Merge branch 'main' into feature/gradvac

Author: ValerianRey

CHANGELOG.md

@@ -10,7 +10,10 @@ changelog does not include internal changes that do not affect the user.
 
 ### Added
 
-- Added `GradVac` and `GradVacWeighting` from [Gradient Vaccine: Investigating and Improving Multi-task Optimization in Massively Multilingual Models](https://arxiv.org/pdf/2010.05874).
+- Added `GradVac` and `GradVacWeighting` from 
+  [Gradient Vaccine: Investigating and Improving Multi-task Optimization in Massively Multilingual Models](https://arxiv.org/pdf/2010.05874).
+- Added a fallback for when the inner optimization of `NashMTL` fails (which can happen for example
+  on the matrix [[0., 0.], [0., 1.]]).
 
 ## [0.9.0] - 2026-02-24
 

src/torchjd/aggregation/_nash_mtl.py

@@ -158,9 +158,10 @@ def _solve_optimization(self, gtg: np.ndarray) -> np.ndarray:
 
             try:
                 self.prob.solve(solver=cp.ECOS, warm_start=True, max_iters=100)
-            except SolverError:
-                # On macOS, this can happen with: Solver 'ECOS' failed.
+            except (SolverError, ValueError):
+                # On macOS, SolverError can happen with: Solver 'ECOS' failed.
                 # No idea why. The corresponding matrix is of shape [9, 11] with rank 5.
+                # ValueError happens with for example matrix [[0., 0.], [0., 1.]].
                 # Maybe other exceptions can happen in other cases.
                 self.alpha_param.value = self.prvs_alpha_param.value
 

tests/unit/aggregation/test_nash_mtl.py

@@ -1,7 +1,7 @@
 from pytest import mark
 from torch import Tensor
 from torch.testing import assert_close
-from utils.tensors import ones_, randn_
+from utils.tensors import ones_, randn_, tensor_
 
 try:
     from torchjd.aggregation import NashMTL
@@ -19,6 +19,10 @@ def _make_aggregator(matrix: Tensor) -> NashMTL:
 
 
 standard_pairs = [(_make_aggregator(matrix), matrix) for matrix in nash_mtl_matrices]
+edge_case_matrices = [
+    tensor_([[0.0, 0.0], [0.0, 1.0]])  # This leads to a (caught) ValueError in _solve_optimization.
+]
+edge_case_pairs = [(_make_aggregator(matrix), matrix) for matrix in edge_case_matrices]
 requires_grad_pairs = [(NashMTL(n_tasks=3), ones_(3, 5, requires_grad=True))]
 
 
@@ -27,8 +31,13 @@ def _make_aggregator(matrix: Tensor) -> NashMTL:
 @mark.filterwarnings(
     "ignore:Solution may be inaccurate.",
     "ignore:You are solving a parameterized problem that is not DPP.",
+    "ignore:divide by zero encountered in divide",
+    "ignore:divide by zero encountered in true_divide",
+    "ignore:overflow encountered in divide",
+    "ignore:overflow encountered in true_divide",
+    "ignore:invalid value encountered in matmul",
 )
-@mark.parametrize(["aggregator", "matrix"], standard_pairs)
+@mark.parametrize(["aggregator", "matrix"], standard_pairs + edge_case_pairs)
 def test_expected_structure(aggregator: NashMTL, matrix: Tensor) -> None:
     assert_expected_structure(aggregator, matrix)