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Model Explorer: intuitive and hierarchical visualization of model graphs

270 points by antognini 2y ago

anvuong 2y ago
Looks cool but seems like it doesn't work on torch 2.0 "AttributeError: module 'torch' has no attribute 'export'" The torch.export API is currently in active development with planned breaking changes. The installation guide for this is still very minimal, anyone knows how to get it working on torch 2.0?
- frontierkodiak 2y ago
  I haven't managed to successfully export my custom ViT model yet, but I've not had an issue accessing the export methods in torch 2.3 within the nvcr.io/nvidia/pytorch:24.02-py3 container. I may have some more time to debug my trace tonight (i.e. remove conditionals from model + make sure everything is on CPU) and will update if I have any new insights. ``` from torch.export import export ... example_args = (dummy_input) exported_program = export(model, args=example_args) ``` Links: - torch.export docs: https://pytorch.org/docs/stable/export.html#serialization https://pytorch.org/docs/stable/export.html#serialization - Using 24.02 container: https://docs.nvidia.com/deeplearning/frameworks/pytorch-release-notes/rel-24-02.html https://docs.nvidia.com/deeplearning/frameworks/pytorch-rele...
swaptr 2y ago
https://github.com/lutzroeder/netron https://github.com/lutzroeder/netron https://netron.app/ https://netron.app/
joaquincabezas 2y ago
I normally use Netron for quickly inspecting models and making this 'mental picture' of the architecture, but this hierarchical approach seems a better fit for my needs. I'm just starting and the first impression is pretty good!
spydum 2y ago
Not to be confused with Microsoft Edge, which replaced Internet Explorer with Chromium (the browser that Google Chrome is based on). /s
deleted 2y ago
[deleted]
brrrrrm 2y ago
I've never really understood the point of these visualizer things. The idea that a model is always well represented by a directed acyclic graph seems extremely dated. I really would love a PyTorch/JAX profiler that shows, in annotated Python, where your code is allocating memory, using compute or doing device copies.
- lamename 2y ago
  It may be that the way you like to think about things and the way others like to are different. I find that quickly grasping a new architecture is easiest with a graph-based diagram first. Then code for details. All with the goal of internalizing the information processing steps. Not memory allocation per se. In my mind, how the network implementation allocates memory is a different question. But I think both of our desires just reflect our jobs, our interests, and simply how our brains conceptualize things differently.
  - brrrrrm 2y ago
    I think it's a trap of visual elegance. When you start thinking of models this way you miss the way a lot of models are actually written. E.g. how do you represent an online fine-tuning process? I want to randomly switch between a reference impl and an approximation method, but when using the approx method I want to back-propagate so that it gets better over time. full disclosure: I've written plenty of these little visualizers and also fallen for the trap of "everything should be a declarative graph."
- gedy 2y ago
  Not mocking you, but I'd make a guess you don't like to draw block diagrams when discussing designs or code architecture with others either? Some folks aren't "visual" thinkers, and took me a long time working to realize that some folks are like that.
- almostgotcaught 2y ago
  Lol I interned inside pytorch a few years ago (you and I even met/talked about tangential things :)) and worked on tracking such allocations (although I didn't hook it up to profiler). Spoiler alert: you can't track such provenance because everything gets muddled in the dispatcher. EDIT: not completely accurate to say you can't do it. I prototyped a little allocator that would stamp every allocation (the pointer itself, in the unused bits, a trick I learned from zach) with the thread id and a timestamp (just an incrementing counter) and then percolate that up to the surface. Obv that didn't land lol.
fabmilo 2y ago
These tools are eye candy and have been around from tensorflow/tensorboard 0.x 10 years ago but never used after just trying them for fun. You need to read the source code no easy way around it.
pdevr 2y ago
Other than visualizing, creating custom nodes seems to be the most interesting available operation (at the time of writing this). API Guide: https://github.com/google-ai-edge/model-explorer/wiki/4.-API-Guide https://github.com/google-ai-edge/model-explorer/wiki/4.-API... Custom Nodes - User Guide: https://github.com/google-ai-edge/model-explorer/wiki/2.-User-Guide#custom-node-data https://github.com/google-ai-edge/model-explorer/wiki/2.-Use...
Veuxdo 2y ago
What is the "AI" part of this...?
- akomtu 2y ago
  Marketing & SEO.
- minimaxir 2y ago
  It's an "AI Model" Explorer, not an AI "Model Explorer."
- Veuxdo 2y ago
  Also, what is the "Google" part of this?
  - nashashmi 2y ago
    And why is this referring to MS Edge?
  - minimaxir 2y ago
    It's owned and operated by Google.
    - deleted 2y ago
      [deleted]
toddmorey 2y ago
Is it becoming more and more common to launch open software like this without accepting contributions?
- deleted 2y ago
  [deleted]
kleiba 2y ago
I'm confused - so it runs only in Edge or what's with the name?!
- inhumantsar 2y ago
  the team is building tools to help run models on edge devices. embedded, mobile, laptops, etc.
  - kleiba 2y ago
    Okay, thanks for the explanation!
tomrod 2y ago
Looks like they have moved to https://ai.google.dev/edge/model-explorer https://ai.google.dev/edge/model-explorer
- dang 2y ago
  I'm not sure which link is more up to date, but since this one appears to give more background, we've switched to it from https://github.com/google-ai-edge/model-explorer https://github.com/google-ai-edge/model-explorer above. Thanks!
  - tomrod 2y ago
    You bet! They reference the github repo, but more info is in the link you moved it to.
westurner 2y ago
google-ai-edge/model-explorer//example_colabs/quick_start.ipynb: https://github.com/google-ai-edge/model-explorer/blob/main/example_colabs/quick_start.ipynb https://github.com/google-ai-edge/model-explorer/blob/main/e... XAI: Explainable AI: https://en.wikipedia.org/wiki/Explainable_artificial_intelligence https://en.wikipedia.org/wiki/Explainable_artificial_intelli...
germanjoey 2y ago
Is there a demo of a model visualized using this somewhere? Even if it's just a short video... it's hard to tell what it's like from screenshots.

270 points by antognini 2y ago

Looks cool but seems like it doesn't work on torch 2.0 "AttributeError: module 'torch' has no attribute 'export'" The torch.export API is currently in active development with planned breaking changes. The installation guide for this is still very minimal, anyone knows how to get it working on torch 2.0?

frontierkodiak 2y ago
I haven't managed to successfully export my custom ViT model yet, but I've not had an issue accessing the export methods in torch 2.3 within the nvcr.io/nvidia/pytorch:24.02-py3 container. I may have some more time to debug my trace tonight (i.e. remove conditionals from model + make sure everything is on CPU) and will update if I have any new insights. ``` from torch.export import export ... example_args = (dummy_input) exported_program = export(model, args=example_args) ``` Links: - torch.export docs: https://pytorch.org/docs/stable/export.html#serialization https://pytorch.org/docs/stable/export.html#serialization - Using 24.02 container: https://docs.nvidia.com/deeplearning/frameworks/pytorch-release-notes/rel-24-02.html https://docs.nvidia.com/deeplearning/frameworks/pytorch-rele...

swaptr 2y ago

https://github.com/lutzroeder/netron https://github.com/lutzroeder/netron https://netron.app/ https://netron.app/

joaquincabezas 2y ago

I normally use Netron for quickly inspecting models and making this 'mental picture' of the architecture, but this hierarchical approach seems a better fit for my needs. I'm just starting and the first impression is pretty good!

spydum 2y ago

Not to be confused with Microsoft Edge, which replaced Internet Explorer with Chromium (the browser that Google Chrome is based on). /s

deleted 2y ago

[deleted]

brrrrrm 2y ago

I've never really understood the point of these visualizer things. The idea that a model is always well represented by a directed acyclic graph seems extremely dated. I really would love a PyTorch/JAX profiler that shows, in annotated Python, where your code is allocating memory, using compute or doing device copies.

lamename 2y ago
It may be that the way you like to think about things and the way others like to are different. I find that quickly grasping a new architecture is easiest with a graph-based diagram first. Then code for details. All with the goal of internalizing the information processing steps. Not memory allocation per se. In my mind, how the network implementation allocates memory is a different question. But I think both of our desires just reflect our jobs, our interests, and simply how our brains conceptualize things differently.
- brrrrrm 2y ago
  I think it's a trap of visual elegance. When you start thinking of models this way you miss the way a lot of models are actually written. E.g. how do you represent an online fine-tuning process? I want to randomly switch between a reference impl and an approximation method, but when using the approx method I want to back-propagate so that it gets better over time. full disclosure: I've written plenty of these little visualizers and also fallen for the trap of "everything should be a declarative graph."
gedy 2y ago
Not mocking you, but I'd make a guess you don't like to draw block diagrams when discussing designs or code architecture with others either? Some folks aren't "visual" thinkers, and took me a long time working to realize that some folks are like that.
almostgotcaught 2y ago
Lol I interned inside pytorch a few years ago (you and I even met/talked about tangential things :)) and worked on tracking such allocations (although I didn't hook it up to profiler). Spoiler alert: you can't track such provenance because everything gets muddled in the dispatcher. EDIT: not completely accurate to say you can't do it. I prototyped a little allocator that would stamp every allocation (the pointer itself, in the unused bits, a trick I learned from zach) with the thread id and a timestamp (just an incrementing counter) and then percolate that up to the surface. Obv that didn't land lol.

fabmilo 2y ago

These tools are eye candy and have been around from tensorflow/tensorboard 0.x 10 years ago but never used after just trying them for fun. You need to read the source code no easy way around it.

pdevr 2y ago

Other than visualizing, creating custom nodes seems to be the most interesting available operation (at the time of writing this). API Guide: https://github.com/google-ai-edge/model-explorer/wiki/4.-API-Guide https://github.com/google-ai-edge/model-explorer/wiki/4.-API... Custom Nodes - User Guide: https://github.com/google-ai-edge/model-explorer/wiki/2.-User-Guide#custom-node-data https://github.com/google-ai-edge/model-explorer/wiki/2.-Use...

Veuxdo 2y ago

What is the "AI" part of this...?

akomtu 2y ago
Marketing & SEO.
minimaxir 2y ago
It's an "AI Model" Explorer, not an AI "Model Explorer."
Veuxdo 2y ago
Also, what is the "Google" part of this?
- nashashmi 2y ago
  And why is this referring to MS Edge?
- minimaxir 2y ago
  It's owned and operated by Google.
  - deleted 2y ago
    [deleted]

toddmorey 2y ago

Is it becoming more and more common to launch open software like this without accepting contributions?

deleted 2y ago
[deleted]

kleiba 2y ago

I'm confused - so it runs only in Edge or what's with the name?!

inhumantsar 2y ago
the team is building tools to help run models on edge devices. embedded, mobile, laptops, etc.
- kleiba 2y ago
  Okay, thanks for the explanation!

tomrod 2y ago

Looks like they have moved to https://ai.google.dev/edge/model-explorer https://ai.google.dev/edge/model-explorer

dang 2y ago
I'm not sure which link is more up to date, but since this one appears to give more background, we've switched to it from https://github.com/google-ai-edge/model-explorer https://github.com/google-ai-edge/model-explorer above. Thanks!
- tomrod 2y ago
  You bet! They reference the github repo, but more info is in the link you moved it to.

westurner 2y ago

google-ai-edge/model-explorer//example_colabs/quick_start.ipynb: https://github.com/google-ai-edge/model-explorer/blob/main/example_colabs/quick_start.ipynb https://github.com/google-ai-edge/model-explorer/blob/main/e... XAI: Explainable AI: https://en.wikipedia.org/wiki/Explainable_artificial_intelligence https://en.wikipedia.org/wiki/Explainable_artificial_intelli...

germanjoey 2y ago

Is there a demo of a model visualized using this somewhere? Even if it's just a short video... it's hard to tell what it's like from screenshots.