134 lines
5.7 KiB
C++
134 lines
5.7 KiB
C++
// Copyright (C) 2018-2025 Intel Corporation
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// SPDX-License-Identifier: Apache-2.0
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//
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#pragma once
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#include <cstddef>
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#include <memory>
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#include <string>
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#include <vector>
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#include "openvino/op/op.hpp"
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#include "openvino/op/util/activation_functions.hpp"
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#include "openvino/op/util/rnn_cell_base.hpp"
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namespace ov {
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namespace op {
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namespace v0 {
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///
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/// \brief Class for single RNN cell node.
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///
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/// \note It follows notation and equations defined as in ONNX standard:
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/// https://github.com/onnx/onnx/blob/master/docs/Operators.md#RNN
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///
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/// \note It calculates following equations:
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///
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/// Ht = f(Xt*(Wi^T) + Ht-1*(Ri^T) + Wbi + Rbi)
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///
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/// * - Is a dot product,
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/// f - is activation functions.
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///
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/// \note This class represents only single *cell* (for current time step)
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/// and not the whole RNN Sequence layer
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///
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/// \sa LSTMSequence, LSTMCell, GRUCell
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///
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/// \ingroup ov_ops_cpp_api
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class OPENVINO_API RNNCell : public util::RNNCellBase {
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public:
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OPENVINO_OP("RNNCell", "opset1", util::RNNCellBase);
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RNNCell();
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///
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/// \brief Constructs RNNCell node.
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///
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/// \param[in] X The input tensor with shape: [batch_size,
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/// input_size].
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/// \param[in] initial_hidden_state The hidden state tensor at current time step
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/// with shape: [batch_size, hidden_size].
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/// \param[in] W The weight tensor with shape: [hidden_size,
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/// input_size].
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/// \param[in] R The recurrence weight tensor with shape:
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/// [hidden_size, hidden_size].
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/// \param[in] hidden_size The number of hidden units for recurrent cell.
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/// \param[in] activations The vector of activation functions used inside
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/// recurrent cell.
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/// \param[in] activations_alpha The vector of alpha parameters for activation
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/// functions in order respective to activation
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/// list.
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/// \param[in] activations_beta The vector of beta parameters for activation
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/// functions in order respective to activation
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/// list.
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/// \param[in] clip The value defining clipping range [-clip,
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/// clip] on input of activation functions.
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///
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RNNCell(const Output<Node>& X,
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const Output<Node>& initial_hidden_state,
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const Output<Node>& W,
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const Output<Node>& R,
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std::size_t hidden_size,
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const std::vector<std::string>& activations = std::vector<std::string>{"tanh"},
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const std::vector<float>& activations_alpha = {},
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const std::vector<float>& activations_beta = {},
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float clip = 0.f);
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///
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/// \brief Constructs RNNCell node.
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///
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/// \param[in] X The input tensor with shape: [batch_size,
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/// input_size].
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/// \param[in] initial_hidden_state The hidden state tensor at current time step
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/// with shape: [batch_size, hidden_size].
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/// \param[in] W The weight tensor with shape: [hidden_size,
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/// input_size].
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/// \param[in] R The recurrence weight tensor with shape:
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/// [hidden_size, hidden_size].
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/// \param[in] B The bias tensor for input gate with shape:
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/// [hidden_size].
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/// \param[in] hidden_size The number of hidden units for recurrent cell.
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/// \param[in] activations The vector of activation functions used inside
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/// recurrent cell.
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/// \param[in] activations_alpha The vector of alpha parameters for activation
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/// functions in order respective to activation
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/// list.
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/// \param[in] activations_beta The vector of beta parameters for activation
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/// functions in order respective to activation
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/// list.
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/// \param[in] clip The value defining clipping range [-clip,
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/// clip] on input of activation functions.
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///
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RNNCell(const Output<Node>& X,
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const Output<Node>& initial_hidden_state,
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const Output<Node>& W,
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const Output<Node>& R,
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const Output<Node>& B,
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std::size_t hidden_size,
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const std::vector<std::string>& activations = std::vector<std::string>{"tanh"},
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const std::vector<float>& activations_alpha = {},
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const std::vector<float>& activations_beta = {},
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float clip = 0.f);
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void validate_and_infer_types() override;
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bool visit_attributes(AttributeVisitor& visitor) override;
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std::shared_ptr<Node> clone_with_new_inputs(const OutputVector& new_args) const override;
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private:
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///
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/// \brief Creates the default bias input initialized with zeros.
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///
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/// \return The object of Output class.
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///
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Output<Node> get_default_bias_input() const;
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///
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/// \brief The Activation function f.
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///
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util::ActivationFunction m_activation_f;
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static constexpr std::size_t s_gates_count{1};
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};
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} // namespace v0
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} // namespace op
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} // namespace ov
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