/* Copyright 2015 Google Inc. 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.
==============================================================================*/

// See docs in ../ops/array_ops.cc.

#define EIGEN_USE_THREADS

#if GOOGLE_CUDA
#define EIGEN_USE_GPU
#endif  // GOOGLE_CUDA

#include "tensorflow/core/kernels/batch_matrix_diag_op.h"

#include <memory>
#include <vector>
#include "third_party/eigen3/unsupported/Eigen/CXX11/Tensor"
#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_shape.h"
#include "tensorflow/core/framework/tensor_types.h"
#include "tensorflow/core/framework/types.h"
#include "tensorflow/core/platform/logging.h"
#include "tensorflow/core/platform/macros.h"

namespace tensorflow {

typedef Eigen::ThreadPoolDevice CPUDevice;
typedef Eigen::GpuDevice GPUDevice;

template <typename T>
inline typename TTypes<T, 3>::ConstTensor flat_inner_dims_matrix(
    const Tensor& t) {
  int64 last_size = t.dims() > 1 ? t.dim_size(t.dims() - 1) : 1;
  int64 but_last_size = t.dims() > 1 ? t.dim_size(t.dims() - 2) : 1;
  if (last_size * but_last_size == 0) {
    DCHECK_EQ(t.NumElements(), 0);
    // Return something empty, avoiding divide by 0
    return t.shaped<T, 3>({0, 0, 0});
  } else {
    return t.shaped<T, 3>({t.NumElements() / (but_last_size * last_size),
                           but_last_size, last_size});
  }
}

template <typename T>
inline typename TTypes<T, 3>::Tensor flat_inner_dims_matrix(Tensor* t) {
  int64 last_size = t->dims() > 1 ? t->dim_size(t->dims() - 1) : 1;
  int64 but_last_size = t->dims() > 1 ? t->dim_size(t->dims() - 2) : 1;
  if (last_size * but_last_size == 0) {
    DCHECK_EQ(t->NumElements(), 0);
    // Return something empty, avoiding divide by 0
    return t->shaped<T, 3>({0, 0, 0});
  } else {
    return t->shaped<T, 3>({t->NumElements() / (but_last_size * last_size),
                            but_last_size, last_size});
  }
}

template <typename Device, typename T>
class BatchMatrixDiagPartOp : public OpKernel {
 public:
  explicit BatchMatrixDiagPartOp(OpKernelConstruction* context)
      : OpKernel(context) {}

  void Compute(OpKernelContext* context) override {
    const Tensor& input = context->input(0);

    const TensorShape& input_shape = input.shape();
    const int rank = input_shape.dims();

    // Preliminary validation of sizes.
    OP_REQUIRES(context, TensorShapeUtils::IsMatrixOrHigher(input_shape),
                errors::InvalidArgument(
                    "input must be at least 2-dim, received shape: ",
                    input.shape().DebugString()));

    // Check to make sure the last two dimensions have the same value
    const int64 k = input_shape.dim_size(rank - 1);
    OP_REQUIRES(
        context, k == input_shape.dim_size(rank - 2),
        errors::InvalidArgument(
            "input's last two dimensions must be equal, received shape: ",
            input.shape().DebugString()));

    auto input_reshaped = flat_inner_dims_matrix<T>(input);

    TensorShape output_shape = input_shape;
    output_shape.RemoveDim(rank - 1);

    Tensor* output = nullptr;
    OP_REQUIRES_OK(context, context->allocate_output(0, output_shape, &output));

    auto output_reshaped = output->flat_inner_dims<T>();

    functor::BatchMatrixDiagPart<Device, T>::Compute(
        context->eigen_device<Device>(), input_reshaped, output_reshaped);
  }

 private:
  TF_DISALLOW_COPY_AND_ASSIGN(BatchMatrixDiagPartOp);
};

template <typename Device, typename T>
class BatchMatrixDiagOp : public OpKernel {
 public:
  explicit BatchMatrixDiagOp(OpKernelConstruction* context)
      : OpKernel(context) {}

  void Compute(OpKernelContext* context) override {
    const Tensor& input = context->input(0);

    const TensorShape& input_shape = input.shape();
    const int rank = input_shape.dims();

    // Preliminary validation of sizes.
    OP_REQUIRES(context, TensorShapeUtils::IsVectorOrHigher(input_shape),
                errors::InvalidArgument(
                    "input must be at least 1-dim, received shape: ",
                    input.shape().DebugString()));

    // Check to make sure the last two dimensions have the same value
    const int64 k = input_shape.dim_size(rank - 1);
    auto input_reshaped = input.flat_inner_dims<T>();

    TensorShape output_shape = input_shape;
    output_shape.AddDim(k);

    Tensor* output = nullptr;
    OP_REQUIRES_OK(context, context->allocate_output(0, output_shape, &output));

    auto output_reshaped = flat_inner_dims_matrix<T>(output);

    functor::BatchMatrixDiag<Device, T>::Compute(
        context->eigen_device<Device>(), input_reshaped, output_reshaped);
  }

 private:
  TF_DISALLOW_COPY_AND_ASSIGN(BatchMatrixDiagOp);
};

#define REGISTER_BATCH_MATRIX_DIAG(type)                                    \
  REGISTER_KERNEL_BUILDER(                                                  \
      Name("BatchMatrixDiag").Device(DEVICE_CPU).TypeConstraint<type>("T"), \
      BatchMatrixDiagOp<CPUDevice, type>);                                  \
  REGISTER_KERNEL_BUILDER(Name("BatchMatrixDiagPart")                       \
                              .Device(DEVICE_CPU)                           \
                              .TypeConstraint<type>("T"),                   \
                          BatchMatrixDiagPartOp<CPUDevice, type>);

TF_CALL_NUMBER_TYPES(REGISTER_BATCH_MATRIX_DIAG);

// Implementation of the functor specialization for CPU.
namespace functor {
template <typename T>
struct BatchMatrixDiag<CPUDevice, T> {
  static void Compute(const CPUDevice& d,
                      typename TTypes<T, 2>::ConstTensor input,
                      typename TTypes<T, 3>::Tensor output) {
    output.device(d) = output.constant(T());
    for (int64 r = 0; r < output.dimension(0); ++r) {
      for (int64 d = 0; d < output.dimension(1); ++d) {
        output(r, d, d) = input(r, d);
      }
    }
  }
};

template <typename T>
struct BatchMatrixDiagPart<CPUDevice, T> {
  static void Compute(const CPUDevice& d,
                      typename TTypes<T, 3>::ConstTensor input,
                      typename TTypes<T, 2>::Tensor output) {
    for (int64 r = 0; r < output.dimension(0); ++r) {
      for (int64 d = 0; d < output.dimension(1); ++d) {
        output(r, d) = input(r, d, d);
      }
    }
  }
};

}  // namespace functor

#if GOOGLE_CUDA

// Forward declarations of the functor specializations for GPU.
namespace functor {
#define DECLARE_GPU_SPEC(T)                                         \
  template <>                                                       \
  void BatchMatrixDiag<GPUDevice, T>::Compute(                      \
      const GPUDevice& d, typename TTypes<T, 2>::ConstTensor input, \
      typename TTypes<T, 3>::Tensor output);                        \
  extern template struct BatchMatrixDiag<GPUDevice, T>;             \
  template <>                                                       \
  void BatchMatrixDiagPart<GPUDevice, T>::Compute(                  \
      const GPUDevice& d, typename TTypes<T, 3>::ConstTensor input, \
      typename TTypes<T, 2>::Tensor output);                        \
  extern template struct BatchMatrixDiagPart<GPUDevice, T>;

TF_CALL_GPU_NUMBER_TYPES(DECLARE_GPU_SPEC);

}  // namespace functor

// Registration of the GPU implementations.
#define REGISTER_BATCH_MATRIX_DIAG_GPU(type)                                \
  REGISTER_KERNEL_BUILDER(                                                  \
      Name("BatchMatrixDiag").Device(DEVICE_GPU).TypeConstraint<type>("T"), \
      BatchMatrixDiagOp<GPUDevice, type>);                                  \
  REGISTER_KERNEL_BUILDER(Name("BatchMatrixDiagPart")                       \
                              .Device(DEVICE_GPU)                           \
                              .TypeConstraint<type>("T"),                   \
                          BatchMatrixDiagPartOp<GPUDevice, type>);

TF_CALL_GPU_NUMBER_TYPES(REGISTER_BATCH_MATRIX_DIAG_GPU);

#undef REGISTER_BATCH_MATRIX_DIAG_GPU

#endif  // GOOGLE_CUDA

}  // namespace tensorflow
