Comments (1)
Other candidates are Alibi or no embeddings at all. For the last one, in order for it to work, do you need to train with a range of sizes so it can learn to length extrapolate well?
from x-transformers.
Related Issues (20)
- [Question] very small attention scores HOT 7
- Was it a clerical error ? ScaleNorm.g init form dim ** -0.5. I think it should be dim ** 0.5 HOT 1
- [Bug] Error when `rotary_pos_emb` set to True in cross attention HOT 3
- Question: problem with xval implementation HOT 6
- Correct interaction between CLS token and RoPE HOT 5
- RoPE inconsistency (2-dim subspaces choice)
- Sinusoidal embedding order choice different from original definition HOT 1
- How to use "src_key_padding_mask" HOT 3
- Enable flash attention does not support BFloat16? HOT 1
- Problem with cache and memory
- Random lack of gradients HOT 1
- Is this the same "X-transformer" that being used in "X-Transformer: A Machine Translation Model Enhanced by the Self-Attention Mechanism" paper? HOT 1
- [Question] Why is RotaryEmbedding not used when cross attending?
- Feature request: Multi-Head Latent Attention Support HOT 1
- Should the mask option to AttentionLayers(boolean) increase memory? HOT 1
- Small paper ideas to be added HOT 3
- Decoder logits are NaN when cross-attention context is all padding HOT 2
- Pytorch warning with autocast
- Allow passing in a pre-existing TokenEmbedding into TransformerWrapper
- [Feature Request] CoPE from Meta HOT 3
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from x-transformers.