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6072d5993e
This is true for mock devices or user specific devices and jax.devices() too. Fix the tests so that the mock devices are hashable. PiperOrigin-RevId: 561103167
320 lines
11 KiB
Python
320 lines
11 KiB
Python
# Copyright 2018 The JAX Authors.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# https://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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"""Definitions of Mesh and ResourceEnv."""
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from __future__ import annotations
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import collections
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from collections.abc import Hashable, Sequence
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import contextlib
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import functools
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import math
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import threading
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from typing import Any, NamedTuple
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import numpy as np
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from jax._src import config as jax_config
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from jax._src import xla_bridge as xb
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from jax._src import util
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from jax._src.lib import xla_client as xc
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from jax._src.lib import xla_extension_version
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MeshAxisName = Any
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ResourceAxisName = Hashable
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class Loop(NamedTuple):
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name: ResourceAxisName
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length: int
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def show_axes(axes):
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return ", ".join(sorted(f"`{a}`" for a in axes))
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class ResourceEnv(NamedTuple):
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physical_mesh: Mesh
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loops: tuple[Loop, ...]
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def with_mesh(self, mesh: Mesh):
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overlap = set(mesh.axis_names) & (self.resource_axes - set(self.physical_mesh.axis_names))
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if overlap:
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raise ValueError(f"Cannot update the mesh of the current resource "
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f"environment. The new mesh shadows already defined axes "
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f"{show_axes(overlap)}")
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return self._replace(physical_mesh=mesh)
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def with_extra_loop(self, loop: Loop):
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if loop.name in self.resource_axes:
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raise ValueError(f"Cannot extend the resource environment with loop named "
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f"`{loop.name}`. An axis of this name is already defined!")
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return self._replace(loops=self.loops + (loop,))
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@property
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def physical_resource_axes(self) -> set[ResourceAxisName]:
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return set(self.physical_mesh.axis_names)
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@property
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def loop_resource_axes(self) -> set[ResourceAxisName]:
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return {loop.name for loop in self.loops}
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@property
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def resource_axes(self) -> set[ResourceAxisName]:
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return self.physical_resource_axes | self.loop_resource_axes
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@property
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def shape(self):
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shape = self.physical_mesh.shape
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shape.update(self.loops)
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return shape
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@property
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def local_shape(self):
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shape = self.physical_mesh.local_mesh.shape
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shape.update(self.loops)
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return shape
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def __repr__(self):
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return f"ResourceEnv({self.physical_mesh!r}, {self.loops!r})"
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@functools.lru_cache(maxsize=128)
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def _get_local_mesh(global_mesh: Mesh, process_index: int) -> Mesh:
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if global_mesh.empty:
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return global_mesh
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is_local_device = np.vectorize(
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lambda d: d.process_index == process_index, otypes=[bool])(global_mesh.devices)
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subcube_indices = []
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# We take the smallest slice of each dimension that doesn't skip any local device.
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for axis in range(global_mesh.devices.ndim):
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other_axes = util.tuple_delete(tuple(range(global_mesh.devices.ndim)), axis)
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# NOTE: This re-reduces over many axes multiple times, so we could definitely
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# optimize it, but I hope it won't be a bottleneck anytime soon.
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local_slices = is_local_device.any(other_axes, keepdims=False)
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nonzero_indices = np.flatnonzero(local_slices)
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start, end = int(np.min(nonzero_indices)), int(np.max(nonzero_indices))
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subcube_indices.append(slice(start, end + 1))
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subcube_indices = tuple(subcube_indices)
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# We only end up with all conditions being true if the local devices formed a
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# subcube of the full array. This is because we were biased towards taking a
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# "hull" spanned by the devices, and in case the local devices don't form a
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# subcube that hull will contain non-local devices.
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if not is_local_device[subcube_indices].all():
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raise ValueError(
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"When passing host local inputs to pjit or xmap, devices "
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"connected to a single host must form a contiguous subcube of the "
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"global device mesh")
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return Mesh(global_mesh.devices[subcube_indices], global_mesh.axis_names)
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_mesh_object_dict = {} # type: ignore
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class Mesh(contextlib.ContextDecorator):
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"""Declare the hardware resources available in the scope of this manager.
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In particular, all ``axis_names`` become valid resource names inside the
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managed block and can be used e.g. in the ``in_axis_resources`` argument of
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:py:func:`jax.experimental.pjit.pjit`. Also see JAX's multi-process programming
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model (https://jax.readthedocs.io/en/latest/multi_process.html)
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and the Distributed arrays and automatic parallelization tutorial
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(https://jax.readthedocs.io/en/latest/notebooks/Distributed_arrays_and_automatic_parallelization.html)
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If you are compiling in multiple threads, make sure that the
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``with Mesh`` context manager is inside the function that the threads will
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execute.
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Args:
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devices: A NumPy ndarray object containing JAX device objects (as
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obtained e.g. from :py:func:`jax.devices`).
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axis_names: A sequence of resource axis names to be assigned to the
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dimensions of the ``devices`` argument. Its length should match the
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rank of ``devices``.
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Example:
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>>> from jax.experimental.pjit import pjit
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>>> from jax.sharding import Mesh
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>>> from jax.sharding import PartitionSpec as P
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>>> import numpy as np
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...
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>>> inp = np.arange(16).reshape((8, 2))
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>>> devices = np.array(jax.devices()).reshape(4, 2)
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...
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>>> # Declare a 2D mesh with axes `x` and `y`.
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>>> global_mesh = Mesh(devices, ('x', 'y'))
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>>> # Use the mesh object directly as a context manager.
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>>> with global_mesh:
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... out = pjit(lambda x: x, in_shardings=None, out_shardings=None)(inp)
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>>> # Initialize the Mesh and use the mesh as the context manager.
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>>> with Mesh(devices, ('x', 'y')) as global_mesh:
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... out = pjit(lambda x: x, in_shardings=None, out_shardings=None)(inp)
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>>> # Also you can use it as `with ... as ...`.
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>>> global_mesh = Mesh(devices, ('x', 'y'))
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>>> with global_mesh as m:
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... out = pjit(lambda x: x, in_shardings=None, out_shardings=None)(inp)
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>>> # You can also use it as `with Mesh(...)`.
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>>> with Mesh(devices, ('x', 'y')):
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... out = pjit(lambda x: x, in_shardings=None, out_shardings=None)(inp)
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"""
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devices: np.ndarray
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axis_names: tuple[MeshAxisName, ...]
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def __new__(cls, devices: np.ndarray | Sequence[xc.Device],
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axis_names: str | Sequence[MeshAxisName]):
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if not isinstance(devices, np.ndarray):
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devices = np.array(devices)
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if isinstance(axis_names, str):
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axis_names = (axis_names,)
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axis_names = tuple(axis_names)
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assert devices.ndim == len(axis_names)
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key = (axis_names, devices.shape, tuple(devices.flat))
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val = _mesh_object_dict.get(key, None)
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if val is not None:
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return val
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self = super(Mesh, cls).__new__(cls)
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self.devices = devices.copy()
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self.devices.flags.writeable = False
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self.axis_names = axis_names
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_mesh_object_dict[key] = self
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return self
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def __reduce__(self):
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return (type(self), (self.devices, self.axis_names))
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def __eq__(self, other):
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if not isinstance(other, Mesh):
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return False
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# This is a performance optimization. Comparing thousands of devices
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# can be expensive.
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if id(self) == id(other):
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return True
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if xla_extension_version >= 185:
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return (self.axis_names == other.axis_names and
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self.devices.shape == other.devices.shape and
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self._internal_device_list == other._internal_device_list)
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else:
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return (self.axis_names == other.axis_names and
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np.array_equal(self.devices, other.devices))
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def __hash__(self):
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if not hasattr(self, '_hash'):
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if xla_extension_version >= 185:
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self._hash = hash(
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(self.axis_names, self._internal_device_list, self.devices.shape))
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else:
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self._hash = hash(
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(self.axis_names, tuple(self.devices.flat), self.devices.shape))
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return self._hash
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def __setattr__(self, name, value):
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if hasattr(self, name):
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raise RuntimeError("Cannot reassign attributes of immutable mesh objects")
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super().__setattr__(name, value)
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def __enter__(self):
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new_env = thread_resources.stack[-1].with_mesh(self)
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thread_resources.stack.append(new_env)
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thread_resources.env = new_env
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jax_config.update_thread_local_jit_state(
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mesh_context_manager=tuple(t.physical_mesh for t in thread_resources.stack
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if not t.physical_mesh.empty))
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return self
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def __exit__(self, exc_type, exc_value, traceback):
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thread_resources.stack.pop()
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thread_resources.env = thread_resources.stack[-1]
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jax_config.update_thread_local_jit_state(
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mesh_context_manager=tuple(t.physical_mesh for t in thread_resources.stack
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if not t.physical_mesh.empty))
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return False
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@property
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def shape(self):
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return collections.OrderedDict(
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(name, size)
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for name, size in util.safe_zip(self.axis_names, self.devices.shape))
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@property
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def size(self):
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return math.prod(self.shape.values())
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@property
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def empty(self):
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return self.devices.ndim == 0
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@functools.cached_property
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def is_multi_process(self):
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return self.devices.size != len(self.local_devices)
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@property
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def local_mesh(self):
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return self._local_mesh(xb.process_index())
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def _local_mesh(self, process_index):
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return _get_local_mesh(self, process_index)
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@functools.cached_property
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def device_ids(self):
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assert not self.empty
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return np.vectorize(lambda d: d.id, otypes=[int])(self.devices)
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@functools.cached_property
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def _local_devices_set(self):
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return set(self.local_devices)
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@functools.cached_property
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def _flat_devices_tuple(self):
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return tuple(self.devices.flat)
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@functools.cached_property
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def _internal_device_list(self):
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return xc.DeviceList(self._flat_devices_tuple)
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@functools.cached_property
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def _flat_devices_set(self):
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return set(self.devices.flat)
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@functools.cached_property
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def _repr(self):
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if self.empty:
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return "Mesh(device_ids=[], axis_names=())"
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return f"Mesh(device_ids={self.device_ids!r}, axis_names={self.axis_names!r})"
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def __repr__(self):
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return self._repr
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@functools.cached_property
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def local_devices(self):
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return [d for d in self.devices.flat
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if d.process_index == d.client.process_index()]
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EMPTY_ENV = ResourceEnv(Mesh(np.empty((), dtype=object), ()), ())
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class _ThreadResourcesLocalState(threading.local):
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def __init__(self):
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self.stack = [EMPTY_ENV]
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self.env = self.stack[-1]
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thread_resources = _ThreadResourcesLocalState()
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