How does the jax2tf.convert function work in practice

Basic Usage

1. Function Conversion: `jax2tf.convert` takes a JAX function as input and returns a wrapped function that accepts TensorFlow tensors or variables. This allows the JAX function to be executed within a TensorFlow context, such as in eager or graph mode, or saved as a TensorFlow SavedModel.

2. Example:

python
   from jax.experimental import jax2tf
   from jax import numpy as jnp
   import numpy as np
   import tensorflow as tf

   def f_jax(x):
       return jnp.sin(jnp.cos(x))

   f_tf = jax2tf.convert(f_jax)
   # Execute f_tf with TensorFlow inputs
   f_tf(np.random.random((10,)))
   

3. Improving Execution: To improve execution speed or save the function as a SavedModel, you can wrap the converted function with `tf.function`. It is recommended to set `autograph=False` to avoid potential issues with Autograph, which may not work well with functions converted from JAX.

python
   f_tf_graph = tf.function(f_tf, autograph=False)
   

Advanced Features

- Native Serialization: As of version 0.4.14, `jax2tf` uses native serialization by default. This involves lowering the JAX function to StableHLO and then invoking it from TensorFlow. This approach offers several advantages, including better performance and compatibility.

- Shape Polymorphism: `jax2tf` supports shape-polymorphic conversion, which allows a single SavedModel to work with different input shapes. This is achieved by specifying `polymorphic_shapes` when calling `jax2tf.convert`. For example, to create a shape-polymorphic function that can handle batches of images of any size, you might use:

python
   f_tf = tf.function(jax2tf.convert(f_jax, polymorphic_shapes=["(b, 28, 28)"]), autograph=False)
   f_tf.get_concrete_function(tf.TensorSpec([None, 28, 28], tf.float32))
   

- Limitations: There are limitations, such as the inability to use `XlaSharding` outside of `tf.function` contexts or on CPUs and GPUs due to lack of end-to-end XLA SPMD support.

Practical Applications

- Model Saving: When saving models, parameters can be wrapped with `tf.Variable` to ensure they are saved separately from the computation graph, avoiding the 2GB limitation for SavedModels.

- Integration with TensorFlow Libraries: `jax2tf` enables the use of TensorFlow libraries within JAX programs by converting JAX functions to TensorFlow graphs, which can then be saved or loaded as SavedModels for further use in TensorFlow contexts.

Overall, `jax2tf.convert` provides a powerful bridge between JAX and TensorFlow, allowing for flexible model development and deployment across different frameworks.