101 lines
2.7 KiB
Python
101 lines
2.7 KiB
Python
|
import pytest
|
||
|
|
import torch
|
||
|
|
import torch.nn as nn
|
||
|
|
import torch.nn.functional as F
|
||
|
|
|
||
|
|
from pytorch_lightning import Trainer, seed_everything, LightningModule, TrainResult
|
||
|
|
from pytorch_lightning.utilities import FLOAT16_EPSILON
|
||
|
|
from tests.base.datamodules import MNISTDataModule
|
||
|
|
from tests.base.develop_utils import set_random_master_port
|
||
|
|
|
||
|
|
|
||
|
|
class SyncBNModule(LightningModule):
|
||
|
|
def __init__(self, gpu_count=1, **kwargs):
|
||
|
|
super().__init__()
|
||
|
|
|
||
|
|
self.gpu_count = gpu_count
|
||
|
|
self.bn_targets = None
|
||
|
|
if 'bn_targets' in kwargs:
|
||
|
|
self.bn_targets = kwargs['bn_targets']
|
||
|
|
|
||
|
|
self.linear = nn.Linear(28 * 28, 10)
|
||
|
|
self.bn_layer = nn.BatchNorm1d(28 * 28)
|
||
|
|
|
||
|
|
def forward(self, x, batch_idx):
|
||
|
|
with torch.no_grad():
|
||
|
|
out_bn = self.bn_layer(x.view(x.size(0), -1))
|
||
|
|
|
||
|
|
if self.bn_targets:
|
||
|
|
bn_target = self.bn_targets[batch_idx]
|
||
|
|
|
||
|
|
# executes on both GPUs
|
||
|
|
bn_target = bn_target[self.trainer.local_rank::self.gpu_count]
|
||
|
|
bn_target = bn_target.to(out_bn.device)
|
||
|
|
assert torch.sum(torch.abs(bn_target - out_bn)) < FLOAT16_EPSILON
|
||
|
|
|
||
|
|
out = self.linear(out_bn)
|
||
|
|
|
||
|
|
return out, out_bn
|
||
|
|
|
||
|
|
def training_step(self, batch, batch_idx):
|
||
|
|
x, y = batch
|
||
|
|
|
||
|
|
y_hat, _ = self(x, batch_idx)
|
||
|
|
loss = F.cross_entropy(y_hat, y)
|
||
|
|
|
||
|
|
return TrainResult(loss)
|
||
|
|
|
||
|
|
def configure_optimizers(self):
|
||
|
|
return torch.optim.Adam(self.linear.parameters(), lr=0.02)
|
||
|
|
|
||
|
|
|
||
|
|
@pytest.mark.skipif(torch.cuda.device_count() < 2, reason="test requires multi-GPU machine")
|
||
|
|
def test_sync_batchnorm_ddp(tmpdir):
|
||
|
|
seed_everything(234)
|
||
|
|
set_random_master_port()
|
||
|
|
|
||
|
|
# define datamodule and dataloader
|
||
|
|
dm = MNISTDataModule()
|
||
|
|
dm.prepare_data()
|
||
|
|
dm.setup(stage=None)
|
||
|
|
|
||
|
|
train_dataloader = dm.train_dataloader()
|
||
|
|
model = SyncBNModule()
|
||
|
|
|
||
|
|
bn_outputs = []
|
||
|
|
|
||
|
|
# shuffle is false by default
|
||
|
|
for batch_idx, batch in enumerate(train_dataloader):
|
||
|
|
x, _ = batch
|
||
|
|
|
||
|
|
_, out_bn = model.forward(x, batch_idx)
|
||
|
|
bn_outputs.append(out_bn)
|
||
|
|
|
||
|
|
# get 3 steps
|
||
|
|
if batch_idx == 2:
|
||
|
|
break
|
||
|
|
|
||
|
|
bn_outputs = [x.cuda() for x in bn_outputs]
|
||
|
|
|
||
|
|
# reset datamodule
|
||
|
|
# batch-size = 16 because 2 GPUs in DDP
|
||
|
|
dm = MNISTDataModule(batch_size=16, dist_sampler=True)
|
||
|
|
dm.prepare_data()
|
||
|
|
dm.setup(stage=None)
|
||
|
|
|
||
|
|
model = SyncBNModule(gpu_count=2, bn_targets=bn_outputs)
|
||
|
|
|
||
|
|
trainer = Trainer(
|
||
|
|
gpus=2,
|
||
|
|
num_nodes=1,
|
||
|
|
distributed_backend='ddp_spawn',
|
||
|
|
max_epochs=1,
|
||
|
|
max_steps=3,
|
||
|
|
sync_batchnorm=True,
|
||
|
|
num_sanity_val_steps=0,
|
||
|
|
replace_sampler_ddp=False,
|
||
|
|
)
|
||
|
|
|
||
|
|
result = trainer.fit(model, dm)
|
||
|
|
assert result == 1, "Sync batchnorm failing with DDP"
|