Add doc for quantizer export_model() (#3473)

docs/en_US/Compression/QuickStart.rst

@@ -110,12 +110,11 @@ Step2. Choose a quantizer and compress the model
 Step3. Export compression result
 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
 
-You can export the quantized model directly by using ``torch.save`` api and the quantized model can be loaded by ``torch.load`` without any extra modification.
+After training and calibration, you can export model weight to a file, and the generated calibration parameters to a file as well. Exporting onnx model is also supported.
 
 .. code-block:: python
 
-   # Save quantized model which is generated by using NNI QAT algorithm
-   torch.save(model.state_dict(), "quantized_model.pth")
+   calibration_config = quantizer.export_model(model_path, calibration_path, onnx_path, input_shape, device)
 
 Plese refer to :githublink:`mnist example <examples/model_compress/quantization/QAT_torch_quantizer.py>` for example code.
 

examples/model_compress/quantization/QAT_torch_quantizer.py

@@ -92,6 +92,14 @@ def main():
         train(model, quantizer, device, train_loader, optimizer)
         test(model, device, test_loader)
 
+    model_path = "mnist_model.pth"
+    calibration_path = "mnist_calibration.pth"
+    onnx_path = "mnist_model.onnx"
+    input_shape = (1, 1, 28, 28)
+    device = torch.device("cuda")
+
+    calibration_config = quantizer.export_model(model_path, calibration_path, onnx_path, input_shape, device)
+    print("Generated calibration config is: ", calibration_config)
 
 if __name__ == '__main__':
     main()