Affine Layers#

Biaffine#

class supar.modules.affine.Biaffine(n_in: int, n_out: int = 1, n_proj: typing.Optional[int] = None, dropout: typing.Optional[float] = 0, scale: int = 0, bias_x: bool = True, bias_y: bool = True, decompose: bool = False, init: typing.Callable = <function zeros_>)[source]#

Biaffine layer for first-order scoring Dozat & Manning (2017).

This function has a tensor of weights \(W\) and bias terms if needed. The score \(s(x, y)\) of the vector pair \((x, y)\) is computed as \(x^T W y / d^s\), where d and s are vector dimension and scaling factor respectively. \(x\) and \(y\) can be concatenated with bias terms.

Parameters
  • n_in (int) – The size of the input feature.

  • n_out (int) – The number of output channels.

  • n_proj (Optional[int]) – If specified, applies MLP layers to reduce vector dimensions. Default: None.

  • dropout (Optional[float]) – If specified, applies a SharedDropout layer with the ratio on MLP outputs. Default: 0.

  • scale (float) – Factor to scale the scores. Default: 0.

  • bias_x (bool) – If True, adds a bias term for tensor \(x\). Default: True.

  • bias_y (bool) – If True, adds a bias term for tensor \(y\). Default: True.

  • decompose (bool) – If True, represents the weight as the product of 2 independent matrices. Default: False.

  • init (Callable) – Callable initialization method. Default: nn.init.zeros_.

forward(x: torch.Tensor, y: torch.Tensor) torch.Tensor[source]#
Parameters
Returns

A scoring tensor of shape [batch_size, n_out, seq_len, seq_len]. If n_out=1, the dimension for n_out will be squeezed automatically.

Return type

Tensor

Triaffine#

class supar.modules.affine.Triaffine(n_in: int, n_out: int = 1, n_proj: typing.Optional[int] = None, dropout: typing.Optional[float] = 0, scale: int = 0, bias_x: bool = False, bias_y: bool = False, decompose: bool = False, init: typing.Callable = <function zeros_>)[source]#

Triaffine layer for second-order scoring Zhang et al. 2020a,Wang et al. (2019).

This function has a tensor of weights \(W\) and bias terms if needed. The score \(s(x, y, z)\) of the vector triple \((x, y, z)\) is computed as \(x^T z^T W y / d^s\), where d and s are vector dimension and scaling factor respectively. \(x\) and \(y\) can be concatenated with bias terms.

Parameters
  • n_in (int) – The size of the input feature.

  • n_out (int) – The number of output channels.

  • n_proj (Optional[int]) – If specified, applies MLP layers to reduce vector dimensions. Default: None.

  • dropout (Optional[float]) – If specified, applies a SharedDropout layer with the ratio on MLP outputs. Default: 0.

  • scale (float) – Factor to scale the scores. Default: 0.

  • bias_x (bool) – If True, adds a bias term for tensor \(x\). Default: False.

  • bias_y (bool) – If True, adds a bias term for tensor \(y\). Default: False.

  • decompose (bool) – If True, represents the weight as the product of 3 independent matrices. Default: False.

  • init (Callable) – Callable initialization method. Default: nn.init.zeros_.

forward(x: torch.Tensor, y: torch.Tensor, z: torch.Tensor) torch.Tensor[source]#
Parameters
Returns

A scoring tensor of shape [batch_size, n_out, seq_len, seq_len, seq_len]. If n_out=1, the dimension for n_out will be squeezed automatically.

Return type

Tensor