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Suggestion: High-order accuracy forward model for avoiding inverse crime #36
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Hi again Muhammad,
The finite difference stencil size is unfortunately currently hard
coded. I did an experiment in the past to increase it
(https://github.com/ar4/deepwave/tree/8th-order) but the runtime
increased substantially without seeming to significantly reduce
numerical dispersion, so I abandoned it. As a quick fix to avoid being
an exact inversion crime, perhaps you could reduce the time step size
when generating your data? If that is not good enough, then there are
some other wave propagators to consider for generating data, such as
Devito and DENISE Black Edition. I will look again into making the
finite difference stencil size user controllable in the future.
|
Hi Alan,
Thank you for the suggestion! I will look into it. :)
…On Wed, Sep 29, 2021 at 7:10 PM Alan Richardson ***@***.***> wrote:
Hi again Muhammad,
The finite difference stencil size is unfortunately currently hard
coded. I did an experiment in the past to increase it
(https://github.com/ar4/deepwave/tree/8th-order
<https://urldefense.com/v3/__https://github.com/ar4/deepwave/tree/8th-order__;!!Nmw4Hv0!itwM2KoO2CiDjJGibrgAoHeC8cLKjpmsKY3x3eHWNef2R_qbRt3rUSLS8syMZK9fHDpBppK8Dh-E$>)
but the runtime
increased substantially without seeming to significantly reduce
numerical dispersion, so I abandoned it. As a quick fix to avoid being
an exact inversion crime, perhaps you could reduce the time step size
when generating your data? If that is not good enough, then there are
some other wave propagators to consider for generating data, such as
Devito and DENISE Black Edition. I will look again into making the
finite difference stencil size user controllable in the future.
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Dear Muhammad, I can help you to implement the function if you need it really. Keting. |
Hi Keting,
Thank you so much! I highly really appreciate it! It is indeed important
for my research and I want to use DeepWave for my next project. That would
be great if you could do it. :)
Thank you!
…On Wed, Sep 29, 2021 at 10:37 PM Keting Guo ***@***.***> wrote:
Dear Muhammad,
I can help you to implement the function if you need it really.
Keting.
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Dear Muhammad,
I guess the high order finite difference codes will be achieved a few days later. Are you in a hurry to get the codes? If you want to get them soon, I try to finish them at tomorrow.
Keting
…------------------ 原始邮件 ------------------
发件人: "ar4/deepwave" ***@***.***>;
发送时间: 2021年9月29日(星期三) 晚上11:59
***@***.***>;
***@***.******@***.***>;
主题: Re: [ar4/deepwave] Suggestion: High-order accuracy forward model for avoiding inverse crime (#36)
Hi Keting,
Thank you so much! I highly really appreciate it! It is indeed important
for my research and I want to use DeepWave for my next project. That would
be great if you could do it. :)
Thank you!
On Wed, Sep 29, 2021 at 10:37 PM Keting Guo ***@***.***>
wrote:
> Dear Muhammad,
>
> I can help you to implement the function if you need it really.
>
> Keting.
>
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Dear Keting, Thank you for your kind contribution. I highly appreciate that. No worries, take your time. :) Muhammad |
Hi Muhammad,
You should be able to use the 8th order version that I already
created. Is that sufficient for your need (the normal version uses 4th
order)? I suggest creating a new Python virtual environment for it, so
that it does not conflict with the normal version. If you are not
familiar with creating a new environment, it can be done with Anaconda
(https://docs.conda.io/projects/conda/en/latest/user-guide/tasks/manage-environments.html)
or using venv (https://docs.python.org/3/tutorial/venv.html). Once you
have switched to the new environment, you should then be able to
install the 8th order version using pip install
***@***.***
A bit messy, but hopefully sufficient until there is a more elegant solution
|
Hi Alan and Keting,
Great thanks for the implementation! I really appreciate that, really!
Yes, that's more than enough. May I know, how can I make a choice between
the accuracy orders?
Thank you so much!!
Best regards,
Muhammad
…On Fri, Oct 1, 2021 at 2:04 AM Alan Richardson ***@***.***> wrote:
Hi Muhammad,
You should be able to use the 8th order version that I already
created. Is that sufficient for your need (the normal version uses 4th
order)? I suggest creating a new Python virtual environment for it, so
that it does not conflict with the normal version. If you are not
familiar with creating a new environment, it can be done with Anaconda
(
https://docs.conda.io/projects/conda/en/latest/user-guide/tasks/manage-environments.html
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)
or using venv (https://docs.python.org/3/tutorial/venv.html
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Once you
have switched to the new environment, you should then be able to
install the 8th order version using pip install
***@***.***
A bit messy, but hopefully sufficient until there is a more elegant
solution
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Hi Muhammad,
The "messy" part of my proposal is that to select between the
different orders you need to switch your Python environment (if you
are in the new virtual environment that you installed the 8th order
version of Deepwave in, then it will use the 8th order propagator, but
if you are in the environment where you installed the normal version
of Deepwave, then it will use the 4th order propagator). I
unfortunately do not think it is possible to change between the two
within the same Python session, but maybe you could generate the
forward data using the 8th order one and save it, and then load the
saved data when using the 4th order version.
I will hopefully have time to make a more elegant solution in a month
or two, or maybe Keting will come up with one before then.
|
No worries, Alan. Thank you so much!
I am just a bit confused about the installation. Shall I just create a new
environment and install DeepWave as usual (pip install deepwave)?
…On Fri, Oct 1, 2021 at 2:21 AM Alan Richardson ***@***.***> wrote:
Hi Muhammad,
The "messy" part of my proposal is that to select between the
different orders you need to switch your Python environment (if you
are in the new virtual environment that you installed the 8th order
version of Deepwave in, then it will use the 8th order propagator, but
if you are in the environment where you installed the normal version
of Deepwave, then it will use the 4th order propagator). I
unfortunately do not think it is possible to change between the two
within the same Python session, but maybe you could generate the
forward data using the 8th order one and save it, and then load the
saved data when using the 4th order version.
I will hopefully have time to make a more elegant solution in a month
or two, or maybe Keting will come up with one before then.
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Hi Muhammad,
No, you have to use the command "pip install
***@***.***" so that it will
install the "8th-order" branch that I made.
|
Sorry, Alan. I cannot see the command. Everything converted to stars (censored). Another way to communicate? |
Ah! Apologies. GitHub was overzealous in hiding what it thinks are email addresses! Hopefully it will work this time: pip install git+https://github.com/ar4/deepwave.git@8th-order |
Got it! Done with installation. Thanks a lot! :) |
I also meet the problem yesterday, the recever record is wrong but the wavefield is seem right. I'll test it today to find the solver.
keting
…---Original---
From: "Muhammad ***@***.***>
Date: Fri, Oct 1, 2021 04:32 AM
To: ***@***.***>;
Cc: "Keting ***@***.******@***.***>;
Subject: Re: [ar4/deepwave] Suggestion: High-order accuracy forward model for avoiding inverse crime (#36)
Hi Alan and Keting,
It seems like the 8th order not providing the correct solution. Kindly refer to the figure below for a comparison between 4th order and 8th order data.
.
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That is a pity. I'm sorry.
I haven't managed to reproduce the problem yet, which makes it hard to
identify the cause. Would it be possible for you to provide an example
script where the problem occurs?
|
Hi Alan and Keting, Kindly find the python notebook below to reproduce the error. |
Hi Muhammad,
Did you run on a CPU or GPU?
|
It was on the GPU via Google Colab.
…On Fri, Oct 1, 2021 at 6:02 PM Alan Richardson ***@***.***> wrote:
Hi Muhammad,
Did you run on a CPU or GPU?
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I ran the CPU version and it seemed fine, so it must be a bug in the
GPU one. I am running it now to confirm.
|
I confirm that the problem occurs in the GPU version, and I think I
have already found the bug. I am testing it now.
|
Glad to hear it! Thanks, Alan!
…On Fri, Oct 1, 2021 at 6:18 PM Alan Richardson ***@***.***> wrote:
I confirm that the problem occurs in the GPU version, and I think I
have already found the bug. I am testing it now.
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I have pushed the fix to GitHub, so hopefully it will work if you
reinstall the 8th-order version.
|
Awesome, thanks!
…On Fri, Oct 1, 2021 at 6:27 PM Alan Richardson ***@***.***> wrote:
I have pushed the fix to GitHub, so hopefully it will work if you
reinstall the 8th-order version.
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Dear Muhammad, I have modified the deepwave to make the order of finite difference can be chosen,for example: Keting |
Dear Keting,
Wow, that was fast! Thank you for your work on it.
|
Dear Keting,
That's super fast! Thanks a lot!
Here is my email, hopefully GitHub will not censor it.
muhammad.izzatullah kaust.edu.sa (fill up the missing place :))
…On Fri, Oct 1, 2021 at 8:30 PM Alan Richardson ***@***.***> wrote:
Dear Keting,
Wow, that was fast! Thank you for your work on it.
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Hi Keting,
Yes that address is correct. If so, you can send to my gmail. miutp11
gmail.com.
Thanks a lot!
On Sat, 2 Oct 2021 at 10:28 AM Keting Guo ***@***.***> wrote:
Dear Muhammad,
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Is that your email address? I can't send to this address because some
errors. If that is true address, Do you have another email?
Keting
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Hi Alan and Keting, I want to know, how many CPU cores DeepWave using if we're running on CPU? I'm thinking to perform the wave propagation on a multi-cores CPU and send the results to GPU for another computation. I'm trying to avoid GPU from being out of memory. Do guys have better suggestions or advice? Thanks! |
Hi Muhammad,
On the CPU, Deepwave will use OpenMP's default number of cores. I
think this is normally all of the cores on your computer. You should
be able to control the number of cores that are used with the
OMP_NUM_THREADS environment variable, if you need to. To be sure that
I understand correctly what you want to do, are you able to share a
bit about how this relates to memory usage or running part of the
computation on a GPU?
Have you already tried running Deepwave on one shot at a time?
|
Hi Alan,
I want to run particle filter (EnKF). At the moment, to avoid out of
memory, I ran 10 batches for each particle which equivalent to 3 shots per
batch per one particle size. Since, I send everything to GPU, I need to use
very small number of shots at a time to compute the misfit function. This
turned out to be 4 for loops. To complete one epoch it takes 4 minutes for
30 particle size, 30 shots, and 60 receivers.
On Sat, 2 Oct 2021 at 7:08 PM Alan Richardson ***@***.***> wrote:
Hi Muhammad,
On the CPU, Deepwave will use OpenMP's default number of cores. I
think this is normally all of the cores on your computer. You should
be able to control the number of cores that are used with the
OMP_NUM_THREADS environment variable, if you need to. To be sure that
I understand correctly what you want to do, are you able to share a
bit about how this relates to memory usage or running part of the
computation on a GPU?
Have you already tried running Deepwave on one shot at a time?
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Hi Muhammad,
That is not a topic that I know much about. Are you just running
forward problems, and applying the filters on the output receiver
data, or are you doing FWI and maybe applying the filters on the
gradients, or something else?
At the moment, I think the only easy way of reducing memory usage in
normal FWI applications with Deepwave is to reduce the batch size
(maybe down to just one shot) and then accumulate gradients over
several shots before updating the model. I would like to add the
ability to save snapshots of the forward wavefield to disk and reload
them during backpropagation, rather than storing them all in memory,
but it's complicated to do that in a way that performs well on every
system, especially when running on GPUs. To help me decide whether it
is worth the effort of trying to do it one day, would storing the
forward snapshots like this help for your application?
|
Hi Alan,
You can imagine solving an ensemble of FWI simultaneously one time. For
example, an ensemble has 10 particles (10 initial models), so that we will
perform 10 FWI procedures simultaneously for 10 different initial models.
The gradients from all these 10 models will be used to construct a
preconditioning matrix at each iteration. In this case, every time to
compute the gradient, we need to save the wavefields from all the 10
models. This is the challenge I'm facing, actually. We can discuss this
further on Zoom if you're interested.
I don't know regarding computation on GPU. As far as I know, the strategy
you mentioned is implemented if we are running on the CPU. Honestly, it is
worth trying because it will be helpful for large-scale Deep Learning and
Uncertainty Quantification applications.
…On Sat, Oct 2, 2021 at 9:18 PM Alan Richardson ***@***.***> wrote:
Hi Muhammad,
That is not a topic that I know much about. Are you just running
forward problems, and applying the filters on the output receiver
data, or are you doing FWI and maybe applying the filters on the
gradients, or something else?
At the moment, I think the only easy way of reducing memory usage in
normal FWI applications with Deepwave is to reduce the batch size
(maybe down to just one shot) and then accumulate gradients over
several shots before updating the model. I would like to add the
ability to save snapshots of the forward wavefield to disk and reload
them during backpropagation, rather than storing them all in memory,
but it's complicated to do that in a way that performs well on every
system, especially when running on GPUs. To help me decide whether it
is worth the effort of trying to do it one day, would storing the
forward snapshots like this help for your application?
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Hi Muhammad,
That sounds like an interesting idea. A Zoom call would be good - I'll
send you an email.
|
Hi Muhammad, Thank you again for the suggestion to make it possible for users to select the finite difference order of accuracy. I have finally included this in the master branch. I hope that it works for you. -Alan |
Hi Alan,
I hope to find you well and in the best of health.
I'm wondering if DeepWave has the functionality to change the order of accuracy of the forward problem. Is such functionality available?
This would be a nice improvement since it could help us avoiding the inverse crime in the inversion.
Thanks! :)
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