Revert "Rework OpenCL Depthwise Convolution"

This reverts commit 561c176598cd14245e2e7918fdf136d1c888d1da.

Reason for revert: <validation>

Change-Id: I6f2d61c27520439bb538e9265736532104b24cf8
Reviewed-on: https://review.mlplatform.org/c/ml/ComputeLibrary/+/5127
Tested-by: Arm Jenkins <bsgcomp@arm.com>
Reviewed-by: Georgios Pinitas <georgios.pinitas@arm.com>
Comments-Addressed: Arm Jenkins <bsgcomp@arm.com>

1 files changed, 432 insertions, 0 deletions

diff --git a/src/core/CL/kernels/CLDepthwiseConvolutionLayer3x3NCHWKernel.cpp b/src/core/CL/kernels/CLDepthwiseConvolutionLayer3x3NCHWKernel.cpp
new file mode 100644
index 0000000000..dda70d2231
--- /dev/null
+++ b/src/core/CL/kernels/CLDepthwiseConvolutionLayer3x3NCHWKernel.cpp
@@ -0,0 +1,432 @@
+/*
+ * Copyright (c) 2018-2021 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/CL/kernels/CLDepthwiseConvolutionLayer3x3NCHWKernel.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/TensorInfo.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/CLValidate.h"
+#include "src/core/CL/ICLKernel.h"
+#include "src/core/helpers/AutoConfiguration.h"
+#include "src/core/helpers/WindowHelpers.h"
+#include "support/StringSupport.h"
+
+namespace arm_compute
+{
+using namespace arm_compute::misc::shape_calculator;
+
+namespace
+{
+Status validate_arguments(const ITensorInfo *input, const ITensorInfo *weights, const ITensorInfo *biases, const ITensorInfo *output,
+                          const PadStrideInfo &conv_info, unsigned int depth_multiplier, const ActivationLayerInfo &act_info, const Size2D dilation,
+                          const ITensorInfo *output_multipliers, const ITensorInfo *output_shifts)
+{
+    ARM_COMPUTE_RETURN_ERROR_ON_F16_UNSUPPORTED(input);
+    ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::QASYMM8, DataType::QASYMM8_SIGNED, DataType::F16, DataType::F32);
+    ARM_COMPUTE_RETURN_ERROR_ON_MSG((act_info.enabled()) && (input->data_type() == DataType::QASYMM8 || input->data_type() == DataType::QASYMM8_SIGNED)
+                                    && (act_info.activation() != ActivationLayerInfo::ActivationFunction::LU_BOUNDED_RELU)
+                                    && (act_info.activation() != ActivationLayerInfo::ActivationFunction::BOUNDED_RELU)
+                                    && (act_info.activation() != ActivationLayerInfo::ActivationFunction::RELU)
+                                    && (act_info.activation() != ActivationLayerInfo::ActivationFunction::LOGISTIC),
+                                    "For QASYMM8 only logistic, relu, lower bounded relu and lower-upper bounded relu are supported");
+    ARM_COMPUTE_RETURN_ERROR_ON(weights->dimension(0) != 3 || weights->dimension(1) != 3);
+    ARM_COMPUTE_RETURN_ERROR_ON(conv_info.stride().first < 1 || conv_info.stride().first > 3);
+
+    ARM_COMPUTE_RETURN_ERROR_ON((dilation.x() < 1) || (dilation.y() < 1));
+
+    const bool is_qasymm = is_data_type_quantized_asymmetric(input->data_type());
+
+    if(biases != nullptr)
+    {
+        if(is_qasymm)
+        {
+            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((biases->dimension(0) != weights->dimension(2)) && (weights->dimension(2) != 1 || biases->dimension(0) != weights->dimension(3)));
+        ARM_COMPUTE_RETURN_ERROR_ON(biases->num_dimensions() > 1);
+    }
+
+    if(is_qasymm)
+    {
+        ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(output_multipliers, output_shifts);
+        ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(output_multipliers, 1, DataType::S32);
+        ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(output_shifts, 1, DataType::S32);
+        ARM_COMPUTE_RETURN_ERROR_ON(output_multipliers->num_dimensions() > 1);
+        ARM_COMPUTE_RETURN_ERROR_ON(output_shifts->num_dimensions() > 1);
+
+        if(is_data_type_quantized_per_channel(weights->data_type()))
+        {
+            ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(weights, 1, DataType::QSYMM8_PER_CHANNEL);
+            ARM_COMPUTE_RETURN_ERROR_ON(weights->dimension(2) != output_multipliers->dimension(0));
+            ARM_COMPUTE_RETURN_ERROR_ON(weights->dimension(2) != output_shifts->dimension(0));
+        }
+        else
+        {
+            ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input, weights);
+            ARM_COMPUTE_RETURN_ERROR_ON(1 != output_multipliers->dimension(0));
+            ARM_COMPUTE_RETURN_ERROR_ON(1 != output_shifts->dimension(0));
+        }
+    }
+    else
+    {
+        ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input, weights);
+    }
+
+    if(output->total_size() != 0)
+    {
+        const ConvolutionInfo info{ conv_info, depth_multiplier, ActivationLayerInfo(), dilation };
+        const TensorShape     output_shape = compute_depthwise_convolution_shape(*input, *weights, info);
+        ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DIMENSIONS(output->tensor_shape(), output_shape);
+    }
+
+    return Status{};
+}
+
+std::pair<Status, Window> validate_and_configure_window(ITensorInfo *input, ITensorInfo *weights, ITensorInfo *output, const PadStrideInfo &conv_info,
+                                                        unsigned int depth_multiplier, std::string &kernel_name, const Size2D dilation)
+{
+    // Output auto inizialitation if not yet initialized
+    const ConvolutionInfo info
+    {
+        conv_info, depth_multiplier, ActivationLayerInfo(), dilation
+    };
+    const TensorShape output_shape = compute_depthwise_convolution_shape(*input, *weights, info);
+    auto_init_if_empty(*output, input->clone()->set_tensor_shape(output_shape).set_quantization_info(output->quantization_info()));
+
+    const unsigned int conv_stride_x = conv_info.stride().first;
+    const unsigned int conv_stride_y = conv_info.stride().second;
+    const bool         is_qasymm     = is_data_type_quantized_asymmetric(input->data_type());
+
+    // Configure kernel window
+    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;
+
+    if(input->data_type() == DataType::F16)
+    {
+        kernel_name                       = "depthwise_convolution_3x3_f16";
+        num_elems_written_per_iteration_x = 8 / data_size_from_type(input->data_type());
+        num_elems_written_per_iteration_y = 1;
+        num_elems_read_per_iteration_y    = 3;
+        switch(conv_stride_x)
+        {
+            case 1:
+                num_elems_read_per_iteration_x = 8;
+                break;
+            case 2:
+                num_elems_read_per_iteration_x = 9;
+                break;
+            case 3:
+                num_elems_read_per_iteration_x = 16;
+                break;
+            default:
+                num_elems_read_per_iteration_x = 3 + (num_elems_written_per_iteration_x - 1) * conv_stride_x;
+                break;
+        }
+        if(conv_stride_x == 1 && conv_stride_y == 1)
+        {
+            kernel_name                       = "depthwise_convolution_3x3_stridex1_stridey1_f16";
+            num_elems_read_per_iteration_x    = 8;
+            num_elems_written_per_iteration_x = 4;
+            num_elems_read_per_iteration_y    = 6;
+            num_elems_written_per_iteration_y = 4;
+        }
+        else if(conv_stride_x == 2 && conv_stride_y == 2)
+        {
+            kernel_name                       = "depthwise_convolution_3x3_stridex2_stridey2_f16";
+            num_elems_read_per_iteration_x    = 10;
+            num_elems_written_per_iteration_x = 4;
+            num_elems_read_per_iteration_y    = 5;
+            num_elems_written_per_iteration_y = 2;
+        }
+    }
+    else if(input->data_type() == DataType::F32)
+    {
+        if(conv_stride_x == 1 && conv_stride_y == 1)
+        {
+            kernel_name                       = "depthwise_convolution_3x3_stridex1_stridey1_f32";
+            num_elems_read_per_iteration_x    = 4;
+            num_elems_read_per_iteration_y    = 6;
+            num_elems_written_per_iteration_x = 2;
+            num_elems_written_per_iteration_y = 4;
+        }
+        else if(conv_stride_x == 2 && conv_stride_y == 2)
+        {
+            kernel_name                       = "depthwise_convolution_3x3_stridex2_stridey2_f32";
+            num_elems_read_per_iteration_x    = 6;
+            num_elems_read_per_iteration_y    = 5;
+            num_elems_written_per_iteration_x = 2;
+            num_elems_written_per_iteration_y = 2;
+        }
+        else
+        {
+            kernel_name                       = "depthwise_convolution_3x3";
+            num_elems_written_per_iteration_x = 8 / data_size_from_type(input->data_type());
+            num_elems_written_per_iteration_y = 1;
+            num_elems_read_per_iteration_x    = 3 + (num_elems_written_per_iteration_x - 1) * conv_stride_x;
+            num_elems_read_per_iteration_y    = 3;
+        }
+    }
+    else
+    {
+        const bool is_dot8_supported = dot8_supported(CLKernelLibrary::get().get_device()) && !is_data_type_quantized_per_channel(weights->data_type());
+
+        kernel_name = is_qasymm ? "dwc_3x3_native_quantized8" : "depthwise_convolution_3x3";
+        kernel_name += (is_qasymm && is_dot8_supported ? "_dot8" : "");
+        kernel_name += (is_qasymm ? "_nchw" : "");
+
+        num_elems_written_per_iteration_x = 8 / data_size_from_type(input->data_type());
+        num_elems_written_per_iteration_y = (is_qasymm && conv_stride_y == 1 && dilation.y() == 1) ? 2 : 1;
+        num_elems_read_per_iteration_x    = 3 + (num_elems_written_per_iteration_x - 1) * conv_stride_x + (conv_stride_x > 1 ? 1 : 0);
+        num_elems_read_per_iteration_y    = num_elems_written_per_iteration_y + 2;
+    }
+    // The OpenCL routine convolution1x3 does loadn(addr), loadn(addr + dilation_x) and loadn(addr + 2 * dilation_x) on the input.
+    // Each of the three convolution1x3 gets called by passing addr, (addr + dilation_y) and (addr + 2 * dilation_y)
+    // Hence we must add 2 * dilation.x/y() to the number of elements read in those axes per thread
+    num_elems_read_per_iteration_x += 2 * dilation.x();
+    num_elems_read_per_iteration_y += 2 * dilation.y();
+
+    // Create window and update padding
+    Window win = calculate_max_window(*output, Steps(num_elems_written_per_iteration_x, num_elems_written_per_iteration_y));
+
+    AccessWindowRectangle input_access(input, -conv_info.pad_left(), -conv_info.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, 3, 3);
+    AccessWindowRectangle output_access(output, 0, 0, num_elems_written_per_iteration_x, num_elems_written_per_iteration_y);
+
+    bool window_changed = update_window_and_padding(win, input_access, weights_access, output_access);
+
+    Status err = (window_changed) ? ARM_COMPUTE_CREATE_ERROR(ErrorCode::RUNTIME_ERROR, "Insufficient Padding!") : Status{};
+    return std::make_pair(err, win);
+}
+} // namespace
+
+CLDepthwiseConvolutionLayer3x3NCHWKernel::CLDepthwiseConvolutionLayer3x3NCHWKernel()
+    : _border_size(0), _input(), _output(), _weights(), _biases(), _conv_stride_y(1), _output_multipliers(), _output_shifts(), _is_quantized(false), _conv_stride_x(0), _conv_pad_top(0), _conv_pad_left(0)
+{
+}
+
+BorderSize CLDepthwiseConvolutionLayer3x3NCHWKernel::border_size() const
+{
+    return _border_size;
+}
+
+void CLDepthwiseConvolutionLayer3x3NCHWKernel::configure(const ICLTensor *input, const ICLTensor *weights, const ICLTensor *biases, ICLTensor *output,
+                                                         const PadStrideInfo &conv_info, unsigned int depth_multiplier, ActivationLayerInfo act_info, const Size2D &dilation,
+                                                         const ICLTensor *output_multipliers, const ICLTensor *output_shifts)
+{
+    configure(CLKernelLibrary::get().get_compile_context(), input, weights, biases, output, conv_info, depth_multiplier, act_info, dilation, output_multipliers, output_shifts);
+}
+
+void CLDepthwiseConvolutionLayer3x3NCHWKernel::configure(const CLCompileContext &compile_context, const ICLTensor *input, const ICLTensor *weights, const ICLTensor *biases, ICLTensor *output,
+                                                         const PadStrideInfo &conv_info, unsigned int depth_multiplier, ActivationLayerInfo act_info, const Size2D &dilation,
+                                                         const ICLTensor *output_multipliers, const ICLTensor *output_shifts)
+{
+    ARM_COMPUTE_ERROR_ON_NULLPTR(input, weights, output);
+    ARM_COMPUTE_ERROR_THROW_ON(validate_arguments(input->info(), weights->info(), (biases != nullptr) ? biases->info() : nullptr, output->info(),
+                                                  conv_info, depth_multiplier, act_info, dilation,
+                                                  (output_multipliers != nullptr) ? output_multipliers->info() : nullptr,
+                                                  (output_shifts != nullptr) ? output_shifts->info() : nullptr));
+
+    _input              = input;
+    _output             = output;
+    _weights            = weights;
+    _biases             = biases;
+    _conv_stride_x      = conv_info.stride().first;
+    _conv_stride_y      = conv_info.stride().second;
+    _conv_pad_left      = conv_info.pad_left();
+    _conv_pad_top       = conv_info.pad_top();
+    _output_multipliers = output_multipliers;
+    _output_shifts      = output_shifts;
+    _is_quantized       = is_data_type_quantized_asymmetric(input->info()->data_type());
+
+    // Configure kernel window
+    std::string kernel_name;
+
+    auto win_config = validate_and_configure_window(input->info(), weights->info(), output->info(), conv_info, depth_multiplier, kernel_name, dilation);
+    ARM_COMPUTE_ERROR_THROW_ON(win_config.first);
+    ICLKernel::configure_internal(win_config.second);
+
+    _border_size = BorderSize(input->info()->padding());
+
+    // Set build options
+    CLBuildOptions build_opts;
+    build_opts.add_option("-DACTIVATION_TYPE=" + lower_string(string_from_activation_func(act_info.activation())));
+    build_opts.add_option("-DDST_CHANNELS=" + support::cpp11::to_string(_output->info()->tensor_shape().z()));
+    build_opts.add_option("-DDEPTH_MULTIPLIER=" + support::cpp11::to_string(depth_multiplier));
+    build_opts.add_option("-DCONV_STRIDE_X=" + support::cpp11::to_string(_conv_stride_x));
+    build_opts.add_option("-DDILATION_X=" + support::cpp11::to_string(dilation.x()));
+    build_opts.add_option("-DDILATION_Y=" + support::cpp11::to_string(dilation.y()));
+    build_opts.add_option_if(_biases != nullptr, "-DHAS_BIAS");
+
+    if(_is_quantized)
+    {
+        const UniformQuantizationInfo iq_info = _input->info()->quantization_info().uniform();
+        const UniformQuantizationInfo wq_info = _weights->info()->quantization_info().uniform();
+        const UniformQuantizationInfo oq_info = _output->info()->quantization_info().uniform();
+
+        const bool is_quantized_per_channel = is_data_type_quantized_per_channel(weights->info()->data_type());
+        const bool is_dot8_supported        = dot8_supported(CLKernelLibrary::get().get_device()) && !is_quantized_per_channel;
+        build_opts.add_option("-DCONV_STRIDE_Y=" + support::cpp11::to_string(_conv_stride_y));
+        build_opts.add_option("-DINPUT_OFFSET=" + support::cpp11::to_string(-iq_info.offset));
+        build_opts.add_option("-DWEIGHTS_OFFSET=" + support::cpp11::to_string(-wq_info.offset));
+        build_opts.add_option("-DOUTPUT_OFFSET=" + support::cpp11::to_string(oq_info.offset));
+        build_opts.add_option("-DK_OFFSET=" + support::cpp11::to_string(9 * iq_info.offset * wq_info.offset));
+        build_opts.add_option_if(is_quantized_per_channel, "-DPER_CHANNEL_QUANTIZATION");
+        build_opts.add_option_if(is_dot8_supported, "-DIS_DOT8");
+
+        // Compute non-per-channel multiplier and shift anyway to make OpenCL kernel simpler
+        float multiplier        = iq_info.scale * wq_info.scale / oq_info.scale;
+        int   output_multiplier = 0;
+        int   output_shift      = 0;
+        quantization::calculate_quantized_multiplier(multiplier, &output_multiplier, &output_shift);
+        build_opts.add_option("-DOUTPUT_MULTIPLIER=" + support::cpp11::to_string(output_multiplier));
+        build_opts.add_option("-DOUTPUT_SHIFT=" + support::cpp11::to_string(output_shift));
+
+        if(act_info.enabled())
+        {
+            int a_val{};
+            int b_val{};
+            std::tie(b_val, a_val) = get_quantized_activation_min_max(act_info, input->info()->data_type(), oq_info);
+
+            const int o1 = oq_info.offset;
+
+            build_opts.add_option("-DA_VAL=" + support::cpp11::to_string(a_val));
+            build_opts.add_option("-DB_VAL=" + support::cpp11::to_string(b_val));
+            build_opts.add_option("-DCONST_0=" + support::cpp11::to_string(o1));
+
+            const float s1 = iq_info.scale;
+            build_opts.add_option("-DS1_VAL=" + float_to_string_with_full_precision(s1));
+            build_opts.add_option("-DO1_VAL=" + support::cpp11::to_string(o1));
+        }
+
+        build_opts.add_option("-DDATA_TYPE=" + get_cl_type_from_data_type(input->info()->data_type()));
+        build_opts.add_option("-DWEIGHTS_TYPE=" + get_cl_type_from_data_type(weights->info()->data_type()));
+        build_opts.add_option("-DWEIGHTS_PROMOTED_TYPE=" + get_cl_promoted_type_from_data_type(weights->info()->data_type()));
+    }
+    else
+    {
+        build_opts.add_option_if(act_info.enabled(), "-DA_VAL=" + float_to_string_with_full_precision(act_info.a()));
+        build_opts.add_option_if(act_info.enabled(), "-DB_VAL=" + float_to_string_with_full_precision(act_info.b()));
+        build_opts.add_option_if(act_info.enabled(), "-DDATA_TYPE=" + get_cl_type_from_data_type(input->info()->data_type()));
+        build_opts.add_option("-DVEC_SIZE=" + support::cpp11::to_string(win_config.second.x().step()));
+    }
+
+    build_opts.add_option_if(input->info()->data_type() == DataType::F16, "-DIS_F16");
+    build_opts.add_option_if(input->info()->data_type() == DataType::F32, "-DIS_F32");
+
+    _kernel = create_kernel(compile_context, kernel_name, build_opts.options());
+
+    // Set config_id for enabling LWS tuning
+    _config_id = kernel_name;
+    _config_id += "_";
+    _config_id += lower_string(string_from_data_type(input->info()->data_type()));
+    _config_id += "_";
+    _config_id += support::cpp11::to_string(input->info()->dimension(0));
+    _config_id += "_";
+    _config_id += support::cpp11::to_string(input->info()->dimension(1));
+    _config_id += "_";
+    _config_id += support::cpp11::to_string(input->info()->dimension(2));
+    _config_id += "_";
+    _config_id += support::cpp11::to_string(output->info()->dimension(0));
+    _config_id += "_";
+    _config_id += support::cpp11::to_string(output->info()->dimension(1));
+}
+
+Status CLDepthwiseConvolutionLayer3x3NCHWKernel::validate(const ITensorInfo *input, const ITensorInfo *weights, const ITensorInfo *biases, const ITensorInfo *output,
+                                                          const PadStrideInfo &conv_info, unsigned int depth_multiplier, ActivationLayerInfo act_info,
+                                                          const Size2D &dilation, const ITensorInfo *output_multipliers, const ITensorInfo *output_shifts)
+{
+    std::string kernel_name;
+    ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments(input, weights, biases, output, conv_info, depth_multiplier, act_info, dilation, output_multipliers, output_shifts));
+    ARM_COMPUTE_RETURN_ON_ERROR(validate_and_configure_window(input->clone().get(), weights->clone().get(), output->clone().get(),
+                                                              conv_info, depth_multiplier, kernel_name, dilation)
+                                .first);
+
+    return Status{};
+}
+
+void CLDepthwiseConvolutionLayer3x3NCHWKernel::run(const Window &window, cl::CommandQueue &queue)
+{
+    ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this);
+    ARM_COMPUTE_ERROR_ON_INVALID_SUBWINDOW(IKernel::window(), window);
+
+    Window collapsed = window.collapse_if_possible(ICLKernel::window(), Window::DimZ);
+
+    // Create input window and adjust
+    Window collapsed_in = collapsed;
+    collapsed_in.adjust(Window::DimX, -_conv_pad_left, true);
+    collapsed_in.adjust(Window::DimY, -_conv_pad_top, true);
+    collapsed_in.set_dimension_step(Window::DimX, collapsed_in.x().step() * _conv_stride_x);
+    collapsed_in.set_dimension_step(Window::DimY, collapsed_in.y().step() * _conv_stride_y);
+
+    Window slice_in      = collapsed_in.first_slice_window_3D();
+    Window slice_out     = collapsed.first_slice_window_3D();
+    Window slice_weights = window.first_slice_window_3D();
+    slice_weights.set_dimension_step(Window::DimX, 0);
+    slice_weights.set_dimension_step(Window::DimY, 0);
+
+    unsigned int idx = 3 * num_arguments_per_3D_tensor();
+
+    // Set output multipliers in case of quantized data type
+    if(_is_quantized)
+    {
+        Window slice;
+        slice.use_tensor_dimensions(_output_multipliers->info()->tensor_shape());
+        add_1D_tensor_argument(idx, _output_multipliers, slice);
+        add_1D_tensor_argument(idx, _output_shifts, slice);
+    }
+
+    // Set biases
+    if(_biases != nullptr)
+    {
+        Window slice_biases;
+        slice_biases.use_tensor_dimensions(_biases->info()->tensor_shape());
+        add_1D_tensor_argument(idx, _biases, slice_biases);
+    }
+
+    do
+    {
+        idx = 0;
+        add_3D_tensor_argument(idx, _input, slice_in);
+        add_3D_tensor_argument(idx, _output, slice_out);
+        add_3D_tensor_argument(idx, _weights, slice_weights);
+
+        enqueue(queue, *this, slice_out, lws_hint());
+    }
+    while(collapsed.slide_window_slice_3D(slice_out) && collapsed_in.slide_window_slice_3D(slice_in));
+}
+} // namespace arm_compute