Does lightning.gpu really have acceleration

PennyLane Help
1

I am using lightning.gpu and torchlayer for image classification tasks, but using lightning.gpu seems to have no acceleration effect, and may not even be as good as default.qubit. Why is this?

2

Hey @HotFrog!

It depends on how big your circuit is. Lightning GPU doesn’t offer much of a speedup when you have a small number of qubits because of computational overheads. When you start going to upwards of 20 qubits, you’ll notice a speedup :slight_smile:.

4

when i am using lightning.gpu and pytorch run resnet152 , it is really slow.
when i use default.qubit and cpu (not cuda) ,it is much more faster than lightning.gpu with cuda.
if i just use classic method to train resnet152, it takes me 5 mins on cuda, 1.8 hours on cpu. but with quantum , it takes 16 hours on cuda , 2. 2hours on cpu.
This makes me doubt whether the mixture of quantum and classical networks cannot fully utilize the GPU.

5

Hey @HotFrog, you might want to try using adjoint differentiation instead:

@qml.qnode(dev, diff_method="adjoint")
def circuit(params):
    ...

Let me know if that helps!

6

ok，thank you , i will have a try.
i am wondering that different diff_method will have different result?

7

Great question! All differentiation methods should be very very close to each other in that the results should be nearly identical! The performance, however, will vary :slight_smile:

8

Hello @HotFrog, some benchmarking studies, compatibility depends on the model:

9

Hi @isaacdevlugt,
In my case, I noticed that diff_method="adjoint" took twice as long to run as compared to the default diff_method. Also, the performance seemed to be off by 5-10%.

So it seems to me that diff_method="adjoint" doesn’t always improve performance for lightning.gpu. Can you help me understand in what situations would it prove beneficial?

10

Hi @vishwa, welcome to the Forum!

Could you please share your code and the output of qml.about()?

Also, to be sure I understand your problem, are you doing the comparison between not specifying any diff_method and using diff_method="adjoint"? Or are you doing a different comparison?

diff_method="adjoint" is generally the best method when you’re simulating circuits. If you want to learn how adjoint differentiation works you can take a look at our demo on this!

11

Yes, I was comparing performance when not specifying any diff_method versus specifying diff_method="adjoint"

Here is my output of qml.about()

Name: PennyLane
Version: 0.33.1
Summary: PennyLane is a Python quantum machine learning library by Xanadu Inc.
Home-page: https://github.com/PennyLaneAI/pennylane
Author: 
Author-email: 
License: Apache License 2.0
Location: /home/QC/.venv/lib/python3.10/site-packages
Requires: appdirs, autograd, autoray, cachetools, networkx, numpy, pennylane-lightning, requests, rustworkx, scipy, semantic-version, toml, typing-extensions
Required-by: PennyLane-Lightning, PennyLane-Lightning-GPU

Platform info:           Linux-5.15.133.1-microsoft-standard-WSL2-x86_64-with-glibc2.35
Python version:          3.10.12
Numpy version:           1.26.1
Scipy version:           1.11.3
Installed devices:
- lightning.gpu (PennyLane-Lightning-GPU-0.33.1)
- default.gaussian (PennyLane-0.33.1)
- default.mixed (PennyLane-0.33.1)
- default.qubit (PennyLane-0.33.1)
- default.qubit.autograd (PennyLane-0.33.1)
- default.qubit.jax (PennyLane-0.33.1)
- default.qubit.legacy (PennyLane-0.33.1)
- default.qubit.tf (PennyLane-0.33.1)
- default.qubit.torch (PennyLane-0.33.1)
- default.qutrit (PennyLane-0.33.1)
- null.qubit (PennyLane-0.33.1)
- lightning.qubit (PennyLane-Lightning-0.33.1)

Thanks for sharing the demo! :smile:

12

Thanks for sharing this information @vishwa !

Could you please also share the code that you’re using which exhibits the performance difference that you mentioned before?