clang-format comments: /src/ngraph/op/util (#3498)

* New clang-format rules for src/ngraph/op/util

* Restore root .clang-format

* Remove redundant .clang-formats

* Revert "Remove redundant .clang-formats"

This reverts commit e158df1e507570b55348ce326fd7e8b4ae819f36.

* Remove redundant .clang-formats

.clang-format

@@ -24,7 +24,6 @@ BreakBeforeBraces: Allman
 BreakConstructorInitializersBeforeComma: true
 
 ColumnLimit: 100
-CommentPragmas: '.*'
 
 IndentCaseLabels: false
 IndentWrappedFunctionNames: true

src/.clang-format

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-  - Regex:           '^".*'
-    Priority:        3
-  - Regex:           '^<.*'
-    Priority:        2
-SortIncludes: true

src/ngraph/op/util/arithmetic_reduction.hpp

@@ -24,8 +24,9 @@ namespace ngraph
     {
         namespace util
         {
-            /// \brief Abstract base class for arithmetic reduction operations, i.e., operations where chosen axes of the input tensors
-            ///        are eliminated (reduced out) by repeated application of a particular binary arithmetic operation.
+            /// \brief Abstract base class for arithmetic reduction operations, i.e., operations
+            ///        where chosen axes of the input tensors are eliminated (reduced out) by
+            ///        repeated application of a particular binary arithmetic operation.
             class ArithmeticReduction : public Op
             {
             protected:

src/ngraph/op/util/binary_elementwise_arithmetic.hpp

@@ -25,12 +25,17 @@ namespace ngraph
     {
         namespace util
         {
-            /// \brief Abstract base class for elementwise binary arithmetic operations, i.e., operations where the same
-            ///        scalar binary arithmetic operation is applied to each corresponding pair of elements in the two
-            ///        input tensors. Implicit broadcast of input tensors is supported through one of the AutoBroadcast modes
+            // clang-format off
+            /// \brief Abstract base class for elementwise binary arithmetic operations, i.e.,
+            ///        operations where the same scalar binary arithmetic operation is applied to
+            ///        each corresponding pair of elements in the two input tensors. Implicit
+            ///        broadcast of input tensors is supported through one of the AutoBroadcast
+            ///        modes.
             ///
-            /// For example, if the underlying arithmetic operation (determined by the subclass) is \f$\mathit{op}(x,y)\f$, the input tensors
-            /// \f$[[x_0,y_0],[z_0,w_0]]\f$ and \f$[[x_1,y_1],[z_1,w_1]]\f$ will be mapped to \f$[[\mathit{op}(x_0,x_1),\mathit{op}(y_0,y_1)],[\mathit{op}(z_0,z_1),\mathit{op}(w_0,w_1)]]\f$.
+            /// For example, if the underlying arithmetic operation (determined by the subclass) is
+            /// \f$\mathit{op}(x,y)\f$, the input tensors
+            /// \f$[[x_0,y_0],[z_0,w_0]]\f$ and \f$[[x_1,y_1],[z_1,w_1]]\f$ will be mapped to
+            /// \f$[[\mathit{op}(x_0,x_1),\mathit{op}(y_0,y_1)],[\mathit{op}(z_0,z_1),\mathit{op}(w_0,w_1)]]\f$.
             ///
             /// ## Inputs
             ///
@@ -43,8 +48,9 @@ namespace ngraph
             /// ## Output
             ///
             /// | Type                   | Description                                                                                                                                                                                                                      |
-            /// | ---------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ |
+            /// | ---------------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
             /// | \f$N[d_1,\dots,d_n]\f$ | The tensor \f$T\f$, where \f$T[i_1,\dots,i_n] = \mathit{op}(\texttt{arg0}[i_1,\dots,i_n],\texttt{arg1}[i_1,\dots,i_n])\f$. This will always have the same shape and element type as the input tensors (after auto broadcasting). |
+            // clang-format on
             class BinaryElementwiseArithmetic : public Op
             {
             protected:

src/ngraph/op/util/binary_elementwise_comparison.hpp

@@ -25,12 +25,17 @@ namespace ngraph
     {
         namespace util
         {
-            /// \brief Abstract base class for elementwise binary comparison operations, i.e., operations where the same
-            ///        scalar binary comparison operation is applied to each corresponding pair of elements in two
-            ///        input tensors. Implicit broadcast of input tensors is supported through one of the AutoBroadcast modes
+            // clang-format off
+            /// \brief Abstract base class for elementwise binary comparison operations, i.e.,
+            ///        operations where the same scalar binary comparison operation is applied to
+            ///        each corresponding pair of elements in two input tensors. Implicit
+            ///        broadcast of input tensors is supported through one of the AutoBroadcast
+            ///        modes.
             ///
-            /// For example, if the underlying comparison operation (determined by the subclass) is \f$\mathit{op}(x,y)\f$, the input tensors
-            /// \f$[[x_0,y_0],[z_0,w_0]]\f$ and \f$[[x_1,y_1],[z_1,w_1]]\f$ will be mapped to \f$[[\mathit{op}(x_0,x_1),\mathit{op}(y_0,y_1)],[\mathit{op}(z_0,z_1),\mathit{op}(w_0,w_1)]]\f$.
+            /// For example, if the underlying comparison operation (determined by the subclass) is
+            /// \f$\mathit{op}(x,y)\f$, the input tensors \f$[[x_0,y_0],[z_0,w_0]]\f$ and
+            /// \f$[[x_1,y_1],[z_1,w_1]]\f$ will be mapped to
+            /// \f$[[\mathit{op}(x_0,x_1),\mathit{op}(y_0,y_1)],[\mathit{op}(z_0,z_1),\mathit{op}(w_0,w_1)]]\f$.
             ///
             /// ## Inputs
             ///
@@ -45,6 +50,7 @@ namespace ngraph
             /// | Type                               | Description                                                                                                                                                                                                        |
             /// | ---------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ |
             /// | \f$\texttt{bool}[d_1,\dots,d_n]\f$ | The tensor \f$T\f$, where \f$T[i_1,\dots,i_n] = \mathit{op}(\texttt{arg0}[i_1,\dots,i_n],\texttt{arg1}[i_1,\dots,i_n])\f$. This will always have the same shape as the input tensors, and the element type `bool`. |
+            // clang-format on
             class BinaryElementwiseComparison : public Op
             {
             protected:

src/ngraph/op/util/binary_elementwise_logical.hpp

@@ -24,13 +24,17 @@ namespace ngraph
     {
         namespace util
         {
-            /// \brief Abstract base class for elementwise binary logical operations, i.e., operations where the same
-            ///        scalar binary logical operation is applied to each corresponding pair of elements in two
-            ///        boolean input tensors. Implicit broadcast of input tensors is supported through one of the AutoBroadcast modes
-            ///        boolean input tensors.
+            // clang-format off
+            /// \brief Abstract base class for elementwise binary logical operations, i.e.,
+            ///        operations where the same scalar binary logical operation is applied to
+            ///        each corresponding pair of elements in two boolean input tensors. Implicit
+            ///        broadcast of input tensors is supported through one of the AutoBroadcast
+            ///        modes.
             ///
-            /// For example, if the underlying operation (determined by the subclass) is \f$\mathit{op}(x,y)\f$, the input tensors
-            /// \f$[[x_0,y_0],[z_0,w_0]]\f$ and \f$[[x_1,y_1],[z_1,w_1]]\f$ will be mapped to \f$[[\mathit{op}(x_0,x_1),\mathit{op}(y_0,y_1)],[\mathit{op}(z_0,z_1),\mathit{op}(w_0,w_1)]]\f$.
+            /// For example, if the underlying operation (determined by the subclass) is
+            /// \f$\mathit{op}(x,y)\f$, the input tensors \f$[[x_0,y_0],[z_0,w_0]]\f$ and
+            /// \f$[[x_1,y_1],[z_1,w_1]]\f$ will be mapped to
+            /// \f$[[\mathit{op}(x_0,x_1),\mathit{op}(y_0,y_1)],[\mathit{op}(z_0,z_1),\mathit{op}(w_0,w_1)]]\f$.
             ///
             /// ## Inputs
             ///
@@ -45,6 +49,7 @@ namespace ngraph
             /// | Type                               | Description                                                                                                                                                                                                        |
             /// | ---------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ |
             /// | \f$\texttt{bool}[d_1,\dots,d_n]\f$ | The tensor \f$T\f$, where \f$T[i_1,\dots,i_n] = \mathit{op}(\texttt{arg0}[i_1,\dots,i_n],\texttt{arg1}[i_1,\dots,i_n])\f$. This will always have the same shape as the input tensors, and the element type `bool`. |
+            // clang-format on
             class BinaryElementwiseLogical : public Op
             {
             protected:

src/ngraph/op/util/broadcasting.cpp

@@ -60,9 +60,9 @@ static ngraph::Shape calculate_broadcast_shape(ngraph::Shape left_shape, ngraph:
 /// \brief Calculate the output shape of numpy-style broadcast operation for all input shapes.
 ///
 /// This function finds the maximum tensor shape that will be the result of element-wise operation
-/// that will be applied to the input shapes vector. The function also prepares the shape of each input
-/// for the element-wise operation by left-padding those shapes so that their rank is equal to
-/// the left_shape's rank.
+/// that will be applied to the input shapes vector. The function also prepares the shape of each
+/// input for the element-wise operation by left-padding those shapes so that their rank is equal
+/// to the left_shape's rank.
 ///
 /// \param input_shapes A vector of input shapes for which a common shape should be found
 /// \return A pair that contains the target shape as its first object and a vector of padded
@@ -421,7 +421,8 @@ namespace ngraph
         {
             std::vector<std::size_t> result(output_shape.size() - input_shape.size());
             // Populate the result vector with monotonic increasing series from 0 until
-            // output_shape_size, excluding values in range [start_match_axis, start_match_axis + input_shape.size()
+            // output_shape_size, excluding values in range:
+            // [start_match_axis, start_match_axis + input_shape.size()]
             std::iota(std::begin(result), std::begin(result) + start_match_axis, 0);
             std::iota(std::begin(result) + start_match_axis,
                       std::end(result),

src/ngraph/op/util/broadcasting.hpp

@@ -27,7 +27,8 @@ namespace ngraph
 {
     namespace op
     {
-        /// \brief Cast shape of all input nodes for an element-wise operation that requires shape-compatibility
+        /// \brief Cast shape of all input nodes for an element-wise operation that requires
+        ///        shape-compatibility
         ///
         /// \param inputs Original list of inputs
         ///
@@ -35,14 +36,16 @@ namespace ngraph
         NodeVector numpy_style_broadcast(const NodeVector& inputs)
             NGRAPH_DEPRECATED("Replace with numpy_style_broadcast_values");
 
-        /// \brief Cast shape of all input nodes for an element-wise operation that requires shape-compatibility
+        /// \brief Cast shape of all input nodes for an element-wise operation that requires
+        ///        shape-compatibility
         ///
         /// \param values Original list of inputs
         ///
         /// \return Numpy-style broadcasted list of nodes.
         OutputVector numpy_style_broadcast_values(const OutputVector& values);
 
-        /// \brief Cast shape of an output to the requested output shape using NumPy's broadcasting rules
+        /// \brief Cast shape of an output to the requested output shape using NumPy's broadcasting
+        ///        rules
         ///
         /// \param value original value
         /// \param shape requested output shape
@@ -50,7 +53,8 @@ namespace ngraph
         /// \return Broadcast output.
         std::shared_ptr<Node> numpy_style_broadcast(const Output<Node>& value, const Shape& shape);
 
-        /// \brief Cast shape of two outputs to make them compatible for an element-wise binary operation.
+        /// \brief Cast shape of two outputs to make them compatible for an element-wise binary
+        ///        operation.
         ///
         /// If necessary the right-hand-side argument will be broadcast to match the shape
         /// of left-hand-side argument. The starting of the mutually equal shape is
@@ -70,7 +74,8 @@ namespace ngraph
                                                                size_t start_match_axis)
             NGRAPH_DEPRECATED("Replace with legacy_style_broadcast_values_for_binary_operation");
 
-        /// \brief Cast shape of two outputs to make them compatible for an element-wise binary operation.
+        /// \brief Cast shape of two outputs to make them compatible for an element-wise binary
+        ///        operation.
         ///
         /// If necessary the right-hand-side argument will be broadcast to match the shape
         /// of left-hand-side argument. The starting of the mutually equal shape is
@@ -89,15 +94,19 @@ namespace ngraph
                                                                         const Output<Node>& right,
                                                                         size_t start_match_axis);
 
-        /// \brief      Broadcast shape of two nodes to make them compatible for a matrix multiplication.
+        /// \brief      Broadcast shape of two nodes to make them compatible for a matrix
+        ///             multiplication.
         ///
-        /// \note       This function is reflecting broadcasting behaviour of NumPy's `matmul` operation
-        ///             (https://docs.scipy.org/doc/numpy/reference/generated/numpy.matmul.html)
-        ///             This mean that only \"stack of matrices\" axes are bidirectionally broadcasted.
-        ///             The last two dimension are left untouched.
+        /// \note       This function is reflecting broadcasting behaviour of NumPy's `matmul`
+        ///             operation.
+        ///             (https://docs.scipy.org/doc/numpy/reference/generated/numpy.matmul.html).
+        ///             This mean that only \"stack of matrices\" axes are bidirectionally
+        ///             broadcasted. The last two dimension are left untouched.
         ///
-        /// \param[in]  left   The Node providing data for the left-hand side of matrix multiplication.
-        /// \param[in]  right  The Node providing data for the right-hand side of matrix multiplication.
+        /// \param[in]  left   The Node providing data for the left-hand side of matrix
+        ///                    multiplication.
+        /// \param[in]  right  The Node providing data for the right-hand side of matrix
+        ///                    multiplication.
         ///
         /// \return     The vector containing both nodes broadcasted.
         ///
@@ -105,15 +114,19 @@ namespace ngraph
                                                               const std::shared_ptr<Node>& right)
             NGRAPH_DEPRECATED("Replace with numpy_style_broadcast_values_for_matmul_operation.");
 
-        /// \brief      Broadcast shape of two nodes to make them compatible for a matrix multiplication.
+        /// \brief      Broadcast shape of two nodes to make them compatible for a matrix
+        ///             multiplication.
         ///
-        /// \note       This function is reflecting broadcasting behaviour of NumPy's `matmul` operation
+        /// \note       This function is reflecting broadcasting behaviour of NumPy's `matmul`
+        ///             operation.
         ///             (https://docs.scipy.org/doc/numpy/reference/generated/numpy.matmul.html)
-        ///             This mean that only \"stack of matrices\" axes are bidirectionally broadcasted.
-        ///             The last two dimension are left untouched.
+        ///             This mean that only \"stack of matrices\" axes are bidirectionally
+        ///             broadcasted. The last two dimension are left untouched.
         ///
-        /// \param[in]  left   The Node providing data for the left-hand side of matrix multiplication.
-        /// \param[in]  right  The Node providing data for the right-hand side of matrix multiplication.
+        /// \param[in]  left   The Node providing data for the left-hand side of matrix
+        ///                    multiplication.
+        /// \param[in]  right  The Node providing data for the right-hand side of matrix
+        ///                    multiplication.
         ///
         /// \return     The vector containing both outputs broadcasted.
         ///

src/ngraph/op/util/fused_op.hpp

@@ -24,7 +24,8 @@ namespace ngraph
     {
         namespace util
         {
-            /// \brief Abstract base class for fused ops, i.e ops that can be broken down into core ngraph ops
+            /// \brief Abstract base class for fused ops, i.e ops that can be broken down into core
+            ///        ngraph ops
             ///
             class FusedOp : public Op
             {

src/ngraph/op/util/logical_reduction.hpp

@@ -24,8 +24,9 @@ namespace ngraph
     {
         namespace util
         {
-            /// \brief Abstract base class for logical reduction operations, i.e., operations where chosen axes of the input tensors
-            ///        are eliminated (reduced out) by repeated application of a particular binary logical operation.
+            /// \brief Abstract base class for logical reduction operations, i.e., operations where
+            ///        chosen axes of the input tensors are eliminated (reduced out) by repeated
+            ///        application of a particular binary logical operation.
             class LogicalReduction : public Op
             {
             protected:

src/ngraph/op/util/rnn_cell_base.hpp

@@ -74,7 +74,8 @@ namespace ngraph
                 ///
                 ActivationFunction get_activation_function(std::size_t idx) const;
                 ///
-                /// \brief      Creates node with element-wise add operation with numpy broadcasting.
+                /// \brief      Creates node with element-wise add operation with numpy
+                ///             broadcasting.
                 ///
                 /// \param[in]  lhs   The left hand side argument node.
                 /// \param[in]  rhs   The right hand side argument node.
@@ -83,7 +84,8 @@ namespace ngraph
                 ///
                 static std::shared_ptr<Node> add(const Output<Node>& lhs, const Output<Node>& rhs);
                 ///
-                /// \brief      Creates node with element-wise subtract operation with numpy broadcasting.
+                /// \brief      Creates node with element-wise subtract operation with numpy
+                ///             broadcasting.
                 ///
                 /// \param[in]  lhs   The left hand side argument node.
                 /// \param[in]  rhs   The right hand side argument node.
@@ -92,7 +94,8 @@ namespace ngraph
                 ///
                 static std::shared_ptr<Node> sub(const Output<Node>& lhs, const Output<Node>& rhs);
                 ///
-                /// \brief      Creates node with element-wise multiply operation with numpy broadcasting.
+                /// \brief      Creates node with element-wise multiply operation with numpy
+                ///             broadcasting.
                 ///
                 /// \param[in]  lhs   The left hand side argument node.
                 /// \param[in]  rhs   The right hand side argument node.
@@ -101,7 +104,8 @@ namespace ngraph
                 ///
                 static std::shared_ptr<Node> mul(const Output<Node>& lhs, const Output<Node>& rhs);
                 ///
-                /// \brief      Creates node with element-wise clip operation with numpy broadcasting.
+                /// \brief      Creates node with element-wise clip operation with numpy
+                ///             broadcasting.
                 ///
                 /// \param[in]  data   The input tensor for clipping.
                 ///

src/ngraph/op/util/unary_elementwise_arithmetic.hpp

@@ -24,11 +24,14 @@ namespace ngraph
     {
         namespace util
         {
-            /// \brief Abstract base class for elementwise unary arithmetic operations, i.e., operations where the same
-            ///        scalar arithmetic operation is applied to each element.
+            // clang-format off
+            /// \brief Abstract base class for elementwise unary arithmetic operations, i.e.,
+            ///        operations where the same scalar arithmetic operation is applied to each
+            ///        element.
             ///
-            /// For example, if the underlying operation (determined by the subclass) is \f$\mathit{op}(x)\f$, the input tensor
-            /// \f$[[x,y],[z,w]]\f$ will be mapped to \f$[[\mathit{op}(x),\mathit{op}(y)],[\mathit{op}(z),\mathit{op}(w)]]\f$.
+            /// For example, if the underlying operation (determined by the subclass) is
+            /// \f$\mathit{op}(x)\f$, the input tensor \f$[[x,y],[z,w]]\f$ will be mapped to
+            /// \f$[[\mathit{op}(x),\mathit{op}(y)],[\mathit{op}(z),\mathit{op}(w)]]\f$.
             ///
             /// ## Inputs
             ///
@@ -41,6 +44,7 @@ namespace ngraph
             /// | Type                   | Description                                                                                                                                                             |
             /// | ---------------------- | ----------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
             /// | \f$N[d_1,\dots,d_n]\f$ | The tensor \f$T\f$, where \f$T[i_1,\dots,i_n] = \mathit{op}(\texttt{arg}[i_1,\dots,i_n])\f$. This will always have the same shape and element type as the input tensor. |
+            // clang-format on
             class UnaryElementwiseArithmetic : public Op
             {
             protected:

test/.clang-format

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-    Priority:        3
-  - Regex:           '^<.*'
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