diff --git a/examples/models/llama/static_attention.py b/examples/models/llama/static_attention.py
index 849718527ed..1880a09f5c6 100644
--- a/examples/models/llama/static_attention.py
+++ b/examples/models/llama/static_attention.py
@@ -764,6 +764,39 @@ def __init__(
             self.q_norm = torch.nn.Identity()
             self.k_norm = torch.nn.Identity()
 
+    @classmethod
+    def from_attention_mha(
+        cls,
+        other: AttentionMHA,
+        split_mha: bool = True,
+        rms_norm_class=torch.nn.RMSNorm,
+        **kwargs: Any,
+    ) -> "StaticAttention":
+        config = ModelArgs(
+            dim=other.dim,
+            n_layers=1,  # Not used in attention layer
+            n_heads=other.n_heads,
+            n_kv_heads=other.n_kv_heads,
+            head_dim=other.head_dim,
+            max_batch_size=other.max_batch_size,
+            max_context_len=other.max_context_len,
+            attention_qkv_bias=other.attention_qkv_bias,
+            use_qk_norm=other.use_qk_norm,
+            qk_norm_before_rope=other.qk_norm_before_rope,
+            norm_eps=other.q_norm_fn.eps if other.use_qk_norm else 1e-5,
+        )
+
+        instance = cls(
+            config=config,
+            layer_id=other.layer_id,
+            rope=other.rope,
+            split_mha=split_mha,
+            **kwargs,
+        )
+        instance.load_weights_from_attention_mha(other, rms_norm_class=rms_norm_class)
+
+        return instance
+
     def forward(
         self,
         x: torch.Tensor,
@@ -1059,3 +1092,37 @@ def transfer_weight(linear, conv2d):
 class StaticAttentionMHA(StaticAttention):
     def __init__(self, config: ModelArgs, layer_id: int, rope: Rope, **kwargs: Any):
         super().__init__(config, layer_id, rope, split_mha=False, **kwargs)
+
+
+def transform_attention_mha_to_static_attention(
+    model: nn.Module,
+    split_mha: bool = True,
+    inplace: bool = True,
+    use_conv2d: bool = False,
+    use_hf_rope: bool = False,
+    **kwargs: Any,
+) -> nn.Module:
+    if not inplace:
+        import copy
+
+        model = copy.deepcopy(model)
+
+    def helper(m):
+        for name, child in list(m.named_children()):
+            if isinstance(child, AttentionMHA):
+                static_attn = StaticAttention.from_attention_mha(
+                    child, split_mha=split_mha, **kwargs
+                )
+                # Note: HF RoPE needs to be applied before linear to conv2d
+                if use_hf_rope:
+                    static_attn.adopt_hf_rope()
+                if use_conv2d:
+                    static_attn.linear_to_conv2d()
+
+                setattr(m, name, static_attn)
+            else:
+                helper(child)
+
+        return m
+
+    return helper(model)
diff --git a/examples/models/llama/tests/test_static_attention.py b/examples/models/llama/tests/test_static_attention.py
index 8786c70da11..0d407968c0e 100644
--- a/examples/models/llama/tests/test_static_attention.py
+++ b/examples/models/llama/tests/test_static_attention.py
@@ -14,6 +14,7 @@
     StaticAttentionMask,
     StaticKCache,
     StaticKVCache,
+    transform_attention_mha_to_static_attention,
 )
 
 
@@ -76,7 +77,6 @@ def test(
             layer_id = 0
             rope = Rope(config)
             attn_mha = AttentionMHA(config, layer_id, rope).eval()
-            static_attn = StaticAttention(config, layer_id, rope).eval()
             if use_qk_norm:
                 with torch.no_grad():
                     attn_mha.q_norm_fn.weight.copy_(
@@ -85,7 +85,9 @@ def test(
                     attn_mha.k_norm_fn.weight.copy_(
                         torch.rand(config.head_dim) * 0.2 + 0.9
                     )
-            static_attn.load_weights_from_attention_mha(attn_mha)
+            static_attn = StaticAttention.from_attention_mha(
+                attn_mha, split_mha=split_mha
+            ).eval()
             if adopt_hf_rope:
                 static_attn.adopt_hf_rope()
             if use_conv2d:
@@ -131,8 +133,7 @@ def test_with_cache(self):
         layer_id = 0
         rope = Rope(config)
         attn_mha = AttentionMHA(config, layer_id, rope).eval()
-        static_attn = StaticAttention(config, layer_id, rope).eval()
-        static_attn.load_weights_from_attention_mha(attn_mha)
+        static_attn = StaticAttention.from_attention_mha(attn_mha).eval()
         static_attn.adopt_hf_rope()
 
         x = torch.rand(1, config.max_seq_len, config.dim)
@@ -198,17 +199,16 @@ def test_with_style(style):
     def _get_test_transformers(self, config, attention_type="static", use_conv2d=False):
         mha_transformer = construct_transformer(config).eval()
 
+        static_transformer = transform_attention_mha_to_static_attention(
+            mha_transformer,
+            split_mha=(attention_type == "static"),
+            inplace=False,
+            use_conv2d=use_conv2d,
+            use_hf_rope=True,
+        ).eval()
+
         config = copy.copy(config)
         config.attention_type = attention_type
-        static_transformer = construct_transformer(config).eval()
-        static_transformer.load_state_dict(mha_transformer.state_dict(), strict=False)
-        for mha_layer, static_layer in zip(
-            mha_transformer.layers, static_transformer.layers
-        ):
-            static_layer.attention.load_weights_from_attention_mha(mha_layer.attention)
-            static_layer.attention.adopt_hf_rope()
-            if use_conv2d:
-                static_layer.linear_to_conv2d()
         config.use_hf_rope = True
 
         return mha_transformer, static_transformer, config