inference from a model with different types of layers based on forwar… #989
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Thanks for the PR! This approach unfortunately isn't general. It assumes that T for a given layer is in the 1st indexed dimension, which is architecture dependent.
The idea with forwardWithPadMask is that the padding will be computed for a given sequential, but that the user will resize the mask for each layer depending on its input size and expected dimensions.
You could reimplement this function in your user-space code, or you might consider creating a module that does this resizing for you in a way that you'd expect given a wrapped module (e.g. Conv2D). If you're able to think up a way to do this in a clean way, I'm happy to take a look at an updated PR.
| auto ntwrkSeq = std::dynamic_pointer_cast<fl::Sequential>(ntwrk); | ||
| auto output = input; | ||
| for (auto& module : ntwrkSeq->modules()) { | ||
| auto tr = std::dynamic_pointer_cast<fl::Transformer>(module); | ||
| auto cfr = std::dynamic_pointer_cast<fl::Conformer>(module); | ||
| if (tr != nullptr || cfr != nullptr) { | ||
| /* input dims of Transformer module should be CxTxBx1 */ | ||
| int T = output.dims(1); |
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As mentioned -- this is a big assumption that's architecture and layer dependent. Better to resize the padding mask per layer to fit whatever layers are in the architecture or add some abstraction to automatically do this if interested.
Summary
The function
forwardSequentialModuleWithPadMaskwork for those networks which are built based on justTransformerfamily. If the network has non-Transformerbased layer, e.g. Conv2D, then the length of the input sequence is changed however the length of the sequence is computed based initial length which raise the following runtime error:I compute the length of the sequence based on itself after applying each layer on the input.
Test Plan (required)
Create networks with different combinations of modules
Transformersand non-Transformersand applying the networks on random inputs.