Results of the summarizing process for the arXiv paper: 2208.07339v1

The development of large language models has revolutionized natural language processing, but their inference requires significant GPU memory. To address this issue, a team of researchers has developed LLM.int8(), a procedure for Int8 matrix multiplication for feed-forward and attention projection layers in transformers. This method cuts the memory needed for inference by half while retaining full precision performance. The researchers were able to load a 175B parameter 16/32-bit checkpoint, convert it to Int8, and use it immediately without any performance degradation. The success of LLM.int8() is due to the researchers' understanding and working around properties of highly systematic emergent features in transformer language models that dominate attention and transformer predictive performance. They developed a two-part quantization procedure using vector-wise quantization with separate normalization constants for each inner product in the matrix multiplication to quantize most of the features. However, for the emergent outliers, they also included a new mixed-precision decomposition scheme that isolates the outlier feature dimensions into a 16-bit matrix multiplication while still more than 99.9% of values are multiplied in 8-bit. To find these outlier features, the researchers used an empirical approach and defined outliers based on their magnitude compared to other features within the model. By analyzing these outlier features, they were able to gain insight into how they affect model performance. Overall, LLM.int8() makes large language models much more accessible by reducing GPU memory requirements during inference which makes it possible to use OPT-175B/BLOOM on a single server with consumer GPUs without any performance degradation.

• Large language models have revolutionized natural language processing but require significant GPU memory for inference
• LLM.int8() is a procedure developed by researchers to address this issue
• LLM.int8() cuts the memory needed for inference by half while retaining full precision performance
• The researchers were able to load a 175B parameter checkpoint, convert it to Int8, and use it immediately without any performance degradation
• LLM.int8() uses a two-part quantization procedure using vector-wise quantization with separate normalization constants for each inner product in the matrix multiplication to quantize most of the features
• For emergent outliers, a new mixed-precision decomposition scheme isolates outlier feature dimensions into a 16-bit matrix multiplication while still more than 99.9% of values are multiplied in 8-bit
• Outlier features were defined based on their magnitude compared to other features within the model and analyzed for their effect on model performance
• LLM.int8() makes large language models more accessible by reducing GPU memory requirements during inference

LLM.int8(): A Procedure for Int8 Matrix Multiplication in Transformer Language Models

The development of large language models has revolutionized natural language processing, but their inference requires significant GPU memory. To address this issue, a team of researchers has developed LLM.int8(), a procedure for Int8 matrix multiplication for feed-forward and attention projection layers in transformers. This method cuts the memory needed for inference by half while retaining full precision performance.

Background

Large transformer models are becoming increasingly popular due to their ability to capture complex relationships between words and phrases in natural language processing tasks such as machine translation, question answering, and text summarization. However, these models require significant amounts of GPU memory during inference which makes them difficult to deploy on consumer GPUs or even multiple servers with consumer GPUs.

The LLM.int8() Methodology

To reduce the amount of GPU memory required during inference, the researchers developed LLM.int8(), a two-part quantization procedure that uses vector-wise quantization with separate normalization constants for each inner product in the matrix multiplication to quantize most of the features while also including a new mixed-precision decomposition scheme that isolates outlier feature dimensions into a 16-bit matrix multiplication while still more than 99.9% of values are multiplied in 8-bit. The success of this method is due to the researchers' understanding and working around properties of highly systematic emergent features in transformer language models that dominate attention and transformer predictive performance which they identified using an empirical approach based on magnitude compared to other features within the model.

Results

The researchers were able to load a 175B parameter 16/32-bit checkpoint, convert it to Int8 using LLM.int8(), and use it immediately without any performance degradation making large language models much more accessible by reducing GPU memory requirements during inference which makes it possible to use OPT-175B/BLOOM on a single server with consumer GPUs without any performance degradation as well as gaining insight into how emergent outliers affect model performance through analysis after identification via their empirical approach..

Conclusion

In conclusion, LLM int 8 is an effective way to reduce GPU memory requirements during inference while maintaining full precision performance when running large transformer language models allowing them to be deployed on consumer GPUs or even multiple servers with consumer GPUs making them much more accessible than before while also providing insight into how emergent outliers affect model performance through analysis after identification via their empirical approach