306 lines
12 KiB
Python
306 lines
12 KiB
Python
import os
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from unittest import mock
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from unittest.mock import Mock
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import pytest
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import torch
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from pytorch_lightning import LightningModule, Trainer
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from pytorch_lightning.callbacks import Callback
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from pytorch_lightning.plugins import DDPShardedPlugin, DDPSpawnShardedPlugin
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from pytorch_lightning.trainer.states import TrainerFn
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from pytorch_lightning.utilities import _FAIRSCALE_AVAILABLE
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from tests.helpers.boring_model import BoringModel
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from tests.helpers.runif import RunIf
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if _FAIRSCALE_AVAILABLE:
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from fairscale.nn.data_parallel.sharded_ddp import ShardedDataParallel
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@pytest.mark.parametrize("clip_val", [0, 10])
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@RunIf(min_gpus=1, skip_windows=True, fairscale=True)
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@mock.patch("fairscale.optim.oss.OSS.clip_grad_norm")
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def test_ddp_sharded_precision_16_clip_gradients(mock_oss_clip_grad_norm, clip_val, tmpdir):
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"""Ensure that clip gradients is only called if the value is greater than 0."""
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model = BoringModel()
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trainer = Trainer(strategy="ddp_sharded", gpus=1, precision=16, fast_dev_run=True, gradient_clip_val=clip_val)
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trainer.fit(model)
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if clip_val > 0:
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mock_oss_clip_grad_norm.assert_called()
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else:
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mock_oss_clip_grad_norm.assert_not_called()
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@RunIf(fairscale=True)
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@pytest.mark.parametrize(["strategy"], [("ddp_sharded",), ("ddp_sharded_spawn",)])
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def test_sharded_ddp_choice(tmpdir, strategy):
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"""Test to ensure that plugin is correctly chosen."""
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class CB(Callback):
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def on_fit_start(self, trainer, pl_module):
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if strategy == "ddp_sharded":
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assert isinstance(trainer.accelerator.training_type_plugin, DDPShardedPlugin)
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elif strategy == "ddp_sharded_spawn":
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assert isinstance(trainer.accelerator.training_type_plugin, DDPSpawnShardedPlugin)
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raise SystemExit()
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model = BoringModel()
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trainer = Trainer(fast_dev_run=True, strategy=strategy, callbacks=[CB()])
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with pytest.raises(SystemExit):
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trainer.fit(model)
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@RunIf(min_gpus=1, fairscale=True)
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@pytest.mark.parametrize(["strategy"], [("ddp_sharded",), ("ddp_sharded_spawn",)])
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def test_ddp_choice_sharded_amp(tmpdir, strategy):
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"""Test to ensure that plugin native amp plugin is correctly chosen when using sharded."""
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class CB(Callback):
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def on_fit_start(self, trainer, pl_module):
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if strategy == "ddp_sharded":
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assert isinstance(trainer.accelerator.training_type_plugin, DDPShardedPlugin)
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elif strategy == "ddp_sharded_spawn":
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assert isinstance(trainer.accelerator.training_type_plugin, DDPSpawnShardedPlugin)
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raise SystemExit()
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model = BoringModel()
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trainer = Trainer(fast_dev_run=True, gpus=1, precision=16, strategy=strategy, callbacks=[CB()])
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with pytest.raises(SystemExit):
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trainer.fit(model)
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@RunIf(skip_windows=True, fairscale=True)
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def test_ddp_sharded_plugin_checkpoint_cpu(tmpdir):
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"""Test to ensure that checkpoint is saved correctly."""
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model = BoringModel()
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trainer = Trainer(strategy="ddp_sharded_spawn", num_processes=2, fast_dev_run=True)
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trainer.fit(model)
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checkpoint_path = os.path.join(tmpdir, "model.pt")
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trainer.save_checkpoint(checkpoint_path)
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saved_model = BoringModel.load_from_checkpoint(checkpoint_path)
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# Assert model parameters are identical after loading
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for ddp_param, shard_param in zip(model.parameters(), saved_model.parameters()):
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assert torch.equal(ddp_param.to("cpu"), shard_param)
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@RunIf(min_gpus=2, skip_windows=True, fairscale=True)
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def test_ddp_sharded_plugin_checkpoint_multi_gpu(tmpdir):
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"""Test to ensure that checkpoint is saved correctly when using multiple GPUs."""
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model = BoringModel()
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trainer = Trainer(gpus=2, strategy="ddp_sharded_spawn", fast_dev_run=True)
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trainer.fit(model)
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checkpoint_path = os.path.join(tmpdir, "model.pt")
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trainer.save_checkpoint(checkpoint_path)
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saved_model = BoringModel.load_from_checkpoint(checkpoint_path)
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# Assert model parameters are identical after loading
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for ddp_param, shard_param in zip(model.parameters(), saved_model.parameters()):
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assert torch.equal(ddp_param.to("cpu"), shard_param)
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@RunIf(min_gpus=2, skip_windows=True, fairscale=True)
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def test_ddp_sharded_plugin_finetune(tmpdir):
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"""Test to ensure that we can save and restart training (simulate fine-tuning)"""
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model = BoringModel()
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trainer = Trainer(gpus=2, strategy="ddp_sharded_spawn", fast_dev_run=True)
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trainer.fit(model)
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checkpoint_path = os.path.join(tmpdir, "model.pt")
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trainer.save_checkpoint(checkpoint_path)
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saved_model = BoringModel.load_from_checkpoint(checkpoint_path)
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trainer = Trainer(fast_dev_run=True)
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trainer.fit(saved_model)
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@RunIf(skip_windows=True, fairscale=True)
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def test_ddp_sharded_plugin_fit_ckpt_path(tmpdir):
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"""Test to ensure that resuming from checkpoint works."""
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model = BoringModel()
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trainer = Trainer(strategy="ddp_sharded_spawn", num_processes=2, fast_dev_run=True)
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trainer.fit(model)
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checkpoint_path = os.path.join(tmpdir, "model.pt")
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trainer.save_checkpoint(checkpoint_path)
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model = BoringModel()
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trainer = Trainer(strategy="ddp_sharded_spawn", num_processes=2, fast_dev_run=True)
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trainer.fit(model, ckpt_path=checkpoint_path)
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@pytest.mark.skip(reason="Not a critical test, skip till drone CI performance improves.") # todo
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@pytest.mark.skip(reason="Currently unsupported restarting training on different number of devices.")
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@RunIf(min_gpus=2, skip_windows=True, fairscale=True)
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def test_ddp_sharded_plugin_fit_ckpt_path_downsize_gpus(tmpdir):
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"""Test to ensure that resuming from checkpoint works when downsizing number of GPUS."""
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model = BoringModel()
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trainer = Trainer(strategy="ddp_sharded_spawn", fast_dev_run=True, gpus=2)
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trainer.fit(model)
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checkpoint_path = os.path.join(tmpdir, "model.pt")
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trainer.save_checkpoint(checkpoint_path)
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model = BoringModel()
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trainer = Trainer(strategy="ddp_sharded_spawn", fast_dev_run=True, gpus=1)
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trainer.fit(model, ckpt_path=checkpoint_path)
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@RunIf(min_gpus=1, skip_windows=True, fairscale=True)
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def test_ddp_sharded_plugin_fit_ckpt_path_gpu_to_cpu(tmpdir):
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"""Test to ensure that resuming from checkpoint works when going from GPUs- > CPU."""
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model = BoringModel()
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trainer = Trainer(strategy="ddp_sharded_spawn", gpus=1, fast_dev_run=True)
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trainer.fit(model)
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checkpoint_path = os.path.join(tmpdir, "model.pt")
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trainer.save_checkpoint(checkpoint_path)
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model = BoringModel()
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trainer = Trainer(strategy="ddp_sharded_spawn", num_processes=2, fast_dev_run=True)
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trainer.fit(model, ckpt_path=checkpoint_path)
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@RunIf(skip_windows=True, standalone=True, fairscale=True)
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@pytest.mark.parametrize("trainer_kwargs", (dict(num_processes=2), pytest.param(dict(gpus=2), marks=RunIf(min_gpus=2))))
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def test_ddp_sharded_plugin_test_multigpu(tmpdir, trainer_kwargs):
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"""Test to ensure we can use validate and test without fit."""
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model = BoringModel()
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trainer = Trainer(strategy="ddp_sharded_spawn", fast_dev_run=True, **trainer_kwargs)
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trainer.validate(model)
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trainer.test(model)
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class ManualBoringModel(BoringModel):
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def __init__(self):
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super().__init__()
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self.automatic_optimization = False
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def training_step(self, batch, batch_idx):
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opt = self.optimizers()
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opt.zero_grad()
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output = self(batch)
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loss = self.loss(batch, output)
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self.manual_backward(loss)
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opt.step()
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return {"loss": loss}
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@RunIf(skip_windows=True, standalone=True, fairscale=True, min_gpus=2)
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def test_ddp_sharded_plugin_manual_optimization_spawn(tmpdir):
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# todo (sean): this test has been split out as running both tests using parametrize causes "Address in use"
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model = ManualBoringModel()
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trainer = Trainer(default_root_dir=tmpdir, strategy="ddp_sharded_spawn", fast_dev_run=2, gpus=2)
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trainer.fit(model)
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@RunIf(skip_windows=True, standalone=True, fairscale=True, min_gpus=2)
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def test_ddp_sharded_plugin_manual_optimization(tmpdir):
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model = ManualBoringModel()
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trainer = Trainer(default_root_dir=tmpdir, strategy="ddp_sharded", fast_dev_run=2, gpus=2)
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trainer.fit(model)
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class BoringModelSharded(BoringModel):
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def on_train_start(self) -> None:
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"""Check if trainer module is wrapped as ShardedDataParallel during training stage."""
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assert isinstance(self.trainer.model, ShardedDataParallel)
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def on_test_start(self) -> None:
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"""Check if trainer module remains as LightningModule during test stage."""
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assert isinstance(self.trainer.model, LightningModule)
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def on_validation_start(self) -> None:
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"""Check if trainer module remains as LightningModule during test stage."""
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if self.trainer.state.fn == TrainerFn.FITTING:
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assert isinstance(self.trainer.model, ShardedDataParallel)
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else:
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assert isinstance(self.trainer.model, LightningModule)
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def on_predict_start(self) -> None:
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"""Check if trainer module remains as LightningModule during prediction stage."""
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assert isinstance(self.trainer.model, LightningModule)
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@RunIf(skip_windows=True, fairscale=True)
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def test_configure_ddp(tmpdir):
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"""Tests with ddp sharded plugin."""
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trainer = Trainer(default_root_dir=tmpdir, strategy="ddp_sharded", fast_dev_run=True)
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model = BoringModelSharded()
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trainer.fit(model)
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trainer.test(model, dataloaders=model.test_dataloader())
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trainer.validate(model, dataloaders=model.val_dataloader())
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trainer.predict(model, dataloaders=model.predict_dataloader())
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@RunIf(skip_windows=True, fairscale=True)
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@mock.patch("pytorch_lightning.plugins.DDPShardedPlugin._wrap_optimizers", autospec=True)
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@pytest.mark.parametrize("cls", [DDPShardedPlugin, DDPSpawnShardedPlugin])
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def test_custom_kwargs_sharded(tmpdir, cls):
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"""Tests to ensure that if custom kwargs are passed, they are set correctly."""
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plugin = cls(reduce_fp16=True)
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plugin.model = Mock(spec=LightningModule)
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plugin.model.trainer = Mock()
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class_name = "sharded" if isinstance(plugin, DDPShardedPlugin) else "sharded_spawn"
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with mock.patch(
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f"pytorch_lightning.plugins.training_type.{class_name}.ShardedDataParallel", autospec=True
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) as mock_sharded:
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plugin.configure_ddp()
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args, kwargs = mock_sharded.call_args
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assert "reduce_fp16" in kwargs
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assert kwargs["reduce_fp16"]
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@RunIf(skip_windows=True, fairscale=True)
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@mock.patch("pytorch_lightning.plugins.DDPShardedPlugin._wrap_optimizers", autospec=True)
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@pytest.mark.parametrize(["params", "expected_buffer_size"], [(dict(), 0), (dict(reduce_buffer_size=128), 128)])
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@pytest.mark.parametrize("num_nodes", [1, 2])
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def test_custom_kwargs_sharded_reduce_buffer_size(tmpdir, params, expected_buffer_size, num_nodes):
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"""Tests to ensure that ``reduce_buffer_size`` is correctly set based on user kwargs."""
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plugin = DDPShardedPlugin(**params)
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plugin.num_nodes = num_nodes
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plugin.model = Mock(spec=LightningModule)
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plugin.model.trainer = Mock()
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with mock.patch(
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"pytorch_lightning.plugins.training_type.sharded.ShardedDataParallel", autospec=True
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) as mock_sharded:
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plugin.configure_ddp()
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args, kwargs = mock_sharded.call_args
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assert "reduce_buffer_size" in kwargs
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if num_nodes > 1 and len(params) == 0:
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# If user has not specified a buffer size and we're using multiple nodes, check to see if default is set
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assert kwargs["reduce_buffer_size"] == DDPShardedPlugin._REDUCE_BUFFER_SIZE_DEFAULT
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else:
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assert kwargs["reduce_buffer_size"] == expected_buffer_size
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@RunIf(skip_windows=True, fairscale=True)
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def test_block_backward_sync(tmpdir):
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plugin = DDPShardedPlugin()
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model = mock.MagicMock(spec=ShardedDataParallel)
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with mock.patch.object(plugin, "_model", model):
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with plugin.block_backward_sync():
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pass
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model.no_sync.assert_called_once()
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