# Copyright 2018 The JAX 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
#
# https://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.
"""Definitions of Mesh and ResourceEnv."""
from __future__ import annotations
import collections
from collections.abc import Hashable, Sequence
import contextlib
import functools
import math
import threading
from typing import Any, NamedTuple
import numpy as np
from jax._src import config as jax_config
from jax._src import xla_bridge as xb
from jax._src import util
from jax._src.lib import xla_client as xc
MeshAxisName = Any
ResourceAxisName = Hashable
class Loop(NamedTuple):
name: ResourceAxisName
length: int
def show_axes(axes):
return ", ".join(sorted(f"`{a}`" for a in axes))
class ResourceEnv(NamedTuple):
physical_mesh: Mesh
loops: tuple[Loop, ...]
def with_mesh(self, mesh: Mesh):
overlap = set(mesh.axis_names) & (self.resource_axes - set(self.physical_mesh.axis_names))
if overlap:
raise ValueError(f"Cannot update the mesh of the current resource "
f"environment. The new mesh shadows already defined axes "
f"{show_axes(overlap)}")
return self._replace(physical_mesh=mesh)
def with_extra_loop(self, loop: Loop):
if loop.name in self.resource_axes:
raise ValueError(f"Cannot extend the resource environment with loop named "
f"`{loop.name}`. An axis of this name is already defined!")
return self._replace(loops=self.loops + (loop,))
@property
def physical_resource_axes(self) -> set[ResourceAxisName]:
return set(self.physical_mesh.axis_names)
@property
def loop_resource_axes(self) -> set[ResourceAxisName]:
return {loop.name for loop in self.loops}
@property
def resource_axes(self) -> set[ResourceAxisName]:
return self.physical_resource_axes | self.loop_resource_axes
@property
def shape(self):
shape = self.physical_mesh.shape
shape.update(self.loops)
return shape
@property
def local_shape(self):
shape = self.physical_mesh.local_mesh.shape
shape.update(self.loops)
return shape
def __repr__(self):
mesh_repr = ", ".join(
f"'{k}': {v}" for k, v in self.physical_mesh.shape.items())
return f"ResourceEnv(mesh=Mesh({mesh_repr}), {self.loops!r})"
@functools.lru_cache(maxsize=128)
def _get_local_mesh(global_mesh: Mesh, process_index: int) -> Mesh:
if global_mesh.empty:
return global_mesh
is_local_device = np.vectorize(
lambda d: d.process_index == process_index, otypes=[bool])(global_mesh.devices)
subcube_indices = []
# We take the smallest slice of each dimension that doesn't skip any local device.
for axis in range(global_mesh.devices.ndim):
other_axes = util.tuple_delete(tuple(range(global_mesh.devices.ndim)), axis)
# NOTE: This re-reduces over many axes multiple times, so we could definitely
# optimize it, but I hope it won't be a bottleneck anytime soon.
local_slices = is_local_device.any(other_axes, keepdims=False)
nonzero_indices = np.flatnonzero(local_slices)
start, end = int(np.min(nonzero_indices)), int(np.max(nonzero_indices))
subcube_indices.append(slice(start, end + 1))
subcube_indices_tuple = tuple(subcube_indices)
# We only end up with all conditions being true if the local devices formed a
# subcube of the full array. This is because we were biased towards taking a
# "hull" spanned by the devices, and in case the local devices don't form a
# subcube that hull will contain non-local devices.
if not is_local_device[subcube_indices_tuple].all():
raise ValueError(
"When passing host local inputs to pjit or xmap, devices "
"connected to a single host must form a contiguous subcube of the "
"global device mesh")
return Mesh(global_mesh.devices[subcube_indices_tuple], global_mesh.axis_names)
_mesh_object_dict = {} # type: ignore
[docs]
class Mesh(contextlib.ContextDecorator):
"""Declare the hardware resources available in the scope of this manager.
In particular, all ``axis_names`` become valid resource names inside the
managed block and can be used e.g. in the ``in_axis_resources`` argument of
:py:func:`jax.experimental.pjit.pjit`. Also see JAX's multi-process programming
model (https://jax.readthedocs.io/en/latest/multi_process.html)
and the Distributed arrays and automatic parallelization tutorial
(https://jax.readthedocs.io/en/latest/notebooks/Distributed_arrays_and_automatic_parallelization.html)
If you are compiling in multiple threads, make sure that the
``with Mesh`` context manager is inside the function that the threads will
execute.
Args:
devices: A NumPy ndarray object containing JAX device objects (as
obtained e.g. from :py:func:`jax.devices`).
axis_names: A sequence of resource axis names to be assigned to the
dimensions of the ``devices`` argument. Its length should match the
rank of ``devices``.
Example:
>>> from jax.experimental.pjit import pjit
>>> from jax.sharding import Mesh
>>> from jax.sharding import PartitionSpec as P
>>> import numpy as np
...
>>> inp = np.arange(16).reshape((8, 2))
>>> devices = np.array(jax.devices()).reshape(4, 2)
...
>>> # Declare a 2D mesh with axes `x` and `y`.
>>> global_mesh = Mesh(devices, ('x', 'y'))
>>> # Use the mesh object directly as a context manager.
>>> with global_mesh:
... out = pjit(lambda x: x, in_shardings=None, out_shardings=None)(inp)
>>> # Initialize the Mesh and use the mesh as the context manager.
>>> with Mesh(devices, ('x', 'y')) as global_mesh:
... out = pjit(lambda x: x, in_shardings=None, out_shardings=None)(inp)
>>> # Also you can use it as `with ... as ...`.
>>> global_mesh = Mesh(devices, ('x', 'y'))
>>> with global_mesh as m:
... out = pjit(lambda x: x, in_shardings=None, out_shardings=None)(inp)
>>> # You can also use it as `with Mesh(...)`.
>>> with Mesh(devices, ('x', 'y')):
... out = pjit(lambda x: x, in_shardings=None, out_shardings=None)(inp)
"""
devices: np.ndarray
axis_names: tuple[MeshAxisName, ...]
def __new__(cls, devices: np.ndarray | Sequence[xc.Device],
axis_names: str | Sequence[MeshAxisName]):
if not isinstance(devices, np.ndarray):
devices = np.array(devices)
if isinstance(axis_names, str):
axis_names = (axis_names,)
axis_names = tuple(axis_names)
if devices.ndim != len(axis_names):
raise ValueError(
"Mesh requires the ndim of its first argument (`devices`) to equal "
"the length of its second argument (`axis_names`), but got "
f"devices.ndim == {devices.ndim} and "
f"len(axis_names) == {len(axis_names)}.")
key = (axis_names, devices.shape, tuple(devices.flat))
val = _mesh_object_dict.get(key, None)
if val is not None:
return val
self = super().__new__(cls)
self.devices = devices.copy()
self.devices.flags.writeable = False
self.axis_names = axis_names
_mesh_object_dict[key] = self
return self
def __reduce__(self):
return (type(self), (self.devices, self.axis_names))
def __eq__(self, other):
if not isinstance(other, Mesh):
return False
# This is a performance optimization. Comparing thousands of devices
# can be expensive.
if id(self) == id(other):
return True
return (self.axis_names == other.axis_names and
self.devices.shape == other.devices.shape and
self._internal_device_list == other._internal_device_list)
def __hash__(self):
if not hasattr(self, '_hash'):
self._hash = hash(
(self.axis_names, self._internal_device_list, self.devices.shape))
return self._hash
def __setattr__(self, name, value):
if hasattr(self, name):
if getattr(self, name) == value:
# This can to happen if two threads race, for example if two threads
# are trying to hash the same Mesh instance.
return
raise RuntimeError(
f"Cannot reassign attributes ({name}) of immutable mesh objects"
)
super().__setattr__(name, value)
def __enter__(self):
new_env = thread_resources.stack[-1].with_mesh(self)
thread_resources.stack.append(new_env)
thread_resources.env = new_env
jax_config.update_thread_local_jit_state(
mesh_context_manager=tuple(t.physical_mesh for t in thread_resources.stack
if not t.physical_mesh.empty))
return self
def __exit__(self, exc_type, exc_value, traceback):
thread_resources.stack.pop()
thread_resources.env = thread_resources.stack[-1]
jax_config.update_thread_local_jit_state(
mesh_context_manager=tuple(t.physical_mesh for t in thread_resources.stack
if not t.physical_mesh.empty))
return False
@property
def shape(self):
return collections.OrderedDict(
(name, size)
for name, size in util.safe_zip(self.axis_names, self.devices.shape))
@property
def size(self):
return math.prod(self.shape.values())
@property
def empty(self):
return self.devices.ndim == 0
@functools.cached_property
def is_multi_process(self):
return self.devices.size != len(self.local_devices)
@property
def local_mesh(self):
return self._local_mesh(xb.process_index())
def _local_mesh(self, process_index):
return _get_local_mesh(self, process_index)
@functools.cached_property
def device_ids(self):
assert not self.empty
return np.vectorize(lambda d: d.id, otypes=[int])(self.devices)
@functools.cached_property
def _local_devices_set(self):
return set(self.local_devices)
@functools.cached_property
def _flat_devices_tuple(self):
return tuple(self.devices.flat)
@functools.cached_property
def _internal_device_list(self):
return xc.DeviceList(self._flat_devices_tuple)
@functools.cached_property
def _flat_devices_set(self):
return set(self.devices.flat)
def __str__(self):
mesh_str = ", ".join(f"'{k}': {v}" for k, v in self.shape.items())
return f"Mesh({mesh_str})"
@functools.cached_property
def _repr(self):
if self.empty:
return "Mesh(device_ids=[], axis_names=())"
return f"Mesh(device_ids={self.device_ids!r}, axis_names={self.axis_names!r})"
def __repr__(self):
return self._repr
@functools.cached_property
def local_devices(self):
return [d for d in self.devices.flat
if d.process_index == d.client.process_index()]
EMPTY_ENV = ResourceEnv(Mesh(np.empty((), dtype=object), ()), ())
class _ThreadResourcesLocalState(threading.local):
def __init__(self):
self.stack = [EMPTY_ENV]
self.env = self.stack[-1]
thread_resources = _ThreadResourcesLocalState()
