153 строки
6.3 KiB
Python
153 строки
6.3 KiB
Python
# coding=utf-8
|
|
# Copyright 2018 The Google AI Language Team Authors.
|
|
#
|
|
# Licensed under the Apache License, Version 2.0 (the "License");
|
|
# you may not use this file except in compliance with the License.
|
|
# You may obtain a copy of the License at
|
|
#
|
|
# http://www.apache.org/licenses/LICENSE-2.0
|
|
#
|
|
# Unless required by applicable law or agreed to in writing, software
|
|
# distributed under the License is distributed on an "AS IS" BASIS,
|
|
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
|
# See the License for the specific language governing permissions and
|
|
# limitations under the License.
|
|
|
|
|
|
import os
|
|
import tempfile
|
|
import unittest
|
|
|
|
from transformers import is_torch_available
|
|
|
|
from .utils import require_torch
|
|
|
|
|
|
if is_torch_available():
|
|
import torch
|
|
|
|
from transformers import (
|
|
AdamW,
|
|
get_constant_schedule,
|
|
get_constant_schedule_with_warmup,
|
|
get_cosine_schedule_with_warmup,
|
|
get_cosine_with_hard_restarts_schedule_with_warmup,
|
|
get_linear_schedule_with_warmup,
|
|
)
|
|
|
|
|
|
def unwrap_schedule(scheduler, num_steps=10):
|
|
lrs = []
|
|
for _ in range(num_steps):
|
|
scheduler.step()
|
|
lrs.append(scheduler.get_lr())
|
|
return lrs
|
|
|
|
|
|
def unwrap_and_save_reload_schedule(scheduler, num_steps=10):
|
|
lrs = []
|
|
for step in range(num_steps):
|
|
scheduler.step()
|
|
lrs.append(scheduler.get_lr())
|
|
if step == num_steps // 2:
|
|
with tempfile.TemporaryDirectory() as tmpdirname:
|
|
file_name = os.path.join(tmpdirname, "schedule.bin")
|
|
torch.save(scheduler.state_dict(), file_name)
|
|
|
|
state_dict = torch.load(file_name)
|
|
scheduler.load_state_dict(state_dict)
|
|
return lrs
|
|
|
|
|
|
@require_torch
|
|
class OptimizationTest(unittest.TestCase):
|
|
def assertListAlmostEqual(self, list1, list2, tol):
|
|
self.assertEqual(len(list1), len(list2))
|
|
for a, b in zip(list1, list2):
|
|
self.assertAlmostEqual(a, b, delta=tol)
|
|
|
|
def test_adam_w(self):
|
|
w = torch.tensor([0.1, -0.2, -0.1], requires_grad=True)
|
|
target = torch.tensor([0.4, 0.2, -0.5])
|
|
criterion = torch.nn.MSELoss()
|
|
# No warmup, constant schedule, no gradient clipping
|
|
optimizer = AdamW(params=[w], lr=2e-1, weight_decay=0.0)
|
|
for _ in range(100):
|
|
loss = criterion(w, target)
|
|
loss.backward()
|
|
optimizer.step()
|
|
w.grad.detach_() # No zero_grad() function on simple tensors. we do it ourselves.
|
|
w.grad.zero_()
|
|
self.assertListAlmostEqual(w.tolist(), [0.4, 0.2, -0.5], tol=1e-2)
|
|
|
|
|
|
@require_torch
|
|
class ScheduleInitTest(unittest.TestCase):
|
|
m = torch.nn.Linear(50, 50) if is_torch_available() else None
|
|
optimizer = AdamW(m.parameters(), lr=10.0) if is_torch_available() else None
|
|
num_steps = 10
|
|
|
|
def assertListAlmostEqual(self, list1, list2, tol):
|
|
self.assertEqual(len(list1), len(list2))
|
|
for a, b in zip(list1, list2):
|
|
self.assertAlmostEqual(a, b, delta=tol)
|
|
|
|
def test_constant_scheduler(self):
|
|
scheduler = get_constant_schedule(self.optimizer)
|
|
lrs = unwrap_schedule(scheduler, self.num_steps)
|
|
expected_learning_rates = [10.0] * self.num_steps
|
|
self.assertEqual(len(lrs[0]), 1)
|
|
self.assertListEqual([l[0] for l in lrs], expected_learning_rates)
|
|
|
|
scheduler = get_constant_schedule(self.optimizer)
|
|
lrs_2 = unwrap_and_save_reload_schedule(scheduler, self.num_steps)
|
|
self.assertListEqual([l[0] for l in lrs], [l[0] for l in lrs_2])
|
|
|
|
def test_warmup_constant_scheduler(self):
|
|
scheduler = get_constant_schedule_with_warmup(self.optimizer, num_warmup_steps=4)
|
|
lrs = unwrap_schedule(scheduler, self.num_steps)
|
|
expected_learning_rates = [2.5, 5.0, 7.5, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0]
|
|
self.assertEqual(len(lrs[0]), 1)
|
|
self.assertListEqual([l[0] for l in lrs], expected_learning_rates)
|
|
|
|
scheduler = get_constant_schedule_with_warmup(self.optimizer, num_warmup_steps=4)
|
|
lrs_2 = unwrap_and_save_reload_schedule(scheduler, self.num_steps)
|
|
self.assertListEqual([l[0] for l in lrs], [l[0] for l in lrs_2])
|
|
|
|
def test_warmup_linear_scheduler(self):
|
|
scheduler = get_linear_schedule_with_warmup(self.optimizer, num_warmup_steps=2, num_training_steps=10)
|
|
lrs = unwrap_schedule(scheduler, self.num_steps)
|
|
expected_learning_rates = [5.0, 10.0, 8.75, 7.5, 6.25, 5.0, 3.75, 2.5, 1.25, 0.0]
|
|
self.assertEqual(len(lrs[0]), 1)
|
|
self.assertListEqual([l[0] for l in lrs], expected_learning_rates)
|
|
|
|
scheduler = get_linear_schedule_with_warmup(self.optimizer, num_warmup_steps=2, num_training_steps=10)
|
|
lrs_2 = unwrap_and_save_reload_schedule(scheduler, self.num_steps)
|
|
self.assertListEqual([l[0] for l in lrs], [l[0] for l in lrs_2])
|
|
|
|
def test_warmup_cosine_scheduler(self):
|
|
scheduler = get_cosine_schedule_with_warmup(self.optimizer, num_warmup_steps=2, num_training_steps=10)
|
|
lrs = unwrap_schedule(scheduler, self.num_steps)
|
|
expected_learning_rates = [5.0, 10.0, 9.61, 8.53, 6.91, 5.0, 3.08, 1.46, 0.38, 0.0]
|
|
self.assertEqual(len(lrs[0]), 1)
|
|
self.assertListAlmostEqual([l[0] for l in lrs], expected_learning_rates, tol=1e-2)
|
|
|
|
scheduler = get_cosine_schedule_with_warmup(self.optimizer, num_warmup_steps=2, num_training_steps=10)
|
|
lrs_2 = unwrap_and_save_reload_schedule(scheduler, self.num_steps)
|
|
self.assertListEqual([l[0] for l in lrs], [l[0] for l in lrs_2])
|
|
|
|
def test_warmup_cosine_hard_restart_scheduler(self):
|
|
scheduler = get_cosine_with_hard_restarts_schedule_with_warmup(
|
|
self.optimizer, num_warmup_steps=2, num_cycles=2, num_training_steps=10
|
|
)
|
|
lrs = unwrap_schedule(scheduler, self.num_steps)
|
|
expected_learning_rates = [5.0, 10.0, 8.53, 5.0, 1.46, 10.0, 8.53, 5.0, 1.46, 0.0]
|
|
self.assertEqual(len(lrs[0]), 1)
|
|
self.assertListAlmostEqual([l[0] for l in lrs], expected_learning_rates, tol=1e-2)
|
|
|
|
scheduler = get_cosine_with_hard_restarts_schedule_with_warmup(
|
|
self.optimizer, num_warmup_steps=2, num_cycles=2, num_training_steps=10
|
|
)
|
|
lrs_2 = unwrap_and_save_reload_schedule(scheduler, self.num_steps)
|
|
self.assertListEqual([l[0] for l in lrs], [l[0] for l in lrs_2])
|