Efficient $1$-bit tensor approximations
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arXiv
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| Format: | Preprint |
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2024
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| _version_ | 1866917793274789888 |
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| author | Riasanovsky, Alex W. Neal Kazdadi, Sarah El |
| author_facet | Riasanovsky, Alex W. Neal Kazdadi, Sarah El |
| contents | We present a spatially efficient decomposition of matrices and arbitrary-order tensors as linear combinations of tensor products of $\{-1, 1\}$-valued vectors. For any matrix $A \in \mathbb{R}^{m \times n}$, $$A - R_w = S_w C_w T_w^\top = \sum_{j=1}^w c_j \cdot \mathbf{s}_j \mathbf{t}_j^\top$$ is a {\it $w$-width signed cut decomposition of $A$}. Here $C_w = "diag"(\mathbf{c}_w)$ for some $\mathbf{c}_w \in \mathbb{R}^w,$ and $S_w, T_w$, and the vectors $\mathbf{s}_j, \mathbf{t}_j$ are $\{-1, 1\}$-valued. To store $(S_w, T_w, C_w)$, we may pack $w \cdot (m + n)$ bits, and require only $w$ floating point numbers. As a function of $w$, $\|R_w\|_F$ exhibits exponential decay when applied to #f32 matrices with i.i.d. $\mathcal N (0, 1)$ entries. Choosing $w$ so that $(S_w, T_w, C_w)$ has the same memory footprint as a \textit{f16} or \textit{bf16} matrix, the relative error is comparable. Our algorithm yields efficient signed cut decompositions in $20$ lines of pseudocode. It reflects a simple modification from a celebrated 1999 paper [1] of Frieze and Kannan. As a first application, we approximate the weight matrices in the open \textit{Mistral-7B-v0.1} Large Language Model to a $50\%$ spatial compression. Remarkably, all $226$ remainder matrices have a relative error $<6\%$ and the expanded model closely matches \textit{Mistral-7B-v0.1} on the {\it huggingface} leaderboard [2]. Benchmark performance degrades slowly as we reduce the spatial compression from $50\%$ to $25\%$. We optimize our open source \textit{rust} implementation [3] with \textit{simd} instructions on \textit{avx2} and \textit{avx512} architectures. We also extend our algorithm from matrices to tensors of arbitrary order and use it to compress a picture of the first author's cat Angus. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_01799 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Efficient $1$-bit tensor approximations Riasanovsky, Alex W. Neal Kazdadi, Sarah El Combinatorics Machine Learning Mathematical Software Numerical Analysis We present a spatially efficient decomposition of matrices and arbitrary-order tensors as linear combinations of tensor products of $\{-1, 1\}$-valued vectors. For any matrix $A \in \mathbb{R}^{m \times n}$, $$A - R_w = S_w C_w T_w^\top = \sum_{j=1}^w c_j \cdot \mathbf{s}_j \mathbf{t}_j^\top$$ is a {\it $w$-width signed cut decomposition of $A$}. Here $C_w = "diag"(\mathbf{c}_w)$ for some $\mathbf{c}_w \in \mathbb{R}^w,$ and $S_w, T_w$, and the vectors $\mathbf{s}_j, \mathbf{t}_j$ are $\{-1, 1\}$-valued. To store $(S_w, T_w, C_w)$, we may pack $w \cdot (m + n)$ bits, and require only $w$ floating point numbers. As a function of $w$, $\|R_w\|_F$ exhibits exponential decay when applied to #f32 matrices with i.i.d. $\mathcal N (0, 1)$ entries. Choosing $w$ so that $(S_w, T_w, C_w)$ has the same memory footprint as a \textit{f16} or \textit{bf16} matrix, the relative error is comparable. Our algorithm yields efficient signed cut decompositions in $20$ lines of pseudocode. It reflects a simple modification from a celebrated 1999 paper [1] of Frieze and Kannan. As a first application, we approximate the weight matrices in the open \textit{Mistral-7B-v0.1} Large Language Model to a $50\%$ spatial compression. Remarkably, all $226$ remainder matrices have a relative error $<6\%$ and the expanded model closely matches \textit{Mistral-7B-v0.1} on the {\it huggingface} leaderboard [2]. Benchmark performance degrades slowly as we reduce the spatial compression from $50\%$ to $25\%$. We optimize our open source \textit{rust} implementation [3] with \textit{simd} instructions on \textit{avx2} and \textit{avx512} architectures. We also extend our algorithm from matrices to tensors of arbitrary order and use it to compress a picture of the first author's cat Angus. |
| title | Efficient $1$-bit tensor approximations |
| topic | Combinatorics Machine Learning Mathematical Software Numerical Analysis |
| url | https://arxiv.org/abs/2410.01799 |