COMPMID-417: Cleanup NEON FullyConnectedLayer
Change-Id: Ie02a0a1a28ca2771e29a5e6552242caf0f6db1cf
Reviewed-on: http://mpd-gerrit.cambridge.arm.com/83555
Tested-by: Kaizen <jeremy.johnson+kaizengerrit@arm.com>
Reviewed-by: Anthony Barbier <anthony.barbier@arm.com>
diff --git a/arm_compute/core/TensorShape.h b/arm_compute/core/TensorShape.h
index 6cf08de..8d15c50 100644
--- a/arm_compute/core/TensorShape.h
+++ b/arm_compute/core/TensorShape.h
@@ -138,17 +138,28 @@
}
/** Collapses given dimension and above.
*
- * @note Precondition: dimension < TensorShape::num_max_dimensions
- *
* @param[in] dimension Size of the wanted dimension
*
* @return The linear size of the collapsed dimensions
*/
size_t total_size_upper(size_t dimension) const
{
+ ARM_COMPUTE_ERROR_ON(dimension >= TensorShape::num_max_dimensions);
return std::accumulate(_id.begin() + dimension, _id.end(), 1, std::multiplies<size_t>());
}
+ /** Compute size of dimensions lower than the given one.
+ *
+ * @param[in] dimension Upper boundary.
+ *
+ * @return The linear size of the collapsed dimensions.
+ */
+ size_t total_size_lower(size_t dimension) const
+ {
+ ARM_COMPUTE_ERROR_ON(dimension > TensorShape::num_max_dimensions);
+ return std::accumulate(_id.begin(), _id.begin() + dimension, 1, std::multiplies<size_t>());
+ }
+
private:
/** Remove trailing dimensions of size 1 from the reported number of dimensions. */
void apply_dimension_correction()
diff --git a/arm_compute/runtime/NEON/functions/NEFullyConnectedLayer.h b/arm_compute/runtime/NEON/functions/NEFullyConnectedLayer.h
index af571d1..08099b8 100644
--- a/arm_compute/runtime/NEON/functions/NEFullyConnectedLayer.h
+++ b/arm_compute/runtime/NEON/functions/NEFullyConnectedLayer.h
@@ -97,11 +97,6 @@
void run() override;
private:
- void configure_fc_fc_wb(const ITensor *input, const ITensor *weights, ITensor *output);
- void configure_fc_fc_nb(const ITensor *input, const ITensor *weights, ITensor *output);
- void configure_conv_fc_wb(const ITensor *input, const ITensor *weights, ITensor *output);
- void configure_conv_fc_nb(const ITensor *input, const ITensor *weights, ITensor *output);
-
NEIm2ColKernel _im2col_kernel;
NEFullyConnectedLayerReshapeWeights _reshape_weights_kernel;
NEGEMMInterleave4x4Kernel _interleave4x4_kernel;
@@ -111,8 +106,8 @@
Tensor _interleave4x4_output;
Tensor _reshape_weights_output;
bool _are_weights_reshaped;
- bool _is_fc_after_conv;
bool _is_batched_fc_layer;
+ bool _linearize_input;
bool _accumulate_biases;
};
}
diff --git a/src/core/NEON/kernels/NEGEMMMatrixAccumulateBiasesKernel.cpp b/src/core/NEON/kernels/NEGEMMMatrixAccumulateBiasesKernel.cpp
index a4fc494..6ed3791 100644
--- a/src/core/NEON/kernels/NEGEMMMatrixAccumulateBiasesKernel.cpp
+++ b/src/core/NEON/kernels/NEGEMMMatrixAccumulateBiasesKernel.cpp
@@ -48,7 +48,7 @@
ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(accum, 1, DataType::QS8, DataType::QS16, DataType::F16, DataType::F32);
ARM_COMPUTE_ERROR_ON_MISMATCHING_DATA_TYPES(biases, accum);
ARM_COMPUTE_ERROR_ON_MISMATCHING_FIXED_POINT_POSITION(biases, accum);
- ARM_COMPUTE_ERROR_ON(biases->info()->num_dimensions() != 1);
+ ARM_COMPUTE_ERROR_ON(biases->info()->num_dimensions() > 1);
_biases = biases;
_accum = accum;
diff --git a/src/core/NEON/kernels/NEGEMMMatrixMultiplyKernel.cpp b/src/core/NEON/kernels/NEGEMMMatrixMultiplyKernel.cpp
index 8381dd8..8a2a481 100644
--- a/src/core/NEON/kernels/NEGEMMMatrixMultiplyKernel.cpp
+++ b/src/core/NEON/kernels/NEGEMMMatrixMultiplyKernel.cpp
@@ -23,6 +23,7 @@
*/
#include "arm_compute/core/NEON/kernels/NEGEMMMatrixMultiplyKernel.h"
+#include "arm_compute/core/AccessWindowStatic.h"
#include "arm_compute/core/AccessWindowTranspose.h"
#include "arm_compute/core/Error.h"
#include "arm_compute/core/Helpers.h"
@@ -1462,7 +1463,7 @@
AccessWindowHorizontal output_access(output->info(), 0, num_elems_processed_per_iteration_x);
update_window_and_padding(win,
- AccessWindowHorizontal(input0->info(), 0, num_elems_processed_per_iteration_x),
+ AccessWindowStatic(input0->info(), 0, 0, input0->info()->tensor_shape().x(), 1),
AccessWindowHorizontal(input1->info(), 0, num_elems_processed_per_iteration_x),
output_access);
diff --git a/src/core/NEON/kernels/NEIm2ColKernel.cpp b/src/core/NEON/kernels/NEIm2ColKernel.cpp
index e4de60d..6e15f82 100644
--- a/src/core/NEON/kernels/NEIm2ColKernel.cpp
+++ b/src/core/NEON/kernels/NEIm2ColKernel.cpp
@@ -291,7 +291,10 @@
_conv_info);
_has_bias = has_bias;
- unsigned int pad_x, pad_y, stride_x, stride_y = 0;
+ unsigned int pad_x = 0;
+ unsigned int pad_y = 0;
+ unsigned int stride_x = 0;
+ unsigned int stride_y = 0;
std::tie(pad_x, pad_y) = conv_info.pad();
std::tie(stride_x, stride_y) = conv_info.stride();
diff --git a/src/runtime/NEON/functions/NEFullyConnectedLayer.cpp b/src/runtime/NEON/functions/NEFullyConnectedLayer.cpp
index 4d9ee85..39983bf 100644
--- a/src/runtime/NEON/functions/NEFullyConnectedLayer.cpp
+++ b/src/runtime/NEON/functions/NEFullyConnectedLayer.cpp
@@ -30,8 +30,8 @@
#include <algorithm>
#include <cmath>
-using namespace arm_compute;
-
+namespace arm_compute
+{
NEFullyConnectedLayerReshapeWeights::NEFullyConnectedLayerReshapeWeights()
: _transpose_kernel(), _transpose1xW_kernel(), _transpose_output(), _transpose_weights(false), _is_batched_fc_layer(false)
{
@@ -40,11 +40,11 @@
void NEFullyConnectedLayerReshapeWeights::configure(const ITensor *input, ITensor *output, bool transpose_weights, bool is_batched_fc_layer)
{
ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::QS8, DataType::QS16, DataType::F16, DataType::F32);
+ ARM_COMPUTE_ERROR_ON(input->info()->num_dimensions() > 2);
ARM_COMPUTE_ERROR_ON(output == nullptr);
- ARM_COMPUTE_ERROR_ON(input->info()->num_dimensions() != 2);
- ARM_COMPUTE_ERROR_ON((transpose_weights == false) && (is_batched_fc_layer == false));
+ ARM_COMPUTE_ERROR_ON(!transpose_weights && !is_batched_fc_layer);
- const DataType dt = input->info()->data_type();
+ const DataType data_type = input->info()->data_type();
const int fixed_point_position = input->info()->fixed_point_position();
_transpose_weights = transpose_weights;
@@ -57,7 +57,7 @@
{
// Initialize the output tensor for transpose
TensorShape shape_transposed(input->info()->dimension(1), input->info()->dimension(0));
- _transpose_output.allocator()->init(TensorInfo(shape_transposed, 1, dt, fixed_point_position));
+ _transpose_output.allocator()->init(TensorInfo(shape_transposed, 1, data_type, fixed_point_position));
_transpose_kernel.configure(input, &_transpose_output);
// Configure transpose 1xW kernel
@@ -91,6 +91,7 @@
{
NEScheduler::get().schedule(&_transpose_kernel, Window::DimY);
}
+
if(_is_batched_fc_layer)
{
NEScheduler::get().schedule(&_transpose1xW_kernel, Window::DimY);
@@ -99,216 +100,142 @@
NEFullyConnectedLayer::NEFullyConnectedLayer()
: _im2col_kernel(), _reshape_weights_kernel(), _interleave4x4_kernel(), _mm_kernel(), _accumulate_biases_kernel(), _im2col_output(), _interleave4x4_output(), _reshape_weights_output(),
- _are_weights_reshaped(false), _is_fc_after_conv(false), _is_batched_fc_layer(false), _accumulate_biases(false)
+ _are_weights_reshaped(false), _is_batched_fc_layer(false), _linearize_input(false), _accumulate_biases(false)
{
}
-void NEFullyConnectedLayer::configure_conv_fc_wb(const ITensor *input, const ITensor *weights, ITensor *output)
-{
- ARM_COMPUTE_ERROR_ON(weights->info()->dimension(0) != (input->info()->dimension(0) * input->info()->dimension(1) * input->info()->dimension(2) * (16 / weights->info()->element_size())));
-
- const DataType dt = input->info()->data_type();
- const int fixed_point_position = input->info()->fixed_point_position();
-
- // If the fully connected layer is called after a convolution layer, the input tensor must be linearized
-
- // Initialize output tensor for im2col
- TensorShape shape_im2col;
- shape_im2col.set(0, input->info()->dimension(0) * input->info()->dimension(1) * input->info()->dimension(2));
- shape_im2col.set(1, input->info()->dimension(3));
- shape_im2col.set(2, input->info()->dimension(4));
- shape_im2col.set(3, input->info()->dimension(5));
- _im2col_output.allocator()->init(TensorInfo(shape_im2col, 1, dt, fixed_point_position));
-
- // Initialize output tensor for interleave 4x4
- TensorShape shape_interleaved = _im2col_output.info()->tensor_shape();
- shape_interleaved.set(0, shape_interleaved.x() * 4);
- shape_interleaved.set(1, std::ceil(static_cast<float>(shape_interleaved.y()) / 4));
- _interleave4x4_output.allocator()->init(TensorInfo(shape_interleaved, 1, dt, fixed_point_position));
-
- // Configure im2col kernel
- _im2col_kernel.configure(input, &_im2col_output, Size2D(1, 1), PadStrideInfo(1, 1, 0, 0), false);
-
- // Configure interleave4x4 kernel
- _interleave4x4_kernel.configure(&_im2col_output, &_interleave4x4_output);
-
- // Configure matrix multiply kernel
- _mm_kernel.configure(&_interleave4x4_output, weights, output, 1.0f);
-
- // Allocate the tensors once all the configure methods have been called
- _im2col_output.allocator()->allocate();
- _interleave4x4_output.allocator()->allocate();
-}
-
-void NEFullyConnectedLayer::configure_fc_fc_wb(const ITensor *input, const ITensor *weights, ITensor *output)
-{
- const DataType dt = input->info()->data_type();
- const int fixed_point_position = input->info()->fixed_point_position();
-
- // Initialize output tensor for interleave 4x4
- TensorShape shape_interleaved = input->info()->tensor_shape();
- shape_interleaved.set(0, shape_interleaved.x() * 4);
- shape_interleaved.set(1, std::ceil(static_cast<float>(shape_interleaved.y()) / 4));
- _interleave4x4_output.allocator()->init(TensorInfo(shape_interleaved, 1, dt, fixed_point_position));
-
- // Configure interleave4x4 kernel
- _interleave4x4_kernel.configure(input, &_interleave4x4_output);
-
- // Configure matrix multiply kernel
- _mm_kernel.configure(&_interleave4x4_output, weights, output, 1.0f);
-
- // Allocate the tensors once all the configure methods have been called
- _interleave4x4_output.allocator()->allocate();
-}
-
-void NEFullyConnectedLayer::configure_conv_fc_nb(const ITensor *input, const ITensor *weights, ITensor *output)
-{
- ARM_COMPUTE_ERROR_ON((weights->info()->dimension(1) != (input->info()->dimension(0) * input->info()->dimension(1) * input->info()->dimension(2))));
-
- const DataType dt = input->info()->data_type();
- const int fixed_point_position = input->info()->fixed_point_position();
-
- // If the fully connected layer is called after a convolution layer, the input tensor must be linearized
-
- // Initialize output tensor for im2col
- TensorShape shape_im2col;
- shape_im2col.set(0, input->info()->dimension(0) * input->info()->dimension(1) * input->info()->dimension(2));
- shape_im2col.set(1, 1);
- _im2col_output.allocator()->init(TensorInfo(shape_im2col, 1, dt, fixed_point_position));
-
- // Configure im2col kernel
- _im2col_kernel.configure(input, &_im2col_output, Size2D(1, 1), PadStrideInfo(1, 1, 0, 0), false);
-
- // Configure matrix multiply kernel
- _mm_kernel.configure(&_im2col_output, weights, output, 1.0f);
-
- // Allocate the output tensor for im2col once all the configure methods have been called
- _im2col_output.allocator()->allocate();
-}
-
-void NEFullyConnectedLayer::configure_fc_fc_nb(const ITensor *input, const ITensor *weights, ITensor *output)
-{
- ARM_COMPUTE_ERROR_ON(input->info()->dimension(0) != weights->info()->dimension(1));
-
- // Configure matrix multiply kernel
- _mm_kernel.configure(input, weights, output, 1.0f);
-}
-
void NEFullyConnectedLayer::configure(const ITensor *input, const ITensor *weights, const ITensor *biases, ITensor *output, bool transpose_weights, bool are_weights_reshaped)
{
- ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::QS8, DataType::QS16, DataType::F16, DataType::F32);
- ARM_COMPUTE_ERROR_ON_MISMATCHING_DATA_TYPES(input, weights, output);
- ARM_COMPUTE_ERROR_ON_MISMATCHING_FIXED_POINT_POSITION(input, weights, output);
- ARM_COMPUTE_ERROR_ON(weights->info()->num_dimensions() != 2);
-
- const DataType dt = input->info()->data_type();
- const int fixed_point_position = input->info()->fixed_point_position();
-
- _are_weights_reshaped = are_weights_reshaped;
- _is_fc_after_conv = true;
- _is_batched_fc_layer = false;
- _accumulate_biases = false;
-
- if(biases != nullptr)
- {
- ARM_COMPUTE_ERROR_ON_MISMATCHING_DATA_TYPES(input, biases);
-
- _accumulate_biases = true;
-
- // Configure accumulate biases kernel
- _accumulate_biases_kernel.configure(output, biases);
- }
-
// With the Fully Connected layer we can have 4 different cases:
// 1) Convolution layer -> Fully Connected layer without batches
// 2) Fully Connected layer -> Fully Connected layer without batches
// 3) Convolution layer -> Fully Connected layer with batches
// 4) Fully Connected layer -> Fully Connected layer with batches
- // Check if we have a fully connected layer with batches
- _is_batched_fc_layer = (output->info()->dimension(1) > 1);
+ // Expected shape before transpose and reshaping
+ // Input: In x B (In and B can be multi-dimensional)
+ // Weights: flat(In) x Out
+ // Biases: Out
+ // Output: Out x B (B can be multi-dimensional)
- const ITensor *weights_to_use = weights;
+ ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::QS8, DataType::QS16, DataType::F16, DataType::F32);
+ ARM_COMPUTE_ERROR_ON_MISMATCHING_DATA_TYPES(input, weights, output);
+ ARM_COMPUTE_ERROR_ON_MISMATCHING_FIXED_POINT_POSITION(input, weights, output);
- if(!are_weights_reshaped)
+ const DataType data_type = input->info()->data_type();
+ const int fixed_point_position = input->info()->fixed_point_position();
+ const int num_batch_dimensions = std::max(0, static_cast<int>(output->info()->tensor_shape().num_dimensions()) - 1);
+ const int num_input_dimensions = input->info()->tensor_shape().num_dimensions() - num_batch_dimensions;
+ const size_t linear_input_size = input->info()->tensor_shape().total_size_lower(num_input_dimensions);
+
+ _linearize_input = input->info()->tensor_shape().x() != linear_input_size;
+ _are_weights_reshaped = are_weights_reshaped;
+ _accumulate_biases = biases != nullptr;
+ _is_batched_fc_layer = num_batch_dimensions > 0;
+
+ // Check if number of batches match
+ ARM_COMPUTE_ERROR_ON(input->info()->tensor_shape().total_size_upper(num_input_dimensions) != output->info()->tensor_shape().total_size_upper(1));
+ ARM_COMPUTE_ERROR_ON(weights->info()->num_dimensions() > 2);
+
+ const size_t interleave_width = 16 / input->info()->element_size();
+ const ITensor *weights_to_use = weights;
+
+ if(!are_weights_reshaped && (transpose_weights || _is_batched_fc_layer))
{
- if((transpose_weights || _is_batched_fc_layer))
+ weights_to_use = &_reshape_weights_output;
+
+ TensorShape reshaped_weights_shape(weights->info()->tensor_shape());
+
+ // Transpose weights if the user hasn't done it
+ if(transpose_weights)
{
- weights_to_use = &_reshape_weights_output;
-
- if(transpose_weights)
- {
- if(_is_batched_fc_layer)
- {
- const float transpose_width = 16.0f / input->info()->element_size();
- TensorShape shape_wt(weights->info()->dimension(0) * static_cast<unsigned int>(transpose_width), static_cast<unsigned int>(std::ceil(weights->info()->dimension(1) / transpose_width)));
- TensorInfo info_wt(shape_wt, 1, dt, fixed_point_position);
- _reshape_weights_output.allocator()->init(info_wt);
- }
- else
- {
- TensorShape shape_wt(weights->info()->dimension(1), weights->info()->dimension(0));
- TensorInfo info_wt(shape_wt, 1, dt, fixed_point_position);
- _reshape_weights_output.allocator()->init(info_wt);
- }
- }
- else
- {
- ARM_COMPUTE_ERROR_ON(!_is_batched_fc_layer);
-
- const float transpose_width = 16.0f / input->info()->element_size();
- TensorShape shape_wt(weights->info()->dimension(1) * static_cast<unsigned int>(transpose_width), static_cast<unsigned int>(std::ceil(weights->info()->dimension(0) / transpose_width)));
- TensorInfo info_wt(shape_wt, 1, dt, fixed_point_position);
- _reshape_weights_output.allocator()->init(info_wt);
- }
-
- // Reshape the weights
- _reshape_weights_kernel.configure(weights, &_reshape_weights_output, transpose_weights, _is_batched_fc_layer);
+ const size_t shape_x = reshaped_weights_shape.x();
+ reshaped_weights_shape.set(0, reshaped_weights_shape.y());
+ reshaped_weights_shape.set(1, shape_x);
}
+
+ // If the we run multiple batches we need 1xW transpose, too.
+ if(_is_batched_fc_layer)
+ {
+ const float shape_x = reshaped_weights_shape.x();
+ reshaped_weights_shape.set(0, reshaped_weights_shape.y() * interleave_width);
+ reshaped_weights_shape.set(1, static_cast<unsigned int>(std::ceil(shape_x / interleave_width)));
+ }
+
+ _reshape_weights_output.allocator()->init(TensorInfo(reshaped_weights_shape, 1, data_type, fixed_point_position));
+
+ // Reshape the weights
+ _reshape_weights_kernel.configure(weights, &_reshape_weights_output, transpose_weights, _is_batched_fc_layer);
+ }
+
+ // Check correct shape of weights
+ if(_is_batched_fc_layer)
+ {
+ // Transpose + Transpose1xW
+ ARM_COMPUTE_ERROR_ON(weights_to_use->info()->tensor_shape().x() != linear_input_size * interleave_width);
+ ARM_COMPUTE_ERROR_ON(weights_to_use->info()->tensor_shape().y() != static_cast<unsigned int>(std::ceil(static_cast<float>(output->info()->tensor_shape().x()) / interleave_width)));
+ }
+ else
+ {
+ // Transpose
+ ARM_COMPUTE_ERROR_ON(weights_to_use->info()->tensor_shape().x() != output->info()->tensor_shape().x());
+ ARM_COMPUTE_ERROR_ON(weights_to_use->info()->tensor_shape().y() != linear_input_size);
+ }
+
+ const ITensor *multiply_input = input;
+
+ if(_linearize_input)
+ {
+ TensorShape shape_im2col(input->info()->tensor_shape());
+ shape_im2col.collapse(num_input_dimensions);
+ _im2col_output.allocator()->init(TensorInfo(shape_im2col, 1, data_type, fixed_point_position));
+
+ // Configure im2col kernel
+ _im2col_kernel.configure(input, &_im2col_output, Size2D(1, 1), PadStrideInfo(1, 1, 0, 0), false);
+
+ multiply_input = &_im2col_output;
}
if(_is_batched_fc_layer)
{
- _is_fc_after_conv = (TensorShape::num_max_dimensions >= 4) && (std::equal(input->info()->tensor_shape().cbegin() + 3,
- input->info()->tensor_shape().cend(),
- output->info()->tensor_shape().cbegin() + 1));
+ TensorShape shape_interleaved(multiply_input->info()->tensor_shape());
+ shape_interleaved.set(0, shape_interleaved.x() * 4);
+ shape_interleaved.set(1, std::ceil(shape_interleaved.y() / 4.f));
+ _interleave4x4_output.allocator()->init(TensorInfo(shape_interleaved, 1, data_type, fixed_point_position));
- if(_is_fc_after_conv)
- {
- // Fully Connected layer after a Convolution Layer with batches
- configure_conv_fc_wb(input, weights_to_use, output);
- }
- else
- {
- // Fully Connected layer after a Fully Connected Layer with batches
- configure_fc_fc_wb(input, weights_to_use, output);
- }
+ // Configure interleave4x4 kernel
+ _interleave4x4_kernel.configure(multiply_input, &_interleave4x4_output);
+
+ multiply_input = &_interleave4x4_output;
}
- else
- {
- // In case of not batched fully connected layer, the weights will not be reshaped using transposed1xW
- _is_fc_after_conv = ((weights_to_use->info()->dimension(1)) == (input->info()->dimension(0) * input->info()->dimension(1) * input->info()->dimension(2)));
- if(_is_fc_after_conv)
- {
- // Fully Connected layer after a Convolution Layer without batches
- configure_conv_fc_nb(input, weights_to_use, output);
- }
- else
- {
- // Fully Connected layer after a Fully Connected Layer without batches
- configure_fc_fc_nb(input, weights_to_use, output);
- }
+ // Configure matrix multiply kernel
+ _mm_kernel.configure(multiply_input, weights_to_use, output, 1.0f);
+
+ if(_accumulate_biases)
+ {
+ ARM_COMPUTE_ERROR_ON_MISMATCHING_DATA_TYPES(input, biases);
+ ARM_COMPUTE_ERROR_ON(biases->info()->tensor_shape().x() != output->info()->tensor_shape().x());
+
+ // Configure accumulate biases kernel
+ _accumulate_biases_kernel.configure(output, biases);
}
// Allocate the transpose tensor if the are_weights_reshaped flag is false and once all the configure methods have been called
- if(!are_weights_reshaped)
+ if(!are_weights_reshaped && (transpose_weights || _is_batched_fc_layer))
{
- if(transpose_weights || _is_batched_fc_layer)
- {
- // Allocate the tensor for the weights reshaped
- _reshape_weights_output.allocator()->allocate();
- }
+ // Allocate the tensor for the weights reshaped
+ _reshape_weights_output.allocator()->allocate();
+ }
+
+ if(_linearize_input)
+ {
+ _im2col_output.allocator()->allocate();
+ }
+
+ if(_is_batched_fc_layer)
+ {
+ _interleave4x4_output.allocator()->allocate();
}
}
@@ -321,8 +248,8 @@
_reshape_weights_kernel.run();
}
- // Linearize input if comes from a convolutional layer
- if(_is_fc_after_conv)
+ // Linearize input if it comes from a convolutional layer
+ if(_linearize_input)
{
NEScheduler::get().schedule(&_im2col_kernel, Window::DimY);
}
@@ -342,3 +269,4 @@
NEScheduler::get().schedule(&_accumulate_biases_kernel, Window::DimY);
}
}
+} // namespace arm_compute