Add shape check for quantizedinputwrapper

backends/cadence/aot/compiler_funcs.py

@@ -19,6 +19,29 @@
 QuantArgs = tuple[float, int, int, int, torch.dtype]
 
 
+def extract_input_shapes_from_graph(
+    module: GraphModule,
+) -> dict[int, tuple[int, ...]]:
+    """
+    Extract input shapes from the FX graph placeholder nodes.
+
+    Returns a dict mapping input index to expected shape tuple.
+    """
+    input_shapes: dict[int, tuple[int, ...]] = {}
+    idx = 0
+    for node in module.graph.nodes:
+        if node.op == "placeholder":
+            # Get the tensor_meta from the node if available
+            if "val" in node.meta:
+                val = node.meta["val"]
+                if isinstance(val, torch.Tensor):
+                    input_shapes[idx] = tuple(val.shape)
+                elif hasattr(val, "shape"):
+                    input_shapes[idx] = tuple(val.shape)
+            idx += 1
+    return input_shapes
+
+
 @torch.no_grad()
 def trace(
     model: torch.nn.Module,
@@ -138,6 +161,9 @@ def __init__(
         super().__init__()
         self.module: GraphModule = module
         self.quant_args: dict[int, QuantArgs] = {}
+        self.expected_shapes: dict[int, tuple[int, ...]] = (
+            extract_input_shapes_from_graph(module)
+        )
 
         if input_args is not None:
             logger.warning(
@@ -151,6 +177,18 @@ def __init__(
 
     def forward(self, *args: torch.Tensor) -> Any:
         """Run inference, dequantizing configured inputs."""
+        # Validate input shapes for quantized inputs
+        for index in self.quant_args:
+            if index < len(args):
+                actual_shape = tuple(args[index].shape)
+                if index in self.expected_shapes:
+                    expected_shape = self.expected_shapes[index]
+                    if actual_shape != expected_shape:
+                        raise ValueError(
+                            f"Shape mismatch for quantized input at index {index}: "
+                            f"expected {expected_shape}, got {actual_shape}"
+                        )
+
         dequantized_args = []
         for index, node in enumerate(args):
             if index in self.quant_args: