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Using EigenTensor to reshape tensor.

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Adaptive_data_structure_for_SwitchOrderLayer
yangyaming 8 years ago
parent a4df3f5bd8
commit f8b885f27f
2 changed files with 157 additions and 35 deletions
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64
paddle/operators/squared_l2_distance_op.cc
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@ -22,36 +22,52 @@ class SquaredL2DistanceOp : public framework::OperatorWithKernel {
using framework::OperatorWithKernel::OperatorWithKernel;
protected:
void InferShape(const framework::InferShapeContext &ctx) const override {
void InferShape(const framework::InferShapeContext& ctx) const override {
PADDLE_ENFORCE_NOT_NULL(ctx.InputVar("X"),
"Input of SquaredL2DistanceOp "
"must be initialized.");
PADDLE_ENFORCE_EQ(ctx.Input<Tensor>("X")->dims(),
ctx.Input<Tensor>("Y")->dims(),
"Dimensions of SquaredL2DistanceOp's two inputs "
"must be same.")
framework::DDim dims = ctx.Input<Tensor>("X")->dims();
ctx.Output<Tensor>("sub_result")->Resize(dims);
ctx.Output<Tensor>("Out")->Resize(framework::make_ddim({dims[0], 1}));
PADDLE_ENFORCE_NOT_NULL(ctx.InputVar("Y"),
"Target of SquaredL2DistanceOp "
"must be initialized.");
auto* X = ctx.Input<Tensor>("X");
auto xDims = X->dims();
auto* Y = ctx.Input<Tensor>("Y");
auto yDims = Y->dims();
PADDLE_ENFORCE_EQ(framework::arity(xDims), framework::arity(yDims),
"Tensor rank of both SquaredL2DistanceOp's "
"inputs must be same.");
int rank = framework::arity(xDims);
PADDLE_ENFORCE(rank >= 2 || rank <= 6, "Tensor rank should be in [2, 6].");
PADDLE_ENFORCE(yDims[0] == 1 || yDims[0] == xDims[0],
"First dimension of target must be equal to input "
"or to 1.");
ctx.Output<Tensor>("sub_result")->Resize(xDims);
ctx.Output<Tensor>("Out")->Resize({xDims[0], 1});
}
};
class SquaredL2DistanceOpMaker : public framework::OpProtoAndCheckerMaker {
public:
SquaredL2DistanceOpMaker(framework::OpProto *proto,
framework::OpAttrChecker *op_checker)
SquaredL2DistanceOpMaker(framework::OpProto* proto,
framework::OpAttrChecker* op_checker)
: OpProtoAndCheckerMaker(proto, op_checker) {
AddInput("X", "Input value.");
AddInput("Y", "Target value.");
AddInput("X", "Input of SquaredL2DistanceOp.");
AddInput("Y", "Target of SquaredL2DistanceOp.");
AddOutput("sub_result",
"Buffering substraction result which "
"will be reused in backward.")
.AsIntermediate();
AddOutput("Out", "Squared l2 distance between input and target.");
AddComment(R"DOC(
SquaredL2DistanceOp will cacluate the squared L2 distances for
SquaredL2DistanceOp will cacluate the squared L2 distance for
input and target. Number of distance value equals to the
first dimension of input.
first dimension of input. First dimension of target could be equal to
input or to 1. If the first dimension of target is 1, SquaredL2DistanceOp
will broadcast the first dimension to the first dimension of input.
You can decide whether calculate the gradient of target.
)DOC");
}
};
@ -61,9 +77,23 @@ class SquaredL2DistanceGradOp : public framework::OperatorWithKernel {
using framework::OperatorWithKernel::OperatorWithKernel;
protected:
void InferShape(const framework::InferShapeContext &ctx) const override {
ctx.Output<Tensor>(framework::GradVarName("X"))
->Resize(ctx.Input<Tensor>("X")->dims());
void InferShape(const framework::InferShapeContext& ctx) const override {
PADDLE_ENFORCE_NOT_NULL(ctx.InputVar(framework::GradVarName("Out")),
"Gradient of Out should not be null");
// check out grad dimensions
auto outDims = ctx.Input<Tensor>(framework::GradVarName("Out"))->dims();
auto xDims = ctx.Input<Tensor>("X")->dims();
auto yDims = ctx.Input<Tensor>("Y")->dims();
PADDLE_ENFORCE_EQ(outDims[0], xDims[0],
"First dimension of output gradient and "
"input value must be equal.");
PADDLE_ENFORCE_EQ(outDims[1], 1,
"Second dimension of output gradient "
"must be 1.");
auto* xGrad = ctx.Output<Tensor>(framework::GradVarName("X"));
auto* yGrad = ctx.Output<Tensor>(framework::GradVarName("Y"));
if (xGrad != nullptr) xGrad->Resize(xDims);
if (yGrad != nullptr) yGrad->Resize(yDims);
}
};

128
paddle/operators/squared_l2_distance_op.h
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@ -20,17 +20,44 @@ namespace paddle {
namespace operators {
using Tensor = framework::Tensor;
template <typename T, int MajorType = Eigen::RowMajor,
template <typename T, size_t D, int MajorType = Eigen::RowMajor,
typename IndexType = Eigen::DenseIndex>
using EigenMatrix = framework::EigenMatrix<T, MajorType, IndexType>;
using EigenTensor = framework::EigenTensor<T, D, MajorType, IndexType>;
template <typename T, int MajorType = Eigen::RowMajor,
typename IndexType = Eigen::DenseIndex>
using EigenVector = framework::EigenVector<T, MajorType, IndexType>;
using EigenMatrix = framework::EigenMatrix<T, MajorType, IndexType>;
template <typename Place, typename T>
class SquaredL2DistanceKernel : public framework::OpKernel {
public:
void Compute(const framework::ExecutionContext& context) const override {
auto* input0 = context.Input<Tensor>("X");
const int rank = framework::arity(input0->dims());
switch (rank) {
case 2:
Operate<2>(context);
break;
case 3:
Operate<3>(context);
break;
case 4:
Operate<4>(context);
break;
case 5:
Operate<5>(context);
break;
case 6:
Operate<6>(context);
break;
default:
// already asserted in SquaredL2DistanceOpMaker
break;
}
}
private:
template <int Dims>
void Operate(const framework::ExecutionContext& context) const {
auto* input0 = context.Input<Tensor>("X");
auto* input1 = context.Input<Tensor>("Y");
auto* output0 = context.Output<Tensor>("sub_result");
@ -39,17 +66,28 @@ class SquaredL2DistanceKernel : public framework::OpKernel {
output0->mutable_data<T>(context.GetPlace());
output1->mutable_data<T>(context.GetPlace());
auto X = EigenMatrix<T>::From(*input0);
auto Y = EigenMatrix<T>::From(*input1);
auto subResult = EigenMatrix<T>::From(*output0);
auto X = EigenTensor<T, Dims>::From(*input0);
auto Y = EigenTensor<T, Dims>::From(*input1);
auto subResult = EigenTensor<T, Dims>::From(*output0);
auto Z = EigenMatrix<T>::From(*output1);
auto xDims = X.dimensions();
auto yDims = Y.dimensions();
auto place = context.GetEigenDevice<Place>();
// buffer the substraction result
subResult.device(place) = X - Y;
const auto& inDims = X.dimensions();
if (yDims[0] == 1 && xDims[0] != yDims[0]) {
auto yBroadcastDims = yDims;
yBroadcastDims[0] = xDims[0];
subResult.device(place) = X - Y.broadcast(yBroadcastDims);
} else {
subResult.device(place) = X - Y;
}
// create matrix view for substraction result
const auto& subResMat = subResult.reshape(Eigen::array<int, 2>(
{static_cast<int>(inDims[0]), static_cast<int>(X.size() / inDims[0])}));
{static_cast<int>(xDims[0]), static_cast<int>(X.size() / xDims[0])}));
Z.device(place) = subResMat.pow(2).sum(Eigen::array<int, 1>({1}));
}
};
@ -59,24 +97,78 @@ class SquaredL2DistanceGradKernel : public framework::OpKernel {
public:
void Compute(const framework::ExecutionContext& context) const override {
auto* input0 = context.Input<Tensor>("sub_result");
auto* OG = context.Input<Tensor>(framework::GradVarName("Out"));
auto* IG = context.Output<Tensor>(framework::GradVarName("X"));
const int rank = framework::arity(input0->dims());
switch (rank) {
case 2:
Operate<2>(context);
break;
case 3:
Operate<3>(context);
break;
case 4:
Operate<4>(context);
break;
case 5:
Operate<5>(context);
break;
case 6:
Operate<6>(context);
break;
default:
// already asserted in SquaredL2DistanceOpMaker
break;
}
}
IG->mutable_data<T>(context.GetPlace());
private:
template <int Dims>
void Operate(const framework::ExecutionContext& context) const {
auto* input0 = context.Input<Tensor>("sub_result");
auto* OG = context.Input<Tensor>(framework::GradVarName("Out"));
auto* XG = context.Output<Tensor>(framework::GradVarName("X"));
auto* YG = context.Output<Tensor>(framework::GradVarName("Y"));
auto subResult = EigenMatrix<T>::From(*input0);
auto subResult = EigenTensor<T, Dims>::From(*input0);
auto outGrad = EigenMatrix<T>::From(*OG);
auto inGrad = EigenMatrix<T>::From(*IG);
const auto& subResDims = subResult.dimensions();
auto subResDims = subResult.dimensions();
int firstDim = static_cast<int>(subResDims[0]);
int cols = subResult.size() / firstDim;
const auto subResMat =
subResult.reshape(Eigen::array<int, 2>({firstDim, cols}));
// create a matrix view for input gradient tensor
auto inGradMat = inGrad.reshape(Eigen::array<int, 2>({firstDim, cols}));
inGradMat.device(context.GetEigenDevice<Place>()) =
// calculate gradient
auto gradMat =
2 * (outGrad.broadcast(Eigen::array<int, 2>({1, cols}))) * subResMat;
// propagate back to input
auto eigenPlace = context.GetEigenDevice<Place>();
if (XG != nullptr) {
XG->mutable_data<T>(context.GetPlace());
auto xGrad = EigenTensor<T, Dims>::From(*XG);
// dimensions are same with subResult
auto xGradMat = xGrad.reshape(Eigen::array<int, 2>({firstDim, cols}));
xGradMat.device(eigenPlace) = gradMat;
}
if (YG != nullptr) {
YG->mutable_data<T>(context.GetPlace());
auto yGrad = EigenTensor<T, Dims>::From(*YG);
auto dimsYGrad = yGrad.dimensions();
auto yGradMat = yGrad.reshape(Eigen::array<int, 2>(
{static_cast<int>(dimsYGrad[0]),
static_cast<int>(yGrad.size() / dimsYGrad[0])}));
PADDLE_ENFORCE(dimsYGrad[0] <= firstDim,
"First dimension of gradient must be greater or "
"equal than first dimension of target");
if (dimsYGrad[0] == firstDim) {
yGradMat.device(eigenPlace) = -1 * gradMat;
} else {
yGradMat.device(eigenPlace) =
-1 * (gradMat.sum(Eigen::array<int, 2>({0})));
}
}
}
};

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