`jax.eval_shape` modifies `nnx.Module` state when using `nnx.vmap` and `nnx.scan`

[ytsmiling]

jax.eval_shape unexpectedly modifies the internal state of an nnx.Module when nnx.vmap and nnx.scan are used in the model definition. Specifically, the type of the parameter values within the nnx.Module's state changes from jaxlib.xla_extension.ArrayImpl to jax._src.interpreters.partial_eval.DynamicJaxprTracer. This change occurs even though eval_shape is intended to be a side-effect-free function and only calculate shapes.

Code to reproduce:

Colab link

import jax
import jax.numpy as jnp
import nnx

class Model(nnx.Module):
  def __init__(self, rngs):
    self.stem = nnx.Linear(16, 32, rngs=rngs)
    @nnx.split_rngs(splits=3)
    @nnx.vmap(in_axes=(0,), out_axes=0)
    def create_block(rngs: nnx.Rngs):
      return nnx.Linear(32, 32, rngs=rngs)
    
    self.backbone = create_block(rngs)
    self.head = nnx.Linear(32, 10, rngs=rngs)

  def __call__(self, x):
    @nnx.scan(in_axes=(nnx.Carry, 0), out_axes=nnx.Carry)
    def forward(x: jax.Array, model: nnx.Module):
      return model(x)

    return self.head(forward(self.stem(x), self.backbone))

model = Model(nnx.Rngs(0))
print(jax.tree.map(lambda x: str(type(x)), nnx.split(model)[1]))  # Initial state
_ = jax.eval_shape(model, jax.ShapeDtypeStruct(shape=(8, 16), dtype=jnp.float32))
print(jax.tree.map(lambda x: str(type(x)), nnx.split(model)[1]))  # State after eval_shape

Output:

State({
  'backbone': {
    'bias': VariableState(
      type=Param,
      value="<class 'jaxlib.xla_extension.ArrayImpl'>"
    ),
    'kernel': VariableState(
      type=Param,
      value="<class 'jaxlib.xla_extension.ArrayImpl'>"
    )
  },
  'head': {
    'bias': VariableState(
      type=Param,
      value="<class 'jaxlib.xla_extension.ArrayImpl'>"
    ),
    'kernel': VariableState(
      type=Param,
      value="<class 'jaxlib.xla_extension.ArrayImpl'>"
    )
  },
  'stem': {
    'bias': VariableState(
      type=Param,
      value="<class 'jaxlib.xla_extension.ArrayImpl'>"
    ),
    'kernel': VariableState(
      type=Param,
      value="<class 'jaxlib.xla_extension.ArrayImpl'>"
    )
  }
})
State({
  'backbone': {
    'bias': VariableState(
      type=Param,
      value="<class 'jax._src.interpreters.partial_eval.DynamicJaxprTracer'>"
    ),
    'kernel': VariableState(
      type=Param,
      value="<class 'jax._src.interpreters.partial_eval.DynamicJaxprTracer'>"
    )
  },
  'head': {
    'bias': VariableState(
      type=Param,
      value="<class 'jaxlib.xla_extension.ArrayImpl'>"
    ),
    'kernel': VariableState(
      type=Param,
      value="<class 'jaxlib.xla_extension.ArrayImpl'>"
    )
  },
  'stem': {
    'bias': VariableState(
      type=Param,
      value="<class 'jaxlib.xla_extension.ArrayImpl'>"
    ),
    'kernel': VariableState(
      type=Param,
      value="<class 'jaxlib.xla_extension.ArrayImpl'>"
    )
  }
})

Expected behavior:

jax.eval_shape should not modify the internal state of the nnx.Module. The types of the parameter values should remain jaxlib.xla_extension.ArrayImpl after calling eval_shape.

Actual behavior:

The types of the parameter values within the nnx.Module's state are changed to jax._src.interpreters.partial_eval.DynamicJaxprTracer after calling eval_shape.

Environment:

• JAX version: 0.4.33
• Flax version: 0.10.2
• Python version: 3.11.11

[cgarciae]

Hey @ytsmiling, two things:

1. Use nnx.eval_shape instead.
2. Pass Modules as explicit inputs to transformed functions.

y_abstract = nnx.eval_shape(Model.__call__, model, jax.ShapeDtypeStruct(shape=(8, 16), dtype=jnp.float32))

[cgarciae]

To clarify, mutating Modules passed as a capture will be an error again in the future, it has been briefly made valid so the JAX team could perform a refactor:

flax/flax/nnx/tracers.py

Lines 41 to 43 in f71ce6c

	def is_valid(self) -> bool:
	# TODO: re-enable when we update nnx to use stackless trace context
	return True