diff --git a/python/llm/src/ipex_llm/transformers/convert.py b/python/llm/src/ipex_llm/transformers/convert.py
index 7a02cc05..c0d94b6e 100644
--- a/python/llm/src/ipex_llm/transformers/convert.py
+++ b/python/llm/src/ipex_llm/transformers/convert.py
@@ -1259,12 +1259,19 @@ def _optimize_post(model, lightweight_bmm=False):
     elif model.config.model_type == "internlmxcomposer2":
         modeling_module_name = model.model.__class__.__module__
         module = importlib.import_module(modeling_module_name)
-        from ipex_llm.transformers.models.internlm import internlm_xcomposser2_attention_forward
+        from ipex_llm.transformers.models.internlm import (
+            internlm_xcomposser2_attention_forward,
+            internlm_xcomposser2_mlp_forward,
+            internlm_xcomposser2_model_forward_wrapper,
+            internlm_xcomposser2_chat
+        )
         convert_forward(model, module.InternLM2Attention, internlm_xcomposser2_attention_forward)
-        from ipex_llm.transformers.models.internlm import internlm_xcomposser2_mlp_forward
         convert_forward(model, module.InternLM2MLP, internlm_xcomposser2_mlp_forward)
         convert_forward(model, module.InternLM2RMSNorm, llama_rms_norm_forward)
-        from ipex_llm.transformers.models.internlm import internlm_xcomposser2_chat
+        internlm_xcomposser2_model_forward = internlm_xcomposser2_model_forward_wrapper(
+            module.InternLM2Model.forward
+        )
+        convert_forward(model, module.InternLM2Model, internlm_xcomposser2_model_forward)
         model.chat = MethodType(internlm_xcomposser2_chat, model)
     elif model.config.model_type == "qwen":
         if hasattr(model.config, "visual"):
diff --git a/python/llm/src/ipex_llm/transformers/models/internlm.py b/python/llm/src/ipex_llm/transformers/models/internlm.py
index df0ffd5d..a3dd0cb8 100644
--- a/python/llm/src/ipex_llm/transformers/models/internlm.py
+++ b/python/llm/src/ipex_llm/transformers/models/internlm.py
@@ -310,7 +310,7 @@ def add_lora(x: torch.Tensor, result: torch.Tensor,
              Plora_A: torch.nn.Linear = None, Plora_B: torch.nn.Linear = None):
     invalidInputError(x.dim() == 3 and result.dim() == 3,
                       "`x` and `result` should have 3 dims")
-    if len(im_mask) == 0 or x.size(1) == 1:
+    if isinstance(im_mask, torch.Tensor) or len(im_mask) == 0:
         return result
     else:
         for start_idx, end_idx in im_mask:
@@ -320,6 +320,56 @@ def add_lora(x: torch.Tensor, result: torch.Tensor,
         return result
 
 
+def internlm_xcomposser2_model_forward_wrapper(origin_forward):
+    def internlm_xcomposser2_model_forward(
+        self,
+        input_ids: torch.LongTensor = None,
+        attention_mask: Optional[torch.Tensor] = None,
+        position_ids: Optional[torch.LongTensor] = None,
+        past_key_values: Optional[List[torch.FloatTensor]] = None,
+        inputs_embeds: Optional[torch.FloatTensor] = None,
+        use_cache: Optional[bool] = None,
+        output_attentions: Optional[bool] = None,
+        output_hidden_states: Optional[bool] = None,
+        return_dict: Optional[bool] = None,
+        **kwargs
+    ):
+        im_mask = kwargs.get('im_mask', None)
+        if im_mask is None or im_mask.size(-1) <= 1 or im_mask.sum() == 0:
+            # decoding or no image input, `im_mask` is not needed
+            kwargs['im_mask'] = []
+        else:
+            # replace im_mask with start_idx and end_idx to improve performance
+            im_mask = im_mask.cpu().flatten().tolist()
+            length = len(im_mask)
+            new_mask = []
+            i = 0
+            while i < length:
+                while i < length and not im_mask[i]:
+                    i = i + 1
+                start_idx = i
+                while i < length and im_mask[i]:
+                    i = i + 1
+                end_idx = i
+                if start_idx != end_idx:
+                    new_mask.append((start_idx, end_idx))
+            kwargs['im_mask'] = new_mask
+        return origin_forward(
+            self=self,
+            input_ids=input_ids,
+            attention_mask=attention_mask,
+            position_ids=position_ids,
+            past_key_values=past_key_values,
+            inputs_embeds=inputs_embeds,
+            use_cache=use_cache,
+            output_attentions=output_attentions,
+            output_hidden_states=output_hidden_states,
+            return_dict=return_dict,
+            **kwargs
+        )
+    return internlm_xcomposser2_model_forward
+
+
 def internlm_xcomposser2_attention_forward(
     self,
     hidden_states: torch.Tensor,
@@ -466,32 +516,26 @@ def internlm_xcomposser2_chat(
     **kwargs,
 ):
     # ipex-llm changes start: fix device and dtype conversion
-    # replace im_mask with start_idx and end_idx to improve performance
     if image is None:
         inputs = self.build_inputs(tokenizer, query, history, meta_instruction)
-        im_mask = []
+        im_mask = torch.zeros(inputs['input_ids'].shape[:2]).bool()
     else:
         image = self.encode_img(image)
         inputs, im_mask = self.interleav_wrap_chat(tokenizer, query, image,
                                                    history, meta_instruction)
-        mask = im_mask.cpu().flatten().tolist()
-        length = len(mask)
-        im_mask = []
-        i = 0
-        while i < length:
-            while i < length and not mask[i]:
-                i = i + 1
-            start_idx = i
-            while i < length and mask[i]:
-                i = i + 1
-            end_idx = i
-            if start_idx != end_idx:
-                im_mask.append((start_idx, end_idx))
 
-    inputs = {
-        k: v.to(device=self.device, dtype=self.dtype)
-        for k, v in inputs.items() if torch.is_tensor(v)
-    }
+    new_inputs = {}
+    for k, v in inputs.items():
+        if torch.is_tensor(v):
+            if v.dtype.is_floating_point:
+                new_inputs[k] = v.to(device=self.device, dtype=self.dtype)
+            else:
+                # input_ids, don't convert its dtype
+                new_inputs[k] = v.to(device=self.device)
+        else:
+            new_inputs[k] = v
+    inputs = new_inputs
+    im_mask = im_mask.to(self.device)
     # ipex-llm changes end
 
     # also add end-of-assistant token in eos token id to avoid unnecessary generation