const size_t image_h     = tensor->tensor_shape()[1] * tensor->tensor_shape()[2] * tensor->tensor_shape()[3];
+    const size_t max_image_w = CLKernelLibrary::get().get_device().getInfo<CL_DEVICE_IMAGE2D_MAX_WIDTH>();
+    const size_t max_image_h = CLKernelLibrary::get().get_device().getInfo<CL_DEVICE_IMAGE2D_MAX_HEIGHT>();
+
+    if(image_w > max_image_w || image_h > max_image_h)
+    {
+        return false;
+    }
+
+    return true;
+}
+
+} // namespace
+
+BorderSize ClDirectConv2dKernel::border_size() const
+{
+    return _border_size;
+}
+
+void ClDirectConv2dKernel::configure(const CLCompileContext &compile_context, ITensorInfo *src, ITensorInfo *weights, ITensorInfo *biases, ITensorInfo *dst,
+                                     const PadStrideInfo &conv_info)
+{
+    ARM_COMPUTE_ERROR_ON_NULLPTR(src, weights, dst);
+
+    // Perform validation
+    ARM_COMPUTE_ERROR_THROW_ON(validate_arguments(src,
+                                                  weights,
+                                                  (biases != nullptr) ? biases : nullptr,
+                                                  dst,
+                                                  conv_info));
+
+    const int conv_stride_x = std::get<0>(conv_info.stride());
+    const int conv_stride_y = std::get<1>(conv_info.stride());
+
+    _data_layout = src->data_layout();
+    _conv_info   = conv_info;
+
+    const unsigned int width_idx   = get_data_layout_dimension_index(_data_layout, DataLayoutDimension::WIDTH);
+    const unsigned int height_idx  = get_data_layout_dimension_index(_data_layout, DataLayoutDimension::HEIGHT);
+    const unsigned int channel_idx = get_data_layout_dimension_index(_data_layout, DataLayoutDimension::CHANNEL);
+    const unsigned int kernel_size = weights->dimension(width_idx);
+    const DataType     data_type   = src->data_type();
+
+    const GPUTarget gpu_target = get_target();
+
+    // Configure kernel window
+    auto win_config = validate_and_configure_window(src, weights, dst, conv_info, gpu_target);
+    ARM_COMPUTE_ERROR_THROW_ON(win_config.first);
+    ICLKernel::configure_internal(win_config.second);
+
+    std::stringstream kernel_name;
+    CLBuildOptions    build_options;
+
+    if(_data_layout == DataLayout::NHWC)
+    {
+        _border_size = BorderSize();
+
+        kernel_name << "direct_convolution_nhwc";
+
+        const unsigned int n0                 = win_config.second.x().step();
+        const unsigned int m0                 = win_config.second.y().step();
+        const unsigned int k0                 = adjust_vec_size(is_data_type_quantized(data_type) ? 16u : 8u, src->dimension(channel_idx));
+        const unsigned int partial_store_n0   = dst->dimension(channel_idx) % n0;
+        const unsigned int pad_left           = conv_info.pad_left();
+        const unsigned int pad_top            = conv_info.pad_top();
+        const bool         export_to_cl_image = export_to_cl_image_support(weights, gpu_target, _data_layout);
+
+        // Update the padding for the weights tensor if we can export to cl_image
+        if(export_to_cl_image)
+        {
+            gemm::update_padding_for_cl_image(weights);
+        }
+
+        if(biases != nullptr)
+        {
+            build_options.add_option(std::string("-DHAS_BIAS"));
+            build_options.add_option(std::string("-DBIA_DATA_TYPE=" + get_cl_type_from_data_type(biases->data_type())));
+        }
+
+        build_options.add_option("-cl-fast-relaxed-math");
+        build_options.add_option("-DSRC_TENSOR_TYPE=BUFFER");
+        build_options.add_option("-DSRC_WIDTH=" + support::cpp11::to_string(src->dimension(width_idx)));
+        build_options.add_option("-DSRC_HEIGHT=" + support::cpp11::to_string(src->dimension(height_idx)));
+        build_options.add_option("-DSRC_CHANNELS=" + support::cpp11::to_string(src->dimension(channel_idx)));
+        build_options.add_option("-DSRC_DATA_TYPE=" + get_cl_type_from_data_type(src->data_type()));
+        build_options.add_option("-DDST_TENSOR_TYPE=BUFFER");
+        build_options.add_option("-DDST_WIDTH=" + support::cpp11::to_string(dst->dimension(width_idx)));
+        build_options.add_option("-DDST_HEIGHT=" + support::cpp11::to_string(dst->dimension(height_idx)));
+        build_options.add_option("-DDST_CHANNELS=" + support::cpp11::to_string(dst->dimension(channel_idx)));
+        build_options.add_option("-DDST_DATA_TYPE=" + get_cl_type_from_data_type(dst->data_type()));
+        build_options.add_option_if_else(export_to_cl_image, "-DWEI_TENSOR_TYPE=IMAGE", "-DWEI_TENSOR_TYPE=BUFFER");
+        build_options.add_option("-DWEI_WIDTH=" + support::cpp11::to_string(weights->dimension(width_idx)));
+        build_options.add_option("-DWEI_HEIGHT=" + support::cpp11::to_string(weights->dimension(height_idx)));
+        build_options.add_option("-DWEI_DATA_TYPE=" + get_cl_type_from_data_type(weights->data_type()));
+        build_options.add_option("-DSTRIDE_X=" + support::cpp11::to_string(conv_stride_x));
+        build_options.add_option("-DSTRIDE_Y=" + support::cpp11::to_string(conv_stride_y));
+        build_options.add_option("-DPAD_LEFT=" + support::cpp11::to_string(pad_left));
+        build_options.add_option("-DPAD_TOP=" + support::cpp11::to_string(pad_top));
+        build_options.add_option("-DN0=" + support::cpp11::to_string(n0));
+        build_options.add_option("-DM0=" + support::cpp11::to_string(m0));
+        build_options.add_option("-DK0=" + support::cpp11::to_string(k0));
+        build_options.add_option("-DPARTIAL_N0=" + support::cpp11::to_string(partial_store_n0));
+
+        if(is_data_type_quantized(data_type))
+        {
+            const UniformQuantizationInfo iqinfo = src->quantization_info().uniform();
+            const UniformQuantizationInfo wqinfo = weights->quantization_info().uniform();
+            const UniformQuantizationInfo oqinfo = dst->quantization_info().uniform();
+
+            PixelValue zero_value = PixelValue(0, src->data_type(), src->quantization_info());
+            int        zero_value_s32;
+            zero_value.get(zero_value_s32);
+
+            float multiplier        = iqinfo.scale * wqinfo.scale / oqinfo.scale;
+            int   output_multiplier = 0;
+            int   output_shift      = 0;
+            quantization::calculate_quantized_multiplier(multiplier, &output_multiplier, &output_shift);
+            build_options.add_option("-DIS_QUANTIZED");
+            build_options.add_option("-DDST_MULTIPLIER=" + support::cpp11::to_string(output_multiplier));
+            build_options.add_option("-DDST_SHIFT=" + support::cpp11::to_string(output_shift));
+            build_options.add_option("-DSRC_OFFSET=" + support::cpp11::to_string(-iqinfo.offset));
+            build_options.add_option("-DWEI_OFFSET=" + support::cpp11::to_string(-wqinfo.offset));
+            build_options.add_option("-DDST_OFFSET=" + support::cpp11::to_string(oqinfo.offset));
+            build_options.add_option("-DZERO_VALUE=" + support::cpp11::to_string(zero_value_s32));
+            build_options.add_option("-DACC_DATA_TYPE=" + get_cl_type_from_data_type(DataType::S32));
+        }
+        else
+        {
+            build_options.add_option("-DACC_DATA_TYPE=" + get_cl_type_from_data_type(data_type));
+            build_options.add_option("-DZERO_VALUE=" + support::cpp11::to_string(0));
+            build_options.add_option("-DSRC_OFFSET=" + support::cpp11::to_string(0));
+            build_options.add_option("-DWEI_OFFSET=" + support::cpp11::to_string(0));
+            build_options.add_option("-DDST_OFFSET=" + support::cpp11::to_string(0));
+        }
+    }
+    else
+    {
+        _border_size = BorderSize(src->padding());
+
+        kernel_name << "direct_convolution" << kernel_size << "x" << kernel_size;
+
+        build_options.add_option_if(biases != nullptr, std::string("-DHAS_BIAS"));
+
+        const bool run_optimized_for_bifrost = can_run_optimized_kernel_for_bifrost_nchw(gpu_target, conv_stride_x, conv_stride_y, kernel_size, data_type, _data_layout);
+
+        if(run_optimized_for_bifrost)
+        {
+            build_options.add_option(std::string("-DWEIGHTS_DEPTH=" + support::cpp11::to_string(weights->dimension(channel_idx))));
+
+            kernel_name << "_f32_bifrost";
+        }
+        else
+        {
+            build_options.add_option(std::string("-DDATA_TYPE=" + get_cl_type_from_data_type(data_type)));
+            build_options.add_option(std::string("-DDATA_SIZE=" + get_data_size_from_data_type(data_type)));
+            build_options.add_option(std::string("-DWEIGHTS_DEPTH=" + support::cpp11::to_string(weights->dimension(channel_idx))));
+            build_options.add_option(std::string("-DSTRIDE_X=" + support::cpp11::to_string(conv_stride_x)));
+            build_options.add_option(std::string("-DDATA_TYPE_PROMOTED=" + get_cl_type_from_data_type(data_type)));
+
+            if(is_data_type_quantized(data_type))
+            {
+                const UniformQuantizationInfo iqinfo = src->quantization_info().uniform();
+                const UniformQuantizationInfo wqinfo = weights->quantization_info().uniform();
+                const UniformQuantizationInfo oqinfo = dst->quantization_info().uniform();
+
+                float multiplier        = iqinfo.scale * wqinfo.scale / oqinfo.scale;
+                int   output_multiplier = 0;
+                int   output_shift      = 0;
+                quantization::calculate_quantized_multiplier(multiplier, &output_multiplier, &output_shift);
+                build_options.add_option("-DOUTPUT_MULTIPLIER=" + support::cpp11::to_string(output_multiplier));
+                build_options.add_option("-DOUTPUT_SHIFT=" + support::cpp11::to_string(output_shift));
+                build_options.add_option("-DKERNEL_SIZE=" + support::cpp11::to_string(kernel_size));
+                build_options.add_option("-DINPUT_OFFSET=" + support::cpp11::to_string(-iqinfo.offset));
+                build_options.add_option("-DWEIGHTS_OFFSET=" + support::cpp11::to_string(-wqinfo.offset));
+                build_options.add_option("-DOUTPUT_OFFSET=" + support::cpp11::to_string(oqinfo.offset));
+
+                kernel_name.str("direct_convolution_quantized");
+            }
+        }
+    }
+
+    _kernel = create_kernel(compile_context, kernel_name.str(), build_options.options());
+
+    // Set config_id for enabling LWS tuning
+    _config_id = kernel_name.str();
+    _config_id += "_";
+    _config_id += lower_string(string_from_data_type(data_type));
+    _config_id += "_";
+    _config_id += support::cpp11::to_string(kernel_size);
+    _config_id += "_";
+    _config_id += support::cpp11::to_string(border_size().left);
+    _config_id += "_";
+    _config_id += support::cpp11::to_string(border_size().top);
+    _config_id += "_";
+    _config_id += support::cpp11::to_string(border_size().right);
+    _config_id += "_";
+    _config_id += support::cpp11::to_string(border_size().bottom);
+    _config_id += "_";
+    _config_id += support::cpp11::to_string(conv_stride_x);
+    _config_id += "_";
+    _config_id += support::cpp11::to_string(conv_stride_y);
+    _config_id += "_";
+    _config_id += support::cpp11::to_string(dst->dimension(width_idx));
+    _config_id += "_";
+    _config_id += support::cpp11::to_string(dst->dimension(height_idx));
+    _config_id += "_";
+    _config_id += lower_string(string_from_data_layout(_data_layout));
+}
+
+Status ClDirectConv2dKernel::validate(const ITensorInfo *src, const ITensorInfo *weights, const ITensorInfo *biases, const ITensorInfo *dst, const PadStrideInfo &conv_info,
+                                      const GPUTarget target)
+{
+    ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments(src, weights, biases, dst, conv_info));
+    ARM_COMPUTE_RETURN_ON_ERROR(validate_and_configure_window(src->clone().get(), weights->clone().get(), dst->clone().get(), conv_info, target).first);
+
+    return Status{};
+}
+
+void ClDirectConv2dKernel::run_op(ITensorPack &tensors, const Window &window, cl::CommandQueue &queue)
+{
+    ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this);
+    ARM_COMPUTE_ERROR_ON_INVALID_SUBWINDOW(IKernel::window(), window);
+
+    // Get initial windows
+    Window slice = window.first_slice_window_3D();
+
+    const auto src     = utils::cast::polymorphic_downcast<const ICLTensor *>(tensors.get_const_tensor(TensorType::ACL_SRC_0));
+    const auto weights = utils::cast::polymorphic_downcast<const ICLTensor *>(tensors.get_const_tensor(TensorType::ACL_SRC_1));
+    const auto biases  = utils::cast::polymorphic_downcast<const ICLTensor *>(tensors.get_const_tensor(TensorType::ACL_SRC_2));
+    auto       dst     = utils::cast::polymorphic_downcast<ICLTensor *>(tensors.get_tensor(TensorType::ACL_DST));
+
+    if(_data_layout == DataLayout::NHWC)
+    {
+        cl::Image2D weights_cl_image;
+
+        const size_t dim_y_collapsed    = ceil_to_multiple(dst->info()->dimension(1) * dst->info()->dimension(2), slice.y().step());
+        const bool   export_to_cl_image = export_to_cl_image_support(weights->info(), get_target(), _data_layout);
+
+        slice.set(Window::DimY, Window::Dimension(0, dim_y_collapsed, slice.y().step()));
+        slice.set(Window::DimZ, Window::Dimension(0, dst->info()->dimension(3), 1));
+
+        if(export_to_cl_image)
+        {
+            const size_t      image_w = weights->info()->dimension(0) / 4;
+            const size_t      image_h = weights->info()->dimension(1) * weights->info()->dimension(2) * weights->info()->dimension(3);
+            const TensorShape shape2d(image_w, image_h);
+            const size_t      image_row_pitch = weights->info()->strides_in_bytes()[1];
+
+            // Export cl_buffer to cl_image
+            weights_cl_image = create_image2d_from_buffer(CLKernelLibrary::get().context(), weights->cl_buffer(), shape2d, weights->info()->data_type(), image_row_pitch);
+        }
+
+        unsigned int idx = 0;
+        add_4D_tensor_argument(idx, src, slice);
+        add_4D_tensor_argument(idx, dst, slice);
+        if(export_to_cl_image)
+        {
+            _kernel.setArg(idx++, weights_cl_image);
+        }
+        add_4D_tensor_argument(idx, weights, slice);
+        if(biases != nullptr)
+        {
+            add_1D_tensor_argument(idx, biases, slice);
+        }
+        enqueue(queue, *this, slice, lws_hint());
+    }
+    else
+    {
+        Window win_in = window;
+
+        win_in.adjust(Window::DimX, -_conv_info.pad_left(), true);
+        win_in.adjust(Window::DimY, -_conv_info.pad_top(), true);
+
+        const int width_idx  = get_data_layout_dimension_index(_data_layout, DataLayoutDimension::WIDTH);
+        const int height_idx = get_data_layout_dimension_index(_data_layout, DataLayoutDimension::HEIGHT);
+
+        const int conv_stride_x = std::get<0>(_conv_info.stride());
+        const int conv_stride_y = std::get<1>(_conv_info.stride());
+
+        win_in.set_dimension_step(width_idx, window[width_idx].step() * conv_stride_x);
+        win_in.set_dimension_step(height_idx, window[height_idx].step() * conv_stride_y);
+
+        Window       slice_in = win_in.first_slice_window_3D();
+        unsigned int idx1     = 2 * num_arguments_per_3D_tensor();
+        add_3D_tensor_argument(idx1, weights, slice);
+
+        if(biases != nullptr)
+        {
+            Window slice_biases;
+            slice_biases.use_tensor_dimensions(biases->info()->tensor_shape());
+            add_1D_tensor_argument(idx1, biases, slice_biases);
+        }
+
+        _kernel.setArg(idx1++, static_cast<unsigned int>(weights->info()->strides_in_bytes()[3]));
+
+        do
+        {
+            unsigned int idx = 0;
+            add_3D_tensor_argument(idx, src, slice_in);
+            add_3D_tensor_argument(idx, dst, slice);
+            enqueue(queue, *this, slice, lws_hint());
+        }
+        while(window.slide_window_slice_3D(slice) && win_in.slide_window_slice_3D(slice_in));
+    }
+}
+} // namespace kernels
+} // namespace opencl
+} // namespace arm_compute