PyTorch CUDA allocator optimization for dynamic batch shape dataloading in ASR #9061
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What does this PR do ?
I was profiling a particularly unlucky run that had dynamic batch shapes and operated close to max GPU RAM. The profile revealed it was re-allocating memory for every mini-batch, generating about 30% overhead in training. This can be resolved gracefully by turning on
expandable_segments
option in PyTorch CUDA allocator which instead of reallocating, extends existing allocations as needed, removing this significant overhead.In this PR I'm proposing to automatically set this option during dataloader instantiation. It can be disabled via configuration.
For documentation purposes, the profile before the change (red blocks in CUDA API timelines indicates malloc/free):
and the profile after the fix:
The blue bars at the top of the profiles in CUDA HW kernel utilization timelines are more condensed in the new profile, indicating improved GPU utilization.
Collection: ASR
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