def _compute_model_jacobians(self):
"""Compute Jacobians with respect to each model parameter
If last_layer_only is True, this computes the jacobian only with respect
to the parameters of the last layer of the model.
"""
Z = self.logits.split(1, dim=-1)
# Store partial Jacobian for each parameter
jacobians = {}
# dL/dW_i = sum_j (dL/dP_j * dP_j/dW_i)
with crypten.no_grad():
# TODO: Async / parallelize this
for z in Z:
z.backward(torch.ones(z.size()), retain_graph=True)
params = self.model.parameters()
for param in params:
grad = param.grad.flatten().unsqueeze(-1)
# Accumulate partial gradients: dL/dZ_j * dP_j/dW_i
if param in jacobians.keys():
jacobians[param] = torch.cat([jacobians[param], grad],
dim=-1)
else:
jacobians[param] = grad
param.grad = None # Reset grad for next p_j.backward()
return jacobians
def _backward_layer_estimation(self, grad_output=None):
with crypten.no_grad():
# Find dLdW for last layer weights
dLdW = self._compute_last_layer_grad(grad_output=grad_output)
# Run backprop in plaintext
if self.is_feature_src():
dLdZ = self._solve_dLdZ(dLdW)
self.logits.backward(dLdZ)
def _add_dp_if_necessary(self, grad):
if self.noise_magnitude is None or self.noise_magnitude == 0.0:
return grad
# Determine noise generation function
generate_noise = (self._generate_noise_from_src
if self.noise_src else self._generate_noise_no_src)
noise = generate_noise(grad.size())
with crypten.no_grad():
grad += noise
return grad
def backward(self, grad_output=None):
protocol = cfg.nn.dpsmpc.protocol
with crypten.no_grad():
if protocol == "full_jacobian":
self._backward_full_jacobian(grad_output=grad_output)
raise NotImplementedError(
"DPS protocol full_jacobian must be fixed before use.")
elif protocol == "layer_estimation":
with torch.no_grad():
self._backward_layer_estimation(grad_output=grad_output)
else:
raise ValueError(
f"Unrecognized DPSplitMPC backward protocol: {protocol}")
def step(self, closure=None):
"""Performs a single optimization step.
Arguments:
closure (callable, optional): A closure that reevaluates the model
and returns the loss.
"""
with crypten.no_grad():
loss = None
if closure is not None:
with crypten.enable_grad():
loss = closure()
for group in self.param_groups:
weight_decay = group["weight_decay"]
momentum = group["momentum"]
dampening = group["dampening"]
nesterov = group["nesterov"]
for p in group["params"]:
if p.grad is None:
continue
d_p = p.grad
if weight_decay != 0:
d_p = d_p.add(p.mul(weight_decay))
if momentum != 0:
param_state = self.state[id(p)]
if "momentum_buffer" not in param_state:
buf = param_state["momentum_buffer"] = d_p.clone(
).detach()
else:
buf = param_state["momentum_buffer"]
buf.mul_(momentum).add_(d_p.mul(1 - dampening))
if nesterov:
d_p = d_p.add(buf.mul(momentum))
else:
d_p = buf
p.sub_(d_p.mul(group["lr"]))
return loss
def test_batchnorm(self):
"""
Tests batchnorm forward and backward steps with training on / off.
"""
tolerance = 0.1
sizes = [(8, 5), (16, 3), (32, 5), (8, 6, 4), (8, 4, 3, 5)]
for size in sizes:
for is_training in (False, True):
# sample input data, weight, and bias:
tensor = get_random_test_tensor(size=size, is_float=True)
encrypted_input = crypten.cryptensor(tensor)
C = size[1]
weight = get_random_test_tensor(size=[C],
max_value=1,
is_float=True)
bias = get_random_test_tensor(size=[C],
max_value=1,
is_float=True)
weight.requires_grad = True
bias.requires_grad = True
# dimensions over which means and variances are computed:
stats_dimensions = list(range(tensor.dim()))
stats_dimensions.pop(1)
# dummy running mean and variance:
running_mean = tensor.mean(stats_dimensions).detach()
running_var = tensor.var(stats_dimensions).detach()
enc_running_mean = crypten.cryptensor(running_mean)
enc_running_var = crypten.cryptensor(running_var)
# compute reference output:
tensor.requires_grad = True
reference = torch.nn.functional.batch_norm(
tensor,
running_mean,
running_var,
weight=weight,
bias=bias,
training=is_training,
)
# compute CrypTen output:
encrypted_input.requires_grad = True
ctx = AutogradContext()
batch_norm_fn, _ = crypten.gradients.get_grad_fn("batchnorm")
with crypten.no_grad():
encrypted_out = batch_norm_fn.forward(
ctx,
encrypted_input,
weight,
bias,
training=is_training,
running_mean=enc_running_mean,
running_var=enc_running_var,
)
# check forward
self._check(
encrypted_out,
reference,
"batchnorm forward failed with training "
f"{is_training} on {tensor.dim()}-D",
tolerance=tolerance,
)
# check backward (input, weight, and bias gradients):
reference.backward(reference)
with crypten.no_grad():
encrypted_grad = batch_norm_fn.backward(ctx, encrypted_out)
TorchGrad = namedtuple("TorchGrad", ["name", "value"])
torch_gradients = [
TorchGrad("input gradient", tensor.grad),
TorchGrad("weight gradient", weight.grad),
TorchGrad("bias gradient", bias.grad),
]
for i, torch_gradient in enumerate(torch_gradients):
self._check(
encrypted_grad[i],
torch_gradient.value,
f"batchnorm backward {torch_gradient.name} failed "
f"with training {is_training} on {tensor.dim()}-D",
tolerance=tolerance,
)
