COMPMID-687: Winograd layer.
Change-Id: Ica682d08e851491bf4a26b8d17908c014844055e
Reviewed-on: https://eu-gerrit-1.euhpc.arm.com/110990
Reviewed-by: Anthony Barbier <anthony.barbier@arm.com>
Tested-by: BSG Visual Compute Jenkins server to access repositories on http://mpd-gerrit.cambridge.arm.com <bsgcomp@arm.com>
diff --git a/src/runtime/NEON/functions/NEWinogradLayer.cpp b/src/runtime/NEON/functions/NEWinogradLayer.cpp
new file mode 100644
index 0000000..a9dec4e
--- /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