COMPMID-784: Winograd tramsforms refactoring
1) Removed the example files winograd_layer.hpp/cpp
2) Teplatized winograd transform kernels
Change-Id: I7045fa0b801b9d30a11275914aaa2dafd254aed2
Reviewed-on: https://eu-gerrit-1.euhpc.arm.com/118332
Tested-by: Jenkins <bsgcomp@arm.com>
Reviewed-by: Anthony Barbier <anthony.barbier@arm.com>
diff --git a/src/core/NEON/kernels/NEWinogradLayerKernel.cpp b/src/core/NEON/kernels/NEWinogradLayerKernel.cpp
index e2e4e40..b0a36ff 100644
--- a/src/core/NEON/kernels/NEWinogradLayerKernel.cpp
+++ b/src/core/NEON/kernels/NEWinogradLayerKernel.cpp
@@ -29,173 +29,193 @@
#include "arm_compute/core/TensorInfo.h"
#include "support/ToolchainSupport.h"
-#include "arm_compute/core/NEON/kernels/winograd/winograd_layer.hpp"
-
-namespace
-{
-using T = WinogradConvolutionLayer<2, 2, 3, 3, float, float>;
-} // namespace
-
namespace arm_compute
{
-class Winograd3x3F32::Private
-{
-public:
- Private(
- const int n_batches, /** Number of batches in the input and output tensors. */
- const int n_input_channels, /** Number of feature maps in a batch of the input tensor. */
- const int n_input_rows, /** Number of rows in a feature map of the input tensor. */
- const int n_input_cols, /** Number of columns in a feature map of the input tensor. */
- const int n_output_channels, /** Number of feature maps in the output tensor. */
- const bool same_padding, /** Use "SAME" padding, otherwise use "VALID". */
- const float *const weights, /** Pointer to weight tensor in spatial domain. Must be ordered as "Height x Rows x Input Feature Maps x Output Feature Maps. */
- float *const weights_storage, /** Pointer to storage for weight tensor in the Winograd domain. Must be at least the size returned by `get_weight_storage_size`. */
- const float *const input, /** Pointer to NHWC ordered input tensor, in the spatial domain. */
- float *const winograd_input, /** Pointer to working space for the input tensor in the Winograd domain. Must be at least the size returned by `get_input_storage_size`. */
- float *const output, /** Pointer to NHWC ordered output tensor, in the spatial domain. */
- float *const winograd_output /** Pointer to working space for the output tensor in the Winograd domain. Must be at least the size returned by `get_output_storage_size`. */
- )
- : convolver(n_batches, n_input_channels, n_input_rows, n_input_cols, n_output_channels, same_padding, weights, weights_storage, input, winograd_input, nullptr, output, winograd_output)
- {
- }
- T convolver;
-};
-
-Winograd3x3F32::~Winograd3x3F32()
+//Batched Gemms
+template <int OutputTileRows, int OutputTileCols, int KernelRows, int KernelCols>
+NEWinogradLayerKernel<OutputTileRows, OutputTileCols, KernelRows, KernelCols>::NEWinogradLayerKernel()
+ : _gemms()
{
}
-Winograd3x3F32::Winograd3x3F32(
- const int n_batches, /** Number of batches in the input and output tensors. */
- const int n_input_channels, /** Number of feature maps in a batch of the input tensor. */
- const int n_input_rows, /** Number of rows in a feature map of the input tensor. */
- const int n_input_cols, /** Number of columns in a feature map of the input tensor. */
- const int n_output_channels, /** Number of feature maps in the output tensor. */
- const bool same_padding, /** Use "SAME" padding, otherwise use "VALID". */
- const float *const weights, /** Pointer to weight tensor in spatial domain. Must be ordered as "Height x Rows x Input Feature Maps x Output Feature Maps. */
- float *const weights_storage, /** Pointer to storage for weight tensor in the Winograd domain. Must be at least the size returned by `get_weight_storage_size`. */
- const float *const input, /** Pointer to NHWC ordered input tensor, in the spatial domain. */
- float *const winograd_input, /** Pointer to working space for the input tensor in the Winograd domain. Must be at least the size returned by `get_input_storage_size`. */
- float *const output, /** Pointer to NHWC ordered output tensor, in the spatial domain. */
- float *const winograd_output /** Pointer to working space for the output tensor in the Winograd domain. Must be at least the size returned by `get_output_storage_size`. */
-)
- : _pimpl(support::cpp14::make_unique<Private>(n_batches, n_input_channels, n_input_rows, n_input_cols, n_output_channels, same_padding, weights, weights_storage, input, winograd_input, output,
- winograd_output))
+template <int OutputTileRows, int OutputTileCols, int KernelRows, int KernelCols>
+void NEWinogradLayerKernel<OutputTileRows, OutputTileCols, KernelRows, KernelCols>::configure(
+ const unsigned int n_gemms,
+ const int M, const int K, const int N,
+ const int a_matrix_stride,
+ const int a_row_stride,
+ const int b_matrix_stride,
+ const int b_row_stride,
+ const int c_matrix_stride,
+ const int c_row_stride,
+ const float *const a_ptr,
+ const float *const b_ptr,
+ float *const c_ptr)
{
-}
-
-unsigned int NEWinogradLayerKernel::get_input_storage_size(const int n_batches, const int n_channels, const int n_rows, const int n_cols, const bool same_padding)
-{
- return T::get_input_storage_size(n_batches, n_channels, n_rows, n_cols, same_padding);
-}
-
-unsigned int NEWinogradLayerKernel::get_output_storage_size(
- const int n_batches, /** Number of batches in the output tensor. */
- const int n_rows, /** Number of rows in each feature map of the input tensor. */
- const int n_cols, /** Number of columns in each feature map of the input tensor. */
- const int n_output_channels, /** Number of feature maps in the output tensor. */
- const bool same_padding /** Use "SAME" padding, otherwise use "VALID". */
-)
-{
- return T::get_output_storage_size(n_batches, n_rows, n_cols, n_output_channels, same_padding);
-}
-
-unsigned int NEWinogradLayerKernel::get_weight_storage_size(const int n_output_channels, const int n_input_channels)
-{
- return T::get_weight_storage_size(n_output_channels, n_input_channels);
-}
-
-NEWinogradLayerKernel::NEWinogradLayerKernel()
- : _convolver(nullptr)
-{
-}
-
-void NEWinogradLayerKernel::configure(Winograd3x3F32 *convolver)
-{
- ARM_COMPUTE_ERROR_ON_NULLPTR(convolver);
- _convolver = convolver;
+ _gemms = support::cpp14::make_unique<MultiGEMM>(n_gemms, M, K, N, a_matrix_stride, a_row_stride, b_matrix_stride, b_row_stride, c_matrix_stride, c_row_stride, a_ptr, b_ptr, c_ptr);
Window win;
- auto win_last = _convolver->_pimpl->convolver.gemms.get_window();
+ auto win_last = _gemms->get_window();
win.set(Window::DimX, Window::Dimension(0, win_last, 1));
INEKernel::configure(win);
}
-void NEWinogradLayerKernel::run(const Window &window, const ThreadInfo &info)
+template <int OutputTileRows, int OutputTileCols, int KernelRows, int KernelCols>
+void NEWinogradLayerKernel<OutputTileRows, OutputTileCols, KernelRows, KernelCols>::run(const Window &window, const ThreadInfo &info)
{
ARM_COMPUTE_UNUSED(info);
ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this);
const size_t first_gemm = window.x().start();
const size_t last_gemm = window.x().end();
- _convolver->_pimpl->convolver.gemms.run(first_gemm, last_gemm);
+ _gemms->run(first_gemm, last_gemm);
}
-INEWinogradLayerTransformKernel::INEWinogradLayerTransformKernel()
- : _convolver(nullptr)
-{
-}
-
-void INEWinogradLayerTransformKernel::configure(Winograd3x3F32 *convolver)
-{
- ARM_COMPUTE_ERROR_ON_NULLPTR(convolver);
- _convolver = convolver;
-}
+template class NEWinogradLayerKernel<2, 2, 3, 3>;
// Weights transform
-void NEWinogradLayerTransformWeightsKernel::configure(Winograd3x3F32 *convolver)
+template <int OutputTileRows, int OutputTileCols, int KernelRows, int KernelCols>
+unsigned int NEWinogradLayerTransformWeightsKernel<OutputTileRows, OutputTileCols, KernelRows, KernelCols>::get_weight_storage_size(int n_output_channels, int n_input_channels)
{
- INEWinogradLayerTransformKernel::configure(convolver);
+ const KernelShape shape(n_output_channels, KernelRows, KernelCols, n_input_channels);
+ return static_cast<unsigned int>(
+ // WinogradConv returns the size in bytes, we divide by `sizeof(float)` to
+ // express that in units of float.
+ WinogradConv::get_kernel_storage_size(shape) / sizeof(float));
+}
+
+template <int OutputTileRows, int OutputTileCols, int KernelRows, int KernelCols>
+NEWinogradLayerTransformWeightsKernel<OutputTileRows, OutputTileCols, KernelRows, KernelCols>::NEWinogradLayerTransformWeightsKernel()
+ : _transform()
+{
+}
+
+template <int OutputTileRows, int OutputTileCols, int KernelRows, int KernelCols>
+void NEWinogradLayerTransformWeightsKernel<OutputTileRows, OutputTileCols, KernelRows, KernelCols>::configure(
+ const ITensor *weights_hwio,
+ float *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_row_stride = roundup(n_output_channels, WinogradConv::N_BLOCK);
+ _transform = support::cpp14::make_unique<WeightsTransform>(reinterpret_cast<float *>(weights_hwio->buffer()), output, matrix_stride, matrix_row_stride, n_output_channels,
+ n_input_channels);
Window win;
- auto win_last = _convolver->_pimpl->convolver.weights_transform.get_window();
+ auto win_last = _transform->get_window();
win.set(Window::DimX, Window::Dimension(0, win_last, 1));
INEKernel::configure(win);
}
-void NEWinogradLayerTransformWeightsKernel::run(const Window &window, const ThreadInfo &info)
+template <int OutputTileRows, int OutputTileCols, int KernelRows, int KernelCols>
+void NEWinogradLayerTransformWeightsKernel<OutputTileRows, OutputTileCols, KernelRows, KernelCols>::run(const Window &window, const ThreadInfo &info)
{
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();
- _convolver->_pimpl->convolver.weights_transform.run(fst, lst);
+ _transform->run(fst, lst);
}
-bool NEWinogradLayerTransformWeightsKernel::is_parallelisable() const
+template <int OutputTileRows, int OutputTileCols, int KernelRows, int KernelCols>
+bool NEWinogradLayerTransformWeightsKernel<OutputTileRows, OutputTileCols, KernelRows, KernelCols>::is_parallelisable() const
{
return false;
}
+template class NEWinogradLayerTransformWeightsKernel<2, 2, 3, 3>;
+
// Input transform
-void NEWinogradLayerTransformInputKernel::configure(Winograd3x3F32 *convolver)
+template <int OutputTileRows, int OutputTileCols, int KernelRows, int KernelCols>
+unsigned int NEWinogradLayerTransformInputKernel<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". */
+)
{
- INEWinogradLayerTransformKernel::configure(convolver);
+ // 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 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(float));
+}
+
+template <int OutputTileRows, int OutputTileCols, int KernelRows, int KernelCols>
+NEWinogradLayerTransformInputKernel<OutputTileRows, OutputTileCols, KernelRows, KernelCols>::NEWinogradLayerTransformInputKernel()
+ : _transform()
+{
+}
+
+template <int OutputTileRows, int OutputTileCols, int KernelRows, int KernelCols>
+void NEWinogradLayerTransformInputKernel<OutputTileRows, OutputTileCols, KernelRows, KernelCols>::configure(
+ const float *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. */
+ float *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 = _convolver->_pimpl->convolver.input_transform.get_window();
+ auto win_last = _transform->get_window();
win.set(Window::DimX, Window::Dimension(0, win_last, 1));
INEKernel::configure(win);
}
-void NEWinogradLayerTransformInputKernel::run(const Window &window, const ThreadInfo &info)
+template <int OutputTileRows, int OutputTileCols, int KernelRows, int KernelCols>
+void NEWinogradLayerTransformInputKernel<OutputTileRows, OutputTileCols, KernelRows, KernelCols>::run(const Window &window, const ThreadInfo &info)
{
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();
- _convolver->_pimpl->convolver.input_transform.run(fst, lst);
+ _transform->run(fst, lst);
}
-bool NEWinogradLayerTransformInputKernel::is_parallelisable() const
+
+template <int OutputTileRows, int OutputTileCols, int KernelRows, int KernelCols>
+bool NEWinogradLayerTransformInputKernel<OutputTileRows, OutputTileCols, KernelRows, KernelCols>::is_parallelisable() const
{
return false;
}
+template class NEWinogradLayerTransformInputKernel<2, 2, 3, 3>;
+
// Output transform
-NEWinogradLayerTransformOutputKernel::NEWinogradLayerTransformOutputKernel()
+
+template <int OutputTileRows, int OutputTileCols, int KernelRows, int KernelCols>
+unsigned int NEWinogradLayerTransformOutputKernel<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". */
+)
+{
+ // 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 PaddingType padding = (same_padding) ? PADDING_SAME : PADDING_VALID;
+
+ // Return the size, converted into units of TOut
+ return static_cast<unsigned int>(
+ WinogradConv::get_output_storage_size(kern_shape, input_shape, padding) / sizeof(float));
+}
+
+template <int OutputTileRows, int OutputTileCols, int KernelRows, int KernelCols>
+NEWinogradLayerTransformOutputKernel<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)
{
}
-void NEWinogradLayerTransformOutputKernel::configure(
+template <int OutputTileRows, int OutputTileCols, int KernelRows, int KernelCols>
+void NEWinogradLayerTransformOutputKernel<OutputTileRows, OutputTileCols, KernelRows, KernelCols>::configure(
const ITensor *biases,
const float *const output_workingspace,
const int matrix_stride,
@@ -205,13 +225,10 @@
const int n_cols,
const int n_channels)
{
- using WinogradBase = winograd::WinogradGEMM<2, 2, 3, 3>;
- using OutputTransform = typename WinogradBase::template OutputTransform<float>;
-
_biases = biases;
_output_workspace = output_workingspace;
_matrix_stride = matrix_stride;
- _matrix_row_stride = roundup(n_channels, WinogradBase::Convolution<float, float>::N_BLOCK);
+ _matrix_row_stride = roundup(n_channels, WinogradConv::N_BLOCK);
_output = output;
_n_batches = n_batches;
_n_rows = n_rows;
@@ -226,7 +243,8 @@
INEKernel::configure(win);
}
-void NEWinogradLayerTransformOutputKernel::run(const Window &window, const ThreadInfo &info)
+template <int OutputTileRows, int OutputTileCols, int KernelRows, int KernelCols>
+void NEWinogradLayerTransformOutputKernel<OutputTileRows, OutputTileCols, KernelRows, KernelCols>::run(const Window &window, const ThreadInfo &info)
{
ARM_COMPUTE_UNUSED(info);
ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this);
@@ -234,9 +252,6 @@
ARM_COMPUTE_ERROR_ON_NULLPTR(_output_workspace);
ARM_COMPUTE_ERROR_ON_NULLPTR(_output);
- using WinogradBase = winograd::WinogradGEMM<2, 2, 3, 3>;
- using OutputTransform = typename WinogradBase::template OutputTransform<float>;
-
OutputTransform output_transform(_output_workspace, _matrix_stride, _matrix_row_stride,
reinterpret_cast<float *>(_biases->buffer()), _output,
_n_batches, _n_rows, _n_cols, _n_channels);
@@ -247,9 +262,12 @@
output_transform.run(fst, lst);
}
-bool NEWinogradLayerTransformOutputKernel::is_parallelisable() const
+template <int OutputTileRows, int OutputTileCols, int KernelRows, int KernelCols>
+bool NEWinogradLayerTransformOutputKernel<OutputTileRows, OutputTileCols, KernelRows, KernelCols>::is_parallelisable() const
{
return false;
}
+template class NEWinogradLayerTransformOutputKernel<2, 2, 3, 3>;
+
} // namespace arm_compute