Raise InternalError for non-CPU constant storage in the emitter

exir/emit/_emitter.py

@@ -471,6 +471,21 @@ def _tensor_spec_to_evalue(
                 ctypes.c_char * typing.cast(torch.UntypedStorage, spec.storage).nbytes()
             )
 
+            # data_ptr() is dereferenced from the host below; for non-CPU storage
+            # (e.g. CUDA) that read can segfault instead of raising (#21545).
+            storage_device = typing.cast(torch.UntypedStorage, spec.storage).device
+            if spec.allocated_memory != 0 and storage_device.type != "cpu":
+                raise InternalError(
+                    self._emit_node_specific_error(
+                        self.node,
+                        f"Constant tensor storage is on device '{storage_device}', "
+                        "but constants serialized into the program must be in CPU "
+                        "memory. Move them to CPU before export (e.g. model.cpu()), "
+                        "or delegate the ops that use them to a backend that "
+                        "accepts device tensors.",
+                    )
+                )
+
             buffer_data = (
                 bytes(
                     ctypes.cast(

exir/emit/test/test_emit.py

@@ -1833,6 +1833,15 @@ def forward(self, x):
         self.assertEqual(len(program.constant_buffer[1].storage), 4)
         self.assertEqual(len(program.constant_buffer[2].storage), 4)
 
+    def test_emit_non_cpu_constant_raises(self) -> None:
+        # Like CUDA storage, "meta" storage is not host-readable, but needs no
+        # accelerator. Unguarded, it already fails with a wrapped "NULL pointer
+        # access" InternalError, so match on the device name.
+        model = torch.nn.Linear(4, 3).to("meta")
+        edge = to_edge(export(model, (torch.ones(2, 4, device="meta"),), strict=True))
+        with self.assertRaisesRegex(InternalError, "on device 'meta'"):
+            edge.to_executorch()
+
     def test_non_persistent_buffer(self) -> None:
         class NonPersistentBuffer(nn.Module):
             def __init__(self):