Google JAX (or Just-In-Time Accelerator for XLA) is an open source framework developed by Google to optimize the performance of Python programs for machine learning, neural networks, scientific computing and other computationally intensive workflows.
Created in 2018, JAX leverages techniques like just-in-time compilation, automatic differentiation and vectorization to accelerate numerical computations often written in Python/NumPy. The key goal is to speed up and scale deep learning, ML training and research workloads leveraging hardware accelerators like GPUs and TPUs.
Some of the standout capabilities provided by JAX include:
- Auto Differentiation – Efficiently compute gradients and high-order derivatives.
- JIT Compilation – Lazily compile and optimize Python functions.
- Vectorization – Auto-vectorize computations over arrays.
- Parallelization – Scaling code across multiple accelerators.
While JAX is still relatively new, it enables impressive performance gains through these composable transformations and integrations with Google‘s XLA compiler. Let‘s explore the technical details behind each one.
Key Features and Benefits of JAX
JAX introduces domain-specific language features to augment regular Python/NumPy code with additional semantics that enable powerful optimizations. These automatic transformations work by adding information to source code, allowing the JAX compiler to generate more efficient native instructions for hardware accelerators.
Some of the major techniques used by JAX include:
Auto-Differentiation with grad
Auto-differentiation allows deriving analytical gradients and higher-order derivatives for numeric functions written in Python. The grad module lets users take gradients without needing to write derivative computations manually.
For example, take a function:
def f(x):
return x**2 + 3*x + 1The gradient can be computed easily with:
from jax import grad
df_dx = grad(f)
print(df_dx(5)) # 16Internally, JAX implements reverse-mode auto-diff using operator overloading and is able to optimize gradient computations using the power of just-in-time compilation discussed next. This makes it very efficient compared to symbolic differentiation.
Benchmarking on more complex functions shows significant performance gains from auto-differentiation compared to numerical approximations using finite differences:
Just-In-Time Compilation with jit
JIT or just-in-time compilation is the process of lazily compiling functions to efficient native code at runtime right before execution, rather than interpreting code like traditional Python.
This helps performance in two ways:
- The compilation step applies optimizations like fused kernels
- It avoids paying interpreter overhead every invocation
Using jit is simple:
from jax import jit
@jit
def f(x):
return x + 1
print(f(1)) # 2 (fast the first time)
print(f(2)) # 3 (very fast subsequently) Below benchmarks show >100x faster invocation after the first time:
Auto-Vectorization with vmap
Vectorization is the process of turning operations on scalars into operations on vectors for performance. The vmap function automatically lifts scalar operations over arrays into fast vectorized code.
For example, to compute square of an array:
from jax import vmap
import jax.numpy as jnp
x = jnp.arange(10)
# Vectorized
vmap(lambda x: x**2)(x)
# [0, 1, 4, 9, 16, 25, 36, 49, 64, 81]Rather than writing explicit loops, vmap parallelizes the computation across all elements. This provides large speedups as computation gets pushed down to low-level vector instructions.
On real-world machine learning training, auto-vectorization speeds up major bottlenecks like loss function and gradient computations. It also composes nicely with auto-differentiation.
Auto-Parallelization with pmap
In addition to vectorization across data samples, JAX can also partition computations across multiple devices like GPUs and TPUs.
The pmap API enables single-program multiple-data (SPMD) style parallelism:
from jax import pmap
@pmap
def parallel_add(x):
return x + 1
x = np.array([1, 2]) # Sharded across devices
parallel_add(x) # [2, 3] computed in parallel On each device, pmap executes the function on a shard of the input data. Results from each device are aggregated at the end. This provides easy scale-out to multiple accelerators with minimal code changes.
In typicalusage, pmap is combined with vmap and jit to fully leverage all levels of parallelism offered by hardware like TPU pods.
Additional JAX Features
Beyond these core transformations, JAX provides other helpful features for accelerated computing:
Efficient NumPy Integration – The jax.numpy module (often aliased as jnp) provides compute-optimized drop-in replacements for NumPy array operations. Any libraries using NumPy can get speedups from switching over.
XLA Integration – JAX taps into XLA for additional memory and cluster optimizations when targeting accelerators like TPUs. XLA handles device-specific compilation for peak hardware efficiency.
Pythonic Function Transformations – By building on Python instead of forcing graph frameworks, JAX transformations fit cleanly into existing code. This avoids a separate model definition language.
First-Class Differentiation Support – In addition to grad, JAX provides value_and_grad, jacfwd, jacrev and other differentiation helpers as usable Python functions instead of requiring special tapes.
Custom Differentiation Rules – For numeric primitives not supported out-of-the-box, JAX allows registering custom vjp (vector-Jacobian product) functions for differentiation via the custom_vjp decorator.
Staging Optimizations – Applying transformations creates "staged" programs, allowing analysis and optimizations before runtime code generation.
Installing JAX and Environment Setup
JAX runs wherever there is a Python environment and compiler toolchain, including Linux, MacOS and Cloud VMs.
For local use, make sure Python 3.7+ is installed then:
# CPU-only install
pip install jax jaxlib --upgrade
# GPU/TPU - follow https://github.com/google/jax#installationFor convenience, importing JAX modules is often done as:
import jax
import jax.numpy as jnp
from typing import Any, Callable, Sequence, Generic, TypeVar
from jax import grad, jit, vmapThis sets up the namespaces without NumPy conflicts.
When running on hardware accelerators, additional runtime dependencies are needed like CUDA, XLA packages etc. Follow the JAX GitHub README for full details on setting up GPUs and TPUs.
Prebuilt containers like the JAX Docker image are also available as an alternate option.
Real-World Usage Examples
Let‘s go through some end-to-end examples of applying JAX to speed up common numerical computing workloads:
Machine Learning Training
Here is a simple neural network model definition and training loop implemented with JAX:
from jax import grad, jit, vmap
import jax.numpy as jnp
# Neural Network Model
def predict(weights, inputs):
return jnp.tanh(jnp.dot(inputs, weights))
def loss(weights, inputs, targets):
predictions = predict(weights, inputs)
return jnp.mean((predictions - targets)**2)
# Training Loop w/ Composable Transformations
@jit
def step(weights, batch_inputs, batch_targets):
gw, loss = grad(loss)(weights, batch_inputs, batch_targets)
return loss, weights - 0.1 * gw
@jit
@vmap
def train_epoch(weights, all_inputs, all_targets):
losses = []
for i in range(10):
loss, weights = step(weights, all_inputs[i], all_targets[i])
losses.append(loss)
return weights, lossesHere @vmap and @jit accelerate the per-sample gradient computation and training step execution respectively. By fusing together just-in-time compilation, auto-differentiation and auto-vectorization, JAX is able to optimize the full machine learning pipeline for much faster training iterations.
This combination of transformation semantics is very performant for modern ML algorithms.
Physics Simulations
For scientific workloads, the differentiable programming model of JAX excels at building differentiable simulators. By transforming the dynamics equations into a neural network, it allows high-performance gradient-based analysis.
As an example, here is an n-body gravitational physics simulator that optimizes trajectories by taking gradients through the entire simulation:
from jax import grad, jit, vmap
import jax.numpy as jnp
@jit
def body_effects(positions):
# Compute gravitational forces
return accelerations
@jit
def move(positions, accelerations):
return positions + dt * accelerations
@jit
def simulate(initial_state):
pos = initial_state
for _ in range(num_steps):
acc = body_effects(pos)
pos = move(pos, acc)
return final_positions
# Get derivatives through simulation
d_final_d_init = grad(simulate)(initial_state)Here running the dynamics equations efficiently on accelerators allows the simulation state to be defined within a neural network graph for easy optimization. There are many such applications of differentiable programming.
Overall as shown via these examples, JAX makes it easy to accelerate numerical Python code with minimal changes while unlocking state-of-the-art hardware performance.
Additional Resources for Learning JAX
For those looking to pick up JAX, here are some useful materials:
- Official JAX Documentation – Very complete set of usage guides and API references.
- JAX GitHub Repo – Example notebooks, performance benchmarks and release notes.
- Introduction to JAX Video Series – Video tutorials covering the basics.
- Deep Learning with JAX Book – In-depth practitioner focused book from JAX team.
- Stanford CS330 Lectures – Several lectures discuss JAX usage in ML systems.
In the machine learning applied research community, JAX has very quickly become one of the most popular frameworks for model experimentation due to its composability and flexibility. The aml3 conference highlights many of these cutting-edge applications.
Overall there is no shortage of informative references to leverage as you explore building high performance pipelines with JAX!
Conclusion
Google JAX provides a unique set of optimizations like auto-differentiation, just-in-time compilation and vectorization to accelerate Python code when running on hardware accelerators.
By staying in the high-productivity NumPy/Python environment without forcing graph abstractions, JAX makes it simpler to boost performance of existing code. Transformations compose cleanly allowing program analysis and code generation optimized across compiler toolchains, in-memory buffers, parallel execution primitives, and hardware accelerators.
While the project is still relatively new (as of 2023), JAX has already seen impressive adoption in the ML community for tasks like neural network training, experimental research and differentiable programming applications. The development velocity has also increased significantly.
As support for more hardware backends, integration with additional compiler toolkits like TVM, and optimizations across longer pipeline fragments progress further, expect JAX to cement itself as one of the most popular high-performance computing frameworks for machine learning and computational science going forward!
