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@ -26,7 +26,8 @@ logger = logging.getLogger(__name__)
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def warmup_cosine(x, warmup=0.002):
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if x < warmup:
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return x/warmup
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return 0.5 * (1.0 + torch.cos(math.pi * x))
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x_ = (x - warmup) / (1 - warmup) # progress after warmup -
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return 0.5 * (1. + math.cos(math.pi * x_))
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def warmup_constant(x, warmup=0.002):
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""" Linearly increases learning rate over `warmup`*`t_total` (as provided to BertAdam) training steps.
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@ -26,7 +26,8 @@ logger = logging.getLogger(__name__)
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def warmup_cosine(x, warmup=0.002):
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if x < warmup:
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return x/warmup
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return 0.5 * (1.0 + torch.cos(math.pi * x))
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x_ = (x - warmup) / (1 - warmup) # progress after warmup
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return 0.5 * (1. + math.cos(math.pi * x_))
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def warmup_constant(x, warmup=0.002):
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""" Linearly increases learning rate over `warmup`*`t_total` (as provided to OpenAIAdam) training steps.
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