[shardformer] support pipeline for deepseek v3 and optimize lora save (#6188)

* [shardformer] support pipeline for deepseek v3

* [checkpointio] fix lora save

* [devops] update ci env

* [booster] optimize lora

* fix test

* fix test

colossalai/booster/plugin/low_level_zero_plugin.py

@@ -359,23 +359,10 @@ class LowLevelZeroCheckpointIO(TorchDDPCheckpointIO):
             model, checkpoint_path, gather_dtensor, prefix, max_shard_size, use_safetensors, use_async=use_async
         )
 
-    def save_lora_as_pretrained(self, model, checkpoint, use_safetensors):
-        if os.path.isfile(checkpoint):
-            self.logger.error(f"Provided path ({checkpoint}) should be a directory, not a file", ranks=[0])
-            return
-        from peft import PeftModel
-
-        assert isinstance(model, ModelWrapper), "Please boost the model before saving!"
+    def save_lora_as_pretrained(self, model, checkpoint, use_safetensors, state_dict: Optional[dict] = None):
+        assert isinstance(model, LowLevelZeroModel), "Please boost the model before saving!"
         model._force_wait_all_gather()
-        peft_model = model.unwrap()
-        assert isinstance(
-            peft_model, PeftModel
-        ), "The model doesn't have lora adapters, please enable lora before saving."
-        return peft_model.save_pretrained(
-            checkpoint,
-            safe_serialization=use_safetensors,
-            state_dict=tree_map(lambda x: x.data if torch.is_tensor(x) else x, peft_model.state_dict()),
-        )
+        super().save_lora_as_pretrained(model, checkpoint, use_safetensors, state_dict=state_dict)
 
 
 class LowLevelZeroPlugin(DPPluginBase):