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What? That's the wrong paper. It is not even from Microsoft. This is it: https://www.microsoft.com/en-us/research/publication/bitnet-scaling-1-bit-transformers-

• llm_trw 2y ago
  Section 2.2 from your paper, with less clarity and more obfuscation: >While the weights and the activations are quantized to low precision, the gradients and the optimizer states are stored in high precision to ensure training stability and accuracy. Following the previous work [ LSL+21 ], we maintain a latent weight in a high-precision format for the learnable parameters to accumulate the parameter updates. The latent weights are binarized on the fly during the forward pass and never used for the inference process. https://arxiv.org/pdf/2310.11453 https://arxiv.org/pdf/2310.11453 The other paper had a much nicer and clearer introduction to bitlinear than the original Microsoft paper, which is why I used it. Uncharitably you might say that they aren't hiding the lead 10 paragraphs in.
  • sigmoid10 2y ago
    They are not hiding anything, because this is standard behaviour for all current optimisers. You still get a massive memory improvement from lower bit model weights during training.
    • llm_trw 2y ago
      I'm sorry but you just don't understand what the paper is saying.