diff options
Diffstat (limited to 'src/runtime/CL')
-rw-r--r-- | src/runtime/CL/functions/CLConvolutionLayer.cpp | 37 | ||||
-rw-r--r-- | src/runtime/CL/functions/CLWinogradConvolutionLayer.cpp | 89 |
2 files changed, 79 insertions, 47 deletions
diff --git a/src/runtime/CL/functions/CLConvolutionLayer.cpp b/src/runtime/CL/functions/CLConvolutionLayer.cpp index 97ef895434..83281e1747 100644 --- a/src/runtime/CL/functions/CLConvolutionLayer.cpp +++ b/src/runtime/CL/functions/CLConvolutionLayer.cpp @@ -43,32 +43,33 @@ CLConvolutionLayer::CLConvolutionLayer(std::shared_ptr<IMemoryManager> memory_ma } void CLConvolutionLayer::configure(ICLTensor *input, const ICLTensor *weights, const ICLTensor *biases, ICLTensor *output, const PadStrideInfo &conv_info, const WeightsInfo &weights_info, - const Size2D &dilation, const ActivationLayerInfo &act_info) + const Size2D &dilation, const ActivationLayerInfo &act_info, bool enable_fast_math) { ARM_COMPUTE_ERROR_ON_NULLPTR(input, weights, output); - ARM_COMPUTE_ERROR_THROW_ON(CLConvolutionLayer::validate(input->info(), weights->info(), ((biases != nullptr) ? biases->info() : nullptr), output->info(), conv_info, weights_info, dilation, act_info)); + ARM_COMPUTE_ERROR_THROW_ON(CLConvolutionLayer::validate(input->info(), weights->info(), ((biases != nullptr) ? biases->info() : nullptr), output->info(), conv_info, weights_info, dilation, act_info, + enable_fast_math)); switch(CLConvolutionLayer::get_convolution_method(input->info(), weights->info(), output->info(), conv_info, - weights_info, act_info, CLScheduler::get().target(), dilation)) + weights_info, act_info, CLScheduler::get().target(), dilation, enable_fast_math)) { case ConvolutionMethod::WINOGRAD: { auto f = arm_compute::support::cpp14::make_unique<CLWinogradConvolutionLayer>(_memory_manager); - f->configure(input, weights, biases, output, conv_info); + f->configure(input, weights, biases, output, conv_info, act_info, enable_fast_math); _function = std::move(f); break; } case ConvolutionMethod::DIRECT: { auto f = arm_compute::support::cpp14::make_unique<CLDirectConvolutionLayer>(); - f->configure(input, weights, biases, output, conv_info); + f->configure(input, weights, biases, output, conv_info, act_info); _function = std::move(f); break; } case ConvolutionMethod::GEMM: { auto f = arm_compute::support::cpp14::make_unique<CLGEMMConvolutionLayer>(_memory_manager); - f->configure(input, weights, biases, output, conv_info, weights_info, dilation); + f->configure(input, weights, biases, output, conv_info, weights_info, dilation, act_info); _function = std::move(f); break; } @@ -79,19 +80,18 @@ void CLConvolutionLayer::configure(ICLTensor *input, const ICLTensor *weights, c } Status CLConvolutionLayer::validate(const ITensorInfo *input, const ITensorInfo *weights, const ITensorInfo *biases, const ITensorInfo *output, const PadStrideInfo &conv_info, - const WeightsInfo &weights_info, const Size2D &dilation, const ActivationLayerInfo &act_info) + const WeightsInfo &weights_info, const Size2D &dilation, const ActivationLayerInfo &act_info, bool enable_fast_math) { ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(input, weights, output); - //Configure if the parameters match the direct convolution or the gemm-based const GPUTarget gpu_target = CLScheduler::get().target(); - switch(CLConvolutionLayer::get_convolution_method(input, weights, output, conv_info, weights_info, act_info, gpu_target, dilation)) + switch(CLConvolutionLayer::get_convolution_method(input, weights, output, conv_info, weights_info, act_info, gpu_target, dilation, enable_fast_math)) { case ConvolutionMethod::WINOGRAD: { //Validate Winograd - CLWinogradConvolutionLayer::validate(input, weights, biases, output, conv_info); + CLWinogradConvolutionLayer::validate(input, weights, biases, output, conv_info, act_info, enable_fast_math); break; } case ConvolutionMethod::DIRECT: @@ -115,25 +115,22 @@ Status CLConvolutionLayer::validate(const ITensorInfo *input, const ITensorInfo } ConvolutionMethod CLConvolutionLayer::get_convolution_method(const ITensorInfo *input, const ITensorInfo *weights, const ITensorInfo *output, const PadStrideInfo &conv_info, - const WeightsInfo &weights_info, const ActivationLayerInfo &act_info, const GPUTarget gpu_target, const Size2D &dilation) + const WeightsInfo &weights_info, const ActivationLayerInfo &act_info, const GPUTarget gpu_target, const Size2D &dilation, bool enable_fast_math) { ARM_COMPUTE_ERROR_ON_NULLPTR(input); ARM_COMPUTE_ERROR_ON_NULLPTR(output); ARM_COMPUTE_ERROR_ON_NULLPTR(weights); - ARM_COMPUTE_UNUSED(output); ARM_COMPUTE_UNUSED(weights_info); ARM_COMPUTE_UNUSED(gpu_target); - const size_t idx_w = get_data_layout_dimension_index(input->data_layout(), DataLayoutDimension::WIDTH); - const size_t idx_h = get_data_layout_dimension_index(input->data_layout(), DataLayoutDimension::HEIGHT); - const size_t idx_c = get_data_layout_dimension_index(input->data_layout(), DataLayoutDimension::CHANNEL); - - if((input->data_type() == DataType::F32) && (input->data_layout() == DataLayout::NCHW) && (input->dimension(idx_c) > 3) && (weights->dimension(idx_w) == 3) && (weights->dimension(idx_h) == 3) - && (weights->num_dimensions() <= 4) && (conv_info.stride().first == 1) && (conv_info.stride().second == 1) && (dilation == Size2D(1U, 1U)) && (!act_info.enabled())) + if(dilation != Size2D(1U, 1U)) + { + return ConvolutionMethod::GEMM; + } + else { - return ConvolutionMethod::WINOGRAD; + return bool(CLWinogradConvolutionLayer::validate(input, weights, nullptr, output, conv_info, act_info, enable_fast_math)) ? ConvolutionMethod::WINOGRAD : ConvolutionMethod::GEMM; } - return ConvolutionMethod::GEMM; } void CLConvolutionLayer::run() diff --git a/src/runtime/CL/functions/CLWinogradConvolutionLayer.cpp b/src/runtime/CL/functions/CLWinogradConvolutionLayer.cpp index 65747cf5d7..5ff4fbceee 100644 --- a/src/runtime/CL/functions/CLWinogradConvolutionLayer.cpp +++ b/src/runtime/CL/functions/CLWinogradConvolutionLayer.cpp @@ -31,33 +31,69 @@ using namespace arm_compute; +namespace +{ +Size2D winograd_output_tile(const Size2D &input_dims, const Size2D &kernel_dims, bool enable_fast_math) +{ + Size2D output_tile = Size2D{}; + + if(kernel_dims == Size2D(3U, 3U)) + { + output_tile = ((input_dims.width <= 4 && input_dims.height <= 4) || !enable_fast_math) ? Size2D(2U, 2U) : Size2D(4U, 4U); + } + else if(kernel_dims == Size2D(5U, 5U)) + { + output_tile = Size2D(4U, 4U); + } + + return output_tile; +} + +bool check_support_fast_math(const Size2D &output_tile, const Size2D &kernel_size) +{ + // 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>>; + + std::vector<WinogradConfiguration> fast_math_winograd = + { + WinogradConfiguration(std::pair<int, int>(4, 4), std::pair<int, int>(3, 3)), + 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)); + + return std::find(fast_math_winograd.begin(), fast_math_winograd.end(), p) != fast_math_winograd.end(); +} +} // namespace + CLWinogradConvolutionLayer::CLWinogradConvolutionLayer(std::shared_ptr<IMemoryManager> memory_manager) : _memory_group(memory_manager), _batched_mm(memory_manager), _input_transform(), _filter_transform(), _output_transform(), _activationlayer_function(), _input0(), _input1(), _batched_mm_output(), _is_first_run(true), _is_activationlayer_enabled(false) { } -void CLWinogradConvolutionLayer::configure(ICLTensor *input, const ICLTensor *weights, const ICLTensor *biases, ICLTensor *output, const PadStrideInfo &conv_info, const ActivationLayerInfo &act_info) +void CLWinogradConvolutionLayer::configure(ICLTensor *input, const ICLTensor *weights, const ICLTensor *biases, ICLTensor *output, const PadStrideInfo &conv_info, const ActivationLayerInfo &act_info, + bool enable_fast_math) { // Get indices for the width and height const size_t idx_width = get_data_layout_dimension_index(input->info()->data_layout(), DataLayoutDimension::WIDTH); const size_t idx_height = get_data_layout_dimension_index(input->info()->data_layout(), DataLayoutDimension::HEIGHT); - // Input shape - const TensorShape input_shape = input->info()->tensor_shape(); - const unsigned int input_w = input->info()->tensor_shape()[idx_width]; - const unsigned int input_h = input->info()->tensor_shape()[idx_height]; - - // Kernel size - const unsigned int kernel_w = weights->info()->tensor_shape()[idx_width]; - const unsigned int kernel_h = weights->info()->tensor_shape()[idx_height]; + // Input shape, kernel size and output tile + const Size2D input_dims = Size2D(input->info()->tensor_shape()[idx_width], input->info()->tensor_shape()[idx_height]); + const Size2D kernel_size = Size2D(weights->info()->tensor_shape()[idx_width], weights->info()->tensor_shape()[idx_height]); + const Size2D output_tile = winograd_output_tile(input_dims, kernel_size, enable_fast_math); - //Winograd output tile - const Size2D output_tile = (Size2D(kernel_w, kernel_h) == Size2D(3U, 3U) && input_w <= 4 && input_h <= 4) ? Size2D(2U, 2U) : Size2D(4U, 4U); + // Check if the Winograd configuration requires fast math + if(!enable_fast_math) + { + ARM_COMPUTE_ERROR_ON_MSG(check_support_fast_math(output_tile, kernel_size), "This Winograd configuration requires enable_fast_math=true"); + } const WinogradInfo winograd_info = WinogradInfo(output_tile, - Size2D(kernel_w, kernel_h), - Size2D(input_shape[idx_width], input_shape[idx_height]), + kernel_size, + input_dims, conv_info, input->info()->data_layout()); @@ -93,27 +129,26 @@ void CLWinogradConvolutionLayer::configure(ICLTensor *input, const ICLTensor *we } Status CLWinogradConvolutionLayer::validate(const ITensorInfo *input, const ITensorInfo *weights, const ITensorInfo *biases, const ITensorInfo *output, const PadStrideInfo &conv_info, - const ActivationLayerInfo &act_info) + const ActivationLayerInfo &act_info, bool enable_fast_math) { // Get indeces for the width and height 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); - // Input shape - const TensorShape input_shape = input->tensor_shape(); - const unsigned int input_w = input->tensor_shape()[idx_width]; - const unsigned int input_h = input->tensor_shape()[idx_height]; - - // Kernel size - const unsigned int kernel_w = weights->tensor_shape()[idx_width]; - const unsigned int kernel_h = weights->tensor_shape()[idx_height]; + // Input shape, kernel size and output tile + const Size2D input_dims = Size2D(input->tensor_shape()[idx_width], input->tensor_shape()[idx_height]); + const Size2D kernel_size = Size2D(weights->tensor_shape()[idx_width], weights->tensor_shape()[idx_height]); + const Size2D output_tile = winograd_output_tile(input_dims, kernel_size, enable_fast_math); - //Winograd output tile - const Size2D output_tile = (Size2D(kernel_w, kernel_h) == Size2D(3U, 3U) && input_w <= 4 && input_h <= 4) ? Size2D(2U, 2U) : Size2D(4U, 4U); + // 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), "This Winograd configuration requires enable_fast_math=true"); + } const WinogradInfo winograd_info = WinogradInfo(output_tile, - Size2D(kernel_w, kernel_h), - Size2D(input_shape[idx_width], input_shape[idx_height]), + kernel_size, + input_dims, conv_info, input->data_layout()); @@ -139,7 +174,7 @@ Status CLWinogradConvolutionLayer::validate(const ITensorInfo *input, const ITen // Validate Activation Layer if(act_info.enabled()) { - CLActivationLayer::validate(output, nullptr, act_info); + ARM_COMPUTE_RETURN_ON_ERROR(CLActivationLayer::validate(output, nullptr, act_info)); } return Status{}; |