Quickstart | Transformations | Install guide | Neural net libraries | Change logs | Reference docs
JAX is a Python library for accelerator-oriented array computation and program transformation, designed for high-performance numerical computing and large-scale machine learning.
With its updated version of Autograd,
JAX can automatically differentiate native
Python and NumPy functions. It can differentiate through loops, branches,
recursion, and closures, and it can take derivatives of derivatives of
derivatives. It supports reverse-mode differentiation (a.k.a. backpropagation)
via grad as well as forward-mode differentiation,
and the two can be composed arbitrarily to any order.
What’s new is that JAX uses XLA
to compile and run your NumPy programs on GPUs and TPUs. Compilation happens
under the hood by default, with library calls getting just-in-time compiled and
executed. But JAX also lets you just-in-time compile your own Python functions
into XLA-optimized kernels using a one-function API,
jit. Compilation and automatic differentiation can be
composed arbitrarily, so you can express sophisticated algorithms and get
maximal performance without leaving Python. You can even program multiple GPUs
or TPU cores at once using pmap, and
differentiate through the whole thing.
Dig a little deeper, and you'll see that JAX is really an extensible system for
composable function transformations. Both
grad and jit
are instances of such transformations. Others are
vmap for automatic vectorization and
pmap for single-program multiple-data (SPMD)
parallel programming of multiple accelerators, with more to come.
This is a research project, not an official Google product. Expect bugs and sharp edges. Please help by trying it out, reporting bugs, and letting us know what you think!
import jax.numpy as jnp
from jax import grad, jit, vmap
def predict(params, inputs):
for W, b in params:
outputs = jnp.dot(inputs, W) + b
inputs = jnp.tanh(outputs) # inputs to the next layer
return outputs # no activation on last layer
def loss(params, inputs, targets):
preds = predict(params, inputs)
return jnp.sum((preds - targets)**2)
grad_loss = jit(grad(loss)) # compiled gradient evaluation function
perex_grads = jit(vmap(grad_loss, in_axes=(None, 0, 0))) # fast per-example grads- Quickstart: Colab in the Cloud
- Transformations
- Current gotchas
- Installation
- Neural net libraries
- Citing JAX
- Reference documentation
Jump right in using a notebook in your browser, connected to a Google Cloud GPU. Here are some starter notebooks:
- The basics: NumPy on accelerators,
gradfor differentiation,jitfor compilation, andvmapfor vectorization - Training a Simple Neural Network, with TensorFlow Dataset Data Loading
JAX now runs on Cloud TPUs. To try out the preview, see the Cloud TPU Colabs.
For a deeper dive into JAX:
- The Autodiff Cookbook, Part 1: easy and powerful automatic differentiation in JAX
- Common gotchas and sharp edges
- See the full list of notebooks.
At its core, JAX is an extensible system for transforming numerical functions.
Here are four transformations of primary interest: grad, jit, vmap, and
pmap.
JAX has roughly the same API as Autograd.
The most popular function is
grad
for reverse-mode gradients:
from jax import grad
import jax.numpy as jnp
def tanh(x): # Define a function
y = jnp.exp(-2.0 * x)
return (1.0 - y) / (1.0 + y)
grad_tanh = grad(tanh) # Obtain its gradient function
print(grad_tanh(1.0)) # Evaluate it at x = 1.0
# prints 0.4199743You can differentiate to any order with grad.
print(grad(grad(grad(tanh)))(1.0))
# prints 0.62162673For more advanced autodiff, you can use
jax.vjp for
reverse-mode vector-Jacobian products and
jax.jvp for
forward-mode Jacobian-vector products. The two can be composed arbitrarily with
one another, and with other JAX transformations. Here's one way to compose those
to make a function that efficiently computes full Hessian
matrices:
from jax import jit, jacfwd, jacrev
def hessian(fun):
return jit(jacfwd(jacrev(fun)))As with Autograd, you're free to use differentiation with Python control structures:
def abs_val(x):
if x > 0:
return x
else:
return -x
abs_val_grad = grad(abs_val)
print(abs_val_grad(1.0)) # prints 1.0
print(abs_val_grad(-1.0)) # prints -1.0 (abs_val is re-evaluated)See the reference docs on automatic differentiation and the JAX Autodiff Cookbook for more.
You can use XLA to compile your functions end-to-end with
jit,
used either as an @jit decorator or as a higher-order function.
import jax.numpy as jnp
from jax import jit
def slow_f(x):
# Element-wise ops see a large benefit from fusion
return x * x + x * 2.0
x = jnp.ones((5000, 5000))
fast_f = jit(slow_f)
%timeit -n10 -r3 fast_f(x) # ~ 4.5 ms / loop on Titan X
%timeit -n10 -r3 slow_f(x) # ~ 14.5 ms / loop (also on GPU via JAX)You can mix jit and grad and any other JAX transformation however you like.
Using jit puts constraints on the kind of Python control flow
the function can use; see
the Gotchas
Notebook
for more.
vmap is
the vectorizing map.
It has the familiar semantics of mapping a function along array axes, but
instead of keeping the loop on the outside, it pushes the loop down into a
function’s primitive operations for better performance.
Using vmap can save you from having to carry around batch dimensions in your
code. For example, consider this simple unbatched neural network prediction
function:
def predict(params, input_vec):
assert input_vec.ndim == 1
activations = input_vec
for W, b in params:
outputs = jnp.dot(W, activations) + b # `activations` on the right-hand side!
activations = jnp.tanh(outputs) # inputs to the next layer
return outputs # no activation on last layerWe often instead write jnp.dot(activations, W) to allow for a batch dimension on the
left side of activations, but we’ve written this particular prediction function to
apply only to single input vectors. If we wanted to apply this function to a
batch of inputs at once, semantically we could just write
from functools import partial
predictions = jnp.stack(list(map(partial(predict, params), input_batch)))But pushing one example through the network at a time would be slow! It’s better to vectorize the computation, so that at every layer we’re doing matrix-matrix multiplication rather than matrix-vector multiplication.
The vmap function does that transformation for us. That is, if we write
from jax import vmap
predictions = vmap(partial(predict, params))(input_batch)
# or, alternatively
predictions = vmap(predict, in_axes=(None, 0))(params, input_batch)then the vmap function will push the outer loop inside the function, and our
machine will end up executing matrix-matrix multiplications exactly as if we’d
done the batching by hand.
It’s easy enough to manually batch a simple neural network without vmap, but
in other cases manual vectorization can be impractical or impossible. Take the
problem of efficiently computing per-example gradients: that is, for a fixed set
of parameters, we want to compute the gradient of our loss function evaluated
separately at each example in a batch. With vmap, it’s easy:
per_example_gradients = vmap(partial(grad(loss), params))(inputs, targets)Of course, vmap can be arbitrarily composed with jit, grad, and any other
JAX transformation! We use vmap with both forward- and reverse-mode automatic
differentiation for fast Jacobian and Hessian matrix calculations in
jax.jacfwd, jax.jacrev, and jax.hessian.
For parallel programming of multiple accelerators, like multiple GPUs, use
pmap.
With pmap you write single-program multiple-data (SPMD) programs, including
fast parallel collective communication operations. Applying pmap will mean
that the function you write is compiled by XLA (similarly to jit), then
replicated and executed in parallel across devices.
Here's an example on an 8-GPU machine:
from jax import random, pmap
import jax.numpy as jnp
# Create 8 random 5000 x 6000 matrices, one per GPU
keys = random.split(random.key(0), 8)
mats = pmap(lambda key: random.normal(key, (5000, 6000)))(keys)
# Run a local matmul on each device in parallel (no data transfer)
result = pmap(lambda x: jnp.dot(x, x.T))(mats) # result.shape is (8, 5000, 5000)
# Compute the mean on each device in parallel and print the result
print(pmap(jnp.mean)(result))
# prints [1.1566595 1.1805978 ... 1.2321935 1.2015157]In addition to expressing pure maps, you can use fast collective communication operations between devices:
from functools import partial
from jax import lax
@partial(pmap, axis_name='i')
def normalize(x):
return x / lax.psum(x, 'i')
print(normalize(jnp.arange(4.)))
# prints [0. 0.16666667 0.33333334 0.5 ]You can even nest pmap functions for more
sophisticated communication patterns.
It all composes, so you're free to differentiate through parallel computations:
from jax import grad
@pmap
def f(x):
y = jnp.sin(x)
@pmap
def g(z):
return jnp.cos(z) * jnp.tan(y.sum()) * jnp.tanh(x).sum()
return grad(lambda w: jnp.sum(g(w)))(x)
print(f(x))
# [[ 0. , -0.7170853 ],
# [-3.1085174 , -0.4824318 ],
# [10.366636 , 13.135289 ],
# [ 0.22163185, -0.52112055]]
print(grad(lambda x: jnp.sum(f(x)))(x))
# [[ -3.2369726, -1.6356447],
# [ 4.7572474, 11.606951 ],
# [-98.524414 , 42.76499 ],
# [ -1.6007166, -1.2568436]]When reverse-mode differentiating a pmap function (e.g. with grad), the
backward pass of the computation is parallelized just like the forward pass.
See the SPMD Cookbook and the SPMD MNIST classifier from scratch example for more.
For a more thorough survey of current gotchas, with examples and explanations, we highly recommend reading the Gotchas Notebook. Some standouts:
- JAX transformations only work on pure functions, which don't have side-effects and respect referential transparency (i.e. object identity testing with
isisn't preserved). If you use a JAX transformation on an impure Python function, you might see an error likeException: Can't lift Traced...orException: Different traces at same level. - In-place mutating updates of
arrays, like
x[i] += y, aren't supported, but there are functional alternatives. Under ajit, those functional alternatives will reuse buffers in-place automatically. - Random numbers are different, but for good reasons.
- If you're looking for convolution
operators,
they're in the
jax.laxpackage. - JAX enforces single-precision (32-bit, e.g.
float32) values by default, and to enable double-precision (64-bit, e.g.float64) one needs to set thejax_enable_x64variable at startup (or set the environment variableJAX_ENABLE_X64=True). On TPU, JAX uses 32-bit values by default for everything except internal temporary variables in 'matmul-like' operations, such asjax.numpy.dotandlax.conv. Those ops have aprecisionparameter which can be used to approximate 32-bit operations via three bfloat16 passes, with a cost of possibly slower runtime. Non-matmul operations on TPU lower to implementations that often emphasize speed over accuracy, so in practice computations on TPU will be less precise than similar computations on other backends. - Some of NumPy's dtype promotion semantics involving a mix of Python scalars
and NumPy types aren't preserved, namely
np.add(1, np.array([2], np.float32)).dtypeisfloat64rather thanfloat32. - Some transformations, like
jit, constrain how you can use Python control flow. You'll always get loud errors if something goes wrong. You might have to usejit'sstatic_argnumsparameter, structured control flow primitives likelax.scan, or just usejiton smaller subfunctions.
| Linux x86_64 | Linux aarch64 | Mac x86_64 | Mac ARM | Windows x86_64 | Windows WSL2 x86_64 | |
|---|---|---|---|---|---|---|
| CPU | yes | yes | yes | yes | yes | yes |
| NVIDIA GPU | yes | yes | no | n/a | no | experimental |
| Google TPU | yes | n/a | n/a | n/a | n/a | n/a |
| AMD GPU | experimental | no | no | n/a | no | no |
| Apple GPU | n/a | no | experimental | experimental | n/a | n/a |
| Hardware | Instructions |
|---|---|
| CPU | pip install -U jax |
| NVIDIA GPU | pip install -U "jax[cuda12]" |
| Google TPU | pip install -U "jax[tpu]" -f https://storage.googleapis.com/jax-releases/libtpu_releases.html |
| AMD GPU | Use Docker or build from source. |
| Apple GPU | Follow Apple's instructions. |
See the documentation for information on alternative installation strategies. These include compiling from source, installing with Docker, using other versions of CUDA, a community-supported conda build, and answers to some frequently-asked questions.
Multiple Google research groups develop and share libraries for training neural networks in JAX. If you want a fully featured library for neural network training with examples and how-to guides, try Flax. Check out the new NNX API for a simplified development experience.
Google X maintains the neural network library Equinox. This is used as the foundation for several other libraries in the JAX ecosystem.
In addition, DeepMind has open-sourced an ecosystem of libraries around JAX including Optax for gradient processing and optimization, RLax for RL algorithms, and chex for reliable code and testing. (Watch the NeurIPS 2020 JAX Ecosystem at DeepMind talk here)
To cite this repository:
@software{jax2018github,
author = {James Bradbury and Roy Frostig and Peter Hawkins and Matthew James Johnson and Chris Leary and Dougal Maclaurin and George Necula and Adam Paszke and Jake Vander{P}las and Skye Wanderman-{M}ilne and Qiao Zhang},
title = {{JAX}: composable transformations of {P}ython+{N}um{P}y programs},
url = {http://github.com/google/jax},
version = {0.3.13},
year = {2018},
}
In the above bibtex entry, names are in alphabetical order, the version number is intended to be that from jax/version.py, and the year corresponds to the project's open-source release.
A nascent version of JAX, supporting only automatic differentiation and compilation to XLA, was described in a paper that appeared at SysML 2018. We're currently working on covering JAX's ideas and capabilities in a more comprehensive and up-to-date paper.
For details about the JAX API, see the reference documentation.
For getting started as a JAX developer, see the developer documentation.