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/*
* 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/CL/functions/CLSoftmaxLayer.h"
#include "arm_compute/core/CL/CLHelpers.h"
#include "arm_compute/core/CL/ICLKernel.h"
#include "arm_compute/core/CL/kernels/CLSoftmaxLayerKernel.h"
#include "arm_compute/core/Helpers.h"
#include "arm_compute/core/Types.h"
#include "arm_compute/core/Utils.h"
#include "arm_compute/runtime/CL/CLMemoryGroup.h"
#include "arm_compute/runtime/CL/CLScheduler.h"
using namespace arm_compute;
CLSoftmaxLayer::CLSoftmaxLayer(std::shared_ptr<IMemoryManager> memory_manager)
: _memory_group(std::move(memory_manager)), _max_kernel(), _shift_exp_sum_kernel(), _max_shift_exp_sum_kernel(), _norm_kernel(), _max(), _sum(), _tmp(), _run_legacy_path(false)
{
}
void CLSoftmaxLayer::configure(const ICLTensor *input, ICLTensor *output, float beta)
{
// Perform validation step
ARM_COMPUTE_ERROR_ON_NULLPTR(input, output);
ARM_COMPUTE_ERROR_THROW_ON(CLSoftmaxLayer::validate(input->info(), output->info()));
// Create intermediate tensors shapes
const TensorInfo input_info = input->info()->clone()->reset_padding().set_is_resizable(true);
DataType tmp_data_type = is_data_type_quantized_asymmetric(input->info()->data_type()) ? DataType::S32 : input->info()->data_type();
TensorInfo tensor_info_tmp(input_info.clone()->set_data_type(tmp_data_type));
_tmp.allocator()->init(tensor_info_tmp);
TensorShape max_sum_shape = input->info()->tensor_shape();
max_sum_shape.set(0, 1);
_max.allocator()->init(input_info.clone()->set_tensor_shape(max_sum_shape));
_sum.allocator()->init(input_info.clone()->set_tensor_shape(max_sum_shape).set_data_type(tmp_data_type));
// Set GPU target to kernels
_max_shift_exp_sum_kernel.set_target(CLScheduler::get().target());
// Manage intermediate buffers
_memory_group.manage(&_tmp);
_memory_group.manage(&_max);
_memory_group.manage(&_sum);
// Configure kernels
// TODO (COMPMID-661): Remove legacy path once the new one is properly validated
_run_legacy_path = is_data_type_quantized_asymmetric(input->info()->data_type());
if(_run_legacy_path)
{
_max_kernel.configure(input, &_max);
_shift_exp_sum_kernel.configure(input, &_max, &_tmp, &_sum, beta);
}
else
{
_max_shift_exp_sum_kernel.configure(input, &_max, &_tmp, &_sum, beta);
}
_norm_kernel.configure(&_tmp, &_sum, output, beta);
// Allocate intermediate buffers
_tmp.allocator()->allocate();
_max.allocator()->allocate();
_sum.allocator()->allocate();
}
Error CLSoftmaxLayer::validate(const ITensorInfo *input, const ITensorInfo *output)
{
ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(input, output);
// Create intermediate tensor info
DataType tmp_data_type = is_data_type_quantized_asymmetric(input->data_type()) ? DataType::S32 : input->data_type();
TensorInfo tensor_info_tmp(input->clone()->set_data_type(tmp_data_type));
TensorShape max_sum_shape = input->tensor_shape();
max_sum_shape.set(0, 1);
TensorInfo tensor_info_max(input->clone()->set_tensor_shape(max_sum_shape));
TensorInfo tensor_info_sum(input->clone()->set_tensor_shape(max_sum_shape).set_data_type(tmp_data_type).set_quantization_info(QuantizationInfo()));
bool run_legacy_path = is_data_type_quantized_asymmetric(input->data_type());
if(run_legacy_path)
{
ARM_COMPUTE_RETURN_ON_ERROR(CLLogits1DMaxKernel::validate(input, &tensor_info_max));
ARM_COMPUTE_RETURN_ON_ERROR(CLLogits1DShiftExpSumKernel::validate(input, &tensor_info_max, &tensor_info_tmp, &tensor_info_sum));
}
else
{
ARM_COMPUTE_RETURN_ON_ERROR(CLLogits1DMaxShiftExpSumKernel::validate(input, &tensor_info_max, &tensor_info_tmp, &tensor_info_sum));
}
ARM_COMPUTE_RETURN_ON_ERROR(CLLogits1DNormKernel::validate(&tensor_info_tmp, &tensor_info_sum, output));
return Error{};
}
void CLSoftmaxLayer::run()
{
_memory_group.acquire();
// Force to use the new fused kernel
if(_run_legacy_path)
{
CLScheduler::get().enqueue(_max_kernel, false);
CLScheduler::get().enqueue(_shift_exp_sum_kernel, false);
}
else
{
CLScheduler::get().enqueue(_max_shift_exp_sum_kernel, false);
}
CLScheduler::get().enqueue(_norm_kernel);
_memory_group.release();
}
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