# Copyright (c) 2020, NVIDIA CORPORATION. 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 json import math import os import re from pathlib import Path from typing import Any from unittest.mock import patch import lightning.pytorch as pl import pytest import torch from lightning.pytorch import Callback from lightning.pytorch.loops import _TrainingEpochLoop from omegaconf import OmegaConf from omegaconf.errors import OmegaConfBaseException from nemo.collections.common.parts.nlp_overrides import NLPDDPStrategy from nemo.constants import NEMO_ENV_VARNAME_VERSION from nemo.core.classes import ModelPT from nemo.utils.app_state import AppState from nemo.utils.callbacks import NeMoModelCheckpoint from nemo.utils.exp_manager import ( CheckpointMisconfigurationError, LoggerMisconfigurationError, NotFoundError, exp_manager, ) class MyTestOptimizer(torch.optim.Optimizer): def __init__(self, params): self._step = 0 super().__init__(params, {}) @torch.no_grad() def step(self, closure=None): loss = None if closure is not None: with torch.enable_grad(): loss = closure() for group in self.param_groups: for p in group['params']: if self._step == 0: p.data = 0.1 * torch.ones(p.shape) elif self._step == 1: p.data = 0.0 * torch.ones(p.shape) else: p.data = 0.01 * torch.ones(p.shape) self._step += 1 return loss class DoNothingOptimizer(torch.optim.Optimizer): def __init__(self, params): self._step = 0 super().__init__(params, {}) @torch.no_grad() def step(self, closure=None): loss = None if closure is not None: with torch.enable_grad(): loss = closure() self._step += 1 return loss class OnesDataset(torch.utils.data.Dataset): def __init__(self, dataset_len): super().__init__() self.__dataset_len = dataset_len def __getitem__(self, *args): return torch.ones(2) def __len__(self): return self.__dataset_len class ExampleModel(ModelPT): def __init__(self, *args, **kwargs): cfg = OmegaConf.structured({}) super().__init__(cfg) pl.seed_everything(1234) self.l1 = torch.nn.modules.Linear(in_features=2, out_features=1) def train_dataloader(self): dataset = OnesDataset(2) return torch.utils.data.DataLoader(dataset, batch_size=2, num_workers=8) def val_dataloader(self): dataset = OnesDataset(10) return torch.utils.data.DataLoader(dataset, batch_size=2, num_workers=8) def forward(self, batch): output = self.l1(batch) output = torch.nn.functional.l1_loss(output, torch.zeros(output.size()).to(output.device)) return output def validation_step(self, batch, batch_idx): self.loss = self(batch) return self.loss def training_step(self, batch, batch_idx): return self(batch) def configure_optimizers(self): return MyTestOptimizer(self.parameters()) # return torch.optim.Adam(self.parameters(), lr=0.1) def list_available_models(self): pass def setup_training_data(self): pass def setup_validation_data(self): pass def on_validation_epoch_end(self): self.log("val_loss", torch.stack([self.loss]).mean()) class ExampleMCoreModel(ExampleModel): def sharded_state_dict(self): return {'a': 3} class DoNothingModel(ExampleModel): def configure_optimizers(self): return DoNothingOptimizer(self.parameters()) class TestExpManager: @pytest.fixture(autouse=True, scope="class") def _mock_onelogger_update_config(self): with patch('nemo.lightning.callback_group.CallbackGroup.update_config', return_value=None): yield @pytest.mark.unit def test_omegaconf(self): """Ensure omegaconf raises an error when an unexcepted argument is passed""" with pytest.raises(OmegaConfBaseException): exp_manager(pl.Trainer(accelerator='cpu'), {"unused": 1}) @pytest.mark.unit def test_trainer_loggers(self, tmp_path): """Test that a trainer with logger errors out with a number of arguments. Test that it works with create_tensorboard_logger set to False """ test_trainer = pl.Trainer(accelerator='cpu') # Should create logger and modelcheckpoint with pytest.raises(LoggerMisconfigurationError): # Fails because exp_manager defaults to trainer exp_manager(test_trainer, {"exp_dir": str(tmp_path)}) with pytest.raises(LoggerMisconfigurationError): # Fails because exp_manager defaults to trainer exp_manager(test_trainer, {"explicit_log_dir": str(tmp_path)}) with pytest.raises(LoggerMisconfigurationError): # Fails because exp_manager defaults to trainer exp_manager(test_trainer, {"resume_if_exists": True}) # Check that exp_manager uses trainer.logger, it's exp_dir, name, and version log_dir = exp_manager(test_trainer, {"create_tensorboard_logger": False, "create_checkpoint_callback": False}) assert log_dir.resolve() == Path("./lightning_logs/version_0").resolve() assert Path("./lightning_logs").exists() assert Path("./lightning_logs/version_0").exists() # Check that a trainer without a logger gets a logger attached to it test_trainer = pl.Trainer(accelerator='cpu', logger=False) log_dir = exp_manager( test_trainer, {"create_tensorboard_logger": True, "create_checkpoint_callback": False, "exp_dir": str(tmp_path)}, ) assert isinstance(test_trainer.logger, pl.loggers.TensorBoardLogger) test_trainer = pl.Trainer(accelerator='cpu', logger=False) # Check that a create_wandb_logger=True errors out unless wandb_logger_kwargs is passed. with pytest.raises(ValueError): log_dir = exp_manager( test_trainer, { "create_tensorboard_logger": False, "create_checkpoint_callback": False, "exp_dir": str(tmp_path), "create_wandb_logger": True, }, ) # Check that a WandbLogger is attached to logger if create_wandb_logger=True and wandb_logger_kwargs has name # and project log_dir = exp_manager( test_trainer, { "create_tensorboard_logger": False, "create_checkpoint_callback": False, "exp_dir": str(tmp_path), "create_wandb_logger": True, "wandb_logger_kwargs": {"name": "", "project": "", "offline": True}, }, ) assert isinstance(test_trainer.logger, pl.loggers.WandbLogger) @pytest.mark.unit def test_trainer_neptune_logger(self, tmp_path): pytest.importorskip("neptune", reason="could not import `neptune`, use `pip install neptune` to run this test") test_trainer = pl.Trainer(accelerator='cpu', logger=False) # Check that a create_neptune_logger=True errors out unless neptune_logger_kwargs is passed. with pytest.raises(ValueError): _ = exp_manager( test_trainer, { "create_tensorboard_logger": False, "create_checkpoint_callback": False, "exp_dir": str(tmp_path), "create_neptune_logger": True, }, ) # Check that a NeptuneLogger is attached to logger if create_neptune_logger=True and neptune_logger_kwargs has name # and project _ = exp_manager( test_trainer, { "create_tensorboard_logger": False, "create_checkpoint_callback": False, "exp_dir": str(tmp_path), "create_neptune_logger": True, "neptune_logger_kwargs": {"name": "", "project": "", "api_key": ""}, }, ) assert isinstance(test_trainer.logger, pl.loggers.NeptuneLogger) @pytest.mark.unit def test_checkpoint_configurations(self): """Test that trainer creating modelcheckpoint and asking exp_manager to do it too results in errors, but is error free if only one is asked to do so. """ disable_tb_logger = {"create_tensorboard_logger": False} test_trainer = pl.Trainer(accelerator='cpu') # Should create logger and modelcheckpoint with pytest.raises(CheckpointMisconfigurationError): # Fails because both try to create modelcheckpoint exp_manager(test_trainer, disable_tb_logger) # Should succeed without error exp_manager(test_trainer, {"create_checkpoint_callback": False, "create_tensorboard_logger": False}) test_trainer_2 = pl.Trainer(accelerator='cpu', enable_checkpointing=False) exp_manager(test_trainer_2, disable_tb_logger) # Should succeed without error @pytest.mark.unit def test_default_log_dir(self): """Check the default of ./nemo_experiments/default/datetime works as intended""" test_trainer = pl.Trainer(accelerator='cpu', enable_checkpointing=False, logger=False) log_dir = exp_manager(test_trainer, {"create_tensorboard_logger": False, "create_checkpoint_callback": False}) assert (log_dir / "..").resolve() == Path("./nemo_experiments/default/").resolve() assert Path("./nemo_experiments").exists() assert Path("./nemo_experiments/default/").exists() sub_dirs = [x for x in Path("./nemo_experiments/default/").iterdir() if x.is_dir()] assert len(sub_dirs) == 1 assert re.match(r"[0-9]{4}-[0-9]{2}-[0-9]{2}_[0-9]{2}-[0-9]{2}-[0-9]{2}", sub_dirs[0].name) @pytest.mark.unit def test_log_dir_overrides(self, monkeypatch, tmp_path): """Check a variety of trainer options with exp_manager""" # Checks that explicit_log_dir ignores exp_dir, name, and version test_trainer = pl.Trainer(accelerator='cpu', enable_checkpointing=False, logger=False) log_dir = exp_manager(test_trainer, {"explicit_log_dir": str(tmp_path / "test_log_dir_overrides")}) assert log_dir.resolve() == (tmp_path / "test_log_dir_overrides").resolve() assert Path(tmp_path).exists() assert Path(tmp_path / "test_log_dir_overrides").exists() # Checks that exp_manager uses exp_dir, default name, and explicit version test_trainer = pl.Trainer(accelerator='cpu', enable_checkpointing=False, logger=False) log_dir = exp_manager(test_trainer, {"exp_dir": str(tmp_path / "test_no_name"), "version": 957}) assert log_dir.resolve() == (tmp_path / "test_no_name" / "default" / "957").resolve() assert Path(tmp_path).exists() assert Path(tmp_path / "test_no_name" / "default" / "957").exists() monkeypatch.delenv(NEMO_ENV_VARNAME_VERSION, raising=False) # Checks that use_datetime_version False toggle works test_trainer = pl.Trainer(accelerator='cpu', enable_checkpointing=False, logger=False) log_dir = exp_manager(test_trainer, {"exp_dir": str(tmp_path / "test_no_name"), "use_datetime_version": False}) assert log_dir.resolve() == (tmp_path / "test_no_name" / "default" / "version_0").resolve() assert Path(tmp_path).exists() assert Path(tmp_path / "test_no_name" / "default" / "version_0").exists() monkeypatch.delenv(NEMO_ENV_VARNAME_VERSION, raising=False) # Checks that use_datetime_version False toggle works and version increments test_trainer = pl.Trainer(accelerator='cpu', enable_checkpointing=False, logger=False) log_dir = exp_manager(test_trainer, {"exp_dir": str(tmp_path / "test_no_name"), "use_datetime_version": False}) assert log_dir.resolve() == (tmp_path / "test_no_name" / "default" / "version_1").resolve() assert Path(tmp_path).exists() assert Path(tmp_path / "test_no_name" / "default" / "version_1").exists() @pytest.mark.unit def test_resume(self, tmp_path): """Tests the resume capabilities of exp_manager""" test_trainer = pl.Trainer(accelerator='cpu', enable_checkpointing=False, logger=False) # Error because explicit_log_dir does not exist with pytest.raises(NotFoundError): exp_manager( test_trainer, { "exp_dir": str(tmp_path / "test_resume"), "resume_if_exists": True, "explicit_log_dir": "Does_not_exist", }, ) # Error because checkpoints folder does not exist with pytest.raises(NotFoundError): exp_manager(test_trainer, {"resume_if_exists": True, "exp_dir": str(tmp_path / "test_resume")}) # No error because we tell exp_manager to ignore notfounderror exp_manager( test_trainer, { "resume_if_exists": True, "exp_dir": str(tmp_path / "test_resume_2"), "resume_ignore_no_checkpoint": True, }, ) test_trainer = pl.Trainer(accelerator='cpu', enable_checkpointing=False, logger=False) Path(tmp_path / "test_resume" / "default" / "version_0" / "checkpoints").mkdir(parents=True) # Error because checkpoints do not exist in folder with pytest.raises(NotFoundError): exp_manager( test_trainer, { "resume_if_exists": True, "explicit_log_dir": str(tmp_path / "test_resume" / "default" / "version_0"), }, ) Path(tmp_path / "test_resume" / "default" / "version_0" / "checkpoints" / "mymodel--end.ckpt").touch() # Error because *end.ckpt is in folder indicating that training has already finished with pytest.raises(ValueError): exp_manager( test_trainer, { "resume_if_exists": True, "explicit_log_dir": str(tmp_path / "test_resume" / "default" / "version_0"), }, ) Path(tmp_path / "test_resume" / "default" / "version_0" / "checkpoints" / "mymodel--end.ckpt").unlink() Path(tmp_path / "test_resume" / "default" / "version_0" / "checkpoints" / "mymodel--last.ckpt").touch() Path(tmp_path / "test_resume" / "default" / "version_0" / "checkpoints" / "mymodel2--last.ckpt").touch() # Error because multiple *last.ckpt is in folder. If more than one, don't know which to restore with pytest.raises(ValueError): exp_manager( test_trainer, { "resume_if_exists": True, "explicit_log_dir": str(tmp_path / "test_resume" / "default" / "version_0"), }, ) # Finally succeed Path(tmp_path / "test_resume" / "default" / "version_0" / "checkpoints" / "mymodel2--last.ckpt").unlink() log_dir = exp_manager( test_trainer, {"resume_if_exists": True, "explicit_log_dir": str(tmp_path / "test_resume" / "default" / "version_0")}, ) checkpoint = Path(tmp_path / "test_resume" / "default" / "version_0" / "checkpoints" / "mymodel--last.ckpt") assert Path(test_trainer.ckpt_path).resolve() == checkpoint.resolve() # Succeed again and make sure that run_0 exists and previous log files were moved test_trainer = pl.Trainer(accelerator='cpu', enable_checkpointing=False, logger=False) exp_manager(test_trainer, {"resume_if_exists": True, "explicit_log_dir": str(log_dir)}) checkpoint = Path(tmp_path / "test_resume" / "default" / "version_0" / "checkpoints" / "mymodel--last.ckpt") assert Path(test_trainer.ckpt_path).resolve() == checkpoint.resolve() prev_run_dir = Path(tmp_path / "test_resume" / "default" / "version_0" / "run_0") assert prev_run_dir.exists() prev_log = Path(tmp_path / "test_resume" / "default" / "version_0" / "run_0" / "lightning_logs.txt") assert prev_log.exists() # Error becasue `dirpath` specified and has no checkpoint test_trainer = pl.Trainer(accelerator='cpu', enable_checkpointing=False, logger=False) dirpath_checkpoint_dir = Path(tmp_path / "test_resume" / "dirpath_test" / "ckpts") dirpath_checkpoint_dir.mkdir(parents=True) with pytest.raises(NotFoundError): exp_manager( test_trainer, { "resume_if_exists": True, "checkpoint_callback_params": {"dirpath": str(dirpath_checkpoint_dir)}, "explicit_log_dir": str(log_dir), }, ) # Check that model loads from `dirpath` and not /checkpoints dirpath_log_dir = Path(tmp_path / "test_resume" / "dirpath_test" / "logs") dirpath_log_dir.mkdir(parents=True) dirpath_checkpoint = Path(dirpath_checkpoint_dir / "mymodel--last.ckpt") dirpath_checkpoint.touch() exp_manager( test_trainer, { "resume_if_exists": True, "checkpoint_callback_params": {"dirpath": str(dirpath_checkpoint_dir)}, "explicit_log_dir": str(dirpath_log_dir), }, ) assert Path(test_trainer.ckpt_path).resolve() == dirpath_checkpoint.resolve() @pytest.mark.unit def test_nemo_checkpoint_save_best_model_1(self, tmp_path): test_trainer = pl.Trainer(accelerator='cpu', enable_checkpointing=False, logger=False, max_epochs=4) exp_manager( test_trainer, {"checkpoint_callback_params": {"save_best_model": True}, "explicit_log_dir": str(tmp_path / "test")}, ) model = ExampleModel() test_trainer.fit(model) assert Path(str(tmp_path / "test" / "checkpoints" / "default.nemo")).exists() model = ExampleModel.restore_from(str(tmp_path / "test" / "checkpoints" / "default.nemo")) assert float(model(torch.tensor([1.0, 1.0], device=model.device))) == 0.0 @pytest.mark.unit def test_nemo_checkpoint_save_best_model_2(self, tmp_path): test_trainer = pl.Trainer(accelerator='cpu', enable_checkpointing=False, logger=False, max_epochs=4) exp_manager( test_trainer, {"explicit_log_dir": str(tmp_path / "test")}, ) model = ExampleModel() test_trainer.fit(model) assert Path(str(tmp_path / "test" / "checkpoints" / "default.nemo")).exists() model = ExampleModel.restore_from(str(tmp_path / "test" / "checkpoints" / "default.nemo")) assert math.fabs(float(model(torch.tensor([1.0, 1.0], device=model.device))) - 0.03) < 1e-5 @pytest.mark.unit def test_nemo_checkpoint_always_save_nemo(self, tmp_path): test_trainer = pl.Trainer(accelerator='cpu', enable_checkpointing=False, logger=False, max_epochs=4) exp_manager( test_trainer, { "checkpoint_callback_params": {"save_best_model": True, "always_save_nemo": True}, "explicit_log_dir": str(tmp_path / "test"), }, ) model = ExampleModel() test_trainer.fit(model) assert Path(str(tmp_path / "test" / "checkpoints" / "default.nemo")).exists() model = ExampleModel.restore_from(str(tmp_path / "test" / "checkpoints" / "default.nemo")) assert float(model(torch.tensor([1.0, 1.0], device=model.device))) == 0.0 @pytest.mark.unit def test_nemo_checkpoint_doesnt_produce_too_many_nemo_ckpts(self, tmp_path): test_trainer = pl.Trainer(accelerator='cpu', enable_checkpointing=False, logger=False, max_epochs=4) exp_manager( test_trainer, { "checkpoint_callback_params": {"save_best_model": True, "always_save_nemo": True, "save_top_k": 2}, "explicit_log_dir": str(tmp_path / "test"), }, ) model = ExampleModel() test_trainer.fit(model) assert Path(str(tmp_path / "test" / "checkpoints" / "default.nemo")).exists() assert ( len(list((tmp_path / "test" / "checkpoints").glob("default*.nemo"))) == 1 ) # check number of `.nemo` checkpoints model = ExampleModel.restore_from(str(tmp_path / "test" / "checkpoints" / "default.nemo")) assert float(model(torch.tensor([1.0, 1.0], device=model.device))) == 0.0 @pytest.mark.unit def test_nemo_checkpoint_make_checkpoint_dir(self, tmp_path): test_trainer = pl.Trainer( accelerator='cpu', enable_checkpointing=False, logger=False, max_epochs=4, check_val_every_n_epoch=5 ) exp_manager( test_trainer, { "checkpoint_callback_params": {"save_best_model": True, "always_save_nemo": True}, "explicit_log_dir": str(tmp_path / "test"), }, ) model = ExampleModel() test_trainer.fit(model) assert Path(str(tmp_path / "test" / "checkpoints" / "default.nemo")).exists() @pytest.mark.unit def test_nemo_checkpoint_restore_model(self, tmp_path): test_trainer = pl.Trainer(accelerator='cpu', enable_checkpointing=False, logger=False, max_epochs=4) exp_manager( test_trainer, { "checkpoint_callback_params": {"save_top_k": 1, "save_last": True}, "explicit_log_dir": str(tmp_path / "test"), }, ) model = ExampleModel() test_trainer.fit(model) checkpoint = list(Path(str(tmp_path / "test" / "checkpoints")).glob("*.ckpt")) # Make sure that only the best and last checkpoint is saved assert len(checkpoint) == 2 assert math.fabs(float(model(torch.tensor([1.0, 1.0], device=model.device))) - 0.03) < 1e-5 test_trainer = pl.Trainer(accelerator='cpu', enable_checkpointing=False, logger=False, max_epochs=5) exp_manager( test_trainer, { "checkpoint_callback_params": {"save_top_k": 1, "save_last": False}, "explicit_log_dir": str(tmp_path / "test"), "resume_if_exists": True, "resume_past_end": True, }, ) model = DoNothingModel() model.l1.weight = torch.nn.Parameter(torch.tensor((0.0, 0.0)).unsqueeze(0)) model.l1.bias = torch.nn.Parameter(torch.tensor(1.0)) assert math.fabs(float(model(torch.tensor([1.0, 1.0], device=model.device))) - 1.0) < 1e-5 test_trainer.fit(model) assert math.fabs(float(model(torch.tensor([1.0, 1.0], device=model.device))) - 0.03) < 1e-5 @pytest.mark.run_only_on('GPU') @pytest.mark.parametrize('test_dist_ckpt', [False, True]) @pytest.mark.pleasefixme def test_base_checkpoints_are_not_overwritten(self, tmp_path, test_dist_ckpt): """Simulates already existing checkpoints in the ckpt directory and tests non-nemo ckpt versioning""" strategy = NLPDDPStrategy() if test_dist_ckpt else 'auto' test_trainer = pl.Trainer( accelerator='cpu', enable_checkpointing=False, logger=False, max_epochs=4, strategy=strategy ) exp_manager( test_trainer, { "checkpoint_callback_params": {"save_nemo_on_train_end": True}, "explicit_log_dir": str(tmp_path / "test"), }, ) model = ExampleMCoreModel() if test_dist_ckpt else ExampleModel() ckpt_dir = Path(tmp_path / "test" / "checkpoints") assert not ckpt_dir.exists() # Fake existing 1st and last checkpoint suffix = '' if test_dist_ckpt else '.ckpt' ckpt_dir.mkdir(parents=True) ckpt_1 = ckpt_dir / f'default--val_loss=0.0000-epoch=1{suffix}' ckpt_2 = ckpt_dir / f'default--val_loss=0.0300-epoch=2{suffix}' if test_dist_ckpt: ckpt_1.mkdir() with open(ckpt_1 / 'metadata.json', 'w') as f: json.dump({'sharded_backend': 'xxx'}, f) else: ckpt_1.touch() # don't create 2nd checkpoint ckpt_nemo = ckpt_dir / 'default.nemo' ckpt_nemo.touch() # Train test_trainer.fit(model) # Check base checkpoint (without versioning) all_checkpoints = [p.name for p in Path(str(tmp_path / "test" / "checkpoints")).glob("*")] assert ckpt_1.exists(), all_checkpoints # existed before assert ckpt_2.exists(), all_checkpoints assert ckpt_nemo.exists(), all_checkpoints # existed before # Versioned checkpoints def _get_versioned_name(ckpt_name: Path, nemo: bool = False): if test_dist_ckpt and not nemo: # no suffix at all return ckpt_name.with_name(ckpt_name.name + '-v1') return ckpt_name.with_stem(ckpt_name.stem + '-v1') assert _get_versioned_name(ckpt_1).exists(), all_checkpoints assert not _get_versioned_name(ckpt_2).exists(), all_checkpoints # ckpt2 didn't exist before # .nemo checkpoints are not versioned: assert not _get_versioned_name(ckpt_nemo, nemo=True).exists(), all_checkpoints @pytest.mark.unit def test_last_checkpoint_saved(self, tmp_path): max_steps = 64 tmp_path = tmp_path / "test_1" class TestModel(ExampleModel): def train_dataloader(self): dataset = OnesDataset(64) return torch.utils.data.DataLoader(dataset, batch_size=1) trainer = pl.Trainer( accelerator='cpu', enable_checkpointing=False, logger=False, max_steps=max_steps, val_check_interval=0.33 ) exp_manager( trainer, { "explicit_log_dir": str(tmp_path), "checkpoint_callback_params": {"filename": f"{{val_loss:.4f}}-{{epoch}}-{{step}}"}, }, ) model = TestModel() trainer.fit(model) checkpoint_dir = Path(str(tmp_path / "checkpoints")) model_path = checkpoint_dir / "val_loss=0.0300-epoch=1-step=64-last.ckpt" last_saved_checkpoint = torch.load(model_path, weights_only=False) assert max_steps == last_saved_checkpoint['global_step'] # restart training, ensure global step starts correctly class AssertCallback(Callback): def on_train_start(self, trainer: "pl.Trainer", pl_module: "pl.LightningModule") -> None: assert trainer.global_step == max_steps def on_train_batch_end( self, trainer: "pl.Trainer", pl_module: "pl.LightningModule", outputs, batch: Any, batch_idx: int ) -> None: # we should only be running for one more step. assert trainer.global_step == max_steps + 1 trainer = pl.Trainer( accelerator='cpu', enable_checkpointing=False, logger=False, max_steps=65, val_check_interval=0.33, callbacks=AssertCallback(), ) exp_manager( trainer, { "explicit_log_dir": str(tmp_path), "checkpoint_callback_params": {"filename": f"{{val_loss:.4f}}-{{epoch}}-{{step}}"}, }, ) model = TestModel() trainer.fit(model, ckpt_path=model_path) @pytest.mark.unit def test_resume_checkpoint_skip_validation(self, tmp_path): """Test to ensure that when we resume from a checkpoint, we do not re-run validation unnecessarily.""" tmp_path = tmp_path / "test_2" def run_training(resume_path=None): class TestModel(ExampleModel): def train_dataloader(self): dataset = OnesDataset(10) return torch.utils.data.DataLoader(dataset, batch_size=1) class AssertCallback(Callback): recorded_validations = 0 recorded_train_steps = 0 def on_validation_start(self, trainer: "pl.Trainer", pl_module: "pl.LightningModule") -> None: self.recorded_validations += 1 def on_train_batch_end( self, trainer: "pl.Trainer", pl_module: "pl.LightningModule", outputs, batch: Any, batch_idx: int ) -> None: self.recorded_train_steps += 1 def on_train_end(self, trainer: "pl.Trainer", pl_module: "pl.LightningModule") -> None: if resume_path is not None: # we should only run validation at the end of training. assert self.recorded_validations == 1 # we continue from half way assert self.recorded_train_steps == len(pl_module.train_dataloader()) // 2 else: # we've run validation within the middle of training and at the end of training. assert self.recorded_validations == 2 assert self.recorded_train_steps == len(pl_module.train_dataloader()) model = TestModel() trainer = pl.Trainer( accelerator='cpu', enable_checkpointing=False, logger=False, callbacks=[AssertCallback()], val_check_interval=0.5, num_sanity_val_steps=0, max_epochs=1, ) exp_manager( trainer, {"explicit_log_dir": str(tmp_path), "checkpoint_callback_params": {"filename": f"{{epoch}}-{{step}}"}}, ) trainer.fit(model, ckpt_path=resume_path) run_training() resume_path = tmp_path / 'checkpoints/epoch=0-step=5.ckpt' run_training(resume_path) def test_warning_validation_skipping_when_custom_epoch_loop(self, tmp_path): """When using validation skipping on restart with a custom epoch loop, we warn the user that we skip support to not interfere with their custom logic. """ tmp_path = tmp_path / "test_3" class CustomLoop(_TrainingEpochLoop): ... trainer = pl.Trainer( accelerator='cpu', enable_checkpointing=False, logger=False, max_epochs=1, val_check_interval=0.33 ) ## _TrainingEpochLoop in PTL 2.0 takes trainer as an arg loop = CustomLoop(trainer) trainer.fit_loop.epoch_loop = loop with pytest.warns(UserWarning, match="Detected custom epoch loop"): exp_manager(trainer, {"explicit_log_dir": str(tmp_path)}) def _write_fake_checkpoint(self, path, isdir, add_unfinished_marker): path = Path(path) if isdir: # fake distributed checkpoint path.mkdir(parents=True, exist_ok=True) (path / "dummy.txt").touch() else: # fake checkpoint file path.parent.mkdir(parents=True, exist_ok=True) path.touch() if add_unfinished_marker: NeMoModelCheckpoint.set_checkpoint_unfinished_marker(path) @pytest.mark.unit def test_skipped_unfinished_checkpoints_when_restoring(self, tmp_path): """ Check if unfinished checkpoints are skipped during last checkpoint lookup. Logic of the test: - write multiple last checkpoints, some of them incomplete - ensure that the last complete checkpoint is found """ test_dir = tmp_path / "test" checkpoints_dir = test_dir / "checkpoints" self._write_fake_checkpoint( checkpoints_dir / "megatron_gpt--val_loss=5.01-step=900-consumed_samples=1000.0.ckpt", isdir=False, add_unfinished_marker=False, ) # not last self._write_fake_checkpoint( checkpoints_dir / "megatron_gpt--val_loss=5.01-step=900-consumed_samples=1000.0-last.ckpt", isdir=False, add_unfinished_marker=True, ) # incomplete self._write_fake_checkpoint( checkpoints_dir / "mp_rank_00" / "megatron_gpt--val_loss=5.01-step=1100-consumed_samples=17600.0-last.ckpt", isdir=False, add_unfinished_marker=True, ) # incomplete self._write_fake_checkpoint( checkpoints_dir / "mp_rank_01" / "megatron_gpt--val_loss=5.01-step=1100-consumed_samples=17600.0-last.ckpt", isdir=False, add_unfinished_marker=True, ) # incomplete self._write_fake_checkpoint( checkpoints_dir / "mp_rank_00" / "megatron_gpt--val_loss=5.01-step=1000-consumed_samples=16000.0-last.ckpt", isdir=False, add_unfinished_marker=False, ) # ok self._write_fake_checkpoint( checkpoints_dir / "mp_rank_01" / "megatron_gpt--val_loss=5.01-step=1000-consumed_samples=16000.0-last.ckpt", isdir=False, add_unfinished_marker=False, ) # ok restored_trainer = pl.Trainer(accelerator='cpu', enable_checkpointing=False, logger=False) exp_manager( restored_trainer, {"resume_if_exists": True, "explicit_log_dir": str(test_dir)}, ) # Check that last complete (w/o unifinished marker) checkpoint was found assert ( Path(restored_trainer.ckpt_path).name == 'megatron_gpt--val_loss=5.01-step=1000-consumed_samples=16000.0-last.ckpt' ) @pytest.mark.unit def test_skipped_unfinished_dist_checkpoints_when_restoring(self, tmp_path): """ Check if unfinished distributed checkpoints are skipped during last checkpoint lookup. Logic of the test: - write multiple last checkpoints, some of them incomplete - ensure that the last complete checkpoint is found """ test_dir = tmp_path / "test" checkpoints_dir = test_dir / "checkpoints" self._write_fake_checkpoint( checkpoints_dir / "megatron_gpt--val_loss=5.01-step=1000-consumed_samples=16000.0", isdir=True, add_unfinished_marker=False, ) self._write_fake_checkpoint( checkpoints_dir / "megatron_gpt--val_loss=5.01-step=1000-consumed_samples=16000.0-last", isdir=True, add_unfinished_marker=False, ) self._write_fake_checkpoint( checkpoints_dir / "megatron_gpt--val_loss=5.01-step=1100-consumed_samples=17600.0", isdir=True, add_unfinished_marker=False, ) self._write_fake_checkpoint( checkpoints_dir / "megatron_gpt--val_loss=5.01-step=1100-consumed_samples=17600.0-last", isdir=True, add_unfinished_marker=True, ) restored_trainer = pl.Trainer(accelerator='cpu', enable_checkpointing=False, logger=False) exp_manager( restored_trainer, {"resume_if_exists": True, "explicit_log_dir": str(test_dir)}, ) # Check that last complete (w/o unifinished marker) checkpoint was found assert ( Path(restored_trainer.ckpt_path).name == 'megatron_gpt--val_loss=5.01-step=1000-consumed_samples=16000.0-last' ) @pytest.mark.unit def test_incomplete_checkpoints_cleanup(self, tmp_path): """ Check if unfinished checkpoints are cleaned up when training starts Complete checkpoints should be left intact. """ test_dir = tmp_path / "test" checkpoints_dir = test_dir / "checkpoints" complete_ckpts = { checkpoints_dir / "step=1-epoch=0.ckpt", checkpoints_dir / "step=2-epoch=0-last.ckpt", checkpoints_dir / "mp_rank_00" / "step=3-epoch=0-last.ckpt", checkpoints_dir / "tp_rank_00_pp_rank_000" / "step=4-epoch=0-last.ckpt", checkpoints_dir / "tp_rank_00_pp_rank_001" / "step=4-epoch=0-last.ckpt", } for ckpt_filepath in complete_ckpts: self._write_fake_checkpoint(ckpt_filepath, isdir=False, add_unfinished_marker=False) incomplete_ckpts = { checkpoints_dir / "step=11-epoch=1.ckpt", checkpoints_dir / "step=12-epoch=1-last.ckpt", checkpoints_dir / "mp_rank_00" / "step=13-epoch=1-last.ckpt", checkpoints_dir / "tp_rank_00_pp_rank_000" / "step=14-epoch=1-last.ckpt", checkpoints_dir / "tp_rank_00_pp_rank_001" / "step=14-epoch=1-last.ckpt", } for ckpt_filepath in incomplete_ckpts: self._write_fake_checkpoint(ckpt_filepath, isdir=False, add_unfinished_marker=True) # sanity check remaining_ckpts = {f for f in (test_dir / "checkpoints").rglob("*.ckpt") if f.is_file()} assert remaining_ckpts == (complete_ckpts | incomplete_ckpts) # marker without corresponding checkpoint should be removed during cleanup in exp_manager (checkpoints_dir / f"orphan-marker001-{NeMoModelCheckpoint.UNFINISHED_CHECKPOINT_SUFFIX}").touch() # unfinished checkpoint with EMA part, both parts should be removed self._write_fake_checkpoint( checkpoints_dir / "incomplete01-EMA.ckpt", isdir=False, add_unfinished_marker=False, ) self._write_fake_checkpoint(checkpoints_dir / "incomplete01.ckpt", isdir=False, add_unfinished_marker=True) # just EMA part - should be removed. NOTE marker path is the same for base part and for EMA part self._write_fake_checkpoint( checkpoints_dir / "incomplete02-EMA.ckpt", isdir=False, add_unfinished_marker=False, ) (checkpoints_dir / f"incomplete02{NeMoModelCheckpoint.UNFINISHED_CHECKPOINT_SUFFIX}").touch() test_trainer = pl.Trainer(accelerator='cpu', enable_checkpointing=False, logger=False, max_epochs=1) exp_manager( test_trainer, { "checkpoint_callback_params": {"save_top_k": 0, "save_last": False}, "explicit_log_dir": str(test_dir), }, ) model = ExampleModel() test_trainer.fit(model) remaining_ckpts = {f for f in (test_dir / "checkpoints").rglob("*.ckpt") if f.is_file()} assert remaining_ckpts == complete_ckpts remaining_markers = list(checkpoints_dir.rglob(f"*{NeMoModelCheckpoint.UNFINISHED_CHECKPOINT_SUFFIX}")) assert remaining_markers == [] @pytest.mark.unit def test_incomplete_dist_checkpoints_cleanup(self, tmp_path): """ Check if unfinished distributed checkpoints are cleaned up when training starts. Complete distributed checkpoints should be left intact. """ test_dir = tmp_path / "test" checkpoints_dir = test_dir / "checkpoints" complete_dist_ckpts = { checkpoints_dir / "step=5-epoch=0", checkpoints_dir / "step=6-epoch=0-last", } for ckpt_dirpath in complete_dist_ckpts: self._write_fake_checkpoint(ckpt_dirpath, isdir=True, add_unfinished_marker=False) incomplete_dist_ckpts = { checkpoints_dir / "step=15-epoch=1", checkpoints_dir / "step=16-epoch=1-last", } for ckpt_dirpath in incomplete_dist_ckpts: self._write_fake_checkpoint(ckpt_dirpath, isdir=True, add_unfinished_marker=True) # marker without corresponding checkpoint should be removed during cleanup in exp_manager (checkpoints_dir / f"orphan-marker001-{NeMoModelCheckpoint.UNFINISHED_CHECKPOINT_SUFFIX}").touch() remaining_dist_ckpts = {f for f in (test_dir / "checkpoints").glob("*") if f.is_dir()} assert remaining_dist_ckpts == (complete_dist_ckpts | incomplete_dist_ckpts) test_trainer = pl.Trainer(accelerator='cpu', enable_checkpointing=False, logger=False, max_epochs=1) exp_manager( test_trainer, { "checkpoint_callback_params": {"save_top_k": 0, "save_last": False}, "explicit_log_dir": str(test_dir), }, ) model = ExampleModel() test_trainer.fit(model) remaining_dist_ckpts = {f for f in (test_dir / "checkpoints").glob("*") if f.is_dir()} assert remaining_dist_ckpts == complete_dist_ckpts remaining_markers = list(checkpoints_dir.rglob(f"*{NeMoModelCheckpoint.UNFINISHED_CHECKPOINT_SUFFIX}")) assert remaining_markers == [] _chkpt_path_and_marker_path_pairs = [ ('a=1_b=1.c.d.e', f'a=1_b=1.c.d.e{NeMoModelCheckpoint.UNFINISHED_CHECKPOINT_SUFFIX}'), ('a=1_b=1.c.d.e-last', f'a=1_b=1.c.d.e-last{NeMoModelCheckpoint.UNFINISHED_CHECKPOINT_SUFFIX}'), ('.ckpt/a=1_b=1.c.d.e.ckpt', f'.ckpt/a=1_b=1.c.d.e{NeMoModelCheckpoint.UNFINISHED_CHECKPOINT_SUFFIX}'), ('.ckpt/a=1_b=1.c.d.e-EMA.ckpt', f'.ckpt/a=1_b=1.c.d.e{NeMoModelCheckpoint.UNFINISHED_CHECKPOINT_SUFFIX}'), ( '.ckpt/a=1_b=1.c.d.e-last.ckpt', f'.ckpt/a=1_b=1.c.d.e-last{NeMoModelCheckpoint.UNFINISHED_CHECKPOINT_SUFFIX}', ), ( '/tmp/mp_rank_00/a=1_b=1.c.d.e.ckpt', f'/tmp/a=1_b=1.c.d.e{NeMoModelCheckpoint.UNFINISHED_CHECKPOINT_SUFFIX}', ), ( '/tmp/tp_rank_00_pp_rank_000/a=1_b=1.c.d.e.ckpt', f'/tmp/a=1_b=1.c.d.e{NeMoModelCheckpoint.UNFINISHED_CHECKPOINT_SUFFIX}', ), ('nemo/a=1_b=1.c.d.e.nemo', f'nemo/a=1_b=1.c.d.e{NeMoModelCheckpoint.UNFINISHED_CHECKPOINT_SUFFIX}'), ('nemo/a=1_b=1.c.d.e-last.nemo', f'nemo/a=1_b=1.c.d.e-last{NeMoModelCheckpoint.UNFINISHED_CHECKPOINT_SUFFIX}'), ] @pytest.mark.unit @pytest.mark.parametrize("chkpt_path, expected_marker_path", _chkpt_path_and_marker_path_pairs) def test_incomplete_checkpoints_marker_path(self, chkpt_path, expected_marker_path): """ Ensure that unfinished checkpoint marker path is correctly formed. """ marker_path = NeMoModelCheckpoint.format_checkpoint_unfinished_marker_path(chkpt_path) assert str(marker_path) == str(expected_marker_path) @pytest.mark.unit def test_invalid_checkpoints_removed_from_topk(self, tmp_path): """ Ensure that invalid (unfinished, deleted) checkpoints are removed from topk when resuming. - Do few training steps and save checkpoints - Delete some checkpoints, mark some as unfinished - Resume training and verify that topk checkpoints are correct """ test_dir = tmp_path / "test" checkpoints_dir = test_dir / "checkpoints" test_trainer = pl.Trainer(accelerator='cpu', enable_checkpointing=False, logger=False, max_epochs=7) exp_manager( test_trainer, { "checkpoint_callback_params": { "save_top_k": 3, "save_last": True, "mode": 'max', "monitor": 'epoch', "filename": f"{{epoch}}", }, "explicit_log_dir": str(tmp_path / "test"), }, ) model = ExampleModel() test_trainer.fit(model) ckpt_filenames = {f.name for f in checkpoints_dir.rglob("*.ckpt") if f.is_file()} assert len(ckpt_filenames) == 4 # 3 top + 1 last assert 'epoch=7-last.ckpt' in ckpt_filenames assert 'epoch=6.ckpt' in ckpt_filenames assert 'epoch=5.ckpt' in ckpt_filenames assert 'epoch=4.ckpt' in ckpt_filenames # Mark 6th epoch checkpoint as unfinished and remove 5th epoch checkpoint, # so last valid candidate for topk is 4th epoch checkpoint NeMoModelCheckpoint.set_checkpoint_unfinished_marker(checkpoints_dir / 'epoch=6.ckpt') (checkpoints_dir / 'epoch=5.ckpt').unlink() test_trainer2 = pl.Trainer(accelerator='cpu', enable_checkpointing=False, logger=False, max_epochs=9) exp_manager( test_trainer2, { "resume_if_exists": True, "checkpoint_callback_params": { "save_top_k": 3, "save_last": True, "mode": 'max', "monitor": 'epoch', "filename": f"{{epoch}}", }, "explicit_log_dir": str(tmp_path / "test"), }, ) model = ExampleModel() test_trainer2.fit(model) ckpt_filenames = {f.name for f in checkpoints_dir.rglob("*.ckpt") if f.is_file()} # 3 top + 1 last assert len(ckpt_filenames) == 4 assert 'epoch=9-last.ckpt' in ckpt_filenames assert 'epoch=8.ckpt' in ckpt_filenames assert 'epoch=7.ckpt' in ckpt_filenames assert 'epoch=4.ckpt' in ckpt_filenames @pytest.mark.unit def test_doesnt_silently_start_from_scratch(self, tmp_path): """ Ensure that if the last checkpoint is unfinished it wont silently start from scratch. This is to avoid a training that is not actually making any progress. """ test_dir = tmp_path / "test" checkpoints_dir = test_dir / "checkpoints" self._write_fake_checkpoint( checkpoints_dir / "megatron_gpt--val_loss=5.01-step=900-consumed_samples=1000.0-last.ckpt", isdir=False, add_unfinished_marker=True, ) # incomplete last restored_trainer = pl.Trainer(accelerator='cpu', enable_checkpointing=False, logger=False) with pytest.raises(Exception): exp_manager( restored_trainer, {"resume_if_exists": True, "resume_ignore_no_checkpoint": True, "explicit_log_dir": str(test_dir)}, ) @pytest.mark.unit def test_doesnt_silently_start_from_scratch_dist(self, tmp_path): """ Ensure that if the last distributed checkpoint is unfinished it wont silently start from scratch. This is to avoid a training that is not actually making any progress. """ test_dir = tmp_path / "test" checkpoints_dir = test_dir / "checkpoints" self._write_fake_checkpoint( checkpoints_dir / "megatron_gpt--val_loss=5.01-step=1100-consumed_samples=17600.0-last", isdir=True, add_unfinished_marker=True, ) # incomplete last restored_trainer = pl.Trainer(accelerator='cpu', enable_checkpointing=False, logger=False) with pytest.raises(Exception): exp_manager( restored_trainer, {"resume_if_exists": True, "resume_ignore_no_checkpoint": True, "explicit_log_dir": str(test_dir)}, ) @pytest.mark.unit def test_save_nemo_not_comp_with_model_parallel(self, tmp_path): """ Ensure that always_save_nemo is not compatible with model parallelism. """ test_dir = tmp_path / "test" with pytest.raises(LoggerMisconfigurationError): appstate = AppState() appstate.tensor_model_parallel_size = 2 appstate.pipeline_model_parallel_size = 1 appstate.context_parallel_size = 1 test_trainer = pl.Trainer(accelerator='cpu', enable_checkpointing=False, logger=False, max_epochs=1) exp_manager( test_trainer, { "checkpoint_callback_params": { "always_save_nemo": True, }, "explicit_log_dir": str(test_dir), }, ) with pytest.raises(LoggerMisconfigurationError): appstate = AppState() appstate.tensor_model_parallel_size = 1 appstate.pipeline_model_parallel_size = 2 appstate.context_parallel_size = 1 test_trainer = pl.Trainer(accelerator='cpu', enable_checkpointing=False, logger=False, max_epochs=1) exp_manager( test_trainer, { "checkpoint_callback_params": { "always_save_nemo": True, }, "explicit_log_dir": str(test_dir), }, ) with pytest.raises(LoggerMisconfigurationError): appstate = AppState() appstate.tensor_model_parallel_size = 1 appstate.pipeline_model_parallel_size = 1 appstate.context_parallel_size = 2 test_trainer = pl.Trainer(accelerator='cpu', enable_checkpointing=False, logger=False, max_epochs=1) exp_manager( test_trainer, { "checkpoint_callback_params": { "always_save_nemo": True, }, "explicit_log_dir": str(test_dir), }, ) appstate = AppState() appstate.tensor_model_parallesl_size = 1 appstate.pipeline_model_parallel_size = 1 appstate.context_parallel_size = 1 test_trainer = pl.Trainer(accelerator='cpu', enable_checkpointing=False, logger=False, max_epochs=1) exp_manager( test_trainer, { "checkpoint_callback_params": { "always_save_nemo": True, }, "explicit_log_dir": str(test_dir), }, )