FrozenGene commented on issue #4828: [QNN][TFLite] TFLite rounding mode support URL: https://github.com/apache/incubator-tvm/pull/4828#issuecomment-583285949 > I thought little more about the bit exact problem. One source of discrepancy for certain is QNN add, and QNN concatenate ops. These call Requantize internally, and they will have default rounding in C++ (UPWARD). For testing, @LiangHao151941 , you can set the C++ default to TFLIte to see if it helps. Meanwhile, we can also think how to can make that C++ rounding visible at python level. Maybe we could use `@register_func` to register one function named as `@register_func(qnn.requantize.rounding)` in TFLite parser, then we could get the value in `RequantizeQnnCanonicalize` from C++. However, this will have one problem that if users specify the rounding value, it won't work, because we don't know whether the rounding value is setting by default or by user explicitly. So, if we support it this, we will constraint we will only one rounding setting by TFLite parser. Another way is we provide `rounding` args for `qnn.add / qnn.mul / qnn.conconcate`, because they use `requantize` in fact too, so they need `rounding`.
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