refactor(autojac): Stop cloning in Accumulate

CHANGELOG.md

@@ -8,6 +8,11 @@ changelog does not include internal changes that do not affect the user.
 
 ## [Unreleased]
 
+### Changed
+
+- Removed an unnecessary internal cloning of gradient. This should slightly improve the memory
+  efficiency of `autojac`.
+
 ## [0.8.1] - 2026-01-07
 
 ### Added

src/torchjd/autojac/_transform/_accumulate.py

@@ -15,11 +15,16 @@ def __call__(self, gradients: TensorDict) -> TensorDict:
             if hasattr(key, "grad") and key.grad is not None:
                 key.grad += gradients[key]
             else:
-                # We clone the value because we do not want subsequent accumulations to also affect
-                # this value (in case it is still used outside). We do not detach from the
-                # computation graph because the value can have grad_fn that we want to keep track of
-                # (in case it was obtained via create_graph=True and a differentiable aggregator).
-                key.grad = gradients[key].clone()
+                # We do not clone the value to save memory and time, so subsequent modifications of
+                # the value of key.grad (subsequent accumulations) will also affect the value of
+                # gradients[key] and outside changes to the value of gradients[key] will also affect
+                # the value of key.grad. So to be safe, the values of gradients should not be used
+                # anymore after being passed to this function.
+                #
+                # We do not detach from the computation graph because the value can have grad_fn
+                # that we want to keep track of (in case it was obtained via create_graph=True and a
+                # differentiable aggregator).
+                key.grad = gradients[key]
 
         return {}
 

tests/unit/autojac/_transform/test_accumulate.py

@@ -45,11 +45,12 @@ def test_multiple_accumulation(iterations: int):
     value1 = ones_([])
     value2 = ones_([1])
     value3 = ones_([2, 3])
-    input = {key1: value1, key2: value2, key3: value3}
 
     accumulate = Accumulate()
 
     for i in range(iterations):
+        # Clone values to ensure that we accumulate values that are not ever used afterwards
+        input = {key1: value1.clone(), key2: value2.clone(), key3: value3.clone()}
         accumulate(input)
 
     grads = {key1: key1.grad, key2: key2.grad, key3: key3.grad}