Eagerly bind submodules

flax/linen/module.py

@@ -490,6 +490,10 @@ def _get_unbound_fn(method_or_fn: Callable[..., Any]) -> Callable[..., Any]:
 
   return method_or_fn
 
+def _map_submodules(fn: Callable[['Module'], Any], tree):
+  """Map a function over all submodules in a tree."""
+  g = lambda _, x: fn(x) if isinstance(x, Module) else x
+  return _freeze_attr(_map_over_modules_in_tree(g, tree))
 
 class SetupState(enum.IntEnum):
   # setup() has not been called.
@@ -1000,6 +1004,12 @@ def __post_init__(self) -> None:
     else:
       raise ValueError('parent must be None, Module or Scope')
 
+    # eagerly bind submodules if scope is available
+    if self.scope is not None:
+        for field in dataclasses.fields(self):
+          if field.name not in ('parent', 'name') and field.init:
+            self._register_submodules(field.name, getattr(self, field.name))
+
     self._state.is_initialized = True
 
   def __repr__(self) -> str:
@@ -1048,8 +1058,8 @@ def _register_submodules(self, name, val):
     _caches[root] = cache
     queue = []
     preserve_adopted_names = config.flax_preserve_adopted_names
-    if hasattr(self, 'preserve_adopted_names'):
-      preserve_adopted_names = self.preserve_adopted_names
+    if hasattr(type(self), 'preserve_adopted_names'):
+      preserve_adopted_names = type(self).preserve_adopted_names
     def adopt_attr_modules(cache, queue, suffix, subvalue):
       if isinstance(subvalue, Module):
         adopted_name = None
@@ -1092,7 +1102,7 @@ def _try_setup(self, shallow: bool = False) -> None:
         # not call the user's setup. This avoids running before a
         # transformation.
         for field in dataclasses.fields(self):
-          if field.name != 'parent' and field.init:
+          if field.name not in ('parent', 'name') and field.init:
             self._register_submodules(field.name, getattr(self, field.name))
         if not shallow:
           self.setup()
@@ -1137,23 +1147,61 @@ def _name_taken(self,
 
   @property
   def _initialization_allowed(self):
-    return self._state.in_setup or self._state.in_compact_method
+    return (not self._state.is_initialized  # allow eager attachment in post-init
+            or self._state.in_setup
+            or self._state.in_compact_method)
 
   def clone(self: M, *,
             parent: Optional[Union[Scope, 'Module']] = None,
+            _deep_clone: Union[bool, weakref.WeakValueDictionary] = False,
             **updates) -> M:
     """Creates a clone of this Module, with optionally updated arguments.
 
     Args:
       parent: The parent of the clone. The clone will have no parent if no
         explicit parent is specified.
+      _deep_clone: A boolean or a weak value dictionary to control deep cloning
+        of submodules. If True, submodules will be cloned recursively. If a
+        weak value dictionary is passed, it will be used to cache cloned
+        submodules. This flag is used by init/apply/bind to avoid scope
+        leakage.
       **updates: Attribute updates.
     Returns:
       A clone of the this Module with the updated attributes and parent.
     """
     attrs = {f.name: getattr(self, f.name) for f in dataclasses.fields(self) if f.init}
+
     attrs.update(parent=parent, **updates)
-    return self.__class__(**attrs)
+
+    # Here we implement deep cloning of submodules, this is necessary to avoid scope leakage
+    # from external submodules into init/apply/bind while preserving sharing-by-reference
+    # relationships between submodules.
+    if _deep_clone != False:
+      # We use a weak value dictionary to cache cloned submodules. When a shared
+      # submodule is cloned, its only cloned once else its fetched from the cache.
+      cache = weakref.WeakValueDictionary() if isinstance(_deep_clone, bool) else _deep_clone
+      def clone_fn(m: Module) -> Module:
+        if hasattr(m, '_id'):
+          key = m._id
+          if key in cache:
+            return cache[key]
+          else:
+            clone = m.clone(_deep_clone=cache)
+            cache[key] = clone
+            return clone
+        else:
+          # If the module doesn't have an _id attribute it could be a mock object
+          # so we return it as is.
+          return m
+
+      # _map_submodules will map over all submodules inside attrs
+      # value here can be any pytree, non-module values are ignored
+      for field_name, value in attrs.items():
+        attrs[field_name] = _map_submodules(clone_fn, value)
+
+    module = self.__class__(**attrs)
+
+    return module
 
   def variable(self, col: str, name: str,
                init_fn: Optional[Callable[..., Any]] = None,
@@ -1371,7 +1419,7 @@ def __call__(self, x):
 
     del args
     scope = core.bind(variables, rngs=rngs, mutable=mutable)
-    return self.clone(parent=scope)
+    return self.clone(parent=scope, _deep_clone=True)
 
   def unbind(self: M) -> Tuple[M, VariableDict]:
     """Returns an unbound copy of a Module and its variables.
@@ -2057,7 +2105,7 @@ def f(foo, x):
   def scope_fn(scope, *args, **kwargs):
     _context.capture_stack.append(capture_intermediates)
     try:
-      return fn(module.clone(parent=scope), *args, **kwargs)
+      return fn(module.clone(parent=scope, _deep_clone=True), *args, **kwargs)
     finally:
       _context.capture_stack.pop()
 
@@ -2118,7 +2166,7 @@ def f(foo, x):
   def scope_fn(scope, *args, **kwargs):
     _context.capture_stack.append(capture_intermediates)
     try:
-      return fn(module.clone(parent=scope), *args, **kwargs)
+      return fn(module.clone(parent=scope, _deep_clone=True), *args, **kwargs)
     finally:
       _context.capture_stack.pop()
 

pyproject.toml

@@ -131,7 +131,11 @@ filterwarnings = [
     "ignore:file system plugins are not loaded.*:UserWarning",
     "ignore:unable to load libtensorflow_io_plugins.so.*:UserWarning",
     # Remove this after next Optax release after 3/27/2023
-    "ignore:jax.numpy.DeviceArray is deprecated. Use jax.Array.*:DeprecationWarning"
+    "ignore:jax.numpy.DeviceArray is deprecated. Use jax.Array.*:DeprecationWarning",
+    # DeprecationWarning: pkg_resources is deprecated as an API
+    "ignore:.*pkg_resources is deprecated.*:DeprecationWarning",
+    # DeprecationWarning: Deprecated call to `pkg_resources.declare_namespace('google')`.
+    "ignore:.*Deprecated call to `pkg_resources.declare_namespace.*:DeprecationWarning",
 ]
 
 [tool.coverage.report]

tests/linen/linen_module_test.py

@@ -2000,6 +2000,180 @@ def __call__(self):
                                 'Trying to access a property that'):
       foo.apply({})
 
+  def test_nested_external_modules(self):
+    class Baz(nn.Module):
+      a: int
+
+      def setup(self):
+        self.b = self.param('b', lambda k: 2)
+
+      def __call__(self, x):
+        return x + self.a * self.b
+
+    class Bar(nn.Module):
+      baz: Baz
+
+      def __call__(self, x):
+        return self.baz(x)
+
+    class Foo(nn.Module):
+      def setup(self):
+        self.bar = Bar(baz=Baz(a=1))
+
+      def __call__(self, x):
+        return self.bar.baz(x)
+
+    module = Foo()
+    y, variables = module.init_with_output(jax.random.PRNGKey(0), 1)
+    self.assertEqual(y, 3)
+
+  def test_getattribute_triggers_setup(self):
+    class B(nn.Module):
+      def setup(self):
+        self.p1 = self.param('p1', lambda k: jnp.ones((2,)))
+      def fn1(self, x):
+        return self.p1 + x
+    class A(nn.Module):
+      b: nn.Module
+      def __call__(self, x):
+        return self.b.fn1(x)
+    a = A(b=B())
+    k = random.PRNGKey(0)
+    x = jnp.zeros((2,))
+    vs = nn.init(lambda a,x: a(x), a)(k, x)
+    y = nn.apply(lambda a,x: a.b.fn1(x), a)(vs, x)
+    np.testing.assert_array_equal(y, jnp.ones((2,)))
+
+  def test_nested_sequential_in_call(self):
+    class Foo(nn.Module):
+      def setup(self):
+        self.seq = nn.Sequential([nn.Dense(10) for i in range(10)])
+
+      def __call__(self, x):
+        # try calling only the first layer
+        return self.seq.layers[0](x)
+
+    module = Foo()
+    variables = module.init(jax.random.PRNGKey(0), jnp.ones((1, 10)))
+
+  def test_setup_called_bounded_submodules(self):
+    module = nn.Sequential([
+      nn.Sequential([
+        nn.Dense(2),
+        nn.relu,
+        nn.Dense(2),
+      ]),
+      nn.relu,
+      nn.Dense(2),
+    ])
+    x = jnp.ones((1, 3))
+    variables = module.init(jax.random.PRNGKey(0), x)
+    bound_module = module.bind(variables)
+
+    self.assertIsNotNone(bound_module.layers[0].layers[0].scope)
+    self.assertIsNotNone(bound_module.layers[0].layers[2].scope)
+    self.assertIsNotNone(bound_module.layers[2].scope)
+
+  def test_call_bounded_toplevel_mutable(self):
+    class Bar(nn.Module):
+      a: int
+
+      def setup(self):
+        self.b = self.param('b', lambda k: 1)
+
+      def __call__(self, x):
+        return x + self.a * self.b
+
+    class Foo(nn.Module):
+      bars: Sequence[Bar]
+
+      def __call__(self, x):
+        for bar in self.bars:
+          x = bar(x)
+        return x
+
+
+    module = Foo(bars=[])
+    module.bars = [Bar(a=1)]
+
+    variables = module.init(jax.random.PRNGKey(0), jnp.ones(()))
+    bound_module = module.bind(variables)
+
+    bar1 = bound_module.bars[0]
+    self.assertIsNotNone(bar1.scope)
+
+  def test_nested_init(self):
+    class Baz(nn.Module):
+      a: int
+
+      def setup(self):
+        self.b = self.param('b', lambda k: jnp.ones(()))
+
+      def __call__(self, x):
+        return x + self.a * self.b
+
+    class Bar(nn.Module):
+      baz: Baz
+
+      def setup(self):
+        a = 1
+
+      def __call__(self, x):
+        return self.baz(x)
+
+    class Foo(nn.Module):
+
+      def setup(self):
+        self.bar: Bar = Bar(baz=Baz(a=1))
+
+      def __call__(self, x):
+        # y = self.bar(x)
+        y, bar_vars = self.bar.init_with_output(jax.random.PRNGKey(0), x)
+        return y, bar_vars
+
+    # create foo
+    module = Foo()
+
+    # run foo
+    (y, bar_vars), variables = module.init_with_output(
+      jax.random.PRNGKey(0), jnp.ones(()))
+
+    self.assertIn('params', bar_vars)
+
+  def test_nested_shared(self):
+    class Shared(nn.Module):
+      @nn.compact
+      def __call__(self, x):
+        return nn.Dense(1)(x)
+
+    class Unshared(nn.Module):
+      shared: nn.Module
+      def __call__(self, x):
+        return self.shared(x)
+
+    class Super(nn.Module):
+      a: nn.Module
+      b: nn.Module
+      def run_a(self, x):
+        return self.a(x)
+      def run_b(self, x):
+        return self.b(x)
+      def __call__(self, x):
+        return self.a(x) + self.b(x)
+
+
+    sh = Shared()
+    a = Unshared(shared=sh)
+    b = Unshared(shared=sh)
+    module = Super(a=a, b=b)
+
+    rng = jax.random.PRNGKey(0)
+    params = module.init(rng, jnp.ones(1))["params"]
+
+    module.apply({"params": params}, jnp.ones(1))  # works as expected
+    module.apply({"params": params}, jnp.ones(1), method="run_a")  # works as expected
+    module.apply({"params": params}, jnp.ones(1), method="run_b")  # ScopeParamNotFoundError: Could not find parameter named "kernel" in scope "/b/shared/Dense_0"
+
   def test_repr(self):
 
     class Base1(nn.Module):