Multi-Layer Transformers Gradient Can be Approximated in Almost Linear
  Time
The quadratic computational complexity in the self-attention mechanism of
popular transformer architectures poses significant challenges for training and
inference, particularly in terms of efficiency and memory requirements. Towards
addressing these challenges, this paper introduces a novel fast computation
method for gradient calculation in multi-layer transformer models. Our approach
enables the computation of gradients for the entire multi-layer transformer
model in almost linear time n^{1+o(1)}, where n is the input sequence
length. This breakthrough significantly reduces the computational bottleneck
associated with the traditional quadratic time complexity. Our theory holds for
any loss function and maintains a bounded approximation error across the entire
model. Furthermore, our analysis can hold when the multi-layer transformer
model contains many practical sub-modules, such as residual connection, casual
mask, and multi-head attention. By improving the efficiency of gradient
computation in large language models, we hope that our work will facilitate the
more effective training and deployment of long-context language models based on
our theoretical results.
66cc05aed20991c2a015a6bc
Yingyu Liang
66cc05aed20991c2a015a6bd
Zhizhou Sha
claimed_verified
2024-08-27T07:42:34.868Z
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Zhizhou Sha
user
JamesSand
66cc05aed20991c2a015a6be
Zhenmei Shi
claimed_verified
2024-09-30T08:07:40.297Z
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Zhenmei Shi
user
Zhenmei
66cc05aed20991c2a015a6bf
Zhao Song
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Yufa Zhou
claimed_verified
2024-08-27T07:42:27.200Z
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YUFA ZHOU
user
MasterZhou
Multi-Layer Transformers Gradient Can be Approximated in Almost Linear
  Time
The quadratic computational complexity in the self-attention mechanism of
popular transformer architectures poses significant challenges for training and
inference, particularly in terms of efficiency and memory requirements. Towards
addressing these challenges, this paper introduces a novel fast computation
method for gradient calculation in multi-layer transformer models. Our approach
enables the computation of gradients for the entire multi-layer transformer
model in almost linear time n^{1+o(1)}, where n is the input sequence
length. This breakthrough significantly reduces the computational bottleneck
associated with the traditional quadratic time complexity. Our theory holds for
any loss function and maintains a bounded approximation error across the entire
model. Furthermore, our analysis can hold when the multi-layer transformer
model contains many practical sub-modules, such as residual connection, casual
mask, and multi-head attention. By improving the efficiency of gradient
computation in large language models, we hope that our work will facilitate the
more effective training and deployment of long-context language models based on
our theoretical results.
A novel fast computation method for gradient calculation in multi-layer transformer models reduces computational complexity from quadratic to almost linear, improving efficiency and enabling better handling of long-context language models.
self-attention mechanism transformer architectures gradient calculation multi-layer transformer models residual connection casual mask multi-head attention