/* 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/math_ops.cc.

#define EIGEN_USE_THREADS

#include <numeric>

#include "tensorflow/core/kernels/aggregate_ops.h"
#include "tensorflow/core/kernels/aggregate_ops_cpu.h"

#include "tensorflow/core/framework/numeric_op.h"
#include "tensorflow/core/framework/register_types.h"
#include "tensorflow/core/framework/variant.h"
#include "tensorflow/core/framework/variant_encode_decode.h"
#include "tensorflow/core/framework/variant_op_registry.h"
#include "tensorflow/core/lib/gtl/inlined_vector.h"
#include "tensorflow/core/platform/logging.h"

namespace tensorflow {

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

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

  void Compute(OpKernelContext* ctx) override {
    if (!ctx->ValidateInputsAreSameShape(this)) return;

    const Tensor& input0 = ctx->input(0);
    const int num = ctx->num_inputs();

    if (num == 1) {
      ctx->set_output(0, input0);
      return;
    }

    // Try to forward and accumulate the result in one of the input buffers.
    int reused_input = -1;
    gtl::InlinedVector<int, 8> input_indices(num);
    std::iota(input_indices.begin(), input_indices.end(), 0);
    Tensor* output = nullptr;
    for (int input_idx = 0; input_idx < num; ++input_idx) {
      if (ctx->forward_input_to_output_with_shape(input_idx, 0, input0.shape(),
                                                  &output)) {
        reused_input = input_idx;
        break;
      }
    }
    if (reused_input == -1) {
      OP_REQUIRES_OK(ctx, ctx->allocate_output(0, input0.shape(), &output));
    } else if (reused_input > 0) {
      // Move the forwarded buffer to the front so we don't double count
      // anything if there are more than 8 inputs.
      input_indices[0] = reused_input;
      input_indices[reused_input] = 0;
    }
    auto To = output->flat<T>();

#define I(IDX) ctx->input(input_indices[IDX]).flat<T>()

#if defined(__ANDROID_TYPES_SLIM__)
    // On Android by default,we only support additions of two arguments, so we
    // can reduce the number of template instantiations.
    OP_REQUIRES(ctx, num == 2,
                errors::InvalidArgument("Only additions of two arguments "
                                        "supported. Num inputs: ",
                                        num));
    functor::Add2Functor<Device, T> functor2;
    functor2(ctx->template eigen_device<Device>(), To, I(0), I(1));
#else
    static const int kWidth = 8;
    int r = num % kWidth;

    switch (r) {
      case 2: {
        functor::Add2Functor<Device, T> functor2;
        functor2(ctx->template eigen_device<Device>(), To, I(0), I(1));
        break;
      }
      case 3: {
        functor::Add3Functor<Device, T> functor3;
        functor3(ctx->template eigen_device<Device>(), To, I(0), I(1), I(2));
        break;
      }
      case 4: {
        functor::Add4Functor<Device, T> functor4;
        functor4(ctx->template eigen_device<Device>(), To, I(0), I(1), I(2),
                 I(3));
        break;
      }
      case 5: {
        functor::Add5Functor<Device, T> functor5;
        functor5(ctx->template eigen_device<Device>(), To, I(0), I(1), I(2),
                 I(3), I(4));
        break;
      }
      case 6: {
        functor::Add6Functor<Device, T> functor6;
        functor6(ctx->template eigen_device<Device>(), To, I(0), I(1), I(2),
                 I(3), I(4), I(5));
        break;
      }
      case 7: {
        functor::Add7Functor<Device, T> functor7;
        functor7(ctx->template eigen_device<Device>(), To, I(0), I(1), I(2),
                 I(3), I(4), I(5), I(6));
        break;
      }
      case 0: {
        functor::Add8Functor<Device, T> functor8;
        functor8(ctx->template eigen_device<Device>(), To, I(0), I(1), I(2),
                 I(3), I(4), I(5), I(6), I(7));
        r = 8;
        break;
      }
      case 1: {
        functor::Add9Functor<Device, T> functor9;
        functor9(ctx->template eigen_device<Device>(), To, I(0), I(1), I(2),
                 I(3), I(4), I(5), I(6), I(7), I(8));
        r = 9;
        break;
      }
    }

    for (; r < num; r += kWidth) {
      functor::Add8pFunctor<Device, T> functor8p;
      functor8p(ctx->template eigen_device<Device>(), To, I(r), I(r + 1),
                I(r + 2), I(r + 3), I(r + 4), I(r + 5), I(r + 6), I(r + 7));
    }
#endif  // defined(__ANDROID_TYPES_SLIM__)

#undef I
  }
};

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

  void Compute(OpKernelContext* ctx) override {
    if (!ctx->ValidateInputsAreSameShape(this)) return;

    const Tensor& input0 = ctx->input(0);
    const int num = ctx->num_inputs();

    if (num == 1) {
      ctx->set_output(0, input0);
      return;
    }

    for (int i = 0; i < num; ++i) {
      // Step 1: ensure unary variants.
      OP_REQUIRES(
          ctx, ctx->input(i).dims() == 0,
          errors::InvalidArgument(
              "AddN of non-scalar Tensor with dtype=DT_VARIANT is not "
              "supported; inputs[",
              i, " has shape: ", ctx->input(i).shape().DebugString(), "."));
    }

    TensorShape common_shape;
    OP_REQUIRES_OK(ctx, GetUnaryVariantShape(ctx->input(0), &common_shape));
    // Step 2: access all variants and ensure shapes match.
    for (int i = 1; i < num; ++i) {
      TensorShape check_shape;
      OP_REQUIRES_OK(ctx, GetUnaryVariantShape(ctx->input(i), &check_shape));
      OP_REQUIRES(ctx, common_shape == check_shape,
                  errors::InvalidArgument(
                      "AddN of Variants of differing shapes; inputs[0] shape: ",
                      common_shape.DebugString(), ", inputs[", i,
                      "] shape: ", check_shape.DebugString()));
    }

    // Step 3: attempt to add using
    //   BinaryOpVariants(ADD_VARIANT_BINARY_OP, ...)
    //   For the output create a default-constructed variant object.
    // TODO(ebrevdo): Perform summation in a tree-structure.
    Tensor out(cpu_allocator(), DT_VARIANT, TensorShape({}));
    Variant* v_out = &(out.scalar<Variant>()());
    OP_REQUIRES_OK(
        ctx, BinaryOpVariants<Device>(
                 ctx, ADD_VARIANT_BINARY_OP, ctx->input(0).scalar<Variant>()(),
                 ctx->input(1).scalar<Variant>()(), v_out));
    for (int i = 2; i < num; ++i) {
      const Variant tmp = std::move(*v_out);
      const Variant& inp = ctx->input(i).scalar<Variant>()();
      OP_REQUIRES_OK(ctx, BinaryOpVariants<Device>(ctx, ADD_VARIANT_BINARY_OP,
                                                   inp, tmp, v_out));
    }
    ctx->set_output(0, out);
  }
};

#define REGISTER_ADDN(type, dev)                                   \
  REGISTER_KERNEL_BUILDER(                                         \
      Name("AddN").Device(DEVICE_##dev).TypeConstraint<type>("T"), \
      AddNOp<dev##Device, type>)

#define REGISTER_ADDN_CPU(type) REGISTER_ADDN(type, CPU)

TF_CALL_NUMBER_TYPES(REGISTER_ADDN_CPU);
REGISTER_ADDN_CPU(Variant);

#undef REGISTER_ADDN_CPU

#if GOOGLE_CUDA
#define REGISTER_ADDN_GPU(type) REGISTER_ADDN(type, GPU)
TF_CALL_GPU_NUMBER_TYPES(REGISTER_ADDN_GPU);
TF_CALL_complex64(REGISTER_ADDN_GPU);
TF_CALL_complex128(REGISTER_ADDN_GPU);
#undef REGISTER_ADDN_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("AddN")
                            .Device(DEVICE_GPU)
                            .TypeConstraint<int32>("T")
                            .HostMemory("inputs")
                            .HostMemory("sum"),
                        AddNOp<CPUDevice, int32>);

// TODO(ebrevdo): Once rendezvous has been properly set up for
// Variants, we'll no longer need a HostMemory attribute for this case.
REGISTER_KERNEL_BUILDER(Name("AddN")
                            .Device(DEVICE_GPU)
                            .TypeConstraint<Variant>("T")
                            .HostMemory("inputs")
                            .HostMemory("sum"),
                        AddNOp<GPUDevice, Variant>);

#endif  // GOOGLE_CUDA

#ifdef TENSORFLOW_USE_SYCL
REGISTER_ADDN(float, SYCL);
REGISTER_ADDN(double, 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("AddN")
                            .Device(DEVICE_SYCL)
                            .TypeConstraint<int32>("T")
                            .HostMemory("inputs")
                            .HostMemory("sum"),
                        AddNOp<CPUDevice, int32>);
#endif  // TENSORFLOW_USE_SYCL

#undef REGISTER_ADDN

}  // namespace tensorflow
