graph#

flax.nnx.split(node, *filters)[source]#

Split a graph node into a GraphDef and one or more State`s. State is a ``Mapping` from strings or integers to Variables, Arrays or nested States. GraphDef contains all the static information needed to reconstruct a Module graph, it is analogous to JAX’s PyTreeDef. split() is used in conjunction with merge() to switch seamlessly between stateful and stateless representations of the graph.

Example usage:

>>> from flax.experimental import nnx
>>> import jax, jax.numpy as jnp
...
>>> class Foo(nnx.Module):
...   def __init__(self, rngs):
...     self.batch_norm = nnx.BatchNorm(2, rngs=rngs)
...     self.linear = nnx.Linear(2, 3, rngs=rngs)
...
>>> node = Foo(nnx.Rngs(0))
>>> graphdef, params, batch_stats = nnx.split(node, nnx.Param, nnx.BatchStat)
...
>>> jax.tree.map(jnp.shape, params)
State({
  'batch_norm': {
    'bias': VariableState(
      type=Param,
      value=(2,)
    ),
    'scale': VariableState(
      type=Param,
      value=(2,)
    )
  },
  'linear': {
    'bias': VariableState(
      type=Param,
      value=(3,)
    ),
    'kernel': VariableState(
      type=Param,
      value=(2, 3)
    )
  }
})
>>> jax.tree.map(jnp.shape, batch_stats)
State({
  'batch_norm': {
    'mean': VariableState(
      type=BatchStat,
      value=(2,)
    ),
    'var': VariableState(
      type=BatchStat,
      value=(2,)
    )
  }
})

split() and merge() are primarily used to interact directly with JAX transformations, see Functional API for more information.

Parameters
  • node – graph node to split.

  • *filters – some optional filters to group the state into mutually exclusive substates.

Returns

GraphDef and one or more States equal to the number of filters passed. If no filters are passed, a single State is returned.

flax.nnx.merge(graphdef, state, /, *states)[source]#

The inverse of split().

merge takes a GraphDef and one or more State’s and creates a new node with the same structure as the original node.

Example usage:

>>> from flax.experimental import nnx
>>> import jax, jax.numpy as jnp
...
>>> class Foo(nnx.Module):
...   def __init__(self, rngs):
...     self.batch_norm = nnx.BatchNorm(2, rngs=rngs)
...     self.linear = nnx.Linear(2, 3, rngs=rngs)
...
>>> node = Foo(nnx.Rngs(0))
>>> graphdef, params, batch_stats = nnx.split(node, nnx.Param, nnx.BatchStat)
...
>>> new_node = nnx.merge(graphdef, params, batch_stats)
>>> assert isinstance(new_node, Foo)
>>> assert isinstance(new_node.batch_norm, nnx.BatchNorm)
>>> assert isinstance(new_node.linear, nnx.Linear)

split() and merge() are primarily used to interact directly with JAX transformations, see Functional API for more information.

Parameters
  • graphdef – A GraphDef object.

  • state – A State object.

  • *states – Additional State objects.

Returns

The merged Module.

flax.nnx.update(node, state, /, *states)[source]#

Update the given graph node with a new State in-place.

Example usage:

>>> from flax import nnx
>>> import jax, jax.numpy as jnp

>>> x = jnp.ones((1, 2))
>>> y = jnp.ones((1, 3))
>>> model = nnx.Linear(2, 3, rngs=nnx.Rngs(0))

>>> def loss_fn(model, x, y):
...   return jnp.mean((y - model(x))**2)
>>> prev_loss = loss_fn(model, x, y)

>>> grads = nnx.grad(loss_fn)(model, x, y)
>>> new_state = jax.tree.map(lambda p, g: p - 0.1*g, nnx.state(model), grads)
>>> nnx.update(model, new_state)
>>> assert loss_fn(model, x, y) < prev_loss
Parameters
  • node – A graph node to update.

  • state – A State object.

  • *states – Additional State objects.

flax.nnx.pop(node, *filters)[source]#

Pop one or more Variable types from the graph node.

Example usage:

>>> from flax import nnx
>>> import jax.numpy as jnp

>>> class Model(nnx.Module):
...   def __init__(self, rngs):
...     self.linear1 = nnx.Linear(2, 3, rngs=rngs)
...     self.linear2 = nnx.Linear(3, 4, rngs=rngs)
...   def __call__(self, x):
...     x = self.linear1(x)
...     self.sow(nnx.Intermediate, 'i', x)
...     x = self.linear2(x)
...     return x

>>> x = jnp.ones((1, 2))
>>> model = Model(rngs=nnx.Rngs(0))
>>> assert not hasattr(model, 'i')
>>> y = model(x)
>>> assert hasattr(model, 'i')

>>> intermediates = nnx.pop(model, nnx.Intermediate)
>>> assert intermediates['i'].value[0].shape == (1, 3)
>>> assert not hasattr(model, 'i')
Parameters
  • node – A graph node object.

  • *filters – One or more Variable objects to filter by.

Returns

The popped State containing the Variable objects that were filtered for.

flax.nnx.state(node, *filters)[source]#

Similar to split() but only returns the State’s indicated by the filters.

Example usage:

>>> from flax import nnx

>>> class Model(nnx.Module):
...   def __init__(self, rngs):
...     self.batch_norm = nnx.BatchNorm(2, rngs=rngs)
...     self.linear = nnx.Linear(2, 3, rngs=rngs)
...   def __call__(self, x):
...     return self.linear(self.batch_norm(x))

>>> model = Model(rngs=nnx.Rngs(0))
>>> # get the learnable parameters from the batch norm and linear layer
>>> params = nnx.state(model, nnx.Param)
>>> # get the batch statistics from the batch norm layer
>>> batch_stats = nnx.state(model, nnx.BatchStat)
>>> # get them separately
>>> params, batch_stats = nnx.state(model, nnx.Param, nnx.BatchStat)
>>> # get them together
>>> state = nnx.state(model)
Parameters
  • node – A graph node object.

  • *filters – One or more Variable objects to filter by.

Returns

One or more State mappings.

flax.nnx.graphdef(node, /)[source]#

Get the GraphDef of the given graph node.

Example usage:

>>> from flax import nnx

>>> model = nnx.Linear(2, 3, rngs=nnx.Rngs(0))
>>> graphdef, _ = nnx.split(model)
>>> assert graphdef == nnx.graphdef(model)
Parameters

node – A graph node object.

Returns

The GraphDef of the Module object.

flax.nnx.iter_graph(node, /)[source]#

Iterates over all nested nodes and leaves of the given graph node, including the current node.

iter_graph creates a generator that yields path and value pairs, where the path is a tuple of strings or integers representing the path to the value from the root. Repeated nodes are visited only once. Leaves include static values.

Example::
>>> from flax import nnx
>>> import jax.numpy as jnp
...
>>> class Linear(nnx.Module):
...   def __init__(self, din, dout, *, rngs: nnx.Rngs):
...     self.din, self.dout = din, dout
...     self.w = nnx.Param(jax.random.uniform(rngs.next(), (din, dout)))
...     self.b = nnx.Param(jnp.zeros((dout,)))
...
>>> module = Linear(3, 4, rngs=nnx.Rngs(0))
>>> graph = [module, module]
...
>>> for path, value in nnx.iter_graph(graph):
...   print(path, type(value).__name__)
...
(0, 'b') Param
(0, 'din') int
(0, 'dout') int
(0, 'w') Param
(0,) Linear
() list
flax.nnx.clone(node)[source]#

Create a deep copy of the given graph node.

Example usage:

>>> from flax import nnx

>>> model = nnx.Linear(2, 3, rngs=nnx.Rngs(0))
>>> cloned_model = nnx.clone(model)
>>> model.bias.value += 1
>>> assert (model.bias.value != cloned_model.bias.value).all()
Parameters

node – A graph node object.

Returns

A deep copy of the Module object.

class flax.nnx.GraphDef(nodedef, index_mapping)[source]#

A dataclass that denotes the tree structure of a Module. A GraphDef can be generated by either calling split() or graphdef() on the Module.

class flax.nnx.UpdateContext(tag, refmap, idxmap)[source]#

A context manager for handling complex state updates.

merge(graphdef, state, *states)[source]#
split(node, *filters)[source]#

Split a graph node into a GraphDef and one or more State`s. State is a ``Mapping` from strings or integers to Variables, Arrays or nested States. GraphDef contains all the static information needed to reconstruct a Module graph, it is analogous to JAX’s PyTreeDef. split() is used in conjunction with merge() to switch seamlessly between stateful and stateless representations of the graph.

Example usage:

>>> from flax.experimental import nnx
>>> import jax, jax.numpy as jnp
...
>>> class Foo(nnx.Module):
...   def __init__(self, rngs):
...     self.batch_norm = nnx.BatchNorm(2, rngs=rngs)
...     self.linear = nnx.Linear(2, 3, rngs=rngs)
...
>>> node = Foo(nnx.Rngs(0))
>>> graphdef, params, batch_stats = nnx.split(node, nnx.Param, nnx.BatchStat)
...
>>> jax.tree.map(jnp.shape, params)
State({
  'batch_norm': {
    'bias': VariableState(
      type=Param,
      value=(2,)
    ),
    'scale': VariableState(
      type=Param,
      value=(2,)
    )
  },
  'linear': {
    'bias': VariableState(
      type=Param,
      value=(3,)
    ),
    'kernel': VariableState(
      type=Param,
      value=(2, 3)
    )
  }
})
>>> jax.tree.map(jnp.shape, batch_stats)
State({
  'batch_norm': {
    'mean': VariableState(
      type=BatchStat,
      value=(2,)
    ),
    'var': VariableState(
      type=BatchStat,
      value=(2,)
    )
  }
})
Parameters
  • node – graph node to split.

  • *filters – some optional filters to group the state into mutually exclusive substates.

Returns

GraphDef and one or more State’s equal to the number of filters passed. If no filters are passed, a single State is returned.

flax.nnx.update_context(tag)[source]#

Creates an UpdateContext context manager which can be used to handle more complex state updates beyond what nnx.update can handle, including updates to static properties and graph structure.

UpdateContext exposes a split and merge API with the same signature as nnx.split / nnx.merge but performs some bookkeeping to have the necessary information in order to perfectly update the input objects based on the changes made inside the transform. The UpdateContext must call split and merge a total of 4 times, the first and last calls happen outside the transform and the second and third calls happen inside the transform as shown in the diagram below:

                      idxmap
(2) merge ─────────────────────────────► split (3)
      β–²                                    β”‚
      β”‚               inside               β”‚
      β”‚. . . . . . . . . . . . . . . . . . β”‚ index_mapping
      β”‚               outside              β”‚
      β”‚                                    β–Ό
(1) split──────────────────────────────► merge (4)
                      refmap

The first call to split (1) creates a refmap which keeps track of the outer references, and the first call to merge (2) creates an idxmap which keeps track of the inner references. The second call to split (3) combines the refmap and idxmap to produce the index_mapping which indicates how the outer references map to the inner references. Finally, the last call to merge (4) uses the index_mapping and the refmap to reconstruct the output of the transform while reusing/updating the inner references. To avoid memory leaks, the idxmap is cleared after (3) and the refmap is cleared after (4), and both are cleared after the context manager exits.

Here is a simple example showing the use of update_context:

>>> from flax import nnx
...
>>> m1 = nnx.Dict({})
>>> with nnx.update_context('example') as ctx:
...   graphdef, state = ctx.split(m1)
...   @jax.jit
...   def f(graphdef, state):
...     m2 = ctx.merge(graphdef, state)
...     m2.a = 1
...     m2.ref = m2  # create a reference cycle
...     return ctx.split(m2)
...   graphdef_out, state_out = f(graphdef, state)
...   m3 = ctx.merge(graphdef_out, state_out)
...
>>> assert m1 is m3
>>> assert m1.a == 1
>>> assert m1.ref is m1

Note that update_context takes in a tag argument which is used primarily as a safety mechanism reduce the risk of accidentally using the wrong UpdateContext when using current_update_context() to access the current active context. current_update_context can be used as a way of accessing the current active context without having to pass it as a capture:

>>> from flax import nnx
...
>>> m1 = nnx.Dict({})
>>> @jax.jit
... def f(graphdef, state):
...   ctx = nnx.current_update_context('example')
...   m2 = ctx.merge(graphdef, state)
...   m2.a = 1     # insert static attribute
...   m2.ref = m2  # create a reference cycle
...   return ctx.split(m2)
...
>>> @nnx.update_context('example')
... def g(m1):
...   ctx = nnx.current_update_context('example')
...   graphdef, state = ctx.split(m1)
...   graphdef_out, state_out = f(graphdef, state)
...   return ctx.merge(graphdef_out, state_out)
...
>>> m3 = g(m1)
>>> assert m1 is m3
>>> assert m1.a == 1
>>> assert m1.ref is m1

As shown in the code above, update_context can also be used as a decorator that creates/activates an UpdateContext context for the duration of the function. The context can be accessed using current_update_context().

Parameters

tag – A string tag to identify the context.

flax.nnx.current_update_context(tag)[source]#

Returns the current active UpdateContext for the given tag.