QuantizationCalibrationDatasetError
quantization.errors.QuantizationCalibrationDatasetError
Stack trace
Traceback (most recent call last):
File "quantize.py", line 45, in <module>
quantizer.calibrate(calibration_dataset)
File "quantization/quantizer.py", line 102, in calibrate
raise QuantizationCalibrationDatasetError("Invalid calibration dataset provided.")
quantization.errors.QuantizationCalibrationDatasetError: Invalid calibration dataset provided. Why it happens
Quantization requires a representative calibration dataset to estimate activation ranges and scale factors. This error occurs when the dataset is missing, empty, or contains incompatible data types or shapes that the quantizer cannot process. Without a valid calibration dataset, the quantization algorithm cannot correctly calibrate the model's parameters.
Detection
Validate the calibration dataset before quantization by checking its presence, non-emptiness, and compatibility with the model input shape and data types. Log dataset properties and catch QuantizationCalibrationDatasetError to identify issues early.
Causes & fixes
Calibration dataset is empty or None
Ensure the calibration dataset is loaded correctly and contains samples before passing it to the quantizer.
Calibration dataset samples have incorrect input shape or data type
Preprocess the dataset to match the model's expected input shape and data type exactly.
Calibration dataset contains corrupted or invalid data entries
Clean the dataset by removing or fixing corrupted samples to ensure all data is valid for calibration.
Using a dataset incompatible with the quantization library's expected format
Convert or wrap the dataset into the format required by the quantization API, such as a specific tensor type or data loader.
Code: broken vs fixed
from quantization import Quantizer
quantizer = Quantizer(model)
calibration_dataset = None # Missing dataset
quantizer.calibrate(calibration_dataset) # This line raises QuantizationCalibrationDatasetErrorimport os
from quantization import Quantizer
# Load calibration dataset properly
calibration_dataset = load_calibration_data() # Implement this to load valid data
quantizer = Quantizer(model)
quantizer.calibrate(calibration_dataset) # Fixed: dataset is valid and non-empty
print("Calibration successful")Workaround
Catch QuantizationCalibrationDatasetError and fallback to a smaller valid subset of the dataset or use synthetic calibration data temporarily.
Prevention
Integrate dataset validation and preprocessing steps in your quantization pipeline to guarantee calibration data correctness before quantization runs.