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/*
* Copyright (c) 2019 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/core/NEON/kernels/NEDepthwiseConvolutionLayerNativeKernel.h"
#include "arm_compute/core/AccessWindowStatic.h"
#include "arm_compute/core/NEON/wrapper/traits.h"
#include "arm_compute/core/NEON/wrapper/wrapper.h"
#include "arm_compute/core/utils/misc/ShapeCalculator.h"
#include "support/ToolchainSupport.h"
namespace arm_compute
{
namespace
{
template <typename T, int S, bool has_biases>
void depthwise_loop_multiplier1(const ITensor *input, const ITensor *weights, const ITensor *biases, ITensor *output, const PadStrideInfo &conv_info,
const Size2D &dilation, const Window &window)
{
using VectorType = typename wrapper::traits::neon_vector<T, S>::type;
using TagType = typename wrapper::traits::neon_vector<T, S>::tag_type;
const size_t input_stride_y = input->info()->strides_in_bytes().y();
const size_t input_stride_z = input->info()->strides_in_bytes().z();
const size_t input_max_offset = input->info()->strides_in_bytes().z() * input->info()->dimension(2) - (input->info()->padding().bottom + input->info()->padding().top) *
input->info()->strides_in_bytes().y();
const size_t weights_width = weights->info()->dimension(1);
const size_t weights_height = weights->info()->dimension(2);
const size_t weights_stride_y = weights->info()->strides_in_bytes().y();
const size_t weights_stride_z = weights->info()->strides_in_bytes().z();
const size_t conv_stride_x = conv_info.stride().first;
const size_t conv_stride_y = conv_info.stride().second;
const size_t conv_pad_left = conv_info.pad_left();
const size_t conv_pad_top = conv_info.pad_top();
Window win_input = window;
win_input.set(Window::DimY, Window::Dimension(0, 0, 0));
win_input.set(Window::DimZ, Window::Dimension(0, 0, 0));
Window win_weights = win_input;
win_weights.set(3, Window::Dimension(0, 0, 0));
Iterator input_it(input, win_input);
Iterator weights_it(weights, win_weights);
Iterator output_it(output, window);
Iterator biases_it{};
if(has_biases)
{
biases_it = Iterator(biases, win_weights);
}
execute_window_loop(window, [&](const Coordinates & id)
{
VectorType acc = wrapper::vdup_n(static_cast<T>(0), TagType{});
const int input_y = id.y() * conv_stride_x - conv_pad_left;
const int input_z = id.z() * conv_stride_y - conv_pad_top;
int input_offset = input_y * input_stride_y + input_z * input_stride_z;
auto weights_ptr = weights_it.ptr();
for(size_t h = 0; h < weights_height; ++h)
{
int offs = input_offset;
for(size_t w = 0; w < weights_width; ++w)
{
const auto input_vals = wrapper::vload(reinterpret_cast<T *>(input_it.ptr() + std::min(static_cast<size_t>(offs), input_max_offset)));
const auto weights_vals = wrapper::vload(reinterpret_cast<T *>(weights_ptr + w * weights_stride_y));
acc = wrapper::vmla(acc, weights_vals, input_vals);
offs += dilation.x() * input_stride_y;
}
weights_ptr += weights_stride_z;
input_offset += dilation.y() * input_stride_z;
}
if(has_biases)
{
const auto biases_vals = wrapper::vload(reinterpret_cast<T *>(biases_it.ptr()));
acc = wrapper::vadd(acc, biases_vals);
}
wrapper::vstore(reinterpret_cast<T *>(output_it.ptr()), acc);
},
input_it, weights_it, biases_it, output_it);
}
template <typename T, bool has_biases>
void depthwise_loop_generic(const ITensor *input, const ITensor *weights, const ITensor *biases, ITensor *output, const PadStrideInfo &conv_info,
const Size2D &dilation, unsigned int depth_multiplier, const Window &window)
{
const size_t input_stride_y = input->info()->strides_in_bytes().y();
const size_t input_stride_z = input->info()->strides_in_bytes().z();
const size_t input_max_offset = input->info()->strides_in_bytes().z() * input->info()->dimension(2) - (input->info()->padding().bottom + input->info()->padding().top) *
input->info()->strides_in_bytes().y();
const size_t weights_width = weights->info()->dimension(1);
const size_t weights_height = weights->info()->dimension(2);
const size_t weights_stride_y = weights->info()->strides_in_bytes().y();
const size_t weights_stride_z = weights->info()->strides_in_bytes().z();
const size_t conv_stride_x = conv_info.stride().first;
const size_t conv_stride_y = conv_info.stride().second;
const size_t conv_pad_left = conv_info.pad_left();
const size_t conv_pad_top = conv_info.pad_top();
Window win_input = window;
win_input.set(Window::DimY, Window::Dimension(0, 0, 0));
win_input.set(Window::DimZ, Window::Dimension(0, 0, 0));
Window win_weights = win_input;
win_weights.set(3, Window::Dimension(0, 0, 0));
win_input.set_dimension_step(Window::DimX, 1);
Iterator input_it(input, win_input);
Iterator weights_it(weights, win_weights);
Iterator output_it(output, window);
Iterator biases_it{};
if(has_biases)
{
biases_it = Iterator(biases, win_weights);
}
execute_window_loop(window, [&](const Coordinates & id)
{
std::vector<T> acc(depth_multiplier, static_cast<T>(0));
const int input_y = id.y() * conv_stride_x - conv_pad_left;
const int input_z = id.z() * conv_stride_y - conv_pad_top;
int input_offset = input_y * input_stride_y + input_z * input_stride_z;
auto weights_ptr = weights_it.ptr();
for(size_t h = 0; h < weights_height; ++h)
{
int offs = input_offset;
for(size_t w = 0; w < weights_width; ++w)
{
const auto input_val = *(reinterpret_cast<T *>(input_it.ptr() + std::min(static_cast<size_t>(offs), input_max_offset)));
for(size_t m = 0; m < depth_multiplier; ++m)
{
const auto weights_val = *(reinterpret_cast<T *>(weights_ptr + m * sizeof(T) + w * weights_stride_y));
acc.at(m) = support::cpp11::fma(weights_val, input_val, acc.at(m));
}
offs += dilation.x() * input_stride_y;
}
weights_ptr += weights_stride_z;
input_offset += dilation.y() * input_stride_z;
}
if(has_biases)
{
for(size_t m = 0; m < depth_multiplier; ++m)
{
const auto biases_val = *(reinterpret_cast<T *>(biases_it.ptr() + m * sizeof(T)));
*(reinterpret_cast<T *>(output_it.ptr() + m * sizeof(T))) = acc.at(m) + biases_val;
}
}
else
{
for(size_t m = 0; m < depth_multiplier; ++m)
{
*(reinterpret_cast<T *>(output_it.ptr() + m * sizeof(T))) = acc.at(m);
}
}
},
input_it, weights_it, biases_it, output_it);
}
Status validate_arguments(const ITensorInfo *input, const ITensorInfo *weights, const ITensorInfo *biases, const ITensorInfo *output, const PadStrideInfo &conv_info, unsigned int depth_multiplier,
const Size2D &dilation)
{
ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(input, weights, 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, output);
ARM_COMPUTE_RETURN_ERROR_ON(depth_multiplier == 0);
ARM_COMPUTE_RETURN_ERROR_ON((input->dimension(0) * depth_multiplier) != weights->dimension(0));
ARM_COMPUTE_RETURN_ERROR_ON((dilation.x() < 1) || (dilation.y() < 1));
ARM_COMPUTE_RETURN_ERROR_ON((conv_info.stride().first < 1) || (conv_info.stride().second < 1));
if(biases != nullptr)
{
ARM_COMPUTE_RETURN_ERROR_ON(biases->num_dimensions() > 1);
ARM_COMPUTE_RETURN_ERROR_ON(biases->dimension(0) != weights->dimension(0));
}
if(output->total_size() != 0)
{
const TensorShape output_shape = misc::shape_calculator::compute_depthwise_convolution_shape(*input, *weights, conv_info, depth_multiplier, dilation);
ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DIMENSIONS(output->tensor_shape(), output_shape);
}
return Status{};
}
} // namespace
std::pair<Status, Window> validate_and_configure_window(ITensorInfo *input, ITensorInfo *weights, ITensorInfo *biases,
ITensorInfo *output, const PadStrideInfo &conv_info,
unsigned int depth_multiplier, const Size2D &dilation)
{
// Get convolved dimensions
const TensorShape output_shape = misc::shape_calculator::compute_depthwise_convolution_shape(*input, *weights, conv_info, depth_multiplier, dilation);
// Output auto inizialitation if not yet initialized
auto_init_if_empty(*output, input->clone()->set_is_resizable(true).reset_padding().set_tensor_shape(output_shape));
// Configure kernel window (generic)
const unsigned int num_elems_read_per_iteration = (depth_multiplier == 1) ? 8 / element_size_from_data_type(input->data_type()) : 1;
const unsigned int num_elems_written_per_iteration = num_elems_read_per_iteration * depth_multiplier;
// Configure kernel window
Window win = calculate_max_window(*output, Steps(num_elems_written_per_iteration));
AccessWindowStatic input_access(input, 0, -conv_info.pad_left(), ceil_to_multiple(num_elems_read_per_iteration, input->dimension(0)),
input->dimension(1) + std::max(std::max(conv_info.pad_right(), conv_info.pad_bottom()), conv_info.pad_top()));
AccessWindowHorizontal weights_access(weights, 0, num_elems_written_per_iteration);
AccessWindowHorizontal output_access(output, 0, num_elems_written_per_iteration);
bool window_changed = update_window_and_padding(win, input_access, weights_access, output_access);
if(biases != nullptr)
{
AccessWindowHorizontal biases_access(biases, 0, num_elems_written_per_iteration);
window_changed |= update_window_and_padding(win, biases_access);
}
output_access.set_valid_region(win, ValidRegion(Coordinates(), output->tensor_shape()));
Status err = (window_changed) ? ARM_COMPUTE_CREATE_ERROR(ErrorCode::RUNTIME_ERROR, "Insufficient Padding!") : Status{};
return std::make_pair(err, win);
}
NEDepthwiseConvolutionLayerNativeKernel::NEDepthwiseConvolutionLayerNativeKernel()
: _func(), _border_size(0), _input(), _weights(), _biases(), _output(), _conv_info(), _depth_multiplier(1), _dilation()
{
}
BorderSize NEDepthwiseConvolutionLayerNativeKernel::border_size() const
{
return _border_size;
}
void NEDepthwiseConvolutionLayerNativeKernel::configure(const ITensor *input, const ITensor *weights, const ITensor *biases, ITensor *output,
const PadStrideInfo &conv_info, unsigned int depth_multiplier, const Size2D &dilation)
{
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, depth_multiplier, dilation));
_input = input;
_weights = weights;
_biases = biases;
_output = output;
_conv_info = conv_info;
_depth_multiplier = depth_multiplier;
_border_size = BorderSize(_conv_info.pad_left(), 0, std::max(std::max(conv_info.pad_right(), conv_info.pad_bottom()), conv_info.pad_top()), 0);
_dilation = dilation;
switch(_input->info()->data_type())
{
case DataType::F32:
_func = (biases != nullptr) ? &NEDepthwiseConvolutionLayerNativeKernel::run_depthwise<float, 2, true> : &NEDepthwiseConvolutionLayerNativeKernel::run_depthwise<float, 2, false>;
break;
default:
ARM_COMPUTE_ERROR("Data type not supported");
break;
}
auto win_config = validate_and_configure_window(_input->info(), _weights->info(), (biases != nullptr) ? biases->info() : nullptr, _output->info(), _conv_info, _depth_multiplier, dilation);
ARM_COMPUTE_ERROR_THROW_ON(win_config.first);
INEKernel::configure(win_config.second);
}
Status NEDepthwiseConvolutionLayerNativeKernel::validate(const ITensorInfo *input, const ITensorInfo *weights, const ITensorInfo *biases, const ITensorInfo *output, const PadStrideInfo &conv_info,
unsigned int depth_multiplier,
const Size2D &dilation)
{
ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments(input, weights, biases, output, conv_info, depth_multiplier, dilation));
ARM_COMPUTE_RETURN_ON_ERROR(validate_and_configure_window(input->clone().get(), weights->clone().get(), (biases != nullptr) ? biases->clone().get() : nullptr, output->clone().get(), conv_info,
depth_multiplier, dilation)
.first);
return Status{};
}
void NEDepthwiseConvolutionLayerNativeKernel::run(const Window &window, const ThreadInfo &info)
{
ARM_COMPUTE_UNUSED(info);
ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this);
ARM_COMPUTE_ERROR_ON_INVALID_SUBWINDOW(INEKernel::window(), window);
(this->*_func)(window);
}
template <typename T, int S, bool has_biases>
void NEDepthwiseConvolutionLayerNativeKernel::run_depthwise(const Window &window)
{
ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this);
ARM_COMPUTE_ERROR_ON_INVALID_SUBWINDOW(INEKernel::window(), window);
if(_depth_multiplier == 1)
{
depthwise_loop_multiplier1<T, S, has_biases>(_input, _weights, _biases, _output, _conv_info, _dilation, window);
}
else
{
depthwise_loop_generic<T, has_biases>(_input, _weights, _biases, _output, _conv_info, _dilation, _depth_multiplier, window);
}
}
} // namespace arm_compute