Spaces:
Running
Running
File size: 9,292 Bytes
650c5f6 |
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 |
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import contextlib
import logging
import unittest
from io import StringIO
from unittest.mock import MagicMock, patch
import torch
from fairseq import checkpoint_utils, data
from omegaconf import OmegaConf
def mock_trainer(epoch, num_updates, iterations_in_epoch):
trainer = MagicMock()
trainer.load_checkpoint.return_value = {
"train_iterator": {
"epoch": epoch,
"iterations_in_epoch": iterations_in_epoch,
"shuffle": False,
},
}
trainer.get_num_updates.return_value = num_updates
return trainer
def mock_dict():
d = MagicMock()
d.pad.return_value = 1
d.eos.return_value = 2
d.unk.return_value = 3
return d
def get_trainer_and_epoch_itr(epoch, epoch_size, num_updates, iterations_in_epoch):
tokens = torch.LongTensor(list(range(epoch_size))).view(1, -1)
tokens_ds = data.TokenBlockDataset(
tokens,
sizes=[tokens.size(-1)],
block_size=1,
pad=0,
eos=1,
include_targets=False,
)
trainer = mock_trainer(epoch, num_updates, iterations_in_epoch)
dataset = data.LanguagePairDataset(
tokens_ds, tokens_ds.sizes, mock_dict(), shuffle=False
)
epoch_itr = data.EpochBatchIterator(
dataset=dataset,
collate_fn=dataset.collater,
batch_sampler=[[i] for i in range(epoch_size)],
)
return trainer, epoch_itr
def get_mock_cfg(finetune_from_model):
cfg_mock = OmegaConf.create(
{
"checkpoint": {
"save_dir": None,
"optimizer_overrides": "{}",
"reset_dataloader": False,
"reset_meters": False,
"reset_optimizer": False,
"reset_lr_scheduler": False,
"finetune_from_model": finetune_from_model,
"model_parallel_size": 1,
"restore_file": "checkpoint_last.pt",
},
"common": {
"model_parallel_size": 1,
},
}
)
return cfg_mock
class TestLoadCheckpoint(unittest.TestCase):
def setUp(self):
self.cfg_mock = get_mock_cfg(None)
self.patches = {
"os.makedirs": MagicMock(),
"os.path.join": MagicMock(),
"os.path.isfile": MagicMock(return_value=True),
"os.path.isabs": MagicMock(return_value=False),
"fairseq.file_io.PathManager.exists": MagicMock(return_value=False),
}
self.applied_patches = [patch(p, d) for p, d in self.patches.items()]
[p.start() for p in self.applied_patches]
logging.disable(logging.CRITICAL)
def tearDown(self):
patch.stopall()
logging.disable(logging.NOTSET)
def test_load_partial_checkpoint(self):
with contextlib.redirect_stdout(StringIO()):
trainer, epoch_itr = get_trainer_and_epoch_itr(2, 150, 200, 50)
trainer.get_train_iterator = MagicMock(return_value=epoch_itr)
_, epoch_itr = checkpoint_utils.load_checkpoint(
self.cfg_mock.checkpoint, trainer
)
self.assertEqual(epoch_itr.epoch, 2)
self.assertEqual(epoch_itr.iterations_in_epoch, 50)
itr = epoch_itr.next_epoch_itr(shuffle=False)
self.assertEqual(epoch_itr.epoch, 2)
self.assertEqual(epoch_itr.iterations_in_epoch, 50)
self.assertEqual(next(itr)["net_input"]["src_tokens"][0].item(), 50)
self.assertEqual(epoch_itr.iterations_in_epoch, 51)
for _ in range(150 - 52):
next(itr)
self.assertEqual(epoch_itr.iterations_in_epoch, 149)
self.assertTrue(itr.has_next())
next(itr)
self.assertFalse(itr.has_next())
itr = epoch_itr.next_epoch_itr(shuffle=False)
self.assertTrue(itr.has_next())
self.assertEqual(epoch_itr.epoch, 3)
self.assertEqual(epoch_itr.iterations_in_epoch, 0)
def test_load_full_checkpoint(self):
with contextlib.redirect_stdout(StringIO()):
trainer, epoch_itr = get_trainer_and_epoch_itr(2, 150, 300, 150)
trainer.get_train_iterator = MagicMock(return_value=epoch_itr)
_, epoch_itr = checkpoint_utils.load_checkpoint(
self.cfg_mock.checkpoint, trainer
)
itr = epoch_itr.next_epoch_itr(shuffle=False)
self.assertEqual(epoch_itr.epoch, 3)
self.assertEqual(epoch_itr.iterations_in_epoch, 0)
self.assertEqual(next(itr)["net_input"]["src_tokens"][0].item(), 0)
def test_load_no_checkpoint(self):
with contextlib.redirect_stdout(StringIO()):
trainer, epoch_itr = get_trainer_and_epoch_itr(1, 150, 0, 0)
trainer.get_train_iterator = MagicMock(return_value=epoch_itr)
self.patches["os.path.isfile"].return_value = False
_, epoch_itr = checkpoint_utils.load_checkpoint(
self.cfg_mock.checkpoint, trainer
)
itr = epoch_itr.next_epoch_itr(shuffle=False)
self.assertEqual(epoch_itr.epoch, 1)
self.assertEqual(epoch_itr.iterations_in_epoch, 0)
self.assertEqual(next(itr)["net_input"]["src_tokens"][0].item(), 0)
def test_finetune_from_model_args_conflict(self):
with contextlib.redirect_stdout(StringIO()):
trainer, epoch_itr = get_trainer_and_epoch_itr(1, 150, 0, 0)
trainer.get_train_iterator = MagicMock(return_value=epoch_itr)
for arg in [
"reset_optimizer",
"reset_lr_scheduler",
"reset_meters",
"reset_dataloader",
]:
with self.subTest(arg=arg):
cfg_mock = get_mock_cfg("/temp/checkpoint_pretrained.pt")
cfg_mock["checkpoint"][arg] = True
with self.assertRaises(Exception) as context:
_, _ = checkpoint_utils.load_checkpoint(
cfg_mock.checkpoint, trainer
)
self.assertTrue(
"--finetune-from-model can not be set together with either --reset-optimizer"
" or reset_lr_scheduler or reset_meters or reset_dataloader"
in str(context.exception)
)
def test_finetune_from_model(self):
with contextlib.redirect_stdout(StringIO()):
trainer, epoch_itr = get_trainer_and_epoch_itr(1, 150, 0, 0)
trainer.get_train_iterator = MagicMock(return_value=epoch_itr)
from_model_path = "/temp/checkpoint_pretrained.pt"
def mock_finetune_exist(path):
if path == from_model_path:
return True
else:
return False
self.patches[
"fairseq.file_io.PathManager.exists"
].side_effect = mock_finetune_exist
cfg_mock = get_mock_cfg(from_model_path)
cfg_mock.checkpoint.restore_file = "checkpoint_last.pt"
_, _ = checkpoint_utils.load_checkpoint(cfg_mock.checkpoint, trainer)
(
checkpoint_path,
reset_optimizer,
reset_lr_scheduler,
optimizer_overrides,
) = trainer.load_checkpoint.call_args[0]
reset_meters = trainer.load_checkpoint.call_args[1]["reset_meters"]
self.assertTrue(reset_optimizer)
self.assertTrue(reset_lr_scheduler)
self.assertTrue(reset_meters)
def test_finetune_from_model_resume(self):
with contextlib.redirect_stdout(StringIO()):
trainer, epoch_itr = get_trainer_and_epoch_itr(1, 150, 0, 0)
trainer.get_train_iterator = MagicMock(return_value=epoch_itr)
from_model_path = "/temp/checkpoint_pretrained.pt"
# launch second time
# both restore_file=checkpoint_last.pt and finetune_from_model are set
def mock_finetune_exist(path):
if path == from_model_path or path.endsWith("checkpoint_last.pt"):
return True
else:
return False
self.patches[
"fairseq.file_io.PathManager.exists"
].side_effect = mock_finetune_exist
cfg_mock = get_mock_cfg(from_model_path)
cfg_mock.checkpoint.restore_file = "checkpoint_last.pt"
_, _ = checkpoint_utils.load_checkpoint(cfg_mock.checkpoint, trainer)
(
checkpoint_path,
reset_optimizer,
reset_lr_scheduler,
optimizer_overrides,
) = trainer.load_checkpoint.call_args[0]
reset_meters = trainer.load_checkpoint.call_args[1]["reset_meters"]
self.assertFalse(reset_optimizer)
self.assertFalse(reset_lr_scheduler)
self.assertFalse(reset_meters)
if __name__ == "__main__":
unittest.main()
|