"""
Auxillary functions and variables for dealing with MPI multiprocessing
Warning:
These functions are mostly no-ops unless MPI is properly installed and python code
was started using :code:`mpirun` or :code:`mpiexec`. Please refer to the
documentation of your MPI distribution for details.
.. autosummary::
:nosignatures:
mpi_send
mpi_recv
mpi_allreduce
.. codeauthor:: David Zwicker <david.zwicker@ds.mpg.de>
"""
from __future__ import annotations
import os
import sys
from typing import TYPE_CHECKING
import numba as nb
import numpy as np
from numba import types
from numba.extending import overload, register_jitable
if TYPE_CHECKING:
from numba_mpi import Operator
# Initialize assuming that we run serial code if `numba_mpi` is not available
initialized: bool = False
"""bool: Flag determining whether mpi was initialized (and is available)"""
size: int = 1
"""int: Total process count"""
rank: int = 0
"""int: ID of the current process"""
# read state of the current MPI node
try:
from mpi4py import MPI
except ImportError:
# package `numba_mpi` could not be loaded
if int(os.environ.get("PMI_SIZE", "1")) > 1:
# environment variable indicates that we are in a parallel program
sys.exit(
"WARNING: Detected multiprocessing run, but could not import python "
"package `numba_mpi`"
)
else:
initialized = MPI.Is_initialized()
size = MPI.COMM_WORLD.size
rank = MPI.COMM_WORLD.rank
class _OperatorRegistry:
"""collection of operators that MPI supports"""
_name_ids: dict[str, int]
_ids_operators: dict[int, MPI.Op]
def __init__(self):
self._name_ids = {}
self._ids_operators = {}
def register(self, name: str, op: MPI.Op):
op_id = int(MPI._addressof(op))
self._name_ids[name] = op_id
self._ids_operators[op_id] = op
def id(self, name_or_id: int | str) -> int:
if isinstance(name_or_id, int):
return name_or_id
else:
return self._name_ids[name_or_id]
def operator(self, name_or_id: int | str) -> MPI.Op:
if isinstance(name_or_id, str):
name_or_id = self._name_ids[name_or_id]
return self._ids_operators[name_or_id]
def __getattr__(self, name: str):
try:
return self._name_ids[name]
except KeyError:
raise AttributeError
Operator = _OperatorRegistry()
Operator.register("MAX", MPI.MAX)
Operator.register("MIN", MPI.MIN)
Operator.register("SUM", MPI.SUM)
parallel_run: bool = size > 1
"""bool: Flag indicating whether the current run is using multiprocessing"""
is_main: bool = rank == 0
"""bool: Flag indicating whether the current process is the main process (with ID 0)"""
[docs]
def mpi_send(data, dest: int, tag: int) -> None:
"""send data to another MPI node
Args:
data: The data being send
dest (int): The ID of the receiving node
tag (int): A numeric tag identifying the message
"""
MPI.COMM_WORLD.send(data, dest=dest, tag=tag)
[docs]
@overload(mpi_send)
def ol_mpi_send(data, dest: int, tag: int):
"""overload the `mpi_send` function"""
import numba_mpi
def impl(data, dest: int, tag: int) -> None:
"""reduce a single number across all cores"""
status = numba_mpi.send(data, dest, tag)
assert status == 0
return impl
[docs]
def mpi_recv(data, source, tag) -> None:
"""receive data from another MPI node
Args:
data: A buffer into which the received data is written
dest (int): The ID of the sending node
tag (int): A numeric tag identifying the message
"""
data[...] = MPI.COMM_WORLD.recv(source=source, tag=tag)
[docs]
@overload(mpi_recv)
def ol_mpi_recv(data, source: int, tag: int):
"""overload the `mpi_recv` function"""
import numba_mpi
def impl(data, source: int, tag: int) -> None:
"""receive data from another MPI node
Args:
data: A buffer into which the received data is written
dest (int): The ID of the sending node
tag (int): A numeric tag identifying the message
"""
status = numba_mpi.recv(data, source, tag)
assert status == 0
return impl
[docs]
def mpi_allreduce(data, operator: int | str | None = None):
"""combines data from all MPI nodes
Note that complex datatypes and user-defined functions are not properly supported.
Args:
data:
Data being send from this node to all others
operator:
The operator used to combine all data. Possible options are summarized in
the IntEnum :class:`numba_mpi.Operator`.
Returns:
The accumulated data
"""
if operator:
return MPI.COMM_WORLD.allreduce(data, op=Operator.operator(operator))
else:
return MPI.COMM_WORLD.allreduce(data)
[docs]
@overload(mpi_allreduce)
def ol_mpi_allreduce(data, operator: int | str | None = None):
"""overload the `mpi_allreduce` function"""
import numba_mpi
if operator is None or isinstance(operator, nb.types.NoneType):
op_id = -1 # value will not be used
elif isinstance(operator, nb.types.misc.StringLiteral):
op_id = Operator.id(operator.literal_value)
elif isinstance(operator, nb.types.misc.Literal):
op_id = int(operator)
else:
raise RuntimeError("`operator` must be a literal type")
@register_jitable
def _allreduce(sendobj, recvobj, operator: int | str | None = None) -> int:
"""helper function that calls `numba_mpi.allreduce`"""
if operator is None:
return numba_mpi.allreduce(sendobj, recvobj) # type: ignore
else:
return numba_mpi.allreduce(sendobj, recvobj, op_id) # type: ignore
if isinstance(data, types.Number):
def impl(data, operator: int | str | None = None):
"""reduce a single number across all cores"""
sendobj = np.array([data])
recvobj = np.empty((1,), sendobj.dtype)
status = _allreduce(sendobj, recvobj, operator)
assert status == 0
return recvobj[0]
elif isinstance(data, types.Array):
def impl(data, operator: int | str | None = None):
"""reduce an array across all cores"""
recvobj = np.empty(data.shape, data.dtype)
status = _allreduce(data, recvobj, operator)
assert status == 0
return recvobj
else:
raise TypeError(f"Unsupported type {data.__class__.__name__}")
return impl