jax  Composable transformations of Python+NumPy programs: differentiate, vectorize, JIT to GPU/TPU, and m  Machine Learning library
kandi XRAY  jax Summary
kandi XRAY  jax Summary
JAX is Autograd and XLA, brought together for highperformance machine learning research. 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 reversemode differentiation (a.k.a. backpropagation) via grad as well as forwardmode 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 justintime compiled and executed. But JAX also lets you justintime compile your own Python functions into XLAoptimized kernels using a onefunction 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 singleprogram multipledata (SPMD) parallel programming of multiple accelerators, with more to come.
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Top functions reviewed by kandi  BETA
 Apply a function to a function .
 Convert a function to a function .
 Apply a function to each axis .
 Wrapper around pjit .
 Compute an XLA computation .
 Gather the gather index .
 Helper function for matplotlib .
 Turn a jaxpr expression into a Function Dialect .
 Rewrite the expression .
 Applies a function to each axis .
jax Key Features
jax Examples and Code Snippets
y, f_lin = linearize(f, x)
y_dot = f_lin(x_dot)
y, y_dot = jvp(f, (x,), (x_dot,))
jvp : (a > b) > (UnrestrictedUse a, T a) o (UnrestrictedUse b, T b)
def split_half(lst: List[Any]) > Tuple[List[Any], List[Any]]:
assert not len(lst)
def jit(f):
def f_jitted(*args):
avals_in = [raise_to_shaped(get_aval(x)) for x in args]
jaxpr, consts, out_tree = make_jaxpr(f, *avals_in)
outs = bind(xla_call_p, *consts, *args, jaxpr=jaxpr, num_consts=len(consts))
return tree_unf
def split_list(lst: List[Any], n: int) > Tuple[List[Any], List[Any]]:
assert 0 <= n <= len(lst)
return lst[:n], lst[n:]
def partition_list(bs: List[bool], l: List[Any]) > Tuple[List[Any], List[Any]]:
assert len(bs) == len(l)
li
# Copyright 2019 The JAX Authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses
# Copyright 2018 The JAX Authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses
# Copyright 2018 The JAX Authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses
Community Discussions
Trending Discussions on jax
QUESTION
I am translating some of my R codes to Python as a learning process, especially trying JAX
for autodiff.
In functions to implement nonlinear least square, when I set tolerance at 1e8, the estimated parameters are nearly identical after several iterations, but the algorithm never appear to converge.
However, the R codes converge at the 12th inter at tol=1e8 and 14th inter at tol=1e9. The estimated parameters are almost the same as the ones resulted from Python implementation.
I think this has something to do with floating point, but not sure which step I could improve to make the converge as quickly as seen in R.
Here are my codes, and most steps are the same as in R
...ANSWER
Answered 2022Apr17 at 14:20One thing to be aware of is that by default, JAX performs computations in 32bit, while tools like R and numpy perform computations in 64bit. Since 1E8
is at the edge of 32bit floating point precision, I suspect this is why your program is failing to converge.
You can enable 64bit computation by putting this at the beginning of your script:
QUESTION
I'm having troubles getting bean validation to work with the following minimalised project consisting only of this three java files plus pom.xml. I'm using Apache TomEE 8.0.10.
LoginMessage.java
...ANSWER
Answered 2022Mar15 at 15:29This appears to be a bug in OpenWebBeans or TomEE. So what's happening is the first the actual instance of the bean is managed by JAXRS, and the second, the bean is managed by the CDI container. In the second case, there needs to be some sort of interceptor the invokes the Bean Validation framework.
I would start a discussion on the mailing list and open a bug on in the JIRA. If you can create a sample project that reproduces the problem it helps the devs out tremendously.
As a workaround, you can @Inject private Validator validator
and if there are any constraint violations returned, throw new ConstraintViolationException(constraintViolations);
.
QUESTION
I have a JAXRS application deployed in WildFly. The application's endpoints shall be protected by Keycloak with Access Type: beareronly
. This works perfectly fine for WildFly versions up to 24.
Starting from WildFly 25 the Keycloak adapter is deprecated and one should migrate to the new Elytron subsystem. According to this WildFly issue https://issues.redhat.com/browse/WFLY15485 however the OIDC adapter is not ready yet to work with beareronly
. But it is mentioned that it should still be possible using the Keycloak Wildfly adapter.
Also the latest Keycloak documentation and this thread in Google Groups states this.
So I installed the adapter from this location and ran the installation script:
./bin/jbosscli.sh file=bin/adapterelytroninstalloffline.cli Dserver.config=standalonefull.xml
When deploying the application I get thte following error message:
java.lang.IllegalStateException: The required mechanism 'KEYCLOAK' is not available in mechanisms [BASIC, CLIENT_CERT, DIGEST, FORM] from the HttpAuthenticationFactory
Setup
 WildFly 26 (Jakarta EE 8)
 Keycloak 16.1.1
web.xml
...ANSWER
Answered 2022Feb01 at 07:31I finally got it working without the Keycloak adapter, i.e. using the new builtin Elytron subsystem.
oidc.json (located in the WEBINF
directory)
QUESTION
I have two numpy arrays like :
...ANSWER
Answered 2022Mar07 at 16:57You can do a cumulated sum
QUESTION
There is a libary to convert Jax functions to Tensorflow functions. Is there a similar library to convert TensorFlow functions to Jax functions?
...ANSWER
Answered 2021Dec14 at 22:16No, there is no library supported by the JAX team to convert tensorflow into JAX in a manner similar to how jax.experimental.jax2tf
converts JAX code to tensorflow, and I have not seen any such library developed by others.
QUESTION
Hello I have a web application running on apachetomeeplus8.0.1. My problem is about getting an Environment variable from a runnable in a custom executor. The variable is defined in /conf/context.xml:
...ANSWER
Answered 2022Feb07 at 22:45JNDI lookups depend on some context information on the running thread, usually the context class loader.
On a Java EE/Jakarta EE server you should not spawn new (unmanaged) threads yourself, but use the ManagedExecutorService
provided by the container. This service automatically propagates some kinds of contexts from the calling thread:
The types of contexts to be propagated from a contextualizing application component include JNDI naming context, classloader, and security information. Containers must support propagation of these context types.
(Jakarta Concurrency Specification, emphasis mine)
You can inject a ManagedExecutorService
using a @Resource
annotation:
QUESTION
In JAX's Quickstart tutorial I found that the Hessian matrix can be computed efficiently for a differentiable function fun
using the following lines of code:
ANSWER
Answered 2022Jan04 at 14:16The answer to your question is within the JAX documentation; see for example this section: https://jax.readthedocs.io/en/latest/notebooks/autodiff_cookbook.html#jacobiansandhessiansusingjacfwdandjacrev
To quote its discussion of jacrev
and jacfwd
:
These two functions compute the same values (up to machine numerics), but differ in their implementation:
jacfwd
uses forwardmode automatic differentiation, which is more efficient for “tall” Jacobian matrices, whilejacrev
uses reversemode, which is more efficient for “wide” Jacobian matrices. For matrices that are nearsquare,jacfwd
probably has an edge overjacrev
.
and further down,
To implement hessian, we could have used
jacfwd(jacrev(f))
orjacrev(jacfwd(f))
or any other composition of the two. But forwardoverreverse is typically the most efficient. That’s because in the inner Jacobian computation we’re often differentiating a function wide Jacobian (maybe like a loss function 𝑓:ℝⁿ→ℝ), while in the outer Jacobian computation we’re differentiating a function with a square Jacobian (since ∇𝑓:ℝⁿ→ℝⁿ), which is where forwardmode wins out.
Since your function looks like 𝑓:ℝⁿ→ℝ, then jit(jacfwd(jacrev(fun)))
is likely the most efficient approach.
As for why you can't implement a hessian with grad
, this is because grad
is only designed for derivatives of functions with scalar outputs. A hessian by definition is a composition of vectorvalued jacobians, not a composition of scalar gradients.
QUESTION
I am having trouble using the Jacobian from JAX with scipy.root
. In the below example, the root
works without the Jacobian, while it fails with the Jacobian. Any ideas on what I need to rewrite in order to get the code below working with the Jacobian?
ANSWER
Answered 2021Dec19 at 14:01There are two issues:
 to perform automatic differentiation, JAX relies on replacing values with tracers. This means your approach of printing and evaluating the string representation of the value will not work.
 additionally, you are attempting to assign traced values to a standard numpy array. You should use a JAX array instead, as it knows how to handle traced values.
With this in mind, you can rewrite your function this way and it should work, so long as your equations only use Python arithmetic operations and jax functions (not things like np.exp
):
QUESTION
Here's my problem. I have two matrices A
and B
, with complex entries, of dimensions (n,n,m,m)
and (n,n)
respectively.
Below is the operation I perform to get a matrix C

ANSWER
Answered 2021Dec17 at 15:19It looks like you want einsum
:
QUESTION
I'm new to automatic differentiation programming, so this maybe a naive question. Below is a simplified version of what I'm trying to solve.
I have two input arrays  a vector A
of size N
and a matrix B
of shape (N, M)
, as well a parameter vector theta
of size M
. I define a new array C(theta) = B * theta
to get a new vector of size N
. I then obtain the indices of elements that fall in the upper and lower quartile of C
, and use them to create a new array A_low(theta) = A[lower quartile indices of C]
and A_high(theta) = A[upper quartile indices of C]
. Clearly these two do depend on theta
, but is it possible to differentiate A_low
and A_high
w.r.t theta
?
My attempts so far seem to suggest no  I have using the python libraries of autograd, JAX and tensorflow, but they all return a gradient of zero. (The approaches I have tried so far involve using argsort or extracting the relevant subarrays using tf.top_k
.)
What I'm seeking help with is either a proof that the derivative is not defined (or cannot be analytically computed) or if it does exist, a suggestion on how to estimate it. My eventual goal is to minimize some function f(A_low, A_high)
wrt theta
.
ANSWER
Answered 2021Dec03 at 16:44This is the JAX computation that I wrote based on your description:
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