torch.func.jacfwd¶
- torch.func.jacfwd(func, argnums=0, has_aux=False, *, randomness='error')¶
Computes the Jacobian of
func
with respect to the arg(s) at indexargnum
using forward-mode autodiff- Parameters
func (function) – A Python function that takes one or more arguments, one of which must be a Tensor, and returns one or more Tensors
argnums (int or Tuple[int]) – Optional, integer or tuple of integers, saying which arguments to get the Jacobian with respect to. Default: 0.
has_aux (bool) – Flag indicating that
func
returns a(output, aux)
tuple where the first element is the output of the function to be differentiated and the second element is auxiliary objects that will not be differentiated. Default: False.randomness (str) – Flag indicating what type of randomness to use. See
vmap()
for more detail. Allowed: “different”, “same”, “error”. Default: “error”
- Returns
Returns a function that takes in the same inputs as
func
and returns the Jacobian offunc
with respect to the arg(s) atargnums
. Ifhas_aux is True
, then the returned function instead returns a(jacobian, aux)
tuple wherejacobian
is the Jacobian andaux
is auxiliary objects returned byfunc
.
Note
You may see this API error out with “forward-mode AD not implemented for operator X”. If so, please file a bug report and we will prioritize it. An alternative is to use
jacrev()
, which has better operator coverage.A basic usage with a pointwise, unary operation will give a diagonal array as the Jacobian
>>> from torch.func import jacfwd >>> x = torch.randn(5) >>> jacobian = jacfwd(torch.sin)(x) >>> expected = torch.diag(torch.cos(x)) >>> assert torch.allclose(jacobian, expected)
jacfwd()
can be composed with vmap to produce batched Jacobians:>>> from torch.func import jacfwd, vmap >>> x = torch.randn(64, 5) >>> jacobian = vmap(jacfwd(torch.sin))(x) >>> assert jacobian.shape == (64, 5, 5)
If you would like to compute the output of the function as well as the jacobian of the function, use the
has_aux
flag to return the output as an auxiliary object:>>> from torch.func import jacfwd >>> x = torch.randn(5) >>> >>> def f(x): >>> return x.sin() >>> >>> def g(x): >>> result = f(x) >>> return result, result >>> >>> jacobian_f, f_x = jacfwd(g, has_aux=True)(x) >>> assert torch.allclose(f_x, f(x))
Additionally,
jacrev()
can be composed with itself orjacrev()
to produce Hessians>>> from torch.func import jacfwd, jacrev >>> def f(x): >>> return x.sin().sum() >>> >>> x = torch.randn(5) >>> hessian = jacfwd(jacrev(f))(x) >>> assert torch.allclose(hessian, torch.diag(-x.sin()))
By default,
jacfwd()
computes the Jacobian with respect to the first input. However, it can compute the Jacboian with respect to a different argument by usingargnums
:>>> from torch.func import jacfwd >>> def f(x, y): >>> return x + y ** 2 >>> >>> x, y = torch.randn(5), torch.randn(5) >>> jacobian = jacfwd(f, argnums=1)(x, y) >>> expected = torch.diag(2 * y) >>> assert torch.allclose(jacobian, expected)
Additionally, passing a tuple to
argnums
will compute the Jacobian with respect to multiple arguments>>> from torch.func import jacfwd >>> def f(x, y): >>> return x + y ** 2 >>> >>> x, y = torch.randn(5), torch.randn(5) >>> jacobian = jacfwd(f, argnums=(0, 1))(x, y) >>> expectedX = torch.diag(torch.ones_like(x)) >>> expectedY = torch.diag(2 * y) >>> assert torch.allclose(jacobian[0], expectedX) >>> assert torch.allclose(jacobian[1], expectedY)