166 lines
7.6 KiB
C++
166 lines
7.6 KiB
C++
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// 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 v3 {
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///
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/// \brief Class for GRU cell node.
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///
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/// \note Note this class represents only single *cell* and not whole GRU *layer*.
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///
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/// \ingroup ov_ops_cpp_api
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class OPENVINO_API GRUCell : public util::RNNCellBase {
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public:
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OPENVINO_OP("GRUCell", "opset3", op::util::RNNCellBase);
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GRUCell();
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///
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/// \brief Constructs GRUCell 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:
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/// [gates_count * hidden_size, input_size].
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/// \param[in] R The recurrence weight tensor with shape:
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/// [gates_count * hidden_size, hidden_size].
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/// \param[in] hidden_size The number of hidden units for recurrent cell.
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///
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GRUCell(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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///
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/// \brief Constructs GRUCell 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:
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/// [gates_count * hidden_size, input_size].
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/// \param[in] R The recurrence weight tensor with shape:
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/// [gates_count * 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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GRUCell(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,
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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,
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bool linear_before_reset);
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///
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/// \brief Constructs GRUCell 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: [gates_count *
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/// hidden_size, input_size].
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/// \param[in] R The recurrence weight tensor with shape:
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/// [gates_count * 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] B The sum of biases (weight and recurrence) for
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/// update, reset and hidden gates.
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/// If linear_before_reset := true then biases for
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/// hidden gates are
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/// placed separately (weight and recurrence).
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/// Shape: [gates_count * hidden_size] if
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/// linear_before_reset := false
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/// Shape: [(gates_count + 1) * hidden_size] if
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/// linear_before_reset := true
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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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/// \param[in] linear_before_reset Whether or not to apply the linear
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/// transformation before multiplying by the
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/// output of the reset gate.
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///
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GRUCell(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>{"sigmoid", "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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bool linear_before_reset = false);
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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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bool get_linear_before_reset() const {
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return m_linear_before_reset;
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}
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void set_linear_before_reset(bool linear_before_reset) {
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m_linear_before_reset = linear_before_reset;
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}
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private:
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/// brief Add and initialize bias input to all zeros.
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void add_default_bias_input();
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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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///
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/// \brief The Activation function g.
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///
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util::ActivationFunction m_activation_g;
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static constexpr std::size_t s_gates_count{3};
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///
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/// \brief Control whether or not apply the linear transformation.
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///
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/// \note The linear transformation may be applied when computing the output of
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/// hidden gate. It's done before multiplying by the output of the reset gate.
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///
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bool m_linear_before_reset;
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};
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} // namespace v3
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} // namespace op
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} // namespace ov
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