Comments (4)
Hi @einarbmag, sorry for the long delay.
I could be wrong, but I think mixture-of-experts might have an implementation for this to use different experts within a batch. @muqeeth might know more about this.
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Hi @einarbmag, here is one possible implementation we can use for a batch containing examples from multiple tasks:
Assume B
is the batch size, N
is the number of tasks, and H
is the hidden dimension at which IA^3 is applied.
- Task indices
T
are represented by aB x N
tensor. This tensor is one-hot, where the index corresponding to the task index is set to 1 for each example. - IA^3 vectors
V
are defined as anN x H
tensor.
We can obtain the required IA^3 vectors for each example by using L_batch = torch.matmul(T, V)
.
Then, we modify the input activations, which have the shape (B x num_tokens x H)
, by multiplying them with L_batch
unsqueezing along the sequence dimension.
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Hi I have to use the mixed task batch so I'll do it if I need to,..
Did you implement IA3 mixed task batch?
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Hi @dodoyeon sorry we did not, but Muqeeth's sketch above can provide a starting point!
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