What coding techniques allow for efficient cross-entropy loss calculation when working with large token vocabularies

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Name the coding techniques allowing for efficient cross-entropy loss calculation when working with large token vocabulary.
Nov 17 in Generative AI by Ashutosh
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For efficient cross-entropy loss calculation with large token vocabularies, You can refer to the following:

  • Sparse Softmax Cross-Entropy: You can avoid computing softmax probabilities for the entire vocabulary by focusing only on the target tokens.

          

  • Negative Sampling: Instead of calculating probabilities for all tokens, use sampled negatives for approximation (e.g., in Word2Vec).

  • Softmax Approximation: For large vocabularies, techniques like hierarchical softmax or noise contrastive estimation (NCE) can be used.

  • Mixed Precision Training: Use torch.cuda.amp for lower precision (e.g., float16) to speed up operations.

  • Logits Masking: Mask irrelevant tokens to reduce unnecessary computations in specific scenarios.


    In the above techniques, Sparse softmax cross-entropy and softmax approximations like NCE are highly efficient for large token vocabularies.

    Hence, using these techniques will allow you efficient cross-entropy loss calculation when working with large token vocabularies.

    answered Nov 17 by ankit thapa

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