Injecting Multiple Modalities into a Transformer Decoder via Cross-Attention

🤗Transformers
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What is the best way to inject two different modalities/inputs into a transformer decoder using cross-attention?

For example, suppose I want my decoder to be conditioned on both text tokens and image patch tokens, or alternatively, on outputs from two different text encoders. In my specific use case, I prefer to incorporate these conditions through cross-attention rather than directly modifying the decoder’s inputs.

Does this approach have standard implementations or best practices?

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Since I had no idea, I asked Hugging Chat.:sweat_smile:

To inject two different modalities/inputs into a transformer decoder using cross-attention in a multimodal setup (e.g., text and images or outputs from two different text encoders), you can leverage the Hugging Face Transformers library, which provides extensive support for multimodal architectures and cross-attention mechanisms. Below is a structured approach to achieve this, along with references to relevant implementations and best practices:

1. Preprocessing and Encoding

  • Each modality (e.g., text tokens and image patches) is processed through separate encoders to generate embeddings. For example:
    • Text is tokenized and processed through a text encoder (e.g., BERT or RoBERTa).
    • Images are converted into patch tokens and processed through a vision encoder (e.g., ViT or ResNet + patch embedding).
  • Ensure that the embeddings from both encoders are of compatible dimensions. If they differ, use projection layers to align them.

2. Multimodal Architecture

  • Use a transformer decoder that can handle multiple sources of cross-attention. This can be achieved by:
    • Concatenating encoder outputs: Combine the outputs of both encoders into a single sequence of key-value pairs. The decoder’s cross-attention layer will attend to both modalities collectively.
    • Separate cross-attention layers: Implement individual cross-attention layers for each modality. For example, one cross-attention layer for text encodings and another for image encodings. This provides finer control over how each modality contributes to the decoder’s output.

3. Implementation in Hugging Face Transformers

  • The Hugging Face Transformers library supports multimodal setups through:

    • PrefixLM-style architectures: Injecting multimodal context as prefixes or external memory in the decoder. For example, Flava or BLIPv2.
    • Custom decoder layers: Modify the decoder layers to include multiple cross-attention modules. For example, you can extend the T5ForConditionalGeneration or BartForConditionalGeneration models to handle multiple encoders.

  • Example code outline:

    from transformers import AutoModel, AutoTokenizer
import torch
import torch.nn as nn

class MultimodalDecoder(nn.Module):
    def __init__(self, decoder, encoder_text, encoder_image, projection_dim):
        super().__init__()
        self.decoder = decoder
        self.encoder_text = encoder_text
        self.encoder_image = encoder_image
        self.text_projection = nn.Linear(text_dim, projection_dim)
        self.image_projection = nn.Linear(image_dim, projection_dim)

    def forward(self, input_ids, attention_mask, image_patches):
        # Encode text and image
        text_embeddings = self.encoder_text(input_ids, attention_mask)
        image_embeddings = self.encoder_image(image_patches)

        # Project to compatible dimensions
        text_embeddings = self.text_projection(text_embeddings.last_hidden_state)
        image_embeddings = self.image_projection(image_embeddings.last_hidden_state)

        # Concatenate encodings
        encoder_outputs = torch.cat([text_embeddings, image_embeddings], dim=1)

        # Pass through decoder
        outputs = self.decoder(
            input_ids=input_ids,
            encoder_outputs=encoder_outputs,
            attention_mask=attention_mask,
        )

        return outputs

4. References and Best Practices

  • For multimodal setups, the work in [1] on the X-Embodiment collaboration and Palm-e highlights the importance of cross-attention mechanisms in integrating diverse modalities.
  • The CLIP model [2] provides a strong foundation for handling text and images in a unified framework, inspiring ways to inject different modalities into decoders.
  • In [3], the iterative generation of images from text demonstrates how cross-attention can be used to fuse information from multiple sources effectively.

5. Hugging Face Model Examples

  • Flava: A vision-language model that processes text and images in a unified framework.
  • BLIPv2: An image-text retrieval model that leverages cross-attention between text and image encodings.
  • MUMT: Multimodal Unsupervised Translation: Combines text and images for translation tasks, showcasing how to inject different modalities into a decoder.

Conclusion

Injecting two different modalities into a transformer decoder using cross-attention is a realistic and feasible approach in multimodal setups. By leveraging the flexibility of the Hugging Face Transformers library, you can implement this by either concatenating encoder outputs or using separate cross-attention layers for each modality. This approach aligns with existing research in multimodal architectures and provides a powerful way to condition the decoder on multiple sources of information.

For further details, you can explore implementations like Flava and BLIPv2, which demonstrate state-of-the-art multimodal architectures.