def setUp_clipped_model(self, clip_value=0.003, run_clipper_step=True):
# Deep copy
self.clipped_model = SampleConvNet() # create the structure
self.clipped_model.load_state_dict(
self.original_model.state_dict()) # fill it
# Intentionally clipping to a very small value
norm_clipper = (ConstantFlatClipper(clip_value)
if not isinstance(clip_value, list) else
ConstantPerLayerClipper(clip_value))
self.clipper = PerSampleGradientClipper(
self.clipped_model,
norm_clipper,
)
for x, y in self.dl:
logits = self.clipped_model(x)
loss = self.criterion(logits, y)
loss.backward() # puts grad in self.clipped_model.parameters()
if run_clipper_step:
self.clipper.clip_and_accumulate()
self.clipper.pre_step()
self.clipped_grads_norms = torch.stack(
[
p.grad.norm()
for p in self.clipped_model.parameters() if p.requires_grad
],
dim=-1,
)
def _check_one_layer(self, layer, input):
if hasattr(layer, "autograd_grad_sample_hooks"):
raise ValueError(f"Input layer already has hooks attached."
f"Please provide freshly constructed layer")
nn.init.uniform_(layer.weight)
nn.init.uniform_(layer.bias)
output = layer(input)
output.norm().backward()
vanilla_run_grads = [
p.grad.detach().clone() for p in layer.parameters()
if p.requires_grad
]
clipper = PerSampleGradientClipper(layer, 999)
output = layer(input)
output.norm().backward()
clipper.step()
private_run_grads = [
p.grad.detach().clone() for p in layer.parameters()
if p.requires_grad
]
for vanilla_grad, private_grad in zip(vanilla_run_grads,
private_run_grads):
self.assertTrue(
torch.allclose(vanilla_grad,
private_grad,
atol=10e-5,
rtol=10e-3))
def _check_one_layer(self, layer, *args, **kwargs):
if hasattr(layer, "autograd_grad_sample_hooks"):
raise ValueError(f"Input layer already has hooks attached."
f"Please provide freshly constructed layer")
self.validator.validate(layer)
if hasattr(layer, "weight"):
nn.init.uniform_(layer.weight)
if hasattr(layer, "bias"):
nn.init.uniform_(layer.bias)
# run without DP
self._reset_seeds()
layer.zero_grad()
output = layer(*args)
if isinstance(output, tuple):
output = output[0]
output.norm().backward()
vanilla_run_grads = [
p.grad.detach().clone() for p in layer.parameters()
if p.requires_grad
]
# run with DP
clipper = PerSampleGradientClipper(layer,
999,
batch_dim=kwargs.get(
"batch_dim", 0))
self._reset_seeds()
layer.zero_grad()
output = layer(*args)
if isinstance(output, tuple):
output = output[0]
output.norm().backward()
for param_name, param in layer.named_parameters():
if param.requires_grad:
self.assertTrue(
hasattr(param, "grad_sample"),
f"Per-sample gradients hasn't been computed for {param_name}",
)
clipper.step()
private_run_grads = [
p.grad.detach().clone() for p in layer.parameters()
if p.requires_grad
]
# compare
for vanilla_grad, private_grad in zip(vanilla_run_grads,
private_run_grads):
self.assertTrue(
torch.allclose(vanilla_grad,
private_grad,
atol=10e-5,
rtol=10e-3))
def _check_one_layer_with_criterion(self, layer, criterion, *args, **kwargs):
self.validator.validate(layer)
for name, param in layer.named_parameters():
if ("weight" in name) or ("bias" in name):
nn.init.uniform_(param, -1.0, 1.0)
# run without DP
self._run_once(layer, criterion, *args)
vanilla_run_grads = [
(name, p.grad.detach())
for (name, p) in layer.named_parameters()
if p.requires_grad
]
# run with DP
clipper = PerSampleGradientClipper(
layer,
ConstantFlatClipper(1e9),
batch_first=kwargs.get("batch_first", True),
loss_reduction=criterion.reduction,
)
self._run_once(layer, criterion, *args)
for param_name, param in layer.named_parameters():
if param.requires_grad:
self.assertTrue(
hasattr(param, "grad_sample"),
f"Per-sample gradients haven't been computed for {param_name}",
)
clipper.clip_and_accumulate()
clipper.pre_step()
private_run_grads = [
(name, p.grad.detach())
for (name, p) in layer.named_parameters()
if p.requires_grad
]
# compare
for (vanilla_name, vanilla_grad), (private_name, private_grad) in zip(
vanilla_run_grads, private_run_grads
):
assert vanilla_name == private_name
self.assertTrue(
torch.allclose(vanilla_grad, private_grad, atol=10e-5, rtol=10e-3),
f"Gradient mismatch. Parameter: {layer}.{vanilla_name}, loss: {criterion.reduction}",
)
clipper.close()
def setUp_clipped_model(self, clip_value=0.003, run_clipper_step=True):
# Deep copy
self.clipped_model = SampleConvNet() # create the structure
self.clipped_model.load_state_dict(
self.original_model.state_dict()) # fill it
# Intentionally clipping to a very small value
self.clipper = PerSampleGradientClipper(self.clipped_model, clip_value)
for x, y in self.dl:
logits = self.clipped_model(x)
loss = self.criterion(logits, y)
loss.backward() # puts grad in self.clipped_model.parameters()
if run_clipper_step:
self.clipper.step()
self.clipped_grads_norms = torch.stack(
[p.grad.norm() for p in self.clipped_model.parameters()], dim=-1)
def _check_one_layer_with_criterion(self,
layer,
criterion,
data,
batch_first=True):
clipper = PerSampleGradientClipper(layer,
ConstantFlatClipper(1e9),
batch_first=batch_first,
loss_reduction=criterion.reduction)
self._run_once(layer, criterion, data)
computed_sample_grads = {}
for (param_name, param) in layer.named_parameters():
computed_sample_grads[param_name] = param.grad_sample.detach()
clipper.clip_and_accumulate()
clipper.pre_step()
clipper.close()
batch_dim = 0 if batch_first else 1
data = data.transpose(0, batch_dim)
for i, sample in enumerate(data):
# simulate batch_size = 1
sample_data = sample.unsqueeze(batch_dim)
self._run_once(layer, criterion, sample_data)
for (param_name, param) in layer.named_parameters():
# grad we just computed with batch_size = 1
vanilla_per_sample_grad = param.grad
# i-th line in grad_sample computed before
computed_per_sample_grad = computed_sample_grads[param_name][i]
self.assertTrue(
torch.allclose(
vanilla_per_sample_grad,
computed_per_sample_grad,
atol=10e-5,
rtol=10e-3,
),
f"Gradient mismatch. Parameter: {layer}.{param_name}, loss: {criterion.reduction}",
)
def _check_one_layer(self, layer, *args, **kwargs):
if hasattr(layer, "autograd_grad_sample_hooks"):
raise ValueError(f"Input layer already has hooks attached."
f"Please provide freshly constructed layer")
self.validator.validate(layer)
for name, param in layer.named_parameters():
if ("weight" in name) or ("bias" in name):
nn.init.uniform_(param, -1.0, 1.0)
# run without DP
self._reset_seeds()
layer.zero_grad()
output = layer(*args)
if isinstance(output, tuple):
output = output[0]
output.norm().backward()
vanilla_run_grads = [
p.grad.detach().clone() for p in layer.parameters()
if p.requires_grad
]
# run with DP
clipper = PerSampleGradientClipper(layer, ConstantFlatClipper(1999),
kwargs.get('batch_first', True))
# The test outcome is sensitive to the threshold here. This test verifies that our backward
# hooks populate sample specific gradients correctly and that when aggregated, they agree
# with the aggregated gradients from vanilla pytorch. The clipper currently does both clipping
# and aggregation, whereas we only want to do the latter. As a work around, we simply set a very
# high threshold for clipping so that it effectively becomes a no-op.
self._reset_seeds()
layer.zero_grad()
output = layer(*args)
if isinstance(output, tuple):
output = output[0]
output.norm().backward()
for param_name, param in layer.named_parameters():
if param.requires_grad:
self.assertTrue(
hasattr(param, "grad_sample"),
f"Per-sample gradients hasn't been computed for {param_name}",
)
clipper.clip_and_accumulate()
clipper.pre_step()
private_run_grads = [
p.grad.detach().clone() for p in layer.parameters()
if p.requires_grad
]
# compare
for vanilla_grad, private_grad in zip(vanilla_run_grads,
private_run_grads):
self.assertTrue(
torch.allclose(vanilla_grad,
private_grad,
atol=10e-5,
rtol=10e-3))