/*
 * SPDX-FileCopyrightText: Copyright (c) 1993-2024 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
 * SPDX-License-Identifier: Apache-2.0
 *
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 */
#include "common/kernels/kernel.h"
namespace nvinfer1
{
namespace plugin
{
#define CUBLAS_CHECK(condition)                                                                                        \
    do                                                                                                                 \
    {                                                                                                                  \
        cublasStatus_t status = condition;                                                                             \
        if (status != CUBLAS_STATUS_SUCCESS)                                                                           \
        {                                                                                                              \
            printf("%s %d CUBLAS FAIL %s\n", __FILE__, __LINE__, cublasGetErrorString(status));                        \
        }                                                                                                              \
    } while (0)

// this scatter kernel works on a 2d table writing rows
// index is 1-D array
// updates is 2-D array
// output is 2-D array
// output[index[i]] = updates[i]
__global__ void scatterKernel(
    char* output,
    const char* updates,
    const int* indices,
    int pitch,
    int rowSize)
{
    int idx = indices[blockIdx.x];
    char* pDst = (char*)output + idx * pitch;
    const char* pSrc = updates + blockIdx.x * rowSize;
    memcpy(pDst, pSrc, rowSize);
}

// Transform nd index to 1 - d index
__global__ void transformIdxKernel(
    int* output,
    const int* transformCoeff, // these are actually the output pitches of the respective dimensions
    const int* indices,
    int sliceRank)
{
    const int* idx = indices + sliceRank * blockIdx.x;
    int transformedIdx = 0;
    for (int i = 0; i < sliceRank; i++)
    {
        transformedIdx += idx[i] * transformCoeff[i];
    }
    output[blockIdx.x] = transformedIdx;
}


pluginStatus_t scatterNDInference(
    cudaStream_t stream,
    int* transformCoeff,
    int nOutputDims,
    int sliceRank,
    int nRows,
    int rowSize,
    int copySize,
    int sizeOfElementInBytes,
    const void* index,
    const void* updates,
    const void* data,
    void* output,
    void* workspace)
{
    const int* _index = (const int*)(index);
    const char* _updates = (const char*)(updates);
    char* _output = (char*)(output);
    int* wo = (int*)(workspace);
    int* transformedIdx = wo + sizeof(int)*nOutputDims;
    int* deviceTransformCoeff = wo;
    CSC(cudaMemcpy(workspace, transformCoeff, sizeof(int) * nOutputDims, cudaMemcpyHostToDevice), STATUS_FAILURE);
    transformIdxKernel<<<nRows, 1, 0, stream>>>(transformedIdx, deviceTransformCoeff, _index, sliceRank);
    CSC(cudaMemcpy(output, data, copySize, cudaMemcpyDeviceToDevice), STATUS_FAILURE);
    // assuming output pitch = rowSize i.e no padding
    scatterKernel<<<nRows, 1, 0, stream>>>(_output, _updates, transformedIdx, rowSize * 4, rowSize * 4);
    return STATUS_SUCCESS;
}
} // namespace plugin
} // namespace nvinfer1