text: /*
   * Licensed to the Apache Software Foundation (ASF) under one
   * or more contributor license agreements.  See the NOTICE file
   * distributed with this work for additional information
   * regarding copyright ownership.  The ASF licenses this file
   * to you 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.
   */
  
  /*!
   * Copyright (c) 2020 by Contributors
   * \file np_polynomial_op.cu
   */
  
  #include "np_polynomial_op-inl.h"
  #include "../../common/cuda/utils.h"
  
  namespace mxnet {
  namespace op {
  
  template<int req>
  struct polyval_backward_gpu {
    template<typename DType>
    MSHADOW_XINLINE static void Map(int i, const DType* p_dptr, const DType* x_dptr,
                                    DType* igrad_x_dptr, DType* igrad_p_dptr,
                                    const DType* ograd_dptr, const index_t p_size) {
    DType igrad_p = 1;
    DType igrad_x = 0;
    index_t j = p_size - 1;
    while (j > 0) {
        // atomic add since different threads could update same variable
        atomicAdd(&igrad_p_dptr[j], igrad_p * ograd_dptr[i]);
        igrad_p *= x_dptr[i];
        igrad_x = igrad_x * x_dptr[i] + p_dptr[p_size - j - 1] * j;
        j--;
    }
    atomicAdd(&igrad_p_dptr[j], igrad_p * ograd_dptr[i]);
    KERNEL_ASSIGN(igrad_x_dptr[i], req, igrad_x * ograd_dptr[i]);
    }
  };
  
  void NumpyPolyvalBackwardGPU(const nnvm::NodeAttrs& attrs,
                               const OpContext& ctx,
                               const std::vector<TBlob>& inputs,
                               const std::vector<OpReqType>& req,
                               const std::vector<TBlob>& outputs) {
    CHECK_EQ(inputs.size(), 3U);
    CHECK_EQ(outputs.size(), 2U);
    CHECK_NE(req[0], kWriteInplace);
  
    if (inputs[1].type_flag_ != inputs[2].type_flag_ ||
      !common::is_float(inputs[1].type_flag_) ||
      !common::is_float(inputs[2].type_flag_)) {
        return;
    }
  
    mshadow::Stream<gpu> *s = ctx.get_stream<gpu>();
    const TBlob& ograd = inputs[0];
    const TBlob& p = inputs[1];
    const TBlob& x = inputs[2];
    const TBlob& igrad_p = outputs[0];
    const TBlob& igrad_x = outputs[1];
    const size_t p_size = p.Size();
  
    using namespace mxnet_op;
    MSHADOW_REAL_TYPE_SWITCH(ograd.type_flag_, DType, {
      MXNET_ASSIGN_REQ_SWITCH(req[0], req_type, {
        Kernel<polyval_backward_gpu<req_type>, gpu>::Launch(
          s, ograd.Size(), p.dptr<DType>(), x.dptr<DType>(),
          igrad_x.dptr<DType>(), igrad_p.dptr<DType>(),
          ograd.dptr<DType>(), p_size);
      });
    });
  }
  
  NNVM_REGISTER_OP(_npi_polyval)
  .set_attr<mxnet::FCompute>("FCompute<gpu>", NumpyPolyvalForward<gpu>);
  
  NNVM_REGISTER_OP(_npi_backward_polyval)
  .set_attr<mxnet::FCompute>("FCompute<gpu>", NumpyPolyvalBackwardGPU);
  
  }  // namespace op
  }  // namespace mxnet
  