旋转嵌入层

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RotaryEmbedding class

keras_nlp.layers.RotaryEmbedding(
    max_wavelength=10000, scaling_factor=1.0, sequence_axis=1, feature_axis=-1, **kwargs
)

Rotary positional encoding layer.

This layer encodes absolute positional information with a rotation matrix. It calculates the rotary encoding with a mix of sine and cosine functions with geometrically increasing wavelengths. Defined and formulated in RoFormer: Enhanced Transformer with Rotary Position Embedding. The input must be a tensor with shape a sequence dimension and a feature dimension. Typically, this will either an input with shape (batch_size, sequence_length, feature_length) or (batch_size, sequence_length, num_heads, feature_length). This layer will return a new tensor with the rotary embedding applied to the input tensor.

Arguments

• max_wavelength: int. The maximum angular wavelength of the sine/cosine curves.
• scaling_factor: float. The scaling factor used to scale positions of the tokens.
• sequence_axis: int. Sequence axis in the input tensor.
• feature_axis: int. Feature axis in the input tensor.
• **kwargs: other keyword arguments passed to keras.layers.Layer, including name, trainable, dtype etc.

Call arguments

• inputs: The tensor inputs to apply the embedding to. This can have any shape, but must contain both a sequence and feature axis. The rotary embedding will be applied to inputs and returned.
• start_index: An integer or integer tensor. The starting position to compute the rotary embedding from. This is useful during cached decoding, where each position is predicted separately in a loop.

Examples

batch_size = 16
feature_length = 18
sequence_length = 256
num_heads = 8

# No multi-head dimension.
tensor = np.ones((batch_size, sequence_length, feature_length))
rot_emb_layer = RotaryEmbedding()
tensor_rot = rot_emb_layer(tensor)

# With multi-head dimension.
tensor = np.ones((batch_size, sequence_length, num_heads, feature_length))
tensor_rot = rot_emb_layer(tensor)

References

• RoFormer: Enhanced Transformer with Rotary Position Embedding