ORTOptimizer for the model type Segformer

[zachmayer]

What does this PR do?

Adds the segformer model to ORTOptimizer. Based on the advice I got in #1761, but I decided to start with segformer.

Fixes # (issue)

Before submitting

• This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case).
• Did you make sure to update the documentation with your changes?
• Did you write any new necessary tests?

Who can review?

@mht-sharma maybe?

[IlyasMoutawwakil]

Thanks for the PR! I left few comments.

[zachmayer]

@mht-sharma @IlyasMoutawwakil the optimizer is definitely doing something to the model. I tested it on the vikp/surya_layout segformer, where I exported the original model to onnx, optimized it, and then quantized the optimized model and counted the number of nodes in the graph:

Original model graph: 2900
Optimized model graph: 1263
Quantized model graph: 1709

The optimizer definitely prunes nodes from the graph and the resulting model is faster for inference when I test it.

[zachmayer]

I tried 3 ways of handling the lists:

• Sum
• Max
• Replace the list with 0 (and let microsoft's optimizer infer based on the graph)

Sum/Max yield pretty similar results, so I went with sum. 0 did not seem to work well.

The tests pass on the PR now, and when I test this optimizer on a real segformer, it definitely makes changes to the model graph.

[zachmayer]

@mht-sharma @IlyasMoutawwakil what do you think? The tests pass when I run them locally, and the optimizer seems to be able to reduce the size of the model a lot. (Almost 60%)

[IlyasMoutawwakil]

@zachmayer to visualize the graphs you can use https://netron.app/
I have ran your code on vikp/surya_layout and sum gives the optimizer the wrong hidden_size/num_attention_heads, which are then ignored in favor of the detected values (from last encoder block)

Optimizing model...
symbolic shape inference disabled or failed.
symbolic shape inference disabled or failed.
--num_heads is 16. Detected value is 8. Using detected value.
--hidden_size is 1024. Detected value is 512. Using detected value.

which are the max values (or last in the lists).
also comparing these two (simple export vs O2 using max).

• https://netron.app/?url=https://huggingface.co/IlyasMoutawwakil/segformers/blob/main/onnx_surya/model.onnx
• https://netron.app/?url=https://huggingface.co/IlyasMoutawwakil/segformers/blob/main/onnx_surya_O2/model.onnx

I don't see any optimizations, @mht-sharma any idea which operators we should be looking for ?

[zachmayer]

huh. I also tried using 0, which the docs said would infer the number of heads based on the model graph.

I changed from sum to max and the values seem correct now: 8 for num_heads and 512 for hidden_size.

In my testing the optimize model definitely has a smaller graph and faster inference. So the optimizer is doing something to the model.

[zachmayer]

@IlyasMoutawwakil I changed the normalized config to use 0 instead of the max. How's it look now?

[IlyasMoutawwakil]

LGTM ! sorry for the late reply, and thanks @zachmayer

[HuggingFaceDocBuilderDev]

The docs for this PR live here. All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.

[zachmayer]

Awesome! No worries and thank you!

[zachmayer]

Hmm looks like I have some tests to fix

[zachmayer]

Looks like at least on test failed because of dependencies which is odd.

Let me rebase off the main branch and see what happens

[IlyasMoutawwakil]

the dep failures are not from this PR, pypi has some issues on some plateforms. merging 🤗

[zachmayer]

awesome! Thank you for the feedback and for helping me get this over the line!