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vasqu
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Apr 14, 2026
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vasqu
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Nice thank you, just thinking if we should have a 4th case with proper check against different gas bsz x step (4:2 vs 8:1 which is tested for both)
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| def test_gradient_accumulation_grad_norm_consistency(self): | ||
| trainer = Trainer(model=RegressionModel(), args=args, train_dataset=RegressionDataset()) | ||
| self.assertEqual(trainer.accelerator.gradient_accumulation_steps, 1) |
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We can add a reference to the issue imo
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| loss_tolerance=0.1, | ||
| model_accepts_loss_kwargs=False, |
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Would it make sense with a tighter tolerance when we accept loss kwargs as last test case?
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do have a tigher tolerance here already test_gradient_accumulation_grad_norm_with_num_items_in_batch
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What does this PR do?
This PR updates the grad acc tests so that we are able to better catch issues. We check the grad norm also in addition to the loss. We are remove some redundant tests. As promised @vasqu. Just read the new test directly, don't bother checking the old tests