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# LoRA训练
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Qwen3-235B-A22B-Instruct-250718 单机8卡H20 LoRA训练的最佳实践参考：[https://github.com/modelscope/ms-swift/pull/5033](https://github.com/modelscope/ms-swift/pull/5033)。
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环境准备请参考Megatron-SWIFT的[快速开始文档](./Quick-start.md)。
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## 传统方式
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### HF转换Mcore
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以下，我们分别介绍使用`swift export`和`megatron export`命令进行权重转换。相比于`swift export`，`megatron export`支持多机和LoRA增量权重转换，但也更加复杂，需要在导出时额外指定并行参数，例如`--tensor_model_parallel_size`, `--export_model_parallel_size`，具体参考[Mcore-Bridge文档](./Mcore-Bridge.md)。若要使用`swift export`命令，参考[快速开始文档](./Quick-start.md)。
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- `swift export`使用单进程，将HF权重放置在gpu中，并使用device_map并行；mcore权重放置在cpu中，且不开启并行。这种方式非常易于debug，并测试HF和mcore的精度对齐情况。
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- `megatron export`使用torchrun启动多进程，mcore权重放置在gpu中，支持开启各种并行、fp8和mtp等功能。如果需测试精度对齐情况，会在第一个rank加载HF权重，并放置在cpu中。
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```shell
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# megatron export
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NPROC_PER_NODE=2 \
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CUDA_VISIBLE_DEVICES=0,1 \
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megatron export \
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--model Qwen/Qwen2.5-7B-Instruct \
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--tensor_model_parallel_size 2 \
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--to_mcore true \
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--torch_dtype bfloat16 \
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--output_dir Qwen2.5-7B-Instruct-mcore \
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--test_convert_precision true
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# swift export
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# CUDA_VISIBLE_DEVICES=0 \
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# swift export \
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# --model Qwen/Qwen2.5-7B-Instruct \
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# --to_mcore true \
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# --torch_dtype bfloat16 \
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# --output_dir Qwen2.5-7B-Instruct-mcore \
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# --test_convert_precision true
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```
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### LoRA训练
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训练脚本：
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```bash
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# full: 2 * 70GiB 0.61s/it
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# lora: 2 * 14GiB 0.45s/it
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PYTORCH_CUDA_ALLOC_CONF='expandable_segments:True' \
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NPROC_PER_NODE=2 \
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CUDA_VISIBLE_DEVICES=0,1 \
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megatron sft \
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--mcore_model Qwen2.5-7B-Instruct-mcore \
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--save_safetensors false \
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--dataset 'AI-ModelScope/alpaca-gpt4-data-zh#500' \
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'AI-ModelScope/alpaca-gpt4-data-en#500' \
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'swift/self-cognition#500' \
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--tuner_type lora \
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--lora_rank 8 \
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--lora_alpha 32 \
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--target_modules all-linear \
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--tensor_model_parallel_size 2 \
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--sequence_parallel true \
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--micro_batch_size 16 \
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--global_batch_size 16 \
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--recompute_granularity full \
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--recompute_method uniform \
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--recompute_num_layers 1 \
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--finetune true \
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--cross_entropy_loss_fusion true \
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--lr 1e-4 \
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--lr_warmup_fraction 0.05 \
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--min_lr 1e-5 \
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--num_train_epochs 1 \
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--output_dir megatron_output/Qwen2.5-7B-Instruct \
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--save_steps 100 \
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--max_length 2048 \
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--system 'You are a helpful assistant.' \
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--dataloader_num_workers 4 \
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--no_save_optim true \
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--no_save_rng true \
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--dataset_num_proc 4 \
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--model_author swift \
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--model_name swift-robot
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```
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- MoE模型的LoRA训练脚本参考[这里](https://github.com/modelscope/ms-swift/tree/main/examples/megatron/lora)。
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### MCore转换HF
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```bash
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# megatron export
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NPROC_PER_NODE=2 \
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CUDA_VISIBLE_DEVICES=0,1 \
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megatron export \
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--mcore_adapter megatron_output/Qwen2.5-7B-Instruct/vx-xxx/checkpoint-xxx \
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--to_hf true \
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--tensor_model_parallel_size 2 \
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--merge_lora false \
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--torch_dtype bfloat16 \
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--output_dir megatron_output/Qwen2.5-7B-Instruct/vx-xxx/checkpoint-xxx-hf \
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--test_convert_precision true
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# swift export
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# CUDA_VISIBLE_DEVICES=0 \
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# swift export \
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# --mcore_adapter megatron_output/Qwen2.5-7B-Instruct/vx-xxx/checkpoint-xxx \
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# --to_hf true \
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# --torch_dtype bfloat16 \
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# --output_dir megatron_output/Qwen2.5-7B-Instruct/vx-xxx/checkpoint-xxx-hf \
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# --test_convert_precision true
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```
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- 注意：`--mcore_adapter`文件夹中包含`args.json`文件，转换过程会读取文件中`--model/--mcore_model`以及LoRA相关的参数信息。`swift export`暂不支持LoRA增量权重的转换。`megatron export`你可以使用`--merge_lora`参数控制是否进行权重合并。
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### 推理
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```shell
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# 如果是全量权重，请将`--adapters`替换为`--model
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CUDA_VISIBLE_DEVICES=0 \
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swift infer \
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--adapters megatron_output/Qwen2.5-7B-Instruct/vx-xxx/checkpoint-xxx-hf \
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--stream true
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```
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### Merge-LoRA
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如果只想merge-lora，而不希望转成HF格式权重，用于后续DPO训练，可以使用以下脚本：
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```shell
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# megatron export
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NPROC_PER_NODE=2 \
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CUDA_VISIBLE_DEVICES=0,1 \
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megatron export \
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--mcore_adapter megatron_output/Qwen2.5-7B-Instruct/vx-xxx/checkpoint-xxx \
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--tensor_model_parallel_size 2 \
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--to_mcore true \
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--merge_lora true \
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--torch_dtype bfloat16 \
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--output_dir megatron_output/Qwen2.5-7B-Instruct/vx-xxx/checkpoint-xxx-mcore \
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--test_convert_precision true
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# swift export
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# CUDA_VISIBLE_DEVICES=0 \
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# swift export \
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# --mcore_adapter megatron_output/Qwen2.5-7B-Instruct/vx-xxx/checkpoint-xxx \
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# --to_mcore true \
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# --torch_dtype bfloat16 \
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# --output_dir megatron_output/Qwen2.5-7B-Instruct/vx-xxx/checkpoint-xxx-mcore \
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# --test_convert_precision true
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```
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## Mcore-Bridge【推荐】
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### 训练
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```shell
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# full: 2 * 70GiB 0.61s/it
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# lora: 2 * 14GiB 0.45s/it
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PYTORCH_CUDA_ALLOC_CONF='expandable_segments:True' \
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NPROC_PER_NODE=2 \
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CUDA_VISIBLE_DEVICES=0,1 \
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megatron sft \
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--model Qwen/Qwen2.5-7B-Instruct \
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--save_safetensors true \
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--merge_lora false \
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--dataset 'AI-ModelScope/alpaca-gpt4-data-zh#500' \
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'AI-ModelScope/alpaca-gpt4-data-en#500' \
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'swift/self-cognition#500' \
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--tuner_type lora \
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--lora_rank 8 \
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--lora_alpha 32 \
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--target_modules all-linear \
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--tensor_model_parallel_size 2 \
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--sequence_parallel true \
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--micro_batch_size 16 \
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--global_batch_size 16 \
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--recompute_granularity full \
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--recompute_method uniform \
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--recompute_num_layers 1 \
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--finetune true \
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--cross_entropy_loss_fusion true \
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--lr 1e-4 \
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--lr_warmup_fraction 0.05 \
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--min_lr 1e-5 \
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--num_train_epochs 1 \
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--output_dir megatron_output/Qwen2.5-7B-Instruct \
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--save_steps 100 \
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--max_length 2048 \
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--system 'You are a helpful assistant.' \
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--dataloader_num_workers 4 \
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--no_save_optim true \
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--no_save_rng true \
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--dataset_num_proc 4 \
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--model_author swift \
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--model_name swift-robot
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```
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### 推理
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```shell
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# 如果是全量权重，请将`--adapters`替换为`--model
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CUDA_VISIBLE_DEVICES=0 \
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swift infer \
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--adapters megatron_output/Qwen2.5-7B-Instruct/vx-xxx/checkpoint-xxx \
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--stream true
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```
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### Merge-LoRA
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由于训练的时候设置了`--merge_lora false`，后续如果想将lora权重合并成全量safetensors权重，可以使用以下脚本：
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```shell
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# 由于lora权重是safetensors格式，你需要使用`--adapters`而不是`--mcore_adapter`
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# megatron export
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NPROC_PER_NODE=2 \
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CUDA_VISIBLE_DEVICES=0,1 \
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megatron export \
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--adapters megatron_output/Qwen2.5-7B-Instruct/vx-xxx/checkpoint-xxx \
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--tensor_model_parallel_size 2 \
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--to_hf true \
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--merge_lora true \
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--torch_dtype bfloat16 \
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--output_dir megatron_output/Qwen2.5-7B-Instruct/vx-xxx/checkpoint-xxx-merged
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```
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