refactor(autojac): Use TypeGuard for TensorWithJac

src/torchjd/autojac/_accumulation.py

@@ -1,5 +1,5 @@
 from collections.abc import Iterable
-from typing import cast
+from typing import TypeGuard
 
 from torch import Tensor
 
@@ -14,6 +14,10 @@ class TensorWithJac(Tensor):
     jac: Tensor
 
 
+def is_tensor_with_jac(t: Tensor) -> TypeGuard[TensorWithJac]:
+    return hasattr(t, "jac")
+
+
 def accumulate_jacs(params: Iterable[Tensor], jacobians: Iterable[Tensor]) -> None:
     for param, jac in zip(params, jacobians, strict=True):
         _check_expects_grad(param, field_name=".jac")
@@ -26,9 +30,8 @@ def accumulate_jacs(params: Iterable[Tensor], jacobians: Iterable[Tensor]) -> No
                 " jacobian are the same size"
             )
 
-        if hasattr(param, "jac"):  # No check for None because jac cannot be None
-            param_ = cast(TensorWithJac, param)
-            param_.jac += jac
+        if is_tensor_with_jac(param):
+            param.jac += jac
         else:
             # We do not clone the value to save memory and time, so subsequent modifications of
             # the value of key.jac (subsequent accumulations) will also affect the value of

src/torchjd/autojac/_jac_to_grad.py

@@ -1,12 +1,11 @@
 from collections.abc import Iterable
-from typing import cast
 
 import torch
 from torch import Tensor
 
 from torchjd.aggregation import Aggregator
 
-from ._accumulation import TensorWithJac, accumulate_grads
+from ._accumulation import TensorWithJac, accumulate_grads, is_tensor_with_jac
 
 
 def jac_to_grad(
@@ -54,13 +53,12 @@ def jac_to_grad(
 
     tensors_ = list[TensorWithJac]()
     for t in tensors:
-        if not hasattr(t, "jac"):
+        if not is_tensor_with_jac(t):
             raise ValueError(
                 "Some `jac` fields were not populated. Did you use `autojac.backward` or "
                 "`autojac.mtl_backward` before calling `jac_to_grad`?"
             )
-        t_ = cast(TensorWithJac, t)
-        tensors_.append(t_)
+        tensors_.append(t)
 
     if len(tensors_) == 0:
         return

tests/utils/asserts.py

@@ -1,26 +1,22 @@
-from typing import cast
-
 import torch
 from torch.testing import assert_close
 
 from torchjd._linalg.matrix import PSDMatrix
-from torchjd.autojac._accumulation import TensorWithJac
+from torchjd.autojac._accumulation import is_tensor_with_jac
 
 
 def assert_has_jac(t: torch.Tensor) -> None:
-    assert hasattr(t, "jac")
-    t_ = cast(TensorWithJac, t)
-    assert t_.jac is not None and t_.jac.shape[1:] == t_.shape
+    assert is_tensor_with_jac(t)
+    assert t.jac is not None and t.jac.shape[1:] == t.shape
 
 
 def assert_has_no_jac(t: torch.Tensor) -> None:
-    assert not hasattr(t, "jac")
+    assert not is_tensor_with_jac(t)
 
 
 def assert_jac_close(t: torch.Tensor, expected_jac: torch.Tensor, **kwargs) -> None:
-    assert hasattr(t, "jac")
-    t_ = cast(TensorWithJac, t)
-    assert_close(t_.jac, expected_jac, **kwargs)
+    assert is_tensor_with_jac(t)
+    assert_close(t.jac, expected_jac, **kwargs)
 
 
 def assert_has_grad(t: torch.Tensor) -> None: