COMPMID-1162: Enable NHWC data layout support for NEWinogradConvolutionLayer - part1
In this first part we reworked the configuration of the kernels as before we
passed the raw pointer to the buffer within the configuration of the function
Change-Id: I83d3cb64c562303093c7f0ae52395ecd080a5d52
Reviewed-on: https://eu-gerrit-1.euhpc.arm.com/133560
Tested-by: Jenkins <bsgcomp@arm.com>
Reviewed-by: Giorgio Arena <giorgio.arena@arm.com>
Reviewed-by: Gian Marco Iodice <gianmarco.iodice@arm.com>
diff --git a/src/core/NEON/kernels/NEWinogradConvolutionLayerKernel.cpp b/src/core/NEON/kernels/NEWinogradConvolutionLayerKernel.cpp
index 672684d..cfd53d7 100644
--- a/src/core/NEON/kernels/NEWinogradConvolutionLayerKernel.cpp
+++ b/src/core/NEON/kernels/NEWinogradConvolutionLayerKernel.cpp
@@ -309,9 +309,9 @@
// Weights transform
template <typename T, int OutputTileRows, int OutputTileCols, int KernelRows, int KernelCols>
-unsigned int NEWinogradLayerTransformWeightsKernel<T, OutputTileRows, OutputTileCols, KernelRows, KernelCols>::get_weight_storage_size(int n_output_channels, int n_input_channels) const
+unsigned int NEWinogradLayerTransformWeightsKernel<T, OutputTileRows, OutputTileCols, KernelRows, KernelCols>::get_weight_storage_size(int num_output_channels, int num_input_channels) const
{
- const KernelShape shape(n_output_channels, KernelRows, KernelCols, n_input_channels);
+ const KernelShape shape(num_output_channels, KernelRows, KernelCols, num_input_channels);
return static_cast<unsigned int>(
// WinogradConv returns the size in bytes, we divide by `sizeof(T)` to express that in units of T
WinogradConv::get_kernel_storage_size(shape) / sizeof(T));
@@ -319,7 +319,8 @@
template <typename T, int OutputTileRows, int OutputTileCols, int KernelRows, int KernelCols>
NEWinogradLayerTransformWeightsKernel<T, OutputTileRows, OutputTileCols, KernelRows, KernelCols>::NEWinogradLayerTransformWeightsKernel()
- : _transform()
+ : _weights_hwio(nullptr), _output(nullptr), _matrix_stride(0), _num_output_channels(0), _num_input_channels(0)
+
{
}
@@ -333,15 +334,20 @@
void NEWinogradLayerTransformWeightsKernel<T, OutputTileRows, OutputTileCols, KernelRows, KernelCols>::configure(
const ITensor *weights_hwio,
T *const output,
- const int matrix_stride, /** Stride across matrices in the output. */
- const int n_output_channels, /** Number of filters. */
- const int n_input_channels) /** Number of channels in each filter. */
+ const int matrix_stride, /** Stride across matrices in the output. */
+ const int num_output_channels, /** Number of filters. */
+ const int num_input_channels) /** Number of channels in each filter. */
{
- const int matrix_row_stride = roundup(n_output_channels, WinogradConv::N_BLOCK);
- _transform = support::cpp14::make_unique<WeightsTransform>(reinterpret_cast<T *>(weights_hwio->buffer()), output, matrix_stride, matrix_row_stride, n_output_channels,
- n_input_channels);
- Window win;
- auto win_last = _transform->get_window();
+ _weights_hwio = weights_hwio;
+ _output = output;
+ _matrix_stride = matrix_stride;
+ _num_output_channels = num_output_channels;
+ _num_input_channels = num_input_channels;
+
+ const int matrix_row_stride = roundup(num_output_channels, WinogradConv::N_BLOCK);
+ WeightsTransform transform(nullptr, output, matrix_stride, matrix_row_stride, num_output_channels, num_input_channels);
+ Window win;
+ auto win_last = transform.get_window();
win.set(Window::DimX, Window::Dimension(0, win_last, 1));
INEKernel::configure(win);
}
@@ -351,9 +357,12 @@
{
ARM_COMPUTE_UNUSED(info);
ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this);
- const size_t fst = window.x().start();
- const size_t lst = window.x().end();
- _transform->run(fst, lst);
+
+ const int matrix_row_stride = roundup(_num_output_channels, WinogradConv::N_BLOCK);
+ WeightsTransform transform(reinterpret_cast<T *>(_weights_hwio->buffer()), _output, _matrix_stride, matrix_row_stride, _num_output_channels, _num_input_channels);
+ const size_t fst = window.x().start();
+ const size_t lst = window.x().end();
+ transform.run(fst, lst);
}
template <typename T, int OutputTileRows, int OutputTileCols, int KernelRows, int KernelCols>
@@ -379,16 +388,16 @@
template <typename T, int OutputTileRows, int OutputTileCols, int KernelRows, int KernelCols>
unsigned int NEWinogradLayerTransformInputKernel<T, OutputTileRows, OutputTileCols, KernelRows, KernelCols>::get_input_storage_size(
- int n_batches, /** Number of batches in the input tensor. */
- int n_channels, /** Number of feature maps in the input tensor. */
- int n_rows, /** Number of rows in each feature map. */
- int n_cols, /** Number of columns in each feature map. */
- bool same_padding /** Use "SAME" padding, otherwise use "VALID". */
+ int num_batches, /* Number of batches in the input tensor. */
+ int num_channels, /* Number of feature maps in the input tensor. */
+ int num_rows, /* Number of rows in each feature map. */
+ int num_cols, /* Number of columns in each feature map. */
+ bool same_padding /* Use "SAME" padding, otherwise use "VALID". */
) const
{
// Construct shapes for the input and kernel tensors.
- const Tensor4DShape input_shape(n_batches, n_rows, n_cols, n_channels);
- const KernelShape kern_shape(1, KernelRows, KernelCols, n_channels);
+ const Tensor4DShape input_shape(num_batches, num_rows, num_cols, num_channels);
+ const KernelShape kern_shape(1, KernelRows, KernelCols, num_channels);
const PaddingType padding = (same_padding) ? PADDING_SAME : PADDING_VALID;
// Return the size, converted into units of TIn
return static_cast<unsigned int>(WinogradConv::get_input_storage_size(kern_shape, input_shape, padding) / sizeof(T));
@@ -403,25 +412,32 @@
template <typename T, int OutputTileRows, int OutputTileCols, int KernelRows, int KernelCols>
NEWinogradLayerTransformInputKernel<T, OutputTileRows, OutputTileCols, KernelRows, KernelCols>::NEWinogradLayerTransformInputKernel()
- : _transform()
+ : _input_nhwc(), _num_batches(0), _num_rows(0), _num_cols(0), _num_channels(0), _padding(), _output(nullptr), _matrix_stride(0)
{
}
template <typename T, int OutputTileRows, int OutputTileCols, int KernelRows, int KernelCols>
void NEWinogradLayerTransformInputKernel<T, OutputTileRows, OutputTileCols, KernelRows, KernelCols>::configure(
- const T *const input, /** Input tensor data */
- const int n_batches, /** Number of batches in input tensor. */
- const int n_rows, /** Number of rows in input tensor. */
- const int n_cols, /** Number of columns in input tensor. */
- const int n_channels, /** Number of channels in input tensor. */
- const PaddingType padding, /** Padding type. */
- T *const output, /** Base of output matrices. */
- const int matrix_stride) /** Stride between output matrices. */
+ const ITensor *input_nhwc,
+ const int num_batches, /* Number of batches in input tensor. */
+ const int num_rows, /* Number of rows in input tensor. */
+ const int num_cols, /* Number of columns in input tensor. */
+ const int num_channels, /* Number of channels in input tensor. */
+ const PaddingType padding, /* Padding type. */
+ T *const output, /* Base of output matrices. */
+ const int matrix_stride) /* Stride between output matrices. */
{
- // _input_matrix_row_stride(n_input_channels),
- _transform = support::cpp14::make_unique<InputTransform>(input, n_batches, n_rows, n_cols, n_channels, padding, output, matrix_stride, n_channels);
- Window win;
- auto win_last = _transform->get_window();
+ _input_nhwc = input_nhwc;
+ _num_batches = num_batches;
+ _num_rows = num_rows;
+ _num_cols = num_cols;
+ _num_channels = num_channels;
+ _padding = padding;
+ _output = output;
+ _matrix_stride = matrix_stride;
+ InputTransform transform(nullptr, num_batches, num_rows, num_cols, num_channels, padding, output, matrix_stride, num_channels);
+ Window win;
+ auto win_last = transform.get_window();
win.set(Window::DimX, Window::Dimension(0, win_last, 1));
INEKernel::configure(win);
}
@@ -431,9 +447,13 @@
{
ARM_COMPUTE_UNUSED(info);
ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this);
+
+ InputTransform input_transform(reinterpret_cast<const T *>(_input_nhwc->buffer()), _num_batches, _num_rows, _num_cols, _num_channels, _padding, _output, _matrix_stride, _num_channels);
+
+ // The code below cannot be moved to configure because biases hasn't been allocated at that point
const size_t fst = window.x().start();
const size_t lst = window.x().end();
- _transform->run(fst, lst);
+ input_transform.run(fst, lst);
}
template <typename T, int OutputTileRows, int OutputTileCols, int KernelRows, int KernelCols>
@@ -453,16 +473,16 @@
template <typename T, int OutputTileRows, int OutputTileCols, int KernelRows, int KernelCols>
unsigned int NEWinogradLayerTransformOutputKernel<T, OutputTileRows, OutputTileCols, KernelRows, KernelCols>::get_output_storage_size(
- int n_batches, /** Number of batches in the output tensor. */
- int n_rows, /** Number of rows in each feature map of the input tensor. */
- int n_cols, /** Number of columns in each feature map of the input tensor. */
- int n_output_channels, /** Number of feature maps in the output tensor. */
- bool same_padding /** Use "SAME" padding, otherwise use "VALID". */
+ int num_batches, /* Number of batches in the output tensor. */
+ int num_rows, /* Number of rows in each feature map of the input tensor. */
+ int num_cols, /* Number of columns in each feature map of the input tensor. */
+ int num_output_channels, /* Number of feature maps in the output tensor. */
+ bool same_padding /* Use "SAME" padding, otherwise use "VALID". */
) const
{
// Construct shapes for the input and kernel tensors.
- const Tensor4DShape input_shape(n_batches, n_rows, n_cols, 1);
- const KernelShape kern_shape(n_output_channels, KernelRows, KernelCols, 1);
+ const Tensor4DShape input_shape(num_batches, num_rows, num_cols, 1);
+ const KernelShape kern_shape(num_output_channels, KernelRows, KernelCols, 1);
const PaddingType padding = (same_padding) ? PADDING_SAME : PADDING_VALID;
// Return the size, converted into units of TOut
@@ -472,7 +492,7 @@
template <typename T, int OutputTileRows, int OutputTileCols, int KernelRows, int KernelCols>
NEWinogradLayerTransformOutputKernel<T, OutputTileRows, OutputTileCols, KernelRows, KernelCols>::NEWinogradLayerTransformOutputKernel()
- : _biases(nullptr), _output_workspace(nullptr), _matrix_stride(0), _matrix_row_stride(0), _output(nullptr), _n_batches(0), _n_rows(0), _n_cols(0), _n_channels(0)
+ : _biases(nullptr), _output_workspace(nullptr), _matrix_stride(0), _matrix_row_stride(0), _output_nhwc(nullptr), _num_batches(0), _num_rows(0), _num_cols(0), _num_channels(0)
{
}
@@ -494,24 +514,24 @@
const ITensor *biases,
const T *const output_workingspace,
const int matrix_stride,
- T *const output,
- const int n_batches,
- const int n_rows,
- const int n_cols,
- const int n_channels)
+ ITensor *const output_nhwc,
+ const int num_batches,
+ const int num_rows,
+ const int num_cols,
+ const int num_channels)
{
_biases = biases;
_output_workspace = output_workingspace;
_matrix_stride = matrix_stride;
- _matrix_row_stride = roundup(n_channels, WinogradConv::N_BLOCK);
- _output = output;
- _n_batches = n_batches;
- _n_rows = n_rows;
- _n_cols = n_cols;
- _n_channels = n_channels;
+ _matrix_row_stride = roundup(num_channels, WinogradConv::N_BLOCK);
+ _output_nhwc = output_nhwc;
+ _num_batches = num_batches;
+ _num_rows = num_rows;
+ _num_cols = num_cols;
+ _num_channels = num_channels;
// We don't have the biases buffer at this stage as it hasn't been allocated, we pass in nullptr OutputTransform is only used here to compute the window
- OutputTransform output_transform(_output_workspace, _matrix_stride, _matrix_row_stride, nullptr, _output, _n_batches, _n_rows, _n_cols, _n_channels);
+ OutputTransform output_transform(_output_workspace, _matrix_stride, _matrix_row_stride, nullptr, nullptr, _num_batches, _num_rows, _num_cols, _num_channels);
Window win;
auto win_last = output_transform.get_window();
win.set(Window::DimX, Window::Dimension(0, win_last, 1));
@@ -524,11 +544,11 @@
ARM_COMPUTE_UNUSED(info);
ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this);
ARM_COMPUTE_ERROR_ON_NULLPTR(_output_workspace);
- ARM_COMPUTE_ERROR_ON_NULLPTR(_output);
+ ARM_COMPUTE_ERROR_ON_NULLPTR(_output_nhwc);
OutputTransform output_transform(_output_workspace, _matrix_stride, _matrix_row_stride,
- (_biases ? reinterpret_cast<T *>(_biases->buffer()) : nullptr), _output,
- _n_batches, _n_rows, _n_cols, _n_channels);
+ (_biases ? reinterpret_cast<T *>(_biases->buffer()) : nullptr), reinterpret_cast<T *>(_output_nhwc->buffer()),
+ _num_batches, _num_rows, _num_cols, _num_channels);
// The code below cannot be moved to configure because biases hasn't been allocated at that point
const size_t fst = window.x().start();