Integrate new winograd APIs from MLTech
Resolves: COMPMID-5400
Signed-off-by: Ramy Elgammal <ramy.elgammal@arm.com>
Change-Id: Ib4428436dd7a6e40d8b2d8a2f8dac1b079154551
Reviewed-on: https://review.mlplatform.org/c/ml/ComputeLibrary/+/7894
Reviewed-by: Pablo Marquez Tello <pablo.tello@arm.com>
Tested-by: Arm Jenkins <bsgcomp@arm.com>
Comments-Addressed: Arm Jenkins <bsgcomp@arm.com>
Benchmark: Arm Jenkins <bsgcomp@arm.com>
diff --git a/src/cpu/kernels/CpuWinogradConv2dKernel.cpp b/src/cpu/kernels/CpuWinogradConv2dKernel.cpp
index 803af09..818d878 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>
-
namespace arm_compute
{
namespace cpu
{
-//Batched Gemms
-
-namespace
+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 }
{
-inline bool is_kernel_size_supported(DataType data_type, Size2D size)
-{
- 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 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());
+
+ _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);
+}
+
+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 }
+{
+}
+
+// Inherited methods overridden:
+void CpuWinogradConv2dTransformOutputKernel::run_op(ITensorPack &tensors, const Window &window, const ThreadInfo &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);
+
+ 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();
+
+ 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_filter_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{};
+ // 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);
}
-std::pair<Status, Window> validate_and_configure_window_winograd_weight_trans(ITensorInfo *input, ITensorInfo *output, const WinogradInfo &winograd_info)
-{
- // 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)
-{
- 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_RETURN_ERROR_ON_MISMATCHING_DIMENSIONS(output->tensor_shape(), output_shape);
- ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input, output);
- }
-
- return Status{};
-}
-
-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 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);
- }
- 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)
-{
-}
-
-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
diff --git a/src/cpu/kernels/CpuWinogradConv2dKernel.h b/src/cpu/kernels/CpuWinogradConv2dKernel.h
index 6909216..0170dca 100644
--- a/src/cpu/kernels/CpuWinogradConv2dKernel.h
+++ b/src/cpu/kernels/CpuWinogradConv2dKernel.h
@@ -24,550 +24,79 @@
#ifndef ARM_COMPUTE_CPUWINOGRADCONV2DKERNEL_H
#define ARM_COMPUTE_CPUWINOGRADCONV2DKERNEL_H
-#include "src/core/NEON/kernels/convolution/common/convolution.hpp"
+#include "arm_compute/core/Helpers.h"
+#include "arm_compute/core/ITensor.h"
+#include "arm_compute/core/ITensorPack.h"
+#include "arm_compute/core/Steps.h"
+#include "arm_compute/core/TensorInfo.h"
+#include "arm_compute/runtime/Tensor.h"
+#include "src/core/NEON/kernels/assembly/winograd.hpp"
#include "src/core/NEON/kernels/convolution/common/tensor.hpp"
#include "src/cpu/ICpuKernel.h"
-#include "src/core/NEON/kernels/convolution/winograd/winograd_layer.hpp"
-
namespace arm_compute
{
namespace cpu
{
-/** Interface for the kernel to perform Winograd input transform. */
-class ICpuWinogradConv2dTransformInputKernel : public ICpuKernel<ICpuWinogradConv2dTransformInputKernel>
-{
-public:
- /** Get the working space required to perform the transformation.
- *
- * Note, the working space is only required when performing the
- * transformation - hence it can be reused whenever the transformation is
- * not running.
- *
- * @param num_threads The greatest number of threads that will be used to execute the transform.
- * @return Size of working space required in bytes.
- */
- virtual unsigned int get_working_space_size(unsigned int num_threads) const = 0;
-
- /** Determine how much memory (in units of TIn) to allocate for the
- * transformed input.
- *
- * @param[in] num_batches Number of batches in the input tensor.
- * @param[in] num_channels Number of feature maps in the input tensor.
- * @param[in] num_rows Number of rows in each feature map.
- * @param[in] num_cols Number of columns in each feature map.
- * @param[in] same_padding Use "SAME" padding, otherwise use "VALID".
- *
- * @return Storage size (in units of TIn) required.
- */
- virtual unsigned int get_input_storage_size(int num_batches, int num_channels, int num_rows, int num_cols, bool same_padding) const = 0;
-
- /** Gets the stride between matrices in the input worspace
- *
- * @param[in] num_batches Number of batches in the input tensor.
- * @param[in] num_channels Number of feature maps in the input tensor.
- * @param[in] num_rows Number of rows in each feature map.
- * @param[in] num_cols Number of columns in each feature map.
- * @param[in] same_padding Use "SAME" padding, otherwise use "VALID".
- *
- * @return Stride expressed in bytes.
- */
- virtual int get_matrix_stride(int num_batches, int num_channels, int num_rows, int num_cols, bool same_padding) const = 0;
-
- /** Configure the output transform kernel.
- *
- * @param[in] input_nhwc Input tensor in NHWC data layout format.
- * @param[in] num_batches Number of batches in input tensor.
- * @param[in] num_rows Number of rows in input tensor.
- * @param[in] num_cols Number of columns in input tensor.
- * @param[in] num_channels Number of channels in input tensor.
- * @param[in] padding Padding type.
- * @param[out] output Base of output matrices.
- * @param[in] matrix_stride Stride between output matrices.
- * @param[in] workspace Tensor to be used as the working space during the computation.
- */
- virtual void configure(const ITensorInfo *input_nhwc, const int num_batches, const int num_rows, const int num_cols, const int num_channels,
- const PaddingType padding, ITensorInfo *output, const int matrix_stride, ITensorInfo *workspace) = 0;
-
- /** Destructor */
- virtual ~ICpuWinogradConv2dTransformInputKernel()
- {
- }
-};
-
-/** Kernel to perform Winograd input transform. */
-template <typename T, int OutputTileRows, int OutputTileCols, int KernelRows, int KernelCols>
-class CpuWinogradConv2dTransformInputKernel : public ICpuWinogradConv2dTransformInputKernel
+class CpuWinogradConv2dTransformInputKernel final : public ICpuKernel<CpuWinogradConv2dTransformInputKernel>
{
public:
/** Prevent instances of this class from being copied (As this class contains pointers) */
CpuWinogradConv2dTransformInputKernel(const CpuWinogradConv2dTransformInputKernel &) = delete;
+
/** Prevent instances of this class from being copied (As this class contains pointers) */
CpuWinogradConv2dTransformInputKernel &operator=(const CpuWinogradConv2dTransformInputKernel &) = delete;
- /** Allow instances of this class to be moved */
- CpuWinogradConv2dTransformInputKernel(CpuWinogradConv2dTransformInputKernel &&) = default;
- /** Allow instances of this class to be moved */
- CpuWinogradConv2dTransformInputKernel &operator=(CpuWinogradConv2dTransformInputKernel &&) = default;
- /** Default destructor */
- ~CpuWinogradConv2dTransformInputKernel() = default;
- /** Determine how much memory (in units of TIn) to allocate for the
- * transformed input.
- *
- * @param[in] num_batches Number of batches in the input tensor.
- * @param[in] num_channels Number of feature maps in the input tensor.
- * @param[in] num_rows Number of rows in each feature map.
- * @param[in] num_cols Number of columns in each feature map.
- * @param[in] same_padding Use "SAME" padding, otherwise use "VALID".
- *
- * @return Storage size (in units of TIn) required.
- */
- unsigned int get_input_storage_size(
- int num_batches,
- int num_channels,
- int num_rows,
- int num_cols,
- bool same_padding) const override;
+ /** Prevent instances of this class from being moved it contains references.*/
+ CpuWinogradConv2dTransformInputKernel(CpuWinogradConv2dTransformInputKernel &&) = delete;
- /** Get the working space required to perform the transformation.
- *
- * Note, the working space is only required when performing the
- * transformation - hence it can be reused whenever the transformation is
- * not running.
- *
- * @param[in] num_threads The greatest number of threads that will be used to execute the transform.
- *
- * @return Size of working space required in bytes.
- */
- unsigned int get_working_space_size(unsigned int num_threads) const override;
+ /** Prevent instances of this class from being moved it contains references.*/
+ CpuWinogradConv2dTransformInputKernel &operator=(CpuWinogradConv2dTransformInputKernel &&) = delete;
- /** Gets the stride between matrices in the input worspace
- *
- * @param[in] num_batches Number of batches in the input tensor.
- * @param[in] num_channels Number of feature maps in the input tensor.
- * @param[in] num_rows Number of rows in each feature map.
- * @param[in] num_cols Number of columns in each feature map.
- * @param[in] same_padding Use "SAME" padding, otherwise use "VALID".
- *
- * @return Stride expressed in bytes.
- */
- int get_matrix_stride(
- int num_batches,
- int num_channels,
- int num_rows,
- int num_cols,
- bool same_padding) const override;
+ CpuWinogradConv2dTransformInputKernel(arm_conv::winograd::WinogradImpl &w_impl, arm_conv::ConvolutionArgs &_c_args, uint32_t nthreads);
- /** Default constructor */
- CpuWinogradConv2dTransformInputKernel();
+ // Inherited methods overridden:
+ void run_op(ITensorPack &tensors, const Window &window, const ThreadInfo &info) override;
const char *name() const override
{
return "CpuWinogradConv2dTransformInputKernel";
}
- /** Configure the output transform kernel.
- *
- * @param[in] input_nhwc Input tensor. Data types supported: F16/F32. Layout supported NHWC.
- * @param[in] num_batches Number of batches in input tensor.
- * @param[in] num_rows Number of rows in input tensor.
- * @param[in] num_cols Number of columns in input tensor.
- * @param[in] num_channels Number of channels in input tensor.
- * @param[in] padding Padding type.
- * @param[out] output Base of output matrices.
- * @param[in] matrix_stride Stride between output matrices.
- * @param[in] workspace Tensor to be used as the working space during the computation.
- */
- void configure(
- const ITensorInfo *input_nhwc,
- const int num_batches,
- const int num_rows,
- const int num_cols,
- const int num_channels,
- const PaddingType padding,
- ITensorInfo *output,
- const int matrix_stride,
- ITensorInfo *workspace) override;
+private:
+ arm_conv::winograd::WinogradImpl &_winograd_impl;
+ arm_conv::ConvolutionArgs &_conv_args;
+ uint32_t _nthreads;
+};
+class CpuWinogradConv2dTransformOutputKernel : public ICpuKernel<CpuWinogradConv2dTransformOutputKernel>
+{
+public:
+ /** Prevent instances of this class from being copied (As this class contains pointers) */
+ CpuWinogradConv2dTransformOutputKernel(const CpuWinogradConv2dTransformOutputKernel &) = delete;
+
+ /** Prevent instances of this class from being copied (As this class contains pointers) */
+ CpuWinogradConv2dTransformOutputKernel &operator=(const CpuWinogradConv2dTransformOutputKernel &) = delete;
+
+ /** Prevent instances of this class from being moved it contains references.*/
+ CpuWinogradConv2dTransformOutputKernel(CpuWinogradConv2dTransformOutputKernel &&) = delete;
+
+ /** Prevent instances of this class from being moved it contains references.*/
+ CpuWinogradConv2dTransformOutputKernel &operator=(CpuWinogradConv2dTransformOutputKernel &&) = delete;
+
+ CpuWinogradConv2dTransformOutputKernel(arm_conv::winograd::WinogradImpl &w_impl, arm_conv::ConvolutionArgs &_c_args, uint32_t nthreads);
// Inherited methods overridden:
void run_op(ITensorPack &tensors, const Window &window, const ThreadInfo &info) override;
- /** Winograd base kernel */
- using WinogradBase = winograd::WinogradGEMM<OutputTileRows, OutputTileCols, KernelRows, KernelCols, winograd::WinogradRoots::Integers>;
- /** Winograd convolution kernel */
- using WinogradConv = typename WinogradBase::template Convolution<T, T>;
-
- /** Static function to check if given info will lead to a valid configuration of @ref CpuWinogradConv2dTransformInputKernel
- *
- * @param[in] input First tensor input info. Data types supported: F16/F32.
- * @param[in] output Output tensor info. Data types supported: same as @p input.
- * @param[in] winograd_info Contains Winograd's information described in @ref WinogradInfo
- *
- * @return a status
- */
- static Status validate(const ITensorInfo *input, const ITensorInfo *output, const WinogradInfo &winograd_info);
-
-private:
- using InputTransform = typename WinogradBase::template InputTransform<T, T>;
-
- std::unique_ptr<InputTransform> _transform{ nullptr };
- int _num_channels; /**< Number of channels in input tensor. */
- int _matrix_stride; /**< Stride between output matrices. */
-};
-
-/** Interface for the kernel to perform Winograd output transform. */
-class ICpuWinogradConv2dTransformOutputKernel : public ICpuKernel<ICpuWinogradConv2dTransformOutputKernel>
-{
-public:
- /** Get the working space required to perform the transformation.
- *
- * Note, the working space is only required when performing the
- * transformation - hence it can be reused whenever the transformation is
- * not running.
- *
- * @param[in] num_threads The greatest number of threads that will be used to execute the transform.
- *
- * @return Size of working space required in bytes.
- */
- virtual unsigned int get_working_space_size(unsigned int num_threads) const = 0;
-
- /** Determine how much memory (in units of TOut) to allocate for the
- * (Winograd domain) output.
- *
- * @param[in] num_batches Number of batches in the output tensor.
- * @param[in] num_rows Number of rows in each feature map of the input tensor.
- * @param[in] num_cols Number of columns in each feature map of the input tensor.
- * @param[in] num_output_channels Number of feature maps in the output tensor.
- *
- * @return Storage size (in units of TOut) required.
- */
- virtual unsigned int get_output_storage_size(int num_batches, int num_rows, int num_cols, int num_output_channels) const = 0;
-
- /** Gets the stride between matrices in the output worspace
- *
- * @param[in] num_batches Number of batches in the output tensor.
- * @param[in] num_rows Number of rows in each feature map of the input tensor.
- * @param[in] num_cols Number of columns in each feature map of the input tensor.
- * @param[in] num_output_channels Number of feature maps in the output tensor.
- *
- * @return Stride expressed in bytes.
- */
- virtual int get_matrix_stride(int num_batches, int num_rows, int num_cols, int num_output_channels) const = 0;
-
- /** Get the output shape of a convolution.
- *
- * @param[in] num_rows Number of rows in each feature map of the input tensor.
- * @param[in] num_cols Number of columns in each feature map of the input tensor.
- * @param[in] padding_same True if padding is SAME, false otherwise
- *
- * @return Shape of the output tensor
- */
- virtual std::pair<unsigned int, unsigned int> 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 /* True if padding is SAME, false otherwise */
- ) const = 0;
-
- /** Configure the output transform kernel.
- *
- * @param[in] biases Pointer to the biases tensor.
- * @param[in] transformed_output Pointer to working space for the output tensor in the Winograd domain.
- * @param[in] matrix_stride Output matrix stride, can be computed with winograd::WinogradGEMM<2, 2, 3, 3>::Convolution<float, float>::get_output_matrix_stride()
- * @param[out] output_nhwc Pointer to a tensor in NHWC data layout ordered output tensor, in the spatial domain.
- * @param[in] num_batches Number of batches in the input tensor.
- * @param[in] num_rows Number of rows in output tensor.
- * @param[in] num_cols Number of columns in output tensor.
- * @param[in] num_channels Number of feature maps in the output tensor.
- * @param[in] workspace Tensor to be used as the working space during the computation.
- * @param[in] activation Activation to be used
- */
- virtual void 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) = 0;
-
- virtual ~ICpuWinogradConv2dTransformOutputKernel()
- {
- }
-};
-
-/** Kernel to perform Winograd output transform. */
-template <typename T, int OutputTileRows, int OutputTileCols, int KernelRows, int KernelCols>
-class CpuWinogradConv2dTransformOutputKernel : public ICpuWinogradConv2dTransformOutputKernel
-{
-public:
const char *name() const override
{
return "CpuWinogradConv2dTransformOutputKernel";
}
- /** Constructor */
- CpuWinogradConv2dTransformOutputKernel();
-
- /** Prevent instances of this class from being copied (As this class contains pointers) */
- CpuWinogradConv2dTransformOutputKernel(const CpuWinogradConv2dTransformOutputKernel &) = delete;
- /** Prevent instances of this class from being copied (As this class contains pointers) */
- CpuWinogradConv2dTransformOutputKernel &operator=(const CpuWinogradConv2dTransformOutputKernel &) = delete;
- /** Allow instances of this class to be moved */
- CpuWinogradConv2dTransformOutputKernel(CpuWinogradConv2dTransformOutputKernel &&) = default;
- /** Allow instances of this class to be moved */
- CpuWinogradConv2dTransformOutputKernel &operator=(CpuWinogradConv2dTransformOutputKernel &&) = default;
- /** Default destructor */
- ~CpuWinogradConv2dTransformOutputKernel() = default;
-
- // Inherited methods overridden:
- /** Determine how much memory (in units of TOut) to allocate for the
- * (Winograd domain) output.
- *
- * @param[in] num_batches Number of batches in the output tensor.
- * @param[in] num_rows Number of rows in each feature map of the input tensor.
- * @param[in] num_cols Number of columns in each feature map of the input tensor.
- * @param[in] num_output_channels Number of feature maps in the output tensor.
- *
- * @return Storage size (in units of TOut) required.
- */
- unsigned int get_output_storage_size(int num_batches, int num_rows, int num_cols, int num_output_channels) const override;
-
- /** Gets the stride between matrices in the output worspace
- *
- * @param[in] num_batches Number of batches in the output tensor.
- * @param[in] num_rows Number of rows in each feature map of the input tensor.
- * @param[in] num_cols Number of columns in each feature map of the input tensor.
- * @param[in] num_output_channels Number of feature maps in the output tensor.
- *
- * @return Stride expressed in bytes.
- */
- int get_matrix_stride(int num_batches, int num_rows, int num_cols, int num_output_channels) const override;
- /** Get the output shape of a convolution.
- *
- * @param[in] num_rows Number of rows in each feature map of the input tensor.
- * @param[in] num_cols Number of columns in each feature map of the input tensor.
- * @param[in] padding_same True if padding is SAME, false otherwise
- *
- * @return Shape of the output tensor
- */
- std::pair<unsigned int, unsigned int> 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 override;
-
- /** Get the working space required to perform the transformation.
- *
- * Note, the working space is only required when performing the
- * transformation - hence it can be reused whenever the transformation is
- * not running.
- *
- * @param[in] num_threads The greatest number of threads that will be used to execute the transform.
- *
- * @return Size of working space required in bytes.
- */
- unsigned int get_working_space_size(unsigned int num_threads) const override;
-
- /** Configure the output transform kernel.
- *
- * @param[in] biases Pointer to the biases tensor.
- * @param[in] transformed_output Pointer to working space for the output tensor in the Winograd domain.
- * @param[in] matrix_stride Output matrix stride, can be computed with winograd::WinogradGEMM<2, 2, 3, 3>::Convolution<float, float>::get_output_matrix_stride()
- * @param[out] output_nhwc Pointer to a tensor with NHWC data layout, in the spatial domain.
- * @param[in] num_batches Number of batches in the input tensor.
- * @param[in] num_rows Number of rows in output tensor.
- * @param[in] num_cols Number of columns in output tensor.
- * @param[in] num_channels Number of feature maps in the output tensor.
- * @param[in] workspace Tensor to be used as the working space during the computation.
- * @param[in] activation Activation to be used
- */
- void 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) override;
-
- void run_op(ITensorPack &tensors, const Window &window, const ThreadInfo &info) override;
-
- /** Static function to check if given info will lead to a valid configuration of @ref CpuWinogradConv2dTransformOutputKernel
- *
- * @param[in] input Source tensor info with shape [C, N, 16, batches] or [C, N, 36, batches]. Data types supported: F16/F32.
- * @param[in] bias Biases tensor info. Shared biases supported. Biases are 1D tensor with dimensions [OFM]. It can be a nullptr. Data type supported: as @p input
- * @param[in] output Destination tensor info with shape [output_convolved_dims.width, output_convolved_dims.height, C, batches]. Data type supported: same as @p input
- * @param[in] winograd_info Contains Winograd's information described in @ref WinogradInfo
- *
- * @return a status
- */
- static Status validate(const ITensorInfo *input, const ITensorInfo *bias, const ITensorInfo *output, const WinogradInfo &winograd_info);
private:
- using WinogradBase = winograd::WinogradGEMM<OutputTileRows, OutputTileCols, KernelRows, KernelCols, winograd::WinogradRoots::Integers>;
- using WinogradConv = typename WinogradBase::template Convolution<T, T>;
- using OutputTransform = typename WinogradBase::template OutputTransform<T, T>;
-
- std::unique_ptr<OutputTransform> _transform{ nullptr };
- int _matrix_stride;
- int _matrix_row_stride;
-};
-
-/** Interface for the kernel to perform Winograd weights transform. */
-class ICpuWinogradConv2dTransformWeightsKernel : public ICpuKernel<ICpuWinogradConv2dTransformWeightsKernel>
-{
-public:
- /** Prevent instances of this class from being copied (As this class contains pointers) */
- ICpuWinogradConv2dTransformWeightsKernel(const ICpuWinogradConv2dTransformWeightsKernel &) = default;
- /** Prevent instances of this class from being copied (As this class contains pointers) */
- ICpuWinogradConv2dTransformWeightsKernel &operator=(const ICpuWinogradConv2dTransformWeightsKernel &) = default;
- /** Allow instances of this class to be moved */
- ICpuWinogradConv2dTransformWeightsKernel(ICpuWinogradConv2dTransformWeightsKernel &&) = default;
- /** Allow instances of this class to be moved */
- ICpuWinogradConv2dTransformWeightsKernel &operator=(ICpuWinogradConv2dTransformWeightsKernel &&) = default;
-
- ICpuWinogradConv2dTransformWeightsKernel()
- {
- }
- virtual ~ICpuWinogradConv2dTransformWeightsKernel()
- {
- }
- /** Determine how much memory (in units of T) to allocate for the
- * transformed weights.
- *
- * @param[in] num_output_channels Number of output feature maps.
- * @param[in] num_input_channels Number of input feature maps.
- *
- * @return Storage size (in units of T) required.
- */
- virtual unsigned int get_weight_storage_size(int num_output_channels, int num_input_channels) const = 0;
- /** Gets the stride between matrices in the kernel worspace
- *
- * @param[in] num_output_channels Number of output feature maps.
- * @param[in] num_input_channels Number of input feature maps.
- *
- * @return Stride expressed in bytes.
- */
- virtual int get_matrix_stride(int num_output_channels, int num_input_channels) const = 0;
-
- /** Configure the weights transform kernel.
- *
- * @param[in] weights_hwio Pointer to the weights tensor info
- * @param[out] output Pointer to working space for the output tensor in the Winograd domain.
- * @param[in] matrix_stride Stride across matrices in the output workspace.
- * @param[in] num_output_channels Number of filters.
- * @param[in] num_input_channels Number of channels in each filter.
- */
-
- virtual void configure(const ITensorInfo *weights_hwio, ITensorInfo *output, const int matrix_stride, const int num_output_channels, const int num_input_channels) = 0;
-
- /** Static function to check if given info will lead to a valid configuration of @ref CpuWinogradConv2dTransformWeightsKernel
- *
- * @param[in] input First tensor input info. Data types supported: F16/F32.
- * @param[in] weights Weights tensor info. Data types supported: same as @p input.
- *
- * @return a status
- */
- static Status validate(const ITensorInfo *input, const ITensorInfo *weights);
-};
-
-/** Kernel to perform Winograd weights transform. */
-template <typename T, int OutputTileRows, int OutputTileCols, int KernelRows, int KernelCols>
-class CpuWinogradConv2dTransformWeightsKernel final : public ICpuWinogradConv2dTransformWeightsKernel
-{
-public:
- /** Prevent instances of this class from being copied (As this class contains pointers) */
- CpuWinogradConv2dTransformWeightsKernel(const CpuWinogradConv2dTransformWeightsKernel &) = delete;
- /** Prevent instances of this class from being copied (As this class contains pointers) */
- CpuWinogradConv2dTransformWeightsKernel &operator=(const CpuWinogradConv2dTransformWeightsKernel &) = delete;
- /** Allow instances of this class to be moved */
- CpuWinogradConv2dTransformWeightsKernel(CpuWinogradConv2dTransformWeightsKernel &&) = default;
- /** Allow instances of this class to be moved */
- CpuWinogradConv2dTransformWeightsKernel &operator=(CpuWinogradConv2dTransformWeightsKernel &&) = default;
- /** Default destructor */
- ~CpuWinogradConv2dTransformWeightsKernel() = default;
-
- /** Default constructor. */
- CpuWinogradConv2dTransformWeightsKernel();
- const char *name() const override
- {
- return "CpuWinogradConv2dTransformWeightsKernel";
- }
-
- /** Static function to check if given info will lead to a valid configuration of @ref CpuWinogradConv2dTransformWeightsKernel
- *
- * @param[in] input Source tensor info. The input is a 4D tensor with dimensions [kernel_x, kernel_y, IFM, OFM] (NCHW data layout).
- * kernel_x must be 3 and equal to kernel_y. Data types supported: F16/F32.
- * @param[in] output Destination tensor info. The output is a 3D tensor with dimensions [OFM, IFM, 16] or [OFM, IFM, 36]. Data type supported: same as @p input
- * @param[in] winograd_info Contains Winograd's information described in @ref WinogradInfo
- *
- * @return a status
- */
- static Status validate(const ITensorInfo *input, const ITensorInfo *output, const WinogradInfo &winograd_info);
-
- // Inherited methods overridden:
-
-#ifndef DOXYGEN_SKIP_THIS
- /** Configure the weights transform kernel.
- *
- * @param[in] weights_hwio Pointer to the weights tensor info
- * @param[out] output Pointer to working space for the output tensor in the Winograd domain.
- * @param[in] matrix_stride Stride across matrices in the output workspace.
- * @param[in] num_output_channels Number of filters.
- * @param[in] num_input_channels Number of channels in each filter.
- */
- void configure(const ITensorInfo *weights_hwio, ITensorInfo *output, const int matrix_stride, const int num_output_channels, const int num_input_channels) override;
-#endif /* DOXYGEN_SKIP_THIS */
-
- /** Determine how much memory (in units of T) to allocate for the
- * transformed weights.
- *
- * @param[in] num_output_channels Number of output feature maps.
- * @param[in] num_input_channels Number of input feature maps.
- *
- * @return Storage size (in units of T) required.
- */
- unsigned int get_weight_storage_size(int num_output_channels, int num_input_channels) const override;
-
- /** Gets the stride between matrices in the input worspace
- *
- * @param[in] num_output_channels Number of output feature maps.
- * @param[in] num_input_channels Number of input feature maps.
- *
- * @return Stride expressed in bytes.
- */
- int get_matrix_stride(int num_output_channels, int num_input_channels) const override;
- void run_op(ITensorPack &tensors, const Window &window, const ThreadInfo &info) override;
- bool is_parallelisable() const override;
-
-private:
- using WinogradBase = winograd::WinogradGEMM<OutputTileRows, OutputTileCols, KernelRows, KernelCols, winograd::WinogradRoots::Integers>;
- using WinogradConv = typename WinogradBase::template Convolution<T, T>;
- using WeightsTransform = typename WinogradBase::template WeightsTransform<T, T>;
-
- std::unique_ptr<WeightsTransform> _transform{ nullptr };
- int _num_output_channels;
- int _matrix_stride;
-};
-
-/** Kernel to perform Winograd. */
-template <typename TIn, typename TOut, int OutputTileRows, int OutputTileCols, int KernelRows, int KernelCols>
-class CpuWinogradConv2dConfiguration
-{
-public:
- /** Winograd base kernel */
- using WinogradBase = winograd::WinogradGEMM<OutputTileRows, OutputTileCols, KernelRows, KernelCols, winograd::WinogradRoots::Integers>;
- /** Winograd convolution kernel */
-
- using WinogradConv = typename WinogradBase::template Convolution<TIn, TOut>;
-
- using TransformInputKernel = CpuWinogradConv2dTransformInputKernel<TIn, OutputTileRows, OutputTileCols, KernelRows, KernelCols>;
- using TransformWeightsKernel = CpuWinogradConv2dTransformWeightsKernel<TIn, OutputTileRows, OutputTileCols, KernelRows, KernelCols>;
- using TransformOutputKernel = CpuWinogradConv2dTransformOutputKernel<TOut, OutputTileRows, OutputTileCols, KernelRows, KernelCols>;
+ arm_conv::winograd::WinogradImpl &_winograd_impl;
+ const arm_conv::ConvolutionArgs &_conv_args;
+ uint32_t _nthreads;
};
} // namespace cpu
diff --git a/src/cpu/kernels/assembly/arm_gemm.hpp b/src/cpu/kernels/assembly/arm_gemm.hpp
index 9920b86..247cb1d 100644
--- a/src/cpu/kernels/assembly/arm_gemm.hpp
+++ b/src/cpu/kernels/assembly/arm_gemm.hpp
@@ -143,12 +143,12 @@
{
public:
const CPUInfo *_ci;
- unsigned int _Msize;
- unsigned int _Nsize;
- unsigned int _Ksize;
+ unsigned int _Msize; // num of tiles
+ unsigned int _Nsize; // output channels
+ unsigned int _Ksize; // input channels
unsigned int _Ksections;
unsigned int _nbatches;
- unsigned int _nmulti;
+ unsigned int _nmulti; // n_gemms to be performed
bool _indirect_input;
Activation _act;
int _maxthreads;
diff --git a/src/cpu/operators/CpuWinogradConv2d.cpp b/src/cpu/operators/CpuWinogradConv2d.cpp
index dcc18ce..7be2d6d 100644
--- a/src/cpu/operators/CpuWinogradConv2d.cpp
+++ b/src/cpu/operators/CpuWinogradConv2d.cpp
@@ -1,5 +1,5 @@
/*
- * Copyright (c) 2021 Arm Limited.
+ * Copyright (c) 2021-2022 Arm Limited.
*
* SPDX-License-Identifier: MIT
*
@@ -31,19 +31,19 @@
#include "arm_compute/runtime/NEON/NEScheduler.h"
#include "src/common/utils/Log.h"
#include "src/core/CPP/Validate.h"
+#include "src/core/NEON/kernels/assembly/winograd.hpp"
+#include "src/core/NEON/kernels/convolution/common/tensor.hpp"
#include "src/core/NEON/kernels/convolution/common/utils.hpp"
-#include "src/core/NEON/kernels/convolution/winograd/winograd.hpp"
#include "src/core/helpers/MemoryHelpers.h"
+#include "src/core/helpers/WindowHelpers.h"
+#include "src/core/utils/AssemblyUtils.h"
#include "src/cpu/kernels/CpuWinogradConv2dKernel.h"
+#include "src/cpu/kernels/assembly/arm_gemm.hpp"
#include "src/cpu/operators/CpuActivation.h"
#include "src/cpu/operators/CpuPermute.h"
-#include "src/cpu/operators/CpuWinogradConv2d.h"
#include "src/cpu/utils/CpuAuxTensorHandler.h"
-
#include "support/Cast.h"
-#include <set>
-
namespace arm_compute
{
namespace cpu
@@ -53,174 +53,20 @@
namespace
{
-arm_gemm::Activation arm_gemm_activation_from_acl_activation(const ActivationLayerInfo &act_info)
+inline Tensor4DShape internal_get_shape(const ITensorInfo *in)
{
- switch(act_info.activation())
- {
- case ActivationLayerInfo::ActivationFunction::RELU:
- {
- return arm_gemm::Activation(arm_gemm::Activation::Type::ReLU, act_info.a(), act_info.b());
- }
- case ActivationLayerInfo::ActivationFunction::BOUNDED_RELU:
- {
- return arm_gemm::Activation(arm_gemm::Activation::Type::BoundedReLU, act_info.a(), act_info.b());
- }
- default:
- {
- return arm_gemm::Activation(arm_gemm::Activation::Type::None);
- }
- }
-}
-
-inline Status validate_kernel_3x3(const Size2D input_dims, const ITensorInfo *src, const TensorInfo *input0, const TensorInfo *input1, const TensorInfo *batched_mm_output,
- const ITensorInfo *weights, const ITensorInfo *biases, const ITensorInfo *dst, const WinogradInfo &winograd_info, const ActivationLayerInfo &act_info)
-{
- ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(src);
- ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(src, 1, DataType::F16, DataType::F32);
-
- if(src->data_type() == DataType::F32)
- {
- if(input_dims.width > 4 && input_dims.height > 4)
- {
- ARM_COMPUTE_RETURN_ON_ERROR((CpuWinogradConv2dTransformInputKernel<float, 4, 4, 3, 3>::validate(src, input0, winograd_info)));
- ARM_COMPUTE_RETURN_ON_ERROR((CpuWinogradConv2dTransformWeightsKernel<float, 4, 4, 3, 3>::validate(weights, input1, winograd_info)));
- ARM_COMPUTE_RETURN_ON_ERROR((CpuWinogradConv2dTransformOutputKernel<float, 4, 4, 3, 3>::validate(batched_mm_output, biases, dst, winograd_info)));
- }
- else
- {
- ARM_COMPUTE_RETURN_ON_ERROR((CpuWinogradConv2dTransformInputKernel<float, 2, 2, 3, 3>::validate(src, input0, winograd_info)));
- ARM_COMPUTE_RETURN_ON_ERROR((CpuWinogradConv2dTransformWeightsKernel<float, 2, 2, 3, 3>::validate(weights, input1, winograd_info)));
- ARM_COMPUTE_RETURN_ON_ERROR((CpuWinogradConv2dTransformOutputKernel<float, 2, 2, 3, 3>::validate(batched_mm_output, biases, dst, winograd_info)));
- }
- }
-#ifdef __ARM_FEATURE_FP16_VECTOR_ARITHMETIC
- else if(src->data_type() == DataType::F16)
- {
- ARM_COMPUTE_RETURN_ON_ERROR((CpuWinogradConv2dTransformInputKernel<__fp16, 4, 4, 3, 3>::validate(src, input0, winograd_info)));
- ARM_COMPUTE_RETURN_ON_ERROR((CpuWinogradConv2dTransformWeightsKernel<__fp16, 4, 4, 3, 3>::validate(weights, input1, winograd_info)));
- ARM_COMPUTE_RETURN_ON_ERROR((CpuWinogradConv2dTransformOutputKernel<__fp16, 4, 4, 3, 3>::validate(batched_mm_output, biases, dst, winograd_info)));
- }
-#endif /* __ARM_FEATURE_FP16_VECTOR_ARITHMETIC */
-
- if(act_info.enabled())
- {
- CpuActivation::validate(dst, nullptr, act_info);
- }
- return Status{};
-}
-
-inline Status validate_kernel_5x5(const ITensorInfo *src, const TensorInfo *input0, const TensorInfo *input1, const TensorInfo *batched_mm_output,
- const ITensorInfo *weights, const ITensorInfo *biases, const ITensorInfo *dst, const WinogradInfo &winograd_info, const ActivationLayerInfo &act_info)
-{
- ARM_COMPUTE_RETURN_ON_ERROR((CpuWinogradConv2dTransformInputKernel<float, 2, 2, 5, 5>::validate(src, input0, winograd_info)));
- ARM_COMPUTE_RETURN_ON_ERROR((CpuWinogradConv2dTransformWeightsKernel<float, 2, 2, 5, 5>::validate(weights, input1, winograd_info)));
- ARM_COMPUTE_RETURN_ON_ERROR((CpuWinogradConv2dTransformOutputKernel<float, 2, 2, 5, 5>::validate(batched_mm_output, biases, dst, winograd_info)));
- if(act_info.enabled())
- {
- CpuActivation::validate(dst, nullptr, act_info);
- }
- return Status{};
-}
-
-inline Status validate_kernel_3x1(const ITensorInfo *src, const TensorInfo *input0, const TensorInfo *input1, const TensorInfo *batched_mm_output,
- const ITensorInfo *weights, const ITensorInfo *biases, const ITensorInfo *dst, const WinogradInfo &winograd_info, const ActivationLayerInfo &act_info)
-{
- ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(src, 1, DataType::F32);
- ARM_COMPUTE_RETURN_ON_ERROR((CpuWinogradConv2dTransformInputKernel<float, 1, 6, 1, 3>::validate(src, input0, winograd_info)));
- ARM_COMPUTE_RETURN_ON_ERROR((CpuWinogradConv2dTransformWeightsKernel<float, 1, 6, 1, 3>::validate(weights, input1, winograd_info)));
- ARM_COMPUTE_RETURN_ON_ERROR((CpuWinogradConv2dTransformOutputKernel<float, 1, 6, 1, 3>::validate(batched_mm_output, biases, dst, winograd_info)));
- if(act_info.enabled())
- {
- CpuActivation::validate(dst, nullptr, act_info);
- }
- return Status{};
-}
-
-inline Status validate_kernel_1x3(const ITensorInfo *src, const TensorInfo *input0, const TensorInfo *input1, const TensorInfo *batched_mm_output,
- const ITensorInfo *weights, const ITensorInfo *biases, const ITensorInfo *dst, const WinogradInfo &winograd_info, const ActivationLayerInfo &act_info)
-{
- ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(src, 1, DataType::F32);
- ARM_COMPUTE_RETURN_ON_ERROR((CpuWinogradConv2dTransformInputKernel<float, 6, 1, 3, 1>::validate(src, input0, winograd_info)));
- ARM_COMPUTE_RETURN_ON_ERROR((CpuWinogradConv2dTransformWeightsKernel<float, 6, 1, 3, 1>::validate(weights, input1, winograd_info)));
- ARM_COMPUTE_RETURN_ON_ERROR((CpuWinogradConv2dTransformOutputKernel<float, 6, 1, 3, 1>::validate(batched_mm_output, biases, dst, winograd_info)));
-
- if(act_info.enabled())
- {
- CpuActivation::validate(dst, nullptr, act_info);
- }
- return Status{};
-}
-
-inline Status validate_kernel_5x1(const ITensorInfo *src, const TensorInfo *input0, const TensorInfo *input1, const TensorInfo *batched_mm_output,
- const ITensorInfo *weights, const ITensorInfo *biases, const ITensorInfo *dst, const WinogradInfo &winograd_info, const ActivationLayerInfo &act_info)
-{
- ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(src, 1, DataType::F32);
- ARM_COMPUTE_RETURN_ON_ERROR((CpuWinogradConv2dTransformInputKernel<float, 1, 4, 1, 5>::validate(src, input0, winograd_info)));
- ARM_COMPUTE_RETURN_ON_ERROR((CpuWinogradConv2dTransformWeightsKernel<float, 1, 4, 1, 5>::validate(weights, input1, winograd_info)));
- ARM_COMPUTE_RETURN_ON_ERROR((CpuWinogradConv2dTransformOutputKernel<float, 1, 4, 1, 5>::validate(batched_mm_output, biases, dst, winograd_info)));
- if(act_info.enabled())
- {
- CpuActivation::validate(dst, nullptr, act_info);
- }
- return Status{};
-}
-inline Status validate_kernel_1x5(const ITensorInfo *src, const TensorInfo *input0, const TensorInfo *input1, const TensorInfo *batched_mm_output,
- const ITensorInfo *weights, const ITensorInfo *biases, const ITensorInfo *dst, const WinogradInfo &winograd_info, const ActivationLayerInfo &act_info)
-{
- ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(src, 1, DataType::F32);
- ARM_COMPUTE_RETURN_ON_ERROR((CpuWinogradConv2dTransformInputKernel<float, 4, 1, 5, 1>::validate(src, input0, winograd_info)));
- ARM_COMPUTE_RETURN_ON_ERROR((CpuWinogradConv2dTransformWeightsKernel<float, 4, 1, 5, 1>::validate(weights, input1, winograd_info)));
- ARM_COMPUTE_RETURN_ON_ERROR((CpuWinogradConv2dTransformOutputKernel<float, 4, 1, 5, 1>::validate(batched_mm_output, biases, dst, winograd_info)));
- if(act_info.enabled())
- {
- CpuActivation::validate(dst, nullptr, act_info);
- }
- return Status{};
-}
-
-inline Status validate_kernel_7x1(const ITensorInfo *src, const TensorInfo *input0, const TensorInfo *input1, const TensorInfo *batched_mm_output,
- const ITensorInfo *weights, const ITensorInfo *biases, const ITensorInfo *dst, const WinogradInfo &winograd_info, const ActivationLayerInfo &act_info)
-{
- ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(src, 1, DataType::F32);
- ARM_COMPUTE_RETURN_ON_ERROR((CpuWinogradConv2dTransformInputKernel<float, 1, 2, 1, 7>::validate(src, input0, winograd_info)));
- ARM_COMPUTE_RETURN_ON_ERROR((CpuWinogradConv2dTransformWeightsKernel<float, 1, 2, 1, 7>::validate(weights, input1, winograd_info)));
- ARM_COMPUTE_RETURN_ON_ERROR((CpuWinogradConv2dTransformOutputKernel<float, 1, 2, 1, 7>::validate(batched_mm_output, biases, dst, winograd_info)));
- if(act_info.enabled())
- {
- CpuActivation::validate(dst, nullptr, act_info);
- }
- return Status{};
-}
-
-inline Status validate_kernel_1x7(const ITensorInfo *src, const TensorInfo *input0, const TensorInfo *input1, const TensorInfo *batched_mm_output,
- const ITensorInfo *weights, const ITensorInfo *biases, const ITensorInfo *dst, const WinogradInfo &winograd_info, const ActivationLayerInfo &act_info)
-{
- ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(src, 1, DataType::F32);
- ARM_COMPUTE_RETURN_ON_ERROR((CpuWinogradConv2dTransformInputKernel<float, 2, 1, 7, 1>::validate(src, input0, winograd_info)));
- ARM_COMPUTE_RETURN_ON_ERROR((CpuWinogradConv2dTransformWeightsKernel<float, 2, 1, 7, 1>::validate(weights, input1, winograd_info)));
- ARM_COMPUTE_RETURN_ON_ERROR((CpuWinogradConv2dTransformOutputKernel<float, 2, 1, 7, 1>::validate(batched_mm_output, biases, dst, winograd_info)));
-
- if(act_info.enabled())
- {
- CpuActivation::validate(dst, nullptr, act_info);
- }
- return Status{};
-}
-
-inline Tensor4DShape internal_get_input_shape(const ITensorInfo *src)
-{
- const DataLayout data_layout = src->data_layout();
- const int in_width = src->dimension(get_data_layout_dimension_index(data_layout, DataLayoutDimension::WIDTH));
- const int in_height = src->dimension(get_data_layout_dimension_index(data_layout, DataLayoutDimension::HEIGHT));
- const int in_channels = src->dimension(get_data_layout_dimension_index(data_layout, DataLayoutDimension::CHANNEL));
- const int in_batches = src->dimension(3);
+ const DataLayout data_layout = in->data_layout();
+ const int in_width = in->dimension(get_data_layout_dimension_index(data_layout, DataLayoutDimension::WIDTH));
+ const int in_height = in->dimension(get_data_layout_dimension_index(data_layout, DataLayoutDimension::HEIGHT));
+ const int in_channels = in->dimension(get_data_layout_dimension_index(data_layout, DataLayoutDimension::CHANNEL));
+ const int in_batches = in->dimension(get_data_layout_dimension_index(data_layout, DataLayoutDimension::BATCHES));
return Tensor4DShape{ in_batches, in_height, in_width, in_channels };
}
Status validate_arguments(const ITensorInfo *src, const ITensorInfo *weights, const ITensorInfo *biases, const ITensorInfo *dst, const PadStrideInfo &conv_info)
{
- ARM_COMPUTE_UNUSED(dst);
+ ARM_COMPUTE_UNUSED(dst, weights);
ARM_COMPUTE_RETURN_ERROR_ON_CPU_F16_UNSUPPORTED(src);
ARM_COMPUTE_RETURN_ERROR_ON_MSG(conv_info.stride().first != 1 || conv_info.stride().second != 1, "Winograd layer only supports unit strides.");
@@ -229,108 +75,85 @@
ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(src, biases);
ARM_COMPUTE_RETURN_ERROR_ON(biases->num_dimensions() > 1);
}
- return ICpuWinogradConv2dTransformWeightsKernel::validate(src, weights);
+ ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(src, 1, DataType::F16, DataType::F32);
+ ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(src, weights);
+ return Status{};
}
-Size2D winograd_output_tile(const Size2D &input_dims, const Size2D &kernel_dims, DataType data_type)
+
+bool get_winograd_kernel_implementation(const ITensorInfo *src, const ITensorInfo *weights, const ITensorInfo *dst,
+ const PadStrideInfo &conv_info, const ActivationLayerInfo &act_info, bool enable_fast_math,
+ arm_conv::winograd::WinogradImpl *winograd_impl, std::unique_ptr<arm_conv::ConvolutionArgs> &conv_args)
{
- Size2D output_tile = Size2D{};
- if(kernel_dims == Size2D(3U, 3U))
+ arm_conv::winograd::WinogradConfig winograd_cfg;
+ arm_gemm::GemmConfig cfg;
+
+ const DataType data_type = src->data_type();
+ Tensor4DShape in_shape{ internal_get_shape(src) };
+ Tensor4DShape out_shape{ internal_get_shape(dst) };
+ Tensor4DShape kernel_shape{ internal_get_shape(weights) };
+ uint32_t nthreads = NEScheduler::get().num_threads();
+ // Get configuration arguments for Winograd
+ winograd_cfg.output_rows = 0;
+ winograd_cfg.output_cols = 0;
+ conv_args = std::make_unique<arm_conv::ConvolutionArgs>(
+ in_shape.n_batches,
+ arm_conv::Shape2D{ static_cast<uint32_t>(in_shape.n_rows), static_cast<uint32_t>(in_shape.n_cols) },
+ in_shape.n_channels,
+ conv_info.pad_top(),
+ conv_info.pad_left(),
+ arm_conv::Shape2D{ static_cast<uint32_t>(out_shape.n_rows), static_cast<uint32_t>(out_shape.n_cols) },
+ out_shape.n_channels,
+ arm_conv::Shape2D{ static_cast<uint32_t>(kernel_shape.n_rows), static_cast<uint32_t>(kernel_shape.n_cols) },
+ assembly_utils::map_to_arm_gemm_activation(act_info));
+
+ bool success = false;
+ if(data_type == DataType::F32)
{
- output_tile = (input_dims.width <= 4 || input_dims.height <= 4) ? Size2D(2U, 2U) : Size2D(4U, 4U);
- if(data_type == DataType::F16)
- {
- output_tile = Size2D(4U, 4U);
- }
+ success = arm_conv::winograd::get_implementation<float>(
+ *winograd_impl, &CPUInfo::get(), *conv_args, nthreads, enable_fast_math, &winograd_cfg, nullptr);
}
- else if(kernel_dims == Size2D(5U, 5U))
+#if defined(__aarch64__) && defined(__ARM_FEATURE_FP16_VECTOR_ARITHMETIC)
+ else if(data_type == DataType::F16)
{
- output_tile = Size2D(2U, 2U);
+ success = arm_conv::winograd::get_implementation<__fp16>(
+ *winograd_impl, &CPUInfo::get(), *conv_args, nthreads, enable_fast_math, &winograd_cfg, nullptr);
}
- else if(kernel_dims == Size2D(1U, 3U))
+#endif // defined(__aarch64__) && defined(__ARM_FEATURE_FP16_VECTOR_ARITHMETIC)
+ else
{
- output_tile = Size2D(1U, 6U);
+ success = false;
}
- else if(kernel_dims == Size2D(3U, 1U))
- {
- output_tile = Size2D(6U, 1U);
- }
- else if(kernel_dims == Size2D(1U, 5U))
- {
- output_tile = Size2D(1U, 4U);
- }
- else if(kernel_dims == Size2D(5U, 1U))
- {
- output_tile = Size2D(4U, 1U);
- }
- else if(kernel_dims == Size2D(7U, 1U))
- {
- output_tile = Size2D(2U, 1U);
- }
- else if(kernel_dims == Size2D(1U, 7U))
- {
- output_tile = Size2D(1U, 2U);
- }
- return output_tile;
+ return success;
}
-
-bool check_support_fast_math(const Size2D &output_tile, const Size2D &kernel_size, DataType data_type)
-{
- // Check if we want to configure a Winograd configuration which requires fast math
- using WinogradConfiguration = std::pair<std::pair<int, int>, std::pair<int, int>>;
-
- const std::vector<WinogradConfiguration> fast_math_winograd_f16 =
- {
- WinogradConfiguration(std::pair<int, int>(4, 4), std::pair<int, int>(3, 3))
- };
-
- const std::vector<WinogradConfiguration> fast_math_winograd_f32 =
- {
- WinogradConfiguration(std::pair<int, int>(2, 2), std::pair<int, int>(5, 5)),
- WinogradConfiguration(std::pair<int, int>(4, 4), std::pair<int, int>(5, 5))
- };
-
- auto p = std::make_pair(std::pair<int, int>(output_tile.width, output_tile.height),
- std::pair<int, int>(kernel_size.width, kernel_size.height));
-
- switch(data_type)
- {
- case DataType::F16:
- return std::find(fast_math_winograd_f16.begin(), fast_math_winograd_f16.end(), p) != fast_math_winograd_f16.end();
- case DataType::F32:
- return std::find(fast_math_winograd_f32.begin(), fast_math_winograd_f32.end(), p) != fast_math_winograd_f32.end();
- default:
- return false;
- }
-}
-
inline bool fuse_function_supported(const ActivationLayerInfo &act_info)
{
return act_info.activation() == ActivationLayerInfo::ActivationFunction::RELU || act_info.activation() == ActivationLayerInfo::ActivationFunction::BOUNDED_RELU;
}
-
} // namespace
CpuWinogradConv2d::CpuWinogradConv2d()
+
: _gemm_function(std::make_unique<CpuGemm>()),
_activation_func(std::make_unique<CpuActivation>()),
+ _transform_input_kernel(nullptr),
+ _transform_output_kernel(nullptr),
_permute_input(std::make_unique<CpuPermute>()),
_permute_output(std::make_unique<CpuPermute>()),
_permute_weights(std::make_unique<CpuPermute>()),
- _transform_input_kernel(nullptr),
- _transform_weights_kernel(nullptr),
- _transform_output_kernel(nullptr),
- _data_layout(),
_aux_mem(AuxTensorIdx::Count),
+ _conv_args{ nullptr },
+ _winograd_impl{},
+ _data_layout(),
+ _winograd_transformed_input{},
+ _winograd_transformed_output{},
+ _winograd_transformed_weights{},
+ _input_workspace(),
+ _output_workspace(),
+ _weights_hwio(),
_input_nhwc(),
_output_nhwc(),
- _input_workspace(),
- _kernel_storage(),
- _output_workspace(),
- _input_transformed(),
- _output_transformed(),
- _weights_hwio(),
- _run_activation(false),
- _is_prepared(false)
+ _is_prepared{ false },
+ _run_activation{ false }
{
}
@@ -342,464 +165,199 @@
ARM_COMPUTE_ERROR_ON_NULLPTR(src, weights, dst);
ARM_COMPUTE_ERROR_THROW_ON(validate_arguments(src, weights, biases, dst, conv_info));
ARM_COMPUTE_LOG_PARAMS(src, weights, biases, dst, conv_info, act_info, enable_fast_math);
+ ARM_COMPUTE_UNUSED(biases);
+ const DataType data_type = src->data_type();
+ uint32_t nthreads = NEScheduler::get().num_threads();
+ _data_layout = src->data_layout();
+ const Tensor4DShape kernel_shape{ internal_get_shape(weights) };
- // Get indices for the width and height
- _data_layout = src->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);
- const unsigned int channel_idx = get_data_layout_dimension_index(_data_layout, DataLayoutDimension::CHANNEL);
+ bool success = get_winograd_kernel_implementation(src, weights, dst, conv_info, act_info, enable_fast_math, &_winograd_impl, _conv_args);
- const Size2D input_dims = Size2D(src->dimension(width_idx), src->dimension(height_idx));
- const Size2D kernel_size = Size2D(weights->dimension(width_idx), weights->dimension(height_idx));
- const DataType data_type = src->data_type();
- const Size2D output_tile = winograd_output_tile(input_dims, kernel_size, data_type);
+ ARM_COMPUTE_EXIT_ON_MSG_VAR(!success, "Unsupported kernel size: %d x %d.\n", kernel_shape.n_rows, kernel_shape.n_cols);
+ ARM_COMPUTE_LOG_MSG_WITH_FORMAT_ACL(arm_compute::logging::LogLevel::INFO, "Using input transform: %s\n", _winograd_impl.input_transform->get_name().c_str());
+ ARM_COMPUTE_LOG_MSG_WITH_FORMAT_ACL(arm_compute::logging::LogLevel::INFO, "Using weight transform: %s\n", _winograd_impl.input_transform->get_name().c_str());
+ ARM_COMPUTE_LOG_MSG_WITH_FORMAT_ACL(arm_compute::logging::LogLevel::INFO, "Using output transform: %s\n", _winograd_impl.input_transform->get_name().c_str());
- // Check if the Winograd configuration requires fast math
- if(!enable_fast_math)
+ const bool has_impl = ((_winograd_impl.input_transform != nullptr) && (_winograd_impl.output_transform != nullptr) && (_winograd_impl.gemm_args != nullptr));
+ if(has_impl)
{
- ARM_COMPUTE_ERROR_ON_MSG(check_support_fast_math(output_tile, kernel_size, data_type),
- "This Winograd configuration requires enable_fast_math=true");
- }
+ // Determine how much working space is required, allocate it.
+ const size_t input_workspace_size = _winograd_impl.input_transform->get_working_space_size(*_conv_args, nthreads);
+ const size_t output_workspace_size = _winograd_impl.output_transform->get_working_space_size(*_conv_args, nthreads);
- _is_prepared = false;
+ TensorInfo input_workspace_info(TensorShape(input_workspace_size), 1, DataType::U8);
+ TensorInfo output_workspace_info(TensorShape(output_workspace_size), 1, DataType::U8);
+ _input_workspace = input_workspace_info;
+ _output_workspace = output_workspace_info;
- std::unique_ptr<ICpuWinogradConv2dTransformInputKernel> transform_input_kernel;
- std::unique_ptr<ICpuWinogradConv2dTransformWeightsKernel> transform_weights_kernel;
- std::unique_ptr<ICpuWinogradConv2dTransformOutputKernel> transform_output_kernel;
+ const auto &wds = _winograd_impl.winograd_spec;
- int n_gemms = 1;
- int N_BLOCK = 1; // Size of block used by GEMM.
- if(data_type == DataType::F32)
- {
- if(kernel_size == Size2D(3, 3))
+ // Preparing winograd transformed input tensor
+ const size_t data_type_size = src->element_size();
+ const uint32_t m = _winograd_impl.gemm_args->_Msize; // Total number of tiles
+ const uint32_t k = _winograd_impl.gemm_args->_Ksize; // Input channels
+ const uint32_t n = _winograd_impl.gemm_args->_Nsize; // Output channels
+ const uint32_t n_gemms = _winograd_impl.gemm_args->_nmulti;
+ const uint32_t n_batches = _winograd_impl.gemm_args->_nbatches;
+ constexpr size_t storage_alignment = 64;
+
+ const TensorShape a_shape(k, m, n_batches, n_gemms);
+ Strides a_strides(data_type_size);
+ a_strides.set(1, data_type_size * _winograd_impl.winograd_spec.input_ld_row);
+ a_strides.set(2, data_type_size * _winograd_impl.winograd_spec.input_ld_batch);
+ a_strides.set(3, data_type_size * _winograd_impl.winograd_spec.input_ld_matrix);
+
+ const TensorShape b_shape(n, k, n_gemms);
+ Strides b_strides(data_type_size);
+ b_strides.set(1, data_type_size * _winograd_impl.winograd_spec.weight_ld_row);
+ b_strides.set(2, data_type_size * _winograd_impl.winograd_spec.weight_ld_matrix);
+
+ const TensorShape d_shape(n, m, n_batches, n_gemms);
+ Strides d_strides(data_type_size);
+ d_strides.set(1, data_type_size * _winograd_impl.winograd_spec.output_ld_row);
+ d_strides.set(2, data_type_size * _winograd_impl.winograd_spec.output_ld_batch);
+ d_strides.set(3, data_type_size * _winograd_impl.winograd_spec.output_ld_matrix);
+
+ TensorInfo a_info{};
+ TensorInfo b_info{};
+ TensorInfo d_info{};
+ a_info.init(a_shape, 1, data_type, a_strides, 0, wds.input_matrix_size_bytes);
+ b_info.init(b_shape, 1, data_type, b_strides, 0, wds.weight_matrix_size_bytes);
+ d_info.init(d_shape, 1, data_type, d_strides, 0, wds.output_matrix_size_bytes);
+
+ _winograd_transformed_input = a_info;
+ _winograd_transformed_weights = b_info;
+ _winograd_transformed_output = d_info;
+
+ PermutationVector weights_permutation_vector(3U, 0U, 1U, 2U);
+
+ // Configure the kernel to transform the input tensor from NCHW -> NHWC
+ if(_data_layout == DataLayout::NCHW)
{
- if(src->dimension(width_idx) > 4 && src->dimension(height_idx) > 4)
- {
- using config = CpuWinogradConv2dConfiguration<float, float, 4, 4, 3, 3>;
- transform_input_kernel = std::make_unique<config::TransformInputKernel>();
- transform_weights_kernel = std::make_unique<config::TransformWeightsKernel>();
- transform_output_kernel = std::make_unique<config::TransformOutputKernel>();
- n_gemms = config::WinogradBase::N_GEMMS;
- N_BLOCK = config::WinogradConv::N_BLOCK;
- }
- else
- {
- using config = CpuWinogradConv2dConfiguration<float, float, 2, 2, 3, 3>;
- transform_input_kernel = std::make_unique<config::TransformInputKernel>();
- transform_weights_kernel = std::make_unique<config::TransformWeightsKernel>();
- transform_output_kernel = std::make_unique<config::TransformOutputKernel>();
- n_gemms = config::WinogradBase::N_GEMMS;
- N_BLOCK = config::WinogradConv::N_BLOCK;
- }
+ _permute_input->configure(src, &_input_nhwc, PermutationVector(2U, 0U, 1U));
+ weights_permutation_vector = PermutationVector(3U, 2U, 0U, 1U);
}
- else if(kernel_size == Size2D(5, 5))
+
+ // Re-order a weight tensor from [Output feature map x Input feature map x Height x Width] to [Height x Width x Input feature map x Output feature map]
+ _permute_weights->configure(weights, &_weights_hwio, weights_permutation_vector);
+
+ // Reorder the convoluted output to ACL's ordering NCHW
+ if(_data_layout == DataLayout::NCHW)
{
- using config = CpuWinogradConv2dConfiguration<float, float, 2, 2, 5, 5>;
- transform_input_kernel = std::make_unique<config::TransformInputKernel>();
- transform_weights_kernel = std::make_unique<config::TransformWeightsKernel>();
- transform_output_kernel = std::make_unique<config::TransformOutputKernel>();
- n_gemms = config::WinogradBase::N_GEMMS;
- N_BLOCK = config::WinogradConv::N_BLOCK;
+ // configure and allocate dst tensor to be used to convert from winograd domain to spatial domain when calling to reshape_output()
+ TensorInfo info(TensorShape(dst->dimension(2), dst->dimension(0),
+ dst->dimension(1), dst->dimension(3)),
+ 1, dst->data_type());
+ _output_nhwc = info;
+ _permute_output->configure(&_output_nhwc, dst, PermutationVector(1U, 2U, 0U));
}
- else if(kernel_size == Size2D(1, 3))
+
+ // Configure GEMM function
+ _gemm_function->configure(&_winograd_transformed_input, &_winograd_transformed_weights, nullptr, &_winograd_transformed_output, 1.0f, 0.f);
+
+ //Configure Activation Layer
+ _run_activation = act_info.enabled() && !fuse_function_supported(act_info);
+ if(_run_activation)
{
- using config = CpuWinogradConv2dConfiguration<float, float, 6, 1, 3, 1>;
- transform_input_kernel = std::make_unique<config::TransformInputKernel>();
- transform_weights_kernel = std::make_unique<config::TransformWeightsKernel>();
- transform_output_kernel = std::make_unique<config::TransformOutputKernel>();
- n_gemms = config::WinogradBase::N_GEMMS;
- N_BLOCK = config::WinogradConv::N_BLOCK;
+ _activation_func->configure(dst, nullptr, act_info);
}
- else if(kernel_size == Size2D(3, 1))
+
+ auto asm_mem_req = _gemm_function->workspace();
+ _aux_mem[GemmWorkspace] = asm_mem_req[GemmWorkspace];
+ _aux_mem[Pretranspose] = asm_mem_req[Pretranspose];
+ _aux_mem[InterleavedLHS] = asm_mem_req[InterleavedLHS];
+ _aux_mem[TransposedRHS] = asm_mem_req[TransposedRHS];
+ _aux_mem[TempResult] = asm_mem_req[TempResult];
+
+ // Request temporary memory. Overlap memory needed for Input/Output transformations as they run on different non-overlapping time-steps.
+ _aux_mem[TransformedInput] = MemoryInfo(offset_int_vec(TransformedInput), MemoryLifetime::Temporary, wds.input_matrix_size_bytes, storage_alignment);
+ _aux_mem[TransformedOutput] = MemoryInfo(offset_int_vec(TransformedOutput), MemoryLifetime::Temporary, wds.output_matrix_size_bytes, storage_alignment);
+ _aux_mem[WorkspaceIO] = MemoryInfo(offset_int_vec(WorkspaceIO), MemoryLifetime::Temporary, std::max(input_workspace_size, output_workspace_size));
+ _aux_mem[PermutedWeights] = MemoryInfo(offset_int_vec(PermutedWeights), MemoryLifetime::Prepare, _weights_hwio.total_size());
+ _aux_mem[TransformedWeights] = MemoryInfo(offset_int_vec(TransformedWeights), MemoryLifetime::Persistent, wds.weight_matrix_size_bytes, storage_alignment);
+ if(_data_layout == DataLayout::NCHW)
{
- using config = CpuWinogradConv2dConfiguration<float, float, 1, 6, 1, 3>;
- transform_input_kernel = std::make_unique<config::TransformInputKernel>();
- transform_weights_kernel = std::make_unique<config::TransformWeightsKernel>();
- transform_output_kernel = std::make_unique<config::TransformOutputKernel>();
- n_gemms = config::WinogradBase::N_GEMMS;
- N_BLOCK = config::WinogradConv::N_BLOCK;
+ _aux_mem[PermutedInput].merge(offset_int_vec(PermutedInput), src->total_size());
+ _aux_mem[PermutedOutput].merge(offset_int_vec(PermutedOutput), dst->total_size());
}
- else if(kernel_size == Size2D(1, 5))
- {
- using config = CpuWinogradConv2dConfiguration<float, float, 4, 1, 5, 1>;
- transform_input_kernel = std::make_unique<config::TransformInputKernel>();
- transform_weights_kernel = std::make_unique<config::TransformWeightsKernel>();
- transform_output_kernel = std::make_unique<config::TransformOutputKernel>();
- n_gemms = config::WinogradBase::N_GEMMS;
- N_BLOCK = config::WinogradConv::N_BLOCK;
- }
- else if(kernel_size == Size2D(5, 1))
- {
- using config = CpuWinogradConv2dConfiguration<float, float, 1, 4, 1, 5>;
- transform_input_kernel = std::make_unique<config::TransformInputKernel>();
- transform_weights_kernel = std::make_unique<config::TransformWeightsKernel>();
- transform_output_kernel = std::make_unique<config::TransformOutputKernel>();
- n_gemms = config::WinogradBase::N_GEMMS;
- N_BLOCK = config::WinogradConv::N_BLOCK;
- }
- else if(kernel_size == Size2D(1, 7))
- {
- using config = CpuWinogradConv2dConfiguration<float, float, 2, 1, 7, 1>;
- transform_input_kernel = std::make_unique<config::TransformInputKernel>();
- transform_weights_kernel = std::make_unique<config::TransformWeightsKernel>();
- transform_output_kernel = std::make_unique<config::TransformOutputKernel>();
- n_gemms = config::WinogradBase::N_GEMMS;
- N_BLOCK = config::WinogradConv::N_BLOCK;
- }
- else if(kernel_size == Size2D(7, 1))
- {
- using config = CpuWinogradConv2dConfiguration<float, float, 1, 2, 1, 7>;
- transform_input_kernel = std::make_unique<config::TransformInputKernel>();
- transform_weights_kernel = std::make_unique<config::TransformWeightsKernel>();
- transform_output_kernel = std::make_unique<config::TransformOutputKernel>();
- n_gemms = config::WinogradBase::N_GEMMS;
- N_BLOCK = config::WinogradConv::N_BLOCK;
- }
- else
- {
- ARM_COMPUTE_ERROR("Not supported.");
- }
- }
-#ifdef __ARM_FEATURE_FP16_VECTOR_ARITHMETIC
- else if(data_type == DataType::F16)
- {
- if(kernel_size == Size2D(3, 3))
- {
- using config = CpuWinogradConv2dConfiguration<__fp16, __fp16, 4, 4, 3, 3>;
- transform_input_kernel = std::make_unique<config::TransformInputKernel>();
- transform_weights_kernel = std::make_unique<config::TransformWeightsKernel>();
- transform_output_kernel = std::make_unique<config::TransformOutputKernel>();
- n_gemms = config::WinogradBase::N_GEMMS;
- N_BLOCK = config::WinogradConv::N_BLOCK;
- }
- else
- {
- ARM_COMPUTE_ERROR("Not supported.");
- }
- }
-#endif // __ARM_FEATURE_FP16_VECTOR_ARITHMETIC
- else
- {
- ARM_COMPUTE_ERROR("Not supported.");
- }
-
- const PaddingType use_padding_type = (conv_info.pad_top() != 0u || conv_info.pad_left() != 0) ? PADDING_SAME : PADDING_VALID;
- const bool use_same_padding = use_padding_type == PADDING_SAME;
-
- // Get convolved dimensions
- const int in_channels = src->dimension(channel_idx);
- const int out_channels = dst->dimension(channel_idx);
-
- const Tensor4DShape in_shape(internal_get_input_shape(src));
- const size_t data_type_size = src->element_size();
- // Get the memory required to instantiate a new Winograd operator.
- constexpr size_t storage_alignment = 64;
-
- // Kernel Storage
- const size_t kernel_storage_size = transform_weights_kernel->get_weight_storage_size(out_channels, in_channels) * data_type_size;
-
- // Input storage
- const size_t input_storage_size = transform_input_kernel->get_input_storage_size(in_shape.n_batches, in_shape.n_channels, in_shape.n_rows, in_shape.n_cols, use_same_padding) * data_type_size;
-
- // Output storage
- const size_t output_storage_size = transform_output_kernel->get_output_storage_size(in_shape.n_batches, in_shape.n_rows, in_shape.n_cols, out_channels) * data_type_size;
- const int kernel_matrix_stride = transform_weights_kernel->get_matrix_stride(out_channels, in_channels);
- const int output_matrix_stride = transform_output_kernel->get_matrix_stride(in_shape.n_batches, in_shape.n_rows, in_shape.n_cols, out_channels);
- const auto output_shape = transform_output_kernel->get_output_shape(in_shape.n_rows, in_shape.n_cols, use_padding_type == PADDING_SAME);
- const int input_matrix_stride = transform_input_kernel->get_matrix_stride(in_shape.n_batches, in_channels, in_shape.n_rows, in_shape.n_cols, use_padding_type == PADDING_SAME);
-
- // Configure GEMM
- const int tile_rows = iceildiv(output_shape.first, output_tile.height);
- const int tile_cols = iceildiv(output_shape.second, output_tile.width);
- 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 kernel_matrix_row_stride = roundup(out_channels, N_BLOCK);
- const int output_matrix_row_stride = kernel_matrix_row_stride;
-
- TensorShape a_shape(k, m, 1, n_gemms);
- Strides a_strides(data_type_size);
- a_strides.set(1, a_strides[0] * k);
- //a_strides.set(2, data_type_size * input_matrix_stride / n_gemms); FIXME: This is the real batch size, but RSH's code crashes if it's not 0.
- a_strides.set(2, 0);
- a_strides.set(3, data_type_size * input_matrix_stride);
-
- TensorShape b_shape(n, k, n_gemms);
- Strides b_strides(data_type_size);
- b_strides.set(1, data_type_size * kernel_matrix_row_stride);
- b_strides.set(2, data_type_size * kernel_matrix_stride);
-
- TensorShape d_shape(n, m, 1, n_gemms);
- Strides d_strides(data_type_size);
- d_strides.set(1, data_type_size * output_matrix_row_stride);
- //d_strides.set(2, data_type_size * output_matrix_stride / n_gemms); FIXME: This is the real batch size, but RSH's code crashes if it's not 0.
- d_strides.set(2, 0);
- d_strides.set(3, data_type_size * output_matrix_stride);
-
- TensorInfo a_info{};
- TensorInfo b_info{};
- TensorInfo d_info{};
- a_info.init(a_shape, 1, data_type, a_strides, 0, input_storage_size);
- b_info.init(b_shape, 1, data_type, b_strides, 0, kernel_storage_size);
- d_info.init(d_shape, 1, data_type, d_strides, 0, output_storage_size);
-
- _input_transformed = a_info;
- _kernel_storage = b_info;
- _output_transformed = d_info;
-
- const ITensorInfo *input_to_use = src;
- ITensorInfo *output_to_use = dst;
- PermutationVector weights_permutation_vector(3U, 0U, 1U, 2U);
- const unsigned int max_num_threads = NEScheduler::get().num_threads();
-
- // Configure the kernel to transform the input tensor from NCHW -> NHWC
- if(_data_layout == DataLayout::NCHW)
- {
- _permute_input->configure(src, &_input_nhwc, PermutationVector(2U, 0U, 1U));
- input_to_use = &_input_nhwc;
- weights_permutation_vector = PermutationVector(3U, 2U, 0U, 1U);
- }
-
- // Configure input transform kernel
- transform_input_kernel->configure(input_to_use, in_shape.n_batches, in_shape.n_rows, in_shape.n_cols, in_shape.n_channels, use_padding_type,
- &_input_transformed, input_matrix_stride, &_input_workspace);
- const size_t input_workspace_size = transform_input_kernel->get_working_space_size(max_num_threads);
- TensorInfo input_workspace_info(TensorShape(input_workspace_size), 1, DataType::U8);
- _input_workspace = input_workspace_info;
-
- // Re-order a weight tensor from [Output feature map x Input feature map x Height x Width] to [Height x Width x Input feature map x Output feature map]
- _permute_weights->configure(weights, &_weights_hwio, weights_permutation_vector);
- transform_weights_kernel->configure(&_weights_hwio, &_kernel_storage, kernel_matrix_stride, out_channels, in_channels);
-
- // Configure GEMM function
- _gemm_function->configure(&_input_transformed, &_kernel_storage, nullptr, &_output_transformed, 1.0f, 0.f);
-
- // Configure output transform function
- // 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
- if(_data_layout == DataLayout::NCHW)
- {
- // configure and allocate dst tensor to be used to convert from winograd domain to spatial domain when calling to reshape_output()
- TensorInfo info(TensorShape(dst->dimension(2), dst->dimension(0),
- dst->dimension(1), dst->dimension(3)),
- 1, dst->data_type());
- _output_nhwc = info;
- output_to_use = &_output_nhwc;
- }
- const arm_gemm::Activation activation = arm_gemm_activation_from_acl_activation(act_info);
-
- transform_output_kernel->configure(biases,
- &_output_transformed,
- output_matrix_stride,
- output_to_use,
- in_shape.n_batches,
- output_shape.first,
- output_shape.second,
- out_channels,
- &_output_workspace,
- activation);
-
- const size_t output_workspace_size = transform_output_kernel->get_working_space_size(max_num_threads);
- TensorInfo output_workspace_info(TensorShape(output_workspace_size), 1, DataType::U8);
- _output_workspace = output_workspace_info;
-
- // Reorder the convoluted output to ACL's ordering NCHW
- if(_data_layout == DataLayout::NCHW)
- {
- _permute_output->configure(&_output_nhwc, dst, PermutationVector(1U, 2U, 0U));
- }
-
- _transform_input_kernel = std::move(transform_input_kernel);
- _transform_weights_kernel = std::move(transform_weights_kernel);
- _transform_output_kernel = std::move(transform_output_kernel);
-
- //Configure Activation Layer
- _run_activation = act_info.enabled() && !fuse_function_supported(act_info);
- if(_run_activation)
- {
- _activation_func->configure(dst, nullptr, act_info);
- }
-
- auto asm_mem_req = _gemm_function->workspace();
- _aux_mem[GemmWorkspace] = asm_mem_req[GemmWorkspace];
- _aux_mem[Pretranspose] = asm_mem_req[Pretranspose];
- _aux_mem[InterleavedLHS] = asm_mem_req[InterleavedLHS];
- _aux_mem[TransposedRHS] = asm_mem_req[TransposedRHS];
- _aux_mem[TempResult] = asm_mem_req[TempResult];
-
- // Request temporary memory. Overlap memory needed for Input/Output transformations as they run on different non-overlapping time-steps.
- _aux_mem[TransformedInput] = MemoryInfo(offset_int_vec(TransformedInput), MemoryLifetime::Temporary, input_storage_size, storage_alignment);
- _aux_mem[TransformedOutput] = MemoryInfo(offset_int_vec(TransformedOutput), MemoryLifetime::Temporary, output_storage_size, storage_alignment);
- _aux_mem[WorkspaceIO] = MemoryInfo(offset_int_vec(WorkspaceIO), MemoryLifetime::Temporary, std::max(input_workspace_size, output_workspace_size));
- _aux_mem[PermutedWeights] = MemoryInfo(offset_int_vec(PermutedWeights), MemoryLifetime::Prepare, _weights_hwio.total_size());
- _aux_mem[TransformedWeights] = MemoryInfo(offset_int_vec(TransformedWeights), MemoryLifetime::Persistent, kernel_storage_size, storage_alignment);
- if(_data_layout == DataLayout::NCHW)
- {
- _aux_mem[PermutedInput].merge(offset_int_vec(PermutedInput), src->total_size());
- _aux_mem[PermutedOutput].merge(offset_int_vec(PermutedOutput), dst->total_size());
}
}
-
Status CpuWinogradConv2d::validate(const ITensorInfo *src, const ITensorInfo *weights, const ITensorInfo *biases, const ITensorInfo *dst,
const PadStrideInfo &conv_info, const ActivationLayerInfo &act_info, bool enable_fast_math)
{
ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(src, weights, dst);
ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments(src, weights, biases, dst, conv_info));
- // Get indices for the width and height
- const size_t idx_width = get_data_layout_dimension_index(src->data_layout(), DataLayoutDimension::WIDTH);
- const size_t idx_height = get_data_layout_dimension_index(src->data_layout(), DataLayoutDimension::HEIGHT);
+ const Tensor4DShape kernel_shape{ internal_get_shape(weights) };
+ arm_conv::winograd::WinogradImpl winograd_impl{};
- // Input shape, kernel size and output tile
- const Size2D input_dims = Size2D(src->dimension(idx_width), src->dimension(idx_height));
- const Size2D kernel_size = Size2D(weights->dimension(idx_width), weights->dimension(idx_height));
- const DataType data_type = src->data_type();
- const Size2D output_tile = winograd_output_tile(input_dims, kernel_size, data_type);
+ std::unique_ptr<arm_conv::ConvolutionArgs> conv_args;
+ const bool success = get_winograd_kernel_implementation(src, weights, dst, conv_info, act_info, enable_fast_math, &winograd_impl, conv_args);
- // Check if the Winograd configuration requires fast math
- if(!enable_fast_math)
- {
- ARM_COMPUTE_RETURN_ERROR_ON_MSG(check_support_fast_math(output_tile, kernel_size, data_type),
- "This Winograd configuration requires enable_fast_math=true");
- }
-
- const WinogradInfo winograd_info = WinogradInfo(output_tile,
- kernel_size,
- input_dims,
- conv_info,
- src->data_layout());
-
- // Validate input transform
- const TensorShape input0_shape = misc::shape_calculator::compute_winograd_input_transform_shape(*src, winograd_info);
- const TensorInfo input0 = src->clone()->set_tensor_shape(input0_shape);
- // Validate filter transform
- const TensorShape input1_shape = misc::shape_calculator::compute_winograd_filter_transform_shape(*weights, winograd_info);
- const TensorInfo input1 = weights->clone()->set_tensor_shape(input1_shape);
- // Validate batched matrix multiply
- TensorShape batched_mm_output_shape = input0.tensor_shape();
- batched_mm_output_shape[0] = input1.tensor_shape()[0];
- const TensorInfo batched_mm_output = input0.clone()->set_tensor_shape(batched_mm_output_shape);
-
- if(kernel_size == Size2D(3, 3))
- {
- ARM_COMPUTE_RETURN_ERROR_ON_MSG(conv_info.pad_top() != 0u && conv_info.pad_top() != 1, "Only SAME or VALID padding supported");
- ARM_COMPUTE_RETURN_ERROR_ON_MSG(conv_info.pad_bottom() != 0u && conv_info.pad_bottom() != 1, "Only SAME or VALID padding supported");
- ARM_COMPUTE_RETURN_ERROR_ON_MSG(conv_info.pad_left() != 0u && conv_info.pad_left() != 1, "Only SAME or VALID padding supported");
- ARM_COMPUTE_RETURN_ERROR_ON_MSG(conv_info.pad_right() != 0u && conv_info.pad_right() != 1, "Only SAME or VALID padding supported");
- ARM_COMPUTE_RETURN_ERROR_ON_MSG(conv_info.pad_right() != conv_info.pad_left(), "Only SAME or VALID padding supported");
- ARM_COMPUTE_RETURN_ERROR_ON_MSG(conv_info.pad_top() != conv_info.pad_bottom(), "Only SAME or VALID padding supported");
- ARM_COMPUTE_RETURN_ERROR_ON_MSG(conv_info.pad_top() != conv_info.pad_left(), "Only SAME or VALID padding supported");
- return validate_kernel_3x3(input_dims, src, &input0, &input1, &batched_mm_output, weights, biases, dst, winograd_info, act_info);
- }
- else if(kernel_size == Size2D(5, 5))
- {
- ARM_COMPUTE_RETURN_ERROR_ON_MSG(conv_info.pad_top() != 0u && conv_info.pad_top() != 2, "Only SAME or VALID padding supported");
- ARM_COMPUTE_RETURN_ERROR_ON_MSG(conv_info.pad_left() != 0u && conv_info.pad_left() != 2, "Only SAME or VALID padding supported");
- ARM_COMPUTE_RETURN_ERROR_ON_MSG(conv_info.pad_bottom() != 0u && conv_info.pad_bottom() != 2, "Only SAME or VALID padding supported");
- ARM_COMPUTE_RETURN_ERROR_ON_MSG(conv_info.pad_right() != 0u && conv_info.pad_right() != 2, "Only SAME or VALID padding supported");
- ARM_COMPUTE_RETURN_ERROR_ON_MSG(conv_info.pad_right() != conv_info.pad_left(), "Only SAME or VALID padding supported");
- ARM_COMPUTE_RETURN_ERROR_ON_MSG(conv_info.pad_top() != conv_info.pad_bottom(), "Only SAME or VALID padding supported");
- ARM_COMPUTE_RETURN_ERROR_ON_MSG(conv_info.pad_top() != conv_info.pad_left(), "Only SAME or VALID padding supported");
- return validate_kernel_5x5(src, &input0, &input1, &batched_mm_output, weights, biases, dst, winograd_info, act_info);
- }
- if(kernel_size == Size2D(3, 1))
- {
- ARM_COMPUTE_RETURN_ERROR_ON_MSG(conv_info.pad_left() != 0u && conv_info.pad_left() != 1, "Only SAME or VALID padding supported");
- ARM_COMPUTE_RETURN_ERROR_ON_MSG(conv_info.pad_right() != 0u && conv_info.pad_right() != 1, "Only SAME or VALID padding supported");
- ARM_COMPUTE_RETURN_ERROR_ON_MSG(conv_info.pad_top() != 0u && conv_info.pad_bottom() != 0, "Only SAME or VALID padding supported");
- return validate_kernel_3x1(src, &input0, &input1, &batched_mm_output, weights, biases, dst, winograd_info, act_info);
- }
- else if(kernel_size == Size2D(1, 3))
- {
- ARM_COMPUTE_RETURN_ERROR_ON_MSG(conv_info.pad_top() != 0u && conv_info.pad_top() != 1, "Only SAME or VALID padding supported");
- ARM_COMPUTE_RETURN_ERROR_ON_MSG(conv_info.pad_bottom() != 0u && conv_info.pad_bottom() != 1, "Only SAME or VALID padding supported");
- ARM_COMPUTE_RETURN_ERROR_ON_MSG(conv_info.pad_left() != 0u && conv_info.pad_right() != 0, "Only SAME or VALID padding supported");
- return validate_kernel_1x3(src, &input0, &input1, &batched_mm_output, weights, biases, dst, winograd_info, act_info);
- }
- else if(kernel_size == Size2D(5, 1))
- {
- ARM_COMPUTE_RETURN_ERROR_ON_MSG(conv_info.pad_left() != 0u && conv_info.pad_left() != 2, "Only SAME or VALID padding supported");
- ARM_COMPUTE_RETURN_ERROR_ON_MSG(conv_info.pad_right() != 0u && conv_info.pad_right() != 2, "Only SAME or VALID padding supported");
- ARM_COMPUTE_RETURN_ERROR_ON_MSG(conv_info.pad_top() != 0u && conv_info.pad_bottom() != 0, "Only SAME or VALID padding supported");
- return validate_kernel_5x1(src, &input0, &input1, &batched_mm_output, weights, biases, dst, winograd_info, act_info);
- }
- else if(kernel_size == Size2D(1, 5))
- {
- ARM_COMPUTE_RETURN_ERROR_ON_MSG(conv_info.pad_top() != 0u && conv_info.pad_top() != 2, "Only SAME or VALID padding supported");
- ARM_COMPUTE_RETURN_ERROR_ON_MSG(conv_info.pad_bottom() != 0u && conv_info.pad_bottom() != 2, "Only SAME or VALID padding supported");
- ARM_COMPUTE_RETURN_ERROR_ON_MSG(conv_info.pad_left() != 0u && conv_info.pad_right() != 0, "Only SAME or VALID padding supported");
- return validate_kernel_1x5(src, &input0, &input1, &batched_mm_output, weights, biases, dst, winograd_info, act_info);
- }
- else if(kernel_size == Size2D(7, 1))
- {
- ARM_COMPUTE_RETURN_ERROR_ON_MSG(conv_info.pad_left() != 0u && conv_info.pad_left() != 3, "Only SAME or VALID padding supported");
- ARM_COMPUTE_RETURN_ERROR_ON_MSG(conv_info.pad_right() != 0u && conv_info.pad_right() != 3, "Only SAME or VALID padding supported");
- ARM_COMPUTE_RETURN_ERROR_ON_MSG(conv_info.pad_top() != 0u && conv_info.pad_bottom() != 0, "Only SAME or VALID padding supported");
- return validate_kernel_7x1(src, &input0, &input1, &batched_mm_output, weights, biases, dst, winograd_info, act_info);
- }
- else if(kernel_size == Size2D(1, 7))
- {
- ARM_COMPUTE_RETURN_ERROR_ON_MSG(conv_info.pad_top() != 0u && conv_info.pad_top() != 3, "Only SAME or VALID padding supported");
- ARM_COMPUTE_RETURN_ERROR_ON_MSG(conv_info.pad_bottom() != 0u && conv_info.pad_bottom() != 3, "Only SAME or VALID padding supported");
- ARM_COMPUTE_RETURN_ERROR_ON_MSG(conv_info.pad_left() != 0u && conv_info.pad_right() != 0, "Only SAME or VALID padding supported");
- return validate_kernel_1x7(src, &input0, &input1, &batched_mm_output, weights, biases, dst, winograd_info, act_info);
- }
- else
- {
- ARM_COMPUTE_RETURN_ERROR_MSG("Kernel shape not supported");
- }
+ ARM_COMPUTE_RETURN_ERROR_ON_MSG_VAR(success == false, "Unsupported kernel size: %d x %d.\n", kernel_shape.n_rows, kernel_shape.n_cols);
+ ARM_COMPUTE_LOG_MSG_WITH_FORMAT_ACL(arm_compute::logging::LogLevel::INFO, "Using input transform: %s\n", winograd_impl.input_transform->get_name().c_str());
+ ARM_COMPUTE_LOG_MSG_WITH_FORMAT_ACL(arm_compute::logging::LogLevel::INFO, "Using weight transform: %s\n", winograd_impl.input_transform->get_name().c_str());
+ ARM_COMPUTE_LOG_MSG_WITH_FORMAT_ACL(arm_compute::logging::LogLevel::INFO, "Using output transform: %s\n", winograd_impl.input_transform->get_name().c_str());
+ return Status{};
}
void CpuWinogradConv2d::run(ITensorPack &tensors)
{
prepare(tensors);
+ auto src = tensors.get_const_tensor(ACL_SRC_0);
+ auto biases = tensors.get_const_tensor(ACL_SRC_2);
+ auto output = tensors.get_tensor(ACL_DST);
+ Window win;
- auto a = tensors.get_const_tensor(ACL_SRC_0);
- auto c = tensors.get_const_tensor(ACL_SRC_2);
- auto d = tensors.get_tensor(ACL_DST);
+ const uint32_t nthreads = NEScheduler::get().num_threads();
+ // The Winograd transform implementation does fine-grain threading inside the transforms. Just pass thread_id and nthreads.
+ win.set(Window::DimX, Window::Dimension(0, nthreads, 1));
+
+ // Wrap the winograd-domain tensorInfos created in configuration in tensors and allocate the required memory.
CpuAuxTensorHandler input_nhwc(offset_int_vec(PermutedInput), _input_nhwc, tensors, true);
- CpuAuxTensorHandler input_transformed(offset_int_vec(TransformedInput), _input_transformed, tensors, true);
+ CpuAuxTensorHandler winograd_input_transformed(offset_int_vec(TransformedInput), _winograd_transformed_input, tensors, true);
CpuAuxTensorHandler input_workspace(offset_int_vec(WorkspaceIO), _input_workspace, tensors, true);
-
- const bool is_nchw = _data_layout == DataLayout::NCHW;
+ const bool is_nchw = _data_layout == DataLayout::NCHW;
if(is_nchw)
{
//Bring channels to the front as Winograd code expects the tensor to be in the format NHWC
- ITensorPack pack{ { ACL_SRC, a }, { ACL_DST, input_nhwc.get() } };
+ ITensorPack pack{ { ACL_SRC, src }, { ACL_DST, input_nhwc.get() } };
_permute_input->run(pack);
}
- // Transform input tensor to the winograd domain
- ITensorPack transform_input_pack{ { ACL_SRC, is_nchw ? input_nhwc.get() : a }, { ACL_DST, input_transformed.get() }, { ACL_INT, input_workspace.get() } };
- NEScheduler::get().schedule_op(_transform_input_kernel.get(), Window::DimX, _transform_input_kernel->window(), transform_input_pack);
+ CpuAuxTensorHandler winograd_output_transformed(offset_int_vec(TransformedOutput), _winograd_transformed_output, tensors, true);
+ CpuAuxTensorHandler output_workspace(offset_int_vec(WorkspaceIO), _output_workspace, tensors, true);
+ CpuAuxTensorHandler output_nhwc(offset_int_vec(PermutedOutput), _output_nhwc, tensors, true);
- CpuAuxTensorHandler output_transformed(offset_int_vec(TransformedOutput), _output_transformed, tensors, true);
- CpuAuxTensorHandler weights_transformed(offset_int_vec(TransformedWeights), _kernel_storage, tensors, true);
+ ITensorPack transform_input_pack{ { ACL_SRC, is_nchw ? input_nhwc.get() : src }, { ACL_DST, winograd_input_transformed.get() }, { ACL_INT, input_workspace.get() } };
+ _transform_input_kernel = std::make_unique<CpuWinogradConv2dTransformInputKernel>(_winograd_impl, *_conv_args, nthreads);
+
+ NEScheduler::get().schedule_op(_transform_input_kernel.get(), Window::DimX, win, transform_input_pack);
+
+ CpuAuxTensorHandler winograd_weights_transformed(offset_int_vec(TransformedWeights), _winograd_transformed_weights, tensors, true);
// Run 16 GEMMs in multiple threads, each kernel runs one or more GEMMs
ITensorPack gemm_pack = tensors;
- gemm_pack.add_const_tensor(ACL_SRC, input_transformed.get());
- gemm_pack.add_const_tensor(ACL_SRC_1, weights_transformed.get());
+ gemm_pack.add_const_tensor(ACL_SRC, winograd_input_transformed.get());
+ gemm_pack.add_const_tensor(ACL_SRC_1, winograd_weights_transformed.get());
gemm_pack.add_const_tensor(ACL_BIAS, nullptr);
- gemm_pack.add_tensor(ACL_DST, output_transformed.get());
+ gemm_pack.add_tensor(ACL_DST, winograd_output_transformed.get());
_gemm_function->run(gemm_pack);
- // Transform output tensor to the spatial domain
- CpuAuxTensorHandler output_workspace(offset_int_vec(WorkspaceIO), _output_workspace, tensors, true);
- CpuAuxTensorHandler output_nhwc(offset_int_vec(PermutedOutput), _output_nhwc, tensors, true);
- ITensorPack transform_output_pack{ { ACL_SRC_0, c }, { ACL_SRC_1, output_transformed.get() }, { ACL_DST, is_nchw ? output_nhwc.get() : d }, { ACL_INT, output_workspace.get() } };
- NEScheduler::get().schedule_op(_transform_output_kernel.get(), Window::DimX, _transform_output_kernel->window(), transform_output_pack);
-
+ // Output transform
+ _transform_output_kernel = std::make_unique<CpuWinogradConv2dTransformOutputKernel>(_winograd_impl, *_conv_args, nthreads);
+ ITensorPack transform_output_pack{ { ACL_SRC_0, winograd_output_transformed.get() }, { ACL_DST, is_nchw ? output_nhwc.get() : output }, { ACL_SRC_1, biases }, { ACL_INT, output_workspace.get() } };
+ NEScheduler::get().schedule_op(_transform_output_kernel.get(), Window::DimX, win, transform_output_pack);
if(is_nchw)
{
// Reorder the convoluted output to ACL's ordering NCHW
- ITensorPack pack{ { ACL_SRC, output_nhwc.get() }, { ACL_DST, d } };
+ ITensorPack pack{ { ACL_SRC, output_nhwc.get() }, { ACL_DST, output } };
_permute_output->run(pack);
}
-
if(_run_activation)
{
- ITensorPack pack{ { ACL_SRC, d }, { ACL_DST, d } };
+ ITensorPack pack{ { ACL_SRC, output }, { ACL_DST, output } };
_activation_func->run(pack);
}
}
@@ -808,34 +366,54 @@
{
if(!_is_prepared)
{
- // Permute weights
const ITensor *weights = tensors.get_const_tensor(ACL_SRC_1);
ITensor *weights_aux = utils::cast::polymorphic_cast<ITensor *>(tensors.get_tensor(offset_int_vec(PermutedWeights)));
- ARM_COMPUTE_ERROR_ON_NULLPTR(weights, weights_aux);
CpuAuxTensorHandler permuted_weights(_weights_hwio, *weights_aux);
ITensorPack permute_tensors{ { ACL_SRC, weights }, { ACL_DST, permuted_weights.get() } };
_permute_weights->run(permute_tensors);
+ const int element_size_in_bytes = permuted_weights.get()->info()->element_size();
+ // Weights were in OHWI format, before being permuted "permuted_weights" to be in HWIO format.
+ const unsigned int height_idx = 3; // H in HWIO
+ const unsigned int width_idx = 2; // W in HWIO
+ const unsigned int channel_idx = 1; // I in HWIO
- // Transform weights
+ const int permuted_weight_row_stride = permuted_weights.get()->info()->strides_in_bytes()[height_idx] / element_size_in_bytes;
+ const int permuted_weight_col_stride = permuted_weights.get()->info()->strides_in_bytes()[width_idx] / element_size_in_bytes;
+ const int permuted_weight_channel_stride = permuted_weights.get()->info()->strides_in_bytes()[channel_idx] / element_size_in_bytes;
+
+ // Wrap the winograd-domain transformed weight TensorInfo in Auxiliary tensor and allocate the required memory.
ITensor *weights_transf = utils::cast::polymorphic_cast<ITensor *>(tensors.get_tensor(offset_int_vec(TransformedWeights)));
ARM_COMPUTE_ERROR_ON_NULLPTR(weights_transf);
+ CpuAuxTensorHandler winograd_transformed_weights(_winograd_transformed_weights, *weights_transf);
- CpuAuxTensorHandler transformed_weights(_kernel_storage, *weights_transf);
- ITensorPack transform_tensors{ { ACL_SRC, permuted_weights.get() }, { ACL_DST, transformed_weights.get() } };
- NEScheduler::get().schedule_op(_transform_weights_kernel.get(), Window::DimX, _transform_weights_kernel->window(), transform_tensors);
+ const void *permuted_weights_ptr;
+ void *win_wght_transf_ptr;
+ permuted_weights_ptr = reinterpret_cast<const void *>(permuted_weights.get()->buffer() + permuted_weights.get()->info()->offset_first_element_in_bytes());
+ win_wght_transf_ptr = reinterpret_cast<void *>(winograd_transformed_weights.get()->buffer() + winograd_transformed_weights.get()->info()->offset_first_element_in_bytes());
+
+ // Prepare Weights
+ _winograd_impl.weight_transform->execute(
+ *_conv_args,
+ permuted_weights_ptr,
+ permuted_weight_row_stride,
+ permuted_weight_col_stride,
+ permuted_weight_channel_stride,
+ win_wght_transf_ptr,
+ _winograd_impl.winograd_spec,
+ 0, 1 // Thread 1 of 1
+ );
ITensorPack gemm_pack = tensors;
- gemm_pack.add_const_tensor(ACL_SRC_1, transformed_weights.get());
+ gemm_pack.add_const_tensor(ACL_SRC_1, winograd_transformed_weights.get());
_gemm_function->prepare(gemm_pack);
-
- _is_prepared = true;
+ _is_prepared = 1;
}
}
-
experimental::MemoryRequirements CpuWinogradConv2d::workspace() const
{
return _aux_mem;
}
+
} // namespace cpu
-} // namespace arm_compute
\ No newline at end of file
+} // namespace arm_compute
diff --git a/src/cpu/operators/CpuWinogradConv2d.h b/src/cpu/operators/CpuWinogradConv2d.h
index 0abd110..e0df34e 100644
--- a/src/cpu/operators/CpuWinogradConv2d.h
+++ b/src/cpu/operators/CpuWinogradConv2d.h
@@ -1,5 +1,5 @@
/*
- * Copyright (c) 2021 Arm Limited.
+ * Copyright (c) 2021-2022 Arm Limited.
*
* SPDX-License-Identifier: MIT
*
@@ -29,6 +29,7 @@
#include "src/core/common/Macros.h"
#include "src/cpu/ICpuOperator.h"
#include "src/cpu/kernels/CpuWinogradConv2dKernel.h"
+#include "src/cpu/kernels/assembly/gemm_common.hpp"
#include "src/cpu/operators/CpuActivation.h"
#include "src/cpu/operators/CpuGemm.h"
#include "src/cpu/operators/CpuPermute.h"
@@ -59,13 +60,13 @@
* |F16 |F16 |F16 |F16 |
* |F32 |F32 |F32 |F32 |
*
- * @param[in] src Source tensor info. 3 lower dimensions represent a single input [width, height, IFM],
+ * @param[in] src Source tensor Info. 3 lower dimensions represent a single input [width, height, IFM],
* while every optional dimension from 4 and above represent a batch of inputs.
* Data types supported: F16/F32.
- * @param[in] weights Weights tensor info. Weights are 4D tensor with dimensions [kernel_x, kernel_y, IFM, OFM]. Data type supported: Same as @p input.
+ * @param[in] weights Weights tensor Info. Weights are 4D tensor with dimensions [kernel_x, kernel_y, IFM, OFM]. Data type supported: Same as @p input.
* Currently only 3x3 and 5x5 kernels are supported.
- * @param[in] biases Biases tensor info. Shared biases supported. Biases are 1D tensor with dimensions [OFM]. Data type supported: Same as @p weights.
- * @param[out] dst Destination tensor info. 3 lower dimensions represent a single output [width, height, OFM], while the rest represent batch of outputs.
+ * @param[in] biases Biases tensor Info. Shared biases supported. Biases are 1D tensor with dimensions [OFM]. Data type supported: Same as @p weights.
+ * @param[out] dst Destination tensor Info. 3 lower dimensions represent a single output [width, height, OFM], while the rest represent batch of outputs.
* Data types supported: Same as @p input.
* @param[in] conv_info Contains padding and stride information described in @ref PadStrideInfo. Currently only unit strides are supported.
* @param[in] act_info (Optional) Activation layer information in case of a fused activation.
@@ -107,28 +108,27 @@
PermutedOutput = TransformedInput,
Count = 10
};
-
- std::unique_ptr<CpuGemm> _gemm_function;
- std::unique_ptr<CpuActivation> _activation_func;
- std::unique_ptr<CpuPermute> _permute_input;
- std::unique_ptr<CpuPermute> _permute_output;
- std::unique_ptr<CpuPermute> _permute_weights;
- std::unique_ptr<ICPPKernel> _transform_input_kernel;
- std::unique_ptr<ICPPKernel> _transform_weights_kernel;
- std::unique_ptr<ICPPKernel> _transform_output_kernel;
-
- DataLayout _data_layout;
- experimental::MemoryRequirements _aux_mem{ Count };
- TensorInfo _input_nhwc;
- TensorInfo _output_nhwc;
- TensorInfo _input_workspace;
- TensorInfo _kernel_storage;
- TensorInfo _output_workspace;
- TensorInfo _input_transformed;
- TensorInfo _output_transformed;
- TensorInfo _weights_hwio;
- bool _run_activation;
- bool _is_prepared;
+ std::unique_ptr<CpuGemm> _gemm_function;
+ std::unique_ptr<CpuActivation> _activation_func;
+ std::unique_ptr<ICPPKernel> _transform_input_kernel;
+ std::unique_ptr<ICPPKernel> _transform_output_kernel;
+ std::unique_ptr<CpuPermute> _permute_input;
+ std::unique_ptr<CpuPermute> _permute_output;
+ std::unique_ptr<CpuPermute> _permute_weights;
+ experimental::MemoryRequirements _aux_mem{ Count };
+ std::unique_ptr<arm_conv::ConvolutionArgs> _conv_args; // Make it unique ptr because this type does not have a default constructor
+ arm_conv::winograd::WinogradImpl _winograd_impl;
+ DataLayout _data_layout;
+ TensorInfo _winograd_transformed_input;
+ TensorInfo _winograd_transformed_output;
+ TensorInfo _winograd_transformed_weights;
+ TensorInfo _input_workspace;
+ TensorInfo _output_workspace;
+ TensorInfo _weights_hwio;
+ TensorInfo _input_nhwc;
+ TensorInfo _output_nhwc;
+ bool _is_prepared;
+ bool _run_activation;
};
} // namespace cpu
} // namespace arm_compute