[Core][Model] torch.compile for layernorm in commandr (#3985)

[Core][Model] Use torch.compile to accelerate layernorm in commandr (#3985)

vllm/model_executor/models/commandr.py

@@ -48,6 +48,18 @@ from vllm.model_executor.weight_utils import (default_weight_loader,
 from vllm.sequence import SamplerOutput
 
 
+@torch.compile
+def layer_norm_func(hidden_states, weight, variance_epsilon):
+    input_dtype = hidden_states.dtype
+    hidden_states = hidden_states.to(torch.float32)
+    mean = hidden_states.mean(-1, keepdim=True)
+    variance = (hidden_states - mean).pow(2).mean(-1, keepdim=True)
+    hidden_states = (hidden_states - mean) * torch.rsqrt(variance +
+                                                         variance_epsilon)
+    hidden_states = weight.to(torch.float32) * hidden_states
+    return hidden_states.to(input_dtype)
+
+
 class LayerNorm(nn.Module):
 
     def __init__(self, param_shape=None, eps=1e-5):
@@ -57,14 +69,9 @@ class LayerNorm(nn.Module):
         set_weight_attrs(self.weight, {"weight_loader": self.weight_loader})
 
     def forward(self, hidden_states, residuals=None):
-        input_dtype = hidden_states.dtype
-        hidden_states = hidden_states.to(torch.float32)
-        mean = hidden_states.mean(-1, keepdim=True)
-        variance = (hidden_states - mean).pow(2).mean(-1, keepdim=True)
-        hidden_states = (hidden_states -
-                         mean) * torch.rsqrt(variance + self.variance_epsilon)
-        hidden_states = self.weight.to(torch.float32) * hidden_states
-        return hidden_states.to(input_dtype), residuals
+        hidden_states = layer_norm_func(hidden_states, self.weight,
+                                        self.variance_epsilon)
+        return hidden_states, residuals
 
     def weight_loader(self, param: Parameter, loaded_weight: torch.Tensor):
         tp_rank = get_tensor_model_parallel_rank()