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Revert optim schedule
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@ -875,16 +875,7 @@ class LearnedDownsamplingModule(nn.Module):
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if random.random() < 0.01 or __name__ == '__main__':
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logging.info(f"mean weight={weights.mean()}, mean-abs-scores={scores.abs().mean()} positive-scores={(scores>0).to(torch.float32).mean()}, discarded-weights={weights_discarded.mean()}, seq_len={seq_len}, seq_len_reduced={seq_len_reduced}")
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# randomly rotate `weights_discarded` on the sequence axis; this is
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# intended to ensure that it doesn't assign the highest scores to
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# not-so-important elements to avoid the randomness of these
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# discarded weights.
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r = random.randint(0, seq_len_reduced - 1)
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weights_discarded = torch.cat((weights_discarded[:, r:],
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weights_discarded[:, :r]),
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dim=1)
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weights_discarded = weights_discarded.flip(dims=1)
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weights = (weights[:, :seq_len_reduced] - weights_discarded)
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else:
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@ -882,7 +882,7 @@ class Eden(LRScheduler):
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warmup_factor = (
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1.0
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if self.batch >= self.warmup_batches
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else 0.1 + 0.9 * (self.batch / self.warmup_batches)
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else 0.5 + 0.5 * (self.batch / self.warmup_batches)
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)
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return [x * factor * warmup_factor for x in self.base_lrs]
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