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#ifndef CAFFE2_OPERATORS_DISTANCE_OP_H_
#define CAFFE2_OPERATORS_DISTANCE_OP_H_
#include "caffe2/core/context.h"
#include "caffe2/core/operator.h"
#include "caffe2/utils/math.h"
#include "c10/util/irange.h"
namespace caffe2 {
template <typename T, class Context>
class SquaredL2DistanceOp : public Operator<Context> {
public:
template <class... Args>
explicit SquaredL2DistanceOp(Args&&... args)
: Operator<Context>(std::forward<Args>(args)...) {}
USE_OPERATOR_CONTEXT_FUNCTIONS;
bool RunOnDevice() override;
protected:
// Input: X, Y; Output: Distance
};
template <typename T, class Context>
class SquaredL2DistanceGradientOp final : public Operator<Context> {
public:
template <class... Args>
explicit SquaredL2DistanceGradientOp(Args&&... args)
: Operator<Context>(std::forward<Args>(args)...) {}
USE_OPERATOR_CONTEXT_FUNCTIONS;
bool RunOnDevice() override {
auto& X = Input(0);
auto& Y = Input(1);
auto& dDistance = Input(2);
int N = X.dim() > 0 ? X.dim32(0) : 1;
int D = N > 0 ? X.numel() / N : 0;
CAFFE_ENFORCE(X.dim() == Y.dim());
for (const auto i : c10::irange(X.dim())) {
CAFFE_ENFORCE(X.dim32(i) == Y.dim32(i));
}
CAFFE_ENFORCE(dDistance.dim() == 1);
CAFFE_ENFORCE(dDistance.dim32(0) == N);
auto* dX = Output(0, X.sizes(), at::dtype<T>());
auto* dY = Output(1, Y.sizes(), at::dtype<T>());
math::Sub<T, Context>(
X.numel(),
X.template data<T>(),
Y.template data<T>(),
dX->template mutable_data<T>(),
&context_);
for (const auto i : c10::irange(N)) {
math::Scale<T, T, Context>(
D,
dDistance.template data<T>() + i,
dX->template data<T>() + i * D,
dX->template mutable_data<T>() + i * D,
&context_);
}
// The gradient of the other side is basically the negative.
math::Scale<T, T, Context>(
X.numel(),
-1,
dX->template data<T>(),
dY->template mutable_data<T>(),
&context_);
return true;
}
protected:
// Input: X, Y, dDistance; Output: dX, dY
};
template <typename T, class Context>
class L1DistanceOp : public Operator<Context> {
public:
template <class... Args>
explicit L1DistanceOp(Args&&... args)
: Operator<Context>(std::forward<Args>(args)...) {}
USE_OPERATOR_CONTEXT_FUNCTIONS;
bool RunOnDevice() override;
protected:
// Input: X, Y; Output: Distance
};
template <typename T, class Context>
class L1DistanceGradientOp : public Operator<Context> {
public:
template <class... Args>
explicit L1DistanceGradientOp(Args&&... args)
: Operator<Context>(std::forward<Args>(args)...) {}
USE_OPERATOR_CONTEXT_FUNCTIONS;
bool RunOnDevice() override;
protected:
// Input: X, Y, dDistance; Output: dX, dY
};
template <typename T, class Context>
class DotProductOp : public Operator<Context> {
public:
template <class... Args>
explicit DotProductOp(Args&&... args)
: Operator<Context>(std::forward<Args>(args)...) {}
USE_OPERATOR_CONTEXT_FUNCTIONS;
bool RunOnDevice() override;
protected:
INPUT_TAGS(X_IN, Y_IN);
OUTPUT_TAGS(DOT_OUT);
};
template <typename T, class Context>
class DotProductGradientOp final : public Operator<Context> {
public:
template <class... Args>
explicit DotProductGradientOp(Args&&... args)
: Operator<Context>(std::forward<Args>(args)...) {}
USE_OPERATOR_CONTEXT_FUNCTIONS;
bool RunOnDevice() override;
protected:
INPUT_TAGS(X_IN, Y_IN, DER_DOT_IN);
OUTPUT_TAGS(DER_X_OUT, DER_Y_OUT);
};
template <typename T, class Context>
class DotProductWithPaddingOp : public Operator<Context> {
public:
template <class... Args>
explicit DotProductWithPaddingOp(Args&&... args)
: Operator<Context>(std::forward<Args>(args)...),
pad_value_(this->template GetSingleArgument<float>("pad_value", 0.0)),
replicate_(this->template GetSingleArgument<bool>("replicate", false)) {
}
USE_OPERATOR_CONTEXT_FUNCTIONS;
bool RunOnDevice() override;
protected:
float pad_value_;
bool replicate_;
INPUT_TAGS(X_IN, Y_IN);
OUTPUT_TAGS(DOT_OUT);
};
template <typename T, class Context>
class CosineSimilarityOp : public Operator<Context> {
public:
template <class... Args>
explicit CosineSimilarityOp(Args&&... args)
: Operator<Context>(std::forward<Args>(args)...) {}
USE_OPERATOR_CONTEXT_FUNCTIONS;
bool RunOnDevice() override;
protected:
INPUT_TAGS(X_IN, Y_IN);
OUTPUT_TAGS(COS_OUT);
private:
Tensor aux_;
};
template <typename T, class Context>
class CosineSimilarityGradientOp final : public Operator<Context> {
public:
template <class... Args>
explicit CosineSimilarityGradientOp(Args&&... args)
: Operator<Context>(std::forward<Args>(args)...) {}
USE_OPERATOR_CONTEXT_FUNCTIONS;
bool RunOnDevice() override;
protected:
INPUT_TAGS(X_IN, Y_IN, DER_COS_IN);
OUTPUT_TAGS(DER_X_OUT, DER_Y_OUT);
private:
Tensor aux_;
};
template <typename T, class Context>
class DotProductWithPaddingGradientOp final : public Operator<Context> {
public:
template <class... Args>
explicit DotProductWithPaddingGradientOp(Args&&... args)
: Operator<Context>(std::forward<Args>(args)...),
pad_value_(this->template GetSingleArgument<float>("pad_value", 0.0)),
replicate_(this->template GetSingleArgument<bool>("replicate", false)) {
}
USE_OPERATOR_CONTEXT_FUNCTIONS;
bool RunOnDevice() override {
auto& X = Input(X_IN);
auto& Y = Input(Y_IN);
auto& dDot = Input(DER_DOT_IN);
int N, D, DX, DY, restD;
if (X.numel() > 0) {
N = X.dim() > 0 ? X.dim32(0) : 1;
DX = X.numel() / N;
DY = Y.numel() / N;
} else {
N = 0;
DX = 0;
DY = 0;
}
CAFFE_ENFORCE(!replicate_ || DX % DY == 0 || DY % DX == 0);
D = std::min(DX, DY);
restD = std::max(DX, DY) - D;
CAFFE_ENFORCE_EQ(X.dim(), Y.dim());
CAFFE_ENFORCE_EQ(X.dim32(0), Y.dim32(0));
CAFFE_ENFORCE_EQ(dDot.dim(), 1);
CAFFE_ENFORCE_EQ(dDot.dim32(0), N);
auto* dX = Output(DER_X_OUT, X.sizes(), at::dtype<T>());
auto* dY = Output(DER_Y_OUT, Y.sizes(), at::dtype<T>());
const auto* X_data = X.template data<T>();
const auto* Y_data = Y.template data<T>();
const auto* dDot_data = dDot.template data<T>();
auto* dX_data = dX->template mutable_data<T>();
auto* dY_data = dY->template mutable_data<T>();
for (const auto i : c10::irange(N)) { // TODO: multithreading
auto offsetX = i * DX;
auto offsetY = i * DY;
if (replicate_) {
// L_ for longer vector and S_ for shorter vector
const T *L_data, *S_data;
T *dL_data, *dS_data;
int DL, DS;
if (DX > DY) {
L_data = X_data + offsetX;
S_data = Y_data + offsetY;
dL_data = dX_data + offsetX;
dS_data = dY_data + offsetY;
DL = DX;
DS = DY;
} else {
L_data = Y_data + offsetY;
S_data = X_data + offsetX;
dL_data = dY_data + offsetY;
dS_data = dX_data + offsetX;
DL = DY;
DS = DX;
}
// TODO: get rid of temp memory use
std::vector<T> tmp_data(DS);
math::Set<T, Context>(DS, 0.0, dS_data, &context_);
for (int j = 0; j < DL / DS; j++) {
math::Scale<T, T, Context>(
DS, dDot_data[i], S_data, dL_data + j * DS, &context_);
math::Scale<T, T, Context>(
DS, dDot_data[i], L_data + j * DS, tmp_data.data(), &context_);
math::Axpy<float, T, Context>(
DS, 1.0, tmp_data.data(), dS_data, &context_);
}
} else {
math::Scale<T, T, Context>(
D, dDot_data[i], X_data + offsetX, dY_data + offsetY, &context_);
math::Scale<T, T, Context>(
D, dDot_data[i], Y_data + offsetY, dX_data + offsetX, &context_);
}
if (!replicate_ && DX != DY) {
T* rest_data;
if (DX > DY) {
rest_data = dX_data + offsetX + D;
} else {
rest_data = dY_data + offsetY + D;
}
auto pad_gradient = dDot_data[i] * pad_value_;
math::Set<T, Context>(restD, pad_gradient, rest_data, &context_);
}
}
return true;
}
protected:
float pad_value_;
bool replicate_;
INPUT_TAGS(X_IN, Y_IN, DER_DOT_IN);
OUTPUT_TAGS(DER_X_OUT, DER_Y_OUT);
};
} // namespace caffe2
#endif // CAFFE2_OPERATORS_DISTANCE_OP_H_
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