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Diffstat (limited to 'src/runtime/NEON/functions/NEWinogradLayer.cpp')
| -rw-r--r-- | src/runtime/NEON/functions/NEWinogradLayer.cpp | 155 |
1 files changed, 155 insertions, 0 deletions
diff --git a/src/runtime/NEON/functions/NEWinogradLayer.cpp b/src/runtime/NEON/functions/NEWinogradLayer.cpp new file mode 100644 index 0000000000..a9dec4ea0d --- /dev/null +++ b/src/runtime/NEON/functions/NEWinogradLayer.cpp @@ -0,0 +1,155 @@ +/* + * 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 = Winograd3x3F32::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 = Winograd3x3F32::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 = Winograd3x3F32::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<float *, float *> 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 |