224 lines
8.3 KiB
Python
224 lines
8.3 KiB
Python
# Copyright The PyTorch Lightning 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 contextlib import contextmanager
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from copy import deepcopy
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from functools import partial
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from typing import Callable, Generator
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import pytest
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import torch
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import torch.distributed
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import torch.multiprocessing as mp
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import torch.nn.functional
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from torch import nn
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from torch.nn.parallel.distributed import DistributedDataParallel
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from torch.utils.data import DataLoader
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from torch.utils.data.distributed import DistributedSampler
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from pytorch_lightning.demos.boring_classes import RandomDataset
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from pytorch_lightning.lite import LightningLite
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from pytorch_lightning.plugins.environments.lightning_environment import find_free_network_port
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from pytorch_lightning.strategies.ddp_spawn import DDPSpawnStrategy
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from pytorch_lightning.utilities.apply_func import apply_to_collection, move_data_to_device
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from pytorch_lightning.utilities.cloud_io import atomic_save
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from tests_pytorch.helpers.runif import RunIf
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class BoringModel(nn.Module):
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def __init__(self):
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super().__init__()
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self.layer = torch.nn.Linear(32, 2, bias=False)
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def forward(self, x):
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x = self.layer(x)
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return torch.nn.functional.mse_loss(x, torch.ones_like(x))
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def configure_optimizers(module: nn.Module):
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return torch.optim.SGD(module.parameters(), lr=0.0001)
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def main(
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move_to_device: Callable,
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model: nn.Module,
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train_dataloader: DataLoader,
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num_epochs: int = 10,
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):
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model = move_to_device(model)
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optimizer = configure_optimizers(model)
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for _ in range(num_epochs):
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model.train()
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for batch in train_dataloader:
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batch = move_to_device(batch)
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optimizer.zero_grad()
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loss = model(batch)
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loss.backward()
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optimizer.step()
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return model.state_dict()
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class LiteRunner(LightningLite):
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def run(self, model: nn.Module, train_dataloader: DataLoader, num_epochs: int = 10, tmpdir: str = None):
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optimizer = configure_optimizers(model)
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model, optimizer = self.setup(model, optimizer)
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train_dataloader = self.setup_dataloaders(train_dataloader)
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model.train()
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for _ in range(num_epochs):
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for batch in train_dataloader:
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batch = self.to_device(batch)
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optimizer.zero_grad()
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loss = model(batch)
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self.backward(loss)
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optimizer.step()
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if isinstance(self._strategy, DDPSpawnStrategy) and tmpdir and self.global_rank == 0:
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checkpoint_path = os.path.join(tmpdir, "model.pt")
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atomic_save(model.state_dict(), checkpoint_path)
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return checkpoint_path
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@contextmanager
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def precision_context(precision, accelerator) -> Generator[None, None, None]:
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if precision == 32:
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yield
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return
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if accelerator == "gpu":
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with torch.cuda.amp.autocast():
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yield
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elif accelerator == "cpu":
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with torch.cpu.amp.autocast():
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yield
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@pytest.mark.parametrize(
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"precision, strategy, devices, accelerator",
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[
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pytest.param(32, None, 1, "cpu"),
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pytest.param(32, None, 1, "gpu", marks=RunIf(min_cuda_gpus=1)),
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pytest.param(16, None, 1, "gpu", marks=RunIf(min_cuda_gpus=1)),
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pytest.param("bf16", None, 1, "gpu", marks=RunIf(min_cuda_gpus=1, min_torch="1.10", bf16_cuda=True)),
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pytest.param(32, None, 1, "mps", marks=RunIf(mps=True)),
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],
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)
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def test_boring_lite_model_single_device(precision, strategy, devices, accelerator, tmpdir):
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LightningLite.seed_everything(42)
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train_dataloader = DataLoader(RandomDataset(32, 8))
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model = BoringModel()
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num_epochs = 1
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state_dict = deepcopy(model.state_dict())
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lite = LiteRunner(precision=precision, strategy=strategy, devices=devices, accelerator=accelerator)
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lite.run(model, train_dataloader, num_epochs=num_epochs)
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lite_state_dict = model.state_dict()
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with precision_context(precision, accelerator):
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model.load_state_dict(state_dict)
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pure_state_dict = main(lite.to_device, model, train_dataloader, num_epochs=num_epochs)
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state_dict = apply_to_collection(state_dict, torch.Tensor, lite.to_device)
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for w_pure, w_lite in zip(state_dict.values(), lite_state_dict.values()):
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assert not torch.equal(w_pure, w_lite)
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for w_pure, w_lite in zip(pure_state_dict.values(), lite_state_dict.values()):
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assert torch.equal(w_pure, w_lite)
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def run(rank, model, train_dataloader, num_epochs, precision, accelerator, tmpdir):
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os.environ["LOCAL_RANK"] = str(rank)
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if torch.distributed.is_available() and not torch.distributed.is_initialized():
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torch.distributed.init_process_group("gloo", rank=rank, world_size=2)
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to_device = partial(move_data_to_device, device=torch.device("cuda", rank))
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model = DistributedDataParallel(
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to_device(model),
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device_ids=[rank],
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)
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train_dataloader = DataLoader(
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train_dataloader.dataset,
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sampler=DistributedSampler(train_dataloader.dataset, rank=rank, num_replicas=2, seed=42, drop_last=False),
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)
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with precision_context(precision, accelerator):
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main(to_device, model, train_dataloader, num_epochs=num_epochs)
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if rank == 0:
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atomic_save(model.state_dict(), os.path.join(tmpdir, "model_spawn.pt"))
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@pytest.mark.skipif(True, reason="Skipping as it takes 80 seconds.")
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@RunIf(min_cuda_gpus=2)
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@pytest.mark.parametrize(
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"precision, strategy, devices, accelerator",
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[
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(32, "ddp_spawn", 2, "gpu"),
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],
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)
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def test_boring_lite_model_ddp_spawn(precision, strategy, devices, accelerator, tmpdir):
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LightningLite.seed_everything(42)
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train_dataloader = DataLoader(RandomDataset(32, 8))
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model = BoringModel()
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num_epochs = 1
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state_dict = deepcopy(model.state_dict())
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lite = LiteRunner(precision=precision, strategy=strategy, devices=devices, accelerator=accelerator)
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checkpoint_path = lite.run(model, train_dataloader, num_epochs=num_epochs, tmpdir=tmpdir)
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spawn_model_state_dict = torch.load(checkpoint_path)
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for w_pure, w_lite in zip(state_dict.values(), spawn_model_state_dict.values()):
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assert not torch.equal(w_pure.cpu(), w_lite.cpu())
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model.load_state_dict(state_dict)
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os.environ["MASTER_ADDR"] = "127.0.0.1"
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os.environ["MASTER_PORT"] = str(find_free_network_port())
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mp.spawn(run, args=(model, train_dataloader, num_epochs, precision, accelerator, tmpdir), nprocs=2)
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spawn_pure_model_state_dict = torch.load(os.path.join(tmpdir, "model_spawn.pt"))
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for w_pure, w_lite in zip(spawn_pure_model_state_dict.values(), spawn_model_state_dict.values()):
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assert torch.equal(w_pure.cpu(), w_lite.cpu())
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@RunIf(min_cuda_gpus=2, standalone=True)
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@pytest.mark.parametrize(
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"precision, strategy, devices, accelerator",
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[
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(32, "ddp", 2, "gpu"),
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],
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)
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def test_boring_lite_model_ddp(precision, strategy, devices, accelerator, tmpdir):
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LightningLite.seed_everything(42)
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train_dataloader = DataLoader(RandomDataset(32, 4))
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model = BoringModel()
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num_epochs = 1
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state_dict = deepcopy(model.state_dict())
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lite = LiteRunner(precision=precision, strategy=strategy, devices=devices, accelerator=accelerator)
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lite.run(model, train_dataloader, num_epochs=num_epochs, tmpdir=tmpdir)
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lite_model_state_dict = model.state_dict()
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for w_pure, w_lite in zip(state_dict.values(), lite_model_state_dict.values()):
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assert not torch.equal(w_pure.cpu(), w_lite.cpu())
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LightningLite.seed_everything(42)
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train_dataloader = DataLoader(RandomDataset(32, 4))
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model = BoringModel()
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run(lite.global_rank, model, train_dataloader, num_epochs, precision, accelerator, tmpdir)
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pure_model_state_dict = model.state_dict()
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for w_pure, w_lite in zip(pure_model_state_dict.values(), lite_model_state_dict.values()):
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assert torch.equal(w_pure.cpu(), w_lite.cpu())
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