/* 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/nn_ops.cc.

#define EIGEN_USE_THREADS

#include "tensorflow/core/kernels/batch_norm_op.h"
#include "third_party/eigen3/unsupported/Eigen/CXX11/Tensor"
#include "tensorflow/core/framework/numeric_op.h"
#include "tensorflow/core/framework/op_kernel.h"
#include "tensorflow/core/framework/register_types.h"
#include "tensorflow/core/framework/tensor.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 BatchNormOp : public OpKernel {
 public:
  explicit BatchNormOp(OpKernelConstruction* context) : OpKernel(context) {
    float variance_epsilon;
    OP_REQUIRES_OK(context,
                   context->GetAttr("variance_epsilon", &variance_epsilon));
    variance_epsilon_ = T(variance_epsilon);
    OP_REQUIRES_OK(context, context->GetAttr("scale_after_normalization",
                                             &scale_after_normalization_));
  }

  void Compute(OpKernelContext* context) override {
    const Tensor& input = context->input(0);
    const Tensor& mean = context->input(1);
    const Tensor& var = context->input(2);
    const Tensor& beta = context->input(3);
    const Tensor& gamma = context->input(4);

    OP_REQUIRES(context, input.dims() == 4,
                errors::InvalidArgument("input must be 4-dimensional",
                                        input.shape().DebugString()));
    OP_REQUIRES(context, mean.dims() == 1,
                errors::InvalidArgument("mean must be 1-dimensional",
                                        mean.shape().DebugString()));
    OP_REQUIRES(context, var.dims() == 1,
                errors::InvalidArgument("var must be 1-dimensional",
                                        var.shape().DebugString()));
    OP_REQUIRES(context, beta.dims() == 1,
                errors::InvalidArgument("beta must be 1-dimensional",
                                        beta.shape().DebugString()));
    OP_REQUIRES(context, gamma.dims() == 1,
                errors::InvalidArgument("gamma must be 1-dimensional",
                                        gamma.shape().DebugString()));

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

    functor::BatchNorm<Device, T>()(
        context->eigen_device<Device>(), input.tensor<T, 4>(), mean.vec<T>(),
        var.vec<T>(), beta.vec<T>(), gamma.vec<T>(), variance_epsilon_,
        scale_after_normalization_, output->tensor<T, 4>());
  }

 private:
  T variance_epsilon_;
  bool scale_after_normalization_;
};

template <typename Device, typename T>
class BatchNormGradOp : public OpKernel {
 public:
  explicit BatchNormGradOp(OpKernelConstruction* context) : OpKernel(context) {
    float variance_epsilon;
    OP_REQUIRES_OK(context,
                   context->GetAttr("variance_epsilon", &variance_epsilon));
    variance_epsilon_ = T(variance_epsilon);
    OP_REQUIRES_OK(context, context->GetAttr("scale_after_normalization",
                                             &scale_after_normalization_));
  }

  void Compute(OpKernelContext* context) override {
    const Tensor& input = context->input(0);
    const Tensor& mean = context->input(1);
    const Tensor& var = context->input(2);
    const Tensor& gamma = context->input(3);
    const Tensor& out_backprop = context->input(4);

    OP_REQUIRES(context, input.dims() == 4,
                errors::InvalidArgument("input must be 4-dimensional",
                                        input.shape().DebugString()));
    OP_REQUIRES(context, mean.dims() == 1,
                errors::InvalidArgument("mean must be 1-dimensional",
                                        mean.shape().DebugString()));
    OP_REQUIRES(context, var.dims() == 1,
                errors::InvalidArgument("var must be 1-dimensional",
                                        var.shape().DebugString()));
    OP_REQUIRES(context, gamma.dims() == 1,
                errors::InvalidArgument("gamma must be 1-dimensional",
                                        gamma.shape().DebugString()));
    OP_REQUIRES(context, out_backprop.dims() == 4,
                errors::InvalidArgument("out_backprop must be 4-dimensional",
                                        out_backprop.shape().DebugString()));

    Tensor* dx = nullptr;
    OP_REQUIRES_OK(context, context->forward_input_or_allocate_output(
                                {0, 4}, 0, input.shape(), &dx));
    Tensor* dm = nullptr;
    OP_REQUIRES_OK(context, context->forward_input_or_allocate_output(
                                {1}, 1, mean.shape(), &dm));
    Tensor* dv = nullptr;
    OP_REQUIRES_OK(context, context->forward_input_or_allocate_output(
                                {2}, 2, var.shape(), &dv));
    Tensor* db = nullptr;
    if (scale_after_normalization_) {
      OP_REQUIRES_OK(context, context->allocate_output(3, mean.shape(), &db));
    } else {
      OP_REQUIRES_OK(context, context->forward_input_or_allocate_output(
                                  {3}, 3, mean.shape(), &db));
    }
    Tensor* dg = nullptr;
    OP_REQUIRES_OK(context, context->allocate_output(4, gamma.shape(), &dg));

    // Scratch buffer of [depth] dimension, aka the 4th dimension of input,
    // which is dim_size(3), for calculating various combinations of
    // (var + epsilon).
    Tensor scratch1;
    OP_REQUIRES_OK(context, context->allocate_temp(
                                DataTypeToEnum<T>::value,
                                TensorShape({input.dim_size(3)}), &scratch1));

    // Scratch buffer of [depth] dimension for saving intermediate calculation
    // values.
    Tensor scratch2;
    OP_REQUIRES_OK(context, context->allocate_temp(
                                DataTypeToEnum<T>::value,
                                TensorShape({input.dim_size(3)}), &scratch2));

    functor::BatchNormGrad<Device, T>()(
        context->eigen_device<Device>(), input.tensor<T, 4>(), mean.vec<T>(),
        var.vec<T>(), gamma.vec<T>(), out_backprop.tensor<T, 4>(),
        variance_epsilon_, scale_after_normalization_, dx->tensor<T, 4>(),
        dm->vec<T>(), dv->vec<T>(), db->vec<T>(), dg->vec<T>(),
        scratch1.vec<T>(), scratch2.vec<T>());
  }

 private:
  T variance_epsilon_;
  bool scale_after_normalization_;
};

#define REGISTER_KERNEL(T)                                         \
  REGISTER_KERNEL_BUILDER(Name("BatchNormWithGlobalNormalization") \
                              .Device(DEVICE_CPU)                  \
                              .TypeConstraint<T>("T"),             \
                          BatchNormOp<CPUDevice, T>);

TF_CALL_half(REGISTER_KERNEL);
TF_CALL_float(REGISTER_KERNEL);
TF_CALL_double(REGISTER_KERNEL);
#undef REGISTER_KERNEL

#if GOOGLE_CUDA || TENSORFLOW_USE_ROCM
// Forward declarations of the functor specializations for GPU.
namespace functor {
#define DECLARE_GPU_SPEC(T)                                                  \
  template <>                                                                \
  void BatchNorm<GPUDevice, T>::operator()(                                  \
      const GPUDevice& d, typename TTypes<T, 4>::ConstTensor input,          \
      typename TTypes<T>::ConstVec mean, typename TTypes<T>::ConstVec var,   \
      typename TTypes<T>::ConstVec beta, typename TTypes<T>::ConstVec gamma, \
      T variance_epsilon, bool scale_after_normalization,                    \
      typename TTypes<T, 4>::Tensor output);                                 \
  extern template struct BatchNorm<GPUDevice, T>;

#define DECLARE_GPU_SPECS(T) DECLARE_GPU_SPEC(T);

TF_CALL_half(DECLARE_GPU_SPECS);
TF_CALL_float(DECLARE_GPU_SPECS);
#undef DECLARE_GPU_SPEC
}  // namespace functor

// Registration of the GPU implementations.
#define REGISTER_GPU_KERNEL(T)                                     \
  REGISTER_KERNEL_BUILDER(Name("BatchNormWithGlobalNormalization") \
                              .Device(DEVICE_GPU)                  \
                              .TypeConstraint<T>("T"),             \
                          BatchNormOp<GPUDevice, T>);

TF_CALL_half(REGISTER_GPU_KERNEL);
TF_CALL_float(REGISTER_GPU_KERNEL);
#undef REGISTER_GPU_KERNEL

#endif  // GOOGLE_CUDA || TENSORFLOW_USE_ROCM

#if TENSORFLOW_USE_SYCL
#define REGISTER_KERNEL(T)                                         \
  REGISTER_KERNEL_BUILDER(Name("BatchNormWithGlobalNormalization") \
                              .Device(DEVICE_SYCL)                 \
                              .TypeConstraint<T>("T"),             \
                          BatchNormOp<SYCLDevice, T>);

TF_CALL_float(REGISTER_KERNEL);
TF_CALL_double(REGISTER_KERNEL);
#undef REGISTER_KERNEL
#endif  // TENSORFLOW_USE_SYCL

#define REGISTER_KERNEL(T)                                             \
  REGISTER_KERNEL_BUILDER(Name("BatchNormWithGlobalNormalizationGrad") \
                              .Device(DEVICE_CPU)                      \
                              .TypeConstraint<T>("T"),                 \
                          BatchNormGradOp<CPUDevice, T>);

TF_CALL_half(REGISTER_KERNEL);
TF_CALL_float(REGISTER_KERNEL);
TF_CALL_double(REGISTER_KERNEL);
#undef REGISTER_KERNEL

#if GOOGLE_CUDA || TENSORFLOW_USE_ROCM
// Forward declarations of the functor specializations for GPU.
namespace functor {
#define DECLARE_GPU_SPEC(T)                                                \
  template <>                                                              \
  void BatchNormGrad<GPUDevice, T>::operator()(                            \
      const GPUDevice& d, typename TTypes<T, 4>::ConstTensor input,        \
      typename TTypes<T>::ConstVec mean, typename TTypes<T>::ConstVec var, \
      typename TTypes<T>::ConstVec gamma,                                  \
      typename TTypes<T, 4>::ConstTensor out_backprop, T variance_epsilon, \
      bool scale_after_normalization, typename TTypes<T, 4>::Tensor dx,    \
      typename TTypes<T>::Vec dm, typename TTypes<T>::Vec dv,              \
      typename TTypes<T>::Vec db, typename TTypes<T>::Vec dg,              \
      typename TTypes<T>::Vec scratch1, typename TTypes<T>::Vec scratch2); \
  extern template struct BatchNormGrad<GPUDevice, T>;

#define DECLARE_GPU_SPECS(T) DECLARE_GPU_SPEC(T);

TF_CALL_half(DECLARE_GPU_SPECS);
TF_CALL_float(DECLARE_GPU_SPECS);
#undef DECLARE_GPU_SPEC
}  // namespace functor

// Registration of the GPU implementations.
#define REGISTER_GPU_KERNEL(T)                                         \
  REGISTER_KERNEL_BUILDER(Name("BatchNormWithGlobalNormalizationGrad") \
                              .Device(DEVICE_GPU)                      \
                              .TypeConstraint<T>("T"),                 \
                          BatchNormGradOp<GPUDevice, T>);

TF_CALL_half(REGISTER_GPU_KERNEL);
TF_CALL_float(REGISTER_GPU_KERNEL);
#undef REGISTER_GPU_KERNEL

#endif  // GOOGLE_CUDA || TENSORFLOW_USE_ROCM

#if TENSORFLOW_USE_SYCL
#define REGISTER_KERNEL(T)                                             \
  REGISTER_KERNEL_BUILDER(Name("BatchNormWithGlobalNormalizationGrad") \
                              .Device(DEVICE_SYCL)                     \
                              .TypeConstraint<T>("T"),                 \
                          BatchNormGradOp<SYCLDevice, T>);

TF_CALL_float(REGISTER_KERNEL);
TF_CALL_double(REGISTER_KERNEL);
#undef REGISTER_KERNEL

#endif  // TENSORFLOW_USE_SYCL

}  // namespace tensorflow