dicee.models.base_model ======================= .. py:module:: dicee.models.base_model Classes ------- .. autoapisummary:: dicee.models.base_model.BaseKGELightning dicee.models.base_model.BaseKGE dicee.models.base_model.IdentityClass Module Contents --------------- .. py:class:: BaseKGELightning(*args, **kwargs) Bases: :py:obj:`lightning.LightningModule` Base class for all neural network modules. Your models should also subclass this class. Modules can also contain other Modules, allowing them to be nested in a tree structure. You can assign the submodules as regular attributes:: import torch.nn as nn import torch.nn.functional as F class Model(nn.Module): def __init__(self) -> None: super().__init__() self.conv1 = nn.Conv2d(1, 20, 5) self.conv2 = nn.Conv2d(20, 20, 5) def forward(self, x): x = F.relu(self.conv1(x)) return F.relu(self.conv2(x)) Submodules assigned in this way will be registered, and will also have their parameters converted when you call :meth:`to`, etc. .. note:: As per the example above, an ``__init__()`` call to the parent class must be made before assignment on the child. :ivar training: Boolean represents whether this module is in training or evaluation mode. :vartype training: bool .. py:attribute:: training_step_outputs :value: [] .. py:method:: mem_of_model() -> Dict Size of model in MB and number of params .. py:method:: training_step(batch, batch_idx=None) Here you compute and return the training loss and some additional metrics for e.g. the progress bar or logger. :param batch: The output of your data iterable, normally a :class:`~torch.utils.data.DataLoader`. :param batch_idx: The index of this batch. :param dataloader_idx: The index of the dataloader that produced this batch. (only if multiple dataloaders used) :returns: - :class:`~torch.Tensor` - The loss tensor - ``dict`` - A dictionary which can include any keys, but must include the key ``'loss'`` in the case of automatic optimization. - ``None`` - In automatic optimization, this will skip to the next batch (but is not supported for multi-GPU, TPU, or DeepSpeed). For manual optimization, this has no special meaning, as returning the loss is not required. In this step you'd normally do the forward pass and calculate the loss for a batch. You can also do fancier things like multiple forward passes or something model specific. Example:: def training_step(self, batch, batch_idx): x, y, z = batch out = self.encoder(x) loss = self.loss(out, x) return loss To use multiple optimizers, you can switch to 'manual optimization' and control their stepping: .. code-block:: python def __init__(self): super().__init__() self.automatic_optimization = False # Multiple optimizers (e.g.: GANs) def training_step(self, batch, batch_idx): opt1, opt2 = self.optimizers() # do training_step with encoder ... opt1.step() # do training_step with decoder ... opt2.step() .. note:: When ``accumulate_grad_batches`` > 1, the loss returned here will be automatically normalized by ``accumulate_grad_batches`` internally. .. py:method:: loss_function(yhat_batch: torch.FloatTensor, y_batch: torch.FloatTensor) :param yhat_batch: :param y_batch: .. py:method:: on_train_epoch_end(*args, **kwargs) Called in the training loop at the very end of the epoch. To access all batch outputs at the end of the epoch, you can cache step outputs as an attribute of the :class:`~lightning.pytorch.LightningModule` and access them in this hook: .. code-block:: python class MyLightningModule(L.LightningModule): def __init__(self): super().__init__() self.training_step_outputs = [] def training_step(self): loss = ... self.training_step_outputs.append(loss) return loss def on_train_epoch_end(self): # do something with all training_step outputs, for example: epoch_mean = torch.stack(self.training_step_outputs).mean() self.log("training_epoch_mean", epoch_mean) # free up the memory self.training_step_outputs.clear() .. py:method:: test_epoch_end(outputs: List[Any]) .. py:method:: test_dataloader() -> None An iterable or collection of iterables specifying test samples. For more information about multiple dataloaders, see this :ref:`section `. For data processing use the following pattern: - download in :meth:`prepare_data` - process and split in :meth:`setup` However, the above are only necessary for distributed processing. .. warning:: do not assign state in prepare_data - :meth:`~lightning.pytorch.trainer.trainer.Trainer.test` - :meth:`prepare_data` - :meth:`setup` .. note:: Lightning tries to add the correct sampler for distributed and arbitrary hardware. There is no need to set it yourself. .. note:: If you don't need a test dataset and a :meth:`test_step`, you don't need to implement this method. .. py:method:: val_dataloader() -> None An iterable or collection of iterables specifying validation samples. For more information about multiple dataloaders, see this :ref:`section `. The dataloader you return will not be reloaded unless you set :paramref:`~lightning.pytorch.trainer.trainer.Trainer.reload_dataloaders_every_n_epochs` to a positive integer. It's recommended that all data downloads and preparation happen in :meth:`prepare_data`. - :meth:`~lightning.pytorch.trainer.trainer.Trainer.fit` - :meth:`~lightning.pytorch.trainer.trainer.Trainer.validate` - :meth:`prepare_data` - :meth:`setup` .. note:: Lightning tries to add the correct sampler for distributed and arbitrary hardware There is no need to set it yourself. .. note:: If you don't need a validation dataset and a :meth:`validation_step`, you don't need to implement this method. .. py:method:: predict_dataloader() -> None An iterable or collection of iterables specifying prediction samples. For more information about multiple dataloaders, see this :ref:`section `. It's recommended that all data downloads and preparation happen in :meth:`prepare_data`. - :meth:`~lightning.pytorch.trainer.trainer.Trainer.predict` - :meth:`prepare_data` - :meth:`setup` .. note:: Lightning tries to add the correct sampler for distributed and arbitrary hardware There is no need to set it yourself. :returns: A :class:`torch.utils.data.DataLoader` or a sequence of them specifying prediction samples. .. py:method:: train_dataloader() -> None An iterable or collection of iterables specifying training samples. For more information about multiple dataloaders, see this :ref:`section `. The dataloader you return will not be reloaded unless you set :paramref:`~lightning.pytorch.trainer.trainer.Trainer.reload_dataloaders_every_n_epochs` to a positive integer. For data processing use the following pattern: - download in :meth:`prepare_data` - process and split in :meth:`setup` However, the above are only necessary for distributed processing. .. warning:: do not assign state in prepare_data - :meth:`~lightning.pytorch.trainer.trainer.Trainer.fit` - :meth:`prepare_data` - :meth:`setup` .. note:: Lightning tries to add the correct sampler for distributed and arbitrary hardware. There is no need to set it yourself. .. py:method:: configure_optimizers(parameters=None) Choose what optimizers and learning-rate schedulers to use in your optimization. Normally you'd need one. But in the case of GANs or similar you might have multiple. Optimization with multiple optimizers only works in the manual optimization mode. :returns: Any of these 6 options. - **Single optimizer**. - **List or Tuple** of optimizers. - **Two lists** - The first list has multiple optimizers, and the second has multiple LR schedulers (or multiple ``lr_scheduler_config``). - **Dictionary**, with an ``"optimizer"`` key, and (optionally) a ``"lr_scheduler"`` key whose value is a single LR scheduler or ``lr_scheduler_config``. - **None** - Fit will run without any optimizer. The ``lr_scheduler_config`` is a dictionary which contains the scheduler and its associated configuration. The default configuration is shown below. .. code-block:: python lr_scheduler_config = { # REQUIRED: The scheduler instance "scheduler": lr_scheduler, # The unit of the scheduler's step size, could also be 'step'. # 'epoch' updates the scheduler on epoch end whereas 'step' # updates it after a optimizer update. "interval": "epoch", # How many epochs/steps should pass between calls to # `scheduler.step()`. 1 corresponds to updating the learning # rate after every epoch/step. "frequency": 1, # Metric to monitor for schedulers like `ReduceLROnPlateau` "monitor": "val_loss", # If set to `True`, will enforce that the value specified 'monitor' # is available when the scheduler is updated, thus stopping # training if not found. If set to `False`, it will only produce a warning "strict": True, # If using the `LearningRateMonitor` callback to monitor the # learning rate progress, this keyword can be used to specify # a custom logged name "name": None, } When there are schedulers in which the ``.step()`` method is conditioned on a value, such as the :class:`torch.optim.lr_scheduler.ReduceLROnPlateau` scheduler, Lightning requires that the ``lr_scheduler_config`` contains the keyword ``"monitor"`` set to the metric name that the scheduler should be conditioned on. .. testcode:: # The ReduceLROnPlateau scheduler requires a monitor def configure_optimizers(self): optimizer = Adam(...) return { "optimizer": optimizer, "lr_scheduler": { "scheduler": ReduceLROnPlateau(optimizer, ...), "monitor": "metric_to_track", "frequency": "indicates how often the metric is updated", # If "monitor" references validation metrics, then "frequency" should be set to a # multiple of "trainer.check_val_every_n_epoch". }, } # In the case of two optimizers, only one using the ReduceLROnPlateau scheduler def configure_optimizers(self): optimizer1 = Adam(...) optimizer2 = SGD(...) scheduler1 = ReduceLROnPlateau(optimizer1, ...) scheduler2 = LambdaLR(optimizer2, ...) return ( { "optimizer": optimizer1, "lr_scheduler": { "scheduler": scheduler1, "monitor": "metric_to_track", }, }, {"optimizer": optimizer2, "lr_scheduler": scheduler2}, ) Metrics can be made available to monitor by simply logging it using ``self.log('metric_to_track', metric_val)`` in your :class:`~lightning.pytorch.core.LightningModule`. .. note:: Some things to know: - Lightning calls ``.backward()`` and ``.step()`` automatically in case of automatic optimization. - If a learning rate scheduler is specified in ``configure_optimizers()`` with key ``"interval"`` (default "epoch") in the scheduler configuration, Lightning will call the scheduler's ``.step()`` method automatically in case of automatic optimization. - If you use 16-bit precision (``precision=16``), Lightning will automatically handle the optimizer. - If you use :class:`torch.optim.LBFGS`, Lightning handles the closure function automatically for you. - If you use multiple optimizers, you will have to switch to 'manual optimization' mode and step them yourself. - If you need to control how often the optimizer steps, override the :meth:`optimizer_step` hook. .. py:class:: BaseKGE(args: dict) Bases: :py:obj:`BaseKGELightning` Base class for all neural network modules. Your models should also subclass this class. Modules can also contain other Modules, allowing them to be nested in a tree structure. You can assign the submodules as regular attributes:: import torch.nn as nn import torch.nn.functional as F class Model(nn.Module): def __init__(self) -> None: super().__init__() self.conv1 = nn.Conv2d(1, 20, 5) self.conv2 = nn.Conv2d(20, 20, 5) def forward(self, x): x = F.relu(self.conv1(x)) return F.relu(self.conv2(x)) Submodules assigned in this way will be registered, and will also have their parameters converted when you call :meth:`to`, etc. .. note:: As per the example above, an ``__init__()`` call to the parent class must be made before assignment on the child. :ivar training: Boolean represents whether this module is in training or evaluation mode. :vartype training: bool .. py:attribute:: args .. py:attribute:: embedding_dim :value: None .. py:attribute:: num_entities :value: None .. py:attribute:: num_relations :value: None .. py:attribute:: num_tokens :value: None .. py:attribute:: learning_rate :value: None .. py:attribute:: apply_unit_norm :value: None .. py:attribute:: input_dropout_rate :value: None .. py:attribute:: hidden_dropout_rate :value: None .. py:attribute:: optimizer_name :value: None .. py:attribute:: feature_map_dropout_rate :value: None .. py:attribute:: kernel_size :value: None .. py:attribute:: num_of_output_channels :value: None .. py:attribute:: weight_decay :value: None .. py:attribute:: loss .. py:attribute:: selected_optimizer :value: None .. py:attribute:: normalizer_class :value: None .. py:attribute:: normalize_head_entity_embeddings .. py:attribute:: normalize_relation_embeddings .. py:attribute:: normalize_tail_entity_embeddings .. py:attribute:: hidden_normalizer .. py:attribute:: param_init .. py:attribute:: input_dp_ent_real .. py:attribute:: input_dp_rel_real .. py:attribute:: hidden_dropout .. py:attribute:: loss_history :value: [] .. py:attribute:: byte_pair_encoding .. py:attribute:: max_length_subword_tokens .. py:attribute:: block_size .. py:method:: forward_byte_pair_encoded_k_vs_all(x: torch.LongTensor) :param x: :type x: B x 2 x T .. py:method:: forward_byte_pair_encoded_triple(x: Tuple[torch.LongTensor, torch.LongTensor]) byte pair encoded neural link predictors :param -------: .. py:method:: init_params_with_sanity_checking() .. py:method:: forward(x: Union[torch.LongTensor, Tuple[torch.LongTensor, torch.LongTensor]], y_idx: torch.LongTensor = None) :param x: :param y_idx: :param ordered_bpe_entities: .. py:method:: forward_triples(x: torch.LongTensor) -> torch.Tensor :param x: .. py:method:: forward_k_vs_all(*args, **kwargs) .. py:method:: forward_k_vs_sample(*args, **kwargs) .. py:method:: get_triple_representation(idx_hrt) .. py:method:: get_head_relation_representation(indexed_triple) .. py:method:: get_sentence_representation(x: torch.LongTensor) :param x shape (b: :param 3: :param t): .. py:method:: get_bpe_head_and_relation_representation(x: torch.LongTensor) -> Tuple[torch.FloatTensor, torch.FloatTensor] :param x: :type x: B x 2 x T .. py:method:: get_embeddings() -> Tuple[numpy.ndarray, numpy.ndarray] .. py:class:: IdentityClass(args=None) Bases: :py:obj:`torch.nn.Module` Base class for all neural network modules. Your models should also subclass this class. Modules can also contain other Modules, allowing them to be nested in a tree structure. You can assign the submodules as regular attributes:: import torch.nn as nn import torch.nn.functional as F class Model(nn.Module): def __init__(self) -> None: super().__init__() self.conv1 = nn.Conv2d(1, 20, 5) self.conv2 = nn.Conv2d(20, 20, 5) def forward(self, x): x = F.relu(self.conv1(x)) return F.relu(self.conv2(x)) Submodules assigned in this way will be registered, and will also have their parameters converted when you call :meth:`to`, etc. .. note:: As per the example above, an ``__init__()`` call to the parent class must be made before assignment on the child. :ivar training: Boolean represents whether this module is in training or evaluation mode. :vartype training: bool .. py:attribute:: args :value: None .. py:method:: __call__(x) .. py:method:: forward(x) :staticmethod: