Authors:
(1) Ben Athiwaratkun, AWS AI Labs;
(2) Sujan Kumar Gonugondla, AWS AI Labs;
(3) Sanjay Krishna Gouda, AWS AI Labs;
(4) Haifeng Qian, AWS AI Labs;
(5) Sanjay Krishna Gouda, AWS AI Labs;
(6) Hantian Ding, AWS AI Labs;
(7) Qing Sun, AWS AI Labs;
(8) Jun Wang, AWS AI Labs;
(9) Jiacheng Guo, AWS AI Labs;
(10 Liangfu Chen, AWS AI Labs;
(11) Parminder Bhatia, GE HealthCare (work done at AWS);
(12) Ramesh Nallapati, Amazon AGI (work done at AWS);
(13) Sudipta Sengupta, AWS AI Labs;
(14) Bing Xiang, Goldman Sachs (work done at AWS). Table of Links Abstract and 1 Introduction 2. Related Work 3. Background 3.1. Notation and 3.2. Language Model Inference 3.3. Multi-Query, Multi-Head and the Generalized Multi-Query Attention 4. Context-Aware Bifurcated Attention and 4.1. Motivation 4.2. Formulation and 4.3. Memory IO Complexity 5. Experiments 5.1. Comparing Capabilities of Multi-Head, Multi-Query, and Multi-Group Attention 5.2. Latencies of Capabilities-Equivalent Models 5.3. Applications 6. Conclusion and References A. FAQs B. Related Work C. Setup D. Multi-Group Attention Family E. Context-Aware Bifurcated Attention F. Applications: Additional Results G. Compatibility with Speculative Decoding and Fast Decoding techniques 3.3. Multi-Query, Multi-Head and the Generalized Multi-Query Attention Multi-query attention, proposed by Shazeer (2019), is an attention mechanism for transformers models that uses a single head for the key and value tensors, compared to h heads in the traditional multi-head attention (Vaswani et al., 2017). This technique effectively reduces the KV memory IO by h times, which leads to higher inference efficiency during incremental decoding. In effect, the single-head key or value tensor is shared and used to attend to all the multi-head query, hence the name multi-query. This corresponds to a compression in representation power of the key and value tensor, which we will see in the scaling laws study (Section 5.1) that it results in a reduced expressiveness in terms of model parameter efficiency. Such reduced expressiveness can be compensated by scaling the model bigger than the multi-head counterpart to match the representation power. We can also extrapolate these insights to a generalized multiquery attention mechanism (Ainslie et al., 2023), which provides a framework to understand both multi-query and multi-head attention, and everything in between. Here, the degree of KV compression is dictated by the number of attention groups g, where we alternatively refer to the generalized multi-query as multi-group. Each attention group can be interpreted as the broadcasted attention between a single head of key or value tensor, and multiple heads of query. In this paradigm, multi-query attention is a special case where the number of groups g = 1; that is, there is exactly one such group. Conversely, multi-head attention is another special case where the number of attention groups matches the number of heads (g = h), in which case each head in the key or value tensor attends to one head in the query. More generally, the number of groups g can lie anywhere between 1 and h, indicating various degrees of compression. For practical purposes, it is most convenient when g divides h. The attention mechanism in this setting can be expressed in terms of Einstein summation as: logits = ⟨q, K⟩ : einsum(bgpnk, bgmk) → bgpnm (1) o = ⟨w, V ⟩ : einsum(bgpmn, bgmv) → bgpnv (2) This generalized multi-group attention mechanism thus provides a unified perspective on the design space of attention architectures. By adjusting the number of attention groups g, one can flexibly tune these trade-offs, potentially yielding new regimes of performance for transformer models. In Section 5.1, we will look into such capability vs latency trade-off. This paper is available on arxiv under CC BY 4.0 DEED license. Authors: (1) Ben Athiwaratkun, AWS AI Labs; (2) Sujan Kumar Gonugondla, AWS AI Labs; (3) Sanjay Krishna Gouda, AWS AI Labs; (4) Haifeng Qian, AWS AI Labs; (5) Sanjay Krishna Gouda, AWS AI Labs; (6) Hantian Ding, AWS AI Labs; (7) Qing Sun, AWS AI Labs; (8) Jun Wang, AWS AI Labs; (9) Jiacheng Guo, AWS AI Labs; (10 Liangfu Chen, AWS AI Labs; (11) Parminder Bhatia, GE HealthCare (work done at AWS); (12) Ramesh Nallapati, Amazon AGI (work done at AWS); (13) Sudipta Sengupta, AWS AI Labs; (14) Bing Xiang, Goldman Sachs (work done at AWS). Authors: Authors: (1) Ben Athiwaratkun, AWS AI Labs; (2) Sujan Kumar Gonugondla, AWS AI Labs; (3) Sanjay Krishna Gouda, AWS AI Labs; (4) Haifeng Qian, AWS AI Labs; (5) Sanjay Krishna Gouda, AWS AI Labs; (6) Hantian Ding, AWS AI Labs; (7) Qing Sun, AWS AI Labs; (8) Jun Wang, AWS AI Labs; (9) Jiacheng Guo, AWS AI Labs; (10 Liangfu Chen, AWS AI Labs; (11) Parminder Bhatia, GE HealthCare (work done at AWS); (12) Ramesh Nallapati, Amazon AGI (work done at AWS); (13) Sudipta Sengupta, AWS AI Labs; (14) Bing Xiang, Goldman Sachs (work done at AWS). Table of Links Abstract and 1 Introduction Abstract and 1 Introduction 2. Related Work 2. Related Work 3. Background 3. Background 3.1. Notation and 3.2. Language Model Inference 3.1. Notation and 3.2. Language Model Inference 3.3. Multi-Query, Multi-Head and the Generalized Multi-Query Attention 3.3. Multi-Query, Multi-Head and the Generalized Multi-Query Attention 4. Context-Aware Bifurcated Attention and 4.1. Motivation 4. Context-Aware Bifurcated Attention and 4.1. Motivation 4.2. Formulation and 4.3. Memory IO Complexity 4.2. Formulation and 4.3. Memory IO Complexity 5. Experiments 5. Experiments 5.1. Comparing Capabilities of Multi-Head, Multi-Query, and Multi-Group Attention 5.1. Comparing Capabilities of Multi-Head, Multi-Query, and Multi-Group Attention 5.2. Latencies of Capabilities-Equivalent Models 5.2. Latencies of Capabilities-Equivalent Models 5.3. Applications 5.3. Applications 6. Conclusion and References 6. Conclusion and References A. FAQs A. FAQs B. Related Work B. Related Work C. Setup C. Setup D. Multi-Group Attention Family D. Multi-Group Attention Family E. Context-Aware Bifurcated Attention E. Context-Aware Bifurcated Attention F. Applications: Additional Results F. Applications: Additional Results G. Compatibility with Speculative Decoding and Fast Decoding techniques G. Compatibility with Speculative Decoding and Fast Decoding techniques 3.3. Multi-Query, Multi-Head and the Generalized Multi-Query Attention Multi-query attention, proposed by Shazeer (2019), is an attention mechanism for transformers models that uses a single head for the key and value tensors, compared to h heads in the traditional multi-head attention (Vaswani et al., 2017). This technique effectively reduces the KV memory IO by h times, which leads to higher inference efficiency during incremental decoding. In effect, the single-head key or value tensor is shared and used to attend to all the multi-head query, hence the name multi-query. This corresponds to a compression in representation power of the key and value tensor, which we will see in the scaling laws study (Section 5.1) that it results in a reduced expressiveness in terms of model parameter efficiency. Such reduced expressiveness can be compensated by scaling the model bigger than the multi-head counterpart to match the representation power. We can also extrapolate these insights to a generalized multiquery attention mechanism (Ainslie et al., 2023), which provides a framework to understand both multi-query and multi-head attention, and everything in between. Here, the degree of KV compression is dictated by the number of attention groups g, where we alternatively refer to the generalized multi-query as multi-group. Each attention group can be interpreted as the broadcasted attention between a single head of key or value tensor, and multiple heads of query. In this paradigm, multi-query attention is a special case where the number of groups g = 1; that is, there is exactly one such group. Conversely, multi-head attention is another special case where the number of attention groups matches the number of heads (g = h), in which case each head in the key or value tensor attends to one head in the query. More generally, the number of groups g can lie anywhere between 1 and h, indicating various degrees of compression. For practical purposes, it is most convenient when g divides h. The attention mechanism in this setting can be expressed in terms of Einstein summation as: logits = ⟨q, K⟩ : einsum(bgpnk, bgmk) → bgpnm (1) o = ⟨w, V ⟩ : einsum(bgpmn, bgmv) → bgpnv (2) This generalized multi-group attention mechanism thus provides a unified perspective on the design space of attention architectures. By adjusting the number of attention groups g, one can flexibly tune these trade-offs, potentially yielding new regimes of performance for transformer models. In Section 5.1, we will look into such capability vs latency trade-off. This paper is available on arxiv under CC BY 4.0 DEED license. This paper is available on arxiv under CC BY 4.0 DEED license. available on arxiv

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Faster AI, Less Lag: A Smarter Way to Process Language Models

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