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Fix a bug causing wrong gradient results in cos_sim op.

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fix_3908
Xinghai Sun 8 years ago
parent ed72af48ce
commit 91215bcef9
4 changed files with 71 additions and 44 deletions
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32
paddle/operators/cos_sim_op.cc
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@ -25,14 +25,16 @@ class CosSimOp : public framework::OperatorWithKernel {
protected:
void InferShape(const framework::InferShapeContext &ctx) const override {
PADDLE_ENFORCE_NOT_NULL(ctx.InputVar("X"), "Input(X) should not be null.");
PADDLE_ENFORCE_NOT_NULL(ctx.InputVar("Y"), "Input(Y) should not be null.");
PADDLE_ENFORCE_NOT_NULL(ctx.InputVar("X"), "Input(X) must not be null.");
PADDLE_ENFORCE_NOT_NULL(ctx.InputVar("Y"), "Input(Y) must not be null.");
PADDLE_ENFORCE_EQ(ctx.Input<Tensor>("X")->dims(),
ctx.Input<Tensor>("Y")->dims(),
"Dimensions of Input(X) and Input(Y) must be the same.");
auto dims = ctx.Input<Tensor>("X")->dims();
ctx.Output<Tensor>("Out")->Resize({dims[0], 1});
ctx.Output<Tensor>("XNorm")->Resize({dims[0], 1});
ctx.Output<Tensor>("YNorm")->Resize({dims[0], 1});
}
};
@ -43,6 +45,9 @@ class CosSimOpMaker : public framework::OpProtoAndCheckerMaker {
AddInput("X", "The first input of cos_sim op.");
AddInput("Y", "The second input of cos_sim op.");
AddOutput("Out", "The output of cos_sim op.");
AddOutput("XNorm", "Row norm of the first input.").AsIntermediate();
AddOutput("YNorm", "Row norm of the second input.").AsIntermediate();
AddComment(R"DOC(
Cosine Similarity Operator.
@ -57,20 +62,31 @@ class CosSimOpGrad : public framework::OperatorWithKernel {
protected:
void InferShape(const framework::InferShapeContext &ctx) const override {
PADDLE_ENFORCE_NOT_NULL(ctx.InputVar("X"), "Input(X) should not be null.");
PADDLE_ENFORCE_NOT_NULL(ctx.InputVar("Y"), "Input(Y) should not be null.");
PADDLE_ENFORCE_NOT_NULL(ctx.InputVar("X"), "Input(X) must not be null.");
PADDLE_ENFORCE_NOT_NULL(ctx.InputVar("Y"), "Input(Y) must not be null.");
PADDLE_ENFORCE_NOT_NULL(ctx.InputVar("XNorm"),
"Input(XNorm) must not be null.");
PADDLE_ENFORCE_NOT_NULL(ctx.InputVar("YNorm"),
"Input(YNorm) must not be null.");
PADDLE_ENFORCE_NOT_NULL(ctx.InputVar(framework::GradVarName("Out")),
"Input(Out@GRAD) should not be null.");
"Input(Out@GRAD) must not be null.");
auto x_dims = ctx.Input<Tensor>("X")->dims();
auto y_dims = ctx.Input<Tensor>("Y")->dims();
auto xnorm_dims = ctx.Input<Tensor>("XNorm")->dims();
auto ynorm_dims = ctx.Input<Tensor>("YNorm")->dims();
auto out_dims = ctx.Input<Tensor>(framework::GradVarName("Out"))->dims();
PADDLE_ENFORCE_EQ(x_dims, y_dims,
"Dimensions of Input(X) and Input(Y) must be the same.");
PADDLE_ENFORCE_EQ(xnorm_dims[0], x_dims[0],
"1st dimension of XNorm must equal that of Input(X).");
PADDLE_ENFORCE_EQ(xnorm_dims[1], 1, "2st dimension of XNorm must be one.");
PADDLE_ENFORCE_EQ(ynorm_dims[0], y_dims[0],
"1st dimension of YNorm must equal that of Input(Y).");
PADDLE_ENFORCE_EQ(ynorm_dims[1], 1, "2st dimension of YNorm must be one.");
PADDLE_ENFORCE_EQ(out_dims[0], x_dims[0],
"1st dimension of Out@GRAD must equal to Input(X)");
PADDLE_ENFORCE_EQ(out_dims[1], 1,
"1st dimension of Out@GRAD must equal to Input(X)");
"1st dimension of Out@GRAD must equal that of Input(X)");
PADDLE_ENFORCE_EQ(out_dims[1], 1, "1st dimension of Out@GRAD must be one.");
auto *x_grad = ctx.Output<Tensor>(framework::GradVarName("X"));
auto *y_grad = ctx.Output<Tensor>(framework::GradVarName("Y"));

34
paddle/operators/cos_sim_op.h
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@ -31,21 +31,27 @@ class CosSimKernel : public framework::OpKernel {
auto* x = context.Input<Tensor>("X");
auto* y = context.Input<Tensor>("Y");
auto* z = context.Output<Tensor>("Out");
auto* x_norm = context.Output<Tensor>("XNorm");
auto* y_norm = context.Output<Tensor>("YNorm");
z->mutable_data<T>(context.GetPlace());
x_norm->mutable_data<T>(context.GetPlace());
y_norm->mutable_data<T>(context.GetPlace());
auto dims = x->dims();
int size = static_cast<int>(framework::product(dims));
auto new_dims = framework::make_ddim({dims[0], size / dims[0]});
auto X = EigenMatrix<T>::From(*x, new_dims);
auto Y = EigenMatrix<T>::From(*y, new_dims);
auto Z = EigenMatrix<T>::From(*z, new_dims);
auto Z = EigenMatrix<T>::From(*z);
auto XNorm = EigenMatrix<T>::From(*x_norm);
auto YNorm = EigenMatrix<T>::From(*y_norm);
auto XY = (X * Y).sum(Eigen::array<int, 1>({1}));
auto XX = (X * X).sum(Eigen::array<int, 1>({1}));
auto YY = (Y * Y).sum(Eigen::array<int, 1>({1}));
auto place = context.GetEigenDevice<Place>();
Z.device(place) = XY / XX.sqrt() / YY.sqrt();
auto XY = (X * Y).sum(Eigen::array<int, 1>({1}));
XNorm.device(place) = (X * X).sum(Eigen::array<int, 1>({1})).sqrt();
YNorm.device(place) = (Y * Y).sum(Eigen::array<int, 1>({1})).sqrt();
Z.device(place) = XY / XNorm / YNorm;
}
};
@ -56,6 +62,8 @@ class CosSimGradKernel : public framework::OpKernel {
auto* x = context.Input<Tensor>("X");
auto* y = context.Input<Tensor>("Y");
auto* z = context.Input<Tensor>("Out");
auto* x_norm = context.Input<Tensor>("XNorm");
auto* y_norm = context.Input<Tensor>("YNorm");
auto* grad_x = context.Output<Tensor>(framework::GradVarName("X"));
auto* grad_y = context.Output<Tensor>(framework::GradVarName("Y"));
auto* grad_z = context.Input<Tensor>(framework::GradVarName("Out"));
@ -69,23 +77,23 @@ class CosSimGradKernel : public framework::OpKernel {
auto X = EigenMatrix<T>::From(*x, new_dims);
auto Y = EigenMatrix<T>::From(*y, new_dims);
auto Z = EigenMatrix<T>::From(*z);
auto X_norm = EigenMatrix<T>::From(*x_norm);
auto Y_norm = EigenMatrix<T>::From(*y_norm);
auto dX = EigenMatrix<T>::From(*grad_x, new_dims);
auto dY = EigenMatrix<T>::From(*grad_y, new_dims);
auto dZ = EigenMatrix<T>::From(*grad_z);
auto XX = (X * X).sum(Eigen::array<int, 1>({1}));
auto YY = (Y * Y).sum(Eigen::array<int, 1>({1}));
Eigen::DSizes<int, 2> bcast(1, dims[1]);
auto denominator_bcast = (XX.sqrt() * YY.sqrt()).broadcast(bcast);
Eigen::DSizes<int, 2> bcast(1, new_dims[1]);
auto Z_bcast = Z.broadcast(bcast);
auto dZ_bcast = dZ.broadcast(bcast);
auto place = context.GetEigenDevice<Place>();
auto X_snorm_bcast = X_norm.square().eval().broadcast(bcast);
auto Y_snorm_bcast = Y_norm.square().eval().broadcast(bcast);
auto norm_prod_bcast = (X_norm * Y_norm).eval().broadcast(bcast);
dX.device(place) =
dZ_bcast * (Y / denominator_bcast - Z_bcast * X / XX.broadcast(bcast));
dZ_bcast * (Y / norm_prod_bcast - Z_bcast * X / X_snorm_bcast);
dY.device(place) =
dZ_bcast * (X / denominator_bcast - Z_bcast * Y / YY.broadcast(bcast));
// dX.device(place) = X;
// Y.device(place) = Y;
dZ_bcast * (X / norm_prod_bcast - Z_bcast * Y / Y_snorm_bcast);
}
};

7
python/paddle/v2/framework/tests/gradient_checker.py
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@ -304,13 +304,6 @@ class GradientChecker(unittest.TestCase):
# get analytical gradients according to different device
analytic_grads = self.__get_gradient(forward_op, backward_op,
input_vars, check_names, place)
#print(numeric_grads[0], numeric_grads[0].shape)
print("dim0: ", numeric_grads[0], numeric_grads[0].shape)
print("dim0: ", analytic_grads[0], analytic_grads[0].shape)
print("---------------------")
print("dim1: ", numeric_grads[1], numeric_grads[1].shape)
print("dim1: ", analytic_grads[1], analytic_grads[1].shape)
assert False
self.__assert_is_close(numeric_grads, analytic_grads, check_names,
max_relative_error,
"Gradient Check On %s" % str(place))

42
python/paddle/v2/framework/tests/test_cos_sim_op.py
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@ -10,30 +10,40 @@ class TestCosSimOp(unittest.TestCase):
def setUp(self):
self.type = "cos_sim"
self.inputs = {
'X': np.random.random((32, 84)).astype("float32"),
'Y': np.random.random((32, 84)).astype("float32")
'X': np.random.random((32, 64)).astype("float32"),
'Y': np.random.random((32, 64)).astype("float32")
}
expect_x_norm = np.linalg.norm(self.inputs['X'], axis=1)
expect_y_norm = np.linalg.norm(self.inputs['Y'], axis=1)
expect_out = (self.inputs['X'] * self.inputs['Y']).sum(axis=1) / \
expect_x_norm / expect_y_norm
self.outputs = {
'XNorm': np.expand_dims(expect_x_norm, 1),
'YNorm': np.expand_dims(expect_y_norm, 1),
'Out': np.expand_dims(expect_out, 1)
}
expect = (self.inputs['X'] * self.inputs['Y']).sum(axis=1) / \
np.linalg.norm(self.inputs['X'], axis=1) / \
np.linalg.norm(self.inputs['Y'], axis=1)
expect = np.expand_dims(expect, 1)
self.outputs = {'Out': expect}
class CosSimGradOpTest(GradientChecker):
def test_cos_sim(self):
def test_cos_sim_2d(self):
op = create_op("cos_sim")
inputs = {
'X': np.random.random((10, 5)).astype("float32"),
'Y': np.random.random((10, 5)).astype("float32")
}
self.compare_grad(op, inputs)
self.check_grad(
op, inputs, set(["X", "Y"]), "Out", max_relative_error=0.05)
def test_cos_sim_3d(self):
op = create_op("cos_sim")
#inputs = {
#'X': np.random.random((2, 2)).astype("float32"),
#'Y': np.random.random((2, 2)).astype("float32")
#}
inputs = {
'X': np.array([[0.9, 0.6], [1.9, 1.6]]).astype("float32"),
'Y': np.array([[0.7, 0.8], [1.7, 1.8]]).astype("float32")
'X': np.random.random((10, 5, 2)).astype("float32"),
'Y': np.random.random((10, 5, 2)).astype("float32")
}
print(inputs)
self.compare_grad(op, inputs)
self.check_grad(
op, inputs, set(["X", "Y"]), "Out", max_relative_error=0.5)
op, inputs, set(["X", "Y"]), "Out", max_relative_error=0.05)
if __name__ == '__main__':

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