Comments (1)
In MultiGPULoss function, the variable out
is cloned as a leaf node of computational graph, which means in this function the computational graph is built from out(o1) and ends at o2. So l.backward()
only calculates the gradient of o2 wrt. o1. Due to the chain rule we should multiply do2/do1 and do1/dw and the correct gradient will be stored at w.grad.
from annotated-transformer.
Related Issues (20)
- Some doubts about SublayerConnection HOT 5
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- TypeError: dropout(): argument 'input' (position 1) must be Tensor, not NoneType HOT 1
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from annotated-transformer.