rehydrax 0.1.0

Rehydrax: A utility library to rehydrate stablehlo into JAX.

Rehydrax

Rehydrate stablehlo into jax.

[!WARNING] This code is still experimental, all ops are not handled yet.

Install

Jax only

pip install rehydrax

with torch

pip install rehydrax[torch] torch torch-xla-cuda-plugin@https://storage.googleapis.com/pytorch-xla-releases/wheels/cuda/12.1/torch_xla_cuda_plugin-2.5.0-py3-none-any.whl

Examples

Loading pytorch model into jax

model = MyModel()
inputs_torch = (torch.randn((16,), dtype=torch.float32),)

# Convert out model to jax
model_state, model_f = rehydrax.rehydrate_torch_module(model, inputs_torch)
inputs_jax = (jax.numpy.array(inputs_torch[0].detach().numpy()),)

# Using our model as any other jax functions
model_grad = jax.grad(lambda state, x: jnp.sum(model_f(state, x)))
model_grad_jitted = jax.jit(model_grad)
grad = model_grad_jitted(model_state, *inputs_jax)

Loading model from other project

You may wan't to just try to use model from another project but don't wan't to depend on that project for any reason. You can do that by just exporting the model from project A and just rehydrate the model on project B.

Project A

@jax.jit
def init(rng):
    model = Model(rngs=nnx.Rngs(rng))
    return nnx.split(model)[1]

@jax.jit
def forward(state, x):
    model = Model(nnx.Rngs(0))
    model_graph = nnx.split(model)[0]
    model = nnx.merge(model_graph, state)
    return model(x)

key = jax.random.PRNGKey(0)

init_lowered = init.lower(key)
init_stablehlo = init_lowered.as_text()
state_abstract = jax.eval_shape(init, key)

sample = jax.random.uniform(key, (1, 2), jnp.float32)
forward_lowered = forward.lower(state_abstract, sample)
forward_stablehlo = forward_lowered.as_text()

Project B

init_rehydrated = rehydrax.rehydrate_stablehlo(init_stablehlo)
state = init_rehydrated(key)
forward_rehydrated = rehydrax.rehydrate_stablehlo(forward_stablehlo)
y2 = forward_rehydrated(*state, sample)