diff --git a/vllm/compilation/backends.py b/vllm/compilation/backends.py
index a1eec7d74483f..2fb6265560b19 100644
--- a/vllm/compilation/backends.py
+++ b/vllm/compilation/backends.py
@@ -520,6 +520,7 @@ class VllmBackend:
         self,
         vllm_config: VllmConfig,
         prefix: str = "",
+        is_encoder: bool = False,
     ):
         # if the model is initialized with a non-empty prefix,
         # then usually it's enough to use that prefix,
@@ -530,7 +531,7 @@ class VllmBackend:
         self.prefix = prefix or model_tag
 
         # Mark compilation for encoder.
-        self.is_encoder = model_is_encoder
+        self.is_encoder = is_encoder or model_is_encoder
 
         # Passes to run on the graph post-grad.
         self.pass_manager = resolve_obj_by_qualname(
@@ -797,7 +798,7 @@ class VllmBackend:
             or not self.compilation_config.cudagraph_copy_inputs
         ):
             return VllmSerializableFunction(
-                graph, example_inputs, self.prefix, self.split_gm
+                graph, example_inputs, self.prefix, self.split_gm, self.is_encoder
             )
 
         # index of tensors that have symbolic shapes (batch size)
@@ -835,5 +836,5 @@ class VllmBackend:
             return self.split_gm(*list_args)
 
         return VllmSerializableFunction(
-            graph, example_inputs, self.prefix, copy_and_call
+            graph, example_inputs, self.prefix, copy_and_call, self.is_encoder
         )
diff --git a/vllm/compilation/caching.py b/vllm/compilation/caching.py
index fc02a08f74265..8c9ec87bcad56 100644
--- a/vllm/compilation/caching.py
+++ b/vllm/compilation/caching.py
@@ -37,12 +37,15 @@ class VllmSerializableFunction(SerializableCallable):
     serializing the Dynamo fx graph plus example inputs.
     """
 
-    def __init__(self, graph_module, example_inputs, prefix, optimized_call):
+    def __init__(
+        self, graph_module, example_inputs, prefix, optimized_call, is_encoder=False
+    ):
         assert isinstance(graph_module, torch.fx.GraphModule)
         self.graph_module = graph_module
         self.example_inputs = example_inputs
         self.prefix = prefix
         self.optimized_call = optimized_call
+        self.is_encoder = is_encoder
         self.shape_env = None
         sym_input = next(
             (i for i in self.example_inputs if isinstance(i, torch.SymInt)), None
@@ -106,7 +109,10 @@ class VllmSerializableFunction(SerializableCallable):
         state["graph_module"] = GraphPickler.loads(state["graph_module"], fake_mode)
         state["graph_module"].recompile()
         state["example_inputs"] = GraphPickler.loads(state["example_inputs"], fake_mode)
-        vllm_backend = VllmBackend(get_current_vllm_config(), state["prefix"])
+        is_encoder = state.get("is_encoder", False)
+        vllm_backend = VllmBackend(
+            get_current_vllm_config(), state["prefix"], is_encoder
+        )
 
         def optimized_call(*example_inputs):
             """
diff --git a/vllm/compilation/piecewise_backend.py b/vllm/compilation/piecewise_backend.py
index 58d3e2a14b22a..12cc49971e08b 100644
--- a/vllm/compilation/piecewise_backend.py
+++ b/vllm/compilation/piecewise_backend.py
@@ -170,8 +170,7 @@ class PiecewiseBackend:
         range_entry = self._find_range_for_shape(runtime_shape)
 
         assert range_entry is not None, (
-            f"Shape out of considered range: {runtime_shape} "
-            "[1, max_num_batched_tokens]"
+            f"Shape: {runtime_shape} out of considered ranges: {self.compile_ranges}"
         )
 
         self._maybe_compile_for_range_entry(range_entry, args)