binary_elementwise_arithmetic.hpp

// Copyright (C) 2018-2021 Intel Corporation
// SPDX-License-Identifier: Apache-2.0
//
 
#pragma once
 
#include "ngraph/op/op.hpp"
#include "ngraph/op/util/attr_types.hpp"
 
namespace ngraph
{
    namespace op
    {
        namespace util
        {
            // 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$[[\mathit{op}(x_0,x_1),\mathit{op}(y_0,y_1)],[\mathit{op}(z_0,z_1),\mathit{op}(w_0,w_1)]]\f$.
            ///
            /// ## Inputs
            ///
            /// |        | Type                              | Description                                                              |
            /// | ------ | --------------------------------- | ------------------------------------------------------------------------ |
            /// | `arg0` | \f$N[d_1,\dots,d_n]~(n \geq 0)\f$ | A tensor of any shape. The element type \f$N\f$ may be any numeric type. |
            /// | `arg1` | \f$N[d_1,\dots,d_n]~(n \geq 0)\f$ | A tensor of the same element type as `arg0`.                             |
            /// | `autob`| AutoBroadcastSpec                 | Auto broadcast specification.                                            |
            ///
            /// ## 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 NGRAPH_API BinaryElementwiseArithmetic : public Op
            {
            protected:
                BinaryElementwiseArithmetic(const AutoBroadcastSpec& autob);
 
                /// \brief Constructs a binary elementwise arithmetic operation.
                ///
                /// \param arg0 Output that produces the first input tensor.
                /// \param arg1 Output that produces the second input tensor.
                BinaryElementwiseArithmetic(const Output<Node>& arg0,
                                            const Output<Node>& arg1,
                                            const AutoBroadcastSpec& autob);
 
            public:
                NGRAPH_RTTI_DECLARATION;
 
                void validate_and_infer_types() override;
 
                const AutoBroadcastSpec& get_autob() const override { return m_autob; }
                void set_autob(const AutoBroadcastSpec& autob) { m_autob = autob; }
                bool visit_attributes(AttributeVisitor& visitor) override;
                bool evaluate_lower(const HostTensorVector& outputs) const override;
                bool evaluate_upper(const HostTensorVector& outputs) const override;
 
            private:
                AutoBroadcastSpec m_autob;
                void validate_and_infer_elementwise_arithmetic(const op::AutoBroadcastSpec& autob);
            };
        } // namespace util
    }     // namespace op
} // namespace ngraph