Add qpth solver

pyproject.toml

@@ -113,9 +113,13 @@ nash_mtl = [
 cagrad = [
     "cvxpy>=1.3.0",  # No Clarabel solver before 1.3.0
 ]
+qpth = [
+    "qpth>=0.0.15",
+]
 full = [
     "cvxpy>=1.3.0",  # No Clarabel solver before 1.3.0
     "ecos>=2.0.14",  # Does not work before 2.0.14
+    "qpth>=0.0.15",
 ]
 
 [tool.pytest.ini_options]

src/torchjd/aggregation/_dualproj.py

@@ -22,7 +22,9 @@ class DualProjWeighting(GramianWeighting):
         numerical errors when computing the gramian, it might not exactly be positive definite.
         This issue can make the optimization fail. Adding ``reg_eps`` to the diagonal of the gramian
         ensures that it is positive definite.
-    :param solver: The solver used to optimize the underlying optimization problem.
+    :param solver: The solver used to optimize the underlying optimization problem. Use
+        ``"quadprog"`` (default) for a CPU-based solver or ``"qpth"`` to solve natively on the
+        device of the input tensors (requires the optional ``qpth`` package).
     """
 
     def __init__(
@@ -90,7 +92,9 @@ class DualProj(GramianWeightedAggregator):
         numerical errors when computing the gramian, it might not exactly be positive definite.
         This issue can make the optimization fail. Adding ``reg_eps`` to the diagonal of the gramian
         ensures that it is positive definite.
-    :param solver: The solver used to optimize the underlying optimization problem.
+    :param solver: The solver used to optimize the underlying optimization problem. Use
+        ``"quadprog"`` (default) for a CPU-based solver or ``"qpth"`` to solve natively on the
+        device of the input tensors (requires the optional ``qpth`` package).
     """
 
     gramian_weighting: DualProjWeighting

src/torchjd/aggregation/_upgrad.py

@@ -23,7 +23,9 @@ class UPGradWeighting(GramianWeighting):
         numerical errors when computing the gramian, it might not exactly be positive definite.
         This issue can make the optimization fail. Adding ``reg_eps`` to the diagonal of the gramian
         ensures that it is positive definite.
-    :param solver: The solver used to optimize the underlying optimization problem.
+    :param solver: The solver used to optimize the underlying optimization problem. Use
+        ``"quadprog"`` (default) for a CPU-based solver or ``"qpth"`` to solve natively on the
+        device of the input tensors (requires the optional ``qpth`` package).
     """
 
     def __init__(
@@ -93,7 +95,9 @@ class UPGrad(GramianWeightedAggregator):
         numerical errors when computing the gramian, it might not exactly be positive definite.
         This issue can make the optimization fail. Adding ``reg_eps`` to the diagonal of the gramian
         ensures that it is positive definite.
-    :param solver: The solver used to optimize the underlying optimization problem.
+    :param solver: The solver used to optimize the underlying optimization problem. Use
+        ``"quadprog"`` (default) for a CPU-based solver or ``"qpth"`` to solve natively on the
+        device of the input tensors (requires the optional ``qpth`` package).
     """
 
     gramian_weighting: UPGradWeighting

src/torchjd/aggregation/_utils/dual_cone.py

@@ -5,7 +5,7 @@
 from qpsolvers import solve_qp
 from torch import Tensor
 
-SUPPORTED_SOLVER: TypeAlias = Literal["quadprog"]
+SUPPORTED_SOLVER: TypeAlias = Literal["quadprog", "qpth"]
 
 
 def project_weights(U: Tensor, G: Tensor, solver: SUPPORTED_SOLVER) -> Tensor:
@@ -15,10 +15,15 @@
 
     :param U: The tensor of weights corresponding to the vectors to project, of shape `[..., m]`.
     :param G: The Gramian matrix of shape `[m, m]`. It must be symmetric and positive definite.
-    :param solver: The quadratic programming solver to use.
+    :param solver: The quadratic programming solver to use. ``"quadprog"`` converts tensors to
+        CPU numpy arrays and uses qpsolvers. ``"qpth"`` solves natively on the same device as
+        the input tensors (e.g. CUDA) using the ``qpth`` package (optional dependency).
     :return: A tensor of projection weights with the same shape as `U`.
     """
 
+    if solver == "qpth":
+        return _project_weights_qpth(U, G)
+
     G_ = _to_array(G)
     U_ = _to_array(U)
 
@@ -27,6 +32,50 @@
     return torch.as_tensor(W, device=G.device, dtype=G.dtype)
 
 
+def _project_weights_qpth(U: Tensor, G: Tensor) -> Tensor:
+    r"""
+    Computes the tensor of projection weights using qpth, keeping computation on the device of
+    the input tensors and running without gradient tracking.
+
+    :param U: The tensor of weights to project, of shape `[..., m]`.
+    :param G: The Gramian matrix of shape `[m, m]`. It must be symmetric and positive definite.
+    """
+    from qpth.qp import QPFunction  # lazy import: qpth is an optional dependency
+
+    shape = U.shape
+    m = shape[-1]
+    batch_size = U.numel() // m
+    device = G.device
+    original_dtype = G.dtype
+
+    # Use float64 for numerical precision, matching the quadprog solver's behavior.
+    U_flat = U.reshape(batch_size, m).double()
+    G_double = G.double()
+
+    # QP formulation: minimize (1/2) v^T (2G) v + 0^T v subject to -I v <= -u (i.e., u <= v)
+    Q = (2.0 * G_double).unsqueeze(0).expand(batch_size, m, m).contiguous()
+    p = torch.zeros(batch_size, m, device=device, dtype=torch.float64)
+    G_ineq = (
+        (-torch.eye(m, device=device, dtype=torch.float64))
+        .unsqueeze(0)
+        .expand(batch_size, m, m)
+        .contiguous()
+    )
+    h_ineq = -U_flat
+    A = torch.zeros(batch_size, 0, m, device=device, dtype=torch.float64)
+    b = torch.zeros(batch_size, 0, device=device, dtype=torch.float64)
+
+    with torch.no_grad():
+        W_flat = QPFunction(verbose=False, maxIter=10, check_Q_spd=False, notImprovedLim=1)(
+            Q, p, G_ineq, h_ineq, A, b
+        )
+
+    if torch.any(torch.isnan(W_flat)):
+        raise ValueError("Failed to solve the quadratic programming problem.")
+
+    return W_flat.to(original_dtype).reshape(shape)
+
+
 def _project_weight_vector(u: np.ndarray, G: np.ndarray, solver: SUPPORTED_SOLVER) -> np.ndarray:
     r"""
     Computes the weights `w` of the projection of `J^T u` onto the dual cone of the rows of `J`,

tests/unit/aggregation/test_dualproj.py

@@ -1,5 +1,7 @@
+import importlib.util
+
 import torch
-from pytest import mark, raises
+from pytest import mark, param, raises
 from torch import Tensor
 from utils.tensors import ones_
 
@@ -15,28 +17,44 @@
 )
 from ._inputs import non_strong_matrices, scaled_matrices, typical_matrices
 
+_has_qpth = importlib.util.find_spec("qpth") is not None
+_skip_no_qpth = mark.skipif(not _has_qpth, reason="qpth not installed")
+
 scaled_pairs = [(DualProj(), matrix) for matrix in scaled_matrices]
 typical_pairs = [(DualProj(), matrix) for matrix in typical_matrices]
 non_strong_pairs = [(DualProj(), matrix) for matrix in non_strong_matrices]
 requires_grad_pairs = [(DualProj(), ones_(3, 5, requires_grad=True))]
 
+_qpth_typical_pairs = [
+    param(DualProj(solver="qpth"), matrix, marks=_skip_no_qpth) for matrix in typical_matrices
+]
+_qpth_non_strong_pairs = [
+    param(DualProj(solver="qpth"), matrix, marks=_skip_no_qpth) for matrix in non_strong_matrices
+]
+_qpth_scaled_pairs = [
+    param(DualProj(solver="qpth"), matrix, marks=_skip_no_qpth) for matrix in scaled_matrices
+]
 
-@mark.parametrize(["aggregator", "matrix"], scaled_pairs + typical_pairs)
+
+@mark.parametrize(
+    ["aggregator", "matrix"],
+    scaled_pairs + typical_pairs + _qpth_scaled_pairs + _qpth_typical_pairs,
+)
 def test_expected_structure(aggregator: DualProj, matrix: Tensor) -> None:
     assert_expected_structure(aggregator, matrix)
 
 
-@mark.parametrize(["aggregator", "matrix"], typical_pairs)
+@mark.parametrize(["aggregator", "matrix"], typical_pairs + _qpth_typical_pairs)
 def test_non_conflicting(aggregator: DualProj, matrix: Tensor) -> None:
     assert_non_conflicting(aggregator, matrix, atol=1e-04, rtol=1e-04)
 
 
-@mark.parametrize(["aggregator", "matrix"], typical_pairs)
+@mark.parametrize(["aggregator", "matrix"], typical_pairs + _qpth_typical_pairs)
 def test_permutation_invariant(aggregator: DualProj, matrix: Tensor) -> None:
     assert_permutation_invariant(aggregator, matrix, n_runs=5, atol=2e-07, rtol=2e-07)
 
 
-@mark.parametrize(["aggregator", "matrix"], non_strong_pairs)
+@mark.parametrize(["aggregator", "matrix"], non_strong_pairs + _qpth_non_strong_pairs)
 def test_strongly_stationary(aggregator: DualProj, matrix: Tensor) -> None:
     assert_strongly_stationary(aggregator, matrix, threshold=3e-03)
 
@@ -66,6 +84,13 @@ def test_representations() -> None:
     assert str(A) == "DualProj([1., 2., 3.])"
 
 
+@_skip_no_qpth
+def test_representations_qpth() -> None:
+    A = DualProj(pref_vector=None, norm_eps=0.0001, reg_eps=0.0001, solver="qpth")
+    assert repr(A) == "DualProj(pref_vector=None, norm_eps=0.0001, reg_eps=0.0001, solver='qpth')"
+    assert str(A) == "DualProj"
+
+
 def test_pref_vector_setter_updates_value() -> None:
     A = DualProj()
     new_pref = torch.tensor([1.0, 2.0, 3.0])

tests/unit/aggregation/test_upgrad.py

@@ -1,5 +1,7 @@
+import importlib.util
+
 import torch
-from pytest import mark, raises
+from pytest import mark, param, raises
 from torch import Tensor
 from utils.tensors import ones_
 
@@ -16,33 +18,49 @@
 )
 from ._inputs import non_strong_matrices, scaled_matrices, typical_matrices
 
+_has_qpth = importlib.util.find_spec("qpth") is not None
+_skip_no_qpth = mark.skipif(not _has_qpth, reason="qpth not installed")
+
 scaled_pairs = [(UPGrad(), matrix) for matrix in scaled_matrices]
 typical_pairs = [(UPGrad(), matrix) for matrix in typical_matrices]
 non_strong_pairs = [(UPGrad(), matrix) for matrix in non_strong_matrices]
 requires_grad_pairs = [(UPGrad(), ones_(3, 5, requires_grad=True))]
 
+_qpth_typical_pairs = [
+    param(UPGrad(solver="qpth"), matrix, marks=_skip_no_qpth) for matrix in typical_matrices
+]
+_qpth_non_strong_pairs = [
+    param(UPGrad(solver="qpth"), matrix, marks=_skip_no_qpth) for matrix in non_strong_matrices
+]
+_qpth_scaled_pairs = [
+    param(UPGrad(solver="qpth"), matrix, marks=_skip_no_qpth) for matrix in scaled_matrices
+]
 
-@mark.parametrize(["aggregator", "matrix"], scaled_pairs + typical_pairs)
+
+@mark.parametrize(
+    ["aggregator", "matrix"],
+    scaled_pairs + typical_pairs + _qpth_scaled_pairs + _qpth_typical_pairs,
+)
 def test_expected_structure(aggregator: UPGrad, matrix: Tensor) -> None:
     assert_expected_structure(aggregator, matrix)
 
 
-@mark.parametrize(["aggregator", "matrix"], typical_pairs)
+@mark.parametrize(["aggregator", "matrix"], typical_pairs + _qpth_typical_pairs)
 def test_non_conflicting(aggregator: UPGrad, matrix: Tensor) -> None:
     assert_non_conflicting(aggregator, matrix, atol=4e-04, rtol=4e-04)
 
 
-@mark.parametrize(["aggregator", "matrix"], typical_pairs)
+@mark.parametrize(["aggregator", "matrix"], typical_pairs + _qpth_typical_pairs)
 def test_permutation_invariant(aggregator: UPGrad, matrix: Tensor) -> None:
     assert_permutation_invariant(aggregator, matrix, n_runs=5, atol=5e-07, rtol=5e-07)
 
 
-@mark.parametrize(["aggregator", "matrix"], typical_pairs)
+@mark.parametrize(["aggregator", "matrix"], typical_pairs + _qpth_typical_pairs)
 def test_linear_under_scaling(aggregator: UPGrad, matrix: Tensor) -> None:
     assert_linear_under_scaling(aggregator, matrix, n_runs=5, atol=6e-02, rtol=6e-02)
 
 
-@mark.parametrize(["aggregator", "matrix"], non_strong_pairs)
+@mark.parametrize(["aggregator", "matrix"], non_strong_pairs + _qpth_non_strong_pairs)
 def test_strongly_stationary(aggregator: UPGrad, matrix: Tensor) -> None:
     assert_strongly_stationary(aggregator, matrix, threshold=5e-03)
 
@@ -70,6 +88,13 @@ def test_representations() -> None:
     assert str(A) == "UPGrad([1., 2., 3.])"
 
 
+@_skip_no_qpth
+def test_representations_qpth() -> None:
+    A = UPGrad(pref_vector=None, norm_eps=0.0001, reg_eps=0.0001, solver="qpth")
+    assert repr(A) == "UPGrad(pref_vector=None, norm_eps=0.0001, reg_eps=0.0001, solver='qpth')"
+    assert str(A) == "UPGrad"
+
+
 def test_pref_vector_setter_updates_value() -> None:
     A = UPGrad()
     new_pref = torch.tensor([1.0, 2.0, 3.0])