# 自定义Megatron模型 这里介绍如何在[Mcore-Bridge](https://github.com/modelscope/mcore-bridge)中注册模型，以支持新模型在Megatron-SWIFT中的训练。我们将以MiniMax-M2.7为例子介绍。 ## 下载模型 首先，你需要下载模型配置。 ```python from swift import safe_snapshot_download model_dir = safe_snapshot_download('MiniMax/MiniMax-M2.7', download_model=False) print(f'model_dir: {model_dir}') ``` 由于模型权重很大，为了加速支持模型的效率，我们采用懒下载的方式，并只下载`num_layers`层的权重，构建mini版本的模型，用于做接入测试。以MiniMax-M2.7为例，我们构建了一层的BF16版本的权重。若有些模型出现前3层为Dense，之后为MoE，则你可以构建4层的权重。若出现Attention交替的情况，例如Qwen3.5采用linear-attention和full-attention交替，你也需要更多的层数。 ```python import os import torch from modelscope.hub.file_download import model_file_download from safetensors.torch import safe_open from swift import safe_snapshot_download from mcore_bridge.utils import Fp8Dequantizer, SafetensorLazyLoader, StreamingSafetensorSaver model_id = 'MiniMax/MiniMax-M2.7' # 有些模型会出现前几层为dense，后面为moe的情况，需合理设置该值 num_layers = 1 # 只下载`num_layers`层，节约磁盘占用和运行时显存占用 model_dir = safe_snapshot_download(model_id, download_model=False) loader = SafetensorLazyLoader(model_dir) state_dict = loader.get_state_dict() saver = StreamingSafetensorSaver(save_dir=model_dir) new_state_dict = {} fp8_dequantizer = Fp8Dequantizer() # 用于将fp8权重转成bf16 def _open_file(self, filename: str): if filename not in self._file_handles: file_path = os.path.join(self.hf_model_dir, filename) tmp_dir = os.path.join(self.hf_model_dir, 'tmp') if not os.path.exists(file_path): file_path = os.path.join(tmp_dir, filename) if not os.path.exists(file_path): file_path = model_file_download( model_id=model_id, file_path=filename, local_dir=tmp_dir, ) self._file_handles[filename] = safe_open(file_path, framework='pt') return self._file_handles[filename] SafetensorLazyLoader._open_file = _open_file # monkey patch (懒下载) for k, v in state_dict.items(): if k.startswith('model.layers.'): idx = int(k[len('model.layers.'):].split('.', 1)[0]) if idx >= num_layers: continue if k.endswith('.weight_scale_inv'): continue elif k.endswith('.weight'): weight_scale_inv = k.replace('.weight', '.weight_scale_inv') if weight_scale_inv in state_dict: v = fp8_dequantizer.convert(v.load(), state_dict[weight_scale_inv].load()).to(torch.bfloat16) new_state_dict[k] = v if isinstance(v, torch.Tensor) else v.load() for k, v in new_state_dict.items(): saver.add_tensor(k, v) saver.finalize() ``` 保存完权重后，你需要修改'config.json'，将`num_hidden_layers`修改为1（与上面的代码对应），并删除`quantization_config`配置（因为权重为BF16的，而不是FP8）。FP8的训练大多数模型会自动适配，但有些模型可能需要额外适配，例如：Qwen3.5的FP8的适配参考[这个PR](https://github.com/modelscope/mcore-bridge/pull/30)。 ## 注册模型 以下提供debug代码，你需要修改代码，以确保huggingface transformers库的forward与megatron的forward对齐。 ```python import os os.environ['CUDA_VISIBLE_DEVICES'] = '0,1,2,3' os.environ['SWIFT_TEST_CONVERT_PRECISION'] = '1' from swift import export_main, ExportArguments, safe_snapshot_download model_id = 'MiniMax/MiniMax-M2.7' model_dir = safe_snapshot_download(model_id, download_model=False) export_main( ExportArguments( model=model_dir, to_mcore=True, exist_ok=True, test_convert_precision=True, torch_dtype='bfloat16', )) ``` 'minimax_m2'的注册可以查看[这个文件](https://github.com/modelscope/mcore-bridge/blob/main/src/mcore_bridge/model/gpts/minimax_m2.py)。我们注册时指定了模型对应的GPTBridge类和模型加载器loader。 ```python register_model(ModelMeta( ModelType.minimax_m2, ['minimax_m2'], bridge_cls=MinimaxM2Bridge, loader=MinimaxM2Loader, )) ``` 参数的总和对齐： ``` [INFO:swift] n_parameter: 522 [INFO:swift] total_sum: 106747128.72671509 [INFO:swift] zero_count: 0 [INFO:swift] n_parameter: 780 [INFO:swift] total_sum: 106747129.32046509 [INFO:swift] zero_count: 0 ``` 模型forward的logits对齐。（当然我们还需要对模型进行训练，训练后再测试forward的精度，避免出现这里输出tokens都是同一个的情况）。 ``` mean_diff: 2.8353377274470404e-05, max_diff: 0.0015382766723632812 mean_diff (with loss): 2.1664049199898727e-05, max_diff (with loss): 0.00021076202392578125 (Please check that mean_diff (with loss) is less than 0.1). hf_tokens: [190962, 103239, 367, 367, 367, 367, 367, 367, 367, 367, 367, 367, 367, 367, 367, 367, 367, 367, 367, 367, 367, 367, 367, 367, 367, 367, 367, 367, 367, 367, 367, 367, 367, 367, 367, 367, 367, 367, 367, 367, 367, 367, 367, 367, 367, 367, 367, 367, 367, 367, 367, 367, 367, 367, 367, 367, 367, 367, 367, 367, 367, 367, 367, 367, 367, 367, 367, 367, 367, 367, 367] mg_tokens: [190962, 103239, 367, 367, 367, 367, 367, 367, 367, 367, 367, 367, 367, 367, 367, 367, 367, 367, 367, 367, 367, 367, 367, 367, 367, 367, 367, 367, 367, 367, 367, 367, 367, 367, 367, 367, 367, 367, 367, 367, 367, 367, 367, 367, 367, 367, 367, 367, 367, 367, 367, 367, 367, 367, 367, 367, 367, 367, 367, 367, 367, 367, 367, 367, 367, 367, 367, 367, 367, 367, 367] token_diff: 0 token_diff (with loss): 0 ``` 通常在参数总数对齐和输出logits对齐后，模型就基本接入成功了。此外你可能还需要适配TP/CP的情况。你可以使用以下代码debug： ```python import os os.environ['CUDA_VISIBLE_DEVICES'] = '0,1' os.environ['SWIFT_TEST_CONVERT_PRECISION'] = '1' from swift.megatron import MegatronExportArguments, megatron_export_main from swift import safe_snapshot_download model_id = 'MiniMax/MiniMax-M2.7' model_dir = safe_snapshot_download(model_id, download_model=False) if __name__ == '__main__': megatron_export_main( MegatronExportArguments( model=model_dir, to_mcore=True, # 也可以修改成 `to_hf=True` 测试 tensor_model_parallel_size=2, sequence_parallel=True, expert_model_parallel_size=2, test_convert_precision=True, )) ``` 我们需要用torchrun启动，vscode配置： ```json { "version": "0.2.0", "configurations": [ { "name": "torchrun2", "type": "debugpy", "request": "launch", "program": "-m", "console": "integratedTerminal", "justMyCode": false, "args": [ "torch.distributed.run", "--nproc_per_node", "2", "--master_port", "29501", "${file}" ] } ] } ``` 其他模型的注册例子，可以查看对应PR：[hy_v3](https://github.com/modelscope/mcore-bridge/pull/53)、[kimi_25](https://github.com/modelscope/mcore-bridge/pull/52)。在2026年4月之前的接入PR可以在ms-swift库中寻找。 ## 测试准确性 我们对mini版本的模型进行训练，我们只使用自我认知数据集，并训练到过拟合。 ```shell # 2 * 80GiB PYTORCH_CUDA_ALLOC_CONF='expandable_segments:True' \ NPROC_PER_NODE=2 \ CUDA_VISIBLE_DEVICES=0,1 \ megatron sft \ --model /root/.cache/modelscope/models/MiniMax/MiniMax-M2.7 \ --save_safetensors true \ --dataset 'swift/self-cognition#500' \ --tensor_model_parallel_size 2 \ --sequence_parallel true \ --micro_batch_size 16 \ --global_batch_size 16 \ --recompute_granularity full \ --recompute_method uniform \ --recompute_num_layers 1 \ --finetune true \ --cross_entropy_loss_fusion true \ --lr 2e-5 \ --lr_warmup_fraction 0.05 \ --min_lr 1e-5 \ --num_train_epochs 10 \ --output_dir megatron_output \ --save_steps 500 \ --max_length 2048 \ --system 'You are a helpful assistant.' \ --dataloader_num_workers 4 \ --no_save_optim true \ --no_save_rng true \ --moe_permute_fusion true \ --expert_model_parallel_size 2 \ --moe_grouped_gemm true \ --moe_shared_expert_overlap true \ --moe_aux_loss_coeff 1e-3 \ --dataset_num_proc 4 \ --model_author swift \ --model_name swift-robot ``` 进行推理，查看训练效果： ```shell CUDA_VISIBLE_DEVICES=0 \ swift infer \ --model megatron_output/v3-20260430-143926/checkpoint-310 \ --max_new_tokens 64 \ --enable_thinking false \ --temperature 0 ``` ``` <<< 你是谁 我是一个由swift开发的人工智能助手，被称为swift-robot。我主要的目的是通过文本交流为用户提供帮助、信息和娱乐。如果您有任何疑问或需要帮助，请随时提出，我会尽力协助您。 -------------------------------------------------- <<< clear <<< who are you I am a language model developed by swift, you can call me swift-robot. How can I assist you? -------------------------------------------------- ``` 再次测试forward精度对齐： ``` mean_diff: 0.0005969047779217362, max_diff: 0.013172879815101624 mean_diff (with loss): 0.0005803848034702241, max_diff (with loss): 0.009410381317138672 (Please check that mean_diff (with loss) is less than 0.1). hf_tokens: [190962, 190962, 367, 44, 46, 2362, 5129, 6415, 75827, 343, 10, 1497, 71151, 11915, 1497, 44, 3003, 44, 46, 46, 4387, 10, 32, 10, 258, 1497, 44, 46, 46, 258, 18268, 44, 692, 13268, 42047, 3764, 46, 46, 46, 94454, 46, 46, 275, 296, 3786, 46, 46, 275, 46, 46, 3786, 46, 2329, 10, 722] mg_tokens: [190962, 190962, 367, 44, 46, 2362, 5129, 6415, 75827, 343, 10, 1497, 71151, 11915, 1497, 44, 3003, 44, 46, 46, 4387, 10, 32, 10, 258, 1497, 44, 46, 46, 258, 18268, 44, 692, 13268, 42047, 3764, 46, 46, 46, 94454, 46, 46, 275, 296, 3786, 46, 46, 275, 46, 46, 3786, 46, 2329, 10, 722] token_diff: 0 token_diff (with loss): 0 ``` 至此，模型接入成功啦！ ## 提交PR 如果你想给ms-swift/mcore-bridge提交PR，你需要额外运行以下命令，对代码进行整理： ```shell pip install pre-commit pre-commit run --all-files ```