Calibration Methods

AMD Quark for PyTorch supports the following calibration methods:

• MinMax Calibration Method: The MinMax calibration method for computing the quantization parameters based on the running min and max values. This method uses the tensor min/max statistics to compute the quantization parameters. The module records the running minimum and maximum of incoming tensors and uses these statistics to compute the quantization parameters.

• Percentile Calibration Method: The Percentile calibration method, often used in robust scaling, involves scaling features based on percentile information from a static histogram, rather than using the absolute minimum and maximum values. This method is particularly useful for managing outliers in data.

• MSE Calibration Method: The MSE (Mean Squared Error) calibration method refers to a method where calibration is performed by minimizing the mean squared error between the predicted outputs and the actual outputs. This method is typically used in regression contexts where the goal is to adjust model parameters or data transformations to reduce the average squared difference between estimated values and the true values. MSE calibration helps in refining model accuracy by fine-tuning predictions to be as close as possible to the real data points.