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# 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)
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