feat: Add autojac.jac

CHANGELOG.md

@@ -10,6 +10,9 @@ changes that do not affect the user.
 
 ### Added
 
+- Added the function `torchjd.autojac.jac` to compute the Jacobian of some outputs with respect to
+  some inputs, without doing any aggregation. Its interface is very similar to
+  `torch.autograd.grad`.
 - Added `__all__` in the `__init__.py` of packages. This should prevent PyLance from triggering warnings when importing from `torchjd`.
 
 ## [0.8.0] - 2025-11-13

docs/source/docs/autojac/index.rst

@@ -10,3 +10,4 @@ autojac
 
     backward.rst
     mtl_backward.rst
+    jac.rst

docs/source/docs/autojac/jac.rst

@@ -0,0 +1,6 @@
+:hide-toc:
+
+jac
+===
+
+.. autofunction:: torchjd.autojac.jac

src/torchjd/autojac/__init__.py

@@ -6,6 +6,7 @@
 """
 
 from ._backward import backward
+from ._jac import jac
 from ._mtl_backward import mtl_backward
 
-__all__ = ["backward", "mtl_backward"]
+__all__ = ["backward", "jac", "mtl_backward"]

src/torchjd/autojac/_jac.py

@@ -0,0 +1,114 @@
+from collections.abc import Sequence
+from typing import Iterable
+
+from torch import Tensor
+
+from torchjd.autojac._transform._base import Transform
+from torchjd.autojac._transform._diagonalize import Diagonalize
+from torchjd.autojac._transform._init import Init
+from torchjd.autojac._transform._jac import Jac
+from torchjd.autojac._transform._ordered_set import OrderedSet
+from torchjd.autojac._utils import (
+    as_checked_ordered_set,
+    check_optional_positive_chunk_size,
+    get_leaf_tensors,
+)
+
+
+def jac(
+    outputs: Sequence[Tensor] | Tensor,
+    inputs: Iterable[Tensor] | None = None,
+    retain_graph: bool = False,
+    parallel_chunk_size: int | None = None,
+) -> tuple[Tensor, ...]:
+    r"""
+    Computes the Jacobian of all values in ``outputs`` with respect to all ``inputs``. Returns the
+    result as a tuple, with one element per input tensor.
+
+    :param outputs: The tensor or tensors to differentiate. Should be non-empty. The Jacobian
+        matrices will have one row for each value of each of these tensors.
+    :param inputs: The tensors with respect to which the Jacobian must be computed. These must have
+        their ``requires_grad`` flag set to ``True``. If not provided, defaults to the leaf tensors
+        that were used to compute the ``outputs`` parameter.
+    :param retain_graph: If ``False``, the graph used to compute the grad will be freed. Defaults to
+        ``False``.
+    :param parallel_chunk_size: The number of scalars to differentiate simultaneously in the
+        backward pass. If set to ``None``, all coordinates of ``outputs`` will be differentiated in
+        parallel at once. If set to ``1``, all coordinates will be differentiated sequentially. A
+        larger value results in faster differentiation, but also higher memory usage. Defaults to
+        ``None``.
+
+    .. admonition::
+        Example
+
+        The following example shows how to use ``jac``.
+
+            >>> import torch
+            >>>
+            >>> from torchjd.autojac import jac
+            >>>
+            >>> param = torch.tensor([1., 2.], requires_grad=True)
+            >>> # Compute arbitrary quantities that are function of param
+            >>> y1 = torch.tensor([-1., 1.]) @ param
+            >>> y2 = (param ** 2).sum()
+            >>>
+            >>> jacobians = jac([y1, y2], [param])
+            >>>
+            >>> jacobians
+            (tensor([-1., 1.],
+                    [ 2., 4.]]),)
+
+        The returned tuple contains a single tensor (because there is a single param), that is the
+        Jacobian of :math:`\begin{bmatrix}y_1 \\ y_2\end{bmatrix}` with respect to ``param``.
+
+    .. warning::
+        To differentiate in parallel, ``jac`` relies on ``torch.vmap``, which has some
+        limitations: `it does not work on the output of compiled functions
+        <https://github.com/pytorch/pytorch/issues/138422>`_, `when some tensors have
+        <https://github.com/TorchJD/torchjd/issues/184>`_ ``retains_grad=True`` or `when using an
+        RNN on CUDA <https://github.com/TorchJD/torchjd/issues/220>`_, for instance. If you
+        experience issues with ``backward`` try to use ``parallel_chunk_size=1`` to avoid relying on
+        ``torch.vmap``.
+    """
+
+    check_optional_positive_chunk_size(parallel_chunk_size)
+    outputs_ = as_checked_ordered_set(outputs, "outputs")
+
+    if inputs is None:
+        inputs_ = get_leaf_tensors(tensors=outputs_, excluded=set())
+    else:
+        inputs_ = OrderedSet(inputs)
+
+    if len(outputs_) == 0:
+        raise ValueError("`outputs` cannot be empty")
+
+    if len(inputs_) == 0:
+        raise ValueError("`inputs` cannot be empty")
+
+    jac_transform = _create_transform(
+        outputs=outputs_,
+        inputs=inputs_,
+        retain_graph=retain_graph,
+        parallel_chunk_size=parallel_chunk_size,
+    )
+
+    result = jac_transform({})
+    return tuple(val for val in result.values())
+
+
+def _create_transform(
+    outputs: OrderedSet[Tensor],
+    inputs: OrderedSet[Tensor],
+    retain_graph: bool,
+    parallel_chunk_size: int | None,
+) -> Transform:
+    # Transform that creates gradient outputs containing only ones.
+    init = Init(outputs)
+
+    # Transform that turns the gradients into Jacobians.
+    diag = Diagonalize(outputs)
+
+    # Transform that computes the required Jacobians.
+    jac = Jac(outputs, inputs, parallel_chunk_size, retain_graph)
+
+    return jac << diag << init

tests/doc/test_jac.py

@@ -0,0 +1,20 @@
+"""
+This file contains the test of the jac usage example, with a verification of the value of the obtained jacobians tuple.
+"""
+
+from torch.testing import assert_close
+
+
+def test_jac():
+    import torch
+
+    from torchjd.autojac import jac
+
+    param = torch.tensor([1.0, 2.0], requires_grad=True)
+    # Compute arbitrary quantities that are function of param
+    y1 = torch.tensor([-1.0, 1.0]) @ param
+    y2 = (param**2).sum()
+    jacobians = jac([y1, y2], [param])
+
+    assert len(jacobians) == 1
+    assert_close(jacobians[0], torch.tensor([[-1.0, 1.0], [2.0, 4.0]]), rtol=0.0, atol=1e-04)

tests/unit/autojac/test_jac.py

@@ -0,0 +1,44 @@
+from utils.tensors import tensor_
+
+from torchjd.autojac import jac
+from torchjd.autojac._jac import _create_transform
+from torchjd.autojac._transform import OrderedSet
+
+
+def test_check_create_transform():
+    """Tests that _create_transform creates a valid Transform."""
+
+    a1 = tensor_([1.0, 2.0], requires_grad=True)
+    a2 = tensor_([3.0, 4.0], requires_grad=True)
+
+    y1 = tensor_([-1.0, 1.0]) @ a1 + a2.sum()
+    y2 = (a1**2).sum() + a2.norm()
+
+    transform = _create_transform(
+        outputs=OrderedSet([y1, y2]),
+        inputs=OrderedSet([a1, a2]),
+        retain_graph=False,
+        parallel_chunk_size=None,
+    )
+
+    output_keys = transform.check_keys(set())
+    assert output_keys == {a1, a2}
+
+
+def test_jac():
+    """Tests that jac works."""
+
+    a1 = tensor_([1.0, 2.0], requires_grad=True)
+    a2 = tensor_([3.0, 4.0], requires_grad=True)
+    inputs = [a1, a2]
+
+    y1 = tensor_([-1.0, 1.0]) @ a1 + a2.sum()
+    y2 = (a1**2).sum() + a2.norm()
+    outputs = [y1, y2]
+
+    jacobians = jac(outputs, inputs)
+
+    assert len(jacobians) == len([a1, a2])
+    for jacobian, a in zip(jacobians, [a1, a2]):
+        assert jacobian.shape[0] == len([y1, y2])
+        assert jacobian.shape[1:] == a.shape