diff options
Diffstat (limited to 'src/core/NEON/kernels/NEWinogradLayerKernel.cpp')
| -rw-r--r-- | src/core/NEON/kernels/NEWinogradLayerKernel.cpp | 204 |
1 files changed, 128 insertions, 76 deletions
diff --git a/src/core/NEON/kernels/NEWinogradLayerKernel.cpp b/src/core/NEON/kernels/NEWinogradLayerKernel.cpp index b0a36ff46a..b2e44f8e09 100644 --- a/src/core/NEON/kernels/NEWinogradLayerKernel.cpp +++ b/src/core/NEON/kernels/NEWinogradLayerKernel.cpp @@ -32,25 +32,25 @@ namespace arm_compute { //Batched Gemms -template <int OutputTileRows, int OutputTileCols, int KernelRows, int KernelCols> -NEWinogradLayerKernel<OutputTileRows, OutputTileCols, KernelRows, KernelCols>::NEWinogradLayerKernel() +template <typename TIn, typename TOut, int OutputTileRows, int OutputTileCols, int KernelRows, int KernelCols> +NEWinogradLayerBatchedGEMMKernel<TIn, TOut, OutputTileRows, OutputTileCols, KernelRows, KernelCols>::NEWinogradLayerBatchedGEMMKernel() : _gemms() { } -template <int OutputTileRows, int OutputTileCols, int KernelRows, int KernelCols> -void NEWinogradLayerKernel<OutputTileRows, OutputTileCols, KernelRows, KernelCols>::configure( +template <typename TIn, typename TOut, int OutputTileRows, int OutputTileCols, int KernelRows, int KernelCols> +void NEWinogradLayerBatchedGEMMKernel<TIn, TOut, OutputTileRows, OutputTileCols, KernelRows, KernelCols>::configure( const unsigned int n_gemms, const int M, const int K, const int N, - const int a_matrix_stride, - const int a_row_stride, - const int b_matrix_stride, - const int b_row_stride, - const int c_matrix_stride, - const int c_row_stride, - const float *const a_ptr, - const float *const b_ptr, - float *const c_ptr) + const int a_matrix_stride, + const int a_row_stride, + const int b_matrix_stride, + const int b_row_stride, + const int c_matrix_stride, + const int c_row_stride, + const TIn *const a_ptr, + const TIn *const b_ptr, + TOut *const c_ptr) { _gemms = support::cpp14::make_unique<MultiGEMM>(n_gemms, M, K, N, a_matrix_stride, a_row_stride, b_matrix_stride, b_row_stride, c_matrix_stride, c_row_stride, a_ptr, b_ptr, c_ptr); Window win; @@ -59,8 +59,8 @@ void NEWinogradLayerKernel<OutputTileRows, OutputTileCols, KernelRows, KernelCol INEKernel::configure(win); } -template <int OutputTileRows, int OutputTileCols, int KernelRows, int KernelCols> -void NEWinogradLayerKernel<OutputTileRows, OutputTileCols, KernelRows, KernelCols>::run(const Window &window, const ThreadInfo &info) +template <typename TIn, typename TOut, int OutputTileRows, int OutputTileCols, int KernelRows, int KernelCols> +void NEWinogradLayerBatchedGEMMKernel<TIn, TOut, OutputTileRows, OutputTileCols, KernelRows, KernelCols>::run(const Window &window, const ThreadInfo &info) { ARM_COMPUTE_UNUSED(info); ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this); @@ -69,36 +69,66 @@ void NEWinogradLayerKernel<OutputTileRows, OutputTileCols, KernelRows, KernelCol _gemms->run(first_gemm, last_gemm); } -template class NEWinogradLayerKernel<2, 2, 3, 3>; +template <typename TIn, typename TOut, int OutputTileRows, int OutputTileCols, int KernelRows, int KernelCols> +unsigned int NEWinogradLayerBatchedGEMMKernel<TIn, TOut, OutputTileRows, OutputTileCols, KernelRows, KernelCols>::get_number_gemms() const +{ + return WinogradBase::N_GEMMS; +} + +template <typename TIn, typename TOut, int OutputTileRows, int OutputTileCols, int KernelRows, int KernelCols> +int NEWinogradLayerBatchedGEMMKernel<TIn, TOut, OutputTileRows, OutputTileCols, KernelRows, KernelCols>::get_output_tile_rows() const +{ + return _output_tile_rows; +} + +template <typename TIn, typename TOut, int OutputTileRows, int OutputTileCols, int KernelRows, int KernelCols> +int NEWinogradLayerBatchedGEMMKernel<TIn, TOut, OutputTileRows, OutputTileCols, KernelRows, KernelCols>::get_output_tile_cols() const +{ + return _output_tile_cols; +} + +template <typename TIn, typename TOut, int OutputTileRows, int OutputTileCols, int KernelRows, int KernelCols> +int NEWinogradLayerBatchedGEMMKernel<TIn, TOut, OutputTileRows, OutputTileCols, KernelRows, KernelCols>::get_number_blocks() const +{ + return WinogradConv::N_BLOCK; +} + +template class NEWinogradLayerBatchedGEMMKernel<float, float, 2, 2, 3, 3>; +template class NEWinogradLayerBatchedGEMMKernel<float, float, 2, 2, 5, 5>; // Weights transform -template <int OutputTileRows, int OutputTileCols, int KernelRows, int KernelCols> -unsigned int NEWinogradLayerTransformWeightsKernel<OutputTileRows, OutputTileCols, KernelRows, KernelCols>::get_weight_storage_size(int n_output_channels, int n_input_channels) +template <typename T, int OutputTileRows, int OutputTileCols, int KernelRows, int KernelCols> +unsigned int NEWinogradLayerTransformWeightsKernel<T, OutputTileRows, OutputTileCols, KernelRows, KernelCols>::get_weight_storage_size(int n_output_channels, int n_input_channels) const { const KernelShape shape(n_output_channels, KernelRows, KernelCols, n_input_channels); return static_cast<unsigned int>( - // WinogradConv returns the size in bytes, we divide by `sizeof(float)` to - // express that in units of float. - WinogradConv::get_kernel_storage_size(shape) / sizeof(float)); + // WinogradConv returns the size in bytes, we divide by `sizeof(T)` to express that in units of T + WinogradConv::get_kernel_storage_size(shape) / sizeof(T)); } -template <int OutputTileRows, int OutputTileCols, int KernelRows, int KernelCols> -NEWinogradLayerTransformWeightsKernel<OutputTileRows, OutputTileCols, KernelRows, KernelCols>::NEWinogradLayerTransformWeightsKernel() +template <typename T, int OutputTileRows, int OutputTileCols, int KernelRows, int KernelCols> +NEWinogradLayerTransformWeightsKernel<T, OutputTileRows, OutputTileCols, KernelRows, KernelCols>::NEWinogradLayerTransformWeightsKernel() : _transform() { } -template <int OutputTileRows, int OutputTileCols, int KernelRows, int KernelCols> -void NEWinogradLayerTransformWeightsKernel<OutputTileRows, OutputTileCols, KernelRows, KernelCols>::configure( +template <typename T, int OutputTileRows, int OutputTileCols, int KernelRows, int KernelCols> +int NEWinogradLayerTransformWeightsKernel<T, OutputTileRows, OutputTileCols, KernelRows, KernelCols>::get_matrix_stride(const KernelShape &kernel_shape) const +{ + return WinogradConv::get_kernel_matrix_stride(kernel_shape); +} + +template <typename T, int OutputTileRows, int OutputTileCols, int KernelRows, int KernelCols> +void NEWinogradLayerTransformWeightsKernel<T, OutputTileRows, OutputTileCols, KernelRows, KernelCols>::configure( const ITensor *weights_hwio, - float *const output, + T *const output, const int matrix_stride, /** Stride across matrices in the output. */ const int n_output_channels, /** Number of filters. */ const int n_input_channels) /** Number of channels in each filter. */ { const int matrix_row_stride = roundup(n_output_channels, WinogradConv::N_BLOCK); - _transform = support::cpp14::make_unique<WeightsTransform>(reinterpret_cast<float *>(weights_hwio->buffer()), output, matrix_stride, matrix_row_stride, n_output_channels, + _transform = support::cpp14::make_unique<WeightsTransform>(reinterpret_cast<T *>(weights_hwio->buffer()), output, matrix_stride, matrix_row_stride, n_output_channels, n_input_channels); Window win; auto win_last = _transform->get_window(); @@ -106,8 +136,8 @@ void NEWinogradLayerTransformWeightsKernel<OutputTileRows, OutputTileCols, Kerne INEKernel::configure(win); } -template <int OutputTileRows, int OutputTileCols, int KernelRows, int KernelCols> -void NEWinogradLayerTransformWeightsKernel<OutputTileRows, OutputTileCols, KernelRows, KernelCols>::run(const Window &window, const ThreadInfo &info) +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) { ARM_COMPUTE_UNUSED(info); ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this); @@ -116,50 +146,57 @@ void NEWinogradLayerTransformWeightsKernel<OutputTileRows, OutputTileCols, Kerne _transform->run(fst, lst); } -template <int OutputTileRows, int OutputTileCols, int KernelRows, int KernelCols> -bool NEWinogradLayerTransformWeightsKernel<OutputTileRows, OutputTileCols, KernelRows, KernelCols>::is_parallelisable() const +template <typename T, int OutputTileRows, int OutputTileCols, int KernelRows, int KernelCols> +bool NEWinogradLayerTransformWeightsKernel<T, OutputTileRows, OutputTileCols, KernelRows, KernelCols>::is_parallelisable() const { return false; } -template class NEWinogradLayerTransformWeightsKernel<2, 2, 3, 3>; +template class NEWinogradLayerTransformWeightsKernel<float, 2, 2, 3, 3>; +template class NEWinogradLayerTransformWeightsKernel<float, 2, 2, 5, 5>; // Input transform -template <int OutputTileRows, int OutputTileCols, int KernelRows, int KernelCols> -unsigned int NEWinogradLayerTransformInputKernel<OutputTileRows, OutputTileCols, KernelRows, KernelCols>::get_input_storage_size( +template <typename T, int OutputTileRows, int OutputTileCols, int KernelRows, int KernelCols> +unsigned int NEWinogradLayerTransformInputKernel<T, OutputTileRows, OutputTileCols, KernelRows, KernelCols>::get_input_storage_size( int n_batches, /** Number of batches in the input tensor. */ int n_channels, /** Number of feature maps in the input tensor. */ int n_rows, /** Number of rows in each feature map. */ int n_cols, /** Number of columns in each feature map. */ bool same_padding /** Use "SAME" padding, otherwise use "VALID". */ -) +) const { // Construct shapes for the input and kernel tensors. const Tensor4DShape input_shape(n_batches, n_rows, n_cols, n_channels); const KernelShape kern_shape(1, KernelRows, KernelCols, n_channels); const PaddingType padding = (same_padding) ? PADDING_SAME : PADDING_VALID; // Return the size, converted into units of TIn - return static_cast<unsigned int>( - WinogradConv::get_input_storage_size(kern_shape, input_shape, padding) / sizeof(float)); + return static_cast<unsigned int>(WinogradConv::get_input_storage_size(kern_shape, input_shape, padding) / sizeof(T)); +} + +template <typename T, int OutputTileRows, int OutputTileCols, int KernelRows, int KernelCols> +int NEWinogradLayerTransformInputKernel<T, OutputTileRows, OutputTileCols, KernelRows, KernelCols>::get_matrix_stride( + const KernelShape &kernel_shape, const Tensor4DShape &input_shape, const PaddingType padding_type) const +{ + return WinogradConv::get_input_matrix_stride(kernel_shape, input_shape, padding_type); } -template <int OutputTileRows, int OutputTileCols, int KernelRows, int KernelCols> -NEWinogradLayerTransformInputKernel<OutputTileRows, OutputTileCols, KernelRows, KernelCols>::NEWinogradLayerTransformInputKernel() +template <typename T, int OutputTileRows, int OutputTileCols, int KernelRows, int KernelCols> +NEWinogradLayerTransformInputKernel<T, OutputTileRows, OutputTileCols, KernelRows, KernelCols>::NEWinogradLayerTransformInputKernel() : _transform() { } -template <int OutputTileRows, int OutputTileCols, int KernelRows, int KernelCols> -void NEWinogradLayerTransformInputKernel<OutputTileRows, OutputTileCols, KernelRows, KernelCols>::configure( - const float *const input, /** Input tensor data */ - const int n_batches, /** Number of batches in input tensor. */ - const int n_rows, /** Number of rows in input tensor. */ - const int n_cols, /** Number of columns in input tensor. */ - const int n_channels, /** Number of channels in input tensor. */ - const PaddingType padding, /** Padding type. */ - float *const output, /** Base of output matrices. */ - const int matrix_stride) /** Stride between output matrices. */ +template <typename T, int OutputTileRows, int OutputTileCols, int KernelRows, int KernelCols> +void NEWinogradLayerTransformInputKernel<T, OutputTileRows, OutputTileCols, KernelRows, KernelCols>::configure( + const T *const input, /** Input tensor data */ + const int n_batches, /** Number of batches in input tensor. */ + const int n_rows, /** Number of rows in input tensor. */ + const int n_cols, /** Number of columns in input tensor. */ + const int n_channels, /** Number of channels in input tensor. */ + const PaddingType padding, /** Padding type. */ + T *const output, /** Base of output matrices. */ + const int matrix_stride) /** Stride between output matrices. */ { // _input_matrix_row_stride(n_input_channels), _transform = support::cpp14::make_unique<InputTransform>(input, n_batches, n_rows, n_cols, n_channels, padding, output, matrix_stride, n_channels); @@ -169,8 +206,8 @@ void NEWinogradLayerTransformInputKernel<OutputTileRows, OutputTileCols, KernelR INEKernel::configure(win); } -template <int OutputTileRows, int OutputTileCols, int KernelRows, int KernelCols> -void NEWinogradLayerTransformInputKernel<OutputTileRows, OutputTileCols, KernelRows, KernelCols>::run(const Window &window, const ThreadInfo &info) +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) { ARM_COMPUTE_UNUSED(info); ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this); @@ -179,24 +216,25 @@ void NEWinogradLayerTransformInputKernel<OutputTileRows, OutputTileCols, KernelR _transform->run(fst, lst); } -template <int OutputTileRows, int OutputTileCols, int KernelRows, int KernelCols> -bool NEWinogradLayerTransformInputKernel<OutputTileRows, OutputTileCols, KernelRows, KernelCols>::is_parallelisable() const +template <typename T, int OutputTileRows, int OutputTileCols, int KernelRows, int KernelCols> +bool NEWinogradLayerTransformInputKernel<T, OutputTileRows, OutputTileCols, KernelRows, KernelCols>::is_parallelisable() const { return false; } -template class NEWinogradLayerTransformInputKernel<2, 2, 3, 3>; +template class NEWinogradLayerTransformInputKernel<float, 2, 2, 3, 3>; +template class NEWinogradLayerTransformInputKernel<float, 2, 2, 5, 5>; // Output transform -template <int OutputTileRows, int OutputTileCols, int KernelRows, int KernelCols> -unsigned int NEWinogradLayerTransformOutputKernel<OutputTileRows, OutputTileCols, KernelRows, KernelCols>::get_output_storage_size( +template <typename T, int OutputTileRows, int OutputTileCols, int KernelRows, int KernelCols> +unsigned int NEWinogradLayerTransformOutputKernel<T, OutputTileRows, OutputTileCols, KernelRows, KernelCols>::get_output_storage_size( int n_batches, /** Number of batches in the output tensor. */ int n_rows, /** Number of rows in each feature map of the input tensor. */ int n_cols, /** Number of columns in each feature map of the input tensor. */ int n_output_channels, /** Number of feature maps in the output tensor. */ bool same_padding /** Use "SAME" padding, otherwise use "VALID". */ -) +) const { // Construct shapes for the input and kernel tensors. const Tensor4DShape input_shape(n_batches, n_rows, n_cols, 1); @@ -205,25 +243,38 @@ unsigned int NEWinogradLayerTransformOutputKernel<OutputTileRows, OutputTileCols // Return the size, converted into units of TOut return static_cast<unsigned int>( - WinogradConv::get_output_storage_size(kern_shape, input_shape, padding) / sizeof(float)); + WinogradConv::get_output_storage_size(kern_shape, input_shape, padding) / sizeof(T)); } -template <int OutputTileRows, int OutputTileCols, int KernelRows, int KernelCols> -NEWinogradLayerTransformOutputKernel<OutputTileRows, OutputTileCols, KernelRows, KernelCols>::NEWinogradLayerTransformOutputKernel() +template <typename T, int OutputTileRows, int OutputTileCols, int KernelRows, int KernelCols> +NEWinogradLayerTransformOutputKernel<T, OutputTileRows, OutputTileCols, KernelRows, KernelCols>::NEWinogradLayerTransformOutputKernel() : _biases(nullptr), _output_workspace(nullptr), _matrix_stride(0), _matrix_row_stride(0), _output(nullptr), _n_batches(0), _n_rows(0), _n_cols(0), _n_channels(0) { } -template <int OutputTileRows, int OutputTileCols, int KernelRows, int KernelCols> -void NEWinogradLayerTransformOutputKernel<OutputTileRows, OutputTileCols, KernelRows, KernelCols>::configure( - const ITensor *biases, - const float *const output_workingspace, - const int matrix_stride, - float *const output, - const int n_batches, - const int n_rows, - const int n_cols, - const int n_channels) +template <typename T, int OutputTileRows, int OutputTileCols, int KernelRows, int KernelCols> +int NEWinogradLayerTransformOutputKernel<T, OutputTileRows, OutputTileCols, KernelRows, KernelCols>::get_matrix_stride( + const KernelShape &kernel_shape, const Tensor4DShape &input_shape, const PaddingType padding_type) const +{ + return WinogradConv::get_output_matrix_stride(kernel_shape, input_shape, padding_type); +} +template <typename T, int OutputTileRows, int OutputTileCols, int KernelRows, int KernelCols> +Tensor4DShape NEWinogradLayerTransformOutputKernel<T, OutputTileRows, OutputTileCols, KernelRows, KernelCols>::get_output_shape( + const KernelShape &kernel_shape, const Tensor4DShape &in_shape, const PaddingType padding) const +{ + return WinogradConv::get_output_shape(kernel_shape, in_shape, padding); +} + +template <typename T, int OutputTileRows, int OutputTileCols, int KernelRows, int KernelCols> +void NEWinogradLayerTransformOutputKernel<T, OutputTileRows, OutputTileCols, KernelRows, KernelCols>::configure( + const ITensor *biases, + const T *const output_workingspace, + const int matrix_stride, + T *const output, + const int n_batches, + const int n_rows, + const int n_cols, + const int n_channels) { _biases = biases; _output_workspace = output_workingspace; @@ -243,8 +294,8 @@ void NEWinogradLayerTransformOutputKernel<OutputTileRows, OutputTileCols, Kernel INEKernel::configure(win); } -template <int OutputTileRows, int OutputTileCols, int KernelRows, int KernelCols> -void NEWinogradLayerTransformOutputKernel<OutputTileRows, OutputTileCols, KernelRows, KernelCols>::run(const Window &window, const ThreadInfo &info) +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) { ARM_COMPUTE_UNUSED(info); ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this); @@ -253,7 +304,7 @@ void NEWinogradLayerTransformOutputKernel<OutputTileRows, OutputTileCols, Kernel ARM_COMPUTE_ERROR_ON_NULLPTR(_output); OutputTransform output_transform(_output_workspace, _matrix_stride, _matrix_row_stride, - reinterpret_cast<float *>(_biases->buffer()), _output, + reinterpret_cast<T *>(_biases->buffer()), _output, _n_batches, _n_rows, _n_cols, _n_channels); // The code below cannot be moved to configure because biases hasn't been allocated at that point @@ -262,12 +313,13 @@ void NEWinogradLayerTransformOutputKernel<OutputTileRows, OutputTileCols, Kernel output_transform.run(fst, lst); } -template <int OutputTileRows, int OutputTileCols, int KernelRows, int KernelCols> -bool NEWinogradLayerTransformOutputKernel<OutputTileRows, OutputTileCols, KernelRows, KernelCols>::is_parallelisable() const +template <typename T, int OutputTileRows, int OutputTileCols, int KernelRows, int KernelCols> +bool NEWinogradLayerTransformOutputKernel<T, OutputTileRows, OutputTileCols, KernelRows, KernelCols>::is_parallelisable() const { return false; } -template class NEWinogradLayerTransformOutputKernel<2, 2, 3, 3>; +template class NEWinogradLayerTransformOutputKernel<float, 2, 2, 3, 3>; +template class NEWinogradLayerTransformOutputKernel<float, 2, 2, 5, 5>; } // namespace arm_compute |