mirror of https://github.com/InternLM/InternLM
85 lines
3.3 KiB
Python
85 lines
3.3 KiB
Python
from typing import Dict, Tuple
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from internlm.core.context.parallel_context import global_context as gpc
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from internlm.model.utils import is_gate_param, is_moe_param, is_norm_param
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def split_params_into_different_groups_for_optimizer(param_groups: Tuple[Dict]) -> Tuple[Dict]:
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"""Split parameters into different MoE groups for optimizer
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Compatiable with muiltiple param groups, each should have a name
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Args:
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param_groups (Tuple[Dict]): The list of parameter groups to split
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Output Example:
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>>> (
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>>> {'name': 'default', 'params': [tensor], 'weight_decay' :xxx},
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>>> ...,
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>>> )
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Returns:
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Tuple[Dict]: list of params groups for optimizer
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Output Example:
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>>> (
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>>> {'name': 'default','params': [tensor],'weight_decay' :xxx},
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>>> {'name': 'norm', 'norm': True, 'params': [tensor],'weight_decay' :xxx},
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>>> {'name': 'gate', 'gate': True, 'params': [tensor],'weight_decay' :xxx},
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>>> {'name': 'moe_ep_size_4', 'moe': True, 'params': [tensor],'weight_decay' :xxx},
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>>> ...,
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>>> )
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"""
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if isinstance(param_groups, tuple):
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param_groups = list(param_groups) # Tuple cannot be modified
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elif isinstance(param_groups, dict):
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param_groups = [param_groups]
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elif not isinstance(param_groups, list):
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raise ValueError(f"Unknown param group type of {type(param_groups)}")
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new_groups = {}
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for pgroup in param_groups:
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new_groups[pgroup["name"]] = {}
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# create new groups for gate and norm
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for key in ["gate", "norm"]:
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new_groups[pgroup["name"]][key] = {}
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new_groups[pgroup["name"]][key]["name"] = key
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new_groups[pgroup["name"]][key][key] = True
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# create moe groups
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for key in gpc.expert_parallel_group_names:
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new_groups[pgroup["name"]][key] = {}
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new_groups[pgroup["name"]][key]["name"] = key
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new_groups[pgroup["name"]][key]["moe"] = True
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# copy attribute from origin group
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for ori_key in pgroup.keys():
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for key in new_groups[pgroup["name"]].keys():
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if ori_key != "name":
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if ori_key == "params":
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new_groups[pgroup["name"]][key][ori_key] = []
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else:
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new_groups[pgroup["name"]][key][ori_key] = pgroup[ori_key]
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# Assign param
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origin_params = []
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for param in pgroup["params"]:
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if is_moe_param(param):
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new_groups[pgroup["name"]][param.group_name]["params"].append(param)
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elif is_norm_param(param):
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new_groups[pgroup["name"]]["norm"]["params"].append(param)
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elif is_gate_param(param):
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new_groups[pgroup["name"]]["gate"]["params"].append(param)
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else:
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origin_params.append(param)
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pgroup["params"] = origin_params
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for _, v in new_groups.items():
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for _, v1 in v.items():
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param_groups.append(v1)
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return tuple(param_groups)
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def create_param_groups(model, weight_decay):
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parameters = {"params": list(model.parameters()), "name": "default", "weight_decay": weight_decay}
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return split_params_into_different_groups_for_optimizer(parameters)
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