Embracing the Period of 1-Bit LLMs: Microsoft & UCAS’s BitNet b1.58 Redefines Effectivity


The latest explosive development of Massive Language Fashions (LLMs) has showcased their distinctive efficiency throughout a spectrum of pure language processing duties. Nonetheless, this development comes with challenges, primarily stemming from their increasing dimension, which presents hurdles for deployment and sparks issues relating to their environmental footprint and financial implications as a result of heightened vitality calls for.

In response to those challenges, one common technique includes leveraging post-training quantization to develop low-bit fashions for inference. This method reduces the precision of weights and activations, thus considerably reducing down the reminiscence and computational calls for of LLMs. Moreover, latest exploration into 1-bit mannequin architectures, exemplified by BitNet, affords a promising avenue for mitigating the prices related to LLMs whereas upholding their efficiency requirements.

Aligned with this trajectory, in a brand new paper The Period of 1-bit LLMs: All Massive Language Fashions are in 1.58 Bits, a analysis staff from Microsoft Analysis and College of Chinese language Academy of Sciences introduces a brand new variant of 1-bit LLMs known as BitNet b1.58. This variant preserves the benefits of the unique 1-bit BitNet whereas ushering in a novel computational paradigm that considerably enhances cost-effectiveness by way of latency, reminiscence utilization, throughput, and vitality consumption.

BitNet b1.58 builds upon the BitNet structure, a Transformer mannequin that substitutes nn.Linear with BitLinear. On this iteration, each parameter is ternary, assuming values of {-1, 0, 1}. The addition of an additional worth, 0, to the unique 1-bit BitNet ends in 1.58 bits within the binary system.

In comparison with its predecessor, BitNet b1.58 incorporates a number of enhancements:

Quantization Perform: The analysis staff adopts an absmean quantization operate, which proves extra handy and easy for each implementation and system-level optimization, whereas introducing negligible efficiency impacts in experiments.

LLaMA-alike Elements: BitNet b1.58 integrates LLaMA-alike parts, using RMSNorm, SwiGLU, rotary embedding, and eliminating all biases. This design facilitates seamless integration into common open-source software program with minimal effort.

The researchers carried out comparative evaluations of BitNet b1.58 towards FP16 LLaMA LLM throughout varied sizes. Outcomes point out that BitNet b1.58 begins to match the efficiency of full-precision LLaMA LLM at a mannequin dimension of 3B by way of perplexity, whereas exhibiting 2.71 instances sooner efficiency and using 3.55 instances much less GPU reminiscence.

Furthermore, BitNet b1.58 retains the important thing advantages of the unique 1-bit BitNet, together with its modern computation paradigm that requires minimal multiplication operations for matrix multiplication, thereby enabling excessive optimization. It additionally maintains equal vitality consumption to the unique 1-bit BitNet whereas vastly bettering effectivity by way of reminiscence consumption, throughput, and latency.

Moreover, BitNet b1.58 affords two further benefits. Firstly, its modeling functionality is strengthened by express assist for function filtering, facilitated by the inclusion of 0 within the mannequin weights, which considerably enhances the efficiency of 1-bit LLMs. Secondly, experimental outcomes display that BitNet b1.58 can match full-precision (i.e., FP16) baselines in each perplexity and end-task efficiency, ranging from a 3B mannequin dimension, utilizing equivalent configurations.

In abstract, BitNet b1.58 establishes a brand new scaling legislation and framework for coaching next-generation LLMs which might be each high-performance and cost-effective. Furthermore, it introduces a novel computational paradigm and lays the inspiration for designing specialised {hardware} optimized for 1-bit LLMs.

The paper The Period of 1-bit LLMs: All Massive Language Fashions are in 1.58 Bits is on arXiv.

Writer: Hecate He | Editor: Chain Zhang

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