How to generate calib.table file while generating int8 engine file

AI & Data Science Deep Learning (Training & Inference) TensorRT
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1

Can you please let us know how to generate calib.table while generating int8 quantized engine…

int8-calib-file=calib.table.

Thank you

4

To generate a calibration table (calib.table) for INT8 quantization while creating a TensorRT engine, you can follow these detailed steps:

Steps to Generate Calibration Table for INT8 Quantization:

  1. Prepare a Calibration Dataset:

    • Gather a representative dataset that reflects the kind of data your model will see during inference. This dataset should cover the full range of values your model is expected to process.

  2. Create a Calibration Script:

    • Implement a calibration script using TensorRT’s calibration API. This script will load your model and the calibration dataset, run inference on the data, and generate the calibration table.

    Here’s a high-level outline of what the calibration script might look like:

    import tensorrt as trt
import numpy as np

class MyCalibrator(trt.IInt8Calibrator):
    def __init__(self, calibration_data):
        super(MyCalibrator, self).__init__()
        self.calibration_data = calibration_data
        self.current_index = 0

    def get_batch_size(self):
        return 1  # Adjust based on your setup

    def get_batch(self, inputs, sizes):
        if self.current_index >= len(self.calibration_data):
            return None

        # Load your calibration data here
        d_input = self.calibration_data[self.current_index]
        inputs[0] = d_input
        self.current_index += 1
        return inputs

    def set_calibration_table(self, table):
        # Save the calibration table to a file
        with open("calib.table", "wb") as f:
            f.write(table)

def main():
    # Load your ONNX model
    # ...

    # Prepare calibration data
    calibration_data = [...]  # Load your calibration dataset here

    calibrator = MyCalibrator(calibration_data)
    
    # Build INT8 engine
    builder = trt.Builder(TRT_LOGGER)
    builder.int8_mode = True
    builder.int8_calibrator = calibrator

    # Create the engine
    engine = builder.build_cuda_engine(network)

    # Serialize and save the engine
    with open("model_int8.engine", "wb") as f:
        f.write(engine.serialize())

  3. Update the Builder with the Calibrator:

    • While building the TensorRT engine, specify the calibrator to the builder using the builder.int8_calibrator method. This instructs TensorRT to use your calibrator during the engine construction.

  4. Run the Calibration:

    • Execute the calibration script to run inference on your calibration dataset. During this step, TensorRT will collect activation statistics and create the calib.table file, which contains the necessary scaling information for quantization.

  5. Build the INT8 Engine:

    • After generating the calibration table, you can build your INT8 engine, which will use calib.table to quantize weights and activations accurately.

Example Command to Build INT8 Engine:

Once you have your calib.table, you can build your INT8 engine with a command like this (using trtexec):

trtexec --onnx=model.onnx --int8 --int8-calib-file=calib.table --saveEngine=model_int8.engine

By following these steps, you should be able to generate a calibration table for your model and create an optimized INT8 TensorRT engine suitable for deployment. If you encounter any errors during this process, please provide the error message for further assistance.