PreLNTransformerLayers

class rectools.models.nn.transformers.net_blocks.PreLNTransformerLayers(n_blocks: int, n_factors: int, n_heads: int, dropout_rate: float, ff_factors_multiplier: int = 4, **kwargs: Any)

Bases: TransformerLayersBase

Pre-LN Transformer blocks.

Parameters

• n_blocks (int) – Number of transformer blocks.

• 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.

• kwargs (Any) –

Methods

forward(seqs, timeline_mask, attn_mask, ...)

Forward pass through transformer blocks.

Attributes

forward(seqs: Tensor, timeline_mask: Tensor, attn_mask: Optional[Tensor], key_padding_mask: Optional[Tensor], **kwargs: Any) → Tensor

Forward pass through transformer blocks.

Parameters

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

• timeline_mask (torch.Tensor) – Mask indicating padding elements.

• 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.

• kwargs (Any) –

Returns

User sequences passed through transformer layers.

Return type

torch.Tensor