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[quant] Implement PTQ for APoT FakeQuant (#81040)
### Summary:
This PR implements PTQ for APoT FakeQuant. It runs models (Resnet-18 pre-trained model, ImageNet dataset) to compare accuracy metrics for different qconfig settings of uniform vs. APoT quantized activation and weight.
According to the collected accuracy stats, model #2 (uniform activation and APoT weight) appears to have a slight improvement in accuracy compared to model #1 (uniform activation and uniform weight) for 8-bit and significant improvement for 4-bit (see "Accuracy Stats" section below).
### Test Plan:
Run models with: `python test/quantization/core/experimental/fx_graph_mode_apot.py`
### Accuracy Stats:
8-bit (Uniform int8, APoT b = 8 k = 2)
**Model #1:** Uniform activation, uniform weight (FX Graph Mode quantized)
Evaluation accuracy on test dataset: 64.43% (Top-1), 85.62% (Top-5)
**Model #2:** Uniform activation, APoT weight (FX Graph Mode quantized)
Evaluation accuracy on test dataset: 64.51% (Top-1), 85.78% (Top-5)
**Model #3:** APoT activation, APoT weight (FX Graph Mode quantized)
Evaluation accuracy on test dataset: 64.32% (Top-1), 85.78% (Top-5)
4-bit (Uniform int4, APoT b = 4 k = 2)
**Model #1:** Uniform activation, uniform weight (FX Graph Mode quantized)
Evaluation accuracy on test dataset: 45.63% (Top-1), 71.96% (Top-5)
**Model #2:** Uniform activation, APoT weight (FX Graph Mode quantized)
Evaluation accuracy on test dataset: 64.24% (Top-1), 85.56% (Top-5)
**Model #3:** APoT activation, APoT weight (FX Graph Mode quantized)
Evaluation accuracy on test dataset: 45.40% (Top-1), 76.21% (Top-5)
**Full Precision model (FX Graph Mode quantized)**
Evaluation accuracy on test dataset: 69.76% (Top-1), 89.08% (Top-5)
**Eager mode quantized model**
Evaluation accuracy on test dataset: 69.49% (Top-1), 88.90% (Top-5)
Pull Request resolved: https://github.com/pytorch/pytorch/pull/81040
Approved by: https://github.com/jerryzh168
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2022-07-28 07:21:31 +00:00 |
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