mirror of
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106 lines
4.3 KiB
Python
106 lines
4.3 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_transformers import (AdamW, ConstantLRSchedule, WarmupConstantSchedule,
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WarmupCosineSchedule, WarmupCosineWithHardRestartsSchedule, WarmupLinearSchedule)
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import numpy as np
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def unwrap_schedule(scheduler, num_steps=10):
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lrs = []
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for _ in range(num_steps):
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scheduler.step()
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lrs.append(scheduler.get_lr())
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return lrs
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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_w(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 = AdamW(params=[w], lr=2e-1, weight_decay=0.0)
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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 ScheduleInitTest(unittest.TestCase):
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m = torch.nn.Linear(50, 50)
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optimizer = AdamW(m.parameters(), lr=10.)
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num_steps = 10
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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_constant_scheduler(self):
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scheduler = ConstantLRSchedule(self.optimizer)
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lrs = unwrap_schedule(scheduler, self.num_steps)
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expected_learning_rates = [10.] * self.num_steps
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self.assertEqual(len(lrs[0]), 1)
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self.assertListEqual([l[0] for l in lrs], expected_learning_rates)
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def test_warmup_constant_scheduler(self):
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scheduler = WarmupConstantSchedule(self.optimizer, warmup_steps=4)
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lrs = unwrap_schedule(scheduler, self.num_steps)
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expected_learning_rates = [2.5, 5.0, 7.5, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0]
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self.assertEqual(len(lrs[0]), 1)
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self.assertListEqual([l[0] for l in lrs], expected_learning_rates)
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def test_warmup_linear_scheduler(self):
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scheduler = WarmupLinearSchedule(self.optimizer, warmup_steps=2, t_total=10)
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lrs = unwrap_schedule(scheduler, self.num_steps)
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expected_learning_rates = [5.0, 10.0, 8.75, 7.5, 6.25, 5.0, 3.75, 2.5, 1.25, 0.0]
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self.assertEqual(len(lrs[0]), 1)
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self.assertListEqual([l[0] for l in lrs], expected_learning_rates)
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def test_warmup_cosine_scheduler(self):
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scheduler = WarmupCosineSchedule(self.optimizer, warmup_steps=2, t_total=10)
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lrs = unwrap_schedule(scheduler, self.num_steps)
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expected_learning_rates = [5.0, 10.0, 9.61, 8.53, 6.91, 5.0, 3.08, 1.46, 0.38, 0.0]
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self.assertEqual(len(lrs[0]), 1)
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self.assertListAlmostEqual([l[0] for l in lrs], expected_learning_rates, tol=1e-2)
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def test_warmup_cosine_hard_restart_scheduler(self):
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scheduler = WarmupCosineWithHardRestartsSchedule(self.optimizer, warmup_steps=2, cycles=2, t_total=10)
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lrs = unwrap_schedule(scheduler, self.num_steps)
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expected_learning_rates = [5.0, 10.0, 8.53, 5.0, 1.46, 10.0, 8.53, 5.0, 1.46, 0.0]
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self.assertEqual(len(lrs[0]), 1)
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self.assertListAlmostEqual([l[0] for l in lrs], expected_learning_rates, tol=1e-2)
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if __name__ == "__main__":
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unittest.main()
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