mikepapadim opened a new pull request, #12287:
URL: https://github.com/apache/tvm/pull/12287
Prior implementation caused the FQI pass to produce the following graph when
`abs` was used:
```
%14 = qnn.dequantize(%13, 6.4712f /* ty=float32 */, 119 /* ty=int32 */,
axis=1) /* ty=Tensor[(1, 960, 512, 64), float32] */;
%15 = abs(%14) /* ty=Tensor[(1, 960, 512, 64), float32] */;
%16 = qnn.quantize(%15, 3.46196f /* ty=float32 */, 0 /* ty=int32 */,
out_dtype="uint8", axis=1) /* ty=Tensor[(1, 960, 512, 64), uint8] */;
```
It seems that the output scale and zero point are different from the input
scale and zero point. So, we doubled the precision.
By adding a qnn op for `abs` we manage requantize on the right values.
```
def @main(%x: Tensor[(1, 960, 512, 64), int8] /* ty=Tensor[(1, 960, 512,
64), int8] */) -> Tensor[(1, 960, 512, 64), int8] {
%0 = qnn.abs(%x, 6.4712f /* ty=float32 */, 119 /* ty=int32 */, 6.4712f /*
ty=float32 */, 0 /* ty=int32 */) /* ty=Tensor[(1, 960, 512, 64), int8] */;
qnn.requantize(%0, 6.4712f /* ty=float32 */, 119 /* ty=int32 */, 3.46196f
/* ty=float32 */, 0 /* ty=int32 */, out_dtype="int8") /* ty=Tensor[(1, 960,
512, 64), int8] */
}
```
@sfvaroglu @mbrookhart @AndrewZhaoLuo @anwang2009
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