Issue about static quantization (prepare and convert)

quantization
1

Background

I tried to quantize a simple model like ResNet18 (from torchvision), and didn’t like to fuse conv-bn-relu pattern, so I comment the fusing part in convert_fx.py, by which I can get a model like:

image
image925×225 15 KB

(As one can see, the Conv2d modules have been replaced to the referenced ones)

and the graph is like:

image
image1361×247 8.41 KB

(As one can see, the conv-bn-relu is not fused and there also has no observer between them)

Issue

Once I run convert function to convert this prepared_model to quantized one, the graph would be like:

image
image1530×404 16 KB

As expected, the reference convolution would be convert to QuantConv and there would be some nodes like conv1_scale_0 and conv1_zero_point_0, so when acting lower_to_fbgemm() these reference modules would not be convert to QuantConv, is something missing or some configs I set are wrong?

2

The output of the convert is the reference quantized model so you wouldn’t see the actual quantized ops in the graph. I’m not sure exactly what your question is.
Can you share a simple repro (maybe with a Conv+BN+Relu module) along with the quantization API calls you use, that exhibits the same issue you are describing?

cc @jerryzh168

3

I simplified my question as here: Question about prepare_fx - #2 by supriyar

Thanks anyway!