Jerry Zhang c98896b76f
[quant][pt2e] Add more precise representation for quantized add ( #104130 ) ...
Summary:
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
Pull Request resolved: https://github.com/pytorch/pytorch/pull/104130
Approved by: https://github.com/kimishpatel
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add Half support for logsigmoid, threshold, elu, gelu, hardtanh, hardsigmoid, hardswish, hardshrink, softshrink, leakyrelu, softplus, glu, silu, mish, and prelu on CPU ( #98745 )
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c98896b76f