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Add an Encoder

1. Add a new encoder class

Source code for encoders lives under ludwig/encoders/. Encoders are grouped into modules by their input feature type. For instance, all new sequence encoders should be added to ludwig/encoders/


An encoder may support multiple types, if so it should be defined in the module corresponding to its most generic supported type. If an encoder is generic with respect to input type, add it to ludwig/encoders/

To create a new encoder:

  1. Define a new encoder class. Inherit from ludwig.encoders.base.Encoder or one of its subclasses.
  2. Create all layers and state in the __init__ method, after calling super().__init__().
  3. Implement your encoder's forward pass in def forward(self, inputs, mask=None):.
  4. Define @property input_shape and @property output_shape.
  5. Define a schema class.

Note: Encoder inherits from LudwigModule, which is itself a torch.nn.Module, so all the usual concerns of developing Torch modules apply.

All encoder parameters should be provided as keyword arguments to the constructor, and must have a default value. For example the StackedRNN encoder takes the following list of parameters in its constructor:

from ludwig.constants import AUDIO, SEQUENCE, TEXT, TIMESERIES
from ludwig.encoders.base import Encoder
from ludwig.encoders.registry import register_encoder

@register_encoder("rnn", [AUDIO, SEQUENCE, TEXT, TIMESERIES])
class StackedRNN(Encoder):
    def __init__(
    # Initialize any modules, layers, or variable state

2. Implement forward, input_shape, and output_shape

Actual computation of activations takes place inside the forward method of the encoder. All encoders should have the following signature:

    def forward(self, inputs: torch.Tensor, mask: Optional[torch.Tensor] = None):
        # perform forward pass
        # ...
        # output_tensor = result of forward pass
        return {"encoder_output": output_tensor}


  • inputs (torch.Tensor): input tensor.
  • mask (torch.Tensor, default: None): binary tensor indicating which values in inputs should be masked out. Note: mask is not required, and is not implemented for most encoder types.


  • (dict): A dictionary containing the key encoder_output whose value is the encoder output tensor. {"encoder_output": output_tensor}

The input_shape and output_shape properties must return the fully-specified shape of the encoder's expected input and output, without batch dimension:

    def input_shape(self) -> torch.Size:
        return torch.Size([self.max_sequence_length])

    def output_shape(self) -> torch.Size:
        return self.recurrent_stack.output_shape

3. Add the new encoder class to the encoder registry

Mapping between encoder names in the model definition and encoder classes is made by registering the class in an encoder registry. The encoder registry is defined in ludwig/encoders/ To register your class, add the @register_encoder decorator on the line above its class definition, specifying the name of the encoder and a list of supported input feature types:

@register_encoder("rnn", [AUDIO, SEQUENCE, TEXT, TIMESERIES])
class StackedRNN(Encoder):

4. Define a schema class

In order to ensure that user config validation for your custom defined encoder functions as desired, we need to define a schema class to go along with the newly defined encoder. To do this, we use a marshmallow_dataclass decorator on a class definition that contains all the inputs to your custom encoder as attributes. For each attribute, we use utility functions from the ludwig.schema.utils directory to validate that input. Lastly, we need to put a reference to this schema class on the custom encoder class. For example:

from marshmallow_dataclass import dataclass

from ludwig.constants import SEQUENCE, TEXT
from ludwig.schema.encoders.base import BaseEncoderConfig
from ludwig.schema.encoders.utils import register_encoder_config
import ludwig.schema.utils as schema_utils

@register_encoder_config("stacked_rnn", [SEQUENCE, TEXT])
class StackedRNNConfig(BaseEncoderConfig):
        type: str = schema_utils.StringOptions(options=["stacked_rnn"], default="stacked_rnn")
        should_embed: bool = schema_utils.Boolean(default=True, description="")
        vocab: list = schema_utils.List(list_type=str, default=None, description="")
        representation: str = schema_utils.StringOptions(options=["sparse", "dense"], default="dense", description="")
        embedding_size: int = schema_utils.Integer(default=256, description="")
        embeddings_trainable: bool = schema_utils.Boolean(default=True, description="")
        pretrained_embeddings: str = schema_utils.String(default=None, description="")
        embeddings_on_cpu: bool = schema_utils.Boolean(default=False, description="")
        num_layers: int = schema_utils.Integer(default=1, description="")
        max_sequence_length: int = schema_utils.Integer(default=None, description="")
        state_size: int = schema_utils.Integer(default=256, description="")
        cell_type: str = schema_utils.StringOptions(
            options=["rnn", "lstm", "lstm_block", "ln", "lstm_cudnn", "gru", "gru_block", "gru_cudnn"], 
            default="rnn", description=""
        bidirectional: bool = schema_utils.Boolean(default=False, description="")
        activation: str = schema_utils.ActivationOptions(default="tanh", description="")
        recurrent_activation: str = schema_utils.activations(default="sigmoid", description="")
        unit_forget_bias: bool = schema_utils.Boolean(default=True, description="")
        recurrent_initializer: str = schema_utils.InitializerOptions(default="orthogonal", description="")
        dropout: float = schema_utils.FloatRange(default=0.0, min=0, max=1, description="")
        recurrent_dropout: float = schema_utils.FloatRange(default=0.0, min=0, max=1, description="")
        fc_layers: list = schema_utils.DictList(default=None, description="")
        num_fc_layers: int = schema_utils.NonNegativeInteger(default=0, description="")
        output_size: int = schema_utils.Integer(default=256, description="")
        use_bias: bool = schema_utils.Boolean(default=True, description="")
        weights_initialize: str = schema_utils.InitializerOptions(default="xavier_uniform", description="")
        bias_initializer: str = schema_utils.InitializerOptions(default="zeros", description="")
        norm: str = schema_utils.StringOptions(options=["batch", "layer"], default=None, description="")
        norm_params: dict = schema_utils.Dict(default=None, description="")
        fc_activation: str = schema_utils.ActivationOptions(default="relu", description="")
        fc_dropout: float = schema_utils.FloatRange(default=0.0, min=0, max=1, description="")
        reduce_output: str = schema_utils.ReductionOptions(default="last", description="")

And lastly you should add a reference to the schema class on the custom encoder:

    def get_schema_cls():
        return StackedRNNConfig