Hi, BPT and RingAttention are awesome works! Thanks a lot for open-sourcing the code.
I have a question about the 2nd equation in the following snapshot taken from the paper. I am having difficulty deriving the LHS and RHS as equal.
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Should the scaling factor be $$\exp(max(Q_i K_j^T) - \max_i)$$ instead of $$\exp(Q_i K_j^T - \max_i)$$, i.e., the maximum symbol is missing?
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Even fixing the above issue, following Online Normalizer Calculation for Softmax, should the scaling factor be applied to both the numerator and the denominator as done in both the pseudo-code in the paper (L43-45) as well as the implementation in the following?
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correction = rearrange(jnp.exp(prev_max_score_chunk - max_score_chunk), 'b h q -> b q h')[..., None] |
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numerator_chunk = numerator_chunk * correction + exp_values |
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denominator_chunk = denominator_chunk * jnp.exp(prev_max_score_chunk - max_score_chunk) + exp_weights.sum(axis=-1) |
But I may miss something in the paper. Any guidance would be much appreciated.
Thanks a lot in advance.
Hi, BPT and RingAttention are awesome works! Thanks a lot for open-sourcing the code.
I have a question about the 2nd equation in the following snapshot taken from the paper. I am having difficulty deriving the LHS and RHS as equal.
Should the scaling factor be$$\exp(max(Q_i K_j^T) - \max_i)$$ instead of $$\exp(Q_i K_j^T - \max_i)$$ , i.e., the maximum symbol is missing?
Even fixing the above issue, following Online Normalizer Calculation for Softmax, should the scaling factor be applied to both the numerator and the denominator as done in both the pseudo-code in the paper (L43-45) as well as the implementation in the following?
ringattention/ringattention/ringattention_jax.py
Lines 142 to 144 in aef108a
But I may miss something in the paper. Any guidance would be much appreciated.
Thanks a lot in advance.