mindspore.communication.management 源代码

# Copyright 2020 Huawei Technologies Co., Ltd
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# http://www.apache.org/licenses/LICENSE-2.0
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"""Communication management API"""
import os
from mindspore import context
from mindspore import log as logger
from mindspore.parallel._ps_context import _is_ps_mode, _is_role_pserver, _is_role_sched, _get_ps_context
from mindspore.communication._comm_helper import Backend, _get_rank_helper, _get_size_helper, \
    _get_world_rank_from_group_rank_helper, _get_group_rank_from_world_rank_helper, \
    _create_group_helper, _destroy_group_helper, HCCL_WORLD_COMM_GROUP, NCCL_WORLD_COMM_GROUP, \
    MCCL_WORLD_COMM_GROUP, _get_local_rank_helper, _get_local_size_helper, GlobalComm, \
    _check_mpi_envs, _set_elegant_exit_handle
from mindspore._c_expression import init_hccl, finalize_hccl, init_cluster, MSContext, ms_ctx_param


__all__ = ["init", "release", "get_rank", "get_local_rank", "get_group_size",
           "get_local_rank_size", "get_world_rank_from_group_rank",
           "get_group_rank_from_world_rank", "create_group", "destroy_group",
           "HCCL_WORLD_COMM_GROUP", "NCCL_WORLD_COMM_GROUP", "MCCL_WORLD_COMM_GROUP"]

DEFAULT_WORLD_COMM_GROUP = HCCL_WORLD_COMM_GROUP


def _set_rank_from_mpi():
    """Set environment variable according to OMPI"""
    ompi_rank_id = os.getenv("OMPI_COMM_WORLD_RANK")
    ompi_device_id = os.getenv("OMPI_COMM_WORLD_LOCAL_RANK")
    ompi_rank_size = os.getenv("OMPI_COMM_WORLD_SIZE")
    if ompi_rank_id and os.getenv("MS_ROLE"):
        logger.warning("Launching distributed job using both dynamic cluster and OpenMPI at the same time. "
                       "MindSpore will prioritize the use of dynamic cluster. Do not set env from OpenMPI.")
    if ompi_rank_id:
        os.environ["RANK_ID"] = ompi_rank_id
    if ompi_device_id:
        os.environ["DEVICE_ID"] = ompi_device_id
        MSContext.get_instance().set_param(ms_ctx_param.device_id, int(ompi_device_id))
    if ompi_rank_size:
        os.environ["RANK_SIZE"] = ompi_rank_size


_set_rank_from_mpi()


def _get_group(group):
    """Return the world communication group if the `group` is `DEFAULT_WORLD_COMM_GROUP`."""
    if group == DEFAULT_WORLD_COMM_GROUP:
        return GlobalComm.WORLD_COMM_GROUP
    return group


def _host_distribute():
    """Check whether host distribute needed."""
    return os.getenv("MS_ROLE") or _check_mpi_envs()


def _check_parallel_envs():
    """
    Check whether parallel environment variables have been exported or not.

    Raises:
        RuntimeError: If parallel environment variables have not been exported or have been exported to wrong values.
    """
    if not GlobalComm.CHECK_ENVS:
        return
    rank_id_str = os.getenv("RANK_ID")
    if not rank_id_str:
        raise RuntimeError("Environment variables RANK_ID has not been exported, please export variables 'RANK_ID'.")
    try:
        int(rank_id_str)
    except ValueError:
        print("Environment variables 'RANK_ID' should be number, but got the type : {}".format(type(rank_id_str)))
    finally:
        pass
    rank_table_file_str = os.getenv("MINDSPORE_HCCL_CONFIG_PATH")
    rank_table_file_str_old = os.getenv("RANK_TABLE_FILE")
    help_cluster = os.getenv("HELP_CLUSTER")
    if not rank_table_file_str and not rank_table_file_str_old and not help_cluster:
        raise RuntimeError("Get hccl rank_table_file failed, "
                           "please export MINDSPORE_HCCL_CONFIG_PATH or RANK_TABLE_FILE.")


[文档]def init(backend_name=None): """ Initialize distributed backends required by communication services, e.g. HCCL/NCCL. It is usually used in distributed parallel scenarios and set before using communication services. Note: - The full name of HCCL is Huawei Collective Communication Library. - The full name of NCCL is NVIDIA Collective Communication Library. - The full name of MCCL is MindSpore Collective Communication Library. Args: backend_name (str): Backend, using HCCL/NCCL/MCCL. HCCL should be used for Ascend hardware platforms and NCCL for GPU hardware platforms. If not set, inference is automatically made based on the hardware platform type (device_target). Default: ``None`` . Raises: TypeError: If `backend_name` is not a string. RuntimeError: If device target is invalid, or backend is invalid, or distributed initialization fails, or the environment variables RANK_ID/MINDSPORE_HCCL_CONFIG_PATH have not been exported when backend is HCCL. Supported Platforms: ``Ascend`` ``GPU`` ``CPU`` Examples: .. note:: Before running the following examples, you need to configure the communication environment variables. For the Ascend devices, users need to prepare the rank table, set rank_id and device_id. Please see the `Ascend tutorial <https://www.mindspore.cn/tutorials/experts/en/r2.1/parallel/train_ascend.html#preparations>`_ for more details. For the GPU devices, users need to prepare the host file and mpi, please see the `GPU tutorial <https://www.mindspore.cn/tutorials/experts/en/r2.1/parallel/train_gpu.html#preparation>`_ . >>> from mindspore.communication import init >>> init() """ host_init = _host_distribute() device_target = context.get_context("device_target") if backend_name is None: if device_target == "Ascend": backend_name = "hccl" elif device_target == "GPU": backend_name = "nccl" elif device_target == "CPU": backend_name = "mccl" else: raise RuntimeError("For 'set_context', the argument 'device_target' {} is not supported in " "parallel initialization, please use Ascend, GPU or CPU.".format(device_target)) if not isinstance(backend_name, str): raise TypeError("For 'init', the argument 'backend_name' must be a string, " "but got the type : {}".format(type(backend_name))) if backend_name == "hccl": if _is_ps_mode(): # Use MindSpore cluster to build network for Parameter Server traning. init_cluster() if _is_role_sched() or _is_role_pserver(): raise RuntimeError("Parameter server and scheduler should use 'CPU' as backend instead of 'Ascend'") if _get_ps_context("worker_num") == 1: GlobalComm.INITED = True _set_elegant_exit_handle() return if device_target != "Ascend": raise RuntimeError("For 'init', the argument 'backend_name' should be 'Ascend' to init hccl, " "but got {}".format(device_target)) if not host_init: _check_parallel_envs() GlobalComm.BACKEND = Backend("hccl") init_hccl() GlobalComm.WORLD_COMM_GROUP = HCCL_WORLD_COMM_GROUP elif backend_name == "nccl": init_cluster() GlobalComm.WORLD_COMM_GROUP = NCCL_WORLD_COMM_GROUP elif backend_name == "mccl": init_cluster() GlobalComm.WORLD_COMM_GROUP = MCCL_WORLD_COMM_GROUP else: raise RuntimeError("For 'init', the argument 'backend_name' must be nccl while 'device_target' is GPU, " "but got the 'backend_name' : hccl.") GlobalComm.INITED = True _set_elegant_exit_handle()
[文档]def release(): """ Release distributed resource. e.g. HCCL/NCCL. Note: This method should be used after init(). Raises: RuntimeError: If failed to release distributed resource. Supported Platforms: ``Ascend`` ``GPU`` Examples: .. note:: Before running the following examples, you need to configure the communication environment variables. For the Ascend devices, users need to prepare the rank table, set rank_id and device_id. Please see the `Ascend tutorial <https://www.mindspore.cn/tutorials/experts/en/r2.1/parallel/train_ascend.html#preparations>`_ for more details. For the GPU devices, users need to prepare the host file and mpi, please see the `GPU tutorial <https://www.mindspore.cn/tutorials/experts/en/r2.1/parallel/train_gpu.html#preparation>`_ . >>> from mindspore.communication import init, release >>> init() >>> release() """ finalize_hccl()
[文档]def get_rank(group=GlobalComm.WORLD_COMM_GROUP): """ Get the rank ID for the current device in the specified collective communication group. Note: This method should be used after init(). Args: group (str): The communication group to work on. Normally, the group should be created by create_group, otherwise, using the default group. Default: ``GlobalComm.WORLD_COMM_GROUP`` . Returns: int, the rank ID of the calling process within the group. Raises: TypeError: If group is not a string. ValueError: If backend is invalid. RuntimeError: If HCCL/NCCL is not available. Supported Platforms: ``Ascend`` ``GPU`` ``CPU`` Examples: .. note:: Before running the following examples, you need to configure the communication environment variables. For the Ascend devices, users need to prepare the rank table, set rank_id and device_id. Please see the `Ascend tutorial <https://www.mindspore.cn/tutorials/experts/en/r2.1/parallel/train_ascend.html#preparations>`_ for more details. For the GPU devices, users need to prepare the host file and mpi, please see the `GPU tutorial <https://www.mindspore.cn/tutorials/experts/en/r2.1/parallel/train_gpu.html#preparation>`_ . >>> from mindspore.communication import init, get_rank >>> init() >>> rank_id = get_rank() >>> print(rank_id) >>> # the result is the rank_id in world_group """ if not isinstance(group, str): raise TypeError("For 'get_rank', the argument 'group' must be type of string, " "but got 'group' type : {}.".format(type(group))) return _get_rank_helper(group=_get_group(group))
[文档]def get_local_rank(group=GlobalComm.WORLD_COMM_GROUP): """ Gets local rank ID for current device in specified collective communication group. Note: GPU version of MindSpore doesn't support this method. This method should be used after init(). Args: group (str): The communication group to work on. Normally, the group should be created by create_group, otherwise, using the default group. Default: ``GlobalComm.WORLD_COMM_GROUP``. Returns: int, the local rank ID of the calling process within the group. Raises: TypeError: If group is not a string. ValueError: If backend is invalid. RuntimeError: If HCCL is not available or MindSpore is GPU version. Supported Platforms: ``Ascend`` Examples: .. note:: Before running the following examples, you need to configure the communication environment variables. For the Ascend devices, users need to prepare the rank table, set rank_id and device_id. Please see the `Ascend tutorial <https://www.mindspore.cn/tutorials/experts/en/r2.1/parallel/train_ascend.html#preparations>`_ for more details. For the GPU devices, users need to prepare the host file and mpi, please see the `GPU tutorial <https://www.mindspore.cn/tutorials/experts/en/r2.1/parallel/train_gpu.html#preparation>`_ . >>> import mindspore as ms >>> from mindspore.communication.management import init, get_rank, get_local_rank >>> ms.set_context(device_target="Ascend") >>> ms.set_auto_parallel_context(device_num=16) # 2 server, each server with 8 NPU. >>> init() >>> world_rank = get_rank() >>> local_rank = get_local_rank() >>> print("local_rank is: {}, world_rank is {}".format(local_rank, world_rank)) local_rank is: 1, world_rank is 9 """ if not isinstance(group, str): raise TypeError("For 'get_local_rank', the argument 'group' must be type of string, " "but got 'group' type : {}.".format(type(group))) return _get_local_rank_helper(group=_get_group(group))
[文档]def get_group_size(group=GlobalComm.WORLD_COMM_GROUP): """ Get the rank size of the specified collective communication group. Note: This method should be used after init(). Args: group (str): The communication group to work on. Normally, the group should be created by create_group, otherwise, using the default group. Default: ``GlobalComm.WORLD_COMM_GROUP``. Returns: int, the rank size of the group. Raises: TypeError: If group is not a string. ValueError: If backend is invalid. RuntimeError: If HCCL/NCCL is not available. Supported Platforms: ``Ascend`` ``GPU`` ``CPU`` Examples: .. note:: Before running the following examples, you need to configure the communication environment variables. For the Ascend devices, users need to prepare the rank table, set rank_id and device_id. Please see the `Ascend tutorial <https://www.mindspore.cn/tutorials/experts/en/r2.1/parallel/train_ascend.html#preparations>`_ for more details. For the GPU devices, users need to prepare the host file and mpi, please see the `GPU tutorial <https://www.mindspore.cn/tutorials/experts/en/r2.1/parallel/train_gpu.html#preparation>`_ . >>> import mindspore as ms >>> from mindspore.communication.management import init, get_group_size >>> ms.set_auto_parallel_context(device_num=8) >>> init() >>> group_size = get_group_size() >>> print("group_size is: ", group_size) group_size is: 8 """ if not isinstance(group, str): raise TypeError("For 'get_group_size', the argument 'group' must be type of string, " "but got 'group' type : {}.".format(type(group))) return _get_size_helper(group=_get_group(group))
[文档]def get_local_rank_size(group=GlobalComm.WORLD_COMM_GROUP): """ Gets local rank size of the specified collective communication group. Note: GPU version of MindSpore doesn't support this method. This method should be used after init(). Args: group (str): The communication group to work on. The group is created by create_group or the default world communication group. Default: ``GlobalComm.WORLD_COMM_GROUP`` . Returns: int, the local rank size where the calling process is within the group. Raises: TypeError: If group is not a string. ValueError: If backend is invalid. RuntimeError: If HCCL is not available or MindSpore is GPU version. Supported Platforms: ``Ascend`` Examples: .. note:: Before running the following examples, you need to configure the communication environment variables. For the Ascend devices, users need to prepare the rank table, set rank_id and device_id. Please see the `Ascend tutorial <https://www.mindspore.cn/tutorials/experts/en/r2.1/parallel/train_ascend.html#preparations>`_ for more details. For the GPU devices, users need to prepare the host file and mpi, please see the `GPU tutorial <https://www.mindspore.cn/tutorials/experts/en/r2.1/parallel/train_gpu.html#preparation>`_ . >>> import mindspore as ms >>> from mindspore.communication.management import init, get_local_rank_size >>> ms.set_context(device_target="Ascend") >>> ms.set_auto_parallel_context(device_num=16) # 2 server, each server with 8 NPU. >>> init() >>> local_rank_size = get_local_rank_size() >>> print("local_rank_size is: ", local_rank_size) local_rank_size is: 8 """ if not isinstance(group, str): raise TypeError("For 'get_local_rank_size', the argument 'group' must be type of string, " "but got 'group' type : {}.".format(type(group))) return _get_local_size_helper(group=_get_group(group))
[文档]def get_world_rank_from_group_rank(group, group_rank_id): """ Get the rank ID in the world communication group corresponding to the rank ID in the specified user communication group. Note: GPU version of MindSpore doesn't support this method. The parameter group should not be "hccl_world_group". This method should be used after init(). Args: group (str): The communication group to work on. The group is created by create_group. group_rank_id (int): A rank ID in the communication group. Returns: int, the rank ID in world communication group. Raises: TypeError: If `group_rank_id` is not an integer or the group is not a string. ValueError: If group is 'hccl_world_group' or backend is invalid. RuntimeError: If HCCL is not available or MindSpore is GPU version. Supported Platforms: ``Ascend`` Examples: .. note:: Before running the following examples, you need to configure the communication environment variables. For the Ascend devices, users need to prepare the rank table, set rank_id and device_id. Please see the `Ascend tutorial <https://www.mindspore.cn/tutorials/experts/en/r2.1/parallel/train_ascend.html#preparations>`_ for more details. For the GPU devices, users need to prepare the host file and mpi, please see the `GPU tutorial <https://www.mindspore.cn/tutorials/experts/en/r2.1/parallel/train_gpu.html#preparation>`_ . >>> from mindspore import set_context >>> from mindspore.communication.management import init, create_group, get_world_rank_from_group_rank >>> set_context(device_target="Ascend") >>> init() >>> group = "0-4" >>> rank_ids = [0,4] >>> create_group(group, rank_ids) >>> world_rank_id = get_world_rank_from_group_rank(group, 1) >>> print("world_rank_id is: ", world_rank_id) world_rank_id is: 4 """ if not isinstance(group, str): raise TypeError("For 'get_world_rank_from_group_rank', the argument 'group' must be type of string, " "but got 'group' type : {}.".format(type(group))) return _get_world_rank_from_group_rank_helper(group=group, group_rank_id=group_rank_id)
[文档]def get_group_rank_from_world_rank(world_rank_id, group): """ Get the rank ID in the specified user communication group corresponding to the rank ID in the world communication group. Note: GPU version of MindSpore doesn't support this method. The parameter group should not be "hccl_world_group". This method should be used after init(). Args: world_rank_id (int): A rank ID in the world communication group. group (str): The communication group to work on. The group is created by create_group. Returns: int, the rank ID in the user communication group. Raises: TypeError: If world_rank_id is not an integer or the group is not a string. ValueError: If group is 'hccl_world_group' or backend is invalid. RuntimeError: If HCCL is not available or MindSpore is GPU version. Supported Platforms: ``Ascend`` Examples: .. note:: Before running the following examples, you need to configure the communication environment variables. For the Ascend devices, users need to prepare the rank table, set rank_id and device_id. Please see the `Ascend tutorial <https://www.mindspore.cn/tutorials/experts/en/r2.1/parallel/train_ascend.html#preparations>`_ for more details. For the GPU devices, users need to prepare the host file and mpi, please see the `GPU tutorial <https://www.mindspore.cn/tutorials/experts/en/r2.1/parallel/train_gpu.html#preparation>`_ . >>> from mindspore import set_context >>> from mindspore.communication.management import init, create_group, get_group_rank_from_world_rank >>> set_context(device_target="Ascend") >>> init() >>> group = "0-4" >>> rank_ids = [0,4] >>> create_group(group, rank_ids) >>> group_rank_id = get_group_rank_from_world_rank(4, group) >>> print("group_rank_id is: ", group_rank_id) group_rank_id is: 1 """ if not isinstance(group, str): raise TypeError("For 'get_group_rank_from_world_rank', the argument 'group' must be type of string, " "but got 'group' type : {}.".format(type(group))) return _get_group_rank_from_world_rank_helper(world_rank_id=world_rank_id, group=group)
[文档]def create_group(group, rank_ids): """ Create a user collective communication group. Note: GPU version of MindSpore doesn't support this method. The size of rank_ids should be larger than 1, rank_ids should not have duplicate data. This method should be used after init(). Only support global single communication group in PyNative mode if you do not start with mpirun. Args: group (str): The name of the communication group to be created. rank_ids (list): A list of device IDs. Raises: TypeError: If group is not a string or `rank_ids` is not a list. ValueError: If `rank_ids` size is not larger than 1, or `rank_ids` has duplicate data, or backend is invalid. RuntimeError: If HCCL is not available or MindSpore is GPU version. Supported Platforms: ``Ascend`` Examples: .. note:: Before running the following examples, you need to configure the communication environment variables. For the Ascend devices, users need to prepare the rank table, set rank_id and device_id. Please see the `Ascend tutorial <https://www.mindspore.cn/tutorials/experts/en/r2.1/parallel/train_ascend.html#preparations>`_ for more details. For the GPU devices, users need to prepare the host file and mpi, please see the `GPU tutorial <https://www.mindspore.cn/tutorials/experts/en/r2.1/parallel/train_gpu.html#preparation>`_ . >>> from mindspore import set_context >>> import mindspore.ops as ops >>> from mindspore.communication.management import init, create_group >>> set_context(device_target="Ascend") >>> init() >>> group = "0-8" >>> rank_ids = [0,8] >>> create_group(group, rank_ids) >>> allreduce = ops.AllReduce(group) """ if not isinstance(group, str): raise TypeError("For 'create_group', the argument 'group' must be type of string, " "but got 'group' type : {}.".format(type(group))) _create_group_helper(group, rank_ids)
[文档]def destroy_group(group): """ Destroy the user collective communication group. Note: GPU version of MindSpore doesn't support this method. The parameter group should not be "hccl_world_group". This method should be used after init(). Args: group (str): The communication group to destroy, the group should be created by create_group. Raises: TypeError: If group is not a string. ValueError: If group is "hccl_world_group" or backend is invalid. RuntimeError: If HCCL is not available or MindSpore is GPU version. Supported Platforms: ``Ascend`` """ if not isinstance(group, str): raise TypeError("For 'destroy_group', the argument 'group' must be type of string, " "but got 'group' type : {}.".format(type(group))) _destroy_group_helper(group)