[Graph Optimization][CINN] Use CINN in PaddleOCR-VL ViT part (#5223)

---------

Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com>

fastdeploy/model_executor/models/paddleocr_vl/siglip.py

@@ -281,7 +281,6 @@ class SiglipMLP(nn.Layer):
     def __init__(self, config):
         super().__init__()
         self.config = config
-        self.activation_fn = get_activation_fn(config.hidden_act)
         self.fc1 = nn.Linear(config.hidden_size, config.intermediate_size)
         self.fc1.weight.weight_loader = self.weight_loader
         self.fc2 = nn.Linear(config.intermediate_size, config.hidden_size)
@@ -304,7 +303,7 @@ class SiglipMLP(nn.Layer):
 
     def forward(self, hidden_states: paddle.Tensor) -> paddle.Tensor:
         hidden_states = self.fc1(hidden_states)
-        hidden_states = self.activation_fn(hidden_states[0])
+        hidden_states = get_activation_fn(self.config.hidden_act)(hidden_states[0])
         hidden_states = self.fc2(hidden_states)
         return hidden_states
 
@@ -318,7 +317,6 @@ class SiglipEncoderLayer(paddle.nn.Layer):
         self.layer_norm2 = paddle.nn.LayerNorm(self.embed_dim, epsilon=config.layer_norm_eps)
         self.mlp = SiglipMLP(config)
 
-    # @paddle.jit.to_static
     def forward(
         self,
         hidden_states,
@@ -527,7 +525,37 @@ class SiglipEncoder(nn.Layer):
         else:
             attn_cu_seqlens = cu_seqlens
 
-        max_seqlen = (attn_cu_seqlens[1:] - attn_cu_seqlens[:-1]).max().item()
+        return self._run_encoder_layer(
+            encoder_states=encoder_states,
+            all_attentions=all_attentions,
+            attn_cu_seqlens=attn_cu_seqlens,
+            output_hidden_states=output_hidden_states,
+            reversed_window_indices=reversed_window_indices if output_hidden_states else None,
+            use_window_attn=use_window_attn,
+            hidden_states=hidden_states,
+            attention_mask=attention_mask,
+            output_attentions=output_attentions,
+            cos_emb=cos_emb,
+            sin_emb=sin_emb,
+        )
+
+    # This function will be compiled with CINN when graph_opt_level >= 2
+    # TODO(SigureMo): Use a new decorator to mark the function for CINN compilation
+    def _run_encoder_layer(
+        self,
+        encoder_states: Optional[Tuple[()]],
+        all_attentions: Optional[Tuple[()]],
+        attn_cu_seqlens: Optional[paddle.Tensor],
+        output_hidden_states: Optional[bool],
+        reversed_window_indices: paddle.Tensor,
+        use_window_attn: bool,
+        hidden_states: paddle.Tensor,
+        attention_mask: Optional[paddle.Tensor],
+        output_attentions: bool,
+        cos_emb: Optional[paddle.Tensor],
+        sin_emb: Optional[paddle.Tensor],
+    ) -> paddle.Tensor:
+        max_seqlen = (attn_cu_seqlens[1:] - attn_cu_seqlens[:-1]).max().cpu()
 
         for encoder_layer in self.layers:
             if output_hidden_states: