395 lines
18 KiB
C++
395 lines
18 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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enum class LSTMWeightsFormat {
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FICO, // IE
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ICOF, // PyTorch
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IFCO, // DNNL, TF, MxNet
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IFOC, // Caffe
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IOFC, // ONNX
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};
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ov::op::util::LSTMWeightsFormat convert_lstm_weights_enums(LSTMWeightsFormat format);
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namespace v0 {
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///
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/// \brief Class for single lstm cell node.
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///
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/// \note Following implementation supports:
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/// \li \c peepholes Gers & Schmidhuber (2000)
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/// https://ieeexplore.ieee.org/document/861302
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/// \li Coupling input and forget gates.
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///
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/// \note It calculates following equations:
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///
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/// it = f(Xt*(Wi^T) + Ht-1*(Ri^T) + Pi (.) Ct-1 + Wbi + Rbi)
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/// ft = f(Xt*(Wf^T) + Ht-1*(Rf^T) + Pf (.) Ct-1 + Wbf + Rbf)
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/// ct = g(Xt*(Wc^T) + Ht-1*(Rc^T) + Wbc + Rbc)
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/// Ct = ft (.) Ct-1 + it (.) ct
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/// ot = f(Xt*(Wo^T) + Ht-1*(Ro^T) + Po (.) Ct + Wbo + Rbo)
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/// Ht = ot (.) h(Ct)
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///
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/// * - Is a dot product,
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/// (.) - is a Hadamard product (element-wise),
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/// f, g, h - are activation functions.
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///
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/// \note This class represents only single *cell* (for current time step) and not
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/// the whole LSTM Sequence layer
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///
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/// \sa LSTMSequence, RNNCell, GRUCell
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///
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/// \ingroup ov_ops_cpp_api
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class OPENVINO_API LSTMCell : public util::RNNCellBase {
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public:
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OPENVINO_OP("LSTMCell", "opset1", op::util::RNNCellBase);
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LSTMCell();
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///
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/// \brief Constructs LSTMCell 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] initial_cell_state The cell state tensor at current time step
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/// with shape: [batch_size, hidden_size].
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/// \param[in] W The gate weights tensor with shape:
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/// [4*hidden_size, input_size].
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/// \param[in] R The recurrence weights tensor with shape:
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/// [4*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] weights_format The order of gates in weights tensors. The
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/// default format is IFCO since it is used by
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/// DNNL.
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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] input_forget Controls coupling input and forget gates.
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///
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LSTMCell(const Output<Node>& X,
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const Output<Node>& initial_hidden_state,
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const Output<Node>& initial_cell_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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LSTMWeightsFormat weights_format = LSTMWeightsFormat::IFCO,
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const std::vector<std::string>& activations = std::vector<std::string>{"sigmoid", "tanh", "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 input_forget = false);
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///
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/// \brief Constructs LSTMCell 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] initial_cell_state The cell 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: [4*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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/// [4*hidden_size, hidden_size].
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/// \param[in] B The bias tensor for gates with shape:
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/// [4*hidden_size].
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/// \param[in] hidden_size The number of hidden units for recurrent cell.
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/// \param[in] weights_format The order of gates in weights tensors. The
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/// default format is IFCO since it is used by
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/// DNNL.
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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] input_forget Controls coupling input and forget gates.
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///
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LSTMCell(const Output<Node>& X,
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const Output<Node>& initial_hidden_state,
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const Output<Node>& initial_cell_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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LSTMWeightsFormat weights_format = LSTMWeightsFormat::IFCO,
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const std::vector<std::string>& activations = std::vector<std::string>{"sigmoid", "tanh", "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 input_forget = false);
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///
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/// \brief Constructs LSTMCell 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] initial_cell_state The cell 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: [4*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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/// [4*hidden_size, hidden_size].
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/// \param[in] B The bias tensor for gates with shape:
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/// [4*hidden_size].
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/// \param[in] P The weight tensor for peepholes with shape:
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/// [3*hidden_size] - 3 equals to only iof gates.
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/// The order is: input, output, forget gates.
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/// \param[in] hidden_size The number of hidden units for recurrent cell.
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/// \param[in] weights_format The order of gates in weights tensors. The
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/// default format is IFCO since it is used by
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/// DNNL.
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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] input_forget Controls coupling input and forget gates.
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///
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LSTMCell(const Output<Node>& X,
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const Output<Node>& initial_hidden_state,
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const Output<Node>& initial_cell_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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const Output<Node>& P,
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std::size_t hidden_size,
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LSTMWeightsFormat weights_format = LSTMWeightsFormat::IFCO,
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const std::vector<std::string>& activations = std::vector<std::string>{"sigmoid", "tanh", "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 input_forget = 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_input_forget() const {
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return m_input_forget;
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}
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LSTMWeightsFormat get_weights_format() const {
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return m_weights_format;
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}
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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 Creates the default peepholes 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_peepholes_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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///
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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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///
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/// \brief The Activation function h.
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///
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util::ActivationFunction m_activation_h;
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///
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/// \brief Controls whether to couple input and forget gates.
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///
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bool m_input_forget = false;
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///
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/// \brief The order of gates in weights tensors.
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///
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LSTMWeightsFormat m_weights_format;
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};
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} // namespace v0
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namespace v4 {
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///
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/// \brief Class for single lstm cell node.
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///
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/// \note Following implementation supports:
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/// \li \c peepholes Gers & Schmidhuber (2000)
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/// https://ieeexplore.ieee.org/document/861302
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/// \li Coupling input and forget gates.
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///
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/// \note It calculates following equations:
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///
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/// it = f(Xt*(Wi^T) + Ht-1*(Ri^T) + Wbi + Rbi)
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/// ft = f(Xt*(Wf^T) + Ht-1*(Rf^T) + Wbf + Rbf)
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/// ct = g(Xt*(Wc^T) + Ht-1*(Rc^T) + Wbc + Rbc)
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/// Ct = ft (.) Ct-1 + it (.) ct
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/// ot = f(Xt*(Wo^T) + Ht-1*(Ro^T) + Wbo + Rbo)
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/// Ht = ot (.) h(Ct)
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///
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/// * - Is a dot product,
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/// (.) - is a Hadamard product (element-wise),
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/// f, g, h - are activation functions.
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///
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/// \note This class represents only single *cell* (for current time step) and not
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/// the whole LSTM Sequence layer
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///
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/// \sa LSTMSequence, RNNCell, GRUCell
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///
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/// \ingroup ov_ops_cpp_api
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class OPENVINO_API LSTMCell : public util::RNNCellBase {
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public:
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OPENVINO_OP("LSTMCell", "opset4", op::util::RNNCellBase);
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LSTMCell();
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///
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/// \brief Constructs LSTMCell 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] initial_cell_state The cell state tensor at current time step
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/// with shape: [batch_size, hidden_size].
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/// \param[in] W The gate weights tensor with shape:
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/// [4*hidden_size, input_size].
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/// \param[in] R The recurrence weights tensor with shape:
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/// [4*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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LSTMCell(const Output<Node>& X,
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const Output<Node>& initial_hidden_state,
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const Output<Node>& initial_cell_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>{"sigmoid", "tanh", "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 LSTMCell 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] initial_cell_state The cell 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: [4*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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/// [4*hidden_size, hidden_size].
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/// \param[in] B The bias tensor for gates with shape:
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/// [4*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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LSTMCell(const Output<Node>& X,
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const Output<Node>& initial_hidden_state,
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const Output<Node>& initial_cell_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", "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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///
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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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///
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/// \brief The Activation function h.
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///
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util::ActivationFunction m_activation_h;
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};
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} // namespace v4
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} // namespace op
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OPENVINO_API
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std::ostream& operator<<(std::ostream& s, const op::LSTMWeightsFormat& type);
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template <>
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class OPENVINO_API AttributeAdapter<op::LSTMWeightsFormat> : public EnumAttributeAdapterBase<op::LSTMWeightsFormat> {
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public:
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AttributeAdapter(op::LSTMWeightsFormat& value) : EnumAttributeAdapterBase<op::LSTMWeightsFormat>(value) {}
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OPENVINO_RTTI("AttributeAdapter<ov::op::LSTMWeightsFormat>");
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~AttributeAdapter() override;
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};
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} // namespace ov
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