deeplearning/modulus/modulus-core-v010/_modules/modulus/distributed/manager.html

Source code for modulus.distributed.manager

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# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
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#     http://www.apache.org/licenses/LICENSE-2.0
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# Unless required by applicable law or agreed to in writing, software
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import torch
import torch.distributed as dist
import os
import numpy as np

from warnings import warn


[docs]class DistributedManager(object): """Distributed Manager for setting up distributed training enviroment. This is a singleton that creates a persistance class instance for storing parallel environment information through out the life time of the program. This should be used to help set up Distributed Data Parallel and parallel datapipes. Note ---- One should call `DistributedManager.initialize()` prior to constructing a manager object Example ------- >>> DistributedManager.initialize() >>> manager = DistributedManager() >>> manager.rank 0 >>> manager.world_size 1 """ _shared_state = {} def __new__(cls): obj = super(DistributedManager, cls).__new__(cls) obj.__dict__ = cls._shared_state # Set the defaults if not hasattr(obj, "_rank"): obj._rank = 0 if not hasattr(obj, "_world_size"): obj._world_size = 1 if not hasattr(obj, "_local_rank"): obj._local_rank = 0 if not hasattr(obj, "_distributed"): obj._distributed = False if not hasattr(obj, "_device"): obj._device = torch.device( f"cuda:0" if torch.cuda.is_available() else "cpu" ) if not hasattr(obj, "_cuda"): obj._cuda = torch.cuda.is_available() if not hasattr(obj, "_broadcast_buffers"): obj._broadcast_buffers = False if not hasattr(obj, "_find_unused_parameters"): obj._find_unused_parameters = False if not hasattr(obj, "_initialization_method"): obj._initialization_method = "None" return obj @property def rank(self): """Process rank""" return self._rank @property def local_rank(self): """Process rank on local machine""" return self._local_rank @property def world_size(self): """Number of processes in distributed enviroment""" return self._world_size @property def device(self): """Process device""" return self._device @property def distributed(self): """Distributed enviroment""" return self._distributed @property def cuda(self): """If cuda is available""" return self._cuda @property def group_names(self): """ Returns a list of all named process groups created """ return self._groups.keys()
[docs] def group(self, name=None): """ Returns a process group with the given name If name is None, group is also None indicating the default process group If named group does not exist, returns None also """ if name in self._groups.keys(): return self._groups[name] else: return None
[docs] def group_size(self, name=None): """ Returns the size of named process group """ if name is None: return self._world_size group = self.group(name) return dist.get_world_size(group=group)
[docs] def group_rank(self, name=None): """ Returns the rank in named process group """ if name is None: return self._rank group = self.group(name) if group is None: return 0 else: return dist.get_rank(group=group)
[docs] def group_name(self, group=None): """ Returns the name of process group """ if group is None: return None return self._group_names[group]

@property def broadcast_buffers(self): """broadcast_buffers in PyTorch DDP""" return self._broadcast_buffers @broadcast_buffers.setter def broadcast_buffers(self, broadcast: bool): """Setter for broadcast_buffers""" self._broadcast_buffers = broadcast @property def find_unused_parameters(self): """find_unused_parameters in PyTorch DDP""" return self._find_unused_parameters @find_unused_parameters.setter def find_unused_parameters(self, find_params: bool): """Setter for find_unused_parameters""" if find_params: warn( "Setting `find_unused_parameters` in DDP to true, use only if necessary." ) self._find_unused_parameters = find_params def __str__(self): output = ( f"Initialized process {self.rank} of {self.world_size} using " f"method '{self._initialization_method}'. Device set to {str(self.device)}" ) return output

[docs] @classmethod def is_initialized(cls) -> bool: """If manager singleton has been initialized""" return len(cls._shared_state) > 0
[docs] @staticmethod def get_available_backend(): """Get communication backend""" if torch.cuda.is_available() and torch.distributed.is_nccl_available(): return "nccl" else: return "gloo"
[docs] @staticmethod def initialize_env(): """Setup method using generic initialization""" rank = int(os.environ.get("RANK")) world_size = int(os.environ.get("WORLD_SIZE")) if "LOCAL_RANK" in os.environ: local_rank = int(os.environ.get("LOCAL_RANK")) else: local_rank = rank % torch.cuda.device_count() addr = os.environ.get("MASTER_ADDR") port = os.environ.get("MASTER_PORT") DistributedManager.setup( rank=rank, world_size=world_size, local_rank=local_rank, addr=addr, port=port, backend=DistributedManager.get_available_backend(), )
[docs] @staticmethod def initialize_open_mpi(addr, port): """Setup method using OpenMPI initialization""" rank = int(os.environ.get("OMPI_COMM_WORLD_RANK")) world_size = int(os.environ.get("OMPI_COMM_WORLD_SIZE")) local_rank = int(os.environ.get("OMPI_COMM_WORLD_LOCAL_RANK")) DistributedManager.setup( rank=rank, world_size=world_size, local_rank=local_rank, addr=addr, port=port, backend=DistributedManager.get_available_backend(), method="openmpi", )
[docs] @staticmethod def initialize_slurm(port): """Setup method using SLURM initialization""" rank = int(os.environ.get("SLURM_PROCID")) world_size = int(os.environ.get("SLURM_NPROCS")) local_rank = int(os.environ.get("SLURM_LOCALID")) addr = os.environ.get("SLURM_LAUNCH_NODE_IPADDR") DistributedManager.setup( rank=rank, world_size=world_size, local_rank=local_rank, addr=addr, port=port, backend=DistributedManager.get_available_backend(), method="slurm", )
[docs] @staticmethod def initialize(): """Initialize distributed manager""" if DistributedManager.is_initialized(): warn("Distributed manager is already intialized") return addr = os.getenv("MASTER_ADDR", "localhost") port = os.getenv("MASTER_PORT", "12355") # https://pytorch.org/docs/master/notes/cuda.html#id5 os.environ["NCCL_ASYNC_ERROR_HANDLING"] = "0" try: DistributedManager.initialize_env() except: if "SLURM_PROCID" in os.environ: DistributedManager.initialize_slurm(port) elif "OMPI_COMM_WORLD_RANK" in os.environ: DistributedManager.initialize_open_mpi(addr, port) # Set per rank numpy random seed for data sampling np.random.seed(seed=DistributedManager().rank)
[docs] @staticmethod def setup( rank=0, world_size=1, local_rank=None, addr="localhost", port="12355", backend="nccl", method="env", ): """Set up PyTorch distributed process group and update manager attributes""" os.environ["MASTER_ADDR"] = addr os.environ["MASTER_PORT"] = str(port) manager = DistributedManager() manager._distributed = (world_size > 1) and torch.distributed.is_available() if manager._distributed: # Update rank and world_size if using distributed manager._rank = rank manager._world_size = world_size if local_rank is None: manager._local_rank = rank % torch.cuda.device_count() else: manager._local_rank = local_rank # Setup distributed process group # time.sleep(1) dist.init_process_group( backend, rank=manager.rank, world_size=manager.world_size ) manager._groups = {} manager._group_ranks = {} manager._group_names = {} manager._device = torch.device( f"cuda:{manager.local_rank}" if torch.cuda.is_available() else "cpu" ) # Needed for cuda graphs if torch.cuda.is_available(): torch.cuda.set_device(manager.local_rank) manager._initialization_method = method # Set device for this process and empty cache to optimize memory usage torch.cuda.device(manager.device) torch.cuda.empty_cache()

# @staticmethod # def create_process_subgroup(name: str, size: int, group_name=None, verbose=False): # """TODO add documentation""" # manager = DistributedManager() # if not manager.distributed: # return None # assert name not in manager._groups, f"Group with name {name} already exists" # # Get parent group's params # group = manager._group[group_name] if group_name else None # group_size = dist.get_world_size(group=group) # group_rank = dist.get_rank(group=group) # num_groups = manager.world_size // group_size # # Get number of sub-groups per parent group # assert ( # group_size % size == 0 # ), f"Cannot divide group size {group_size} evenly into subgroups of size {size}" # num_subgroups = group_size // size # # Create all the sub-groups # # Note: all ranks in the job need to create all sub-groups in # # the same order even if a rank is not part of a sub-group # manager._group_ranks[name] = [] # for g in range(num_groups): # for i in range(num_subgroups): # # Get global ranks that are part of this sub-group # start = i * size # end = start + size # if group_name: # ranks = manager._group_ranks[group_name][g][start:end] # else: # ranks = list(range(start, end)) # # Create sub-group and keep track of ranks # tmp_group = dist.new_group(ranks=ranks) # manager._group_ranks[name].append(ranks) # if manager.rank in ranks: # # Set group in manager only if this rank is part of the group # manager._groups[name] = tmp_group # manager._group_names[tmp_group] = name # if verbose and manager.rank == 0: # print(f"Process group '{name}':") # for grp in manager._group_ranks[name]: # print(" ", grp) # @staticmethod # def create_orthogonal_process_group(name: str, group_name: str, verbose=False): # """TODO add documentation""" # manager = DistributedManager() # if not manager.distributed: # return None # assert ( # group_name in manager._groups # ), f"Group with name {group_name} does not exist" # assert name not in manager._groups, f"Group with name {name} already exists" # group_ranks = manager._group_ranks[group_name] # orthogonal_ranks = [list(i) for i in zip(*group_ranks)] # for ranks in orthogonal_ranks: # tmp_group = dist.new_group(ranks=ranks) # if manager.rank in ranks: # # Set group in manager only if this rank is part of the group # manager._groups[name] = tmp_group # manager._group_names[tmp_group] = name # manager._group_ranks[name] = orthogonal_ranks # if verbose and manager.rank == 0: # print(f"Process group '{name}':") # for grp in manager._group_ranks[name]: # print(" ", grp)

[docs] @staticmethod def cleanup(): """Clean up distributed group and singleton""" dist.destroy_process_group() DistributedManager._shared_state = {}
© Copyright 2023, NVIDIA Modulus Team. Last updated on Aug 8, 2023.