# 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,
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"""Utilities for working with pjit and partitioned models.
This module introduces `axis_rules`, `logical_to_mesh_axes`,
`logical_to_mesh`, `with_logical_constraint` for appyling pjit
sharding constraints in terms of "logical named axes" rather than
pjit's default mesh axes.
Additionally the `LogicallyPartitioned` metadata wrapper is defined as
well as the initializer function wrapper `with_logical_partitioning` for
introducing logical axis metadata into a model's variables.
"""
import collections
import contextlib
import dataclasses
import enum
import functools
import threading
from typing import Any, Callable, List, Optional, Sequence, Tuple, Union
import jax
from jax.experimental import maps, pjit
from flax import struct
from flax.core import meta
from flax.core.lift import In as ScanIn # pylint: disable=unused-import
from flax.core.lift import Out as ScanOut # pylint: disable=unused-import
# Real types and dummy aliases for documentation
LogicalRules = Sequence[Tuple[str, Union[str, Tuple[str], None]]]
Array = Any # pylint: disable=invalid-name
ArrayPytree = Any # pylint: disable=invalid-name
LogicalPartitionSpec = Any # pylint: disable=invalid-name
LogicalPartitionSpecPytree = Any # pylint: disable=invalid-name
PartitionSpecPytree = Any # pylint: disable=invalid-name
# Dynamic Axis Mapping Context
# ------------------------------------------------------------------------------
@dataclasses.dataclass
class _AxisRules(threading.local):
"""Dynamic logical axis to mesh axis binding context."""
rules: LogicalRules = ()
# Global axis binding context.
_axis_rules = _AxisRules()
[docs]def set_logical_axis_rules(rules: LogicalRules):
"""Sets the global logical axis to mesh axis binding."""
_axis_rules.rules = rules
[docs]def get_logical_axis_rules() -> LogicalRules:
"""Returns the global logical axis to mesh axis binding."""
return _axis_rules.rules
[docs]@contextlib.contextmanager
def logical_axis_rules(rules: LogicalRules):
"""Context manager for setting the logical to mesh axis bindings."""
old_rules = _axis_rules.rules
try:
_axis_rules.rules = rules
yield
finally:
_axis_rules.rules = old_rules
class _UnassignedAxis:
"""Sentinel class for unassigned logical axis name."""
def __repr__(self):
return 'UnassignedAxis'
def __bool__(self):
return False
_unassigned_axis = _UnassignedAxis()
def _mesh_assignment_free(new_assignment, existing_assignments):
"""Determines if a given mesh axis has already been assigned."""
new = set(jax.tree_util.tree_leaves(new_assignment))
existing = set(jax.tree_util.tree_leaves(existing_assignments))
if existing.intersection(new):
return False
return True
def _logical_to_mesh_axes(
array_dim_names: Optional[Sequence[Optional[str]]],
rules: Optional[LogicalRules] = None,
) -> Optional[List[Union[_UnassignedAxis, None, str, Tuple[str]]]]:
"""Same as logical_to_mesh_axes, but doesn't fill in _unassigned_axis."""
if array_dim_names is None:
return None
if rules is None:
rules = _axis_rules.rules
axis_name_counts = collections.Counter(array_dim_names)
dups = tuple(
k for k, v in axis_name_counts.items() if v > 1 and k is not None)
if dups:
raise ValueError(
f'Unsupported: Dimensions {dups} occur more than once in array names.')
if not isinstance(rules, (tuple, list)):
raise ValueError('Unknown axis rule specification type.')
# We assign mesh axes using a priority based ruleset over logical axis names.
result: List[Union[_UnassignedAxis, None, str, Tuple[str]]]
result = [_unassigned_axis] * len(array_dim_names)
for rule_model_name, rule_mesh_names in rules:
if rule_model_name in array_dim_names:
pos = array_dim_names.index(rule_model_name)
if (_mesh_assignment_free(rule_mesh_names, result) and
result[pos] == _unassigned_axis):
result[pos] = rule_mesh_names
return result
[docs]def logical_to_mesh_axes(
array_dim_names: Optional[Sequence[Optional[str]]],
rules: Optional[LogicalRules] = None,
) -> Optional[jax.sharding.PartitionSpec]:
"""Compute layout for an array.
The rules are in order of precedence, and consist of pairs:
(ArrayDimensionName, MeshDimensionName), meaning that the given array
dimension (if present and unused) should be sharded across the given
mesh dimension (if present and unused).
A Layout of an Array is expressed as a tuple with one element for each
dimension in the Array. The element is either None, or is the name of a
mesh-dimension, meaning that this dimension of the array is sharded across
this dimension of the mesh.
For example, given an array with
array_dim_names = ('batch', 'length', 'heads', 'features')
and the layout rules are:
rules = (('batch', 'X'),
('features', 'X'),
('heads', 'Y'),
('batch', 'Z'))
then this function will return
PartitionSpec('X', None, 'Y', None)
Args:
array_dim_names: Tuple of array dimension names or None.
rules: Optional logical to mesh rules override. Defaults to using the
rules defined in the dynamic context set from the `axis_rules` function.
Returns:
PartitionSpec for the parameter.
"""
result = _logical_to_mesh_axes(array_dim_names, rules)
if result is None:
return None
# We default to None - ie unsharded along the dimension.
result = [None if x is _unassigned_axis else x for x in result]
return jax.sharding.PartitionSpec(*result)
[docs]def logical_to_mesh(
tree: Any,
rules: Optional[LogicalRules] = None
) -> Any:
"""Applies logical_to_mesh_axes to pytrees of logical PartitionSpecs."""
return jax.tree_map(
lambda x: logical_to_mesh_axes(x, rules),
tree,
is_leaf=lambda x: isinstance(x, jax.sharding.PartitionSpec),
)
def _global_mesh_defined() -> bool:
"""Checks if global xmap/pjit mesh resource environment is defined."""
maps_env = maps.thread_resources.env
return maps_env.physical_mesh.devices.shape != () # pylint: disable=g-explicit-bool-comparison
class RulesFallback(enum.Enum):
"""How a sharding constraint should behave when no matching rule is found."""
AXIS_IS_UNSHARDED = 'axis_is_unsharded'
RAISE_ERROR = 'raise_error'
NO_CONSTRAINT = 'no_constraint'
def _with_sharding_constraint(
x: Array,
axis_resources: Optional[jax.sharding.PartitionSpec],
mesh: Optional[jax.sharding.Mesh] = None):
"""Wrapper for pjit with_sharding_constraint, no-op on cpu or outside pjit."""
if jax.devices()[0].platform == 'cpu' or (not _global_mesh_defined() and mesh is None):
return x
else:
if mesh is not None and axis_resources is not None:
sharding = jax.sharding.NamedSharding(mesh, axis_resources)
return pjit.with_sharding_constraint(x, sharding)
return pjit.with_sharding_constraint(x, axis_resources)
def _with_sharding_constraint_one_fallback(
axis_resources: LogicalPartitionSpec,
x: Array,
fallback: RulesFallback = RulesFallback.AXIS_IS_UNSHARDED,
rules: Optional[LogicalRules] = None,
mesh: Optional[jax.sharding.Mesh] = None):
"""Either imposes a sharding constraint or applies fallback."""
mesh_axes = _logical_to_mesh_axes(axis_resources, rules)
if mesh_axes is None:
return _with_sharding_constraint(x, None, mesh=mesh)
if fallback == RulesFallback.AXIS_IS_UNSHARDED:
mesh_axes = [None if x is _unassigned_axis else x for x in mesh_axes]
else:
if any(x is _unassigned_axis for x in mesh_axes):
if fallback == RulesFallback.RAISE_ERROR:
raise ValueError(f'Axis names {axis_resources} did not match a rule')
else:
return x
return _with_sharding_constraint(x, jax.sharding.PartitionSpec(*mesh_axes), mesh=mesh)
def _is_logical_spec(x):
return x is None or (
isinstance(x, tuple) and all(isinstance(e, str) or e is None for e in x))
[docs]def with_logical_constraint(
x: ArrayPytree,
logical_axis_resources: LogicalPartitionSpecPytree,
rules: Optional[LogicalRules] = None,
mesh: Optional[jax.sharding.Mesh] = None,
fallback: RulesFallback = RulesFallback.AXIS_IS_UNSHARDED):
"""Version of pjit's with_sharding_constraint that uses logical axis names."""
# If no axis binding is set, this is a no-op.
if rules is None:
rules = _axis_rules.rules
if not rules or logical_axis_resources is None:
return x
# Translate logical names to mesh assignments.
return jax.tree_util.tree_map(
functools.partial(
_with_sharding_constraint_one_fallback, fallback=fallback,
rules=rules, mesh=mesh),
logical_axis_resources,
x,
is_leaf=_is_logical_spec)
# Logical Partitioning Axis Metadata
# ------------------------------------------------------------------------------
[docs]class LogicallyPartitioned(meta.Partitioned):
rules: Optional[LogicalRules] = struct.field(default=None, pytree_node=False)
def unbox(self, apply_constraint=True) -> Any:
"""Returns the wrapped value with the partitioning constraint applied."""
if apply_constraint and (_global_mesh_defined() or self.mesh is not None):
return with_logical_constraint(
self.value, self.get_partition_spec(),
rules=self.rules, mesh=self.mesh)
else:
return self.value
[docs]def with_logical_partitioning(
fn: Callable[..., Any],
names: meta.LogicalNames,
mesh: Optional[jax.sharding.Mesh] = None,
rules: Optional[LogicalRules] = None,
) -> Callable[..., LogicallyPartitioned]:
"""Wraps a function's return value with LogicallyPartitioned.
Example::
kernel_init = with_logical_partitioning(
nn.initializers.lecun_normal, (None, "data"))
partitioned_dense = nn.Dense(features, kernel_init=kernel_init)
Args:
fn: The function to be wrapped. Typically this is an initializer.
names: The logical axis passed to ``LogicallyPartitioned``.
mesh: The mesh to use for the partitioning. If None, the global mesh
resource is used if available.
rules: Optional logical to mesh rules use. If None, the global rules
are used if available.
Returns:
A function wrapping ``fn`` that will return an instance of
``LogicallyPartitioned``.
"""
@functools.wraps(fn)
def wrapper(*args, **kwargs):
return LogicallyPartitioned(fn(*args, **kwargs), names,
rules=rules, mesh=mesh)
return wrapper
