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#!/usr/bin/env fbpython
# (c) Meta Platforms, Inc. and affiliates. Confidential and proprietary.
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,
},
"FakeTask": checkpoint_dict()["FakeTask"],
}
trainer.get_num_updates.return_value = num_updates
trainer.task.__class__.__name__ = "FakeTask"
trainer.task.get_checkpoint_dict.return_value = checkpoint_dict()
trainer.task.set_checkpoint_dict = MagicMock()
return trainer
def checkpoint_dict():
return {
"FakeTask": {
"observer_stats": {
(
4,
16,
"MovingAveragePerChannelMinMax",
"MovingAveragePerChannelMinMax",
): {"mod1": 1, "mod2": 2, "mod3": 3}
}
}
}
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",
"no_save": False,
"save_interval_updates": 0,
"no_last_checkpoints": False,
"keep_interval_updates": 0,
"keep_last_epochs": 0,
"keep_best_checkpoints": 0,
},
"common": {
"model_parallel_size": 1,
},
}
)
return cfg_mock
class TestCheckpointsForTaskLevelAttributes(unittest.TestCase):
def setUp(self) -> None:
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)
self.trainer, self.epoch_itr = get_trainer_and_epoch_itr(2, 150, 200, 50)
self.trainer.get_train_iterator = MagicMock(return_value=self.epoch_itr)
self.epoch_itr.next_epoch_itr(shuffle=False)
checkpoint_utils.save_checkpoint(
self.cfg_mock.checkpoint, self.trainer, self.epoch_itr, None
)
def tearDown(self):
patch.stopall()
logging.disable(logging.NOTSET)
def test_verify_checkpoint(self) -> None:
cp_dict = self.trainer.task.get_checkpoint_dict()
self.assertTrue(len(cp_dict) == 1)
self.assertTrue("FakeTask" in cp_dict)
self.assertTrue("observer_stats" in cp_dict["FakeTask"])
self.assertTrue(len(cp_dict["FakeTask"]["observer_stats"]) == 1)
self.assertTrue(
(
4,
16,
"MovingAveragePerChannelMinMax",
"MovingAveragePerChannelMinMax",
)
in cp_dict["FakeTask"]["observer_stats"]
)
self.assertTrue(
cp_dict["FakeTask"]["observer_stats"][
(
4,
16,
"MovingAveragePerChannelMinMax",
"MovingAveragePerChannelMinMax",
)
]
== {"mod1": 1, "mod2": 2, "mod3": 3}
)
def test_load_checkpoint(self) -> None:
with contextlib.redirect_stdout(StringIO()):
# Now, load checkpoint to ensure the respective logic works as expected
_, epoch_itr = checkpoint_utils.load_checkpoint(
self.cfg_mock.checkpoint, self.trainer
)
self.trainer.task.set_checkpoint_dict.assert_called_once_with(
checkpoint_dict()["FakeTask"]
)
if __name__ == "__main__":
unittest.main()
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