megatron/core/optimizer/__init__.py

# Copyright (c) 2024, NVIDIA CORPORATION. All rights reserved.
import copy
import logging
import warnings
from dataclasses import astuple
from typing import Any, Callable, Dict, List, Optional, Tuple, Union

import torch
from torch.optim import SGD as CPUSGD
from torch.optim import AdamW as CPUAdam

try:
    from transformer_engine.pytorch.optimizers import FusedAdam as Adam
    from transformer_engine.pytorch.optimizers import FusedSGD as SGD

    USING_PYTORCH_OPTIMIZER = False
except ImportError:
    try:
        from apex.optimizers import FusedAdam as Adam
        from apex.optimizers import FusedSGD as SGD

        USING_PYTORCH_OPTIMIZER = False
    except ImportError:
        warnings.warn(
            f'Transformer Engine and Apex are not installed. Falling back to Torch optimizers.'
        )

        # Apex's FusedAdam is a drop-in replacement for torch's AdamW.
        # pylint: disable-next=line-too-long.
        # See https://github.com/NVIDIA/apex/blob/7b73b12361068a10b0f44844534613f252a5ea75/apex/optimizers/fused_adam.py#L16.
        from torch.optim import SGD
        from torch.optim import AdamW as Adam

        USING_PYTORCH_OPTIMIZER = True

from megatron.core import parallel_state
from megatron.core.optimizer.cpu_offloading.hybrid_optimizer import HybridDeviceOptimizer
from megatron.core.optimizer_param_scheduler import (
    ParamGroupOverride,
    combine_param_group_overrides,
    param_group_override_to_tuple,
)
from megatron.core.process_groups_config import ProcessGroupCollection
from megatron.core.transformer.fsdp_dtensor_checkpoint import get_global_unique_param_name

from ..distributed.param_and_grad_buffer import _ParamAndGradBuffer
from ..transformer.module import MegatronModule
from ..utils import get_model_config, get_pg_rank, get_pg_size, is_te_min_version, log_single_rank
from .distrib_optimizer import DistributedOptimizer
from .grad_scaler import ConstantGradScaler, DynamicGradScaler
from .optimizer import (
    ChainedOptimizer,
    Float16OptimizerWithFloat16Params,
    FP32Optimizer,
    MegatronOptimizer,
    param_group_identifier_keys,
)
from .optimizer_config import (
    AdamOptimizerConfig,
    OptimizerConfig,
    ParamKey,
    ParamPredicate,
    ParamWithNamePredicate,
    SGDOptimizerConfig,
)

logger = logging.getLogger(__name__)


def get_standard_config_overrides(config: OptimizerConfig) -> Dict[ParamKey, ParamGroupOverride]:
    """Get standard config overrides for the optimizer, handling decoupled LR and common wd skips.

    Args:
        config (OptimizerConfig): optimizer configuration object.

    Returns:
        Dict[ParamKey, ParamGroupOverride]: standard config overrides.
    """
    config_overrides: Optional[Dict[ParamKey, ParamGroupOverride]] = {}
    # First, figure out how we are going to do wd skipping. The two main approaches are:
    #  1. The classic megatron approach of skipping all len 1 and bias parameters.
    #  2. The Qwen3-Next approach of doing 1, other than qk layernorm parameters.
    if config.apply_wd_to_qk_layernorm:
        shape_1_not_qkln_param = ParamWithNamePredicate(
            name="s1_not_qkln",
            fn=lambda param, name: (len(param.shape) == 1 or name.endswith(".bias"))
            and not ("q_layernorm." in name or "k_layernorm." in name),
        )
        param_wd_mult_key = ParamKey(with_name_predicate=shape_1_not_qkln_param)
    else:
        param_length_1_match = ParamPredicate(
            name="param_len_1", fn=lambda param: len(param.shape) == 1
        )
        param_wd_mult_key = ParamKey(name="*.bias", predicate=param_length_1_match)

    config_overrides[param_wd_mult_key] = ParamGroupOverride(wd_mult=0.0)

    if config.decoupled_lr is not None:
        decoupled_lr_config: ParamGroupOverride = {"max_lr": config.decoupled_lr}
        decoupled_param_key = ParamKey(attr="is_embedding_or_output_parameter")
        if config.decoupled_min_lr is not None:
            decoupled_lr_config["min_lr"] = config.decoupled_min_lr
        config_overrides[decoupled_param_key] = decoupled_lr_config

    return config_overrides


def _get_param_groups(
    model_chunks: List[MegatronModule],
    config: OptimizerConfig,
    config_overrides: Optional[Dict[ParamKey, ParamGroupOverride]],
) -> List[Dict]:
    """Create parameter groups for optimizer.

    Creates parameter groups from provided optimizer config object.

    NOTE There can be more than one match between a ParamKey and a parameter.
        What we do is merge all of the matching ParamKey overrides into a single ParamGroupOverride
        for that parameter and use that as the key for that parameter. Any parameters that get
        the same set of merged overrides will be mapped into the same parameter group.

    Args:
        model_chunks (List[MegatronModule]): model chunks to create parameter
            groups for.
        config (OptimizerConfig): optimizer configuration object.
        config_overrides (Optional[Dict[ParamKey, ParamGroupOverride]): optimizer overrides,
            specified on a per-layer basis. NOTE: if you want to skip applying weight decay on bias
            and length 1 parameters, and also do not want to do any other overrides, set this to an
            empty dictionary rather than the default value of None.
    Returns:
        List of parameter groups.
    """

    # Map (pg_overrides, is_expert_parallel) to params.
    params_map = {}

    if config_overrides is None:
        # TODO remove this default behavior eventually.
        #  This is only needed for backwards compatibility with the old config overrides API where
        #  the config_overrides argument by default lead to bias parameters and length 1 parameters.
        #  We assume that users of decoupled LR already provide config overrides so will adapt
        #  to the new API.
        config_overrides = get_standard_config_overrides(config=config)

    for model_chunk in model_chunks:
        for name, param in model_chunk.named_parameters():
            if not param.requires_grad:
                continue

            uses_default_config = False
            # Get optimizer config overrides for this parameter.
            param_overrides_list: list[ParamGroupOverride] = []
            if config_overrides is not None:
                for param_key, param_override in config_overrides.items():
                    if param_key.matches(param, name):
                        param_overrides_list.append(param_override)

            if param_overrides_list:
                param_override: ParamGroupOverride | None = combine_param_group_overrides(
                    param_overrides_list
                )
            else:
                param_override = None

            is_expert_parallel = not getattr(param, 'allreduce', True)

            # Create config_tuple that is hash-able, and has a consistent ordering of the keys.
            param_override_tuple: tuple[tuple[str, Any], ...] | None = (
                param_group_override_to_tuple(param_override)
            )
            key = (param_override_tuple, is_expert_parallel)
            if key not in params_map:
                params_map[key] = []
            params_map[key].append(param)

    # Distributed checkpoint requires all ranks to have the same param groups,
    # so we need to align the param groups across ranks, otherwise we may have
    # runtime error when loading the checkpoint or numerical error when resuming training.
    params_key = list(params_map.keys())
    gathered_params_key = [None for _ in range(torch.distributed.get_world_size())]
    torch.distributed.all_gather_object(gathered_params_key, params_key)
    for keys in gathered_params_key:
        for key in keys:
            if key not in params_key:
                params_key.append(key)
    # Need to pick one of the param_override_tuples to use for the param group.
    param_groups = []
    # Sort keys, None first.
    for key in sorted(params_key, key=lambda x: (x[0] is not None, x[0])):
        param_override_tuple, is_expert_parallel = key
        params = params_map[key] if key in params_map else []
        if param_override_tuple is None:
            param_override: ParamGroupOverride = {}
        else:
            param_override: ParamGroupOverride = {k: v for (k, v) in param_override_tuple}

        # False if param_group_override is None or empty tuple or if we do not modify the
        #  LR schedule.
        #  NOTE: "default_config" is used for logging the learning rate in training.py.
        #   so set to True if we do not modify the learning rate.
        #  if param_group['default_config']:
        #    learning_rate = param_group['lr']
        uses_default_lr_schedule: bool = (not bool(param_override_tuple)) or not any(
            ["lr" in k for k in param_override]
        )

        # TODO: Remove "backwards compatible" fields below eventually.
        default_config: ParamGroupOverride = {
            'wd_mult': 1.0,
            'lr_mult': 1.0,
            'is_decoupled_lr': False,
            # The following two fields may be important to keep even when we remove the
            #   above "backwards compatible" fields.
            "max_lr": config.lr,  # user may override this in param_override
            "min_lr": config.min_lr,  # user may override this in param_override
        }
        assert (
            "params" not in param_override
        ), "'params' should not be in param_override, this is a protected key"
        param_group = {
            'params': params,
            'is_expert_parallel': is_expert_parallel,
            'default_config': uses_default_lr_schedule,
            **default_config,
            **param_override,  # keep **param_override last so that users can override other fields.
        }
        param_groups.append(param_group)

    return param_groups


def _get_param_groups_and_buffers(
    model_chunks: List[MegatronModule],
    model_chunk_offset: int,
    config: OptimizerConfig,
    config_overrides: Optional[Dict[ParamKey, ParamGroupOverride]],
    filter_fn: Callable,
    buffer_name: str,
) -> Tuple[List[Dict], Dict[int, List[_ParamAndGradBuffer]]]:
    """Returns parameter groups and buffer for optimizer.

    Args:
        model_chunks (List[MegatronModule]): model chunks to create parameter
            groups for.
        model_chunk_offset (int): offset of model_chunks in global model_chunks list.
        config (OptimizerConfig): optimizer configuration object.
        config_overrides (Optional[Dict[ParamKey, ParamGroupOverride]): optimizer/scheduler
            overrides, specified on the basis of ParamKey matches with each parameter.
        lr (float): learning rate.
        min_lr (float): minimum learning rate.
        filter_fn (callable): filtering function for param_groups.
        buffer_name (str): name of buffer.

    Returns:
        List of parameter groups and dictionary of model chunk IDs to buffers.
    """
    param_groups = _get_param_groups(model_chunks, config, config_overrides)
    param_groups = list(filter(filter_fn, param_groups))
    buffers = {}
    for model_chunk_idx, model_chunk in enumerate(model_chunks):
        if hasattr(model_chunk, buffer_name):
            buffers[model_chunk_idx + model_chunk_offset] = getattr(model_chunk, buffer_name)

    return param_groups, buffers


def _get_megatron_optimizer_based_on_param_groups(
    config: OptimizerConfig,
    model_chunks: List[MegatronModule],
    param_groups: List,
    per_model_buffers: Optional[Dict[int, List[_ParamAndGradBuffer]]] = None,
    model_parallel_group: Optional[torch.distributed.ProcessGroup] = None,
    data_parallel_group: Optional[torch.distributed.ProcessGroup] = None,
    data_parallel_group_gloo: Optional[torch.distributed.ProcessGroup] = None,
    data_parallel_group_idx: Optional[int] = None,
    intra_dist_opt_group: Optional[torch.distributed.ProcessGroup] = None,
    distributed_optimizer_instance_id: Optional[int] = 0,
    pg_collection: Optional[ProcessGroupCollection] = None,
) -> MegatronOptimizer:
    """Get Megatron optimizer based on parameter groups.

    Args:
        config (OptimizerConfig): optimizer configuration object.
        model_chunks (list): list of model chunks.
        param_groups (list): list of parameter groups.
        per_model_buffers (dict, optional): buffers for distributed optimizer. Defaults to None.
        data_parallel_group (torch.distributed.ProcessGroup, optional): data-parallel group for
            distributed optimizer. Defaults to None.
        data_parallel_group_gloo (torch.distributed.ProcessGroup, optional): gloo data-parallel
            group for distributed optimizer. Defaults to None.
        data_parallel_group_idx (int, optional): data-parallel group index for distributed
            optimizer. Defaults to None.
        distributed_optimizer_instance_id (int, optional): Distributed optimizer instance. Defaults
            0.

    Returns:
        Instance of MegatronOptimizer.
    """
    # TODO: Logic needs to be updated to handle different optimizer types (i.e., param_groups
    # passed into this function need to correspond to the same optimizer).

    # When freezing sub-models we may have no trainable parameters on a rank and
    # hence an empty param_groups. However, we still need to create an optimizer
    # for the purposes of grad stats reductions.
    if param_groups:
        if config.optimizer_cpu_offload:
            if torch.__version__ < '2.3.0':
                warnings.warn(
                    "CPU offload is recommended for PyTorch >= 2.3.0, "
                    "untested versions below this may have convergence issues."
                )
            assert (
                config.decoupled_weight_decay
            ), "CPU offloading only supported with decoupled_weight_decay enabled (AdamW mode)."
            gpu_optimizer_cls = Adam if config.optimizer == 'adam' else SGD
            cpu_optimizer_cls = CPUAdam if config.optimizer == 'adam' else CPUSGD
            if config.use_torch_optimizer_for_cpu_offload:
                gpu_optimizer_cls = cpu_optimizer_cls
            if config.optimizer == 'adam':
                gpu_optimizer_cls = Adam
                cpu_optimizer_cls = CPUAdam
                optimizer_defaults = dict(
                    lr=config.lr,
                    weight_decay=config.weight_decay,
                    betas=(config.adam_beta1, config.adam_beta2),
                    eps=config.adam_eps,
                    bias_correction=True,
                    fused=True,  # this flag is used to improve the performance of the cpu optimizer
                )
            else:
                gpu_optimizer_cls = SGD
                cpu_optimizer_cls = CPUSGD
                optimizer_defaults = dict(
                    lr=config.lr, weight_decay=config.weight_decay, momentum=config.sgd_momentum
                )
            optimizer = HybridDeviceOptimizer(
                param_groups,
                offload_fraction=config.optimizer_offload_fraction,
                cpu_optimizer_cls=cpu_optimizer_cls,
                gpu_optimizer_cls=gpu_optimizer_cls,
                overlap_cpu_optimizer_d2h_h2d=config.overlap_cpu_optimizer_d2h_h2d,
                pin_cpu_grads=config.pin_cpu_grads,
                pin_cpu_params=config.pin_cpu_params,
                # param_update_in_fp32=True,
                param_update_in_fp32=config.pure_bf16_optimizer, # Pure bf16 optimizer
                **optimizer_defaults,
            )
            init_state_fn = None
        elif config.optimizer == 'adam':
            kwargs = {
                "params": param_groups,
                "lr": config.lr,
                "weight_decay": config.weight_decay,
                "betas": (config.adam_beta1, config.adam_beta2),
                "eps": config.adam_eps,
            }

            # set Adam class and weight decay mode depending
            # on source of optimizer (Torch or TE/Apex)
            # if USING_PYTORCH_OPTIMIZER:
            #     adam_cls = torch.optim.AdamW if config.decoupled_weight_decay else torch.optim.Adam
            # else:
            #     kwargs["adam_w_mode"] = config.decoupled_weight_decay
            #     adam_cls = Adam

            # set Adam class and weight decay mode depending
            # on source of optimizer (Torch or TE/Apex)
            # NOTE: pure bf16 optimizer states are incompatible with TE FusedAdam,
            # which requires fp32 exp_avg/exp_avg_sq internally.
            # Pure bf16 optimizer
            # 原来是: use_torch_adam = USING_PYTORCH_OPTIMIZER or config.pure_bf16_optimizer
            use_torch_adam = USING_PYTORCH_OPTIMIZER or config.pure_bf16_optimizer
            if use_torch_adam:
                adam_cls = torch.optim.AdamW if config.decoupled_weight_decay else torch.optim.Adam
            else:
                kwargs["adam_w_mode"] = config.decoupled_weight_decay
                adam_cls = Adam

            if config.use_precision_aware_optimizer:
                kwargs.update(
                    {
                        "exp_avg_dtype": config.exp_avg_dtype,
                        "exp_avg_sq_dtype": config.exp_avg_sq_dtype,
                    }
                )
                # Master weight is managed by MCore when main_params_dtype is fp32. This is
                # because we want to use fp8 primary weight with precision aware optimizer.
                # Otherwise, master weight will be managed by TransformerEngine.
                # Delayed scaling is an exception because casting as well as the computation
                # of the scaling factor can be conducted in the adam kernel.
                if config.use_precision_aware_optimizer_no_fp8_or_ds_fp8:
                    kwargs.update(
                        {
                            "master_weights": True,
                            "use_decoupled_grad": True,
                            "master_weight_dtype": config.main_params_dtype,
                        }
                    )

                if is_te_min_version("2.1.0.dev0"):
                    kwargs.update({"store_param_remainders": config.store_param_remainders})

            optimizer = adam_cls(**kwargs)

            # def init_state_fn(opt, config=None):
            #     for group in opt.param_groups:
            #         for p in group['params']:
            #             if len(opt.state[p]) == 0:
            #                 if config is None or not config.use_precision_aware_optimizer:
            #                     opt.state[p]['exp_avg'] = torch.zeros_like(p.data)
            #                     opt.state[p]['exp_avg_sq'] = torch.zeros_like(p.data)
            #                 else:
            #                     opt.initialize_state(p)
            # Pure bf16 optimizer
            def init_state_fn(opt, config=None):
                for group in opt.param_groups:
                    for p in group['params']:
                        if len(opt.state[p]) == 0:
                            if config is None or not config.use_precision_aware_optimizer:
                                # Pure bf16 optimizer
                                # 原来的是: opt.state[p]['exp_avg'] = torch.zeros_like(p.data)
                                # opt.state[p]['exp_avg_sq'] = torch.zeros_like(p.data)
                                state_dtype = (
                                    torch.bfloat16
                                    if (config is not None and config.pure_bf16_optimizer)
                                    else p.data.dtype
                                )
                                opt.state[p]['exp_avg'] = torch.zeros_like(p.data, dtype=state_dtype)
                                opt.state[p]['exp_avg_sq'] = torch.zeros_like(
                                    p.data, dtype=state_dtype
                                )
                            else:
                                opt.initialize_state(p)

        elif config.optimizer == 'sgd':
            optimizer = SGD(
                param_groups,
                lr=config.lr,
                weight_decay=config.weight_decay,
                momentum=config.sgd_momentum,
            )
            init_state_fn = None
        else:
            raise Exception('{} optimizer is not supported.'.format(config.optimizer))
    else:
        optimizer = None
        init_state_fn = None

    # Mixed precision optimizer.
    # - Note: both the Float16Optimizer and the DistributedOptimizer inherit
    #   from the MixedPrecisionOptimizer, which manages any optimizer where
    #   the model params and main params are distinct.
    if config.fp16 or config.bf16 or config.use_distributed_optimizer:

        # Grad scaler:
        #    if loss-scale is provided, instantiate the constant scaler.
        #    if we are using fp16 and loss-scale is not present, use a
        #       dynamic scaler.
        #    otherwise we are running in bf16 with no loss-scale so
        #       leave it as None.
        grad_scaler = None

        # Constant loss scale.
        if config.loss_scale:
            grad_scaler = ConstantGradScaler(config.loss_scale)

        # Dynamic loss scale.
        else:
            if config.fp16:
                grad_scaler = DynamicGradScaler(
                    initial_scale=config.initial_loss_scale,
                    min_scale=config.min_loss_scale,
                    growth_factor=2.0,
                    backoff_factor=0.5,
                    growth_interval=config.loss_scale_window,
                    hysteresis=config.hysteresis,
                )

        optimizer_args = [optimizer, config, grad_scaler, init_state_fn]
        if config.use_distributed_optimizer:
            optimizer = DistributedOptimizer(
                *optimizer_args,
                model_chunks=model_chunks,
                per_model_buffers=per_model_buffers,
                data_parallel_group=data_parallel_group,
                data_parallel_group_gloo=data_parallel_group_gloo,
                data_parallel_group_idx=data_parallel_group_idx,
                distributed_optimizer_instance_id=distributed_optimizer_instance_id,
            )
            # This is needed for case where num_distributed_optimizer_instances > 1. In this case,
            # weight gradients are all-reduced across optimizer instances, so each instance has
            # the duplicated weight gradients, need to reduce gradient stats inside each instance.
            setattr(optimizer, 'grad_stats_parallel_group', intra_dist_opt_group)
        else:
            optimizer = Float16OptimizerWithFloat16Params(*optimizer_args)
            setattr(optimizer, 'grad_stats_parallel_group', model_parallel_group)
    else:
        # FP32 optimizer.
        optimizer = FP32Optimizer(optimizer, config, init_state_fn)
        setattr(optimizer, 'grad_stats_parallel_group', model_parallel_group)

    if pg_collection is None or not hasattr(pg_collection, 'tp'):
        tp_group = parallel_state.get_tensor_model_parallel_group()
    else:
        tp_group = pg_collection.tp
    # TODO(M4): plumb tp_group through optimizer constructors so this setattr disappears.
    setattr(optimizer, 'tp_group', tp_group)

    return optimizer


def check_config_overrides_consistency(
    config: OptimizerConfig, config_overrides: Optional[Dict[ParamKey, ParamGroupOverride]]
):
    """Check if the config overrides are consistent with the config."""

    # TODO: Remove `optimizer` from this eventually (e.g., if we use Muon for some layers and
    # Adam for other layers). This would need some more refactoring to work though (param_groups
    # filtered by optimizer passed into _get_megatron_optimizer_based_on_param_groups).
    if config_overrides is not None:
        fields_to_check_for_consistency = [
            'overlap_param_gather_with_optimizer_step',
            'optimizer',
            'optimizer_cpu_offload',
        ]
        for field_name in fields_to_check_for_consistency:
            base_field = getattr(config, field_name, None)
            all_config_overrides = list(config_overrides.values())
            for config_override in all_config_overrides:
                if field_name in config_override:
                    field = config_override[field_name]
                    if field != base_field:
                        raise ValueError(
                            f"Field {field_name} should not be overriden in a config override."
                        )
    return True


def get_megatron_optimizer(
    config: OptimizerConfig,
    model_chunks: List[MegatronModule],
    config_overrides: Optional[Dict[ParamKey, ParamGroupOverride]] = None,
    use_gloo_process_groups: bool = True,
    pg_collection: Optional[ProcessGroupCollection] = None,
    dump_param_to_param_group_map: Optional[str] = None,
) -> MegatronOptimizer:
    """Retrieve the Megatron optimizer for model chunks.

    We use separate optimizers for expert parameters and non-expert parameters.

    Args:
        config (OptimizerConfig): optimizer configuration object.
        model_chunks (List[MegatronModule]): model chunks to get optimizer for.
        config_overrides (Optional[Dict[ParamKey, OptimizerConfig]]): optional dictionary of
            optimizer configuration objects to override default optimizer behavior for different
            subsets of parameters (identified by ParamKey).
        use_gloo_process_groups (bool): if false, disable use of Gloo process groups
            in underlying Megatron optimizers.
        pg_collection: Optional unified process group for distributed training.
        dump_param_to_param_group_map (Optional[str]): path to dump parameter to param group map.

    Returns:
        Instance of MegatronOptimizer.
    """

    log_single_rank(logger, logging.INFO, f'Setting up optimizer with config {config}')

    check_config_overrides_consistency(config, config_overrides)

    # Separate out first model chunk if overlapping param AG with optimizer step.
    if config.overlap_param_gather_with_optimizer_step:
        all_dense_model_chunks = [[model_chunks[0]], model_chunks[1:]]
        overlap_param_gather_with_optimizer_step_flags = [True, False]
    else:
        all_dense_model_chunks = [model_chunks]
        overlap_param_gather_with_optimizer_step_flags = [False]

    # Setup process groups using helper method
    process_groups_dict = ProcessGroupCollection.setup_process_groups_for_optimizer(
        pg_collection, model_chunks, use_gloo_process_groups
    )

    dp_cp_group = process_groups_dict['dp_cp_group']
    intra_dp_cp_group = process_groups_dict['intra_dp_cp_group']
    intra_expt_dp_group = process_groups_dict['intra_expt_dp_group']
    mp_group = process_groups_dict['mp_group']
    expt_tp_pp_group = process_groups_dict['expt_tp_pp_group']
    intra_dp_cp_group_gloo = process_groups_dict['intra_dp_cp_group_gloo']
    intra_expt_dp_group_gloo = process_groups_dict['intra_expt_dp_group_gloo']
    intra_dist_opt_group = process_groups_dict['intra_dist_opt_group']

    model_parallel_rank = get_pg_rank(mp_group)

    if get_pg_size(dp_cp_group) > get_pg_size(intra_dp_cp_group):
        inter_dist_opt_group = process_groups_dict['inter_dist_opt_group']
        distributed_optimizer_instance_id = get_pg_rank(inter_dist_opt_group)
    else:
        distributed_optimizer_instance_id = 0

    optimizers = []
    model_chunk_offset = 0
    ddp_config = model_chunks[0].ddp_config  # Use the first model chunk's DDP config
    if ddp_config.use_megatron_fsdp:
        for model_chunk, overlap_param_gather_with_optimizer_step in zip(
            all_dense_model_chunks, overlap_param_gather_with_optimizer_step_flags
        ):
            param_groups, buffers = _get_param_groups_and_buffers(
                model_chunk,
                model_chunk_offset=model_chunk_offset,
                config=config,
                config_overrides=config_overrides,
                filter_fn=lambda g: True,
                buffer_name='buffers',
            )

            optimizers.append(
                _get_megatron_optimizer_based_on_param_groups(
                    config=config,
                    model_chunks=model_chunk,
                    param_groups=param_groups,
                    per_model_buffers=buffers,
                    model_parallel_group=mp_group,
                    data_parallel_group=dp_cp_group,
                    data_parallel_group_gloo=intra_dp_cp_group_gloo,
                    data_parallel_group_idx=model_parallel_rank,
                    intra_dist_opt_group=intra_dist_opt_group,
                    distributed_optimizer_instance_id=distributed_optimizer_instance_id,
                    pg_collection=pg_collection,
                )
            )
            model_chunk_offset += 1

        if len(optimizers) == 1:
            return optimizers[0]

        return ChainedOptimizer(optimizers)

    if dump_param_to_param_group_map is not None:
        param_to_param_group = {}
        param_group_id = 0
    for dense_model_chunks, overlap_param_gather_with_optimizer_step in zip(
        all_dense_model_chunks, overlap_param_gather_with_optimizer_step_flags
    ):
        param_groups, buffers = _get_param_groups_and_buffers(
            dense_model_chunks,
            model_chunk_offset=model_chunk_offset,
            config=config,
            config_overrides=config_overrides,
            filter_fn=lambda g: not g['is_expert_parallel'],
            buffer_name='buffers',
        )
        for model_chunk in dense_model_chunks:
            model_chunk.overlap_param_gather_with_optimizer_step = (
                overlap_param_gather_with_optimizer_step
            )
        if dump_param_to_param_group_map is not None:
            for param_group in param_groups:
                for param in param_group["params"]:
                    param_name = get_global_unique_param_name(model_chunks, param)
                    param_to_param_group[param_name] = param_group_id
                param_group_id += 1

        # Pass Gloo process groups into optimizer only if needed.
        optimizers.append(
            _get_megatron_optimizer_based_on_param_groups(
                config=config,
                model_chunks=dense_model_chunks,
                param_groups=param_groups,
                per_model_buffers=buffers,
                model_parallel_group=mp_group,
                data_parallel_group=intra_dp_cp_group,
                data_parallel_group_gloo=intra_dp_cp_group_gloo,
                data_parallel_group_idx=model_parallel_rank,
                intra_dist_opt_group=intra_dist_opt_group,
                distributed_optimizer_instance_id=distributed_optimizer_instance_id,
                pg_collection=pg_collection,
            )
        )
        model_chunk_offset += 1

    moe_param_groups, moe_buffers = _get_param_groups_and_buffers(
        model_chunks,
        model_chunk_offset=0,
        config=config,
        config_overrides=config_overrides,
        filter_fn=lambda g: g['is_expert_parallel'],
        buffer_name='expert_parallel_buffers',
    )
    if dump_param_to_param_group_map is not None:
        for param_group in moe_param_groups:
            for param in param_group["params"]:
                param_name = get_global_unique_param_name(model_chunks, param)
                param_to_param_group[param_name] = param_group_id
            param_group_id += 1
    if len(moe_param_groups) > 0:
        expt_model_parallel_rank = get_pg_rank(expt_tp_pp_group)
        # Pass Gloo process groups into optimizer only if needed.
        if use_gloo_process_groups:
            expt_data_parallel_group_gloo = intra_expt_dp_group_gloo
        else:
            expt_data_parallel_group_gloo = None
        optimizers.append(
            _get_megatron_optimizer_based_on_param_groups(
                config=config,
                model_chunks=model_chunks,
                param_groups=moe_param_groups,
                per_model_buffers=moe_buffers,
                model_parallel_group=expt_tp_pp_group,
                data_parallel_group=intra_expt_dp_group,
                data_parallel_group_gloo=expt_data_parallel_group_gloo,
                data_parallel_group_idx=expt_model_parallel_rank,
                intra_dist_opt_group=intra_dist_opt_group,
                distributed_optimizer_instance_id=distributed_optimizer_instance_id,
                pg_collection=pg_collection,
            )
        )

    if dump_param_to_param_group_map is not None:
        torch.distributed.checkpoint.save(
            state_dict=param_to_param_group, checkpoint_id=dump_param_to_param_group_map
        )

    return ChainedOptimizer(optimizers)