def brute_force_hessian(layer_idx, parallel_idx, which):
"""Compute Hessian of loss w.r.t. parameter in layer."""
if layer_idx > 2:
raise ValueError("Works only for indices from 0 to 2")
parallel = example_sequence_parallel()
_, loss = forward(parallel, random_input())
if which == "weight":
return exact_hessian(
loss,
[
list(parallel.children())[layer_idx]
._get_parallel_module(parallel_idx)
.weight
],
)
elif which == "bias":
return exact_hessian(
loss,
[
list(parallel.children())[layer_idx]
._get_parallel_module(parallel_idx)
.bias
],
)
else:
raise ValueError
def brute_force_parameter_hessian(which):
"""Compute Hessian of loss w.r.t. the weights."""
layer = example_linear_parallel()
input = random_input()
_, loss = forward(layer, input)
if which == "weight":
return [
exact_hessian(loss, [child.weight])
for child in layer.parallel_children()
]
elif which == "bias":
return [
exact_hessian(loss, [child.bias])
for child in layer.parallel_children()
]
def _torch_input_hvp(self):
"""Create Hessian-vector product routine for torch layer."""
layer = self._create_torch_layer()
x, y = self._create_input()
x.requires_grad = True
out = layer(x, y)
hessian_x = exact_hessian(out, [x]).detach().to(self.DEVICE)
return hessian_x.matmul
def _torch_parameter_hvp(self):
"""Yield block-wise HVP with the parameter Hessians"""
layer = self._create_torch_layer()
x = self._create_input()
x.requires_grad = True
out = layer(x)
loss = self._loss_fn(out)
for p in layer.parameters():
yield exact_hessian(loss, [p]).detach().to(self.DEVICE)
def _cvp_after_hessian_backward(self):
"""Return the CVP layer after performing CVP."""
layer = self._create_cvp_layer()
x = self._create_input()
x.requires_grad = True
out = layer(x)
loss = self._loss_fn(out)
# required for nonlinear layers (need to save backprop quantities)
loss_hessian_vp = exact_hessian(loss, [out]).detach().to(
self.DEVICE).matmul
loss.backward()
layer.backward_hessian(loss_hessian_vp)
return layer
def _cvp_input_hvp(self):
"""Create Hessian-vector product routine for CVP layer."""
layer = self._create_cvp_layer()
x = self._create_input()
x.requires_grad = True
out = layer(x)
loss = self._loss_fn(out)
# required for nonlinear layers (need to save backprop quantities)
loss_hessian_vp = exact_hessian(loss, [out]).detach().to(
self.DEVICE).matmul
loss.backward()
hessian_x = layer.backward_hessian(loss_hessian_vp,
compute_input_hessian=True)
return hessian_x
def brute_force_input_hessian():
"""Compute the Hessian with respect to the input by brute force."""
layer = example_sequence_parallel()
input = random_input()
_, loss = forward(layer, input)
return exact_hessian(loss, [input])
def layer_input_hessian():
"""Compute the Hessian with respect to the input."""
layer, x, out, loss = layer_with_input_output_and_loss()
input_hessian = exact_hessian(loss, [x])
return input_hessian
def brute_force_input_hessian():
"""Compute Hessian of loss w.r.t. input."""
parallel = example_linear_parallel()
input = random_input()
_, loss = forward(parallel, input)
return exact_hessian(loss, [input])
