136 lines
4.6 KiB
Python
136 lines
4.6 KiB
Python
# Copyright The PyTorch Lightning team.
|
|
#
|
|
# Licensed under the Apache License, Version 2.0 (the "License");
|
|
# you may not use this file except in compliance with the License.
|
|
# You may obtain a copy of the License at
|
|
#
|
|
# http://www.apache.org/licenses/LICENSE-2.0
|
|
#
|
|
# Unless required by applicable law or agreed to in writing, software
|
|
# distributed under the License is distributed on an "AS IS" BASIS,
|
|
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
|
# See the License for the specific language governing permissions and
|
|
# limitations under the License.
|
|
|
|
import numpy as np
|
|
import torch
|
|
|
|
from lightning_lite.utilities.distributed import _AllGather
|
|
from lightning_lite.utilities.seed import seed_everything
|
|
from pytorch_lightning import Trainer
|
|
from pytorch_lightning.demos.boring_classes import BoringModel
|
|
from tests_pytorch.core.test_results import spawn_launch
|
|
from tests_pytorch.helpers.runif import RunIf
|
|
|
|
|
|
def all_gather_ddp_spawn_fn(strategy):
|
|
rank = strategy.local_rank
|
|
world_size = strategy.num_processes
|
|
|
|
tensor1 = torch.ones(8, requires_grad=True)
|
|
tensor2 = torch.ones((8, 16, 32), requires_grad=True)
|
|
|
|
tensor1_gathered = _AllGather.apply(tensor1)
|
|
tensor2_gathered = _AllGather.apply(tensor2)
|
|
|
|
tensor1_gathered = tensor1_gathered * rank
|
|
tensor2_gathered = tensor2_gathered * rank
|
|
|
|
tensor1_gathered.sum().backward()
|
|
tensor2_gathered.sum().backward()
|
|
|
|
grad1 = torch.zeros_like(tensor1.grad).fill_(torch.arange(world_size).sum().float())
|
|
grad2 = torch.zeros_like(tensor2.grad).fill_(torch.arange(world_size).sum().float())
|
|
|
|
assert torch.allclose(grad1, tensor1.grad)
|
|
assert torch.allclose(grad2, tensor2.grad)
|
|
|
|
|
|
@RunIf(skip_windows=True)
|
|
def test_all_gather_ddp_spawn():
|
|
spawn_launch(all_gather_ddp_spawn_fn, [torch.device("cpu")] * 3)
|
|
|
|
|
|
@RunIf(min_cuda_gpus=2, skip_windows=True, standalone=True)
|
|
def test_all_gather_collection(tmpdir):
|
|
class TestModel(BoringModel):
|
|
|
|
training_epoch_end_called = False
|
|
|
|
def training_epoch_end(self, outputs) -> None:
|
|
losses = torch.stack([x["loss"] for x in outputs])
|
|
gathered_loss = self.all_gather(
|
|
{
|
|
"losses_tensor_int": torch.rand(2, 2).int().t(),
|
|
"losses_tensor_float": torch.rand(2, 2).t(),
|
|
"losses_np_ndarray": np.array([1, 2, 3]),
|
|
"losses_bool": [True, False],
|
|
"losses_float": [0.0, 1.0, 2.0],
|
|
"losses_int": [0, 1, 2],
|
|
"losses": losses,
|
|
"losses_list": [losses, losses],
|
|
}
|
|
)
|
|
assert gathered_loss["losses_tensor_int"][0].dtype == torch.int32
|
|
assert gathered_loss["losses_tensor_float"][0].dtype == torch.float
|
|
assert gathered_loss["losses_np_ndarray"][0].dtype == torch.int64
|
|
# torch.bool can't be all_gathered
|
|
assert gathered_loss["losses_bool"][0].dtype == torch.uint8
|
|
assert gathered_loss["losses_float"][0].dtype == torch.float
|
|
assert gathered_loss["losses_int"][0].dtype == torch.int
|
|
assert gathered_loss["losses_list"][0].numel() == 2 * len(losses)
|
|
assert gathered_loss["losses"].numel() == 2 * len(losses)
|
|
self.training_epoch_end_called = True
|
|
|
|
seed_everything(42)
|
|
|
|
model = TestModel()
|
|
|
|
limit_train_batches = 8
|
|
trainer = Trainer(
|
|
default_root_dir=tmpdir,
|
|
limit_train_batches=limit_train_batches,
|
|
limit_val_batches=2,
|
|
max_epochs=1,
|
|
log_every_n_steps=1,
|
|
accumulate_grad_batches=2,
|
|
accelerator="gpu",
|
|
devices=2,
|
|
strategy="ddp",
|
|
enable_progress_bar=False,
|
|
enable_model_summary=False,
|
|
)
|
|
|
|
trainer.fit(model)
|
|
assert model.training_epoch_end_called
|
|
|
|
|
|
@RunIf(min_cuda_gpus=2, skip_windows=True, standalone=True)
|
|
def test_all_gather_sync_grads(tmpdir):
|
|
class TestModel(BoringModel):
|
|
|
|
training_step_called = False
|
|
|
|
def training_step(self, batch, batch_idx):
|
|
self.training_step_called = True
|
|
tensor = torch.rand(2, 2, requires_grad=True, device=self.device)
|
|
gathered_tensor = self.all_gather(tensor, sync_grads=True)
|
|
assert gathered_tensor.shape == torch.Size([2, 2, 2])
|
|
|
|
loss = gathered_tensor.sum()
|
|
|
|
return loss
|
|
|
|
model = TestModel()
|
|
trainer = Trainer(
|
|
default_root_dir=tmpdir,
|
|
fast_dev_run=True,
|
|
accelerator="gpu",
|
|
devices=2,
|
|
strategy="ddp",
|
|
enable_progress_bar=False,
|
|
enable_model_summary=False,
|
|
)
|
|
trainer.fit(model)
|
|
assert model.training_step_called
|