[nnx] simpllify unflatten

benchmarks/nnx_graph_overhead.py

@@ -30,25 +30,44 @@
 flags.DEFINE_integer('depth', 5, 'Depth of the model')
 
 
-
 class Linear(nnx.Module):
   def __init__(self, din: int, dout: int, *, rngs: nnx.Rngs):
-    self.list = [
-      nnx.Param(jax.random.uniform(rngs.params(), (din, dout))),
-      nnx.Param(jnp.zeros((dout,))),
-    ]
-    self.dict = {
-      'w': nnx.Param(jax.random.uniform(rngs.params(), (din, dout))),
-      'b': nnx.Param(jnp.zeros((dout,))),
-    }
+    self.w = nnx.Param(jax.random.uniform(rngs.params(), (din, dout)))
+    self.b = nnx.Param(jnp.zeros((dout,)))
+
+  def __call__(self, x):
+    return x @ self.w + self.b
+
+
+class Block(nnx.Module):
+  def __init__(self, din: int, dout: int, *, rngs: nnx.Rngs):
+    self.linear = Linear(din, dout, rngs=rngs)
+    self.bn = nnx.BatchNorm(dout, rngs=rngs)
+
+  def __call__(self, x):
+    return nnx.relu(self.bn(self.linear(x)))
 
 
+class Count(nnx.Variable):
+  pass
+
 
 class MLP(nnx.Module):
-  def __init__(self, depth, *, rngs: nnx.Rngs):
+  def __init__(self, din, dhidden, dout, depth, *, rngs: nnx.Rngs):
+    self.count = Count(jnp.array(0))
+    self.linear_in = Block(din, dhidden, rngs=rngs)
     self.intermediates = [
-      Linear(10, 10, rngs=rngs) for _ in range(depth)
+      Block(dhidden, dhidden, rngs=rngs) for _ in range(depth - 2)
     ]
+    self.linear_out = Block(dhidden, dout, rngs=rngs)
+
+  def __call__(self, x):
+    self.count.value += 1
+    x = nnx.relu(self.linear_in(x))
+    for layer in self.intermediates:
+      x = nnx.relu(layer(x))
+    x = self.linear_out(x)
+    return x
 
 
 def main(argv):
@@ -63,14 +82,15 @@ def main(argv):
   X = np.linspace(0, 1, 100)[:, None]
   Y = 0.8 * X**2 + 0.1 + np.random.normal(0, 0.1, size=X.shape)
 
-  model = MLP(depth=depth, rngs=nnx.Rngs(0))
-  tx = optax.sgd(1e-3)
-  optimizer = nnx.Optimizer(model, tx)
-
   #------------------------------------------------------------
   # NNX
   #------------------------------------------------------------
   if mode in ['all', 'nnx']:
+    model = MLP(din=1, dhidden=width, dout=1, depth=depth, rngs=nnx.Rngs(0))
+    tx = optax.sgd(1e-3)
+    optimizer = nnx.Optimizer(model, tx)
+    t0 = time()
+
     @nnx.jit
     def step_nnx(model: MLP, optimizer: nnx.Optimizer):
       pass
@@ -93,6 +113,11 @@ def step_nnx(model: MLP, optimizer: nnx.Optimizer):
   #------------------------------------------------------------
 
   if mode in ['all', 'jax']:
+    model = MLP(din=1, dhidden=width, dout=1, depth=depth, rngs=nnx.Rngs(0))
+    tx = optax.sgd(1e-3)
+    optimizer = nnx.Optimizer(model, tx)
+    t0 = time()
+
     @jax.jit
     def step_jax(graphdef, state):
       return graphdef, state

flax/nnx/bridge/variables.py

@@ -18,10 +18,9 @@
 import jax
 from flax import struct
 from flax.core import meta
-from flax.nnx import spmd
+from flax.nnx import graph, spmd
 from flax.nnx import traversals
 from flax.nnx import variablelib as variableslib
-from flax.nnx.module import GraphDef
 import typing as tp
 
 
@@ -192,9 +191,11 @@ def linen_vars_to_nnx_attrs(variables: tp.Mapping[str, Any]) -> dict[str, Any]:
 def nnx_attrs_to_linen_vars(nnx_attrs: dict) -> dict:
   linen_structured = {}
   for kp, v in traversals.flatten_mapping(
-      nnx_attrs,
-      is_leaf=lambda _, x: isinstance(x, variableslib.Variable | GraphDef),
-      ).items():
+    nnx_attrs,
+    is_leaf=lambda _, x: isinstance(
+      x, variableslib.Variable | graph.NodeDef | graph.NodeRef
+    ),
+  ).items():
     if isinstance(v, variableslib.Variable):
       col_name = variable_type_name(type(v))
     else:

flax/nnx/extract.py

@@ -251,10 +251,13 @@ class GraphDefState(struct.PyTreeNode):
   graphdef: graph.GraphDef[tp.Any] = struct.field(pytree_node=False)
   state: graph.GraphState = struct.field(pytree_node=True)
 
+S = tp.TypeVar(
+  'S', bound=graph.GraphState | graph.GraphFlatState | list[tp.Any]
+)
 
-class NodeStates(struct.PyTreeNode):
+class NodeStates(struct.PyTreeNode, tp.Generic[S]):
   _graphdef: graph.GraphDef[tp.Any] | None
-  states: tuple[graph.GraphState | graph.GraphFlatState, ...]
+  states: tuple[S, ...]
   metadata: tp.Any = struct.field(pytree_node=False)
 
   @property
@@ -264,7 +267,7 @@ def graphdef(self) -> graph.GraphDef[tp.Any]:
     return self._graphdef
 
   @property
-  def state(self) -> graph.GraphState | graph.GraphFlatState:
+  def state(self) -> S:
     if len(self.states) != 1:
       raise ValueError(
         f'Expected exactly one GraphDefState, got {len(self.states)}'
@@ -275,18 +278,18 @@ def state(self) -> graph.GraphState | graph.GraphFlatState:
   def from_split(
     cls,
     graphdef: graph.GraphDef[tp.Any],
-    state: graph.GraphState | graph.GraphFlatState,
+    state: S,
     /,
-    *states: graph.GraphState | graph.GraphFlatState,
+    *states: S,
     metadata: tp.Any = None,
   ):
     return cls(_graphdef=graphdef, states=(state, *states), metadata=metadata)
 
   @classmethod
   def from_states(
     cls,
-    state: graph.GraphState | graph.GraphFlatState,
-    *states: graph.GraphState | graph.GraphFlatState,
+    state: S,
+    *states: S,
   ):
     return cls(_graphdef=None, states=(state, *states), metadata=None)
 
@@ -319,6 +322,15 @@ def to_tree(
   ctxtag: str | None = None,
   check_aliasing: bool = True,
 ) -> tp.Any:
+  if prefix is Missing or prefix is None:
+    # fast path, no need for prefix broadcasting or consistent aliasing checks
+    with graph.split_context(ctxtag) as split_ctx:
+      return jax.tree.map(
+        lambda x: split_fn(split_ctx, (), prefix, x)
+        if map_non_graph_nodes or graph.is_graph_node(x)
+        else x,
+        tree,
+      )
   leaf_prefixes = broadcast_prefix(
     prefix,
     tree,
@@ -373,6 +385,16 @@ def from_tree(
   map_non_graph_nodes: bool = False,
   ctxtag: str | None = None,
 ) -> tp.Any:
+  if prefix is Missing or prefix is None:
+    # fast path, no need for prefix broadcasting or consistent aliasing checks
+    with graph.merge_context(ctxtag) as merge_ctx:
+      return jax.tree.map(
+        lambda x: merge_fn(merge_ctx, (), prefix, x)
+        if map_non_graph_nodes or is_node_leaf(x)
+        else x,
+        tree,
+        is_leaf=is_leaf,
+      )
   leaf_prefixes = broadcast_prefix(
     prefix,
     tree,
@@ -387,13 +409,9 @@ def from_tree(
 
   with graph.merge_context(ctxtag) as merge_ctx:
     for (keypath, leaf), leaf_prefix in zip(leaf_keys, leaf_prefixes):
-      if is_node_leaf(leaf):
-        leaf_out = merge_fn(merge_ctx, keypath, leaf_prefix, leaf)
-        leaves_out.append(leaf_out)
-      else:
-        if map_non_graph_nodes:
-          leaf = merge_fn(merge_ctx, keypath, leaf_prefix, leaf)
-        leaves_out.append(leaf)
+      if map_non_graph_nodes or is_node_leaf(leaf):
+        leaf = merge_fn(merge_ctx, keypath, leaf_prefix, leaf)
+      leaves_out.append(leaf)
 
   pytree_out = jax.tree.unflatten(treedef, leaves_out)
   return pytree_out