[Relax][UnitTest] Validate IRModule with multiple targets

This commit adds a unit test to verify that a single `IRModule` can
contain functions that will be used on multiple distinct targets.
Previously, this test case caused errors when running the
`LegalizeOps` and `ApplyDefaultSchedule` transforms.

tests/python/relax/test_vm_build.py

@@ -1246,5 +1246,64 @@ def test_set_input_get_failure_rpc(exec_mode):
         run_on_rpc(TestVMSetInput, set_input_attempt_get, exec_mode)
 
 
+@tvm.testing.requires_gpu
+def test_relax_module_with_multiple_targets(exec_mode):
+    """Relax functions may contain kernels for multiple targets
+
+    In this example, the module contains one function to execute on
+    LLVM, and one function to execute on CUDA.
+
+    """
+
+    @I.ir_module
+    class Module:
+        I.module_global_infos({"vdevice": [I.vdevice("llvm")]})
+
+        @R.function
+        def func_cuda(A: R.Tensor([32, 32], "float32"), B: R.Tensor([32, 32], "float32")):
+            C = R.add(A, B)
+            return C
+
+        @R.function
+        def func_llvm(
+            A: R.Tensor([32, 32], "float32", "llvm"), B: R.Tensor([32, 32], "float32", "llvm")
+        ):
+            C = R.add(A, B)
+            return C
+
+    seq = tvm.ir.transform.Sequential(
+        [
+            tvm.relax.transform.LegalizeOps(),
+            tvm.dlight.ApplyDefaultSchedule(tvm.dlight.gpu.Fallback()),
+        ],
+        name="LegalizeAndSchedule",
+    )
+    with tvm.target.Target("cuda"):
+        built = tvm.relax.build(seq(Module))
+
+    np_A = np.random.random([32, 32]).astype("float32")
+    np_B = np.random.random([32, 32]).astype("float32")
+
+    dev_llvm = tvm.device("llvm")
+    vm_llvm = tvm.relax.VirtualMachine(built, device=dev_llvm)
+    llvm_output = vm_llvm["func_llvm"](
+        tvm.nd.array(np_A, dev_llvm),
+        tvm.nd.array(np_B, dev_llvm),
+    )
+
+    dev_cuda = tvm.device("cuda")
+    vm_cuda = tvm.relax.VirtualMachine(built, device=dev_cuda)
+
+    cuda_output = vm_cuda["func_cuda"](
+        tvm.nd.array(np_A, dev_cuda),
+        tvm.nd.array(np_B, dev_cuda),
+    )
+
+    np_C = np_A + np_B
+
+    tvm.testing.assert_allclose(llvm_output.numpy(), np_C)
+    tvm.testing.assert_allclose(cuda_output.numpy(), np_C)
+
+
 if __name__ == "__main__":
     tvm.testing.main()