| /* |
| * Copyright (c) 2017 ARM Limited. |
| * |
| * SPDX-License-Identifier: MIT |
| * |
| * Permission is hereby granted, free of charge, to any person obtaining a copy |
| * of this software and associated documentation files (the "Software"), to |
| * deal in the Software without restriction, including without limitation the |
| * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or |
| * sell copies of the Software, and to permit persons to whom the Software is |
| * furnished to do so, subject to the following conditions: |
| * |
| * The above copyright notice and this permission notice shall be included in all |
| * copies or substantial portions of the Software. |
| * |
| * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR |
| * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, |
| * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE |
| * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER |
| * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, |
| * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE |
| * SOFTWARE. |
| */ |
| #include "arm_compute/runtime/NEON/functions/NEWinogradLayer.h" |
| |
| #include "arm_compute/core/Utils.h" |
| #include "arm_compute/core/Validate.h" |
| #include "arm_compute/runtime/NEON/NEScheduler.h" |
| #include "support/ToolchainSupport.h" |
| |
| namespace |
| { |
| inline Tensor4DShape internal_get_input_shape(const arm_compute::ITensor *input) |
| { |
| const int in_width = input->info()->dimension(0); |
| const int in_height = input->info()->dimension(1); |
| const int in_batches = input->info()->dimension(3); |
| const int in_channels = input->info()->dimension(2); |
| return Tensor4DShape({ in_batches, in_height, in_width, in_channels }); |
| } |
| } /* namespace */ |
| |
| namespace arm_compute |
| { |
| NEWinogradLayer::NEWinogradLayer(std::shared_ptr<IMemoryManager> memory_manager) |
| : _memory_group(std::move(memory_manager)), _winograd_kernel(), _weights_workspace(), _workspace(), _kernel_storage(), _input(), _weights(), _output(), _reshaped_kernel(false), _conv() |
| { |
| } /* arm_compute */ |
| |
| void NEWinogradLayer::configure(const ITensor *input, const ITensor *weights, const ITensor *biases, ITensor *output, const PadStrideInfo &conv_info) |
| { |
| ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::F32); |
| ARM_COMPUTE_ERROR_ON_MISMATCHING_DATA_TYPES(input, weights); |
| ARM_COMPUTE_ERROR_ON_MSG(weights->info()->dimension(1) != 3 || weights->info()->dimension(0) != 3, "Only 3x3 kernels are supported"); |
| ARM_COMPUTE_ERROR_ON(weights->info()->num_dimensions() > 4); |
| |
| if(biases != nullptr) |
| { |
| ARM_COMPUTE_ERROR_ON_MISMATCHING_DATA_TYPES(input, biases); |
| ARM_COMPUTE_ERROR_ON(biases->info()->num_dimensions() > 1); |
| } |
| |
| _weights = weights; |
| _input = input; |
| _output = output; |
| |
| // Get parameters from conv_info |
| unsigned int stride_x = 0; |
| unsigned int stride_y = 0; |
| std::tie(stride_x, stride_y) = conv_info.stride(); |
| ARM_COMPUTE_ERROR_ON_MSG(stride_y != 1 || stride_x != 1, "Winograd layer only supports unit strides."); |
| |
| // Get convolved dimensions |
| auto padding = PADDING_VALID; |
| const int in_channels = input->info()->dimension(2); |
| |
| const int out_channels = output->info()->dimension(2); |
| const int weights_width = weights->info()->dimension(0); |
| const int weights_height = weights->info()->dimension(1); |
| |
| const KernelShape kernel_shape({ out_channels, weights_height, weights_width, in_channels }); |
| const Tensor4DShape in_shape(internal_get_input_shape(input)); |
| |
| // Get the memory required to instantiate a new Winograd operator. |
| constexpr size_t kstore_alignment = 64; |
| const size_t kernel_storage_per_thread = NEWinogradLayerKernel::get_kernel_storage_size(kernel_shape); |
| _kernel_storage.allocator()->init(TensorInfo(TensorShape{ (kernel_storage_per_thread + kstore_alignment - 1) }, 1, DataType::U8)); |
| _memory_group.manage(&_kernel_storage); |
| |
| // Get workbench size and allocate memory |
| constexpr size_t wspace_alignment = 64; |
| const size_t ws_size = NEWinogradLayerKernel::get_working_space_size(in_shape, kernel_shape, padding); |
| _workspace.allocator()->init(TensorInfo(TensorShape{ (ws_size + wspace_alignment - 1) }, 1, DataType::U8)); |
| _memory_group.manage(&_workspace); |
| |
| // Workspace for weights transform |
| const size_t weights_transform_size = NEWinogradLayerKernel::get_kernel_transform_working_size(kernel_shape); |
| _weights_workspace.allocator()->init(TensorInfo(TensorShape{ (weights_transform_size + wspace_alignment - 1) }, 1, DataType::U8)); |
| _memory_group.manage(&_weights_workspace); |
| |
| _kernel_storage.allocator()->allocate(); |
| _workspace.allocator()->allocate(); |
| _weights_workspace.allocator()->allocate(); |
| |
| // Create Winograd operator object |
| _conv = support::cpp14::make_unique<Winograd3x3F32>(kernel_shape, in_shape, padding, _kernel_storage.buffer()); |
| |
| // Configure the kernel, padding not needed so it's safe to call configure after allocare |
| _winograd_kernel.configure(output, _conv.get()); |
| } |
| |
| void NEWinogradLayer::run() |
| { |
| #if defined(__aarch64__) |
| _memory_group.acquire(); |
| if(!_reshaped_kernel) |
| { |
| _conv->transform_weights(reinterpret_cast<const float *>(_weights->buffer()), reinterpret_cast<float *>(_weights_workspace.buffer())); |
| _reshaped_kernel = true; |
| } |
| const Tensor4DShape in_shape(internal_get_input_shape(_input)); |
| auto padding = PADDING_VALID; |
| |
| //Bring channels to the front as Winograd code expects the tensor to be in the format NHWC |
| _conv->nchw2nhwc(in_shape, padding, _workspace.buffer(), reinterpret_cast<const float *>(_input->buffer())); |
| |
| //Get ptrs into the workspace |
| std::pair<void *, void *> nhwc_ptrs = _conv->get_nhwc_ptrs(in_shape, padding, _workspace.buffer()); |
| |
| //Setup matrices ptrs and transfor the input tensor to the appropriate form before running GEMM. |
| _conv->reshape_input(in_shape, padding, nhwc_ptrs.second, _workspace.buffer()); |
| |
| //Run 16 GEMMs in multiple threads, each kernel runs one or more GEMMs |
| NEScheduler::get().schedule(&_winograd_kernel, Window::DimY); |
| |
| //Transform the output to the appropriate form |
| _conv->reshape_output(in_shape, padding, nhwc_ptrs.first); |
| |
| //Transform back to NCHW |
| _conv->nhwc2nchw(in_shape, padding, _workspace.buffer(), reinterpret_cast<float *>(_output->buffer())); |
| |
| _memory_group.release(); |
| #else /* __aarch64__ */ |
| ARM_COMPUTE_UNUSED(_winograd_kernel); |
| ARM_COMPUTE_UNUSED(_workspace); |
| ARM_COMPUTE_UNUSED(_kernel_storage); |
| ARM_COMPUTE_UNUSED(_input); |
| ARM_COMPUTE_UNUSED(_weights); |
| ARM_COMPUTE_UNUSED(_output); |
| ARM_COMPUTE_UNUSED(_reshaped_kernel); |
| ARM_COMPUTE_UNUSED(_conv); |
| ARM_COMPUTE_ERROR("Winograd only supported for aarch64, recompile with arch=arm64-v8a."); |
| #endif /* __aarch64__ */ |
| } |
| } // namespace arm_compute |