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c13737d | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 | # Copyright 2022 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 logging
import os
import random
import tempfile
import unittest
import torch
from torch import nn
from torch.utils.data import DataLoader, TensorDataset
from accelerate import Accelerator
from accelerate.utils import ProjectConfiguration, set_seed
logger = logging.getLogger(__name__)
def dummy_dataloaders(a=2, b=3, batch_size=16, n_train_batches: int = 10, n_valid_batches: int = 2):
"Generates a tuple of dummy DataLoaders to test with"
def get_dataset(n_batches):
x = torch.randn(batch_size * n_batches, 1)
return TensorDataset(x, a * x + b + 0.1 * torch.randn(batch_size * n_batches, 1))
train_dataset = get_dataset(n_train_batches)
valid_dataset = get_dataset(n_valid_batches)
train_dataloader = DataLoader(train_dataset, shuffle=True, batch_size=batch_size, num_workers=4)
valid_dataloader = DataLoader(valid_dataset, shuffle=False, batch_size=batch_size, num_workers=4)
return (train_dataloader, valid_dataloader)
def train(num_epochs, model, dataloader, optimizer, accelerator, scheduler=None):
"Trains for `num_epochs`"
rands = []
for epoch in range(num_epochs):
# Train quickly
model.train()
for batch in dataloader:
x, y = batch
outputs = model(x)
loss = torch.nn.functional.mse_loss(outputs, y)
accelerator.backward(loss)
optimizer.step()
optimizer.zero_grad()
rands.append(random.random()) # Introduce some randomness
if scheduler is not None:
scheduler.step()
return rands
class DummyModel(nn.Module):
"Simple model to do y=mx+b"
def __init__(self):
super().__init__()
self.a = nn.Parameter(torch.randn(1))
self.b = nn.Parameter(torch.randn(1))
def forward(self, x):
return x * self.a + self.b
class CheckpointTest(unittest.TestCase):
def test_with_save_limit(self):
with tempfile.TemporaryDirectory() as tmpdir:
set_seed(42)
model = DummyModel()
optimizer = torch.optim.Adam(params=model.parameters(), lr=1e-3)
train_dataloader, valid_dataloader = dummy_dataloaders()
project_config = ProjectConfiguration(total_limit=1, project_dir=tmpdir, automatic_checkpoint_naming=True)
# Train baseline
accelerator = Accelerator(project_config=project_config)
model, optimizer, train_dataloader, valid_dataloader = accelerator.prepare(
model, optimizer, train_dataloader, valid_dataloader
)
# Save initial
accelerator.save_state()
# Save second state
accelerator.save_state()
self.assertEqual(len(os.listdir(accelerator.project_dir)), 1)
def test_can_resume_training_with_folder(self):
with tempfile.TemporaryDirectory() as tmpdir:
set_seed(42)
model = DummyModel()
optimizer = torch.optim.Adam(params=model.parameters(), lr=1e-3)
train_dataloader, valid_dataloader = dummy_dataloaders()
# Train baseline
accelerator = Accelerator()
model, optimizer, train_dataloader, valid_dataloader = accelerator.prepare(
model, optimizer, train_dataloader, valid_dataloader
)
# Save initial
initial = os.path.join(tmpdir, "initial")
accelerator.save_state(initial)
(a, b) = model.a.item(), model.b.item()
opt_state = optimizer.state_dict()
ground_truth_rands = train(3, model, train_dataloader, optimizer, accelerator)
(a1, b1) = model.a.item(), model.b.item()
opt_state1 = optimizer.state_dict()
# Train partially
set_seed(42)
model = DummyModel()
optimizer = torch.optim.Adam(params=model.parameters(), lr=1e-3)
train_dataloader, valid_dataloader = dummy_dataloaders()
accelerator = Accelerator()
model, optimizer, train_dataloader, valid_dataloader = accelerator.prepare(
model, optimizer, train_dataloader, valid_dataloader
)
accelerator.load_state(initial)
(a2, b2) = model.a.item(), model.b.item()
opt_state2 = optimizer.state_dict()
self.assertEqual(a, a2)
self.assertEqual(b, b2)
self.assertEqual(opt_state, opt_state2)
test_rands = train(2, model, train_dataloader, optimizer, accelerator)
# Save everything
checkpoint = os.path.join(tmpdir, "checkpoint")
accelerator.save_state(checkpoint)
# Load everything back in and make sure all states work
accelerator.load_state(checkpoint)
test_rands += train(1, model, train_dataloader, optimizer, accelerator)
(a3, b3) = model.a.item(), model.b.item()
opt_state3 = optimizer.state_dict()
self.assertEqual(a1, a3)
self.assertEqual(b1, b3)
self.assertEqual(opt_state1, opt_state3)
self.assertEqual(ground_truth_rands, test_rands)
def test_can_resume_training(self):
with tempfile.TemporaryDirectory() as tmpdir:
set_seed(42)
model = DummyModel()
optimizer = torch.optim.Adam(params=model.parameters(), lr=1e-3)
train_dataloader, valid_dataloader = dummy_dataloaders()
project_config = ProjectConfiguration(automatic_checkpoint_naming=True)
# Train baseline
accelerator = Accelerator(project_dir=tmpdir, project_config=project_config)
model, optimizer, train_dataloader, valid_dataloader = accelerator.prepare(
model, optimizer, train_dataloader, valid_dataloader
)
# Save initial
accelerator.save_state()
(a, b) = model.a.item(), model.b.item()
opt_state = optimizer.state_dict()
ground_truth_rands = train(3, model, train_dataloader, optimizer, accelerator)
(a1, b1) = model.a.item(), model.b.item()
opt_state1 = optimizer.state_dict()
# Train partially
set_seed(42)
model = DummyModel()
optimizer = torch.optim.Adam(params=model.parameters(), lr=1e-3)
train_dataloader, valid_dataloader = dummy_dataloaders()
project_config = ProjectConfiguration(iteration=1, automatic_checkpoint_naming=True)
accelerator = Accelerator(project_dir=tmpdir, project_config=project_config)
model, optimizer, train_dataloader, valid_dataloader = accelerator.prepare(
model, optimizer, train_dataloader, valid_dataloader
)
accelerator.load_state(os.path.join(tmpdir, "checkpoints", "checkpoint_0"))
(a2, b2) = model.a.item(), model.b.item()
opt_state2 = optimizer.state_dict()
self.assertEqual(a, a2)
self.assertEqual(b, b2)
self.assertEqual(opt_state, opt_state2)
test_rands = train(2, model, train_dataloader, optimizer, accelerator)
# Save everything
accelerator.save_state()
# Load everything back in and make sure all states work
accelerator.load_state(os.path.join(tmpdir, "checkpoints", "checkpoint_1"))
test_rands += train(1, model, train_dataloader, optimizer, accelerator)
(a3, b3) = model.a.item(), model.b.item()
opt_state3 = optimizer.state_dict()
self.assertEqual(a1, a3)
self.assertEqual(b1, b3)
self.assertEqual(opt_state1, opt_state3)
self.assertEqual(ground_truth_rands, test_rands)
def test_invalid_registration(self):
t = torch.tensor([1, 2, 3])
t1 = torch.tensor([2, 3, 4])
net = DummyModel()
opt = torch.optim.Adam(net.parameters())
accelerator = Accelerator()
with self.assertRaises(ValueError) as ve:
accelerator.register_for_checkpointing(t, t1, net, opt)
message = str(ve.exception)
self.assertTrue("Item at index 0" in message)
self.assertTrue("Item at index 1" in message)
self.assertFalse("Item at index 2" in message)
self.assertFalse("Item at index 3" in message)
def test_with_scheduler(self):
with tempfile.TemporaryDirectory() as tmpdir:
set_seed(42)
model = DummyModel()
optimizer = torch.optim.Adam(params=model.parameters(), lr=1e-3)
scheduler = torch.optim.lr_scheduler.StepLR(optimizer, step_size=1, gamma=0.99)
train_dataloader, valid_dataloader = dummy_dataloaders()
project_config = ProjectConfiguration(automatic_checkpoint_naming=True)
# Train baseline
accelerator = Accelerator(project_dir=tmpdir, project_config=project_config)
model, optimizer, train_dataloader, valid_dataloader, scheduler = accelerator.prepare(
model, optimizer, train_dataloader, valid_dataloader, scheduler
)
# Save initial
accelerator.save_state()
scheduler_state = scheduler.state_dict()
train(3, model, train_dataloader, optimizer, accelerator, scheduler)
self.assertNotEqual(scheduler_state, scheduler.state_dict())
# Load everything back in and make sure all states work
accelerator.load_state(os.path.join(tmpdir, "checkpoints", "checkpoint_0"))
self.assertEqual(scheduler_state, scheduler.state_dict())
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