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gradient_accumulation
performance-gradient-accumulatio-gradient-accumulation-in-language-model-training-r-39d96261
IFGradient accumulation in language model training results in incorrect gradient norms because the loss calculation uses the total number of non-padding labels across all micro-batches instead of per micro-batch.
THENModify the Trainer to return a list of num_items_in_batch for each micro-batch. Then in the training loop, pass the appropriate num_items_in_batch to compute_loss for each sample. This ensures the cross-entropy loss is normalized by the correct number of tokens per micro-batch.
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