diff options
Diffstat (limited to 'src/runtime/NEON/functions/NEWinogradConvolutionLayer.cpp')
| -rw-r--r-- | src/runtime/NEON/functions/NEWinogradConvolutionLayer.cpp | 68 |
1 files changed, 50 insertions, 18 deletions
diff --git a/src/runtime/NEON/functions/NEWinogradConvolutionLayer.cpp b/src/runtime/NEON/functions/NEWinogradConvolutionLayer.cpp index e699ad1815..6983c1c01b 100644 --- a/src/runtime/NEON/functions/NEWinogradConvolutionLayer.cpp +++ b/src/runtime/NEON/functions/NEWinogradConvolutionLayer.cpp @@ -33,6 +33,7 @@ #include "arm_compute/runtime/NEON/functions/NEGEMMAssemblyDispatch.h" #include "support/ToolchainSupport.h" +#include "arm_compute/core/NEON/kernels/convolution/common/utils.hpp" #include "arm_compute/core/NEON/kernels/convolution/winograd/winograd.hpp" namespace arm_compute @@ -232,6 +233,31 @@ bool check_support_fast_math(const Size2D &output_tile, const Size2D &kernel_siz return std::find(fast_math_winograd.begin(), fast_math_winograd.end(), p) != fast_math_winograd.end(); } +inline bool fuse_function_supported(const ActivationLayerInfo &act_info) +{ + return act_info.activation() == ActivationLayerInfo::ActivationFunction::RELU || + act_info.activation() == ActivationLayerInfo::ActivationFunction::BOUNDED_RELU; +} + +arm_gemm::Activation arm_gemm_activation_from_acl_activation(const ActivationLayerInfo &act_info) +{ + 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); + } + } +} + } //namespace NEWinogradConvolutionLayer::NEWinogradConvolutionLayer(const std::shared_ptr<IMemoryManager> &memory_manager) @@ -257,6 +283,8 @@ void NEWinogradConvolutionLayer::configure(const ITensor *input, const ITensor * const Size2D kernel_size = Size2D(weights->info()->dimension(width_idx), weights->info()->dimension(height_idx)); const Size2D output_tile = winograd_output_tile(input_dims, kernel_size); + + // Check if the Winograd configuration requires fast math if(!enable_fast_math) { @@ -388,21 +416,15 @@ void NEWinogradConvolutionLayer::configure(const ITensor *input, const ITensor * * 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, - use_same_padding) - * data_type_size; - ; - const KernelShape kernel_shape({ out_channels, static_cast<int>(kernel_size.height), static_cast<int>(kernel_size.width), in_channels }); - const int kernel_matrix_stride = transform_weights_kernel->get_matrix_stride(kernel_shape); - - const int output_matrix_stride = transform_output_kernel->get_matrix_stride(kernel_shape, in_shape, use_padding_type); - const auto output_shape(transform_output_kernel->get_output_shape(kernel_shape, in_shape, use_padding_type)); - - const int input_matrix_stride = transform_input_kernel->get_matrix_stride(kernel_shape, in_shape, use_padding_type); + 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.n_rows, output_tile.height); - const int tile_cols = iceildiv(output_shape.n_cols, output_tile.width); + 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; @@ -489,9 +511,19 @@ void NEWinogradConvolutionLayer::configure(const ITensor *input, const ITensor * _memory_group.manage(&_output_nhwc); output_to_use = &_output_nhwc; } - transform_output_kernel->configure(biases, &_output_transformed, - output_matrix_stride, output_to_use, - in_shape.n_batches, output_shape.n_rows, output_shape.n_cols, out_channels, &_output_workspace); + 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, _output->info()->data_type()); _output_workspace.allocator()->init(output_workspace_info); @@ -510,7 +542,7 @@ void NEWinogradConvolutionLayer::configure(const ITensor *input, const ITensor * _transform_output_kernel = std::move(transform_output_kernel); //Configure Activation Layer - _is_activationlayer_enabled = act_info.enabled(); + _is_activationlayer_enabled = act_info.enabled() && ! fuse_function_supported(act_info); if(_is_activationlayer_enabled) { _activationlayer_function.configure(_output, nullptr, act_info); @@ -546,7 +578,7 @@ void NEWinogradConvolutionLayer::run() _permute_output.run(); } - if(_is_activationlayer_enabled) + if(_is_activationlayer_enabled ) { _activationlayer_function.run(); } |