# 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 six import warnings from multiprocessing import Process, Manager import time import sys from paddle import compat as cpt # deprecated module import from paddle.fluid import core from paddle.fluid.framework import _set_expected_place from paddle.fluid.dygraph import parallel_helper from paddle.fluid.dygraph.parallel import ParallelEnv from paddle.distributed.fleet.base.private_helper_function import wait_server_ready __all__ = ["init_parallel_env"] 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 init_parallel_env(): """ Initialize parallel training environment in dynamic graph mode. .. note:: Now initialize both `NCCL` and `GLOO` contexts for communication. Returns: None Examples: .. code-block:: python 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(LinearNet, self).__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 # 1. gpu xpu check, must be gpu or xpu if not core.is_compiled_with_cuda() and not core.is_compiled_with_xpu(): raise NotImplementedError( "Cannot initialize parallel environment in CPU-only version, now only " "supports initializing the GPU and XPU parallel environment. Please recompile " "or reinstall paddle with GPU or XPU support.") # 2. check env 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) if core.is_compiled_with_cuda(): _check_var_exists("FLAGS_selected_gpus") elif core.is_compiled_with_xpu(): _check_var_exists('FLAGS_selected_xpus') _check_var_exists("PADDLE_TRAINER_ID") _check_var_exists("PADDLE_CURRENT_ENDPOINT") _check_var_exists("PADDLE_TRAINERS_NUM") _check_var_exists("PADDLE_TRAINER_ENDPOINTS") # 3: init gloo context (step 1: httpsever start) init_gloo = int(os.getenv("PADDLE_WITH_GLOO", "0")) if init_gloo: ep_rank_0 = parallel_env.trainer_endpoints[0].split(":") ep_rank = parallel_env.trainer_endpoints[parallel_env.rank].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} 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 # 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 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) _set_expected_place(place) # init nccl or bkcl context if 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)) 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 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() def get_rank(): """ Returns the rank of current trainer. Its value is equal to the value of the environment variable ``PADDLE_TRAINER_ID`` . The default value is 0. Returns: (int) The rank of current trainer. Examples: .. code-block:: python import paddle import paddle.distributed as dist # execute this command in terminal: export PADDLE_TRAINER_ID=0 print("The rank is %d" % dist.get_rank()) # The rank is 0 """ return _get_global_parallel_env().rank def get_world_size(): """ Returns the number of trainers (number of processes participating in current job). Its value is equal to the value of the environment variable ``PADDLE_TRAINERS_NUM`` . The default value is 1. Returns: (int) The number of trainers. Examples: .. code-block:: python import paddle import paddle.distributed as dist # execute this command in terminal: export PADDLE_TRAINERS_NUM=4 print("The world_size is %d" % dist.get_world_size()) # The world_size is 4 """ return _get_global_parallel_env().world_size