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// See docs in ../ops/nn_ops.cc.
#define EIGEN_USE_THREADS
#include "tensorflow/core/framework/numeric_op.h"
#include "tensorflow/core/framework/op_kernel.h"
#include "tensorflow/core/framework/register_types.h"
#include "tensorflow/core/kernels/batch_norm_op.h"
#include "tensorflow/core/public/tensor.h"
#include "third_party/eigen3/unsupported/Eigen/CXX11/Tensor"
namespace tensorflow {
typedef Eigen::ThreadPoolDevice CPUDevice;
typedef Eigen::GpuDevice GPUDevice;
template <typename Device, typename T>
class BatchNormOp : public OpKernel {
public:
explicit BatchNormOp(OpKernelConstruction* context) : OpKernel(context) {
OP_REQUIRES_OK(context,
context->GetAttr("variance_epsilon", &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().ShortDebugString()));
OP_REQUIRES(context, mean.dims() == 1,
errors::InvalidArgument("mean must be 1-dimensional",
mean.shape().ShortDebugString()));
OP_REQUIRES(context, var.dims() == 1,
errors::InvalidArgument("var must be 1-dimensional",
var.shape().ShortDebugString()));
OP_REQUIRES(context, beta.dims() == 1,
errors::InvalidArgument("beta must be 1-dimensional",
beta.shape().ShortDebugString()));
OP_REQUIRES(context, gamma.dims() == 1,
errors::InvalidArgument("gamma must be 1-dimensional",
gamma.shape().ShortDebugString()));
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:
float variance_epsilon_;
bool scale_after_normalization_;
};
template <typename Device, typename T>
class BatchNormGradOp : public OpKernel {
public:
explicit BatchNormGradOp(OpKernelConstruction* context) : OpKernel(context) {
OP_REQUIRES_OK(context,
context->GetAttr("variance_epsilon", &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().ShortDebugString()));
OP_REQUIRES(context, mean.dims() == 1,
errors::InvalidArgument("mean must be 1-dimensional",
mean.shape().ShortDebugString()));
OP_REQUIRES(context, var.dims() == 1,
errors::InvalidArgument("var must be 1-dimensional",
var.shape().ShortDebugString()));
OP_REQUIRES(context, gamma.dims() == 1,
errors::InvalidArgument("gamma must be 1-dimensional",
gamma.shape().ShortDebugString()));
OP_REQUIRES(
context, out_backprop.dims() == 4,
errors::InvalidArgument("out_backprop must be 4-dimensional",
out_backprop.shape().ShortDebugString()));
Tensor* dx = nullptr;
OP_REQUIRES_OK(context, context->allocate_output(0, input.shape(), &dx));
Tensor* dm = nullptr;
OP_REQUIRES_OK(context, context->allocate_output(1, mean.shape(), &dm));
Tensor* dv = nullptr;
OP_REQUIRES_OK(context, context->allocate_output(2, var.shape(), &dv));
Tensor* db = nullptr;
OP_REQUIRES_OK(context, context->allocate_output(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:
float variance_epsilon_;
bool scale_after_normalization_;
};
#define REGISTER_KERNEL(T) \
REGISTER_KERNEL_BUILDER(Name("BatchNormWithGlobalNormalization") \
.Device(DEVICE_CPU) \
.TypeConstraint<T>("T"), \
BatchNormOp<CPUDevice, T>);
REGISTER_KERNEL(float);
REGISTER_KERNEL(double);
#undef REGISTER_KERNEL
#if GOOGLE_CUDA
// 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, \
float 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);
DECLARE_GPU_SPECS(float);
#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>);
REGISTER_GPU_KERNEL(float);
#undef REGISTER_GPU_KERNEL
#endif // GOOGLE_CUDA
#define REGISTER_KERNEL(T) \
REGISTER_KERNEL_BUILDER(Name("BatchNormWithGlobalNormalizationGrad") \
.Device(DEVICE_CPU) \
.TypeConstraint<T>("T"), \
BatchNormGradOp<CPUDevice, T>);
REGISTER_KERNEL(float);
REGISTER_KERNEL(double);
#undef REGISTER_KERNEL
#if GOOGLE_CUDA
// 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, float 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);
DECLARE_GPU_SPECS(float);
#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>);
REGISTER_GPU_KERNEL(float);
#undef REGISTER_GPU_KERNEL
#endif // GOOGLE_CUDA
} // namespace tensorflow
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