Joint Pruning, Quantization and Distillation for Efficient Inference of Transformers

Authors: Nikita Savelyev, Alexander Kozlov, Ekaterina Aidova, Maxim Proshin

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Introduction

Whisper is a general-purpose speech recognition model from OpenAI. The model can transcribe speech across dozens of languages and even handle poor audio quality or excessive background noise. You can find more information about this model in the research paper, OpenAI blog, model card and GitHub repository.

Recently, a distilled variant of the model called Distil-Whisper has been proposed in the paper Robust Knowledge Distillation via Large-Scale Pseudo Labelling. Compared to Whisper, Distil-Whisper runs several times faster with 50% fewer parameters, while performing to within 1% word error rate (WER) on out-of-distribution evaluation data.

Whisper is a Transformer-based encoder-decoder model, also referred to as a sequence-to-sequence model. It maps a sequence of audio spectrogram features to a sequence of text tokens. First, the raw audio inputs are converted to a log-Mel spectrogram by action of the feature extractor. Then, the Transformer encoder encodes the spectrogram to form a sequence of encoder hidden states. Finally, the decoder autoregressively predicts text tokens, conditional on both the previous tokens and the encoder's hidden states.

You can see the model architecture in the diagram below:

In this article, we would like to demonstrate how to improve Whisper and Distil-Whisper inference speed with OpenVINO for Intel hardware. Additionally, we show how to make models even faster by applying 8-bit Post-training Quantization with Neural Network Compression Framework (NNCF). In the end we present evaluation results from accuracy and performance standpoints on a large-scale dataset.

All code snippets presented in this article are from the Automatic speech recognition using Distil-Whisper and OpenVINO Jupyter notebook, so you can follow along.

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Converting Model to OpenVINO format

We are going to load models from Hugging Face Hub with the help of Optimum Intel library which makes it easier to load and run OpenVINO-optimized models. For more details, pleaes refer to the Hugging Face Optimum documentation.

For example, the following code loads the Distil-Whisper large-v2 model ready for inference with OpenVINO.

from optimum.intel.openvino import OVModelForSpeechSeq2Seq

model_id = "distil-whisper/distil-large-v2"
model_path = Path(model_id)
if not model_path.exists():
    ov_model = OVModelForSpeechSeq2Seq.from_pretrained(
        model_id, export=True, compile=False, load_in_8bit=False)
    ov_model.half()
    ov_model.save_pretrained(model_path)
else:
    ov_model = OVModelForSpeechSeq2Seq.from_pretrained(
        model_path, compile=False)

Models from the Distil-Whisper family are available at Distil-Whisper Models collection and Whisper models are available at OpenAI Hugging Face page.

To transcribe an input audio with the loaded model, we first compile the model to the device of choice and then call generate() method on input features prepared by corresponding processor.

from transformers import AutoProcessor

processor = AutoProcessor.from_pretrained(model_id)

ov_model.to("AUTO")
ov_model.compile()

# ... load input audio and reference text
input_features = processor(input_audio).input_features
predicted_ids = ov_model.generate(input_features)
transcription = processor.batch_decode(predicted_ids, skip_special_tokens=True)[0]
print(f"Reference: {reference_text}")
print(f"Result: {transcription}")

The output is the following. As you can see the transcription equals the reference text.

Reference: MISTER QUILTER IS THE APOSTLE OF THE MIDDLE CLASSES AND WE ARE GLAD TO WELCOME HIS GOSPEL
Result:  Mr. Quilter is the apostle of the middle classes, and we are glad to welcome his gospel.

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Running Post-Training Quantization with NNCF

NNCF enables post-training quantization by adding quantization layers into the model graph and then using a subset of the training dataset to initialize parameters of these additional quantization layers. During quantization, some layers (e.g., MatMuls, Convolutions) are transformed to be executed in INT8 instead of FP16/FP32. If a quantized operation is parameterized then its corresponding weight variable is also converted to INT8.

In general, the optimization process contains the following steps:

1. Create a calibration dataset for quantization.
2. Run nncf.quantize() to obtain quantized encoder and decoder models.
3. Serialize the INT8 models using openvino.save_model() function.

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Whisper model consists of an encoder and decoder submodels. Furthermore, for the decoder model its forward() signature is different for the first call compared to all subsequent calls. During the first call, key-value cache is empty and is not needed for decoder inference. Starting from the second call, key-value cache is fed to the decoder. Because of this, these two cases are represented by two separate OpenVINO models: openvino_decoder_model.xml and openvino_decoder_with_past_model.xml. Since the first decoder model is inferred only once it does not make much sense to quantize it. So, we apply quantization to the encoder and the decoder with past models.

The first step towards quantization is collecting calibration data. For that, we need to collect some number of model inputs for both models. To do that, we patch OpenVINO model request objects with an InferRequestWrapper class instance that will intercept model inputs during inference and store them in a list. We infer the model on about 50 samples from validation split of librispeech_asr dataset.

def collect_calibration_dataset(ov_model: OVModelForSpeechSeq2Seq, calibration_dataset_size: int):
    # Overwrite model request properties, saving the original ones for restoring later
    original_encoder_request = ov_model.encoder.request
    original_decoder_with_past_request = ov_model.decoder_with_past.request
    encoder_calibration_data = []
    decoder_calibration_data = []
    ov_model.encoder.request = InferRequestWrapper(original_encoder_request, encoder_calibration_data)
    ov_model.decoder_with_past.request = InferRequestWrapper(original_decoder_with_past_request,
                                                             decoder_calibration_data)

    calibration_dataset = load_dataset("librispeech_asr", "clean", split="validation", streaming=True)
    for sample in islice(calibration_dataset, calibration_dataset_size):
        input_features = extract_input_features(sample)
        ov_model.generate(input_features)

    ov_model.encoder.request = original_encoder_request
    ov_model.decoder_with_past.request = original_decoder_with_past_request

    return encoder_calibration_data, decoder_calibration_data

With the collected calibration data for encoder and decoder models we can proceed to quantization itself. Let's examine the quantization call for the encoder model. For the decoder model, it is similar.

quantized_encoder = nncf.quantize(
    ov_model.encoder.model,                     # ov.Model object of the encoder model
    nncf.Dataset(encoder_calibration_data),     # calibration data wrapped in a nncf.Dataset object
    subset_size=len(encoder_calibration_data),  # number of samples to calibrate on (all are chosen)
    model_type=nncf.ModelType.TRANSFORMER,      # providing the information that Whisper encoder is of
    # a Transformer architecture
    advanced_parameters=nncf.AdvancedQuantizationParameters(smooth_quant_alpha=0.50)    # Smooth Quant 
    # algorithm reduces activation quantization error; optimal alpha was obtained through grid search
)
ov.save_model(quantized_encoder, quantized_model_path / "openvino_encoder_model.xml")

After both models are quantized and saved, the quantized Whisper model can be loaded and run the same way as shown previously. Comparing the transcriptions produced by original and quantized models results in the following.

Original :  Mr. Quilter is the apostle of the middle classes, and we are glad to welcome his gospel.
Quantized:  Mr. Quilter is the apostle of the middle classes, and we are glad to welcome his gospel.

As you can see for the quantized distil-whisper-large-v2 transcription is the same.

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Evaluating on Common Voice Dataset

We evaluate Whisper and Distil-Whisper large-v2 model variants on a Common Voice 13.0 speech-to-text dataset. We use en/test split containing 16372 audio samples amounting to about 27 hours of recordings.

The evaluation is done across three model types: original PyTorch model, original OpenVINO model and quantized OpenVINO model. Additionally, we run tests on three Intel CPUs: Cascade Lake Intel(R) Core(TM) i9-10980XE, Ice Lake Intel(R) Xeon(R) Gold 6338 and Sapphire Rapids Intel(R) Xeon(R) Gold 6430L.

For all combinations above we measure transcription time and accuracy. When measuring time for a model we sum up generate() call durations for all audio samples. Transcription accuracy is represented as Accuracy = (100 - WER), WER stands for Word Error Rate. We compute accuracy for each audio sample and then take the average value across the dataset. The results are given in the table below.

Please note that we report transcription time in relative terms such that the values for each CPU are normalized over its corresponding column. The duration of audio data in the dataset is 27.06 hours and the absolute transcription time values for Whisper large-v2 PyTorch on each CPU are:

• 20.35 hours for Core i9-10980XE
• 14.09 hours for Xeon Gold 6338
• 15.03 hours for Xeon Gold 6430L

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Based on the results we can conclude that:

1. OpenVINO models execute 1.4x - 5.1x faster than PyTorch models with pretty much the same accuracy across all cases.
2. When compared to original PyTorch models, quantized OpenVINO models provide 2.1x - 6.1x performance boost with 1-2% accuracy drop.

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NOTE: in terms of this article we focus on presenting performance values. Accuracy of quantized models can be improved with a more careful selection of calibration data.

Notices and Disclaimers:

Performance varies by use, configuration, and other factors. Learn more at www.intel.com/PerformanceIndex. Performance results are based on testing as of dates shown in configurations and may not reflect all publicly available updates. No product or component can be absolutely secure. Intel technologies may require enabled hardware, software or service activation.

The products described may contain design defects or errors known as errata which may cause the product to deviate from published specifications. Current characterized errata are available on request.

Test Configuration: Intel® Core™ i9-10980XE CPU Processor at 3.00GHz with DDR4 128 GB at 3000MHz, OS: Ubuntu 20.04.3 LTS; Intel® Xeon® Gold 6338 CPU Processor at 2.00GHz with DDR4 256 GB at 3200MHz, OS: Ubuntu 20.04.3 LTS; Intel® Xeon® Gold 6430L CPU Processor at 1.90GHz with DDR5 1024 GB at 4800MHz, OS: Ubuntu 20.04.6 LTS. Testing was performed using distil-whisper-asr notebook for model export and whisper evaluation notebook for model evaluation.

The test was conducted by Intel in December 2023.

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Conclusion

We demonstrated how to load and run Whisper and Distil-Whisper models for audio transcription task with OpenVINO and Optimum Intel, and how to perform INT8 post-training quantization of these models with NNCF. Further we evaluated these models on a large scale speech-to-text dataset across multiple CPU devices. The evaluation results show a significant performance boost of OpenVINO vs PyTorch models without loss of transcription quality, and even a larger boost with a tolerable accuracy drop when we apply INT8 quantization.

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Authors

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

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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.‍

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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 (https://arxiv.org/pdf/2311.01792.pdf).Authors propose accurate asymmetric schema for the floating-point quantization. Instead of using typical asymmetric schema with scale and zero point, they use just2 scales: one is for positive values and another - for negative ones. It gives better accuracy NF4/NF3 quantization on different LLAMA models with no memory overhead. Code is available: https://github.com/zhangsichengsjtu/AFPQ.

• 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) Clipped 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.

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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.

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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.

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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.

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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.

Author:Xiake Sun, Wenyi Zou, Churkin Andrey

Introduction

With the advent of e-commerce and online websites, image retrieval applications have been increasing all along around our daily life. Top e-commerce platform such as Amazon and Alibaba have been heavily utilizing image retrieval to put forward what they think is the most suitable product based on what we have seen just now.

Image retrieval is the process of finding an image from a collection or database from the traits of a query image. The traits are usually visual similarities between the images. The top retrieved images can provide hypotheses about which parts of the scene are likely visible in the query image.

Since images in their original form don’t reflect these traits in their pixel-based data, we need to transform this pixel data into a latent space where the representation of the image will reflect the traits. Naver Corporation proposed Combination of Multiple Global Descriptors (CGD) for Image Retrieval task. The CGD framework exploits multiple global descriptors to get an ensemble effect when it can be trained in an end-to-end manner. Quantitative and qualitative analysis results show that exploiting multiple global descriptors led to higher performance over the single global descriptor.

Neural Network CompressionFramework (NNCF) provides a suite of post-training and training-time algorithms for neural network inference optimization in OpenVINO™ with minimal accuracy drop. NNCF is designed to work with models from PyTorch, TensorFlow, ONNX, and OpenVINO™. In this blog, we use NNCF Post-Training Quantization (PTQ) to quantize CGD model, which can further boost inference while keeping acceptable accuracy without fine-tuning.

Figure1. shows the CGD framework. The framework is described with ResNet-50 backbone where Stage 3 down sampling is removed. From the last feature map, each of n global descriptor branches outputs a k-dimensional embedding vector, which is concatenated into the combined descriptor for ranking loss. Exclusively the first global descriptor is used for auxiliary classification loss where M denotes the number of classes.

CGD framework utilizes the following global descriptors with different focuses:

• Sum pooling of convolutions (SPoC): activates larger regions on the image representation.
• Generalized mean pooling (GeM): generalizes max and average pooling with a pooling parameter.
• Maximum activation of convolutions (MAC): activates more focused regions.

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In this blog, we choose CGD ResNet50(SG) model with ResNet50 backbone that combines SPoC and GeM type of global descriptors. Figure 2 shows some retrieval results of CGD Pytorch model based on Standard Online Products (SOP) dataset. The left most query image serves as input to retrieve the 8 most similar image from the database, where the green bounding box means that the predicted class match the query image class, while the red bounding box means a mismatch of image class. Therefore, the retrieved image can be further filtered out with class information.

CGD Model Enabling and Quantization with OpenVINO™ and NNCF

To leverage efficient inference with OpenVINO™ runtime on the intel platform, we proposed the following workflow in Figure 3 for CGD model enabling and quantization with OpenVINO™ and NNCF PTQ, which is implemented in a single Python script run_quantize.py.

CGD model uses ResNet50 backbone extracted latent feature to create multiple global descriptors, then the global descriptors will be normalized and concatenated as output.

For INT8 quantization, we found some useful tricks to mitigate accuracy issue caused by accuracy sensitive layers, e.g., YOLOv8 OpenVINO Notebook proposes to keep several accuracy sensitive layers in post-processing subgraph as FP32 precision to better preserve accuracy after NNCF PTQ.

For INT8 quantization of CGD model, the left part of Figure 4 shows the subgraph of CGD for global descriptor combination and normalization. Original torch.nn.functional.normalize is accuracy sensitive, which are converted to OpenVINO™ operators (e.g. Power, Divide). Quantization of these operators from FP32 to INT8 weights can lead to accuracy degradation. Here we marked all accuracy-sensitive layers in the right part of Figure 4.

Furthermore, we can use ignored_scopes in NNCF configuration to skip these layers for INT8 quantization to remain FP32 precision as follows:

def quantize_ov_model(fp32_xml_path: str, int8_xml_path: str):
    core = ov.Core()
    ov_model = core.read_model(fp32_xml_path)

    def transform_fn(data_item):
            images, _ = data_item
            return images.numpy()

    calibration_dataset = nncf.Dataset(test_data_loader, transform_fn)
    ignored_scope = nncf.IgnoredScope(
        names=[
            # bottom
            "/Pow",
            "/Pow_1",
            "561",

            # left
            "/main_modules.0/main_modules.0.1/Pow",
            "/main_modules.0/main_modules.0.1/Pow_1",
            "/main_modules.0/main_modules.0.1/Div",

            # right
            "/main_modules.1/main_modules.1.1/Pow",
            "/main_modules.1/main_modules.1.1/Pow_1",
            "/main_modules.1/main_modules.1.1/Div",

            # right
            "/global_descriptors.1/Pow",
            "/global_descriptors.1/ReduceMean",
            "/global_descriptors.1/Pow_1",
            "/global_descriptors.1/Mul",

            # left
            "/global_descriptors.0/ReduceMean",
        ]
    )
    quantized_model = nncf.quantize(ov_model,
                          calibration_dataset,
                          ignored_scope=ignored_scope)
    ov.serialize(quantized_model, int8_xml_path)

CGD OpenVINO™ Demo

Here we can run a CGD demo with CGD_OpenVINO_Demo as follows:

Setup Environment

conda create -n CGD python=3.8
pip install openvino==2023.0.1 openvino-dev[pytorch,onnx]==2023.0.1 nncf==2.5.0 torch==2.0.1

Prepare dataset based on Standard Online Products (SOP)

sudo mkdir -p /home/data/sop
sudo chmod -R 777 /home/data/sop
python data_utils.py --data_path /home/data

Download pre-trained Pytorch CGD ResNet50(SG) model trained on SOP dataset to the results directory.

cp /sop_uncropped_resnet50_SG_1536_0.1_0.5_0.1_128_model.pth results
cp /sop_uncropped_resnet50_SG_1536_0.1_0.5_0.1_128_data_base.pth results

Verify Pytorch FP32 Model Image Retrieval Results

python test.py --query_img_name /home/data/sop/uncropped/281602463529_2.JPG \
        --data_base sop_uncropped_resnet50_SG_1536_0.1_0.5_0.1_128_data_base.pth \
        --retrieval_num 8

Run NNCF PTQ for default quantization with ignore scopes

mkdir -p models
python run_quantize.py

Generated FP32 ONNX model and FP32/INT8 OpenVINO™ model will be saved in the “models” directory. Besides, we also store evaluation results of OpenVINO™ FP32/INT8 model as a Database in the “results” directory respectively. The database can be directly used for image retrieval via input query image.

Verify OpenVINO™ FP32 Model Image Retrieval Results

python test.py --query_img_name /home/data/sop/uncropped/281602463529_2.JPG \
        --data_base ov_fp32_model_data_base.pth  \
        --retrieval_num 8

Verify OpenVINO™ INT8 Model Image Retrieval Results

python test.py --query_img_name /home/data/sop/uncropped/281602463529_2.JPG \
        --data_base ov_int8_model_data_base.pth  \
        --retrieval_num 8

Table 1 shows CGD OpenVINO™ FP32 and INT8 accuracy verification and performance evaluation results with OpenVINO™ 2023.0 on Intel® Xeon® Platinum 8358 Processor.

From an accuracy perspective, test_recall@1/2/4/8 measures if the top n image retrieval results match with the query image. OpenVINO™ INT8 PTQ quantizes all FP32 layers to INT8, which leads to ~1.2% accuracy degradation compared with OpenVINO™ FP32 Model. OpenVINO™ INT8 PTQ (w/ IgnoreScope) skips quantization of accuracy sensitive layers via ignore scopes, which controls the accuracy difference between OpenVINO™ INT8 model and OpenVINO™FP32 model within 0.16%.

Compared with OpenVINO™ FP32 model, both OpenVINO™ INT8 PTQ and OpenVINO™ INT8 PTQ (w/ Ignore Scopes) can reach ~4x performance boost. Results show that keeping serval layers as FP32 precision has minimal impact on OpenVINO™ INT8 model.

Figure 5 shows the CGD Image Retrieval Results of Pytorch FP32, OpenVINO™ FP32/INT8 models with the same query image. The Pytorch and OpenVINO™ FP32 retrieved images are the same. Although the 7th image of OpenVINO™ INT8 model results is not matched with FP32 model's results, it can be further filtered out with predicted class information.

Conclusion

In this blog, we introduced how to enable and quantize the CGD model with OpenVINO™ runtime and NNCF:

• Proposed INT8 quantization NNCF PTQ with ignore scopes to reach ~4x performance boost while keeping minimal accuracy degradation (<0.16%) compared to FP32 model.
• Provided a demo repository for CGD model enabling, quantization, accuracy verification, and deployment with OpenVINO™ and NNCF.

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Reference

• ‍Combination of Multiple Global Descriptors for Image Retrieval
• CGD OpenVINO Demo‍
• A PyTorch implementation of CGD by leftthomas‍
• Neural Network Compression Framework (NNCF)