Summary 2-Bit Quantization of Large Language Models arxiv.org
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QuIP is a quantization method that enhances runtime efficiency in large language models by utilizing the incoherence between weight and proxy Hessian matrices.
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Key Points
- QuIP is a two-bit quantization method for large language models that improves runtime efficiency.
- Quantization is most effective when weight and proxy Hessian matrices are incoherent.
- LDLQ is an optimal adaptive rounding method for large language models that updates columns of the weight matrix using a linear function of the rounding residuals.
- QuIP computes the quantization range based on the spectrum of the weight matrix instead of the maximum value.
- Incoherence processing in QuIP greatly improves the performance of all quantization methods at lower weight bits.
Summaries
24 word summary
QuIP is a two-bit quantization method for large language models that improves runtime efficiency by leveraging the incoherence between weight and proxy Hessian matrices.
36 word summary
This work presents QuIP, a two-bit quantization method for large language models (LLMs) that improves runtime efficiency. The method leverages the incoherence between weight and proxy Hessian matrices to achieve effective quantization. The document discusses the
692 word summary
This work introduces QuIP, a two-bit quantization method for large language models (LLMs) that improves runtime efficiency. The key insight is that quantization is most effective when weight and proxy Hessian matrices are incoherent. QuIP consists
The document discusses the LDLQ method, an optimal adaptive rounding method for large language models. The method iteratively updates columns of the weight matrix by rounding them using a linear function of the rounding residuals. The final rounded weight matrix satisfies a matrix equation that
We derive explicit proxy losses for plain nearest and stochastic rounding, comparing them to what LDLQ gets via Lemma 2. In the worst case, stochastic rounding achieves (m/4) tr(H), while in the average case, nearest and stochastic rounding
A method for quantizing large language models using a random matrix is described. Additional heuristics are outlined, including rescaling matrices to minimize the spectrum and computing the quantization range based on the spectrum instead of the maximum value. Greedy local search
The paper discusses the quantization of large language models using a method called QuIP. The authors present Theorem 7, which states that there exists an assignment of hyperparameters in the quantization algorithm that ensures all quantized weights are within the desired
QuIP's incoherence processing greatly improves the performance of all quantization methods at lower weight bits, including nearest quantization at two bits. QuIP-RG modifications may provide additional improvements but require further study. The relative contributions of QuIP's
This excerpt contains a list of references to various papers and conferences related to the topic of quantization for large language models. The references include papers on post-training quantization, low-bit vision transformers, block reconstruction, noisy bias-enhanced activation quantization,
Pytorch, an imperative style deep learning library, is referenced in the document. The paper aims to push the quantization of large language models (LLMs) into the 2 bits per weight regime for more efficient running of powerful LLMs.
The authors propose a method called QuIP for quantizing large language models. They compute the quantization range based on the spectrum of the weight matrix, rather than the typical maximum value. They use a parameter, ? = 2.4, consistently
When using 2-bit quantization for large language models, multiple passes of greedy updates are typically run. The paper compares the proxy loss of LDLQ and nearest rounding by analyzing the trace of matrices D and H. Bounds on the proxy loss for LDL
Table 5, 6, 7, 8, and 9 show the results of quantizing the OPT-30b, OPT-13b, OPT-6.7b, OPT-2.7b, and OPT-
Our study demonstrates that our incoherence processing allows for a significant change in quantization at 2 bits with all rounding methods. We provide detailed tables comparing the performance of different quantization and pre-post processing methods on language generation and zeroshot tasks for
OPTQ/LDLQ, LDLQ-RG, and Greedy perform similarly at 2 bits and outperform Nearest. In the evaluation of Adaptive Rounding with Linear feedback, biased rounding is typically used. Weighted averages of proxy loss show
Table 13 shows the average perplexity difference between unbiased and biased rounding for LDLQ/OPTQ on WikiText2, PTB, and C4. The results indicate that biased rounding performs better than unbiased rounding, particularly at lower bits. Table
The text excerpt discusses the quantization of large language models. It presents a proof showing that a global minimum of a certain function occurs when a specific condition is met. It then introduces a lemma related to the LDL Cholesky decomposition of a matrix and
The text discusses the LDL decomposition of a matrix H and its connection to the worst and average-case losses. It then presents upper and lower bounds derived from these calculations. The text also includes proofs for the incoherence processing step and the incoherence
The text excerpt discusses the quantization of large language models. It introduces a proxy loss function and explains how it can be written in block form. The text also discusses the use of the LDL decomposition of a matrix and its application in the quantization procedure
By applying the union bound and setting the right side equal to ?, it is shown that the inequality I(w?-w)uI ? ?Lu?1 log2 ! 2 ? ? holds. The second statement follows from L