[code-reflection] RFR: Onnx subgraphs, lambda execution and BB removal [v3]

[Adam Sotona]

On Wed, 26 Feb 2025 17:19:28 GMT, Paul Sandoz <psandoz at openjdk.org> wrote:

>> Adam Sotona has updated the pull request incrementally with one additional commit since the last revision:
>> 
>>   removal of Tensor::asByteBuffer
>
> cr-examples/onnx/src/test/java/oracle/code/onnx/MNISTDemo.java line 52:
> 
>> 50: 
>> 51:     public static Tensor<Float> cnn(Tensor<Float> inputImage) {
>> 52:         return OnnxRuntime.execute(() -> {
> 
> I think it's preferable to eventually express the following:
> 
> OnnxRuntime.execute(() -> cnn(inputImage));
> 
> i.e. i would like to see that expression embedded in the drawing code.
> 
> This generally requires that we support invocations to other ONNX functions (or we inline when transforming), which we anyway should eventually work out.
> 
> For the case of a single method invocation it should be possible to take a short cut for now (for clues see `LambdaOp.methodReference` which we could refine). So i would recommend following up on the short cut and leaning into that so we can pull out the execute from the `cnn` method.
> 
> With this approach we still need to eventually workout the difference between initializers and inputs. I believe the former is the generally accepted way of representing constant input rather than more directly as constants in the graph.

Initializers are now on top of my ToDo list. They are technically just differently serialized Constant ops. I'm still not sure how we should model them - maybe Initializer op?

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PR Review Comment: https://git.openjdk.org/babylon/pull/326#discussion_r1972146598