COMPMID-1074: Rename WinograLayer.cpp to WinogradConvolutionLayer.cpp

Change-Id: Iccac7cd6cb458469568d0cd6fb36b262353f4188
Reviewed-on: https://eu-gerrit-1.euhpc.arm.com/129261
Tested-by: Jenkins <bsgcomp@arm.com>
Reviewed-by: Pablo Tello <pablo.tello@arm.com>

1 files changed, 390 insertions, 0 deletions

diff --git a/src/runtime/NEON/functions/NEWinogradConvolutionLayer.cpp b/src/runtime/NEON/functions/NEWinogradConvolutionLayer.cpp
new file mode 100644
index 0000000000..a1256ac8cb
--- /dev/null
+++ b/src/runtime/NEON/functions/NEWinogradConvolutionLayer.cpp
@@ -0,0 +1,390 @@
+/*
+ * Copyright (c) 2017-2018 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/NEWinogradConvolutionLayer.h"
+
+#include "arm_compute/core/Error.h"
+#include "arm_compute/core/Utils.h"
+#include "arm_compute/core/Validate.h"
+#include "arm_compute/core/Validate.h"
+#include "arm_compute/core/utils/misc/ShapeCalculator.h"
+#include "arm_compute/runtime/NEON/AssemblyHelper.h"
+#include "arm_compute/runtime/NEON/NEScheduler.h"
+#include "support/ToolchainSupport.h"
+
+#include "arm_compute/core/NEON/kernels/NEWinogradConvolutionLayerKernel.h"
+
+#include "arm_compute/core/NEON/kernels/convolution/winograd/winograd_gemm.hpp"
+
+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
+{
+namespace
+{
+Status validate_arguments(const ITensorInfo *input, const ITensorInfo *weights, const ITensorInfo *biases, const ITensorInfo *output, const PadStrideInfo &conv_info)
+{
+    ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(input);
+    ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(weights);
+    ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(output);
+    ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::F32);
+    ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input, weights);
+    ARM_COMPUTE_RETURN_ERROR_ON_MSG(weights->dimension(0) != 3 && weights->dimension(0) != 5, "Only 3 and 5 kernels are supported");
+    ARM_COMPUTE_RETURN_ERROR_ON(weights->num_dimensions() > 4);
+
+    if(biases != nullptr)
+    {
+        ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input, biases);
+        ARM_COMPUTE_RETURN_ERROR_ON(biases->num_dimensions() > 1);
+    }
+
+    // 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_RETURN_ERROR_ON_MSG(stride_y != 1 || stride_x != 1, "Winograd layer only supports unit strides.");
+
+    ARM_COMPUTE_UNUSED(output);
+    return Status{};
+}
+} //namespace
+
+NEWinogradConvolutionLayer::NEWinogradConvolutionLayer(std::shared_ptr<IMemoryManager> memory_manager)
+    : _memory_group(std::move(memory_manager)), _arm_gemm(nullptr), _gemm_kernel(nullptr), _transform_input_kernel(nullptr), _transform_output_kernel(nullptr), _transform_weights_kernel(nullptr),
+      _activationlayer_function(), _permute_input(), _permute_weights(), _permute_output(), _input_workspace(), _output_workspace(), _kernel_storage(), _input_nhwc(), _output_nhwc(), _weights_hwio(),
+      _workspace(), _input(), _weights(), _output(), _reshaped_kernel(false), _is_activationlayer_enabled(false)
+{
+} /* arm_compute */
+
+void NEWinogradConvolutionLayer::configure(const ITensor *input, const ITensor *weights, const ITensor *biases, ITensor *output, const PadStrideInfo &conv_info, const ActivationLayerInfo &act_info)
+{
+    ARM_COMPUTE_ERROR_ON_NULLPTR(input, weights, output);
+    ARM_COMPUTE_UNUSED(conv_info);
+    ARM_COMPUTE_ERROR_THROW_ON(validate_arguments(input->info(), weights->info(), (biases != nullptr) ? biases->info() : nullptr, output->info(), conv_info));
+
+    _weights = weights;
+    _input   = input;
+    _output  = output;
+
+    std::unique_ptr<INEWinogradLayerTransformInputKernel<float>>   transform_input_kernel;
+    std::unique_ptr<INEWinogradLayerTransformWeightsKernel<float>> transform_weights_kernel;
+    std::unique_ptr<INEWinogradLayerTransformOutputKernel<float>>  transform_output_kernel;
+
+    const int weights_width  = weights->info()->dimension(0);
+    const int weights_height = weights->info()->dimension(1);
+
+    int output_tile_rows = 0;
+    int output_tile_cols = 0;
+    int n_gemms          = 0;
+    int N_BLOCK          = 0; // Size of block used by GEMM.
+
+    switch(weights_width)
+    {
+        case 3:
+        {
+            transform_input_kernel   = support::cpp14::make_unique<NEWinogradLayerTransformInputKernel<float, 2, 2, 3, 3>>();
+            transform_weights_kernel = support::cpp14::make_unique<NEWinogradLayerTransformWeightsKernel<float, 2, 2, 3, 3>>();
+            transform_output_kernel  = support::cpp14::make_unique<NEWinogradLayerTransformOutputKernel<float, 2, 2, 3, 3>>();
+            output_tile_rows         = 2;
+            output_tile_cols         = 2;
+            n_gemms                  = NEWinogradLayerBatchedGEMMKernel<float, float, 2, 2, 3, 3>::WinogradBase::N_GEMMS;
+            N_BLOCK                  = NEWinogradLayerBatchedGEMMKernel<float, float, 2, 2, 3, 3>::WinogradConv::N_BLOCK;
+            break;
+        }
+        case 5:
+        {
+            transform_input_kernel   = support::cpp14::make_unique<NEWinogradLayerTransformInputKernel<float, 2, 2, 5, 5>>();
+            transform_weights_kernel = support::cpp14::make_unique<NEWinogradLayerTransformWeightsKernel<float, 2, 2, 5, 5>>();
+            transform_output_kernel  = support::cpp14::make_unique<NEWinogradLayerTransformOutputKernel<float, 2, 2, 5, 5>>();
+            output_tile_rows         = 2;
+            output_tile_cols         = 2;
+            n_gemms                  = NEWinogradLayerBatchedGEMMKernel<float, float, 2, 2, 5, 5>::WinogradBase::N_GEMMS;
+            N_BLOCK                  = NEWinogradLayerBatchedGEMMKernel<float, float, 2, 2, 5, 5>::WinogradConv::N_BLOCK;
+            break;
+        }
+        default:
+        {
+            ARM_COMPUTE_ERROR("Not supported.");
+            break;
+        }
+    }
+
+    const PaddingType use_padding_type = (conv_info.pad_left() != 0u) ? PADDING_SAME : PADDING_VALID;
+    const bool        use_same_padding = use_padding_type == PADDING_SAME;
+
+    // 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
+    const int in_channels  = input->info()->dimension(2);
+    const int out_channels = output->info()->dimension(2);
+
+    const Tensor4DShape in_shape(internal_get_input_shape(input));
+    const size_t        data_type_size = input->info()->element_size();
+    // Get the memory required to instantiate a new Winograd operator.
+    constexpr size_t storage_alignment   = 64;
+    const size_t     kernel_storage_size = transform_weights_kernel->get_weight_storage_size(out_channels, in_channels) * data_type_size;
+    _kernel_storage.allocator()->init(TensorInfo(TensorShape{ (kernel_storage_size + storage_alignment - 1) }, 1, DataType::U8));
+    _kernel_storage.allocator()->allocate();
+    // Input storage
+    const size_t input_storage_size = transform_input_kernel->get_input_storage_size(in_shape.n_batches, in_shape.n_channels, in_shape.n_rows, in_shape.n_cols, use_same_padding) * data_type_size;
+    _input_workspace.allocator()->init(TensorInfo(TensorShape{ (input_storage_size + storage_alignment - 1) }, 1, DataType::U8));
+    _input_workspace.allocator()->allocate();
+
+    // 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;
+    _output_workspace.allocator()->init(TensorInfo(TensorShape{ (output_storage_size + storage_alignment - 1) }, 1, DataType::U8));
+    _output_workspace.allocator()->allocate();
+
+    // configure and allocate dst tensor to be used to convert from winograd domain to spatial domain when calling to reshape_output()
+    TensorInfo info(TensorShape(_output->info()->dimension(2), _output->info()->dimension(0),
+                                _output->info()->dimension(1), _output->info()->dimension(3)),
+                    1, _output->info()->data_type());
+    _output_nhwc.allocator()->init(info);
+    _output_nhwc.allocator()->allocate();
+
+    // Re-order a weight tensor from [Output feature map x Input feature map x Height x Width] to [Height x Width x Input feature map x Output feature map]
+    _permute_weights.configure(weights, &_weights_hwio, PermutationVector(3U, 2U, 0U, 1U));
+    _weights_hwio.allocator()->allocate();
+
+    // configure the kernel to transform the input tensor from NCHW -> NHWC
+    _permute_input.configure(input, &_input_nhwc, PermutationVector(2U, 0U, 1U));
+    _input_nhwc.allocator()->allocate();
+
+    const KernelShape kernel_shape({ out_channels, weights_height, weights_width, in_channels });
+
+    // Configure the InputTransform
+    const int input_matrix_stride = transform_input_kernel->get_matrix_stride(kernel_shape, in_shape, use_padding_type);
+    transform_input_kernel->configure(reinterpret_cast<float *>(_input_nhwc.buffer()), in_shape.n_batches, in_shape.n_rows, in_shape.n_cols, in_shape.n_channels, use_padding_type,
+                                      reinterpret_cast<float *>(_input_workspace.buffer()), input_matrix_stride);
+
+    // Configure WeightsTransform
+    const int kernel_matrix_stride = transform_weights_kernel->get_matrix_stride(kernel_shape);
+    transform_weights_kernel->configure(&_weights_hwio, reinterpret_cast<float *>(_kernel_storage.buffer()), kernel_matrix_stride, out_channels, in_channels);
+
+    // Configure OutputTransform
+    //The biases tensor has not been allocated at this point in time, the output transform will add the biases to the final result in the run() method
+    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));
+
+    transform_output_kernel->configure(biases, reinterpret_cast<float *>(_output_workspace.buffer()),
+                                       output_matrix_stride, reinterpret_cast<float *>(_output_nhwc.buffer()),
+                                       in_shape.n_batches, output_shape.n_rows, output_shape.n_cols, out_channels);
+
+    // Configure GEMM
+    const int    tile_rows                = iceildiv(output_shape.n_rows, output_tile_rows);
+    const int    tile_cols                = iceildiv(output_shape.n_cols, output_tile_cols);
+    const int    m                        = in_shape.n_batches * tile_rows * tile_cols;
+    const int    k                        = in_shape.n_channels;
+    const int    n                        = out_channels;
+    const int    input_matrix_row_stride  = in_shape.n_channels;
+    const int    kernel_matrix_row_stride = roundup(out_channels, N_BLOCK);
+    const int    output_matrix_row_stride = kernel_matrix_row_stride;
+    unsigned int num_threads              = NEScheduler::get().num_threads();
+
+    _arm_gemm = arm_gemm::gemm<float, float>(NEScheduler::get().cpu_info(), m, n, k, 1, n_gemms, false, false, 1.f, 0.f, num_threads, false);
+    _arm_gemm->set_arrays(reinterpret_cast<float *>(_input_workspace.buffer()), input_matrix_row_stride, 0, input_matrix_stride, reinterpret_cast<float *>(_kernel_storage.buffer()),
+                          kernel_matrix_row_stride, kernel_matrix_stride, reinterpret_cast<float *>(_output_workspace.buffer()), output_matrix_row_stride, 0, output_matrix_stride);
+
+    auto acl_gemm_wrapper = support::cpp14::make_unique<NEGEMMAssemblyWrapper<arm_gemm::GemmCommon<float, float>>>();
+    acl_gemm_wrapper->configure(_arm_gemm.get());
+    const size_t workspace_size = _arm_gemm->get_working_size();
+
+    // Allocate workspace
+    if(workspace_size > 0)
+    {
+        const unsigned int alignment = 4096;
+        allocate_workspace(workspace_size, _workspace, _memory_group, alignment, 1);
+        _arm_gemm->set_working_space(reinterpret_cast<float *>(_workspace.buffer()));
+    }
+
+    const unsigned int window_size = _arm_gemm->get_window_size();
+    if(window_size < num_threads)
+    {
+        num_threads = window_size;
+        _arm_gemm->set_nthreads(num_threads);
+    }
+
+    _gemm_kernel = std::move(acl_gemm_wrapper);
+
+    // Reorder the convoluted output to ACL's ordering NCHW
+    _permute_output.configure(&_output_nhwc, _output, PermutationVector(1U, 2U, 0U));
+
+    _transform_input_kernel   = std::move(transform_input_kernel);
+    _transform_weights_kernel = std::move(transform_weights_kernel);
+    _transform_output_kernel  = std::move(transform_output_kernel);
+
+    //Configure Activation Layer
+    _is_activationlayer_enabled = act_info.enabled();
+    if(_is_activationlayer_enabled)
+    {
+        _activationlayer_function.configure(output, nullptr, act_info);
+    }
+}
+
+void NEWinogradConvolutionLayer::run()
+{
+    _memory_group.acquire();
+    if(!_reshaped_kernel)
+    {
+        _reshaped_kernel = true;
+        _permute_weights.run();
+        NEScheduler::get().schedule(_transform_weights_kernel.get(), Window::DimX);
+    }
+    //Bring channels to the front as Winograd code expects the tensor to be in the format NHWC
+    _permute_input.run();
+
+    // Transform input tensor to the winograd domain
+    NEScheduler::get().schedule(_transform_input_kernel.get(), Window::DimX);
+
+    //Run 16 GEMMs in multiple threads, each kernel runs one or more GEMMs
+    NEScheduler::get().schedule(_gemm_kernel.get(), Window::DimX);
+
+    // Transform output tensor to the spatial domain
+    NEScheduler::get().schedule(_transform_output_kernel.get(), Window::DimX);
+
+    // Reorder the convoluted output to ACL's ordering NCHW
+    _permute_output.run();
+
+    if(_is_activationlayer_enabled)
+    {
+        _activationlayer_function.run();
+    }
+    _memory_group.release();
+}
+
+Status NEWinogradConvolutionLayer::validate(const ITensorInfo *input, const ITensorInfo *weights, const ITensorInfo *biases, const ITensorInfo *output, const PadStrideInfo &conv_info,
+                                            const ActivationLayerInfo &act_info)
+{
+    ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments(input, weights, biases, output, conv_info));
+
+    // Get indices 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();
+
+    // Kernel size
+    const unsigned int kernel_w = weights->tensor_shape()[idx_width];
+    const unsigned int kernel_h = weights->tensor_shape()[idx_height];
+
+    const WinogradInfo winograd_info = WinogradInfo(Size2D(2, 2),
+                                                    Size2D(kernel_w, kernel_h),
+                                                    Size2D(input_shape[idx_width], input_shape[idx_height]),
+                                                    conv_info,
+                                                    input->data_layout());
+
+    // Validate input transform
+    const TensorShape input0_shape = misc::shape_calculator::compute_winograd_input_transform_shape(*input, winograd_info);
+    const TensorInfo  input0       = input->clone()->set_tensor_shape(input0_shape);
+    switch(weights->dimension(0))
+    {
+        case 3:
+        {
+            ARM_COMPUTE_RETURN_ON_ERROR((NEWinogradLayerTransformInputKernel<float, 2, 2, 3, 3>::validate(input, &input0, winograd_info)));
+            break;
+        }
+        case 5:
+        {
+            ARM_COMPUTE_RETURN_ON_ERROR((NEWinogradLayerTransformInputKernel<float, 2, 2, 5, 5>::validate(input, &input0, winograd_info)));
+            break;
+        }
+        default:
+        {
+            ARM_COMPUTE_RETURN_ERROR_MSG("Only 3x3 and 5x5 kernels supported.");
+            break;
+        }
+    }
+    // Validate filter transform
+    const TensorShape input1_shape = misc::shape_calculator::compute_winograd_filter_transform_shape(*weights, winograd_info);
+    const TensorInfo  input1       = weights->clone()->set_tensor_shape(input1_shape);
+
+    switch(weights->dimension(0))
+    {
+        case 3:
+        {
+            ARM_COMPUTE_RETURN_ON_ERROR((NEWinogradLayerTransformWeightsKernel<float, 2, 2, 3, 3>::validate(weights, &input1, winograd_info)));
+            break;
+        }
+        case 5:
+        {
+            ARM_COMPUTE_RETURN_ON_ERROR((NEWinogradLayerTransformWeightsKernel<float, 2, 2, 5, 5>::validate(weights, &input1, winograd_info)));
+            break;
+        }
+        default:
+        {
+            ARM_COMPUTE_RETURN_ERROR_MSG("Only 3x3 and 5x5 kernels supported.");
+            break;
+        }
+    }
+    // Validate batched matrix multiply
+    TensorShape batched_mm_output_shape = input0.tensor_shape();
+    batched_mm_output_shape[0]          = input1.tensor_shape()[0];
+    const TensorInfo batched_mm_output  = input0.clone()->set_tensor_shape(batched_mm_output_shape);
+    switch(weights->dimension(0))
+    {
+        case 3:
+        {
+            ARM_COMPUTE_RETURN_ON_ERROR((NEWinogradLayerBatchedGEMMKernel<float, float, 2, 2, 3, 3>::validate(&input0, &input1, nullptr, &batched_mm_output, 1.0f, 0.0f, GEMMInfo(false, false,
+                                                                                                              true /* Reshape weights only for the first run*/))));
+            // Validate output transform
+            ARM_COMPUTE_RETURN_ON_ERROR((NEWinogradLayerTransformOutputKernel<float, 2, 2, 3, 3>::validate(&batched_mm_output, biases, output, winograd_info)));
+            break;
+        }
+        case 5:
+        {
+            ARM_COMPUTE_RETURN_ON_ERROR((NEWinogradLayerBatchedGEMMKernel<float, float, 2, 2, 5, 5>::validate(&input0, &input1, nullptr, &batched_mm_output, 1.0f, 0.0f, GEMMInfo(false, false,
+                                                                                                              true /* Reshape weights only for the first run*/))));
+            // Validate output transform
+            ARM_COMPUTE_RETURN_ON_ERROR((NEWinogradLayerTransformOutputKernel<float, 2, 2, 5, 5>::validate(&batched_mm_output, biases, output, winograd_info)));
+            break;
+        }
+        default:
+        {
+            ARM_COMPUTE_RETURN_ERROR_MSG("Only 3x3 and 5x5 kernels supported.");
+            break;
+        }
+    }
+
+    // Validate Activation Layer
+    if(act_info.enabled())
+    {
+        NEActivationLayer::validate(output, nullptr, act_info);
+    }
+    return Status{};
+}
+
+} // namespace arm_compute