Argmax Softmax

[bo3z]

A# Description

:memo: A new implementation of Softmax activation for Vivado and Quartus

• Softmax is a monotonically increasing function; therefore, in classification problems, the output is equivalent to the largest output before Softmax activation (i.e. Softmax
• Default implementation is still stable, as we are sometimes interested in the normalized output probability.

Two implementations are added:

• ArgMax - Returns a one-hot encoded vector. This would be set through the hls4ml config, so for example hls_config['LayerName']['softmax']['strategy'] = 'argmax' (very similar to what we do now with stable and latency implementations of Softmax).
• Logits - Removes the Softmax layer. Again handled through hls4ml config, through an optional boolean attribute skip (defaults to false), so for example: hls_config['LayerName']['softmax']['skip'] = True. There would be an optimizer that removes the Softmax node from the model graph and rewires the network.

Type of change

• [x] New feature (non-breaking change which adds functionality)

Tests

• Expanded test/pytest/test_softmax.py with new implementation

Checklist

• [x] I have read the guidelines for contributing.
• [] I have commented my code, particularly in hard-to-understand areas.
• [] I have made corresponding changes to the documentation.
• [x] My changes generate no new warnings.
• [x] I have added tests that prove my fix is effective or that my feature works.

[thesps]

I like the idea, but I think the implementation could be handled differently.

Am I right thinking that this "strategy argmax" effectively replaces the softmax function with a linear activation? If so, this feels like something that should be done with an optimizer pass rather than in the HLS, i.e. one that looks for a softmax at the end of the model and deletes it. And it's confusing to still call this softmax. From the description I also expected a layer that returns the actual argmax value, I think that would be nice. There you'd need some HLS to do that function, and I'd say also an optimizer pass to insert it. We could even implement a Python 'argmax layer' for Keras, such that users can add it to the model at the end to explicitly say that this is how the model should be converted.

I think also it would not only be softmax that can be replaced in this way, but any activation function that meets the criteria you described (which is most of them).

[jmduarte]

I agree with Sioni's comments and I would add there could be two different optimizer passes depending on the choice of strategy:

• "argmax" -> replaces softmax with an argmax layer (returning either a 1-hot encoded vector or the max index)
• "logits" -> just removes softmax layer entirely and returns the logits

[bo3z]

I agree with the comments, the reason I didn't remove the Softmax layer completely is because in the test we have a simple one-layer network, which if removed, wouldn't really work. @thesps you're right, Softmax is essentially replaced with linear activation in this PR. My proposed change is then two "implementations":

1. ArgMax - Returns a one-hot encoded vector. This would be set through the hls4ml config, so for example hls_config['LayerName']['softmax']['strategy'] = 'argmax' (very similar to what we do now with stable and latency implementations of Softmax). I would still handle this implementation through HLS, as it acts as a (rough) approximation of Sofmtax. I would avoid only returning the index of ArgMax, as it changes the dimensionality of the output (can be done, but doesn't feel correct)
2. Logits - Removes the Softmax layer. Again handled through hls4ml config, through an optional boolean attribute skip (defaults to false), so for example: hls_config['LayerName']['softmax']['skip'] = True. There would be an optimizer that removes the Softmax node from the model graph and rewires the network.

I would avoid having a custom Keras layer, for two reasons:

1. Users might not be aware of our implementation (we should include it in the documentation & tutorials) and they might already have a pre-trained model using Keras Softmax (this is a very weak point, as replacing a Keras layer with a custom one is a few lines of code)
2. hls4ml is not Keras specific, there are converters for PyTorch and ONNX as well

[thesps]

Apologies for the late followup review. This looks really nice now, all these implemented options are useful. The problems now are:

• some tests fail, including ones testing the new feature. Could you look into that?
• there are some merge conflicts, although they look easy enough to resolve

[vloncar]

Since Benjamin is back at university now, I resolved the two issues, can you merge now @thesps ?