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Numpyro | 1,716 | 4 | 27 | 3 days ago | 19 | July 04, 2022 | 55 | apache-2.0 | Python | |
Probabilistic programming with NumPy powered by JAX for autograd and JIT compilation to GPU/TPU/CPU. | ||||||||||
Transonic | 99 | 3 months ago | 6 | bsd-3-clause | Python | |||||
:rocket: Make your Python code fly at transonic speeds! | ||||||||||
Batch First | 26 | 3 years ago | gpl-3.0 | Python | ||||||
A JIT compiled chess engine which traverses the search tree in batches in a best-first manner, allowing for neural network batching, asynchronous GPU use, and vectorized CPU computations. | ||||||||||
Dali Cython | 9 | 7 years ago | 3 | mit | Jupyter Notebook | |||||
:lollipop: Dali in Python | ||||||||||
Chitrapy | 4 | 2 years ago | 6 | gpl-3.0 | Jupyter Notebook | |||||
Mini Image Processing Library in Python with JIT Compiler for Fast computation. | ||||||||||
Numbaflow | 4 | 4 years ago | mit | C | ||||||
A Numba language binding for TensorFlow, designed for inference. | ||||||||||
Wireless Access Point Optimization | 4 | a year ago | 9 | gpl-3.0 | Python | |||||
Python script implementation that suggests the allocation of wireless access points within a floor plan using GPU and Simulated Annealing features as the chosen meta-heuristic. | ||||||||||
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Best tips and tricks to make your Python code run immensely faster. | ||||||||||
Python Computing Acceleration | 2 | 7 years ago | Python | |||||||
Dynamic language and software development |
Probabilistic programming powered by JAX for autograd and JIT compilation to GPU/TPU/CPU.
NumPyro is a lightweight probabilistic programming library that provides a NumPy backend for Pyro. We rely on JAX for automatic differentiation and JIT compilation to GPU / CPU. NumPyro is under active development, so beware of brittleness, bugs, and changes to the API as the design evolves.
NumPyro is designed to be lightweight and focuses on providing a flexible substrate that users can build on:
sample
and param
. The model code should look very similar to Pyro except for some minor differences between PyTorch and Numpy's API. See the example below.jit
and grad
to compile the entire integration step into an XLA optimized kernel. We also eliminate Python overhead by JIT compiling the entire tree building stage in NUTS (this is possible using Iterative NUTS). There is also a basic Variational Inference implementation together with many flexible (auto)guides for Automatic Differentiation Variational Inference (ADVI). The variational inference implementation supports a number of features, including support for models with discrete latent variables (see TraceGraph_ELBO and TraceEnum_ELBO).torch.distributions
. In addition to distributions, constraints
and transforms
are very useful when operating on distribution classes with bounded support. Finally, distributions from TensorFlow Probability (TFP) can directly be used in NumPyro models.sample
and param
can be provided nonstandard interpretations using effect-handlers from the numpyro.handlers module, and these can be easily extended to implement custom inference algorithms and inference utilities.Let us explore NumPyro using a simple example. We will use the eight schools example from Gelman et al., Bayesian Data Analysis: Sec. 5.5, 2003, which studies the effect of coaching on SAT performance in eight schools.
The data is given by:
>>> import numpy as np
>>> J = 8
>>> y = np.array([28.0, 8.0, -3.0, 7.0, -1.0, 1.0, 18.0, 12.0])
>>> sigma = np.array([15.0, 10.0, 16.0, 11.0, 9.0, 11.0, 10.0, 18.0])
, where y
are the treatment effects and sigma
the standard error. We build a hierarchical model for the study where we assume that the group-level parameters theta
for each school are sampled from a Normal distribution with unknown mean mu
and standard deviation tau
, while the observed data are in turn generated from a Normal distribution with mean and standard deviation given by theta
(true effect) and sigma
, respectively. This allows us to estimate the population-level parameters mu
and tau
by pooling from all the observations, while still allowing for individual variation amongst the schools using the group-level theta
parameters.
>>> import numpyro
>>> import numpyro.distributions as dist
>>> # Eight Schools example
... def eight_schools(J, sigma, y=None):
... mu = numpyro.sample('mu', dist.Normal(0, 5))
... tau = numpyro.sample('tau', dist.HalfCauchy(5))
... with numpyro.plate('J', J):
... theta = numpyro.sample('theta', dist.Normal(mu, tau))
... numpyro.sample('obs', dist.Normal(theta, sigma), obs=y)
Let us infer the values of the unknown parameters in our model by running MCMC using the No-U-Turn Sampler (NUTS). Note the usage of the extra_fields
argument in MCMC.run. By default, we only collect samples from the target (posterior) distribution when we run inference using MCMC
. However, collecting additional fields like potential energy or the acceptance probability of a sample can be easily achieved by using the extra_fields
argument. For a list of possible fields that can be collected, see the HMCState object. In this example, we will additionally collect the potential_energy
for each sample.
>>> from jax import random
>>> from numpyro.infer import MCMC, NUTS
>>> nuts_kernel = NUTS(eight_schools)
>>> mcmc = MCMC(nuts_kernel, num_warmup=500, num_samples=1000)
>>> rng_key = random.PRNGKey(0)
>>> mcmc.run(rng_key, J, sigma, y=y, extra_fields=('potential_energy',))
We can print the summary of the MCMC run, and examine if we observed any divergences during inference. Additionally, since we collected the potential energy for each of the samples, we can easily compute the expected log joint density.
>>> mcmc.print_summary() # doctest: +SKIP
mean std median 5.0% 95.0% n_eff r_hat
mu 4.14 3.18 3.87 -0.76 9.50 115.42 1.01
tau 4.12 3.58 3.12 0.51 8.56 90.64 1.02
theta[0] 6.40 6.22 5.36 -2.54 15.27 176.75 1.00
theta[1] 4.96 5.04 4.49 -1.98 14.22 217.12 1.00
theta[2] 3.65 5.41 3.31 -3.47 13.77 247.64 1.00
theta[3] 4.47 5.29 4.00 -3.22 12.92 213.36 1.01
theta[4] 3.22 4.61 3.28 -3.72 10.93 242.14 1.01
theta[5] 3.89 4.99 3.71 -3.39 12.54 206.27 1.00
theta[6] 6.55 5.72 5.66 -1.43 15.78 124.57 1.00
theta[7] 4.81 5.95 4.19 -3.90 13.40 299.66 1.00
Number of divergences: 19
>>> pe = mcmc.get_extra_fields()['potential_energy']
>>> print('Expected log joint density: {:.2f}'.format(np.mean(-pe))) # doctest: +SKIP
Expected log joint density: -54.55
The values above 1 for the split Gelman Rubin diagnostic (r_hat
) indicates that the chain has not fully converged. The low value for the effective sample size (n_eff
), particularly for tau
, and the number of divergent transitions looks problematic. Fortunately, this is a common pathology that can be rectified by using a non-centered paramaterization for tau
in our model. This is straightforward to do in NumPyro by using a TransformedDistribution instance together with a reparameterization effect handler. Let us rewrite the same model but instead of sampling theta
from a Normal(mu, tau)
, we will instead sample it from a base Normal(0, 1)
distribution that is transformed using an AffineTransform. Note that by doing so, NumPyro runs HMC by generating samples theta_base
for the base Normal(0, 1)
distribution instead. We see that the resulting chain does not suffer from the same pathology — the Gelman Rubin diagnostic is 1 for all the parameters and the effective sample size looks quite good!
>>> from numpyro.infer.reparam import TransformReparam
>>> # Eight Schools example - Non-centered Reparametrization
... def eight_schools_noncentered(J, sigma, y=None):
... mu = numpyro.sample('mu', dist.Normal(0, 5))
... tau = numpyro.sample('tau', dist.HalfCauchy(5))
... with numpyro.plate('J', J):
... with numpyro.handlers.reparam(config={'theta': TransformReparam()}):
... theta = numpyro.sample(
... 'theta',
... dist.TransformedDistribution(dist.Normal(0., 1.),
... dist.transforms.AffineTransform(mu, tau)))
... numpyro.sample('obs', dist.Normal(theta, sigma), obs=y)
>>> nuts_kernel = NUTS(eight_schools_noncentered)
>>> mcmc = MCMC(nuts_kernel, num_warmup=500, num_samples=1000)
>>> rng_key = random.PRNGKey(0)
>>> mcmc.run(rng_key, J, sigma, y=y, extra_fields=('potential_energy',))
>>> mcmc.print_summary(exclude_deterministic=False) # doctest: +SKIP
mean std median 5.0% 95.0% n_eff r_hat
mu 4.08 3.51 4.14 -1.69 9.71 720.43 1.00
tau 3.96 3.31 3.09 0.01 8.34 488.63 1.00
theta[0] 6.48 5.72 6.08 -2.53 14.96 801.59 1.00
theta[1] 4.95 5.10 4.91 -3.70 12.82 1183.06 1.00
theta[2] 3.65 5.58 3.72 -5.71 12.13 581.31 1.00
theta[3] 4.56 5.04 4.32 -3.14 12.92 1282.60 1.00
theta[4] 3.41 4.79 3.47 -4.16 10.79 801.25 1.00
theta[5] 3.58 4.80 3.78 -3.95 11.55 1101.33 1.00
theta[6] 6.31 5.17 5.75 -2.93 13.87 1081.11 1.00
theta[7] 4.81 5.38 4.61 -3.29 14.05 954.14 1.00
theta_base[0] 0.41 0.95 0.40 -1.09 1.95 851.45 1.00
theta_base[1] 0.15 0.95 0.20 -1.42 1.66 1568.11 1.00
theta_base[2] -0.08 0.98 -0.10 -1.68 1.54 1037.16 1.00
theta_base[3] 0.06 0.89 0.05 -1.42 1.47 1745.02 1.00
theta_base[4] -0.14 0.94 -0.16 -1.65 1.45 719.85 1.00
theta_base[5] -0.10 0.96 -0.14 -1.57 1.51 1128.45 1.00
theta_base[6] 0.38 0.95 0.42 -1.32 1.82 1026.50 1.00
theta_base[7] 0.10 0.97 0.10 -1.51 1.65 1190.98 1.00
Number of divergences: 0
>>> pe = mcmc.get_extra_fields()['potential_energy']
>>> # Compare with the earlier value
>>> print('Expected log joint density: {:.2f}'.format(np.mean(-pe))) # doctest: +SKIP
Expected log joint density: -46.09
Note that for the class of distributions with loc,scale
parameters such as Normal
, Cauchy
, StudentT
, we also provide a LocScaleReparam reparameterizer to achieve the same purpose. The corresponding code will be
with numpyro.handlers.reparam(config={'theta': LocScaleReparam(centered=0)}):
theta = numpyro.sample('theta', dist.Normal(mu, tau))
Now, let us assume that we have a new school for which we have not observed any test scores, but we would like to generate predictions. NumPyro provides a Predictive class for such a purpose. Note that in the absence of any observed data, we simply use the population-level parameters to generate predictions. The Predictive
utility conditions the unobserved mu
and tau
sites to values drawn from the posterior distribution from our last MCMC run, and runs the model forward to generate predictions.
>>> from numpyro.infer import Predictive
>>> # New School
... def new_school():
... mu = numpyro.sample('mu', dist.Normal(0, 5))
... tau = numpyro.sample('tau', dist.HalfCauchy(5))
... return numpyro.sample('obs', dist.Normal(mu, tau))
>>> predictive = Predictive(new_school, mcmc.get_samples())
>>> samples_predictive = predictive(random.PRNGKey(1))
>>> print(np.mean(samples_predictive['obs'])) # doctest: +SKIP
3.9886456
For some more examples on specifying models and doing inference in NumPyro:
lax.scan
primitive for fast inference.Pyro users will note that the API for model specification and inference is largely the same as Pyro, including the distributions API, by design. However, there are some important core differences (reflected in the internals) that users should be aware of. e.g. in NumPyro, there is no global parameter store or random state, to make it possible for us to leverage JAX's JIT compilation. Also, users may need to write their models in a more functional style that works better with JAX. Refer to FAQs for a list of differences.
We provide an overview of most of the inference algorithms supported by NumPyro and offer some guidelines about which inference algorithms may be appropriate for different classes of models.
Like HMC/NUTS, all remaining MCMC algorithms support enumeration over discrete latent variables if possible (see restrictions). Enumerated sites need to be marked with infer={'enumerate': 'parallel'}
like in the annotation example.
Trace_ELBO
but computes part of the ELBO analytically if doing so is possible.See the docs for more details.
Limited Windows Support: Note that NumPyro is untested on Windows, and might require building jaxlib from source. See this JAX issue for more details. Alternatively, you can install Windows Subsystem for Linux and use NumPyro on it as on a Linux system. See also CUDA on Windows Subsystem for Linux and this forum post if you want to use GPUs on Windows.
To install NumPyro with the latest CPU version of JAX, you can use pip:
pip install numpyro
In case of compatibility issues arise during execution of the above command, you can instead force the installation of a known compatible CPU version of JAX with
pip install numpyro[cpu]
To use NumPyro on the GPU, you need to install CUDA first and then use the following pip command:
pip install numpyro[cuda] -f https://storage.googleapis.com/jax-releases/jax_cuda_releases.html
If you need further guidance, please have a look at the JAX GPU installation instructions.
To run NumPyro on Cloud TPUs, you can look at some JAX on Cloud TPU examples.
For Cloud TPU VM, you need to setup the TPU backend as detailed in the Cloud TPU VM JAX Quickstart Guide.
After you have verified that the TPU backend is properly set up,
you can install NumPyro using the pip install numpyro
command.
Default Platform: JAX will use GPU by default if CUDA-supported
jaxlib
package is installed. You can use set_platform utilitynumpyro.set_platform("cpu")
to switch to CPU at the beginning of your program.
You can also install NumPyro from source:
git clone https://github.com/pyro-ppl/numpyro.git
cd numpyro
# install jax/jaxlib first for CUDA support
pip install -e .[dev] # contains additional dependencies for NumPyro development
You can also install NumPyro with conda:
conda install -c conda-forge numpyro
Unlike in Pyro, numpyro.sample('x', dist.Normal(0, 1))
does not work. Why?
You are most likely using a numpyro.sample
statement outside an inference context. JAX does not have a global random state, and as such, distribution samplers need an explicit random number generator key (PRNGKey) to generate samples from. NumPyro's inference algorithms use the seed handler to thread in a random number generator key, behind the scenes.
Your options are:
Call the distribution directly and provide a PRNGKey
, e.g. dist.Normal(0, 1).sample(PRNGKey(0))
Provide the rng_key
argument to numpyro.sample
. e.g. numpyro.sample('x', dist.Normal(0, 1), rng_key=PRNGKey(0))
.
Wrap the code in a seed
handler, used either as a context manager or as a function that wraps over the original callable. e.g.
with handlers.seed(rng_seed=0): # random.PRNGKey(0) is used
x = numpyro.sample('x', dist.Beta(1, 1)) # uses a PRNGKey split from random.PRNGKey(0)
y = numpyro.sample('y', dist.Bernoulli(x)) # uses different PRNGKey split from the last one
, or as a higher order function:
def fn():
x = numpyro.sample('x', dist.Beta(1, 1))
y = numpyro.sample('y', dist.Bernoulli(x))
return y
print(handlers.seed(fn, rng_seed=0)())
Can I use the same Pyro model for doing inference in NumPyro?
As you may have noticed from the examples, NumPyro supports all Pyro primitives like sample
, param
, plate
and module
, and effect handlers. Additionally, we have ensured that the distributions API is based on torch.distributions
, and the inference classes like SVI
and MCMC
have the same interface. This along with the similarity in the API for NumPy and PyTorch operations ensures that models containing Pyro primitive statements can be used with either backend with some minor changes. Example of some differences along with the changes needed, are noted below:
torch
operation in your model will need to be written in terms of the corresponding jax.numpy
operation. Additionally, not all torch
operations have a numpy
counterpart (and vice-versa), and sometimes there are minor differences in the API.pyro.sample
statements outside an inference context will need to be wrapped in a seed
handler, as mentioned above.numpyro.param
outside an inference context will have no effect. To retrieve the optimized parameter values from SVI, use the SVI.get_params method. Note that you can still use param
statements inside a model and NumPyro will use the substitute effect handler internally to substitute values from the optimizer when running the model in SVI.For most small models, changes required to run inference in NumPyro should be minor. Additionally, we are working on pyro-api which allows you to write the same code and dispatch it to multiple backends, including NumPyro. This will necessarily be more restrictive, but has the advantage of being backend agnostic. See the documentation for an example, and let us know your feedback.
How can I contribute to the project?
Thanks for your interest in the project! You can take a look at beginner friendly issues that are marked with the good first issue tag on Github. Also, please feel to reach out to us on the forum.
In the near term, we plan to work on the following. Please open new issues for feature requests and enhancements:
The motivating ideas behind NumPyro and a description of Iterative NUTS can be found in this paper that appeared in NeurIPS 2019 Program Transformations for Machine Learning Workshop.
If you use NumPyro, please consider citing:
@article{phan2019composable,
title={Composable Effects for Flexible and Accelerated Probabilistic Programming in NumPyro},
author={Phan, Du and Pradhan, Neeraj and Jankowiak, Martin},
journal={arXiv preprint arXiv:1912.11554},
year={2019}
}
as well as
@article{bingham2019pyro,
author = {Eli Bingham and
Jonathan P. Chen and
Martin Jankowiak and
Fritz Obermeyer and
Neeraj Pradhan and
Theofanis Karaletsos and
Rohit Singh and
Paul A. Szerlip and
Paul Horsfall and
Noah D. Goodman},
title = {Pyro: Deep Universal Probabilistic Programming},
journal = {J. Mach. Learn. Res.},
volume = {20},
pages = {28:1--28:6},
year = {2019},
url = {http://jmlr.org/papers/v20/18-403.html}
}