COMPMID-1959: Implements 2D FFT on OpenCL

Change-Id: I73cf3984a5463acc854c8a59dc2bd9a5234cd99c
Signed-off-by: Georgios Pinitas <georgios.pinitas@arm.com>
Reviewed-on: https://review.mlplatform.org/c/936
Comments-Addressed: Arm Jenkins <bsgcomp@arm.com>
Tested-by: Arm Jenkins <bsgcomp@arm.com>
Reviewed-by: Gian Marco Iodice <gianmarco.iodice@arm.com>

4 files changed, 539 insertions, 14 deletions

diff --git a/src/runtime/CL/functions/CLFFT1D.cpp b/src/runtime/CL/functions/CLFFT1D.cpp
index d893cd3d1b..67111e7e5c 100644
--- a/src/runtime/CL/functions/CLFFT1D.cpp
+++ b/src/runtime/CL/functions/CLFFT1D.cpp
@@ -31,7 +31,7 @@
 namespace arm_compute
 {
 CLFFT1D::CLFFT1D(std::shared_ptr<IMemoryManager> memory_manager)
-    : _memory_group(std::move(memory_manager)), _digit_reversed_input(), _digit_reverse_indices(), _digit_reverse_kernel(), _fft_kernels(), _num_ffts(0)
+    : _memory_group(std::move(memory_manager)), _digit_reverse_kernel(), _fft_kernels(), _scale_kernel(), _digit_reversed_input(), _digit_reverse_indices(), _num_ffts(0), _run_scale(false)
 {
 }
 
@@ -46,11 +46,18 @@ void CLFFT1D::configure(const ICLTensor *input, ICLTensor *output, const FFT1DIn
     const auto         decomposed_vector = arm_compute::helpers::fft::decompose_stages(N, supported_radix);
     ARM_COMPUTE_ERROR_ON(decomposed_vector.empty());
 
+    // Flags
+    _run_scale        = config.direction == FFTDirection::Inverse;
+    const bool is_c2r = input->info()->num_channels() == 2 && output->info()->num_channels() == 1;
+
     // Configure digit reverse
+    FFTDigitReverseKernelInfo digit_reverse_config;
+    digit_reverse_config.axis      = config.axis;
+    digit_reverse_config.conjugate = config.direction == FFTDirection::Inverse;
     TensorInfo digit_reverse_indices_info(TensorShape(input->info()->tensor_shape()[config.axis]), 1, DataType::U32);
     _digit_reverse_indices.allocator()->init(digit_reverse_indices_info);
     _memory_group.manage(&_digit_reversed_input);
-    _digit_reverse_kernel.configure(input, &_digit_reversed_input, &_digit_reverse_indices, config.axis);
+    _digit_reverse_kernel.configure(input, &_digit_reversed_input, &_digit_reverse_indices, digit_reverse_config);
 
     // Create and configure FFT kernels
     unsigned int Nx = 1;
@@ -60,16 +67,25 @@ void CLFFT1D::configure(const ICLTensor *input, ICLTensor *output, const FFT1DIn
     {
         const unsigned int radix_for_stage = decomposed_vector.at(i);
 
-        FFTRadixStageKernelDescriptor fft_kernel_desc;
-        fft_kernel_desc.axis           = config.axis;
-        fft_kernel_desc.radix          = radix_for_stage;
-        fft_kernel_desc.Nx             = Nx;
-        fft_kernel_desc.is_first_stage = (i == 0);
-        _fft_kernels[i].configure(&_digit_reversed_input, i == (_num_ffts - 1) ? output : nullptr, fft_kernel_desc);
+        FFTRadixStageKernelInfo fft_kernel_info;
+        fft_kernel_info.axis           = config.axis;
+        fft_kernel_info.radix          = radix_for_stage;
+        fft_kernel_info.Nx             = Nx;
+        fft_kernel_info.is_first_stage = (i == 0);
+        _fft_kernels[i].configure(&_digit_reversed_input, ((i == (_num_ffts - 1)) && !is_c2r) ? output : nullptr, fft_kernel_info);
 
         Nx *= radix_for_stage;
     }
 
+    // Configure scale kernel
+    if(_run_scale)
+    {
+        FFTScaleKernelInfo scale_config;
+        scale_config.scale     = static_cast<float>(N);
+        scale_config.conjugate = config.direction == FFTDirection::Inverse;
+        is_c2r ? _scale_kernel.configure(&_digit_reversed_input, output, scale_config) : _scale_kernel.configure(output, nullptr, scale_config);
+    }
+
     // Allocate tensors
     _digit_reversed_input.allocator()->allocate();
     _digit_reverse_indices.allocator()->allocate();
@@ -84,8 +100,9 @@ void CLFFT1D::configure(const ICLTensor *input, ICLTensor *output, const FFT1DIn
 Status CLFFT1D::validate(const ITensorInfo *input, const ITensorInfo *output, const FFT1DInfo &config)
 {
     ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(input, output);
-    ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 2, DataType::F32);
-    ARM_COMPUTE_RETURN_ERROR_ON(config.axis != 0);
+    ARM_COMPUTE_RETURN_ERROR_ON(input->data_type() != DataType::F32);
+    ARM_COMPUTE_RETURN_ERROR_ON(input->num_channels() != 1 && input->num_channels() != 2);
+    ARM_COMPUTE_RETURN_ERROR_ON(std::set<unsigned int>({ 0, 1 }).count(config.axis) == 0);
 
     // Check if FFT is decomposable
     const auto         supported_radix   = CLFFTRadixStageKernel::supported_radix();
@@ -96,6 +113,8 @@ Status CLFFT1D::validate(const ITensorInfo *input, const ITensorInfo *output, co
     // Checks performed when output is configured
     if((output != nullptr) && (output->total_size() != 0))
     {
+        ARM_COMPUTE_RETURN_ERROR_ON(output->num_channels() == 1 && input->num_channels() == 1);
+        ARM_COMPUTE_RETURN_ERROR_ON(output->num_channels() != 1 && output->num_channels() != 2);
         ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES(input, output);
         ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input, output);
     }
@@ -107,11 +126,19 @@ void CLFFT1D::run()
 {
     MemoryGroupResourceScope scope_mg(_memory_group);
 
+    // Run digit reverse
     CLScheduler::get().enqueue(_digit_reverse_kernel, false);
 
+    // Run radix kernels
     for(unsigned int i = 0; i < _num_ffts; ++i)
     {
-        CLScheduler::get().enqueue(_fft_kernels[i], i == (_num_ffts - 1));
+        CLScheduler::get().enqueue(_fft_kernels[i], i == (_num_ffts - 1) && !_run_scale);
+    }
+
+    // Run output scaling
+    if(_run_scale)
+    {
+        CLScheduler::get().enqueue(_scale_kernel, true);
     }
 }
 } // namespace arm_compute
diff --git a/src/runtime/CL/functions/CLFFT2D.cpp b/src/runtime/CL/functions/CLFFT2D.cpp
new file mode 100644
index 0000000000..4300fb4e32
--- /dev/null
+++ b/src/runtime/CL/functions/CLFFT2D.cpp
@@ -0,0 +1,95 @@
+/*
+ * Copyright (c) 2019 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 "arm_compute/runtime/CL/functions/CLFFT2D.h"
+
+#include "arm_compute/core/CL/ICLTensor.h"
+#include "arm_compute/core/Validate.h"
+#include "arm_compute/runtime/CL/CLScheduler.h"
+
+namespace arm_compute
+{
+CLFFT2D::CLFFT2D(std::shared_ptr<IMemoryManager> memory_manager)
+    : _memory_group(memory_manager), _first_pass_func(memory_manager), _second_pass_func(memory_manager), _first_pass_tensor()
+{
+}
+
+void CLFFT2D::configure(const ICLTensor *input, ICLTensor *output, const FFT2DInfo &config)
+{
+    ARM_COMPUTE_ERROR_ON_NULLPTR(input, output);
+    ARM_COMPUTE_ERROR_THROW_ON(CLFFT2D::validate(input->info(), output->info(), config));
+
+    // Setup first pass
+    FFT1DInfo first_pass_config;
+    first_pass_config.axis      = config.axes.first;
+    first_pass_config.direction = config.direction;
+    _memory_group.manage(&_first_pass_tensor);
+    _first_pass_func.configure(input, &_first_pass_tensor, first_pass_config);
+
+    // Setup second pass
+    FFT1DInfo second_pass_config;
+    second_pass_config.axis      = config.axes.second;
+    second_pass_config.direction = config.direction;
+    _second_pass_func.configure(&_first_pass_tensor, output, second_pass_config);
+    _first_pass_tensor.allocator()->allocate();
+}
+
+Status CLFFT2D::validate(const ITensorInfo *input, const ITensorInfo *output, const FFT2DInfo &config)
+{
+    ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(input, output);
+
+    // Create intermediate tensor info
+    TensorInfo first_pass_tensor(input->clone()->set_is_resizable(true).reset_padding().set_num_channels(2));
+
+    // Validate first pass
+    FFT1DInfo first_pass_config;
+    first_pass_config.axis      = config.axes.first;
+    first_pass_config.direction = config.direction;
+    ARM_COMPUTE_RETURN_ON_ERROR(CLFFT1D::validate(input, &first_pass_tensor, first_pass_config));
+
+    // Validate second pass
+    FFT1DInfo second_pass_config;
+    second_pass_config.axis      = config.axes.second;
+    second_pass_config.direction = config.direction;
+    ARM_COMPUTE_RETURN_ON_ERROR(CLFFT1D::validate(&first_pass_tensor, output, second_pass_config));
+
+    // Checks performed when output is configured
+    if((output != nullptr) && (output->total_size() != 0))
+    {
+        ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES(input, output);
+        ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input, output);
+    }
+
+    return Status{};
+}
+
+void CLFFT2D::run()
+{
+    _memory_group.acquire();
+
+    _first_pass_func.run();
+    _second_pass_func.run();
+
+    _memory_group.release();
+}
+} // namespace arm_compute
diff --git a/src/runtime/CL/functions/CLFFTConvolutionLayer.cpp b/src/runtime/CL/functions/CLFFTConvolutionLayer.cpp
new file mode 100644
index 0000000000..441c1c7214
--- /dev/null
+++ b/src/runtime/CL/functions/CLFFTConvolutionLayer.cpp
@@ -0,0 +1,380 @@
+/*
+ * Copyright (c) 2019 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 "arm_compute/runtime/CL/functions/CLFFTConvolutionLayer.h"
+
+#include "arm_compute/core/CL/ICLTensor.h"
+#include "arm_compute/core/Utils.h"
+#include "arm_compute/core/Validate.h"
+#include "arm_compute/core/utils/helpers/fft.h"
+#include "arm_compute/core/utils/misc/ShapeCalculator.h"
+#include "arm_compute/runtime/CL/CLScheduler.h"
+#include "arm_compute/runtime/CPP/CPPScheduler.h"
+
+namespace arm_compute
+{
+namespace
+{
+int pad_decomposable(int N)
+{
+    const auto supported_radix = CLFFTRadixStageKernel::supported_radix();
+
+    int  pad           = 0;
+    bool is_decomposed = false;
+    while(!is_decomposed)
+    {
+        const auto decomposed_vector = arm_compute::helpers::fft::decompose_stages(N++, supported_radix);
+        is_decomposed                = !decomposed_vector.empty();
+        if(!is_decomposed)
+        {
+            ++pad;
+        }
+    }
+    return pad;
+}
+} // namespace
+CLFFTConvolutionLayer::CLFFTConvolutionLayer(std::shared_ptr<IMemoryManager> memory_manager)
+    : _memory_group(memory_manager),
+      _flip_weights_func(),
+      _permute_input_func(),
+      _permute_output_func(),
+      _permute_weights_func(),
+      _permute_bias_func(),
+      _pad_input_func(),
+      _pad_weights_func(),
+      _transform_input_func(memory_manager),
+      _transform_weights_func(memory_manager),
+      _itransform_output_func(memory_manager),
+      _prod_func(),
+      _reduce_func(),
+      _extract_output_func(),
+      _bias_add_func(),
+      _activation_layer_func(),
+      _permuted_input(),
+      _permuted_weights(),
+      _permuted_bias(),
+      _permuted_output(),
+      _padded_input(),
+      _padded_weights(),
+      _flip_axis(),
+      _flipped_weights(),
+      _transformed_input(),
+      _transformed_weights(),
+      _input_weights_product(),
+      _output_product(),
+      _output_reduced(),
+      _itransformed_output(),
+      _reshaped_output(),
+      _bias_output(),
+      _original_weights(nullptr),
+      _original_bias(nullptr),
+      _is_activationlayer_enabled(false),
+      _needs_permute(false),
+      _has_bias(false),
+      _is_prepared(false)
+{
+}
+
+void CLFFTConvolutionLayer::configure(ICLTensor *input, const ICLTensor *weights, const ICLTensor *biases, ICLTensor *output, const PadStrideInfo &conv_info,
+                                      const ActivationLayerInfo &act_info)
+{
+    _original_weights = weights;
+    _original_bias    = biases;
+
+    // Flat if bias addition is required
+    _has_bias = biases != nullptr;
+
+    // Get indices for the width and height
+    const size_t idx_width  = get_data_layout_dimension_index(input->info()->data_layout(), DataLayoutDimension::WIDTH);
+    const size_t idx_height = get_data_layout_dimension_index(input->info()->data_layout(), DataLayoutDimension::HEIGHT);
+
+    // Input shape, kernel size and output tile
+    const Size2D input_dims  = Size2D(input->info()->tensor_shape()[idx_width], input->info()->tensor_shape()[idx_height]);
+    const Size2D kernel_size = Size2D(weights->info()->tensor_shape()[idx_width], weights->info()->tensor_shape()[idx_height]);
+    const Size2D pad_valid   = Size2D(pad_decomposable(input_dims.x() + kernel_size.x() - 1),
+                                      pad_decomposable(input_dims.y() + kernel_size.y() - 1));
+    // Tensors to use
+    ICLTensor       *input_to_use   = input;
+    const ICLTensor *weights_to_use = weights;
+    ICLTensor       *output_to_use  = _has_bias ? &_bias_output : output;
+
+    // Permute bias
+    _permute_bias_func.configure(biases, &_permuted_bias, PermutationVector(1U, 2U, 0U));
+    _permuted_bias.info()->set_data_layout(DataLayout::NCHW);
+
+    // Permute input if needed
+    _needs_permute = input->info()->data_layout() == DataLayout::NHWC;
+    if(_needs_permute)
+    {
+        _memory_group.manage(&_permuted_input);
+        // Configure the function to transform the input tensor from NHWC -> NCHW
+        _permute_input_func.configure(input, &_permuted_input, PermutationVector(1U, 2U, 0U));
+        _permuted_input.info()->set_data_layout(DataLayout::NCHW);
+
+        // Configure the function to transform the weights tensor from HWI -> IHW
+        _permute_weights_func.configure(weights, &_permuted_weights, PermutationVector(1U, 2U, 0U));
+        _permuted_weights.info()->set_data_layout(DataLayout::NCHW);
+
+        input_to_use   = &_permuted_input;
+        weights_to_use = &_permuted_weights;
+    }
+
+    // Flip weights
+    _flipped_weights.allocator()->init(weights_to_use->info()->clone()->set_is_resizable(true).reset_padding());
+    _flip_axis.allocator()->init(TensorInfo(TensorShape(2U), 1, DataType::U32));
+    _flip_weights_func.configure(weights_to_use, &_flipped_weights, &_flip_axis);
+
+    // Pad weights
+    const PaddingList padding_w = { { 0, input_dims.x() + pad_valid.x() - 1 }, { 0, input_dims.y() + pad_valid.y() - 1 } };
+    _pad_weights_func.configure(&_flipped_weights, &_padded_weights, padding_w);
+
+    // Transform weights
+    _transform_weights_func.configure(&_padded_weights, &_transformed_weights, FFT2DInfo());
+
+    // Pad input
+    const PaddingList padding_in = { { 0, kernel_size.x() + pad_valid.x() - 1 }, { 0, kernel_size.y() + pad_valid.y() - 1 } };
+    _memory_group.manage(&_padded_input);
+    _pad_input_func.configure(input_to_use, &_padded_input, padding_in);
+    if(_needs_permute)
+    {
+        _permuted_input.allocator()->allocate();
+    }
+
+    // Transform input
+    _memory_group.manage(&_transformed_input);
+    _transform_input_func.configure(&_padded_input, &_transformed_input, FFT2DInfo());
+    _padded_input.allocator()->allocate();
+
+    // Perform product
+    _memory_group.manage(&_output_product);
+    _prod_func.configure(&_transformed_input, &_transformed_weights, &_output_product);
+    _transformed_input.allocator()->allocate();
+
+    // Perform reduction
+    _memory_group.manage(&_output_reduced);
+    _reduce_func.configure(&_output_product, &_output_reduced, 2, ReductionOperation::SUM);
+    _output_product.allocator()->allocate();
+
+    // Transform output
+    _memory_group.manage(&_itransformed_output);
+    FFT2DInfo itranform_info;
+    itranform_info.direction = FFTDirection::Inverse;
+    _itransformed_output.allocator()->init(_output_reduced.info()->clone()->set_is_resizable(true).set_num_channels(1).reset_padding());
+    _itransform_output_func.configure(&_output_reduced, &_itransformed_output, itranform_info);
+    _output_reduced.allocator()->allocate();
+
+    // Reshape output
+    TensorShape reshaped_shape = _itransformed_output.info()->tensor_shape();
+    reshaped_shape.remove_dimension(2);
+    _reshaped_output.allocator()->init(_itransformed_output.info()->clone()->set_tensor_shape(reshaped_shape));
+
+    // Extract correct region
+    const int start_left = kernel_size.x() - conv_info.pad_left() - 1;
+    const int start_top  = kernel_size.y() - conv_info.pad_top() - 1;
+    const int end_right  = _reshaped_output.info()->tensor_shape().x() - (kernel_size.x() - conv_info.pad_right() - 1) - pad_valid.x();
+    const int end_botton = _reshaped_output.info()->tensor_shape().y() - (kernel_size.y() - conv_info.pad_bottom() - 1) - pad_valid.y();
+    if(_has_bias)
+    {
+        _memory_group.manage(&_bias_output);
+    }
+    else if(_needs_permute)
+    {
+        output_to_use = &_permuted_output;
+        _memory_group.manage(&_permuted_output);
+    }
+    _extract_output_func.configure(&_reshaped_output, output_to_use, Coordinates(start_left, start_top), Coordinates(end_right, end_botton));
+    _itransformed_output.allocator()->allocate();
+
+    // Add bias
+    if(biases != nullptr)
+    {
+        output_to_use = output;
+        if(_needs_permute)
+        {
+            output_to_use = &_permuted_output;
+            _memory_group.manage(&_permuted_output);
+        }
+        auto_init_if_empty(*output_to_use->info(), *_bias_output.info());
+        _bias_add_func.configure(&_bias_output, &_permuted_bias, output_to_use, ConvertPolicy::WRAP);
+        _bias_output.allocator()->allocate();
+    }
+
+    // Permute output
+    if(_needs_permute)
+    {
+        // Configure the function to transform the convoluted output to ACL's native ordering format NCHW
+        _permuted_output.info()->set_data_layout(DataLayout::NCHW);
+        _permute_output_func.configure(&_permuted_output, output, PermutationVector(2U, 0U, 1U));
+
+        // Allocate tensors
+        _permuted_output.allocator()->allocate();
+    }
+
+    // Configure Activation Layer
+    _is_activationlayer_enabled = act_info.enabled();
+    if(_is_activationlayer_enabled)
+    {
+        _activation_layer_func.configure(output, nullptr, act_info);
+    }
+
+    // Setup flip axis data
+    _flip_axis.allocator()->allocate();
+    _flip_axis.map(true);
+    auto axis_data = reinterpret_cast<uint32_t *>(_flip_axis.buffer());
+    axis_data[0]   = 0;
+    axis_data[1]   = 1;
+    _flip_axis.unmap();
+}
+
+Status CLFFTConvolutionLayer::validate(const ITensorInfo *input, const ITensorInfo *weights, const ITensorInfo *biases, const ITensorInfo *output, const PadStrideInfo &conv_info,
+                                       const ActivationLayerInfo &act_info)
+{
+    ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::F32);
+    ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input, weights);
+
+    // Get indices for the width and height
+    const size_t idx_width  = get_data_layout_dimension_index(input->data_layout(), DataLayoutDimension::WIDTH);
+    const size_t idx_height = get_data_layout_dimension_index(input->data_layout(), DataLayoutDimension::HEIGHT);
+
+    // Input shape, kernel size and output tile
+    const Size2D kernel_size = Size2D(weights->tensor_shape()[idx_width], weights->tensor_shape()[idx_height]);
+
+    // Strides
+    const auto strides = conv_info.stride();
+    ARM_COMPUTE_RETURN_ERROR_ON(strides.first != strides.second && strides.first != 1);
+    ARM_COMPUTE_RETURN_ERROR_ON(kernel_size.x() != kernel_size.y());
+    ARM_COMPUTE_RETURN_ERROR_ON(conv_info.pad_left() != (kernel_size.x() / 2) || conv_info.pad_right() != (kernel_size.x() / 2));
+    ARM_COMPUTE_RETURN_ERROR_ON(conv_info.pad_top() != (kernel_size.y() / 2) || conv_info.pad_bottom() != (kernel_size.y() / 2));
+
+    // Validate biases
+    if(biases != nullptr)
+    {
+        const size_t idx_channels = get_data_layout_dimension_index(input->data_layout(), DataLayoutDimension::CHANNEL);
+        ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input, biases);
+        ARM_COMPUTE_RETURN_ERROR_ON(input->tensor_shape()[idx_channels] != biases->tensor_shape().x());
+    }
+
+    // Checks performed when output is configured
+    if((output != nullptr) && (output->total_size() != 0))
+    {
+        ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input, output);
+        ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES(input, output);
+
+        // Validate Activation Layer
+        if(act_info.enabled())
+        {
+            ARM_COMPUTE_RETURN_ON_ERROR(CLActivationLayer::validate(output, nullptr, act_info));
+        }
+    }
+
+    return Status{};
+}
+
+void CLFFTConvolutionLayer::run()
+{
+    prepare();
+
+    _memory_group.acquire();
+
+    // Transform input
+    if(_needs_permute)
+    {
+        _permute_input_func.run();
+    }
+    _pad_input_func.run();
+    _transform_input_func.run();
+
+    // Perform operations to frequency domain
+    _prod_func.run();
+    _reduce_func.run();
+
+    // Transform output
+    _itransform_output_func.run();
+    _reshaped_output.allocator()->import_memory(_itransformed_output.cl_buffer());
+    _extract_output_func.run();
+    // Add bias
+    if(_has_bias)
+    {
+        _bias_add_func.run();
+    }
+    if(_needs_permute)
+    {
+        _permute_output_func.run();
+    }
+
+    // Run activation layer
+    if(_is_activationlayer_enabled)
+    {
+        _activation_layer_func.run();
+    }
+
+    _memory_group.release();
+}
+
+void CLFFTConvolutionLayer::prepare()
+{
+    if(!_is_prepared)
+    {
+        // Permute bias to NCHW
+        if(_original_bias != nullptr)
+        {
+            _permuted_bias.allocator()->allocate();
+            _permute_bias_func.run();
+            _original_bias->mark_as_unused();
+        }
+
+        const ICLTensor *cur_weights = _original_weights;
+        // Permute weights
+        if(_needs_permute)
+        {
+            ARM_COMPUTE_ERROR_ON(!cur_weights->is_used());
+
+            _permuted_weights.allocator()->allocate();
+            _permute_weights_func.run();
+            cur_weights->mark_as_unused();
+            cur_weights = &_permuted_weights;
+        }
+
+        // Flip weights
+        _flipped_weights.allocator()->allocate();
+        _flip_weights_func.run();
+        cur_weights->mark_as_unused();
+
+        // Pad weights
+        _padded_weights.allocator()->allocate();
+        _pad_weights_func.run();
+        _flipped_weights.mark_as_unused();
+        CLScheduler::get().queue().finish();
+        _flipped_weights.allocator()->free();
+
+        // Transform weights to frequence domain
+        _transformed_weights.allocator()->allocate();
+        _transform_weights_func.run();
+        _padded_weights.mark_as_unused();
+        CLScheduler::get().queue().finish();
+        _padded_weights.allocator()->free();
+
+        _is_prepared = true;
+    }
+}
+} // namespace arm_compute
diff --git a/src/runtime/CL/functions/CLPixelWiseMultiplication.cpp b/src/runtime/CL/functions/CLPixelWiseMultiplication.cpp
index b4c20db3da..959464ce14 100644
--- a/src/runtime/CL/functions/CLPixelWiseMultiplication.cpp
+++ b/src/runtime/CL/functions/CLPixelWiseMultiplication.cpp
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2016-2018 ARM Limited.
+ * Copyright (c) 2016-2019 ARM Limited.
  *
  * SPDX-License-Identifier: MIT
  *
@@ -29,8 +29,8 @@
 
 #include <utility>
 
-using namespace arm_compute;
-
+namespace arm_compute
+{
 void CLPixelWiseMultiplication::configure(ICLTensor *input1, ICLTensor *input2, ICLTensor *output, float scale,
                                           ConvertPolicy overflow_policy, RoundingPolicy rounding_policy)
 {
@@ -54,3 +54,26 @@ Status CLPixelWiseMultiplication::validate(const ITensorInfo *input1, const ITen
 {
     return CLPixelWiseMultiplicationKernel::validate(input1, input2, output, scale, overflow_policy, rounding_policy);
 }
+
+void CLComplexPixelWiseMultiplication::configure(ICLTensor *input1, ICLTensor *input2, ICLTensor *output)
+{
+    auto k = arm_compute::support::cpp14::make_unique<CLComplexPixelWiseMultiplicationKernel>();
+    k->configure(input1, input2, output);
+    _kernel = std::move(k);
+
+    if(output->info()->dimension(0) > 1)
+    {
+        ICLTensor *broadcasted_info = (input1->info()->dimension(0) == 1) ? input1 : input2;
+
+        if(broadcasted_info->info()->dimension(0) == 1)
+        {
+            _border_handler.configure(broadcasted_info, _kernel->border_size(), BorderMode::REPLICATE);
+        }
+    }
+}
+
+Status CLComplexPixelWiseMultiplication::validate(const ITensorInfo *input1, const ITensorInfo *input2, const ITensorInfo *output)
+{
+    return CLComplexPixelWiseMultiplicationKernel::validate(input1, input2, output);
+}
+} // namespace arm_compute
\ No newline at end of file