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Add a Decoder

1. Add a new decoder class

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


A decoder may support multiple output types, if so it should be defined in the module corresponding to its most generic supported type. If a decoder is generic with respect to output type, add it to ludwig/decoders/

To create a new decoder:

  1. Define a new decoder class. Inherit from ludwig.decoders.base.Decoder or one of its subclasses.
  2. Create all layers and state in the __init__ method, after calling super().__init__().
  3. Implement your decoder's forward pass in def forward(self, combiner_outputs, **kwargs):.
  4. Define a schema class.

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

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

from ludwig.constants import SEQUENCE, TEXT
from ludwig.decoders.base import Decoder
from ludwig.decoders.registry import register_decoder

@register_decoder("generator", [SEQUENCE, TEXT])
class SequenceGeneratorDecoder(Decoder):
    def __init__(
        vocab_size: int,
        max_sequence_length: int,
        cell_type: str = "gru",
        input_size: int = 256,
        reduce_input: str = "sum",
        num_layers: int = 1,
    # Initialize any modules, layers, or variable state

2. Implement forward

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

    def forward(self, combiner_outputs, **kwargs):
        # perform forward pass
        # combiner_hidden_output = combiner_outputs[HIDDEN]
        # ...
        # logits = result of decoder forward pass
        return {LOGITS: logits}


  • combiner_outputs (Dict[str, torch.Tensor]): The input tensor, which is the output of a combiner or the combination of combiner and the activations of any dependent output decoders. The dictionary of combiner outputs includes a tensor of shape b x h, where b is the batch size and h is the embedding size, or a sequence of embeddings b x s x h where s is the sequence length.


  • (Dict[str, torch.Tensor]): A dictionary of decoder output tensors. Typical decoders will return values for the keys LOGITS, PREDICTION, or both (defined in ludwig.constants).

3. Add the new decoder class to the corresponding decoder registry

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

@register_decoder("generator", [SEQUENCE, TEXT])
class SequenceGeneratorDecoder(Decoder):

4. Define a schema class

In order to ensure that user config validation for your custom defined decoder functions as desired, we need to define a schema class to go along with the newly defined decoder. To do this, we use a marshmallow_dataclass decorator on a class definition that contains all the inputs to your custom decoder 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 decoder class. For example:

from marshmallow_dataclass import dataclass

from ludwig.constants import SEQUENCE, TEXT
from ludwig.schema.decoders.base import BaseDecoderConfig
from ludwig.schema.decoders.utils import register_decoder_config
import ludwig.schema.utils as schema_utils

@register_decoder_config("generator", [SEQUENCE, TEXT])
class SequenceGeneratorDecoderConfig(BaseDecoderConfig):

    type: str = schema_utils.StringOptions(options=["generator"], default="generator")
    vocab_size: int = schema_utils.Integer(default=None, description="")
    max_sequence_length: int = schema_utils.Integer(default=None, description="")
    cell_type: str = schema_utils.String(default="gru", description="")
    input_size: int = schema_utils.Integer(default=256, description="")
    reduce_input: str = schema_utils.ReductionOptions(default="sum")
    num_layers: int = schema_utils.Integer(default=1, description="")

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

    def get_schema_cls():
        return SequenceGeneratorDecoderConfig