Source code for flax.linen.transforms

# Copyright 2022 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.

"""JAX transformations on Modules.

Jax functional transformations operate on pure functions.
Flax extends these transformations to also operate on Module's which
have stateful variables and PRNG sequences. We refer to these extended
versions as "lifted transformations".

A lifted transformation can be applied to a ``Module`` class or a
function that takes a ``Module`` instance as its first argument.
"""
import dataclasses
import functools
import inspect
from typing import (Any, Callable, Dict, Iterable, Mapping, Optional, Sequence,
                    Tuple, Type, TypeVar, Union)

from flax import errors
from flax import struct
from flax import traceback_util
from flax.core import lift
from flax.core import Scope
from flax.core.frozen_dict import FrozenDict
from flax.linen import module as linen_module
from flax.linen.module import Module
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__)

# pylint: disable=protected-access

# Utils
# -----------------------------------------------------------------------------
def clean_clone(x):
  """Remove scopes and tracers from children."""
  if isinstance(x, Module):
    object.__setattr__(
        x, 'children',
        {k: clean_clone(v) for k, v in x.children.items()})
    object.__setattr__(x, 'scope', None)
  return x


@struct.dataclass
class VariablePlaceholder:
  """Used to mark Variables in a JAX-compatible way when lifting arguments."""
  collection: str = struct.field(pytree_node=False)
  name: str = struct.field(pytree_node=False)
  id: int = struct.field(pytree_node=False)


@struct.dataclass
class InstancePlaceholder:
  """Marks module instances in a JAX-compatible way when lifting arguments."""
  cls: Type[Any] = struct.field(pytree_node=False)
  attrs: Dict[Any, Any] = struct.field(pytree_node=False)
  id: int = struct.field(pytree_node=False)


def _memoize_by_id(fn, refs):
  """Memoization by module/variable id to handle aliasing in traversal."""
  @functools.wraps(fn)
  def wrapped_fn(x):
    nonlocal refs
    if isinstance(x, (VariablePlaceholder, InstancePlaceholder)):
      x_id = x.id
    elif isinstance(x, (Variable, Module)):
      x_id = x._id
    else:
      return fn(x)
    if x_id not in refs:
      refs[x_id] = fn(x)
    return refs[x_id]
  return wrapped_fn


def get_module_scopes(module, args=None, kwargs=None):
  """Get all scopes on module, including constructor Module arguments.

  To properly functionalize a Module that has other bound Modules passed in
  "from the outside" as dataclass attributes, we need to traverse all dataclass
  fields to find the Scopes associated with the Module.  Additionally, because
  we allow Modules to be passed inside pytrees on the dataclass attributes, we
  must traverse all dataclass attributes as pytrees to find all Modules.  We
  additionally handle lifting Variables (which are just references to data in
  particular scopes) and Module instances that are passed as arguments to
  methods.

  Args:
    module: a bound flax Module.
    args: an *args list possibly containing Variables or Module instances
      referencing a scope.
    kwargs: a **kwargs dict possibly containing Variables or Module instances
      referencing a scope.

  Returns:
    A list of all functional-core Scopes bound on self and inside dataclass
    fields as well as any Scopes passed via argument Variables, an updated args
    list, and an updated kwargs dict that have both had Variables replaced with
    VariablePlaceholders and Module instances replaced with InstancePlaceholders
    that are compatible with jax functions.
  """
  scopes = []
  refs = {}
  # Gather scopes associated with Variables and Module instances passed as
  # positional and keyword arguments.
  @functools.partial(_memoize_by_id, refs=refs)
  def get_arg_scope(x):
    nonlocal scopes
    if isinstance(x, Variable) and isinstance(x.scope, Scope):
      scopes.append(x.scope)
      return VariablePlaceholder(x.collection, x.name, x._id)
    elif isinstance(x, Module) and isinstance(x.scope, Scope):
      x._try_setup(shallow=True)
      scopes.append(x.scope)
      attrs = {
          f.name: getattr(x, f.name)
          for f in dataclasses.fields(x)
          if f.name != 'parent' and f.init
      }
      attrs = jax.tree_util.tree_map(get_arg_scope, attrs)
      return InstancePlaceholder(x.__class__, attrs, x._id)
    return x
  new_args, new_kwargs = jax.tree_util.tree_map(get_arg_scope, (args, kwargs))

  # Gather scopes in Variables and Submodules passed as Module attributes.
  @functools.partial(_memoize_by_id, refs=refs)
  def get_scopes(module):
    nonlocal scopes
    module._try_setup(shallow=True)
    def get_scopes_inner(x):
      nonlocal scopes
      if isinstance(x, Module) and isinstance(x.scope, Scope):
        get_scopes(x)
      elif isinstance(x, Variable) and isinstance(x.scope, Scope):
        scopes.append(x.scope)

    attrs = {
        f.name: getattr(module, f.name)
        for f in dataclasses.fields(module)
        if f.name != 'parent' and f.init
    }
    jax.tree_util.tree_map(get_scopes_inner, attrs)
    scopes.append(module.scope)
  get_scopes(module)
  return scopes, new_args, new_kwargs


def set_module_scopes(module, args, kwargs, scopes):
  """Set all scopes on module, including those on Modules in dataclass fields.

  To properly functionalize a Module we must also "rehydrate" it with Scopes
  from `get_module_scopes`.  We need to set scopes not just on the Module but
  also on any Module living inside dataclass attributes or even pytrees in its
  dataclass attributes.  We additionally handle restoring Variables and Module
  instances from their placeholders in the method positional and keyword
  arguments.  The order of traversal through this method is the same as in
  `get_module_scopes`, guaranteeing the correct Scopes are applied to each
  Module.

  Args:
    module: a flax Module.
    args: an *args list possibly containing VariablePlaceholder or
      InstancePlaceholder members.
    kwargs: a **kwargs dict possibly containing VariablePlaceholder or
      InstancePlaceholder members.
    scopes: a list of Scopes corresponding to this Module and its arguments that
      was created by the `get_module_scopes` function.

  Returns:
    A copy of the module with it and its attributes bound to the scopes passed
    to this function, an updated args list, and an updated kwargs dict with
    updated Variable and Module instance references.
  """
  idx = 0
  refs = {}
  # Set scopes associated with Variables and Module instances passed as
  # positional and keyword arguments.
  @functools.partial(_memoize_by_id, refs=refs)
  def set_arg_scope(x):
    nonlocal idx
    if isinstance(x, VariablePlaceholder):
      new_x = Variable(scope=scopes[idx], collection=x.collection, name=x.name)
      idx += 1
      return new_x
    elif isinstance(x, InstancePlaceholder):
      instance_scope = scopes[idx]
      idx += 1
      instance_attrs = jax.tree_util.tree_map(set_arg_scope, x.attrs)
      return x.cls(parent=instance_scope, **instance_attrs)
    return x

  def is_placeholder(x):
    return isinstance(x, (VariablePlaceholder, InstancePlaceholder))

  new_args, new_kwargs = jax.tree_util.tree_map(
      set_arg_scope, (args, kwargs), is_leaf=is_placeholder)

  # set scopes in Variables and Submodules passed as Module attributes
  @functools.partial(_memoize_by_id, refs=refs)
  def set_scopes(module):
    nonlocal idx
    def set_scopes_inner(x):
      nonlocal idx
      if isinstance(x, Module) and isinstance(x.scope, Scope):
        return set_scopes(x)
      elif isinstance(x, Variable) and isinstance(x.scope, Scope):
        new_x = Variable(scope=scopes[idx],
                         collection=x.collection,
                         name=x.name)
        idx += 1
        return new_x
      else:
        return x

    attrs = {
        f.name: getattr(module, f.name)
        for f in dataclasses.fields(module)
        if f.name != 'parent' and f.init
    }
    new_attrs = jax.tree_util.tree_map(set_scopes_inner, attrs)
    new_module = module.clone(parent=scopes[idx], **new_attrs)
    idx += 1
    return new_module
  new_module = set_scopes(module)
  assert len(scopes) == idx, f'scope list mismatch {len(scopes)} != {idx}'
  return new_module, new_args, new_kwargs


def _test_transformed_return_values(tree, method_name):
  """Tests whether the return value contains any Modules or Variables."""
  impure = any(map(lambda x: isinstance(x, (Module, Variable)),
                   jax.tree_util.tree_leaves(tree)))
  if impure:
    raise errors.TransformedMethodReturnValueError(method_name)


# Class lifting
# -----------------------------------------------------------------------------
def module_class_lift_transform(
    transform,
    module_class,
    *trafo_args,
    methods=None,
    **trafo_kwargs):
  """Module class lift transform."""
  # TODO(marcvanzee): Improve docstrings (#1977).
  # TODO(levskaya): find nicer argument convention for multi-method case?

  # Prepare per-method transform args, kwargs.
  if methods is None:
    # Default case, just transform __call__
    class_trafo_args = {'__call__': (trafo_args, trafo_kwargs)}
  elif isinstance(methods, (list, tuple)):
    # Transform every method in methods with given args, kwargs.
    class_trafo_args = {m: (trafo_args, trafo_kwargs) for m in methods}
  elif isinstance(methods, dict):
    # Pass different trafo args per each method.
    class_trafo_args = {k: ((), v) for k, v in methods.items()}
  else:
    raise ValueError(
        'transform methods argument must be None, tuple, list, or dict.')

  # Handle partially initialized module class constructors.
  if (isinstance(module_class, functools.partial) and
      issubclass(module_class.func, Module)):
    partial_object = module_class
    module_class = module_class.func
  else:
    partial_object = None

  def create_trans_fn(fn_name, fn_trafo_args):
    # get existing unbound method from class
    fn = getattr(module_class, fn_name)
    trafo_args, trafo_kwargs = fn_trafo_args
    # we need to create a scope-function from our class for the given method
    @functools.wraps(fn)
    def wrapped_fn(self, *args, **kwargs):
      state = self._state.export()
      # make a scope-function to transform
      def core_fn(scopes, *args, **kwargs):
        # make a clone of self using its arguments
        attrs = {
            f.name: getattr(self, f.name)
            for f in dataclasses.fields(self)
            if f.name != 'parent' and f.init
        }
        # we reference module_class, not self.__class__ to avoid infinite loop
        cloned = module_class(parent=None, **attrs)
        cloned, args, kwargs = set_module_scopes(cloned, args, kwargs, scopes)
        object.__setattr__(cloned, '_state', state.export())
        res = 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 = transform(core_fn, *trafo_args, **trafo_kwargs)
      module_scopes, args, kwargs = get_module_scopes(self, args, kwargs)
      ret = trafo_fn(module_scopes, *args, **kwargs)
      return ret
    return wrapped_fn
  transformed_fns = {fn_name: create_trans_fn(fn_name, fn_trafo_args)
                     for fn_name, fn_trafo_args in class_trafo_args.items()}
  # construct new dynamic class w. transformed methods
  transformed_cls = type(
      transform.__name__.capitalize() + module_class.__name__,
      (module_class,),
      transformed_fns)
  # Handle partially initialized module class constructors.
  if partial_object is not None:
    transformed_cls = functools.partial(transformed_cls,
                                        *partial_object.args,
                                        **partial_object.keywords)
  return transformed_cls


# Function lifting as decorator on methods __inside__ class definition.
# -----------------------------------------------------------------------------
def decorator_lift_transform(transform, class_fn, *trafo_args,
                             multi_scope=True, **trafo_kwargs):
  """Decorator for lifted transform."""
  # TODO(marcvanzee): Improve docstrings (#1977).
  # 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.
  if isinstance(class_fn, tuple):
    class_fns = class_fn
  else:
    class_fns = (class_fn,)
  prewrapped_fns = [wrap_method_once(class_fn) for class_fn in class_fns]
  @functools.wraps(prewrapped_fns[0])
  def wrapped_fn(self, *args, **kwargs):
    state = self._state.export()
    # make a scope-function to transform
    def core_fn(prewrapped_fn, class_fn, scopes, *args, **kwargs):
      if not multi_scope:
        scopes = [scopes]
      cloned, args, kwargs = set_module_scopes(self, args, kwargs, scopes)
      object.__setattr__(cloned, '_state', state.export())
      res = prewrapped_fn(cloned, *args, **kwargs)
      self._state.reimport(cloned._state)
      _test_transformed_return_values(res, getattr(class_fn, '__name__', None))
      return res
    core_fns = [functools.partial(core_fn, prewrapped_fn, class_fn)
                for prewrapped_fn, class_fn in zip(prewrapped_fns, class_fns)]
    # here we apply the given lifting transform to the scope-ingesting fn
    trafo_fn = transform(*core_fns, *trafo_args, **trafo_kwargs)
    module_scopes, args, kwargs = get_module_scopes(self, args, kwargs)
    if not multi_scope:
      if len(module_scopes) != 1:
        # TODO(levskaya): transforms like jvp & vjp have args that follow the
        # pytree structure of scopes. The user doesn't explicitly control shared
        # modules passed as arguments to methods or as attributes to Module
        # constructors. Therefore, there is no obvious API for specifying
        # arguments per lifted Module.
        raise NotImplementedError(
            'This transform does not yet support'
            ' Modules that include other Modules passed as arguments.')
      module_scopes = module_scopes[0]
    return trafo_fn(module_scopes, *args, **kwargs)
  return wrapped_fn


# Utility to wrap a class or to use as decorator in def of class method.
# -----------------------------------------------------------------------------

TransformTarget = Union[Type[Module], Callable[..., Any]]
Target = TypeVar('Target', bound=TransformTarget)


def _is_module_class(target: TransformTarget) -> bool:
  return (inspect.isclass(target) and issubclass(target, Module) or
          (isinstance(target, functools.partial)) and
          _is_module_class(target.func))


def lift_transform(transform,
                   target,
                   *trafo_args,
                   methods=None,
                   **trafo_kwargs):
  """Applies to class or as a decorator on class fns."""
  # TODO(marcvanzee): Improve docstrings (#1977).
  if _is_module_class(target):
    return module_class_lift_transform(
        transform, target, *trafo_args, methods=methods, **trafo_kwargs)
  # we presume this is being used as a function decorator in class definition
  elif callable(target) and not isinstance(target, Module):
    return decorator_lift_transform(
        transform, target, *trafo_args, **trafo_kwargs)
  else:
    raise errors.TransformTargetError(target)


def lift_direct_transform(transform: Callable[..., Any],
                          targets: Tuple[Callable[..., Any], ...],
                          mdl: Module,
                          *args, multi_scope=True, **kwargs):
  """Lift direct transform."""
  # TODO(marcvanzee): Improve docstrings (#1977).
  for target in targets:
    if _is_module_class(target):
      raise ValueError(
          f'The {transform.__name__} transform can only be applied on a Module method.'
          ' That is function that takes a Module instance as its first arg.')
    elif not callable(target):
      raise ValueError('transform target must be callable')
  # normalize self.foo bound methods to class.foo unbound methods.
  targets = tuple(_get_unbound_fn(target) for target in targets)
  aug_transform = lambda *fns: functools.partial(transform, *fns)
  return decorator_lift_transform(
      aug_transform, targets, multi_scope=multi_scope)(mdl, *args, **kwargs)


[docs]def vmap(target: Target, variable_axes: Mapping[lift.CollectionFilter, lift.InOutAxis] = FrozenDict(), split_rngs: Mapping[lift.PRNGSequenceFilter, bool] = FrozenDict(), in_axes=0, out_axes=0, axis_size: Optional[int] = None, axis_name: Optional[str] = None, spmd_axis_name: Optional[str] = None, methods=None) -> Target: """A lifted version of ``jax.vmap``. See ``jax.vmap`` for the unlifted batch transform in Jax. ``vmap`` can be used to add a batch axis to a ``Module``. For example we could create a version of ``Dense`` with a batch axis that does not share parameters:: BatchDense = nn.vmap( nn.Dense, in_axes=0, out_axes=0, variable_axes={'params': 0}, split_rngs={'params': True}) By using ``variable_axes={'params': 0}``, we indicate that the parameters themselves are mapped over and therefore not shared along the mapped axis. Consequently, we also split the 'params' RNG, otherwise the parameters would be initialized identically along the mapped axis. Similarly, ``vmap`` could be use to add a batch axis with parameter sharing:: BatchFoo = nn.vmap( Foo, in_axes=0, out_axes=0, variable_axes={'params': None}, split_rngs={'params': False}) Here we use ``variable_axes={'params': None}`` to indicate the parameter variables are shared along the mapped axis. Consequently, the 'params' RNG must also be shared. Args: target: a ``Module`` or a function taking a ``Module`` as its first argument. variable_axes: the variable collections that are lifted into the batching transformation. Use `None` to indicate a broadcasted collection or an integer to map over an axis. split_rngs: Split PRNG sequences will be different for each index of the batch dimension. Unsplit PRNGs will be broadcasted. in_axes: Specifies the mapping of the input arguments (see `jax.vmap`). out_axes: Specifies the mapping of the return value (see `jax.vmap`). axis_size: Specifies the size of the batch axis. This only needs to be specified if it cannot be derived from the input arguments. axis_name: Specifies a name for the batch axis. Can be used together with parallel reduction primitives (e.g. `jax.lax.pmean`, `jax.lax.ppermute`, etc.) methods: If `target` is a `Module`, the methods of `Module` to vmap over. spmd_axis_name: Axis name added to any pjit sharding constraints appearing in `fn`. See also https://github.com/google/flax/blob/main/flax/linen/partitioning.py. Returns: A batched/vectorized version of ``target``, with the same arguments but with extra axes at positions indicated by ``in_axes``, and the same return value, but with extra axes at positions indicated by ``out_axes``. """ return lift_transform( lift.vmap, target, variable_axes, split_rngs, methods=methods, in_axes=in_axes, out_axes=out_axes, axis_size=axis_size, axis_name=axis_name, spmd_axis_name=spmd_axis_name)
[docs]def jit(target: Target, variables: lift.CollectionFilter = True, rngs: lift.PRNGSequenceFilter = True, static_argnums: Union[int, Iterable[int]] = (), donate_argnums: Union[int, Iterable[int]] = (), device=None, backend: Union[str, None] = None, methods=None) -> Target: """Lifted version of ``jax.jit``. Args: target: a ``Module`` or a function taking a ``Module`` as its first argument. variables: The variable collections that are lifted. By default all collections are lifted. rngs: The PRNG sequences that are lifted. By default all PRNG sequences are lifted. static_argnums: An int or collection of ints specifying which positional arguments to treat as static (compile-time constant). Operations that only depend on static arguments will be constant-folded in Python (during tracing), and so the corresponding argument values can be any Python object. Static arguments should be hashable, meaning both ``__hash__`` and ``__eq__`` are implemented, and immutable. Calling the jitted function with different values for these constants will trigger recompilation. If the jitted function is called with fewer positional arguments than indicated by ``static_argnums`` then an error is raised. Arguments that are not arrays or containers thereof must be marked as static. Defaults to (). donate_argnums: Specify which arguments are "donated" to the computation. It is safe to donate arguments if you no longer need them once the computation has finished. In some cases XLA can make use of donated buffers to reduce the amount of memory needed to perform a computation, for example recycling one of your input buffers to store a result. You should not reuse buffers that you donate to a computation, JAX will raise an error if you try to. device: This is an experimental feature and the API is likely to change. Optional, the Device the jitted function will run on. (Available devices can be retrieved via :py:func:`jax.devices`.) The default is inherited from XLA's DeviceAssignment logic and is usually to use ``jax.devices()[0]``. backend: a string representing the XLA backend: ``'cpu'``, ``'gpu'``, or ``'tpu'``. methods: If `target` is a `Module`, the methods of `Module` to jit. Returns: A wrapped version of target, set up for just-in-time compilation. """ return lift_transform( lift.jit, target, variables=variables, rngs=rngs, static_argnums=static_argnums, donate_argnums=donate_argnums, device=device, backend=backend, methods=methods)
def checkpoint(target: Target, variables: lift.CollectionFilter = True, rngs: lift.PRNGSequenceFilter = True, concrete: bool = False, prevent_cse: bool = True, static_argnums: Union[int, Tuple[int, ...]] = (), policy: Optional[Callable[..., bool]] = None, methods=None) -> Target: """Lifted version of ``jax.checkpoint``. This function is aliased to ``lift.remat`` just like ``jax.remat``. Args: target: a ``Module`` or a function taking a ``Module`` as its first argument. intermediate computations will be re-computed when computing gradients for the target. variables: The variable collections that are lifted. By default all collections are lifted. rngs: The PRNG sequences that are lifted. By default all PRNG sequences are lifted. concrete: Optional, boolean indicating whether ``fun`` may involve value-dependent Python control flow (default False). Support for such control flow is optional, and disabled by default, because in some edge-case compositions with :func:`jax.jit` it can lead to some extra computation. prevent_cse: Optional, boolean indicating whether to prevent common subexpression elimination (CSE) optimizations in the HLO generated from differentiation. This CSE prevention has costs because it can foil other optimizations, and because it can incur high overheads on some backends, especially GPU. The default is True because otherwise, under a ``jit`` or ``pmap``, CSE can defeat the purpose of this decorator. But in some settings, like when used inside a ``scan``, this CSE prevention mechanism is unnecessary, in which case ``prevent_cse`` should be set to False. static_argnums: Optional, int or sequence of ints, indicates which argument values on which to specialize for tracing and caching purposes. Specifying arguments as static can avoid ConcretizationTypeErrors when tracing, but at the cost of more retracing overheads. policy: Experimental checkpoint policy, see ``jax.checkpoint``. methods: If `target` is a `Module`, the methods of `Module` to checkpoint. Returns: A wrapped version of ``target``. When computing gradients intermediate computations will be re-computed on the backward pass. """ # subtract 1 from each static_argnums because 'self' is not passed to the # lifted function static_argnums = jax.tree_util.tree_map(lambda x: x - 1, static_argnums) return lift_transform( lift.checkpoint, target, variables=variables, rngs=rngs, concrete=concrete, static_argnums=static_argnums, prevent_cse=prevent_cse, policy=policy, methods=methods) remat = checkpoint
[docs]def remat_scan( target: Target, lengths: Optional[Sequence[int]] = (), policy: Optional[Callable[..., bool]] = None, variable_broadcast: lift.CollectionFilter = False, variable_carry: lift.CollectionFilter = False, variable_axes: Mapping[lift.CollectionFilter, lift.InOutScanAxis] = FrozenDict({True: 0}), split_rngs: Mapping[lift.PRNGSequenceFilter, bool] = FrozenDict({True: True}) ) -> Target: """Combines remat and scan for memory efficiency and constant time compilation. ``remat_scan`` allows for constant compile times and sublinear memory usage with respect to model depth. At a small constant penalty. This is typically beneficial for very deep models. Example:: class BigModel(nn.Module): @nn.compact def __call__(self, x): DenseStack = nn.remat_scan(nn.Dense, lengths=(10, 10)) # 100x dense with O(sqrt(N)) memory for gradient computation return DenseStack(8, name="dense_stack")(x) Args: target: a ``Module`` or a function taking a ``Module`` as its first argument. lengths: number of loop iterations at the given level. The total number of iterations `n = prod(lengths)`. each loop is rematerialized. This way the memory consumption is proportional to `n^(1 / d)` where `d = len(lengths)`. Minimal memory consumptions requires tuning the lengths such that the same amount of memory is consumed at each level of the nested loop. policy: Experimental checkpoint policy, see ``jax.checkpoint``. variable_broadcast: Specifies the broadcasted variable collections. A broadcasted variable should not depend on any computation that cannot be lifted out of the loop. This is typically used to define shared parameters inside the fn. variable_carry: Specifies the variable collections that are carried through the loop. Mutations to these variables are carried to the next iteration and will be preserved when the scan finishes. variable_axes: the variable collections that are scanned over. Defaults to ``{True: 0}``. split_rngs: Split PRNG sequences will be different for each loop iterations. If split is False the PRNGs will be the same across iterations. Defaults to ``{True: True}``. Returns: A wrapped version of ``target`` that repeats itself prod(lengths) times. """ return lift_transform( lift.remat_scan, target, lengths=lengths, variable_broadcast=variable_broadcast, variable_carry=variable_carry, variable_axes=variable_axes, split_rngs=split_rngs, policy=policy, )
[docs]def scan(target: Target, variable_axes: Mapping[lift.CollectionFilter, lift.InOutScanAxis] = FrozenDict(), variable_broadcast: lift.CollectionFilter = False, variable_carry: lift.CollectionFilter = False, split_rngs: Mapping[lift.PRNGSequenceFilter, bool] = FrozenDict(), in_axes=0, out_axes=0, length: Optional[int] = None, reverse: bool = False, unroll: int = 1, data_transform: Optional[Callable[..., Any]] = None, methods=None) -> Target: """A lifted version of ``jax.lax.scan``. See ``jax.lax.scan`` for the unlifted scan in Jax. To improve consistency with ``vmap``, this version of scan uses ``in_axes`` and ``out_axes`` to determine which arguments are scanned over and along which axis. ``scan`` distinguishes between 3 different types of values inside the loop: #. **scan**: a value that is iterated over in a loop. All scan values must have the same size in the axis they are scanned over. Scanned outputs will be stacked along the scan axis. #. **carry**: A carried value is updated at each loop iteration. It must have the same shape and dtype throughout the loop. #. **broadcast**: a value that is closed over by the loop. When a variable is broadcasted they are typically initialized inside the loop body but independent of the loop variables. The loop body should have the signature ``(scope, body, carry, *xs) -> (carry, ys)``, where ``xs`` and ``ys`` are the scan values that go in and out of the loop. Example:: import flax import flax.linen as nn from jax import random class SimpleScan(nn.Module): @nn.compact def __call__(self, c, xs): LSTM = nn.scan(nn.LSTMCell, variable_broadcast="params", split_rngs={"params": False}, in_axes=1, out_axes=1) return LSTM()(c, xs) seq_len, batch_size, in_feat, out_feat = 20, 16, 3, 5 key_1, key_2, key_3 = random.split(random.PRNGKey(0), 3) xs = random.uniform(key_1, (batch_size, seq_len, in_feat)) init_carry = nn.LSTMCell.initialize_carry(key_2, (batch_size,), out_feat) model = SimpleScan() variables = model.init(key_3, init_carry, xs) out_carry, out_val = model.apply(variables, init_carry, xs) assert out_val.shape == (batch_size, seq_len, out_feat) Note that when providing a function to ``nn.scan``, the scanning happens over all arguments starting from the third argument, as specified by ``in_axes``. So in the following example, the input that are being scanned over are ``xs``, ``*args*``, and ``**kwargs``:: def body_fn(cls, carry, xs, *args, **kwargs): extended_states = cls.some_fn(xs, carry, *args, **kwargs) return extended_states scan_fn = nn.scan( body_fn, in_axes=0, # scan over axis 0 from third arg of body_fn onwards. variable_axes=SCAN_VARIABLE_AXES, split_rngs=SCAN_SPLIT_RNGS) Args: target: a ``Module`` or a function taking a ``Module`` as its first argument. variable_axes: the variable collections that are scanned over. variable_broadcast: Specifies the broadcasted variable collections. A broadcasted variable should not depend on any computation that cannot be lifted out of the loop. This is typically used to define shared parameters inside the fn. variable_carry: Specifies the variable collections that are carried through the loop. Mutations to these variables are carried to the next iteration and will be preserved when the scan finishes. split_rngs: Split PRNG sequences will be different for each loop iterations. If split is False the PRNGs will be the same across iterations. in_axes: Specifies the axis to scan over for the arguments. Should be a prefix tree of the arguments. Use `flax.core.broadcast` to feed an entire input to each iteration of the scan body. out_axes: Specifies the axis to scan over for the return value. Should be a prefix tree of the return value. length: Specifies the number of loop iterations. This only needs to be specified if it cannot be derivied from the scan arguments. reverse: If true, scan from end to start in reverse order. unroll: how many scan iterations to unroll within a single iteration of a loop (default: 1). data_transform: optional function to transform raw functional-core variable and rng groups inside lifted scan body_fn, intended for inline SPMD annotations. methods: If `target` is a `Module`, the methods of `Module` to scan over. Returns: The scan function with the signature ``(scope, carry, *xxs) -> (carry, yys)``, where ``xxs`` and ``yys`` are the scan values that go in and out of the loop. """ return lift_transform( lift.scan, target, variable_axes=variable_axes, variable_broadcast=variable_broadcast, variable_carry=variable_carry, split_rngs=split_rngs, in_axes=in_axes, out_axes=out_axes, length=length, reverse=reverse, unroll=unroll, data_transform=data_transform, methods=methods)
[docs]def map_variables( target: Target, mapped_collections: lift.CollectionFilter = True, trans_in_fn: Callable[..., Any] = lift.id_fn, trans_out_fn: Callable[..., Any] = lift.id_fn, init: bool = False, mutable: bool = False, rngs: lift.PRNGSequenceFilter = True, variables: lift.CollectionFilter = True, methods=None) -> Target: """Map Variables inside a module. Example:: class OneBitDense(nn.Module): @nn.compact def __call__(self, x): def sign(x): return jax.tree_util.tree_map(jnp.sign, x) MapDense = nn.map_variables(nn.Dense, "params", sign, init=True) return MapDense(4)(x) Args: target: the function to be transformed. mapped_collections: the collection(s) to be transformed. trans_in_fn: creates a view of the target variables. trans_out_fn: transforms the updated variables in the view after mutation. init: If True, variables are initialized before transformation. mutable: If True, the mapped variable collections will be mutable. rngs: PRNGSequences added to the transformed scope (default: all). variables: Additional Variable collections added to the transformed scope. Besides those specified by `target` (default: all). methods: If `target` is a `Module`, the methods of `Module` to map variables for. Returns: a wrapped version of ``target`` that will map the specificied collections. """ return lift_transform( lift.map_variables, target, mapped_collections, trans_in_fn, trans_out_fn, init, mutable, rngs, variables, methods=methods, )
[docs]def vjp( fn: Callable[..., Any], mdl: Module, *primals, has_aux: bool = False, reduce_axes=(), vjp_variables: lift.CollectionFilter = 'params', variables: lift.CollectionFilter = True, rngs: lift.PRNGSequenceFilter = True, ) -> Tuple[Any, Any]: """A lifted version of ``jax.vjp``. See ``jax.vjp`` for the unlifted vector-Jacobiam product (backward gradient). Note that a gradient is returned for all variables in the collections specified by `vjp_variables`. However, the backward funtion only expects a cotangent for the return value of `fn`. If variables require a co-tangent as well they can be returned from `fn` using `Module.variables`. Example:: class LearnScale(nn.Module): @nn.compact def __call__(self, x, y): p = self.param('scale', nn.initializers.zeros, ()) return p * x * y class Foo(nn.Module): @nn.compact def __call__(self, x, y): z, bwd = nn.vjp(lambda mdl, x, y: mdl(x, y), LearnScale(), x, y) params_grad, x_grad, y_grad = bwd(jnp.ones(z.shape)) return z, params_grad, x_grad, y_grad Args: fn: Function to be differentiated. Its arguments should be arrays, scalars, or standard Python containers of arrays or scalars. It should return an array, scalar, or standard Python container of arrays or scalars. It will receive the scope and primals as arguments. mdl: The module of which the variables will be differentiated. *primals: A sequence of primal values at which the Jacobian of ``fn`` should be evaluated. The length of ``primals`` should be equal to the number of positional parameters to ``fn``. Each primal value should be a tuple of arrays, scalar, or standard Python containers thereof. has_aux: Optional, bool. Indicates whether ``fn`` returns a pair where the first element is considered the output of the mathematical function to be differentiated and the second element is auxiliary data. Default False. reduce_axes: Optional, tuple of axis names. If an axis is listed here, and ``fn`` implicitly broadcasts a value over that axis, the backward pass will perform a ``psum`` of the corresponding gradient. Otherwise, the VJP will be per-example over named axes. For example, if ``'batch'`` is a named batch axis, ``vjp(f, *args, reduce_axes=('batch',))`` will create a VJP function that sums over the batch while ``vjp(f, *args)`` will create a per-example VJP. vjp_variables: The vjpfun will return a cotangent vector for all variable collections specified by this filter. variables: other variables collections that are available inside `fn` but do not receive a cotangent. rngs: the prngs that are available inside `fn`. Returns: If ``has_aux`` is ``False``, returns a ``(primals_out, vjpfun)`` pair, where ``primals_out`` is ``fn(*primals)``. ``vjpfun`` is a function from a cotangent vector with the same shape as ``primals_out`` to a tuple of cotangent vectors with the same shape as ``primals``, representing the vector-Jacobian product of ``fn`` evaluated at ``primals``. If ``has_aux`` is ``True``, returns a ``(primals_out, vjpfun, aux)`` tuple where ``aux`` is the auxiliary data returned by ``fn``. """ return lift_direct_transform( lift.vjp, (fn,), mdl, *primals, multi_scope=False, has_aux=has_aux, reduce_axes=reduce_axes, vjp_variables=vjp_variables, variables=variables, rngs=rngs)
[docs]def jvp( fn: Callable[..., Any], mdl: Module, primals, tangents, variable_tangents, variables: lift.CollectionFilter = True, rngs: lift.PRNGSequenceFilter = True, ) -> Union[Tuple[Any, Callable[..., Any]], Tuple[Any, Callable[..., Any], Any]]: """A lifted version of ``jax.jvp``. See ``jax.jvp`` for the unlifted Jacobian-vector product (forward gradient). Note that no tangents are returned for variables. When variable tangents are required their value should be returned explicitly by `fn` using `Module.variables`:: class LearnScale(nn.Module): @nn.compact def __call__(self, x): p = self.param('test', nn.initializers.zeros, ()) return p * x class Foo(nn.Module): @nn.compact def __call__(self, x): scale = LearnScale() vars_t = jax.tree_util.tree_map(jnp.ones_like, scale.variables.get('params', {})) _, out_t = nn.jvp( lambda mdl, x: mdl(x), scale, (x,), (jnp.zeros_like(x),), variable_tangents={'params': vars_t}) return out_t Example:: def learn_scale(scope, x): p = scope.param('scale', nn.initializers.zeros, ()) return p * x def f(scope, x): vars_t = jax.tree_util.tree_map(jnp.ones_like, scope.variables().get('params', {})) x, out_t = lift.jvp( learn_scale, scope, (x,), (jnp.zeros_like(x),), variable_tangents={'params': vars_t}) return out_t Args: fn: Function to be differentiated. Its arguments should be arrays, scalars, or standard Python containers of arrays or scalars. It should return an array, scalar, or standard Python container of arrays or scalars. It will receive the scope and primals as arguments. mdl: The module of which the variables will be differentiated. primals: The primal values at which the Jacobian of ``fun`` should be evaluated. Should be either a tuple or a list of arguments, and its length should be equal to the number of positional parameters of ``fun``. tangents: The tangent vector for which the Jacobian-vector product should be evaluated. Should be either a tuple or a list of tangents, with the same tree structure and array shapes as ``primals``. variable_tangents: A dict or PyTree fo dicts with the same structure as scopes. Each entry in the dict specifies the tangents for a variable collection. Not specifying a collection in variable_tangents is equivalent to passing a zero vector as the tangent. variables: other variables collections that are available in `fn` but do not receive a tangent. rngs: the prngs that are available inside `fn`. Returns: A ``(primals_out, tangents_out)`` pair, where ``primals_out`` is ``fun(*primals)``, and ``tangents_out`` is the Jacobian-vector product of ``function`` evaluated at ``primals`` with ``tangents``. The ``tangents_out`` value has the same Python tree structure and shapes as ``primals_out``. """ return lift_direct_transform( lift.jvp, (fn,), mdl, primals, tangents, variable_tangents, multi_scope=False, variables=variables, rngs=rngs)
ModuleT = TypeVar('ModuleT', bound=Module) C = TypeVar('C')
[docs]def while_loop( cond_fn: Callable[[ModuleT, C], bool], body_fn: Callable[[ModuleT, C], C], mdl: ModuleT, init: C, carry_variables: lift.CollectionFilter = False, broadcast_variables: lift.CollectionFilter = True, split_rngs: Mapping[lift.PRNGSequenceFilter, bool] = FrozenDict()) -> C: """Lifted version of jax.lax.while_loop. The lifted scope is passed to `cond_fn` and `body_fn`. Broadcasted variables are immutable. The carry variable are mutable but cannot change shape and dtype. This also means you cannot initialize variables inside the body. Consider calling `body_fn` once manually before calling `while_loop` if variable initialization is required. Example:: class WhileLoopExample(nn.Module): @nn.compact def __call__(self, x): def cond_fn(mdl, c): return mdl.variables['state']['acc'] < 10 def body_fn(mdl, c): acc = mdl.variable('state', 'acc', lambda: jnp.array(0)) acc.value += 1 y = nn.Dense(c.shape[-1])(c) return y c = x if self.is_mutable_collection('params'): return body_fn(self, c) else: return nn.while_loop(cond_fn, body_fn, self, c, carry_variables='state') k = random.PRNGKey(0) x = jnp.ones((2, 2)) intial_vars = WhileLoopExample().init(k, x) result, state = WhileLoopExample().apply(intial_vars, x, mutable=['state']) Args: cond_fn: Should return True as long as the loop should continue. body_fn: The body of the while loop. mdl: The Module which should be lifted into the loop. init: The initial state passed to the loop carry_variables: collections that are carried through the loop and are therefore mutable (default: none). broadcast_variables: collections that are closed over and are therefore read-only (default: all collections) split_rngs: Split PRNG sequences will be different for each loop iterations. If split is False the PRNGs will be the same across iterations. Returns: The final state after executing the while loop. """ return lift_direct_transform( lift.while_loop, (cond_fn, body_fn), mdl, init, carry_variables, broadcast_variables, split_rngs)
def _cond_wrapper(t_fn, f_fn, scope, pred, *ops, variables, rngs): return lift.cond(pred, t_fn, f_fn, scope, *ops, variables=variables, rngs=rngs)
[docs]def cond( pred: Any, true_fun: Callable[..., C], false_fun: Callable[..., C], mdl: Module, *operands, variables: lift.CollectionFilter = True, rngs: lift.PRNGSequenceFilter = True) -> C: """Lifted version of ``jax.lax.cond``. The returned values from ``true_fun`` and ``false_fun`` must have the same Pytree structure, shapes, and dtypes. The variables created or updated inside the branches must also have the same structure. Note that this constraint is violated when creating variables or submodules in only one branch. Because initializing variables in just one branch causes the parameter structure to be different. Example:: class CondExample(nn.Module): @nn.compact def __call__(self, x, pred): self.variable('state', 'true_count', lambda: 0) self.variable('state', 'false_count', lambda: 0) def true_fn(mdl, x): mdl.variable('state', 'true_count').value += 1 return nn.Dense(2, name='dense')(x) def false_fn(mdl, x): mdl.variable('state', 'false_count').value += 1 return -nn.Dense(2, name='dense')(x) return nn.cond(pred, true_fn, false_fn, self, x) Args: pred: determines if true_fun or false_fun is evaluated. true_fun: The function evalauted when ``pred`` is `True`. The signature is (module, *operands) -> T. false_fun: The function evalauted when ``pred`` is `False`. The signature is (module, *operands) -> T. mdl: A Module target to pass. *operands: The arguments passed to ``true_fun`` and ``false_fun`` variables: The variable collections passed to the conditional branches (default: all) rngs: The PRNG sequences passed to the conditionals (default: all) Returns: The result of the evaluated branch (``true_fun`` or ``false_fun``). """ return lift_direct_transform( _cond_wrapper, (true_fun, false_fun), mdl, pred, *operands, variables=variables, rngs=rngs)
def _switch_wrapper(*args, variables, rngs, n_branches): # first n_branches arguments are branches. # then scope, index, and the rest are *operands branches = args[:n_branches] scope, index, *operands = args[n_branches:] return lift.switch(index, branches, scope, *operands, variables=variables, rngs=rngs)
[docs]def switch( index: Any, branches: Sequence[Callable[..., C]], mdl: Module, *operands, variables: lift.CollectionFilter = True, rngs: lift.PRNGSequenceFilter = True) -> C: """Lifted version of ``jax.lax.switch``. The returned values from ``branches`` must have the same Pytree structure, shapes, and dtypes. The variables created or updated inside the branches must also have the same structure. Note that this constraint is violated when creating variables or submodules in only one branch. Because initializing variables in just one branch causes the parameter structure to be different. Example:: class SwitchExample(nn.Module): @nn.compact def __call__(self, x, index): self.variable('state', 'a_count', lambda: 0) self.variable('state', 'b_count', lambda: 0) self.variable('state', 'c_count', lambda: 0) def a_fn(mdl, x): mdl.variable('state', 'a_count').value += 1 return nn.Dense(2, name='dense')(x) def b_fn(mdl, x): mdl.variable('state', 'b_count').value += 1 return -nn.Dense(2, name='dense')(x) def c_fn(mdl, x): mdl.variable('state', 'c_count').value += 1 return nn.Dense(2, name='dense')(x) return nn.switch(index, [a_fn, b_fn, c_fn], self, x) If you want to have a different parameter structure for each branch you should run all branches on initialization before calling switch:: class MultiHeadSwitchExample(nn.Module): def setup(self) -> None: self.heads = [ nn.Sequential([nn.Dense(10), nn.Dense(7), nn.Dense(5)]), nn.Sequential([nn.Dense(11), nn.Dense(5)]), nn.Dense(5), ] @nn.compact def __call__(self, x, index): def head_fn(i): return lambda mdl, x: mdl.heads[i](x) branches = [head_fn(i) for i in range(len(self.heads))] # run all branches on init if self.is_mutable_collection('params'): for branch in branches: _ = branch(self, x) return nn.switch(index, branches, self, x) Args: index: Integer scalar type, indicating which branch function to apply. branches: Sequence of functions to be applied based on index. The signature of each function is (module, *operands) -> T. mdl: A Module target to pass. *operands: The arguments passed to the branches. variables: The variable collections passed to the conditional branches (default: all) rngs: The PRNG sequences passed to the conditionals (default: all) Returns: The result of the evaluated branch. """ return lift_direct_transform( _switch_wrapper, tuple(branches), mdl, index, *operands, variables=variables, rngs=rngs, n_branches=len(branches))
# a version of lift.custom_vjp with a single scope function # this avoids having to lift multiple functions in # lift_transform. def _custom_vjp_single_scope_fn( fn: Callable[..., Any], backward_fn: Callable[..., Any], grad_vars: lift.CollectionFilter = 'params', nondiff_argnums=()): nodiff_fn = functools.partial(fn, needs_residual=False) forward_fn = functools.partial(fn, needs_residual=True) return lift.custom_vjp(nodiff_fn, forward_fn, backward_fn, grad_vars, nondiff_argnums)
[docs]def custom_vjp(fn: Callable[..., Any], forward_fn: Callable[..., Any], backward_fn: Callable[..., Any], grad_vars: lift.CollectionFilter = 'params', nondiff_argnums=()): """Lifted version of `jax.custom_vjp`. `forward_fn` and `backward_fn` together define a custom vjp for `fn`. The original `fn` will run in case a vjp (backward gradient) is not computed. The `forward_fn` receives the same arguments as `fn` but is expected to return a tuple containing the output of `fn(mdl, *args)` and the residuals that are passed to `backward_fn`. The `backward_fn` receives the nondiff arguments, residuals, and the output tangents. It should return a tuple containing the variable and input tangents. Note that the vjp function returned by `nn.vjp` can be passed as residual and used in the `backward_fn`. The scope is unavailable during the backward pass. If the module is required in `backward_fn`, a snapshot of the variables can be taken and returned as a residual in the `forward_fn`. Example:: class Foo(nn.Module): @nn.compact def __call__(self, x): def f(mdl, x): return mdl(x) def fwd(mdl, x): return nn.vjp(f, mdl, x) def bwd(vjp_fn, y_t): params_t, *inputs_t = vjp_fn(y_t) params_t = jax.tree_util.tree_map(jnp.sign, params_t) return (params_t, *inputs_t) sign_grad = nn.custom_vjp( f, forward_fn=fwd, backward_fn=bwd) return sign_grad(nn.Dense(1), x).reshape(()) x = jnp.ones((2,)) variables = Foo().init(random.PRNGKey(0), x) grad = jax.grad(Foo().apply)(variables, x) Args: fn: The function to define a custom_vjp for. forward_fn: A function with the same arguments as ``fn`` returning an tuple with the original output and the residuals that will be passsed to ``backward_fn``. backward_fn: arguments are passed as ``(*nondiff_args, residuals, tangents)`` The function should return a tuple containing the tangents for the variable in the collections specified by `grad_vars` and the input arguments (except the module and nondiff args). grad_vars: The collections for which a vjp will be computed (default: "params"). nondiff_argnums: arguments for which no vjp is computed. Returns: A function with the same signature as `fn` with the custom vjp. """ def shared_forward_fn(*args, needs_residual, **kwargs): if needs_residual: return forward_fn(*args, **kwargs) else: return fn(*args, ** kwargs) return decorator_lift_transform( _custom_vjp_single_scope_fn, shared_forward_fn, backward_fn=backward_fn, grad_vars=grad_vars, nondiff_argnums=nondiff_argnums, multi_scope=False)
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. """ # 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