New Transformer structure for highly effective LLMs with out GPUs

Matrix multiplications (MatMul) are probably the most computationally costly operations in massive language fashions (LLM) utilizing the Transformer structure. As LLMs scale to bigger sizes, the price of MatMul grows considerably, rising reminiscence utilization and latency throughout coaching and inference. 

Now, researchers on the College of California, Santa Cruz, Soochow College and College of California, Davis have developed a novel structure that utterly eliminates matrix multiplications from language fashions whereas sustaining robust efficiency at massive scales.

Of their paper, the researchers introduce MatMul-free language fashions that obtain efficiency on par with state-of-the-art Transformers whereas requiring far much less reminiscence throughout inference. 

MatMul

Matrix multiplication is a basic operation in deep studying, the place it’s used to mix knowledge and weights in neural networks. MatMul is essential for duties like reworking enter knowledge by way of layers of a neural community to make predictions throughout coaching and inference. 

GPUs are designed to carry out many MatMul operations concurrently, due to their extremely parallel structure. This parallelism permits GPUs to deal with the large-scale computations required in deep studying a lot quicker than conventional CPUs, making them important for coaching and operating advanced neural community fashions effectively. 

Nonetheless, with LLMs scaling to lots of of billions of parameters, MatMul operations have develop into a bottleneck, requiring very massive GPU clusters throughout each coaching and inference phases. Changing MatMul with an easier operation can lead to big financial savings in reminiscence and computation. However earlier efforts to interchange MatMul operations have produced combined outcomes, decreasing reminiscence consumption however slowing down operations as a result of they don’t carry out effectively on GPUs.

Changing MatMul with ternary operations

Within the new paper, the researchers recommend changing the standard 16-bit floating level weights utilized in Transformers with 3-bit ternary weights that may take one in every of three states: -1, 0 and +1. In addition they exchange MatMul with additive operations that present equally good outcomes at a lot much less computational prices. The fashions are composed of “BitLinear layers” that use ternary weights. 

“By constraining the weights to the set {−1, 0, +1} and applying additional quantization techniques, MatMul operations are replaced with addition and negation operations,” the researchers write.

In addition they make extra profound modifications to the language mannequin structure. Transformer blocks include two foremost parts: a token mixer and a channel mixer. The token mixer is answerable for integrating data throughout completely different tokens in a sequence. In conventional Transformer fashions, that is usually achieved utilizing self-attention mechanisms, which use MatMul operations to compute relationships between all pairs of tokens to seize dependencies and contextual data. 

Nonetheless, within the MatMul-free structure described within the paper, the token mixer is applied utilizing a MatMul-free Linear Gated Recurrent Unit (MLGRU). The GRU is a deep studying for sequence modeling that was standard earlier than the arrival of Transformers. The MLGRU processes the sequence of tokens by updating hidden states by way of easy ternary operations with out the necessity for costly matrix multiplications.

The channel mixer is answerable for integrating data throughout completely different characteristic channels inside a single token’s illustration. The researchers applied their channel mixer utilizing a Gated Linear Unit (GLU), which can also be utilized in Llama-2 and Mistral. Nonetheless, they modified the GLU to additionally work with ternary weights as an alternative of MatMul operations. This enabled them to cut back computational complexity and reminiscence utilization whereas sustaining the effectiveness of characteristic integration 

“By combining the MLGRU token mixer and the GLU channel mixer with ternary weights, our proposed architecture relies solely on addition and element-wise products,” the researchers write.

Evaluating MatMul-free language fashions

The researchers in contrast two variants of their MatMul-free LM towards the superior Transformer++ structure, utilized in Llama-2, on a number of mannequin sizes. 

Curiously, their scaling projections present that the MatMul-free LM is extra environment friendly in leveraging extra compute sources to enhance efficiency compared to the Transformer++ structure.

The researchers additionally evaluated the standard of the fashions on a number of language duties. The two.7B MatMul-free LM outperformed its Transformer++ counterpart on two superior benchmarks, ARC-Problem and OpenbookQA, whereas sustaining comparable efficiency on the opposite duties.

“These results highlight that MatMul-free architectures are capable achieving strong zero-shot performance on a diverse set of language tasks, ranging from question answering and commonsense reasoning to physical understanding,” the researchers write.

Expectedly, MatMul-free LM has decrease reminiscence utilization and latency in comparison with Transformer++, and its reminiscence and latency benefits develop into extra pronounced because the mannequin measurement will increase. For the 13B mannequin, the MatMul-free LM used solely 4.19 GB of GPU reminiscence at a latency of 695.48 ms, whereas Transformer++ required 48.50 GB of reminiscence at a latency of 3183.10 ms.

Optimized implementations

The researchers created an optimized GPU implementation and a customized FPGA configuration for MatMul-free language fashions. With the GPU implementation of the ternary dense layers, they had been capable of speed up coaching by 25.6% and scale back reminiscence consumption by as much as 61.0% over an unoptimized baseline implementation. 

“This work goes beyond software-only implementations of lightweight models and shows how scalable, yet lightweight, language models can both reduce computational demands and energy use in the real-world,” the researchers write.

The researchers consider their work can pave the best way for the event of extra environment friendly and hardware-friendly deep studying architectures.

Attributable to computational constraints, they weren’t capable of check the MatMul-free structure on very massive fashions with greater than 100 billion parameters. Nonetheless, they hope their work will function a name to motion for establishments and organizations which have the sources to construct the most important language fashions to put money into accelerating light-weight fashions.

Ideally, this structure will make language fashions a lot much less depending on high-end GPUs like these from Nvidia, and can allow researchers to run highly effective fashions on different, cheaper and fewer provide constrained varieties of processors. The researchers have launched the code for the algorithm and fashions for the analysis neighborhood to construct on.

“By prioritizing the development and deployment of MatMul-free architectures such as this one, the future of LLMs will only become more accessible, efficient, and sustainable,” the researchers write.