fix PriorDumpDataLoader to correctly handle num_datapoints

[kursatfelsen]

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[gemini-code-assist]

Summary of Changes

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This pull request addresses an issue in the PriorDumpDataLoader class to accurately handle the num_datapoints parameter when loading data from HDF5 files. The changes ensure that the maximum number of data points is correctly identified and used for slicing input features (X) and targets (y), accommodating datasets with variable sequence lengths more robustly. This prevents potential data loading errors and optimizes memory usage by only loading necessary data.

Highlights

• Corrected num_datapoints_max calculation: The num_datapoints_max attribute is now correctly derived from the second dimension of the X dataset, representing the maximum sequence length within a sample, rather than the number of samples.
• Dynamic num_datapoints handling: The data loader now checks for a dedicated num_datapoints field in the HDF5 file. If present, it uses this field to dynamically determine the maximum sequence length (max_seq_in_batch) for each batch, allowing for variable-length sequences.
• Improved data slicing: The X and y tensors are now sliced using the dynamically determined max_seq_in_batch. This ensures that only the relevant data points are loaded for each sample, preventing potential shape mismatches and optimizing memory usage.

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[gemini-code-assist]

Code Review

This pull request fixes an issue in PriorDumpDataLoader related to handling the number of datapoints. The changes correctly determine the maximum number of datapoints from the HDF5 file shape and introduce logic to handle datasets with variable sequence lengths by reading num_datapoints if available. This makes data loading more efficient and robust by only loading data up to the maximum sequence length within a batch. The slicing of both features x and labels y is now consistent. I've added one suggestion to improve code consistency.