add XPU LinearAddAdd op (#1017)

Signed-off-by: Liu, Kaixuan <[email protected]>

optimum/exporters/ipex/modeling_utils.py

@@ -118,6 +118,21 @@ def forward(
         return hidden_states
 
 
+class XPUlinearAddAdd(torch.nn.Module):
+    def __init__(self, module: torch.nn.Module):
+        super().__init__()
+        self.weight = module.weight.transpose(0, 1).contiguous()
+        self.bias = module.bias
+
+    def forward(self, x, y, z):
+        if self.bias is not None:
+            x = torch.ops.torch_ipex.mm_bias_resadd(x, self.weight, self.bias, 1.0, y, 1.0)
+            x += z
+        else:
+            x = torch.ops.torch_ipex.mm_bias_resadd(x, self.weight, z, 1.0, y, 1.0)
+        return x
+
+
 # Adapted from https://github.com/huggingface/transformers/blob/v4.38.2/src/transformers/models/llama/modeling_llama.py#L83
 def _ipex_rms_layer_norm_forward(self, hidden_states):
     return rms_norm(hidden_states, self.weight, self.variance_epsilon)
@@ -703,7 +718,10 @@ def __init__(self, module, config) -> None:
         elif self.module_device == "xpu":
             self.linear_gelu = XPULinearGelu(module.dense_h_to_4h)
         if module.dense_4h_to_h.__class__.__name__ not in ["LinearAllreduce"]:
-            self.linear_add_add = LinearAddAdd(module.dense_4h_to_h)
+            if self.module_device == "cpu":
+                self.linear_add_add = LinearAddAdd(module.dense_4h_to_h)
+            elif self.module_device == "xpu":
+                self.linear_add_add = XPUlinearAddAdd(module.dense_4h_to_h)
 
     def forward(
         self,