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Use SimpleDistributedPerLayerClipping optimizer in hooks mode #750

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Use SimpleDistributedPerLayerClipping optimizer in hooks mode #750

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1562415
Replace register_backward_hook with register_full_backward_hook (#720)
Apr 3, 2025
658b9ed
Use SimpleDistributedPerLayerClipping optimizer in hooks mode (#750)
iden-kalemaj Apr 3, 2025
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2 changes: 1 addition & 1 deletion opacus/grad_sample/grad_sample_module.py
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Original file line number Diff line number Diff line change
Expand Up @@ -207,7 +207,7 @@ def add_hooks(
)

self.autograd_grad_sample_hooks.append(
module.register_backward_hook(
module.register_full_backward_hook(
partial(
self.capture_backprops_hook,
loss_reduction=loss_reduction,
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10 changes: 2 additions & 8 deletions opacus/optimizers/__init__.py
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Expand Up @@ -13,10 +13,7 @@
# limitations under the License.

from .adaclipoptimizer import AdaClipDPOptimizer
from .ddp_perlayeroptimizer import (
DistributedPerLayerOptimizer,
SimpleDistributedPerLayerOptimizer,
)
from .ddp_perlayeroptimizer import SimpleDistributedPerLayerOptimizer
from .ddpoptimizer import DistributedDPOptimizer
from .ddpoptimizer_fast_gradient_clipping import (
DistributedDPOptimizerFastGradientClipping,
Expand All @@ -28,7 +25,6 @@

__all__ = [
"AdaClipDPOptimizer",
"DistributedPerLayerOptimizer",
"DistributedDPOptimizer",
"DPOptimizer",
"DPOptimizerFastGradientClipping",
Expand All @@ -55,9 +51,7 @@ def get_optimizer_class(clipping: str, distributed: bool, grad_sample_mode: str
elif clipping == "per_layer" and distributed is False:
return DPPerLayerOptimizer
elif clipping == "per_layer" and distributed is True:
if grad_sample_mode == "hooks":
return DistributedPerLayerOptimizer
elif grad_sample_mode == "ew":
if grad_sample_mode == "hooks" or grad_sample_mode == "ew":
return SimpleDistributedPerLayerOptimizer
else:
raise ValueError(f"Unexpected grad_sample_mode: {grad_sample_mode}")
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10 changes: 2 additions & 8 deletions opacus/tests/multigpu_gradcheck.py
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Expand Up @@ -26,10 +26,7 @@
from opacus import PrivacyEngine
from opacus.distributed import DifferentiallyPrivateDistributedDataParallel as DPDDP
from opacus.grad_sample import GradSampleModuleFastGradientClipping
from opacus.optimizers.ddp_perlayeroptimizer import (
DistributedPerLayerOptimizer,
SimpleDistributedPerLayerOptimizer,
)
from opacus.optimizers.ddp_perlayeroptimizer import SimpleDistributedPerLayerOptimizer
from opacus.optimizers.ddpoptimizer import DistributedDPOptimizer
from opacus.optimizers.ddpoptimizer_fast_gradient_clipping import (
DistributedDPOptimizerFastGradientClipping,
Expand Down Expand Up @@ -165,10 +162,7 @@ def demo_basic(rank, weight, world_size, dp, clipping, grad_sample_mode):
grad_sample_mode=grad_sample_mode,
)
if clipping == "per_layer":
assert isinstance(
optimizer,
(DistributedPerLayerOptimizer, SimpleDistributedPerLayerOptimizer),
)
assert isinstance(optimizer, SimpleDistributedPerLayerOptimizer)
else:
assert isinstance(optimizer, DistributedDPOptimizer)

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