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/*
* Copyright (c) 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_NEQLSTMLAYERNORMALIZATIONKERNEL_H
#define ARM_COMPUTE_NEQLSTMLAYERNORMALIZATIONKERNEL_H
#include "arm_compute/core/NEON/INEKernel.h"
#include <functional>
namespace arm_compute
{
class ITensor;
/** NEON kernel to perform layer normalization */
class NEQLSTMLayerNormalizationKernel : public INEKernel
{
public:
const char *name() const override
{
return "NEQLSTMLayerNormalizationKernel";
}
/** Default constructor */
NEQLSTMLayerNormalizationKernel() = default;
/** Prevent instances of this class from being copied (As this class contains pointers) */
NEQLSTMLayerNormalizationKernel(const NEQLSTMLayerNormalizationKernel &) = delete;
/** Prevent instances of this class from being copied (As this class contains pointers) */
NEQLSTMLayerNormalizationKernel &operator=(const NEQLSTMLayerNormalizationKernel &) = delete;
/** Default Move Constructor. */
NEQLSTMLayerNormalizationKernel(NEQLSTMLayerNormalizationKernel &&) = default;
/** Default move assignment operator */
NEQLSTMLayerNormalizationKernel &operator=(NEQLSTMLayerNormalizationKernel &&) = default;
/** Default destructor */
~NEQLSTMLayerNormalizationKernel() = default;
/** Set the input and output tensors.
*
* @param[in] input Source tensor. Data types supported: QSYMM16.
* @param[out] output Destination tensor. Data types supported: Same as @p input.
* @param[in] weight Weight tensor. Data types supported: Same as @p input.
* @param[in] bias Bias tensor. Data types supported: S32
*/
void configure(const ITensor *input, ITensor *output, const ITensor *weight, const ITensor *bias);
/** Static function to check if given info will lead to a valid configuration of @ref NEQLSTMLayerNormalizationKernel
*
* @param[in] input Source tensor info. Data types supported: QSYMM16.
* @param[in] output Destination tensor info. Data types supported: Same as @p input.
* @param[in] weight Weight tensor info. Data types supported: Same as @p input.
* @param[in] bias Bias tensor info. Data types supported: S32
*
* @return a status
*/
static Status validate(const ITensorInfo *input, const ITensorInfo *output, const ITensorInfo *weight, const ITensorInfo *bias);
// Inherited methods overridden:
void run(const Window &window, const ThreadInfo &info) override;
private:
// constants
static constexpr uint32_t max_input_dimension{ 2 }; /**< The maximum input dimension supported */
static constexpr uint32_t max_weight_dimension{ 1 }; /**< The maximum weight dimension supported */
static constexpr uint32_t max_bias_dimension{ 1 }; /**< The maximum bias dimension supported */
static constexpr uint32_t vector_size_byte{ 16 }; /**< Computation vector size in byte */
using ComputeFuncType = std::function<void(NEQLSTMLayerNormalizationKernel &)>;
ComputeFuncType _fn{}; /**< Function pointer to computation function */
const ITensor *_input{ nullptr }; /**< Input tensor */
const ITensor *_weight{ nullptr }; /**< Weight tensor */
const ITensor *_bias{ nullptr }; /**< Bias tensor */
ITensor *_output{ nullptr }; /**< Output tensor */
int32_t _output_multiplier{}; /**< Multiplier for output values */
int32_t _output_shift{}; /**< Shift value for output values */
int32_t _window_start_x{}; /**< The beginning of x-axis iteration */
int32_t _window_end_x{}; /**< The end of x-axis iteration */
int32_t _window_step_x{}; /**< The size of x-axis iteration's step */
Window _inout_window{}; /**< Window for input and output tensor */
Window _weight_window{}; /**< Window for weight and bias tensor */
/** Function to configure initial windows for destination of computation
*
* @param[in] Target destination tensor to use for output window
*
* @return configured window
*/
Window configure_window(ITensor *target);
// Function to compute for data type QSYMM16
void compute_qsymm16();
/** Function to compute summation and summation of squared input of the given input pointer
*
* @param[in] Input_ptr pointer to input array
*
*/
std::pair<int64_t, int64_t> sum_qsymm16(const int16_t *input_ptr);
/** Function to normalize values using computed mean and standard deviation
*
* @param[in] input_ptr Pointer to input array
* @param[in] output_ptr Pointer to output array
* @param[in] weight_ptr Pointer to weight array
* @param[in] bias_ptr Pointer to bias array
* @param[in] mean Mean value
* @param[in] inv_std_mul Quantized multiplier for standard deviation
* @param[in] inv_std_shift Shift for standard deviation
*
*/
void normalize_qasymm16(const int16_t *input_ptr,
int16_t *output_ptr,
const int16_t *weight_ptr,
const int32_t *bias_ptr,
int32_t mean, int32_t inv_std_mul, int32_t inv_std_shift);
/** Function to compute output quantization information */
QuantizationInfo compute_output_qinfo();
};
} // namespace arm_compute
#endif /* ARM_COMPUTE_NEQLSTMLAYERNORMALIZATIONKERNEL_H */
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