PreLNTransformerLayer

class rectools.models.nn.transformers.net_blocks.PreLNTransformerLayer(n_factors: int, n_heads: int, dropout_rate: float, ff_factors_multiplier: int = 4)[source]

Bases: Module

Pre-LN Transformer Layer as described in “On Layer Normalization in the Transformer Architecture” https://arxiv.org/pdf/2002.04745

Parameters

  • n_factors (int) – Latent embeddings size.

  • n_heads (int) – Number of attention heads.

  • dropout_rate (float) – Probability of a hidden unit to be zeroed.

  • ff_factors_multiplier (int) – Feed-forward layers latent embedding size multiplier.

Methods

forward(seqs, attn_mask, key_padding_mask)

Forward pass through transformer block.

Attributes

forward(seqs: Tensor, attn_mask: Optional[Tensor], key_padding_mask: Optional[Tensor]) → Tensor[source]

Forward pass through transformer block.

Parameters

  • seqs (torch.Tensor) – User sequences of item embeddings.

  • attn_mask (torch.Tensor, optional) – Optional mask to use in forward pass of multi-head attention as attn_mask.

  • key_padding_mask (torch.Tensor, optional) – Optional mask to use in forward pass of multi-head attention as key_padding_mask.

Returns

User sequences passed through transformer layers.

Return type

torch.Tensor