70 lines
2.9 KiB
Python
70 lines
2.9 KiB
Python
# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved.
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# Redistribution and use in source and binary forms, with or without
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# modification, are permitted provided that the following conditions are met:
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# * Redistributions of source code must retain the above copyright
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# notice, this list of conditions and the following disclaimer.
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# * Redistributions in binary form must reproduce the above copyright
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# notice, this list of conditions and the following disclaimer in the
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# documentation and/or other materials provided with the distribution.
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# * Neither the name of the NVIDIA CORPORATION nor the
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# names of its contributors may be used to endorse or promote products
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# derived from this software without specific prior written permission.
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# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
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# ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
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# WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
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# DISCLAIMED. IN NO EVENT SHALL NVIDIA CORPORATION BE LIABLE FOR ANY
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# DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
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# (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
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# LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND
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# ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
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# (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
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# SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
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import numpy as np
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class ScheduledOptim():
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''' A simple wrapper class for learning rate scheduling '''
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def __init__(self, optimizer, d_model, n_warmup_steps, current_steps):
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self._optimizer = optimizer
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self.n_warmup_steps = n_warmup_steps
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self.n_current_steps = current_steps
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self.init_lr = np.power(d_model, -0.5)
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def step_and_update_lr_frozen(self, learning_rate_frozen):
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for param_group in self._optimizer.param_groups:
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param_group['lr'] = learning_rate_frozen
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self._optimizer.step()
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def step_and_update_lr(self):
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self._update_learning_rate()
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self._optimizer.step()
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def get_learning_rate(self):
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learning_rate = 0.0
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for param_group in self._optimizer.param_groups:
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learning_rate = param_group['lr']
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return learning_rate
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def zero_grad(self):
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# print(self.init_lr)
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self._optimizer.zero_grad()
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def _get_lr_scale(self):
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return np.min([
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np.power(self.n_current_steps, -0.5),
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np.power(self.n_warmup_steps, -1.5) * self.n_current_steps])
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def _update_learning_rate(self):
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''' Learning rate scheduling per step '''
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self.n_current_steps += 1
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lr = self.init_lr * self._get_lr_scale()
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for param_group in self._optimizer.param_groups:
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param_group['lr'] = lr
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