69 lines
2.2 KiB
Python
69 lines
2.2 KiB
Python
# SPDX-FileCopyrightText: Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
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# SPDX-License-Identifier: Apache-2.0
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import torch
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from omegaconf import ListConfig
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from torch import nn
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from chronoedit._ext.imaginaire.utils import log
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def get_regular_param_group(net: nn.Module):
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"""
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seperate the parameters of the network into two groups: decay and no_decay.
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based on nano_gpt codebase.
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"""
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param_dict = {pn: p for pn, p in net.named_parameters()}
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param_dict = {pn: p for pn, p in param_dict.items() if p.requires_grad}
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decay_params = [p for n, p in param_dict.items() if p.dim() >= 2]
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nodecay_params = [p for n, p in param_dict.items() if p.dim() < 2]
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return decay_params, nodecay_params
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def get_base_optimizer(
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model: nn.Module,
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lr: float,
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weight_decay: float,
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optim_type: str = "adamw",
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**kwargs,
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) -> torch.optim.Optimizer:
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net_decay_param, net_nodecay_param = get_regular_param_group(model)
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num_decay_params = sum(p.numel() for p in net_decay_param)
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num_nodecay_params = sum(p.numel() for p in net_nodecay_param)
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net_param_total = num_decay_params + num_nodecay_params
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log.critical(f"total num parameters : {net_param_total:,}")
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param_group = [
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{
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"params": net_decay_param + net_nodecay_param,
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"lr": lr,
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"weight_decay": weight_decay,
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},
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]
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if optim_type == "adamw":
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opt_cls = torch.optim.AdamW
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else:
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raise ValueError(f"Unknown optimizer type: {optim_type}")
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for k, v in kwargs.items():
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if isinstance(v, ListConfig):
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kwargs[k] = list(v)
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return opt_cls(param_group, **kwargs)
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