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/*
* Copyright (c) 2017-2020 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/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/CLScheduler.h"
#include "src/core/CL/ICLKernel.h"
#include "src/core/CL/kernels/CLFillBorderKernel.h"
#include "src/core/CL/kernels/CLSoftmaxLayerKernel.h"
#include "src/core/helpers/SoftmaxHelpers.h"
#include "support/MemorySupport.h"
namespace arm_compute
{
template <bool IS_LOG>
CLSoftmaxLayerGeneric<IS_LOG>::CLSoftmaxLayerGeneric(std::shared_ptr<IMemoryManager> memory_manager)
: _memory_group(std::move(memory_manager)),
_permute_input(),
_permute_output(),
_max_shift_exp_sum_kernel(support::cpp14::make_unique<CLLogits1DMaxShiftExpSumKernel>()),
_norm_kernel(support::cpp14::make_unique<CLLogits1DNormKernel>()),
_max(),
_sum(),
_tmp(),
_input_permuted(),
_output_permuted(),
_needs_permute()
{
}
template <bool IS_LOG>
CLSoftmaxLayerGeneric<IS_LOG>::~CLSoftmaxLayerGeneric() = default;
template <bool IS_LOG>
void CLSoftmaxLayerGeneric<IS_LOG>::configure(const ICLTensor *input, ICLTensor *output, float beta, int32_t axis)
{
configure(CLKernelLibrary::get().get_compile_context(), input, output, beta, axis);
}
template <bool IS_LOG>
void CLSoftmaxLayerGeneric<IS_LOG>::configure(const CLCompileContext &compile_context, const ICLTensor *input, ICLTensor *output, float beta, int32_t axis)
{
// Perform validation step
ARM_COMPUTE_ERROR_ON_NULLPTR(input, output);
ARM_COMPUTE_ERROR_THROW_ON(CLSoftmaxLayerGeneric<IS_LOG>::validate(input->info(), output->info(), beta, axis));
const size_t actual_axis = static_cast<size_t>(wrap_around(axis, static_cast<int32_t>(input->info()->num_dimensions())));
_needs_permute = actual_axis != 0;
ICLTensor *tmp_output = output;
const ICLTensor *tmp_input = _needs_permute ? &_input_permuted : input;
if(_needs_permute)
{
_memory_group.manage(&_input_permuted);
_memory_group.manage(&_output_permuted);
_permute_input.configure(compile_context, input, &_input_permuted, softmax_helpers::get_permutation_vector_from_softmax_axis(actual_axis));
tmp_output = &_output_permuted;
}
// Create intermediate tensors
DataType tmp_data_type = is_data_type_quantized_asymmetric(tmp_input->info()->data_type()) ? DataType::S32 : tmp_input->info()->data_type();
TensorInfo tensor_info_tmp(tmp_input->info()->clone()->set_data_type(tmp_data_type));
_tmp.allocator()->init(tensor_info_tmp);
TensorShape max_sum_shape = tmp_input->info()->tensor_shape();
max_sum_shape.set(0, 1);
_max.allocator()->init(tmp_input->info()->clone()->set_tensor_shape(max_sum_shape));
_sum.allocator()->init(tmp_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);
SoftmaxKernelInfo softmax_info;
softmax_info.beta = beta;
softmax_info.is_log = IS_LOG;
softmax_info.input_data_type = tmp_input->info()->data_type();
// Configure kernels
_max_shift_exp_sum_kernel->configure(compile_context, tmp_input, &_max, &_tmp, &_sum, softmax_info);
_norm_kernel->configure(compile_context, &_tmp, &_sum, tmp_output, softmax_info);
// Allocate intermediate buffers
_tmp.allocator()->allocate();
_max.allocator()->allocate();
_sum.allocator()->allocate();
if(_needs_permute)
{
_permute_output.configure(compile_context, &_output_permuted, output, softmax_helpers::get_permutation_vector_from_softmax_axis(actual_axis));
_input_permuted.allocator()->allocate();
_output_permuted.allocator()->allocate();
}
}
template <bool IS_LOG>
Status CLSoftmaxLayerGeneric<IS_LOG>::validate(const ITensorInfo *input, const ITensorInfo *output, float beta, int32_t axis)
{
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");
ARM_COMPUTE_UNUSED(beta);
ARM_COMPUTE_RETURN_ERROR_ON(axis < static_cast<int32_t>(-input->num_dimensions()) || static_cast<int32_t>(input->num_dimensions()) <= axis);
const size_t actual_axis = static_cast<size_t>(wrap_around(axis, static_cast<int32_t>(input->num_dimensions())));
const bool needs_permute = actual_axis != 0;
if(needs_permute)
{
const PermutationVector permutation_vector = softmax_helpers::get_permutation_vector_from_softmax_axis(actual_axis);
const TensorShape permuted_shape = misc::shape_calculator::compute_permutation_output_shape(*input, permutation_vector);
TensorInfo input_permuted(input->clone()->set_tensor_shape(permuted_shape));
ARM_COMPUTE_RETURN_ON_ERROR(CLPermute::validate(input, &input_permuted, permutation_vector));
TensorInfo output_permuted(output->clone()->set_tensor_shape(permuted_shape));
ARM_COMPUTE_RETURN_ON_ERROR(CLPermute::validate(&output_permuted, output, permutation_vector));
}
// 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));
SoftmaxKernelInfo softmax_info;
softmax_info.beta = beta;
softmax_info.is_log = IS_LOG;
softmax_info.input_data_type = input->data_type();
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, softmax_info));
return Status{};
}
template <bool IS_LOG>
void CLSoftmaxLayerGeneric<IS_LOG>::run()
{
MemoryGroupResourceScope scope_mg(_memory_group);
if(_needs_permute)
{
_permute_input.run();
}
CLScheduler::get().enqueue(*_max_shift_exp_sum_kernel, false);
CLScheduler::get().enqueue(*_norm_kernel, !_needs_permute);
if(_needs_permute)
{
_permute_output.run();
}
}
template class CLSoftmaxLayerGeneric<false>;
template class CLSoftmaxLayerGeneric<true>;
} // namespace arm_compute
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