/****************************************************************************
*
*    Copyright (c) 2020 Vivante Corporation
*
*    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 <stdint.h>
#include <stdlib.h>
#include <string.h>
#include "vsi_nn_types.h"
#include "vsi_nn_tensor.h"
#include "vsi_nn_graph.h"
#include "vsi_nn_log.h"
#include "vsi_nn_error.h"
#include "vsi_nn_prv.h"
#include "vsi_nn_tensor_util.h"
#include "utils/vsi_nn_util.h"
#include "kernel/vsi_nn_kernel.h"
#include "libnnext/vx_lib_nnext.h"

__BEGIN_DECLS

/*
 * Define kernel meta.
 */
#define _INPUT_NUM          (2)
#define _OUTPUT_NUM         (1)
#define _KERNEL_NAME        CVIVANTE_NAMESPACE("cpu.l2normalizescale")


/*
 * Kernel params
 */
static vx_param_description_t _l2normalizescale_kernel_param_def[] =
{
    {VX_INPUT,  VX_TYPE_TENSOR, VX_PARAMETER_STATE_REQUIRED},
    {VX_INPUT,  VX_TYPE_TENSOR, VX_PARAMETER_STATE_REQUIRED},
    {VX_OUTPUT, VX_TYPE_TENSOR, VX_PARAMETER_STATE_REQUIRED},
    {VX_INPUT,  VX_TYPE_SCALAR, VX_PARAMETER_STATE_REQUIRED},
};

#define _L2NORMALIZESCALE_PARAM_NUM  _cnt_of_array( _l2normalizescale_kernel_param_def )

#define SCALAR_INPUT_AXIS          (3)

/*
 * Kernel function
 */
DEF_KERNEL_EXECUTOR(_compute)
    (
    vsi_nn_kernel_node_t                node,
    const vsi_nn_kernel_node_param_t  * param,
    size_t                              param_size
    )
{
    vsi_status status = VSI_FAILURE;
    vsi_nn_kernel_tensor_t input[_INPUT_NUM] = {NULL};
    vsi_nn_kernel_tensor_t output[_OUTPUT_NUM] = {NULL};
    float *f32_in_buffer[_INPUT_NUM] = {NULL};
    float *f32_out_buffer[_OUTPUT_NUM] = {NULL};
    vsi_nn_kernel_tensor_attr_t *in_attr[_INPUT_NUM];
    vsi_nn_kernel_tensor_attr_t *out_attr[_OUTPUT_NUM];
    size_t   out_stride_size[_OUTPUT_NUM][VSI_NN_MAX_DIM_NUM] = {{1}};
    size_t   out_elements[_OUTPUT_NUM] = {0};
    size_t   out_bytes[_OUTPUT_NUM] = {0};
    uint32_t i, index;
    int32_t  axis        = 0;
    int32_t  outerSize   = 1;
    int32_t  axisSize    = 1;
    int32_t  innerSize   = 1;
    int32_t  inner       = 0;
    int32_t  outer       = 0;
    float    rsqrt = 0.0f, scaleValue = 0.0f;
    float    epsilon = (float)10e-12;
    float    l2Value = 0.0f, tmpValue = 0.0f;
    for(i = 0; i < _INPUT_NUM; i ++)
    {
        input[i] = (vsi_nn_kernel_tensor_t)param[i];
        in_attr[i] = vsi_nn_kernel_tensor_attr_create( input[i] );
        f32_in_buffer[i] = (float*)vsi_nn_kernel_tensor_create_buffer( input[i], in_attr[i], TRUE );
        CHECK_PTR_FAIL_GOTO( f32_in_buffer[i], "Create input0 buffer fail.", final );

    }
    for(i = 0; i < _OUTPUT_NUM; i ++)
    {
        output[i] = (vsi_nn_kernel_tensor_t)param[i + _INPUT_NUM];
        out_attr[i] = vsi_nn_kernel_tensor_attr_create( output[i] );
        vsi_nn_kernel_tensor_attr_get_stride( out_attr[i], out_stride_size[i] );
        out_elements[i] = vsi_nn_kernel_tensor_attr_get_size( out_attr[i] );
        out_bytes[i] = out_elements[i] * sizeof(float);
        f32_out_buffer[i] = (float *)malloc( out_bytes[i] );
        CHECK_PTR_FAIL_GOTO( f32_out_buffer[i], "Create output buffer fail.", final );
        memset( f32_out_buffer[i], 0, out_bytes[i] );
    }

    status = vsi_nn_kernel_scalar_read_int32((vsi_nn_kernel_scalar_t)param[SCALAR_INPUT_AXIS], &axis);
    CHECK_STATUS_FAIL_GOTO(status, final );

    for (i = 0; i < (uint32_t)axis; i++)
    {
        innerSize *= in_attr[0]->shape->data[i];
    }

    axisSize = in_attr[0]->shape->data[axis];

    for (i = (uint32_t)axis + 1; i < in_attr[0]->shape->size; i++)
    {
        outerSize *= in_attr[0]->shape->data[i];
    }

    for (outer = 0; outer < outerSize; ++outer) {
        for (inner = 0; inner < innerSize; ++inner) {
            float sum      = 0.0f;

            for (i = 0; i < (uint32_t)axisSize; ++i) {
                index     = (outer * axisSize + i) * innerSize + inner;
                tmpValue  = f32_in_buffer[0][index];
                sum      += tmpValue * tmpValue;
            }
            rsqrt = 1.0f / sqrtf(vsi_nn_max(sum, epsilon));
            for (i = 0; i < (uint32_t)axisSize; ++i) {
                index      = (outer * axisSize + i) * innerSize + inner;
                tmpValue   = f32_in_buffer[0][index];;
                scaleValue = f32_in_buffer[1][i];;
                l2Value    = tmpValue * rsqrt * scaleValue;
                f32_out_buffer[0][index] = l2Value;
            }
        }
    }

    /* save data */
    for(i = 0; i < _OUTPUT_NUM; i++)
    {
        status = vsi_nn_kernel_tensor_write_from_float( output[i], out_attr[i],
                f32_out_buffer[i], out_elements[i] );
        CHECK_STATUS_FAIL_GOTO( status, final );
    }

final:
    for (i = 0; i < _INPUT_NUM; i++)
    {
        if (f32_in_buffer[i])
        {
            free(f32_in_buffer[i]);
            f32_in_buffer[i] = NULL;
        }
        if (in_attr[i])
        {
            vsi_nn_kernel_tensor_attr_release( &in_attr[i] );
        }
    }
    for(i = 0; i < _OUTPUT_NUM; i++)
    {
        if (f32_out_buffer[i])
        {
            free(f32_out_buffer[i]);
            f32_out_buffer[i] = NULL;
        }
        if (out_attr[i])
        {
            vsi_nn_kernel_tensor_attr_release( &out_attr[i] );
        }
    }

    return status;
} /* _compute() */


/*
 * Query kernel
 */
static vsi_status _query_kernel
    (
    vsi_nn_kernel_t * kernel,
    vsi_nn_tensor_t * const * const inputs,
    vsi_nn_tensor_t * const * const outputs
    )
{
    vsi_status status = VSI_FAILURE;
    snprintf( kernel->info.name, VX_MAX_KERNEL_NAME, "%s",  _KERNEL_NAME );
    kernel->info.function    = _compute;
    kernel->info.parameters  = _l2normalizescale_kernel_param_def;
    kernel->info.numParams   = _cnt_of_array( _l2normalizescale_kernel_param_def );
    status = VSI_SUCCESS;
    return status;
} /* _query_kernel() */


static vsi_nn_kernel_node_t _setup
    (
    vsi_nn_graph_t              * graph,
    vsi_nn_tensor_t            ** inputs,
    size_t                        input_num,
    vsi_nn_tensor_t            ** outputs,
    size_t                        output_num,
    const vsi_nn_kernel_param_t * params,
    vsi_nn_kernel_t             * kernel
    )
{
    vsi_status status = VSI_FAILURE;
    vsi_nn_kernel_node_param_t node_params[_L2NORMALIZESCALE_PARAM_NUM] = {NULL};
    vsi_nn_kernel_node_t node = NULL;
    int32_t axis = 0;

    axis   = vsi_nn_kernel_param_get_int32(params, "axis");
    status = _query_kernel( kernel, inputs, outputs );
    if( VSI_SUCCESS == status)
    {
        node = vsi_nn_kernel_create_node( graph, kernel );
        if( node )
        {
            /* Set inputs and outputs */
            vsi_nn_kernel_node_pack_io( node_params, _L2NORMALIZESCALE_PARAM_NUM,
                    inputs, input_num, outputs, output_num );
            node_params[SCALAR_INPUT_AXIS] = vsi_nn_kernel_scalar_create(
                    graph, I32, &axis );
            /* Pass parameters to node. */
            status  = vsi_nn_kernel_node_pass_param( node, node_params, _L2NORMALIZESCALE_PARAM_NUM );
            VSI_ASSERT( status == VSI_SUCCESS );
            vsi_nn_kernel_scalar_release( &node_params[SCALAR_INPUT_AXIS] );
        }
    }
    return node;
} /* _setup() */

__END_DECLS

REGISTER_BACKEND_CPU( l2normalizescale, _setup )

