115 lines
4.4 KiB
Python
115 lines
4.4 KiB
Python
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import os
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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 pytorch_lightning import Trainer
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from pytorch_lightning.plugins import DDP2Plugin, DDPPlugin, DDPShardedPlugin, DeepSpeedPlugin, RPCSequentialPlugin
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from pytorch_lightning.plugins.environments import LightningEnvironment, SLURMEnvironment, TorchElasticEnvironment
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from pytorch_lightning.utilities import rank_zero_only
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from tests.helpers.runif import RunIf
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def environment_combinations():
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expected = dict(global_rank=3, local_rank=1, node_rank=1, world_size=4)
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# Lightning
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variables = {
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"CUDA_VISIBLE_DEVICES": "0,1,2,4",
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"LOCAL_RANK": "1",
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"NODE_RANK": "1",
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"WORLD_SIZE": "8",
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}
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environment = LightningEnvironment()
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yield environment, variables, expected
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# SLURM
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variables = {
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"CUDA_VISIBLE_DEVICES": "0,1,2,4",
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"SLURM_JOB_NAME": "SOME_NAME",
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"SLURM_LOCALID": "1",
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"SLURM_NODEID": "1",
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"SLURM_PROCID": "3",
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"SLURM_NTASKS": "4",
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}
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environment = SLURMEnvironment()
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yield environment, variables, expected
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# TorchElastic
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variables = {
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"CUDA_VISIBLE_DEVICES": "0,1,2,4",
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"LOCAL_RANK": "1",
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"GROUP_RANK": "1",
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"RANK": "3",
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"WORLD_SIZE": "4",
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"LOCAL_WORLD_SIZE": "2",
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}
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environment = TorchElasticEnvironment()
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yield environment, variables, expected
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@pytest.mark.parametrize(
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"plugin_cls", [
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DDPPlugin,
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DDPShardedPlugin,
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DDP2Plugin,
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pytest.param(DeepSpeedPlugin, marks=RunIf(deepspeed=True)),
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pytest.param(RPCSequentialPlugin, marks=RunIf(fairscale_pipe=True)),
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]
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)
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def test_ranks_available_manual_plugin_selection(plugin_cls):
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""" Test that the rank information is readily available after Trainer initialization. """
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num_nodes = 2
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for cluster, variables, expected in environment_combinations():
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if plugin_cls == DDP2Plugin:
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expected.update(global_rank=expected["node_rank"], world_size=num_nodes)
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with mock.patch.dict(os.environ, variables):
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plugin = plugin_cls(
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parallel_devices=[torch.device("cuda", 1), torch.device("cuda", 2)],
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num_nodes=num_nodes,
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cluster_environment=cluster,
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)
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trainer = Trainer(plugins=[plugin])
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assert rank_zero_only.rank == expected["global_rank"]
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assert trainer.global_rank == expected["global_rank"]
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assert trainer.local_rank == expected["local_rank"]
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assert trainer.node_rank == expected["node_rank"]
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assert trainer.world_size == expected["world_size"]
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@pytest.mark.parametrize(
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"trainer_kwargs", [
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dict(accelerator="ddp", gpus=[1, 2]),
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dict(accelerator="ddp_sharded", gpus=[1, 2]),
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dict(accelerator="ddp2", gpus=[1, 2]),
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dict(accelerator="ddp_cpu", num_processes=2),
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dict(accelerator="ddp_spawn", gpus=[1, 2]),
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]
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)
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@mock.patch("torch.cuda.is_available", return_value=True)
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@mock.patch("torch.cuda.device_count", return_value=4)
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def test_ranks_available_automatic_plugin_selection(mock0, mock1, trainer_kwargs):
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""" Test that the rank information is readily available after Trainer initialization. """
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num_nodes = 2
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trainer_kwargs.update(num_nodes=num_nodes)
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for cluster, variables, expected in environment_combinations():
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if trainer_kwargs["accelerator"] == "ddp2":
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expected.update(global_rank=expected["node_rank"], world_size=num_nodes)
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if trainer_kwargs["accelerator"] in ("ddp_cpu", "ddp_spawn"):
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if isinstance(cluster, (SLURMEnvironment, TorchElasticEnvironment)):
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# slurm and torchelastic do not work with spawn plugins
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continue
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# when using spawn, we don't reach rank > 0 until we call Trainer.fit()
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expected.update(global_rank=(expected["node_rank"] * 2), local_rank=0)
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with mock.patch.dict(os.environ, variables):
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trainer = Trainer(**trainer_kwargs)
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assert type(trainer.training_type_plugin.cluster_environment) == type(cluster)
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assert rank_zero_only.rank == expected["global_rank"]
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assert trainer.global_rank == expected["global_rank"]
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assert trainer.local_rank == expected["local_rank"]
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assert trainer.node_rank == expected["node_rank"]
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assert trainer.world_size == expected["world_size"]
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