# 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": { "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()