fix grpo resume_from_checkpoint (#4035)

Author: Jintao-Huang

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

@@ -1,7 +1,7 @@
 
 # Megatron-SWIFT训练
 
-SWIFT引入了Megatron的并行技术来加速大模型的训练，包括数据并行、张量并行、流水线并行、序列并行，上下文并行，专家并行。支持Qwen3、Qwen3-MoE、Llama3、Deepseek-R1蒸馏系等模型的预训练和微调。完整支持的模型可以参考[支持的模型与数据集文档](./支持的模型和数据集.md)。
+SWIFT引入了Megatron的并行技术来加速大模型的训练，包括数据并行、张量并行、流水线并行、序列并行，上下文并行，专家并行。支持Qwen3、[Qwen3-MoE](https://github.com/modelscope/ms-swift/blob/main/examples/train/megatron/qwen3_moe.sh)、Qwen2.5、Llama3、Deepseek-R1蒸馏系等模型的预训练和微调。完整支持的模型可以参考[支持的模型与数据集文档](./支持的模型和数据集.md)。
 
 ## 环境准备
 使用Megatron-SWIFT，除了安装swift依赖外，还需要安装以下内容：
@@ -174,6 +174,7 @@ I am a language model developed by swift, you can call me swift-robot. How can I
 
 **checkpoint参数**:
 - 🔥save: checkpoint的输出目录，默认None。在训练中，若未设置该参数，则默认为`f'megatron_output/{model_suffix}'`，例如`'megatron_output/Qwen2.5-7B-Instruct'`。
+  - 注意：若在多机训练时，请确保每个节点的保存路径指向相同位置。否则你需要在训练后手动集中这些权重。
 - 🔥save_interval: checkpoint保存的间隔（steps），默认为500。
   - 注意：训练结束时一定会保存权重。
 - 🔥no_save_optim: 不保存optimizer，默认为False。

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

@@ -1,7 +1,7 @@
 
 # Megatron-SWIFT Training
 
-SWIFT incorporates Megatron's parallelization techniques to accelerate the training of large models, including data parallelism, tensor parallelism, pipeline parallelism, sequence parallelism, context parallelism, and expert parallelism. It supports the pre-training and fine-tuning of models such as Qwen3, Qwen3-MoE, Llama3, and the Deepseek-R1 distillation series. For a complete list of supported models, please refer to the [Supported Models and Datasets documentation](./Supported-models-and-datasets.md).
+SWIFT incorporates Megatron's parallelization techniques to accelerate the training of large models, including data parallelism, tensor parallelism, pipeline parallelism, sequence parallelism, context parallelism, and expert parallelism. It supports the pre-training and fine-tuning of models such as Qwen3, [Qwen3-MoE](https://github.com/modelscope/ms-swift/blob/main/examples/train/megatron/qwen3_moe.sh), Qwen2.5, Llama3, and the Deepseek-R1 distillation series. For a complete list of supported models, please refer to the [Supported Models and Datasets documentation](./Supported-models-and-datasets.md).
 
 ## Environment Setup
 
@@ -181,6 +181,7 @@ seq_length: Defaults to None, meaning it is set to `max_length`. To restrict the
 **Checkpoint Parameters**:
 
 - 🔥save: Output directory for checkpoints, default is None. During training, if this parameter is not set, it defaults to `f'megatron_output/{model_suffix}'`, e.g., `'megatron_output/Qwen2.5-7B-Instruct'`.
+  - Note: When training on multiple machines, ensure that the save paths on each node point to the same location. Otherwise, you will need to manually consolidate these weights after training.
 - 🔥save_interval: Checkpoint saving interval (steps), default is 500.
   - Note: Weights will always be saved at the end of training.
 - 🔥no_save_optim: Do not save optimizer, default is False.

swift/llm/argument/train_args.py

@@ -141,10 +141,11 @@ def __post_init__(self) -> None:
                            'Please specify `--attn_impl flash_attn`.')
         if self.resume_from_checkpoint:
             self.resume_from_checkpoint = to_abspath(self.resume_from_checkpoint, True)
-            if self.train_type == 'full':
-                self.model = self.resume_from_checkpoint
-            else:
-                self.adapters = [self.resume_from_checkpoint]
+            if self.resume_only_model:
+                if self.train_type == 'full':
+                    self.model = self.resume_from_checkpoint
+                else:
+                    self.adapters = [self.resume_from_checkpoint]
         BaseArguments.__post_init__(self)
         Seq2SeqTrainingOverrideArguments.__post_init__(self)
         TunerArguments.__post_init__(self)