The Seq2SeqAttentionDecoder can be verified by instantiating a test configuration with vocab_size $$= 10$$, embed_size $$= 8$$, num_hiddens $$= 16$$, and num_layers $$= 2$$, then feeding a minibatch of $$4$$ sequences each with $$7$$ time steps. After running the encoder and initializing the decoder state, the forward pass produces an output tensor of shape (batch_size, num_steps, vocab_size) $$= (4, 7, 10)$$. The returned state contains the encoder outputs of shape (batch_size, num_steps, num_hiddens) $$= (4, 7, 16)$$ and a per-layer decoder hidden state where each layer has shape (batch_size, num_hiddens) $$= (4, 16)$$.

Claude

The Seq2SeqAttentionDecoder class implements a concrete RNN decoder that integrates Bahdanau-style additive attention into the sequence-to-sequence framework. Its architecture consists of four components: an AdditiveAttention module, an embedding layer that maps target token indices to dense vectors, a multilayer GRU whose input size equals embed_size + num_hiddens (to accommodate the concatenated context and embedding), and a fully connected output layer that projects hidden states to the target vocabulary size. All parameters are initialized using Xavier initialization.

Seq2SeqAttentionDecoder Implementation

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The decoder state in the Seq2SeqAttentionDecoder is initialized from the encoder outputs as a three-element tuple: (i) the encoder's last-layer hidden states at all time steps, transposed to shape (batch_size, num_steps, num_hiddens), which serve as both the keys and values for the attention mechanism; (ii) the encoder's hidden states across all layers at the final time step, with shape (num_layers, batch_size, num_hiddens), used to initialize the decoder's GRU hidden state; and (iii) the valid lengths of the encoder inputs, used to mask padding tokens during attention pooling.

Seq2SeqAttentionDecoder State Initialization

During the forward pass of the Seq2SeqAttentionDecoder, the target token indices are first embedded and transposed to (num_steps, batch_size, embed_size). The decoder then iterates over each time step. At each step: (1) the last-layer hidden state from the previous time step is reshaped into a query of shape (batch_size, $$1$$, num_hiddens); (2) the additive attention mechanism computes a context vector of shape (batch_size, $$1$$, num_hiddens) by attending over all encoder outputs (keys and values), with padding excluded via valid lengths; (3) this context vector is concatenated with the current embedded input along the feature dimension; (4) the concatenated tensor, of size embed_size + num_hiddens, is fed through the GRU, which updates the hidden state. After processing all time steps, the collected GRU outputs are concatenated and projected through a dense layer to produce predictions of shape (batch_size, num_steps, vocab_size). The attention weights from every step are stored for later inspection.

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