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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/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/core/utils/misc/ShapeCalculator.h"
#include "arm_compute/runtime/CL/CLMemoryGroup.h"
#include "arm_compute/runtime/CL/CLScheduler.h"
namespace arm_compute
{
CLSoftmaxLayer::CLSoftmaxLayer(std::shared_ptr<IMemoryManager> memory_manager)
: _memory_group(std::move(memory_manager)), _max_shift_exp_sum_kernel(), _norm_kernel(), _flatten_kernel(), _reshape_kernel(), _max(), _sum(), _tmp(), _input_flat(), _output_flat(),
_needs_flattening(false)
{
}
void CLSoftmaxLayer::configure_flatten_kernel(const ICLTensor *input, const ICLTensor *output)
{
// Flatten the input
const TensorShape shape_flatten = misc::shape_calculator::compute_flatten_shape(input->info());
// Initialize the flat input
_input_flat.allocator()->init(input->info()->clone()->set_is_resizable(true).reset_padding().set_tensor_shape(shape_flatten));
// Configure the flatten_kernel
_flatten_kernel.configure(input, &_input_flat);
// We need to init the output tensor here. Indeed, the reshape kernel expects
// both tensors to be already initialized
auto_init_if_empty(*output->info(), *input->info()->clone());
}
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()));
_needs_flattening = input->info()->num_dimensions() > 2;
// If we are dealing with a 4D tensor, we will:
// - Flatten the input, so that we end up with a [width*height*depth] * batches 2D tensor
// - Execute all the pipeline (reduction + normalization) on the flattened tensor
// - Reshape the flattened output into the real output
if(_needs_flattening)
{
// Add to the memory manager _input_flat
_memory_group.manage(&_input_flat);
// Cofigure _flatten_kernel and _input_flat
configure_flatten_kernel(input, output);
}
// We want to deal with a 2D input. Either it is the flattened version of the original input (4D case)
// or it is the original input case (2D case)
const ICLTensor *input_2D = (_needs_flattening ? &_input_flat : input);
// Create intermediate tensors shapes
TensorInfo input_info = input_2D->info()->clone()->reset_padding().set_is_resizable(true);
DataType tmp_data_type = is_data_type_quantized_asymmetric(input_2D->info()->data_type()) ? DataType::S32 : input_2D->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_2D->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
_max_shift_exp_sum_kernel.configure(input_2D, &_max, &_tmp, &_sum, beta);
if(_needs_flattening)
{
// Add to the memory manager _output_flat
_memory_group.manage(&_output_flat);
// The normalization kernel stores the result in a flat output tensor
_norm_kernel.configure(&_tmp, &_sum, &_output_flat, beta);
// Reshape the flat output into a the requested (4D) output
_reshape_kernel.configure(&_output_flat, output);
// Allocate the intermediate flat tensors
_input_flat.allocator()->allocate();
_output_flat.allocator()->allocate();
}
else
{
// Softmax 2D case
_norm_kernel.configure(&_tmp, &_sum, output, beta);
}
// Allocate intermediate buffers
_tmp.allocator()->allocate();
_max.allocator()->allocate();
_sum.allocator()->allocate();
}
Status CLSoftmaxLayer::validate(const ITensorInfo *input, const ITensorInfo *output)
{
ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(input, output);
ARM_COMPUTE_RETURN_ERROR_ON_MSG(input->num_dimensions() > 4, "Only up to 4 dimensions are supported");
// 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).set_is_resizable(true));
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).set_is_resizable(true));
TensorInfo tensor_info_sum(input->clone()->set_tensor_shape(max_sum_shape).set_data_type(tmp_data_type).set_quantization_info(QuantizationInfo()).set_is_resizable(true));
const TensorShape shape_flatten = misc::shape_calculator::compute_flatten_shape(input);
TensorInfo tensor_info_flat(input->clone()->set_tensor_shape(shape_flatten).set_is_resizable(true));
if(input->num_dimensions() > 2) // needs flattening
{
ARM_COMPUTE_RETURN_ON_ERROR(CLFlattenLayerKernel::validate(input, &tensor_info_flat));
}
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 Status{};
}
void CLSoftmaxLayer::run()
{
_memory_group.acquire();
if(_needs_flattening)
{
CLScheduler::get().enqueue(_flatten_kernel, false);
}
CLScheduler::get().enqueue(_max_shift_exp_sum_kernel, false);
CLScheduler::get().enqueue(_norm_kernel, !_needs_flattening);
if(_needs_flattening)
{
CLScheduler::get().enqueue(_reshape_kernel, true);
}
// Relase intermediate buffers
_memory_group.release();
}
} // namespace arm_compute
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