Rewriting hypothesis_utils (#22830)
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/22830
Separating the tensor generation and the generation of the quantization parameters
- Introducing hypothesis filter `assume_not_overflowing`, which makes sure that the generated tensor and qparams play well with each other. **Note: This is an expensive filter!**
- `qtensor` -> Renameed to `tensor`
- `qtensor_conv` -> Renamed to `tensor_conv2d`
- The tensors don't return the quantization parameters anymore, use `qparams` for it
- The `dtypes` argument is just a quantized dtype now.
- The enforcement for zero_point is predefined as before. As before, if set to `None` the zero_point will be sampled. However, if `None`, you can override sampling with `zero_point_min` and `zero_point_max`
- Scale sampling can also be overriden using `scale_min` and `scale_max`
Reviewed By: jerryzh168
Differential Revision: D16234314
fbshipit-source-id: 5b538a5aa9772b7add4f2ce5eff6fd0decd48f8e
