Question about onnx graph generation for differentiable QPLayer #390

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1. jcarpent commented on Apr 5, 2025

   

   jcarpent

   on Apr 5, 2025

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   Basically avoids dynamic control flow, so that the solver iterations can be unrolled easily?

   Why do you want to unfold if you can compute forward and backward derivatives analytically?
2. gulshan216 commented on Apr 7, 2025

   

   gulshan216

   on Apr 7, 2025

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   My main question is around if a model containing QPLayer would work with ONNX? We need to be able to convert the model to ONNX format primarily for inference. I tried exporting the model in the examples/python/qplayer_sudoku.py example to ONNX by the adding the following snippet of code below, but the exported model onnx_model.graph.input is always empty.

   My initial intuition was that the QPLayer may not be supporting ONNX conversion, since ONNX is a static graph representation that may require the underlying QP solver iterations to be unfolded, but I may be wrong?

       model.eval()
       # Create a dummy input (batch size 1)
       dummy_input = torch.randn(1, trainX.size(1), trainX.size(2), trainX.size(3))
   
       import torch.onnx
       onnx_filename = "qplayer_sudoku.onnx"
   
       # Export the model
       torch.onnx.export(
           model,               # The PyTorch model
           dummy_input,         # The dummy input to trace the model
           onnx_filename,       # The output ONNX file path
           export_params=True,  # Export the model parameters (weights)
           opset_version=12,    # ONNX opset version (choose an appropriate version)
           do_constant_folding=True,  # Apply optimizations (constant folding)
           input_names=['input'],     # Input name (useful for later inference)
           output_names=['output'],   # Output name
       )
   
       import onnx
   
       # Load the ONNX model
       onnx_model = onnx.load("qplayer_sudoku.onnx")
       # Iterate over input nodes to check their details
       for input_tensor in onnx_model.graph.input:
           print(f"Input Name: {input_tensor.name}")
           print(f"Input Type: {input_tensor.type}")
           for dim in input_tensor.type.tensor_type.shape.dim:
               print(f"Dimension: {dim.dim_value}")
   
3. gulshan216 commented on Apr 7, 2025

   

   gulshan216

   on Apr 7, 2025

   Last edited by gulshan216

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   If QPLayer doesn't support ONNX conversion, my less than ideal alternative would be to skip the QPLayer during the ONNX graph conversion by splitting the model into 2 sub-graphs. And replace the QPLayer with the same formulation (with same solver settings) running using the proxqp c++ interface at inference time. But the manual burden of maintaining the python QP formulation and C++ formulation with proxqp in sync would be bad. Really appreciate your inputs here!