243 lines
7.7 KiB
Python
243 lines
7.7 KiB
Python
# Copyright The PyTorch Lightning team.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import os
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import pytest
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import torch
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from pytorch_lightning import Callback, seed_everything, Trainer
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from pytorch_lightning.accelerators import HPUAccelerator
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from pytorch_lightning.strategies.hpu_parallel import HPUParallelStrategy
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from pytorch_lightning.strategies.single_hpu import SingleHPUStrategy
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from pytorch_lightning.utilities import _HPU_AVAILABLE
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from pytorch_lightning.utilities.exceptions import MisconfigurationException
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from tests.helpers.boring_model import BoringModel
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from tests.helpers.datamodules import ClassifDataModule
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from tests.helpers.runif import RunIf
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from tests.helpers.simple_models import ClassificationModel
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@RunIf(hpu=True)
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def test_availability():
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assert HPUAccelerator.is_available()
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@pytest.mark.skipif(_HPU_AVAILABLE, reason="test requires non-HPU machine")
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def test_fail_if_no_hpus():
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with pytest.raises(MisconfigurationException, match="HPUAccelerator can not run on your system"):
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Trainer(accelerator="hpu", devices=1)
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@RunIf(hpu=True)
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def test_accelerator_selected():
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trainer = Trainer(accelerator="hpu")
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assert isinstance(trainer.accelerator, HPUAccelerator)
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@RunIf(hpu=True)
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def test_all_stages(tmpdir, hpus):
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"""Tests all the model stages using BoringModel on HPU."""
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model = BoringModel()
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trainer = Trainer(
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default_root_dir=tmpdir,
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fast_dev_run=True,
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accelerator="hpu",
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devices=hpus,
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precision=16,
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)
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trainer.fit(model)
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trainer.validate(model)
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trainer.test(model)
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trainer.predict(model)
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@RunIf(hpu=True)
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def test_optimization(tmpdir):
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seed_everything(42)
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dm = ClassifDataModule(length=1024)
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model = ClassificationModel()
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trainer = Trainer(default_root_dir=tmpdir, max_epochs=1, accelerator="hpu", devices=1)
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# fit model
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trainer.fit(model, dm)
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assert trainer.state.finished, f"Training failed with {trainer.state}"
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assert dm.trainer is not None
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# validate
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result = trainer.validate(datamodule=dm)
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assert dm.trainer is not None
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assert result[0]["val_acc"] > 0.7
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# test
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result = trainer.test(model, datamodule=dm)
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assert dm.trainer is not None
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test_result = result[0]["test_acc"]
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assert test_result > 0.6
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# test saved model
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model_path = os.path.join(tmpdir, "model.pt")
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trainer.save_checkpoint(model_path)
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model = ClassificationModel.load_from_checkpoint(model_path)
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trainer = Trainer(default_root_dir=tmpdir, accelerator="hpu", devices=1)
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result = trainer.test(model, datamodule=dm)
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saved_result = result[0]["test_acc"]
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assert saved_result == test_result
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@RunIf(hpu=True)
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def test_stages_correct(tmpdir):
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"""Ensure all stages correctly are traced correctly by asserting the output for each stage."""
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class StageModel(BoringModel):
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def training_step(self, batch, batch_idx):
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loss = super().training_step(batch, batch_idx)
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loss = loss.get("loss")
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# tracing requires a loss value that depends on the model.
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# force it to be a value but ensure we use the loss.
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loss = (loss - loss) + torch.tensor(1)
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return {"loss": loss}
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def validation_step(self, batch, batch_idx):
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loss = super().validation_step(batch, batch_idx)
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x = loss.get("x")
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x = (x - x) + torch.tensor(2)
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return {"x": x}
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def test_step(self, batch, batch_idx):
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loss = super().test_step(batch, batch_idx)
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y = loss.get("y")
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y = (y - y) + torch.tensor(3)
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return {"y": y}
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def predict_step(self, batch, batch_idx, dataloader_idx=None):
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output = super().predict_step(batch, batch_idx)
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return (output - output) + torch.tensor(4)
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class TestCallback(Callback):
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def on_train_batch_end(self, trainer, pl_module, outputs, batch, batch_idx) -> None:
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assert outputs["loss"].item() == 1
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def on_validation_batch_end(self, trainer, pl_module, outputs, batch, batch_idx, dataloader_idx) -> None:
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assert outputs["x"].item() == 2
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def on_test_batch_end(self, trainer, pl_module, outputs, batch, batch_idx, dataloader_idx) -> None:
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assert outputs["y"].item() == 3
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def on_predict_batch_end(self, trainer, pl_module, outputs, batch, batch_idx, dataloader_idx) -> None:
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assert torch.all(outputs == 4).item()
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model = StageModel()
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trainer = Trainer(
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default_root_dir=tmpdir, fast_dev_run=True, accelerator="hpu", devices=1, callbacks=TestCallback()
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)
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trainer.fit(model)
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trainer.test(model)
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trainer.validate(model)
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trainer.predict(model)
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@RunIf(hpu=True)
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def test_accelerator_hpu():
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trainer = Trainer(accelerator="hpu", devices=1)
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assert isinstance(trainer.accelerator, HPUAccelerator)
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assert trainer.num_devices == 1
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trainer = Trainer(accelerator="hpu")
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assert isinstance(trainer.accelerator, HPUAccelerator)
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assert trainer.num_devices == 8
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trainer = Trainer(accelerator="auto", devices=8)
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assert isinstance(trainer.accelerator, HPUAccelerator)
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assert trainer.num_devices == 8
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@RunIf(hpu=True)
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def test_accelerator_hpu_with_single_device():
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trainer = Trainer(accelerator="hpu", devices=1)
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assert isinstance(trainer.strategy, SingleHPUStrategy)
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assert isinstance(trainer.accelerator, HPUAccelerator)
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@RunIf(hpu=True)
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def test_accelerator_hpu_with_multiple_devices():
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trainer = Trainer(accelerator="hpu", devices=8)
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assert isinstance(trainer.strategy, HPUParallelStrategy)
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assert isinstance(trainer.accelerator, HPUAccelerator)
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@RunIf(hpu=True)
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def test_accelerator_auto_with_devices_hpu():
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trainer = Trainer(accelerator="auto", devices=8)
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assert isinstance(trainer.strategy, HPUParallelStrategy)
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@RunIf(hpu=True)
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def test_strategy_choice_hpu_plugin():
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trainer = Trainer(strategy=SingleHPUStrategy(device=torch.device("hpu")), accelerator="hpu", devices=1)
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assert isinstance(trainer.strategy, SingleHPUStrategy)
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trainer = Trainer(accelerator="hpu", devices=1)
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assert isinstance(trainer.strategy, SingleHPUStrategy)
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@RunIf(hpu=True)
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def test_strategy_choice_hpu_parallel_plugin():
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trainer = Trainer(
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strategy=HPUParallelStrategy(parallel_devices=[torch.device("hpu")] * 8), accelerator="hpu", devices=8
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)
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assert isinstance(trainer.strategy, HPUParallelStrategy)
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trainer = Trainer(accelerator="hpu", devices=8)
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assert isinstance(trainer.strategy, HPUParallelStrategy)
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@RunIf(hpu=True)
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def test_devices_auto_choice_hpu():
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trainer = Trainer(accelerator="auto", devices="auto")
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assert trainer.num_devices == 8
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@RunIf(hpu=True)
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@pytest.mark.parametrize("hpus", [1])
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def test_inference_only(tmpdir, hpus):
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model = BoringModel()
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trainer = Trainer(default_root_dir=tmpdir, fast_dev_run=True, accelerator="hpu", devices=hpus)
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trainer.validate(model)
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trainer.test(model)
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trainer.predict(model)
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def test_hpu_auto_device_count():
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assert HPUAccelerator.auto_device_count() == 8
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@RunIf(hpu=True)
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def test_hpu_unsupported_device_type():
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with pytest.raises(MisconfigurationException, match="`devices` for `HPUAccelerator` must be int, string or None."):
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Trainer(accelerator="hpu", devices=[1])
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