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