docker/bloom13b/Megatron-DeepSpeed/megatron/optimizer/__init__.py

# Copyright (C) 2024 Habana Labs, Ltd. an Intel Company.
# Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved.

from deepspeed.accelerator import get_accelerator
if get_accelerator().device_name() == 'cuda':
    from apex.optimizers import FusedAdam as Adam
    from apex.optimizers import FusedSGD as SGD
else:
    from torch.optim import Adam
    from torch.optim import SGD

import torch
from torch.optim import AdamW

from megatron import get_args

from .distrib_optimizer import DistributedOptimizer
from .grad_scaler import ConstantGradScaler, DynamicGradScaler
from .optimizer import Float16OptimizerWithFloat16Params, FP32Optimizer


def get_param_groups(modules,
                     no_weight_decay_cond,
                     scale_lr_cond,
                     lr_mult):
    """creates param groups based on weight decay condition (regularized vs non regularized)
       and learning rate scale condition (args.lr vs lr_mult * args.lr)
       scale_lr_cond is used during finetuning where head of the network requires a scaled
       version of the base learning rate. 
    """
    args = get_args()
    wd_no_scale_lr = []
    wd_scale_lr = []
    no_wd_no_scale_lr = []
    no_wd_scale_lr = []
    for module in modules:
        for name, param in module.named_parameters():
            if not param.requires_grad:
                continue

            no_wd = None
            if no_weight_decay_cond is not None:
                no_wd = no_weight_decay_cond(name, param)
            elif not args.do_norm_bias_weight_decay:
                # do not regularize biases nor Norm parameters
                no_wd = name.endswith(".bias") or len(param.shape) == 1

            if scale_lr_cond is not None:
                scale_lr = scale_lr_cond(name, param)
            else:
                scale_lr = False

            if not no_wd and not scale_lr:
                wd_no_scale_lr.append(param)
            elif not no_wd and scale_lr:
                wd_scale_lr.append(param)
            elif no_wd and not scale_lr:
                no_wd_no_scale_lr.append(param)
            else:
                no_wd_scale_lr.append(param)

    param_groups = []
    if len(wd_no_scale_lr):
        param_groups.append({'name': 'wd_no_scale_lr', 'params': wd_no_scale_lr, 'wd_mult': 1.0, 'lr_mult': 1.0})
    if len(wd_scale_lr):
        param_groups.append({'name': 'wd_scale_lr', 'params': wd_scale_lr, 'wd_mult': 1.0, 'lr_mult': lr_mult})
    if len(no_wd_no_scale_lr):
        param_groups.append({'name': 'no_wd_no_scale_lr', 'params': no_wd_no_scale_lr, 'wd_mult': 0.0, 'lr_mult': 1.0})
    if len(no_wd_scale_lr):
        param_groups.append({'name': 'no_wd_scale_lr', 'params': no_wd_scale_lr, 'wd_mult': 0.0, 'lr_mult': lr_mult})

    return param_groups

def get_megatron_optimizer(model,
                           no_weight_decay_cond=None,
                           scale_lr_cond=None,
                           lr_mult=1.0):
    args = get_args()

    # Base optimizer.
    param_groups = get_param_groups(model,
                                    no_weight_decay_cond,
                                    scale_lr_cond,
                                    lr_mult)
    if args.create_moe_param_group:
        from deepspeed.moe.utils import split_params_into_different_moe_groups_for_optimizer
        param_groups = split_params_into_different_moe_groups_for_optimizer(param_groups)

    if args.cpu_optimizer:
        assert args.optimizer == 'adam', 'CPU offloading is for Adam'
        if args.cpu_torch_adam:
            cpu_adam_optimizer = torch.optim.AdamW
        else:
            from deepspeed.ops.adam import DeepSpeedCPUAdam
            cpu_adam_optimizer = DeepSpeedCPUAdam
        optimizer = cpu_adam_optimizer(param_groups,
                                       lr=args.lr,
                                       weight_decay=args.weight_decay,
                                       betas=(args.adam_beta1, args.adam_beta2),
                                       eps=args.adam_eps)
    else:
        if args.optimizer == 'adam':
            optimizer = Adam(param_groups,
                            lr=args.lr,
                            weight_decay=args.weight_decay,
                            betas=(args.adam_beta1, args.adam_beta2),
                            eps=args.adam_eps)
        elif args.optimizer == 'sgd':
            optimizer = SGD(param_groups,
                            lr=args.lr,
                            weight_decay=args.weight_decay,
                            momentum=args.sgd_momentum)
        elif args.optimizer == 'adamw':
            optimizer = AdamW(param_groups,
                            lr=args.lr,
                            weight_decay=args.weight_decay,
                            betas=(args.adam_beta1, args.adam_beta2),
                            eps=args.adam_eps)
        elif args.optimizer == 'fusedadamw':
            assert get_accelerator().device_name() == "hpu", "FusedAdamW optimizer is supported only when using HPU"
            from habana_frameworks.torch.hpex.optimizers import FusedAdamW
            optimizer = FusedAdamW(param_groups,
                            lr=args.lr,
                            weight_decay=args.weight_decay,
                            betas=(args.adam_beta1, args.adam_beta2),
                            eps=args.adam_eps)
        else:
            raise Exception('{} optimizer is not supported.'.format(
            args.optimizer))

    if args.deepspeed:
        return optimizer

    # Determine whether the params have main-grad field.
    params_have_main_grad = False
    if args.use_contiguous_buffers_in_local_ddp:
        params_have_main_grad = True

    # 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 args.fp16 or args.bf16 or args.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 args.loss_scale:
            grad_scaler = ConstantGradScaler(args.loss_scale)

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

        # Megatron optimizer.
        opt_ty = DistributedOptimizer \
            if args.use_distributed_optimizer else \
            Float16OptimizerWithFloat16Params
        return opt_ty(optimizer,
                      args.clip_grad,
                      args.log_num_zeros_in_grad,
                      params_have_main_grad,
                      args.use_contiguous_buffers_in_local_ddp,
                      args.fp16,
                      args.bf16,
                      args.params_dtype,
                      grad_scaler,
                      model)

    # FP32.
    return FP32Optimizer(optimizer, args.clip_grad,
                         args.log_num_zeros_in_grad,
                         params_have_main_grad,
                         args.use_contiguous_buffers_in_local_ddp,
                         model)