420 lines
13 KiB
Python
420 lines
13 KiB
Python
import os
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import pytest
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import torch
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import tests.utils as tutils
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from pytorch_lightning import Trainer
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from pytorch_lightning.callbacks import (
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ModelCheckpoint,
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)
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from pytorch_lightning.core import memory
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from pytorch_lightning.testing import (
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LightningTestModel,
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)
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from pytorch_lightning.trainer.distrib_parts import (
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parse_gpu_ids,
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determine_root_gpu_device,
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)
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from pytorch_lightning.utilities.debugging import MisconfigurationException
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PRETEND_N_OF_GPUS = 16
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def test_multi_gpu_model_ddp2(tmpdir):
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"""Make sure DDP2 works."""
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if not tutils.can_run_gpu_test():
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return
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tutils.reset_seed()
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tutils.set_random_master_port()
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model, hparams = tutils.get_model()
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trainer_options = dict(
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default_save_path=tmpdir,
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show_progress_bar=True,
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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=2,
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weights_summary=None,
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distributed_backend='ddp2'
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)
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tutils.run_model_test(trainer_options, model)
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def test_multi_gpu_model_ddp(tmpdir):
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"""Make sure DDP works."""
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if not tutils.can_run_gpu_test():
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return
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tutils.reset_seed()
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tutils.set_random_master_port()
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model, hparams = tutils.get_model()
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trainer_options = dict(
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default_save_path=tmpdir,
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show_progress_bar=False,
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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=[0, 1],
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distributed_backend='ddp'
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)
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tutils.run_model_test(trainer_options, model)
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def test_optimizer_return_options():
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tutils.reset_seed()
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trainer = Trainer()
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model, hparams = tutils.get_model()
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# single optimizer
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opt_a = torch.optim.Adam(model.parameters(), lr=0.002)
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opt_b = torch.optim.SGD(model.parameters(), lr=0.002)
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optim, lr_sched = trainer.init_optimizers(opt_a)
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assert len(optim) == 1 and len(lr_sched) == 0
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# opt tuple
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opts = (opt_a, opt_b)
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optim, lr_sched = trainer.init_optimizers(opts)
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assert len(optim) == 2 and optim[0] == opts[0] and optim[1] == opts[1]
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assert len(lr_sched) == 0
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# opt list
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opts = [opt_a, opt_b]
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optim, lr_sched = trainer.init_optimizers(opts)
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assert len(optim) == 2 and optim[0] == opts[0] and optim[1] == opts[1]
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assert len(lr_sched) == 0
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# opt tuple of lists
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opts = ([opt_a], ['lr_scheduler'])
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optim, lr_sched = trainer.init_optimizers(opts)
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assert len(optim) == 1 and len(lr_sched) == 1
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assert optim[0] == opts[0][0] and lr_sched[0] == 'lr_scheduler'
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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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tutils.reset_seed()
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hparams = tutils.get_hparams()
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model = LightningTestModel(hparams)
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# logger file to get meta
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logger = tutils.get_test_tube_logger(tmpdir, False)
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version = logger.version
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trainer_options = dict(
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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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# fit model
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trainer = Trainer(**trainer_options)
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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, 'amp + ddp 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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for dataloader in model.test_dataloader():
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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_test_tube_logger(tmpdir, False, version=version)
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trainer_options = dict(
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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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trainer = Trainer(**trainer_options)
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model = LightningTestModel(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_multi_gpu_none_backend(tmpdir):
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"""Make sure when using multiple GPUs the user can't use `distributed_backend = None`."""
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tutils.reset_seed()
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if not tutils.can_run_gpu_test():
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return
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model, hparams = tutils.get_model()
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trainer_options = dict(
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default_save_path=tmpdir,
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show_progress_bar=False,
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max_epochs=1,
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train_percent_check=0.1,
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val_percent_check=0.1,
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gpus='-1'
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)
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with pytest.raises(MisconfigurationException):
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tutils.run_model_test(trainer_options, model)
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def test_multi_gpu_model_dp(tmpdir):
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"""Make sure DP works."""
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tutils.reset_seed()
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if not tutils.can_run_gpu_test():
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return
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model, hparams = tutils.get_model()
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trainer_options = dict(
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default_save_path=tmpdir,
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show_progress_bar=False,
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distributed_backend='dp',
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max_epochs=1,
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train_percent_check=0.1,
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val_percent_check=0.1,
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gpus='-1'
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)
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tutils.run_model_test(trainer_options, model)
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# test memory helper functions
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memory.get_memory_profile('min_max')
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def test_ddp_sampler_error(tmpdir):
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"""Make sure DDP + AMP work."""
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if not tutils.can_run_gpu_test():
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return
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tutils.reset_seed()
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tutils.set_random_master_port()
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hparams = tutils.get_hparams()
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model = LightningTestModel(hparams, force_remove_distributed_sampler=True)
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logger = tutils.get_test_tube_logger(tmpdir, True)
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trainer = Trainer(
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logger=logger,
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show_progress_bar=False,
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max_epochs=1,
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gpus=[0, 1],
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distributed_backend='ddp',
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use_amp=True
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)
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with pytest.warns(UserWarning):
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trainer.get_dataloaders(model)
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@pytest.fixture
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def mocked_device_count(monkeypatch):
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def device_count():
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return PRETEND_N_OF_GPUS
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monkeypatch.setattr(torch.cuda, 'device_count', device_count)
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@pytest.fixture
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def mocked_device_count_0(monkeypatch):
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def device_count():
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return 0
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monkeypatch.setattr(torch.cuda, 'device_count', device_count)
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test_num_gpus_data = [
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pytest.param(None, 0, None, id="None - expect 0 gpu to use."),
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pytest.param(0, 0, None, id="Oth gpu, expect 1 gpu to use."),
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pytest.param(1, 1, None, id="1st gpu, expect 1 gpu to use."),
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pytest.param(-1, PRETEND_N_OF_GPUS, "ddp", id="-1 - use all gpus"),
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pytest.param('-1', PRETEND_N_OF_GPUS, "ddp", id="'-1' - use all gpus"),
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pytest.param(3, 3, "ddp", id="3rd gpu - 1 gpu to use (backend:ddp)")
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]
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@pytest.mark.gpus_param_tests
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@pytest.mark.parametrize(["gpus", "expected_num_gpus", "distributed_backend"], test_num_gpus_data)
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def test_trainer_gpu_parse(mocked_device_count, gpus, expected_num_gpus, distributed_backend):
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assert Trainer(gpus=gpus, distributed_backend=distributed_backend).num_gpus == expected_num_gpus
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test_num_gpus_data_0 = [
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pytest.param(None, 0, None, id="None - expect 0 gpu to use."),
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pytest.param(None, 0, "ddp", id="None - expect 0 gpu to use."),
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]
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@pytest.mark.gpus_param_tests
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@pytest.mark.parametrize(["gpus", "expected_num_gpus", "distributed_backend"], test_num_gpus_data_0)
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def test_trainer_num_gpu_0(mocked_device_count_0, gpus, expected_num_gpus, distributed_backend):
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assert Trainer(gpus=gpus, distributed_backend=distributed_backend).num_gpus == expected_num_gpus
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test_root_gpu_data = [
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pytest.param(None, None, "ddp", id="None is None"),
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pytest.param(0, None, "ddp", id="O gpus, expect gpu root device to be None."),
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pytest.param(1, 0, "ddp", id="1 gpu, expect gpu root device to be 0."),
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pytest.param(-1, 0, "ddp", id="-1 - use all gpus, expect gpu root device to be 0."),
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pytest.param('-1', 0, "ddp", id="'-1' - use all gpus, expect gpu root device to be 0."),
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pytest.param(3, 0, "ddp", id="3 gpus, expect gpu root device to be 0.(backend:ddp)")]
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@pytest.mark.gpus_param_tests
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@pytest.mark.parametrize(['gpus', 'expected_root_gpu', "distributed_backend"], test_root_gpu_data)
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def test_root_gpu_property(mocked_device_count, gpus, expected_root_gpu, distributed_backend):
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assert Trainer(gpus=gpus, distributed_backend=distributed_backend).root_gpu == expected_root_gpu
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test_root_gpu_data_for_0_devices_passing = [
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pytest.param(None, None, None, id="None is None"),
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pytest.param(None, None, "ddp", id="None is None"),
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pytest.param(0, None, "ddp", id="None is None"),
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]
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@pytest.mark.gpus_param_tests
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@pytest.mark.parametrize([
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'gpus', 'expected_root_gpu', "distributed_backend"], test_root_gpu_data_for_0_devices_passing)
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def test_root_gpu_property_0_passing(
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mocked_device_count_0, gpus, expected_root_gpu, distributed_backend):
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assert Trainer(gpus=gpus, distributed_backend=distributed_backend).root_gpu == expected_root_gpu
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# Asking for a gpu when non are available will result in a MisconfigurationException
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test_root_gpu_data_for_0_devices_raising = [
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pytest.param(1, None, "ddp"),
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pytest.param(3, None, "ddp"),
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pytest.param(3, None, "ddp"),
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pytest.param([1, 2], None, "ddp"),
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pytest.param([0, 1], None, "ddp"),
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pytest.param(-1, None, "ddp"),
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pytest.param('-1', None, "ddp")
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]
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@pytest.mark.gpus_param_tests
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@pytest.mark.parametrize([
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'gpus', 'expected_root_gpu', "distributed_backend"], test_root_gpu_data_for_0_devices_raising)
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def test_root_gpu_property_0_raising(
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mocked_device_count_0, gpus, expected_root_gpu, distributed_backend):
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with pytest.raises(MisconfigurationException):
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Trainer(gpus=gpus, distributed_backend=distributed_backend).root_gpu
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test_determine_root_gpu_device_data = [
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pytest.param(None, None, id="No gpus, expect gpu root device to be None"),
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pytest.param([0], 0, id="Oth gpu, expect gpu root device to be 0."),
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pytest.param([1], 1, id="1st gpu, expect gpu root device to be 1."),
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pytest.param([3], 3, id="3rd gpu, expect gpu root device to be 3."),
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pytest.param([1, 2], 1, id="[1, 2] gpus, expect gpu root device to be 1."),
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]
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@pytest.mark.gpus_param_tests
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@pytest.mark.parametrize(['gpus', 'expected_root_gpu'], test_determine_root_gpu_device_data)
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def test_determine_root_gpu_device(gpus, expected_root_gpu):
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assert determine_root_gpu_device(gpus) == expected_root_gpu
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test_parse_gpu_ids_data = [
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pytest.param(None, None),
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pytest.param(0, None),
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pytest.param(1, [0]),
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pytest.param(3, [0, 1, 2]),
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pytest.param(-1, list(range(PRETEND_N_OF_GPUS)), id="-1 - use all gpus"),
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pytest.param([0], [0]),
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pytest.param([1, 3], [1, 3]),
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pytest.param('0', [0]),
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pytest.param('3', [3]),
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pytest.param('1, 3', [1, 3]),
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pytest.param('-1', list(range(PRETEND_N_OF_GPUS)), id="'-1' - use all gpus"),
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]
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@pytest.mark.gpus_param_tests
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@pytest.mark.parametrize(['gpus', 'expected_gpu_ids'], test_parse_gpu_ids_data)
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def test_parse_gpu_ids(mocked_device_count, gpus, expected_gpu_ids):
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assert parse_gpu_ids(gpus) == expected_gpu_ids
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test_parse_gpu_invalid_inputs_data = [
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pytest.param(0.1),
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pytest.param(-2),
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pytest.param(False),
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pytest.param([]),
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pytest.param([-1]),
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pytest.param([None]),
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pytest.param(['0']),
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pytest.param((0, 1)),
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]
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@pytest.mark.gpus_param_tests
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@pytest.mark.parametrize(['gpus'], test_parse_gpu_invalid_inputs_data)
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def test_parse_gpu_fail_on_unsupported_inputs(mocked_device_count, gpus):
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with pytest.raises(MisconfigurationException):
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parse_gpu_ids(gpus)
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@pytest.mark.gpus_param_tests
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@pytest.mark.parametrize("gpus", [''])
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def test_parse_gpu_fail_on_empty_string(mocked_device_count, gpus):
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# This currently results in a ValueError instead of MisconfigurationException
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with pytest.raises(ValueError):
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parse_gpu_ids(gpus)
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@pytest.mark.gpus_param_tests
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@pytest.mark.parametrize("gpus", [[1, 2, 19], -1, '-1'])
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def test_parse_gpu_fail_on_non_existant_id(mocked_device_count_0, gpus):
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with pytest.raises(MisconfigurationException):
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parse_gpu_ids(gpus)
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@pytest.mark.gpus_param_tests
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def test_parse_gpu_fail_on_non_existant_id_2(mocked_device_count):
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with pytest.raises(MisconfigurationException):
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parse_gpu_ids([1, 2, 19])
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@pytest.mark.gpus_param_tests
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@pytest.mark.parametrize("gpus", [-1, '-1'])
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def test_parse_gpu_returns_None_when_no_devices_are_available(mocked_device_count_0, gpus):
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with pytest.raises(MisconfigurationException):
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parse_gpu_ids(gpus)
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# if __name__ == '__main__':
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# pytest.main([__file__])
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