text: /* Copyright 2015 The TensorFlow Authors. All Rights Reserved.
  
  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.
  ==============================================================================*/
  
  #define EIGEN_USE_THREADS
  
  #include "tensorflow/core/kernels/sparse_reorder_op.h"
  
  #include <algorithm>
  #include <numeric>
  #include <unordered_map>
  #include <utility>
  
  #include "tensorflow/core/framework/op_kernel.h"
  #include "tensorflow/core/framework/register_types.h"
  #include "tensorflow/core/framework/tensor.h"
  #include "tensorflow/core/framework/tensor_util.h"
  #include "tensorflow/core/framework/types.h"
  #include "tensorflow/core/lib/gtl/inlined_vector.h"
  #include "tensorflow/core/util/sparse/sparse_tensor.h"
  
  namespace tensorflow {
  
  using CPUDevice = Eigen::ThreadPoolDevice;
  
  #if GOOGLE_CUDA || TENSORFLOW_USE_ROCM
  using GPUDevice = Eigen::GpuDevice;
  #endif  // GOOGLE_CUDA || TENSORFLOW_USE_ROCM
  
  namespace functor {
  
  template <typename T>
  struct SparseReorderFunctor<CPUDevice, T> {
    void operator()(OpKernelContext* context, const Tensor& input_ind,
                    const Tensor& input_val, const Tensor& input_shape_in) {
      gtl::ArraySlice<int64> input_shape(input_shape_in.vec<int64>().data(),
                                         input_shape_in.NumElements());
  
      gtl::InlinedVector<int64, 8> std_order(input_shape.size());
      std::iota(std_order.begin(), std_order.end(), 0);
  
      // Check if the sparse tensor is already ordered correctly
      sparse::SparseTensor input_sp;
      OP_REQUIRES_OK(
          context, sparse::SparseTensor::Create(input_ind, input_val, input_shape,
                                                std_order, &input_sp));
  
      if (input_sp.IndicesValid().ok()) {
        context->set_output(0, input_sp.indices());
        context->set_output(1, input_sp.values());
      } else {
        // Deep-copy the input Tensors, then reorder in-place
        sparse::SparseTensor reordered_sp;
        OP_REQUIRES_OK(context,
                       sparse::SparseTensor::Create(tensor::DeepCopy(input_ind),
                                                    tensor::DeepCopy(input_val),
                                                    input_shape, &reordered_sp));
        reordered_sp.Reorder<T>(std_order);
        context->set_output(0, reordered_sp.indices());
        context->set_output(1, reordered_sp.values());
      }
    }
  };
  
  }  // namespace functor
  
  template <typename Device, typename T>
  class SparseReorderOp : public OpKernel {
   public:
    explicit SparseReorderOp(OpKernelConstruction* context) : OpKernel(context) {}
  
    void Compute(OpKernelContext* context) override {
      const Tensor& input_ind = context->input(0);
      OP_REQUIRES(context, TensorShapeUtils::IsMatrix(input_ind.shape()),
                  errors::InvalidArgument(
                      "Input indices should be a matrix but received shape ",
                      input_ind.shape().DebugString()));
  
      const Tensor& input_val = context->input(1);
      OP_REQUIRES(context, TensorShapeUtils::IsVector(input_val.shape()),
                  errors::InvalidArgument(
                      "Input values should be a vector but received shape ",
                      input_val.shape().DebugString()));
  
      const Tensor& input_shape_in = context->input(2);
      OP_REQUIRES(context, TensorShapeUtils::IsVector(input_shape_in.shape()),
                  errors::InvalidArgument(
                      "Input shape should be a vector but received shape ",
                      input_shape_in.shape().DebugString()));
  
      functor::SparseReorderFunctor<Device, T>()(context, input_ind, input_val,
                                                 input_shape_in);
    }
  };
  
  #define REGISTER_KERNELS(type)                                            \
    REGISTER_KERNEL_BUILDER(                                                \
        Name("SparseReorder").Device(DEVICE_CPU).TypeConstraint<type>("T"), \
        SparseReorderOp<CPUDevice, type>)
  
  TF_CALL_ALL_TYPES(REGISTER_KERNELS);
  #undef REGISTER_KERNELS
  
  #if GOOGLE_CUDA || TENSORFLOW_USE_ROCM
  
  #define REGISTER_GPU_KERNELS(type)                                        \
    REGISTER_KERNEL_BUILDER(                                                \
        Name("SparseReorder").Device(DEVICE_GPU).TypeConstraint<type>("T"), \
        SparseReorderOp<GPUDevice, type>)
  
  TF_CALL_GPU_NUMBER_TYPES(REGISTER_GPU_KERNELS);
  TF_CALL_INTEGRAL_TYPES(REGISTER_GPU_KERNELS);
  REGISTER_GPU_KERNELS(bool);
  #undef REGISTER_GPU_KERNELS
  
  #endif  // GOOGLE_CUDA || TENSORFLOW_USE_ROCM
  
  }  // namespace tensorflow
  