fix booboo
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@ -273,7 +273,7 @@ class TransformerDiffusion(nn.Module):
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return groups
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def before_step(self, step):
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scaled_grad_parameters = list(itertools.chain.from_iterable([lyr.out.parameters() for lyr in self.diff.layers]))
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scaled_grad_parameters = list(itertools.chain.from_iterable([lyr.out.parameters() for lyr in self.layers]))
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# Scale back the gradients of the blkout and prenorm layers by a constant factor. These get two orders of magnitudes
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# higher gradients. Ideally we would use parameter groups, but ZeroRedundancyOptimizer makes this trickier than
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# directly fiddling with the gradients.
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