Use jax.named_scope for name stack rather than named_call.

CHANGELOG.md

@@ -6,7 +6,8 @@ vNext
 (Add your change to a random empty line to avoid merge conflicts)
 -
 -
--
+- Switched to using `jax.named_scope` for all profile naming, cut some pointless
+  stack traces out.
 -
 -
 -

flax/config.py

@@ -21,8 +21,8 @@
 
 .. data:: flax_profile
 
-    Whether to automatically wrap Module methods with named_call for profiles.
-    Set by the FLAX_PROFILE environment variable.  Defaults to False.
+    Whether to automatically wrap Module methods with jax.named_scope for
+    profiles.  Set by the FLAX_PROFILE environment variable.  Defaults to True.
 """
 
 import os
@@ -38,6 +38,8 @@ def bool_env(varname: str, default: bool) -> bool:
   Args:
     varname: the name of the variable
     default: the default boolean value
+  Returns:
+    boolean return value derived from defaults and environment.
   Raises: ValueError if the environment variable is anything else.
   """
   val = os.getenv(varname, str(default))
@@ -56,10 +58,8 @@ def bool_env(varname: str, default: bool) -> bool:
 # Whether to hide flax-internal stack frames from tracebacks.
 flax_filter_frames = bool_env('FLAX_FILTER_FRAMES', True)
 
-# Whether to automatically wrap Module methods with named_call for profiles.
-# We profile by default if JAX's dynamic name-stack based named_call is used.
-flax_profile = (bool_env('FLAX_PROFILE', False) or
-                bool_env('JAX_EXPERIMENTAL_NAME_STACK', False))
+# Whether to run Module methods under jax.named_scope for profiles.
+flax_profile = bool_env('FLAX_PROFILE', True)
 
 # Whether to use the lazy rng implementation
 flax_lazy_rng = bool_env('FLAX_LAZY_RNG', True)

flax/core/lift.py

@@ -1345,22 +1345,6 @@ def inner_loop(scope, carry):
   return fn
 
 
-def named_call(fn: Callable[..., Any], name: str) -> Callable[..., Any]:
-  """Adds a name scope to `fn` during profiling."""
-  def inner(scope_fn, repack_fn, variable_groups, rng_groups, args, kwargs):
-    @functools.wraps(fn)
-    def named(variable_groups, rng_groups):
-      scope = scope_fn(variable_groups, rng_groups)
-      y = fn(scope, *args, **kwargs)
-      return y, repack_fn(scope)
-    named = jax.named_call(named, name=name)
-    return named(variable_groups, rng_groups)
-  lifted = pack(inner, (True,), (True,), (True,))
-  def wrapper(scope, *args, **kwargs):
-    return lifted(scope, args, kwargs)
-  return wrapper
-
-
 def _unzip2(xs):
   ys = tuple(zip(*xs))
   return ys if ys else ((), ())

flax/core/scope.py

@@ -581,7 +581,7 @@ def child(self,
       fn: the function to partially apply the child Scope to.
       name: optional name of the child.
       prefix: prefix used for generating name if it is `None`.
-      named_call: if true, `fn` will be wrapped with `lift.named_call`. The XLA
+      named_call: if true, `fn` will be run under `jax.named_scope`. The XLA
         profiler will use this to name tag the computation.
       **partial_kwargs: additional kwargs partially applied to `fn`.
 
@@ -593,15 +593,16 @@ def child(self,
         prefix = fn.__name__ + '_' if hasattr(fn, '__name__') else ''
       name = self.default_name(prefix)
     scope = self.push(name)
-    if named_call:
-      # We import named_call at runtime to avoid a circular import issue.
-      from . import lift  # pylint: disable=g-import-not-at-top
-      fn = lift.named_call(fn, name)
 
     @functools.wraps(fn)
     def wrapper(*args, **kwargs):
       kwargs = dict(partial_kwargs, **kwargs)
-      return fn(scope.rewound(), *args, **kwargs)
+      if named_call:
+        with jax.named_scope(name):
+          res = fn(scope.rewound(), *args, **kwargs)
+      else:
+        res = fn(scope.rewound(), *args, **kwargs)
+      return res
 
     return wrapper
 

flax/errors.py

@@ -588,19 +588,11 @@ def __init__(self, path, step):
 #################################################
 
 class TransformedMethodReturnValueError(FlaxError):
-  """
-  Transformed Module methods cannot return other Modules or Variables.
+  """Transformed Module methods cannot return other Modules or Variables."""
 
-  This commonly occurs when ``@nn.named_call`` is automatically applied to
-  helper constructor methods when profiling is enabled (``FLAX_PROFILE=true``
-  environment variable or via ``nn.enable_named_call()``), and can be mitigated
-  by using the ``@nn.nowrap`` decorator to prevent automatic wrapping.
-  """
   def __init__(self, name):
     super().__init__(
-      f'Transformed module method {name} cannot return Modules or Variables. '
-      f'For helper constructor methods use the @nn.nowrap decorator to prevent '
-      f'decoration by the automatic named_call transform.')
+        f'Transformed module method {name} cannot return Modules or Variables.')
 
 
 class TransformTargetError(FlaxError):

flax/linen/module.py

@@ -142,15 +142,26 @@ class _Sentinel:
 _use_named_call = config.flax_profile
 
 
+def _derive_profiling_name(module, fn):
+  def _get_fn_name(fn):
+    if isinstance(fn, functools.partial):
+      return _get_fn_name(fn.func)
+    return fn.__name__
+  fn_name = _get_fn_name(fn)
+  method_suffix = f'.{fn_name}' if fn_name != '__call__' else ''
+  module_name = module.name or module.__class__.__name__
+  return f'{module_name}{method_suffix}'
+
+
 def enable_named_call():
   """Enables named call wrapping for labelling profile traces.
 
   When named call wrapping is enabled all JAX ops executed in a Module
-  will be wrapped with ``jax.named_call``. The ``Module`` class name will
+  will be run under ``jax.named_scope``. The ``Module`` class name will
   show up around the operations belonging to that Module in the
   Tensorboard profiling UI, simplifying the profiling process.
 
-  Note that ``jax.named_call`` only works for
+  Note that ``jax.named_scope`` only works for
   compiled functions (e.g.: using jax.jit or jax.pmap).
   """
   global _use_named_call
@@ -281,9 +292,6 @@ def nowrap(fun: _CallableT) -> _CallableT:
   with the state handler or a separate named_call transform.
 
   This is needed in several concrete instances:
-   - if you have a helper method that returns Modules or Variables to prevent
-     it from being functionalized by named_call. (Functionalized methods
-     can't return Modules/Variables.)
    - if you're subclassing a method like Module.param and don't want this
      overriden core function decorated with the state management wrapper.
    - If you want a method to be callable from an unbound Module (e.g.: a
@@ -608,12 +616,7 @@ def _wrap_module_methods(cls):
       method = getattr(cls, key)
       if hasattr(method, 'nowrap'):
         continue
-      wrapped_method = wrap_method_once(method)
-      if key != 'setup':
-        # We import named_call at runtime to avoid a circular import issue.
-        from flax.linen.transforms import named_call  # pylint: disable=g-import-not-at-top
-        wrapped_method = named_call(wrapped_method, force=False)
-      setattr(cls, key, wrapped_method)
+      setattr(cls, key, wrap_method_once(method))
     return cls
 
   def _call_wrapped_method(self, fun, args, kwargs):
@@ -649,7 +652,11 @@ def _call_wrapped_method(self, fun, args, kwargs):
       self._state.in_compact_method = True
     _context.module_stack.append(self)
     try:
-      y = fun(self, *args, **kwargs)
+      if _use_named_call:
+        with jax.named_scope(_derive_profiling_name(self, fun)):
+          y = fun(self, *args, **kwargs)
+      else:
+        y = fun(self, *args, **kwargs)
       if _context.capture_stack:
         filter_fn = _context.capture_stack[-1]
         if filter_fn and filter_fn(self, fun_name):
@@ -880,7 +887,7 @@ def run_setup_only(x):
   def _name_taken(self,
                   name: str,
                   module: 'Module' = None,
-                  reuse_scopes : bool = False) -> bool:
+                  reuse_scopes: bool = False) -> bool:
     if name in _all_names_on_object(self):
       val = getattr(self, name, None)
       if module is not None and val is module:

flax/linen/transforms.py

@@ -39,6 +39,7 @@
 from flax.linen.module import Variable
 from flax.linen.module import wrap_method_once
 from flax.linen.module import _get_unbound_fn
+from flax.linen.module import _derive_profiling_name
 import jax
 
 traceback_util.register_exclusion(__file__)
@@ -1296,55 +1297,25 @@ def shared_forward_fn(*args, needs_residual, **kwargs):
       multi_scope=False)
 
 
-# Special case of decorator_lift_transform to handle named calls for profiling.
 def named_call(class_fn, force=True):
   """Labels a method for labelled traces in profiles.
 
+  Note that it is better to use the `jax.named_scope` context manager directly
+  to add names to JAX's metadata name stack.
+
   Args:
     class_fn: The class method to label.
     force: If True, the named_call transform is applied even if it is globally
       disabled. (e.g.: by calling `flax.linen.disable_named_call()`)
   Returns:
     A wrapped version of ``class_fn`` that is labeled.
   """
-  if (hasattr(jax.config, 'jax_experimental_name_stack') and
-      jax.config.jax_experimental_name_stack):
-    # Use JAX's improved dynamic name-stack named_call.
-    # No transform boundary needed!
-    @functools.wraps(class_fn)
-    def wrapped_fn(self, *args, **kwargs):
-      fn_name = class_fn.__name__
-      method_suffix = f'.{fn_name}' if fn_name != '__call__' else ''
-      module_name = self.name or self.__class__.__name__
-      full_name = f'{module_name}{method_suffix}'
-      return jax.named_call(class_fn, name=full_name)(self, *args, **kwargs)
-  else:
-    # Use JAX's old purely-functional call-based named_call.
-    # Due to the ordering of method decorators, we must wrap the class_fn
-    # with the module state management wrapper first to maintain Module state
-    # correctly.
-    prewrapped_fn = wrap_method_once(class_fn)
-    @functools.wraps(prewrapped_fn)
-    def wrapped_fn(self, *args, **kwargs):
-      if ((not force and not linen_module._use_named_call)
-          or self._state.in_setup):
-        return prewrapped_fn(self, *args, **kwargs)
-      fn_name = class_fn.__name__
-      method_suffix = f'.{fn_name}' if fn_name != '__call__' else ''
-      module_name = self.name or self.__class__.__name__
-      full_name = f'{module_name}{method_suffix}'
-      # make a scope-function to transform
-      def core_fn(scopes, *args, **kwargs):
-        cloned, args, kwargs = set_module_scopes(self, args, kwargs, scopes)
-        object.__setattr__(cloned, '_state', self._state.export())
-        res = prewrapped_fn(cloned, *args, **kwargs)
-        self._state.reimport(cloned._state)
-        _test_transformed_return_values(res, fn_name)
-        return res
-      # here we apply the given lifting transform to the scope-ingesting fn
-      trafo_fn = lift.named_call(core_fn, full_name)
-      module_scopes, args, kwargs = get_module_scopes(self, args, kwargs)
-      return trafo_fn(module_scopes, *args, **kwargs)
-
+  # We use JAX's dynamic name-stack named_call. No transform boundary needed!
+  @functools.wraps(class_fn)
+  def wrapped_fn(self, *args, **kwargs):
+    if ((not force and not linen_module._use_named_call)  # pylint: disable=protected-access
+        or self._state.in_setup):  # pylint: disable=protected-access
+      return class_fn(self, *args, **kwargs)
+    full_name = _derive_profiling_name(self, class_fn)
+    return jax.named_call(class_fn, name=full_name)(self, *args, **kwargs)
   return wrapped_fn
-

setup.py

@@ -26,7 +26,7 @@
 
 install_requires = [
     "numpy>=1.12",
-    "jax>=0.3.2",
+    "jax>=0.3.14",
     "matplotlib",  # only needed for tensorboard export
     "msgpack",
     "optax",

tests/linen/linen_module_test.py

@@ -26,7 +26,6 @@
 from flax import struct
 from flax.core import Scope, freeze, tracers
 from flax.linen import compact
-from flax.linen.module import override_named_call
 import jax
 from jax import random
 from jax.nn import initializers
@@ -1584,13 +1583,12 @@ class MyComponent2(Generic[T], nn.Module):
     class MyModule2(nn.Module):
       submodule: MyComponent2[jnp.ndarray]
 
-  def test_named_call_rng_equivalance(self):
+  def test_jit_rng_equivalance(self):
     model = nn.Dense(1, use_bias=False)
-    with override_named_call(False):
-      param = model.init(random.PRNGKey(0), np.ones((1, 1)))['params']['kernel']
-    with override_named_call(True):
-      param_2 = model.init(random.PRNGKey(0), np.ones(
-          (1, 1)))['params']['kernel']
+    jit_model = nn.jit(nn.Dense)(1, use_bias=False)
+    param = model.init(random.PRNGKey(0), np.ones((1, 1)))['params']['kernel']
+    param_2 = jit_model.init(random.PRNGKey(0), np.ones(
+        (1, 1)))['params']['kernel']
     self.assertEqual(param, param_2)
 
   def test_rng_reuse_after_rewind(self):