[Utils] Offloaded cache size #1714
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Signed-off-by: Kyle Sayers <[email protected]>
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Summary of Changes
Hello @kylesayrs, I'm Gemini Code Assist1! I'm currently reviewing this pull request and will post my feedback shortly. In the meantime, here's a summary to help you and other reviewers quickly get up to speed!
I've added a new utility to help debug memory usage of the offloaded cache. This new functionality provides a way to inspect the memory footprint of cached values, broken down by the device they reside on, making it easier to identify and manage memory consumption within the cache.
Highlights
- New
size()method: Introduced asize()method to theCacheclass insrc/llmcompressor/pipelines/cache.pyto calculate the memory used by cached values. - Device-specific memory tracking: The
size()method reports memory usage pertorch.device, allowing for granular insights into where cached data resides. - Recursive memory calculation: Implemented a recursive helper function
_size_helperto accurately sum up memory for complex nested data structures liketorch.Tensors, dataclasses, tuples, and dictionaries within the cache.
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Code Review
This pull request adds a size() method to IntermediatesCache to calculate the memory usage of the cache, which is a helpful utility for debugging. The current recursive implementation has a couple of issues: it can fail with a RecursionError on deeply nested data, and it doesn't correctly calculate the size of lists. I've provided a suggestion for an iterative implementation that is more robust and correctly handles lists, which should improve both correctness and reliability.
Purpose