set xavier_uniform_ as lora_A's init function

auto_gptq/__init__.py

@@ -1,2 +1,3 @@
 from .modeling import BaseQuantizeConfig
 from .modeling import AutoGPTQForCausalLM
+from .utils.peft_utils import get_gptq_peft_model

auto_gptq/utils/peft_utils.py

@@ -47,7 +47,7 @@ class QuantLoraLinear(torch.nn.Linear, LoraLayer):
 
     def reset_lora_parameters(self, adapter_name):
         if adapter_name in self.lora_A.keys():
-            torch.nn.init.ones_(self.lora_A[adapter_name].weight)
+            torch.nn.init.xavier_uniform_(self.lora_A[adapter_name].weight)
             torch.nn.init.zeros_(self.lora_B[adapter_name].weight)
 
     def merge(self):
@@ -277,9 +277,6 @@ def get_gptq_peft_model(
     model_id: str = None,
     adapter_name: str = "default"
 ):
-    if not peft_config.task_type == TaskType.CAUSAL_LM:
-        raise TypeError("only support CAUSAL_LM task type.")
-
     PEFT_TYPE_TO_MODEL_MAPPING[PeftType.LORA] = QuantLoraModel
 
     try: