377 lines
12 KiB
Python
377 lines
12 KiB
Python
# Copyright The Lightning AI 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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from functools import partial
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from unittest import mock
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import pytest
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import torch
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from torch.utils.data import DataLoader
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import tests_pytorch.helpers.pipelines as tpipes
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from lightning.pytorch import Trainer
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from lightning.pytorch.accelerators import XLAAccelerator
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from lightning.pytorch.callbacks import EarlyStopping
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from lightning.pytorch.demos.boring_classes import BoringModel, RandomDataset
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from lightning.pytorch.strategies import XLAStrategy
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from lightning.pytorch.strategies.launchers.xla import _XLALauncher
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from lightning.pytorch.trainer.connectors.logger_connector.result import _Sync
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from lightning.pytorch.utilities.exceptions import MisconfigurationException
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from tests_pytorch.helpers.runif import RunIf
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class SerialLoaderBoringModel(BoringModel):
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def train_dataloader(self):
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return DataLoader(RandomDataset(32, 2000), batch_size=32)
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def val_dataloader(self):
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return DataLoader(RandomDataset(32, 2000), batch_size=32)
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@RunIf(tpu=True, standalone=True)
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@mock.patch.dict(os.environ, os.environ.copy(), clear=True)
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def test_model_tpu_devices_1(tmpdir):
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"""Make sure model trains on TPU."""
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trainer_options = {
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"default_root_dir": tmpdir,
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"enable_progress_bar": False,
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"max_epochs": 2,
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"accelerator": "tpu",
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"devices": 1,
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"limit_train_batches": 4,
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"limit_val_batches": 4,
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}
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model = BoringModel()
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tpipes.run_model_test(trainer_options, model, with_hpc=False)
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@pytest.mark.parametrize("tpu_core", [1, 3])
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@RunIf(tpu=True, standalone=True)
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@mock.patch.dict(os.environ, os.environ.copy(), clear=True)
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def test_model_tpu_index(tmpdir, tpu_core):
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"""Make sure model trains on TPU."""
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trainer_options = {
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"default_root_dir": tmpdir,
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"enable_progress_bar": False,
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"max_epochs": 2,
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"accelerator": "tpu",
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"devices": [tpu_core],
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"limit_train_batches": 4,
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"limit_val_batches": 4,
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}
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model = BoringModel()
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tpipes.run_model_test(trainer_options, model, with_hpc=False)
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import torch_xla
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from torch_xla.experimental import pjrt
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expected = tpu_core if pjrt.using_pjrt() else tpu_core + 1
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assert torch_xla._XLAC._xla_get_default_device() == f"xla:{expected}"
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@RunIf(tpu=True)
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@mock.patch.dict(os.environ, os.environ.copy(), clear=True)
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def test_model_multiple_tpu_devices(tmpdir):
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"""Make sure model trains on TPU."""
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trainer_options = {
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"default_root_dir": tmpdir,
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"enable_progress_bar": False,
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"max_epochs": 1,
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"accelerator": "tpu",
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"devices": "auto",
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"limit_train_batches": 4,
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"limit_val_batches": 4,
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}
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# multiple cores needs a big dataset
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model = SerialLoaderBoringModel()
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tpipes.run_model_test(trainer_options, model, with_hpc=False, min_acc=0.05)
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@RunIf(tpu=True, standalone=True)
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@mock.patch.dict(os.environ, os.environ.copy(), clear=True)
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def test_model_16bit_tpu_devices_1(tmpdir):
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"""Make sure model trains on TPU."""
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trainer_options = {
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"default_root_dir": tmpdir,
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"precision": "16-mixed",
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"enable_progress_bar": False,
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"max_epochs": 2,
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"accelerator": "tpu",
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"devices": 1,
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"limit_train_batches": 8,
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"limit_val_batches": 2,
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}
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model = BoringModel()
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tpipes.run_model_test(trainer_options, model)
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@pytest.mark.parametrize("tpu_core", [1, 3])
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@RunIf(tpu=True, standalone=True)
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@mock.patch.dict(os.environ, os.environ.copy(), clear=True)
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def test_model_16bit_tpu_index(tmpdir, tpu_core):
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"""Make sure model trains on TPU."""
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trainer_options = {
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"default_root_dir": tmpdir,
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"precision": "16-mixed",
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"enable_progress_bar": False,
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"max_epochs": 2,
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"accelerator": "tpu",
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"devices": [tpu_core],
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"limit_train_batches": 4,
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"limit_val_batches": 2,
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}
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model = BoringModel()
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tpipes.run_model_test(trainer_options, model)
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import torch_xla
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from torch_xla.experimental import pjrt
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expected = tpu_core if pjrt.using_pjrt() else tpu_core + 1
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assert torch_xla._XLAC._xla_get_default_device() == f"xla:{expected}"
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@RunIf(tpu=True)
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@mock.patch.dict(os.environ, os.environ.copy(), clear=True)
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def test_model_16bit_multiple_tpu_devices(tmpdir):
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"""Make sure model trains on TPU."""
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trainer_options = {
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"default_root_dir": tmpdir,
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"precision": "16-mixed",
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"enable_progress_bar": False,
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"max_epochs": 1,
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"accelerator": "tpu",
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"devices": "auto",
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"limit_train_batches": 4,
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"limit_val_batches": 4,
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}
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# multiple cores needs a big dataset
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model = SerialLoaderBoringModel()
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tpipes.run_model_test(trainer_options, model, with_hpc=False, min_acc=0.05)
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class CustomBoringModel(BoringModel):
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def validation_step(self, *args, **kwargs):
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out = super().validation_step(*args, **kwargs)
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self.log("val_loss", out["x"])
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return out
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@RunIf(tpu=True)
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@mock.patch.dict(os.environ, os.environ.copy(), clear=True)
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def test_model_tpu_early_stop(tmpdir):
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"""Test if single TPU core training works."""
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model = CustomBoringModel()
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trainer = Trainer(
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callbacks=[EarlyStopping(monitor="val_loss")],
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default_root_dir=tmpdir,
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enable_progress_bar=False,
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max_epochs=2,
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limit_train_batches=2,
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limit_val_batches=2,
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accelerator="tpu",
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devices="auto",
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)
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trainer.fit(model)
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trainer.test(dataloaders=DataLoader(RandomDataset(32, 2000), batch_size=32))
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@RunIf(tpu=True, standalone=True)
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@mock.patch.dict(os.environ, os.environ.copy(), clear=True)
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def test_tpu_grad_norm(tmpdir):
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"""Test if grad_norm works on TPU."""
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trainer_options = {
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"default_root_dir": tmpdir,
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"enable_progress_bar": False,
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"max_epochs": 4,
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"accelerator": "tpu",
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"devices": 1,
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"limit_train_batches": 0.4,
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"limit_val_batches": 0.4,
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"gradient_clip_val": 0.5,
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}
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model = BoringModel()
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tpipes.run_model_test(trainer_options, model, with_hpc=False)
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@RunIf(tpu=True, standalone=True)
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@mock.patch.dict(os.environ, os.environ.copy(), clear=True)
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def test_tpu_clip_grad_by_value(tmpdir):
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"""Test if clip_gradients by value works on TPU."""
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trainer_options = {
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"default_root_dir": tmpdir,
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"enable_progress_bar": False,
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"max_epochs": 4,
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"accelerator": "tpu",
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"devices": 1,
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"limit_train_batches": 10,
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"limit_val_batches": 10,
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"gradient_clip_val": 0.5,
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"gradient_clip_algorithm": "value",
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}
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model = BoringModel()
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tpipes.run_model_test(trainer_options, model, with_hpc=False)
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@RunIf(tpu=True)
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@mock.patch.dict(os.environ, os.environ.copy(), clear=True)
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def test_dataloaders_passed_to_fit(tmpdir):
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"""Test if dataloaders passed to trainer works on TPU."""
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model = BoringModel()
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trainer = Trainer(default_root_dir=tmpdir, max_epochs=1, accelerator="tpu", devices="auto")
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trainer.fit(model, train_dataloaders=model.train_dataloader(), val_dataloaders=model.val_dataloader())
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@pytest.mark.parametrize("devices", [[1, 8], "9, ", [9], [-1], 2, 10])
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def test_tpu_misconfiguration(devices, tpu_available):
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with pytest.raises(ValueError, match="`devices` can only be"):
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Trainer(accelerator="tpu", devices=devices)
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@pytest.mark.skipif(XLAAccelerator.is_available(), reason="test requires missing TPU")
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def test_exception_when_no_tpu_found(xla_available):
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"""Test if exception is thrown when xla devices are not available."""
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with pytest.raises(MisconfigurationException, match="XLAAccelerator` can not run on your system"):
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Trainer(accelerator="tpu", devices=8)
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@pytest.mark.parametrize("devices", [1, 4, [1]])
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@RunIf(tpu=True, standalone=True)
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@mock.patch.dict(os.environ, os.environ.copy(), clear=True)
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def test_accelerator_set_when_using_tpu(devices):
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"""Test if the accelerator is set to `tpu` when devices is not None."""
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assert isinstance(Trainer(accelerator="tpu", devices=devices).accelerator, XLAAccelerator)
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@RunIf(tpu=True)
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@mock.patch.dict(os.environ, os.environ.copy(), clear=True)
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def test_if_test_works_with_checkpoint_false(tmpdir):
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"""Ensure that model trains properly when `enable_checkpointing` is set to False."""
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# Train a model on TPU
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model = BoringModel()
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trainer = Trainer(
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max_epochs=1,
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accelerator="tpu",
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devices="auto",
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default_root_dir=tmpdir,
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fast_dev_run=True,
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enable_checkpointing=False,
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)
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trainer.fit(model)
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assert trainer.state.finished, f"Training failed with {trainer.state}"
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def wrap_launch_function(fn, strategy, *args, **kwargs):
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# the launcher does not manage this automatically. explanation available in:
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# https://github.com/Lightning-AI/lightning/pull/14926#discussion_r982976718
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strategy.setup_environment()
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return fn(*args, **kwargs)
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def xla_launch(fn):
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# TODO: the accelerator should be optional to just launch processes, but this requires lazy initialization
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accelerator = XLAAccelerator()
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strategy = XLAStrategy(
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accelerator=accelerator,
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parallel_devices=XLAAccelerator.get_parallel_devices(XLAAccelerator.auto_device_count()),
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)
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launcher = _XLALauncher(strategy=strategy)
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wrapped = partial(wrap_launch_function, fn, strategy)
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return launcher.launch(wrapped, strategy)
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def tpu_sync_dist_fn(strategy):
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sync = _Sync(strategy.reduce, _should=True, _op=torch.distributed.ReduceOp.SUM)
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value = torch.tensor([1.0])
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value = sync(value)
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world_size = XLAAccelerator.auto_device_count()
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assert value.item() == world_size
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@RunIf(tpu=True)
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@mock.patch.dict(os.environ, os.environ.copy(), clear=True)
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def test_tpu_sync_dist():
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"""Test tpu spawn sync dist operation."""
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xla_launch(tpu_sync_dist_fn)
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class AssertXLADebugModel(BoringModel):
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def on_train_start(self):
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assert os.environ.get("PT_XLA_DEBUG") == "1", "PT_XLA_DEBUG was not set in environment variables"
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def teardown(self, stage):
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assert "PT_XLA_DEBUG" not in os.environ
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@RunIf(tpu=True)
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@mock.patch.dict(os.environ, os.environ.copy(), clear=True)
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def test_tpu_debug_mode(tmpdir):
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"""Test if debug mode works on TPU."""
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trainer_options = {
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"default_root_dir": tmpdir,
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"enable_progress_bar": False,
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"max_epochs": 4,
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"accelerator": "tpu",
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"devices": "auto",
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"limit_train_batches": 0.4,
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"limit_val_batches": 0.4,
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"strategy": XLAStrategy(debug=True),
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}
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model = AssertXLADebugModel()
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tpipes.run_model_test(trainer_options, model, with_hpc=False)
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class AssertXLAWorldSizeModel(BoringModel):
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def on_train_start(self):
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assert os.environ.get("XRT_HOST_WORLD_SIZE") == str(1)
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@RunIf(tpu=True)
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@mock.patch.dict(os.environ, os.environ.copy(), clear=True)
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def test_tpu_host_world_size(tmpdir):
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"""Test Host World size env setup on TPU."""
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from torch_xla.experimental import pjrt
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if pjrt.using_pjrt():
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pytest.skip("PJRT doesn't set 'XRT_HOST_WORLD_SIZE'")
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trainer_options = {
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"default_root_dir": tmpdir,
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"enable_progress_bar": False,
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"max_epochs": 4,
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"accelerator": "tpu",
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"devices": "auto",
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"limit_train_batches": 0.4,
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"limit_val_batches": 0.4,
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}
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model = AssertXLAWorldSizeModel()
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assert "XRT_HOST_WORLD_SIZE" not in os.environ
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tpipes.run_model_test(trainer_options, model, with_hpc=False)
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assert "XRT_HOST_WORLD_SIZE" not in os.environ
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@RunIf(tpu=True)
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def test_device_type_when_tpu_strategy_passed(tmpdir):
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trainer = Trainer(default_root_dir=tmpdir, strategy=XLAStrategy(), accelerator="tpu", devices="auto")
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assert isinstance(trainer.strategy, XLAStrategy)
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assert isinstance(trainer.accelerator, XLAAccelerator)
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