#!/usr/bin/env python
# -*- encoding: utf-8 -*-
import argparse
import os
import pprint
from pathlib import Path
from typing import Callable, Dict, Iterable, List, Optional, Tuple, Union
import torch
import torch.nn as nn
from torch.nn.modules.loss import _Loss
from torch.nn.parallel import DistributedDataParallel as DDP
from torch.optim.lr_scheduler import _LRScheduler
from torch.optim.optimizer import Optimizer
from torch.utils.data import DataLoader
from colossalai.amp import AMP_TYPE, convert_to_amp
from colossalai.amp.naive_amp import NaiveAMPModel
from colossalai.builder.builder import build_gradient_handler
from colossalai.context import Config, ConfigException, ParallelMode
from colossalai.context.moe_context import MOE_CONTEXT
from colossalai.core import global_context as gpc
from colossalai.engine import Engine
from colossalai.engine.gradient_accumulation import accumulate_gradient
from colossalai.engine.schedule import (
InterleavedPipelineSchedule,
NonPipelineSchedule,
PipelineSchedule,
get_tensor_shape,
)
from colossalai.logging import get_dist_logger
from colossalai.nn.optimizer.colossalai_optimizer import ColossalaiOptimizer
from colossalai.utils import get_current_device, is_using_ddp, is_using_pp, is_using_sequence, sync_model_param
from colossalai.utils.moe import sync_moe_model_param
from colossalai.zero.legacy import ShardedOptimizerV2, convert_to_zero_v2
from colossalai.zero.legacy.gemini.ophooks import BaseOpHook
def get_default_parser():
"""Reads user command line and uses an argument parser to parse the input arguments.
Input arguments include configuration, host, port, world size, local rank, backend for torch.distributed.
Returns:
Namespace: Returns the parser with the default arguments, the user may add customized arguments into this parser.
"""
parser = argparse.ArgumentParser()
parser.add_argument('--config', type=str, help='path to the config file')
parser.add_argument('--host', type=str, help='the master address for distributed training')
parser.add_argument('--port', type=int, help='the master port for distributed training')
parser.add_argument('--world_size', type=int, help='world size for distributed training')
parser.add_argument('--rank', type=int, help='rank for the default process group')
parser.add_argument('--local_rank', type=int, help='local rank on the node')
parser.add_argument('--backend', type=str, default='nccl', help='backend for distributed communication')
return parser
def launch(config: Union[str, Path, Config, Dict],
rank: int,
world_size: int,
host: str,
port: int,
backend: str = 'nccl',
local_rank: int = None,
seed: int = 1024,
verbose: bool = True):
"""This function first parses the configuration arguments, using :func:`parse_args()` in case one of the input
arguments are not given. Then initialize and set distributed environment by calling global_context's functions.
Args:
config (Union[str, dict, Config]): Config file or config file path are both acceptable
rank (int): Rank for the default process group
world_size (int): World size of the default process group
host (str): The master address for distributed training
port (str): The master port for distributed training
backend (str, optional): Backend for ``torch.distributed``, defaults to ``nccl``
local_rank (int, optional):
Rank for the process on the node and is used to set the default CUDA device,
defaults to None. If local_rank = None, the default device ordinal will be calculated automatically.
seed (int, optional): Specified random seed for every process. Defaults to 1024.
verbose (bool, optional): Whether to print logs. Defaults to True.
Raises:
Exception: Raise exception when config type is wrong
"""
gpc.verbose = verbose
# set config
assert isinstance(config, (Config, str, Path, dict)), \
f'expected argument config to be Config, str or Path, but got {type(config)}'
if not isinstance(config, Config) and isinstance(config, dict):
config = Config(config)
if isinstance(config, (str, Path)):
config = Config.from_file(config)
gpc.load_config(config)
# init default process group
gpc.init_global_dist(rank, world_size, backend, host, port)
# init process groups for different parallel modes from config
gpc.init_parallel_groups()
# set cuda device
if torch.cuda.is_available():
# if local rank is not given, calculate automatically
gpc.set_device(local_rank)
# set the number of processes running on the same node
gpc.detect_num_processes_on_current_node()
gpc.set_seed(seed)
if verbose:
logger = get_dist_logger()
logger.info(
f'Distributed environment is initialized, '
f'data parallel size: {gpc.data_parallel_size}, pipeline parallel size: {gpc.pipeline_parallel_size}, '
f'tensor parallel size: {gpc.tensor_parallel_size}',
ranks=[0])
def launch_from_slurm(config: Union[str, Path, Config, Dict],
host: str,
port: int,
backend: str = 'nccl',
seed: int = 1024,
verbose: bool = True):
"""A wrapper for colossalai.launch for SLURM launcher by reading rank and world size from the environment variables
set by SLURM
Args:
config (Union[str, dict, Config]): Config file or config file path are both acceptable
host (str): The master address for distributed training
port (str): The master port for distributed training
backend (str, optional): Backend for ``torch.distributed``, defaults to ``nccl``
seed (int, optional): Specified random seed for every process. Defaults to 1024.
verbose (bool, optional): Whether to print logs. Defaults to True.
"""
try:
rank = int(os.environ['SLURM_PROCID'])
world_size = int(os.environ['SLURM_NPROCS'])
except KeyError as e:
raise RuntimeError(
f"Could not find {e} in the SLURM environment, visit https://www.colossalai.org/ for more information on launching with SLURM"
)
launch(config=config,
rank=rank,
world_size=world_size,
host=host,
port=port,
backend=backend,
seed=seed,
verbose=verbose)
def launch_from_openmpi(config: Union[str, Path, Config, Dict],
host: str,
port: int,
backend: str = 'nccl',
seed: int = 1024,
verbose: bool = True):
"""A wrapper for colossalai.launch for OpenMPI launcher by reading rank and world size from the environment variables
set by OpenMPI
Args:
config (Union[str, dict, Config]): Config file or config file path are both acceptable
host (str): The master address for distributed training
port (str): The master port for distributed training
backend (str, optional): Backend for ``torch.distributed``, defaults to ``nccl``
seed (int, optional): Specified random seed for every process. Defaults to 1024.
verbose (bool, optional): Whether to print logs. Defaults to True.
"""
try:
rank = int(os.environ['OMPI_COMM_WORLD_RANK'])
local_rank = int(os.environ['OMPI_COMM_WORLD_LOCAL_RANK'])
world_size = int(os.environ['OMPI_COMM_WORLD_SIZE'])
except KeyError as e:
raise RuntimeError(
f"Could not find {e} in the OpenMPI environment, visit https://www.colossalai.org/ for more information on launching with OpenMPI"
)
launch(config=config,
local_rank=local_rank,
rank=rank,
world_size=world_size,
host=host,
port=port,
backend=backend,
seed=seed,
verbose=verbose)
def launch_from_torch(config: Union[str, Path, Config, Dict],
backend: str = 'nccl',
seed: int = 1024,
verbose: bool = True):
"""A wrapper for colossalai.launch for torchrun or torch.distributed.launch by reading rank and world size
from the environment variables set by PyTorch
Args:
config (Union[str, dict, Config]): Config file or config file path are both acceptable
backend (str, optional): Backend for ``torch.distributed``, defaults to ``nccl``
seed (int, optional): Specified random seed for every process. Defaults to 1024.
verbose (bool, optional): Whether to print logs. Defaults to True.
"""
try:
rank = int(os.environ['RANK'])
local_rank = int(os.environ['LOCAL_RANK'])
world_size = int(os.environ['WORLD_SIZE'])
host = os.environ['MASTER_ADDR']
port = int(os.environ['MASTER_PORT'])
except KeyError as e:
raise RuntimeError(
f"Could not find {e} in the torch environment, visit https://www.colossalai.org/ for more information on launching with torch"
)
launch(config=config,
local_rank=local_rank,
rank=rank,
world_size=world_size,
host=host,
port=port,
backend=backend,
seed=seed,
verbose=verbose)
def initialize(model: nn.Module,
optimizer: Optimizer,
criterion: Optional[_Loss] = None,
train_dataloader: Optional[Iterable] = None,
test_dataloader: Optional[Iterable] = None,
lr_scheduler: Optional[_LRScheduler] = None,
ophooks: Optional[List[BaseOpHook]] = None,
verbose: bool = True) -> Tuple[Engine, DataLoader, DataLoader, _LRScheduler]:
"""Core function to wrap the essential training components with our functionality based on the config which is
loaded into gpc.config.
Args:
model (:class:`torch.nn.Module` or Callbale): Your model instance or a function to build the model.
optimizer (:class:`torch.optim.optimizer.Optimizer` or :class:`Type[torch.optim.optimizer]`):
Your optimizer instance.
criterion (:class:`torch.nn.modules.loss._Loss`, optional): Your criterion instance.
train_dataloader (:class:`torch.utils.data.DataLoader`, optional): Dataloader for training.
test_dataloader (:class:`torch.utils.data.DataLoader`, optional): Dataloader for testing.
lr_scheduler (:class:`torch.nn.lr_scheduler._LRScheduler`, optional): Your lr scheduler instance, optional.
verbose (bool, optional): Whether to print logs.
Returns:
Tuple (engine, train_dataloader, test_dataloader, lr_scheduler):
A tuple of ``(engine, train_dataloader, test_dataloader, lr_scheduler)``
where only ``engine`` could not be None.
"""
# get logger
logger = get_dist_logger()
gpc.verbose = verbose
# get config from gpc
config = gpc.config
# print config
if verbose:
logger.info(
f"\n========== Your Config ========\n"
f"{pprint.pformat(gpc.config)}\n"
f"================================\n",
ranks=[0])
# cudnn
cudnn_benchmark = config.get('cudnn_benchmark', False)
cudnn_deterministic = config.get('cudnn_deterministic', False)
torch.backends.cudnn.benchmark = cudnn_benchmark
torch.backends.cudnn.deterministic = cudnn_deterministic
if verbose:
logger.info(f"cuDNN benchmark = {cudnn_benchmark}, deterministic = {cudnn_deterministic}", ranks=[0])
# zero
use_zero = hasattr(gpc.config, 'zero')
if use_zero:
zero_cfg = gpc.config.get('zero', None)
if zero_cfg is not None:
cfg_ = zero_cfg.copy()
else:
cfg_ = {}
optimizer_config = zero_cfg.get('optimizer_config', None)
model_config = zero_cfg.get('model_config', None)
model, optimizer = convert_to_zero_v2(model,
optimizer,
model_config=model_config,
optimizer_config=optimizer_config)
logger.info("Initializing ZeRO model and optimizer finished!", ranks=[0])
else:
if isinstance(model, nn.Module):
# first sync model across dp ranks
model.to(get_current_device())
elif isinstance(model, Callable):
model = model().to(get_current_device())
# optimizer maybe a optimizer_cls
if isinstance(optimizer, Callable):
optimizer = optimizer(model.parameters())
logger.warning("Initializing an non ZeRO model with optimizer class")
if not use_zero:
if is_using_sequence():
sync_model_param(model, ParallelMode.SEQUENCE_DP)
elif MOE_CONTEXT.is_initialized:
sync_moe_model_param(model)
elif is_using_ddp():
sync_model_param(model, ParallelMode.DATA)
else:
logger.warning(
"The parameters of models is not automatically synchronized.\n"
"Please make sure that all parameters are the same in data parallel group.",
ranks=[0])
# check amp and zero
fp16_cfg = gpc.config.get('fp16', None)
if fp16_cfg is not None and fp16_cfg.mode is not None and use_zero:
raise ConfigException(
"It is not allowed to set fp16 and zero configuration in your config file at the same time")
# clip grad norm
clip_grad_norm = gpc.config.get('clip_grad_norm', 0.0)
# initialize amp
amp_mode = None
if fp16_cfg is not None and fp16_cfg.mode is not None:
cfg_ = fp16_cfg.copy()
amp_mode = cfg_.pop('mode')
if is_using_pp():
assert amp_mode == AMP_TYPE.NAIVE, 'Pipeline only support NaiveAMP currently'
if amp_mode == AMP_TYPE.NAIVE:
cfg_['clip_grad_norm'] = clip_grad_norm
model, optimizer, criterion = convert_to_amp(model=model,
optimizer=optimizer,
criterion=criterion,
mode=amp_mode,
amp_config=cfg_)
# get torch ddp config
torch_ddp_cfg = gpc.config.get('torch_ddp', dict())
# gradient handler
gradient_handler_cfg = gpc.config.get('gradient_handler', None)
if gradient_handler_cfg is None:
# if gradient handler is not specified in the configuration file,
# check in the following order
# 1. if optimizer is ZERO, then use zero grad handler
# 2. if dp size is larger than 1 and pipeline is not used, use pytorch ddp
# 3. if using pipeline and dp size larger than 1, use data parallel grad handler
if isinstance(optimizer, ShardedOptimizerV2):
gradient_handler_cfg = [dict(type='ZeROGradientHandler')]
if verbose:
logger.info(
"Training with zero is detected, ZeROGradientHandler is automatically "
"added even though not specified in the configuration",
ranks=[0])
elif is_using_ddp() and MOE_CONTEXT.is_initialized:
gradient_handler_cfg = [dict(type='MoeGradientHandler')]
if verbose:
logger.info(
"Data parallel training is detected with moe parallel, MoeGradientHandler is automatically "
"added even though not specified in the configuration",
ranks=[0])
elif is_using_sequence():
model = DDP(model,
process_group=gpc.get_group(ParallelMode.SEQUENCE_DP),
device_ids=[torch.cuda.current_device()],
**torch_ddp_cfg)
if verbose:
logger.info('Model is using torch.nn.parallel.DistributedDataParallel for Sequence Parallelism',
ranks=[0])
elif is_using_ddp() and not is_using_pp() and amp_mode != AMP_TYPE.NAIVE:
model = DDP(model,
process_group=gpc.get_group(ParallelMode.DATA),
device_ids=[torch.cuda.current_device()],
**torch_ddp_cfg)
if verbose:
logger.info('Model is using torch.nn.parallel.DistributedDataParallel for Data Parallelism', ranks=[0])
elif is_using_ddp():
gradient_handler_cfg = [dict(type='DataParallelGradientHandler')]
if verbose:
logger.info(
"Data parallel training is detected when using pipeline parallel, "
"DataParallelGradientHandler is automatically "
"added even though not specified in the configuration",
ranks=[0])
# add pipeline parallel gradient handler, if pipeline shared module is detected
for param in model.parameters():
if getattr(param, 'pipeline_shared_module_pg', None) is not None:
if gradient_handler_cfg is None:
gradient_handler_cfg = [dict(type='PipelineSharedModuleGradientHandler')]
else:
gradient_handler_cfg.append(dict(type='PipelineSharedModuleGradientHandler'))
if verbose:
logger.info(
"pipeline_shared_module is detected, PipelineSharedModuleGradientHandler is automatically "
"added even though not specified in the configuration",
ranks=[0])
break
else:
if not isinstance(gradient_handler_cfg, list):
raise ConfigException(
f"expected gradient_handler in the configuration file to be a list but got {type(gradient_handler_cfg)}"
)
# turn off sync buffer for NaiveAMPModel if using torch DDP and NaiveAMPModel at the same time
# to avoid duplicated buffer synchronization
if isinstance(model, DDP) and isinstance(model.module, NaiveAMPModel):
model.module.sync_buffer = False
# initialize schedule for engine
if is_using_pp():
tensor_shape = get_tensor_shape()
use_interleaved = hasattr(gpc.config, 'model') and hasattr(gpc.config.model, 'num_chunks')
if gpc.is_initialized(ParallelMode.PARALLEL_1D):
scatter_gather = True
else:
scatter_gather = False
if use_interleaved:
if isinstance(model, nn.Sequential):
model = nn.ModuleList([model])
schedule = InterleavedPipelineSchedule(gpc.config.NUM_MICRO_BATCHES,
gpc.config.model.num_chunks,
tensor_shape=tensor_shape,
scatter_gather_tensors=scatter_gather)
else:
schedule = PipelineSchedule(gpc.config.NUM_MICRO_BATCHES,
tensor_shape=tensor_shape,
scatter_gather_tensors=scatter_gather)
else:
schedule = NonPipelineSchedule()
if gradient_handler_cfg is None:
gradient_handlers = None
if verbose and not isinstance(model, DDP):
logger.warning(
"No PyTorch DDP or gradient handler is set up, please make sure you do not need "
"to all-reduce the gradients after a training step.",
ranks=[0])
else:
gradient_handlers = [build_gradient_handler(cfg, model, optimizer) for cfg in gradient_handler_cfg]
# check if optimizer is ColossalaiOptimizer
if not isinstance(optimizer, (ColossalaiOptimizer, ShardedOptimizerV2)):
optimizer = ColossalaiOptimizer(optim=optimizer)
# gradient accumulation
grad_accum_size = gpc.config.get('gradient_accumulation', None)
if grad_accum_size is not None:
optimizer, train_dataloader, gradient_handlers, lr_scheduler = accumulate_gradient(
model=model,
optimizer=optimizer,
dataloader=train_dataloader,
accumulate_size=grad_accum_size,
gradient_handlers=gradient_handlers,
lr_scheduler=lr_scheduler)
engine = Engine(model=model,
optimizer=optimizer,
criterion=criterion,
gradient_handlers=gradient_handlers,
clip_grad_norm=clip_grad_norm,
ophook_list=ophooks,
schedule=schedule)
return engine, train_dataloader, test_dataloader, lr_scheduler