def batch_l2_grad(self):
with backpack(new_ext.BatchL2Grad()):
_, _, loss = self.problem.forward_pass()
loss.backward()
batch_l2_grad = [
p.batch_l2 for p in self.problem.model.parameters()
]
return batch_l2_grad
lossfunc = CrossEntropyLoss()
model = extend(model)
lossfunc = extend(lossfunc)
# %%
# We can now evaluate the loss and do a backward pass with Backpack
# -----------------------------------------------------------------
loss = lossfunc(model(X), y)
with backpack(
extensions.BatchGrad(),
extensions.Variance(),
extensions.SumGradSquared(),
extensions.BatchL2Grad(),
extensions.DiagGGNMC(mc_samples=1),
extensions.DiagGGNExact(),
extensions.DiagHessian(),
extensions.KFAC(mc_samples=1),
extensions.KFLR(),
extensions.KFRA(),
):
loss.backward()
# %%
# And here are the results
# -----------------------------------------------------------------
for name, param in model.named_parameters():
print(name)
from torch.nn import CrossEntropyLoss, Flatten, Linear, Sequential
from backpack import backpack, extend, extensions
from backpack.utils.examples import load_mnist_data
B = 4
X, y = load_mnist_data(B)
print(
"# Gradient with PyTorch, individual gradients' L2 norms with BackPACK | B =",
B)
model = Sequential(
Flatten(),
Linear(784, 10),
)
lossfunc = CrossEntropyLoss()
model = extend(model)
lossfunc = extend(lossfunc)
loss = lossfunc(model(X), y)
with backpack(extensions.BatchL2Grad()):
loss.backward()
for name, param in model.named_parameters():
print(name)
print(".grad.shape: ", param.grad.shape)
print(".batch_l2.shape: ", param.batch_l2.shape)
def batch_l2(self):
with backpack(new_ext.BatchL2Grad()):
self.loss().backward()
batch_l2s = [p.batch_l2 for p in self.model.parameters()]
return batch_l2s
def batch_l2_grad(self) -> List[Tensor]: # noqa:D102
with backpack(new_ext.BatchL2Grad()):
_, _, loss = self.problem.forward_pass()
loss.backward()
return self.problem.collect_data("batch_l2")