/*
 * 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.
 */
#include <mshadow/tensor.h>
#include "./index_array-inl.h"

namespace mxnet {
namespace op {

using namespace mshadow::cuda;

void IndexArrayForwardGPU(const nnvm::NodeAttrs& attrs,
                          const OpContext& ctx,
                          const std::vector<TBlob>& inputs,
                          const std::vector<OpReqType>& req,
                          const std::vector<TBlob>& outputs) {
  using namespace mshadow;
  CHECK_EQ(inputs.size(), 1U);
  CHECK_EQ(outputs.size(), 1U);
  CHECK_EQ(req.size(), 1U);
  const TBlob& in_data  = inputs[0];
  const TBlob& out_data = outputs[0];

  const IndexArrayParam& param = nnvm::get<IndexArrayParam>(attrs.parsed);

  const TShape inshape = in_data.shape_;
  const int ndim       = inshape.ndim();

  Stream<gpu>* s      = ctx.get_stream<gpu>();
  cudaStream_t stream = Stream<gpu>::GetStream(s);

  using namespace mxnet_op;

  if (param.axes.has_value()) {
    const mxnet::Tuple<int>& axes = param.axes.value();
    const int naxes               = axes.ndim();

    std::vector<int64_t> index_products = IndexArrayComputeIndexProducts(inshape);

    std::vector<int64_t> cpu_workspace(2 * naxes);
    IndexArrayBuildSelectedAxesWorkspace(axes, index_products, cpu_workspace.data(), ndim);

    Tensor<gpu, 1, int64_t> workspace =
        ctx.requested[0].get_space_typed<gpu, 1, int64_t>(Shape1(2 * naxes), s);

    CUDA_CALL(cudaMemcpyAsync(workspace.dptr_,
                              cpu_workspace.data(),
                              sizeof(int64_t) * (2 * naxes),
                              cudaMemcpyHostToDevice,
                              stream));

    MXNET_ASSIGN_REQ_SWITCH(req[0], req_type, {
      Kernel<IndexArrayKernel<req_type>, gpu>::Launch(
          s, in_data.Size(), out_data.dptr<int64_t>(), naxes, workspace.dptr_);
    });
  } else {
    Tensor<gpu, 1, dim_t> workspace =
        ctx.requested[0].get_space_typed<gpu, 1, dim_t>(Shape1(ndim), s);

    CUDA_CALL(cudaMemcpyAsync(
        workspace.dptr_, inshape.data(), sizeof(dim_t) * ndim, cudaMemcpyHostToDevice, stream));

    MXNET_ASSIGN_REQ_SWITCH(req[0], req_type, {
      Kernel<IndexArrayDefaultKernel<req_type>, gpu>::Launch(
          s, in_data.Size(), out_data.dptr<int64_t>(), ndim, workspace.dptr_);
    });
  }
}

NNVM_REGISTER_OP(_contrib_index_array)
    .set_attr<FIsCUDAGraphsCompatible>("FIsCUDAGraphsCompatible",
                                       [](const NodeAttrs& attrs, const bool) { return false; })
    .set_attr<FCompute>("FCompute<gpu>", IndexArrayForwardGPU);

}  // namespace op
}  // namespace mxnet