153 lines
6.3 KiB
Python
153 lines
6.3 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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import os
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import tempfile
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import unittest
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from transformers import is_torch_available
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from .utils import require_torch
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if is_torch_available():
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import torch
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from transformers import (
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AdamW,
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get_constant_schedule,
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get_constant_schedule_with_warmup,
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get_cosine_schedule_with_warmup,
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get_cosine_with_hard_restarts_schedule_with_warmup,
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get_linear_schedule_with_warmup,
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)
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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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def unwrap_and_save_reload_schedule(scheduler, num_steps=10):
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lrs = []
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for step in range(num_steps):
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scheduler.step()
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lrs.append(scheduler.get_lr())
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if step == num_steps // 2:
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with tempfile.TemporaryDirectory() as tmpdirname:
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file_name = os.path.join(tmpdirname, "schedule.bin")
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torch.save(scheduler.state_dict(), file_name)
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state_dict = torch.load(file_name)
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scheduler.load_state_dict(state_dict)
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return lrs
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@require_torch
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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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@require_torch
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class ScheduleInitTest(unittest.TestCase):
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m = torch.nn.Linear(50, 50) if is_torch_available() else None
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optimizer = AdamW(m.parameters(), lr=10.0) if is_torch_available() else None
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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 = get_constant_schedule(self.optimizer)
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lrs = unwrap_schedule(scheduler, self.num_steps)
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expected_learning_rates = [10.0] * 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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scheduler = get_constant_schedule(self.optimizer)
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lrs_2 = unwrap_and_save_reload_schedule(scheduler, self.num_steps)
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self.assertListEqual([l[0] for l in lrs], [l[0] for l in lrs_2])
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def test_warmup_constant_scheduler(self):
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scheduler = get_constant_schedule_with_warmup(self.optimizer, num_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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scheduler = get_constant_schedule_with_warmup(self.optimizer, num_warmup_steps=4)
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lrs_2 = unwrap_and_save_reload_schedule(scheduler, self.num_steps)
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self.assertListEqual([l[0] for l in lrs], [l[0] for l in lrs_2])
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def test_warmup_linear_scheduler(self):
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scheduler = get_linear_schedule_with_warmup(self.optimizer, num_warmup_steps=2, num_training_steps=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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scheduler = get_linear_schedule_with_warmup(self.optimizer, num_warmup_steps=2, num_training_steps=10)
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lrs_2 = unwrap_and_save_reload_schedule(scheduler, self.num_steps)
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self.assertListEqual([l[0] for l in lrs], [l[0] for l in lrs_2])
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def test_warmup_cosine_scheduler(self):
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scheduler = get_cosine_schedule_with_warmup(self.optimizer, num_warmup_steps=2, num_training_steps=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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scheduler = get_cosine_schedule_with_warmup(self.optimizer, num_warmup_steps=2, num_training_steps=10)
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lrs_2 = unwrap_and_save_reload_schedule(scheduler, self.num_steps)
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self.assertListEqual([l[0] for l in lrs], [l[0] for l in lrs_2])
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def test_warmup_cosine_hard_restart_scheduler(self):
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scheduler = get_cosine_with_hard_restarts_schedule_with_warmup(
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self.optimizer, num_warmup_steps=2, num_cycles=2, num_training_steps=10
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)
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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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scheduler = get_cosine_with_hard_restarts_schedule_with_warmup(
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self.optimizer, num_warmup_steps=2, num_cycles=2, num_training_steps=10
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)
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lrs_2 = unwrap_and_save_reload_schedule(scheduler, self.num_steps)
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self.assertListEqual([l[0] for l in lrs], [l[0] for l in lrs_2])
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