![]() ![]() The decoder then adds the token to the output sequence, and repeats this autoregressive process until the EOS token is generated. Such that it can predict the next output sequence token. Transformer decoding starts with full input sequence, but empty decoding sequence.Ĭross-attention introduces information from the input sequence to the layers of the decoder, Cross-Attention in Transformer DecoderĬross-attention was described in the Transformer paper, but it was not given this name yet. bmm ( attention_probs, value ) Cross-Attention in Popular ArchitecturesĬross-attention is widely used in encoder-decoder or multi-modality use cases. get_attention_scores ( query, key, attention_mask ) hidden_states = torch. head_to_batch_dim ( value ) attention_probs = attn. to_v ( encoder_hidden_states ) key = attn. to_k ( encoder_hidden_states ) value = attn. head_to_batch_dim ( query ) encoder_hidden_states = encoder_hidden_states if encoder_hidden_states is not None else hidden_states key = attn. The constructor shows, how we can also have different dimensions and if you step through with a debugger, you will also see the different sequence length between the two modalities. In this case the cross-attention is used to condition transformers inside a UNet layer with a text prompt for image generation. Have a look at CrossAttention implementation in Diffusers library, which can generate images with Stable Diffusion. In an equation: \( \mathbf((W_Q S_2) (W_K S_1)^\intercal) W_V S_1 \) Cross-attention Implementation Output sequence has dimension and length of sequence S2.Calculate attention matrix from Keys and Queries.Calculate Key and Value from sequence S1.Let us have embeddings (token) sequences S1 and S2.The feed forward layer is related to cross-attention, except the feed forward layer does use softmax and one of the input sequences is static.Īugmenting Self-attention with Persistent Memory paper shows that Feed Forward layer calculation made the same as self-attention. One of the sequences serves as a query input, while the other as a key and value inputs.Īlternative cross-attention in SelfDoc, uses query and value from one sequence, and key from the other. machine translation: cross-attention helps decoder predict next token of the translated textĮxcept for inputs, cross-attention calculation is the same as self-attention.Ĭross-attention combines asymmetrically two separate embedding sequences of same dimension, in contrast self-attention input is a single embedding sequence.image-text classification with Perceiver.the other sequence then produces key and value input. ![]() one of the sequences defines the output length as it plays a role of a query input.the two sequences can be of different modalities (e.g.the two sequences must have the same dimension.an attention mechanism in Transformer architecture that mixes two different embedding sequences.Watch Cross-Attention in Transformer Architecture on Youtube ![]()
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