[Trainer] Deeper length checks for IterableDatasetShard #15539
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sgugger
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Feb 7, 2022
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
stevhliu
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Feb 18, 2022
…15539) * Unused import * Make `has_length()` torch-independent to use in callbacks * Update src/transformers/trainer_utils.py Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com> Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
ManuelFay
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Mar 31, 2022
…15539) * Unused import * Make `has_length()` torch-independent to use in callbacks * Update src/transformers/trainer_utils.py Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com> Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
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What does this PR do?
As discussed in #15309 (comment) , in cases where
IterableDatasetShardwraps a non-sized dataset, the check forisinstance(dataset, Sized)passes, whilelen(dataset)fails.This PR adds a universal
has_length(dataset)that tries to get the dataset's length explicitly without relying on type checking.