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#ifndef CAFFE2_OPERATORS_REDUCE_OPS_H_
#define CAFFE2_OPERATORS_REDUCE_OPS_H_
#include "caffe2/core/context.h"
#include "caffe2/core/operator.h"
#include "caffe2/core/types.h"
#include "caffe2/utils/math.h"
#include <c10/util/irange.h>
#include <algorithm>
#include <functional>
#include <vector>
namespace caffe2 {
template <typename InputTypes, class Context, class Reducer>
class ReduceOp final : public Operator<Context> {
public:
USE_OPERATOR_CONTEXT_FUNCTIONS;
template <class... Args>
explicit ReduceOp(Args&&... args)
: Operator<Context>(std::forward<Args>(args)...),
axes_(this->template GetRepeatedArgument<int>("axes")),
OP_SINGLE_ARG(bool, "keepdims", keep_dims_, true),
reducer_{this->template GetSingleArgument<bool>("allow_broadcast_fastpath", false)} {}
bool RunOnDevice() override {
return DispatchHelper<InputTypes>::call(this, Input(0));
}
template <typename T>
bool DoRunWithType() {
const auto& X = Input(0);
const int ndim = X.dim();
const std::vector<int> X_dims(X.sizes().cbegin(), X.sizes().cend());
if (axes_.empty()) {
axes_.resize(ndim);
std::iota(axes_.begin(), axes_.end(), 0);
} else {
for (auto& axis : axes_) {
axis = X.canonical_axis_index(axis);
}
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.");
}
std::vector<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* Y = Output(0, output_dims, at::dtype<T>());
std::vector<int> Y_dims = X_dims;
for (const int axis : axes_) {
Y_dims[axis] = 1;
}
return reducer_.template Forward<T>(
X_dims,
Y_dims,
X.template data<T>(),
Y->template mutable_data<T>(),
&context_);
}
private:
std::vector<int> axes_;
const int keep_dims_;
const Reducer reducer_;
};
template <typename InputTypes, class Context, class Reducer>
class ReduceGradientOp final : public Operator<Context> {
public:
USE_OPERATOR_CONTEXT_FUNCTIONS;
template <class... Args>
explicit ReduceGradientOp(Args&&... args)
: Operator<Context>(std::forward<Args>(args)...),
axes_(this->template GetRepeatedArgument<int>("axes")),
reducer_{this->template GetSingleArgument<bool>("allow_broadcast_fastpath", false)} {}
bool RunOnDevice() override {
return DispatchHelper<InputTypes>::call(this, Input(0));
}
template <typename T>
bool DoRunWithType() {
const auto& dY = Input(0);
const auto& X = Input(1);
const auto& Y = Input(2);
const int ndim = X.dim();
if (axes_.empty()) {
axes_.resize(ndim);
std::iota(axes_.begin(), axes_.end(), 0);
} else {
for (auto& axis : axes_) {
axis = X.canonical_axis_index(axis);
}
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 reducer_.template Backward<T>(
dY_dims,
dX_dims,
dY.template data<T>(),
X.template data<T>(),
Y.template data<T>(),
dX->template mutable_data<T>(),
&context_);
}
private:
std::vector<int> axes_;
const Reducer reducer_;
};
template <class Context>
struct MinReducer {
explicit MinReducer(bool allow_broadcast_fastpath)
: allow_broadcast_fastpath_(allow_broadcast_fastpath) {}
template <typename T>
bool Forward(
const std::vector<int>& X_dims,
const std::vector<int>& Y_dims,
const T* X_data,
T* Y_data,
Context* context) const {
math::ReduceMin<T, Context>(
X_dims.size(),
X_dims.data(),
Y_dims.data(),
T(1),
X_data,
Y_data,
context,
allow_broadcast_fastpath_);
return true;
}
template <typename T>
bool Backward(
const std::vector<int>& dY_dims,
const std::vector<int>& dX_dims,
const T* dY_data,
const T* X_data,
const T* Y_data,
T* dX_data,
Context* context) const;
const bool allow_broadcast_fastpath_;
};
template <class Context>
struct MaxReducer {
explicit MaxReducer(bool allow_broadcast_fastpath)
: allow_broadcast_fastpath_(allow_broadcast_fastpath) {}
template <typename T>
bool Forward(
const std::vector<int>& X_dims,
const std::vector<int>& Y_dims,
const T* X_data,
T* Y_data,
Context* context) const {
math::ReduceMax<T, Context>(
X_dims.size(),
X_dims.data(),
Y_dims.data(),
T(1),
X_data,
Y_data,
context,
allow_broadcast_fastpath_);
return true;
}
template <typename T>
bool Backward(
const std::vector<int>& dY_dims,
const std::vector<int>& dX_dims,
const T* dY_data,
const T* X_data,
const T* Y_data,
T* dX_data,
Context* context) const;
const bool allow_broadcast_fastpath_;
};
template <class Context>
struct SumReducer {
explicit SumReducer(bool allow_broadcast_fastpath)
: allow_broadcast_fastpath_(allow_broadcast_fastpath) {}
template <typename T>
bool Forward(
const std::vector<int>& X_dims,
const std::vector<int>& Y_dims,
const T* X_data,
T* Y_data,
Context* context) const {
math::ReduceSum<T, Context>(
X_dims.size(),
X_dims.data(),
Y_dims.data(),
T(1),
X_data,
Y_data,
context,
allow_broadcast_fastpath_);
return true;
}
template <typename T>
bool Backward(
const std::vector<int>& dY_dims,
const std::vector<int>& dX_dims,
const T* dY_data,
const T* /* X_data */,
const T* /* Y_data */,
T* dX_data,
Context* context) const {
math::Broadcast(
dY_dims.size(),
dY_dims.data(),
dX_dims.size(),
dX_dims.data(),
T(1),
dY_data,
dX_data,
context,
allow_broadcast_fastpath_);
return true;
}
const bool allow_broadcast_fastpath_;
};
template <class Context>
struct MeanReducer {
explicit MeanReducer(bool allow_broadcast_fastpath)
: allow_broadcast_fastpath_(allow_broadcast_fastpath) {}
template <typename T>
bool Forward(
const std::vector<int>& X_dims,
const std::vector<int>& Y_dims,
const T* X_data,
T* Y_data,
Context* context) const {
math::ReduceMean<T, Context>(
X_dims.size(),
X_dims.data(),
Y_dims.data(),
T(1),
X_data,
Y_data,
context,
allow_broadcast_fastpath_);
return true;
}
template <typename T>
bool Backward(
const std::vector<int>& dY_dims,
const std::vector<int>& dX_dims,
const T* dY_data,
const T* /* X_data */,
const T* /* Y_data */,
T* dX_data,
Context* context) const {
const int dY_size = std::accumulate(
dY_dims.cbegin(), dY_dims.cend(), 1, std::multiplies<int>());
const int dX_size = std::accumulate(
dX_dims.cbegin(), dX_dims.cend(), 1, std::multiplies<int>());
math::Broadcast(
dY_dims.size(),
dY_dims.data(),
dX_dims.size(),
dX_dims.data(),
static_cast<T>(dY_size) / static_cast<T>(dX_size),
dY_data,
dX_data,
context,
allow_broadcast_fastpath_);
return true;
}
const bool allow_broadcast_fastpath_;
};
template <class Context>
struct L1Reducer {
explicit L1Reducer(bool allow_broadcast_fastpath)
: allow_broadcast_fastpath_(allow_broadcast_fastpath) {}
template <typename T>
bool Forward(
const std::vector<int>& X_dims,
const std::vector<int>& Y_dims,
const T* X_data,
T* Y_data,
Context* context) const {
math::ReduceL1<T, Context>(
X_dims.size(),
X_dims.data(),
Y_dims.data(),
T(1),
X_data,
Y_data,
context,
allow_broadcast_fastpath_);
return true;
}
template <typename T>
bool Backward(
const std::vector<int>& dY_dims,
const std::vector<int>& dX_dims,
const T* dY_data,
const T* X_data,
const T* Y_data,
T* dX_data,
Context* context) const;
const bool allow_broadcast_fastpath_;
};
template <class Context>
struct L2Reducer {
explicit L2Reducer(bool allow_broadcast_fastpath)
: allow_broadcast_fastpath_(allow_broadcast_fastpath) {}
template <typename T>
bool Forward(
const std::vector<int>& X_dims,
const std::vector<int>& Y_dims,
const T* X_data,
T* Y_data,
Context* context) const {
math::ReduceL2<T, Context>(
X_dims.size(),
X_dims.data(),
Y_dims.data(),
T(1),
X_data,
Y_data,
context,
allow_broadcast_fastpath_);
return true;
}
template <typename T>
bool Backward(
const std::vector<int>& dY_dims,
const std::vector<int>& dX_dims,
const T* dY_data,
const T* X_data,
const T* Y_data,
T* dX_data,
Context* context) const;
const bool allow_broadcast_fastpath_;
};
} // namespace caffe2
#endif // CAFFE2_OPERATORS_REDUCE_OPS_H_
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