Add broadcasting support (e.g. matrix-vector) for cos sim operator.

paddle/operators/cos_sim_op.cc

@@ -25,16 +25,29 @@ class CosSimOp : public framework::OperatorWithKernel {
 
  protected:
   void InferShape(const framework::InferShapeContext &ctx) const override {
+    // notnull check
     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});
+
+    // shape check
+    auto x_dims = ctx.Input<Tensor>("X")->dims();
+    auto y_dims = ctx.Input<Tensor>("Y")->dims();
+    PADDLE_ENFORCE_EQ(framework::arity(x_dims), framework::arity(y_dims),
+                      "Ranks of Input(X) and Input(Y) must be equal.");
+    PADDLE_ENFORCE_GE(framework::arity(x_dims), 2,
+                      "Rank of Input(X) must not be less than 2.");
+    PADDLE_ENFORCE_EQ(
+        framework::slice_ddim(x_dims, 1, framework::arity(x_dims)),
+        framework::slice_ddim(y_dims, 1, framework::arity(y_dims)),
+        "All dimensions except 1st of Input(X) and Input(Y) must be equal.");
+    PADDLE_ENFORCE(x_dims[0] == y_dims[0] || y_dims[0] == 1,
+                   "1st dimension of Input(Y) must be equal to Input(X) or "
+                   "just 1 (which will be broadcasted to match Input(X)).");
+
+    // resize tensor
+    ctx.Output<Tensor>("Out")->Resize({x_dims[0], 1});
+    ctx.Output<Tensor>("XNorm")->Resize({x_dims[0], 1});
+    ctx.Output<Tensor>("YNorm")->Resize({y_dims[0], 1});
   }
 };
 
@@ -42,8 +55,8 @@ class CosSimOpMaker : public framework::OpProtoAndCheckerMaker {
  public:
   CosSimOpMaker(framework::OpProto *proto, framework::OpAttrChecker *op_checker)
       : OpProtoAndCheckerMaker(proto, op_checker) {
-    AddInput("X", "The first input of cos_sim op.");
-    AddInput("Y", "The second input of cos_sim op.");
+    AddInput("X", "The 1st input of cos_sim op.");
+    AddInput("Y", "The 2nd 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();
@@ -51,7 +64,12 @@ class CosSimOpMaker : public framework::OpProtoAndCheckerMaker {
     AddComment(R"DOC(
 Cosine Similarity Operator.
 
-The equation is: Out = X^T * Y / (sqrt(X^T * X) * sqrt(Y^T * Y))
+The equation is: Out = X^T * Y / (sqrt(X^T * X) * sqrt(Y^T * Y)).
+
+Input(X) and Input(Y) must have the same shape, except that the 1st dimension
+of Input(Y) could be just 1 (different from Input(X)), which will be
+broadcasted to match the shape of Input(X) before computing their cosine
+similarity.
 )DOC");
   }
 };
@@ -62,32 +80,47 @@ class CosSimOpGrad : public framework::OperatorWithKernel {
 
  protected:
   void InferShape(const framework::InferShapeContext &ctx) const override {
+    // notnull check
     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("Out"),
+                            "Input(Out) must not be null.");
     PADDLE_ENFORCE_NOT_NULL(ctx.InputVar(framework::GradVarName("Out")),
                             "Input(Out@GRAD) must not be null.");
 
+    // shape check
     auto x_dims = ctx.Input<Tensor>("X")->dims();
     auto y_dims = ctx.Input<Tensor>("Y")->dims();
+    PADDLE_ENFORCE_GE(framework::arity(x_dims), framework::arity(y_dims),
+                      "Ranks of Input(X) and Input(Y) must be equal.");
+    PADDLE_ENFORCE_GE(framework::arity(x_dims), 2,
+                      "Rank of Input(X) must not be less than 2.");
+    PADDLE_ENFORCE_EQ(
+        framework::slice_ddim(x_dims, 1, framework::arity(x_dims)),
+        framework::slice_ddim(y_dims, 1, framework::arity(y_dims)),
+        "All dimensions except 1st of Input(X) and Input(Y) must be equal.");
+    PADDLE_ENFORCE(x_dims[0] == y_dims[0] || y_dims[0] == 1,
+                   "1st dimension of Input(Y) must be equal to Input(X) or "
+                   "just 1 (which will be broadcasted to match Input(X)).");
     auto xnorm_dims = ctx.Input<Tensor>("XNorm")->dims();
+    PADDLE_ENFORCE_EQ(xnorm_dims, framework::make_ddim({x_dims[0], 1}),
+                      "Shape of Input(XNorm) must be [X.Dim(0), 1].");
     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 that of Input(X)");
-    PADDLE_ENFORCE_EQ(out_dims[1], 1, "1st dimension of Out@GRAD must be one.");
-
+    PADDLE_ENFORCE_EQ(ynorm_dims, framework::make_ddim({y_dims[0], 1}),
+                      "Shape of Input(YNorm) must be [Y.Dim(0), 1].");
+    auto out_dims = ctx.Input<Tensor>("Out")->dims();
+    PADDLE_ENFORCE_EQ(out_dims, framework::make_ddim({x_dims[0], 1}),
+                      "Shape of Input(Out) must be [X.Dim(0), 1].");
+    auto out_grad_dims =
+        ctx.Input<Tensor>(framework::GradVarName("Out"))->dims();
+    PADDLE_ENFORCE_EQ(out_grad_dims, framework::make_ddim({x_dims[0], 1}),
+                      "Shape of Input(Out@Grad) must be [X.Dim(0), 1].");
+
+    // resize tensor
     auto *x_grad = ctx.Output<Tensor>(framework::GradVarName("X"));
     auto *y_grad = ctx.Output<Tensor>(framework::GradVarName("Y"));
     if (x_grad) x_grad->Resize(x_dims);

paddle/operators/cos_sim_op.h

@@ -28,30 +28,38 @@ template <typename Place, typename T>
 class CosSimKernel : public framework::OpKernel {
  public:
   void Compute(const framework::ExecutionContext& context) const override {
-    auto* input_x = context.Input<Tensor>("X");
-    auto* input_y = context.Input<Tensor>("Y");
-    auto* output_z = context.Output<Tensor>("Out");
-    auto* output_x_norm = context.Output<Tensor>("XNorm");
-    auto* output_y_norm = context.Output<Tensor>("YNorm");
+    // get Tensor
+    auto* in_x = context.Input<Tensor>("X");
+    auto* in_y = context.Input<Tensor>("Y");
+    auto* out_z = context.Output<Tensor>("Out");
+    auto* out_x_norm = context.Output<Tensor>("XNorm");
+    auto* out_y_norm = context.Output<Tensor>("YNorm");
+    out_z->mutable_data<T>(context.GetPlace());
+    out_x_norm->mutable_data<T>(context.GetPlace());
+    out_y_norm->mutable_data<T>(context.GetPlace());
 
-    output_z->mutable_data<T>(context.GetPlace());
-    output_x_norm->mutable_data<T>(context.GetPlace());
-    output_y_norm->mutable_data<T>(context.GetPlace());
-
-    auto dims = input_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(*input_x, new_dims);
-    auto y = EigenMatrix<T>::From(*input_y, new_dims);
-    auto z = EigenMatrix<T>::From(*output_z);
-    auto x_norm = EigenMatrix<T>::From(*output_x_norm);
-    auto y_norm = EigenMatrix<T>::From(*output_y_norm);
+    // convert Tensor to Eigen Tensor
+    int rows_x = in_x->dims()[0];
+    int rows_y = in_y->dims()[0];
+    int cols = framework::product(in_x->dims()) / rows_x;
+    auto x = EigenMatrix<T>::From(*in_x, framework::make_ddim({rows_x, cols}));
+    auto y = EigenMatrix<T>::From(*in_y, framework::make_ddim({rows_y, cols}));
+    auto z = EigenMatrix<T>::From(*out_z);
+    auto x_norm = EigenMatrix<T>::From(*out_x_norm);
+    auto y_norm = EigenMatrix<T>::From(*out_y_norm);
 
+    // compute
     auto place = context.GetEigenDevice<Place>();
-    auto xy = (x * y).sum(Eigen::array<int, 1>({1}));
     x_norm.device(place) = x.square().sum(Eigen::array<int, 1>({1})).sqrt();
     y_norm.device(place) = y.square().sum(Eigen::array<int, 1>({1})).sqrt();
-    z.device(place) = xy / x_norm / y_norm;
+    if (rows_x == rows_y) {
+      auto xy = (x * y).sum(Eigen::array<int, 1>({1}));
+      z.device(place) = xy / x_norm / y_norm;
+    } else {
+      Eigen::DSizes<int, 2> bcast(rows_x, 1);
+      auto xy = (x * y.broadcast(bcast)).sum(Eigen::array<int, 1>({1}));
+      z.device(place) = xy / x_norm / y_norm.broadcast(bcast);
+    }
   }
 };
 
@@ -59,43 +67,75 @@ template <typename Place, typename T>
 class CosSimGradKernel : public framework::OpKernel {
  public:
   void Compute(const framework::ExecutionContext& context) const override {
-    auto* input_x = context.Input<Tensor>("X");
-    auto* input_y = context.Input<Tensor>("Y");
-    auto* input_z = context.Input<Tensor>("Out");
-    auto* input_x_norm = context.Input<Tensor>("XNorm");
-    auto* input_y_norm = context.Input<Tensor>("YNorm");
-    auto* output_grad_x = context.Output<Tensor>(framework::GradVarName("X"));
-    auto* output_grad_y = context.Output<Tensor>(framework::GradVarName("Y"));
-    auto* input_grad_z = context.Input<Tensor>(framework::GradVarName("Out"));
+    // get Tensor
+    auto* in_x = context.Input<Tensor>("X");
+    auto* in_y = context.Input<Tensor>("Y");
+    auto* in_z = context.Input<Tensor>("Out");
+    auto* in_x_norm = context.Input<Tensor>("XNorm");
+    auto* in_y_norm = context.Input<Tensor>("YNorm");
+    auto* out_grad_x = context.Output<Tensor>(framework::GradVarName("X"));
+    auto* out_grad_y = context.Output<Tensor>(framework::GradVarName("Y"));
+    auto* in_grad_z = context.Input<Tensor>(framework::GradVarName("Out"));
 
-    auto dims = input_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(*input_x, new_dims);
-    auto y = EigenMatrix<T>::From(*input_y, new_dims);
-    auto z = EigenMatrix<T>::From(*input_z);
-    auto x_norm = EigenMatrix<T>::From(*input_x_norm);
-    auto y_norm = EigenMatrix<T>::From(*input_y_norm);
-    auto dz = EigenMatrix<T>::From(*input_grad_z);
+    // convert Tensor to Eigen Tensor
+    int rows_x = in_x->dims()[0];
+    int rows_y = in_y->dims()[0];
+    int cols = framework::product(in_x->dims()) / rows_x;
+    auto x = EigenMatrix<T>::From(*in_x, framework::make_ddim({rows_x, cols}));
+    auto y = EigenMatrix<T>::From(*in_y, framework::make_ddim({rows_y, cols}));
+    auto z = EigenMatrix<T>::From(*in_z);
+    auto x_norm = EigenMatrix<T>::From(*in_x_norm);
+    auto y_norm = EigenMatrix<T>::From(*in_y_norm);
+    auto dz = EigenMatrix<T>::From(*in_grad_z);
 
-    Eigen::DSizes<int, 2> bcast(1, new_dims[1]);
+    // compute gradident
+    Eigen::DSizes<int, 2> bcast(1, cols);
     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);
-    if (output_grad_x) {
-      output_grad_x->mutable_data<T>(context.GetPlace());
-      auto dx = EigenMatrix<T>::From(*output_grad_x, new_dims);
-      dx.device(place) =
-          dz_bcast * (y / norm_prod_bcast - z_bcast * x / x_snorm_bcast);
-    }
-    if (output_grad_y) {
-      output_grad_y->mutable_data<T>(context.GetPlace());
-      auto dy = EigenMatrix<T>::From(*output_grad_y, new_dims);
-      dy.device(place) =
-          dz_bcast * (x / norm_prod_bcast - z_bcast * y / y_snorm_bcast);
+    auto place = context.GetEigenDevice<Place>();
+    if (rows_x == rows_y) {
+      auto y_snorm_bcast = y_norm.square().eval().broadcast(bcast);
+      auto norm_prod_bcast = (x_norm * y_norm).eval().broadcast(bcast);
+      // compute dx
+      if (out_grad_x) {
+        out_grad_x->mutable_data<T>(context.GetPlace());
+        auto dx = EigenMatrix<T>::From(*out_grad_x,
+                                       framework::make_ddim({rows_x, cols}));
+        auto grad = y / norm_prod_bcast - z_bcast * x / x_snorm_bcast;
+        dx.device(place) = dz_bcast * grad;
+      }
+      // compute dy
+      if (out_grad_y) {
+        out_grad_y->mutable_data<T>(context.GetPlace());
+        auto dy = EigenMatrix<T>::From(*out_grad_y,
+                                       framework::make_ddim({rows_y, cols}));
+        auto grad = x / norm_prod_bcast - z_bcast * y / y_snorm_bcast;
+        dy.device(place) = dz_bcast * grad;
+      }
+    } else {
+      Eigen::DSizes<int, 2> bcast_row(rows_x, 1);
+      auto y_bcast =  y.broadcast(bcast_row);
+      auto y_snorm_bcast =
+          y_norm.square().eval().broadcast(bcast_row).eval().broadcast(bcast);
+      auto norm_prod_bcast =
+          (x_norm * y_norm.broadcast(bcast_row)).eval().broadcast(bcast);
+      // compute dx
+      if (out_grad_x) {
+        out_grad_x->mutable_data<T>(context.GetPlace());
+        auto dx = EigenMatrix<T>::From(
+          *out_grad_x, framework::make_ddim({rows_x, cols}));
+        auto grad = y_bcast / norm_prod_bcast - z_bcast * x / x_snorm_bcast;
+        dx.device(place) = dz_bcast * grad;
+      }
+      // compute dy
+      if (out_grad_y) {
+        out_grad_y->mutable_data<T>(context.GetPlace());
+        auto dy = EigenMatrix<T>::From(
+          *out_grad_y, framework::make_ddim({rows_y, cols}));
+        auto grad = x / norm_prod_bcast - z_bcast * y_bcast / y_snorm_bcast;
+        dy.device(place) = (dz_bcast * grad).sum(Eigen::array<int, 1>({0}));
+      }
     }
   }
 };

python/paddle/v2/framework/tests/test_cos_sim_op.py

@@ -4,7 +4,7 @@ from gradient_checker import GradientChecker, create_op
 from op_test_util import OpTestMeta
 
 
-class TestCosSimOp(unittest.TestCase):
+class TestCosSimOpWithRank2(unittest.TestCase):
     __metaclass__ = OpTestMeta
 
     def setUp(self):
@@ -24,12 +24,72 @@ class TestCosSimOp(unittest.TestCase):
         }
 
 
+class TestCosSimOpWithRank2Bcast(unittest.TestCase):
+    __metaclass__ = OpTestMeta
+
+    def setUp(self):
+        self.type = "cos_sim"
+        self.inputs = {
+            'X': np.random.random((32, 64)).astype("float32"),
+            'Y': np.random.random((1, 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)
+        }
+
+
+class TestCosSimOpWithRank3(unittest.TestCase):
+    __metaclass__ = OpTestMeta
+
+    def setUp(self):
+        self.type = "cos_sim"
+        self.inputs = {
+            'X': np.random.random((32, 64, 10)).astype("float32"),
+            'Y': np.random.random((32, 64, 10)).astype("float32")
+        }
+        expect_x_norm = np.linalg.norm(self.inputs['X'], axis=(1, 2))
+        expect_y_norm = np.linalg.norm(self.inputs['Y'], axis=(1, 2))
+        expect_out = (self.inputs['X'] * self.inputs['Y']).sum(axis=(1, 2)) / \
+            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)
+        }
+
+
+class TestCosSimOpWithRank3Bcast(unittest.TestCase):
+    __metaclass__ = OpTestMeta
+
+    def setUp(self):
+        self.type = "cos_sim"
+        self.inputs = {
+            'X': np.random.random((32, 64, 10)).astype("float32"),
+            'Y': np.random.random((1, 64, 10)).astype("float32")
+        }
+        expect_x_norm = np.linalg.norm(self.inputs['X'], axis=(1, 2))
+        expect_y_norm = np.linalg.norm(self.inputs['Y'], axis=(1, 2))
+        expect_out = (self.inputs['X'] * self.inputs['Y']).sum(axis=(1, 2)) / \
+            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)
+        }
+
+
 class TestCosSimGradOp(GradientChecker):
     def setUp(self):
         self.op = create_op("cos_sim")
         self.inputs = {
-            'X': np.random.random((10, 5)).astype("float32"),
-            'Y': np.random.random((10, 5)).astype("float32")
+            'X': np.random.random((6, 5)).astype("float32"),
+            'Y': np.random.random((6, 5)).astype("float32")
         }
 
     def test_cpu_gpu_compare(self):
@@ -56,5 +116,32 @@ class TestCosSimGradOp(GradientChecker):
             no_grad_set={"Y"})
 
 
+class TestCosSimGradOpWithRank2Bcast(TestCosSimGradOp):
+    def setUp(self):
+        self.op = create_op("cos_sim")
+        self.inputs = {
+            'X': np.random.random((6, 5)).astype("float32"),
+            'Y': np.random.random((1, 5)).astype("float32")
+        }
+
+
+class TestCosSimGradOpWithRank3(TestCosSimGradOp):
+    def setUp(self):
+        self.op = create_op("cos_sim")
+        self.inputs = {
+            'X': np.random.random((6, 5, 2)).astype("float32"),
+            'Y': np.random.random((6, 5, 2)).astype("float32")
+        }
+
+
+class TestCosSimGradOpWithRank3Bcast(TestCosSimGradOp):
+    def setUp(self):
+        self.op = create_op("cos_sim")
+        self.inputs = {
+            'X': np.random.random((6, 5, 2)).astype("float32"),
+            'Y': np.random.random((1, 5, 2)).astype("float32")
+        }
+
+
 if __name__ == '__main__':
     unittest.main()