[train] support target_parameters

docs/source/Instruction/Megatron-SWIFT训练.md

@@ -432,6 +432,7 @@ lora训练：
 - adapter_load: 加载adapter的权重路径，用于lora断点续训，默认为None。lora断点续训方式与全参数一致，请关注`--finetune`参数的含义。
 - 🔥target_modules: 指定lora模块的后缀, 默认为`['all-linear']`。
 - 🔥target_regex: 指定lora模块的regex表达式，默认为`None`。如果该值传入，则target_modules参数失效。
+- target_parameters: 要替换为LoRA的参数名称列表。该参数的行为与 `target_modules` 类似，但传入的应是参数名称。该特性需要安装"peft>=0.17.0"。
 - 🔥modules_to_save: 在已附加tuner后，额外指定一部分原模型模块参与训练和存储。默认为`[]`。
 - 🔥lora_rank: 默认为`8`。
 - 🔥lora_alpha: 默认为`32`。

docs/source/Instruction/命令行参数.md

@@ -212,6 +212,7 @@
 - 🔥target_modules: 指定lora模块, 默认为`['all-linear']`。你也可以设置为module的后缀，例如：`--target_modules q_proj k_proj v_proj`。该参数不限于LoRA，可用于其他tuners。
   - 注意：在LLM和多模态LLM中，'all-linear'的行为有所不同。若是LLM则自动寻找除lm_head外的linear并附加tuner；若是多模态LLM，则默认只在LLM上附加tuner，该行为可以被`freeze_llm`、`freeze_vit`、`freeze_aligner`控制。
 - 🔥target_regex: 指定lora模块的regex表达式，默认为`None`。如果该值传入，则target_modules参数失效。该参数不限于LoRA，可用于其他tuners。
+- target_parameters: 要替换为LoRA的参数名称列表。该参数的行为与 `target_modules` 类似，但传入的应是参数名称。该特性需要安装"peft>=0.17.0"。
 - init_weights: 初始化weights的方法，LoRA可以指定为`true`、`false`、`gaussian`、`pissa`、`pissa_niter_[number of iters]`，Bone可以指定为`true`、`false`、`bat`。默认值`true`。
 - 🔥modules_to_save: 在已附加tuner后，额外指定一部分原模型模块参与训练和存储。默认为`[]`。该参数不限于LoRA，可用于其他tuners。
 

docs/source_en/Instruction/Command-line-parameters.md

@@ -216,6 +216,7 @@ Other important parameters:
 - 🔥 target_modules: Specifies the LoRA modules. The default is `['all-linear']`, but you can also pass layer-name suffixes, e.g. `--target_modules q_proj k_proj v_proj`. This argument is not restricted to LoRA and can be used with other tuners as well.
   - Note: The behavior of the special value `'all-linear'` differs between plain LLMs and multimodal LLMs. For a standard LLM, it automatically locates every linear layer except `lm_head` and attaches a tuner. For a multimodal LLM, it attaches the tuner only to the LLM component by default. This default can be changed with the `freeze_llm`, `freeze_vit`, and `freeze_aligner` options.
 - 🔥target_regex: Specifies a regex expression for LoRA modules, with a default of `None`. If this value is provided, the target_modules parameter becomes ineffective. This parameter is not limited to LoRA and can be used for other tuners.
+- target_parameters: List of parameter names to be replaced with LoRA. This argument behaves similarly to target_modules, but you should pass parameter names instead. This feature requires "peft>=0.17.0".
 - init_weights: Specifies the method for initializing weights. LoRA can specify `true`, `false`, `gaussian`, `pissa`, `pissa_niter_[number of iters]`. Bone can specify `true`, `false`, `bat`. The default is `true`.
 - 🔥modules_to_save: After attaching a tuner, explicitly specifies additional original model modules to participate in training and storage. The default is `[]`. This parameter is not limited to LoRA and can be used for other tuners.
 

docs/source_en/Instruction/Megatron-SWIFT-Training.md

@@ -453,6 +453,7 @@ LoRA Training:
 - adapter_load: The path to the adapter weights for loading, used for resuming LoRA training from a checkpoint. The default is None. The method for resuming LoRA training from a checkpoint is the same as for full-parameter training. Please pay attention to the meaning of the `--finetune` parameter.
 - 🔥target_modules: Suffixes of modules to apply LoRA to. Default is `['all-linear']`.
 - 🔥target_regex: Regex expression to specify LoRA modules. Default is `None`. If this value is provided, the `target_modules` parameter will be ignored.
+- target_parameters: List of parameter names to be replaced with LoRA. This argument behaves similarly to target_modules, but you should pass parameter names instead. This feature requires "peft>=0.17.0".
 - 🔥modules_to_save: After attaching a tuner, explicitly specifies additional original model modules to participate in training and storage. The default is `[]`.
 - 🔥lora_rank: Default is `8`.
 - 🔥lora_alpha: Default is `32`.

requirements/framework.txt

@@ -19,7 +19,7 @@ numpy
 openai
 oss2
 pandas
-peft>=0.11,<0.17
+peft>=0.11,<0.18
 pillow
 PyYAML>=5.4
 requests

swift/llm/argument/tuner_args.py

@@ -108,6 +108,7 @@ class TunerArguments:
     # tuners
     target_modules: List[str] = field(default_factory=lambda: ['all-linear'])
     target_regex: Optional[str] = None
+    target_parameters: Optional[list[str]] = None
     # e.g. ['wte', 'ln_1', 'ln_2', 'ln_f', 'lm_head']
     modules_to_save: List[str] = field(default_factory=list)
 

swift/llm/train/tuner.py

@@ -173,6 +173,8 @@ def prepare_adapter(args: TrainArguments, model, *, template=None, train_dataset
                 task_type = 'SEQ_CLS'
             elif task_type == 'GENERATIVE_RERANKER':
                 task_type = 'CAUSAL_LM'
+            if args.target_parameters is not None:
+                lora_kwargs['target_parameters'] = args.target_parameters
             lora_config = LoraConfig(task_type=task_type, lora_dtype=args.lora_dtype, **lora_kwargs)
             if args.init_weights == 'lora-ga':
                 try:

swift/megatron/argument/megatron_args.py

@@ -40,6 +40,7 @@ class MegatronTunerMixin:
     adapter_load: Optional[str] = None
     target_modules: List[str] = field(default_factory=lambda: ['all-linear'])
     target_regex: Optional[str] = None
+    target_parameters: Optional[List[str]] = None
     modules_to_save: List[str] = field(default_factory=list)
 
     # lora

swift/megatron/utils/utils.py

@@ -71,6 +71,8 @@ def prepare_adapter(model):
         'modules_to_save': modules_to_save,
         'use_rslora': args.use_rslora,
     }
+    if args.target_parameters is not None:
+        lora_kwargs['target_parameters'] = args.target_parameters
     lora_config = LoraConfig(task_type='CAUSAL_LM', lora_dtype=args.lora_dtype, **lora_kwargs)
     logger.info(f'lora_config: {lora_config}')
     return Swift.prepare_model(model, lora_config)