diff options
author | Michalis Spyrou <michalis.spyrou@arm.com> | 2021-07-01 12:20:56 +0100 |
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committer | Michalis Spyrou <michalis.spyrou@arm.com> | 2021-07-13 13:42:25 +0000 |
commit | 96f977e43f452a75f2658b820791cb3d3da9c0a3 (patch) | |
tree | fe279f0573d871c051bb49acf4b83f50b29a1647 /src/core | |
parent | 04b39e8e56112dabf6f5746117354680a9985841 (diff) | |
download | ComputeLibrary-96f977e43f452a75f2658b820791cb3d3da9c0a3.tar.gz |
Port NEWinogradConvolutionLayer
Rename to CpuWinogradConv2d
Allow memory to be injected externally
Change-Id: I1f0a26ea533e326a7c63df86e708895c31752a39
Signed-off-by: Michalis Spyrou <michalis.spyrou@arm.com>
Reviewed-on: https://review.mlplatform.org/c/ml/ComputeLibrary/+/5926
Comments-Addressed: Arm Jenkins <bsgcomp@arm.com>
Tested-by: Arm Jenkins <bsgcomp@arm.com>
Reviewed-by: Michele Di Giorgio <michele.digiorgio@arm.com>
Diffstat (limited to 'src/core')
-rw-r--r-- | src/core/NEON/NEKernels.h | 1 | ||||
-rw-r--r-- | src/core/cpu/kernels/CpuWinogradConv2dKernel.cpp (renamed from src/core/NEON/kernels/NEWinogradConvolutionLayerKernel.cpp) | 282 | ||||
-rw-r--r-- | src/core/cpu/kernels/CpuWinogradConv2dKernel.h (renamed from src/core/NEON/kernels/NEWinogradConvolutionLayerKernel.h) | 180 |
3 files changed, 222 insertions, 241 deletions
diff --git a/src/core/NEON/NEKernels.h b/src/core/NEON/NEKernels.h index cd09544d31..6c6c51dd87 100644 --- a/src/core/NEON/NEKernels.h +++ b/src/core/NEON/NEKernels.h @@ -66,6 +66,5 @@ #include "src/core/NEON/kernels/NEStridedSliceKernel.h" #include "src/core/NEON/kernels/NETileKernel.h" #include "src/core/NEON/kernels/NEWeightsReshapeKernel.h" -#include "src/core/NEON/kernels/NEWinogradConvolutionLayerKernel.h" #endif /* ARM_COMPUTE_NEKERNELS_H */ diff --git a/src/core/NEON/kernels/NEWinogradConvolutionLayerKernel.cpp b/src/core/cpu/kernels/CpuWinogradConv2dKernel.cpp index be34980663..74b031b226 100644 --- a/src/core/NEON/kernels/NEWinogradConvolutionLayerKernel.cpp +++ b/src/core/cpu/kernels/CpuWinogradConv2dKernel.cpp @@ -21,7 +21,7 @@ * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE * SOFTWARE. */ -#include "src/core/NEON/kernels/NEWinogradConvolutionLayerKernel.h" +#include "src/core/cpu/kernels/CpuWinogradConv2dKernel.h" #include "arm_compute/core/Error.h" #include "arm_compute/core/Helpers.h" @@ -39,6 +39,8 @@ namespace arm_compute { +namespace cpu +{ //Batched Gemms namespace @@ -175,7 +177,7 @@ std::pair<Status, Window> validate_and_configure_window_winograd_output_trans(IT } } // namespace -Status INEWinogradLayerTransformWeightsKernel::validate(const ITensorInfo *input, const ITensorInfo *weights) +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); @@ -189,7 +191,7 @@ Status INEWinogradLayerTransformWeightsKernel::validate(const ITensorInfo *input } template <typename T, int OutputTileRows, int OutputTileCols, int KernelRows, int KernelCols> -unsigned int NEWinogradLayerTransformWeightsKernel<T, OutputTileRows, OutputTileCols, KernelRows, KernelCols>::get_weight_storage_size(int num_output_channels, int num_input_channels) const +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); return static_cast<unsigned int>( @@ -198,89 +200,94 @@ unsigned int NEWinogradLayerTransformWeightsKernel<T, OutputTileRows, OutputTile } template <typename T, int OutputTileRows, int OutputTileCols, int KernelRows, int KernelCols> -NEWinogradLayerTransformWeightsKernel<T, OutputTileRows, OutputTileCols, KernelRows, KernelCols>::NEWinogradLayerTransformWeightsKernel() - : _transform(nullptr), _weights_hwio(nullptr), _output(nullptr), _matrix_stride(0), _num_output_channels(0), _num_input_channels(0) +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 NEWinogradLayerTransformWeightsKernel<T, OutputTileRows, OutputTileCols, KernelRows, KernelCols>::get_matrix_stride(int num_output_channels, int num_input_channels) const +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 NEWinogradLayerTransformWeightsKernel<T, OutputTileRows, OutputTileCols, KernelRows, KernelCols>::configure( - const ITensor *weights_hwio, - ITensor *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. */ -{ - _weights_hwio = weights_hwio; - _output = output; - _matrix_stride = matrix_stride; - _num_output_channels = num_output_channels; - _num_input_channels = num_input_channels; +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)); - INEKernel::configure(win); + ICpuKernel::configure(win); } #endif /* DOXYGEN_SKIP_THIS */ template <typename T, int OutputTileRows, int OutputTileCols, int KernelRows, int KernelCols> -void NEWinogradLayerTransformWeightsKernel<T, OutputTileRows, OutputTileCols, KernelRows, KernelCols>::run(const Window &window, const ThreadInfo &info) +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(); - _transform->set_weight_tensor(_weights_hwio->buffer()); + + 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->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 NEWinogradLayerTransformWeightsKernel<T, OutputTileRows, OutputTileCols, KernelRows, KernelCols>::is_parallelisable() const +bool CpuWinogradConv2dTransformWeightsKernel<T, OutputTileRows, OutputTileCols, KernelRows, KernelCols>::is_parallelisable() const { return false; } template <typename T, int OutputTileRows, int OutputTileCols, int KernelRows, int KernelCols> -Status NEWinogradLayerTransformWeightsKernel<T, OutputTileRows, OutputTileCols, KernelRows, KernelCols>::validate(const ITensorInfo *input, const ITensorInfo *output, - const WinogradInfo &winograd_info) +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 NEWinogradLayerTransformWeightsKernel<float, 2, 2, 3, 3>; -template class NEWinogradLayerTransformWeightsKernel<float, 4, 4, 3, 3>; -template class NEWinogradLayerTransformWeightsKernel<float, 2, 2, 5, 5>; -template class NEWinogradLayerTransformWeightsKernel<float, 1, 6, 1, 3>; -template class NEWinogradLayerTransformWeightsKernel<float, 6, 1, 3, 1>; +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 NEWinogradLayerTransformWeightsKernel<float, 1, 4, 1, 5>; -template class NEWinogradLayerTransformWeightsKernel<float, 4, 1, 5, 1>; -template class NEWinogradLayerTransformWeightsKernel<float, 1, 2, 1, 7>; -template class NEWinogradLayerTransformWeightsKernel<float, 2, 1, 7, 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 NEWinogradLayerTransformWeightsKernel<__fp16, 4, 4, 3, 3>; +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 NEWinogradLayerTransformInputKernel<T, OutputTileRows, OutputTileCols, KernelRows, KernelCols>::get_input_storage_size( +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. */ @@ -296,13 +303,13 @@ unsigned int NEWinogradLayerTransformInputKernel<T, OutputTileRows, OutputTileCo } template <typename T, int OutputTileRows, int OutputTileCols, int KernelRows, int KernelCols> -unsigned int NEWinogradLayerTransformInputKernel<T, OutputTileRows, OutputTileCols, KernelRows, KernelCols>::get_working_space_size(unsigned int num_threads) const +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) / sizeof(T); } template <typename T, int OutputTileRows, int OutputTileCols, int KernelRows, int KernelCols> -int NEWinogradLayerTransformInputKernel<T, OutputTileRows, OutputTileCols, KernelRows, KernelCols>::get_matrix_stride( +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. */ @@ -313,38 +320,32 @@ int NEWinogradLayerTransformInputKernel<T, OutputTileRows, OutputTileCols, Kerne } template <typename T, int OutputTileRows, int OutputTileCols, int KernelRows, int KernelCols> -NEWinogradLayerTransformInputKernel<T, OutputTileRows, OutputTileCols, KernelRows, KernelCols>::NEWinogradLayerTransformInputKernel() - : _transform(nullptr), _input_nhwc(nullptr), _num_batches(0), _num_rows(0), _num_cols(0), _num_channels(0), _padding(), _output(nullptr), _matrix_stride(0), _padding_top(), _padding_left(), - _padding_right(), _padding_bottom(), _workspace(nullptr) +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 NEWinogradLayerTransformInputKernel<T, OutputTileRows, OutputTileCols, KernelRows, KernelCols>::configure( - const ITensor *input_nhwc, - const int num_batches, /* Number of batches in input tensor. */ - const int num_rows, /* Number of rows in input tensor. */ - const int num_cols, /* Number of columns in input tensor. */ - const int num_channels, /* Number of channels in input tensor. */ - const PaddingType padding, /* Padding type. */ - ITensor *output, /* Base of output matrices. */ - const int matrix_stride, /* Stride between output matrices. */ - ITensor *workspace) -{ - _input_nhwc = input_nhwc; - _num_batches = num_batches; - _num_rows = num_rows; - _num_cols = num_cols; +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; - _padding = padding; - _output = output; _matrix_stride = matrix_stride; - _workspace = workspace; - _padding_top = (padding == PADDING_SAME) ? (KernelRows - 1) / 2 : 0; - _padding_left = (padding == PADDING_SAME) ? (KernelCols - 1) / 2 : 0; - _padding_bottom = (padding == PADDING_SAME) ? iceildiv(KernelRows - 1, 2) : 0; - _padding_right = (padding == PADDING_SAME) ? iceildiv(KernelCols - 1, 2) : 0; + 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, @@ -353,37 +354,41 @@ void NEWinogradLayerTransformInputKernel<T, OutputTileRows, OutputTileCols, Kern 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. */ + 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)); - INEKernel::configure(win); + ICpuKernel::configure(win); } template <typename T, int OutputTileRows, int OutputTileCols, int KernelRows, int KernelCols> -void NEWinogradLayerTransformInputKernel<T, OutputTileRows, OutputTileCols, KernelRows, KernelCols>::run(const Window &window, const ThreadInfo &info) +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_NULLPTR(_workspace); - - 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(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()); + _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(); @@ -392,7 +397,8 @@ void NEWinogradLayerTransformInputKernel<T, OutputTileRows, OutputTileCols, Kern } template <typename T, int OutputTileRows, int OutputTileCols, int KernelRows, int KernelCols> -Status NEWinogradLayerTransformInputKernel<T, OutputTileRows, OutputTileCols, KernelRows, KernelCols>::validate(const ITensorInfo *input, const ITensorInfo *output, const WinogradInfo &winograd_info) +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); @@ -400,25 +406,25 @@ Status NEWinogradLayerTransformInputKernel<T, OutputTileRows, OutputTileCols, Ke return Status{}; } -template class NEWinogradLayerTransformInputKernel<float, 2, 2, 3, 3>; -template class NEWinogradLayerTransformInputKernel<float, 4, 4, 3, 3>; -template class NEWinogradLayerTransformInputKernel<float, 2, 2, 5, 5>; -template class NEWinogradLayerTransformInputKernel<float, 1, 6, 1, 3>; -template class NEWinogradLayerTransformInputKernel<float, 6, 1, 3, 1>; +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 NEWinogradLayerTransformInputKernel<float, 1, 4, 1, 5>; -template class NEWinogradLayerTransformInputKernel<float, 4, 1, 5, 1>; -template class NEWinogradLayerTransformInputKernel<float, 1, 2, 1, 7>; -template class NEWinogradLayerTransformInputKernel<float, 2, 1, 7, 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 NEWinogradLayerTransformInputKernel<__fp16, 4, 4, 3, 3>; +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 NEWinogradLayerTransformOutputKernel<T, OutputTileRows, OutputTileCols, KernelRows, KernelCols>::get_output_storage_size( +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. */ @@ -434,20 +440,19 @@ unsigned int NEWinogradLayerTransformOutputKernel<T, OutputTileRows, OutputTileC } template <typename T, int OutputTileRows, int OutputTileCols, int KernelRows, int KernelCols> -NEWinogradLayerTransformOutputKernel<T, OutputTileRows, OutputTileCols, KernelRows, KernelCols>::NEWinogradLayerTransformOutputKernel() - : _transform(nullptr), _biases(nullptr), _transformed_output(nullptr), _workspace(nullptr), _matrix_stride(0), _matrix_row_stride(0), _output_nhwc(nullptr), _num_batches(0), _num_rows(0), - _num_cols(0), _num_channels(0) +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 NEWinogradLayerTransformOutputKernel<T, OutputTileRows, OutputTileCols, KernelRows, KernelCols>::get_working_space_size(unsigned int num_threads) const +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) / sizeof(T); } template <typename T, int OutputTileRows, int OutputTileCols, int KernelRows, int KernelCols> -int NEWinogradLayerTransformOutputKernel<T, OutputTileRows, OutputTileCols, KernelRows, KernelCols>::get_matrix_stride( +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. */ @@ -458,7 +463,7 @@ int NEWinogradLayerTransformOutputKernel<T, OutputTileRows, OutputTileCols, Kern } template <typename T, int OutputTileRows, int OutputTileCols, int KernelRows, int KernelCols> -std::pair<unsigned int, unsigned int> NEWinogradLayerTransformOutputKernel<T, OutputTileRows, OutputTileCols, KernelRows, KernelCols>::get_output_shape( +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 @@ -467,54 +472,52 @@ std::pair<unsigned int, unsigned int> NEWinogradLayerTransformOutputKernel<T, Ou } template <typename T, int OutputTileRows, int OutputTileCols, int KernelRows, int KernelCols> -void NEWinogradLayerTransformOutputKernel<T, OutputTileRows, OutputTileCols, KernelRows, KernelCols>::configure( - const ITensor *biases, - const ITensor *transformed_output, +void CpuWinogradConv2dTransformOutputKernel<T, OutputTileRows, OutputTileCols, KernelRows, KernelCols>::configure( + const ITensorInfo *biases, + const ITensorInfo *transformed_output, const int matrix_stride, - ITensor *output_nhwc, + ITensorInfo *output_nhwc, const int num_batches, const int num_rows, const int num_cols, const int num_channels, - ITensor *workspace, + ITensorInfo *workspace, const arm_gemm::Activation &activation) { - _biases = biases; - _workspace = workspace; - _transformed_output = transformed_output; - _matrix_stride = matrix_stride; - _matrix_row_stride = roundup(num_channels, WinogradConv::N_BLOCK); - _output_nhwc = output_nhwc; - _num_batches = num_batches; - _num_rows = num_rows; - _num_cols = num_cols; - _num_channels = num_channels; + 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)); - INEKernel::configure(win); + ICpuKernel::configure(win); } template <typename T, int OutputTileRows, int OutputTileCols, int KernelRows, int KernelCols> -void NEWinogradLayerTransformOutputKernel<T, OutputTileRows, OutputTileCols, KernelRows, KernelCols>::run(const Window &window, const ThreadInfo &info) +void CpuWinogradConv2dTransformOutputKernel<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_NULLPTR(_workspace); - ARM_COMPUTE_ERROR_ON_NULLPTR(_transformed_output); - ARM_COMPUTE_ERROR_ON_NULLPTR(_output_nhwc); - - const int out_batch_stride = _output_nhwc->info()->strides_in_bytes()[3] / sizeof(T); - const int out_row_stride = _output_nhwc->info()->strides_in_bytes()[2] / sizeof(T); - const int out_col_stride = _output_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(_output_nhwc->buffer() + _output_nhwc->info()->offset_first_element_in_bytes(), out_batch_stride, out_row_stride, out_col_stride); - _transform->set_working_space(_workspace->buffer()); + 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(); @@ -522,8 +525,8 @@ void NEWinogradLayerTransformOutputKernel<T, OutputTileRows, OutputTileCols, Ker } template <typename T, int OutputTileRows, int OutputTileCols, int KernelRows, int KernelCols> -Status NEWinogradLayerTransformOutputKernel<T, OutputTileRows, OutputTileCols, KernelRows, KernelCols>::validate(const ITensorInfo *input, const ITensorInfo *bias, const ITensorInfo *output, - const WinogradInfo &winograd_info) +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); @@ -531,18 +534,19 @@ Status NEWinogradLayerTransformOutputKernel<T, OutputTileRows, OutputTileCols, K return Status{}; } -template class NEWinogradLayerTransformOutputKernel<float, 2, 2, 3, 3>; -template class NEWinogradLayerTransformOutputKernel<float, 4, 4, 3, 3>; -template class NEWinogradLayerTransformOutputKernel<float, 2, 2, 5, 5>; -template class NEWinogradLayerTransformOutputKernel<float, 1, 6, 1, 3>; -template class NEWinogradLayerTransformOutputKernel<float, 6, 1, 3, 1>; +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 NEWinogradLayerTransformOutputKernel<float, 1, 4, 1, 5>; -template class NEWinogradLayerTransformOutputKernel<float, 4, 1, 5, 1>; -template class NEWinogradLayerTransformOutputKernel<float, 1, 2, 1, 7>; -template class NEWinogradLayerTransformOutputKernel<float, 2, 1, 7, 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 NEWinogradLayerTransformOutputKernel<__fp16, 4, 4, 3, 3>; +template class CpuWinogradConv2dTransformOutputKernel<__fp16, 4, 4, 3, 3>; #endif // __ARM_FEATURE_FP16_VECTOR_ARITHMETIC +} // namespace cpu } // namespace arm_compute diff --git a/src/core/NEON/kernels/NEWinogradConvolutionLayerKernel.h b/src/core/cpu/kernels/CpuWinogradConv2dKernel.h index 75d257de4b..b5a29ffd02 100644 --- a/src/core/NEON/kernels/NEWinogradConvolutionLayerKernel.h +++ b/src/core/cpu/kernels/CpuWinogradConv2dKernel.h @@ -21,22 +21,21 @@ * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE * SOFTWARE. */ -#ifndef ARM_COMPUTE_NEGEMMWINOGRADCONVOLUTIONLAYERKERNEL_H -#define ARM_COMPUTE_NEGEMMWINOGRADCONVOLUTIONLAYERKERNEL_H +#ifndef ARM_COMPUTE_CPUWINOGRADCONV2DKERNEL_H +#define ARM_COMPUTE_CPUWINOGRADCONV2DKERNEL_H -#include "src/core/NEON/INEKernel.h" #include "src/core/NEON/kernels/convolution/common/convolution.hpp" #include "src/core/NEON/kernels/convolution/common/tensor.hpp" +#include "src/core/cpu/ICpuKernel.h" #include "src/core/NEON/kernels/convolution/winograd/winograd_layer.hpp" namespace arm_compute { -// Forward declarations -class ITensor; - +namespace cpu +{ /** Interface for the kernel to perform Winograd input transform. */ -class INEWinogradLayerTransformInputKernel : public INEKernel +class ICpuWinogradConv2dTransformInputKernel : public ICpuKernel { public: /** Get the working space required to perform the transformation. @@ -87,30 +86,30 @@ public: * @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 ITensor *input_nhwc, const int num_batches, const int num_rows, const int num_cols, const int num_channels, - const PaddingType padding, ITensor *output, const int matrix_stride, ITensor *workspace) = 0; + 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 ~INEWinogradLayerTransformInputKernel() + virtual ~ICpuWinogradConv2dTransformInputKernel() { } }; /** Kernel to perform Winograd input transform. */ template <typename T, int OutputTileRows, int OutputTileCols, int KernelRows, int KernelCols> -class NEWinogradLayerTransformInputKernel : public INEWinogradLayerTransformInputKernel +class CpuWinogradConv2dTransformInputKernel : public ICpuWinogradConv2dTransformInputKernel { public: /** Prevent instances of this class from being copied (As this class contains pointers) */ - NEWinogradLayerTransformInputKernel(const NEWinogradLayerTransformInputKernel &) = delete; + CpuWinogradConv2dTransformInputKernel(const CpuWinogradConv2dTransformInputKernel &) = delete; /** Prevent instances of this class from being copied (As this class contains pointers) */ - NEWinogradLayerTransformInputKernel &operator=(const NEWinogradLayerTransformInputKernel &) = delete; + CpuWinogradConv2dTransformInputKernel &operator=(const CpuWinogradConv2dTransformInputKernel &) = delete; /** Allow instances of this class to be moved */ - NEWinogradLayerTransformInputKernel(NEWinogradLayerTransformInputKernel &&) = default; + CpuWinogradConv2dTransformInputKernel(CpuWinogradConv2dTransformInputKernel &&) = default; /** Allow instances of this class to be moved */ - NEWinogradLayerTransformInputKernel &operator=(NEWinogradLayerTransformInputKernel &&) = default; + CpuWinogradConv2dTransformInputKernel &operator=(CpuWinogradConv2dTransformInputKernel &&) = default; /** Default destructor */ - ~NEWinogradLayerTransformInputKernel() = default; + ~CpuWinogradConv2dTransformInputKernel() = default; /** Determine how much memory (in units of TIn) to allocate for the * transformed input. @@ -160,11 +159,11 @@ public: bool same_padding) const override; /** Default constructor */ - NEWinogradLayerTransformInputKernel(); + CpuWinogradConv2dTransformInputKernel(); const char *name() const override { - return "NEWinogradLayerTransformInputKernel"; + return "CpuWinogradConv2dTransformInputKernel"; } /** Configure the output transform kernel. @@ -180,25 +179,25 @@ public: * @param[in] workspace Tensor to be used as the working space during the computation. */ void configure( - const ITensor *input_nhwc, - const int num_batches, - const int num_rows, - const int num_cols, - const int num_channels, - const PaddingType padding, - ITensor *output, - const int matrix_stride, - ITensor *workspace) override; + 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; // Inherited methods overridden: - void run(const Window &window, const ThreadInfo &info) override; + 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 NEWinogradLayerTransformInputKernel + /** 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. @@ -212,23 +211,12 @@ private: using InputTransform = typename WinogradBase::template InputTransform<T, T>; std::unique_ptr<InputTransform> _transform{ nullptr }; - const ITensor *_input_nhwc; - int _num_batches; /**< Number of batches in input tensor. */ - int _num_rows; /**< Number of rows in input tensor. */ - int _num_cols; /**< Number of columns in input tensor. */ - int _num_channels; /**< Number of channels in input tensor. */ - PaddingType _padding; /**< Padding type. */ - ITensor *_output; /**< Base of output matrices. */ - int _matrix_stride; /**< Stride between output matrices. */ - int _padding_top; /**< Padding to apply to the top of the image. */ - int _padding_left; /**< Padding to apply to the left of the image. */ - int _padding_right; /**< Padding to apply to the right of the image. */ - int _padding_bottom; /**< Padding to apply to the bottom of the image. */ - ITensor *_workspace; + 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 INEWinogradLayerTransformOutputKernel : public INEKernel +class ICpuWinogradConv2dTransformOutputKernel : public ICpuKernel { public: /** Get the working space required to perform the transformation. @@ -294,44 +282,44 @@ public: * @param[in] activation Activation to be used */ virtual void configure( - const ITensor *biases, - const ITensor *transformed_output, + const ITensorInfo *biases, + const ITensorInfo *transformed_output, const int matrix_stride, - ITensor *output_nhwc, + ITensorInfo *output_nhwc, const int num_batches, const int num_rows, const int num_cols, const int num_channels, - ITensor *workspace, + ITensorInfo *workspace, const arm_gemm::Activation &activation) = 0; - virtual ~INEWinogradLayerTransformOutputKernel() + virtual ~ICpuWinogradConv2dTransformOutputKernel() { } }; /** Kernel to perform Winograd output transform. */ template <typename T, int OutputTileRows, int OutputTileCols, int KernelRows, int KernelCols> -class NEWinogradLayerTransformOutputKernel : public INEWinogradLayerTransformOutputKernel +class CpuWinogradConv2dTransformOutputKernel : public ICpuWinogradConv2dTransformOutputKernel { public: const char *name() const override { - return "NEWinogradLayerTransformOutputKernel"; + return "CpuWinogradConv2dTransformOutputKernel"; } /** Constructor */ - NEWinogradLayerTransformOutputKernel(); + CpuWinogradConv2dTransformOutputKernel(); /** Prevent instances of this class from being copied (As this class contains pointers) */ - NEWinogradLayerTransformOutputKernel(const NEWinogradLayerTransformOutputKernel &) = delete; + CpuWinogradConv2dTransformOutputKernel(const CpuWinogradConv2dTransformOutputKernel &) = delete; /** Prevent instances of this class from being copied (As this class contains pointers) */ - NEWinogradLayerTransformOutputKernel &operator=(const NEWinogradLayerTransformOutputKernel &) = delete; + CpuWinogradConv2dTransformOutputKernel &operator=(const CpuWinogradConv2dTransformOutputKernel &) = delete; /** Allow instances of this class to be moved */ - NEWinogradLayerTransformOutputKernel(NEWinogradLayerTransformOutputKernel &&) = default; + CpuWinogradConv2dTransformOutputKernel(CpuWinogradConv2dTransformOutputKernel &&) = default; /** Allow instances of this class to be moved */ - NEWinogradLayerTransformOutputKernel &operator=(NEWinogradLayerTransformOutputKernel &&) = default; + CpuWinogradConv2dTransformOutputKernel &operator=(CpuWinogradConv2dTransformOutputKernel &&) = default; /** Default destructor */ - ~NEWinogradLayerTransformOutputKernel() = default; + ~CpuWinogradConv2dTransformOutputKernel() = default; // Inherited methods overridden: /** Determine how much memory (in units of TOut) to allocate for the @@ -395,20 +383,20 @@ public: * @param[in] activation Activation to be used */ void configure( - const ITensor *biases, - const ITensor *transformed_output, + const ITensorInfo *biases, + const ITensorInfo *transformed_output, const int matrix_stride, - ITensor *output_nhwc, + ITensorInfo *output_nhwc, const int num_batches, const int num_rows, const int num_cols, const int num_channels, - ITensor *workspace, + ITensorInfo *workspace, const arm_gemm::Activation &activation) override; - void run(const Window &window, const ThreadInfo &info) 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 NEWinogradLayerTransformOutputKernel + /** 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 @@ -425,35 +413,27 @@ private: using OutputTransform = typename WinogradBase::template OutputTransform<T, T>; std::unique_ptr<OutputTransform> _transform{ nullptr }; - const ITensor *_biases; - const ITensor *_transformed_output; - ITensor *_workspace; int _matrix_stride; int _matrix_row_stride; - ITensor *_output_nhwc; - int _num_batches; - int _num_rows; - int _num_cols; - int _num_channels; }; /** Interface for the kernel to perform Winograd weights transform. */ -class INEWinogradLayerTransformWeightsKernel : public INEKernel +class ICpuWinogradConv2dTransformWeightsKernel : public ICpuKernel { public: /** Prevent instances of this class from being copied (As this class contains pointers) */ - INEWinogradLayerTransformWeightsKernel(const INEWinogradLayerTransformWeightsKernel &) = default; + ICpuWinogradConv2dTransformWeightsKernel(const ICpuWinogradConv2dTransformWeightsKernel &) = default; /** Prevent instances of this class from being copied (As this class contains pointers) */ - INEWinogradLayerTransformWeightsKernel &operator=(const INEWinogradLayerTransformWeightsKernel &) = default; + ICpuWinogradConv2dTransformWeightsKernel &operator=(const ICpuWinogradConv2dTransformWeightsKernel &) = default; /** Allow instances of this class to be moved */ - INEWinogradLayerTransformWeightsKernel(INEWinogradLayerTransformWeightsKernel &&) = default; + ICpuWinogradConv2dTransformWeightsKernel(ICpuWinogradConv2dTransformWeightsKernel &&) = default; /** Allow instances of this class to be moved */ - INEWinogradLayerTransformWeightsKernel &operator=(INEWinogradLayerTransformWeightsKernel &&) = default; + ICpuWinogradConv2dTransformWeightsKernel &operator=(ICpuWinogradConv2dTransformWeightsKernel &&) = default; - INEWinogradLayerTransformWeightsKernel() + ICpuWinogradConv2dTransformWeightsKernel() { } - virtual ~INEWinogradLayerTransformWeightsKernel() + virtual ~ICpuWinogradConv2dTransformWeightsKernel() { } /** Determine how much memory (in units of T) to allocate for the @@ -476,16 +456,16 @@ public: /** Configure the weights transform kernel. * - * @param[in] weights_hwio Pointer to the weights tensor + * @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 ITensor *weights_hwio, ITensor *output, const int matrix_stride, const int num_output_channels, const int num_input_channels) = 0; + 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 NEWinogradLayerTransformWeightsKernel + /** 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. @@ -497,28 +477,28 @@ public: /** Kernel to perform Winograd weights transform. */ template <typename T, int OutputTileRows, int OutputTileCols, int KernelRows, int KernelCols> -class NEWinogradLayerTransformWeightsKernel final : public INEWinogradLayerTransformWeightsKernel +class CpuWinogradConv2dTransformWeightsKernel final : public ICpuWinogradConv2dTransformWeightsKernel { public: /** Prevent instances of this class from being copied (As this class contains pointers) */ - NEWinogradLayerTransformWeightsKernel(const NEWinogradLayerTransformWeightsKernel &) = delete; + CpuWinogradConv2dTransformWeightsKernel(const CpuWinogradConv2dTransformWeightsKernel &) = delete; /** Prevent instances of this class from being copied (As this class contains pointers) */ - NEWinogradLayerTransformWeightsKernel &operator=(const NEWinogradLayerTransformWeightsKernel &) = delete; + CpuWinogradConv2dTransformWeightsKernel &operator=(const CpuWinogradConv2dTransformWeightsKernel &) = delete; /** Allow instances of this class to be moved */ - NEWinogradLayerTransformWeightsKernel(NEWinogradLayerTransformWeightsKernel &&) = default; + CpuWinogradConv2dTransformWeightsKernel(CpuWinogradConv2dTransformWeightsKernel &&) = default; /** Allow instances of this class to be moved */ - NEWinogradLayerTransformWeightsKernel &operator=(NEWinogradLayerTransformWeightsKernel &&) = default; + CpuWinogradConv2dTransformWeightsKernel &operator=(CpuWinogradConv2dTransformWeightsKernel &&) = default; /** Default destructor */ - ~NEWinogradLayerTransformWeightsKernel() = default; + ~CpuWinogradConv2dTransformWeightsKernel() = default; /** Default constructor. */ - NEWinogradLayerTransformWeightsKernel(); + CpuWinogradConv2dTransformWeightsKernel(); const char *name() const override { - return "NEWinogradLayerTransformWeightsKernel"; + return "CpuWinogradConv2dTransformWeightsKernel"; } - /** Static function to check if given info will lead to a valid configuration of @ref NEWinogradLayerTransformWeightsKernel + /** 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. @@ -534,13 +514,13 @@ public: #ifndef DOXYGEN_SKIP_THIS /** Configure the weights transform kernel. * - * @param[in] weights_hwio Pointer to the weights tensor + * @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 ITensor *weights_hwio, ITensor *output, const int matrix_stride, const int num_output_channels, const int num_input_channels) override; + 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 @@ -561,7 +541,7 @@ public: * @return Stride expressed in bytes. */ int get_matrix_stride(int num_output_channels, int num_input_channels) const override; - void run(const Window &window, const ThreadInfo &info) override; + void run_op(ITensorPack &tensors, const Window &window, const ThreadInfo &info) override; bool is_parallelisable() const override; private: @@ -570,16 +550,13 @@ private: using WeightsTransform = typename WinogradBase::template WeightsTransform<T, T>; std::unique_ptr<WeightsTransform> _transform{ nullptr }; - const ITensor *_weights_hwio; - ITensor *_output; - int _matrix_stride; int _num_output_channels; - int _num_input_channels; + int _matrix_stride; }; /** Kernel to perform Winograd. */ template <typename TIn, typename TOut, int OutputTileRows, int OutputTileCols, int KernelRows, int KernelCols> -class NEWinogradLayerConfiguration +class CpuWinogradConv2dConfiguration { public: /** Winograd base kernel */ @@ -588,10 +565,11 @@ public: using WinogradConv = typename WinogradBase::template Convolution<TIn, TOut>; - using TransformInputKernel = NEWinogradLayerTransformInputKernel<TIn, OutputTileRows, OutputTileCols, KernelRows, KernelCols>; - using TransformWeightsKernel = NEWinogradLayerTransformWeightsKernel<TIn, OutputTileRows, OutputTileCols, KernelRows, KernelCols>; - using TransformOutputKernel = NEWinogradLayerTransformOutputKernel<TOut, OutputTileRows, OutputTileCols, KernelRows, KernelCols>; + using TransformInputKernel = CpuWinogradConv2dTransformInputKernel<TIn, OutputTileRows, OutputTileCols, KernelRows, KernelCols>; + using TransformWeightsKernel = CpuWinogradConv2dTransformWeightsKernel<TIn, OutputTileRows, OutputTileCols, KernelRows, KernelCols>; + using TransformOutputKernel = CpuWinogradConv2dTransformOutputKernel<TOut, OutputTileRows, OutputTileCols, KernelRows, KernelCols>; }; +} // namespace cpu } // namespace arm_compute -#endif /*ARM_COMPUTE_NEGEMMWINOGRADCONVOLUTIONLAYERKERNEL_H*/ +#endif /*ARM_COMPUTE_CPUWINOGRADCONV2DKERNEL_H*/ |