209 lines
8.4 KiB
Python
209 lines
8.4 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 ANY, MagicMock, Mock
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import pytest
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import torch
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from lightning.fabric.accelerators.xla import _XLA_GREATER_EQUAL_2_1, XLAAccelerator
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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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from lightning.fabric.utilities.seed import seed_everything
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from torch.utils.data import DataLoader
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from tests_fabric.helpers.datasets import RandomDataset
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from tests_fabric.helpers.runif import RunIf
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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, strategy=None):
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# TODO: the accelerator should be optional to just launch processes, but this requires lazy initialization
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if not strategy:
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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 broadcast_on_tpu_fn(strategy):
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# test broadcasting a tensor
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obj = torch.tensor(strategy.global_rank)
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assert obj.device.type == "cpu"
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# In PjRT, the local rank and global rank have no solid relation.
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# global rank may not even be contiguous on a host, because it depends on the 3D mesh structure that is formed by
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# the TPUs on all hosts in a pod. So checking a different src is not reliable
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# https://github.com/pytorch/xla/blob/v2.0.0/torch_xla/experimental/pjrt.py#L161-L163
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src = 0
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result = strategy.broadcast(obj, src)
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assert result.item() == src
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assert result.device.type == "cpu" # the original device is preserved
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# test broadcasting an arbitrary object
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tensor = torch.tensor(strategy.global_rank, device=strategy.root_device, dtype=torch.bfloat16)
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obj = ("ver_0.5", "logger_name", strategy.global_rank, tensor)
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result = strategy.broadcast(obj, src=src)
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assert result == ("ver_0.5", "logger_name", src, ANY)
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assert result[3].device.type == "xla" # the original device is preserved
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assert result[3].dtype == torch.bfloat16
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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 broadcast 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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def tpu_all_gather_fn(strategy):
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with pytest.raises(NotImplementedError, match="only implemented for tensors"):
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strategy.all_gather([1])
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for sync_grads in (True, False):
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tensor = torch.tensor(1.0, 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 summed.device.type == "cpu" # the original device is preserved
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assert torch.equal(summed, torch.tensor(strategy.world_size, dtype=torch.float32))
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if not _XLA_GREATER_EQUAL_2_1:
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summed.backward()
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if sync_grads:
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if _XLA_GREATER_EQUAL_2_1:
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# in 2.1, sync_grads=False makes it so that you cannot call .backward even if it originally had set
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# requires_grad=True
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summed.backward()
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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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def tpu_sync_module_states_fn(sync_module_states, strategy):
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seed_everything(strategy.local_rank) # force the model to have different weights across ranks
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model = torch.nn.Linear(1, 1).to(strategy.root_device)
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model = strategy.setup_module(model)
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gathered = strategy.all_gather(model.weight)
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for t in gathered[1:]:
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if sync_module_states:
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assert torch.equal(gathered[0], t)
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else:
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assert not torch.equal(gathered[0], t)
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@RunIf(tpu=True)
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@pytest.mark.parametrize("sync_module_states", [True, False])
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@mock.patch.dict(os.environ, os.environ.copy(), clear=True)
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def test_tpu_sync_module_states(sync_module_states):
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"""Test sync_module_states."""
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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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sync_module_states=sync_module_states,
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)
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partial_fn = partial(tpu_sync_module_states_fn, sync_module_states)
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xla_launch(partial_fn, strategy)
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@mock.patch.dict(os.environ, os.environ.copy(), clear=True)
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def test_rank_properties_access(xla_available):
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"""Test that the strategy returns the expected values depending on whether we're in the main process or not."""
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strategy = XLAStrategy()
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strategy.cluster_environment = Mock()
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# we're in the main process, no processes have been launched yet
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assert not strategy._launched
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assert strategy.global_rank == 0
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assert strategy.local_rank == 0
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assert strategy.node_rank == 0
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assert strategy.world_size == 1
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# simulate we're in a worker process
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strategy._launched = True
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assert strategy.global_rank == strategy.cluster_environment.global_rank()
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assert strategy.local_rank == strategy.cluster_environment.local_rank()
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assert strategy.node_rank == strategy.cluster_environment.node_rank()
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assert strategy.world_size == strategy.cluster_environment.world_size()
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