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52 lines
1.6 KiB
52 lines
1.6 KiB
"""
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optimizer learning rate scheduling helpers
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"""
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from math import ceil
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def noam_schedule(step, warmup_step=4000):
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if step <= warmup_step:
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return step / warmup_step
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return (warmup_step ** 0.5) * (step ** -0.5)
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def warmup_linear(step, warmup_step, tot_step):
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if step < warmup_step:
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return step / warmup_step
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return max(0, (tot_step-step)/(tot_step-warmup_step))
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def vqa_schedule(step, warmup_interval, decay_interval,
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decay_start, decay_rate):
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""" VQA schedule from MCAN """
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if step < warmup_interval:
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return 1/4
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elif step < 2 * warmup_interval:
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return 2/4
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elif step < 3 * warmup_interval:
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return 3/4
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elif step >= decay_start:
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num_decay = ceil((step - decay_start) / decay_interval)
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return decay_rate ** num_decay
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else:
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return 1
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def get_lr_sched(global_step, opts):
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# learning rate scheduling
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if opts.decay == 'linear':
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lr_this_step = opts.learning_rate * warmup_linear(
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global_step, opts.warmup_steps, opts.num_train_steps)
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elif opts.decay == 'invsqrt':
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lr_this_step = opts.learning_rate * noam_schedule(
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global_step, opts.warmup_steps)
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elif opts.decay == 'constant':
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lr_this_step = opts.learning_rate
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elif opts.decay == 'vqa':
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lr_this_step = opts.learning_rate * vqa_schedule(
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global_step, opts.warm_int, opts.decay_int,
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opts.decay_st, opts.decay_rate)
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if lr_this_step <= 0:
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# save guard for possible miscalculation of train steps
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lr_this_step = 1e-8
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return lr_this_step
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