Transformer Layers

Transformer layers use self-attention mechanisms to process sequences in parallel, enabling efficient training on long sequences. They are the foundation of modern NLP models (BERT, GPT) and increasingly used in vision and other domains.

• Transformer: Complete encoder-decoder architecture
• TransformerEncoder/Decoder: Standalone encoder or decoder stacks
• TransformerEncoderLayer/DecoderLayer: Individual transformer blocks
• MultiheadAttention: Core attention mechanism used throughout

Key parameters:

• d_model: Dimension of the model (embedding dimension)
• nhead: Number of attention heads
• num_encoder_layers/num_decoder_layers: Number of stacked layers
• dim_feedforward: Dimension of feedforward network
• dropout: Dropout rate for regularization

Transformer

Complete encoder-decoder transformer architecture.

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Example:

auto transformer = torch::nn::Transformer(
    torch::nn::TransformerOptions()
        .d_model(512)
        .nhead(8)
        .num_encoder_layers(6)
        .num_decoder_layers(6)
        .dim_feedforward(2048)
        .dropout(0.1));

TransformerEncoder

Stack of encoder layers for processing source sequences.

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TransformerDecoder

Stack of decoder layers for generating target sequences.

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TransformerEncoderLayer

Single encoder layer with self-attention and feedforward network.

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TransformerDecoderLayer

Single decoder layer with self-attention, cross-attention, and feedforward network.

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MultiheadAttention

Scaled dot-product attention with multiple parallel heads.

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