Source code for lambeq.training.model

# Copyright 2021-2024 Cambridge Quantum Computing Ltd.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
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#     http://www.apache.org/licenses/LICENSE-2.0
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"""
Model
=====
Module containing the base class for a lambeq model.

"""
from __future__ import annotations

from abc import ABC, abstractmethod
from collections.abc import Collection
from typing import Any

from sympy import default_sort_key, Symbol as SymPySymbol

from lambeq.ansatz.base import Symbol
from lambeq.backend.tensor import Diagram
from lambeq.training.checkpoint import Checkpoint
from lambeq.typing import StrPathT




class Model(ABC):
    """Model base class.

    Attributes
    ----------
    symbols : list of symbols
        A sorted list of all :py:class:`Symbols <.Symbol>` occuring in
        the data.
    weights : Collection
        A data structure containing the numeric values of
        the model's parameters.

    """



    def __init__(self) -> None:
        """Initialise an instance of :py:class:`Model` base class."""
        self.symbols: list[Symbol | SymPySymbol] = []
        self.weights: Collection = []




    def __call__(self, *args: Any, **kwds: Any) -> Any:
        return self.forward(*args, **kwds)




    @abstractmethod
    def initialise_weights(self) -> None:
        """Initialise the weights of the model."""




    @classmethod
    def from_checkpoint(cls,
                        checkpoint_path: StrPathT,
                        **kwargs: Any) -> Model:
        """Load the weights and symbols from a training checkpoint.

        Parameters
        ----------
        checkpoint_path : str or PathLike
            Path that points to the checkpoint file.

        Other Parameters
        ----------------
        backend_config : dict
            Dictionary containing the backend configuration for the
            :py:class:`TketModel`. Must include the fields `'backend'`,
            `'compilation'` and `'shots'`.

        """

        model = cls(**kwargs)
        checkpoint = Checkpoint.from_file(checkpoint_path)
        model._load_checkpoint(checkpoint)
        return model


    @abstractmethod
    def _load_checkpoint(self, checkpoint: Checkpoint) -> None:
        """Load the model weights and symbols from a lambeq
        :py:class:`.Checkpoint`.

        Parameters
        ----------
        checkpoint : Checkpoint
            :py:class:`.Checkpoint` containing the model weights,
            symbols and additional information.

        """

    @abstractmethod
    def _make_checkpoint(self) -> Checkpoint:
        """Create checkpoint that contains the model weights and symbols.

        Returns
        -------
        Checkpoint
            :py:class:`.Checkpoint` containing the model weights,
            symbols and additional information.

        """



    def save(self, checkpoint_path: StrPathT) -> None:
        """Create a lambeq :py:class:`.Checkpoint` and save to a path.

        Example:
        >>> from lambeq import PytorchModel
        >>> model = PytorchModel()
        >>> model.save('my_checkpoint.lt')

        Parameters
        ----------
        checkpoint_path : str or PathLike
            Path that points to the checkpoint file.

        """
        checkpoint = self._make_checkpoint()
        checkpoint.to_file(checkpoint_path)




    def load(self, checkpoint_path: StrPathT) -> None:
        """Load model data from a path pointing to a lambeq checkpoint.

        Checkpoints that are created by a lambeq :py:class:`Trainer`
        usually have the extension `.lt`.

        Parameters
        ----------
        checkpoint_path : str or PathLike
            Path that points to the checkpoint file.

        """
        checkpoint = Checkpoint.from_file(checkpoint_path)
        self._load_checkpoint(checkpoint)




    @abstractmethod
    def get_diagram_output(self, diagrams: list[Diagram]) -> Any:
        """Return the diagram prediction.

        Parameters
        ----------
        diagrams : list of :py:class:`~lambeq.tensor.Diagram`
            The tensor or circuit diagrams to be evaluated.

        """




    @abstractmethod
    def forward(self, x: list[Any]) -> Any:
        """The forward pass of the model."""




    @classmethod
    def from_diagrams(cls, diagrams: list[Diagram], **kwargs: Any) -> Model:
        """Build model from a list of
        :py:class:`Diagrams <lambeq.tensor.Diagram>`.

        Parameters
        ----------
        diagrams : list of :py:class:`~lambeq.tensor.Diagram`
            The tensor or circuit diagrams to be evaluated.

        Other Parameters
        ----------------
        backend_config : dict
            Dictionary containing the backend configuration for the
            :py:class:`TketModel`. Must include the fields `'backend'`,
            `'compilation'` and `'shots'`.
        use_jit : bool, default: False
            Whether to use JAX's Just-In-Time compilation in
            :py:class:`NumpyModel`.

        """
        model = cls(**kwargs)
        model.symbols = sorted(
            {sym for circ in diagrams for sym in circ.free_symbols},
            key=default_sort_key)
        return model