Click here to download the project base paper of large language models.
Abstract:
In this work of advanced deep Learning, we propose a Retentive Network (RetNet) as a foundation architecture for large language models, simultaneously achieving training parallelism, low-cost inference, and good performance of Project in Visakhapatnam, Hyderabad. We theoretically derive the connection between recurrence and attention. Then we propose the retention mechanism for sequence modelling, which supports three computation paradigms, i.e., parallel, recurrent, and chunkwise recurrent. Specifically, the parallel representation allows for training parallelism. The recurrent representation enables low-cost inference, which improves decoding throughput, latency, and GPU memory without sacrificing performance. The chunkwise recurrent representation facilitates efficient long-sequence modelling with linear complexity, where each chunk is encoded parallelly while recurrently summarizing the chunks. Experimental results on language modelling show that RetNet achieves favourable scaling results, parallel training, low-cost deployment, and efficient inference.
Numerous efforts have been made to design the next-generation architecture, to maintain training parallelism and competitive performance as transformers while having efficient inference. Itischallengingtoachievetheabovegoalssimultaneously, i.e., the so-called “impossible” First, linearizedattention[KVPF20] approximates standard attention scores exp(qk) with kernels, allowing autoregressive inference to be written in a continuous form It. The second strand restores current models for efficient inference while foregoing training parallelism. As a treatment, element-wise operators are utilized for acceleration; however, representation capacity and performance suffer as a result. The third line of research looks toward replacing attention with other processes, such as S4 and its variants. None of the preceding work could break through the theoretical triangle, resulting in a clear winner when compared to Transformers. The intriguing properties make RetNet a strong successor to Transformer for models. The code will be available Click Here
