/****************************************************************************
*
*    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"

__BEGIN_DECLS

#define HASH_L2NORMALIZESCALE_HASH_KEY(AXIS, IN0_DTYPE, IN1_DTYPE, OUT_DTYPE, _image_2d) \
    ((AXIS << 28) | (IN1_DTYPE << 20) | (IN0_DTYPE << 12) | (OUT_DTYPE << 4) | (_image_2d))

 #define HASH_L2NORMALIZESCALE_KERNEL_SOURCE_NAME(AXIS) \
    "l2normalizescale_axis"#AXIS

#define HASH_L2NORMALIZESCALE_KERNELS_2D( AXIS, IN0_DTYPE, IN1_DTYPE, OUT_DTYPE) \
        { HASH_L2NORMALIZESCALE_HASH_KEY(AXIS, IN0_DTYPE, IN1_DTYPE, OUT_DTYPE, 1), \
        CVIVANTE_NAMESPACE("cl.l2normalizescale_axis"#AXIS"_"#IN0_DTYPE"_"#IN1_DTYPE"to"#OUT_DTYPE"_2D"), \
        HASH_L2NORMALIZESCALE_KERNEL_SOURCE_NAME(AXIS) },

typedef struct
{
    uint32_t key;
    char * function_name;
    const char * source_name;
} _kernel_map_type;

static const _kernel_map_type _l2normalizescale_kernel_map[] =
{
    HASH_L2NORMALIZESCALE_KERNELS_2D( 0, F32, F32, F32 )
    HASH_L2NORMALIZESCALE_KERNELS_2D( 0, U8 , F32, U8  )
    HASH_L2NORMALIZESCALE_KERNELS_2D( 1, F32, F32, F32 )
    HASH_L2NORMALIZESCALE_KERNELS_2D( 1, U8 , F32, U8  )
};


/*
 * 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},
    {VX_INPUT,  VX_TYPE_SCALAR, VX_PARAMETER_STATE_REQUIRED},
    {VX_INPUT,  VX_TYPE_SCALAR, VX_PARAMETER_STATE_REQUIRED},
    {VX_INPUT,  VX_TYPE_SCALAR, VX_PARAMETER_STATE_REQUIRED},
    {VX_INPUT,  VX_TYPE_SCALAR, VX_PARAMETER_STATE_REQUIRED},
    {VX_INPUT,  VX_TYPE_SCALAR, 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)
#define SCALAR_AXIS_SIZE            (4)
#define SCALAR_EPS_VALUE            (5)
#define SCALAR_INPUT_SCALE          (6)
#define SCALAR_INPUT_TAIL           (7)
#define SCALAR_OUTPUT_SCALE         (8)
#define SCALAR_OUTPUT_TAIL          (9)

#define L2NORMSCALE_PARAM_NUM         6
#define L2NORMSCALE_QUANT_PARAM_NUM   _cnt_of_array( _l2normalizescale_kernel_param_def )

/*
 * Kernel initializer
 */
DEF_KERNEL_INITIALIZER(_l2normalizescale_initializer)
    (
    vsi_nn_kernel_node_t                node,
    const vsi_nn_kernel_node_param_t  * param,
    size_t                              param_size
    )
{
    vsi_status status = VSI_FAILURE;
    gpu_param_t gpu_param = {
        2,
        {0, 0, 0},
        {0, 0, 0},
        {0, 0, 0},
        {0, 0, 0}
        };
    int32_t     axis                           = 0;
    vsi_nn_kernel_tensor_attr_t *output_attr   = NULL;
    vsi_size_array_t * output_shape             = NULL;

    output_attr = vsi_nn_kernel_tensor_attr_create( (vsi_nn_kernel_tensor_t)param[2] );
    CHECK_PTR_FAIL_GOTO( output_attr, "Create tensor attr buffer fail.", final );
    status = vsi_nn_kernel_scalar_read_int32((vsi_nn_kernel_scalar_t)param[3], &axis);
    CHECK_STATUS_FAIL_GOTO(status, final );
    output_shape  = output_attr->shape;

    if (1 == axis)
    {
        gpu_param.global_offset[0] = 0;
        gpu_param.global_offset[1] = 0;
        gpu_param.global_scale[0]  = 1;
        gpu_param.global_scale[1]  = 1;
        gpu_param.local_size[0]    = 1;
        gpu_param.local_size[1]    = 16;
        gpu_param.global_size[0]   = gpu_align_p2((output_shape->data[0] + gpu_param.global_scale[0] - 1)
            / gpu_param.global_scale[0], 4);
        gpu_param.global_size[1]   = 16;
    }
    else if (0 == axis)
    {
        gpu_param.global_offset[0] = 0;
        gpu_param.global_offset[1] = 0;
        gpu_param.global_scale[0]  = 1;
        gpu_param.global_scale[1]  = 1;
        gpu_param.local_size[0]    = 16;
        gpu_param.local_size[1]    = 1;
        gpu_param.global_size[0]   = 16;
        gpu_param.global_size[1]   = output_shape->data[1];
    }
    else
    {
        status = VSI_FAILURE;
        CHECK_STATUS_FAIL_GOTO(status, final );
    }

    status = vsi_nn_kernel_gpu_config( node, &gpu_param );
final:
    if (output_attr) vsi_nn_kernel_tensor_attr_release( &output_attr );
    return status;
} /* _l2normalizescale_initializer() */



/*
 * 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,
    int32_t axis,
    vsi_bool image_2d,
    vsi_bool *is_use_u8_kernel
    )
{
    vsi_status status = VSI_FAILURE;
    vsi_nn_kernel_dtype_e in0_dtype;
    vsi_nn_kernel_dtype_e in1_dtype;
    vsi_nn_kernel_dtype_e out_dtype;
    const _kernel_map_type * kernel_map = _l2normalizescale_kernel_map;
    size_t kernel_map_size              = _cnt_of_array( _l2normalizescale_kernel_map );
    vx_param_description_t * param_def  = _l2normalizescale_kernel_param_def;
    size_t param_def_size               = _cnt_of_array( _l2normalizescale_kernel_param_def );
    vx_kernel_initialize_f  initializer = _l2normalizescale_initializer;

    uint32_t key;
    uint32_t i;

    in0_dtype  = vsi_nn_kernel_map_dtype( inputs[0]->attr.dtype.vx_type );
    in1_dtype  = vsi_nn_kernel_map_dtype( inputs[1]->attr.dtype.vx_type );
    out_dtype  = vsi_nn_kernel_map_dtype( outputs[0]->attr.dtype.vx_type );

    if (F16 == in0_dtype)
    {
        in0_dtype = F32;
    }

    if (F16 == in1_dtype)
    {
        in1_dtype = F32;
    }

    if (F16 == out_dtype)
    {
        out_dtype = F32;
    }

   if ((U8 == in0_dtype) || (U8 == out_dtype))
    {
        param_def_size = L2NORMSCALE_QUANT_PARAM_NUM;
        *is_use_u8_kernel = TRUE;
    }
    else
    {
        param_def_size = L2NORMSCALE_PARAM_NUM;
        *is_use_u8_kernel = FALSE;
    }

    key = HASH_L2NORMALIZESCALE_HASH_KEY(axis, in0_dtype, in1_dtype, out_dtype, image_2d);

    for( i = 0; i < kernel_map_size; i ++ )
    {
        if( kernel_map[i].key == key )
        {
            break;
        }
    }
    if( i < kernel_map_size )
    {
        snprintf( kernel->info.name, VX_MAX_KERNEL_NAME, "%s",  kernel_map[i].function_name );
        kernel->info.parameters  = param_def;
        kernel->info.numParams   = (uint32_t)param_def_size;
        kernel->info.initialize  = initializer;
        // Register code source
        vsi_nn_kernel_add_source( kernel, VSI_NN_GPU_SOURCE_FMT_CODE, 1,
                kernel_map[i].source_name );
        // Register binary source
        vsi_nn_kernel_add_source( kernel, VSI_NN_GPU_SOURCE_FMT_EXECUTABLE, 1,
                kernel_map[i].source_name );
        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;
    vsi_bool image_2d = FALSE;
    int32_t  axis = 0;
    vsi_size_t  axis_size = 0;
    float    outputScale  = vsi_nn_get_tensor_scale(outputs[0]);
    float    outputTail   = (float)vsi_nn_get_tensor_zero_point(outputs[0]);
    float    inputScale   = vsi_nn_get_tensor_scale(inputs[0]);
    float    inputTail    = (float)vsi_nn_get_tensor_zero_point(inputs[0]);
    float    epsilon      = (float)10e-12;
    float    rsEps        = 1.0f / sqrtf(epsilon);
    vsi_bool is_use_u8_kernel = FALSE;

    outputScale = 1.0f / outputScale;
    inputTail   = -(inputTail * inputScale);

    axis = vsi_nn_kernel_param_get_int32(params, "axis");

    if( !vsi_nn_kernel_gpu_check_shape( inputs[0]->attr.size,
                inputs[0]->attr.dim_num )
     || !vsi_nn_kernel_gpu_check_shape( outputs[0]->attr.size,
                outputs[0]->attr.dim_num )
     || axis > 2)
    {
        return NULL;
    }

    image_2d = (inputs[0]->attr.dim_num == 2 || inputs[0]->attr.size[2] == 1);
    status = _query_kernel( kernel, inputs, outputs, axis, image_2d, &is_use_u8_kernel );
    axis_size = inputs[0]->attr.size[axis];


    if( VSI_SUCCESS == status)
    {
        node = vsi_nn_kernel_create_node( graph, kernel );
        if( node )
        {
            size_t node_params_num = L2NORMSCALE_PARAM_NUM;
            /* 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 );
            node_params[SCALAR_AXIS_SIZE] = vsi_nn_kernel_scalar_create(
                    graph, I32, &axis_size );
            node_params[SCALAR_EPS_VALUE] = vsi_nn_kernel_scalar_create(
                    graph, F32, &rsEps );
            if (is_use_u8_kernel)
            {
                node_params[SCALAR_INPUT_SCALE]  = vsi_nn_kernel_scalar_create( graph, F32, &inputScale );
                node_params[SCALAR_INPUT_TAIL]   = vsi_nn_kernel_scalar_create(graph, F32, &inputTail );
                node_params[SCALAR_OUTPUT_SCALE] = vsi_nn_kernel_scalar_create( graph, F32, &outputScale );
                node_params[SCALAR_OUTPUT_TAIL]  = vsi_nn_kernel_scalar_create(graph, F32, &outputTail );
                node_params_num = L2NORMSCALE_QUANT_PARAM_NUM;
            }
            /* Pass parameters to node. */
            status  = vsi_nn_kernel_node_pass_param( node, node_params, node_params_num );
            VSI_ASSERT( status == VSI_SUCCESS );
            vsi_nn_kernel_scalar_release( &node_params[SCALAR_INPUT_AXIS] );
            vsi_nn_kernel_scalar_release( &node_params[SCALAR_AXIS_SIZE] );
            vsi_nn_kernel_scalar_release( &node_params[SCALAR_EPS_VALUE] );
            if (is_use_u8_kernel)
            {
                vsi_nn_kernel_scalar_release( &node_params[SCALAR_INPUT_SCALE] );
                vsi_nn_kernel_scalar_release( &node_params[SCALAR_INPUT_TAIL] );
                vsi_nn_kernel_scalar_release( &node_params[SCALAR_OUTPUT_SCALE] );
                vsi_nn_kernel_scalar_release( &node_params[SCALAR_OUTPUT_TAIL] );
            }
        }
    }
    return node;
} /* _setup() */

__END_DECLS

REGISTER_BACKEND_CL( l2normalizescale, _setup )
