tfm.nlp.networks.FunnelTransformerEncoder

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Funnel Transformer-based encoder network.

tfm.nlp.networks.FunnelTransformerEncoder(
    vocab_size: int,
    hidden_size: int = 768,
    num_layers: int = 12,
    num_attention_heads: int = 12,
    max_sequence_length: int = 512,
    type_vocab_size: int = 16,
    inner_dim: int = 3072,
    inner_activation: _Activation = _approx_gelu,
    output_dropout: float = 0.1,
    attention_dropout: float = 0.1,
    pool_type: str = _MAX,
    pool_stride: int = 2,
    unpool_length: int = 0,
    initializer: _Initializer = tf.keras.initializers.TruncatedNormal(stddev=0.02),
    output_range: Optional[int] = None,
    embedding_width: Optional[int] = None,
    embedding_layer: Optional[tf.keras.layers.Layer] = None,
    norm_first: bool = False,
    transformer_cls: Union[str, tf.keras.layers.Layer] = tfm.nlp.layers.TransformerEncoderBlock,
    share_rezero: bool = False,
    **kwargs
)

Funnel Transformer Implementation of https://arxiv.org/abs/2006.03236 This implementation utilizes the base framework with Bert (https://arxiv.org/abs/1810.04805). Its output is compatible with BertEncoder.

vocab_size The size of the token vocabulary.
hidden_size The size of the transformer hidden layers.
num_layers The number of transformer layers.
num_attention_heads The number of attention heads for each transformer. The hidden size must be divisible by the number of attention heads.
max_sequence_length The maximum sequence length that this encoder can consume. If None, max_sequence_length uses the value from sequence length. This determines the variable shape for positional embeddings.
type_vocab_size The number of types that the 'type_ids' input can take.
inner_dim The output dimension of the first Dense layer in a two-layer feedforward network for each transformer.
inner_activation The activation for the first Dense layer in a two-layer feedforward network for each transformer.
output_dropout Dropout probability for the post-attention and output dropout.
attention_dropout The dropout rate to use for the attention layers within the transformer layers.
pool_type Pooling type. Choose from ['max', 'avg', 'truncated_avg'].
pool_stride An int or a list of ints. Pooling stride(s) to compress the sequence length. If set to int, each layer will have the same stride size. If set to list, the number of elements needs to match num_layers.
unpool_length Leading n tokens to be skipped from pooling.
initializer The initialzer to use for all weights in this encoder.
output_range The sequence output range, [0, output_range), by slicing the target sequence of the last transformer layer. None means the entire target sequence will attend to the source sequence, which yields the full output.
embedding_width The width of the word embeddings. If the embedding width is not equal to hidden size, embedding parameters will be factorized into two matrices in the shape of ['vocab_size', 'embedding_width'] and 'embedding_width', 'hidden_size'.
embedding_layer An optional Layer instance which will be called to generate embeddings for the input word IDs.
norm_first Whether to normalize inputs to attention and intermediate dense layers. If set False, output of attention and intermediate dense layers is normalized. This does not apply to ReZero.
transformer_cls str or a keras Layer. This is the base TransformerBlock the funnel encoder relies on.
share_rezero bool. Whether to share ReZero alpha between the attention layer and the ffn layer. This option is specific to ReZero.
with_dense_inputs Whether to accept dense embeddings as the input.

pooler_layer The pooler dense layer after the transformer layers.
transformer_layers List of Transformer layers in the encoder.

Methods

call

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call(
    inputs, output_range: Optional[tf.Tensor] = None
)

This is where the layer's logic lives.

The call() method may not create state (except in its first invocation, wrapping the creation of variables or other resources in tf.init_scope()). It is recommended to create state, including tf.Variable instances and nested Layer instances, in __init__(), or in the build() method that is called automatically before call() executes for the first time.

Args
inputs Input tensor, or dict/list/tuple of input tensors. The first positional inputs argument is subject to special rules:

  • inputs must be explicitly passed. A layer cannot have zero arguments, and inputs cannot be provided via the default value of a keyword argument.
  • NumPy array or Python scalar values in inputs get cast as tensors.
  • Keras mask metadata is only collected from inputs.
  • Layers are built (build(input_shape) method) using shape info from inputs only.
  • input_spec compatibility is only checked against inputs.
  • Mixed precision input casting is only applied to inputs. If a layer has tensor arguments in *args or **kwargs, their casting behavior in mixed precision should be handled manually.
  • The SavedModel input specification is generated using inputs only.
  • Integration with various ecosystem packages like TFMOT, TFLite, TF.js, etc is only supported for inputs and not for tensors in positional and keyword arguments.
*args Additional positional arguments. May contain tensors, although this is not recommended, for the reasons above.
**kwargs Additional keyword arguments. May contain tensors, although this is not recommended, for the reasons above. The following optional keyword arguments are reserved:
  • training: Boolean scalar tensor of Python boolean indicating whether the call is meant for training or inference.
  • mask: Boolean input mask. If the layer's call() method takes a mask argument, its default value will be set to the mask generated for inputs by the previous layer (if input did come from a layer that generated a corresponding mask, i.e. if it came from a Keras layer with masking support).

    • Returns
    A tensor or list/tuple of tensors.

    get_embedding_layer

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    get_embedding_layer()

    get_embedding_table

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    get_embedding_table()