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
diff --git a/src/core/NEON/kernels/winograd/winograd_gemm.cpp b/src/core/NEON/kernels/winograd/winograd_gemm.cpp
index b45f6f5..0542645 100644
--- a/src/core/NEON/kernels/winograd/winograd_gemm.cpp
+++ b/src/core/NEON/kernels/winograd/winograd_gemm.cpp
@@ -36,8 +36,8 @@
{
return Tensor4DShape {
in_shape.n_batches,
- (padding == PADDING_SAME) ? in_shape.n_rows : in_shape.n_rows - (kernel_rows - 2),
- (padding == PADDING_SAME) ? in_shape.n_cols : in_shape.n_cols - (kernel_cols - 2),
+ (padding == PADDING_SAME) ? in_shape.n_rows : in_shape.n_rows - (kernel_rows - 1),
+ (padding == PADDING_SAME) ? in_shape.n_cols : in_shape.n_cols - (kernel_cols - 1),
kernel_shape.n_output_channels,
in_shape.ordering
};
diff --git a/src/core/NEON/kernels/winograd/winograd_layer.cpp b/src/core/NEON/kernels/winograd/winograd_layer.cpp
deleted file mode 100644
index f16d62c..0000000
--- a/src/core/NEON/kernels/winograd/winograd_layer.cpp
+++ /dev/null
@@ -1,206 +0,0 @@
-/*
- * Copyright (c) 2017 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 "convolution.hpp"
-#include "winograd_layer.hpp"
-#include "tensor.hpp"
-
-
-/** Determine how much memory (in units of TIn) to allocate for the transformed
- * weights.
- */
-template <
- int OutputTileRows, int OutputTileCols, int KernelRows, int KernelCols,
- typename TIn, typename TOut
->
-unsigned int WinogradConvolutionLayer<
- OutputTileRows, OutputTileCols, KernelRows, KernelCols, TIn, TOut
->::get_weight_storage_size(
- const int n_output_channels, /** Number of output feature maps. */
- const int n_input_channels /** Number of input feature maps. */
-)
-{
- 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(TIn)` to
- // express that in units of TIn.
- WinogradConv::get_kernel_storage_size(shape) / sizeof(TIn)
- );
-}
-
-
-/** Determine how much memory (in units of TIn) to allocate for the transformed
- * input.
- */
-template <
- int OutputTileRows, int OutputTileCols, int KernelRows, int KernelCols,
- typename TIn, typename TOut
->
-unsigned int WinogradConvolutionLayer<
- OutputTileRows, OutputTileCols, KernelRows, KernelCols, TIn, TOut
->::get_input_storage_size(
- const int n_batches, /** Number of batches in the input tensor. */
- const int n_channels, /** Number of feature maps in the input tensor. */
- const int n_rows, /** Number of rows in each feature map. */
- const int n_cols, /** Number of columns in each feature map. */
- const 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, 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(TIn)
- );
-}
-
-
-/** Determine how much memory (in units of TOut) to allocate for the (Winograd
- * domain) output.
- */
-template <
- int OutputTileRows, int OutputTileCols, int KernelRows, int KernelCols,
- typename TIn, typename TOut
->
-unsigned int WinogradConvolutionLayer<
- OutputTileRows, OutputTileCols, KernelRows, KernelCols, TIn, TOut
->::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". */
-)
-{
- // 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(TOut)
- );
-}
-
-
-/** Get the shape (rows, cols) of a feature map of the output tensor. */
-template <
- int OutputTileRows, int OutputTileCols, int KernelRows, int KernelCols,
- typename TIn, typename TOut
->
-std::pair<int, int> WinogradConvolutionLayer<
- OutputTileRows, OutputTileCols, KernelRows, KernelCols, TIn, TOut
->::get_output_feature_map_shape(
- const int n_input_rows, /** Number of rows in the input feature map. */
- const int n_input_cols, /** Number of columns in the input feature map. */
- const bool same_padding /** Use "SAME" padding, otherwise use "VALID". */
-)
-{
- // Construct shapes for the input and kernel tensors.
- const Tensor4DShape input_shape(1, n_input_rows, n_input_cols, 1);
- const KernelShape kern_shape(1, KernelRows, KernelCols, 1);
- const PaddingType padding = (same_padding) ? PADDING_SAME : PADDING_VALID;
-
- // Compute the new shape
- const auto output_shape = WinogradConv::get_output_shape(
- kern_shape, input_shape, padding
- );
-
- return std::make_pair(output_shape.n_rows, output_shape.n_cols);
-}
-
-
-/** Create a new Winograd convolution layer.
- */
-template <
- int OutputTileRows, int OutputTileCols, int KernelRows, int KernelCols,
- typename TIn, typename TOut
->
-WinogradConvolutionLayer<OutputTileRows, OutputTileCols, KernelRows, KernelCols, TIn, TOut>::
-WinogradConvolutionLayer(
- 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 TIn* const weights, /** Pointer to weight tensor in spatial domain. Must be ordered as "Height x Rows x Input Feature Maps x Output Feature Maps. */
- TIn* const winograd_weights, /** Pointer to storage for weight tensor in the Winograd domain. Must be at least the size returned by `get_weight_storage_size`. */
- const TIn* const input, /** Pointer to NHWC ordered input tensor, in the spatial domain. */
- TIn* 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`. */
- const TOut* const biases, /** Pointer to biases vector. */
- TOut* const output, /** Pointer to NHWC ordered output tensor, in the spatial domain. */
- TOut* 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`. */
-) : _kernel_shape(n_output_channels, KernelRows, KernelCols, n_input_channels),
- _input_shape(n_batches, n_input_rows, n_input_cols, n_input_channels),
- _padding(same_padding ? PADDING_SAME : PADDING_VALID),
- _output_shape(WinogradConv::get_output_shape(_kernel_shape, _input_shape, _padding)),
- _n_output_rows(_output_shape.n_rows),
- _n_output_cols(_output_shape.n_cols),
- _kernel_matrix_stride(WinogradConv::get_kernel_matrix_stride(_kernel_shape)),
- _kernel_matrix_row_stride(roundup(n_output_channels, WinogradConv::N_BLOCK)),
- _input_matrix_stride(WinogradConv::get_input_matrix_stride(_kernel_shape, _input_shape, _padding)),
- _input_matrix_row_stride(n_input_channels),
- _output_matrix_stride(WinogradConv::get_output_matrix_stride(_kernel_shape, _input_shape, _padding)),
- _output_matrix_row_stride(_kernel_matrix_row_stride),
- _tile_rows(iceildiv(_n_output_rows, OutputTileRows)),
- _tile_cols(iceildiv(_n_output_cols, OutputTileCols)),
- _m(n_batches * _tile_rows * _tile_cols),
- _k(n_input_channels),
- _n(n_output_channels),
- weights_transform(
- weights, winograd_weights,
- _kernel_matrix_stride, _kernel_matrix_row_stride,
- n_output_channels, n_input_channels
- ),
- input_transform(
- input, n_batches, n_input_rows, n_input_cols, n_input_channels, _padding,
- winograd_input, _input_matrix_stride, _input_matrix_row_stride
- ),
- gemms(
- WinogradBase::N_GEMMS, _m, _k, _n,
- _input_matrix_stride, _input_matrix_row_stride,
- _kernel_matrix_stride, _kernel_matrix_row_stride,
- _output_matrix_stride, _output_matrix_row_stride,
- winograd_input, winograd_weights, winograd_output
- ),
- output_transform(
- winograd_output, _output_matrix_stride, _output_matrix_row_stride, biases,
- output, n_batches, _n_output_rows, _n_output_cols, n_output_channels
- )
-{
-}
-
-// Instantiate valid implementations.
-template class WinogradConvolutionLayer<2, 2, 3, 3, float, float>;
-template class WinogradConvolutionLayer<4, 4, 3, 3, float, float>;
-template class WinogradConvolutionLayer<2, 2, 5, 5, float, float>;
diff --git a/src/runtime/NEON/functions/NEWinogradLayer.cpp b/src/runtime/NEON/functions/NEWinogradLayer.cpp
index e8c7741..6196c51 100644
--- a/src/runtime/NEON/functions/NEWinogradLayer.cpp
+++ b/src/runtime/NEON/functions/NEWinogradLayer.cpp
@@ -46,7 +46,7 @@
{
NEWinogradLayer::NEWinogradLayer(std::shared_ptr<IMemoryManager> memory_manager)
: _memory_group(std::move(memory_manager)), _winograd_kernel(), _transform_input_kernel(), _transform_output_kernel(), _transform_weights_kernel(), _permute_input(), _permute_weights(),
- _permute_output(), _input_workspace(), _output_workspace(), _kernel_storage(), _input_nhwc(), _output_nhwc(), _weights_hwio(), _input(), _weights(), _output(), _reshaped_kernel(false), _conv()
+ _permute_output(), _input_workspace(), _output_workspace(), _kernel_storage(), _input_nhwc(), _output_nhwc(), _weights_hwio(), _input(), _weights(), _output(), _reshaped_kernel(false)
{
} /* arm_compute */
@@ -81,19 +81,23 @@
const size_t data_type_size = input->info()->element_size();
// Get the memory required to instantiate a new Winograd operator.
constexpr size_t storage_alignment = 64;
- const size_t kernel_storage_size = NEWinogradLayerKernel::get_weight_storage_size(out_channels, in_channels) * data_type_size;
+ const size_t kernel_storage_size = NEWinogradLayerTransformWeightsKernel<2, 2, 3, 3>::get_weight_storage_size(out_channels, in_channels) * data_type_size;
_kernel_storage.allocator()->init(TensorInfo(TensorShape{ (kernel_storage_size + storage_alignment - 1) }, 1, DataType::U8));
_memory_group.manage(&_kernel_storage);
_memory_group.manage(&_input_nhwc);
_kernel_storage.allocator()->allocate();
// Input storage
- const size_t input_storage_size = NEWinogradLayerKernel::get_input_storage_size(in_shape.n_batches, in_shape.n_channels, in_shape.n_rows, in_shape.n_cols, false) * data_type_size;
+ const size_t input_storage_size = NEWinogradLayerTransformInputKernel<2, 2, 3, 3>::get_input_storage_size(in_shape.n_batches, in_shape.n_channels, in_shape.n_rows, in_shape.n_cols,
+ false)
+ * data_type_size;
_input_workspace.allocator()->init(TensorInfo(TensorShape{ (input_storage_size + storage_alignment - 1) }, 1, DataType::U8));
_memory_group.manage(&_input_workspace);
_input_workspace.allocator()->allocate();
// Output storage
- const size_t output_storage_size = NEWinogradLayerKernel::get_output_storage_size(in_shape.n_batches, in_shape.n_rows, in_shape.n_cols, out_channels, false) * data_type_size;
+ const size_t output_storage_size = NEWinogradLayerTransformOutputKernel<2, 2, 3, 3>::get_output_storage_size(in_shape.n_batches, in_shape.n_rows, in_shape.n_cols, out_channels,
+ false)
+ * data_type_size;
_output_workspace.allocator()->init(TensorInfo(TensorShape{ (output_storage_size + storage_alignment - 1) }, 1, DataType::U8));
_memory_group.manage(&_output_workspace);
_output_workspace.allocator()->allocate();
@@ -132,38 +136,46 @@
_permute_input.configure(input, &_input_nhwc, PermutationVector(2U, 0U, 1U));
_input_nhwc.allocator()->allocate();
- // Create Winograd operator object
- _conv = support::cpp14::make_unique<Winograd3x3F32>(
- in_shape.n_batches,
- in_shape.n_channels,
- in_shape.n_rows,
- in_shape.n_cols,
- out_channels,
- false,
- reinterpret_cast<const float *>(_weights_hwio.buffer()),
- reinterpret_cast<float *>(_kernel_storage.buffer()),
- reinterpret_cast<float *>(_input_nhwc.buffer()),
- reinterpret_cast<float *>(_input_workspace.buffer()),
- reinterpret_cast<float *>(_output_nhwc.buffer()),
- reinterpret_cast<float *>(_output_workspace.buffer()));
- // Configure the kernel, padding not needed so it's safe to call configure after allocare
- _winograd_kernel.configure(_conv.get());
- _transform_input_kernel.configure(_conv.get());
- _transform_weights_kernel.configure(_conv.get());
-
- //The biases tensor has not been allocated at this point in time, the output transform will add the biases to the final result in the run() method
using T = winograd::WinogradGEMM<2, 2, 3, 3>::Convolution<float, float>;
const int weights_width = weights->info()->dimension(0);
const int weights_height = weights->info()->dimension(1);
const KernelShape kernel_shape({ out_channels, weights_height, weights_width, in_channels });
- const int output_matrix_stride = T::get_output_matrix_stride(kernel_shape, in_shape, PADDING_VALID);
- const auto output_shape(T::get_output_shape(kernel_shape, in_shape, PADDING_VALID));
+
+ // Configure the InputTransform
+ const int input_matrix_stride = T::get_input_matrix_stride(kernel_shape, in_shape, PADDING_VALID);
+ _transform_input_kernel.configure(reinterpret_cast<float *>(_input_nhwc.buffer()), in_shape.n_batches, in_shape.n_rows, in_shape.n_cols, in_shape.n_channels, PADDING_VALID,
+ reinterpret_cast<float *>(_input_workspace.buffer()), input_matrix_stride);
+
+ // Configure WeightsTransform
+ const int kernel_matrix_stride = T::get_kernel_matrix_stride(kernel_shape);
+ _transform_weights_kernel.configure(&_weights_hwio, reinterpret_cast<float *>(_kernel_storage.buffer()), kernel_matrix_stride, out_channels, in_channels);
+
+ // Configure OutputTransform
+ //The biases tensor has not been allocated at this point in time, the output transform will add the biases to the final result in the run() method
+ const int output_matrix_stride = T::get_output_matrix_stride(kernel_shape, in_shape, PADDING_VALID);
+ const auto output_shape(T::get_output_shape(kernel_shape, in_shape, PADDING_VALID));
_transform_output_kernel.configure(biases, reinterpret_cast<float *>(_output_workspace.buffer()),
output_matrix_stride, reinterpret_cast<float *>(_output_nhwc.buffer()),
in_shape.n_batches, output_shape.n_rows, output_shape.n_cols, out_channels);
+ // Configure Batched GEMMs
+ const int tile_rows = iceildiv(output_shape.n_rows, NEWinogradLayerKernel<2, 2, 3, 3>::_output_tile_rows);
+ const int tile_cols = iceildiv(output_shape.n_cols, NEWinogradLayerKernel<2, 2, 3, 3>::_output_tile_cols);
+ const int m = in_shape.n_batches * tile_rows * tile_cols;
+ const int k = in_shape.n_channels;
+ const int n = out_channels;
+ const int input_matrix_row_stride = in_shape.n_channels;
+ const int kernel_matrix_row_stride = roundup(out_channels, NEWinogradLayerKernel<2, 2, 3, 3>::WinogradConv::N_BLOCK);
+ const int output_matrix_row_stride = kernel_matrix_row_stride;
+
+ _winograd_kernel.configure(NEWinogradLayerKernel<2, 2, 3, 3>::WinogradBase::N_GEMMS, m, k, n,
+ input_matrix_stride, input_matrix_row_stride,
+ kernel_matrix_stride, kernel_matrix_row_stride,
+ output_matrix_stride, output_matrix_row_stride,
+ reinterpret_cast<float *>(_input_workspace.buffer()), reinterpret_cast<float *>(_kernel_storage.buffer()), reinterpret_cast<float *>(_output_workspace.buffer()));
+
// Reorder the convoluted output to ACL's ordering NCHW
_permute_output.configure(&_output_nhwc, _output, PermutationVector(1U, 2U, 0U));
}