Comments (3)
Which I think would be consistent with https://github.com/lucidrains/x-transformers#flash-attention
from recurrent-memory-transformer-pytorch.
Ok, I think I'm looking at a different residual paper. This is implementing ResiDual not residual attention. Sorry
from recurrent-memory-transformer-pytorch.
@lucidrains I've been trying different types of normalisation with your excellent XTransformers library to see how i can accelerate convergence. So far, resiDual hasn't given me any benefits over standard nn.Layernorm. Is there a reason why you added it to this repo? Was it in the original paper? Or is it that you've found success using it generally and thought it fit well in RMT? Cheers
from recurrent-memory-transformer-pytorch.
Related Issues (18)
- causal mask assert hit HOT 25
- Question: configuring scaled_dot_product_attention
- Attend : check mask isn't already 4D HOT 1
- Question: how does memory replay backprogagation work with multiple models in series HOT 8
- Question: why do we need read_memory_emb HOT 6
- Question: masks HOT 3
- Feature request: make JIT and ONNX export work HOT 4
- Question: Global write tokens or recurrent HOT 7
- Question: first read memories HOT 12
- flash attention, and a potentially better improvement HOT 5
- token_shift HOT 4
- bptt depth implementation? HOT 3
- have you had a chance to train it yet? HOT 2
- What happens if texts from the dataset don't have equal lengths HOT 4
- Is rmt compatible with pretrain models like LLaMA? HOT 6
- Question: How to set seq_len ? HOT 1
- Question: how to adapt this for CTC loss HOT 2
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from recurrent-memory-transformer-pytorch.