# Optimization

### Large Language Model Graph Customization with OpenVINO™ Transformations API

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.

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.

- Create a pattern
- Implement a callback
- Register the pattern and Matcher
- 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.

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:

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.

**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:

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.

**Reference**

IntegrateOpenVINO™ with Your Application – Model Representation

### Q4'23: Technology Update – Low Precision and Model Optimization

### Authors

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

## Summary

This quarter we observe that most of the work is still dedicated to the Large Language Model optimization. Researchers try to break through W4A8 quantization setup for LLMs achieving the accuracy results that allows considering such optimized models for deployment scenario. Some teams work on lower-precision settings such as 2-bit weight quantization or even binary weight compression. Interestingly, some teams propose to stick to a higher bit-width (FP6) and data-free optimization approach to avoid overfitting to calibration data. We also see an increasing interest in applying various types of weight sparsity in LLMs. And, of course, we should note the tremendous improvement in the inference time of Diffusion models caused by the decrease in the overall number of iterations in the diffusion process. This allows running variations of Stable Diffusion on mobile devices in the below 1 second.

## Papers with notable results

### Quantization

**AWEQ: Post-Training Quantization with Activation-Weight Equalization for Large Language Models****by Jilin University**(https://arxiv.org/pdf/2311.01305.pdf). Authors apply a known recipe for DL models quantization to LLM models. It contains weight equalization and bias correction methods stacked together. The difference is in how they estimate parameters and where to apply both methods. The method shows good results for W8A8 and W4A8 settings and outperforms GPTQ method on LLAMA and OPT models.**AFPQ: Asymmetric Floating Point Quantization for LLMs**by China Universities and Microsoft Research Asia

**Enhancing Computation Efficiency in Large Language Models through Weight and Activation Quantization****by Hanyang University, SAPEON Korea Inc., Seoul National University**(https://arxiv.org/pdf/2311.05161.pdf). Authors present two techniques: activation-quantization-aware scaling (a trade-off between SQ and AWQ) and sequence-length-aware calibration (adaptation of OPTQ to various sequence lengths) to enhance PTQ by considering the combined effects on weights and activations and aligning calibration sequence lengths to target tasks. They also introduce dINT, a hybrid data format combining integer and denormal representations, to address the underflow issue in W4A8 quantization, where small values are rounded to zero. The combined approach allows for achieving superior results compared to baselines. However, dINT has a limitation of efficient implementation on a general-purpose HW, such as CPU and GPU.**POST-TRAINING QUANTIZATIONWITH LOW-PRECISION MINIFLOATS AND INTEGERS ON FPGAS by AMD Research, National University of Singapore, and Tampere University**(https://arxiv.org/pdf/2311.12359.pdf). Authors compare integer and mini float quantization techniques, encompassing a combination of state-of-the-art PTQ methods, such as weight equalization, bias correction, SmoothQuant, learned rounding, and GPTQ. They explore the accuracy-hardware tradeoffs, providing analysis for three models -ResNet-18, MobileNetV2, and ViT-B32 - based on a custom FPGA implementation. Experiments indicate that mini float quantization typically outperforms integer quantization for bit-widths of four or more, both for weights and activations. However, when compared against FPGA hardware cost model, integer quantization often retains its Pareto optimality due to its smaller hardware footprint at a given precision.

**I&S-ViT: An Inclusive& Stable Method for Pushing the Limit of Post-Training ViTs Quantization****by Xiamen University, Tencent, and Peng Cheng Laboratory**(https://arxiv.org/pdf/2311.10126.pdf). The paper introduces a method that regulates the PTQ of ViTs in an inclusive and stable fashion. It first identifies two issues in the PTQ of ViTs: (1)Quantization inefficiency in the prevalent log2 quantizer for post-Softmax activations; (2) Rugged and magnified loss landscape in coarse-grained quantization granularity for post-LayerNorm activations. Then, the method addresses these issues by introducing: (1) A novel shift-uniform-log2 quantizer(SULQ) that incorporates a shift mechanism followed by uniform quantization to achieve both an inclusive domain representation and accurate distribution approximation; (2) A three-stage smooth optimization strategy that amalgamates the strengths of channel-wise and layer-wise quantization to enable stable learning. The method achieves comparable results in the W4A4 and W3A3 quantization settings.**Quantizable Transformers: Removing Outliers by Helping Attention Heads Do Nothing by Qualcomm AI Research**(https://arxiv.org/pdf/2306.12929.pdf). This work aims to remove outliers by construction (pretraining) so that transformer can be quantized easily without the need of finer quantization granularity (e.g. per channel, per group). The authors root-caused that outliers in trained transformers are essentially the artifact of attention head attenuating uninformative tokens and outliers emerged in the formulation/backpropagation of softmax, residual connections and layer normalization to sustain the effect of these tokens. Two independent solutions are proposed - (1) C*lipped softmax*that allows exact zeros and ones in softmax to avoid growing outliers during training. (2)*Gated attention which*is a tiny neural network (linear+sigmoid) added to the vanilla attention to decouple the needs of large attention output for disregarding the uninformative tokens. Pretraining with the proposed formulation on BERT, OPT and ViT has been shown to converge similarly if not better than baseline recipe. Most notably, the ease of per-tensor int8 static quantization to both weight and activation in post-training fashion has been empirically verified. Code is coming soon at https://github.com/qualcomm-ai-research/outlier-free-transformers**A Speed Odyssey for Deployable Quantization of LLMs****by Meituan (**https://arxiv.org/pdf/2311.09550.pdf).Authors propose a solution for deployable W4A8 quantization that comprises a tailored quantization configuration and a novel Fast GEMM kernel for 4-bitinteger matrix multiplication that reduces the cost, and it achieves 2.23× and1.45× speed boosting over the TensorRT-LLM FP16 and INT8 implementation respectively. The W4A8 recipe is proven mostly on par with the state-of-the-art W8A8 quantization method SmoothQuant on a variety of common language benchmarks for the state-of-the-art LLMs.**TFMQ-DM: Temporal Feature Maintenance Quantization for Diffusion Models****by SenseTime****and universities of US, China and Australia**(https://arxiv.org/pdf/2311.16503.pdf). Authors investigate the problems in quantization of diffusion models and claim that these models heavily depend on the time-step t to achieve satisfactory multi-round denoising when t is encoded to a temporal feature by a few modules totally irrespective of the sampling data. They propose a Temporal Feature Maintenance Quantization framework building upon a Temporal Information Block which is just related to the time-step t and unrelated to the sampling data. Powered by this block design, authors propose temporal information aware reconstruction and finite set calibration to align the full-precision temporal features in a limited time. The method achieves accurate results even in W4A8quantization setting.**QUIK: TOWARDS END-TO-END4-BIT INFERENCE ON GENERATIVE LARGE LANGUAGE MODELS by ETH Zurich, Institute of Science and Technology Austria, Xidian University, KAUST, Neural Magic**(https://arxiv.org/pdf/2310.09259v2.pdf). The paper addresses the problem where both weights and activations should be quantized. Authors show that the majority of inference computations for large generative models such as LLaMA, OPT, and Falcon can be performed with both weights and activations being cast to 4 bits, in a way that leads to practical speedups, while at the same time maintaining good accuracy. They achieve this via a hybrid quantization strategy called QUIK, which compresses most of the weights and activations to 4-bit, while keeping some outlier weights and activations in higher precision which leads to practical end-to-end throughput improvements of up to 3.4x relative to FP16 execution. Code is available at: https://github.com/IST-DASLab/QUIK.**Post-training Quantization with Progressive Calibration and Activation Relaxing for Text-to-Image Diffusion Models****by Tsinghua University**(https://arxiv.org/pdf/2311.06322.pdf). Authors propose a post-training quantization method for text-to-image diffusion models, which consists of a progressive calibration strategy that considers the accumulated quantization error across timesteps, and an activation relaxing strategy that improves the performance with a small cost. They also propose a new QDiffBench benchmark, which utilizes data in the same domain for a more accurate evaluation of the generation accuracy.

**Enabling Fast 2-bit LLM on GPUs: Memory Alignment, Sparse Outlier, and Asynchronous Dequantization****by Shanghai Jiao Tong University and Tsinghua University**(https://arxiv.org/pdf/2311.16442.pdf).The paper proposes range-aware quantization with memory alignment. It points out that the range of weights by groups varies. Thus, only 25% of the weights are quantized using 4-bit with memory alignment. Such a method reduces the accuracy loss for 2-bit Llama2-7b quantization from 8.7% to 2.9%. Authors show as well that only a small fraction of outliers exist in weights quantized using2-bit. These quantize these sparse outliers with < 3% increased average weight bit and improve the accuracy by >0.5%. They also accelerate GPU kernels by introducing asynchronous dequantization achieving 3.92× improvement on a kernel level.**ZeroQuant(4+2): Redefining LLMs Quantization with a New FP6-Centric Strategy for Diverse Generative Tasks****by DeepSpeed**(https://arxiv.org/pdf/2312.08583.pdf).Authors show that popular data-aware LLM compression methods such as GPTQ can overfit to calibrated datasets especially on moderate-size LLMs (<=1B). They also illustrate that FP6, employing a basic round-to-nearest (RTN) algorithm and a per-channel quantization approach, consistently achieves accuracy on par with full-precision models. They propose an unpacking (mapping) scheme for FP8 so that it can be efficiently used with FP16 inference.

### Pruning/Sparsity

**Sparse Fine-tuning for Inference Acceleration of Large Language Models****by IST Austria, Skoltech & Yandex, and Neural Magic**(https://arxiv.org/pdf/2310.06927.pdf). The paper analyses the challenges of LLMs pruning, namely loss spikes leading to divergence, poor recovery from fine-tuning, and overfitting. To overcome these issues, authors incorporate standard cross-entropy, output knowledge distillation, and a type of per-token ℓ2 knowledge distillation on top of SparseGPT method. They show that the resulting sparse models can be executed with inference speedups on CPU and GPU, especially when stacking with INT8quantization. The code is available: https://github.com/IST-DASLab/SparseFinetuning.**ReLU Strikes Back: Exploiting Activation Sparsity in LLMs by****Apple.**(https://arxiv.org/pdf/2310.04564.pdf). This work advocates to reinstate ReLU as main activation function in LLMs due to its intriguing property – high post-ReLU activation sparsity can be translated to computational efficiency with sparse runtime. To overcome training from scratch and for LLMs employing GeLU/SiLU, the paper proposes “Relufication”, a two-stage uptraining by first replacing non-ReLU activations in pre-trained LLMs with ReLU, and then appending ReLU to normalization layers in second stage for more sparsity. With increase of activation sparsity, the authors also observe high overlapping activated neurons during decoding (termed aggregated sparsity) and suggest weight reuse to alleviate memory transfer. The authors show application of aggregated sparsity in speculative decoding and demonstrate 27% speedup of OPT-6.7B at a minor degradation of perplexity.**Deja Vu: Contextual Sparsity for Efficient LLMs at Inference Time****by Rice University, Zhe Jiang University, Stanford University, University of California, ETH Zurich, Adobe Research, MetaAI, Carnegie Mellon University**(https://proceedings.mlr.press/v202/liu23am/liu23am.pdf). Authors propose a contextual dynamic sparsity method for LLMs. Contrary to a usual sparsity approach where a model is pruned once and then inferenced on every input sample in the same pruned state, here authors compute the set of pruned operations on the run resulting in a more flexible pruning scheme. Foreach transformer layer this is achieved by predicting set of MHA heads and MLP matrix columns to exclude based on previous layers activations. Prediction is performed by small separately trained perceptron networks. To remove the performance bottleneck produced by the need to inference of perceptron networks authors propose to make sparsity predictions for (i+1)-th transformer layer based on activations from (i-1)-th layer, resulting in parallel computation of i-th layer and sparsity sets for (i+1)-th layer. This is viable due to shown similarity between activations of neighboring layers in LLMs. For OPT-175Bmodel the approach achieves over 6x performance improvement compared to Hugging Face implementation and over 2x improvement compared to state-of-the-art FasterTransformer model.**SparseByteNN: A Novel Mobile Inference Acceleration Framework Based on Fine-Grained Group Sparsity****by ByteDance**(https://arxiv.org/pdf/2310.19509.pdf). The paper introduces SparseByteNN, consisting of three components: a)compression algorithm component, which provides out-of-the-box pruning capabilities for pre-trained models b) model conversion tool, which converts the model IR of the training framework into Model IR of sparse engine c) sparse inference engine, which provides efficient inference implementation compatible with CPUs for fine-grained kernel group sparsity. Experimental results on Qualcomm 855 show that for 30% sparse MobileNet-v1, SparseByteNN achieves 1.27×speedup over the dense version. The code will be available at: https://github.com/lswzjuer/SparseByteNN.

### Neural Architecture Search

**LoNAS: Elastic Low-Rank Adapters for Efficient Large Language Models by Anonymous**(https://openreview.net/pdf?id=pzB-1OCS6gd). Researchers demonstrate a novel integration of low-rank (LoRA)adapters with Neural Architecture Search. LoNAS efficiently fine-tunes and compress large language models (LLMs). A weight-sharing super-network is generated using the frozen weights of the input model, and the attached elastic low-rank adapters. The reduction in trainable parameters results in less the memory requirements to train the super-network, enabling the manipulation of LLMs in resource-constrained devices, without sacrificing the performance of the resulting compressed models. LoNAS’ high-performing compressed models result in faster inference times, cost savings during the model’s lifetime, and an increase in the range of devices in which large language models can be deployed. Experiments’ results on six reasoning datasets demonstrate the benefits of LoNAS.**Bridging the Gap between Foundation Models and Heterogenous Federated Learning by Iowa State U. and Intel Labs**(https://arxiv.org/abs/2310.00247). This paper explores the application of Neural Architecture Search (NAS) in combination with Federated Learning (FL). The proposed framework, Resource-aware Federated Foundation Models (RaFFM) introduces model compression and salient parameter prioritization in the context of Federated Learning, allowing for the collaborative training of large foundation models using heterogeneous devices. Compared to traditional FL methods, RaFFM yields better resource utilization, without sacrificing in model performance.**Rankitect: Ranking Architecture Search Battling World-class Engineers at Meta Scale by Meta Platforms**(https://arxiv.org/pdf/2311.08430.pdf). Researchers at Meta demonstrate the real-world applications of Neural Architecture Search (NAS). They apply NAS to production models, e.g., Click Through Rate (CTR) model, on a system that serves billions of users. The baseline models explored in this work have already been optimized by world-class engineers. The proposed NAS framework, Rankitect, improves over existing models by exploring search spaces with no inductive bias from the baseline models, and discovers new models from scratch that outperform those hand-crafted by human experts. Rankitect also keeps human engineers in-the-loop by allowing the manual design of search spaces, which results in even more efficient models.**QuadraNet: Improving High-Order Neural Interaction Efficiency with Hardware-Aware Quadratic Neural Networks by George Mason University, University of Maryland, University at Buffalo, Peking University**(https://arxiv.org/pdf/2311.17956.pdf). This paper presents QuadraNet, a new neural network design methodology based on efficient quadratic neurons that captures high-order neural interactions similar to Transformer-based models. The design of this alternative to Transformer-based models is hardware-aware through the application of Neural Architecture Search(NAS). Experiments with QuadraNets show improvements of 1.5x in throughput without any reduction in accuracy compared to their Transformer-based counterparts.

### Other

**Divergent Token Metrics: Measuring degradation to prune away LLM components – and optimize quantization****by****Aleph Alpha, Hessian.AI and German Universities**. (https://arxiv.org/pdf/2311.01544.pdf). The work highlights that the commonly used perplexity (PPL) metric in compression research does not reflect the degradation of compressed model and cannot distinguish subtleties (see figure below). The authors propose a family of divergent token metrics (DTM), namely First Token Divergence, i.e., when the first diverging token happens w.r.t baseline generated text, as well as Share of Divergent Tokens denoting the total number of divergent tokens. In a series of experiments pertaining to layer-wise quantization or pruning, DTM-based ranking consistently outperforms PPL-based ranking methods.

**SIMPLIFYING TRANSFORMERBLOCKS****by ETH Zurich**(https://arxiv.org/pdf/2311.01906.pdf). The paper introduces a set of Transformer block pruning techniques that makes them more lightweight from the number of parameters and computations standpoint. This set includes: removing skip connection both in the Attention sub-block and Feed-Forward sub-block, removing value and projection parameters, removing normalization layers, and model depth scaling. Authors also show how to recover the accuracy after model perturbation using fine-tuning. The proposed method produces the decoder and decoder Transformer models that perform on part with their baselines: 15% faster training throughput, and using 15% fewer parameters.**MobileDiffusion: Subsecond Text-to-Image Generation on Mobile Devices****by Google**(https://arxiv.org/pdf/2311.16567.pdf).The paper presents a comprehensive guide for crafting highly efficient text-to-image diffusion models. Authors applied the following tricks to highly optimize UNet model in the Diffusion pipeline: more transformers in the middle of Unet (at lower resolution), retaining cross-attention layers while discarding only the self-attention layers at high resolutions, sharing key-value projections, replacing gelu with swish, fine-tune softmax into relu, trim feed-forward layers, use separable convolution, prune redundant residual blocks, reduce sampling iterations, knowledge distillation. The resulting model is able to generate 512×512 images in sub-second on mobile devices:**0.2 second on iPhone 15 Pro**.

**Online Speculative Decoding**by**UC Berkeley, UCSD, Sisu Data, SJTU**(https://arxiv.org/pdf/2310.07177.pdf). Practical speedup of speculative decoding is often impeded by the capability gap between draft and target model which can be 10-20X gap in parameters, leading to high rejection of draft predictions and fallback to more forward passes of target model. This work proposes online fine-tuning of draft model by distillation with the readily available rejected predictions. The proposed solution incorporates a replay buffer tracking logits of draft and target model, and distillation backpropagation is executed at a regular interval. Experimental results demonstrate not only significant improvement in acceptance rate, translating up to a theoretical 3X of latency reduction, but also adaptability against distribution shift in input queries.**Token Fusion: Bridging the Gap between Token Pruning and Token Merging****by Michigan State University Samsung Research America**(https://arxiv.org/pdf/2312.01026.pdf).The paper introduces a method (ToFu) that combines token pruning and token merging. ToFu dynamically adapts to each layer’s properties, ensuring optimal performance based on the model’s functional linearity with respect to the interpolation in its input. Authors exploit MLERP merging technique, an enhancement over traditional average merging, inspired by the SLERP method. This approach merges tokens while preserving their norm distribution. Evaluation shows that ToFu outperforms ToMe in terms of accuracy while showing the similar performance gain at inference.

### Deep Learning Software

**Medusa: Simple Framework for Accelerating LLM Generation with Multiple Decoding Heads****by Together.AI**. Contemporary speculative decoding solutions (Leviathan et al., Chen et al.) require multiple models (target and draft models) which often involves intricate optimization& selection of draft models to attain practical acceleration. For simplicity, Together.AI unveils a user-friendly framework, Medusa, built a top of a research work in 2018, "Block wise Parallel Decoding", with multiple enhancements. Medusa simplifies the creation of draft models without separate models by extending base model with multiple decoding heads. By keeping the base model frozen, the Medusa heads are trained in a parameter-efficient way and all on a single GPU. Medusa also features a tree-based attention mechanism for parallel evaluation of the proposed candidates, and a truncated sampling for efficient creative generation. Results and framework can be found at https://github.com/FasterDecoding/Medusa.**HyperAttention: Long-context Attention in Near-Linear Time****by Yale University and Google**https://github.com/insuhan/hyper-attn. Authors propose algorithm that consists of (1) finding heavy entries inattention matrix and (2) column subsampling. For (1), authors use the**sorted locality sensitive hashing**(sortLSH) based on the Hamming distance. Applying sortLSH makes heavy entries in the attention matrix (sorting rows/columns) located in near diagonal hence authors do block-diagonal approximation which can be done fast. The method supports casual masking. Code is available here: https://github.com/insuhan/hyper-attn.**Flash-Decoding for long-context inference****by Stanford University**. Flash Attention v1 & v2 are designed and optimized primarily for training case and exhibit low utilization of compute units when applied for LLM generation, especially for long context. As identified cause is rooted in low- batch size (query tokens) in relative to context length, Flash Decoding extends flash attention by adding 2^{nd}-level tiling over the keys/values to improve compute utilization while retaining memory efficiency of flash attention. On A100, the micro-benchmark for multi-head attention with flash decoding kernel achieves almost constant run-time as the sequence length scales to up to 64k, translating up to 8X speedup of CodeLLaMa-34b over vanilla flash attention at very long sequences. Implementation is available at official flash attention repo & xformers.**LLM in a flash: Efficient Large Language Model Inference with Limited Memory****by Apple**(https://arxiv.org/pdf/2312.11514.pdf).The paper tackles the challenge of efficiently running LLMs that exceed the available DRAM capacity by storing the model parameters on flash memory but bringing them on demand to DRAM. The method involves constructing an inference cost model that harmonizes with the flash memory behavior, guiding to optimize in two critical areas: reducing the volume of data transferred from flash and reading data in larger, more contiguous chunks. Within this flash memory-informed framework, authors introduce two principal techniques. First, “windowing” strategically reduces data transfer by reusing previously activated neurons, and second, “row-column bundling”, tailored to the sequential data access strengths of flash memory, increases the size of data chunks read from flash memory. These methods collectively enable running models up to twice the size of the available DRAM, with a 4-5x and 20-25x increase in inference speed compared to naive loading approaches in CPU and GPU, respectively.

### Accelerate DIEN for Click-Through-Rate Prediction with OpenVINO™

**Author: Xiake Sun, Cecilia Peng**

**Introduction**

A click-through rate (CTR) prediction model is designed to estimate how likely a user will click on an advertisement or item. Deployment of a CTR model is considered one of the core tasks in e-commerce, as its performance not only affects platform revenue but also influences customers’ online shopping experience.

Deep Interest Evolution Network (DIEN) developed by Alibaba Group aims to better predict customer’s CTR to improve the effectiveness of advertisement display. DIEN proposes the following two modules:

- Temporally captures and extracts latent interests based on customer history behaviors.
- Models an evolving process of user interests using GRU with an attentional update gate (AUGRU)

Figure 1 shows the structure of DIEN, with the help of AUGRU, DIEN can overcome the disturbance from interest drifting, which improves the performance of CTR prediction largely in online advertising system.

**DIEN Optimization with OpenVINO**^{TM}

^{TM}

Here we introduce DIEN optimization with OpenVINO^{TM }in two aspects: graph level and dynamism runtime optimization.

**Graph Level Optimization**

Figure 2 shows the AUGRU subgraph of DIEN visualized in Netron.

OpenVINO^{TM} implements internal operations AUGRUCell and AUGRUSequence for better graph-level optimization. Each decomposed subgraph of GRU and AUGRU is fused into a corresponding cell operator respectively. What's more, in case of static sequence length, the group of consecutive cells are further fused into a sequence operator. In case of dynamic sequence length, however, the sequence is processed with a loop of cells due to the limitation of oneDNN RNN primitive. This loop of cells is TensorIterator and (AU)GRUCell. We will introduce the optimizations of TensorIterator in next session.

**TensorIterator Runtime Optimization with Dynamic Shape**

Before we dive into optimization details, let’s first checkout how OpenVINO^{TM} TensorIterator operation works.

The TensorIterator layer performs recurrent execution of thenetwork, which is described in the body, iterating through the data. Figure 3 shows the workflow of OpenVINO^{TM }Operation TensorIterator in a simplified view. For details, please refer to the specification.

Similar to other layers, TensorIterator has regular sections: input and output. It allows connecting TensorIterator to the rest of the IR. TensorIterator also has several special sections: body, port_map, back_edges. The principles of their work are described below.

**body**is a network that will be recurrently executed. The network is described layer by layer as a typical IR network.**port_map**is a set of rules to map input or output data tensors of TensorIterator layer onto body data tensors. The port_map entries can be input and output. Each entry describes a corresponding mapping rule.**back_edges**is a set of rules to transfer tensor values from body outputs at one iteration to body parameters at the next iteration. Back edge connects some Result layers in body to Parameter layer in the same body.

If output entry in the Port map doesn’t have partitioning (axis, begin, end, strides) attributes, then the final value of output of TensorIterator is the value of Result node from the last iteration. Otherwise, the final value of output of TensorIterator is a concatenation of tensors in the Result node for all body iterations.

We use Intel® VTune™ Profiler to run benchmark_app with DIEN FP32 IR model on Intel® Xeon® Gold 6252N Processor for performance profiling.

**Cache internal reorder primitives in TensorIterator**

Figure 4 shows that TensorIterator::prepareDynamicBackEdges() spends nearly 45% CPU time to create the reorder primitives. DIEN FP32 model has 2 TensorIterator, eachTensorIterator runs 100 iterations in body with the same input/output shape regarding the current batch. Besides, each TensorIterator has 7 back edges, which means the reorder primitive are frequently created.

So, we propose to cache internal reorder primitive in TensorIterator to optimize back edge memory copy logic. With this optimization, the performance with dynamic shape can be improved by 8x times.

**Memory allocation and reuse optimization in TensorIterator**

As Figure 3 shows, if we have split input as n_{th }piece to loop in body, at the end, the outputs of TensorIterator will be a concatenation of tensors in the Result node for all body iterations, which can lead to performance overhead. Based on previous optimization we re-run performance profiling using benchmark_app with DIEN FP32 IR model on Intel® Xeon® Gold 6252N Processor as showed in Figure 5.

CPU plugin TensorIterator supports both two operators - TensorIterator and Loop. The outputs of each iteration could be concatenated and return to users. Since the output size is not always known before the execution, the legacy implementation is to dynamically allocate the concatenated output buffer.

We propose two points from the memory allocation standpoint:

- In the case of TensorIterator number of iterations is determined by the size of the axis we are slicing. So, if TensorIterator body one ach iteration will produce the same shape on output we can easily preallocate enough memory before the TI computation, The same for Loop with trip count input - we can just read the value from this input, make shape inference for the body and this determines the required amount of memory.
- More complicated story is when we don't know exact number of iterations before Loop inference (e.g., number of iterations is determined by ExecutionCondition input). In that case do the following: let’s have an output buffer where we put the Loop output. Once the buffer doesn't have enough space, we reallocate it on new size based on a simple and effective dynamic array algorithm.

OpenVINO^{TM} implemented memory allocation and reuse optimization in TensorIterator to significantly reduce the number of reallocations and not to allocate to much memory at the same time. Experiments show that performance can be further improved by more than 20%.

**DIEN OpenVINO**^{TM }Demo

^{TM }Demo

Clone demo repository:

Prepare Amazon dataset:

Setup Python Environment:

Convert original TensorFlow model to OpenVINO^{TM} FP32 IR:

Run the Benchmark with TensorFlow backend:

Run the Benchmark with OpenVINO^{TM} backend using FP32 inference precision:

Run the Benchmark with OpenVINO^{TM} backend using BF16 inference precision:

Please note, Xeon native supports BF16 infer precision since 4th Generation Intel® Xeon® Scalable Processors. Running BF16 on a legacy Xeon platform may lead to performance degradation.

**Conclusion**

In this blog, we introduce inference optimization of DIEN recommendation model with OpenVINO^{TM} runtime as follows:

- For static input sequence length, AUGRU subgraph will be decomposed and fused as AUGRU and AUGRUSequence OpenVINO
^{TM}internal operation. - For dynamic input sequence length, we propose cache internal reorder primitives and memory allocation and re-use optimization in TensorIterator.
- Provide a demo for model enabling and efficient inference of DIEN with OpenVINO
^{TM}runtime.

**Reference**

Deep Interest Evolution Network (DIEN)

### Enable chatGLM by creating OpenVINO™ stateful model and runtime pipeline

### Authors: Zhen Zhao(Fiona), Cheng Luo, Tingqian Li, Wenyi Zou

### Introduction

Since the Large Language Models (LLMs) become the hot topic, a lot Chinese language models have been developed and actively deployed in optimization platforms. chatGLM is one of the popular Chinese LLMs which are widely been evaluated. However, ChatGLM model is not yet a native model in Transformers, which means there remains support gap in official optimum. In this blog, we provide a quick workaround to re-construct the model structure by OpenVINO™ opset contains custom optimized nodes for chatGLM specifically and these nodes has been highly optimized by AMX intrinsic and MHA fusion.

*Please note, this blog only introduces a workaround of optimization method by creating OpenVINO™ stateful model for chatGLM. This workaround has limitation of platform, which requires to use Intel® 4^{th} Xeon Sapphire Rapids with AMX optimization. We do not promise the maintenance of this workaround.

Source link: https://github.com/luo-cheng2021/openvino/tree/luocheng/chatglm_custom/tools/gpt

To support more LLMs, including llama, chatglm2, gpt-neox/dolly, gpt-j and falcon. You can refer this link which not limited on SPR platform, also can compute from Core to Xeon:

Source link: https://github.com/luo-cheng2021/ov.cpu.llm.experimental

### ChatGLM model brief

If we check with original model source of chatGLM, we can find that the ChatGLM is not compatible with Optimum* ModelForCasualML*, it defines the new class *ChatGLMForConditionalGeneration*. This model has 3 main modules (embedding, GLMBlock layers and lm_logits) during the pipeline loop, the structure is like below:

As you can see, the whole pipeline actually require model with two different graphs, the first-time inference with input prompt tokens do not require KV cache as inputs for *GLMBlock *layers. Since the second iteration, the previous results of QKV Attention should become the inputs of current round model inference. Along with the length of generated token increased, there will remain a lot of large sized memory copies between model inputs and outputs during pipeline inference. We can use ChatGLM6b default model configurations as an example, the memory copies between input and output arrays are like below pseudocode:

Therefore, two topics is the most important:

- How we can optimize model inference pipeline to eliminate memory copy between model inputs and outputs
- How we can put optimization efforts on
*GLMBlock*module by reinvent execution graph

### Extremely optimization by OpenVINO™ stateful model

Firstly, we need to analyze the structure of *GLMBlock *layer, and try to encapsulate a class to invoke OpenVINO™ opset with below workflow. Then serialize the graph to IR model(.xml, .bin).

To build an OpenVINO™ stateful model, you can refer to this document to learn.

https://docs.openvino.ai/2022.3/openvino_docs_OV_UG_network_state_intro.html

OpenVINO™ also provide model creation sample to show how to build a model by opset.

https://github.com/openvinotoolkit/openvino/blob/master/samples/cpp/model_creation_sample/main.cpp

It is clear to show that the emphasized optimization block is the custom op of Attention for chatGLM. The main idea is to build up a global context to store and update pastKV results internally, and then use intrinsic optimization for Rotary Embedding and Multi-Head Attentions. In this blog, we provide an optimized the attention structure of chatGLM with AMX intrinsic operators.

At the same time, we use int8 to compress the weights of the Fully Connected layer, you are not required to compress the model by Post Training Quantization (PTQ) or process with framework for Quantization Aware Training(QAT).

### Create OpenVINO™ stateful model for chatGLM

Please prepare your hardware and software environment like below and follow the steps to optimize the chatGLM:

#### Hardware requirements

Intel® 4^{th} Xeon platform(codename Sapphire Rapids) and above

#### Software Validation Environment

Ubuntu 22.04.1 LTS

python 3.10.11 for OpenVINO™ Runtime Python API

GCC 11.3.0 to build OpenVINO™ Runtime

cmake 3.26.4

#### Building OpenVINO™ Source

- Install system dependency and setup environment
- Create and enable python virtual environment

- Install python dependency

- Build OpenVINO™ with GCC 11.3.0
- Clone OpenVINO™ and update submodule

- Install python dependency for building python wheels

- Create build directory

- Build OpenVINO™ with CMake

- Install built python wheel for OpenVINO™ runtime and openvino-dev tools

- Check system gcc version and conda runtime gcc version. If the system gcc version is higher than conda gcc version like below, you should update conda gcc version for OpenVINO runtime. (Optional)

- convert pytorch model to OpenVINO™ IR

### Use OpenVINO Runtime API to build Inference pipeline for chatGLM

We provide a demo by using transformers and OpenVINO™ runtime API to build the inference pipeline. In *test_chatglm.py*, we create a new class which inherit from *transformers.PreTrainedModel*. And we update the forward function by build up model inference pipeline with OpenVINO™ runtime Python API. Other member functions are migrated from *ChatGLMForConditionalGeneration *from modeling_chatglm.py, so that, we can make sure the input preparation work, *set_random_seed*, tokenizer/detokenizer and left pipelined operation can be totally same as original model source.

To enable the int8 weights compress, you just need a simple environment variable USE_INT8_WEIGHT=1. That is because during the model generation, we use int8 to compress the weights of the Fully Connected layer, and then it can use int8 weights to inference on runtime, you are not required to compress the model by framework or quantization tools.

Please follow below steps to test the chatGLM with OpenVINO™ runtime pipeline:

- Run bf16 model

- Run int8 model

### Weights compression reduces memory bandwidth utilization to improve inference speed

We use VTune for performance comparison analysis of model weights bf16 and int8. Comparative analysis of memory bandwidth and CPI rate (Table 1). When model weight is compressed to int8, it can reduce memory bandwidth utilization and CPI rate.

Clockticks per Instructions Retired(CPI) event ratio, also known as Cycles per Instructions, is one of the basic performance metrics for the hardware event-based sampling collection, also known as Performance Monitoring Counter (PMC) analysis in the sampling mode. This ratio is calculated by dividing the number of unhalted processor cycles(Clockticks) by the number of instructions retired. On each processor the exact events used to count clockticks and instructions retired may be different, but VTune Profiler knows the correct ones to use.

A CPI < 1 is typical for instruction bound code, while a CPI > 1 may show up for a stall cycle bound application, also likely memory bound.

### Conclusion

Along with the upgrading of OpenVINO™ main branch, the optimization work in this workaround will be generalized and integrated into official release. It will be helpful to scale more LLMs model usage. Please refer OpenVINO™ official release and Optimum-intel OpenVINO™ backend to get official and efficient support for LLMs.