Why functorch? | Install guide | Transformations | Documentation | Future Plans
This library is currently under heavy development - if you have suggestions on the API or use-cases you'd like to be covered, please open an github issue or reach out. We'd love to hear about how you're using the library.
functorch is JAX-like composable function
transforms for PyTorch.
It aims to provide composable vmap and grad transforms that work with
PyTorch modules and PyTorch autograd with good eager-mode performance.
In addition, there is experimental functionality to trace through these transformations using FX in order to capture the results of these transforms ahead of time. This would allow us to compile the results of vmap or grad to improve performance.
There are a number of use cases that are tricky to do in PyTorch today:
- computing per-sample-gradients (or other per-sample quantities)
- running ensembles of models on a single machine
- efficiently batching together tasks in the inner-loop of MAML
- efficiently computing Jacobians and Hessians
- efficiently computing batched Jacobians and Hessians
Composing vmap, grad, vjp, and jvp transforms allows us to express the above
without designing a separate subsystem for each. This idea of composable function
transforms comes from the JAX framework.
There are two ways to install functorch:
- functorch from source
- functorch beta (compatible with recent PyTorch releases)
We recommend trying out the functorch beta first.
Click to expand
Follow the instructions in this Colab notebook
As of 9/21/2022, functorch comes installed alongside a nightly PyTorch binary.
Please install a Preview (nightly) PyTorch binary; see https://pytorch.org/
for instructions.
Once you've done that, run a quick sanity check in Python:
import torch
from functorch import vmap
x = torch.randn(3)
y = vmap(torch.sin)(x)
assert torch.allclose(y, x.sin())As of 9/21/2022, functorch comes installed alongside PyTorch and is in the
PyTorch source tree. Please install
PyTorch from source, then,
you will be able to import functorch.
Try to run some tests to make sure all is OK:
pytest test/test_vmap.py -v
pytest test/test_eager_transforms.py -vAOTAutograd has some additional optional requirements. You can install them via:
pip install networkxTo run functorch tests, please install our test dependencies (expecttest, pyyaml).
Click to expand
Follow the instructions here
Prerequisite: Install PyTorch
pip install functorchFinally, run a quick sanity check in python:
import torch
from functorch import vmap
x = torch.randn(3)
y = vmap(torch.sin)(x)
assert torch.allclose(y, x.sin())Right now, we support the following transforms:
grad,vjp,jvp,jacrev,jacfwd,hessianvmap
Furthermore, we have some utilities for working with PyTorch modules.
make_functional(model)make_functional_with_buffers(model)
Note: vmap imposes restrictions on the code that it can be used on.
For more details, please read its docstring.
vmap(func)(*inputs) is a transform that adds a dimension to all Tensor
operations in func. vmap(func) returns a new function that maps func over
some dimension (default: 0) of each Tensor in inputs.
vmap is useful for hiding batch dimensions: one can write a function func
that runs on examples and then lift it to a function that can take batches of
examples with vmap(func), leading to a simpler modeling experience:
from functorch import vmap
batch_size, feature_size = 3, 5
weights = torch.randn(feature_size, requires_grad=True)
def model(feature_vec):
# Very simple linear model with activation
assert feature_vec.dim() == 1
return feature_vec.dot(weights).relu()
examples = torch.randn(batch_size, feature_size)
result = vmap(model)(examples)grad(func)(*inputs) assumes func returns a single-element Tensor. It compute
the gradients of the output of func w.r.t. to inputs[0].
from functorch import grad
x = torch.randn([])
cos_x = grad(lambda x: torch.sin(x))(x)
assert torch.allclose(cos_x, x.cos())
# Second-order gradients
neg_sin_x = grad(grad(lambda x: torch.sin(x)))(x)
assert torch.allclose(neg_sin_x, -x.sin())When composed with vmap, grad can be used to compute per-sample-gradients:
from functorch import vmap
batch_size, feature_size = 3, 5
def model(weights,feature_vec):
# Very simple linear model with activation
assert feature_vec.dim() == 1
return feature_vec.dot(weights).relu()
def compute_loss(weights, example, target):
y = model(weights, example)
return ((y - target) ** 2).mean() # MSELoss
weights = torch.randn(feature_size, requires_grad=True)
examples = torch.randn(batch_size, feature_size)
targets = torch.randn(batch_size)
inputs = (weights,examples, targets)
grad_weight_per_example = vmap(grad(compute_loss), in_dims=(None, 0, 0))(*inputs)The vjp transform applies func to inputs and returns a new function that
computes vjps given some cotangents Tensors.
from functorch import vjp
outputs, vjp_fn = vjp(func, inputs); vjps = vjp_fn(*cotangents)The jvp transforms computes Jacobian-vector-products and is also known as
"forward-mode AD". It is not a higher-order function unlike most other transforms,
but it returns the outputs of func(inputs) as well as the jvps.
from functorch import jvp
x = torch.randn(5)
y = torch.randn(5)
f = lambda x, y: (x * y)
_, output = jvp(f, (x, y), (torch.ones(5), torch.ones(5)))
assert torch.allclose(output, x + y)The jacrev transform returns a new function that takes in x and returns the
Jacobian of torch.sin with respect to x using reverse-mode AD.
from functorch import jacrev
x = torch.randn(5)
jacobian = jacrev(torch.sin)(x)
expected = torch.diag(torch.cos(x))
assert torch.allclose(jacobian, expected)Use jacrev to compute the jacobian. This can be composed with vmap to produce
batched jacobians:
x = torch.randn(64, 5)
jacobian = vmap(jacrev(torch.sin))(x)
assert jacobian.shape == (64, 5, 5)jacfwd is a drop-in replacement for jacrev that computes Jacobians using
forward-mode AD:
from functorch import jacfwd
x = torch.randn(5)
jacobian = jacfwd(torch.sin)(x)
expected = torch.diag(torch.cos(x))
assert torch.allclose(jacobian, expected)Composing jacrev with itself or jacfwd can produce hessians:
def f(x):
return x.sin().sum()
x = torch.randn(5)
hessian0 = jacrev(jacrev(f))(x)
hessian1 = jacfwd(jacrev(f))(x)The hessian is a convenience function that combines jacfwd and jacrev:
from functorch import hessian
def f(x):
return x.sin().sum()
x = torch.randn(5)
hess = hessian(f)(x)We can also trace through these transformations in order to capture the results as new code using make_fx. There is also experimental integration with the NNC compiler (only works on CPU for now!).
from functorch import make_fx, grad
def f(x):
return torch.sin(x).sum()
x = torch.randn(100)
grad_f = make_fx(grad(f))(x)
print(grad_f.code)
def forward(self, x_1):
sin = torch.ops.aten.sin(x_1)
sum_1 = torch.ops.aten.sum(sin, None); sin = None
cos = torch.ops.aten.cos(x_1); x_1 = None
_tensor_constant0 = self._tensor_constant0
mul = torch.ops.aten.mul(_tensor_constant0, cos); _tensor_constant0 = cos = None
return mulSometimes you may want to perform a transform with respect to the parameters and/or buffers of an nn.Module. This can happen for example in:
- model ensembling, where all of your weights and buffers have an additional dimension
- per-sample-gradient computation where you want to compute per-sample-grads of the loss with respect to the model parameters
Our solution to this right now is an API that, given an nn.Module, creates a stateless version of it that can be called like a function.
make_functional(model)returns a functional version ofmodeland themodel.parameters()make_functional_with_buffers(model)returns a functional version ofmodeland themodel.parameters()andmodel.buffers().
Here's an example where we compute per-sample-gradients using an nn.Linear layer:
import torch
from functorch import make_functional, vmap, grad
model = torch.nn.Linear(3, 3)
data = torch.randn(64, 3)
targets = torch.randn(64, 3)
func_model, params = make_functional(model)
def compute_loss(params, data, targets):
preds = func_model(params, data)
return torch.mean((preds - targets) ** 2)
per_sample_grads = vmap(grad(compute_loss), (None, 0, 0))(params, data, targets)If you're making an ensemble of models, you may find
combine_state_for_ensemble useful.
For more documentation, see our docs website.
torch._C._functorch.dump_tensor: Dumps dispatch keys on stack
torch._C._functorch._set_vmap_fallback_warning_enabled(False) if the vmap warning spam bothers you.
In the end state, we'd like to upstream this into PyTorch once we iron out the design details. To figure out the details, we need your help -- please send us your use cases by starting a conversation in the issue tracker or trying our project out.
Functorch has a BSD-style license, as found in the LICENSE file.
If you use functorch in your publication, please cite it by using the following BibTeX entry.
@Misc{functorch2021,
author = {Horace He, Richard Zou},
title = {functorch: JAX-like composable function transforms for PyTorch},
howpublished = {\url{https://github.com/pytorch/functorch}},
year = {2021}
}
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