[tmva][sofie] Several Fixes for parsing complex models #22150
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lmoneta
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May 5, 2026
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- After the changes for CLAD the mlpf modek could not be parsed anymore. Handle now correctly the variable defining the number of non zero elements coming from Non_Zero
- Fixes also TMVA::SOFIE::Copy for different types than float making it a template function
- Add also output shape definition in generated code as it is done for the input
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Thanks you very much! Looks good to me once the test is fixed.
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Actually I remember that the RModel has a non-zero class version, and we can't add new non-transient data members without incrementing the class version.
Does the RModel actually need to be serializable?
Same discussion as here:
Test Results 22 files 22 suites 3d 13h 27m 39s ⏱️ For more details on these failures, see this check. Results for commit 9e9a991. ♻️ This comment has been updated with latest results. |
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guitargeek
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Thank you very much for the fixes!
- After the changes for CLAD the mlpf modek could not be parsed anymore. Handle now correctly the variable defining the number of non zero elements coming from Non_Zero - Fixes also TMVA::SOFIE::Copy for different types than float making it a template function - Add also output shape definition in generated code as it is done for the input
The casting to bool was incorrect since it was done a cast to uint8. Fix also the special case of NonZero dynamic parameter which is defindef by NonZero operator. Add at the end a Session data member for the parameter which is then used in creating the output vector Fix a bug introduced in softmax generated code in the generic case Fix the writing of the data in initializer lists for uint8_t types Add correctly new version in RModel.hxx (version 4)
- Fix Where for initialized and Shape tensors. New impelmentation was not taking into account the Shape tensors. This caused a failure to parse the ATLAS Gnn tracking model - Fix Slice for trivial copying. Use now std::copy since we cannot use alias tensor anymore after the change of using a free function with a const Session - Avoid printing tensor names in the comment of Softmax generated code. There is a issue in the function RModel::CollectTensorMemberNames used to get tensor members from Session. The problem if a tensor is gaving as name "tensor_X" and used as member "tensor_tensor_X" the function assume exists a tensor with name "X". This was causing teh Keras parser to crash. - Fix an issue writing the initialized data when are inf or NaN. Use the function from limits in this case
…pe tensor When output is a param shape tensor the tensor values were not assigned in initialization as in a constant tensor, they need to be set at run time in the infer function because they depend on the provided dynamic shale values Fix also a issue on Windows in the new COnvertValuesTOString implementation dealing with inf values. Create a specialisation for float or double which will handle the infinity values in numerical limits.
Fix some operators when input and/or output is a shape tensors Fix also in Gemm when broadcasting dynamic shape for the bias
When computing the last usage of tensors, the loop on the input tensors of the added operator was not performed correctly. The loop was stopping if an initialized tensor was an input
Remove check to include standard library header only if within a list of allowed ones. There is no need for that check and this was excluding addition of extra headers like chrono
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