2025-08-11 12:24:21 +08:00

123 lines
4.4 KiB
Python

# Copyright 2024 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.
"""Colocated Python top-level API."""
from __future__ import annotations
import collections
from typing import Any, Callable, Sequence, Type, overload
import jax
from jax._src import api_util
from jax._src import util
from jax.experimental.colocated_python.func import make_callable
from jax.experimental.colocated_python.obj import wrap_class
import numpy as np
@overload
def colocated_cpu_devices(
devices_or_mesh: Sequence[jax.Device],
) -> Sequence[jax.Device]:
...
@overload
def colocated_cpu_devices(
devices_or_mesh: jax.sharding.Mesh,
) -> jax.sharding.Mesh:
...
def colocated_cpu_devices(devices_or_mesh):
"""Finds devices or a mesh that has CPU devices colocated with the given devices or mesh."""
if isinstance(devices_or_mesh, jax.sharding.Mesh):
return _colocated_cpu_mesh_cached(devices_or_mesh)
if not isinstance(devices_or_mesh, tuple):
devices_or_mesh = tuple(devices_or_mesh)
try:
return _colocated_cpu_devices_cached(devices_or_mesh)
except (ValueError, AttributeError):
return _colocated_cpu_devices_cached_fallback_to_cpu_backend(
devices_or_mesh
)
@util.cache(max_size=1024, trace_context_in_key=False)
def _colocated_cpu_devices_cached(
devices: tuple[jax.Device, ...],
) -> Sequence[jax.Device]:
cpu_devices_by_colocation_id = collections.defaultdict(list)
for device in devices[0].client._get_all_devices(): # pylint: disable=protected-access
if device.device_kind == "cpu":
cpu_devices_by_colocation_id[device.colocation_id].append(device)
if not cpu_devices_by_colocation_id:
raise ValueError("No CPU devices found")
colocated_cpu_devices = []
for device in devices:
matches = cpu_devices_by_colocation_id[device.colocation_id]
if not matches:
raise ValueError(f"Device {device} has no colocated devices")
elif len(matches) > 1:
raise ValueError(
f"Ambiguous colocated devices; device {device} has"
f" {len(matches)} colocated devices: f{matches}"
)
colocated_cpu_devices.append(matches[0])
return colocated_cpu_devices
@util.cache(max_size=1024, trace_context_in_key=False)
def _colocated_cpu_devices_cached_fallback_to_cpu_backend(
devices: tuple[jax.Device, ...],
) -> Sequence[jax.Device]:
# PjRt-IFRT currently defines CPU devices by using a CPU backend.
# TODO(hyeontaek): Remove this fallback path once a PjRt-IFRT backend defines
# CPU devices by its own instead of using a separate CPU backend.
cpu_backend_devices = jax.local_devices(backend="cpu")
device_index_map = {device.id: i for i, device in enumerate(jax.devices())}
available_devices = devices[: min(len(cpu_backend_devices), len(devices))]
return [
cpu_backend_devices[device_index_map[d.id]] for d in available_devices
]
@util.cache(max_size=1024, trace_context_in_key=False)
def _colocated_cpu_mesh_cached(mesh: jax.sharding.Mesh) -> jax.sharding.Mesh:
"""Returns a CPU mesh that is similar to the given mesh but has colocated CPU devices."""
# Finding colocated CPU devices reuses the cache of `colocated_cpu_devices`
# called with devices. `_colocated_cpu_mesh` itself is also cached to avoid
# creating a new `Mesh` object repeatedly.
flat_cpu_devices = colocated_cpu_devices(tuple(mesh.devices.flat))
return jax.sharding.Mesh(
np.array(flat_cpu_devices).reshape(mesh.axis_sizes),
mesh.axis_names,
axis_types=mesh.axis_types,
)
def colocated_python(fun: Callable[..., Any]):
"""Executes the given Python function on the same devices as the arguments."""
return make_callable(
fun, api_util.fun_sourceinfo(fun), api_util.fun_signature(fun)
)
def colocated_python_class(cls: Type[object]) -> Type[object]:
"""Executes the given Python class methods on the same devices as the arguments."""
return wrap_class(cls, api_util.fun_sourceinfo(cls))