parallel.py

# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except jin 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.

import os
import time
import warnings
from multiprocessing import Manager  # noqa: F401
from multiprocessing import Process  # noqa: F401

import paddle
from paddle.distributed.collective import (
    Group,
    _default_group_name,
    _get_group_map_by_name,
    _new_process_group_impl,
    _set_default_backend,
    _set_default_store,
    _set_group_map,
    _set_group_map_backend,
    _set_group_map_by_name,
    _valid_backend_list,
)
from paddle.distributed.communication.group import _add_new_group
from paddle.distributed.fleet.base.private_helper_function import (  # noqa: F401
    wait_server_ready,
)
from paddle.distributed.fleet.launch_utils import check_backend

# deprecated module import
# (TODO: GhostScreaming) It will be removed later.
from paddle.fluid import core

# (TODO: GhostScreaming) It will be removed later.
from paddle.fluid.dygraph.parallel import ParallelEnv

# (TODO: GhostScreaming) It will be removed later.
from paddle.framework import (
    _set_expected_place,
    in_dygraph_mode,
    parallel_helper,
)

__all__ = []

ParallelStrategy = core.ParallelStrategy

# NOTE(chenweihang): Maintain a global parallel env to avoid
# initializing ParallelEnv every time and improve performance
_global_parallel_env = None


def _get_global_parallel_env():
    global _global_parallel_env
    if _global_parallel_env is None:
        _global_parallel_env = ParallelEnv()
    return _global_parallel_env


def _start_kv_server(port, http_server_d, size):
    from paddle.distributed.fleet.utils.http_server import KVServer

    http_server = KVServer(int(port), size=size)
    http_server.start()
    wait_seconds = 3
    while http_server_d.get("running", False) or not http_server.should_stop():
        time.sleep(wait_seconds)
    http_server.stop()


def _is_cpuonly(backend):
    check_backend(backend)
    if (
        backend in ['auto', 'nccl', 'bkcl', 'hccl', 'heter', 'cncl']
        and (
            core.is_compiled_with_cuda()
            or core.is_compiled_with_xpu()
            or core.is_compiled_with_npu()
            or core.is_compiled_with_mlu()
        )
    ) or backend == 'xccl':

        # passes 'auto' and can use cuda or xpu, use the default logics. so return False
        return False
    else:
        return True


def _check_var_exists(var_name):
    var = os.environ.get(var_name, None)
    if var is None:
        raise ValueError(
            "paddle.distributed initialize error, "
            "environment variable %s is needed, but not set." % var_name
        )


def init_parallel_env():
    """

    Initialize parallel training environment in dynamic graph mode.

    Note:
        Now initialize both `NCCL` and `GLOO` contexts for communication.

    Args:
        backend (string): A string represents the backend used by DataParallel,
            should be one of 'gloo'(for cpu), 'nccl'(for cuda), 'bkcl'(for xpu), 'auto'(auto detect).
            The auto detection prefer 'nccl', 'bkcl' than 'gloo'.

    Returns:
        None

    Examples:
        .. code-block:: python

            # required: gpu
            import paddle
            import paddle.nn as nn
            import paddle.optimizer as opt
            import paddle.distributed as dist

            class LinearNet(nn.Layer):
                def __init__(self):
                    super().__init__()
                    self._linear1 = nn.Linear(10, 10)
                    self._linear2 = nn.Linear(10, 1)

                def forward(self, x):
                    return self._linear2(self._linear1(x))

            def train():
                # 1. initialize parallel environment
                dist.init_parallel_env()

                # 2. create data parallel layer & optimizer
                layer = LinearNet()
                dp_layer = paddle.DataParallel(layer)

                loss_fn = nn.MSELoss()
                adam = opt.Adam(
                    learning_rate=0.001, parameters=dp_layer.parameters())

                # 3. run layer
                inputs = paddle.randn([10, 10], 'float32')
                outputs = dp_layer(inputs)
                labels = paddle.randn([10, 1], 'float32')
                loss = loss_fn(outputs, labels)

                loss.backward()

                adam.step()
                adam.clear_grad()

            if __name__ == '__main__':
                dist.spawn(train)

    """

    # 0. get env & check world size
    global _global_parallel_env
    # when call init_parallel_env, need update `_global_parallel_env`
    _global_parallel_env = ParallelEnv()
    parallel_env = _global_parallel_env
    # if not parallel, `init_parallel_env` do nothing
    if parallel_env.world_size < 2:
        warnings.warn(
            "Currently not a parallel execution environment, `paddle.distributed.init_parallel_env` will not do anything."
        )
        return
    # NOTE(xiongkun): support cpu gloo only, add this environment variable to
    #                 enable cpu only gloo prarllel training)
    backend = os.environ.get('PADDLE_DISTRI_BACKEND', 'auto')
    is_cpu_only = _is_cpuonly(backend)
    # 1. gpu xpu check, must be gpu or xpu,
    if not (
        is_cpu_only
        or core.is_compiled_with_cuda()
        or core.is_compiled_with_xpu()
        or core.is_compiled_with_npu()
        or core.is_compiled_with_mlu()
        or backend == "xccl"
    ):
        raise NotImplementedError(
            "If you want to use CPU-only version, please use 'gloo' as backend"
        )

    if backend == "xccl":
        FLAGS_selected_custom_devices = 'FLAGS_selected_{}s'.format(
            parallel_env.device_type
        )
        _check_var_exists(FLAGS_selected_custom_devices)
    else:
        if not is_cpu_only and core.is_compiled_with_cuda():
            _check_var_exists("FLAGS_selected_gpus")
            backend = "nccl" if backend == "auto" else backend
        elif not is_cpu_only and core.is_compiled_with_xpu():
            _check_var_exists('FLAGS_selected_xpus')
            backend = "bkcl" if backend == "auto" else backend
        elif not is_cpu_only and core.is_compiled_with_npu():
            _check_var_exists('FLAGS_selected_npus')
            backend = "hccl" if backend == "auto" else backend
        elif not is_cpu_only and core.is_compiled_with_mlu():
            _check_var_exists('FLAGS_selected_mlus')
            backend = "cncl" if backend == "auto" else backend

    _check_var_exists("PADDLE_TRAINER_ID")
    _check_var_exists("PADDLE_CURRENT_ENDPOINT")
    _check_var_exists("PADDLE_TRAINERS_NUM")
    _check_var_exists("PADDLE_TRAINER_ENDPOINTS")

    # NOTE(chenweihang): [ why config global place here? ]
    # the dygraph mode will be set to default mode,
    # users will not call `dygraph.guard` or `enable_dygraph`
    # directly, if they want to switch default place,
    # they need to call a function to change default place,
    # here just set correctly place to users
    if backend == "xccl":
        place = core.CustomPlace(
            parallel_env.device_type, parallel_env.device_id
        )
    elif is_cpu_only:
        place = core.CPUPlace()
    elif core.is_compiled_with_cuda():
        place = core.CUDAPlace(parallel_env.device_id)
    elif core.is_compiled_with_xpu():
        place = core.XPUPlace(parallel_env.device_id)
    elif core.is_compiled_with_npu():
        place = core.NPUPlace(parallel_env.device_id)
    elif core.is_compiled_with_mlu():
        place = core.MLUPlace(parallel_env.device_id)

    _set_expected_place(place)

    group = None

    if backend in _valid_backend_list and in_dygraph_mode():
        if _default_group_name in _get_group_map_by_name():
            return _get_group_map_by_name()[_default_group_name]
        _set_default_backend(backend)
        rank = int(os.getenv("PADDLE_TRAINER_ID"))
        world_size = int(os.getenv("PADDLE_TRAINERS_NUM"))
        assert rank >= 0 and world_size > rank and world_size > 1, (
            "rank must be non-negative and world_size must be the "
            "maximum rank plus one. Moreover, at least two processes are "
            "required to create a process group."
        )
        master_addr = os.getenv("MASTER_ADDR", None)
        master_port = os.getenv("MASTER_PORT", None)
        endpoints = (
            ":".join([master_addr, master_port])
            if master_addr and master_port
            else None
        )
        if endpoints is None:
            endpoints = os.getenv("PADDLE_MASTER", None)
        if endpoints is None:
            endpoints = os.getenv("PADDLE_TRAINER_ENDPOINTS").split(',')[0]
        assert endpoints, (
            "The environment variable 'MASTER_ADDR' and 'MASTER_PORT' "
            "must be specified, for example 'export MASTER_ADDR=127.0.0.1' "
            "and 'export MASTER_ADDR=54612'. Or you can start your training"
            "with paddle.distributed.run module."
        )
        master_addr, master_port = endpoints.split(":")
        master_port = int(master_port)
        is_master = rank == 0
        stop_check_timeout = int(os.getenv("FLAGS_stop_check_timeout", "900"))
        default_store = core.TCPStore(
            master_addr,
            master_port,
            is_master,
            world_size,
            timeout=stop_check_timeout,
        )
        _set_default_store(default_store)
        pg = _new_process_group_impl(
            backend,
            default_store,
            rank,
            world_size,
            _default_group_name,
            pg_options=None,
        )
        ranks = list(range(world_size))
        group = Group(rank, 0, ranks, pg=pg, name=_default_group_name)
        _set_group_map_by_name(_default_group_name, group)
        _set_group_map(0, group)
        _set_group_map_backend(group, backend)
        _add_new_group(group)
        parallel_helper._set_parallel_ctx(True)

        paddle.distributed.barrier(group=group)
        return group

    node_num = set([i.split(":")[0] for i in parallel_env.trainer_endpoints])
    # 3: init gloo context (step 1: httpsever start)
    init_gloo = int(os.getenv("PADDLE_WITH_GLOO", "0"))
    if is_cpu_only or init_gloo or backend == "heter":
        ep_rank_0 = parallel_env.trainer_endpoints[0].split(":")
        manager = Manager()
        # glboal dict to store status
        http_server_d = manager.dict()
        http_server_d["running"] = False
        if parallel_env.rank == 0:
            # The scope for worker used by http server is '_worker'
            size = {'_worker': parallel_env.world_size}
            if backend == "heter":
                size = {'_worker': len(node_num)}
            http_server = Process(
                target=_start_kv_server,
                args=(int(ep_rank_0[1]), http_server_d, size),
            )
            http_server.daemon = True
            http_server_d["running"] = True
            http_server.start()

    # 4. init NCCL ParallelStrategy
    strategy = ParallelStrategy()
    if parallel_helper._is_parallel_ctx_initialized():
        warnings.warn("The parallel environment has been initialized.")
    strategy.nranks = parallel_env.world_size
    strategy.local_rank = parallel_env.rank
    strategy.trainer_endpoints = parallel_env.trainer_endpoints
    strategy.current_endpoint = parallel_env.current_endpoint
    strategy.nrings = parallel_env.nrings

    # init nccl or hccl or bkcl or heter context
    if is_cpu_only:
        parallel_helper._set_parallel_ctx(
            core.GLOOParallelContext(strategy, place)
        )
    elif backend == "heter":
        parallel_helper._set_parallel_ctx(
            core.HeterParallelContext(strategy, parallel_env.device_id)
        )
    elif core.is_compiled_with_cuda():
        parallel_helper._set_parallel_ctx(
            core.NCCLParallelContext(strategy, place)
        )
    elif core.is_compiled_with_xpu():
        parallel_helper._set_parallel_ctx(
            core.BKCLParallelContext(strategy, place)
        )
    elif core.is_compiled_with_npu():
        parallel_helper._set_parallel_ctx(
            core.HCCLParallelContext(strategy, place)
        )
    elif core.is_compiled_with_mlu():
        parallel_helper._set_parallel_ctx(
            core.CNCLParallelContext(strategy, place)
        )

    if backend != "heter":
        other_endpoints = strategy.trainer_endpoints[:]
        other_endpoints.remove(strategy.current_endpoint)
        if not is_cpu_only and strategy.local_rank == 0:
            wait_server_ready(other_endpoints)

    parallel_helper._init_parallel_ctx()

    # 5: init gloo context (step 2: gloo init)
    # dividing init_gloo into two part beacause nccl and gloo
    # are separately looking for free ports which sometimes
    # leads to port-conflict.
    if (is_cpu_only or backend == "heter") and parallel_env.rank == 0:
        # compare to init_gloo, we don't need to
        # init gloo, because we do this in _init_parallel_ctx;
        http_server_d["running"] = False
        http_server.join()

    elif init_gloo:
        wait_server_ready([parallel_env.trainer_endpoints[0]])
        gloo_strategy = core.GlooParallelStrategy()
        gloo_strategy.rank = parallel_env.rank
        gloo_strategy.rank_num = parallel_env.world_size
        gloo_strategy.ip_address = ep_rank_0[0]
        gloo_strategy.ip_port = int(ep_rank_0[1])
        default_init_timeout_seconds = 3600
        default_run_timeout_seconds = 9999999
        gloo_strategy.init_seconds = default_init_timeout_seconds
        gloo_strategy.run_seconds = default_run_timeout_seconds
        gloo = core.GlooParallelContext(gloo_strategy)
        gloo.init()
        if parallel_env.rank == 0:
            http_server_d["running"] = False
            http_server.join()
    return group


def get_rank(group=None):
    """
    Returns the rank of current trainer in the given group, ranks are consecutive integers in [0, ``world_size``).
    If none of the group is given, the global group will be used as default.

    Args:
        group (Group, optional): The communication group you want to get rank of current trainer, use global group as default if group is None.

    Returns:
        (int) The rank of current trainer in the given group. Return -1 if the process is not part of the given group.

    Warning:
        Argument ``group`` only supports in dygraph mode.

    Examples:
        .. code-block:: python

            # Execute this script using distributed launch with one card configs.
            import paddle
            import paddle.distributed as dist

            dist.init_parallel_env()
            print("The rank is %d" % dist.get_rank())
            # The rank is 0
    """
    if in_dygraph_mode() and group:
        return group.rank

    assert group is None, "Only support group argument in eager mode."
    return _get_global_parallel_env().rank


def get_world_size(group=None):
    """
    Returns the number of trainers (number of processes participating in current job) in the given group.
    If none of the group is given, the global group will be used as default.

    Args:
        group (Group, optional): The communication group you want to check world size, use global group as default if group is None.

    Returns:
        (int) The number of trainers in the given group. Return -1 if the process if not part of the given group.

    Warning:
        Argument ``group`` only supports in dygraph mode.

    Examples:
        .. code-block:: python

            # Execute this script using distributed launch with one card configs.
            import paddle
            import paddle.distributed as dist

            dist.init_parallel_env()
            print("The world_size is %d" % dist.get_world_size())
            # The world_size is 1
    """
    if in_dygraph_mode() and group:
        return group.world_size

    assert group is None, "Only support group argument in eager mode."
    return _get_global_parallel_env().world_size