/****************************************************************************
*
*    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_prv.h"
#include "vsi_nn_error.h"
#include "vsi_nn_tensor_util.h"
#include "utils/vsi_nn_util.h"
#include "utils/vsi_nn_dtype_util.h"
#include "kernel/vsi_nn_kernel.h"
#include "kernel/vsi_nn_kernel_eltwise.h"
#include "libnnext/vsi_nn_vxkernel.h"
__BEGIN_DECLS

#define _CPU_ARG_NUM            (2)
#define _CPU_INPUT_NUM          (1)
#define _CPU_OUTPUT_NUM         (1)
#define _CPU_IO_NUM             (_CPU_INPUT_NUM + _CPU_OUTPUT_NUM)
#define _CPU_PARAM_NUM          (_CPU_ARG_NUM + _CPU_IO_NUM)
#define _KERNEL_NAME            CVIVANTE_NAMESPACE("log_softmax_sw")

DEF_KERNEL_EXECUTOR(_log_softmax_exec)
    (
    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 tensors[_CPU_IO_NUM] = { NULL };
    float * buffer[_CPU_IO_NUM] = { NULL };
    size_t out_elements = 0;
    vsi_nn_kernel_tensor_attr_t * attr[_CPU_IO_NUM] = { NULL };
    int32_t axis = 0;
    float beta = 0;
    vsi_ssize_t outerSize = 1;
    vsi_ssize_t axisSize = 1;
    vsi_ssize_t innerSize = 1;
    vsi_ssize_t i = 0;
    vsi_ssize_t inner = 0;
    vsi_ssize_t outer = 0;

    tensors[0]  = (vsi_nn_kernel_tensor_t)param[0];
    tensors[1]  = (vsi_nn_kernel_tensor_t)param[1];

    attr[0] = vsi_nn_kernel_tensor_attr_create( tensors[0] );
    attr[1] = vsi_nn_kernel_tensor_attr_create( tensors[1] );

    status = vsi_nn_kernel_scalar_read_int32((vsi_nn_kernel_scalar_t)param[2], &axis);
    CHECK_STATUS_FAIL_GOTO(status, final );
    status = vsi_nn_kernel_scalar_read_float32((vsi_nn_kernel_scalar_t)param[3], &beta);
    CHECK_STATUS_FAIL_GOTO(status, final );

    buffer[0] = (float*)vsi_nn_kernel_tensor_create_buffer( tensors[0], attr[0], TRUE );
    CHECK_PTR_FAIL_GOTO( buffer[0], "Create input buffer fail.", final );

    out_elements = vsi_nn_kernel_tensor_attr_get_size( attr[1] );
    buffer[1] = (float *)malloc( out_elements * sizeof(float) );
    CHECK_PTR_FAIL_GOTO( buffer[1], "Create output buffer fail.", final );
    memset( buffer[1], 0, out_elements * sizeof(float) );

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

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

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

    for ( outer = 0; outer < outerSize; ++outer)
    {
        for ( inner = 0; inner < innerSize; ++inner)
        {
            // We subtract the maximum value from each element to ensure
            // numerical stability, taking advantage of the following equality:
            // exp(x[i])/sum(exp(x[i])) == exp(x[i]+C)/sum(exp(x[i]+C))
            float sum = 0;
            float logSum = 0;
            float maxValue = buffer[0][outer * axisSize * innerSize + inner];
            for (i = 1; i < axisSize; ++i)
            {
                maxValue = vsi_nn_max(maxValue, buffer[0][(outer * axisSize + i) * innerSize + inner]);
            }

            sum = 0;
            for (i = 0; i < axisSize; ++i)
            {
                sum += expf((buffer[0][(outer * axisSize + i) * innerSize + inner] - maxValue) * beta);
            }

            logSum = logf(sum);
            for (i = 0; i < axisSize; ++i)
            {
                buffer[1][(outer * axisSize + i) * innerSize + inner] =
                        (buffer[0][(outer * axisSize + i) * innerSize + inner] - maxValue) * beta -
                        logSum;
            }
        }
    }

    status = vsi_nn_kernel_tensor_write_from_float( tensors[1], attr[1],
            buffer[1], out_elements );
    CHECK_STATUS_FAIL_GOTO( status, final );

final:
    for( i = 0; i < _CPU_IO_NUM; i ++ )
    {
        if( buffer[i] )
        {
            free( buffer[i] );
        }
        vsi_nn_kernel_tensor_attr_release( &attr[i] );
    }
    return status;
} /* _log_softmax_exec() */

static vx_param_description_t kernel_param_def[] =
{
    {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}
};

static const vx_kernel_description_t _kernel_info =
{
    KERNEL_ID_PLACEHOLDER,
    _KERNEL_NAME,
    _log_softmax_exec,
    kernel_param_def,
    _cnt_of_array( kernel_param_def ),
    vsi_nn_KernelValidator,
    NULL,
    NULL,
    vsi_nn_KernelInitializer,
    vsi_nn_KernelDeinitializer
};

static vsi_status _query_kernel
    (
    vsi_nn_tensor_t* const* const inputs,
    vsi_nn_tensor_t* const* const outputs,
    vsi_nn_kernel_t* kernel
    )
{
    memmove( &kernel->info, &_kernel_info, sizeof(vx_kernel_description_t) );
    return VSI_SUCCESS;
} /* _query_kernel() */

#define SCALAR_INPUT_AXIS          (2)
#define SCALAR_INPUT_BETA          (3)

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_SUCCESS;
    vsi_nn_kernel_node_param_t backend_params[_CPU_PARAM_NUM] = {NULL};
    vsi_nn_kernel_node_t node = NULL;
    int32_t axis = 0;
    float beta = 1.0f;

    axis = vsi_nn_kernel_param_get_int32(params, "axis");
    beta = vsi_nn_kernel_param_get_float32(params, "beta");

    status = _query_kernel( inputs, outputs, kernel );
    if( VSI_SUCCESS == status)
    {
        node = vsi_nn_kernel_create_node( graph, kernel );
        if( node )
        {
            /* Set inputs and outputs */
            vsi_nn_kernel_node_pack_io( backend_params, _CPU_PARAM_NUM,
                    inputs, _CPU_INPUT_NUM, outputs, _CPU_OUTPUT_NUM );
            backend_params[SCALAR_INPUT_AXIS] = vsi_nn_kernel_scalar_create(
                    graph, I32, &axis );
            backend_params[SCALAR_INPUT_BETA] = vsi_nn_kernel_scalar_create(
                    graph, F32, &beta );

            /* Pass parameters to node. */
            status = vsi_nn_kernel_node_pass_param( node, backend_params, _CPU_PARAM_NUM );

            vsi_nn_kernel_scalar_release( &backend_params[SCALAR_INPUT_AXIS] );
            vsi_nn_kernel_scalar_release( &backend_params[SCALAR_INPUT_BETA] );
        }
        else
        {
            status = VSI_FAILURE;
        }
    }
    return node;
} /* _setup() */

__END_DECLS

REGISTER_BACKEND_CPU( log_softmax, _setup )

