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feat: update lightning example to lightning 2.0 #603

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feat: update lightning example to lightning 2.0 #603

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1b735c6
feat: update lightning example to lightning 2.0
Sep 11, 2023
c314d42
Update examples/mnist_lightning.py
dwahdany Oct 5, 2023
c35262d
Fix BatchMemoryManager length
Mar 22, 2024
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7 changes: 2 additions & 5 deletions examples/mnist_lightning.py
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Original file line number Diff line number Diff line change
Expand Up @@ -99,11 +99,8 @@ def configure_optimizers(self):
optimizer = optim.SGD(self.parameters(), lr=self.lr, momentum=0)

if self.enable_dp:
data_loader = (
# soon there will be a fancy way to access train dataloader,
# see https://github.com/PyTorchLightning/pytorch-lightning/issues/10430
self.trainer._data_connector._train_dataloader_source.dataloader()
)
self.trainer.fit_loop.setup_data()
data_loader = self.trainer.train_dataloader

# transform (model, optimizer, dataloader) to DP-versions
if hasattr(self, "dp"):
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11 changes: 8 additions & 3 deletions opacus/utils/batch_memory_manager.py
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Original file line number Diff line number Diff line change
Expand Up @@ -16,12 +16,13 @@
from typing import List

import numpy as np
from torch.utils.data import BatchSampler, DataLoader, Sampler

from opacus.optimizers import DPOptimizer
from opacus.utils.uniform_sampler import (
DistributedUniformWithReplacementSampler,
UniformWithReplacementSampler,
)
from torch.utils.data import BatchSampler, DataLoader, Sampler


class BatchSplittingSampler(Sampler[List[int]]):
Expand Down Expand Up @@ -71,13 +72,17 @@ def __iter__(self):
def __len__(self):
if isinstance(self.sampler, BatchSampler):
return int(
len(self.sampler) * (self.sampler.batch_size / self.max_batch_size)
np.ceil(
len(self.sampler) * (self.sampler.batch_size / self.max_batch_size)
)
)
elif isinstance(self.sampler, UniformWithReplacementSampler) or isinstance(
self.sampler, DistributedUniformWithReplacementSampler
):
expected_batch_size = self.sampler.sample_rate * self.sampler.num_samples
return int(len(self.sampler) * (expected_batch_size / self.max_batch_size))
return int(
np.ceil(len(self.sampler) * (expected_batch_size / self.max_batch_size))
)

return len(self.sampler)

Expand Down