From Projection to Prediction: Beyond Logits for Scalable Language Models

TL;DR

This paper proposes a unified approach to LLM output projection and loss computation that reduces memory and bandwidth usage, enabling faster training with larger batches and longer sequences.

Contribution

It introduces a novel method that combines projection and loss calculation into a single operation, bypassing explicit logits materialization for improved efficiency.

Findings

Significant memory savings during training.

Measurable speedups over standard methods.

Supports larger batch sizes and longer sequences.

Abstract

Training Large Language Models (LLMs) typically involves a two-stage pipeline at the output layer: hidden states are projected into vocabulary logits via a linear transformation (lm_head), followed by cross-entropy loss computation against target tokens. While conceptually simple, this design incurs substantial overhead. The intermediate logits tensor, with dimensions proportional to batch size, sequence length, and vocabulary size, must be fully materialized in GPU memory, even though only one target token per position is ultimately used. This leads to significant memory footprint and bandwidth comsumption, limiting scalability and slowing training throughput. In this work, we introduce a novel approach to integrates the output projection and loss prediction into a single operation. By directly computing the loss from hidden states and target tokens, our approach bypasses explicit logits materialization. This design reduces memory usage and alleviates bandwidth pressure. Experiments on LLM training demonstrate that our method achieves substantial memory savings and measurable speedups compared to the standard two-stage pipeline, enabling large batch sizes and longer sequences without sacrificing accuracy. Our work highlights the benefits of rethinking the boundary between projection and prediction, offering a practical systems optimization for efficient LLM training.