290 lines
8.5 KiB
Python
290 lines
8.5 KiB
Python
"""XGBoost collective communication related API."""
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import ctypes
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import json
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import logging
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import os
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import pickle
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from enum import IntEnum, unique
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from typing import Any, Dict, List, Optional
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import numpy as np
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from ._typing import _T
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from .core import _LIB, _check_call, build_info, c_str, from_pystr_to_cstr, py_str
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LOGGER = logging.getLogger("[xgboost.collective]")
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def init(**args: Any) -> None:
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"""Initialize the collective library with arguments.
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Parameters
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----------
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args: Dict[str, Any]
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Keyword arguments representing the parameters and their values.
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Accepted parameters:
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- xgboost_communicator: The type of the communicator. Can be set as an environment
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variable.
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* rabit: Use Rabit. This is the default if the type is unspecified.
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* federated: Use the gRPC interface for Federated Learning.
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Only applicable to the Rabit communicator (these are case sensitive):
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-- rabit_tracker_uri: Hostname of the tracker.
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-- rabit_tracker_port: Port number of the tracker.
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-- rabit_task_id: ID of the current task, can be used to obtain deterministic rank
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assignment.
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-- rabit_world_size: Total number of workers.
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-- rabit_hadoop_mode: Enable Hadoop support.
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-- rabit_tree_reduce_minsize: Minimal size for tree reduce.
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-- rabit_reduce_ring_mincount: Minimal count to perform ring reduce.
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-- rabit_reduce_buffer: Size of the reduce buffer.
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-- rabit_bootstrap_cache: Size of the bootstrap cache.
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-- rabit_debug: Enable debugging.
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-- rabit_timeout: Enable timeout.
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-- rabit_timeout_sec: Timeout in seconds.
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-- rabit_enable_tcp_no_delay: Enable TCP no delay on Unix platforms.
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Only applicable to the Rabit communicator (these are case-sensitive, and can be set as
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environment variables):
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-- DMLC_TRACKER_URI: Hostname of the tracker.
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-- DMLC_TRACKER_PORT: Port number of the tracker.
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-- DMLC_TASK_ID: ID of the current task, can be used to obtain deterministic rank
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assignment.
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-- DMLC_ROLE: Role of the current task, "worker" or "server".
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-- DMLC_NUM_ATTEMPT: Number of attempts after task failure.
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-- DMLC_WORKER_CONNECT_RETRY: Number of retries to connect to the tracker.
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Only applicable to the Federated communicator (use upper case for environment variables, use
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lower case for runtime configuration):
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-- federated_server_address: Address of the federated server.
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-- federated_world_size: Number of federated workers.
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-- federated_rank: Rank of the current worker.
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-- federated_server_cert: Server certificate file path. Only needed for the SSL mode.
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-- federated_client_key: Client key file path. Only needed for the SSL mode.
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-- federated_client_cert: Client certificate file path. Only needed for the SSL mode.
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"""
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config = from_pystr_to_cstr(json.dumps(args))
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_check_call(_LIB.XGCommunicatorInit(config))
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def finalize() -> None:
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"""Finalize the communicator."""
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_check_call(_LIB.XGCommunicatorFinalize())
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def get_rank() -> int:
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"""Get rank of current process.
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Returns
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-------
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rank : int
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Rank of current process.
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"""
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ret = _LIB.XGCommunicatorGetRank()
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return ret
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def get_world_size() -> int:
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"""Get total number workers.
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Returns
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-------
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n : int
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Total number of process.
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"""
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ret = _LIB.XGCommunicatorGetWorldSize()
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return ret
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def is_distributed() -> int:
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"""If the collective communicator is distributed."""
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is_dist = _LIB.XGCommunicatorIsDistributed()
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return is_dist
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def communicator_print(msg: Any) -> None:
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"""Print message to the communicator.
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This function can be used to communicate the information of
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the progress to the communicator.
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Parameters
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----------
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msg : str
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The message to be printed to the communicator.
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"""
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if not isinstance(msg, str):
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msg = str(msg)
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is_dist = _LIB.XGCommunicatorIsDistributed()
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if is_dist != 0:
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_check_call(_LIB.XGCommunicatorPrint(c_str(msg.strip())))
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else:
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print(msg.strip(), flush=True)
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def get_processor_name() -> str:
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"""Get the processor name.
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Returns
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-------
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name : str
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the name of processor(host)
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"""
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name_str = ctypes.c_char_p()
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_check_call(_LIB.XGCommunicatorGetProcessorName(ctypes.byref(name_str)))
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value = name_str.value
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assert value
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return py_str(value)
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def broadcast(data: _T, root: int) -> _T:
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"""Broadcast object from one node to all other nodes.
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Parameters
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----------
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data : any type that can be pickled
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Input data, if current rank does not equal root, this can be None
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root : int
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Rank of the node to broadcast data from.
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Returns
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-------
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object : int
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the result of broadcast.
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"""
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rank = get_rank()
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length = ctypes.c_ulong()
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if root == rank:
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assert data is not None, "need to pass in data when broadcasting"
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s = pickle.dumps(data, protocol=pickle.HIGHEST_PROTOCOL)
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length.value = len(s)
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# run first broadcast
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_check_call(
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_LIB.XGCommunicatorBroadcast(
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ctypes.byref(length), ctypes.sizeof(ctypes.c_ulong), root
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)
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)
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if root != rank:
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dptr = (ctypes.c_char * length.value)()
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# run second
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_check_call(
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_LIB.XGCommunicatorBroadcast(
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ctypes.cast(dptr, ctypes.c_void_p), length.value, root
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)
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)
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data = pickle.loads(dptr.raw)
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del dptr
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else:
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_check_call(
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_LIB.XGCommunicatorBroadcast(
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ctypes.cast(ctypes.c_char_p(s), ctypes.c_void_p), length.value, root
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)
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)
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del s
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return data
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# enumeration of dtypes
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DTYPE_ENUM__ = {
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np.dtype("int8"): 0,
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np.dtype("uint8"): 1,
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np.dtype("int32"): 2,
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np.dtype("uint32"): 3,
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np.dtype("int64"): 4,
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np.dtype("uint64"): 5,
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np.dtype("float32"): 6,
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np.dtype("float64"): 7,
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}
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@unique
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class Op(IntEnum):
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"""Supported operations for allreduce."""
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MAX = 0
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MIN = 1
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SUM = 2
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BITWISE_AND = 3
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BITWISE_OR = 4
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BITWISE_XOR = 5
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def allreduce(data: np.ndarray, op: Op) -> np.ndarray: # pylint:disable=invalid-name
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"""Perform allreduce, return the result.
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Parameters
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----------
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data :
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Input data.
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op :
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Reduction operator.
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Returns
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-------
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result :
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The result of allreduce, have same shape as data
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Notes
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-----
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This function is not thread-safe.
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"""
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if not isinstance(data, np.ndarray):
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raise TypeError("allreduce only takes in numpy.ndarray")
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buf = data.ravel()
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if buf.base is data.base:
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buf = buf.copy()
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if buf.dtype not in DTYPE_ENUM__:
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raise TypeError(f"data type {buf.dtype} not supported")
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_check_call(
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_LIB.XGCommunicatorAllreduce(
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buf.ctypes.data_as(ctypes.c_void_p),
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buf.size,
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DTYPE_ENUM__[buf.dtype],
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int(op),
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None,
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None,
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)
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)
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return buf
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class CommunicatorContext:
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"""A context controlling collective communicator initialization and finalization."""
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def __init__(self, **args: Any) -> None:
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self.args = args
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key = "dmlc_nccl_path"
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if args.get(key, None) is not None:
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return
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binfo = build_info()
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if not binfo["USE_DLOPEN_NCCL"]:
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return
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try:
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# PyPI package of NCCL.
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from nvidia.nccl import lib
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# There are two versions of nvidia-nccl, one is from PyPI, another one from
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# nvidia-pyindex. We support only the first one as the second one is too old
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# (2.9.8 as of writing).
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if lib.__file__ is not None:
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dirname: Optional[str] = os.path.dirname(lib.__file__)
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else:
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dirname = None
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if dirname:
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path = os.path.join(dirname, "libnccl.so.2")
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self.args[key] = path
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except ImportError:
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pass
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def __enter__(self) -> Dict[str, Any]:
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init(**self.args)
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assert is_distributed()
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LOGGER.debug("-------------- communicator say hello ------------------")
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return self.args
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def __exit__(self, *args: List) -> None:
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finalize()
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LOGGER.debug("--------------- communicator say bye ------------------")
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