Distributed (multi-GPU) execution

[lnnnn123]

Hello, Professor. I tried to run the elastic wave equation on multiple Gpus, but I encountered some problems. In the attachment is my modified code, but from the results, there are two horizontal lines dividing the speed, and the gradient is not updated. Another problem is that I found that the results of a single shot fire save file and a second shot fire update gradient are relatively poor. There is also coupling between multiple parameters, and I want to know how it works.

Thanks 多参数问题.pdf

[ar4]

Hello,

In Deepwave parallel processing is done by splitting the shot dimension over devices. In your example it looks like there is an attempt to somehow instead split the spatial domain of the model over devices. Is your code similar to the one in the example https://ausargeo.com/deepwave/example_distributed ? If so, please send me your whole code so that I can see what is causing the problem.

Coupling between the parameters in elastic propagation is normal and expected. You may parameterise the model differently, if you wish. You need to pass lambda, mu, and buoyancy as the inputs to Deepwave, but the parameters that you actually optimise can be chosen by you (you just need to convert them into lambda, mu, and buoyancy before passing them to Deepwave).

[lnnnn123]

Hello,

This is my code, the first is a single gun FWI program, the second is the result of running on multiple Gpus.

Thank you very much for taking the time to answer my question.

At 2024-06-06 18:06:20, "Alan Richardson" @.***> wrote:

Hello,

In Deepwave parallel processing is done by splitting the shot dimension over devices. In your example it looks like there is an attempt to somehow instead split the spatial domain of the model over devices. Is your code similar to the one in the example https://ausargeo.com/deepwave/example_distributed ? If so, please send me your whole code so that I can see what is causing the problem.

Coupling between the parameters in elastic propagation is normal and expected. You may parameterise the model differently, if you wish. You need to pass lambda, mu, and buoyancy as the inputs to Deepwave, but the parameters that you actually optimise can be chosen by you (you just need to convert them into lambda, mu, and buoyancy before passing them to Deepwave).

— Reply to this email directly, view it on GitHub, or unsubscribe. You are receiving this because you authored the thread.Message ID: @.***>

[ar4]

Your code unfortunately does not seem to have come through. Perhaps if you reply using the GitHub website you will be able to attach it.

[lnnnn123]

This is my code, because there are too many, I converted it into a file, thank you for your correction. elatasic.txt

[ar4]

Thank you for your code. It looks reasonable, but I do not have multiple GPUs to test it on. Do you still have the problem you mentioned in your first post, with the strange horizontal lines in the optimised results? If so, I suggest first simplifying your code to make it easier to identify the problem by changing to use the code that you get by modifying the Deepwave elastic example as little as possible to run it on multiple GPUs (so just create a Prop module and apply DataParallel to it). Please let me know what the result is. I am sorry that this is causing trouble for you.

[lnnnn123]

Thanks for your guidance, after my testing, I finally found the reason, I ran the program on four Gpus at the same time, one GPU could run, but it could not conduct the gradient well, resulting in the loss of inversion results. This is the result of my latest run, obviously it can conduct the gradient very well, but it still appears two lines, I guess it should be multiple GPU conduction gradient, not as correct as one GPU conduction, I don't know what you think? test.pdf

[ar4]

Thank you for the update. I am glad to hear that you made progress. That result does look better than before, but there is still something strange happening. I can't imagine what is causing it to seemingly only update the model in the middle depths. The result when you run on multiple GPUs should be almost identical to running on a single GPU. Unfortunately, since I do not have multiple GPUs, it is hard for me to debug it, but would you mind sending me your latest code anyway, so that I can check to see if I can find anything that might be causing this behaviour?

[lnnnn123]

This is my code, the main change is to run the code on 0,1,3 Gpus, welcome your correction. elatasic.txt

[ar4]

I think I see the problem. In Deepwave's DataParallel example (https://github.com/ar4/deepwave/blob/master/docs/example_distributed_dp.py) the models are passed to the constructor of the propagator object, not when the propagator is being applied. This is because PyTorch's DataParallel divides input Tensors between GPUs on the specified dimension (0 by default), so if you pass the models when the propagator is applied the models will also be divided among the GPUs on this dimension. By passing them to the constructor instead, you avoid this.

So, I suggest that you modify your Prop class to take vp, vs, and rho as inputs in __init__ rather than passing them when applying the propagator:

class Prop(torch.nn.Module):

    def __init__(self, dx, dt, freq, vp, vs, rho):
        super().__init__()

        self.dx = dx
        self.dt = dt
        self.freq = freq
        self.vp = vp
        self.vs = vs
        self.rho = rho

    def forward(self, source_amplitudes, source_locations, receiver_locations):
        out = elastic(
            *deepwave.common.vpvsrho_to_lambmubuoyancy(self.vp, self.vs,
                                                       self.rho),
            self.dx,
            self.dt,
            source_amplitudes_y=source_amplitudes,
            source_locations_y=source_locations,
            receiver_locations_y=receiver_locations,
            pml_freq=self.freq,
        )
        return out[-2]

I hope that will resolve the problem you encountered, but note that PyTorch's documentation recommends using DistributedDataParallel rather than DataParallel for better performance. Here is an example of using it with Deepwave: https://github.com/ar4/deepwave/blob/master/docs/example_distributed_ddp.py

[lnnnn123]

Thank you for your guidance. My multi GPU running program is running correctly. The gradient can be conducted correctly.

[ar4]

That's wonderful news. Thank you for letting me know. I am glad that I was able to help.

[ar4]

I hope this Issue is resolved, so I am going to close it. Please feel free to reopen it if you have further questions about this.