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Fix bug in choosing layers to drop
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@ -471,11 +471,11 @@ class ConformerEncoder(nn.Module):
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# with their specified probs while reaching this exact target.
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num_to_drop = int(tot) + int(shared_rng.random() < (tot - int(tot)))
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layers = list(range(num_layers))
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independent_rng.shuffle(layers)
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# go through the shuffled layers until we get the required number of samples.
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if num_to_drop > 0:
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for layer in itertools.cycle(layers):
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if independent_rng.random() < layerdrop_probs[layer]:
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ans.add(layer)
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