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transformerlightpredictor

TransformerLightPredictor

Bases: LightningModule

Source code in spotpython/light/transformer/transformerlightpredictor.py 
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class TransformerLightPredictor(L.LightningModule):
    def __init__(
        self,
        l1: int,
        d_mult: int,
        dim_feedforward: int,
        nhead: int,
        num_layers: int,
        epochs: int,
        batch_size: int,
        initialization: str,
        act_fn: nn.Module,
        optimizer: str,
        dropout_prob: float,
        lr_mult: float,
        patience: int,
        _L_in: int,
        _L_out: int,
        model_dim: int,
        num_heads: int,
        lr: float,
        warmup: int,
        max_iters: int,
        input_dropout: float,
        dropout: float,
        *args,
        **kwargs,
    ):
        """
        Initializes the TransformerLightRegression object.

        Args:
            l1 (int): The number of neurons in the first hidden layer.
            epochs (int): The number of epochs to train the model for.
            batch_size (int): The batch size to use during training.
            initialization (str): The initialization method to use for the weights.
            act_fn (nn.Module): The activation function to use in the hidden layers.
            optimizer (str): The optimizer to use during training.
            dropout_prob (float): The probability of dropping out a neuron during training.
            lr_mult (float): The learning rate multiplier for the optimizer.
            patience (int): The number of epochs to wait before early stopping.
            _L_in (int):
                The number of input features. Not a hyperparameter, but needed to create the network. `input_dim`,
                hidden dimensionality of the input.
            _L_out (int):
                The number of output classes. Not a hyperparameter, but needed to create the network. `num_classes`,
                number of classes to predict per sequence element.
            model_dim (int):
                Hidden dimensionality to use inside the Transformer
            num_heads (int):
                Number of heads to use in the Multi-Head Attention blocks
            num_layers (int):
                Number of encoder blocks to use.
            lr (float):
                Learning rate in the optimizer
            warmup (int):
                Number of warmup steps. Usually between 50 and 500
            max_iters (int):
                Number of maximum iterations the model is trained for. This is needed for the CosineWarmup scheduler
            input_dropout (float):
                Dropout to apply on the input features
            dropout (float):
                Dropout to apply inside the Transformer
        """
        super().__init__()
        # Attribute 'act_fn' is an instance of `nn.Module` and is already saved during
        # checkpointing. It is recommended to ignore them
        # using `self.save_hyperparameters(ignore=['act_fn'])`
        # self.save_hyperparameters(ignore=["act_fn"])
        #
        self._L_in = _L_in
        self._L_out = _L_out
        self.d_mult = d_mult
        # _L_in and _L_out are not hyperparameters, but are needed to create the network
        self.save_hyperparameters(ignore=["_L_in", "_L_out"])
        # set dummy input array for Tensorboard Graphs
        # set log_graph=True in Trainer to see the graph (in traintest.py)
        self.example_input_array = torch.zeros((batch_size, self._L_in))
        self._create_model()

    def _create_model(self):
        # Input dim -> Model dim
        self.input_net = nn.Sequential(nn.Dropout(self.hparams.input_dropout), nn.Linear(self.hparams.input_dim, self.hparams.model_dim))
        # Positional encoding for sequences
        self.positional_encoding = PositionalEncodingBasic(d_model=self.hparams.model_dim)
        # Transformer
        self.transformer = TransformerEncoder(
            num_layers=self.hparams.num_layers,
            input_dim=self.hparams.model_dim,
            dim_feedforward=2 * self.hparams.model_dim,
            num_heads=self.hparams.num_heads,
            dropout=self.hparams.dropout,
        )
        # Output classifier per sequence element
        self.output_net = nn.Sequential(
            nn.Linear(self.hparams.model_dim, self.hparams.model_dim),
            nn.LayerNorm(self.hparams.model_dim),
            nn.ReLU(inplace=True),
            nn.Dropout(self.hparams.dropout),
            nn.Linear(self.hparams.model_dim, self.hparams.num_classes),
        )

    def forward(self, x, mask=None, add_positional_encoding=True):
        """
        Inputs:
            x - Input features of shape [Batch, SeqLen, input_dim]
            mask - Mask to apply on the attention outputs (optional)
            add_positional_encoding - If True, we add the positional encoding to the input.
                                      Might not be desired for some tasks.
        """
        x = self.input_net(x)
        if add_positional_encoding:
            x = self.positional_encoding(x)
        x = self.transformer(x, mask=mask)
        x = self.output_net(x)
        return x

    @torch.no_grad()
    def get_attention_maps(self, x, mask=None, add_positional_encoding=True):
        """
        Function for extracting the attention matrices of the whole Transformer for a single batch.
        Input arguments same as the forward pass.
        """
        x = self.input_net(x)
        if add_positional_encoding:
            x = self.positional_encoding(x)
        attention_maps = self.transformer.get_attention_maps(x, mask=mask)
        return attention_maps

    def configure_optimizers(self):
        optimizer = optim.Adam(self.parameters(), lr=self.hparams.lr)

        # Apply lr scheduler per step
        lr_scheduler = CosineWarmupScheduler(optimizer, warmup=self.hparams.warmup, max_iters=self.hparams.max_iters)
        return [optimizer], [{"scheduler": lr_scheduler, "interval": "step"}]

    def training_step(self, batch, batch_idx):
        raise NotImplementedError

    def validation_step(self, batch, batch_idx):
        raise NotImplementedError

    def test_step(self, batch, batch_idx):
        raise NotImplementedError

__init__(l1, d_mult, dim_feedforward, nhead, num_layers, epochs, batch_size, initialization, act_fn, optimizer, dropout_prob, lr_mult, patience, _L_in, _L_out, model_dim, num_heads, lr, warmup, max_iters, input_dropout, dropout, *args, **kwargs)

Initializes the TransformerLightRegression object.

Parameters:

Name Type Description Default
l1 int

The number of neurons in the first hidden layer.

 required
epochs int

The number of epochs to train the model for.

 required
batch_size int

The batch size to use during training.

 required
initialization str

The initialization method to use for the weights.

 required
act_fn Module

The activation function to use in the hidden layers.

 required
optimizer str

The optimizer to use during training.

 required
dropout_prob float

The probability of dropping out a neuron during training.

 required
lr_mult float

The learning rate multiplier for the optimizer.

 required
patience int

The number of epochs to wait before early stopping.

 required
_L_in int

The number of input features. Not a hyperparameter, but needed to create the network. input_dim, hidden dimensionality of the input.

 required
_L_out int

The number of output classes. Not a hyperparameter, but needed to create the network. num_classes, number of classes to predict per sequence element.

 required
model_dim int

Hidden dimensionality to use inside the Transformer

 required
num_heads int

Number of heads to use in the Multi-Head Attention blocks

 required
num_layers int

Number of encoder blocks to use.

 required
lr float

Learning rate in the optimizer

 required
warmup int

Number of warmup steps. Usually between 50 and 500

 required
max_iters int

Number of maximum iterations the model is trained for. This is needed for the CosineWarmup scheduler

 required
input_dropout float

Dropout to apply on the input features

 required
dropout float

Dropout to apply inside the Transformer

 required
Source code in spotpython/light/transformer/transformerlightpredictor.py 
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def __init__(
    self,
    l1: int,
    d_mult: int,
    dim_feedforward: int,
    nhead: int,
    num_layers: int,
    epochs: int,
    batch_size: int,
    initialization: str,
    act_fn: nn.Module,
    optimizer: str,
    dropout_prob: float,
    lr_mult: float,
    patience: int,
    _L_in: int,
    _L_out: int,
    model_dim: int,
    num_heads: int,
    lr: float,
    warmup: int,
    max_iters: int,
    input_dropout: float,
    dropout: float,
    *args,
    **kwargs,
):
    """
    Initializes the TransformerLightRegression object.

    Args:
        l1 (int): The number of neurons in the first hidden layer.
        epochs (int): The number of epochs to train the model for.
        batch_size (int): The batch size to use during training.
        initialization (str): The initialization method to use for the weights.
        act_fn (nn.Module): The activation function to use in the hidden layers.
        optimizer (str): The optimizer to use during training.
        dropout_prob (float): The probability of dropping out a neuron during training.
        lr_mult (float): The learning rate multiplier for the optimizer.
        patience (int): The number of epochs to wait before early stopping.
        _L_in (int):
            The number of input features. Not a hyperparameter, but needed to create the network. `input_dim`,
            hidden dimensionality of the input.
        _L_out (int):
            The number of output classes. Not a hyperparameter, but needed to create the network. `num_classes`,
            number of classes to predict per sequence element.
        model_dim (int):
            Hidden dimensionality to use inside the Transformer
        num_heads (int):
            Number of heads to use in the Multi-Head Attention blocks
        num_layers (int):
            Number of encoder blocks to use.
        lr (float):
            Learning rate in the optimizer
        warmup (int):
            Number of warmup steps. Usually between 50 and 500
        max_iters (int):
            Number of maximum iterations the model is trained for. This is needed for the CosineWarmup scheduler
        input_dropout (float):
            Dropout to apply on the input features
        dropout (float):
            Dropout to apply inside the Transformer
    """
    super().__init__()
    # Attribute 'act_fn' is an instance of `nn.Module` and is already saved during
    # checkpointing. It is recommended to ignore them
    # using `self.save_hyperparameters(ignore=['act_fn'])`
    # self.save_hyperparameters(ignore=["act_fn"])
    #
    self._L_in = _L_in
    self._L_out = _L_out
    self.d_mult = d_mult
    # _L_in and _L_out are not hyperparameters, but are needed to create the network
    self.save_hyperparameters(ignore=["_L_in", "_L_out"])
    # set dummy input array for Tensorboard Graphs
    # set log_graph=True in Trainer to see the graph (in traintest.py)
    self.example_input_array = torch.zeros((batch_size, self._L_in))
    self._create_model()

forward(x, mask=None, add_positional_encoding=True)

Inputs

x - Input features of shape [Batch, SeqLen, input_dim] mask - Mask to apply on the attention outputs (optional) add_positional_encoding - If True, we add the positional encoding to the input. Might not be desired for some tasks.

Source code in spotpython/light/transformer/transformerlightpredictor.py 
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def forward(self, x, mask=None, add_positional_encoding=True):
    """
    Inputs:
        x - Input features of shape [Batch, SeqLen, input_dim]
        mask - Mask to apply on the attention outputs (optional)
        add_positional_encoding - If True, we add the positional encoding to the input.
                                  Might not be desired for some tasks.
    """
    x = self.input_net(x)
    if add_positional_encoding:
        x = self.positional_encoding(x)
    x = self.transformer(x, mask=mask)
    x = self.output_net(x)
    return x

get_attention_maps(x, mask=None, add_positional_encoding=True)

Function for extracting the attention matrices of the whole Transformer for a single batch. Input arguments same as the forward pass.

Source code in spotpython/light/transformer/transformerlightpredictor.py 
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@torch.no_grad()
def get_attention_maps(self, x, mask=None, add_positional_encoding=True):
    """
    Function for extracting the attention matrices of the whole Transformer for a single batch.
    Input arguments same as the forward pass.
    """
    x = self.input_net(x)
    if add_positional_encoding:
        x = self.positional_encoding(x)
    attention_maps = self.transformer.get_attention_maps(x, mask=mask)
    return attention_maps