…rch#104130)

Summary:
Pull Request resolved: pytorch#104130

The planned e2e for quantization in pytorch 2.0 export is the following:

float_model -> prepare_pt2e -> calibration -> convert_pt2e -> ...

inside convert_pt2e, we will first produce a q/dq representation of the quantized model, similar to the previous output of
convert_to_reference_fx in fx grah mode quantization:

```
torch.ops.quantized_decomposed.dequantize_per_tensor -> torch.ops.aten.add -> torch.ops.quantized_decomopsed.quantize_per_tensor
torch.ops.quantized_decomposed.dequantize_per_tensor   /
```

Then we'll rewrite the above to a more precise representation that express the intention in a more precise manner, since
here we actually want to do int8 addition, instead of simulating the int8 addition with fp32 operations, the representation for
quantized add is:

```
def quantized_add(x_i8, x_scale, x_zero_point, y_i8, y_scale, y_zero_point, out_scale, out_zero_point):
    x = (x_scale / out_scale) * x_i8
    y = (y_scale / out_scale) * y_i8
    out = x + y
    out -= (x_zero_point * x_scale - y_zero_point * y_scale) / out_scale
    out += out_zero_point
    return out
```

Test Plan:
```
buck2 test caffe2/test:quantization_pt2e -- --exact 'caffe2/test:quantization_pt2e - test_representation_add (quantization.pt2e.test_quantize_pt2e.TestQuantizePT2E)'
```

Reviewed By: kimishpatel

Differential Revision: D45628032

fbshipit-source-id: b59cdd27be4c2c72a0bb1629351eff4542370d31