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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.
*/
#ifndef ARM_COMPUTE_NESOFTMAXLAYER_H
#define ARM_COMPUTE_NESOFTMAXLAYER_H
#include "arm_compute/core/NEON/kernels/NEFillBorderKernel.h"
#include "arm_compute/core/NEON/kernels/NESoftmaxLayerKernel.h"
#include "arm_compute/runtime/IFunction.h"
#include "arm_compute/runtime/MemoryGroup.h"
#include "arm_compute/runtime/NEON/functions/NEFlattenLayer.h"
#include "arm_compute/runtime/NEON/functions/NEReshapeLayer.h"
#include "arm_compute/runtime/Tensor.h"
namespace arm_compute
{
class ITensor;
/** Basic function to compute a SoftmaxLayer and a Log SoftmaxLayer.
*
* Softmax is calculated by :
* @f[ out = exp((x - max(x)) * beta) / sum(exp((x - max(x)) * beta)) @f]
*
* Log Softmax is calculated by :
* @f[ out = (x - max(x) * beta) - log(\sum{e^{x - max(x) * beta}}) @f]
*
* This function runs the following kernels:
* -# @ref NEFillBorderKernel
* -# @ref NELogits1DMaxKernel
* -# @ref NELogits1DSoftmaxKernel
* And if the reduce_end_axis is not 0 or -input_num_dimensions, the function will use one of the the following kernels
* to reshape the input and perform softmax on the reshaped input:
* -# @ref NEFlattenLayerKernel
* -# @ref NEReshapeLayerKernel
*/
template <bool IS_LOG = false>
class NESoftmaxLayerGeneric : public IFunction
{
public:
/** Constructor */
NESoftmaxLayerGeneric(std::shared_ptr<IMemoryManager> memory_manager = nullptr);
/** Prevent instances of this class from being copied (As this class contains pointers) */
NESoftmaxLayerGeneric(const NESoftmaxLayerGeneric &) = delete;
/** Default move constructor */
NESoftmaxLayerGeneric(NESoftmaxLayerGeneric &&) = default;
/** Prevent instances of this class from being copied (As this class contains pointers) */
NESoftmaxLayerGeneric &operator=(const NESoftmaxLayerGeneric &) = delete;
/** Default move assignment operator */
NESoftmaxLayerGeneric &operator=(NESoftmaxLayerGeneric &&) = default;
/** Set the input and output tensors.
*
* @param[in,out] input Source tensor. Data types supported: QASYMM8/QASYMM8_SIGNED/F16/F32. If the width is not a
* multiple of the internal processing block size, @ref NEFillBorderKernel replicates the
* last value of each row to the nearest multiple.
* @param[out] output Destination tensor. Data types supported: same as @p input.
* @param[in] beta (Optional) A scaling factor for the exponent.
* @param[in] reduce_end_axis (Optional) The last axis of the first n dimensions (inclusive)to reduce. Defaults to 0.
* It has the purpose of squashing together the first n dimensions till (including) the @p reduce_end_axis. For instance, given a [2x3x4x5] image,
* when @p reduce_end_axis is 1, the reduction will be applied to axes 0 and 1, and the Softmax op will be applied on each of the [2x3] planes of the input image.
* Negative index is used to specify axis from the end (e.g. -1 for the last axis).
* Must be in range [-input_num_dimensions, input_num_dimensions).
*/
void configure(ITensor *input, ITensor *output, float beta = 1.0f, int32_t reduce_end_axis = 0);
/** Static function to check if given info will lead to a valid configuration of @ref NESoftmaxLayer
*
* @param[in] input Source tensor info. Data types supported: QASYMM8/QASYMM8_SIGNED/F16/F32.
* @param[in] output Destination tensor info. Data types supported: same as @p input
* @param[in] beta (Optional) A scaling factor for the exponent.
* @param[in] reduce_end_axis (Optional) The last axis of the first n dimensions (inclusive)to reduce. Defaults to 0.
* It has the purpose of squashing together the first n dimensions till (including) the @p reduce_end_axis. For instance, given a [2x3x4x5] image,
* when @p reduce_end_axis is 1, the reduction will be applied to axes 0 and 1, and the Softmax op will be applied on each of the [2x3] planes of the input image.
* Negative index is used to specify axis from the end (e.g. -1 for the last axis).
* Must be in range [-input_num_dimensions, input_num_dimensions).
* @return a status
*/
static Status validate(const ITensorInfo *input, const ITensorInfo *output, float beta = 1.0f, int32_t reduce_end_axis = 0);
// Inherited methods overridden:
void run() override;
private:
/** Utility method to configure the kernels needed to flatten the input
* tensor.
*
* @note This function changes the internal state of this class. In particular,
* it initializes the kernel @p _flatten_kernel and the tensors @p _input_flat and
* @p _output_flat
*
* @param[in] input Original source tensor.
* @param[in] output Original destination tensor.
* @param[in] reduce_end_axis (Optional) The last axis of the first n dimensions (inclusive)to reduce. Defaults to 0.
* It has the purpose of squashing together the first n dimensions till (including) the @p reduce_end_axis. For instance, given a [2x3x4x5] image,
* when @p reduce_end_axis is 1, the reduction will be applied to axes 0 and 1, and the Softmax op will be applied on each of the [2x3] planes of the input image.
* Negative index is used to specify axis from the end (e.g. -1 for the last axis).
* Must be in range [-input_num_dimensions, input_num_dimensions).
*/
void configure_reshape_input_kernel(const ITensor *input, const ITensor *output, int32_t reduce_end_axis);
MemoryGroup _memory_group;
NELogits1DMaxKernel _max_kernel;
NELogits1DSoftmaxKernel<IS_LOG> _softmax_kernel;
std::unique_ptr<IFunction> _flat_or_reshape_ptr;
NEFillBorderKernel _fill_border_kernel;
NEReshapeLayer _reshape;
Tensor _max;
Tensor _tmp;
Tensor _input_flattened;
Tensor _output_flattened;
bool _needs_flattening;
};
using NESoftmaxLayer = NESoftmaxLayerGeneric<false>;
using NELogSoftmaxLayer = NESoftmaxLayerGeneric<true>;
} // namespace arm_compute
#endif /* ARM_COMPUTE_NESOFTMAXLAYER_H */
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