Version: 2.7.x

rasa.utils.tensorflow.rasa_layers

ConcatenateSparseDenseFeatures Objects

class ConcatenateSparseDenseFeatures(tf.keras.layers.Layer)

Combines multiple sparse and dense feature tensors into one dense tensor.

This layer combines features from various featurisers into a single feature array per input example. All features must be of the same feature type, i.e. sentence- level or sequence-level (token-level).

The layer combines a given list of tensors (whether sparse or dense) by:

  1. converting sparse tensors into dense ones
  2. optionally, applying dropout to sparse tensors before and/or after the conversion
  3. concatenating all tensors along the last dimension

Arguments:

  • attribute - Name of attribute (e.g. text or label) whose features will be processed.

  • feature_type - Feature type to be processed -- sequence or sentence.

  • feature_type_signature - A list of signatures for the given attribute and feature type.

  • config - A model config for correctly parametrising the layer.

    Input shape: Tuple containing one list of N-D tensors, each with shape: (batch_size, ..., input_dim). All dense tensors must have the same shape, except possibly the last dimension. All sparse tensors must have the same shape, including the last dimension.

    Output shape: N-D tensor with shape: (batch_size, ..., units) where units is the sum of the last dimension sizes across all input tensors, with sparse tensors instead contributing config[DENSE_DIMENSION][attribute] units each.

Raises:

A TFLayerConfigException if no feature signatures are provided.

Attributes:

  • output_units - The last dimension size of the layer's output.

__init__

| __init__(attribute: Text, feature_type: Text, feature_type_signature: List[FeatureSignature], config: Dict[Text, Any]) -> None

Creates a new ConcatenateSparseDenseFeatures object.

call

| call(inputs: Tuple[List[Union[tf.Tensor, tf.SparseTensor]]], training: bool = False) -> tf.Tensor

Combines sparse and dense feature tensors into one tensor.

Arguments:

  • inputs - Contains the input tensors, all of the same rank.
  • training - A flag indicating whether the layer should behave in training mode (applying dropout to sparse tensors if applicable) or in inference mode (not applying dropout).

Returns:

Single tensor with all input tensors combined along the last dimension.

RasaFeatureCombiningLayer Objects

class RasaFeatureCombiningLayer(tf.keras.layers.Layer)

Combines multiple dense or sparse feature tensors into one.

This layer combines features by following these steps:

  1. Apply a ConcatenateSparseDenseFeatures layer separately to sequence- and sentence-level features, yielding two tensors (one for each feature type).
  2. Concatenate the sequence- and sentence-level tensors along the sequence dimension by appending sentence-level features at the first available token position after the sequence-level (token-level) features.

Arguments:

  • attribute - Name of attribute (e.g. text or label) whose features will be processed.

  • attribute_signature - A dictionary containing two lists of feature signatures, one for each feature type (sequence or sentence) of the given attribute.

  • config - A model config used for correctly parameterising the layer and the ConcatenateSparseDenseFeatures layer it uses internally.

    Input shape: Tuple of three input tensors:

  • sequence_features - List of 3-D dense or sparse tensors, each with shape (batch_size, max_seq_length, input_dim) where input_dim can be different for sparse vs dense tensors. See the input shape of ConcatenateSparseDenseFeatures for more information.

  • sentence_features - List of 3-D dense or sparse tensors, each with shape (batch_size, 1, input_dim) where input_dim can be different for sparse vs dense tensors, and can differ from that in sequence_features. See the input shape of ConcatenateSparseDenseFeatures for more information.

  • sequence_feature_lengths - Dense tensor of shape (batch_size, ).

    Output shape:

  • combined_features - A 3-D tensor with shape (batch_size, sequence_length, units) where units is completely determined by the internally applied ConcatenateSparseDenseFeatures layer and sequence_length is the combined length of sequence- and sentence-level features: max_seq_length + 1 if both feature types are present, max_seq_length if only sequence-level features are present, and 1 if only sentence-level features are present).

  • mask_combined_sequence_sentence - A 3-D tensor with shape (batch_size, sequence_length, 1).

Raises:

A TFLayerConfigException if no feature signatures are provided.

Attributes:

  • output_units - The last dimension size of the layer's combined_features output.

__init__

| __init__(attribute: Text, attribute_signature: Dict[Text, List[FeatureSignature]], config: Dict[Text, Any]) -> None

Creates a new RasaFeatureCombiningLayer object.

call

| call(inputs: Tuple[
| List[Union[tf.Tensor, tf.SparseTensor]],
| List[Union[tf.Tensor, tf.SparseTensor]],
| tf.Tensor,
| ], training: bool = False) -> Tuple[tf.Tensor, tf.Tensor]

Combines multiple 3-D dense/sparse feature tensors into one.

Arguments:

  • inputs - Tuple containing:
  • sequence_features - Dense or sparse tensors representing different token-level features.
  • sentence_features - Dense or sparse tensors representing sentence-level features.
  • sequence_feature_lengths - A tensor containing the real sequence length (the number of real -- not padding -- tokens) for each example in the batch.
  • training - A flag indicating whether the layer should behave in training mode (applying dropout to sparse tensors if applicable) or in inference mode (not applying dropout).

Returns:

combined features: A tensor containing all the features combined.

  • mask_combined_sequence_sentence - A binary mask with 1s in place of real features in the combined feature tensor, and 0s in padded positions with fake features.

RasaSequenceLayer Objects

class RasaSequenceLayer(tf.keras.layers.Layer)

Creates an embedding from all features for a sequence attribute; facilitates MLM.

This layer combines all features for an attribute and embeds them using a transformer, optionally doing masked language modeling. The layer is meant only for attributes with sequence-level features, such as text, response and action_text.

Internally, this layer applies the following steps:

  1. Combine features using RasaFeatureCombiningLayer.
  2. Apply a dense layer(s) to the combined features.
  3. Optionally, and only during training for the text attribute, apply masking to the features and create further helper variables for masked language modeling.
  4. Embed the features using a transformer, effectively reducing variable-length sequences of features to fixed-size embeddings.

Arguments:

  • attribute - Name of attribute (e.g. text or label) whose features will be processed.

  • attribute_signature - A dictionary containing two lists of feature signatures, one for each feature type (sentence or sequence) of the given attribute.

  • config - A model config used for correctly parameterising the underlying layers.

    Input shape: Tuple of three input tensors:

  • sequence_features - List of 3-D dense or sparse tensors, each with shape (batch_size, max_seq_length, input_dim) where input_dim can be different for sparse vs dense tensors. See the input shape of ConcatenateSparseDenseFeatures for more information.

  • sentence_features - List of 3-D dense or sparse tensors, each with shape (batch_size, 1, input_dim) where input_dim can be different for sparse vs dense tensors, and can differ from that in sequence_features. See the input shape of ConcatenateSparseDenseFeatures for more information.

  • sequence_feature_lengths - Dense tensor of shape (batch_size, ).

    Output shape:

  • outputs - (batch_size, seq_length, units) where units matches the underlying transformer's output size (if present), otherwise it matches the output size of the Ffnn block applied to the combined features, or it's the output size of the underlying RasaFeatureCombiningLayer if the Ffnn block has 0 layers. seq_length is the sum of the sequence dimension sizes of sequence- and sentence-level features (for details, see the output shape of RasaFeatureCombiningLayer). If both feature types are present, then seq_length will be 1 + the length of the longest sequence of real tokens across all examples in the given batch.

  • seq_sent_features - (batch_size, seq_length, hidden_dim), where hidden_dim is the output size of the underlying Ffnn block, or the output size of the underlying RasaFeatureCombiningLayer if the Ffnn block has 0 layers.

  • mask_combined_sequence_sentence - (batch_size, seq_length, 1)

  • token_ids - (batch_size, seq_length, id_dim). id_dim is 2 when no dense sequence-level features are present. Otherwise, it's arbitrarily chosen to match the last dimension size of the first dense sequence-level feature in the input list of features.

  • mlm_boolean_mask - (batch_size, seq_length, 1), empty tensor if not doing MLM.

  • attention_weights - (transformer_layers, batch_size, num_transformer_heads, seq_length, seq_length), empty tensor if the transformer has 0 layers.

Raises:

A TFLayerConfigException if no feature signatures for sequence-level features are provided.

Attributes:

  • output_units - The last dimension size of the layer's first output (outputs).

__init__

| __init__(attribute: Text, attribute_signature: Dict[Text, List[FeatureSignature]], config: Dict[Text, Any]) -> None

Creates a new RasaSequenceLayer object.

call

| call(inputs: Tuple[
| List[Union[tf.Tensor, tf.SparseTensor]],
| List[Union[tf.Tensor, tf.SparseTensor]],
| tf.Tensor,
| ], training: bool = False) -> Tuple[tf.Tensor, tf.Tensor, tf.Tensor, tf.Tensor, tf.Tensor, tf.Tensor]

Combines all of an attribute's features and embeds using a transformer.

Arguments:

  • inputs - Tuple containing:
  • sequence_features - Dense or sparse tensors representing different token-level features.
  • sentence_features - Dense or sparse tensors representing different sentence-level features.
  • sequence_feature_lengths - A tensor containing the real sequence length (the number of real -- not padding -- tokens) for each example in the batch.
  • training - A flag indicating whether the layer should behave in training mode (applying dropout to sparse tensors if applicable) or in inference mode (not applying dropout).

Returns:

  • outputs - Tensor with all features combined, masked (if doing MLM) and embedded with a transformer.
  • seq_sent_features - Tensor with all features combined from just before the masking and transformer is applied
  • mask_combined_sequence_sentence - A binary mask with 1s in place of real features in the combined feature tensor, and 0s in padded positions with fake features.
  • token_ids - Tensor with dense token-level features which can serve as IDs (unique embeddings) of all the different tokens found in the batch. Empty tensor if not doing MLM.
  • mlm_boolean_mask - A boolean mask with True where real tokens in outputs were masked and False elsewhere. Empty tensor if not doing MLM.
  • attention_weights - Tensor containing self-attention weights received from the underlying transformer. Empty tensor if the transformer has 0 layers.

compute_mask

compute_mask(sequence_lengths: tf.Tensor) -> tf.Tensor

Computes binary mask given real sequence lengths.

Takes a 1-D tensor of shape (batch_size,) containing the lengths of sequences (in terms of number of tokens) in the batch. Creates a binary mask of shape (batch_size, max_seq_length, 1) with 1s at positions with real tokens and 0s elsewhere.

prepare_transformer_layer

prepare_transformer_layer(attribute_name: Text, config: Dict[Text, Any], num_layers: int, units: int, drop_rate: float, unidirectional: bool) -> Union[
TransformerEncoder,
Callable[
[tf.Tensor, Optional[tf.Tensor], Optional[Union[tf.Tensor, bool]]],
Tuple[tf.Tensor, Optional[tf.Tensor]],
],
]

Creates & returns a transformer encoder, potentially with 0 layers.