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64 lines
2.1 KiB
Python
64 lines
2.1 KiB
Python
# coding=utf-8
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# Copyright 2018 The Google AI Language Team Authors.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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from __future__ import absolute_import
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from __future__ import division
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from __future__ import print_function
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import unittest
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import torch
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from pytorch_pretrained_bert import BertAdam, WarmupCosineWithRestartsSchedule
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from matplotlib import pyplot as plt
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import numpy as np
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class OptimizationTest(unittest.TestCase):
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def assertListAlmostEqual(self, list1, list2, tol):
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self.assertEqual(len(list1), len(list2))
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for a, b in zip(list1, list2):
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self.assertAlmostEqual(a, b, delta=tol)
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def test_adam(self):
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w = torch.tensor([0.1, -0.2, -0.1], requires_grad=True)
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target = torch.tensor([0.4, 0.2, -0.5])
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criterion = torch.nn.MSELoss()
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# No warmup, constant schedule, no gradient clipping
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optimizer = BertAdam(params=[w], lr=2e-1,
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weight_decay=0.0,
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max_grad_norm=-1)
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for _ in range(100):
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loss = criterion(w, target)
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loss.backward()
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optimizer.step()
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w.grad.detach_() # No zero_grad() function on simple tensors. we do it ourselves.
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w.grad.zero_()
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self.assertListAlmostEqual(w.tolist(), [0.4, 0.2, -0.5], tol=1e-2)
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class WarmupCosineWithRestartsTest(unittest.TestCase):
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def test_it(self):
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m = WarmupCosineWithRestartsSchedule(warmup=0.2, t_total=1, cycles=3)
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x = np.arange(0, 1000) / 1000
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y = [m.get_lr_(xe) for xe in x]
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plt.plot(y)
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plt.show()
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if __name__ == "__main__":
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unittest.main()
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