Skip to content

refactor(autogram): Type args as tuple[PyTree, ...] #439

New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Closed
PierreQuinton wants to merge 2 commits into from
Closed

refactor(autogram): Type args as tuple[PyTree, ...] #439

Show file tree
Hide file tree
Changes from all commits
Commits
Show all changes
2 commits
Select commit Hold shift + click to select a range
d6c0b49
Type `args` as `tuple[PyTree, ...]` to highlight both the fact that i…
PierreQuinton Oct 1, 2025
a0c593e
Type `in_dims` as `tuple[PyTree, ...]` to highlight both the fact tha…
PierreQuinton Oct 1, 2025
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
10 changes: 5 additions & 5 deletions src/torchjd/autogram/_module_hook_manager.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -101,7 +101,7 @@ def __init__(
self.gramian_accumulator = gramian_accumulator
self.has_batch_dim = has_batch_dim

def __call__(self, module: nn.Module, args: PyTree, output: PyTree) -> PyTree:
def __call__(self, module: nn.Module, args: tuple[PyTree, ...], output: PyTree) -> PyTree:
if self.gramian_accumulation_phase:
return output

Expand Down Expand Up @@ -154,7 +154,7 @@ def forward(
gramian_accumulation_phase: BoolRef,
output_spec: TreeSpec,
vjp: VJP,
args: PyTree,
args: tuple[PyTree, ...],
gramian_accumulator: GramianAccumulator,
module: nn.Module,
*xs: Tensor,
Expand Down Expand Up @@ -199,7 +199,7 @@ class AccumulateJacobian(torch.autograd.Function):
def forward(
output_spec: TreeSpec,
vjp: VJP,
args: PyTree,
args: tuple[PyTree, ...],
gramian_accumulator: GramianAccumulator,
module: nn.Module,
*flat_grad_outputs: Tensor,
Expand All @@ -213,10 +213,10 @@ def forward(
@staticmethod
def vmap(
_,
in_dims: PyTree,
in_dims: tuple[PyTree, ...],
output_spec: TreeSpec,
vjp: VJP,
args: PyTree,
args: tuple[PyTree, ...],
gramian_accumulator: GramianAccumulator,
module: nn.Module,
*flat_jac_outputs: Tensor,
Expand Down
4 changes: 2 additions & 2 deletions src/torchjd/autogram/_vjp.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -19,7 +19,7 @@ class VJP(ABC):
"""Represents an abstract VJP function."""

@abstractmethod
def __call__(self, grad_outputs: PyTree, args: PyTree) -> dict[str, Tensor]:
def __call__(self, grad_outputs: PyTree, args: tuple[PyTree, ...]) -> dict[str, Tensor]:
"""
Computes and returns the dictionary of parameter names to their gradients for the given
grad_outputs (cotangents) and at the given inputs.
Expand Down Expand Up @@ -56,7 +56,7 @@ def __init__(self, module: nn.Module):
super().__init__(module)
self.vmapped_vjp = torch.vmap(self._call_on_one_instance)

def __call__(self, grad_outputs: PyTree, args: PyTree) -> dict[str, Tensor]:
def __call__(self, grad_outputs: PyTree, args: tuple[PyTree, ...]) -> dict[str, Tensor]:
return self.vmapped_vjp(grad_outputs, args)

def _call_on_one_instance(self, grad_outputs_j: PyTree, args_j: PyTree) -> dict[str, Tensor]:
Expand Down
Loading