diff --git a/python/llm/src/ipex_llm/transformers/models/chatglm.py b/python/llm/src/ipex_llm/transformers/models/chatglm.py
index 51a2026a..77e2ae44 100644
--- a/python/llm/src/ipex_llm/transformers/models/chatglm.py
+++ b/python/llm/src/ipex_llm/transformers/models/chatglm.py
@@ -23,7 +23,7 @@ import torch.utils.checkpoint
 import torch.nn.functional as F
 from typing import Optional, Tuple
 from ipex_llm.transformers.models.utils import init_kv_cache, extend_kv_cache, append_kv_cache
-from ipex_llm.transformers.models.chatglm2 import glm_sdpa
+from ipex_llm.transformers.models.utils import use_flash_attention, use_sdp
 
 
 def rotate_half(x):
@@ -39,6 +39,49 @@ def apply_rotary_pos_emb_index(q, k, cos, sin, position_id):
     q, k = (q * cos) + (rotate_half(q) * sin), (k * cos) + (rotate_half(k) * sin)
     return q, k
 
+
+def glm_sdpa(query, key, value, attention_mask=None, is_causal=False):
+    if use_flash_attention(query, key, attention_mask) or query.device.type == 'cpu':
+        context_layer = F.scaled_dot_product_attention(query.to(key.dtype),
+                                                       key,
+                                                       value,
+                                                       attention_mask,
+                                                       is_causal=is_causal).to(key.dtype)
+    else:
+        # attention_mask is not None only when past_key_value is not None and q_len > 1
+        if attention_mask is not None:
+            attn_bias = torch.zeros(attention_mask.shape, dtype=query.dtype,
+                                    device=query.device)
+            attention_mask = ~attention_mask
+            if attention_mask.dtype == torch.bool:
+                attn_bias.masked_fill_(attention_mask.logical_not(), float("-inf"))
+            else:
+                attn_bias += attention_mask
+        elif is_causal:
+            L, S = query.size(-2), key.size(-2)
+            attn_bias = torch.zeros(L, S, dtype=query.dtype, device=query.device)
+            temp_mask = torch.ones(L, S, dtype=torch.bool, device=query.device).tril(diagonal=0)
+            attn_bias.masked_fill_(temp_mask.logical_not(), float("-inf"))
+            attn_bias.to(key.dtype)
+        else:
+            attn_bias = None
+        if use_sdp(query.shape[2], key.shape[2],
+                   query.shape[-1], query):
+            import xe_addons
+            attn_output = xe_addons.sdp(query, key, value, attn_bias)
+            context_layer = attn_output.view(query.shape)
+        else:
+            head_dim = query.size(-1)
+            attn = torch.matmul(query.to(key.dtype) / math.sqrt(head_dim),
+                                key.transpose(2, 3))
+            if attn_bias is not None:
+                attn += attn_bias
+            attn = F.softmax(attn, dim=-1,
+                             dtype=torch.float32).to(value.dtype)
+            context_layer = torch.matmul(attn, value)
+    return context_layer
+
+
 import os
 
 KV_CACHE_ALLOC_BLOCK_LENGTH = int(os.environ.get("KV_CACHE_ALLOC_BLOCK_LENGTH", 256))