test: Add ExcessMTL aggregator coverage and fix redundant casts

Author: KhusPatel4450

src/torchjd/aggregation/_excess_mtl.py

@@ -124,12 +124,12 @@ def forward(self, matrix: Matrix, /) -> Tensor:
             if self._n_warmup_steps > 0:
                 # Average excess risk observed during warmup (Appendix C.1)
                 self._initial_w = cast(Tensor, self._warmup_w_sum) / self._n_warmup_steps
-                w = w / (cast(Tensor, self._initial_w) + 1e-7)
+                w = w / (self._initial_w + 1e-7)
             else:
                 # Official impl behavior: first call's excess is the baseline; use w raw
                 self._initial_w = w
         else:
-            w = w / (cast(Tensor, self._initial_w) + 1e-7)
+            w = w / (self._initial_w + 1e-7)
 
         # Exponentiated gradient weight update (Equation 9)
         weights = cast(Tensor, self._weights)

tests/unit/aggregation/test_excess_mtl.py

@@ -3,7 +3,7 @@
 from torch.testing import assert_close
 from utils.tensors import randn_, tensor_
 
-from torchjd.aggregation._excess_mtl import ExcessMTLWeighting
+from torchjd.aggregation._excess_mtl import ExcessMTL, ExcessMTLWeighting
 
 
 def test_representations() -> None:
@@ -190,3 +190,34 @@ def test_non_differentiable() -> None:
     W = ExcessMTLWeighting()
     weights = W(J)
     assert not weights.requires_grad
+
+
+# ExcessMTL (aggregator wrapper) tests
+
+
+def test_excess_mtl_representations() -> None:
+    agg = ExcessMTL(robust_step_size=2.0, n_warmup_steps=3)
+    assert repr(agg) == "ExcessMTL(robust_step_size=2.0, n_warmup_steps=3)"
+
+
+def test_excess_mtl_properties_delegate() -> None:
+    agg = ExcessMTL(robust_step_size=1.0, n_warmup_steps=0)
+    assert agg.robust_step_size == 1.0
+    assert agg.n_warmup_steps == 0
+
+    agg.robust_step_size = 0.5
+    assert agg.robust_step_size == 0.5
+    assert agg.weighting.robust_step_size == 0.5
+
+    agg.n_warmup_steps = 5
+    assert agg.n_warmup_steps == 5
+    assert agg.weighting.n_warmup_steps == 5
+
+
+def test_excess_mtl_reset_delegates() -> None:
+    J = randn_((3, 8))
+    agg = ExcessMTL(n_warmup_steps=0)
+    first = agg(J)
+    agg(J)
+    agg.reset()
+    assert_close(first, agg(J))