Minor correction in 'Add & Norm' logic in Block Class in gpt.py #22
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Updating the forward function in Transformer block. The code is simple to example the pull request, but still trying my best to explain below: As per paper: In 'Add & Norm' block of Transformer, Layer Norm is applied on top of input/ residual & output of Self-attention. While in the current code, first layer Norm is applied & then added back to the input/ residual.
AbhishekAshokDubey
changed the title
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See 1:35:33 |
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Updating the forward function in Transformer block.
The change is simple, but still trying my best to explain below:
As per original paper: In 'Add & Norm' block of Transformer, Layer Norm is applied on top of => input/ residual and output of Self-attention. While in the current code, layer Norm is applied first & then added back to the input/ residual.