400 lines
12 KiB
Python
400 lines
12 KiB
Python
import os
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import platform
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from collections import namedtuple
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import pytest
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import torch
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from packaging.version import parse as version_parse
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import tests.base.utils as tutils
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from pytorch_lightning import Trainer
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from pytorch_lightning.callbacks import EarlyStopping
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from tests.base import EvalModelTemplate
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from pytorch_lightning.callbacks import ModelCheckpoint
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def test_cpu_slurm_save_load(tmpdir):
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"""Verify model save/load/checkpoint on CPU."""
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hparams = EvalModelTemplate.get_default_hparams()
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model = EvalModelTemplate(**hparams)
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# logger file to get meta
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logger = tutils.get_default_logger(tmpdir)
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version = logger.version
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# fit model
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trainer = Trainer(
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max_epochs=1,
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logger=logger,
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train_percent_check=0.2,
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val_percent_check=0.2,
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checkpoint_callback=ModelCheckpoint(tmpdir)
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)
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result = trainer.fit(model)
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real_global_step = trainer.global_step
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# traning complete
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assert result == 1, 'cpu model failed to complete'
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# predict with trained model before saving
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# make a prediction
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dataloaders = model.test_dataloader()
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if not isinstance(dataloaders, list):
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dataloaders = [dataloaders]
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for dataloader in dataloaders:
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for batch in dataloader:
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break
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x, y = batch
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x = x.view(x.size(0), -1)
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model.eval()
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pred_before_saving = model(x)
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# test HPC saving
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# simulate snapshot on slurm
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saved_filepath = trainer.hpc_save(tmpdir, logger)
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assert os.path.exists(saved_filepath)
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# new logger file to get meta
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logger = tutils.get_default_logger(tmpdir, version=version)
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trainer = Trainer(
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max_epochs=1,
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logger=logger,
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checkpoint_callback=ModelCheckpoint(tmpdir),
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)
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model = EvalModelTemplate(**hparams)
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# set the epoch start hook so we can predict before the model does the full training
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def assert_pred_same():
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assert trainer.global_step == real_global_step and trainer.global_step > 0
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# predict with loaded model to make sure answers are the same
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trainer.model.eval()
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new_pred = trainer.model(x)
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assert torch.all(torch.eq(pred_before_saving, new_pred)).item() == 1
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model.on_epoch_start = assert_pred_same
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# by calling fit again, we trigger training, loading weights from the cluster
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# and our hook to predict using current model before any more weight updates
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trainer.fit(model)
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def test_early_stopping_cpu_model(tmpdir):
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"""Test each of the trainer options."""
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stopping = EarlyStopping(monitor='val_loss', min_delta=0.1)
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trainer_options = dict(
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default_root_dir=tmpdir,
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early_stop_callback=stopping,
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max_epochs=2,
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gradient_clip_val=1.0,
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overfit_pct=0.20,
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track_grad_norm=2,
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train_percent_check=0.1,
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val_percent_check=0.1,
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)
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model = EvalModelTemplate()
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tutils.run_model_test(trainer_options, model, on_gpu=False)
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# test freeze on cpu
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model.freeze()
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model.unfreeze()
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@pytest.mark.spawn
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@pytest.mark.skipif(platform.system() == "Windows",
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reason="Distributed training is not supported on Windows")
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@pytest.mark.skipif((platform.system() == "Darwin" and
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version_parse(torch.__version__) < version_parse("1.3.0")),
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reason="Distributed training is not supported on MacOS before Torch 1.3.0")
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def test_multi_cpu_model_ddp(tmpdir):
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"""Make sure DDP works."""
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tutils.set_random_master_port()
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trainer_options = dict(
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default_root_dir=tmpdir,
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progress_bar_refresh_rate=0,
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max_epochs=1,
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train_percent_check=0.4,
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val_percent_check=0.2,
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gpus=None,
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num_processes=2,
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distributed_backend='ddp_cpu'
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)
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model = EvalModelTemplate()
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tutils.run_model_test(trainer_options, model, on_gpu=False)
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def test_lbfgs_cpu_model(tmpdir):
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"""Test each of the trainer options."""
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trainer_options = dict(
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default_root_dir=tmpdir,
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max_epochs=1,
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progress_bar_refresh_rate=0,
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weights_summary='top',
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train_percent_check=0.2,
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val_percent_check=0.2,
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)
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hparams = EvalModelTemplate.get_default_hparams()
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hparams.update(optimizer_name='lbfgs',
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learning_rate=0.004)
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model = EvalModelTemplate(**hparams)
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model.configure_optimizers = model.configure_optimizers__lbfgs
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tutils.run_model_test_without_loggers(trainer_options, model, min_acc=0.25)
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def test_default_logger_callbacks_cpu_model(tmpdir):
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"""Test each of the trainer options."""
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trainer_options = dict(
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default_root_dir=tmpdir,
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max_epochs=1,
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gradient_clip_val=1.0,
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overfit_pct=0.20,
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progress_bar_refresh_rate=0,
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train_percent_check=0.01,
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val_percent_check=0.01,
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)
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model = EvalModelTemplate()
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tutils.run_model_test_without_loggers(trainer_options, model)
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# test freeze on cpu
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model.freeze()
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model.unfreeze()
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def test_running_test_after_fitting(tmpdir):
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"""Verify test() on fitted model."""
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model = EvalModelTemplate()
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# logger file to get meta
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logger = tutils.get_default_logger(tmpdir)
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# logger file to get weights
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checkpoint = tutils.init_checkpoint_callback(logger)
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# fit model
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trainer = Trainer(
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default_root_dir=tmpdir,
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progress_bar_refresh_rate=0,
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max_epochs=2,
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train_percent_check=0.4,
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val_percent_check=0.2,
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test_percent_check=0.2,
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checkpoint_callback=checkpoint,
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logger=logger
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)
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result = trainer.fit(model)
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assert result == 1, 'training failed to complete'
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trainer.test()
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# test we have good test accuracy
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tutils.assert_ok_model_acc(trainer, thr=0.5)
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def test_running_test_no_val(tmpdir):
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"""Verify `test()` works on a model with no `val_loader`."""
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model = EvalModelTemplate()
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# logger file to get meta
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logger = tutils.get_default_logger(tmpdir)
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# logger file to get weights
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checkpoint = tutils.init_checkpoint_callback(logger)
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# fit model
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trainer = Trainer(
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progress_bar_refresh_rate=0,
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max_epochs=1,
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train_percent_check=0.4,
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val_percent_check=0.2,
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test_percent_check=0.2,
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checkpoint_callback=checkpoint,
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logger=logger,
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early_stop_callback=False
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)
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result = trainer.fit(model)
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assert result == 1, 'training failed to complete'
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trainer.test()
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# test we have good test accuracy
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tutils.assert_ok_model_acc(trainer)
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@pytest.mark.skipif(not torch.cuda.is_available(), reason="test requires GPU machine")
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def test_single_gpu_batch_parse():
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trainer = Trainer()
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# batch is just a tensor
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batch = torch.rand(2, 3)
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batch = trainer.transfer_batch_to_gpu(batch, 0)
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assert batch.device.index == 0 and batch.type() == 'torch.cuda.FloatTensor'
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# tensor list
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batch = [torch.rand(2, 3), torch.rand(2, 3)]
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batch = trainer.transfer_batch_to_gpu(batch, 0)
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assert batch[0].device.index == 0 and batch[0].type() == 'torch.cuda.FloatTensor'
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assert batch[1].device.index == 0 and batch[1].type() == 'torch.cuda.FloatTensor'
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# tensor list of lists
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batch = [[torch.rand(2, 3), torch.rand(2, 3)]]
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batch = trainer.transfer_batch_to_gpu(batch, 0)
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assert batch[0][0].device.index == 0 and batch[0][0].type() == 'torch.cuda.FloatTensor'
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assert batch[0][1].device.index == 0 and batch[0][1].type() == 'torch.cuda.FloatTensor'
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# tensor dict
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batch = [{'a': torch.rand(2, 3), 'b': torch.rand(2, 3)}]
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batch = trainer.transfer_batch_to_gpu(batch, 0)
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assert batch[0]['a'].device.index == 0 and batch[0]['a'].type() == 'torch.cuda.FloatTensor'
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assert batch[0]['b'].device.index == 0 and batch[0]['b'].type() == 'torch.cuda.FloatTensor'
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# tuple of tensor list and list of tensor dict
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batch = ([torch.rand(2, 3) for _ in range(2)],
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[{'a': torch.rand(2, 3), 'b': torch.rand(2, 3)} for _ in range(2)])
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batch = trainer.transfer_batch_to_gpu(batch, 0)
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assert batch[0][0].device.index == 0 and batch[0][0].type() == 'torch.cuda.FloatTensor'
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assert batch[1][0]['a'].device.index == 0
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assert batch[1][0]['a'].type() == 'torch.cuda.FloatTensor'
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assert batch[1][0]['b'].device.index == 0
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assert batch[1][0]['b'].type() == 'torch.cuda.FloatTensor'
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# namedtuple of tensor
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BatchType = namedtuple('BatchType', ['a', 'b'])
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batch = [BatchType(a=torch.rand(2, 3), b=torch.rand(2, 3)) for _ in range(2)]
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batch = trainer.transfer_batch_to_gpu(batch, 0)
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assert batch[0].a.device.index == 0
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assert batch[0].a.type() == 'torch.cuda.FloatTensor'
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def test_simple_cpu(tmpdir):
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"""Verify continue training session on CPU."""
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model = EvalModelTemplate()
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# fit model
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trainer = Trainer(
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default_root_dir=tmpdir,
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max_epochs=1,
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val_percent_check=0.1,
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train_percent_check=0.1,
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)
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result = trainer.fit(model)
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# traning complete
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assert result == 1, 'amp + ddp model failed to complete'
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def test_cpu_model(tmpdir):
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"""Make sure model trains on CPU."""
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trainer_options = dict(
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default_root_dir=tmpdir,
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progress_bar_refresh_rate=0,
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max_epochs=1,
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train_percent_check=0.4,
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val_percent_check=0.4
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)
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model = EvalModelTemplate()
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tutils.run_model_test(trainer_options, model, on_gpu=False)
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def test_all_features_cpu_model(tmpdir):
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"""Test each of the trainer options."""
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trainer_options = dict(
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default_root_dir=tmpdir,
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gradient_clip_val=1.0,
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overfit_pct=0.20,
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track_grad_norm=2,
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progress_bar_refresh_rate=0,
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accumulate_grad_batches=2,
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max_epochs=1,
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train_percent_check=0.4,
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val_percent_check=0.4
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)
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model = EvalModelTemplate()
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tutils.run_model_test(trainer_options, model, on_gpu=False)
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def test_tbptt_cpu_model(tmpdir):
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"""Test truncated back propagation through time works."""
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truncated_bptt_steps = 2
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sequence_size = 30
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batch_size = 30
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x_seq = torch.rand(batch_size, sequence_size, 1)
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y_seq_list = torch.rand(batch_size, sequence_size, 1).tolist()
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class MockSeq2SeqDataset(torch.utils.data.Dataset):
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def __getitem__(self, i):
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return x_seq, y_seq_list
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def __len__(self):
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return 1
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class BpttTestModel(EvalModelTemplate):
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def __init__(self, *args, **kwargs):
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super().__init__(*args, **kwargs)
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self.test_hidden = None
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def training_step(self, batch, batch_idx, hiddens):
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assert hiddens == self.test_hidden, "Hidden state not persistent between tbptt steps"
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self.test_hidden = torch.rand(1)
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x_tensor, y_list = batch
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assert x_tensor.shape[1] == truncated_bptt_steps, "tbptt split Tensor failed"
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y_tensor = torch.tensor(y_list, dtype=x_tensor.dtype)
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assert y_tensor.shape[1] == truncated_bptt_steps, "tbptt split list failed"
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pred = self(x_tensor.view(batch_size, truncated_bptt_steps))
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loss_val = torch.nn.functional.mse_loss(
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pred, y_tensor.view(batch_size, truncated_bptt_steps))
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return {
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'loss': loss_val,
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'hiddens': self.test_hidden,
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}
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def train_dataloader(self):
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return torch.utils.data.DataLoader(
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dataset=MockSeq2SeqDataset(),
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batch_size=batch_size,
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shuffle=False,
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sampler=None,
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)
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hparams = EvalModelTemplate.get_default_hparams()
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hparams.update(
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batch_size=batch_size,
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in_features=truncated_bptt_steps,
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hidden_dim=truncated_bptt_steps,
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out_features=truncated_bptt_steps
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)
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model = BpttTestModel(**hparams)
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# fit model
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trainer = Trainer(
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default_root_dir=tmpdir,
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max_epochs=1,
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truncated_bptt_steps=truncated_bptt_steps,
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val_percent_check=0,
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weights_summary=None,
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early_stop_callback=False
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)
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result = trainer.fit(model)
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assert result == 1, 'training failed to complete'
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