import random from types import MethodType from typing import Callable, Optional, OrderedDict, Tuple import numpy as np import torch import torch.distributed as dist from torch.distributed import ProcessGroup from torch.nn import Module from torch.optim import Optimizer from torch.optim.lr_scheduler import _LRScheduler as LRScheduler from torch.utils.data import DataLoader from torch.utils.data.distributed import DistributedSampler from colossalai.booster.plugin.hybrid_parallel_plugin import ( HybridParallelAMPOptimizer, HybridParallelModule, HybridParallelNaiveOptimizer, HybridParallelPlugin, get_param_info, init_pipeline_optimizer, ) from colossalai.cluster import ProcessGroupMesh from colossalai.interface import ModelWrapper, OptimizerWrapper from colossalai.moe import MoECheckpintIO from colossalai.pipeline.schedule import OneForwardOneBackwardSchedule from colossalai.pipeline.stage_manager import PipelineStageManager from colossalai.shardformer import ShardConfig from colossalai.shardformer.policies.base_policy import Policy from colossalai.zero.low_level import LowLevelZeroOptimizer PP_AXIS, DP_AXIS, TP_AXIS = 0, 1, 2 class HybridParallelZeroOptimizer(LowLevelZeroOptimizer): def __init__( self, optimizer: Optimizer, model: Module, use_pipeline: bool, param_info: OrderedDict, initial_scale: int = 2**16, # grad scaler config min_scale: int = 1, growth_factor: float = 2.0, backoff_factor: float = 0.5, growth_interval: int = 2000, hysteresis: int = 2, max_scale: int = 2**24, clip_grad_norm: float = 0.0, # grad clipping verbose: bool = False, reduce_bucket_size: int = 1024 * 1024, # communication communication_dtype: Optional[torch.dtype] = None, overlap_communication: bool = True, partition_grad: bool = False, # stage 2 flag cpu_offload: bool = False, # cpu offload dp_process_group: Optional[ProcessGroup] = None, # the dp pg for comm tp_process_group: Optional[ProcessGroup] = None, # if using tp pp_process_group: Optional[ProcessGroup] = None, forced_dtype: Optional[torch.dtype] = None, moe_extra_dp_process_group: Optional[ProcessGroup] = None, ): self.param_info = param_info self.stage_manager = model.stage_manager self.shared_params = model.shared_params self.dp_pg = dp_process_group self.tp_pg = tp_process_group self.pp_pg = pp_process_group if use_pipeline: init_pipeline_optimizer(optimizer, model) super().__init__( optimizer=optimizer, initial_scale=initial_scale, min_scale=min_scale, growth_factor=growth_factor, backoff_factor=backoff_factor, growth_interval=growth_interval, hysteresis=hysteresis, max_scale=max_scale, clip_grad_norm=clip_grad_norm, verbose=verbose, reduce_bucket_size=reduce_bucket_size, communication_dtype=communication_dtype, overlap_communication=overlap_communication, partition_grad=partition_grad, cpu_offload=cpu_offload, dp_process_group=dp_process_group, forced_dtype=forced_dtype, moe_extra_dp_process_group=moe_extra_dp_process_group, ) class MoeHybridParallelPlugin(HybridParallelPlugin): """ Plugin for Moe Hybrid Parallel Training. Tensor parallel, pipeline parallel and data parallel(DDP/ZeRO) can be picked and combined in this plugin. The size of tp and pp should be passed in by user, then the size of dp is automatically calculated from dp_size = world_size / (tp_size * pp_size). Example: >>> from colossalai.booster import Booster >>> from colossalai.booster.plugin import HybridParallelPlugin >>> model, train_dataset, optimizer, criterion = ... >>> plugin = HybridParallelPlugin(tp_size=2, pp_size=2) >>> train_dataloader = plugin.prepare_dataloader(train_dataset, batch_size=8) >>> booster = Booster(plugin=plugin) >>> model, optimizer, criterion, train_dataloader, _ = booster.boost(model, optimizer, criterion, train_dataloader) Args: tp_size (int): The size of tensor parallelism. Tensor parallelism will not be used when tp_size is set to 1. pp_size (int): The number of pipeline stages in pipeline parallelism. Pipeline parallelism will not be used when pp_size is set to 1. precision (str, optional): Specifies the precision of parameters during training. Auto-mixied precision will be used when this argument is set to 'fp16' or 'bf16', otherwise model is trained with 'fp32'. Defaults to 'fp16'. zero_stage (int, optional): The stage of ZeRO for data parallelism. Can only be choosed from [0, 1, 2]. When set to 0, ZeRO will not be used. Defaults to 0. enable_all_optimization (bool, optional): Whether to switch on all the optimizations supported by Shardformer. Currently all the optimization methods include fused normalization, flash attention and JIT. Defaults to False. enable_fused_normalization (bool, optional): Whether to switch on fused normalization in Shardformer. Defaults to False. enable_flash_attention (bool, optional): Whether to switch on flash attention in Shardformer. Defaults to False. enable_jit_fused (bool, optional): Whether to switch on JIT in Shardformer. Default to False. enable_sequence_parallelism (bool): Whether to turn on sequence parallelism in Shardformer. Defaults to False. enable_sequence_overlap (bool): Whether to turn on sequence overlap in Shardformer. Defaults to False. num_microbatches (int, optional): Number of microbatches when using pipeline parallelism. Defaults to None. microbatch_size (int, optional): Microbatch size when using pipeline parallelism. Either ``num_microbatches`` or ``microbatch_size`` should be provided if using pipeline. If ``num_microbatches`` is provided, this will be ignored. Defaults to None. initial_scale (float, optional): The initial loss scale of AMP. Defaults to 2**16. min_scale (float, optional): The minimum loss scale of AMP. Defaults to 1. growth_factor (float, optional): The multiplication factor for increasing loss scale when using AMP. Defaults to 2. backoff_factor (float, optional): The multiplication factor for decreasing loss scale when using AMP. Defaults to 0.5. growth_interval (int, optional): The number of steps to increase loss scale when no overflow occurs when using AMP. Defaults to 1000. hysteresis (int, optional): The number of overflows before decreasing loss scale when using AMP. Defaults to 2. max_scale (float, optional): The maximum loss scale of AMP. Defaults to 2**32. max_norm (float, optional): Maximum norm for gradient clipping. Defaults to 0. broadcast_buffers (bool, optional): Whether to broadcast buffers in the beginning of training when using DDP. Defaults to True. ddp_bucket_cap_mb (int, optional): The bucket size in MB when using DDP. Defaults to 25. find_unused_parameters (bool, optional): Whether to find unused parameters when using DDP. Defaults to False. check_reduction (bool, optional): Whether to check reduction when using DDP. Defaults to False. gradient_as_bucket_view (bool, optional): Whether to use gradient as bucket view when using DDP. Defaults to False. static_graph (bool, optional): Whether to use static graph when using DDP. Defaults to False. zero_bucket_size_in_m (int, optional): Gradient reduce bucket size in million elements when using ZeRO. Defaults to 12. cpu_offload (bool, optional): Whether to open cpu_offload when using ZeRO. Defaults to False. communication_dtype (torch.dtype, optional): Communication dtype when using ZeRO. If not specified, the dtype of param will be used. Defaults to None. overlap_communication (bool, optional): Whether to overlap communication and computation when using ZeRO. Defaults to True. """ def __init__( self, tp_size: int, pp_size: int, extra_dp_size: int = 1, precision: str = "fp16", zero_stage: int = 0, enable_all_optimization: bool = False, enable_fused_normalization: bool = False, enable_flash_attention: bool = False, enable_jit_fused: bool = False, enable_sequence_parallelism: bool = False, enable_sequence_overlap: bool = False, num_microbatches: Optional[int] = None, microbatch_size: Optional[int] = None, initial_scale: float = 2**16, min_scale: float = 1, growth_factor: float = 2, backoff_factor: float = 0.5, growth_interval: int = 1000, hysteresis: int = 2, max_scale: float = 2**32, max_norm: float = 0, broadcast_buffers: bool = True, ddp_bucket_cap_mb: int = 25, find_unused_parameters: bool = False, check_reduction: bool = False, gradient_as_bucket_view: bool = False, static_graph: bool = False, zero_bucket_size_in_m: int = 12, cpu_offload: bool = False, communication_dtype: Optional[torch.dtype] = None, overlap_communication: bool = True, use_ep_inside: bool = True, custom_policy: Policy = None, ) -> None: assert ( dist.get_world_size() % (tp_size * pp_size) == 0 ), f"world size {dist.get_world_size()} is not divisible by tp_size {tp_size} * pp_size {pp_size}" if enable_sequence_parallelism: assert tp_size > 1, "Sequence parallelism must be enabled when using tensor parallelism" self.tp_size = tp_size self.pp_size = pp_size self.dp_size = dist.get_world_size() // (tp_size * pp_size) self.precision = precision self.zero_stage = zero_stage self.cpu_offload = cpu_offload self.enable_all_optimization = enable_all_optimization self.enable_fused_normalization = enable_fused_normalization self.enable_flash_attention = enable_flash_attention self.enable_jit_fused = enable_jit_fused self.enable_sequence_parallelism = enable_sequence_parallelism # we change pg mesh to (pp, dp, tp) for better moe performance self.pg_mesh = ProcessGroupMesh(self.pp_size, self.dp_size, self.tp_size) # sync moe in outer dp group, and sync other param in global dp group if extra_dp_size > 1: ep_size = self.dp_size // extra_dp_size if use_ep_inside: self.pg_mesh_moe = ProcessGroupMesh(self.pp_size, extra_dp_size, ep_size) self.moe_extra_dp_group = self.pg_mesh_moe.get_group_along_axis(1) if dist.get_rank() == 0: print(f"Zero Parallel: pp {self.pp_size}, outer_dp {extra_dp_size}, inner_dp {ep_size}") else: self.pg_mesh_moe = ProcessGroupMesh(self.pp_size, ep_size, extra_dp_size) self.moe_extra_dp_group = self.pg_mesh_moe.get_group_along_axis(2) if dist.get_rank() == 0: print(f"Zero Parallel: pp {self.pp_size}, outer_dp {ep_size}, inner_dp {extra_dp_size}") else: self.moe_extra_dp_group = None self.stage_manager = None self.schedule = None self.custom_policy = custom_policy assert zero_stage in (0, 1, 2) if self.pp_size > 1: assert ( num_microbatches is not None or microbatch_size is not None ), "num_microbatches or microbatch_size must be specified when using pipeline parallelism" assert self.zero_stage <= 1, "zero stage must be 0 or 1 when using pipeline parallelism" self.stage_manager = PipelineStageManager(self.pg_mesh, PP_AXIS) self.schedule = OneForwardOneBackwardSchedule( self.stage_manager, num_microbatches=num_microbatches, microbatch_size=microbatch_size ) self.tp_group = self.pg_mesh.get_group_along_axis(TP_AXIS) self.dp_group = self.pg_mesh.get_group_along_axis(DP_AXIS) self.pp_group = self.pg_mesh.get_group_along_axis(PP_AXIS) self.shard_config = ShardConfig( tensor_parallel_process_group=self.tp_group, pipeline_stage_manager=self.stage_manager, enable_tensor_parallelism=self.tp_size > 1, enable_all_optimization=self.enable_all_optimization, enable_fused_normalization=self.enable_fused_normalization, enable_flash_attention=self.enable_flash_attention, enable_jit_fused=self.enable_jit_fused, enable_sequence_parallelism=enable_sequence_parallelism, enable_sequence_overlap=enable_sequence_overlap, ) self.amp_config = dict( initial_scale=initial_scale, growth_factor=growth_factor, backoff_factor=backoff_factor, growth_interval=growth_interval, hysteresis=hysteresis, min_scale=min_scale, max_scale=max_scale, ) self.ddp_config = dict( broadcast_buffers=broadcast_buffers, bucket_cap_mb=ddp_bucket_cap_mb, find_unused_parameters=find_unused_parameters, check_reduction=check_reduction, gradient_as_bucket_view=gradient_as_bucket_view, static_graph=static_graph, ) self.zero_config = dict( reduce_bucket_size=zero_bucket_size_in_m * 1024 * 1024, communication_dtype=communication_dtype, overlap_communication=overlap_communication, cpu_offload=cpu_offload, partition_grad=(self.zero_stage == 2), ) self.max_norm = max_norm def prepare_dataloader( self, dataset, batch_size, shuffle=False, seed=1024, drop_last=False, pin_memory=False, num_workers=0, **kwargs ): r""" Prepare a dataloader for distributed training. The dataloader will be wrapped by `torch.utils.data.DataLoader` and `torch.utils.data.DistributedSampler`. Args: dataset (`torch.utils.data.Dataset`): The dataset to be loaded. shuffle (bool, optional): Whether to shuffle the dataset. Defaults to False. seed (int, optional): Random worker seed for sampling, defaults to 1024. add_sampler: Whether to add ``DistributedDataParallelSampler`` to the dataset. Defaults to True. drop_last (bool, optional): Set to True to drop the last incomplete batch, if the dataset size is not divisible by the batch size. If False and the size of dataset is not divisible by the batch size, then the last batch will be smaller, defaults to False. pin_memory (bool, optional): Whether to pin memory address in CPU memory. Defaults to False. num_workers (int, optional): Number of worker threads for this dataloader. Defaults to 0. kwargs (dict): optional parameters for ``torch.utils.data.DataLoader``, more details could be found in `DataLoader `_. Returns: :class:`torch.utils.data.DataLoader`: A DataLoader used for training or testing. """ _kwargs = kwargs.copy() sampler = DistributedSampler( dataset, num_replicas=self.pg_mesh.size(DP_AXIS), rank=self.pg_mesh.coordinate(DP_AXIS), shuffle=shuffle ) # Deterministic dataloader def seed_worker(worker_id): worker_seed = seed np.random.seed(worker_seed) torch.manual_seed(worker_seed) random.seed(worker_seed) return DataLoader( dataset, batch_size=batch_size, sampler=sampler, worker_init_fn=seed_worker, drop_last=drop_last, pin_memory=pin_memory, num_workers=num_workers, **_kwargs, ) def get_checkpoint_io(self) -> MoECheckpintIO: self.checkpoint_io = MoECheckpintIO(self.dp_group, self.pp_group, self.tp_group, self.zero_stage) return self.checkpoint_io def configure( self, model: Module, optimizer: Optional[Optimizer] = None, criterion: Optional[Callable] = None, dataloader: Optional[DataLoader] = None, lr_scheduler: Optional[LRScheduler] = None, ) -> Tuple[Module, OptimizerWrapper, Callable, DataLoader, LRScheduler]: param_info = get_param_info(optimizer) if not isinstance(model, ModelWrapper): use_ddp = self.dp_size > 1 and self.pp_size == 1 and self.zero_stage == 0 model = HybridParallelModule( module=model, precision=self.precision, shard_config=self.shard_config, dp_group=self.dp_group, tp_group=self.tp_group, use_ddp=use_ddp, ddp_config=self.ddp_config, custom_policy=self.custom_policy, ) if optimizer is not None and not isinstance(optimizer, OptimizerWrapper): if self.zero_stage == 0: if self.precision in ["fp16", "bf16"]: optimizer = HybridParallelAMPOptimizer( optimizer, model, use_pipeline=self.enable_pipeline_parallelism, param_info=param_info, precision=self.precision, max_norm=self.max_norm, **self.amp_config, ) else: optimizer = HybridParallelNaiveOptimizer( optimizer, model, use_pipeline=self.enable_pipeline_parallelism, param_info=param_info ) else: assert self.dp_size > 1, "Please use Zero when data parallel size is greater than 1." assert self.precision != "fp32", "Please set precision to 'fp16' or 'bf16' when using ZeRO." optimizer = HybridParallelZeroOptimizer( optimizer, model, use_pipeline=self.enable_pipeline_parallelism, param_info=param_info, dp_process_group=self.dp_group, tp_process_group=self.tp_group, pp_process_group=self.pp_group, moe_extra_dp_process_group=self.moe_extra_dp_group, verbose=True, clip_grad_norm=self.max_norm, **self.zero_config, **self.amp_config, ) # inject update_master_params model.update_master_params = MethodType(optimizer.update_master_params, model) return model, optimizer, criterion, dataloader, lr_scheduler