worker.py

# Copyright 2024 Bytedance Ltd. and/or its affiliates
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""
the class for Worker
"""

import os
import socket
from dataclasses import dataclass
from typing import Tuple

import ray
import torch

from .decorator import Dispatch, Execute, register
from .register_center.ray import create_worker_group_register_center


@dataclass
class DistRankInfo:
    tp_rank: int
    dp_rank: int
    pp_rank: int


@dataclass
class DistGlobalInfo:
    tp_size: int
    dp_size: int
    pp_size: int


class WorkerHelper:
    def _get_node_ip(self) -> str:
        host_ipv4 = os.getenv("MY_HOST_IP", None)
        host_ipv6 = os.getenv("MY_HOST_IPV6", None)
        host_ip_by_env = host_ipv4 or host_ipv6
        host_ip_by_sdk = ray._private.services.get_node_ip_address()

        host_ip = host_ip_by_env or host_ip_by_sdk
        return host_ip

    def _get_free_port(self) -> int:
        with socket.socket() as sock:
            sock.bind(("", 0))
            return sock.getsockname()[1]

    def get_availale_master_addr_port(self) -> Tuple[str, str]:
        return self._get_node_ip(), str(self._get_free_port())

    def _get_pid(self):
        return


class WorkerMeta:
    keys = [
        "WORLD_SIZE",
        "RANK",
        "LOCAL_WORLD_SIZE",
        "LOCAL_RANK",
        "MASTER_ADDR",
        "MASTER_PORT",
        "CUDA_VISIBLE_DEVICES",
    ]

    def __init__(self, store) -> None:
        self._store = store

    def to_dict(self):
        return {f"_{key.lower()}": self._store.get(f"_{key.lower()}", None) for key in WorkerMeta.keys}


# we assume that in each WorkerGroup, there is a Master Worker
class Worker(WorkerHelper):
    """A (distributed) worker."""

    _world_size: int
    _rank: int
    _local_world_size: int
    _local_rank: int
    _master_addr: str
    _master_port: str
    _cuda_visible_devices: str

    def __new__(cls, *args, **kwargs):
        instance = super().__new__(cls)

        # note that here we use int to distinguish
        disable_worker_init = int(os.getenv("DISABLE_WORKER_INIT", 0))
        if disable_worker_init:
            return instance

        rank = os.getenv("RANK", None)
        worker_group_prefix = os.getenv("WG_PREFIX", None)

        # when decorator @ray.remote applies, __new__ will be called while we don't want to apply _configure_before_init
        if None not in [rank, worker_group_prefix] and "ActorClass(" not in cls.__name__:
            instance._configure_before_init(f"{worker_group_prefix}_register_center", int(rank))

        return instance

    def _configure_before_init(self, register_center_name: str, rank: int):
        assert isinstance(rank, int), f"rank must be int, instead of {type(rank)}"

        if rank == 0:
            master_addr, master_port = self.get_availale_master_addr_port()
            rank_zero_info = {
                "MASTER_ADDR": master_addr,
                "MASTER_PORT": master_port,
            }
            self.register_center = create_worker_group_register_center(name=register_center_name, info=rank_zero_info)
            os.environ.update(rank_zero_info)

    def __init__(self, cuda_visible_devices=None) -> None:
        # construct a meta from envrionment variable. Note that the import must be inside the class because it is executed remotely
        world_size = int(os.getenv("WORLD_SIZE"))
        rank = int(os.getenv("RANK"))
        self._rank = rank
        self._world_size = world_size

        if "AMD" in torch.cuda.get_device_name():
            os.environ["CUDA_VISIBLE_DEVICES"] = os.getenv("ROCR_VISIBLE_DEVICES")
            os.environ["LOCAL_RANK"] = os.getenv("RAY_LOCAL_RANK")
            cuda_visible_devices = os.getenv("LOCAL_RANK", "0")
            torch.cuda.set_device(int(cuda_visible_devices))

        ## for DCU K100_AI, 通过 torch.cuda.get_device_name() 获取 device_name
        if "K500SM_AI" in torch.cuda.get_device_name():
            print("Init DCU Devices")
            os.environ["CUDA_VISIBLE_DEVICES"] = os.getenv("HIP_VISIBLE_DEVICES")
            os.environ["LOCAL_RANK"] = os.getenv("RAY_LOCAL_RANK")
            cuda_visible_devices = os.getenv("LOCAL_RANK", "0")
            torch.cuda.set_device(int(cuda_visible_devices))

        master_addr = os.getenv("MASTER_ADDR")
        master_port = os.getenv("MASTER_PORT")

        local_world_size = int(os.getenv("LOCAL_WORLD_SIZE", "1"))
        local_rank = int(os.getenv("LOCAL_RANK", "0"))

        store = {
            "_world_size": world_size,
            "_rank": rank,
            "_local_world_size": local_world_size,
            "_local_rank": local_rank,
            "_master_addr": master_addr,
            "_master_port": master_port,
        }
        if cuda_visible_devices is not None:
            store["_cuda_visible_devices"] = cuda_visible_devices

        meta = WorkerMeta(store=store)
        self._configure_with_meta(meta=meta)

    def _configure_with_meta(self, meta: WorkerMeta):
        """
        This function should only be called inside by WorkerGroup
        """
        assert isinstance(meta, WorkerMeta)
        self.__dict__.update(meta.to_dict())  # this is hacky
        # print(f"__dict__: {self.__dict__}")
        for key in WorkerMeta.keys:
            val = self.__dict__.get(f"_{key.lower()}", None)
            if val is not None:
                # print(f"set {key} to {val}")
                os.environ[key] = str(val)

        os.environ["REDIS_STORE_SERVER_HOST"] = (
            str(self._master_addr).replace("[", "").replace("]", "") if self._master_addr else ""
        )

    def get_master_addr_port(self):
        return self._master_addr, self._master_port

    def get_cuda_visible_devices(self):
        cuda_visible_devices = os.getenv("CUDA_VISIBLE_DEVICES", "not set")
        return cuda_visible_devices

    def print_rank0(self, *args, **kwargs):
        if self.rank == 0:
            print(*args, **kwargs)

    @property
    def world_size(self):
        return self._world_size

    @property
    def rank(self):
        return self._rank

    @register(dispatch_mode=Dispatch.DP_COMPUTE_PROTO_WITH_FUNC)
    def execute_with_func_generator(self, func, *args, **kwargs):
        ret_proto = func(self, *args, **kwargs)
        return ret_proto

    @register(dispatch_mode=Dispatch.ALL_TO_ALL, execute_mode=Execute.RANK_ZERO)
    def execute_func_rank_zero(self, func, *args, **kwargs):
        result = func(*args, **kwargs)
        return result