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cd7f03f272
Currently the serialized data and its length are being carried in two separate arrays, a fixed-with bytes array (with a hard-coded max size) and a unit32 array respectively. PiperOrigin-RevId: 734299259
241 lines
8.1 KiB
Python
241 lines
8.1 KiB
Python
# Copyright 2024 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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"""Colocated Python serialization utilities."""
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from __future__ import annotations
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import base64
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import collections
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import functools
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import io
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from typing import Any, Callable, Sequence
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try:
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import cloudpickle # type: ignore[import-not-found]
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except ImportError:
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cloudpickle = None
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import jax
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from jax._src import api
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from jax._src import tree_util
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from jax._src import xla_bridge as xb
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from jax._src.lib import xla_client as xc
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import numpy as np
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DeviceList = xc.DeviceList
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@jax.util.cache(max_size=None)
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def _get_cpu_device_map() -> dict[int, jax.Device]:
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"""Returns a map from a device id to a matching device."""
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cpu_device_map: dict[int, jax.Device] = {}
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# TODO(hyeontaek): We should look up CPU devices for a specific CPU backend.
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# When deserializing a device on the controller, the backend should be the one
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# associated with colocated_python. When deserializing on the colocated_python
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# executor, it should be the CPU backend visible to the user function running
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# under colocated_python.
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# Look for CPU devices in the default backend.
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for d in xb.local_devices()[0].client._get_all_devices(): # pylint: disable=protected-access
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if d.device_kind == "cpu":
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if d.id in cpu_device_map:
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raise ValueError(
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f"Multiple CPU devices with id {d.id} found:"
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f" {cpu_device_map[d.id]} and {d}"
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)
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cpu_device_map[d.id] = d
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if cpu_device_map:
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return cpu_device_map
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# Fall back to searching CPU devices in all backends.
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for backend in xb.backends().values():
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for d in backend._get_all_devices(): # pylint: disable=protected-access
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if d.device_kind == "cpu":
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if d.id in cpu_device_map:
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raise ValueError(
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f"Multiple CPU devices with id {d.id} found:"
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f" {cpu_device_map[d.id]} and {d}"
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)
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cpu_device_map[d.id] = d
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return cpu_device_map
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def _lookup_cpu_device(
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cpu_device_map: dict[int, jax.Device], device_id: int
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) -> jax.Device:
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"""Returns a CPU device with the given device ID."""
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d = cpu_device_map.get(device_id)
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if d is None:
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raise ValueError(
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f"Invalid device ID {device_id}. Device list must contain only CPU"
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" devices."
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)
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return d
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def _reduce_mesh(
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mesh: jax.sharding.Mesh,
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) -> tuple[Callable[..., jax.sharding.Mesh], Any]:
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def make_mesh(
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mesh_device_ids: np.ndarray, axis_names: Any
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) -> jax.sharding.Mesh:
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cpu_device_map = _get_cpu_device_map()
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mesh_devices = np.vectorize(
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functools.partial(_lookup_cpu_device, cpu_device_map)
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)(mesh_device_ids)
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return jax.sharding.Mesh(mesh_devices, axis_names)
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mesh_device_ids = np.vectorize(lambda d: d.id, otypes=[int])(mesh.devices)
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return make_mesh, (mesh_device_ids, mesh.axis_names)
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def _reduce_device_list(
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device_list: DeviceList,
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) -> tuple[Callable[..., DeviceList], Any]:
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def make_device_list(device_ids: Sequence[int]) -> DeviceList:
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cpu_device_map = _get_cpu_device_map()
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devices = np.vectorize(
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functools.partial(_lookup_cpu_device, cpu_device_map)
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)(device_ids)
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return DeviceList(tuple(devices))
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device_ids = [d.id for d in device_list]
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return make_device_list, (device_ids,)
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def _reduce_single_device_sharding(
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sharding: jax.sharding.SingleDeviceSharding,
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) -> tuple[Callable[..., jax.sharding.SingleDeviceSharding], Any]:
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def make_single_device_sharding(device_id: int):
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cpu_device_map = _get_cpu_device_map()
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device = _lookup_cpu_device(cpu_device_map, device_id)
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return jax.sharding.SingleDeviceSharding(device)
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return make_single_device_sharding, (sharding.device_set.pop().id,)
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def _serialize(obj: Any) -> bytes:
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"""Serializes callables and input/output spec objects.
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DO NOT USE THIS FUNCTION EXCEPT FOR THE INTERNAL IMPLEMENTATION OF
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colocated_python.
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This module contains utility functions used internally for implementiong
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`colocated_python` when it ships callables and input/output specs through
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IFRT. The pickled data is produced and consumed in an ephermeral fashion
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without any persistence, and it does not expect any version compatibility
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(which cloudpickle does not guarantee). Furthermore, serialization and
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deserialization is expected to be done on machine(s) that are controlled by a
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single tenant, which allows unpickling done during deserialization to be
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trusted.
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Raises:
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ModuleNotFoundError: If cloudpickle is not available.
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"""
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if cloudpickle is None:
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raise ModuleNotFoundError('No module named "cloudpickle"')
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class _CustomPickler(cloudpickle.Pickler):
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dispatch_table = collections.ChainMap(
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{jax.sharding.Mesh: _reduce_mesh},
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{DeviceList: _reduce_device_list},
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{jax.sharding.SingleDeviceSharding: _reduce_single_device_sharding},
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cloudpickle.CloudPickler.dispatch_table, # pylint: disable=attribute-error
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)
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dispatch = dispatch_table
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with io.BytesIO() as file:
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_CustomPickler(file).dump(obj)
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return file.getvalue()
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def _deserialize(serialized: bytes) -> Any:
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"""Deserializes callables and input/output spec objects.
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DO NOT USE THIS FUNCTION EXCEPT FOR THE INTERNAL IMPLEMENTATION OF
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colocated_python. See serialize() for details.
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Raises:
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ModuleNotFoundError: If cloudpickle is not available.
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"""
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if cloudpickle is None:
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raise ModuleNotFoundError('No module named "cloudpickle"')
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return cloudpickle.loads(serialized)
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def _make_specs_for_serialized_specs(
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devices: DeviceList,
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) -> api.ShapeDtypeStruct:
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"""Makes output specs for serialized specs."""
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mesh = jax.sharding.Mesh(tuple(devices), ("x",))
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replicated_sharding = jax.sharding.NamedSharding(
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mesh, jax.sharding.PartitionSpec()
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)
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return api.ShapeDtypeStruct(
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shape=(), dtype=np.dtypes.StringDType(), sharding=replicated_sharding # type: ignore
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)
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def _serialize_specs(
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specs_treedef: tree_util.PyTreeDef,
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specs_leaves: tuple[api.ShapeDtypeStruct, ...],
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devices: DeviceList,
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) -> jax.Array:
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"""Serializes the output specs into a jax.Array of string type.
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DO NOT USE THIS FUNCTION EXCEPT FOR THE INTERNAL IMPLEMENTATION OF
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colocated_python. See serialize() for details.
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"""
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if not hasattr(np.dtypes, "StringDType"):
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raise TypeError(
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"Serializing Colocated Python requires StringDType. Please use"
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" numpy to 2.0.0 or later, or explicityly provide an output spec"
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" function."
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)
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s_bytes = _serialize((specs_treedef, specs_leaves))
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s_str = base64.b64encode(s_bytes).decode("ascii")
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s_np_array = np.array(s_str, dtype=np.dtypes.StringDType()) # type: ignore
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# TODO(jmudigonda): Revisit this when JAX supports HLO sharding for making
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# jax.Array via make_array_from_single_device_arrays. We should then use a
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# sharding that spans all the execution devices - not just the addressable
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# ones.
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addressable_devices = devices.addressable_device_list
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mesh = jax.sharding.Mesh(tuple(addressable_devices), ("x",))
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replicated_sharding = jax.sharding.NamedSharding(
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mesh, jax.sharding.PartitionSpec()
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)
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out_arrays = [
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jax.device_put(s_np_array, device) for device in addressable_devices
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]
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return jax.make_array_from_single_device_arrays(
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arrays=out_arrays, sharding=replicated_sharding, shape=(),
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)
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def _deserialize_specs(
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serialized_specs: jax.Array,
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) -> tuple[tree_util.PyTreeDef, tuple[api.ShapeDtypeStruct, ...]]:
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"""Deserializes the specs from the serialized specs.
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DO NOT USE THIS FUNCTION EXCEPT FOR THE INTERNAL IMPLEMENTATION OF
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colocated_python. See serialize() for details.
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"""
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data_array = serialized_specs.addressable_shards[0].data
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data = base64.b64decode(data_array.item().encode("ascii"))
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return _deserialize(data)
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