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/*
* Copyright (c) 2016-2018 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/core/NEON/kernels/NEGEMMTranspose1xWKernel.h"
#include "arm_compute/core/AccessWindowTranspose.h"
#include "arm_compute/core/Coordinates.h"
#include "arm_compute/core/Error.h"
#include "arm_compute/core/Helpers.h"
#include "arm_compute/core/ITensor.h"
#include "arm_compute/core/NEON/INEKernel.h"
#include "arm_compute/core/TensorInfo.h"
#include "arm_compute/core/TensorShape.h"
#include "arm_compute/core/Types.h"
#include "arm_compute/core/Validate.h"
#include "arm_compute/core/Window.h"
#include <arm_neon.h>
#include <cstddef>
#include <cstring>
using namespace arm_compute;
namespace
{
TensorShape get_output_shape(const ITensorInfo *input)
{
TensorShape output_shape{ input->tensor_shape() };
const size_t transpose_w = 16 / input->element_size();
output_shape.set(0, input->dimension(1) * transpose_w);
output_shape.set(1, static_cast<size_t>(std::ceil((input->dimension(0) / static_cast<float>(transpose_w)))));
return output_shape;
}
Status validate_arguments(const ITensorInfo *input, const ITensorInfo *output)
{
//Note: ARM_COMPUTE_RETURN_ERROR_ON_CPU_F16_UNSUPPORTED(input) is not needed here as this kernel doesn't use NEON FP16 instructions.
ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::QASYMM8, DataType::U8, DataType::S8,
DataType::U16, DataType::S16, DataType::U32, DataType::S32,
DataType::F16, DataType::F32);
ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input, output);
if(output->total_size() != 0)
{
ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DIMENSIONS(output->tensor_shape(), get_output_shape(input));
ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input, output);
}
return Status{};
}
std::pair<Status, Window> validate_and_configure_window(ITensorInfo *input, ITensorInfo *output)
{
const unsigned int num_elems_processed_per_iteration = 16 / input->element_size();
const int scale_x = num_elems_processed_per_iteration;
bool window_changed = false;
// Configure kernel window
Window win = calculate_max_window(*input, Steps(num_elems_processed_per_iteration));
ARM_COMPUTE_ERROR_ON_MSG((win.x().end() / scale_x) == 0, "Transposed shape would be 0 in the second dimension");
AccessWindowHorizontal input_access(input, 0, num_elems_processed_per_iteration);
window_changed = window_changed || update_window_and_padding(win, input_access);
// Configure window in case of configured output
if(output->total_size() != 0)
{
AccessWindowTranspose output_access(output, 0, 0, num_elems_processed_per_iteration, 1, scale_x, 1.f / scale_x);
window_changed = window_changed || update_window_and_padding(win, output_access);
output_access.set_valid_region(win, ValidRegion(Coordinates(), input->tensor_shape()));
}
Status err = (window_changed) ? ARM_COMPUTE_CREATE_ERROR(ErrorCode::RUNTIME_ERROR, "Insufficient Padding!") : Status{};
return std::make_pair(err, win);
}
} // namespace
void NEGEMMTranspose1xWKernel::configure(const ITensor *input, ITensor *output)
{
ARM_COMPUTE_ERROR_ON_NULLPTR(input, output);
// Output tensor auto inizialitation if not yet initialized
auto_init_if_empty(*output->info(), get_output_shape(input->info()), 1, input->info()->data_type());
// Perform validate step
ARM_COMPUTE_ERROR_THROW_ON(validate_arguments(input->info(), output->info()));
_input = input;
_output = output;
// Configure kernel window
auto win_config = validate_and_configure_window(input->info(), output->info());
ARM_COMPUTE_ERROR_THROW_ON(win_config.first);
INEKernel::configure(win_config.second);
}
Status NEGEMMTranspose1xWKernel::validate(const ITensorInfo *input, const ITensorInfo *output)
{
ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments(input, output));
ARM_COMPUTE_RETURN_ON_ERROR(validate_and_configure_window(input->clone().get(), output->clone().get()).first);
return Status{};
}
void NEGEMMTranspose1xWKernel::run(const Window &window, const ThreadInfo &info)
{
ARM_COMPUTE_UNUSED(info);
ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this);
ARM_COMPUTE_ERROR_ON_INVALID_SUBWINDOW(INESimpleKernel::window(), window);
/*
* Following an example of how the transposition1xW works when the input data type is F32
*
* |a00 a01 a02 a03|
* |a10 a11 a12 a13|
* |a20 a21 a22 a23| = | a00 a01 a02 a03 || a10 a11 a12 a13 || a20 a21 a22 a23 || a30 a31 a32 a33 |
* |a30 a31 a32 a33|
*
* The output matrix will have the following shape: [ height * W, ceil(width / W) ], where W = (16 / element size of the tensor)
*/
// Set window for output tensor. Set to 0 the X and Y dimensions in order to allow multi-threading implementation and future batched matrix multiplications
Window win_out(window);
win_out.set(Window::DimX, Window::Dimension(0, 0, 0));
win_out.set(Window::DimY, Window::Dimension(0, 0, 0));
Iterator in(_input, window);
Iterator out(_output, win_out);
switch(_input->info()->element_size())
{
case 1:
{
const size_t out_stride = _output->info()->strides_in_bytes()[1];
execute_window_loop(window, [&](const Coordinates & id)
{
// Output address = base addr + (y * 16) + (x / 16 ) * stride
const uint8_t *in_ptr = in.ptr();
uint8_t *const out_ptr = out.ptr() + (id.y() << 4) + (id.x() >> 4) * out_stride;
vst1q_u8(out_ptr, vld1q_u8(in_ptr));
},
in, out);
break;
}
case 2:
{
const size_t out_stride = _output->info()->strides_in_bytes()[1] / sizeof(int16_t);
execute_window_loop(window, [&](const Coordinates & id)
{
// Output address = base addr + (y * 8) + (x / 8 ) * stride
const auto in_ptr = reinterpret_cast<const uint16_t *>(in.ptr());
const auto out_ptr = reinterpret_cast<uint16_t *>(out.ptr()) + (id.y() << 3) + (id.x() >> 3) * out_stride;
vst1q_u16(out_ptr, vld1q_u16(in_ptr));
},
in, out);
break;
}
case 4:
{
const size_t out_stride = _output->info()->strides_in_bytes()[1] / sizeof(float);
execute_window_loop(window, [&](const Coordinates & id)
{
// Output address = base addr + (y * 4) + (x / 4 ) * stride
const auto in_ptr = reinterpret_cast<const uint32_t *>(in.ptr());
const auto out_ptr = reinterpret_cast<uint32_t *>(out.ptr()) + (id.y() << 2) + (id.x() >> 2) * out_stride;
vst1q_u32(out_ptr, vld1q_u32(in_ptr));
},
in, out);
break;
}
default:
{
ARM_COMPUTE_ERROR("Element size not supported");
break;
}
}
}
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