https://github.com/freewym/espresso
Tip revision: 660facf088ded9f084cc1a24a1f00f64ce5f6918 authored by freewym on 20 July 2023, 23:05:26 UTC
allows dictionary files w/o the counts column; rename task's
allows dictionary files w/o the counts column; rename task's
Tip revision: 660facf
test_checkpoint_utils.py
# 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 os
import tempfile
import unittest
from io import StringIO
from unittest.mock import patch
from omegaconf import OmegaConf
from fairseq import checkpoint_utils
from tests.utils import (
create_dummy_data,
preprocess_translation_data,
train_translation_model,
)
import torch
class TestCheckpointUtils(unittest.TestCase):
def setUp(self):
logging.disable(logging.CRITICAL)
def tearDown(self):
logging.disable(logging.NOTSET)
@contextlib.contextmanager
def _train_transformer(self, seed, extra_args=None):
if extra_args is None:
extra_args = []
with tempfile.TemporaryDirectory(f"_train_transformer_seed{seed}") as data_dir:
create_dummy_data(data_dir)
preprocess_translation_data(data_dir)
train_translation_model(
data_dir,
"transformer_iwslt_de_en",
[
"--encoder-layers",
"3",
"--decoder-layers",
"3",
"--encoder-embed-dim",
"8",
"--decoder-embed-dim",
"8",
"--seed",
str(seed),
]
+ extra_args,
)
yield os.path.join(data_dir, "checkpoint_last.pt")
def test_load_model_ensemble_and_task(self):
# with contextlib.redirect_stdout(StringIO()):
with self._train_transformer(seed=123) as model1:
with self._train_transformer(seed=456) as model2:
ensemble, cfg, task = checkpoint_utils.load_model_ensemble_and_task(
filenames=[model1, model2]
)
self.assertEqual(len(ensemble), 2)
# after Transformer has been migrated to Hydra, this will probably
# become cfg.common.seed
self.assertEqual(ensemble[0].args.seed, 123)
self.assertEqual(ensemble[1].args.seed, 456)
# the task from the first model should be returned
self.assertTrue("seed123" in task.cfg.data)
# last cfg is saved
self.assertEqual(cfg.common.seed, 456)
def test_prune_state_dict(self):
with contextlib.redirect_stdout(StringIO()):
extra_args = ["--encoder-layerdrop", "0.01", "--decoder-layerdrop", "0.01"]
with self._train_transformer(seed=1, extra_args=extra_args) as model:
ensemble, cfg, task = checkpoint_utils.load_model_ensemble_and_task(
filenames=[model],
arg_overrides={
"encoder_layers_to_keep": "0,2",
"decoder_layers_to_keep": "1",
},
)
self.assertEqual(len(ensemble), 1)
self.assertEqual(len(ensemble[0].encoder.layers), 2)
self.assertEqual(len(ensemble[0].decoder.layers), 1)
def test_torch_persistent_save_async(self):
state_dict = {}
filename = "async_checkpoint.pt"
with patch(f"{checkpoint_utils.__name__}.PathManager.opena") as mock_opena:
with patch(
f"{checkpoint_utils.__name__}._torch_persistent_save"
) as mock_save:
checkpoint_utils.torch_persistent_save(
state_dict, filename, async_write=True
)
mock_opena.assert_called_with(filename, "wb")
mock_save.assert_called()
def test_load_ema_from_checkpoint(self):
dummy_state = {"a": torch.tensor([1]), "b": torch.tensor([0.1])}
with patch(f"{checkpoint_utils.__name__}.PathManager.open") as mock_open, patch(
f"{checkpoint_utils.__name__}.torch.load"
) as mock_load:
mock_load.return_value = {"extra_state": {"ema": dummy_state}}
filename = "ema_checkpoint.pt"
state = checkpoint_utils.load_ema_from_checkpoint(filename)
mock_open.assert_called_with(filename, "rb")
mock_load.assert_called()
self.assertIn("a", state["model"])
self.assertIn("b", state["model"])
self.assertTrue(torch.allclose(dummy_state["a"], state["model"]["a"]))
self.assertTrue(torch.allclose(dummy_state["b"], state["model"]["b"]))
if __name__ == "__main__":
unittest.main()