#!/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.accelerator import get_accelerator
from colossalai.context import Config, ConfigException
from colossalai.interface import OptimizerWrapper
from colossalai.legacy.amp import AMP_TYPE, convert_to_amp
from colossalai.legacy.amp.naive_amp import NaiveAMPModel
from colossalai.legacy.builder.builder import build_gradient_handler
from colossalai.legacy.context import ParallelMode
from colossalai.legacy.core import global_context as gpc
from colossalai.legacy.engine import Engine
from colossalai.legacy.engine.gradient_accumulation import accumulate_gradient
from colossalai.legacy.engine.schedule import (
InterleavedPipelineSchedule,
NonPipelineSchedule,
PipelineSchedule,
get_tensor_shape,
)
from colossalai.legacy.utils import is_using_ddp, is_using_pp, is_using_sequence, sync_model_param
from colossalai.legacy.zero import ShardedOptimizerV2, convert_to_zero_v2
from colossalai.legacy.zero.gemini.ophooks import BaseOpHook
from colossalai.logging import get_dist_logger
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 Callable): 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_accelerator().get_current_device())
elif isinstance(model, Callable):
model = model().to(get_accelerator().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 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_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 OptimizerWrapper
if not isinstance(optimizer, (OptimizerWrapper, ShardedOptimizerV2)):
optimizer = OptimizerWrapper(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