# 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.
# pytype: skip-file
import dataclasses
import functools
import typing as tp
from flax.nnx import (
extract,
filterlib,
graph,
statelib,
variablelib,
)
import jax
import jax.core
import jax.stages
from flax.typing import Missing
F = tp.TypeVar('F', bound=tp.Callable[..., tp.Any])
# -------------------------------
# jit
# -------------------------------
class StateSharding(extract.PrefixMapping):
def __init__(
self,
filter_sharding: statelib.State
| tp.Mapping[filterlib.Filter, tp.Any]
| tp.Iterable[tuple[filterlib.Filter, tp.Any]],
/,
):
if isinstance(filter_sharding, statelib.State):
filter_sharding = statelib.create_path_filters(filter_sharding) # type: ignore
iterable = tuple(
filter_sharding.items()
if isinstance(filter_sharding, tp.Mapping)
else filter_sharding
)
self._filters = tuple(filter for filter, _ in iterable)
self._shardings = tuple(axis for _, axis in iterable)
@property
def filters(self) -> tuple[filterlib.Filter, ...]:
return self._filters
@property
def shardings(self) -> tuple[tp.Any, ...]:
return self._shardings
def map_prefix(
self, path: variablelib.PathParts, variable: variablelib.Variable
) -> tp.Any:
for filter, sharding in zip(self.filters, self.shardings):
predicate = filterlib.to_predicate(filter)
if predicate(path, variable):
return sharding
raise ValueError(f'No axis found for {path=}, {variable=}')
def __repr__(self):
return f'StateSharding({dict(zip(self.filters, self.shardings))})'
def __eq__(self, other):
return (
isinstance(other, StateSharding)
and self.filters == other.filters
and self.shardings == other.shardings
)
def __hash__(self):
return hash((self.filters, self.shardings))
def _jit_split_fn(ctx: graph.SplitContext, path, prefix, x):
if isinstance(prefix, StateSharding):
return extract.NodeStates.from_split(
*ctx.split(x, *prefix.filters), metadata=prefix
)
return extract.NodeStates.from_split(*ctx.split(x))
@dataclasses.dataclass(eq=False)
class JitFn:
f: tp.Callable[..., tp.Any]
in_shardings: tp.Any
out_shardings: tp.Any
kwarg_shardings: tp.Any
def __post_init__(self):
functools.update_wrapper(self, self.f)
def __call__(self, *pure_args, **pure_kwargs):
args, kwargs = extract.from_tree((pure_args, pure_kwargs), ctxtag='jit')
out = self.f(*args, **kwargs)
args_out, kwargs_out = extract.clear_non_graph_nodes((args, kwargs))
pure_args_out, pure_kwargs_out, pure_out = extract.to_tree(
(args_out, kwargs_out, out),
prefix=(self.in_shardings, self.kwarg_shardings, self.out_shardings),
ctxtag='jit',
split_fn=_jit_split_fn,
)
return pure_args_out, pure_kwargs_out, pure_out
@tp.overload
def jit(
*,
in_shardings: tp.Any = None,
out_shardings: tp.Any = None,
static_argnums: int | tp.Sequence[int] | None = None,
static_argnames: str | tp.Iterable[str] | None = None,
donate_argnums: int | tp.Sequence[int] | None = None,
donate_argnames: str | tp.Iterable[str] | None = None,
keep_unused: bool = False,
device: tp.Optional[jax.Device] = None,
backend: tp.Optional[str] = None,
inline: bool = False,
abstracted_axes: tp.Optional[tp.Any] = None,
) -> tp.Callable[[F], F]: ...
@tp.overload
def jit(
fun: F,
*,
in_shardings: tp.Any = None,
out_shardings: tp.Any = None,
static_argnums: int | tp.Sequence[int] | None = None,
static_argnames: str | tp.Iterable[str] | None = None,
donate_argnums: int | tp.Sequence[int] | None = None,
donate_argnames: str | tp.Iterable[str] | None = None,
keep_unused: bool = False,
device: tp.Optional[jax.Device] = None,
backend: tp.Optional[str] = None,
inline: bool = False,
abstracted_axes: tp.Optional[tp.Any] = None,
) -> F: ...
[docs]def jit(
fun: F | type[Missing] = Missing,
*,
in_shardings: tp.Any = None,
out_shardings: tp.Any = None,
static_argnums: int | tp.Sequence[int] | None = None,
static_argnames: str | tp.Iterable[str] | None = None,
donate_argnums: int | tp.Sequence[int] | None = None,
donate_argnames: str | tp.Iterable[str] | None = None,
keep_unused: bool = False,
device: tp.Optional[jax.Device] = None,
backend: tp.Optional[str] = None,
inline: bool = False,
abstracted_axes: tp.Optional[tp.Any] = None,
) -> F | tp.Callable[[F], F]:
"""
Lifted version of ``jax.jit`` that can handle Modules / graph nodes as
arguments.
Args:
fun: Function to be jitted. ``fun`` should be a pure function, as
side-effects may only be executed once.
The arguments and return value of ``fun`` should be arrays,
scalars, or (nested) standard Python containers (tuple/list/dict) thereof.
Positional arguments indicated by ``static_argnums`` can be anything at
all, provided they are hashable and have an equality operation defined.
Static arguments are included as part of a compilation cache key, which is
why hash and equality operators must be defined.
JAX keeps a weak reference to ``fun`` for use as a compilation cache key,
so the object ``fun`` must be weakly-referenceable. Most :class:`Callable`
objects will already satisfy this requirement.
in_shardings: Pytree of structure matching that of arguments to ``fun``,
with all actual arguments replaced by resource assignment specifications.
It is also valid to specify a pytree prefix (e.g. one value in place of a
whole subtree), in which case the leaves get broadcast to all values in
that subtree.
The ``in_shardings`` argument is optional. JAX will infer the shardings
from the input :py:class:`jax.Array`'s and defaults to replicating the input
if the sharding cannot be inferred.
The valid resource assignment specifications are:
- :py:class:`Sharding`, which will decide how the value
will be partitioned. With this, using a mesh context manager is not
required.
- :py:obj:`None`, will give JAX the freedom to choose whatever sharding
it wants.
For in_shardings, JAX will mark is as replicated but this behavior
can change in the future.
For out_shardings, we will rely on the XLA GSPMD partitioner to
determine the output shardings.
The size of every dimension has to be a multiple of the total number of
resources assigned to it. This is similar to pjit's in_shardings.
out_shardings: Like ``in_shardings``, but specifies resource
assignment for function outputs. This is similar to pjit's
out_shardings.
The ``out_shardings`` argument is optional. If not specified, :py:func:`jax.jit`
will use GSPMD's sharding propagation to figure out what the sharding of the
output(s) should be.
static_argnums: An optional int or collection of ints that specify 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.
Arguments that are not arrays or containers thereof must be marked as
static.
If neither ``static_argnums`` nor ``static_argnames`` is provided, no
arguments are treated as static. If ``static_argnums`` is not provided but
``static_argnames`` is, or vice versa, JAX uses
:code:`inspect.signature(fun)` to find any positional arguments that
correspond to ``static_argnames``
(or vice versa). If both ``static_argnums`` and ``static_argnames`` are
provided, ``inspect.signature`` is not used, and only actual
parameters listed in either ``static_argnums`` or ``static_argnames`` will
be treated as static.
static_argnames: An optional string or collection of strings specifying
which named arguments to treat as static (compile-time constant). See the
comment on ``static_argnums`` for details. If not
provided but ``static_argnums`` is set, the default is based on calling
``inspect.signature(fun)`` to find corresponding named arguments.
donate_argnums: Specify which positional argument buffers are "donated" to
the computation. It is safe to donate argument buffers 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. By default, no argument buffers are
donated.
If neither ``donate_argnums`` nor ``donate_argnames`` is provided, no
arguments are donated. If ``donate_argnums`` is not provided but
``donate_argnames`` is, or vice versa, JAX uses
:code:`inspect.signature(fun)` to find any positional arguments that
correspond to ``donate_argnames``
(or vice versa). If both ``donate_argnums`` and ``donate_argnames`` are
provided, ``inspect.signature`` is not used, and only actual
parameters listed in either ``donate_argnums`` or ``donate_argnames`` will
be donated.
For more details on buffer donation see the
`FAQ <https://jax.readthedocs.io/en/latest/faq.html#buffer-donation>`_.
donate_argnames: An optional string or collection of strings specifying
which named arguments are donated to the computation. See the
comment on ``donate_argnums`` for details. If not
provided but ``donate_argnums`` is set, the default is based on calling
``inspect.signature(fun)`` to find corresponding named arguments.
keep_unused: If `False` (the default), arguments that JAX determines to be
unused by `fun` *may* be dropped from resulting compiled XLA executables.
Such arguments will not be transferred to the device nor provided to the
underlying executable. If `True`, unused arguments will not be pruned.
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: This is an experimental feature and the API is likely to change.
Optional, a string representing the XLA backend: ``'cpu'``, ``'gpu'``, or
``'tpu'``.
inline: Specify whether this function should be inlined into enclosing
jaxprs (rather than being represented as an application of the xla_call
primitive with its own subjaxpr). Default False.
Returns:
A wrapped version of ``fun``, set up for just-in-time compilation.
"""
if fun is Missing:
return functools.partial(
jit,
in_shardings=in_shardings,
out_shardings=out_shardings,
static_argnums=static_argnums,
static_argnames=static_argnames,
donate_argnums=donate_argnums,
donate_argnames=donate_argnames,
keep_unused=keep_unused,
device=device,
backend=backend,
inline=inline,
abstracted_axes=abstracted_axes,
) # type: ignore[return-value]
kwarg_shardings = None
jax_in_shardings = jax.tree.map(
lambda x: extract.NodeStates.from_prefixes(x.shardings, metadata=x)
if isinstance(x, StateSharding)
else x,
in_shardings,
)
jax_out_shardings = jax.tree.map(
lambda x: extract.NodeStates.from_prefixes(x.shardings, metadata=x)
if isinstance(x, StateSharding)
else x,
out_shardings,
)
jitted_fn = jax.jit(
JitFn(fun, in_shardings, out_shardings, kwarg_shardings),
in_shardings=jax_in_shardings,
out_shardings=(jax_in_shardings, kwarg_shardings, jax_out_shardings), # type: ignore
static_argnums=static_argnums,
static_argnames=static_argnames,
donate_argnums=donate_argnums,
donate_argnames=donate_argnames,
keep_unused=keep_unused,
device=device,
backend=backend,
inline=inline,
abstracted_axes=abstracted_axes,
)
@functools.wraps(fun)
@graph.update_context('jit')
def jit_wrapper(*args, **kwargs):
pure_args, pure_kwargs = extract.to_tree(
(args, kwargs),
prefix=(in_shardings, kwarg_shardings),
split_fn=_jit_split_fn,
check_aliasing=in_shardings is not None,
ctxtag='jit',
)
pure_args_out, pure_kwargs_out, pure_out = jitted_fn(
*pure_args, **pure_kwargs
)
_args_out, _kwargs_out, out = extract.from_tree(
(pure_args_out, pure_kwargs_out, pure_out), ctxtag='jit'
)
return out
jit_wrapper.inner = jitted_fn # type: ignore
return jit_wrapper # type: ignore
