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|
/*
* 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 arm_compute
{
namespace
{
inline Tensor4DShape internal_get_input_shape(const arm_compute::ITensor *input)
{
const DataLayout data_layout = input->info()->data_layout();
const int in_width = input->info()->dimension(get_data_layout_dimension_index(data_layout, DataLayoutDimension::WIDTH));
const int in_height = input->info()->dimension(get_data_layout_dimension_index(data_layout, DataLayoutDimension::HEIGHT));
const int in_channels = input->info()->dimension(get_data_layout_dimension_index(data_layout, DataLayoutDimension::CHANNEL));
const int in_batches = input->info()->dimension(3);
return Tensor4DShape({ in_batches, in_height, in_width, in_channels });
}
Status validate_arguments(const ITensorInfo *input, const ITensorInfo *weights, const ITensorInfo *biases, const ITensorInfo *output, const PadStrideInfo &conv_info)
{
const DataLayout data_layout = input->data_layout();
const unsigned int width_idx = get_data_layout_dimension_index(data_layout, DataLayoutDimension::WIDTH);
const unsigned int height_idx = get_data_layout_dimension_index(data_layout, DataLayoutDimension::HEIGHT);
ARM_COMPUTE_UNUSED(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(width_idx) != 3 && weights->dimension(height_idx) != 5, "Only 3 and 5 kernels are supported");
ARM_COMPUTE_RETURN_ERROR_ON(data_layout != DataLayout::NCHW); // COMPMID-1287
ARM_COMPUTE_RETURN_ERROR_ON(weights->num_dimensions() > 4);
ARM_COMPUTE_RETURN_ERROR_ON_MSG(conv_info.stride().first != 1 || conv_info.stride().second != 1, "Winograd layer only supports unit strides.");
if(biases != nullptr)
{
ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input, biases);
ARM_COMPUTE_RETURN_ERROR_ON(biases->num_dimensions() > 1);
}
return Status{};
}
Size2D winograd_output_tile(const Size2D &input_dims, const Size2D &kernel_dims)
{
Size2D output_tile = Size2D{};
if(kernel_dims == Size2D(3U, 3U))
{
output_tile = (input_dims.width <= 4 && input_dims.height <= 4) ? Size2D(2U, 2U) : Size2D(4U, 4U);
}
else if(kernel_dims == Size2D(5U, 5U))
{
output_tile = Size2D(2U, 2U);
}
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>(2, 2), std::pair<int, int>(5, 5)),
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
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(), _is_prepared(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,
bool enable_fast_math)
{
ARM_COMPUTE_ERROR_ON_NULLPTR(input, weights, output);
ARM_COMPUTE_ERROR_THROW_ON(validate_arguments(input->info(), weights->info(), (biases != nullptr) ? biases->info() : nullptr, output->info(), conv_info));
// Get indices for the width and height
const DataLayout data_layout = input->info()->data_layout();
const unsigned int width_idx = get_data_layout_dimension_index(data_layout, DataLayoutDimension::WIDTH);
const unsigned int height_idx = get_data_layout_dimension_index(data_layout, DataLayoutDimension::HEIGHT);
const unsigned int channel_idx = get_data_layout_dimension_index(data_layout, DataLayoutDimension::CHANNEL);
const Size2D input_dims = Size2D(input->info()->dimension(width_idx), input->info()->dimension(height_idx));
const Size2D kernel_size = Size2D(weights->info()->dimension(width_idx), weights->info()->dimension(height_idx));
const Size2D output_tile = winograd_output_tile(input_dims, kernel_size);
// 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");
}
_weights = weights;
_input = input;
_output = output;
_is_prepared = false;
std::unique_ptr<INEWinogradLayerTransformInputKernel<float>> transform_input_kernel;
std::unique_ptr<INEWinogradLayerTransformWeightsKernel<float>> transform_weights_kernel;
std::unique_ptr<INEWinogradLayerTransformOutputKernel<float>> transform_output_kernel;
int n_gemms = 0;
int N_BLOCK = 0; // Size of block used by GEMM.
switch(kernel_size.width)
{
case 3:
{
if(input->info()->dimension(width_idx) > 4 && input->info()->dimension(height_idx) > 4)
{
transform_input_kernel = support::cpp14::make_unique<NEWinogradLayerTransformInputKernel<float, 4, 4, 3, 3>>();
transform_weights_kernel = support::cpp14::make_unique<NEWinogradLayerTransformWeightsKernel<float, 4, 4, 3, 3>>();
transform_output_kernel = support::cpp14::make_unique<NEWinogradLayerTransformOutputKernel<float, 4, 4, 3, 3>>();
n_gemms = NEWinogradLayerBatchedGEMMKernel<float, float, 4, 4, 3, 3>::WinogradBase::N_GEMMS;
N_BLOCK = NEWinogradLayerBatchedGEMMKernel<float, float, 4, 4, 3, 3>::WinogradConv::N_BLOCK;
}
else
{
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>>();
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>>();
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 convolved dimensions
const int in_channels = input->info()->dimension(channel_idx);
const int out_channels = output->info()->dimension(channel_idx);
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;
// Kernel Storage
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();
const KernelShape kernel_shape({ out_channels, static_cast<int>(kernel_size.height), static_cast<int>(kernel_size.width), in_channels });
// Configure the InputTransform
const int input_matrix_stride = transform_input_kernel->get_matrix_stride(kernel_shape, in_shape, use_padding_type);
if(data_layout == DataLayout::NCHW)
{
// 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();
transform_input_kernel->configure(&_input_nhwc, 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);
}
else
{
transform_input_kernel->configure(_input, 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);
if(data_layout == DataLayout::NCHW)
{
// 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));
transform_weights_kernel->configure(&_weights_hwio, reinterpret_cast<float *>(_kernel_storage.buffer()), kernel_matrix_stride, out_channels, in_channels);
}
else
{
// 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, 0U, 1U, 2U));
transform_weights_kernel->configure(&_weights_hwio, reinterpret_cast<float *>(_kernel_storage.buffer()), kernel_matrix_stride, out_channels, in_channels);
}
_weights_hwio.allocator()->allocate();
// 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));
if(data_layout == DataLayout::NCHW)
{
transform_output_kernel->configure(biases, reinterpret_cast<float *>(_output_workspace.buffer()),
output_matrix_stride, &_output_nhwc,
in_shape.n_batches, output_shape.n_rows, output_shape.n_cols, out_channels);
}
else
{
transform_output_kernel->configure(biases, reinterpret_cast<float *>(_output_workspace.buffer()),
output_matrix_stride, _output,
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.height);
const int tile_cols = iceildiv(output_shape.n_cols, output_tile.width);
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;
// TODO (COMPMID-1248) : Add support for memory manager in NEWinogradConvolutionLayer
// Warning : Do not set a memory group in allocate_workspace, should be done under COMPMID-1248
allocate_workspace(workspace_size, _workspace, nullptr, 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(data_layout == DataLayout::NCHW && _is_activationlayer_enabled)
{
_activationlayer_function.configure(_output, nullptr, act_info);
}
}
void NEWinogradConvolutionLayer::run()
{
const DataLayout data_layout = _input->info()->data_layout();
prepare();
_memory_group.acquire();
if(data_layout == DataLayout::NCHW)
{
//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);
if(data_layout == DataLayout::NCHW)
{
// 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, bool enable_fast_math)
{
ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(input, weights, output);
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, kernel size and output tile
const Size2D input_dims = Size2D(input->dimension(idx_width), input->dimension(idx_height));
const Size2D kernel_size = Size2D(weights->dimension(idx_width), weights->dimension(idx_height));
const Size2D output_tile = winograd_output_tile(input_dims, kernel_size);
// 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,
kernel_size,
input_dims,
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(idx_width))
{
case 3:
{
if(input_dims.width > 4 && input_dims.height > 4)
{
ARM_COMPUTE_RETURN_ON_ERROR((NEWinogradLayerTransformInputKernel<float, 4, 4, 3, 3>::validate(input, &input0, winograd_info)));
}
else
{
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(idx_width))
{
case 3:
{
if(input_dims.width > 4 && input_dims.height > 4)
{
ARM_COMPUTE_RETURN_ON_ERROR((NEWinogradLayerTransformWeightsKernel<float, 4, 4, 3, 3>::validate(weights, &input1, winograd_info)));
}
else
{
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(idx_width))
{
case 3:
{
if(input_dims.width > 4 && input_dims.height > 4)
{
// Validate output transform
ARM_COMPUTE_RETURN_ON_ERROR((NEWinogradLayerTransformOutputKernel<float, 4, 4, 3, 3>::validate(&batched_mm_output, biases, output, winograd_info)));
}
else
{
// 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:
{
// 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{};
}
void NEWinogradConvolutionLayer::prepare()
{
if(!_is_prepared)
{
// Permute weights
_permute_weights.run();
_weights->mark_as_unused();
// Transform weights
NEScheduler::get().schedule(_transform_weights_kernel.get(), Window::DimX);
_weights_hwio.allocator()->free();
_is_prepared = true;
}
}
} // namespace arm_compute
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