feat(aggregation): Add MoDoWeighting

CHANGELOG.md

@@ -10,6 +10,7 @@ changelog does not include internal changes that do not affect the user.
 
 ### Added
 
+- Added `MoDoWeighting` from [Three-Way Trade-Off in Multi-Objective Learning: Optimization, Generalization and Conflict-Avoidance](https://www.jmlr.org/papers/volume25/23-1287/23-1287.pdf) (JMLR 2024). It is a stateful `Weighting` that maintains task weights across calls via a softmax-projected gradient step on a cross-batch matrix `G = J_1 @ J_2.T`, computed from two independent mini-batches using `autojac.jac`.
 - Added `GeometricMean` (also known as GLS) studied in [MultiNet++: Multi-Stream Feature
   Aggregation and Geometric Loss Strategy for Multi-Task
   Learning](https://openaccess.thecvf.com/content_CVPRW_2019/papers/WAD/Chennupati_MultiNet_Multi-Stream_Feature_Aggregation_and_Geometric_Loss_Strategy_for_Multi-Task_CVPRW_2019_paper.pdf),

docs/source/docs/aggregation/index.rst

@@ -41,6 +41,7 @@ Abstract base classes
     krum.rst
     mean.rst
     mgda.rst
+    modo.rst
     nash_mtl.rst
     pcgrad.rst
     random.rst

docs/source/docs/aggregation/modo.rst

@@ -0,0 +1,7 @@
+:hide-toc:
+
+MoDo
+====
+
+.. autoclass:: torchjd.aggregation.MoDoWeighting
+    :members: __call__, reset

src/torchjd/aggregation/__init__.py

@@ -53,6 +53,7 @@
 from ._mean import Mean, MeanWeighting
 from ._mgda import MGDA, MGDAWeighting
 from ._mixins import Stateful
+from ._modo import MoDoWeighting
 from ._nash_mtl import NashMTL
 from ._pcgrad import PCGrad, PCGradWeighting
 from ._random import Random, RandomWeighting
@@ -87,6 +88,7 @@
     "MeanWeighting",
     "MGDA",
     "MGDAWeighting",
+    "MoDoWeighting",
     "NashMTL",
     "PCGrad",
     "PCGradWeighting",

src/torchjd/aggregation/_modo.py

@@ -0,0 +1,162 @@
+from __future__ import annotations
+
+from typing import cast
+
+import torch
+from torch import Tensor
+
+from torchjd.aggregation._mixins import Stateful, _NonDifferentiable
+from torchjd.linalg import Matrix
+
+from ._weighting_bases import _MatrixWeighting
+
+
+class MoDoWeighting(_MatrixWeighting, Stateful, _NonDifferentiable):
+    r"""
+    :class:`~torchjd.aggregation._mixins.Stateful`
+    :class:`~torchjd.aggregation.Weighting` [:class:`~torchjd.linalg.Matrix`] from `Three-Way
+    Trade-Off in Multi-Objective Learning: Optimization, Generalization and Conflict-Avoidance
+    <https://www.jmlr.org/papers/volume25/23-1287/23-1287.pdf>`_ (JMLR 2024).
+
+    .. warning::
+        The input matrix must be :math:`G = J_1 J_2^\top`, computed from two **independent**
+        mini-batches via :func:`torchjd.autojac.jac`. Using a single-batch Gramian
+        (:math:`J_1 J_1^\top`) breaks the convergence guarantee. See the usage examples below.
+
+    :param gamma: Learning rate of the task-weight update. Must be positive.
+    :param rho: Non-negative :math:`\ell_2` regularisation coefficient.
+
+    .. admonition:: Example (two batches per step)
+
+        The following example reproduces basic MoDo using two independent mini-batches per step.
+
+        .. code-block:: python
+
+            import torch
+            from torch.nn import Linear, MSELoss, ReLU, Sequential
+            from torch.optim import SGD
+
+            from torchjd.aggregation import MoDoWeighting
+            from torchjd.autojac import jac
+
+            model = Sequential(Linear(5, 4), ReLU(), Linear(4, 1))
+            optimizer = SGD(model.parameters())
+            criterion = MSELoss(reduction="none")
+            weighting = MoDoWeighting(gamma=0.1, rho=0.0)
+            params = list(model.parameters())
+
+            # loader_1 and loader_2 must yield independent draws of the same size.
+            for batch_1, batch_2 in zip(loader_1, loader_2):
+                input_1, target_1 = batch_1
+                input_2, target_2 = batch_2
+
+                losses_1 = criterion(model(input_1).squeeze(dim=1), target_1)
+                jacs_1 = jac(losses_1, params)
+                J_1 = torch.cat([j.flatten(1) for j in jacs_1], dim=1)
+
+                # retain_graph=True keeps the graph for the backward step below.
+                losses_2 = criterion(model(input_2).squeeze(dim=1), target_2)
+                jacs_2 = jac(losses_2, params, retain_graph=True)
+                J_2 = torch.cat([j.flatten(1) for j in jacs_2], dim=1)
+
+                G = J_1 @ J_2.T
+                weights = weighting(G)
+
+                losses_2.backward(weights)
+                optimizer.step()
+                optimizer.zero_grad()
+
+    .. admonition:: Example (three batches per step)
+
+        The following example reproduces basic MoDo using three independent mini-batches per step,
+        keeping the :math:`\lambda` update and the parameter update on separate draws.
+
+        .. code-block:: python
+
+            import torch
+            from torch.nn import Linear, MSELoss, ReLU, Sequential
+            from torch.optim import SGD
+
+            from torchjd.aggregation import MoDoWeighting
+            from torchjd.autojac import jac
+
+            model = Sequential(Linear(5, 4), ReLU(), Linear(4, 1))
+            optimizer = SGD(model.parameters())
+            criterion = MSELoss(reduction="none")
+            weighting = MoDoWeighting(gamma=0.1, rho=0.0)
+            params = list(model.parameters())
+
+            for batch_1, batch_2, batch_3 in zip(loader_1, loader_2, loader_3):
+                input_1, target_1 = batch_1
+                input_2, target_2 = batch_2
+                input_3, target_3 = batch_3
+
+                losses_1 = criterion(model(input_1).squeeze(dim=1), target_1)
+                jacs_1 = jac(losses_1, params)
+                J_1 = torch.cat([j.flatten(1) for j in jacs_1], dim=1)
+
+                losses_2 = criterion(model(input_2).squeeze(dim=1), target_2)
+                jacs_2 = jac(losses_2, params)
+                J_2 = torch.cat([j.flatten(1) for j in jacs_2], dim=1)
+
+                G = J_1 @ J_2.T
+                weights = weighting(G)
+
+                losses_3 = criterion(model(input_3).squeeze(dim=1), target_3)
+                losses_3.backward(weights)
+                optimizer.step()
+                optimizer.zero_grad()
+    """
+
+    def __init__(self, gamma: float = 0.1, rho: float = 0.0) -> None:
+        super().__init__()
+        self.gamma = gamma
+        self.rho = rho
+        self._lambda: Tensor | None = None
+        self._state_key: tuple[int, torch.dtype, torch.device] | None = None
+
+    @property
+    def gamma(self) -> float:
+        return self._gamma
+
+    @gamma.setter
+    def gamma(self, value: float) -> None:
+        if value <= 0.0:
+            raise ValueError(f"Attribute `gamma` must be positive. Found gamma={value!r}.")
+        self._gamma = value
+
+    @property
+    def rho(self) -> float:
+        return self._rho
+
+    @rho.setter
+    def rho(self, value: float) -> None:
+        if value < 0.0:
+            raise ValueError(f"Attribute `rho` must be non-negative. Found rho={value!r}.")
+        self._rho = value
+
+    def reset(self) -> None:
+        """Clears the stored task weights so the next forward starts from uniform."""
+
+        self._lambda = None
+        self._state_key = None
+
+    def forward(self, matrix: Matrix, /) -> Tensor:
+        self._ensure_state(matrix)
+        lambd = cast(Tensor, self._lambda)
+
+        grad = matrix @ lambd + self._rho * lambd
+        lambd = torch.softmax(lambd - self._gamma * grad, dim=-1)
+
+        self._lambda = lambd
+        return lambd
+
+    def _ensure_state(self, matrix: Matrix) -> None:
+        key = (matrix.shape[0], matrix.dtype, matrix.device)
+        if self._state_key == key and self._lambda is not None:
+            return
+        self._lambda = matrix.new_full((matrix.shape[0],), 1.0 / matrix.shape[0])
+        self._state_key = key
+
+    def __repr__(self) -> str:
+        return f"{self.__class__.__name__}(gamma={self.gamma!r}, rho={self.rho!r})"

tests/unit/aggregation/test_modo.py

@@ -0,0 +1,159 @@
+import torch
+from pytest import raises
+from torch.testing import assert_close
+from utils.tensors import randn_, tensor_
+
+from torchjd.aggregation._modo import MoDoWeighting
+
+
+def test_representations() -> None:
+    W = MoDoWeighting(gamma=0.1, rho=0.05)
+    assert repr(W) == "MoDoWeighting(gamma=0.1, rho=0.05)"
+
+
+def test_reset_restores_first_step_behavior() -> None:
+    J = randn_((3, 8))
+    G = J @ J.T
+    W = MoDoWeighting(gamma=0.1)
+    first = W(G)
+    W(G)
+    W.reset()
+    assert_close(first, W(G))
+
+
+def test_gamma_setter_accepts_valid() -> None:
+    W = MoDoWeighting()
+    W.gamma = 0.01
+    assert W.gamma == 0.01
+    W.gamma = 0.1
+    assert W.gamma == 0.1
+    W.gamma = 1.0
+    assert W.gamma == 1.0
+
+
+def test_gamma_setter_rejects_non_positive() -> None:
+    W = MoDoWeighting()
+    with raises(ValueError, match="gamma"):
+        W.gamma = 0.0
+    with raises(ValueError, match="gamma"):
+        W.gamma = -0.1
+
+
+def test_rho_setter_accepts_valid() -> None:
+    W = MoDoWeighting()
+    W.rho = 0.0
+    assert W.rho == 0.0
+    W.rho = 0.1
+    assert W.rho == 0.1
+
+
+def test_rho_setter_rejects_negative() -> None:
+    W = MoDoWeighting()
+    with raises(ValueError, match="rho"):
+        W.rho = -0.1
+
+
+def test_output_lies_on_simplex() -> None:
+    """The softmax projection ensures the weights sum to 1 and are non-negative."""
+
+    J = randn_((4, 10))
+    G = J @ J.T
+    W = MoDoWeighting(gamma=0.1, rho=0.05)
+    weights = W(G)
+    assert weights.shape == (4,)
+    assert (weights >= 0).all()
+    assert_close(weights.sum(), tensor_(1.0))
+
+
+def test_small_gamma_stays_near_uniform() -> None:
+    """With a tiny gamma, one step barely moves lambda from the uniform initialisation."""
+
+    J = randn_((3, 8))
+    G = J @ J.T
+    m = J.shape[0]
+    W = MoDoWeighting(gamma=1e-8)
+    uniform = tensor_([1.0 / m] * m)
+    assert_close(W(G), uniform, atol=1e-6, rtol=1e-6)
+
+
+def test_update_recurrence() -> None:
+    """Verify one step of the softmax-projected gradient update by hand."""
+
+    gamma = 0.1
+    rho = 0.05
+    J = randn_((3, 8))
+    G = J @ J.T
+    m = J.shape[0]
+
+    W = MoDoWeighting(gamma=gamma, rho=rho)
+    lambda_0 = tensor_([1.0 / m] * m)
+    grad = G @ lambda_0 + rho * lambda_0
+    expected = torch.softmax(lambda_0 - gamma * grad, dim=-1)
+
+    assert_close(W(G), expected)
+
+
+def test_two_consecutive_steps() -> None:
+    """Verify two consecutive steps of the softmax-projected gradient update."""
+
+    gamma = 0.1
+    rho = 0.0
+    J1 = randn_((3, 8))
+    J2 = randn_((3, 8))
+    G1 = J1 @ J1.T
+    G2 = J2 @ J2.T
+    m = J1.shape[0]
+
+    W = MoDoWeighting(gamma=gamma, rho=rho)
+
+    lambda_0 = tensor_([1.0 / m] * m)
+    grad_1 = G1 @ lambda_0 + rho * lambda_0
+    lambda_1 = torch.softmax(lambda_0 - gamma * grad_1, dim=-1)
+
+    grad_2 = G2 @ lambda_1 + rho * lambda_1
+    lambda_2 = torch.softmax(lambda_1 - gamma * grad_2, dim=-1)
+
+    assert_close(W(G1), lambda_1)
+    assert_close(W(G2), lambda_2)
+
+
+def test_changing_m_auto_resets() -> None:
+    """When the number of objectives changes, the state is re-initialised to uniform."""
+
+    W = MoDoWeighting(gamma=0.1)
+    W(randn_((3, 8)) @ randn_((3, 8)).T)
+    # After a state-resetting call with m=2, the first output should equal the uniform step's output.
+    fresh = MoDoWeighting(gamma=0.1)
+    J = randn_((2, 8))
+    G = J @ J.T
+    assert_close(W(G), fresh(G))
+
+
+def test_non_differentiable() -> None:
+    """The _NonDifferentiable mixin must prevent autograd graph construction."""
+
+    G = randn_((3, 8)) @ randn_((3, 8)).T
+    G.requires_grad_(True)
+    W = MoDoWeighting()
+    weights = W(G)
+    assert not weights.requires_grad
+
+
+def test_non_symmetric_input() -> None:
+    """MoDoWeighting must accept and correctly process a non-symmetric cross-batch matrix."""
+
+    gamma = 0.1
+    rho = 0.05
+    J1 = randn_((3, 8))
+    J2 = randn_((3, 8))
+    G = J1 @ J2.T  # not symmetric, not PSD in general
+    m = J1.shape[0]
+
+    W = MoDoWeighting(gamma=gamma, rho=rho)
+    lambda_0 = tensor_([1.0 / m] * m)
+    grad = G @ lambda_0 + rho * lambda_0
+    expected = torch.softmax(lambda_0 - gamma * grad, dim=-1)
+
+    assert_close(W(G), expected)
+    assert W(G).shape == (m,)
+    assert (W(G) >= 0).all()