145 lines
4.9 KiB
Python
145 lines
4.9 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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import sys
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import numpy as np
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import torch
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from pytorch_lightning import seed_everything, Trainer
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from pytorch_lightning.demos.boring_classes import BoringModel
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from pytorch_lightning.utilities import AllGatherGrad
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from tests_pytorch.helpers.runif import RunIf
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def setup_ddp(rank, world_size):
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"""Setup ddp environment."""
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os.environ["MASTER_ADDR"] = "localhost"
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os.environ["MASTER_PORT"] = "8088"
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if torch.distributed.is_available() and sys.platform not in ("win32", "cygwin"):
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torch.distributed.init_process_group("gloo", rank=rank, world_size=world_size)
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def _test_all_gather_ddp(rank, world_size):
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setup_ddp(rank, world_size)
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tensor1 = torch.ones(8, requires_grad=True)
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tensor2 = torch.ones((8, 16, 32), requires_grad=True)
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tensor1_gathered = AllGatherGrad.apply(tensor1)
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tensor2_gathered = AllGatherGrad.apply(tensor2)
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tensor1_gathered = tensor1_gathered * rank
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tensor2_gathered = tensor2_gathered * rank
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tensor1_gathered.sum().backward()
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tensor2_gathered.sum().backward()
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grad1 = torch.zeros_like(tensor1.grad).fill_(torch.arange(world_size).sum().float())
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grad2 = torch.zeros_like(tensor2.grad).fill_(torch.arange(world_size).sum().float())
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assert torch.allclose(grad1, tensor1.grad)
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assert torch.allclose(grad2, tensor2.grad)
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@RunIf(skip_windows=True)
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def test_all_gather_ddp_spawn():
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world_size = 3
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torch.multiprocessing.spawn(_test_all_gather_ddp, args=(world_size,), nprocs=world_size)
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@RunIf(min_cuda_gpus=2, skip_windows=True, standalone=True)
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def test_all_gather_collection(tmpdir):
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class TestModel(BoringModel):
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training_epoch_end_called = False
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def training_epoch_end(self, outputs) -> None:
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losses = torch.stack([x["loss"] for x in outputs])
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gathered_loss = self.all_gather(
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{
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"losses_tensor_int": torch.rand(2, 2).int().t(),
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"losses_tensor_float": torch.rand(2, 2).t(),
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"losses_np_ndarray": np.array([1, 2, 3]),
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"losses_bool": [True, False],
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"losses_float": [0.0, 1.0, 2.0],
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"losses_int": [0, 1, 2],
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"losses": losses,
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"losses_list": [losses, losses],
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}
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)
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assert gathered_loss["losses_tensor_int"][0].dtype == torch.int32
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assert gathered_loss["losses_tensor_float"][0].dtype == torch.float
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assert gathered_loss["losses_np_ndarray"][0].dtype == torch.int64
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# torch.bool can't be all_gathered
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assert gathered_loss["losses_bool"][0].dtype == torch.uint8
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assert gathered_loss["losses_float"][0].dtype == torch.float
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assert gathered_loss["losses_int"][0].dtype == torch.int
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assert gathered_loss["losses_list"][0].numel() == 2 * len(losses)
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assert gathered_loss["losses"].numel() == 2 * len(losses)
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self.training_epoch_end_called = True
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seed_everything(42)
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model = TestModel()
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limit_train_batches = 8
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trainer = Trainer(
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default_root_dir=tmpdir,
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limit_train_batches=limit_train_batches,
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limit_val_batches=2,
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max_epochs=1,
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log_every_n_steps=1,
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accumulate_grad_batches=2,
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accelerator="gpu",
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devices=2,
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strategy="ddp",
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enable_progress_bar=False,
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enable_model_summary=False,
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)
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trainer.fit(model)
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assert model.training_epoch_end_called
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@RunIf(min_cuda_gpus=2, skip_windows=True, standalone=True)
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def test_all_gather_sync_grads(tmpdir):
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class TestModel(BoringModel):
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training_step_called = False
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def training_step(self, batch, batch_idx):
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self.training_step_called = True
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tensor = torch.rand(2, 2, requires_grad=True, device=self.device)
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gathered_tensor = self.all_gather(tensor, sync_grads=True)
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assert gathered_tensor.shape == torch.Size([2, 2, 2])
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loss = gathered_tensor.sum()
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return loss
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model = TestModel()
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trainer = Trainer(
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default_root_dir=tmpdir,
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fast_dev_run=True,
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accelerator="gpu",
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devices=2,
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strategy="ddp",
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enable_progress_bar=False,
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enable_model_summary=False,
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)
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trainer.fit(model)
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assert model.training_step_called
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