File size: 6,830 Bytes
1e6d67a |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 |
# coding=utf-8
# Copyright 2020 The HuggingFace Team. All rights reserved.
#
# 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 transformers.testing_utils import require_torch
if is_torch_available():
import torch
from torch import nn
from transformers import (
Adafactor,
AdamW,
get_constant_schedule,
get_constant_schedule_with_warmup,
get_cosine_schedule_with_warmup,
get_cosine_with_hard_restarts_schedule_with_warmup,
get_inverse_sqrt_schedule,
get_linear_schedule_with_warmup,
get_polynomial_decay_schedule_with_warmup,
)
def unwrap_schedule(scheduler, num_steps=10):
lrs = []
for _ in range(num_steps):
lrs.append(scheduler.get_lr()[0])
scheduler.step()
return lrs
def unwrap_and_save_reload_schedule(scheduler, num_steps=10):
lrs = []
for step in range(num_steps):
lrs.append(scheduler.get_lr()[0])
scheduler.step()
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 = 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)
def test_adafactor(self):
w = torch.tensor([0.1, -0.2, -0.1], requires_grad=True)
target = torch.tensor([0.4, 0.2, -0.5])
criterion = nn.MSELoss()
# No warmup, constant schedule, no gradient clipping
optimizer = Adafactor(
params=[w],
lr=1e-2,
eps=(1e-30, 1e-3),
clip_threshold=1.0,
decay_rate=-0.8,
beta1=None,
weight_decay=0.0,
relative_step=False,
scale_parameter=False,
warmup_init=False,
)
for _ in range(1000):
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 = 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, msg=None):
self.assertEqual(len(list1), len(list2))
for a, b in zip(list1, list2):
self.assertAlmostEqual(a, b, delta=tol, msg=msg)
def test_schedulers(self):
common_kwargs = {"num_warmup_steps": 2, "num_training_steps": 10}
# schedulers doct format
# function: (sched_args_dict, expected_learning_rates)
scheds = {
get_constant_schedule: ({}, [10.0] * self.num_steps),
get_constant_schedule_with_warmup: (
{"num_warmup_steps": 4},
[0.0, 2.5, 5.0, 7.5, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0],
),
get_linear_schedule_with_warmup: (
{**common_kwargs},
[0.0, 5.0, 10.0, 8.75, 7.5, 6.25, 5.0, 3.75, 2.5, 1.25],
),
get_cosine_schedule_with_warmup: (
{**common_kwargs},
[0.0, 5.0, 10.0, 9.61, 8.53, 6.91, 5.0, 3.08, 1.46, 0.38],
),
get_cosine_with_hard_restarts_schedule_with_warmup: (
{**common_kwargs, "num_cycles": 2},
[0.0, 5.0, 10.0, 8.53, 5.0, 1.46, 10.0, 8.53, 5.0, 1.46],
),
get_polynomial_decay_schedule_with_warmup: (
{**common_kwargs, "power": 2.0, "lr_end": 1e-7},
[0.0, 5.0, 10.0, 7.656, 5.625, 3.906, 2.5, 1.406, 0.625, 0.156],
),
get_inverse_sqrt_schedule: (
{"num_warmup_steps": 2},
[0.0, 5.0, 10.0, 8.165, 7.071, 6.325, 5.774, 5.345, 5.0, 4.714],
),
}
for scheduler_func, data in scheds.items():
kwargs, expected_learning_rates = data
scheduler = scheduler_func(self.optimizer, **kwargs)
self.assertEqual(len([scheduler.get_lr()[0]]), 1)
lrs_1 = unwrap_schedule(scheduler, self.num_steps)
self.assertListAlmostEqual(
lrs_1,
expected_learning_rates,
tol=1e-2,
msg=f"failed for {scheduler_func} in normal scheduler",
)
scheduler = scheduler_func(self.optimizer, **kwargs)
if scheduler_func.__name__ != "get_constant_schedule":
LambdaScheduleWrapper.wrap_scheduler(scheduler) # wrap to test picklability of the schedule
lrs_2 = unwrap_and_save_reload_schedule(scheduler, self.num_steps)
self.assertListEqual(lrs_1, lrs_2, msg=f"failed for {scheduler_func} in save and reload")
class LambdaScheduleWrapper:
"""See https://github.com/huggingface/transformers/issues/21689"""
def __init__(self, fn):
self.fn = fn
def __call__(self, *args, **kwargs):
return self.fn(*args, **kwargs)
@classmethod
def wrap_scheduler(self, scheduler):
scheduler.lr_lambdas = list(map(self, scheduler.lr_lambdas))
|