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-rw-r--r--src/cpu/kernels/CpuWinogradConv2dKernel.cpp568
1 files changed, 66 insertions, 502 deletions
diff --git a/src/cpu/kernels/CpuWinogradConv2dKernel.cpp b/src/cpu/kernels/CpuWinogradConv2dKernel.cpp
index 803af09a67..818d878119 100644
--- a/src/cpu/kernels/CpuWinogradConv2dKernel.cpp
+++ b/src/cpu/kernels/CpuWinogradConv2dKernel.cpp
@@ -1,5 +1,5 @@
/*
- * Copyright (c) 2017-2021 Arm Limited.
+ * Copyright (c) 2017-2022 Arm Limited.
*
* SPDX-License-Identifier: MIT
*
@@ -21,531 +21,95 @@
* OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
* SOFTWARE.
*/
-#include "src/cpu/kernels/CpuWinogradConv2dKernel.h"
-
-#include "arm_compute/core/Error.h"
-#include "arm_compute/core/Helpers.h"
-#include "arm_compute/core/ITensor.h"
-#include "arm_compute/core/TensorInfo.h"
-#include "arm_compute/core/Validate.h"
-#include "arm_compute/core/Window.h"
-#include "arm_compute/core/utils/misc/ShapeCalculator.h"
-#include "src/core/NEON/kernels/convolution/common/utils.hpp"
-#include "src/core/NEON/kernels/convolution/winograd/winograd_layer.hpp"
-#include "src/core/helpers/AutoConfiguration.h"
-#include "src/core/helpers/WindowHelpers.h"
-#include <memory>
+#include "src/cpu/kernels/CpuWinogradConv2dKernel.h"
namespace arm_compute
{
namespace cpu
{
-//Batched Gemms
-
-namespace
-{
-inline bool is_kernel_size_supported(DataType data_type, Size2D size)
+CpuWinogradConv2dTransformInputKernel::CpuWinogradConv2dTransformInputKernel(arm_conv::winograd::WinogradImpl &w_impl, arm_conv::ConvolutionArgs &_c_args, uint32_t nthreads)
+ : _winograd_impl{ w_impl }, _conv_args{ _c_args }, _nthreads{ nthreads }
{
- const std::array<Size2D, 8> f32_support = { { Size2D(1, 3), Size2D(3, 1), Size2D(5, 5), Size2D(3, 3), Size2D(1, 5), Size2D(5, 1), Size2D(7, 1), Size2D(1, 7) } };
- const std::array<Size2D, 8> f16_support = { { Size2D(3, 3) } };
-
- switch(data_type)
- {
- case DataType::F16:
- return std::end(f16_support) != std::find(std::begin(f16_support), std::end(f16_support), size);
- case DataType::F32:
- return std::end(f32_support) != std::find(std::begin(f32_support), std::end(f32_support), size);
- default:
- return false;
- }
}
-Status validate_arguments_winograd_weight_trans(const ITensorInfo *input, const ITensorInfo *output, const WinogradInfo &winograd_info)
+void CpuWinogradConv2dTransformInputKernel::run_op(ITensorPack &tensors, const Window &window, const ThreadInfo &info)
{
- ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(input);
- ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(output);
- ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::F16, DataType::F32);
+ ARM_COMPUTE_UNUSED(window);
+ const ITensor *input_nhwc = tensors.get_const_tensor(TensorType::ACL_SRC);
+ const ITensor *winograd_input_transform = tensors.get_const_tensor(TensorType::ACL_DST);
+ const ITensor *workspace = tensors.get_const_tensor(TensorType::ACL_INT);
- 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);
- const auto input_width = input->dimension(idx_width);
- const auto input_height = input->dimension(idx_height);
- ARM_COMPUTE_RETURN_ERROR_ON_MSG(!is_kernel_size_supported(input->data_type(), Size2D(input_width, input_height)),
- "Only 1x3, 3x1, 1x5, 5x1, 7x1, 1x7, 3x3 and 5x5 kernels are supported");
- ARM_COMPUTE_RETURN_ERROR_ON(input->num_dimensions() > 4);
- const Size2D &output_tile = winograd_info.output_tile_size;
- const std::array<Size2D, 8> supported_tile_sizes = { { Size2D(2U, 2U), Size2D(4U, 4U), Size2D(1U, 6U), Size2D(6U, 1U), Size2D(4, 1), Size2D(1, 4), Size2D(2, 1), Size2D(1, 2) } };
- ARM_COMPUTE_RETURN_ERROR_ON(std::end(supported_tile_sizes) == std::find(std::begin(supported_tile_sizes), std::end(supported_tile_sizes), output_tile));
+ const unsigned int width_idx = 1;
+ const unsigned int height_idx = 2;
+ const unsigned int batch_idx = 3;
+ int element_size_in_bytes = input_nhwc->info()->element_size();
+ const auto src_strides = input_nhwc->info()->strides_in_bytes();
- // Checks performed when output is configured
- if(output->total_size() != 0)
- {
- const TensorInfo tensor_info_output = input->clone()->set_tensor_shape(arm_compute::misc::shape_calculator::compute_winograd_filter_transform_shape(*input, winograd_info));
+ const size_t input_row_stride = src_strides[height_idx] / element_size_in_bytes;
+ const size_t input_col_stride = src_strides[width_idx] / element_size_in_bytes;
+ const size_t input_batch_stride = src_strides[batch_idx] / element_size_in_bytes;
+ const auto input_nhwc_ptr = reinterpret_cast<const void *>(input_nhwc->buffer() + input_nhwc->info()->offset_first_element_in_bytes());
+ auto win_transf_ptr = reinterpret_cast<void *>(winograd_input_transform->buffer() + winograd_input_transform->info()->offset_first_element_in_bytes());
- ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES(output, &tensor_info_output);
- ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input, output);
- }
-
- return Status{};
+ _winograd_impl.input_transform->execute(
+ _conv_args,
+ input_nhwc_ptr,
+ input_batch_stride,
+ input_row_stride,
+ input_col_stride,
+ win_transf_ptr,
+ _winograd_impl.winograd_spec,
+ workspace->buffer(),
+ info.thread_id,
+ _nthreads);
}
-std::pair<Status, Window> validate_and_configure_window_winograd_weight_trans(ITensorInfo *input, ITensorInfo *output, const WinogradInfo &winograd_info)
+CpuWinogradConv2dTransformOutputKernel::CpuWinogradConv2dTransformOutputKernel(arm_conv::winograd::WinogradImpl &w_impl, arm_conv::ConvolutionArgs &_c_args, uint32_t nthreads)
+ : _winograd_impl{ w_impl }, _conv_args{ _c_args }, _nthreads{ nthreads }
{
- // Output tensor auto inizialitation if not yet initialized
- auto_init_if_empty(*output, input->clone()->set_tensor_shape(arm_compute::misc::shape_calculator::compute_winograd_filter_transform_shape(*input, winograd_info)));
- const Window win = calculate_max_window(*input, Steps(), true /* skip border*/);
- return std::make_pair(Status{}, win);
}
-Status validate_arguments_winograd_input_trans(const ITensorInfo *input, const ITensorInfo *output, const WinogradInfo &winograd_info)
+// Inherited methods overridden:
+void CpuWinogradConv2dTransformOutputKernel::run_op(ITensorPack &tensors, const Window &window, const ThreadInfo &info)
{
- const Size2D &kernel_dims = winograd_info.kernel_size;
- const PadStrideInfo &conv_info = winograd_info.convolution_info;
- ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(input);
- ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(output);
- ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::F16, DataType::F32);
- ARM_COMPUTE_RETURN_ERROR_ON_MSG(conv_info.stride().first != 1 || conv_info.stride().second != 1, "Winograd input transform only supports unit strides");
- ARM_COMPUTE_RETURN_ERROR_ON_MSG(!is_kernel_size_supported(input->data_type(), Size2D(kernel_dims.width, kernel_dims.height)),
- "Only 1x3, 3x1, 3x3 and 5x5 kernels are supported");
-
- // Validate configured output
- if(output->total_size() != 0)
- {
- const TensorShape output_shape = misc::shape_calculator::compute_winograd_input_transform_shape(*input, winograd_info);
+ ARM_COMPUTE_UNUSED(window);
+ const ITensor *dst_nhwc = tensors.get_const_tensor(TensorType::ACL_DST);
+ const ITensor *winograd_output_transform = tensors.get_const_tensor(TensorType::ACL_SRC_0);
+ const ITensor *biases = tensors.get_const_tensor(TensorType::ACL_SRC_1);
+ const ITensor *workspace = tensors.get_tensor(TensorType::ACL_INT);
- ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DIMENSIONS(output->tensor_shape(), output_shape);
- ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input, output);
- }
-
- return Status{};
-}
+ const unsigned int width_idx = 1;
+ const unsigned int height_idx = 2;
+ const unsigned int batch_idx = 3;
+ const int element_size_in_bytes = dst_nhwc->info()->element_size();
+ const auto dst_strides = dst_nhwc->info()->strides_in_bytes();
-std::pair<Status, Window> validate_and_configure_window_winograd_input_trans(ITensorInfo *input, ITensorInfo *output, const WinogradInfo &winograd_info)
-{
- const TensorShape output_shape = misc::shape_calculator::compute_winograd_input_transform_shape(*input, winograd_info);
- // Output auto inizialitation if not yet initialized
- auto_init_if_empty(*output, input->clone()->set_tensor_shape(output_shape));
- return std::make_pair(Status{}, calculate_max_window(*input, Steps(), true));
-}
-
-Status validate_arguments_winograd_output_trans(const ITensorInfo *input, const ITensorInfo *bias, const ITensorInfo *output, const WinogradInfo &winograd_info)
-{
- const PadStrideInfo &conv_info = winograd_info.convolution_info;
- const Size2D kernel_dims = winograd_info.kernel_size;
-
- // Number of tiles along the X and Y direction
- const unsigned int num_tiles_x = std::ceil((winograd_info.input_dimensions.x() - (kernel_dims.width - 1) + conv_info.pad_left() + conv_info.pad_right()) / static_cast<float>
- (winograd_info.output_tile_size.width));
- const unsigned int num_tiles_y = std::ceil((winograd_info.input_dimensions.y() - (kernel_dims.height - 1) + conv_info.pad_top() + conv_info.pad_bottom()) / static_cast<float>
- (winograd_info.output_tile_size.height));
- const Size2D num_tiles = Size2D(num_tiles_x, num_tiles_y);
-
- ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(input);
- ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(output);
- ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::F16, DataType::F32);
- ARM_COMPUTE_RETURN_ERROR_ON(input->dimension(1) != num_tiles.area());
- ARM_COMPUTE_RETURN_ERROR_ON_MSG(!is_kernel_size_supported(input->data_type(), Size2D(kernel_dims.width, kernel_dims.height)),
- "Only 1x3, 3x1, 3x3 and 5x5 kernels are supported");
-
- const std::array<unsigned int, 3> supported_gemm_sizes = { { 8U, 16U, 36U } };
- ARM_COMPUTE_RETURN_ERROR_ON(std::end(supported_gemm_sizes) == std::find(std::begin(supported_gemm_sizes), std::end(supported_gemm_sizes), input->dimension(2)));
- ARM_COMPUTE_UNUSED(kernel_dims);
- if(bias != nullptr)
- {
- ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input, bias);
- ARM_COMPUTE_RETURN_ERROR_ON(input->dimension(0) != bias->dimension(0));
- ARM_COMPUTE_RETURN_ERROR_ON(bias->num_dimensions() != size_t(1));
- }
-
- // Checks performed when output is configured
- if(output->total_size() != 0)
+ const size_t out_row_stride = dst_strides[height_idx] / element_size_in_bytes;
+ const size_t out_col_stride = dst_strides[width_idx] / element_size_in_bytes;
+ const size_t out_batch_stride = dst_strides[batch_idx] / element_size_in_bytes;
+ const auto wout_transf_ptr = reinterpret_cast<const void *>(winograd_output_transform->buffer() + winograd_output_transform->info()->offset_first_element_in_bytes());
+ auto dst_nhwc_ptr = reinterpret_cast<void *>(dst_nhwc->buffer() + dst_nhwc->info()->offset_first_element_in_bytes());
+ void *biases_data_ptr = nullptr;
+ if(biases != nullptr)
{
- const TensorInfo tensor_info_output = input->clone()->set_tensor_shape(arm_compute::misc::shape_calculator::compute_winograd_output_transform_shape(*input, winograd_info));
- ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES(output, &tensor_info_output);
- ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input, output);
+ biases_data_ptr = reinterpret_cast<void *>(biases->buffer() + biases->info()->offset_first_element_in_bytes());
}
- return Status{};
-}
-
-std::pair<Status, Window> validate_and_configure_window_winograd_output_trans(ITensorInfo *input, ITensorInfo *output, const WinogradInfo &winograd_info)
-{
- // Output tensor auto initialization if not yet initialized
- auto_init_if_empty(*output, input->clone()->set_tensor_shape(arm_compute::misc::shape_calculator::compute_winograd_output_transform_shape(*input, winograd_info)));
-
- return std::make_pair(Status{}, calculate_max_window(*input, Steps(), true));
-}
-} // namespace
-
-Status ICpuWinogradConv2dTransformWeightsKernel::validate(const ITensorInfo *input, const ITensorInfo *weights)
-{
- ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::F16, DataType::F32);
- ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input, weights);
- const DataLayout data_layout = input->data_layout();
- const unsigned int width_idx = get_data_layout_dimension_index(data_layout, DataLayoutDimension::WIDTH);
- const unsigned int height_idx = get_data_layout_dimension_index(data_layout, DataLayoutDimension::HEIGHT);
- ARM_COMPUTE_RETURN_ERROR_ON_MSG(!is_kernel_size_supported(input->data_type(), Size2D(weights->dimension(width_idx), weights->dimension(height_idx))),
- "Only 1x3, 3x1, 3x3 and 5x5 kernels are supported");
- ARM_COMPUTE_RETURN_ERROR_ON(weights->num_dimensions() > 4);
- return Status{};
-}
-
-template <typename T, int OutputTileRows, int OutputTileCols, int KernelRows, int KernelCols>
-unsigned int CpuWinogradConv2dTransformWeightsKernel<T, OutputTileRows, OutputTileCols, KernelRows, KernelCols>::get_weight_storage_size(int num_output_channels, int num_input_channels) const
-{
- const KernelShape shape(num_output_channels, KernelRows, KernelCols, num_input_channels);
- // WinogradConv returns the size in bytes, we divide by `sizeof(T)` to express that in units of T
- return static_cast<unsigned int>(WinogradConv::get_kernel_storage_size(num_input_channels, num_output_channels) / sizeof(T));
-}
-
-template <typename T, int OutputTileRows, int OutputTileCols, int KernelRows, int KernelCols>
-CpuWinogradConv2dTransformWeightsKernel<T, OutputTileRows, OutputTileCols, KernelRows, KernelCols>::CpuWinogradConv2dTransformWeightsKernel()
- : _transform(nullptr), _num_output_channels(0), _matrix_stride(0)
-{
-}
-
-template <typename T, int OutputTileRows, int OutputTileCols, int KernelRows, int KernelCols>
-int CpuWinogradConv2dTransformWeightsKernel<T, OutputTileRows, OutputTileCols, KernelRows, KernelCols>::get_matrix_stride(int num_output_channels, int num_input_channels) const
-{
- return WinogradConv::get_kernel_matrix_stride(num_input_channels, num_output_channels);
-}
-
-#ifndef DOXYGEN_SKIP_THIS
-template <typename T, int OutputTileRows, int OutputTileCols, int KernelRows, int KernelCols>
-void CpuWinogradConv2dTransformWeightsKernel<T, OutputTileRows, OutputTileCols, KernelRows, KernelCols>::configure(
- const ITensorInfo *weights_hwio,
- ITensorInfo *output,
- 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. */
-{
- ARM_COMPUTE_UNUSED(weights_hwio, output);
-
- _transform = std::make_unique<WeightsTransform>(num_output_channels, num_input_channels);
- _num_output_channels = num_output_channels;
- _matrix_stride = matrix_stride;
-
- Window win;
- auto win_last = _transform->get_window();
- win.set(Window::DimX, Window::Dimension(0, win_last, 1));
- ICpuKernel::configure(win);
-}
-#endif /* DOXYGEN_SKIP_THIS */
-
-template <typename T, int OutputTileRows, int OutputTileCols, int KernelRows, int KernelCols>
-void CpuWinogradConv2dTransformWeightsKernel<T, OutputTileRows, OutputTileCols, KernelRows, KernelCols>::run_op(ITensorPack &tensors, const Window &window, const ThreadInfo &info)
-{
- ARM_COMPUTE_UNUSED(info);
- ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this);
- ARM_COMPUTE_ERROR_ON(tensors.empty());
-
- const size_t fst = window.x().start();
- const size_t lst = window.x().end();
-
- const ITensor *weights_hwio = tensors.get_const_tensor(TensorType::ACL_SRC);
- ITensor *output = tensors.get_tensor(TensorType::ACL_DST);
-
- _transform->set_weight_tensor(weights_hwio->buffer());
- const int matrix_row_stride = roundup(_num_output_channels, WinogradConv::N_BLOCK);
- _transform->set_output_matrices(output->buffer(), _matrix_stride, matrix_row_stride);
- _transform->set_working_space(output->buffer());
-
- _transform->run(fst, lst);
-}
-
-template <typename T, int OutputTileRows, int OutputTileCols, int KernelRows, int KernelCols>
-bool CpuWinogradConv2dTransformWeightsKernel<T, OutputTileRows, OutputTileCols, KernelRows, KernelCols>::is_parallelisable() const
-{
- return false;
-}
-
-template <typename T, int OutputTileRows, int OutputTileCols, int KernelRows, int KernelCols>
-Status CpuWinogradConv2dTransformWeightsKernel<T, OutputTileRows, OutputTileCols, KernelRows, KernelCols>::validate(const ITensorInfo *input, const ITensorInfo *output,
- const WinogradInfo &winograd_info)
-{
- ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments_winograd_weight_trans(input, output, winograd_info));
- ARM_COMPUTE_RETURN_ON_ERROR(validate_and_configure_window_winograd_weight_trans(input->clone().get(), output->clone().get(), winograd_info).first);
- return Status{};
-}
-
-template class CpuWinogradConv2dTransformWeightsKernel<float, 2, 2, 3, 3>;
-template class CpuWinogradConv2dTransformWeightsKernel<float, 4, 4, 3, 3>;
-template class CpuWinogradConv2dTransformWeightsKernel<float, 2, 2, 5, 5>;
-template class CpuWinogradConv2dTransformWeightsKernel<float, 1, 6, 1, 3>;
-template class CpuWinogradConv2dTransformWeightsKernel<float, 6, 1, 3, 1>;
-
-template class CpuWinogradConv2dTransformWeightsKernel<float, 1, 4, 1, 5>;
-template class CpuWinogradConv2dTransformWeightsKernel<float, 4, 1, 5, 1>;
-template class CpuWinogradConv2dTransformWeightsKernel<float, 1, 2, 1, 7>;
-template class CpuWinogradConv2dTransformWeightsKernel<float, 2, 1, 7, 1>;
-
-#ifdef __ARM_FEATURE_FP16_VECTOR_ARITHMETIC
-template class CpuWinogradConv2dTransformWeightsKernel<__fp16, 4, 4, 3, 3>;
-#endif // __ARM_FEATURE_FP16_VECTOR_ARITHMETIC
-
-// Input transform
-
-template <typename T, int OutputTileRows, int OutputTileCols, int KernelRows, int KernelCols>
-unsigned int CpuWinogradConv2dTransformInputKernel<T, OutputTileRows, OutputTileCols, KernelRows, KernelCols>::get_input_storage_size(
- 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(num_batches, num_rows, num_cols, num_channels);
- const KernelShape kern_shape(1, KernelRows, KernelCols, num_channels);
- // Return the size, converted into units of TIn
- return static_cast<unsigned int>(WinogradConv::get_input_storage_size(num_batches, num_rows, num_cols, num_channels, same_padding) / sizeof(T));
-}
-
-template <typename T, int OutputTileRows, int OutputTileCols, int KernelRows, int KernelCols>
-unsigned int CpuWinogradConv2dTransformInputKernel<T, OutputTileRows, OutputTileCols, KernelRows, KernelCols>::get_working_space_size(unsigned int num_threads) const
-{
- return _transform->get_working_space_size(num_threads);
-}
-
-template <typename T, int OutputTileRows, int OutputTileCols, int KernelRows, int KernelCols>
-int CpuWinogradConv2dTransformInputKernel<T, OutputTileRows, OutputTileCols, KernelRows, KernelCols>::get_matrix_stride(
- 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
-{
- return WinogradConv::get_input_matrix_stride(num_batches, num_rows, num_cols, num_channels, same_padding);
-}
-
-template <typename T, int OutputTileRows, int OutputTileCols, int KernelRows, int KernelCols>
-CpuWinogradConv2dTransformInputKernel<T, OutputTileRows, OutputTileCols, KernelRows, KernelCols>::CpuWinogradConv2dTransformInputKernel()
- : _transform(nullptr), _num_channels(0), _matrix_stride(0)
-{
-}
-
-template <typename T, int OutputTileRows, int OutputTileCols, int KernelRows, int KernelCols>
-void CpuWinogradConv2dTransformInputKernel<T, OutputTileRows, OutputTileCols, KernelRows, KernelCols>::configure(
- const ITensorInfo *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. */
- ITensorInfo *output, /* Base of output matrices. */
- const int matrix_stride, /* Stride between output matrices. */
- ITensorInfo *workspace)
-{
- ARM_COMPUTE_UNUSED(input_nhwc, output, matrix_stride, workspace);
-
- _num_channels = num_channels;
- _matrix_stride = matrix_stride;
-
- const int padding_top = (padding == PADDING_SAME) ? (KernelRows - 1) / 2 : 0;
- const int padding_left = (padding == PADDING_SAME) ? (KernelCols - 1) / 2 : 0;
- const int padding_bottom = (padding == PADDING_SAME) ? iceildiv(KernelRows - 1, 2) : 0;
- const int padding_right = (padding == PADDING_SAME) ? iceildiv(KernelCols - 1, 2) : 0;
-
- _transform = std::make_unique<InputTransform>(
- KernelRows,
- KernelCols,
- num_batches,
- num_rows,
- num_cols,
- num_channels,
- padding_top, /**< Padding to apply to the top of the image. */
- padding_left, /**< Padding to apply to the left of the image. */
- padding_bottom, /**< Padding to apply to the bottom of the image. */
- padding_right /**< Padding to apply to the right of the image. */
- );
-
- Window win;
- auto win_last = _transform->get_window();
- win.set(Window::DimX, Window::Dimension(0, win_last, 1));
- ICpuKernel::configure(win);
-}
-
-template <typename T, int OutputTileRows, int OutputTileCols, int KernelRows, int KernelCols>
-void CpuWinogradConv2dTransformInputKernel<T, OutputTileRows, OutputTileCols, KernelRows, KernelCols>::run_op(ITensorPack &tensors, const Window &window, const ThreadInfo &info)
-{
- ARM_COMPUTE_UNUSED(info);
- ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this);
- ARM_COMPUTE_ERROR_ON(tensors.empty());
-
- const ITensor *input_nhwc = tensors.get_const_tensor(TensorType::ACL_SRC);
- const ITensor *workspace = tensors.get_const_tensor(TensorType::ACL_INT);
- ITensor *output = tensors.get_tensor(TensorType::ACL_DST);
-
- const int element_size_in_bytes = input_nhwc->info()->element_size();
- const int input_col_stride = input_nhwc->info()->strides_in_bytes().y() / element_size_in_bytes;
- const int input_row_stride = input_nhwc->info()->strides_in_bytes().z() / element_size_in_bytes;
- const int input_batch_stride = input_nhwc->info()->strides_in_bytes()[3] / element_size_in_bytes;
- const auto input_nhwc_ptr = reinterpret_cast<const T *>(input_nhwc->buffer() + input_nhwc->info()->offset_first_element_in_bytes());
- auto output_ptr = reinterpret_cast<T *>(output->buffer() + output->info()->offset_first_element_in_bytes());
- ARM_COMPUTE_ERROR_ON_NULLPTR(output_ptr);
-
- _transform->set_input_tensor(input_nhwc_ptr, input_batch_stride, input_row_stride, input_col_stride);
- _transform->set_output_matrices(output_ptr, _matrix_stride, _num_channels);
-
- _transform->set_working_space(workspace->buffer());
-
- // 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, info.thread_id);
-}
-
-template <typename T, int OutputTileRows, int OutputTileCols, int KernelRows, int KernelCols>
-Status CpuWinogradConv2dTransformInputKernel<T, OutputTileRows, OutputTileCols, KernelRows, KernelCols>::validate(const ITensorInfo *input, const ITensorInfo *output,
- const WinogradInfo &winograd_info)
-{
- ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments_winograd_input_trans(input, output, winograd_info));
- ARM_COMPUTE_RETURN_ON_ERROR(validate_and_configure_window_winograd_input_trans(input->clone().get(), output->clone().get(), winograd_info).first);
-
- return Status{};
-}
-
-template class CpuWinogradConv2dTransformInputKernel<float, 2, 2, 3, 3>;
-template class CpuWinogradConv2dTransformInputKernel<float, 4, 4, 3, 3>;
-template class CpuWinogradConv2dTransformInputKernel<float, 2, 2, 5, 5>;
-template class CpuWinogradConv2dTransformInputKernel<float, 1, 6, 1, 3>;
-template class CpuWinogradConv2dTransformInputKernel<float, 6, 1, 3, 1>;
-
-template class CpuWinogradConv2dTransformInputKernel<float, 1, 4, 1, 5>;
-template class CpuWinogradConv2dTransformInputKernel<float, 4, 1, 5, 1>;
-template class CpuWinogradConv2dTransformInputKernel<float, 1, 2, 1, 7>;
-template class CpuWinogradConv2dTransformInputKernel<float, 2, 1, 7, 1>;
-
-#ifdef __ARM_FEATURE_FP16_VECTOR_ARITHMETIC
-template class CpuWinogradConv2dTransformInputKernel<__fp16, 4, 4, 3, 3>;
-#endif // __ARM_FEATURE_FP16_VECTOR_ARITHMETIC
-// Output transform
-
-template <typename T, int OutputTileRows, int OutputTileCols, int KernelRows, int KernelCols>
-unsigned int CpuWinogradConv2dTransformOutputKernel<T, OutputTileRows, OutputTileCols, KernelRows, KernelCols>::get_output_storage_size(
- 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. */
-) const
-{
- // Construct shapes for the input and kernel tensors.
- const Tensor4DShape input_shape(num_batches, num_rows, num_cols, 1);
- const KernelShape kern_shape(num_output_channels, KernelRows, KernelCols, 1);
- // Return the size, converted into units of TOut
- return static_cast<unsigned int>(
- WinogradConv::get_output_storage_size(num_batches, num_rows, num_cols, num_output_channels) / sizeof(T));
-}
-
-template <typename T, int OutputTileRows, int OutputTileCols, int KernelRows, int KernelCols>
-CpuWinogradConv2dTransformOutputKernel<T, OutputTileRows, OutputTileCols, KernelRows, KernelCols>::CpuWinogradConv2dTransformOutputKernel()
- : _transform(nullptr), _matrix_stride(0), _matrix_row_stride(0)
-{
+ // Output transform
+ _winograd_impl.output_transform->execute(
+ _conv_args,
+ wout_transf_ptr,
+ _winograd_impl.winograd_spec,
+ biases_data_ptr,
+ dst_nhwc_ptr,
+ out_batch_stride,
+ out_row_stride,
+ out_col_stride,
+ workspace->buffer(),
+ info.thread_id,
+ _nthreads);
}
-template <typename T, int OutputTileRows, int OutputTileCols, int KernelRows, int KernelCols>
-unsigned int CpuWinogradConv2dTransformOutputKernel<T, OutputTileRows, OutputTileCols, KernelRows, KernelCols>::get_working_space_size(unsigned int num_threads) const
-{
- return _transform->get_working_space_size(num_threads);
-}
-
-template <typename T, int OutputTileRows, int OutputTileCols, int KernelRows, int KernelCols>
-int CpuWinogradConv2dTransformOutputKernel<T, OutputTileRows, OutputTileCols, KernelRows, KernelCols>::get_matrix_stride(
- 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. */
-) const
-{
- return WinogradConv::get_output_matrix_stride(num_batches, num_rows, num_cols, num_output_channels);
-}
-
-template <typename T, int OutputTileRows, int OutputTileCols, int KernelRows, int KernelCols>
-std::pair<unsigned int, unsigned int> CpuWinogradConv2dTransformOutputKernel<T, OutputTileRows, OutputTileCols, KernelRows, KernelCols>::get_output_shape(
- 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. */
- bool padding_same) const
-{
- return WinogradConv::get_output_shape(std::make_pair<unsigned int, unsigned int>(num_rows, num_cols), padding_same);
-}
-
-template <typename T, int OutputTileRows, int OutputTileCols, int KernelRows, int KernelCols>
-void CpuWinogradConv2dTransformOutputKernel<T, OutputTileRows, OutputTileCols, KernelRows, KernelCols>::configure(
- const ITensorInfo *biases,
- const ITensorInfo *transformed_output,
- const int matrix_stride,
- ITensorInfo *output_nhwc,
- const int num_batches,
- const int num_rows,
- const int num_cols,
- const int num_channels,
- ITensorInfo *workspace,
- const arm_gemm::Activation &activation)
-{
- ARM_COMPUTE_UNUSED(biases, transformed_output, output_nhwc, num_batches, num_rows, num_cols, workspace, activation);
-
- _matrix_stride = matrix_stride;
- _matrix_row_stride = roundup(num_channels, WinogradConv::N_BLOCK);
-
- // 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
- _transform = std::make_unique<OutputTransform>(num_batches, num_rows, num_cols, num_channels, activation);
- Window win;
- auto win_last = _transform->get_window();
- win.set(Window::DimX, Window::Dimension(0, win_last, 1));
-
- ICpuKernel::configure(win);
-}
-
-template <typename T, int OutputTileRows, int OutputTileCols, int KernelRows, int KernelCols>
-void CpuWinogradConv2dTransformOutputKernel<T, OutputTileRows, OutputTileCols, KernelRows, KernelCols>::run_op(ITensorPack &tensors, const Window &window, const ThreadInfo &info)
-{
- ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this);
- ARM_COMPUTE_ERROR_ON(tensors.empty());
-
- const ITensor *biases = tensors.get_const_tensor(TensorType::ACL_SRC_0);
- const ITensor *transformed_output = tensors.get_const_tensor(TensorType::ACL_SRC_1);
- ITensor *workspace = tensors.get_tensor(TensorType::ACL_INT);
- ITensor *dst_nhwc = tensors.get_tensor(TensorType::ACL_DST);
-
- const int out_batch_stride = dst_nhwc->info()->strides_in_bytes()[3] / sizeof(T);
- const int out_row_stride = dst_nhwc->info()->strides_in_bytes()[2] / sizeof(T);
- const int out_col_stride = dst_nhwc->info()->strides_in_bytes()[1] / sizeof(T);
-
- _transform->set_input_matrices(transformed_output->buffer(), _matrix_stride, _matrix_row_stride);
- _transform->set_bias((biases ? reinterpret_cast<T *>(biases->buffer() + biases->info()->offset_first_element_in_bytes()) : nullptr));
- _transform->set_output_tensor(dst_nhwc->buffer() + dst_nhwc->info()->offset_first_element_in_bytes(), out_batch_stride, out_row_stride, out_col_stride);
- _transform->set_working_space(workspace->buffer());
-
- // 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, info.thread_id);
-}
-
-template <typename T, int OutputTileRows, int OutputTileCols, int KernelRows, int KernelCols>
-Status CpuWinogradConv2dTransformOutputKernel<T, OutputTileRows, OutputTileCols, KernelRows, KernelCols>::validate(const ITensorInfo *input, const ITensorInfo *bias, const ITensorInfo *output,
- const WinogradInfo &winograd_info)
-{
- ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments_winograd_output_trans(input, (bias != nullptr ? bias->clone().get() : nullptr), output, winograd_info));
- ARM_COMPUTE_RETURN_ON_ERROR(validate_and_configure_window_winograd_output_trans(input->clone().get(), output->clone().get(), winograd_info).first);
-
- return Status{};
-}
-
-template class CpuWinogradConv2dTransformOutputKernel<float, 2, 2, 3, 3>;
-template class CpuWinogradConv2dTransformOutputKernel<float, 4, 4, 3, 3>;
-template class CpuWinogradConv2dTransformOutputKernel<float, 2, 2, 5, 5>;
-template class CpuWinogradConv2dTransformOutputKernel<float, 1, 6, 1, 3>;
-template class CpuWinogradConv2dTransformOutputKernel<float, 6, 1, 3, 1>;
-
-template class CpuWinogradConv2dTransformOutputKernel<float, 1, 4, 1, 5>;
-template class CpuWinogradConv2dTransformOutputKernel<float, 4, 1, 5, 1>;
-template class CpuWinogradConv2dTransformOutputKernel<float, 1, 2, 1, 7>;
-template class CpuWinogradConv2dTransformOutputKernel<float, 2, 1, 7, 1>;
-
-#ifdef __ARM_FEATURE_FP16_VECTOR_ARITHMETIC
-template class CpuWinogradConv2dTransformOutputKernel<__fp16, 4, 4, 3, 3>;
-#endif // __ARM_FEATURE_FP16_VECTOR_ARITHMETIC
} // namespace cpu
-} // namespace arm_compute
+} // namespace arm_compute \ No newline at end of file
generated by cgit v1.2.1 (git 2.23.0) at 2024-05-20 03:04:12 +0000