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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/NEON/functions/NESoftmaxLayer.h"
#include "arm_compute/core/Helpers.h"
#include "arm_compute/core/NEON/kernels/NESoftmaxLayerKernel.h"
#include "arm_compute/core/utils/misc/ShapeCalculator.h"
#include "arm_compute/runtime/NEON/NEScheduler.h"
namespace arm_compute
{
template <bool IS_LOG>
NESoftmaxLayerGeneric<IS_LOG>::NESoftmaxLayerGeneric(std::shared_ptr<IMemoryManager> memory_manager)
: _memory_group(std::move(memory_manager)), _max_kernel(), _softmax_kernel(), _flat_or_reshape_ptr(nullptr), _fill_border_kernel(), _reshape(), _max(), _tmp(), _input_flattened(), _output_flattened(),
_needs_flattening(false)
{
}
template <bool IS_LOG>
void NESoftmaxLayerGeneric<IS_LOG>::configure_reshape_input_kernel(const ITensor *input, const ITensor *output, int32_t first_n_reduce_axes)
{
// Flatten the input
const TensorShape shape_flatten = misc::shape_calculator::compute_softmax_shape(input->info(), first_n_reduce_axes);
// Initialize the flat input
_input_flattened.allocator()->init(input->info()->clone()->set_is_resizable(true).reset_padding().set_tensor_shape(shape_flatten));
// Note that the "other cases" include both:
// 1. first_n_reduce_axes < 3: Reduce the first 1 (no need to reduce) or 2 dimensions (inclusive)
// 2. first_n_reduce_axes == 4: Reduce all 4 dimensions. This can only be handled by NEReshapeKernel instead of NEFlattenKernel.
if(first_n_reduce_axes == 3)
{
auto flatten_kernel_ptr = support::cpp14::make_unique<NEFlattenLayer>();
flatten_kernel_ptr->configure(input, &_input_flattened);
_flat_or_reshape_ptr = std::move(flatten_kernel_ptr);
}
else
{
auto reshape_kernel_ptr = support::cpp14::make_unique<NEReshapeLayer>();
reshape_kernel_ptr->configure(input, &_input_flattened);
_flat_or_reshape_ptr = std::move(reshape_kernel_ptr);
}
// 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());
}
template <bool IS_LOG>
void NESoftmaxLayerGeneric<IS_LOG>::configure(ITensor *input, ITensor *output, float beta, int32_t reduce_end_axis)
{
// Perform validation step
ARM_COMPUTE_ERROR_ON_NULLPTR(input, output);
ARM_COMPUTE_ERROR_THROW_ON(NESoftmaxLayerGeneric::validate(input->info(), output->info(), beta, reduce_end_axis));
// Convert reduce-before axis (inclusive) to first n axes to reduce
size_t first_n_reduce_axes = dim_index_2_num_dims(reduce_end_axis, static_cast<int32_t>(input->info()->num_dimensions()));
// We only need flattening when the number of axes to reduce is greater than 1
_needs_flattening = first_n_reduce_axes > 1;
// 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_flattened
_memory_group.manage(&_input_flattened);
// Configure _flatten_kernel and _input_flattened
configure_reshape_input_kernel(input, output, first_n_reduce_axes);
}
// 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)
ITensor *input_2D = (_needs_flattening ? &_input_flattened : input);
// Create intermediate tensors shapes
const 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::F32 : input_2D->info()->data_type();
TensorInfo tensor_info_tmp(input_info.clone()->set_data_type(tmp_data_type));
// Init intermediate tensors
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));
_tmp.allocator()->init(tensor_info_tmp);
// Manage intermediate buffers
_memory_group.manage(&_max);
_memory_group.manage(&_tmp);
// Configure Kernels
_max_kernel.configure(input_2D, &_max);
if(_needs_flattening)
{
// Add to the memory manager _output_flattened
_memory_group.manage(&_output_flattened);
// The normalization kernel stores the result in a flat output tensor
_softmax_kernel.configure(input_2D, &_max, &_output_flattened, beta, &_tmp);
_input_flattened.allocator()->allocate();
// Reshape the flat output into the requested (4D) output
_reshape.configure(&_output_flattened, output);
// Allocate the intermediate flat tensors
_output_flattened.allocator()->allocate();
}
else
{
// Softmax 2D case
_fill_border_kernel.configure(input_2D, _max_kernel.border_size(), BorderMode::REPLICATE);
_softmax_kernel.configure(input_2D, &_max, output, beta, &_tmp);
}
// Allocate intermediate buffers
_max.allocator()->allocate();
_tmp.allocator()->allocate();
}
template <bool IS_LOG>
Status NESoftmaxLayerGeneric<IS_LOG>::validate(const ITensorInfo *input, const ITensorInfo *output, float beta, int32_t reduce_end_axis)
{
// Perform validation step
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(reduce_end_axis < static_cast<int32_t>(-input->num_dimensions()) || static_cast<int32_t>(input->num_dimensions()) <= reduce_end_axis);
// Convert reduce-before axis (inclusive) to first n axes to reduce
size_t first_n_reduce_axes = dim_index_2_num_dims(reduce_end_axis, static_cast<int32_t>(input->num_dimensions()));
// Create intermediate tensor info
DataType tmp_data_type = input->data_type();
const 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);
const TensorInfo tensor_info_max_sum(input->clone()->set_tensor_shape(max_sum_shape).set_data_type(tmp_data_type).set_quantization_info(input->quantization_info()).set_is_resizable(true));
const TensorInfo dont_care;
const bool needs_flattening = (first_n_reduce_axes > 1);
if(needs_flattening)
{
const TensorShape shape_flatten = misc::shape_calculator::compute_softmax_shape(input, first_n_reduce_axes);
TensorInfo tensor_info_flat(input->clone()->set_tensor_shape(shape_flatten).set_is_resizable(true));
if(first_n_reduce_axes == 3)
{
ARM_COMPUTE_RETURN_ON_ERROR(NEFlattenLayer::validate(input, &tensor_info_flat));
}
else
{
ARM_COMPUTE_RETURN_ON_ERROR(NEReshapeLayer::validate(input, &tensor_info_flat));
}
}
ARM_COMPUTE_RETURN_ON_ERROR(NELogits1DMaxKernel::validate(input, &tensor_info_max_sum));
ARM_COMPUTE_RETURN_ON_ERROR(NELogits1DSoftmaxKernel<IS_LOG>::validate(&tensor_info_tmp, &tensor_info_max_sum, output, beta, &dont_care));
return Status{};
}
template <bool IS_LOG>
void NESoftmaxLayerGeneric<IS_LOG>::run()
{
MemoryGroupResourceScope scope_mg(_memory_group);
if(_needs_flattening)
{
_flat_or_reshape_ptr->run();
}
NEScheduler::get().schedule(&_fill_border_kernel, Window::DimY);
NEScheduler::get().schedule(&_max_kernel, Window::DimY);
NEScheduler::get().schedule(&_softmax_kernel, Window::DimY);
if(_needs_flattening)
{
_reshape.run();
}
}
template class NESoftmaxLayerGeneric<false>;
template class NESoftmaxLayerGeneric<true>;
} // namespace arm_compute
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