Q4'24: Technology Update – Low Precision and Model Optimization

Authors

Alexander Kozlov, Nikita Savelyev, Vui Seng Chua, Souvikk Kundu, Nikolay Lyalyushkin,  Andrey Anufriev, Pablo Munoz, Alexander Suslov, Liubov Talamanova, Yury Gorbachev, Nilesh Jain, Maxim Proshin

Summary

This quarter, we continue observing the trendon the optimization of LLM-based pipelines. Besides a high interest in weight quantizationto precisions beyond 4-bits, we see a lot of effort in the optimization of usageof KV-cache during the ScaledDotProduct computation: from KV-cache quantizationand decomposition to sparse attention where only a part of KV-cache is used topredict the next token. This gives the opportunity to design more efficientinference pipelines with heterogeneous execution (see RetrievalAttention work).

Highlights

• ‍SpinQuant: LLM Quantizationwith Learned Rotations by Meta (https://arxiv.org/abs/2405.16406). Develop the idea of rotation by a random orthogonal matrix from QuIP, QuIP#, and QuaRotto reduce outliers in the LLMs and obtain better quality of W4A4KV4 quantization. The authors found that not all rotations help equally, and random rotations produce a significant variance in quantized models. Therefore, it is proposed to search for “good” rotation matrices using optimization with Cayley optimization. The matrix optimization procedure takes a little over an hour on smaller representatives of the LLama family on 8 A100 and half a day for 70B models. Regarding quality, they are ahead of baselines (the closest QuaRot is about 1% on average). Adding a rotation inside FFN gives the most significant gain. Code is available: https://github.com/facebookresearch/SpinQuant.

• ACCURATE COMPRESSION OFTEXT-TO-IMAGE DIFFUSION MODELS VIA VECTOR QUANTIZATION by Yandex Research, HSE University, Skoltech, MIPT, Neural Magic, IST Austria (https://arxiv.org/pdf/2409.00492).The authors explore vector-based PTQ strategies for text-to-image diffusion models and demonstrate that the compressed models yield higher quality text-to-image generation than the scalar alternatives under the same bit-widths. They describe an effective fine-tuning technique that further closes the gap between the full-precision and compressed models, leveraging the flexibility of the vector quantized representation. To showcase the method, they compress the weights of SDXL down to 3 bits per parameter. Extensive human evaluation and automated metrics confirm the superiority of our approach over previous diffusion compression methods under the same bit-widths. The authors illustrate that the approach can be effectively applied to distilled diffusion models, such as SDXL, which achieve nearly lossless 4-bit compression. Code is available at https://github.com/yandex-research/vqdm.

• Sparse  Refinement for Efficient High-Resolution Semantic Segmentation by MIT, NVIDIA, Tsinghua University, University of Toronto, UC Berkeley (https://arxiv.org/pdf/2407.19014). Authors introduce a novel approach that enhances dense low-resolution predictions with sparse high-resolution refinements. Based on coarse low-resolution outputs, the method first uses an entropy selector to identify a sparse set of pixels with high entropy. It then employs a sparse feature extractor to generate the refinements for those pixels of interest. Finally, it leverages a gated ensembler to apply these sparse refinements to the initial coarse predictions. The method can be seamlessly integrated into any existing semantic segmentation model, regardless of CNN- or ViT-based. SparseRefine achieves significant speedup: 1.5 to 3.7 times when applied to HRNet-W48, SegFormer-B5, Mask2Former-T/L and SegNeXt-L on Cityscapes, with negligible to no loss of accuracy.

• RetrievalAttention: Accelerating Long-Context LLM Inference via Vector Retrieval by Microsoft Research, Shanghai Jiao Tong University, Fudan University (https://arxiv.org/pdf/2409.10516). Authors employ dynamic sparse attention during token generation, allowing the most critical tokens to emerge from the extensive context data. To address theOOD issue, the method constructs a vector index tailored for the attention mechanism, focusing on the distribution of queries rather than key similarities. This approach allows for traversal of only a small subset of key vectors (1% to 3%), effectively identifying the most relevant tokens to achieve accurate attention scores and results. To optimize resource utilization, RetrievalAttention retains KV vectors in the GPU memory following static patterns while offloading the majority of KV vectors to CPU memory for index construction. This strategy enables RetrievalAttention to perform attention computation with reduced latency and minimal GPU memory utilization. The method shows SOTA results in terms of latency-performance.

Papers with notable results

Quantization

• ADFQ-ViT: Activation-Distribution-Friendly Post-Training Quantization for Vision Transformers by Chinese universities (https://arxiv.org/pdf/2407.02763). Authors design the Per-Patch Outlier-aware Quantizer and the Shift-Log2 Quantizer, which addresses the challenges of outliers and irregular distributions in post-LayerNorm activations and the non-uniform distribution of positive and negative values in post-GELU activations. They also introduce the attention-score enhanced module-wise optimization, which optimizes the parameters of the weight and activation quantizer to reduce errors before and after quantization. The method shows very good results for various Vision Transformer models and use cases at W4A4 and W6A6 setups.
• How Does Quantization Affect Multilingual LLMs? by Cohere (https://arxiv.org/pdf/2407.03211). The authors investigate the problem of LLM accuracy degradation after quantization. They use automatic benchmarks, LLM-as-a-Judge methods, and human evaluation, finding that (1) harmful effects of quantization are apparent in human evaluation, and automatic metrics severely underestimate the detriment: a 1.7%average drop in Japanese across automatic tasks corresponds to a 16.0% drop reported by human evaluators on realistic prompts; (2) languages are disparately affected by quantization, with non-Latin script languages impacted worst; and (3) challenging tasks such as mathematical reasoning degrade fastest.
• CLAMP-ViT: Contrastive Data-Free Learning for Adaptive Post-Training Quantization of ViTs by Georgia Institute of Technology and Intel Labs (https://arxiv.org/pdf/2407.05266). The authors incorporate a patch-level contrastive learning scheme to generate richer, semantically meaningful data. Furthermore, they leverage contrastive learning in layer-wise evolutionary search for fixed- and mixed-precision quantization to identify optimal quantization parameters while mitigating the effects of a non-smooth loss landscape. Evaluations across various vision tasks demonstrate the superiority of CLAMP-ViT, with performance improvements of up to 3% in top-1 accuracy for classification, 0.6 mAP for object detection, and 1.5 mIoU for segmentation at a similar or better compression ratio over existing alternatives. The code is available at https://github.com/georgia-tech-synergy-lab/CLAMP-ViT.git.
• RoLoRA: Fine-tuning Rotated Outlier-free LLMs for Effective Weight-Activation Quantization by Hong Kong University of Science and Technology and Meta Reality Labs (https://arxiv.org/pdf/2407.08044).The paper proposes RoLoRA, the scheme for weight-activation quantization. RoLoRA utilizes rotation for outlier elimination and proposes rotation-aware fine-tuning to preserve the outlier-free characteristics in rotated LLMs. Experimental results show RoLoRA consistently improves low-bit LoRA convergence and post-training quantization robustness in weight-activation settings. The code is supposed to be available at https://github.com/HuangOwen/RoLoRA.
• LRQ: Optimizing Post-Training Quantization for Large Language Models by Learning Low-Rank Weight-Scaling Matrices by NAVER Cloud, KAIST AI, AITRICS, SNU AI Center (https://arxiv.org/pdf/2407.11534). The authors propose a post-training weight quantization method for LLMs that reconstructs the outputs of an intermediate Transformer block by leveraging low-rank weight-scaling matrices, replacing the conventional full weight-scaling matrices that entail as many learnable scales as their associated weights. Thanks to parameter sharing via low-rank structure, the method only needs to learn significantly fewer parameters while enabling the individual scaling of weights, thus boosting the generalization capability of quantized LLMs. Authors show the superiority of the method over prior LLM PTQ works under (i) 8-bit weight and per-tensor activation quantization, (ii) 4-bitweight and 8-bit per-token activation quantization, and (iii) low-bitweight-only quantization schemes. The code is available at https://github.com/onliwad101/FlexRound_LRQ.
• AdaLog: Post-Training Quantization for Vision Transformers with Adaptive Logarithm Quantizer by Beihang University (https://arxiv.org/pdf/2407.12951). The paper proposes a non-uniform quantizer that optimizes the logarithmic base to accommodate the power-law-like distribution of activations while simultaneously allowing for hardware-friendly quantization and dequantization. By employing the bias reparameterization, the quantizer is applicable to both the post-Softmax and post-GELU activations. The authors also develop an efficient Fast Progressive Combining Search (FPCS) strategy to determine the optimal logarithm base, as well as the scaling factors and zero points for the uniform quantizers. Experimental results on public benchmarks demonstrate promising results for various ViT-based architectures and vision tasks, especially in the W6A6setup. The code is available at https://github.com/GoatWu/AdaLog.
• RECLAIMING RESIDUAL KNOWLEDGE: A NOVEL PARADIGM TO LOW-BITQUANTIZATION by Irish Universities (https://arxiv.org/pdf/2408.00923). The authors present an efficient, low-bit, and PTQ framework for ConvNets by framing optimal quantization as an architecture search problem to re-capture quantization residual knowledge with low-rank adapters. They introduce a differentiable neural combinatorial optimization approach, searching for the optimal low-rank adapters using a smooth, high-order normalized Butterworth kernel. They also show a result, converting the weights of existing high-rank quantization residual convolutional operators to low-rank adapters without training. The method achieves good 4-bit and 3-bit quantization results by using less than 250 iterations on a small calibration set with 1600 images. Code will be open-sourced.
• VQ4DiT: Efficient Post-Training Vector Quantization for Diffusion Transformers by Zhejiang University and vivo Mobile Communication (https://arxiv.org/pdf/2408.17131). The authors explore the Vector Quantization methods for extremely low bit-width DiTs and introduce DiT-specific improvements for better quantization. They calibrate both the codebook and the assignments of each layer simultaneously. The proposed method calculates the candidate assignment set for each weight sub-vector based on Euclidean distance and reconstructs the sub-vector based on the weighted average. Then, using the zero-data and block-wise calibration method, the optimal assignment from the set is efficiently selected while calibrating the codebook. The method achieves competitive evaluation results compared to full-precision models on the ImageNet.
• MobileQuant: Mobile-friendly Quantization for On-device Language Models by Samsung AI Center, Cambridge (https://arxiv.org/pdf/2408.13933). The authors introduce a post-training quantization approach for LLMs that is supported by current mobile hardware implementations (i.e., DSP, NPU), thus being directly deployable on real-edge devices. The method improves upon prior works through simple yet effective methodological extensions that enable us to effectively quantize most activations to a lower bit-width (i.e., 8-bit) with near-lossless performance. They conduct an on-device evaluation of model accuracy, inference latency, and energy consumption. The results indicate that the proposed method reduces inference latency and energy usage by 20%-50% while still maintaining accuracy compared to models using 16-bit activations.
• Low-Bit width Floating Point Quantization for Efficient High-Quality Diffusion Models by the University of Toronto & Vector Institute (https://arxiv.org/pdf/2408.06995).The authors propose a floating-point quantization method for diffusion models that provides better image quality compared to integer quantization methods. They employ a floating-point quantization method by integrating weight rounding learning during the mapping of the full-precision values to the quantized values in the quantization process. The authors also study integer and floating-point quantization methods in state-of-the-art diffusion models. Additionally, they introduce a methodology to evaluate quantization effects, highlighting shortcomings with existing output quality metrics and experimental methodologies. Finally, their floating-point quantization method increases model sparsity by an order of magnitude, enabling further optimization opportunities.
• DopQ-ViT: Towards Distribution-Friendly and Outlier-Aware Post-Training Quantization for Vision Transformers by Institute of Automation and School of Artificial Intelligence of Chinese Academy of Sciences (https://arxiv.org/pdf/2408.03291v2).The paper focuses on the full quantization of Vision Transformers. The authors propose using the Tan Quantizer, which focuses more on values near 1, thereby better fitting the distribution of post-Softmax activations in Transformer layers. Besides, the method selects the median as the optimal scaling factor, effectively addressing the accuracy degradation issue that occurs after parametrizing post-LayerNorm activations. The method achieves very accurate results especially in W6/A6 for various tasks such as ImageNet or MS COCO.
• Differentiable Product Quantization for Memory Efficient Camera Relocalization by Czech Technical University in Prague, Aalto University, University of Oulu (https://arxiv.org/pdf/2407.15540).The authors introduce a simple and standalone metric learning for Differentiable Product Quantization for 3D scene compression that preserves matching properties of the descriptors and the final camera localization performance; ii) the proposed hybrid method enables a better tradeoff between memory complexity and localization; iii) they analyze the tradeoffs between description and map compression and show how localization is more tolerant to description compression on outdoor and indoor datasets. The code will be publicly available at https://github.com/AaltoVision/dpqe.
• Advancing Multimodal Large Language Models with Quantization-Aware Scale Learning for Efficient Adaptation by Xiamen University and SkyWork AI (https://arxiv.org/pdf/2408.03735).The paper introduces a Quantization-aware scale Learning method based on multimodal warmup. This method is grounded in two key innovations: (1) The learning of group-wise scale factors for quantized LLM weights to mitigate the quantization error arising from activation outliers and achieve more effective vision-language instruction tuning; (2) The implementation of a multimodal warmup that progressively integrates linguistic and multimodal training samples, thereby preventing overfitting of the quantized model to multimodal data while ensuring stable adaptation of multimodal large language models to downstream vision-language tasks. The code is supposed to be available at https://github.com/xjjxmu/QSLAW.
• Mamba-PTQ: Outlier Channels in Recurrent Large Language Models by Intel Labs (https://arxiv.org/pdf/2407.12397).This workshop paper is among the first to study post-training quantization on the Mamba architecture. Similar to Transformer models, it observed the presence of outlier channels in activations (those with absolute maximum values exceeding 6 standard deviations from the layer mean) and found that downstream task performance degrades substantially when these channels are removed. The study presents zero-shot results of naïve symmetrical per-tensor quantization of weights and activations across Mamba1 models, ranging from 130M to 2.8B parameters, providing a baseline for future quantization research on this emerging architecture.
• Foundation of Large Language Model Compression – Part 1: Weight Quantization by CSAIL MIT (https://arxiv.org/pdf/2409.02026).This work introduces CVXQ, a post-training weight quantization framework that assigns varying bit widths down to the per-group level, constrained by a target average bit rate per weight element. Formulated through the lens of Lagrangian convex optimization, the framework leads to a dual-ascent methods that alternately update the bit width and the tradeoff variable until all optimality conditions are met. To overcome the non-differentiability arising from discrete bit widths and considering that weight distributions are Gaussian or Laplacian, the framework leverages a well-known result from rate-distortion theory to provide closed-form derivative estimates during optimization. CVXQ adopts an interesting compounding (non-uniform) quantization, where weights are first projected to the sigmoid domain before applying uniform round-to-nearest quantization. A codebook is employed to enable dequantization via simple lookup, avoiding complex inverse computations. Tested across a wide range of model sizes in OPT and Llama2, CVXQ outperforms GPTQ, AWQ, and OWQ at 3- and 4-bit rates per weight in nearly all cases. Full implementation will be available soon here.

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Pruning / Sparsity

• LazyLLM: DYNAMIC TOKEN PRUNING FOR EFFICIENT LONGCONTEXT LLM INFERENCE by Apple and Meta AI (https://arxiv.org/pdf/2407.14057). The paper introduces an LLM acceleration method that selectively computes the KV for tokens important for the next token prediction in both the prefilling and decoding stages. Contrary to static pruning approaches that prune the prompt at once, LazyLLM allows language models to dynamically select different subsets of tokens from the context in different generation steps, even though they might be pruned in previous steps. The method also introduces a concept of AuxCache to store the tokens that are omitted during the previous steps of text generation but required at the current step. Experiments on standard datasets across various tasks demonstrate that LazyLLM can significantly accelerate the generation without fine-tuning, e.g., prefilling stage of the LLama 2 7B model by 2.34x while maintaining accuracy.
• Compact Language Models via Pruning and Knowledge Distillation by Nvidia (https://www.arxiv.org/pdf/2407.14679). Authors propose compression best practices for LLMs that combine depth, width, attention, and MLP pruning with knowledge distillation-based retraining. They arrive at these best practices through a detailed empirical exploration of pruning strategies for each axis, methods to combine axes, distillation strategies, and search techniques for arriving at optimal compressed architectures. They use this guide to compress the Nemotron-4 family of LLMs by a factor of 2-4× and compare their performance to similarly-sized models on a variety of language modeling tasks. Deriving 8B and 4B models from an already pretrained 15B model using this approach requires up to 40x fewer training tokens per model compared to training from scratch; this results in compute cost savings of 1.8x for training the full model family (15B, 8B, and 4B).
• SQFT: Low-cost Model Adaptation in Low-precision Sparse Foundation Models by Intel Labs (https://github.com/IntelLabs/Hardware-Aware-Automated-Machine-Learning). This paper proposes an end-to-end solution for low-precision sparse parameter-efficient fine-tuning of large pre-trained models. It includes an innovative strategy that enables the merging of sparse weights with low-rank adapters without losing the sparsity induced in the base model, overcoming the limitations of previous approaches. SQFT also addresses the challenge of having quantized weights and adapters with different numerical precisions, enabling merging in the desired numerical format without sacrificing accuracy. Multiple adaptation scenarios, models, and comprehensive sparsity levels demonstrate the effectiveness of SQFT. Models and open-source code are available.
• ShadowLLM: Predictor-based Contextual Sparsity for Large Language Models by Cornell University and Google (https://arxiv.org/abs/2406.16635). Contemporary research on contextual sparsity primarily uses magnitude-based metrics to measure the importance of attention heads and neurons in LLMs. This paper aims to assess various importance metrics from the literature, including those based on(1) activation norm, (2) first-order gradient, (3) combination of norm and gradient, (4) second-order gradient, and (5) sensitivity-based metrics. The authors conclude that the PlainAct criterion – the L1-norm of the product of magnitude and gradient – emerges as the better metric by offering a robust sparsity-task tradeoff and learnability in importance rank. The authors also propose using just a single predictor, with the attention scores of the first transformer block as input, to forecast sparsity patterns for the entire LLM, as opposed to DejaVu, which requires predictors at regular intervals of transformer blocks. This innovation simplifies predictor training and implementation while also reducing inference overhead, achieving up to 20% faster generation than DejaVu across sizes of OPT family. Code is here.
• STUN: Structured-Then-Unstructured Pruning for Scalable MoE Pruning by SNU and Snowflake AI Research (https://arxiv.org/pdf/2409.06211).  The work discovers a novel way to prune experts of MoE where the method reduces the complexity of expert selection from combinatorial O(kⁿ/√n) down to O(1) using several greedy assumptions. The authors exploit the structure of router weight, applying clustering based on a so-called behavioral similarity metric to identify (dis)similar experts and utilize the centroid as pruned representation to compute a first-order Taylor approximation of the relative distortion. The entire expert pruning can be effectively run without any calibration data and unnecessarily on GPU, especially for the MoE with large numbers of experts. The work also found that expert pruning followed by unstructured pruning provides a better Pareto front. A key result on Snowflake Arctic, a 480B-parameter MoE with 128 experts, shows that STUN achieves 40% sparsity with minimal performance loss in just two hours using a single H100 GPU where unstructured pruning methods alone fall short.

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Other

• Accuracy is     Not All You Need by Microsoft Research, India (https://arxiv.org/pdf/2407.09141). The authors study the accuracy difference between compressed and source models. They claim that when the accuracy metrics are similar, they observe the phenomenon of flips, wherein answers change from correct to incorrect and vice versa in proportion. The authors conduct a detailed study of metrics across multiple compression techniques, models, and datasets, demonstrating that the behavior of compressed models as visible to end users is often significantly different from the baseline model, even when accuracy is similar. They further evaluate compressed models qualitatively and quantitatively using MT-Bench, showing that compressed models are     significantly worse than baseline models in this free-form generative task. They argue that compression techniques should also be evaluated using distance metrics. Finally, the authors propose two metrics, KL-Divergence and % flips, and show that they are well correlated.
• Scaling LLM Test-Time Compute Optimally can be More Effective than Scaling Model Parameters by UC Berkeley and Google DeepMind (https://arxiv.org/pdf/2408.03314).The paper studies the scaling of inference-time computation in LLMs, focusing on answering the question: If an LLM is allowed to use a fixed but non-trivial amount of inference-time compute, how much can it improve its performance on a challenging prompt? Answering this question has implications not only on the achievable performance of LLMs, but also on the future of LLM pretraining and how one should trade inference-time and pre-training compute. Authors analyze two primary mechanisms to scale test-time computation: (1) searching against dense, process-based verifier reward models; and (2) updating the model’s distribution over a response adaptively, given the prompt at test time. They find that in both cases, the effectiveness of different approaches to scaling test-time compute critically varies depending on the difficulty of the prompt. This observation motivates applying a “compute-optimal” scaling strategy, which acts to most effectively allocate test-time compute adaptively per prompt. Using this compute-optimal strategy, authors can improve the efficiency of test-time compute scaling by more than 4x compared to a best-of-N baseline. Additionally, in a FLOPs-matched evaluation, they find that on problems where a smaller base model attains somewhat non-trivial success rates, test-time compute can be used to outperform a 14x larger model.
• Transformers are SSMs: Generalized Models and Efficient Algorithms Through Structured State Space Dual it by Tri Dao and Albert Gu (https://arxiv.org/abs/2405.21060). This paper discusses improvements to Mamba, the selective structure state space model (SSM) proposed as an alternative to Transformer-based models. The authors provide a framework called State Space Duality (SSD) that connects SSMs and variants of the attention mechanism. The Mamba-2 architecture is proposed, which obtains 2-8x speedup compared to the previous version of Mamba, and it is designed to be friendly to tensor and sequence parallelism. Experiments show that Mamba-2 outperforms Mamba and Transformer-based models in different model sizes. The authors also discuss hybrid models that can benefit from the combination of SSD with components from Transformer blocks.

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Software

• A thorough analysis of performance and bottlenecks when using 4-bit KV cache on Nvidia with PyTorch: https://pytorch.org/blog/int4-decoding. Authors show step-by-step improvement when computing the Self-Attention operation of the Transformer block and compare results with CUDA and Flash Decoding baselines in 4-bit per-row and per-channel quantization settings of KV-cache.
• Llama.cpp introduced SIMD-friendly ternary weights packing/unpacking: https://compilade.net/blog/ternary-packing
• HuggingFace posted an exploratory work and recipes for ternary weights (1.58 bits) LLM weight compression: https://huggingface.co/blog/1_58_llm_extreme_quantization.

Authors

Alexander Kozlov, Nikita Savelyev, Vui Seng Chua, Souvikk Kundu, Nikolay Lyalyushkin,  Andrey Anufriev, Pablo Munoz, Alexander Suslov, Liubov Talamanova, Yury Gorbachev, Nilesh Jain, Maxim Proshin

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Summary

This quarter we see an increasing interest in KV-cache optimization of Large Language and Vision Models. This actually expected as KV-cache is getting a bottleneck after the weight compression problem is solved to some degree. We also believe that KV-cache optimization will continue being a hot topic as it is also involved in the Video Generations scenario where we see a lot of work going on nowadays.

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Highlights

• ‍QServe: W4A8KV4 Quantization and System Co-design for Efficient LLM Serving by MIT, NVIDIA, UMass Amherst, MIT-IBM Watson AI Lab (https://arxiv.org/pdf/2405.04532). A regular work from Song Han Lab which is a comprehensive study of deep LLM optimization and a reference design of a tool for LLM serving. The LLM optimization part includes: W4A8 and 4-bit KV-cache quantization approach; Progressive quantization of weights, to comply with 8-bit compute after dequantizing4-bit weights to 8-bits; SmoothAttention method, to reduce the error of 4-bit quantization of Key cache that is compatible with RoPE operation and can be fused into a preceding Linear layer; ‍‍Progressive quantization of weights, to comply with 8-bit compute after dequantizing4-bit weights to 8-bits. The inference part contains tips and tricks to design efficient inference kernels and execution pipelines on the Nvidia GPUs. The method shows superior results comparing to competitive solutions and demonstrates the ability to substantially reduce LLM serving costs. Some code and pre-compiled binaries are available here: https://github.com/mit-han-lab/qserve.

• ZipCache: Accurate and Efficient KV Cache Quantization with Salient Token Identification by Houmo AI and Chinese universities (https://arxiv.org/pdf/2405.14256).Authors present a KV cache quantization method for LLMs. First, they construct a strong baseline for quantizing KV cache. Through the proposed channel-separable token-wise quantization scheme, the memory overhead of quantization parameters is substantially reduced compared to fine-grained group-wise quantization. To enhance the compression ratio, they propose a normalized attention score. The quantization bit-width for each token is adaptively assigned based on their saliency. The authors also develop an approximation method that decouples the saliency metric from full attention scores compatible with FlashAttention. Experiments demonstrate that the method achieves good compression ratios at fast generation speed, for example, when evaluating Mistral-7B model on GSM8k dataset, the method is capable of compressing the KV cache by 4.98×,with only a 0.38% drop in accuracy.

• BitsFusion: 1.99 bits Weight Quantization of Diffusion Model by Snap Inc. and Rutgers University (https://arxiv.org/pdf/2406.04333). The paper provides a thorough analysis of UNet weight-only quantization of Stable Diffusion 1.5 model. The authors propose an approach for mixed-precision quantization of diffusers. They quantize different layers into different bits according to their quantization error. The authors also introduce several techniques to initialize the quantized model to improve performance, including time embedding pre-computing and caching, adding balance integer, and alternating optimization for scaling factor initialization. Finally, they propose a two-stage Quantization-aware training where distillation is used at the first stage. The quantized model achieves very good results on various benchmarks. Code will be released here: https://github.com/snap-research/BitsFusion.

• Applying t-Distributions to Explore Accurate and Efficient Format[KA1] s for LLMs by Cornell University and Google (https://arxiv.org/abs/2405.03103). The paper investigates non-uniform quantization data formats by profiling the distributions of weight and activation across 30 models, including both LLM and non-LLM models. The authors discovered that Student’s t-Distribution is a better fit than the Gaussian distribution due to its flexible parameterization, which can resemble Gaussian, Cauchy, or other distributions observed indifferent neural networks. The authors derived Student Float (SF4) using a similar design process to Normal Float (NF4). SF4 outperforms NF4, FP4, and Int4 in accuracy retention across most cases and model architectures, making it a strong drop-in replacement for lookup-based datatypes like NF4. The paper proposes using SF4as a reference to extend supernormal support for existing datatypes like E2M1(one variant of FP4) and APoT4,by reassigning negative zero to a useful value, which is otherwise wasted. Additionally, the paper examines the Pareto frontier of datatypes in terms of model accuracy and MAC chip area, concluding that APoT4 and its supernormal extension are Pareto optimal for a set of models smaller than 7B parameters.

• ShiftAddLLM: MatMul-free LLM via Inference Time Reparameterization by Intel, Google Deep Mind, Google, Georgia Tech (https://arxiv.org/pdf/2406.05981).Authors developed an inference time reparameterization for traditional LLMs layers with MatMul ops to convert them to layers with Shift-Add and LUT query-based operations only. Specifically, authors quantize each weight matrix into binary matrices paired with group-wise scaling factors. The associated multiplications are reparameterized into (1) shifts between activations and scaling factors and (2) queries and adds according to the binary matrices. To reduce accuracy loss, they present a multi-objective optimization method to minimize both weight and output activation reparameterization errors. Additionally, based on varying sensitivity across layers to reparameterization, they develop an automated bit allocation strategy to further reduce memory usage and latency. The code is available at: https://github.com/GATECH-EIC/ShiftAddLLM.

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Quantization

• ‍QServe: W4A8KV4 Quantization and System Co-design for Efficient LLM Serving by MIT, NVIDIA, UMass Amherst, MIT-IBM Watson AI Lab (https://arxiv.org/pdf/2405.04532). A regular work from Song Han Lab which is a comprehensive study of deep LLM optimization and a reference design of a tool for LLM serving. The LLM optimization part includes: W4A8 and 4-bit KV-cache quantization approach; Progressive quantization of weights, to comply with 8-bit compute after dequantizing4-bit weights to 8-bits; SmoothAttention method, to reduce the error of 4-bit quantization of Key cache that is compatible with RoPE operation and can be fused into a preceding Linear layer; ‍‍Progressive quantization of weights, to comply with 8-bit compute after dequantizing4-bit weights to 8-bits. The inference part contains tips and tricks to design efficient inference kernels and execution pipelines on the Nvidia GPUs. The method shows superior results comparing to competitive solutions and demonstrates the ability to substantially reduce LLM serving costs. Some code and pre-compiled binaries are available here: https://github.com/mit-han-lab/qserve.‍
• LQER: Low-Rank Quantization Error Reconstruction for LLMs by Imperial College London London and University of Cambridge (https://arxiv.org/pdf/2402.02446). The paper combines quantization and low-rank approximation techniques to achieve accurate and efficient LLM optimization. The method employs MXINT4 datatype (int4 + shared exponent for 4 elements) for weight quantization while quantizing activation into 8 or 6 bits with per-token scaling factors. The method also introduces 8-bit LoRA adapters to restore accuracy after weight quantization. It does not use any kind of fine-tuning. Instead, it introduces the error decomposition into two low-rank matrices. The method achieves very accurate results in W4A8 and W4A6 settings, especially on Llama-2 model family.‍
• LLM-QBench: A Benchmark Towards the Best Practice for Post-training Quantization of Large Language Models by Beihang University, SenseTime Research, and Nanyang Technological University (https://arxiv.org/pdf/2405.06001).The paper focuses on identifying the most effective practices for quantizing LLMs, with the goal of balancing performance with computational efficiency. Fora fair analysis, the authors develop a quantization toolkit LLMC and design four crucial principles considering the inference efficiency, quantized accuracy, calibration cost, and modularization. By benchmarking on various models and datasets with over 500 experiments, three takeaways corresponding to calibration data, quantization algorithm, and quantization schemes are derived. Finally, a best practice of LLM PTQ pipeline is constructed. All the benchmark results and the toolkit can be found at https://github.com/ModelTC/llmc.
• SKVQ: Sliding-window Key and Value Cache Quantization for Large Language Models Houmo AI by Houmo AI and Chinese universities (https://arxiv.org/pdf/2405.06219). The paper addresses the problem of extremely low bit-width KV cache quantization. To achieve this, it proposes a method that rearranges the channels of the KV cache in order to improve the similarity of channels in quantization groups and applies clipped dynamic quantization at the group level. Additionally, the method ensures that the most recent window tokens in the KV cache are preserved with high precision. This helps maintain the accuracy of a small but important portion of the KV cache.  Evaluation on LLMs demonstrates that the method surpasses previous quantization approaches, allowing for quantization of the KV cache to 2-bit keys and 1.5-bit values with minimal loss of accuracy. The code is available at https://github.com/cat538/SKVQ.‍
• Integer Scale: A Free Lunch for Faster Fine-grained Quantization of LLMs by Meituan (https://arxiv.org/pdf/2405.14597). The paper proposes a scheme to use integer scales when computing dot products of W4A8quantized LLMs. It allows keeping group scales for weights in the integer precision as well and using INT32 buffer as the accumulator of partial dot products. An additional floating point scale is required and applied to the super-group of dot products between weights and activations. It brings the proposed method close to the known double quantization approach. The paper provides extensive evaluation data for Llama2 and Llama3 models showing close results to the baseline floating-point scales.
• Mitigating Quantization Errors Due to Activation Spikes in GLU-Based LLMs by Hanyang University (https://arxiv.org/pdf/2405.14428).The paper aims at reducing the accuracy degradation of fully-quantized LLM models (both weights and activations are quantized). Authors propose two empirical methods, Quantization-free Module (QFeM) and Quantization-free Prefix (QFeP), to isolate the activation spikes during quantization that cause most of the accuracy drop. Essentially, they propose a way to identify what layers are more error-prone and keep these layers in the floating-point precision. The code is available at https://github.com/onnoo/activation-spikes.
• AdpQ: A Zero-shot Calibration Free Adaptive Post Training Quantization Method for LLMs by Huawei Noah Lab and McGill University (https://arxiv.org/pdf/2405.13358).This paper presents a novel zero-shot adaptive PTQ method for LLMs that does not require any calibration data. Inspired by Adaptive LASSO regression model, the authors proposed approach that tackles the challenge of outlier activations by separating salient weights using an adaptive soft-thresholding method. Guided by Adaptive LASSO, this method ensures that the quantized weights distribution closely follows the originally trained weights and eliminates the need for calibration data entirely. The method achieves good results at much faster quantization time.
• PTQ4SAM: Post-Training Quantization for Segment Anything by Beihang University (https://arxiv.org/pdf/2405.03144).A practical study on quantization of the Segment Anything model. The authors observe a challenging bimodal distribution for quantization and analyze its characteristics. To overcome it, they propose a Bimodal Integration (BIG)strategy, which automatically detects it and transforms the bimodal distribution to normal distribution equivalently. They also present the Adaptive Granularity Quantization which represents diverse post-Softmax distributions accurately with appropriate granularity. Experiments show that the method can achieve good results even in low-bit quantization settings (6 or4 bits). Code is available at https://github.com/chengtao-lv/PTQ4SAM.
• QNCD: Quantization Noise Correction for Diffusion Models by Kuaishou Technology (https://arxiv.org/pdf/2403.20137). Authors identify two primary quantization challenges for Duffusion models: intra and inter quantization noise. Intra quantization noise, exacerbated by embeddings in the resblock module, extends activation quantization ranges, increasing disturbances in each single denoising step. Besides, inter quantization noise stems from cumulative quantization deviations across the entire denoising process, altering data distributions step-by-step. Authors propose embedding-derived feature smoothing for eliminating intra quantization noise and a runtime noise estimation module for dynamically filtering inter quantization noise. Experiments demonstrate that the method achieves good results in W4A8 and W8A8 quantization settings on ImageNet (LDM-4). Code is available at: https://github.com/huanpengchu/QNCD.
• SliM-LLM: Salience-DrivenMixed-Precision Quantization for Large Language Models by The ETH Zürich, University of Hong Kong, and Beihang University (https://arxiv.org/pdf/2405.14917).The paper focuses on the problem of ultra-low bit weight quantization of LLMs. Specifically, it proposes the method relies on two novel techniques: (1)Salience-Determined Bit Allocation utilizes the clustering characteristics of salience distribution to allocate the bit-widths of each quantization group. This increases the accuracy of quantized LLMs and maintains the inference efficiency high; (2) Salience-Weighted Quantizer Calibration optimizes the parameters of the quantizer by considering the element-wise salience within the group. The method is evaluated in two setups for quantization parameters tuning: greedy search and gradient based search. Evaluation shows good results on Llama 1/2/3 models. Code is available at https://github.com/Aaronhuang-778/SliM-LLM.
• LCQ: Low-Rank Codebook based Quantization for Large Language Models by Nanjing University (https://arxiv.org/pdf/2405.20973). The paper proposes a method for LLM optimization using customized low-ranking codebooks the rank of which can be larger than one, for quantization. A gradient-based optimization algorithm is proposed to optimize the parameters of the codebook. The method also adopts a double quantization strategy for compressing the parameters of the codebook, which can reduce the storage cost of the codebook. Experiments show that achieves better accuracy than existing methods with a negligibly extra storage cost.
• P2 -ViT: Power-of-Two Post-Training Quantization and Acceleration for Fully Quantized Vision Transformer by Nanjing University and Sun Yat-sen University (https://arxiv.org/pdf/2405.19915). The paper introduces a Power-of-Two (PoT) post-training quantization and acceleration framework for ViT models. The authors analyze ViTs’ properties and develop a dedicated quantization scheme. This scheme incorporates techniques such as adaptive PoT rounding and PoT Aware smoothing, allowing for the efficient quantization of ViTs with PoT scaling factors. By doing this, computationally expensive floating-point multiplications and divisions with in the re-quantization process can be traded with hardware-efficient bitwise shift operations. Furthermore, we introduce a coarse-to-fine automatic mixed-precision quantization methodology for better accuracy-efficiency trade-offs. Finally, authors build a dedicated accelerator engine to better everage our algorithmic properties for enhancing hardware efficiency. Code is available at: https://github.com/shihuihong214/P2-ViT.
• QJL: 1-Bit Quantized JLTransform for KV Cache Quantization with Zero Overhead by New York University and Adobe Research (https://arxiv.org/pdf/2406.03482).The paper studies problems of KV-cache quantization of LLMs, specifically the Key part as it is more error-prone when lowering its precision. Authors propose an approach that consists of a Johnson-Lindenstrauss (JL) transform followed by sign-bit quantization for Key cache. They introduce an asymmetric estimator for the inner product of two vectors and demonstrate that applying the method to one vector and a standard JL transform without quantization to the other provides an unbiased estimator with minimal distortion.  They also developed a CUDA-based implementation for optimized computation. When applied across various LLMs and NLP tasks to quantize the KV cache to only 3 bits, the method demonstrates a more than fivefold reduction in KV cache memory usage without an insignificant accuracy drop. Codes will be available at https://github.com/amirzandieh/QJL.
• ViDiT-Q: Efficient and Accurate Quantization of Diffusion Transformers for Image and Video Generation by Tsinghua University, Infinigence AI, 3Microsoft, and Shanghai Jiao Tong University (https://arxiv.org/pdf/2406.02540).The paper tackles the problems of accurate quantization of diffusion vision transformer models. Essentially, authors apply dynamic 8-bit per-token quantization to activations. They also propose to smooth activation with a Smoothquant-like approach but with different α factors tuned to each iteration of the diffusion process. Finally, authors propose to select a per-layer weight bit-width (e.g.W4A8, W6A6, or W8A8) depending on the sensitivity and position of the layer in the Transformer block. All these tricks lead to very good accuracy results in the image and video generation tasks.
• Instance-Aware Group Quantization for Vision Transformers by Yonsei University and Articron (https://arxiv.org/pdf/2404.00928). In this paper an approach for instance-aware group quantization for ViTs(IGQ-ViT) is introduced. According to the approach, channels of activation maps are dynamically split into multiple groups where each group has its own set of quantization parameters. Authors also extend their scheme to quantize softmax attentions across tokens. IGQ-ViT demonstrates superior accuracy results across image classification, object detection and instance segmentation task. Authors claim that performance overhead induced by dynamic quantization is no more than 4% compared to layer-wise quantization.
• Reg-PTQ: Regression-specialized Post-training Quantization for Fully Quantized Object Detector by Beihang University (https://openaccess.thecvf.com/content/CVPR2024/papers/Ding_Reg-PTQ_Regression-specialized_Post-training_Quantization_for_Fully_Quantized_Object_Detector_CVPR_2024_paper.pdf). In this paper authors explore full quantization of object detection models contrary to most existing approaches which quantize only detection backbones and keep detection head in original precision. Based on the findings, the reason behind poor quantization of detector heads is that they are optimized to solve regression tasks. Specifically, authors argue that (1) regressors are more sensitive to perturbation compared to classifiers, (2) minimizing quantization error does not necessarily result in optimal scaling factors for regressor and(3) regressors weights follow non-uniform distribution contrary to classifiers. To tackle these problems a novel Reg-PTQ method is introduced. Based on the results it achieves 7.6x and 5.4x reduction in computation and storage consumption under INT4 precision with little performance degradation.‍
• Towards Accurate Post-training Quantization for Diffusion Models (https://openaccess.thecvf.com/content/CVPR2024/papers/Wang_Towards_Accurate_Post-training_Quantization_for_Diffusion_Models_CVPR_2024_paper.pdf). In this paper authors propose a method for accurate post-training quantization of diffusion models. The main idea is to split diffusion timesteps for each layer into groups where each group corresponds to its own set of quantization parameters. Such split is obtained by minimizing some optimization objective on a calibration dataset.  Besides this, a special timestep selection method is employed for sampling timesteps for calibration. Overall, the method demonstrates superior generation quality results over such baselines as LSQ, PTQ4DM and Q-Diffusion.

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Pruning/Sparsity

• Effective Interplay between Sparsity and Quantization: From Theory to Practice by Google and EcoCloud (https://arxiv.org/pdf/2405.20935). Authors provide the theoretical analysis of how sparsity and quantization interact. Mathematical proofs establish that applying sparsity before quantization (S → Q) is the optimal sequence for compression. Authors demonstrate that sparsity and quantization are not orthogonal operations. Combining them introduces additional errors beyond the sum of their individual errors. They validate theoretical findings through experiments covering a diverse range of models, including prominent LLMs (OPT, LLaMA) and ViTs. The code will be published at: https://github.com/parsa-epfl/quantization-sparsity-interplay.
• Prompt-prompted Mixture of Experts for Efficient LLM Generation by CMU (https://arxiv.org/pdf/2404.01365). Authors introduce GRIFFIN, a training-free MoE that selects unique FF experts at the sequence level for efficient generation across a plethora of LLMs with different non-ReLU activation functions. This is possible due to a critical observation that many trained LLMs naturally produce highly structured FF activation patterns within a sequence, which we call flocking. Despite the method’s simplicity, it shows with 50% of the FF parameters, GRIFFIN maintains the original model’s performance with little to no degradation on a variety of classification and generation tasks, all while improving latency (e.g. 1.25× speed-up in Llama 213B on an NVIDIA L40). Code is available at https://github.com/hdong920/GRIFFIN.
• Sparse maximal update parameterization: A holistic approach to sparse training dynamics by Cerebras Systems (https://arxiv.org/pdf/2405.15743).This paper addresses the common issue in sparse training where hyper parameters from dense training are reused, leading to suboptimal convergence, and requiring extensive tuning for different sparsity ratios. The researchers introduce a novel sparse training methodology called Sparse Maximal Update Parameterization (SuPar), which extends the maximal update parameterization (uP)to sparse training. SuPar involves reparameterizing (see Table 1) weight initialization and learning rates relative to changes in sparsity, effectively preventing exploding or vanishing signals and maintaining stable activation, gradient, and weight update scales across varying sparsity levels and model widths. SuPar reparameterization is remarkable, it allows zero-shot hyperparameter transfer, i.e. practitioners can now tune small proxy models(dense/sparse) and transfer optimal HPs directly to models at scale for any model sparsity, thus enhancing the efficiency and reducing the cost of sparse model development. Experiments demonstrate that SμPar sets the Pareto frontier best loss across all sparsities and widths, including large dense model with width equal to GPT-3 XL.
• Sparse Expansion and Neuronal Disentanglement by MIT, IST Austria, Neural Magic (https://arxiv.org/pdf/2405.15756). Sparse Expansion is an approach of converting dense LLMs to mixture of sparse experts to attain inference efficiency. The method begins with applying dimensionality reduction (PCA) on the inputs of FFN linear layers, followed by a k-means clustering. The intuition is that tokens within a cluster share a sparse expert better without significant distortion. SparseGPT is then used to create a sparse expert for each cluster group. During inference, the PCA and k-means models act as routers, directing tokens to the appropriate sparse expert based on their cluster. While this increases the overall model size, acceleration is achieved through the conditional execution of experts and the sparse execution of these experts, with minimal cost for the routers. The paper includes layer-wise speedup benchmarks and shows that Sparse Expansion outperforms other one-shot sparsification approaches in perplexity for the same inference FLOP budget per token. A significant portion of the paper is dedicated to the concept of neuron entanglement, explaining, and quantifying the efficacy of sparse expansion.‍
• MULTIFLOW: Shifting Towards Task-Agnostic Vision-Language Pruning by University of Trento and Cisco Research (https://arxiv.org/pdf/2404.05621). Authors highlight that existing techniques for pruning of Visual-Language models(VLMs) are task-specific and propose a task-agnostic method for pruning VLMs. The proposed Multimodal Flow Pruning framework has the following properties: (1) the importance of a weight is computed based on saliency of the neurons it connects; and (2) parameters are pruned considering features of which modality they are used to compute allowing to avoid pruning too much from a single modality and too little from another. Experiments show that the proposed MULTIFLOW method outperforms recent more sophisticated competitors.

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Other methods

• Flash Diffusion: Accelerating Any Conditional Diffusion Model for Few Steps Image Generation by Jasper Research (https://arxiv.org/pdf/2406.02347). The paper proposes a LoRA-compatible distillation method aiming at reducing the number of sampling steps required to generate high-quality samples from a trained diffusion model. Authors emphasize the versatility of the method through an extensive experimental study across various tasks (text-to-image, image inpainting, super-resolution, face-swapping), diffusion model architectures (SD1.5, SDXL and Pixart-α) and illustrate its compatibility with adapters. The method is relatively lightweight and can optimize SD1.5 model with 2 Nvidia H100 80GB with 13 hours of fine-tuning. Code is available at https://github.com/gojasper/flash-diffusion.
• GaLore: Memory-Efficient LLM Training by Gradient Low-Rank Projection by California Institute of Technology, Meta AI, University of Texas at Austin, and Carnegie Mellon University (https://arxiv.org/pdf/2403.03507). The paper introduces a Gradient Low-Rank Projection (GaLore), a training strategy that allows full-parameter learning but is more memory-efficient than common low-rank adaptation methods such as LoRA. The idea is to use PCA after a number of training steps to obtain a gradient projection matrix and use it to get a low-rank gradient matrix that is used for weights update. The approach reduces memory usage by up to 65.5% in optimizer states while maintaining both efficiency and performance for pre-training. 8-bit GaLore further reduces optimizer memory by up to 82.5% and total training memory by 63.3%, compared to a BF16 baseline. It demonstrates the feasibility of pre-training a 7B model on consumer GPUs with 24GB memory. The code is available at: https://github.com/jiaweizzhao/GaLore.
• MiniCache: KV Cache Compression in Depth Dimension for Large Language Models by ZIP Lab of Monash and Zhejiang University (https://arxiv.org/pdf/2405.14366).The authors propose a training-free KV cache compression technique by merging KV tokens across every two consecutive transformer layers, based on the observation that KV tokens are highly similar across depth, especially from the middle to the last transformer layers. Specifically, a pair of K/V projections from two consecutive layers can be encoded into respective scaling factors and a shared directional vector computed via Spherical Linear Interpolation(SLERP). To address the information loss from merging dissimilar tokens, the algorithm uses angular-based distance to filter KV positions for retention. The algorithm is straightforward, involving calibration of only two hyperparameters, and it has demonstrated to enhance a 4X compressed KV cache by4-bit quantization to over 5X compression while retaining reasonable accuracy of instruction-tuned Mistral, LLama2-7B across benchmarks.
• Scalable MatMul-free Language Modeling by University of California, Soochow University, LuxiTech (https://arxiv.org/pdf/2406.02528). Authors develop a MatMul-free language model by using additive operations in dense layers and element-wise Hadamard products for self-attention-like functions. Specifically, ternary weights eliminate MatMul in dense layers, similar to BNNs. To remove MatMul from self-attention, they optimize the Gated Recurrent to rely solely on element-wise products and show that this model competes with state-of-the-art Transformers while eliminating all MatMul operations. To quantify the hardware benefits of lightweight models, the authors provide an optimized GPU implementation in addition to a custom FPGA accelerator. By using fused kernels in the GPU implementation of the ternary dense layers, training is accelerated by 25.6% and memory consumption is reduced by up to 61.0% over an unoptimized baseline on GPU. Furthermore, by employing lower-bit optimized CUDA kernels, inference speed is increased by 4.57 times, and memory usage is reduced by a factor of 10 when the model is scaled up to 13B parameters. The code is available at https://github.com/ridgerchu/matmulfreellm.
• Unlocking Efficiency in Large Language Model Inference: A Comprehensive Survey of Speculative Decoding by Hong Kong Polytechnic, Peking University, Microsoft Research Asia and Alibaba (https://arxiv.org/abs/2401.07851). While LLMs are proliferating over the past two years, Speculative Decoding (SD) has emerged as a crucial paradigm to accelerate autoregressive generation. This survey is among the first to provide a comprehensive introduction and overview of the state of the art in SD, highlighting key developments in this space. A main contribution of this work is the introduction of Spec-Bench, a unified benchmark for evaluating SD methods across standardized subtasks such as multi-turn conversation, summarization, RAG, translation, question answering, and mathematical reasoning. The codes and benchmarks for various SD methods on RTX 3090 and A100 GPUs are accessible for further exploration and validation.
• Speculative Decoding via Early-exiting for Faster LLM Inference with Thompson Sampling Control Mechanism by Meituan and Meta AI (https://arxiv.org/pdf/2406.03853).The paper introduces an early-exiting framework for generating draft tokens, which allows a single LLM to fulfill the drafting and verification stages. The model is trained using self-distillation. The authors conceptualize the generation length of draft tokens as a multi-armed bandit problem and propose a control mechanism based on Thompson Sampling, which leverages sampling to devise an optimal strategy. They conducted experiments on three benchmarks and showed that the method can significantly improve the model’s inference speed.
• LayerSkip: Enabling Early Exit Inference and Self-Speculative Decoding by Meta, University of Toronto, Carnegie Mellon University, University of Wisconsin-Madison, Dana-Farber Cancer Institute (https://arxiv.org/pdf/2404.16710).Authors research the idea of early exit in LLMs for speculative decoding. First, during training, they apply layer dropout, with low dropout rates for earlier layers and higher dropout rates for later layers, and an early exit loss where all transformer layers share the same exit. Second, during inference, they show that this training recipe increases the accuracy of early exit at earlier layers, without adding any auxiliary layers or modules to the model. Third, they present a self-speculative decoding solution where we exit at early layers and verify and correct with remaining layers of the model. They run experiments on different Llama model sizes on different types of training: pretraining from scratch, continual pretraining, finetuning on specific data domain, and finetuning on specific task, and show speedups of up to 2.16× on summarization for CNN/DM documents, 1.82× on coding, and 2.0× on TOPv2 semantic parsing task.

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Software

• INT4 Decoding GQA CUDA Optimizations for LLM Inference by Meta(https://pytorch.org/blog/int4-decoding).The authors provide a comprehensive study and ten practical steps, including KV-cache quantization, to improve the performance of Grouped-query Attention. All these optimizations result in performance improvements of up to 1.8x on the NVIDIA A100 GPU and 1.9x on the NVIDIA H100 GPU.
• torchao: PyTorch Architecture Optimization by Meta (https://github.com/pytorch/ao). PyTorch library for quantization and sparsity. Currently-available features contain full models quantization, INT8, INT4, MXFP4,6,8 weight-only quantization and efficient model fine-tuning with GaLore method.
• Introducing Apple’s On-Device and Server Foundation Models by Apple (https://machinelearning.apple.com/research/introducing-apple-foundation-models). Apple has established a set of pre-trained and optimized models for its HW. The claim is that 3B LLM model can be run at 30t/s on iPhone 15 Pro. In terms of optimizations that are being used, authors claim weight palletization to 2 and 4 bits, quantization of embeddings and activations and efficient Key-Value (KV) cache update. They use their own AXLearn  library built on top of JAX  and XLA for model pre-training and fine-tuning.
• BitBLAS by Microsoft (https://github.com/microsoft/BitBLAS).A library to support mixed-precision BLAS operations on GPUs. BitBLAS aims to support efficient mixed-precision DNN model deployment, especially the quantization in large language models (LLMs), for example, the 𝑊4𝐴16 in GPTQ, the 𝑊2𝐴16 in BitDistiller, the 𝑊2𝐴8 in BitNet-b1.58.

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Authors: Xiake Sun, Wenyi Zou, Fiona Zhao

Introduction

A Large Language Model (LLM) is a type of artificial intelligence algorithm that uses deep learning techniques and massively large data sets to understand, summarize, generate and predict new content.

OpenVINO™ optimizes the deployment of LLMs, enhancing their performance and integration into various applications. We already provide general guide to use LLMs with OpenVINO™, from model loading and conversion to advanced use cases.

In this blog, we will introduce some useful method to customize Large Language model’s graph with OpenVINO™ transformation API.

OpenVINO™ Runtime has three main transformation types:

• Modelpass: straightforward way to work with ov::Model directly
• Matcherpass: pattern-based transformation approach
• Graphrewrite pass: container for matcher passes needed for efficient execution.

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In this blog, we mainly use ov::pass::MatcherPassto customize model subgraph via pattern-based transformation.

Here are common steps to implement graph customization using ov::pass::MatcherPass.

1. Create a pattern
2. Implement a callback
3. Register the pattern and Matcher
4. Execute MatcherPass

In this blog, we will use an open-source LLMs Qwen1.5-7B-Chat-GPTQ-Int4 from Alibaba Cloud with guide for model conversion and graph customization methods.

Qwen Pytorch to OpenVINO™ Model conversion

Here we can use openvino.genai repo to convert Qwen1.5 GPTQ INT4 Pytroch model to OpenVINO™model.

conda create -n openvino.genai python=3.10
git clone https://github.com/openvinotoolkit/openvino.genai
cd llm_bench/python
pip install -r requirements.txt
python convert.py –model_id Qwen/Qwen1.5-7B-Chat-GPTQ-Int4 --output_dir  Qwen1.5-7B-Chat-GPTQ-Int4-OV --precision FP16 

Converted model can be find in path “Qwen1.5-7B-Chat-GPTQ-Int4-OV/pytorch/dldt/GPTQ_INT4-FP16/".

Insert custom layer to OpenVINO™ model

Vocabularysize in the context of LLMs refers to the total number of unique words, or tokens, that the model can recognize and use. The larger the vocabulary size,the more nuanced and detailed the model’s understanding of language can be,however, it also requires more computational and memory resources for deployment.  E.g. Qwen’s vocabulary size(151936) is almost 5x that Llama2 (32000), therefore additional optimization is required for efficient deployment.

We found that the following pattern existed in the Qwen model in Figure 2:

To compute the first token generation for the input prompt with shape [1, seq_length], we need to calculate a MatMul operation based on two inputs.

• First input is a reshape node output with shape[1, seq_length, 4096]
• Second input is a constant value that contains the model’s vocabulary with shape [4096,151936]

Then Matmul calculates two inputs [1, seq_length, 4096] * [4096,151936] to output large logits [1, seq_length,151936]. However, for the next token prediction, we only need the last element [1,4096] in 1st dimension from logits for sampling.

The main idea is to insert a slice operation between Reshape and Matmul nodes to extract only the last element in 2nd dimension of reshape node output as the first input with shape [1,4096] for computation. Therefore, Matmul computation can be reduced from [1, seq_len, 4096] * [1, 4096, 151936] = [1, seq_len, 151936] to [1, 1, 4096] *[4096, 151936] = [1, 1, 151936], which can reduce first token latency and memory consumption.

Here is a sample code to implement the workflow defined in Figure2 to reduce Qwen's last Matmul computation and memory usage:

# -*- coding: utf-8 -*-
import numpy as np
import openvino as ov
from openvino.runtime import Core, Type
from openvino.runtime.passes import Manager, MatcherPass, WrapType, Matcher
from openvino.runtime import opset10 as ops
from openvino.preprocess import PrePostProcessor

class InsertSlice(MatcherPass):
    def __init__(self):
        MatcherPass.__init__(self)
        self.model_changed = False

        param = WrapType("opset10.Result")

        def callback(matcher: Matcher) -> bool:
            root = matcher.get_match_root()
            print("root: ", root)
            if root is None:
                return False
            root_output = matcher.get_match_value()
            print("root_output", root_output)
            root_name = root.get_friendly_name()
            if (len(root.get_output_partial_shape(0)) == 3):
                print(f"Find target root node name: {root_name}")
                parent = root.input_value(0).get_node()
                print(f"Find target parent node name: {parent.get_friendly_name()}")
                grand_parent = parent.input_value(0).get_node()
                print(f"Find grandparent node name: {grand_parent.get_friendly_name()}")
                grand_parent_output = parent.input(0).get_source_output()
                print("grand_parent_output: ", grand_parent_output)
                consumers = grand_parent_output.get_target_inputs()
                
                print(f"consumers: {consumers}")
                print("Original reshape node output shape:", grand_parent_output.get_partial_shape())
                start = np.array([0, -1, 0], dtype=np.int32)
                stop = np.array([1, -2, 4096], dtype=np.int32)
                step = np.array([1, -1, 1], dtype=np.int32)
                axes = np.array([0, 1, 2], dtype=np.int32)
                slice = ops.slice(grand_parent, start, stop, step, axes, name="inserted_slice")
                print("After insert slice node, output shape:", slice.output(0).get_partial_shape())

                for consumer in consumers:
                    consumer.replace_source_output(slice.output(0))
                self.model_changed = True
                # Use new operation for additional matching
                self.register_new_node(slice)
                                
                return True

        self.register_matcher(Matcher(param,"InsertSlice"), callback)

if __name__ == "__main__":
    model_path = " Qwen1.5-7B-Chat-GPTQ-Int4-OV/pytorch/dldt/GPTQ_INT4-FP16/ openvino_model.xml"
    modified_model_path = "Qwen1.5-7B-Chat-GPTQ-Int4-OV/pytorch/dldt/GPTQ_INT4-FP16/modified_openvino_model.xml")
    core = Core()
    ov_model = core.read_model(model_path)
    manager = Manager()
    manager.register_pass(InsertSlice())
    manager.run_passes(ov_model)
    ov.save_model(ov_model, modified_model_path)

We defined a OpenVINO™ transformation "InsertSlice" to find the logits (Results) node via ov::pass::MatchPass, then search along root->parent->grandparent node to find the Reshape node. Afterward, we insert a Slice node between the Reshape and Matmul nodes to extract the last element of seq_length with shape [1,1,4096]. In the end, we apply "InsertSlice" transformation to original OpenVINO™ model and save modified model on disk for deployment.

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Modify model weights of specified layer in OpenVINO™ model

In case you want to update certain model layer weights after model training or fine-tuning/compression.

E.g. if you have an INT4 weight-compressed model using another model compression method, e.g. AWQ, you may want to transfer model weights optimized with the quantization method.

The most general method will be to convert the original model to OpenVINO™ model if the model direct conversion works. However, if first option is not works out of box, an alternative option is to replace the model weights from OpenVINO™ models with external fine-tuning model weights.

Here we introduce a common method to modify layer weights of Qwen model via OpenVINO™ transformation API.

As Figure 3 shows, the goal is to replace model weights and scale of the original Constant node with external fine-tuned weights and scale data.

At first, we use ov::pass::MatchPass method to find the Convert node after the target node. Then we create a new constant node with external weight saved as a numpy array. Please note, GPTQ int4 model weight is saved asuint4 (U4) binary format, while numpy can only represent data with numpy.uint8. Therefore, we use a help function to pack 2 uint4 binary data as 1 uint8 binary data. Then we replace the Convert input port from the original Constant node to the new Constant node.  Since the old constant node has no consumers and is neither the Result nor the Sink operation whose shared pointer counter is zero, the operation will be destructed and not be accessible anymore.

Here is a sample code to implement the workflow defined in Figure3 to replace Qwen Constant node via the new Constant node with external data:

# -*- coding: utf-8 -*-
import numpy as np
import openvino as ov
import torch 
from openvino.runtime import Core, Model, Type
from openvino.runtime.passes import Manager, GraphRewrite, MatcherPass, WrapType, Matcher
from openvino.runtime import opset10 as ops
from openvino.helpers import pack_data, unpack_data
                    
pytorch_to_ov_layer_mapping = [{"__module.model.layers.0.mlp.down_proj/aten::to/Convert": }]
packed_layername_tensor_dict_list = [{"name":"__module.model.layers.0.mlp.down_proj/aten::to/Convert","value":np.ones([1376*4, 4096],dtype=np.uint8)}]

class InsertWeights(MatcherPass):
    def __init__(self,packed_layername_tensor_dict_list):
        MatcherPass.__init__(self)
        self.model_changed = False

        param = WrapType("opset10.Convert")

        def callback(matcher: Matcher) -> bool:
            root = matcher.get_match_root()
            if root is None:
                return False
            root_output = matcher.get_match_value()
            for y in packed_layername_tensor_dict_list:
                #root_name = root.get_friendly_name().replace('.','_')
                root_name = root.get_friendly_name()
                print(f"root_name: {root_name}")
                if root_name.find(y["name"]) != -1 :
                    consumers = root.input_value(0).get_target_inputs()
                    unpacked_data = unpack_data(y["value"],Type.u4,y["value"].shape)
                    print(unpacked_data.shape)
                    new_weights = ops.constant(np.zeros(root.get_output_shape(0)),Type.u4,name=y["name"]+"_new_const")
                    print("new_weights: ", new_weights)
                    new_weights.data[:] = unpacked_data.ravel()
                    print(f"new_weights.shape: {new_weights.shape}")
                    
                    for consumer in consumers:
                        consumer.replace_source_output(new_weights.output(0))

                    # For testing purpose
                    self.model_changed = True
                    # Use new operation for additional matching
                    packed_layername_tensor_dict_list.remove(y)

            return True

        self.register_matcher(Matcher(param,"InsertWeights"), callback)

if __name__ == "__main__":
    model_path = "Qwen1.5-7B-Chat-GPTQ-Int4-OV/pytorch/dldt/GPTQ_INT4-FP16/openvino_model.xml"
    modified_model_path = "Qwen1.5-7B-Chat-GPTQ-Int4-OV/pytorch/dldt/GPTQ_INT4-FP16/modified_openvino_model.xml")
    core = Core()
    ov_model = core.read_model(model_path)
    manager = Manager()
    manager.register_pass(InsertWeights(packed_layername_tensor_dict_list))
    manager.run_passes(ov_model)
    ov.save_model(ov_model, modified_model_path)

We defined a OpenVINO™ transformation "InsertWeights" to find the target constant node via ov::pass::MatchPass, then we create a new Constat node with external numpy data and pack it as uint4 OpenVINO™ Tensor to replace original constant node in graph. In the end, we apply "InsertWeights" transformation to original OpenVINO™ model and save modified model on disk for deployment.

Conclusion

In this blog, we introduce how to apply graph customization based on OpenVINO™ model with OpenVINO™ transformation API. Furthermore, we show two examples of inserting layers & modifying layer weights based on Qwen LLM model with simple Python code.

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Reference

QwenLM/Qwen1.5

OpenVINO™Transformation API

IntegrateOpenVINO™ with Your Application – Model Representation

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