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#ifndef CAFFE2_OPERATORS_MOMENTS_OP_H_
#define CAFFE2_OPERATORS_MOMENTS_OP_H_
#include "caffe2/core/context.h"
#include "caffe2/core/operator.h"
#include "caffe2/utils/math.h"
#include <c10/util/irange.h>
#include <algorithm>
#include <vector>
namespace caffe2 {
template <typename T, class Context>
class MomentsOp final : public Operator<Context> {
public:
USE_OPERATOR_CONTEXT_FUNCTIONS;
template <class... Args>
explicit MomentsOp(Args&&... args)
: Operator<Context>(std::forward<Args>(args)...),
axes_(this->template GetRepeatedArgument<int>("axes")),
OP_SINGLE_ARG(bool, "keepdims", keep_dims_, true),
OP_SINGLE_ARG(bool, "allow_broadcast_fastpath", allow_broadcast_fastpath_, true) {}
bool RunOnDevice() override {
const auto& X = Input(0);
const int ndim = X.dim();
if (axes_.empty()) {
axes_.resize(ndim);
std::iota(axes_.begin(), axes_.end(), 0);
} else {
std::sort(axes_.begin(), axes_.end());
CAFFE_ENFORCE_GE(axes_.front(), 0, "Axes ids must be non-negative.");
CAFFE_ENFORCE_LT(
axes_.back(),
ndim,
"Axes ids must be smaller than the dimensions of input.");
}
const std::vector<int> X_dims(X.sizes().cbegin(), X.sizes().cend());
std::vector<int> Y_dims = X_dims;
for (const int axis : axes_) {
Y_dims[axis] = 1;
}
std::vector<std::int64_t> output_dims;
output_dims.reserve(ndim);
std::size_t cur_axis = 0;
for (const auto i : c10::irange(ndim)) {
if (cur_axis < axes_.size() && i == axes_[cur_axis]) {
if (keep_dims_) {
output_dims.push_back(1);
}
++cur_axis;
} else {
output_dims.push_back(X_dims[i]);
}
}
auto* mean = Output(0, output_dims, at::dtype<T>());
auto* var = Output(1, output_dims, at::dtype<T>());
math::Moments<float, Context>(
X_dims.size(),
X_dims.data(),
Y_dims.data(),
X.template data<T>(),
mean->template mutable_data<T>(),
var->template mutable_data<T>(),
&context_,
allow_broadcast_fastpath_);
return true;
}
private:
std::vector<int> axes_;
const int keep_dims_;
const bool allow_broadcast_fastpath_;
};
template <typename T, class Context>
class MomentsGradientOp final : public Operator<Context> {
public:
USE_OPERATOR_CONTEXT_FUNCTIONS;
template <class... Args>
explicit MomentsGradientOp(Args&&... args)
: Operator<Context>(std::forward<Args>(args)...),
axes_(this->template GetRepeatedArgument<int>("axes")),
OP_SINGLE_ARG(bool, "allow_broadcast_fastpath", allow_broadcast_fastpath_, true) {}
bool RunOnDevice() override {
const auto& dmean = Input(0);
const auto& dvariance = Input(1);
const auto& X = Input(2);
const auto& mean = Input(3);
const int ndim = X.dim();
if (axes_.empty()) {
axes_.resize(ndim);
std::iota(axes_.begin(), axes_.end(), 0);
} else {
std::sort(axes_.begin(), axes_.end());
CAFFE_ENFORCE_GE(axes_.front(), 0, "Axes ids must be non-negative.");
CAFFE_ENFORCE_LT(
axes_.back(),
ndim,
"Axes ids must be smaller than the dimensions of input.");
}
const std::vector<int> dX_dims(X.sizes().cbegin(), X.sizes().cend());
std::vector<int> dY_dims = dX_dims;
for (const int axis : axes_) {
dY_dims[axis] = 1;
}
auto* dX = Output(0, X.sizes(), at::dtype<T>());
return Compute(
dY_dims,
dX_dims,
dmean.template data<T>(),
dvariance.template data<T>(),
X.template data<T>(),
mean.template data<T>(),
dX->template mutable_data<T>());
}
private:
bool Compute(
const std::vector<int>& dY_dims,
const std::vector<int>& dX_dims,
const T* dmean_data,
const T* dvariance_data,
const T* X_data,
const T* mean_data,
T* dX_data);
std::vector<int> axes_;
const bool allow_broadcast_fastpath_;
};
} // namespace caffe2
#endif // CAFFE2_OPERATORS_MOMENTS_OP_H_
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