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Remove unnecessary mask operations in test phase for dropout operator.

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update-doc-pybind
Xinghai Sun 8 years ago
parent a2798ff25f
commit ffeeef82f3
4 changed files with 20 additions and 27 deletions
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15
paddle/operators/dropout_op.cc
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@ -26,7 +26,6 @@ class DropoutOp : public framework::OperatorWithKernel {
protected:
void InferShape(const framework::InferShapeContext &ctx) const override {
// validity check
PADDLE_ENFORCE_NOT_NULL(ctx.InputVar("X"), "Input(X) must not be null.");
PADDLE_ENFORCE_GE(ctx.Attr<float>("dropout_prob"), 0);
PADDLE_ENFORCE_LE(ctx.Attr<float>("dropout_prob"), 1);
@ -34,10 +33,11 @@ class DropoutOp : public framework::OperatorWithKernel {
PADDLE_ENFORCE(ctx.Attr<int>("is_training") == 0 ||
ctx.Attr<int>("is_training") == 1);
// resize
auto dims = ctx.Input<Tensor>("X")->dims();
ctx.Output<LoDTensor>("Out")->Resize(dims);
ctx.Output<LoDTensor>("Mask")->Resize(dims);
if (ctx.Attr<int>("is_training") == 1) {
ctx.Output<LoDTensor>("Mask")->Resize(dims);
}
}
};
@ -75,24 +75,27 @@ class DropoutOpGrad : public framework::OperatorWithKernel {
protected:
void InferShape(const framework::InferShapeContext &ctx) const override {
// validity check
PADDLE_ENFORCE_EQ(ctx.Attr<int>("is_training"), 1,
"GradOp is only callable when is_training is true");
PADDLE_ENFORCE_NOT_NULL(ctx.InputVar("X"), "Input(X) must not be null.");
PADDLE_ENFORCE_NOT_NULL(ctx.InputVar("Mask"), "Mask must not be null.");
PADDLE_ENFORCE_NOT_NULL(ctx.InputVar(framework::GradVarName("Out")),
"Input(Out@GRAD) must not be null.");
PADDLE_ENFORCE_GE(ctx.Attr<AttrType>("dropout_prob"), 0);
PADDLE_ENFORCE_LE(ctx.Attr<AttrType>("dropout_prob"), 1);
// TODO(xinghai-sun): remove this check after swtiching to bool
PADDLE_ENFORCE(ctx.Attr<int>("is_training") == 0 ||
ctx.Attr<int>("is_training") == 1);
auto x_dims = ctx.Input<Tensor>("X")->dims();
auto mask_dims = ctx.Input<Tensor>("Mask")->dims();
auto out_dims = ctx.Input<Tensor>(framework::GradVarName("Out"))->dims();
PADDLE_ENFORCE_EQ(x_dims, out_dims,
"Dimensions of Input(X) and Out@Grad must be the same.");
auto mask_dims = ctx.Input<Tensor>("Mask")->dims();
PADDLE_ENFORCE_EQ(x_dims, mask_dims,
"Dimensions of Input(X) and Mask must be the same.");
// resize
auto *x_grad = ctx.Output<LoDTensor>(framework::GradVarName("X"));
x_grad->Resize(x_dims);
}

10
paddle/operators/dropout_op.cu
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@ -53,26 +53,24 @@ class GPUDropoutKernel : public framework::OpKernel {
auto* x = context.Input<Tensor>("X");
auto* y = context.Output<Tensor>("Out");
y->mutable_data<T>(context.GetPlace());
auto* mask = context.Output<Tensor>("Mask");
auto* mask_data = mask->mutable_data<T>(context.GetPlace());
AttrType dropout_prob = context.Attr<AttrType>("dropout_prob");
auto X = EigenMatrix<T>::Reshape(*x, 1);
auto Y = EigenMatrix<T>::Reshape(*y, 1);
auto M = EigenMatrix<T>::Reshape(*mask, 1);
auto place = context.GetEigenDevice<Place>();
int size = framework::product(mask->dims());
if (context.Attr<int>("is_training") == 1) {
auto* mask = context.Output<Tensor>("Mask");
auto* mask_data = mask->mutable_data<T>(context.GetPlace());
int size = framework::product(mask->dims());
int seed = context.Attr<int>("seed");
thrust::counting_iterator<unsigned int> index_sequence_begin(0);
thrust::transform(index_sequence_begin, index_sequence_begin + size,
thrust::device_ptr<T>(mask_data),
MaskGenerator<T, AttrType>(dropout_prob, seed));
auto M = EigenMatrix<T>::Reshape(*mask, 1);
Y.device(place) = X * M;
} else {
cudaMemset(mask_data, 0, sizeof(T) * size);
Y.device(place) = X * dropout_prob;
}
}

12
paddle/operators/dropout_op.h
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@ -31,14 +31,13 @@ class CPUDropoutKernel : public framework::OpKernel {
void Compute(const framework::ExecutionContext& context) const override {
auto* x = context.Input<Tensor>("X");
auto* y = context.Output<Tensor>("Out");
auto* mask = context.Output<Tensor>("Mask");
auto* mask_data = mask->mutable_data<T>(context.GetPlace());
auto* y_data = y->mutable_data<T>(context.GetPlace());
const auto* x_data = x->data<T>();
auto* y_data = y->mutable_data<T>(context.GetPlace());
AttrType dropout_prob = context.Attr<AttrType>("dropout_prob");
if (context.Attr<int>("is_training") == 1) {
auto* mask = context.Output<Tensor>("Mask");
auto* mask_data = mask->mutable_data<T>(context.GetPlace());
int seed = context.Attr<int>("seed");
std::minstd_rand engine;
engine.seed(seed);
@ -54,8 +53,6 @@ class CPUDropoutKernel : public framework::OpKernel {
}
}
} else {
size_t size = framework::product(mask->dims());
memset(mask_data, 0, sizeof(T) * size);
auto X = EigenMatrix<T>::Reshape(*x, 1);
auto Y = EigenMatrix<T>::Reshape(*y, 1);
auto place = context.GetEigenDevice<Place>();
@ -69,7 +66,8 @@ class DropoutGradKernel : public framework::OpKernel {
public:
void Compute(const framework::ExecutionContext& context) const override {
PADDLE_ENFORCE_EQ(context.Attr<int>("is_training"), 1,
"Only callable when is_training is true");
"GradOp is only callable when is_training is true");
auto* grad_x = context.Output<Tensor>(framework::GradVarName("X"));
auto* grad_y = context.Input<Tensor>(framework::GradVarName("Out"));
auto* mask = context.Input<Tensor>("Mask");

10
python/paddle/v2/framework/tests/test_dropout_op.py
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@ -38,10 +38,7 @@ class TestDropoutOp4(OpTest):
self.op_type = "dropout"
self.inputs = {'X': np.random.random((32, 64)).astype("float32")}
self.attrs = {'dropout_prob': 0.35, 'is_training': 0}
self.outputs = {
'Out': self.inputs['X'] * self.attrs['dropout_prob'],
'Mask': np.zeros((32, 64))
}
self.outputs = {'Out': self.inputs['X'] * self.attrs['dropout_prob']}
def test_check_output(self):
self.check_output()
@ -52,10 +49,7 @@ class TestDropoutOp5(OpTest):
self.op_type = "dropout"
self.inputs = {'X': np.random.random((32, 64, 3)).astype("float32")}
self.attrs = {'dropout_prob': 0.75, 'is_training': 0}
self.outputs = {
'Out': self.inputs['X'] * self.attrs['dropout_prob'],
'Mask': np.zeros((32, 64, 3))
}
self.outputs = {'Out': self.inputs['X'] * self.attrs['dropout_prob']}
def test_check_output(self):
self.check_output()

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