151 lines
5.8 KiB
Python
151 lines
5.8 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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from unittest.mock import MagicMock, Mock
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import pytest
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import torch
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from tests_fabric.helpers.dataloaders import CustomNotImplementedErrorDataloader
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from tests_fabric.helpers.models import RandomDataset, RandomIterableDataset
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from tests_fabric.helpers.runif import RunIf
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from torch.utils.data import DataLoader
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from lightning.fabric.accelerators import TPUAccelerator
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from lightning.fabric.strategies import XLAStrategy
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from lightning.fabric.strategies.launchers.xla import _XLALauncher
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from lightning.fabric.utilities.distributed import ReduceOp
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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 = TPUAccelerator()
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strategy = XLAStrategy(accelerator=accelerator, parallel_devices=list(range(8)))
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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 broadcast_on_tpu_fn(strategy):
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obj = ("ver_0.5", "logger_name", strategy.local_rank)
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result = strategy.broadcast(obj)
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assert result == ("ver_0.5", "logger_name", 0)
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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_broadcast_on_tpu():
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"""Checks if an object from the main process is broadcasted to other processes correctly."""
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xla_launch(broadcast_on_tpu_fn)
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def tpu_reduce_fn(strategy):
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with pytest.raises(ValueError, match="XLAStrategy only supports"):
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strategy.all_reduce(1, reduce_op="undefined")
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with pytest.raises(ValueError, match="XLAStrategy only supports"):
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strategy.all_reduce(1, reduce_op=ReduceOp.MAX)
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# it is faster to loop over here than to parameterize the test
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for reduce_op in ("mean", "AVG", "sum", ReduceOp.SUM):
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result = strategy.all_reduce(1, reduce_op=reduce_op)
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if isinstance(reduce_op, str) and reduce_op.lower() in ("mean", "avg"):
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assert result.item() == 1
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else:
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assert result.item() == 8
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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_reduce():
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"""Test tpu spawn all_reduce operation."""
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xla_launch(tpu_reduce_fn)
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@RunIf(tpu=True)
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@mock.patch("lightning.fabric.strategies.xla.XLAStrategy.root_device")
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def test_xla_mp_device_dataloader_attribute(_, monkeypatch):
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dataset = RandomDataset(32, 64)
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dataloader = DataLoader(dataset)
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strategy = XLAStrategy()
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isinstance_return = True
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import torch_xla.distributed.parallel_loader as parallel_loader
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class MpDeviceLoaderMock(MagicMock):
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def __instancecheck__(self, instance):
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# to make `isinstance(dataloader, MpDeviceLoader)` pass with a mock as class
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return isinstance_return
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mp_loader_mock = MpDeviceLoaderMock()
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monkeypatch.setattr(parallel_loader, "MpDeviceLoader", mp_loader_mock)
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processed_dataloader = strategy.process_dataloader(dataloader)
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assert processed_dataloader is dataloader
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mp_loader_mock.assert_not_called() # no-op
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isinstance_return = False
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processed_dataloader = strategy.process_dataloader(dataloader)
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mp_loader_mock.assert_called_with(dataloader, strategy.root_device)
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assert processed_dataloader.dataset == processed_dataloader._loader.dataset
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assert processed_dataloader.batch_sampler == processed_dataloader._loader.batch_sampler
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_loader = DataLoader(RandomDataset(32, 64))
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_iterable_loader = DataLoader(RandomIterableDataset(32, 64))
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_loader_no_len = CustomNotImplementedErrorDataloader(_loader)
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@RunIf(tpu=True)
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@pytest.mark.parametrize("dataloader", [None, _iterable_loader, _loader_no_len])
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@mock.patch("lightning.fabric.strategies.xla.XLAStrategy.root_device")
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def test_xla_validate_unsupported_iterable_dataloaders(_, dataloader, monkeypatch):
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"""Test that the XLAStrategy validates against dataloaders with no length defined on datasets (iterable
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dataset)."""
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import torch_xla.distributed.parallel_loader as parallel_loader
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monkeypatch.setattr(parallel_loader, "MpDeviceLoader", Mock())
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with pytest.raises(TypeError, match="TPUs do not currently support"):
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XLAStrategy().process_dataloader(dataloader)
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def tpu_all_gather_fn(strategy):
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for sync_grads in [True, False]:
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tensor = torch.tensor(1.0, device=strategy.root_device, requires_grad=True)
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result = strategy.all_gather(tensor, sync_grads=sync_grads)
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summed = result.sum()
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assert torch.equal(summed, torch.tensor(8.0))
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summed.backward()
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if sync_grads:
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assert torch.equal(tensor.grad, torch.tensor(1.0))
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else:
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# As gradients are not synced, the original tensor will not have gradients.
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assert tensor.grad is None
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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_all_gather():
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"""Test the all_gather operation on TPU."""
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xla_launch(tpu_all_gather_fn)
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