Fix iterable dataset sharding condition when n_shards == num_processes - #3958
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Use >= instead of > so native HF dataset sharding is used when the shard count exactly matches the process count, instead of falling back to the less efficient IterableDatasetShard wrapper.
Use >= instead of > so native HF dataset sharding is used when the shard count exactly matches the process count, instead of falling back to the less efficient IterableDatasetShard wrapper.
…nto fix-iterable-dataset-sharding
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What does this PR do
Fixes #3635 (comment)
When
n_shards == num_processes, native HF dataset sharding (dataset.shard()) is not used because the condition is strictly >. This causes the code to fall through to theIterableDatasetShardwrapper, which does sample-level skipping instead of file-level assignment.This is suboptimal since
n_shards == num_processesis actually the ideal case — each process gets exactly one file shard.