mirror of https://github.com/InternLM/InternLM
79 lines
3.1 KiB
Python
79 lines
3.1 KiB
Python
from typing import Dict, Tuple
|
|
|
|
import torch
|
|
|
|
from internlm.core.context.parallel_context import global_context as gpc
|
|
from internlm.model.utils import is_gate_param, is_moe_param, is_norm_param
|
|
|
|
|
|
def split_params_into_different_groups_for_optimizer(param_groups: Tuple[Dict]) -> Tuple[Dict]:
|
|
"""Split parameters into different MoE groups for optimizer
|
|
Compatiable with muiltiple param groups, each should have a name
|
|
|
|
Args:
|
|
param_groups (Tuple[Dict]): The list of parameter groups to split
|
|
Input Example:
|
|
>>> (
|
|
>>> {'name': 'default', 'params': [tensor], 'weight_decay' :xxx},
|
|
>>> ...,
|
|
>>> )
|
|
|
|
Returns:
|
|
Tuple[Dict]: list of params groups for optimizer
|
|
Output Example:
|
|
>>> (
|
|
>>> {'name': 'default','params': [tensor],'weight_decay' :xxx},
|
|
>>> {'name': 'norm', 'norm': True, 'params': [tensor],'weight_decay' :xxx},
|
|
>>> {'name': 'gate', 'gate': True, 'params': [tensor],'weight_decay' :xxx},
|
|
>>> {'name': 'moe_ep_size_4', 'moe': True, 'params': [tensor],'weight_decay' :xxx},
|
|
>>> ...,
|
|
>>> )
|
|
"""
|
|
|
|
if isinstance(param_groups, tuple):
|
|
param_groups = list(param_groups) # Tuple cannot be modified
|
|
elif isinstance(param_groups, dict):
|
|
param_groups = [param_groups]
|
|
elif not isinstance(param_groups, list):
|
|
raise ValueError(f"Unknown param group type of {type(param_groups)}")
|
|
|
|
# create new groups for fp32, norm, moe gate and moe expert
|
|
new_groups = {}
|
|
new_groups["fp32"] = {"name": "fp32", "params": []}
|
|
for key in ["gate", "norm"]:
|
|
new_groups[key] = {"name": key, "sync_tp": True, "params": []}
|
|
for key in gpc.expert_parallel_group_names:
|
|
new_groups[key] = {"name": key, "moe": True, "params": []}
|
|
|
|
for pgroup in param_groups:
|
|
# copy attribute from origin group
|
|
for ori_key in pgroup.keys():
|
|
if ori_key not in ("name", "params"):
|
|
for _, group in new_groups.items():
|
|
group[ori_key] = pgroup[ori_key]
|
|
# assign param
|
|
origin_params = []
|
|
# first split the norm and gate groups, then the fp32 group, finally moe group
|
|
for param in pgroup["params"]:
|
|
if is_norm_param(param):
|
|
new_groups["norm"]["params"].append(param)
|
|
elif is_gate_param(param):
|
|
new_groups["gate"]["params"].append(param)
|
|
elif param.dtype == torch.float32:
|
|
new_groups["fp32"]["params"].append(param)
|
|
elif is_moe_param(param):
|
|
new_groups[param.group_name]["params"].append(param)
|
|
else:
|
|
origin_params.append(param)
|
|
# bf16 param group, which is the first group in the param groups
|
|
pgroup["params"] = origin_params
|
|
|
|
param_groups.extend(new_groups.values())
|
|
|
|
return tuple(param_groups)
|
|
|
|
|
|
def create_param_groups(model, weight_decay):
|
|
parameters = {"params": list(model.parameters()), "name": "default", "weight_decay": weight_decay}
|
|
return split_params_into_different_groups_for_optimizer(parameters)
|