[nnx] add tabulate

docs_nnx/api_reference/flax.nnx/summary.rst

@@ -0,0 +1,7 @@
+summary
+------------------------
+
+.. automodule:: flax.nnx
+.. currentmodule:: flax.nnx
+
+.. autofunction:: tabulate

flax/nnx/__init__.py

@@ -167,4 +167,5 @@
 from .extract import to_tree as to_tree
 from .extract import from_tree as from_tree
 from .extract import NodeStates as NodeStates
+from .summary import tabulate as tabulate
 from . import traversals as traversals

flax/nnx/extract.py

@@ -144,7 +144,7 @@ def check_consistent_aliasing(
   for path, value in graph.iter_graph(node):
     if graph.is_graph_node(value) or isinstance(value, graph.Variable):
       if isinstance(value, Object):
-        value.check_valid_context(
+        value._check_valid_context(
           lambda: f'Trying to extract graph node from different trace level, got {value!r}'
         )
       if isinstance(value, graph.Variable):

flax/nnx/object.py

@@ -34,7 +34,7 @@
   visualization,
 )
 from flax.nnx.variablelib import Variable, VariableState
-from flax.typing import SizeBytes, value_stats
+from flax.typing import SizeBytes
 
 G = tp.TypeVar('G', bound='Object')
 
@@ -55,7 +55,7 @@ def _collect_stats(
     var_type = type(node)
     if issubclass(var_type, nnx.RngState):
       var_type = nnx.RngState
-    size_bytes = value_stats(node.value)
+    size_bytes = SizeBytes.from_any(node.value)
     if size_bytes:
       stats[var_type] = size_bytes
 
@@ -134,6 +134,10 @@ class Array(reprlib.Representable):
   shape: tp.Tuple[int, ...]
   dtype: tp.Any
 
+  @staticmethod
+  def from_array(array: jax.Array | np.ndarray) -> Array:
+    return Array(array.shape, array.dtype)
+
   def __nnx_repr__(self):
     yield reprlib.Object(type='Array', same_line=True)
     yield reprlib.Attr('shape', self.shape)
@@ -165,12 +169,12 @@ def __setattr__(self, name: str, value: Any) -> None:
       self._setattr(name, value)
 
   def _setattr(self, name: str, value: tp.Any) -> None:
-    self.check_valid_context(
+    self._check_valid_context(
       lambda: f"Cannot mutate '{type(self).__name__}' from different trace level"
     )
     object.__setattr__(self, name, value)
 
-  def check_valid_context(self, error_msg: tp.Callable[[], str]) -> None:
+  def _check_valid_context(self, error_msg: tp.Callable[[], str]) -> None:
     if not self._object__state.trace_state.is_valid():
       raise errors.TraceContextError(error_msg())
 

flax/nnx/rnglib.py

@@ -64,7 +64,7 @@ def __post_init__(self):
       raise TypeError(f'key must be a jax.Array, got {type(self.key)}')
 
   def __call__(self) -> jax.Array:
-    self.check_valid_context(
+    self._check_valid_context(
       lambda: 'Cannot call RngStream from a different trace level'
     )
     key = jax.random.fold_in(self.key.value, self.count.value)

flax/nnx/variablelib.py

@@ -25,11 +25,7 @@
 
 from flax import errors
 from flax.nnx import filterlib, reprlib, tracers, visualization
-from flax.typing import (
-  Missing,
-  PathParts,
-  value_stats,
-)
+from flax.typing import Missing, PathParts, SizeBytes
 import jax.tree_util as jtu
 
 A = tp.TypeVar('A')
@@ -317,7 +313,7 @@ def to_state(self: Variable[A]) -> VariableState[A]:
     return VariableState(type(self), self.raw_value, **self._var_metadata)
 
   def __nnx_repr__(self):
-    stats = value_stats(self.value)
+    stats = SizeBytes.from_any(self.value)
     if stats:
       comment = f' # {stats}'
     else:
@@ -329,7 +325,7 @@ def __nnx_repr__(self):
       yield reprlib.Attr(name, repr(value))
 
   def __treescope_repr__(self, path, subtree_renderer):
-    size_bytes = value_stats(self.value)
+    size_bytes = SizeBytes.from_any(self.value)
     if size_bytes:
       stats_repr = f' # {size_bytes}'
       first_line_annotation = treescope.rendering_parts.comment_color(
@@ -784,7 +780,7 @@ def __delattr__(self, name: str) -> None:
       del self._var_metadata[name]
 
   def __nnx_repr__(self):
-    stats = value_stats(self.value)
+    stats = SizeBytes.from_any(self.value)
     if stats:
       comment = f' # {stats}'
     else:
@@ -798,7 +794,7 @@ def __nnx_repr__(self):
       yield reprlib.Attr(name, value)
 
   def __treescope_repr__(self, path, subtree_renderer):
-    size_bytes = value_stats(self.value)
+    size_bytes = SizeBytes.from_any(self.value)
     if size_bytes:
       stats_repr = f' # {size_bytes}'
       first_line_annotation = treescope.rendering_parts.comment_color(

flax/typing.py

@@ -194,19 +194,19 @@ class SizeBytes:  # type: ignore[misc]
   size: int
   bytes: int
 
-  @staticmethod
-  def from_array(x: ShapeDtype) -> SizeBytes:
+  @classmethod
+  def from_array(cls, x: ShapeDtype):
     size = int(np.prod(x.shape))
     dtype: jnp.dtype
     if isinstance(x.dtype, str):
       dtype = jnp.dtype(x.dtype)
     else:
       dtype = x.dtype  # type: ignore
     bytes = size * dtype.itemsize  # type: ignore
-    return SizeBytes(size, bytes)
+    return cls(size, bytes)
 
-  def __add__(self, other: SizeBytes) -> SizeBytes:
-    return SizeBytes(self.size + other.size, self.bytes + other.bytes)
+  def __add__(self, other: SizeBytes):
+    return type(self)(self.size + other.size, self.bytes + other.bytes)
 
   def __bool__(self) -> bool:
     return bool(self.size)
@@ -215,12 +215,12 @@ def __repr__(self) -> str:
     bytes_repr = _bytes_repr(self.bytes)
     return f'{self.size:,} ({bytes_repr})'
 
+  @classmethod
+  def from_any(cls, x):
+    leaves = jax.tree.leaves(x)
+    size_bytes = cls(0, 0)
+    for leaf in leaves:
+      if has_shape_dtype(leaf):
+        size_bytes += cls.from_array(leaf)
 
-def value_stats(x):
-  leaves = jax.tree.leaves(x)
-  size_bytes = SizeBytes(0, 0)
-  for leaf in leaves:
-    if has_shape_dtype(leaf):
-      size_bytes += SizeBytes.from_array(leaf)
-
-  return size_bytes
+    return size_bytes

tests/nnx/summary_test.py

@@ -0,0 +1,74 @@
+# Copyright 2024 The Flax Authors.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import jax.numpy as jnp
+from absl.testing import absltest
+
+from flax import nnx
+
+CONSOLE_TEST_KWARGS = dict(force_terminal=False, no_color=True, width=10_000)
+
+
+class SummaryTest(absltest.TestCase):
+  def test_tabulate(self):
+    class Block(nnx.Module):
+      def __init__(self, din, dout, rngs: nnx.Rngs):
+        self.linear = nnx.Linear(din, dout, rngs=rngs)
+        self.bn = nnx.BatchNorm(dout, rngs=rngs)
+        self.dropout = nnx.Dropout(0.2, rngs=rngs)
+
+      def forward(self, x):
+        return nnx.relu(self.dropout(self.bn(self.linear(x))))
+
+    class Foo(nnx.Module):
+      def __init__(self, rngs: nnx.Rngs):
+        self.block1 = Block(32, 128, rngs=rngs)
+        self.block2 = Block(128, 10, rngs=rngs)
+
+      def __call__(self, x):
+        return self.block2.forward(self.block1.forward(x))
+
+    foo = Foo(nnx.Rngs(0))
+    x = jnp.ones((1, 32))
+    table_repr = nnx.tabulate(
+      foo, x, console_kwargs=CONSOLE_TEST_KWARGS
+    ).splitlines()
+
+    self.assertIn('Foo Summary', table_repr[0])
+    self.assertIn('path', table_repr[2])
+    self.assertIn('type', table_repr[2])
+    self.assertIn('BatchStat', table_repr[2])
+    self.assertIn('Param', table_repr[2])
+    self.assertIn('block1/forward', table_repr[6])
+    self.assertIn('Block', table_repr[6])
+    self.assertIn('block1/linear', table_repr[8])
+    self.assertIn('Linear', table_repr[8])
+    self.assertIn('block1/bn', table_repr[13])
+    self.assertIn('BatchNorm', table_repr[13])
+    self.assertIn('block1/dropout', table_repr[18])
+    self.assertIn('Dropout', table_repr[18])
+    self.assertIn('block2/forward', table_repr[20])
+    self.assertIn('Block', table_repr[20])
+    self.assertIn('block2/linear', table_repr[22])
+    self.assertIn('Linear', table_repr[22])
+    self.assertIn('block2/bn', table_repr[27])
+    self.assertIn('BatchNorm', table_repr[27])
+    self.assertIn('block2/dropout', table_repr[32])
+    self.assertIn('Dropout', table_repr[32])
+
+    self.assertIn('Total', table_repr[34])
+    self.assertIn('276 (1.1 KB)', table_repr[34])
+    self.assertIn('5,790 (23.2 KB)', table_repr[34])
+    self.assertIn('2 (12 B)', table_repr[34])
+    self.assertIn('Total Parameters: 6,068 (24.3 KB)', table_repr[37])