performs slightly better than in the k
Posted: Mon Dec 23, 2024 10:38 am
For all methods trained from scratch (including pre-training), the perplexity gets worse once the context length becomes too large. From the above figure, we can see that - performs slightly worse than - for short contexts but performs better than - for long contexts. This observation is in line with the researchers' expectation that - as a hidden state is more expressive than a linear model. Also, all methods have the same training as . . Actual running time Training and inference can be decomposed into forward, backward, and generation. Since both the forward (during training and inference) and the backward can be parallelized, the researchers use the dual form.
Generating new k (also known as decoding) is inherently japan mobile number sequential, so the researchers use the original form. Due to resource constraints, this experiment was written in J and run on . However, since (implemented in , and ) can only run on , the researchers also rewrote the method to run on for a fair comparison. Specifically, the researchers wrote a kernel for the forward pass in K. Historically, it has been inefficient in the forward and backward passes due to improper use of parallelism and matrix multiplication. The goal of this forward kernel is to demonstrate the effectiveness of the dual form of - and these problems.
The left figure of the figure shows the latency of the forward kernel for batch size . All model parameters are . (for .). The time for each k increases linearly with the increase of context length but remains roughly the same for other methods. In addition, the researchers wrote another kernel for generation in and benchmarked the speed of batch size . It can be seen that the latency of - and is almost the same, which is significantly smaller than and -. After seeing the birth of such a powerful new architecture, there is no lack of heated discussion in the community.
Generating new k (also known as decoding) is inherently japan mobile number sequential, so the researchers use the original form. Due to resource constraints, this experiment was written in J and run on . However, since (implemented in , and ) can only run on , the researchers also rewrote the method to run on for a fair comparison. Specifically, the researchers wrote a kernel for the forward pass in K. Historically, it has been inefficient in the forward and backward passes due to improper use of parallelism and matrix multiplication. The goal of this forward kernel is to demonstrate the effectiveness of the dual form of - and these problems.
The left figure of the figure shows the latency of the forward kernel for batch size . All model parameters are . (for .). The time for each k increases linearly with the increase of context length but remains roughly the same for other methods. In addition, the researchers wrote another kernel for generation in and benchmarked the speed of batch size . It can be seen that the latency of - and is almost the same, which is significantly smaller than and -. After seeing the birth of such a powerful new architecture, there is no lack of heated discussion in the community.