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We cover HOW gating is used in technology adoption. #Meritocracy #Gating #TechnologyResearch
Part of HackerNoon's growing list of open-source research papers, promoting free access to academic material.
Authors:
(1) Soham De, Google DeepMind and with Equal contributions;
(2) Samuel L. Smith, Google DeepMind and with Equal contributions;
(3) Anushan Fernando, Google DeepMind and with Equal contributions;
(4) Aleksandar Botev, Google DeepMind and with Equal contributions;
(5) George Cristian-Muraru, Google DeepMind and with Equal contributions;
(6) Albert Gu, Work done while at Google DeepMind;
(7) Ruba Haroun, Google DeepMind;
(8) Leonard Berrada, Google DeepMind;
(9) Yutian Chen, Google DeepMind;
(10) Srivatsan Srinivasan, Google DeepMind;
(11) Guillaume Desjardins, Google DeepMind;
(12) Arnaud Doucet, Google DeepMind;
(13) David Budden, Google DeepMind;
(14) Yee Whye Teh, Google DeepMind;
(15) David Budden, Google DeepMind;
(16) Razvan Pascanu, Google DeepMind;
(17) Nando De Freitas, Google DeepMind;
(18) Caglar Gulcehre, Google DeepMind.
3 Recurrent Models Scale as Efficiently as Transformers
3.2. Evaluation on downstream tasks
4.2. Efficient linear recurrences on device
4.3. Training speed on longer sequences
5.1. A simple model of the decode step
6. Long Context Modeling and 6.1. Improving next token prediction with longer contexts
6.2. Copy and retrieval capabilities
8. Conclusion, Acknowledgements, and References
B. Complex-Gated Linear Recurrent Unit (CG-LRU)
C. Model Scale Hyper-Parameters
D. Efficient Linear Recurrences on Device
E. The Local Attention Window Size of Griffin
G. Improving Next Token Prediction with Longer Contexts: Additional Results
H. Additional Details of the Copy and Retrieval Tasks
Here, cache size refers to either the size of the KV cache at batch size 1 (for Transformers), or to the size of the recurrent state at batch size 1 (for RNNs).
Cache sizes The difference in cache size relative to model parameters has important implications for sampling efficiency. In recurrent and local attention blocks, parameter loading is the primary bottleneck, (because the cache size is substantially smaller). In contrast, global attention’s KV cache scales with the sequence length 𝑇 and can be comparable to, or even exceed, the size of the model parameters. This introduces considerable overhead when the sequence length 𝑇 is large enough (as shown in F.4). Consequently, an equally sized recurrent model can exhibit substantially lower latency than a Transformer when 𝑇 is large. Note however that as the model size grows the sequence length at which we see latency benefits (where the KV cache size is comparable to parameter size) also increases. It is important to note that, as well as improving latency, having a small recurrent state can also increase the largest batch size that fits in memory on a single device, leading to higher throughput.
This paper is available on arxiv under CC BY 4.0 DEED license.