GPU usage zero with Pennylane and PyTorch


I am trying to train two models on a GPU cluster: 1. Hybrid quantum-classical reinforcement learning (RL) model and 2. Classical deep RL model. The model is pretty complex and therefore it’s difficult to share the complete code on this forum. When I train the classical model, the training happens on GPU but, when I train the quantum-classical model, I realized that the GPU usage is 0, thus making each iteration extremely slow. I tried debugging the code to figure out the issue and I realized that the input tensor as well as the parameters of the model (classical and quantum) are on GPU. Following are further details of my implementation. I am using V100-16GB GPU for the

dev = qml.device("default.qubit", wires=4)
# The qnode is created using the following annotation: 
# @qml.qnode(dev, interface='torch')

Please let me know if there are any flags that I should change or any tensor that I should evaluate to figure out the reason why GPU is not being used for quantum simulation. Apologies that I couldn’t share a working code snippet to reproduce the issue.

Output of qml.about()

Name: PennyLane
Version: 0.28.0
Summary: PennyLane is a Python quantum machine learning library by Xanadu Inc.
Home-page: GitHub - PennyLaneAI/pennylane: PennyLane is a cross-platform Python library for differentiable programming of quantum computers. Train a quantum computer the same way as a neural network.
License: Apache License 2.0
Location: /home/sinha/miniconda3/envs/qns/lib/python3.8/site-packages
Requires: appdirs, autograd, autoray, cachetools, networkx, numpy, pennylane-lightning, requests, retworkx, scipy, semantic-version, toml
Required-by: PennyLane-Lightning, PennyLane-Lightning-GPU

Platform info: Linux-5.15.0-1028-nvidia-x86_64-with-glibc2.17
Python version: 3.8.16
Numpy version: 1.22.3
Scipy version: 1.7.3
Installed devices:

  • lightning.gpu (PennyLane-Lightning-GPU-0.30.0)
  • lightning.qubit (PennyLane-Lightning-0.28.1)
  • default.gaussian (PennyLane-0.28.0)
  • default.mixed (PennyLane-0.28.0)
  • default.qubit (PennyLane-0.28.0)
  • default.qubit.autograd (PennyLane-0.28.0)
  • default.qubit.jax (PennyLane-0.28.0)
  • (PennyLane-0.28.0)
  • default.qubit.torch (PennyLane-0.28.0)
  • default.qutrit (PennyLane-0.28.0)
  • null.qubit (PennyLane-0.28.0)

Hi @imakash

Sorry, but we seem to be lacking any information to figure out what exactly is going on here. However, this part of the documentation may be of use to you. If the wire count is small, GPUs are usually much slower. You might be better off using a CPU at these scales, but it might also depend on the code.

Hope this helps!


Hello @Alvaro_Ballon

Thanks for your response. My implementation is actually inspired by the documentation you shared in your answer. I understand that it would be difficult for you to come up with a solution for my problem without the code to reproduce the error. However, if you could suggest some debugging steps to help me out here?

Best Regards,

Hello @imakash

Maybe you could try using one of the lightning devices? If that doesn’t work, the Torch/JAX backprop GPU pipeline could be faster than lightning at those problem sizes. Even so, a CPU is probably your best bet here. I’m sorry, but without more information, these are all the suggestions we can come up with :frowning:



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You can also try 90 core qsim, Best regards. - Kevin

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