@zhiics It looks like the tuple with duplicated tensors is only problematic if
it is the return value of a subfunction (i.e. a function that is lowered to
topi and compiled by TVM). If we lift the tuple out of a subfunction and put it
under the global function, it seems to work fine. The test below works on my
local.
```
import tvm
from tvm import relay
data = relay.var("data", relay.ty.TensorType((1, 32, 32, 3), "float32"))
log = relay.log(data)
func = relay.Function([data], relay.Tuple(tvm.convert([log, log])))
func = relay.ir_pass.infer_type(func)
with relay.build_config(opt_level=3):
graph, lib, params = relay.build(func, target="llvm")
```
The tuple is now lifted out of subfunction %0.
```
fn (%data: Tensor[(1, 32, 32, 3), float32]) -> (Tensor[(1, 32, 32, 3),
float32], Tensor[(1, 32, 32, 3), float32]) {
%0 = fn (%p0: Tensor[(1, 32, 32, 3), float32], __dict__=meta[StrMap][0]) ->
Tensor[(1, 32, 32, 3), float32] {
log(%p0)
}
%1 = %0(%data)
(%1, %1)
}
```
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