/* 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.
==============================================================================*/

// 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/slice_op.h"

#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/kernels/ops_util.h"
#include "tensorflow/core/lib/core/status.h"
#include "tensorflow/core/lib/gtl/array_slice.h"
#include "tensorflow/core/platform/prefetch.h"

namespace tensorflow {

namespace {

gtl::InlinedVector<int64, 4> IntTensorToInt64Vec(const Tensor& tensor) {
  gtl::InlinedVector<int64, 4> out;
  if (tensor.dtype() == DT_INT32) {
    for (int64 i = 0; i < tensor.NumElements(); ++i) {
      out.push_back(tensor.flat<int32>()(i));
    }
  } else if (tensor.dtype() == DT_INT64) {
    for (int64 i = 0; i < tensor.NumElements(); ++i) {
      out.push_back(tensor.flat<int64>()(i));
    }
  } else {
    LOG(FATAL) << "begin must be either int32 or int64";
  }
  return out;
}

}  // namespace

typedef Eigen::ThreadPoolDevice CPUDevice;
typedef Eigen::GpuDevice GPUDevice;
#ifdef TENSORFLOW_USE_SYCL
typedef Eigen::SyclDevice SYCLDevice;
#endif // TENSORFLOW_USE_SYCL

// Shared code that is not dependent on the type of T.  We do this to reduce
// code size by not duplicating all this for all T (float, double, int32, etc.)
static void SharedValidation(OpKernelContext* context,
                             TensorShape* output_shape, bool* is_identity,
                             bool* slice_dim0,
                             gtl::InlinedVector<int64, 4>* begin,
                             gtl::InlinedVector<int64, 4>* size) {
  const Tensor& input = context->input(0);
  const Tensor& begin_tensor = context->input(1);
  const Tensor& size_tensor = context->input(2);

  OP_REQUIRES(
      context, context->op_kernel().IsLegacyVector(begin_tensor.shape()) &&
                   context->op_kernel().IsLegacyVector(size_tensor.shape()) &&
                   begin_tensor.NumElements() == input.dims() &&
                   size_tensor.NumElements() == input.dims(),
      errors::InvalidArgument(
          "Expected begin and size arguments to be 1-D tensors of size ",
          input.dims(), ", but got shapes ", begin_tensor.shape().DebugString(),
          " and ", size_tensor.shape().DebugString(), " instead."));

  const int input_dims = input.dims();
  *begin = IntTensorToInt64Vec(begin_tensor);
  *size = IntTensorToInt64Vec(size_tensor);
  for (int i = 0; i < input_dims; ++i) {
    if ((*size)[i] == -1) {
      // A size[i] of -1 means "all elements from begin[i] to dim_size(i)".
      (*size)[i] = input.dim_size(i) - (*begin)[i];
    }
  }

  *is_identity = true;
  *slice_dim0 = true;
  for (int i = 0; i < input_dims; ++i) {
    int64 b = (*begin)[i];
    int64 s = (*size)[i];
    if (input.dim_size(i) == 0) {
      OP_REQUIRES(
          context, b == 0 && s == 0,
          errors::InvalidArgument("Expected begin[", i, "] == 0 (got ", b,
                                  ") and size[", i, "] == 0 ", "(got ", s,
                                  ") when ", "input.dim_size(", i, ") == 0"));
    } else {
      OP_REQUIRES(context, 0 <= b && b <= input.dim_size(i),
                  errors::InvalidArgument("Expected begin[", i, "] in [0, ",
                                          input.dim_size(i), "], but got ", b));
      OP_REQUIRES(
          context, 0 <= s && b + s <= input.dim_size(i),
          errors::InvalidArgument("Expected size[", i, "] in [0, ",
                                  input.dim_size(i) - b, "], but ", "got ", s));
    }
    output_shape->AddDim(s);
    const bool take_all = (b == 0) && (s == input.dim_size(i));
    (*is_identity) &= take_all;
    (*slice_dim0) &= (i == 0) || take_all;
  }
}

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

  void Compute(OpKernelContext* context) override {
    TensorShape output_shape;
    bool is_identity = true;
    bool slice_dim0 = true;
    gtl::InlinedVector<int64, 4> begin;
    gtl::InlinedVector<int64, 4> size;
    SharedValidation(context, &output_shape, &is_identity, &slice_dim0, &begin,
                     &size);
    if (!context->status().ok()) return;
    const Tensor& input = context->input(0);
    if (is_identity) {
      VLOG(1) << "Slice identity";
      context->set_output(0, input);
      return;
    }

    if (slice_dim0 && IsDim0SliceAligned<T>(input.shape(), begin[0], size[0])) {
      VLOG(1) << "Slice dim 0: " << input.shape().DebugString();
      CHECK_GE(input.dims(), 1);  // Otherwise, is_identity should be true.
      context->set_output(0, input.Slice(begin[0], begin[0] + size[0]));
      return;
    }

    Tensor* result = nullptr;
    OP_REQUIRES_OK(context, context->allocate_output(0, output_shape, &result));
    const int input_dims = input.dims();

    if (output_shape.num_elements() > 0) {
      if (std::is_same<Device, CPUDevice>::value && input_dims == 2 &&
          DataTypeCanUseMemcpy(DataTypeToEnum<T>::v())) {
        auto input = context->input(0).tensor<T, 2>();
        auto output = result->tensor<T, 2>();
        // TODO(agarwal): Consider multi-threading this loop for cases where
        // size[0] is very large.
        for (int i = 0; i < size[0]; ++i) {
          const int64 row = begin[0] + i;
          if (i + 1 < size[0]) {
            port::prefetch<port::PREFETCH_HINT_T0>(&output(i + 1, 0));
            port::prefetch<port::PREFETCH_HINT_T0>(&input(row + 1, begin[1]));
          }
          memcpy(&output(i, 0), &input(row, begin[1]), size[1] * sizeof(T));
        }
        return;
      }
#define HANDLE_DIM(NDIM)                            \
  if (input_dims == NDIM) {                         \
    HandleCase<NDIM>(context, begin, size, result); \
    return;                                         \
  }

      HANDLE_DIM(1);
      HANDLE_DIM(2);
      HANDLE_DIM(3);
      HANDLE_DIM(4);
      HANDLE_DIM(5);
      HANDLE_DIM(6);
      HANDLE_DIM(7);

#undef HANDLE_DIM

      OP_REQUIRES(context, false, errors::Unimplemented(
                                      "SliceOp : Unhandled input dimensions"));
    }
  }

 private:
  template <int NDIM>
  void HandleCase(OpKernelContext* context, const gtl::ArraySlice<int64>& begin,
                  const gtl::ArraySlice<int64>& size, Tensor* result) {
    Eigen::DSizes<Eigen::DenseIndex, NDIM> indices;
    Eigen::DSizes<Eigen::DenseIndex, NDIM> sizes;
    for (int i = 0; i < NDIM; ++i) {
      indices[i] = begin[i];
      sizes[i] = size[i];
    }

    functor::Slice<Device, T, NDIM>()(
        context->eigen_device<Device>(), result->tensor<T, NDIM>(),
        context->input(0).tensor<T, NDIM>(), indices, sizes);
  }
};

// Forward declarations of the functor specializations for declared in the
// sharded source files.
namespace functor {
#define DECLARE_CPU_SPEC(T, NDIM)                                  \
  template <>                                                      \
  void Slice<CPUDevice, T, NDIM>::operator()(                      \
      const CPUDevice& d, typename TTypes<T, NDIM>::Tensor output, \
      typename TTypes<T, NDIM>::ConstTensor input,                 \
      const Eigen::DSizes<Eigen::DenseIndex, NDIM>& indices,       \
      const Eigen::DSizes<Eigen::DenseIndex, NDIM>& sizes);        \
  extern template struct Slice<CPUDevice, T, NDIM>;

#define DECLARE_FOR_N(T)  \
  DECLARE_CPU_SPEC(T, 1); \
  DECLARE_CPU_SPEC(T, 2); \
  DECLARE_CPU_SPEC(T, 3); \
  DECLARE_CPU_SPEC(T, 4); \
  DECLARE_CPU_SPEC(T, 5); \
  DECLARE_CPU_SPEC(T, 6); \
  DECLARE_CPU_SPEC(T, 7);

TF_CALL_ALL_TYPES(DECLARE_FOR_N);
DECLARE_FOR_N(bfloat16);

#undef DECLARE_FOR_N
#undef DECLARE_CPU_SPEC
}  // namespace functor

#define REGISTER_SLICE(type)                             \
  REGISTER_KERNEL_BUILDER(Name("Slice")                  \
                              .Device(DEVICE_CPU)        \
                              .TypeConstraint<type>("T") \
                              .HostMemory("begin")       \
                              .HostMemory("size"),       \
                          SliceOp<CPUDevice, type>)

TF_CALL_POD_STRING_TYPES(REGISTER_SLICE);
TF_CALL_QUANTIZED_TYPES(REGISTER_SLICE);
REGISTER_SLICE(bfloat16);

#undef REGISTER_SLICE

#if GOOGLE_CUDA
// Forward declarations of the functor specializations for GPU.
namespace functor {
#define DECLARE_GPU_SPEC(T, NDIM)                                  \
  template <>                                                      \
  void Slice<GPUDevice, T, NDIM>::operator()(                      \
      const GPUDevice& d, typename TTypes<T, NDIM>::Tensor output, \
      typename TTypes<T, NDIM>::ConstTensor input,                 \
      const Eigen::DSizes<Eigen::DenseIndex, NDIM>& indices,       \
      const Eigen::DSizes<Eigen::DenseIndex, NDIM>& sizes);        \
  extern template struct Slice<GPUDevice, T, NDIM>;

#define DECLARE_FOR_N(T)  \
  DECLARE_GPU_SPEC(T, 1); \
  DECLARE_GPU_SPEC(T, 2); \
  DECLARE_GPU_SPEC(T, 3); \
  DECLARE_GPU_SPEC(T, 4); \
  DECLARE_GPU_SPEC(T, 5); \
  DECLARE_GPU_SPEC(T, 6); \
  DECLARE_GPU_SPEC(T, 7);

TF_CALL_GPU_NUMBER_TYPES(DECLARE_FOR_N);
TF_CALL_complex64(DECLARE_FOR_N);
TF_CALL_complex128(DECLARE_FOR_N);
DECLARE_FOR_N(int32);

#undef DECLARE_FOR_N
#undef DECLARE_GPU_SPEC
}  // namespace functor

#define REGISTER_GPU(type)                                     \
  REGISTER_KERNEL_BUILDER(Name("Slice")                        \
                              .Device(DEVICE_GPU)              \
                              .TypeConstraint<type>("T")       \
                              .HostMemory("begin")             \
                              .HostMemory("size")              \
                              .TypeConstraint<int32>("Index"), \
                          SliceOp<GPUDevice, type>)

TF_CALL_GPU_NUMBER_TYPES(REGISTER_GPU);
TF_CALL_complex64(REGISTER_GPU);
TF_CALL_complex128(REGISTER_GPU);

// A special GPU kernel for int32.
// TODO(b/25387198): Also enable int32 in device memory. This kernel
// registration requires all int32 inputs and outputs to be in host memory.
REGISTER_KERNEL_BUILDER(Name("Slice")
                            .Device(DEVICE_GPU)
                            .TypeConstraint<int32>("T")
                            .TypeConstraint<int32>("Index")
                            .HostMemory("input")
                            .HostMemory("begin")
                            .HostMemory("size")
                            .HostMemory("output"),
                        SliceOp<CPUDevice, int32>);

#undef REGISTER_GPU

#endif  // GOOGLE_CUDA

#ifdef TENSORFLOW_USE_SYCL
// Forward declarations of the functor specializations for SYCL.
namespace functor {
#define DECLARE_SYCL_SPEC(T, NDIM)                                 \
  template <>                                                      \
  void Slice<SYCLDevice, T, NDIM>::operator()(                     \
      const SYCLDevice& d, typename TTypes<T, NDIM>::Tensor output,\
      typename TTypes<T, NDIM>::ConstTensor input,                 \
      const Eigen::DSizes<Eigen::DenseIndex, NDIM>& indices,       \
      const Eigen::DSizes<Eigen::DenseIndex, NDIM>& sizes);        \
  extern template struct Slice<SYCLDevice, T, NDIM>;

#define DECLARE_FOR_N(T)   \
  DECLARE_SYCL_SPEC(T, 1); \
  DECLARE_SYCL_SPEC(T, 2); \
  DECLARE_SYCL_SPEC(T, 3); \
  DECLARE_SYCL_SPEC(T, 4); \
  DECLARE_SYCL_SPEC(T, 5); \
  DECLARE_SYCL_SPEC(T, 6); \
  DECLARE_SYCL_SPEC(T, 7);

TF_CALL_GPU_NUMBER_TYPES(DECLARE_FOR_N);
DECLARE_FOR_N(int32);

#undef DECLARE_FOR_N
#undef DECLARE_SYCL_SPEC
}  // namespace functor

#define REGISTER_SYCL(type)                                    \
  REGISTER_KERNEL_BUILDER(Name("Slice")                        \
                              .Device(DEVICE_SYCL)             \
                              .TypeConstraint<type>("T")       \
                              .HostMemory("begin")             \
                              .HostMemory("size")              \
                              .TypeConstraint<int32>("Index"), \
                          SliceOp<SYCLDevice, type>)

TF_CALL_GPU_NUMBER_TYPES(REGISTER_SYCL);

// A special GPU kernel for int32.
// TODO(b/25387198): Also enable int32 in device memory. This kernel
// registration requires all int32 inputs and outputs to be in host memory.
REGISTER_KERNEL_BUILDER(Name("Slice")
                            .Device(DEVICE_SYCL)
                            .TypeConstraint<int32>("T")
                            .TypeConstraint<int32>("Index")
                            .HostMemory("input")
                            .HostMemory("begin")
                            .HostMemory("size")
                            .HostMemory("output"),
                        SliceOp<CPUDevice, int32>);

#undef REGISTER_SYCL

#endif  // TENSORFLOW_USE_SYCL
}  // namespace tensorflow
